Common inorganic and organic arsenic species [5].
\\n\\n
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
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"8068",leadTitle:null,fullTitle:"Cytotoxicity - Definition, Identification, and Cytotoxic Compounds",title:"Cytotoxicity",subtitle:"Definition, Identification, and Cytotoxic Compounds",reviewType:"peer-reviewed",abstract:"Compensating for cytotoxicity in the multicellular organism by a certain level of cellular proliferation is the primary aim of homeostasis. In addition, the loss of cellular proliferation control (tumorigenesis) is at least as important as cytotoxicity, however, it is a contrasting trauma. With the disruption of the delicate balance between cytotoxicity and proliferation, confrontation with cancer can inevitably occur. This book presents important information pertaining to the molecular control of the mechanisms of cytotoxicity and cellular proliferation as they relate to cancer. It is designed for students and researchers studying cytotoxicity and its control.",isbn:"978-1-78984-755-0",printIsbn:"978-1-78984-754-3",pdfIsbn:"978-1-83962-286-1",doi:"10.5772/intechopen.77899",price:119,priceEur:129,priceUsd:155,slug:"cytotoxicity-definition-identification-and-cytotoxic-compounds",numberOfPages:118,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"20a09223d92829b5478b5f241f6a03ce",bookSignature:"Erman Salih Istifli and Hasan Basri Ila",publishedDate:"October 2nd 2019",coverURL:"https://cdn.intechopen.com/books/images_new/8068.jpg",numberOfDownloads:6585,numberOfWosCitations:7,numberOfCrossrefCitations:9,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:17,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:33,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 14th 2018",dateEndSecondStepPublish:"January 30th 2019",dateEndThirdStepPublish:"March 31st 2019",dateEndFourthStepPublish:"June 19th 2019",dateEndFifthStepPublish:"August 18th 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"179007",title:"Dr.",name:"Erman Salih",middleName:null,surname:"Istifli",slug:"erman-salih-istifli",fullName:"Erman Salih Istifli",profilePictureURL:"https://mts.intechopen.com/storage/users/179007/images/system/179007.JPG",biography:"Dr. Erman Salih İstifli received his Ph.D. from Biology Department of Cukurova University, Insitute of Science and Letter. In his doctoral study, Dr. İstifli focused on the elucidation of the genotoxic and cytotoxic effects of a commonly used anticancer agent (antifolate) on human lymphocytes. During his period of doctoral research, he joined the molecular cytogenetics group at the Max Planck Institute for Molecular Genetics in Berlin, Germany, and he focused there on investigating the molecular cytogenetic causes of some human rare diseases. During these studies, he contributed experimentally to the identification of four candidate genes (GRIA2, GLRB, NPY1R, and NPY5R) responsible for intelligence and obesity. He was assigned as an expert and rapporteur on eight candidate projects in the Marie-Sklodowska Curie-Actions Innovative Training Networks in 2016. In 2017, he completed the online theoretical and practical course 'Introduction to Biology - The Secret of Life', run by the Massachusetts Institute of Technology (MIT) on the edX platform. In April 2019, within the framework of Erasmus+ staff mobility program, he gave seminars on 'DNA microarrays and their use in genotoxicity' at Tirana University in Tirana, Albania. He is a published author of several articles in journals covered by the SCI and SCI-E, and has manuscripts in other refereed scientific journals. He currently serves as a referee in several journals covered by the SCI and SCI-E. His studies mainly fall into the field of genetic toxicology. He continues his current research on the structural biology of COVID-19 as well as identification of novel plant-based hit compounds in the treatment of Alzheimer’s disease.",institutionString:"Çukurova University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Cukurova University",institutionURL:null,country:{name:"Turkey"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"246832",title:"Prof.",name:"Hasan",middleName:"Basri",surname:"İla",slug:"hasan-ila",fullName:"Hasan İla",profilePictureURL:"https://mts.intechopen.com/storage/users/246832/images/system/246832.jpg",biography:"Prof. Dr. Hasan Basri İla received his Ph.D. from the Biology Department of Çukurova University, Institute of Sciences and Letters. During his doctoral