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Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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This book presents a plethora of creative interventions in education, culture, expressions, communications, and other areas. Each chapter brings forth a core idea well attested on the scales of creative interventions. It is a collaborative effort to bring forth multidisciplinary creativity in the ever-evolving world of design, communication, and possibilities. There is really no logical order to the book. You do not necessarily have to start at the beginning, just find a chapter that interests you and read. I hope that you find the book stimulating as well as informative.",isbn:"978-1-83881-041-2",printIsbn:"978-1-83881-040-5",pdfIsbn:"978-1-83881-042-9",doi:"10.5772/intechopen.87355",price:119,priceEur:129,priceUsd:155,slug:"creativity-a-force-to-innovation",numberOfPages:188,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"58f740bc17807d5d88d647c525857b11",bookSignature:"Pooja Jain",publishedDate:"March 3rd 2021",coverURL:"https://cdn.intechopen.com/books/images_new/9560.jpg",numberOfDownloads:5718,numberOfWosCitations:0,numberOfCrossrefCitations:3,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:4,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:7,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 25th 2020",dateEndSecondStepPublish:"June 15th 2020",dateEndThirdStepPublish:"August 14th 2020",dateEndFourthStepPublish:"November 2nd 2020",dateEndFifthStepPublish:"January 1st 2021",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"316765",title:"Dr.",name:"Pooja",middleName:null,surname:"Jain",slug:"pooja-jain",fullName:"Pooja Jain",profilePictureURL:"https://mts.intechopen.com/storage/users/316765/images/system/316765.jpg",biography:"Pooja Jain, Ph.D., is an academician in graphic design and visual communication. She holds a Ph.D. in Advertising from Delhi University, India, and is an avid researcher with several publications in international peer-reviewed journals and paper presentations in national and international conferences. Currently, she is actively working on multiple startups and corporate projects as a freelance design practitioner. Dr. Jain is professionally associated with many eminent Indian institutions of art and design, serving in multiple roles including external examiner, paper setter, Ph.D. advisor, career counselor, advisory board member, and others. At present, Dr. Jain is working as an academic coordinator and faculty member in the Department of Visual Communication at Srishti Institute of Art Design & Technology, Bengaluru, India.",institutionString:"Srishti Institute of Art Design and Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Srishti Institute of Art Design and Technology",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"265",title:"Education",slug:"social-sciences-education"}],chapters:[{id:"73497",title:"Peace Education in Times of Covid-19: Rethinking Other Kind of Logic from the Imagination, Fantasy, Creativity and Utopia",doi:"10.5772/intechopen.93895",slug:"peace-education-in-times-of-covid-19-rethinking-other-kind-of-logic-from-the-imagination-fantasy-cre",totalDownloads:419,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This article aims to reflect on the challenges of peace education in times of Covid-19 global pandemic from a positive perspective, understood as a new opportunity for education to consider the teaching of how to make peace from our daily experiences; and in this way, humanity can forge a more peaceful future. In this task, the use of imagination, fantasy and creativity as educational resources will be revalued. Likewise, utopia is proposed as that unknown horizon, still to come, that will show us, in the face of so many doubts and uncertainties, those possible scenarios which will motivate us to continue working for cultures of peace. This reflection starts from the Reconstructive-Empowering Peace Education approach that I have been proposing in my research as a member of the Interuniversity Institute of Social Development and Peace.",signatures:"Sofia Herrero Rico",downloadPdfUrl:"/chapter/pdf-download/73497",previewPdfUrl:"/chapter/pdf-preview/73497",authors:[{id:"322093",title:"Dr.",name:"Sofia",surname:"Herrero Rico",slug:"sofia-herrero-rico",fullName:"Sofia Herrero Rico"}],corrections:null},{id:"73321",title:"Dare to be Disruptive! The Social Stigma toward Creativity in Higher Education and a Proposed Antidote",doi:"10.5772/intechopen.93663",slug:"dare-to-be-disruptive-the-social-stigma-toward-creativity-in-higher-education-and-a-proposed-antidot",totalDownloads:607,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Despite the fact that creativity has been named one of the top-10 skills necessary for success in the twenty-first century, the current educational system in the developed world stifles creativity through its focus on convergent thinking and standardized testing. We propose that a stigma toward creativity exists among educators, which prevents successful implementation of creative teaching and fostering creativity within the classroom. The proposed root cause of the stigma toward creativity in education – that creativity is perceived as disruptive – is examined through the lens of the Adaptor-Innovator theory of creativity and the implicit and explicit theories of creativity, as well as the psychological factors inherent to the social construction of stigma. Seminal and current research in the fields of creativity studies and communication studies offer insight into this phenomenon. The chapter concludes by proposing an antidote to address and fight this stigma as seen through the lens of Fishbein and Ajzen’s Theory of Reasoned Action.",signatures:"Amanda Lohiser and Gerard J. Puccio",downloadPdfUrl:"/chapter/pdf-download/73321",previewPdfUrl:"/chapter/pdf-preview/73321",authors:[{id:"323876",title:"Prof.",name:"Gerard",surname:"Puccio",slug:"gerard-puccio",fullName:"Gerard Puccio"},{id:"327541",title:"Dr.",name:"Amanda",surname:"Lohiser",slug:"amanda-lohiser",fullName:"Amanda Lohiser"}],corrections:null},{id:"73781",title:"The Light Penetrates Silence: Kolok Dance Study in Bengkala Village, Buleleng, Bali",doi:"10.5772/intechopen.94397",slug:"the-light-penetrates-silence-em-kolok-em-dance-study-in-bengkala-village-buleleng-bali",totalDownloads:358,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Art is an expression of the pure soul of humans that is not limited by physical. Therefore, everyone can express their art in various forms of art acts such as dancing, singing, painting, and others. This occurs to the Kolok (mute) community in Bengkala village where they express their artistic spirit by dancing. The Kolok community in this village dances Janger Kolok Dance, Baris Bebek Bingar Bengkala (Bebila) Dance or Baris Bebila Dance, and Jalak Anguci Starling Dance. This dance illustrates the joy and excitement of the mute people in Bengkala village who are able to surpass their limitations into an opportunity to fill themselves up. The creation of this dance is motivated by esthetic, economic and religious reasons. Structurally, all Kolok dances in Bengkala follow the same pattern of general dance but there are adjustments to the conditions of the dancers. This dance has the meaning of struggle, discipline, hard work, cooperation, entertainment based on excitement through the limitations of those who are deaf Kolok.",signatures:"Ida Ayu Trisnawati",downloadPdfUrl:"/chapter/pdf-download/73781",previewPdfUrl:"/chapter/pdf-preview/73781",authors:[{id:"322558",title:"Dr.",name:"Ida Ayu",surname:"Trisnawati",slug:"ida-ayu-trisnawati",fullName:"Ida Ayu Trisnawati"}],corrections:null},{id:"73320",title:"Creativity in Public Relations: The Case from Croatia – How to Make the History of the Insurance Company “Cool”",doi:"10.5772/intechopen.93689",slug:"creativity-in-public-relations-the-case-from-croatia-how-to-make-the-history-of-the-insurance-compan",totalDownloads:400,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The chapter deals with the role of creativity in public relations. Creativity is usually associated with marketing and design, while public relations is associated with information and communication management, respectively, as the information and educational component, but often as persuasion. However, in modern conditions in which there is a kind of inflation of content transmitted by public relations experts to the media and the public, it is very difficult to fight for media attention and public attention. Therefore, the public relations professional is forced to bring creativity to the way of communicating, presenting key messages, and achieving communication goals. For that reason, creativity is becoming an essential strategic and tactical tool for public relations professionals to shift the task to a higher level. The authors present a case study of the leading Croatian insurance company—Croatia osiguranje, which had the challenging task of using the anniversary to communicate its own identity and values, strengthen its image, and attract new clients. The project “Croatia je Hrvatska” has received a number of national and international awards and can serve as an excellent example of synergy between communication management and creativity in achieving communication and business goals.",signatures:"Bozo Skoko and Dejan Gluvacevic",downloadPdfUrl:"/chapter/pdf-download/73320",previewPdfUrl:"/chapter/pdf-preview/73320",authors:[{id:"322984",title:"Prof.",name:"Bozo",surname:"Skoko",slug:"bozo-skoko",fullName:"Bozo Skoko"},{id:"323694",title:"Dr.",name:"Dejan",surname:"Gluvacevic",slug:"dejan-gluvacevic",fullName:"Dejan Gluvacevic"}],corrections:null},{id:"73747",title:"Assessing Creativity and Innovation in Islam",doi:"10.5772/intechopen.94110",slug:"assessing-creativity-and-innovation-in-islam",totalDownloads:736,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:1,abstract:"The purpose of this study is to understand how Islam assesses creativity—specifically, to determine if an Islamic framework for assessing creativity can be identified. Islam does not provide a framework for assessing creativity, but Islamic scholars continue to assess creativity in the absence of this framework. This study was conducted with the assistance of seven informants from seven leading Islamic traditions based in the UK. Each informant provided a unique insight into their understanding and interpretations of Islamic scriptures and texts in relation to creativity and to determine how creativity in Islam is assessed.",signatures:"Cameron Iqbal",downloadPdfUrl:"/chapter/pdf-download/73747",previewPdfUrl:"/chapter/pdf-preview/73747",authors:[{id:"322647",title:"Dr.",name:"Cameron",surname:"Iqbal",slug:"cameron-iqbal",fullName:"Cameron Iqbal"}],corrections:null},{id:"73601",title:"A Design Thinking Approach for Museum Institutions",doi:"10.5772/intechopen.93950",slug:"a-design-thinking-approach-for-museum-institutions",totalDownloads:666,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In these recent years, museum institutions are facing challenges such as deepening diversity among audiences and within the workforce, shifting authority and keeping pace with the creation of a digital offering to be provided in the new shared economy. Additionally, museums cannot just deliver knowledge as information anymore. They are forced to seek to be relevant and meaningful for the audiences and the society. Thus, a visitor-centered approach needs to be developed. The design thinking framework can help museum professionals to face the challenges they handle in today’s world. Indeed, this approach is focused on people and not on a specific product or service. The goal is to understand the needs of customers, their wishes and, based on this information, find the best solution to respond to the type of problem identified or the strategy to be developed. For this reason, the ratio of this discipline provides that people are stimulated to find alternative, creative, and innovative solutions designed and built on the reality of the facts and not dictated by instinct. The aim of this chapter is to investigate the characteristics of the design thinking approach and to analyze how this framework can be implemented in museum institutions.",signatures:"Luigi Nasta and Luca Pirolo",downloadPdfUrl:"/chapter/pdf-download/73601",previewPdfUrl:"/chapter/pdf-preview/73601",authors:[{id:"168999",title:"Dr.",name:"Luca",surname:"Pirolo",slug:"luca-pirolo",fullName:"Luca Pirolo"},{id:"262571",title:"Ph.D.",name:"Luigi",surname:"Nasta",slug:"luigi-nasta",fullName:"Luigi Nasta"}],corrections:null},{id:"73301",title:"A Framework for Assessing the Creativity Manifested in the Emergent Outcomes of Open-Ended Tasks Based on a “Puzzle”",doi:"10.5772/intechopen.93688",slug:"a-framework-for-assessing-the-creativity-manifested-in-the-emergent-outcomes-of-open-ended-tasks-bas",totalDownloads:391,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In creative disciplines, “basic design” is offered as a foundation course to foster diverse thinking skills and creativity. The tasks are generally framed based on the principles such as “progressive transformation,” “borrowing,” and “deconstruction.” The emergent outcomes of such tasks are unique and very challenging to evaluate. In this context, this chapter aims to discuss a framework for assessing the creativity manifested in the emergent outcomes of generative tasks based on a puzzle. Three tasks based on “TANGRAM,” a dissection puzzle with slight variations, were formulated. The task was introduced as a practicum at a faculty development program conducted at the AMS School of Architecture in association with the Council of Architecture, India. Besides, the framed tasks were introduced as an assignment for a theory course and also as a basic design task at the Department of architecture, Sathyabama Institute of Science and Technology, India. The emergent outcomes are explored, decoded, and analyzed. The findings are triangulated and a framework is developed that can be suitably modified so as to investigate the degrees of creativity manifested in the emergent outcomes of an open-ended task.",signatures:"Arulmalar Ramaraj and Jothilakshmy Nagammal",downloadPdfUrl:"/chapter/pdf-download/73301",previewPdfUrl:"/chapter/pdf-preview/73301",authors:[{id:"321996",title:"Mrs.",name:"Arulmalar",surname:"Ramaraj",slug:"arulmalar-ramaraj",fullName:"Arulmalar Ramaraj"},{id:"328627",title:"Dr.",name:"Jothilakshmy",surname:"Nagammal",slug:"jothilakshmy-nagammal",fullName:"Jothilakshmy Nagammal"}],corrections:null},{id:"73433",title:"Assessment of Creativity: Theories and Methods",doi:"10.5772/intechopen.93971",slug:"assessment-of-creativity-theories-and-methods",totalDownloads:1042,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The history of creativity assessment is as old as the concept itself. Researchers from various cultures and disciplines attempted to define the concept of creativity and offer a valid way to assess it. Creativity is generally defined as the ability to produce work that is novel and appropriate. Researchers in the field attempted to measure creativity from different perspectives and tried to answer the question like “What are the mental processes involved in creative thought?, Which personality traits are associated with creativity?, How can a product can be judged to be creative? and, What are the external forces that affect creativity?”