study, Dr. İla investigated the effects of a commonly used antibiotic on chromosome aberration and micronucleus formation by in vivo tests. He actively took responsibility for 19 national projects as a researcher and/or project leader, and has numerous poster and/or oral scientific presentations in several international conferences. He has also actively been giving lectures on biology, cytology, genetics, evolution, organelle genetics, and cancer genetics. Dr. İla has several publications in internationally indexed journals (SCI, SCI-E), and his articles have been cited 636 times.",institutionString:"Cukurova University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Cukurova University",institutionURL:null,country:{name:"Turkey"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1208",title:"Medical Toxicology",slug:"medical-toxicology"}],chapters:[{id:"66173",title:"In Vitro Cytotoxicity Screening as a Criterion for the Rational Selection of Tear Substitutes",doi:"10.5772/intechopen.85106",slug:"in-vitro-cytotoxicity-screening-as-a-criterion-for-the-rational-selection-of-tear-substitutes",totalDownloads:1044,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"A large number of artificial tears are currently available in the pharmaceutical market. Selecting the right drug for the patient remains a challenge for both the doctor and the patient. Comparing the cytotoxicity of artificial tears is one of the criteria for the rational selection of a drug that promotes maximum clinical efficacy and a higher safety profile. It is known that cells grown in vitro retain many metabolic features of the parent host tissues and at the same time lack tissue and organ interrelations and regulatory effects of the nervous and endocrine systems and have very limited compensatory capabilities. These features of cell cultures provide an opportunity to investigate the interaction of chemical agents directly with the cell itself, to identify changes in cellular and subcellular structures that can be masked in whole-organism settings. This study presents the results of assessing the cytotoxicity of tear substitutes, which demonstrate that these drugs can have a cytostatic effect in vitro and differ in their cytotoxic potential. In recent years, the problem of drug therapy of patients with dry eye syndrome has been attracting increasing attention of ophthalmologists, so screening the cytotoxicity of a wide range of tear substitutes using cell culture-based test systems can promote the rational selection of these drugs.",signatures:"Olga I. Aleksandrova, Igor N. Okolov, Julia I. Khorolskaya, Natalia A. Mikhailova, Diana M. Darvish and Miralda I. Blinova",downloadPdfUrl:"/chapter/pdf-download/66173",previewPdfUrl:"/chapter/pdf-preview/66173",authors:[null],corrections:null},{id:"66560",title:"Study of the Cytotoxic Activity of Haarlem Oil on Different Cell Lines and a Higher Organism, Steinernema feltiae",doi:"10.5772/intechopen.85467",slug:"study-of-the-cytotoxic-activity-of-haarlem-oil-on-different-cell-lines-and-a-higher-organism-em-stei",totalDownloads:928,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Haarlem oil (HO) is a semisynthetic product made by combining terpene oil and sulfur atoms at high temperatures. HO contains organosulfur compounds; these compounds are known to have strong antioxidant properties, such as superoxide dismutase (SOD). This study provides a brief overview of the effects of HO cytotoxicity on several cell lines using several cytotoxicity test methods. The crystal violet (CV) staining assay showed that HO had a strong toxic effect on the A549 cell line. The test results of the trypan blue and celltiter-Glo assay methods showed that HO has a strong cytotoxic effect on HL-60 cells. The results of the MTT and XTT assays indicated that HO produced a fairly strong toxic effect on HL-60 cells and U937 cells. A hoechst staining assay showed that HO was able to increase (induce) apoptotic cell levels and reduce mitotic cell levels after 24 hours of incubation. However, in this study, we were not able to detect any effect of HO on activation and inhibition of the K562 cell line through the NF-κB pathway. Meanwhile, the live and dead assay showed that HO tends to cause apoptosis. The nematicidal assay showed that HO showed moderate activity against Steinernema feltiae.",signatures:"Khairan Khairan, Torsten Burkholz, Mareike Kelkel, Vincent Jamier, Karl-Herbert Schäfer and Claus Jacob",downloadPdfUrl:"/chapter/pdf-download/66560",previewPdfUrl:"/chapter/pdf-preview/66560",authors:[null],corrections:null},{id:"67165",title:"Cytotoxic Activity of Essential Oils of Some Species from Lamiaceae Family",doi:"10.5772/intechopen.86392",slug:"cytotoxic-activity-of-essential-oils-of-some-species-from-lamiaceae-family",totalDownloads:1008,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Cancer is considered one of the most lethal diseases in the world, with a prevalence of 439.2 cases and 163.5 deaths per 100,000 inhabitants, in the period from 2011 to 2015; this disease has a greater impact in underdeveloped countries. For the treatment of this disease, a combination of chemotherapy with surgery or radiation is generally used, however, it is not exempt from adverse effects or resistance of the tumor to this type of treatment, for this reason the search for new treatments is constant. The plants are a possible source to achieve this; Lamiaceae is a family of plants widely distributed on the planet and has been used traditionally for the treatment of different diseases, and various essential oils with the potential for cancer treatment have been isolated from this species. The scope of this review is to present 46 essential oils isolated from different species of Lamiaceae which have been tested against different cancer cell lines.",signatures:"Cuauhtémoc Pérez-González, Julia Pérez-Ramos, Carlos Alberto Méndez-Cuesta, Roberto Serrano-Vega, Miguel Martell-Mendoza and Salud Pérez-Gutiérrez",downloadPdfUrl:"/chapter/pdf-download/67165",previewPdfUrl:"/chapter/pdf-preview/67165",authors:[{id:"224593",title:"Dr.",name:"Salud",surname:"Pérez-Gutiérrez",slug:"salud-perez-gutierrez",fullName:"Salud Pérez-Gutiérrez"},{id:"287822",title:"Dr.",name:"Roberto José",surname:"Serrano-Vega",slug:"roberto-jose-serrano-vega",fullName:"Roberto José Serrano-Vega"},{id:"287825",title:"Dr.",name:"Cuauhtemoc",surname:"Pérez-González",slug:"cuauhtemoc-perez-gonzalez",fullName:"Cuauhtemoc Pérez-González"},{id:"287826",title:"Dr.",name:"Julia",surname:"Pérez-Ramos",slug:"julia-perez-ramos",fullName:"Julia Pérez-Ramos"},{id:"300158",title:"Dr.",name:"Carlos Alberto",surname:"Méndez-Cuesta",slug:"carlos-alberto-mendez-cuesta",fullName:"Carlos Alberto Méndez-Cuesta"},{id:"300160",title:"Mr.",name:"Miguel",surname:"Martell-Mendoza",slug:"miguel-martell-mendoza",fullName:"Miguel Martell-Mendoza"}],corrections:null},{id:"68041",title:"Cytotoxic Effect and Mechanisms from Some Plant-Derived Compounds in Breast Cancer",doi:"10.5772/intechopen.87177",slug:"cytotoxic-effect-and-mechanisms-from-some-plant-derived-compounds-in-breast-cancer",totalDownloads:1161,totalCrossrefCites:3,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Breast cancer (BrC) is a major health problem in women all around the world. A growing knowledge about these alterations and their associated molecular signaling pathways offers opportunities for therapeutic strategies; chemotherapy is one of the most utilized treatments; however, because of the adverse side effects and multidrug resistance that patients may present, there has been great advancement in search of new alternatives as the use of plant-derived natural compounds. This review describes information on the progress and development of cytotoxic compounds against BrC belonging to the families of flavonoids, terpenes, and alkaloids that through in vitro and in vivo studies have demonstrated to induce cellular death mainly through apoptosis, activating the intrinsic pathway. The in vitro IC50 and the in vivo EC50 dose-response relationship can vary depending on various factors, including the choice of cell line and/or the model used. Also, the association of some of these compounds with nanoparticles or paclitaxel with antibodies has clearly shown a potential improvement in its effect. The clinical studies that are being conducted with some of them show promising results; however, it is necessary to continue with the effort to develop new and more effective drugs against different types of BrC.",signatures:"Elvia Pérez-Soto, Cynthia Carolina Estanislao-Gómez, David Guillermo Pérez-Ishiwara, Crisalde Ramirez-Celis and María del Consuelo Gómez-García",downloadPdfUrl:"/chapter/pdf-download/68041",previewPdfUrl:"/chapter/pdf-preview/68041",authors:[null],corrections:null},{id:"67746",title:"Apoptotic Inhibitors as Therapeutic Targets for Cell Survival",doi:"10.5772/intechopen.85465",slug:"apoptotic-inhibitors-as-therapeutic-targets-for-cell-survival",totalDownloads:900,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:0,abstract:"Apoptosis