. The answers of these questions constitute the most commonly used creativity assessment instruments. This chapter presents a brief overview on assessment of creativity through the eyes of the psychometric perspective and discusses the strengths and weaknesses of various instruments used in the field.",signatures:"Esra Kanlı",downloadPdfUrl:"/chapter/pdf-download/73433",previewPdfUrl:"/chapter/pdf-preview/73433",authors:[{id:"323220",title:"Dr.",name:"Esra",surname:"Kanli",slug:"esra-kanli",fullName:"Esra Kanli"}],corrections:null},{id:"74760",title:"Using Ideation Grids to Power Collaborative Creativity in Face-to-Face and Remote Innovation Sessions",doi:"10.5772/intechopen.93850",slug:"using-ideation-grids-to-power-collaborative-creativity-in-face-to-face-and-remote-innovation-session",totalDownloads:590,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"This chapter outlines a design-led approach to ideation. Ideation is a structured way to develop innovative ideas via collaborative workshops. The chapter starts by contextualising ideation within an overview of the ways in which design supports innovation both as a definable mindset as well as via a standardised methodology. People, behavioural approaches and methods for design innovation are described in section three. Design Thinking is positioned from this analysis as a practical asset in the innovators’ toolkit and also as a natural inheritor and embodiment of applied creativity. The chapter concludes by detailing how ideation works in practice and describes an evolved set of techniques, principles and methods for maximising the value of the approach through ideation grids that can be used in face-to-face and remote innovation work.",signatures:"John Knight, Elliot Ross and Dan Fitton",downloadPdfUrl:"/chapter/pdf-download/74760",previewPdfUrl:"/chapter/pdf-preview/74760",authors:[{id:"321992",title:"Dr.",name:"John",surname:"Knight",slug:"john-knight",fullName:"John Knight"},{id:"322330",title:"Dr.",name:"Dan",surname:"Fitton",slug:"dan-fitton",fullName:"Dan Fitton"},{id:"322331",title:"Mr.",name:"Elliot",surname:"Ross",slug:"elliot-ross",fullName:"Elliot Ross"}],corrections:null},{id:"73199",title:"Examining the Structures and Textures of Gender-Based Japanese Advertisements",doi:"10.5772/intechopen.93475",slug:"examining-the-structures-and-textures-of-gender-based-japanese-advertisements",totalDownloads:509,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"For the non-Japanese native speakers, including foreigner Japanese learners, watching a Japanese advertisement commercial (CM) can be entertaining due to their unique language and visual presentation. There are some Japanese commercials based on display, for example, the 2D advertisements in magazines or posters and the 3D video-based advertisements accompanied by side comments that can always be seen on YouTube videos. This research is based on Japanese commercials (CMs), in the form of YouTube videos, with the same product types but showing gender differences for men and women. Thus, the main research objective is to examine the structure and texture of Japanese commercials (CMs) in terms of how they market the same product type for both men and women. This research has find that most of the Japanese advertisements for men’s products use a more straightforward style of speaking with an opening and closing commercial structure, and there is a short description of the commercial content. Advertisements for women’s products, meanwhile, are more varied in their opening, closing, and content, as well as in the product introduction and narration. The tenses used in men’s products usually contain present tenses and imperative sentences, which are also prominent. While some advertisers typically use the present tenses for women’s products as well as men’s products, they also use past tenses and different Japanese dictionary formats. Proper sentences also used in the advertisements for women’s products. As for lexicon, or word choice, used for men’s products, only zo and ore are used. The lexicon in women’s products, meanwhile, is more varied with kashira, wa, wayo, wane, no, noyo, none, koto, and kotoyo that being used. For advertisements with high selling power, the structure comprises just an opening and closing, and they are more straightforward and less wordy.",signatures:"Sri Aju Indrowaty, Djatmika, Dwi Purnanto and Tatang Hariri",downloadPdfUrl:"/chapter/pdf-download/73199",previewPdfUrl:"/chapter/pdf-preview/73199",authors:[{id:"322178",title:"Ph.D. Student",name:"Sri Aju",surname:"Indrowaty",slug:"sri-aju-indrowaty",fullName:"Sri Aju Indrowaty"},{id:"322609",title:"Prof.",name:"Djatmika",surname:null,slug:"djatmika",fullName:"Djatmika null"},{id:"322610",title:"Dr.",name:"Dwi",surname:"Purnanto",slug:"dwi-purnanto",fullName:"Dwi Purnanto"},{id:"322611",title:"Dr.",name:"Tatang",surname:"Hariri",slug:"tatang-hariri",fullName:"Tatang Hariri"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"6674",title:"Contemporary Pedagogies in Teacher Education and 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"60927",title:"Effect of Free Float Ratio on the Behavior of Shares Valuation in Companies Listed in Latin American Capital Market",doi:"10.5772/intechopen.76421",slug:"effect-of-free-float-ratio-on-the-behavior-of-shares-valuation-in-companies-listed-in-latin-american",body:'For several years, explanations have been sought to describe the behavior of stock prices in markets as well as the reasons for their valuation changes. Consequently, the analysis of financial and market information is relevant [1]. Financial information presented by companies allows the elaboration of different ratios that are useful for academicals porpoises and for investors. This analysis can be done to compare variations in valuation over several years for the same company, to compare companies from the same country within a sector or to compare a sector in different countries. Regional analysis is interesting because it allows us to detect different behaviors within the companies that operate in similar environments.
Using financial information from different items of companies’ assets, liabilities and earnings allows us to analyze profitability ratios, returns persistence, debt ratios and companies solvency [2, 3]. The return ratio on equity is usually considered by investors and analysts, since it measures profitability of the resources contributed by the owners. There is a perception that the higher the return on equity, the better will be the performance of share prices. However, this hypothesis is not proven by previous empirical research since high values of ratios do not always lead to higher returns on shares [4].
For the purpose of measuring the size of a company, we use the concept of stock market capitalization, defined as the market value of an enterprise. This provides an interesting fact because it is the capital of the company multiplied by the price of it equities in the stock market, and therefore, it is a way of quantifying the size of the capital market. The total market capitalization is the sum of all companies’ capitalizations that quoted at a certain date.
It is important to measure market capitalization in absolute terms and in relative terms related to the gross product of each country in order to observe its behavior and draw conclusions [1]. Now that capital is owned by different shareholders, with different characteristics. Nevertheless, there is no doubt that the ownership structure of traded shares in markets can have its effects [5].
Hardouvelis and Karalas (2016) [6] mentioned the importance of the presence of institutional investors in markets and the impact of this on prices. They remarked that the percentage owned by institutional shareholders increased dramatically from about 45% average in the mid-90s to about 80% in recent years. Then, different authors [7] asserted that the intervention of governments affects the liquidity in markets because of different measures adopted that are related to the countries economical evolution.
In the case that the shareholders are state agencies, decisions are taken differently from institutional investors in general because they do not act freely in the market. They have to complete with certain standards in each country.
In Argentina, it is observed in companies listed that in addition to the concentration observed in the capital by majority shareholders, there exists the Sustainability Guarantee Fund (FGS) created in 2009 after the National Senate approved the reform of the Argentine Pension System, which implied the disappearance of private retirement and the Retirement and Pension Funds Administrators (AFJP). In 2016, Law No. 27260 in a National Program of Historical Reparation affects the resources of the Sustainability Guarantee Fund of the Argentine Social Security System for Retirees and Pensioners. Therefore, this fund can generate an offer of shares in the market but not with periodicity, affecting market liquidity. For this reason, it is possible to consider the adjusted floating capital by subtracting the holding portion of the FGS.
Other studies quantify the performance of the company as of its accounting profitability, in relation to the property concentration [5], but in this chapter, the analysis is oriented to the magnitude of the floating capital. The aim is to demonstrate the effect of floating capital on the share’s prices in markets, volatility and finally traded volume. The approach adopted follows the methodology of Çalişkan and Kerestecioğlu.
The study is carried out in companies whose shares are part of the most representative indexes of Latin American markets—from Argentina, Brazil, Chile, Peru and Colombia. The reason why shares of these indices are studied is because they are the ones that have greater stock market presence. According to what is mentioned on O’shee et al. in Latin America, two important aspects characterize the ownership and control structures. In the first place, companies show a high degree of ownership concentration and second, many firms are indirectly controlled by industrial and financial conglomerates that operate in Latin America.
The model proposed analyze data corresponding to 181 companies of the selected Latin Americans indices and applies three simple linear regressions, where the explanatory variable is always the magnitude of the free float ratio, and response variables are the share price variation, the share price volatility and the traded volume. Before applying the model, a descriptive analysis of the variables used in each selected country is carried out to contextualize the results obtained. The results show that there are diverse conclusions for each country and for the different regressions implemented. It will be possible to repeat the same analysis next year and verify the conclusions observed.
This chapter has been organized in five parts. In the first part, we introduce the topic that will be investigated, in the second part, we mention the literary revision of works on different capital markets, the third part describes the methodology followed at work, the fourth part shows the results obtained and finally the fifth one presents the conclusions.
Çalişkan y Kerestecioğlu defined free float as the number of outstanding shares minus shares that are restricted from trading. The free float ratio is the quantity of shares available for public trading. Shares that are restricted from trading are called stable shareholdings, and include shares held by a parent company for control of a subsidiary, shares held by the government, and cross-shareholdings among companies. It has been said that the relationship between ownership structure and corporate performance has been a popular subject for researchers recently. Ownership structure studies mostly focus on firm performance, which is typically defined by accounting profit, or other metrics based on financial statements.
On the other hand, free float ratio studies examine the market performance of stocks. Free float ratio gives information about the ownership structure of a company. A low free float ratio indicates a concentrated ownership structure as well as a small and shallow market for stocks of that company. Free float ratio can affect stock prices in two ways. First, if the free float ratio is low, investors will tend to avoid that stock. Secondly, lower free float ratio means that there is less amount of shares in the market, which might cause low liquidity in the market for that stock. Investors dislike illiquidity. The authors examined the effect of free float ratio on market performance of stocks in Turkey. They attempt to answer the following questions; first, how much do free float ratios affect stock prices of selected firms? Second, do free float ratios affect daily trading volume? Third, do free float ratios affect price volatility? For that research, 194 firms were selected from Istanbul Stock Exchange Market for the period from 25.02.2011 to 09.03.2012. The statistical method applied was linear regression. Results showed that there is no evidence of relationship between price return and free float ratio. In other words, investors did not pay more or less for stocks depending on whether free float ratio was considered to be high or low. On the other hand, there seems to be a positive relationship between free float ratio and price volatility. Finally, free float ratio is directly related to trading volumes. In other words, higher free float ratio results in higher liquidity in the market.
Bostancı and Kılıç examined the free float ratios effects on market performance of stocks in Turkey. Their research includes 199 listed firms on Istanbul Stock Exchange Market for the year 2007. The relationship between free float ratio and the dependent variables average daily closing price, price volatility and average daily trading activity is measured by regression models. Findings suggest that the market rewards higher floating ratio, that is, average daily closing price and trading activity is significantly higher for stocks with higher free float ratio. They also notice that price volatility, which is associated with the risk of a stock, increases with free float ratio. Finally, the effect of free float ratio on these variables is measured by controlling size of firms through a multivariable regression model. According to regression results, effects of floating ratio do not increase or decrease as the firm size increases or decreases. As a conclusion, these results are compatible with the previous studies and prove that free float ratio does matter for investors. Higher floating ratio implies higher market value for stocks, higher liquidity in the market and lower financial costs for corporations. They support suggesting initiatives to corporations and policy makers to increase floating ratios that will result on the decrease of financial costs and ensure capital market development. Although the regression results of the study are robust, the regressions depend on 1 year data, which contain all the sectors and eliminate the free float variations within a stock.
Chan et al. asserted that the intervention of the Hong Kong government offers a clear case for examining how market liquidity is affected by a substantial decline in free float. For many companies listed in Asian and emerging markets, government, controlling companies affiliated companies, and majority owners control a large percentage of the shares. As a result, the amount of shares outstanding considered available for trading could be relatively small. When investigating the liquidity of these markets, it is possible to determine the amount of free-floating shares available. The author also indicates that the amount of free-floating shares is often difficult to define, as it is not easy to determine the identity of ultimate beneficial owners. Sometimes, the trail becomes tangled and it is not possible to accurately monitor ownership across thousands of securities.
The same topic is addressed by other authors [8, 9] saying that in August 1998, after an intervention in the stock market by the Hong Kong government, there was a dramatic decrease in the amount of the shares in the market and this caused a decline on the free floats. The intervention of the Hong Kong government in the stock market offers a natural experiment to examining how the market liquidity was adversely affected by a substantial decline in free float in the market. The trading volume of the stocks listed in the Hang Seng Index (HSI) decreased substantially in 1999, while trading volumes of the group of control stocks did not decline. Also, stocks listed on the HSI experienced price increases. This showed that the government intervention affected the liquidity of the Hang Seng Index (HIS) stocks. On the other hand, they did not find a relationship between free float ratio and price variations of the stocks.
O’shee et al. focused their studies on showing the level of ownership concentration in Latin American companies and explained the impact of this characteristic in the performance of the enterprises. For this, they analyzed 271 companies listed in five South American emerging markets with the methodology of panel analysis. They observed that in global markets, near 46% of a company’s stock is concentrated in the hands of the three main shareholders and, for companies that operate in South American markets that percentage rises up to 70%. In the chapter, they concluded that, on an average, a stock portfolio conformed by companies with the greatest concentration of holdings generates higher yields than the yields produced by portfolio conformed by companies with less concentration of shareholdings. This indicates that stock investors require a higher yield, due to the greater risk they face as minority shareholders in companies with an elevated concentration of holdings. The investigation carried out for emerging countries, seem to show more homogeneous results; likewise, the positive interdependence between the concentration of ownership and the returns of the companies appears to be clearer.
Hardouvelis and Karalas studied the relation of expected stock returns with fund style concentration in stock ownership over the period 1997–2015. Their sample consists of common stocks trading on the NYSE, AMEX and NASDAQ between the first quarter of 1997 and the fourth quarter of 2015. The econometric results confirm the positive association and are robust to the inclusion of known risk-factors as determinants of expected stock returns, the returns of the investment styles themselves, plus a set of style-related control variables like liquidity, size, or volatility characteristics of stocks.
Ginglinger and Hamon [10] investigated how ownership concentration and the separation of ownership and control affect secondary-market liquidity in France. They found that firms with a large insider block holder exhibit significantly lower liquidity. However, different methods of enhancing control affect liquidity in different ways. Pyramidal structures impair market liquidity. Double voting right shares, a French specific method to control enhancement rewarding long-term shareholders and restraining insiders from trading their shares, lead to increased liquidity, especially for small firms. Their results suggest that by using double voting rights to enhance their control, a more transparent decoupling mechanism, rather than pyramidal methods, a more doubtfully decoupling mechanism, block holders offer higher secondary-market liquidity to outside investors.
The objective of this chapter is to show if there is a relationship between floating capital ratios and volumes operated in stock markets, volatility of prices and performance of shares. For this, data compiled include Latin American companies at the end 2016. The total number of enterprises studied is 181 and they belong to Argentina, Brazil, Chile, Peru and Colombia. Data were obtained from Thomson Reuters Eikon platform. (For detail, see Appendices).
Information corresponds to companies that are included in the main indices of the countries under study, as shown in Table 1. and the data compiled concerns to the evolution of prices, their volatility and the volume traded during financial year 2016.
COUNTRY | INDEX | Number of companies |
---|---|---|
Argentina | MERVAL | 26 |
Brazil | BOVESPA | 57 |
Chile | IPSA | 39 |
Peru | I GENERAL | 34 |
Colombia | COLCAP | 25 |
Size of sample.
The applied statistical methodology is a simple linear regression, in which the magnitude of the floating capital ratio is used as the explanatory variable of the model. The explained variables are the Neperian logarithm of the annual percentage return (LREA), the annual volatility (DESV) and the annual average traded volume of shares, expressed in millions of the local currency of each country (VOLAM).
Volatility is a measure of the risk of price movements for a security calculated from the standard deviation of the day-to-day logarithmic historical price changes. The 260-day price volatility equals the annualized standard deviation of the relative price change for the 260 most recent daily trading closing prices, expressed as a percentage.
The volume was calculated based on the annual accumulated volume, divided by the number of working days of the year. The applied model is:
The variables are:
FREFik is free float as a percentage of shares outstanding of the company i-th and of the country k-th.
LREAik is the Neperian logarithm of the annual return in percentage of the company i-th and the country k-th.
DESVik is the volatility of the i-th company and the k-th country.
VOLAMik is the annual traded volume in millions of pesos of the i-th company in the country k-th.
The explanatory variable in the developed regressions is floating capital ratio (FREF). Floating capital can be used as a representative measure of market size, understood as the value of all shares outstanding for trading. The explained variable in the first regression (Eq.(1)) of the proposed model is “annual percentage returns” which is defined as the annual variation of prices of each share that compound the selected indices of each country. It is constructed as the Neperian logarithm of annual returns for each company that conforms the sample in each country. The annual returns are obtained as the quotient of the homogeneous prices at the end of 2016, “Price Ait”, of the i-th company and those that correspond to the homogeneous price of the same company at the annual close of the previous year (2015), “Price A i(t-1)“. In order to obtain the percentages of variation, this result is multiplied by 100.
The next variable is annual typical deviation of the variation in prices (Eq. (2)) of shares from each country, which is used as a response variable. (DESVik).
A measure of the risk of price variation for a security is calculated from the standard deviation of day-to-day logarithmic historical price changes. The 260-day price volatility equals the annualized standard deviation of the relative price change for the 260 most recent daily trading closing price, expressed as a percentage.
Regarding the standard deviation, it must be borne in mind that it is a measure of risk in absolute terms. The higher the standard deviation, the greater variability of the assets price and therefore the greater its risk. It is a very useful statistical measure as long as the distribution of probability of the asset’s performance is normal.
The third proposal uses the variable average daily traded volume of each i-th company (VOLAMik), (Eq. (3)) and is considered in millions of the currency of each country. Its calculation is performed as the accumulated annual volume divided by the days susceptible of negotiation between both dates.
The importance of data is potential and it only becomes information when it is associated within a suitable context. Data must be analyzed and transformed; only in this way it produces knowledge and support decision-making.
To begin the analysis of the data the descriptive statistics, although it is very simple, it does become important in many studies. Results allow us to compare experimental evidences with theories and hypotheses, validating empirical arguments from mathematical models designed and adjusted by experts in the corresponding topic. For this reason, descriptive statistics of the variables used in the model proposed in this chapter are carried out.
O’shee et al. mentioned in his article that Latin American companies that are publicly traded are characterized as being highly concentrated. In them, they clearly identified that majority shareholders can be of great strength for the firm due to their active position within it and because they represent a financial source for company in times of crisis.
The first variable to describe is floating capital, which is studied by different authors obtaining interesting results.
In Argentina, the ownership structure changed dramatically in the nineties, when almost all state enterprises were privatized; but even so, high levels of concentration were maintained. This can be seen in the fact that the 20 largest companies show majority shareholders that hold around the 65% of the capital. In Brazil, on average, main shareholder own 41% of the firm, while the most important five hold 61%. In Chile and Peru, it is shown that the first three major shareholders own about three quarters of all shares. Colombia shows the lowest level of concentration and numbers are similar to those held by companies in Europe and Asia [5].
Table 2 gives the descriptive statistics for Free Float ratio divided into quartiles. It is possible to observe 60.22% (15.47 + 44.75%) of the companies included in the sample have less than 50% of their free float listed in the market.
COUNTRY | Sample | Companies with FREF <25% | Companies with FREF >25% ^FREF <50% | Companies with FREF >50% ^FREF <75% | Companies with FREF >75% | ||||
---|---|---|---|---|---|---|---|---|---|
Argentina | 26 | 5 | 19.23% | 12 | 46.15% | 4 | 15.38% | 5 | 19.24% |
Brazil | 57 | 0 | 0.00% | 22 | 38.60% | 10 | 17.54% | 25 | 43.86% |
Chile | 39 | 8 | 20.51% | 26 | 66.67% | 3 | 7.69% | 2 | 5.13% |
Peru | 34 | 9 | 26.47% | 10 | 29.41% | 3 | 8.82% | 12 | 35.30% |
Colombia | 25 | 6 | 24.00% | 11 | 44.00% | 1 | 4.00% | 7 | 28.00% |
TOTAL | 181 | 28 | 15.47% | 81 | 44.75% | 21 | 11.60% | 51 | 28.18% |
Descriptive analysis of FREF in different countries.
In the selected temporal space and in this sample, it is possible to affirm that Brazil is the country where stocks that make up the BOVESPA index have the highest percentage of free float on the market. Since 61.40% (17.54 + 43.86%) of its companies have free float higher than 50% and there is no company with less than 25% of their capital as free float.
The opposite situation is what we found in Chile. In its capital market the 87.18% (20.51 + 66.67%) of the stocks in the 39 companies that make up the IPSA Index are property of the major shareholders.
In Argentina, these numbers change substantially and are more similar to Chile if the holdings of the FGS are incorporated as part of the majority shareholders holdings. Table 3 shows how the FREF is modified if the FGS holding is considered. It is called FREF adjusted (FREF AJUS) to that difference. In Colombia, 68% of the companies that are part of the index have majority shareholding participation in more than 50%. This is similar to the average mentioned in another article written by different authors [5].
Companies | FREF | FGS | FREF AJUS |
---|---|---|---|
Banco Macro SA | 61.59 | 30.97 | 30.62 |
San Miguel SA | 46.86 | 26.96 | 19.90 |
Edenor SA | 51.00 | 26.81 | 24.19 |
Consultatio SA | 31.07 | 26.62 | 4.45 |
Distribuidora Gas Cuyana SA | 30.00 | 26.62 | 3.38 |
Siderar SA | 39.06 | 26.03 | 13.03 |
Telecom SA | 96.14 | 24.99 | 71.15 |
Pampa Energía SA | 84.31 | 23.23 | 61.08 |
Trans. Gas Sur SA | 49.00 | 23.11 | 25.89 |
Mirgor SA | 51.74 | 21.54 | 30.20 |
Grupo Financiero Galicia SA | 88.40 | 21.28 | 67.12 |
Transener SA | 47.35 | 19.57 | 27.78 |
Details of free float adjusted in different companies in Argentina.
Investors obtain returns for their investments in shares by two ways: dividens and price appreciation. In most Latin American markets, the payment of dividends is irregular and it is not of significance, therefore price differential is what is important for investors. Table 4 shows the descriptive statistics for the Neperian logarithm of the annual return (LREA Eq. (4)) in percentage of the companies in different countries. These exposed values are not comparable, since this return is in the countries own currencies but it allows us to observe some extreme values, average and individual deviation of each country individually.
COUNTRY | Sample | Mean | Std. Dev | Min | Max |
---|---|---|---|---|---|
Argentina | 26 | 52.71 | 39.33 | −18.68 | 129.43 |
Brazil | 57 | 36.44 | 37.32 | −62.95 | 137.62 |
Chile | 39 | 13.63 | 23.7 | −33.15 | 99.43 |
Perú | 34 | 51.92 | 37.15 | −8.01 | 137.63 |
Colombia | 25 | 17.81 | 17.01 | −0.22 | 75.32 |
Descriptive analysis of LREA in different countries.
It is observed that in all the indexes analyzed, there were stocks whose prices in 2016 fell compared to the previous year. In the case of Argentina, only 2 shares out of 26 maintained that negative behavior, even including 8 companies whose yields were higher than 100%. The average value of the variable LREA is 52.71 and the standard deviation is 39.33. The country with the smallest range of variation of the variable shown in Table 4 is Colombia. Only one company has a slight negative variation in its price and only one has yields over 100%.
Observing the volatility in Table 5, which is a measure of the risk of price movements for a value calculated from the standard deviation of the historical changes in daily logarithmic prices, it can be seen that the highest volatility is found in Peru and Brazil. The variable DESV reached values of 97.53 and 64.75 respectively. The minimum value is also obtained by Brazil in a magnitude of 8.43.
COUNTRY | Sample | Mean | Std. Dev | Min | Max |
---|---|---|---|---|---|
Argentina | 26 | 36.71 | 6.15 | 28.93 | 48.44 |
Brazil | 57 | 34.16 | 11.35 | 8.43 | 64.75 |
Chile | 39 | 23.74 | 7.72 | 14.68 | 47.45 |
Peru | 34 | 34.35 | 20.16 | 12.90 | 97.53 |
Colombia | 25 | 18.22 | 5.5 | 10.62 | 35.03 |
Descriptive analysis of DESV in different countries.
Once the descriptive analysis of the relevant variables has been carried out, an analysis of the results of the regression is accomplished.
The results of this work, based on an empirical study, seek to assess the relationship between the magnitude of the floating capital ratio and the selected market indicators such as the traded volume, annual returns and the standard deviation of price variation. According to Çalişkan and Kerestecioğlu, a high floating capital ratio is positive for investors in case they need to exercise their rights after buying shares. The results of the regression model proposed in the equation (Eq. (1)) are calculated and shown in Table 6.
Country | LREA | Coef. | Std.Err. | t | P > |t| | [95% Conf.Interval] | |
---|---|---|---|---|---|---|---|
Argentina | FREF | −0.2787 | 0.3127 | −0.89 | 0.382 | −0.9242 | 0.36671 |
_cons | 66.68 | 17.48 | 3.81 | 0.001 | 30.60 | 102.77 | |
Brazil | FREF | 0.1425 | 0.1959 | 0.73 | 0.47 | −0.2501 | 0.5353 |
_cons | 26.91 | 14.00 | 1.92 | 0.06 | −1.15 | 54.98 | |
Chile | FREF | 0.2622 | 0.2181 | 1.2 | 0.237 | −0.1798 | 0.704 |
_cons | 4.00 | 8.85 | 0.45 | 0.654 | −13.93 | 21.95 | |
Perú | FREF | 0.399 | 0.175 | 2.28 | 0.029 | 0.04313 | 0.7565 |
_cons | 30.86 | 11.00 | 2.80 | 0.008 | 8.45 | 53.28 | |
Colombia | FREF | 0.2767 | 0.0939 | 2.95 | 0.007 | 0.08245 | 0.471144 |
_cons | 4.62 | 5.37 | 0.86 | 0.398 | −6.49 | 15.73 |
Estimated coefficients, standard errors and significance of variables. Variable response LREA.
FREF coefficient (β1) (Eq.(1)) is negative and statistically insignificant for Argentina, so it can be concluded that for the analyzed data there is no relationship between price variation and floating capital ratio. For Brazil and Chile, there is direct relationship but no significance.