has revealed an essential function in the development or prevention of oncogenic transformation in the body; however, programmed cell death (PCD) must be tightly controlled since deregulated cell death is involved in the development of a large number of different pathologies. Apoptosis can be decreased in pathological states such as in cancer and autoimmunity or elevated such as in stroke, neurodegeneration, retinal cell death, myocardial and liver ischemia, inflammatory diseases such as sepsis, osteoarthritis (OA), rheumatoid arthritis (RA), and asthma. Different types of apoptotic inhibitors will be discussed in this chapter displaying their mechanism of action, which have been reported to be therapeutic targets for cell survival or at least limiting cell death. These inhibitors are classified according to their nature into natural antiapoptotic proteins that present mainly in the cell and synthetic small molecule inhibitors that are widely used to protect against overexpression of apoptosis mediators and, in turn, to prevent corresponding diseases.",signatures:"El-Shimaa Mohamed Naguib Abdelhafez, Sara Mohamed Naguib Abdelhafez Ali, Mohamed Ramadan Eisa Hassan and Adel Mohammed Abdel-Hakem",downloadPdfUrl:"/chapter/pdf-download/67746",previewPdfUrl:"/chapter/pdf-preview/67746",authors:[null],corrections:null},{id:"68419",title:"Cell Division, Cytotoxicity, and the Assays Used in the Detection of Cytotoxicity",doi:"10.5772/intechopen.88368",slug:"cell-division-cytotoxicity-and-the-assays-used-in-the-detection-of-cytotoxicity",totalDownloads:1544,totalCrossrefCites:3,totalDimensionsCites:6,hasAltmetrics:1,abstract:"Cell division is a phenomenon that is encountered in all cells in nature. While normal cell division results in proliferation in single-celled organisms, and development and repair in multicellular organisms, aberrant and untimely cell division results in tumor formation. Therefore, the understanding of the cell division is hidden in identifying the details of the molecular mechanisms that govern cellular division at the exact time and under right conditions. Sometimes these molecular mechanisms are distorted by both intrinsic and extracellular factors, and the division process halts or deviates to an abnormal pathway. At this point, it is essential that the abnormal cells are removed from the tissue by an appropriate mechanism. In this context, in this review, general and specific information about cell division and its molecular control mechanisms were discussed, and different types of cell death mechanisms were mentioned accordingly. In addition, chemical, biological, and physical cytotoxic agents that negatively affect cell division and their mechanisms of action are explained. Finally, a brief review of the principles of different cytotoxicity (cell viability and proliferation) test systems has been performed to provide a source of information for investigators who study cell viability, proliferation, or different types of cellular death pathways.",signatures:"Erman Salih Istifli, Mehmet Tahir Hüsunet and Hasan Basri Ila",downloadPdfUrl:"/chapter/pdf-download/68419",previewPdfUrl:"/chapter/pdf-preview/68419",authors:[{id:"179007",title:"Dr.",name:"Erman Salih",surname:"Istifli",slug:"erman-salih-istifli",fullName:"Erman Salih Istifli"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"6969",title:"Lymphocytes",subtitle:null,isOpenForSubmission:!1,hash:"1aa8ac01c934ebdeedd5d7813036beef",slug:"lymphocytes",bookSignature:"Erman Salih Istifli and Hasan Basri 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A metalloid is a chemical element that has properties of both metals and nonmetals. Arsenic is from all its features mostly recognized as a poison. Arsenic has a complex chemical behavior since it exists in four different oxidative states [1]. Depending on oxidative state and presence in environment, arsenic species exhibit different toxicity [2]. Arsenic species can be present in all types of environment and can originate from natural and anthropogenic sources [3]. Natural sources of arsenic are: rocks with incorporated arsenic compounds, activity of volcanoes and some biological processes. Anthropogenic sources are numerous, from mining to different types of production (pesticides, wood preservatives, and pigments). When the arsenic compounds reach groundwater, it is hard to distinguish the origin, both natural and anthropogenic arsenic species are released [3].