For the case of Colombia and Peru this relationship, according to the results obtained, is direct and statistically significant. Therefore, it is concluded that the results obtained are not of equal sense and importance in all the countries analyzed.
Price volatility (DESV) regression is performed for the floating capital ratio. Results are shown in Table 7.
Country | DESV | Coef. | Std.Err. | t | P > |t| | [95% Conf.Interval] | |
---|---|---|---|---|---|---|---|
Argentina | FREF | −0.0486 | 0.0487 | −1 | 0.329 | −0.1492 | 0.05198 |
_cons | 39.14 | 2.73 | 14.37 | 33.52 | 44.77 | ||
Brazil | FREF | 0.0132346 | 0.0598 | 0.22 | 0.826 | −0.1067 | 0.1332 |
_cons | 33.28 | 4.28 | 7.78 | 24.70 | 41.86 | ||
Chile | FREF | −0.00305 | 0.0724 | −0.04 | 0.967 | −0.1498 | 0.1437 |
_cons | 23.85 | 2.94 | 8.11 | 17.89 | 29.81 | ||
Peru | FREF | 0.2939 | 0.0959 | 2.13 | 0.041 | 0.0084 | 0.399 |
_cons | 23.61 | 6.03 | 3.92 | 11.33 | 35.89 | ||
Colombia | FREF | 0.00757 | 0.0355 | 0.21 | 0.833 | −0.066 | 0.081199 |
_cons | 17.85 | 2.03 | 8.78 | 13.65 | 22.07 |
Estimated coefficients, standard errors and significance of the variables. Variable response DESV.
FREF coefficients(β1) (Eq.(2)) are negative and statistically insignificant for Argentina and Chile, so it can be concluded that for the analyzed data, there is no relationship between price volatility and floating capital ratio. For Brazil and Colombia, there is direct relationship but no significance.
In the case of Peru, this relationship, according to the results obtained, is direct and statistically significant. Therefore, it is shown again that the results obtained are not of equal sense and important in all the countries studied.
The coefficient of FREF (β1)(Eq. (2)) for Peru is 0.2939 and significant at 5% level as P > |t| is 0.041. This suggests that FREF is significantly positive correlated with price volatility. Higher free float ratio means higher risk for the stock. Peru was the only Latin American country analyzed which shows a coefficient of significance.
Finally, the results of the regression proposed in Eq. (3) that seeks to prove whether there is a relationship between the volume traded and the floating capital ratio are shown in Table 8.
Country | VOLAM | Coef. | Std.Err. | t | P > |t| | [95% Conf.Interval] | |
---|---|---|---|---|---|---|---|
Argentina | FREF | 0.0944 | 0.069 | 1.37 | 0.185 | −0.0482 | 0.237 |
_cons | 3.3073 | 3.863 | 0.86 | 0.4 | −4.66 | 11.28 | |
Brazil | FREF | 0.377 | 0.5232 | 0.72 | 0.473 | −0.6708 | 1.4263 |
_cons | 61.06 | 37.40 | 1.63 | 0.108 | −13.89 | 136.01 | |
Chile | FREF | 2.0296 | 12.76 | 0.16 | 0.875 | −23.8373 | 27.89 |
_cons | 1278.75 | 518.03 | 2.47 | 0.018 | 229.11 | 2328.38 | |
Perú | FREF | 6.39 | 7.167 | 0.89 | 0.379 | −8.2 | 20.9898 |
_cons | 576.87 | 450.35 | 1.28 | 0.209 | −340.47 | 1494.21 | |
Colombia | FREF | 36.7117 | 29.7348 | 1.23 | 0.229 | −24.799 | 98.223 |
_cons | 2490.83 | 1699.5 | 1.73 | 0.097 | −574.85 | 6456.51 |
Estimated coefficients, standard errors and significance of variables. Variable response VOLAM.
The coefficients (Eq. (3)) that result from the regression for all countries are not statistically significant. Unlike what Caliskan and Kerestecioglu (2013) say, we find that for the countries under study and for this temporary space, it is not possible to demonstrate that there is a relationship between both variables.
Floating capital ratio can affect the price of shares in two ways: first, a small quantity of shares can make a stock unattractive to investors, and second, a low floating capital in the market represents less amount of shares available to negotiate, which can cause inadequate liquidity [7]. These statements make it possible to conclude that a low floating capital ratio has the effect of reducing the value of shares due to insufficient demand from investors [11].
Different authors studied the effect of stock property concentration on stock return (7), others address the adverse change in market liquidity of stocks as a result of the decrease in free float (6), or the effect of the free float ratio (FFR) on stock return, risk, and trading activity (8).
This study documented the effects of floating capital ratio on price returns, price volatility and traded volume in Latin American capital markets. Data from 181 firms listed at the end of 2016 was used. These enterprises are part of the most representative indices of each market based on traded volume or market capitalization. Results obtained applying linear regressions methods show different situations in each country.
It is observed, for this temporary space and according to the sample requested, that in the case of Argentina, free float ratio is not statistically significant in the variation share prices. Volatility or the negotiated volume presents an inverse relationship with floating capital.
In the studies made for the countries of Peru and Colombia, we found that greater floating capital affects profitability in a positive way but only in Peru, it is possible to say that a greater floating capital affects volatility in stock prices. For the case of Chile and Brazil, it is not possible to obtain conclusions since results were not significant.
As a conclusion, these findings are compatible with the previous studies and prove that free float ratio does matter for the investors. Higher floating ratio implies higher market value for stocks for the cases of Peru and Colombia. Therefore, these results provide empirical evidence for the growing practice of weighting stocks according to free float ratio for the construction of indexes. They also support designing incentive measures to present to corporations and policy makers for enlarging floating ratios that will decrease cost of capital and ensure capital market development. Although the regression results of this study were robust and clear, it depends on 1-year data, which eliminates the free float variations within a stock. Therefore, examining effects of free float ratio for different sectors or for firms whose floating ratios change substantially within a time horizon may bring interesting results for further studies [11].
The study shows that there is no relationship between floating capital ratio and the traded volume for this temporary space and for the companies selected. It will be possible to repeat the same analysis next year and check whether these conclusions can be different or continue ratifying the current results.
Nevertheless, this study presents two limitations: the first is the use of data from a cross-sectional sample, that is, it takes data corresponding to a set of companies for a moment in time, and the other limitation is that the selected companies are only those that make up the indexes, due to the availability of public information.
Future lines of research can be oriented to confirm if the results obtained in the present study (2016 period of analysis) are maintained over the years and to integrate the findings of effects of stock concentration of property (government holdings and majority shareholders)on stock return (7) with the effects of the floating capital ratio in stock markets.
No. | Companies | FREF | DESV | VOLAM | LREA |
---|---|---|---|---|---|
1 | Agrometal SA | 45.20 | 48.00 | 1.66 | 129.44 |
2 | Petrolera Pampa SA | 36.90 | 33.12 | 1.45 | 110.63 |
3 | Autopistas del Sol SA | 100.00 | 39.10 | 0.86 | 105.61 |
4 | Petróleo Brasilero SA | 49.58 | 39.94 | 41.18 | 101.46 |
5 | Holcim SA | 20.39 | 36.54 | 3.79 | 101.27 |
6 | San Miguel SA | 46.86 | 40.44 | 3.26 | 89.03 |
7 | Central Costanera SA | 24.32 | 48.17 | 2.39 | 80.22 |
8 | Central Puerto SA | 20.98 | 33.35 | 2.58 | 70.39 |
9 | Distribuidora Gas Cuyana | 30.00 | 48.45 | 0.97 | 65.12 |
10 | Pampa Energía | 84.31 | 33.19 | 19.94 | 64.00 |
11 | Trans. Gas Norte | 20.01 | 43.14 | 0.61 | 63.23 |
12 | Transener SA | 47.35 | 43.18 | 4.40 | 59.87 |
13 | Trans. Gas Sur | 49.00 | 36.05 | 2.34 | 56.05 |
14 | Tenaris SA | 39.45 | 28.94 | 9.62 | 56.04 |
15 | Edenor SA | 51.00 | 33.52 | 5.12 | 50.84 |
16 | Mirgor SA | 51.74 | 46.99 | 7.56 | 44.76 |
17 | Cresud SA | 69.19 | 30.86 | 11.78 | 33.00 |
18 | Telecom SA | 96.14 | 32.30 | 4.68 | 28.26 |
19 | Banco Macro SA | 61.59 | 34.65 | 10.58 | 26.37 |
20 | Y.P.F. SA | 48.99 | 33.66 | 19.38 | 17.11 |
21 | Consultatio SA | 31.07 | 31.18 | 3.69 | 15.73 |
22 | Grupo Financiero Galicia SA | 88.40 | 31.23 | 19.96 | 15.16 |
23 | Siderar SA | 39.06 | 32.79 | 9.00 | 14.89 |
24 | Banco Francés SA | 24.05 | 29.71 | 8.27 | 4.56 |
25 | Aluar Aluminio SA | 27.42 | 31.55 | 5.29 | −13.74 |
26 | Comercial del Plata | 100.00 | 34.43 | 8.63 | −18.68 |
Information of companies of the MERVAL-ARGENTINA.
No. | Companies | FREF | DESV | VOLAM | LREA |
---|---|---|---|---|---|
1 | AMBEV ON/d | 28.07 | 18.82 | 235.53 | −4.89 |
2 | BRASIL ON/d | 37.54 | 41.14 | 202.68 | 68.99 |
3 | BRADESCO ON/d | 26.05 | 28.14 | 39.69 | 49.15 |
4 | BRADESCO PN/d | 97.76 | 28.38 | 295.73 | 55.22 |
5 | BBSEGURIDADE O/d | 33.69 | 26.95 | 125.92 | 21.15 |
6 | BRADESPAR PN/d | 98.55 | 47.59 | 25.80 | 109.83 |
7 | BRF FOODS ON/d | 95.31 | 29.14 | 113.18 | −11.06 |
8 | BRASKEM PNA/d | 54.93 | 33.50 | 39.14 | 32.08 |
9 | BR MALLS PAR O/d | 95.55 | 33.33 | 61.78 | 33.62 |
10 | BMF BOVESPA ON/d | 97.56 | 26.12 | 173.57 | 44.64 |
11 | CCR RODOVIAS O/d | 48.78 | 34.04 | 70.43 | 28.66 |
12 | CIELO ON/d | 41.20 | 29.58 | 161.20 | 1.31 |
13 | CEMIG PN/d | 92.57 | 47.51 | 51.03 | 34.89 |
14 | CPFL ENERGIA O/d | 66.11 | 8.43 | 37.14 | 54.28 |
15 | COPEL PNB/d | 78.74 | 39.36 | 18.38 | 16.18 |
16 | COSAN ON/d | 37.71 | 31.31 | 33.85 | 48.44 |
17 | SID NACIONAL O/d | 44.30 | 53.53 | 65.68 | 99.79 |
18 | CYRELA REALT O/d | 64.38 | 37.91 | 24.99 | 34.05 |
19 | ECORODOVIAS ON/d | 35.75 | 38.86 | 23.71 | 51.07 |
20 | ENGIE BRASIL/d | 31.29 | 19.14 | 31.99 | 9.42 |
21 | ELETROBRAS ON/d | 31.67 | 64.75 | 30.69 | 137.63 |
22 | ELETROBRAS PNB/d | 92.78 | 53.89 | 30.49 | 90.82 |
23 | EMBRAER ON/d | 94.59 | 28.90 | 49.50 | −62.96 |
24 | ENERGIAS BR ON/d | 48.73 | 24.15 | 28.92 | 21.02 |
25 | EQUATORIAL ON/d | 100.00 | 21.24 | 51.94 | 48.45 |
26 | ESTACIO PART O/d | 85.56 | 46.02 | 34.04 | 24.55 |
27 | FIBRIA ON/d | 41.46 | 38.56 | 66.09 | −46.93 |
28 | GERDAU PN/d | 80.12 | 43.31 | 106.64 | 84.73 |
29 | GERDAU MET PN/d | 98.30 | 53.56 | 69.32 | 106.18 |
30 | HYPERMARCAS ON/d | 59.15 | 21.69 | 70.54 | 20.28 |
31 | ITAUSA PN/d | 82.95 | 24.34 | 163.42 | 36.01 |
32 | ITAUUNIBANCO P/d | 100.00 | 26.12 | 413.68 | 40.52 |
33 | JBS ON/d | 34.21 | 63.82 | 94.75 | −3.42 |
34 | KLABIN UNT/d | 100.00 | 28.91 | 49.31 | −25.21 |
35 | KROTON ON/d | 93.54 | 36.46 | 123.05 | 36.22 |
36 | LOJAS AMERIC P/d | 64.99 | 35.56 | 37.49 | 5.63 |
37 | LOJAS RENNER O/d | 84.98 | 29.34 | 71.43 | 32.46 |
38 | MARFRIG ON/d | 42.63 | 35.65 | 15.75 | 4.01 |
39 | MRV ON/d | 64.46 | 29.14 | 30.25 | 26.13 |
40 | MULTIPLAN ON/d | 69.33 | 26.80 | 37.64 | 45.48 |
41 | NATURA ON/d | 40.04 | 38.52 | 33.76 | −0.32 |
42 | P.ACUCAR-CBD P/d | 93.82 | 33.47 | 46.56 | 26.87 |
43 | PETROBRAS ON/d | 49.58 | 35.49 | 149.59 | 68.14 |
44 | PETROBRAS/d | 76.06 | 39.92 | 586.91 | 79.72 |
45 | QUALICORP ON/d | 100.00 | 34.98 | 33.75 | 41.50 |
46 | RAIADROGASIL O/d | 67.38 | 24.07 | 63.75 | 55.51 |
47 | RUMO/d | 90.69 | 43.83 | 43.57 | −1.62 |
48 | LOCALIZA ON/d | 71.94 | 32.22 | 34.90 | 34.93 |
49 | SANTANDER BR U/d | 100.00 | 35.14 | 25.78 | 66.88 |
50 | SABESP ON/d | 49.74 | 33.87 | 49.88 | 42.86 |
51 | TRANS ALIANCA /d | 100.00 | 23.43 | 22.76 | 34.80 |
52 | TIM PART ON/d | 33.40 | 23.65 | 21.35 | 15.86 |
53 | ULTRAPAR ON/d | 70.07 | 21.01 | 87.84 | 14.74 |
54 | USIMINAS PNA/d | 79.38 | 55.02 | 70.18 | 98.96 |
55 | VALE ON/d | 39.64 | 41.91 | 126.03 | 68.43 |
56 | TELEF BRASIL P/d | 37.09 | 18.80 | 84.72 | 25.28 |
57 | WEG ON/d | 35.65 | 27.10 | 32.05 | 6.14 |
Information of companies of the BOVESPA- BRAZIL.