According to Science Direct, during the last decade, a significant number of scientific papers reporting the results from arsenic investigations are presented in Figure 1. The focus of these researches was the development and improvement of methods for arsenic detection, extraction, separation and removal.
The number of publications with keyword arsenic, according to Science Direct.
The investigation of arsenic species and their behavior in various samples, especially in natural waters and environment is important for chemistry and environmental protection. The most common arsenic species are presented in Table 1
Arsenic species | Oxidation state | Chemical formula | Group | Presence in the environment |
---|---|---|---|---|
As(V) | +5 | AsO4−3 | iAs | Water |
As(III) | +3 | AsO3−3 | ||
MMA | +5 | CH3AsO(OH)2 | oAs | |
DMA | +5 | (CH3)2AsO(OH) | ||
TMAO | +5 | (CH3)3AsO | Seafood (fish, mussels) | |
TETRA | +3 | (CH3)4As+ | ||
AsB | +3 | (CH3)3As+CH2COO− | ||
AsC | +3 | (CH3)3As+CH2CH2OH |
Common inorganic and organic arsenic species [5].
Depending on the oxido-reduction conditions, microbiological environment, arsenic species can be present in water in solution or in a precipitated form, and they can also adsorb or desorb from the existing precipitates [1, 2]. When arsenic species are soluble in water, they can be present in both inorganic and organic forms. For iAs species both As(III), arsenite, and As(V), arsenate, can be present. For oAs species, MMA and DMA are soluble forms of organic arsenic species. From the value of the chemical equilibrium constants for each molecular or ionic form of arsenic in water, the present species can be recognized [3]. When choosing and analyzing the most dominant form of arsenic in water, the most present is inorganic arsenic as As(V). If As(III) is present, there are two important things that need to be taken into account. As(III) is more poisonous (even at low concentrations) than As(V). Beside the severe toxic effect, As(III) is easily oxidized. In oxidized conditions, stable forms of arsenic are As(V), and MMA and DMA, from oAs species. Many water sources in the world containing high concentration of arsenic cause health problems or diseases such as cancer. The WHO provisional guideline value for arsenic in drinking water is 10 μg L−1 [4]. Water quality analysis usually do not include test on arsenic. Arsenic compounds are colorless and odorless.
Once the presence of arsenic is determined in water, the separation and removal is obligatory. Removal technologies that are efficient, but still need improvement include absorption, precipitation, different electrochemical processes, membrane and hybrid membrane processes [6, 7, 8, 9].
Arsenic enters the water through the dissolution of minerals, ores soil, sediments, water, living organisms and rocks containing high concentrations of arsenic. Drinking water from surface water bodies usually does not contain high concentrations of arsenic. Higher concentrations have only been found in the groundwater. Human activities influence and change the content of arsenic in nature. When using arsenic compounds for different purposes, there is a direct influence. There is also indirect influence that affects the mobility of arsenic from different natural sources. Organic arsenic compounds such as AsB, AsC, TETRA, TMAO, arsenosugars and arsenic-containing lipids are mainly found in marine organisms although some of these compounds have also been found in terrestrial species.