No. | Companies | FREF | DESV | VOLAM | LREA |
---|---|---|---|---|---|
1 | AGUAS ANDINAS /d | 32.60 | 24.46 | 1875.71 | −5.16 |
2 | EMB ANDINA B/d | 39.44 | 24.05 | 642.72 | 16.35 |
3 | ANTAR CHILE/d | 3.94 | 17.53 | 164.13 | 1.50 |
4 | AESGENER/d | 30.42 | 28.01 | 1043.89 | −27.06 |
5 | BANMEDICA/d | 31.58 | 21.65 | 179.14 | 22.92 |
6 | BCI/d | 34.95 | 18.20 | 1686.37 | 25.67 |
7 | CAP/d | 36.70 | 47.46 | 1563.67 | 99.43 |
8 | CMPC/d | 39.36 | 22.08 | 1935.19 | −11.52 |
9 | CERVECERIAS UN/d | 16.41 | 17.40 | 1822.74 | −12.03 |
10 | CENCOSUD/d | 39.74 | 17.24 | 5408.48 | 28.12 |
11 | BANCO DE CHILE/d | 20.78 | 15.87 | 2171.01 | 10.67 |
12 | SM BANCO CHILE/d | 100.00 | 14.69 | 437.37 | 15.04 |
13 | VINA CONCHA TO/d | 31.88 | 16.96 | 741.81 | 0.68 |
14 | COLBUN/d | 35.31 | 20.23 | 1286.89 | −25.31 |
15 | EMPRESAS COPEC/d | 25.24 | 21.82 | 2049.63 | 4.95 |
16 | ENGIE ENERGIA /d | 44.49 | 18.19 | 837.73 | 7.14 |
17 | EMBONOR B/d | 62.27 | 20.89 | 276.32 | 27.81 |
18 | ENEL AMERICAS/d | 43.90 | 19.09 | 3289.75 | 2.88 |
19 | ENEL GENERACIO/d | 34.65 | 20.15 | 2537.08 | −33.15 |
20 | ENTEL/d | 35.89 | 19.93 | 1551.88 | 11.35 |
21 | FALABELLA/d | 33.19 | 17.36 | 6052.37 | 16.21 |
22 | FORUS/d | 26.52 | 25.64 | 353.92 | 25.35 |
23 | IAM SA/d | 24.33 | 26.74 | 422.19 | −3.38 |
24 | INV LA CONSTRU/d | 24.99 | 27.41 | 358.04 | 13.97 |
25 | ITAU CORPBANCA/d | 22.07 | 18.18 | 1847.03 | −4.05 |
26 | LATAM AIRLINES/d | 47.24 | 26.17 | 2830.31 | 42.08 |
27 | MASISA/d | 30.04 | 39.45 | 138.35 | 61.44 |
28 | ORO BLANCO/d | 20.23 | 44.67 | 293.03 | 12.92 |
29 | PARQ ARAUCO/d | 55.80 | 19.09 | 1145.36 | 29.15 |
30 | QUINENCO/d | 14.82 | 23.45 | 217.47 | 23.11 |
31 | RIPLEY CORP/d | 28.79 | 22.70 | 561.83 | 30.36 |
32 | SMSAAM/d | 39.89 | 22.23 | 209.26 | 14.04 |
33 | SALFACORP/d | 76.21 | 38.17 | 146.27 | 25.65 |
34 | GRUPO SECURITY/d | 37.53 | 21.00 | 221.15 | 17.42 |
35 | SIGDO KOPPERS/d | 34.82 | 22.82 | 165.11 | 0.00 |
36 | SONDA/d | 47.88 | 20.89 | 1190.40 | −6.19 |
37 | SOQUIMICH B/d | 69.93 | 30.30 | 2667.48 | 35.55 |
38 | SANTANDER CHIL/d | 29.82 | 18.84 | 2065.53 | 14.44 |
39 | VAPORES/d | 27.98 | 35.02 | 390.35 | 23.39 |
Information of companies of the IPSA-CHILE.
No | Companies | FREF | DESV | VOLAM | LREA |
---|---|---|---|---|---|
1 | ALICORP/d | 75.66 | 16.88 | 6561.95 | 24.71 |
2 | ANDINO INVEST/d | 26.12 | 40.47 | 24.85 | 53.41 |
3 | AUSTRAL GROUP/d | 10.65 | 46.15 | 16.18 | 18.92 |
4 | BCO CONTINENTA/d | 7.76 | 20.10 | 2211.62 | 56.07 |
5 | CEM PACASMAYO/d | 49.99 | 18.76 | 4011.91 | 30.50 |
6 | BUENAVENTURA/d | 49.36 | 36.13 | 282.66 | 67.59 |
7 | ATACOCHA/d | 100.00 | 50.25 | 151.22 | 137.63 |
8 | CREDICORP/d | 65.20 | 20.04 | 1035.22 | 49.67 |
9 | ENGEPE/d | 16.40 | 26.82 | 623.63 | 13.91 |
10 | ENEL DIST PERU/d | 100.00 | 23.29 | 351.87 | 23.19 |
11 | CASA GRANDE/d | 37.57 | 26.96 | 124.55 | 71.69 |
12 | FERREYROS/d | 100.00 | 22.88 | 1720.30 | 29.01 |
13 | GRANA Y MONTER/d | 77.08 | 82.84 | 2513.65 | 89.05 |
14 | INRETAIL PERU/d | 27.31 | 14.06 | 865.85 | 32.52 |
15 | INTERGROUP/d | 42.95 | 17.69 | 2564.01 | 38.68 |
16 | INV CENTENARIO/d | 62.90 | 23.92 | 10.99 | −8.00 |
17 | LUZ DEL SUR/d | 16.07 | 16.49 | 495.57 | 40.53 |
18 | MINSUR/d | 100.00 | 28.88 | 717.87 | 87.55 |
19 | PANORO/d | 82.84 | 57.10 | 13.36 | 76.64 |
20 | RELAPASA/d | 17.62 | 37.21 | 243.67 | 70.61 |
21 | SIDER/d | 9.97 | 36.91 | 78.47 | 109.86 |
22 | CERRO VERDE/d | 5.86 | 29.48 | 84.56 | 32.16 |
23 | MINERA EL BROC/d | 35.27 | 31.55 | 38.58 | 33.35 |
24 | TREVALI MINING/d | 98.99 | 51.46 | 44.58 | 87.31 |
25 | BACKUS JOHNSTO/d | 11.10 | 19.94 | 94.79 | 26.11 |
26 | UNACEM/d | 32.33 | 24.79 | 924.05 | 46.95 |
27 | ACEROS AREQUIP/d | 100.00 | 34.65 | 490.94 | 65.54 |
28 | SOUTHERN COPPE/d | 10.56 | 24.19 | 99.80 | 24.69 |
29 | VOLCAN MINERA/d | 100.00 | 36.03 | 2856.84 | 135.67 |
30 | BOLSA DE VALOR/d | 74.47 | 12.91 | 8.57 | 5.11 |
31 | CANDENTE/d | 95.77 | 97.53 | 5.06 | 35.67 |
32 | ENGIE ENER PER/d | 38.23 | 15.01 | 1776.72 | 14.15 |
33 | PERUV METALS/d | 87.10 | 77.51 | 12.57 | 126.69 |
34 | VOLCAN MINERA/d | 25.75 | 49.13 | 2.72 | 18.23 |
Information of companies of the I GENERAL-PERU.
No | Companies | FREF | DESV | VOLAM | LREA |
---|---|---|---|---|---|
1 | ECOPETROL/d | 10.97 | 22.50 | 15747.98 | 21.77 |
2 | EEB/d | 18.99 | 11.96 | 947.38 | 5.10 |
3 | GRUPOAVAL/d | 2.25 | 19.11 | 291.99 | 7.93 |
4 | PFAVAL/d | 47.19 | 14.68 | 4687.84 | 10.86 |
5 | BCOLOMBIA/d | 34.94 | 19.97 | 7916.54 | 18.41 |
6 | PFBCOLOM/d | 97.55 | 19.69 | 19438.60 | 22.98 |
7 | ISA/d | 36.49 | 15.20 | 3435.78 | 30.18 |
8 | BOGOTA/d | 10.46 | 20.16 | 743.53 | 1.17 |
9 | GRUPOSURA/d | 46.38 | 14.33 | 8267.88 | 6.77 |
10 | PFGRUPSURA/d | 89.93 | 14.63 | 5078.49 | 6.13 |
11 | GRUPOARGOS/d | 74.54 | 16.44 | 4273.79 | 17.41 |
12 | PFGRUPOARG/d | 92.98 | 15.17 | 3074.15 | 17.16 |
13 | CEMARGOS/d | 35.45 | 16.58 | 6257.46 | 19.90 |
14 | PFCEMARGOS/d | 92.02 | 14.26 | 2854.31 | 15.54 |
15 | NUTRESA/d | 41.06 | 10.62 | 3821.21 | 9.60 |
16 | PROMIGAS/d | 11.53 | 28.08 | 41.74 | 3.92 |
17 | EXITO/d | 35.39 | 12.76 | 9485.99 | 9.87 |
18 | CORFICOLCF/d | 36.26 | 24.75 | 3747.63 | −0.22 |
19 | CLH/d | 25.12 | 18.70 | 3531.05 | 9.27 |
20 | PFDAVVNDA/d | 92.40 | 15.97 | 7224.65 | 31.93 |
21 | CELSIA/d | 42.99 | 13.66 | 1293.18 | 36.29 |
22 | ETB/d | 10.57 | 18.28 | 551.91 | 14.50 |
23 | CNEC/d | 80.07 | 24.66 | 1183.16 | 50.88 |
24 | CONCONCRET/d | 26.21 | 18.30 | 322.47 | 2.74 |
25 | PFAVH/d | 100.00 | 35.04 | 3052.93 | 75.33 |
Information of companies of the COLCAP- COLOMBIA.
The most sustainable strategy to accomplish clean and efficient transport is to stimulate the automotive hybrid electric vehicles (HEVs)/electric vehicles (EVs) industry by developing the most advanced battery technologies. There is massive competition in the markets for selling batteries with different chemistry, especially between the most common nickel-metal hydride (Ni-MH), nickel-cadmium (Ni-Cad) and lithium-ion (Li-ion) batteries. More recently, it seems that the most promising future and great potential of development for HEVs/EVs automotive industry worldwide have the Li-ion batteries due to their advantages compared with other strong competitors on the market. They surpass these competitors by features such as lightweight, high-energy-density, little memory effect, and relatively low self-discharge, as is mentioned in [1, 2]. Furthermore, after substantial improvements and research investments, the Li-ion batteries have become safer and less toxic. The battery state of charge (SOC) represents the available capacity of the battery cell that changes corresponding to the fluctuations in the input charging and discharging current profile during a cycle. It is worth mentioning that the SOC plays a crucial role in keeping the battery safe for various operating conditions and significantly extending battery life [3, 4]. Moreover, the SOC is an essential internal battery parameter of great significance constantly monitored by the battery management system (BMS) [1, 2, 3, 4, 5, 6]. In real life, a specialized software package integrated onboard the vehicle estimates the value of the battery SOC due to the lack of an accurate measurement sensor integrated into BMS [1, 2, 3, 4, 5, 6, 7]. Let us see why the battery SOC has become a topic of great interest for researchers working in the field, very dedicated for developing the most suitable estimation techniques and strategies supported today by an impressive number of research papers published in the literature. The most used model-based Kalman filter (KF) can estimate the battery SOC with a high accuracy grade [3, 4, 5, 6, 7]. The BMS monitors the battery system through sensors and state estimation algorithms to detect any abnormalities during the battery system operation [8, 9]. The performance of the battery SOC estimators’ model is highly dependent on the battery model accuracy. If the battery model is accurate, then the different SOC estimation versions will estimate the battery SOC with the same accuracy. Consequently, the battery model is essential for implementing the most suitable SOC estimators. It is always desirable to get a battery model as accurate as the actual battery to reduce the mismatch between the model and the existing battery. Moreover, the battery SOC is “a critical factor
Additionally, over time, the effects of battery aging will be more noticeable in degrading its performance, and the mismatch between the battery model and the actual battery will also increase. In the “real-life” applications subjected to the plant/process identification, fixing the possible mismatches between the plant/process and their corresponding models with repeated effective re-identification procedures is almost inapplicable and time-consuming, as is revealed in [10, 11, 12]. Therefore, mismatch detection is essential for different plants/processes modeling and identification strategies to isolate defective submodules to avoid complete re-identification, as mentioned in [11].
A suitable identification plant/process strategy is developed in [10, 11, 12] that is a polynomial discrete state-space representation of the plant/process models based on a plant/process input-output measurement data set collected in an open loop. The plant/process input-output measurement data set is used to develop and implement two attractive statistical models.
The first model is a linear discrete state-space autoregressive exogenous input (ARX) polynomial representation, beneficial to model a 60 Ah LiFePO4 battery module [10]. Based on this model, an extended Kalman filter (EKF) battery SOC estimator is developed for BMSs. The second model is an auto-regressive moving average with exogenous input (ARMAX) model developed in [12]. The adaptability of ARX battery models developed in [10] for designing a robust and accurate EKF SOC estimator is rigorously assessed in the same reference [10]. Some simulation results indicate that the proposed EKF SOC battery module estimator based on the ARX model shows a “great performance” in terms of robustness and SOC accuracy [10]. Additionally, the proposed EKF battery estimator “increases the model output voltage accuracy, thereby having the potential to be used in real applications, such as EVs and HEVs” [10]. Two MIMO ARMAX models are developed in [12] for modeling and identification of heating, ventilation, and air-conditioning (HVAC) multi-input multi-output (MIMO) centrifugal chiller plant. This model is built and implemented in a MATLAB simulation environment to develop two accurate MIMO proportional integral-plus (PIP) control strategies in a closed loop for temperature control and refrigerant liquid control level. For comparison purposes in [11], ARX and ARMAX polynomial discrete-time plant representations are built as decorrelation models for detecting model-plant mismatch for a column distillation integrated into a model predictive control (MPC) strategy. Detailed simulations in [11] show that the ARMAX models provide:
Higher accuracy
Less computational complexity
Less processing power is required with less model order than ARX.
Moreover, in [12], ARMAX models are developed for an MIMO HVAC centrifugal chiller open-loop control system using the identification techniques presented in MATLAB Identification Toolbox [13]. Also, for the same HVAC plant, an MIMO ARMAX open-loop polynomial model helps implement an interesting closed-loop proportional integral-plus (PIP) control strategy of chiller plant temperature and liquid-level refrigerant. Both ARX and ARMAX models are helpful in [12] for implementing an extended MIMO PIP control strategy as a new modeling approach in a non-minimal discrete-time state-space system representation (NMSS). The MATLAB simulation results show a superior accuracy of the MIMO NMSS centrifugal chiller model compared with the ARMAX models. Therefore, the MIMO PIP closed-loop control strategy based on the MIMO NMMS models performs better than those built on the MIMO ARMAX models of the MIMO chiller plant, as is proved in [12, 13].
Taking advantage of the considerable advances in modeling, identification, and control systems developed in the field of literature, thanks to the latest achievements in artificial intelligence, statistics and machine learning, deep learning, signal process analysis, our research objectives diversify with new approaches. The most recent results in modeling and identification for various industrial applications reported in the literature field motivate us to investigate attractive new modeling approaches. Then remains to adapt these approaches to our research topic of developing new Li-ion battery models. Furthermore, the proposed Li-ion battery SOC estimator for a Rint SAFT model of 6 Ah and 11 V nominal voltage in the selected case study is expected to perform much better in terms of accuracy and robustness of the battery SOC estimates for different operating conditions [7]. For simulation and comparison results purposes, as a case study of Li-ion battery, a third-order resistor-capacitor (RC) equivalent circuit model (ECM) (in abbreviated notation 3RC ECM) is considered. It combines three parallel polarization circuits R-C connected in series with the battery’s internal resistance (Rint) and voltage source, i.e., as a similar 3RC ECM battery model developed in [7]. The model selection is suggested due to its simplicity and ability to describe the static and dynamic behavior of the Li-ion battery accurately.
Since the proposed Li-ion battery’s open-circuit voltage (OCV) has highly nonlinear dependence on the battery SOC, as an alternative block model developed in [7], it is an adaptive neuro-fuzzy inference system (ANFIS) model. It is a hybrid neuro-fuzzy technique that brings the learning capabilities of neural networks to fuzzy inference systems. The learning algorithm tunes the membership functions of a Sugeno-type fuzzy inference system using the training input/output data [14]. More precisely, the learning algorithm teaches the ANFIS to map the input (current driving cycle profile) to the Li-ion battery SOC and terminal voltage through training. At the end of the training, the trained ANFIS network would have learned the input-output map and be ready to be deployed into the Kalman filter SOC estimator solution. The architecture, design, and implementation of the proposed ANFIS battery model are developed in an attractive MATLAB R2021b simulation environment [14, 15, 16]. This new battery model adjusts the design techniques and guidelines inspired from [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33] to the selected model adopted in the case study from [7]. The accuracy of the ANFIS battery model has a significant impact on the SOC Li-ion battery Kalman Filter estimator accuracy performance built on this model. Its effectiveness is proved through extensive simulations and comparisons conducted on the same MATLAB platform. In this research, our motivation for using adaptive neuro-fuzzy training of Sugano-type fuzzy inference system (ANFIS) modeling comes from the preliminary results obtained for similar investigations on the impact of nonlinearities and uncertainties actuators [18]. The ANFIS modeling is well documented in the most recent MATLAB release versions that use the fuzzy logic toolbox and fuzzy inference tuning procedure [14, 15, 16]. Handy tutorials of using ANFIS modeling architectures are presented in [14, 15, 16, 17]. For MATLAB implementation and simulation intent, as well as “proof concept” in this research, the accuracy of the Li-ion battery ANFIS model is tested for a battery urban dynamometer driving schedule (UDDS) input current profile.
In the proposed case study, for both ARX and ANFIS models an adaptive EKF (AEKF) SOC estimator is adopted attached to Li-ion battery used for creating fault detection and isolation (FDI) control strategies in [12], preferred for its simplicity, SOC accuracy, real-time implementation capability, and robustness. Its robustness is tested for four different scenarios, such as to changes in SOC initial values (guess values), ranging 70–40%, 20, 90, and 100%, to federal test procedure for 75 F (FTP-75) degree Fahrenheit driving cycle profile test, changes in measurement-level noise (from 0.001 to 0.01), to changes in the battery capacity value from 6 Ah to 4.8 Ah due to aging effects, and changes in internal resistance due to temperature effects, and also for simultaneous changes [7, 29]. Based on a rigorous performance analysis of SOC residual error compared with the similar results reported in the literature with a typically 2% error, in some situations, the AEKF estimator SOC residual error reached values smaller than 1%, such as shown in [29]. Since of the lack of data in the literature field for similar situations developed in our research for Li-ion battery, it is not easy to make a state-of-the-art analysis of the results reported in the literature related to Li-ion battery SAFT 6 Ah and 11 V nominal voltage AEKF SOC estimators based on ANFIS models analysis. The overall ANFIS battery model consists of two ANFIS models, the first one attached to the battery Rint-3RC active part and the second to OCV(SOC) nonlinear block. The SOC and terminal voltages accuracy of the overall battery ANFIS model and AEKF SOC estimators, as well as their robustness to changes in the initial values of the battery SOC from 70 to 40%, are proved in this research paper based on extensive simulations conducted on MATLAB R2021b platform.
Based on MATLAB simulation results useful information on SOC and battery terminal voltage accuracy can be extracted based on SOC and terminal voltage residuals and based on four statistic criteria values shown in eight tables, defined in [29, 30], and grouped as:
Root mean squared error (RMSE)
Mean squared error (MSE)
Mean absolute error (MAE)
Mean average percentage error (MAPE)
For each Li-ion battery model developed in this research, the SOC and terminal voltage performance are evaluated by simulations conducted on MATLAB Simulink platform. The information extracted from these simulations is beneficial for a rigorous comparison of performance, so that the reader has a better perspective on the modeling, design, and implementation of the battery. From a variety of battery models, the reader has the ability to decide which model and estimator are best for a particular application.
The paper is organized as follows: Section 2 gives a brief description of Rint Li-ion SAFT 6 Ah 11 V nominal voltage and battery selection for the case study, model option, and its validation using the National Renewable Energy Laboratory (NREL) ADVANCED SIMULATOR (ADVISOR) 2003 for HEVs and EVs design. An equivalent electric circuit model (ECM) for the Li-ion battery SAFT Rint model is preferred due to its simplicity and ability to capture all the battery dynamics such that its SOC and the predicted terminal cell voltage are of high accuracy. The dynamics battery part ARX and ANFIS models, the ANFIS battery OCV(SOC) model, their order selection, parameters identification and model implementation, ANFIS models’ generation and performance, and the MATLAB simulation results are shown and discussed in Sections 2 and 3, respectively. For the ARX model and hybrid combinations of the ARX model and ANFIS model of OCV(SOC) nonlinear block, an overall ANFIS battery model consisting of ANFIS dynamic battery part model and ANFIS OCV(SOC) nonlinear battery block models are considered. A rigorous analysis of AEKF SOC estimator adaptability for all these models is evaluated based on simulation results conducted on MATLAB R2021b and Simulink environments that provide valuable information on SOC and battery terminal voltage accuracy and robustness performance. Also, a comparison of the evaluation of the results is made based on the SOC and battery voltage residuals and statistics criteria values summarized in almost eight tables in the last subsection 2.1.3 of Section 2 and all Section 3. Section 4 is dedicated to Conclusions supporting all the previous MATLAB results. In summary, in this research paper an impressive number of investigations are done on the accuracy and adaptability of six alternative Li-ion batteries models to the original NREL Li-ion Rint SAFT-type 6 Ah 11 V rated voltage battery model. The main reason of this selection is that the Li-ion batteries are very common in a wide variety of HEVs/EVs applications in the automotive industry, Also, it is a beneficial option to be used as a baseline model of Li-ion battery for performance comparison and validation of alternative models, among them the linear, simple, and accurate 3RC ECM Li-ion battery model developed in this research work. This alternative model is designed by using one of the most used designing tools very spread in the automotive industry, created by NREL, known as ADVANCED SIMULATOR (ADVISOR) with the final launch in 2003 for HEVs/EVs design. The Li-ion battery model 3RC ECM model is selected for simulation purpose and “proof” concept for developing new alternative battery models. Five alternative models that derivate from 3RC ECM Li-ion battery model are developed in this research work: (a) ARX-ECM that models the dynamic part of the battery represented by the Rint-3RC circuit using an equivalent ARX model; (b) ANFIS-ECM replacing the Rint-3RC circuit with an ANFIS model; (c) ARX-ANFIS hybrid structure that is a combination of ARX model for Rint-3RC dynamic part and an ANFIS model of the nonlinear static block OCV (SOC); (d) Rint-3RC -ANFIS model that keeps the dynamic part of the battery combined with the ANFIS model of OCV(SOC) static block; (e) full ANFIS model structure derived from ARX-ANFIS, which replaces the ARX model with an ANFIS model. The MATLAB simulation results for each model provide a large database stored in 10 useful tables for a rigorous analysis of the performance of the Li-ion battery in terms of SOC accuracy and terminal voltage, as well as the robustness of the AEKF algorithm for estimating the SOC of the selected battery.
In this section, we focus our attention on the Li-ion battery selection for the case study, its description, and developing the most suitable battery model of high accuracy. The selected model is validated through an impressive number of simulations conducted on the MATLAB R2021b platform. Then will compare the MATLAB simulations result to an experimental test performed in a similar MATLAB environment integrated with a specialized simulator ADVISOR for batteries design of different chemistry, which is very spread in the automotive industry. National Renewable Energy Laboratory (NREL) developed this simulator in 1983 and improved its performance until the last release in 2003-00-r0116. A 6 Ah nominal capacity and 11 V nominal voltage SAFT Li-ion battery integrated into the hybrid electric vehicle (HEV) BMS structure, namely a Japanese Toyota Prius, one of the most spread commercial hybrid electric cars on the automotive industry market. It is equipped with an MC-AC75 motor of 75 kW and a powertrain control version, TX-5SPD manual transmission with five speeds and frontal wheel drive, as shown in Figure 1. In this figure on the right side, the reader can see the type of vehicle in the database of the ADVISOR simulator considered one of its primary inputs; also, at the bottom side is shown the open-circuit voltage (OCV) graph of the proposed SAFT Li-ion battery as the most suitable for the HEV car selected in the case study.
The ADVISOR 3.2 simulator -input interface set up.
Figure 2 shows the Simulink diagram of the Toyota Prius HEV car configuration. In Figure 3, you can see the graphical user interface that selects the urban dynamometer driving schedule (UDDS) of cycle speed profile for a driving test, the initial temperature, state of charge (SOC), and the ambiental conditions.
Block diagram of HEV powertrain configuration.
Graphical user interface with the ADVISOR simulator parameters.
In Figure 4, the MATLAB simulations results are displayed. Among these results two of the variables of interest can be emphasized, such as the UDDS driving cycle input current profile (ess_current) and the battery SOC evolution (ess _soc_hist).
MATLAB input-output simulation results.
The UDDS driving cycle of the current profile shown in Figure 5 is the equivalent of the UDDS driving cycle speed profile shown in the graph in Figure 3. It represents the evolution of the input battery current during a repeated sequence of charging and discharging the battery for different periods.