Despite the fact that iAs species are predominant in natural waters, the presence of oAs has also been reported. Even though the main analytical interest is to determine total arsenic in water, it is also important to develop the procedures for As species determination, separation, and removal. The distribution of i As and oAs species is a function of pH value of water [2].
The distribution of arsenic species vs. pH values of water is presented in Figure 2 [2].
The distribution of iAs and oAs species as a function of pH values of water [
As(III) species: H3AsO3, H2AsO3−, HAsO32− and AsO33−, are stable under slightly reducing aqueous conditions. As(V) species: H3AsO4, H2AsO4−, HAsO42− and AsO43−, are stable in oxygenated waters [6]. Two valences of the same element, molecular (ortho, H3AsO3, H3AsO4 and meta forms, HAsO2, HAsO3) and ionic forms with different charges make the research of arsenic removal from water more challenging and indivisible of arsenic chemistry knowledge. Any arsenic removal technology strongly depends on the water conditions and the stability of arsenic forms in the water.
Bearing in mind the fact that arsenic occurs in water in molecular and ionic form depending on water pH, the main goal of many investigations is to select the most efficient exchanger, not only in terms of efficiency, but also in terms of applicability in the wide range of water pH values in real and environmentally friendly water treatment systems. In neutral conditions, As(V) species are completely in ionic form (H2AsO4− and HAsO42−), while As(III) is in molecular (H3AsO3 or HAsO2), as shown in Figure 2 [2].
There are a variety of chemical methods from classical to contemporary analytical techniques that are used for determination of arsenic and arsenic species in water.
There has been several review articles on the speciation of arsenic in a variety of samples [10, 11, 12, 13, 14]. These reviews focus on (1) determination of total content of arsenic and (2) speciation analysis.
A review of contemporary methods for arsenic and arsenic species in water is presented in Table 2. The parameters, as detection limit, advantages and disadvantages are pointed out in order to have an insight into ability and application of available techniques.
Methodology | Detection | Detection limit (μg L−1) | Advantages | Disadvantage | Ref. |
---|---|---|---|---|---|
ICP-AES | Total arsenic | ~30 | Minimal sample volume; no sample pretreatment and short measurement time | Expensive; needs lot of knowledge for operating and interpretation of data | [14] |
ICP-MS | Total arsenic | ~0.1 | Approved by US EPA | Spectral and matrix interferences | [11, 13, 19] |
GF-AAS | Total arsenic | ~0.025 | Approved by US EPA | — | [3, 14] |
HG-AAS | Total arsenic and arsenic speciation | 0.6–6.0 | Approved by US EPA | — | [14] |
HPLC-HG-AAS | Total arsenic and arsenic speciation | 1–47 | No need for sample pretreatment | — | [3, 14] |
HPLC-HF-AAS | Arsenic speciation | 0.05–0.8 | Rapid, inexpensive. No need for sample pretreatment | — | [3, 14] |
IC-ICP-MS | Arsenic speciation | 0.01 | No need for sample pretreatment | — | [19] |
HPLC-ICP-MS | Total arsenic | 0.01 | No need for sample pretreatment | — | [13] |
A review of contemporary methods for arsenic and arsenic species determination in water.
The total concentration of arsenic in drinking water (mostly traces of arsenic, level of μg L−1 or less) can be detected only by sophisticated analytical techniques as ICP-MS, GF-AAS and HG-AAS [3, 14]. For As speciation analysis, well-established methods that involve the coupling of separation techniques, such as HPLC with a sensitive detection system, that is, ICP-MS, are recommended, and they are mostly used [13].
Historically,
To perform speciation analysis properly, the best option is coupling of two analytical techniques. One technique is used for the separation of all chemical forms of arsenic that are present in water, and the other is used for the detection of these species. Besides coupling analytical techniques, there are necessary steps for complete analysis of arsenic. The first one is the extraction of arsenic, which has to be both mild and effective, at the same time. The second step is separation of various forms of arsenic species. The final step is the measuring step which gives the answer to the quantification of each present arsenic compound.