The input UDDS driving cycle current profile (battery charging and discharging periods).
More precisely, the ADVISOR 3.2 Simulator provides an extensive Database of different types of HEV cars, driving cycles speed tests, and input currents profiles for battery charging and discharging cycles.
For simulation purposes and “proof concept,” a starting point for developing new Li-ion battery alternative models might be a linear electrical circuit consisting of one of the combinations of an open-circuit voltage (OCV) controlled source, known in the literature as Thevenin voltage source, connected in series with the internal resistance (Rint) of the battery, followed by one, two, or three parallel resistive and capacitive polarization cells (RC). These combinations lead to a simple electrical equivalent circuit models (ECMs) very spread in the literature field as is shown in Figure 6 [7]. Until now, the ECMs proved that they are of the high simplicity and are the most suitable models to capture the battery’s dynamic electrochemical behavior and increase the model’s accuracy. Since in Figure 6, the ECM has three parallel RC bias polarization cells, it is known in the literature field as a three-order RC (3RC) ECM Li-ion battery model. The ECM schematic is built using the Multisim 14.1 software package provided by the well-known National Instruments (NI) company. The first R1pC1p polarization parallel cell captures the fast transient of the battery compared with the last two RC cells that capture only the slow steady state with a great impact in the increase of the battery model accuracy [7]. Since most HEV/EV technologies are very dependent on batteries nowadays, it is crucial for developing and implementing accurate Li-ion battery models. These models must suit better the BMS requirements to be easily deployed on-board power simulators and electronic on-board power systems. Moreover, the 3RC ECM accuracy performance is a baseline for all other alternative battery models developed in this research paper for comparison purposes. For MATLAB simulation’s goal, a similar setup for the 3RC ECM Li-ion battery model parameters used in [7], shown in Table 1 or directly on the electrical schematics from Figure 6, is considered to prove the effectiveness and the robustness of an adaptive extended Kalman filter SOC estimation strategy, similar to those used in [9] for a generic Li-ion cobalt battery and adapted to the 3RC ECM model, presented in Appendix A. This setup is achieved from a generic ECM by changing only the values of the model parameters in state-space equations.
Electrical schematic of third-order 3RC ECM battery selection (see [
Item | Parameters/Coefficients | Symbol | Value | Unit Measure |
---|---|---|---|---|
1 | Li-ion battery ECM parameters | |||
1.1 | Internal ohmic resistance | Rint+ | 13.333 | mΩ (milliohm) |
1.2 | First cell polarization resistance | Rp1 | 0.65 | mΩ |
1.3 | Second cell polarization resistance | Rp2 | 1.06 | mΩ |
1.4 | Third cell polarization resistance | Rp3 | 0.2 | mΩ |
1.5 | First cell polarization capacitance | Cp1 | 5847.08 | F (Farad) |
1.6 | Second cell polarization capacitance | Cp2 | 47719.07 | F |
1.7 | Third cell polarization capacitance | Cp3 | 8.99e9 | F |
2 | Li-ion battery OCV coefficients | |||
2.1 | k0 | 11.38 | ||
2.2 | k1 | 3.86e-5 | ||
2.3 | k2 | 0.24 | ||
2.4 | k3 | 0.22 | ||
2.5 | k4 | 0.04 |
The Li-ion battery 3RC ECM model parameters and the OCV nonlinear model coefficients are given in Tables 1 and 2. The OCV shown in Figure 7 is a nonlinear function of SOC that combines three additional well-known models, namely Shepherd, Unnewehr universal and Nernst (SUN-OCV) models, defined in [3, 5, 7, 9] with the coefficients set at same values as in [3, 7, 9].
Criteria indices | Acronyms | Values | |
---|---|---|---|
ARX-ECM | ANFIS combined model | ||
IC1 | RMSE | 8% | 2.2% |
IC2 | MSE | 0.64% | 0.051% |
IC3 | MAE | 4.17% | 1.1% |
IC4 | MAPE | 8.12% | 1.73% |
Performance of the Li-ion AEKF SOC ARX model compared with AEKF SOC estimator ANFIS model for UDDS driving cycle test [7].
Battery terminal Rint-3RC ECM voltage versus ARX – ECM for the dynamic part of the battery.
According to the values of the parameters and coefficients set in the Table 1 the Li-ion battery model dynamics is described by the following discrete-time Eqs. [7]:
where
A Simulink model based on these previous equations is shown in Figure 8a, compact in compact form, and in Figure 8b, for a detailed form.
(a) The Simulink model of the 3RC ECM Li-ion battery in compact form; (b) the detailed Simulink model of the 3RC ECM Li-ion battery.
The MATLAB simulation results are shown in Figure 9. In Figure 9a and b are depicted the SOC of the battery 3RC model versus SOC estimated by the ADVISOR simulator. In Figure 9c and d are presented the OCV = f(SOC) curve and the battery SOC for a complete UDDS discharge cycle respectively. In Figure 9e is shown only the terminal voltage for a single UDDS cycle. The SOC residual represented in Figure 9b reveals a good SOC accuracy performance of the 3RC EMC battery model with respect to the estimated battery SOC on the ADVISOR simulator integrated with the MATLAB platform. This excellent result is a realistic argument that validates certainly the proposed 3RC ECM Li-ion battery attached to the generic Rint model of SAFT-type battery.
(a) Li-ion battery 3RC ECM model SOC versus battery ADVISOR simulator estimated; (b) SOC residual error; (c) OCV = f(SOC) curve for 5 hours full discharge of the battery to a UDDS driving multi-cycles; (d) SOC for a full battery discharge SOC; (e) SOC for a single UDDS driving cycle discharging input profile (1370 seconds).
The main goal of this section is to estimate the battery SOC and analyze the AEKF SOC estimator accuracy compared with the actual value of the battery model validated in the previous section. It is essential to prove that an accurate battery model in terms of SOC and terminal voltage is vital for building the most accurate SOC estimator. To accomplish this goal, an adaptive extended Kalman filter (AEKF) SOC estimator is adopted in this research, encouraged by the preliminary results obtained in [9, 29] by using the same AEKF estimator for a similar application. The SOC estimator implementation is performed on the MATLAB R2018b platform, and the simulation results are depicted in Figure 10. Also, in Figure 10a is shown the SOC AEKF estimator accuracy and its robustness to changes in the SOC initial value from SOCini = 0.7 to SOCini = 0.4, compared with the 3RC ECM model values. In Figure 10b, the battery model terminal voltage is compared with the AEKF estimate of the terminal voltage. Both Figure 10a and b reveal that the SOC AEKF estimator performs well with high SOC accuracy, evaluated also based on their errors shown in Figure 10c and d.
(a) SOC voltage; (b) battery terminal voltage versus AEKF estimated. (c) Terminal voltage residual error; (d) SOC residual error.
An alternative to the battery model is a linear polynomial model in discrete-time state-space representation, namely an autoregressive with exogenous terms (ARX) model, which captures the dynamics impact on the series circuit Rint (internal battery resistance) and all three RC polarization cells. The linear discrete-time polynomial model ARX is one of the simplest models that incorporate the stimulus input signal to capture some stochastic dynamics as part of the 3RC ECM dynamics. Since the OCV = f(SOC) curve is of high nonlinearity, an ANFIS model is also assessed. A hybrid battery model structure ARX Rint-3RC ECM – ANFIS OCV(SOC) model will be developed as a challenge in this valuable research for the reader to have a good insight on OCV(SOC) impact on battery SOC accuracy. Finally, a combined ARX Rint-3RC ECM – ANFIS OCV(SOC) model structure is investigated. The Simulink diagram with all these alternative modeling techniques is shown in Figure 8. To build a single-input single-output (SISO) ARX model, the system identification MATLAB toolbox and Simulink are the most precious tools [13]. Also, for good documentation, a piece of valuable information about ARX models is provided in the references [10, 11, 12, 13, 31]. MATLAB’s arx command is helpful to generate and estimate the models’ parameters from the input-output data sets. This MATLAB command is a routine based on a prediction-error least-squares method and specified polynomial orders to estimate the parameters of ARX polynomial discrete-time models. The model properties include covariances (parameter uncertainties) and goodness of fit between the estimated and measured data. Fundamental work on systems identification is done in [31]. The MATLAB implementation and simulations of SISO polynomial ARX models can be performed on any recent MATLAB platforms available online at www.mathworks.com/help/ident/ref/arx.html [13]. A “trial and error” procedure is considered to select the most suitable ARX model order. This procedure is repetitive until the best match of the data set is found, provided by the following status indicators:
Fit to data estimation (prediction focus)
Final prediction error (FPE)
Mean square error (MSE),
where
where the voltages
where
Sample time: 1 seconds, Parameterization: polynomial orders:
0<
The ARX – ECM model of 3RC ECM dynamic part voltage- MATLAB simulation result for validation.
Battery terminal voltage residual error.
According to Eq. (4) and Eq. (6), an overall discrete-state space representation for the integrated ARX model structure of 3RC ECM SAFT Li-ion battery model can be written as follows:
where
where
(a) The battery SOC true values versus SOC AEKF estimated values and SOC ADVISOR estimate for SOCini = 40%.; (b) AEKF battery terminal voltage versus battery ARX ECM and 3RC ECM models terminal voltages; (c) SOC battery residual between ARX model SOC and AEKF SOC estimator; (d) terminal voltage error between ARX model and AEKF estimator.
As an alternative to 3RC ECM and ARX battery models, the ANFIS modeling techniques are based on specific MATLAB commands provided by fuzzy logic toolbox and based on fuzzy inference tuning procedures [14, 15, 16]. The Sugeno-type inference system FIS is tuned based on an input-output training data set collected in open-loop from 3RC ECM Li-ion battery model. From our most recent preliminary results in the Li-ion battery field, modeling and SOC estimators disseminated in [12, 25, 26], an interesting state-of-the-art analysis of similar SOC AEKF estimators performance reported in the literature is done in terms of statistical performance criteria values, such as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), standard deviation (std), mean fundamental percentage error (MAPE), and R2 (R-squared). Among three SOC Li-ion battery estimators, the AEKF, adaptive unscented Kalman filter (AUKF), and particle filter (PF) SOC estimators, the AEKF proved that is the most suitable for HEVs applications [29].
A specific MATLAB function
Step 1: Set up the driving cycle profile for Li-ion battery as input u, and the battery SOC (y1) and terminal voltage (y2) as battery outputs; The Li-ion battery input–output measurements data set will be collected from a 3RC ECM original battery model given by Eq. (1)–Eq. (7) from previous section through several extensive simulations conducted on MATLAB platform.
Step 2: Generate the ANFIS model grid partition method–based options using the specific MATLAB function:
options = genfisOptions(‘GridPartition’)
options. NumMembershipFunctions = 5 or greater than this
Step 3: Construct the FIS input attached to the battery SOC and terminal voltage
in_fis1 = genfis (u, y1, options)
in_fis2 = genfis (u, y2, options)
Step 3: Select for training the ANFIS model options
options = anfisOptions.
options. InitialFIS1 = in_fis1
options. InitialFIS2 = in_fis2
options. EpochNumber = 20 or greater to get a reasonable accuracy
Step 4: Construct the FIS output attached to the battery SOC and terminal voltage
out_fis1 = anfis ([u y1], options)
out_fis2 = anfis ([u y2], options)
Step 5: Plot the input-output measurements data set versus input-output of both ANFIS models
plot (u, y1, u, evalfis (u, out_fis1))
plot (u, y2, u, evalfis (u, out_fis2))
legend (‘trainingData’,‘ANFIS Output’).
The previous steps must be adapted to generate both ANFIS models of the Rint-3RC ECM dynamic part and the OCV(SOC) nonlinear function. The MATLAB simulation results are depicted in Figure. Figure 14a presents the ANFIS Rint-3RC ECM dynamic part model output and voltage training data set measurements, and Figure 14b shows only the ANFIS model output. The impact on battery terminal voltage accuracy using the ANFIS -ECM model compared with ARX -ECM developed in the last Section 2.1.4 is shown in Figure 14c. The accuracy of ANFIS -ECM model is revealed in Figure 14d, which presents the battery terminal voltage residual.
(a) ANFIS ECM dynamic part model output and voltage training data set measurements; (b) Rint-3RC ECM dynamic part ANFIS model yd. circuit output; (c) terminal battery voltage Vbat with Rint-3RC ECM dynamic part ANFIS model (ANFIS-ECM); (d) terminal voltage residual error.
Also, for building some interesting Li-ion SAFT battery structures, an ANFIS model is developed for OCV(SOC) nonlinear function block in Figure 15a for training data phase, and in Figure 15b for OCV(SOC) ANFIS output model. Both ANFIS models are based on a repeated UDDS driving cycles input current profile for almost 5 hours to assure a large interval of input-output data set measurements for SOC, OCV, and battery dynamic part voltage. The impact of OCV(SOC) ANFIS block on battery terminal voltage accuracy based on 3RC ECM is revealed in Figure 15c and d. In Figure 15c it is very difficult to distinguish between ECM battery terminal voltage graph and the second one ECM Li-ion battery terminal voltage that integrates the OCV(SOC) block ANFIS model due to the high ANFIS OCV(SOC) model block accuracy. The reader can have a better insight on the battery terminal voltage accuracy in Figure 15d that reveals a very small battery terminal voltage residual error compared with the ARX ECM dynamic part battery model terminal voltage shown in previous Figure 14d.
(a) OCV = f(SOC) ANFIS model versus OCV measurements; (b) OCV = f(SOC) ANFIS model; (d) ECM Li-ion SAFT battery terminal voltage versus ECM-OCV(SOC) ANFIS model (Rint-3RC -ANFIS) terminal battery voltage; (e) ECM Li-ion SAFT battery terminal voltage residual error versus ECM-OCV(SOC) ANFIS model (Rint-3RC – ANFIS) integrated structure.
Let us discuss why the ANFIS battery integrated model is the most suitable to build hybrid integrated battery Li-ion structures in terms of high accuracy.
An exciting hybrid battery Li-ion structure can incorporate into the ARX ECM dynamic part model and an ANFIS OCV(SOC) nonlinear block model. The MATLAB simulations result of the Li-ion battery hybrid structure is presented in Figure 16a and b.
(a). ECM Li-ion SAFT hybrid structure – ARX Rint-3RC dynamic part and ANFIS OCV (SOC) nonlinear block model (ARX-ANFIS; (b) Li-ion SAFT battery terminal residual voltage error for hybrid structure.
A rigorous analysis of MATLAB simulation results from Figure 15c and Figure 15d shows a high battery terminal voltage accuracy compared with the battery hybrid structure, as can be seen in Figure 16a and b.
The last combined battery structure consists of two ANFIS models, the first one for Rint-3RC ECM active battery part and the second one that replaces the Li-ion SAFT ECM SUN OCV(SOC) nonlinear block with an ANFIS model block. The MATLAB simulation results are depicted in the Figure 17a and b.
(a) ECM Li-ion SAFT battery terminal voltage versus ECM ANFIS combined structure terminal voltage; (b) ECM Li-ion SAFT battery terminal residual voltage error for ECM ANFIS combined structure.
The voltage accuracy performance revealed by simulation results from Figure 17a and b seems to be better than the previous hybrid ARX and ANFIS battery structure. Still, it is slightly inferior compared with the design that integrates only the ANFIS model for SOC(OCV) nonlinear battery block.
For simplification purpose and SOC and battery terminal voltage accuracy, as alternative Li-ion 3RC ECM structure required to implement the AEKF SOC estimator on a MATLAB R2021b platform is considered the ANFIS 3RC ECM SAFT Li-ion battery model consisting of Rint-3RC ECM dynamic part block, and second ANFIS model attached to OCV(SOC) nonlinear block. The overall simplified ANFIS 3RC ECM battery model structure is described by the following equations:
In all the MATLAB simulations for implementing the AEKF SOC estimator are considered the following parameters values:
SOC initial value = 0.4,
Covariance of estimated value of SOC, Phat = 1e-10,
Covariance process noise Qw = 0.01,
Measurement noise Rv = 0.001.
α =0.791, r = 5.
The MATLAB simulations results are presented in Figure 18a–c. Similar to ARX model developed in previous chapter 2, in Figure 18 the robustness of AEKF SOC estimator to changes in the battery SOC initial values from SOCini = 0.7 to SOCini = 0.4 is shown. In Figure 18b the predicted values of battery terminal and OCV voltages cell by AEKF and ANFIS are compared with 3RC ECM true values.
(a) Robustness of ANFIS AEKF SOC estimator to changes in SOC initial values from SOCini = 0.7 to SOCini = 0.4; (b) the ANFIS 3RC ECM Li-ion battery OCV voltage accuracy.
The battery SOC and terminal voltage accuracy are revealed in Figure 19a and b, respectively, based on SOC and battery terminal voltage residuals.
(a) The ANFIS 3RC ECM Li-ion battery SOC residual error; (b) the ANFIS 3RC ECM Li-ion battery terminal voltage residual error with respect with the battery terminal voltage estimated by AEKF.
Based on the information accessible from the battery SOC and terminal voltage residual errors presented in the first two subsections of Section 3, more precisely the MATLAB simulation results and the statistics criteria values RMSE, RSE, MAE, MAPE, collected in Table 2, can be made a rigorous performance analysis of both ANFIS models and AEKF SOC estimator.
The performance analysis is made on the information provided by the battery terminal voltage residuals errors. The MATLAB simulation results reveal a battery terminal voltage prediction accuracy for ANFIS Rint-3RC ECM dynamic part model of an absolute residual error less than 0.0 3 volts and greater than −0.04 volts, compared with ARX model of same structure that is situated in the range (−0.2, 0.15) volts. The voltage error of OCV(SOC) ANFIS model is very small ranged inside the interval (−1.5 × 10e-4, 1.5 × 10e-4) volts. For the ANFIS combined structure (ANFIS-ANFIS), the residual error remains in the same range as Rint-3RC ECM dynamic part model, i.e., (−0.04, 0.03) volts.
The performance analysis is made on the information provided by the battery SOC and terminal voltage residuals errors shown in Figure 19a and b. During the steady state, more precisely after 347 seconds, the SOC residual error is less than 1% smaller than usual SOC residual error of 2% value reported in the literature field. Since the SOC residual error of AEKF based on ARX ECM battery model that is less than 1% during the steady state after 600 s, as is shown in Figure 13c, is obviously that the AEKF based on ANFIS battery model performs better. For a complete information about the suitability of AEKF SOC estimator based on ANFIS battery model is built in the Table 2, which incorporates all the statistics of criteria values RMSE (IC1), MSE (IC2), MAE (IC3), and MAPE (IC4), the most common criteria that have been used in the literature field to measure model performance and select the best model from a set of potential candidate models [7, 29, 30]. Comparing both statistics criteria values (third and fourth columns) is straightforward that the AEKF SOC estimator based on ANFIS battery model performs better than AEKF SOC estimator based on ARX battery model.
By comparing the terminal battery voltage residuals shown in Figure 12 for AEKF based on ARX model and AEKF based on ANFIS model depicted in Figure 19b, they are ranged inside the intervals (−0.2, 0.3) volts and (−0.01, 0.08) volts, respectively; thus, the second SOC estimator based ANFIS battery model performs better than the first one. Based on the performance analysis of SOC accuracy, robustness to changes in SOCini value and terminal battery voltage prediction accuracy it can conclude that the AEKF SOC estimator based on ANFIS model is the most suitable SOC estimator for HEVs/EVs applications.
A system-level flowchart/flow diagram is shown in Figure 20. It indicates the major steps involved in the key sections of the last two chapters to provide an overview of the differences in steps between generic ECM, ARX ECM, ANFIS ECM, hybrid (ARX-ANFIS), and combined (ANFIS-ANFIS) models.
System-level flowchart diagram.
A detailed description for each small block of the overall diagram of the models along with a flow of the equations is also considered in the overall diagram shown in Figure 20.
This research paper has opened a new Li-ion battery modeling research direction in the HEV BMS applications field by performing several investigations on ARX and ANFIS alternative accurate battery models with a high impact on improving the battery SOC estimators’ accuracy and their robustness, design, and real-time implementation in MATLAB and Simulink environments.
The effectiveness of the modeling and SOC estimation strategies is demonstrated through an extensive number of simulations in a MATLAB R2021b software environment. The preliminary simulation results are encouraging, and extensive investigations will be done in future work to extend the applications area. The performance analysis from the last section reveals that ANFIS battery models overpass the second-order linear ARX polynomial battery model in terms of SOC and terminal voltage accuracy and by their capability and suitability to simplify the battery model structure and build robust and accurate SOC Li-ion battery estimators with a high terminal voltage prediction accuracy. The AEKF SOC estimator accuracy based on combined ANFIS model structure is also very accurate compared with AEKF SOC estimator based on ARX dynamic part model with the SOC absolute value lower than 1%, better than the usual 2% SOC value reported in the literature field. Both alternative models are based only on the measurement input-output data set collected by a data acquisition (DAQ) system incorporated in the BMS of HEVs. Besides, the battery SOC and output voltage signals’ accuracy is not affected by noise as long as the AEKF SOC estimator is very robust.
The authors declare no conflict of interest.
electric vehicle
hybrid electric vehicle
battery management system
Federal test procedure at 75 F
Urban Dynamometer Driving Schedule
open-circuit voltage
Shepherd, Unnewehr universal and Nernst open-circuit voltage
state of charge
autoregressive exogenous
ADVISOR National Renewable Energy Laboratory Advanced Vehicle Simulator
adaptive extended Kalman filter
adaptive neuro-fuzzy inference system
equivalent circuit model
model predictive control
ECM third-order RC ECM
ECM third-order internal resistance RC ECM
fault detection isolation
root mean square error
mean square error
mean absolute error
mean absolute percentage error
standard deviation
R-squared
Rint-3RC replaced by ARX model
OCV(SOC) block replaced by ANSIM model
Rint-3RC replaced by ARX and OCV (SOC) by ANFIS (hybrid structure)
Rint-3RC replaced by ANFIS and OCV (SOC) by ANFIS (combined structure)
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He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. 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His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. 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Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:6,paginationItems:[{id:"82526",title:"Deep Multiagent Reinforcement Learning Methods Addressing the Scalability Challenge",doi:"10.5772/intechopen.105627",signatures:"Theocharis Kravaris and George A. 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He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). 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In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. 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The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. 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She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. 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He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"117248",title:"Dr.",name:"Andrew",middleName:null,surname:"Macnab",slug:"andrew-macnab",fullName:"Andrew Macnab",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"322007",title:"Dr.",name:"Maria Elizbeth",middleName:null,surname:"Alvarez-Sánchez",slug:"maria-elizbeth-alvarez-sanchez",fullName:"Maria Elizbeth Alvarez-Sánchez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",country:{name:"Mexico"}}},{id:"337443",title:"Dr.",name:"Juan",middleName:null,surname:"A. 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A dynamic career research platform which is based on the thematic areas of comparative vertebrate physiology, stress endocrinology, reproductive endocrinology, animal health and welfare, and conservation biology. \nEdward has supervised 40 research students and published over 60 peer reviewed research.",institutionString:null,institution:{name:"University of Queensland",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null,series:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517"},editorialBoard:[{id:"258334",title:"Dr.",name:"Carlos Eduardo",middleName:null,surname:"Fonseca-Alves",slug:"carlos-eduardo-fonseca-alves",fullName:"Carlos Eduardo Fonseca-Alves",profilePictureURL:"https://mts.intechopen.com/storage/users/258334/images/system/258334.jpg",institutionString:null,institution:{name:"Universidade Paulista",institutionURL:null,country:{name:"Brazil"}}},{id:"191123",title:"Dr.",name:"Juan José",middleName:null,surname:"Valdez-Alarcón",slug:"juan-jose-valdez-alarcon",fullName:"Juan José Valdez-Alarcón",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSBfcQAG/Profile_Picture_1631354558068",institutionString:"Universidad Michoacana de San Nicolás de Hidalgo",institution:{name:"Universidad Michoacana de San Nicolás de Hidalgo",institutionURL:null,country:{name:"Mexico"}}},{id:"161556",title:"Dr.",name:"Maria Dos Anjos",middleName:null,surname:"Pires",slug:"maria-dos-anjos-pires",fullName:"Maria Dos Anjos Pires",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS8q2QAC/Profile_Picture_1633432838418",institutionString:null,institution:{name:"University of Trás-os-Montes and Alto Douro",institutionURL:null,country:{name:"Portugal"}}},{id:"209839",title:"Dr.",name:"Marina",middleName:null,surname:"Spinu",slug:"marina-spinu",fullName:"Marina Spinu",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRLXpQAO/Profile_Picture_1630044895475",institutionString:null,institution:{name:"University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca",institutionURL:null,country:{name:"Romania"}}},{id:"92185",title:"Dr.",name:"Sara",middleName:null,surname:"Savic",slug:"sara-savic",fullName:"Sara Savic",profilePictureURL:"https://mts.intechopen.com/storage/users/92185/images/system/92185.jfif",institutionString:'Scientific Veterinary Institute "Novi Sad"',institution:{name:'Scientific Veterinary Institute "Novi Sad"',institutionURL:null,country:{name:"Serbia"}}}]},onlineFirstChapters:{paginationCount:18,paginationItems:[{id:"83115",title:"Fungi and Oomycetes–Allies in Eliminating Environmental Pathogens",doi:"10.5772/intechopen.106498",signatures:"Iasmina Luca",slug:"fungi-and-oomycetes-allies-in-eliminating-environmental-pathogens",totalDownloads:0,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Animal Welfare - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11579.jpg",subseries:{id:"19",title:"Animal Science"}}},{id:"82991",title:"Diseases of the Canine Prostate Gland",doi:"10.5772/intechopen.105835",signatures:"Sabine Schäfer-Somi",slug:"diseases-of-the-canine-prostate-gland",totalDownloads:8,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Recent Advances in Canine Medicine",coverURL:"https://cdn.intechopen.com/books/images_new/11580.jpg",subseries:{id:"19",title:"Animal Science"}}},{id:"82773",title:"Canine Transmissible Venereal Tumor: An Infectious Neoplasia in Dogs",doi:"10.5772/intechopen.106150",signatures:"Chanokchon Setthawongsin, Somporn Techangamsuwan and Anudep Rungsipipat",slug:"canine-transmissible-venereal-tumor-an-infectious-neoplasia-in-dogs",totalDownloads:17,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Recent Advances in Canine Medicine",coverURL:"https://cdn.intechopen.com/books/images_new/11580.jpg",subseries:{id:"19",title:"Animal Science"}}},{id:"82797",title:"Anatomical Guide to the Paranasal Sinuses of Domestic Animals",doi:"10.5772/intechopen.106157",signatures:"Mohamed A.M. 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