Analytical methods for determining different arsenic species have become increasingly important due to different toxicity and chemical behavior of various arsenic forms. Methods that involve the coupling of separation techniques, such as IC and HPLC with a sensitive detection system, such as ICP-MS, HG-AFS, HG-AAS and GF-AAS [3, 11, 13, 14, 19]. HPLC has been a preferred technique used for separation of arsenic compounds. Coupled with ICP-MS for determination, as HPLC-ICP-MS system it is a method of choice for separation and measurements all arsenic species in water. In addition, applying IC coupled with ICP-MS, it is possible to separate and estimate arsenic species in water: iAs(III), iAs(V), DMA, MMA, AsBet. A representative result is presented in Figure 3 [19].
Determination of five arsenic species by IC-ICP-MS. Mobile phase: NaOH [
The evaluation of analytical method is based on defining: selectivity, repeatability, accuracy, specific features of the method and defining the limits of detection and quantification (LoD and LoQ). These limits, these numbers give the information on the smallest concentration that can be detected and quantified with certain accuracy that has been defined [10]. The LoD was discussed and determined for the induced coupled plasma-mass spectrometry (ICP-MS) measurements of arsenic [11]. Thorough analysis has shown that the best option for LoD would be experiments, which would include the repetition many times. If experiments would be repeated 100 times, it is expected that only five measurements would be inadequate. Although this is ideal, the time consumption for the repetitive measurements is not acceptable. The most important conclusions were that LoD is not permanent and constant value, and it has to be verified and adopted for each new case. LoD is a basic parameter for estimation of the LoQ. It was concluded in [11] that the traditional (IUPAC) method is the one that could be applied.
Different methods can be applied for arsenic removal from water. Arsenic (V) is more effectively removed than As(III) by both conventional and nonconventional methods. Pretreatmen (preoxidation) of As(III) to As(V) is an essential step for better removal [2]. Methods that have been successfully applied in water treatment plants are: precipitation and coprecipitation, electrochemical (such as electrocoagulation), ion exchange and MST (reverse osmosis, ultrafiltration and other membrane techniques) [6, 7, 8, 9, 20, 21].
A wide range of sorbent materials for aqueous arsenic removal has been tested and used: biological materials, mineral oxides, activated carbons and polymer resins. Even some agricultural and industrial by-products such as red mud, fly ash, waste iron slag from steel production plant and waste filter sand from water treatment plant, have proved to be good and inexpensive arsenic sorbents [6, 7]. The potential use and application of industrial wastes in water treatment is in favor of the eco-friendly concept that preserves natural resources and supports the reuse-recycle concept. The technology of arsenic adsorption is based on materials which have a high affinity for dissolved arsenic. Adsorption of arsenic by iron modified sorbents has been established by several authors [6, 7]. There are numerous scientific and professional investigations with intention to develop a small and efficient system for arsenic removal based on natural and artificial sorption materials [20, 21]. Large amount of chemicals used for
A step forward has been made by investigations that were devoted to the evaluation of selective multifunctional sorbents including ion-exchange resins for SPE and chromatographic columns connected with a sensitive measurements system [2]. The need to determine As species in water resulted in developing new materials for arsenic separation and removal. A simple procedure for selective separation (in pretreatment) of arsenic species in water using chemically modified and unmodified ion-exchange resins is presented in Figure 4 [2].
Procedure for selective separation arsenic species in water using ion-exchange resins [
For separation of As species in water, two types of resins, strong base anion exchange resin (SBAE), hybrid resins (HY) and hybrid resin chemically modified (HY-Fe and HY-AgCl), were tested and used. The HY-Fe resin retained all arsenic species except DMAs(V). This is recognized as an advantage because this makes direct measurement of this species in the effluent possible. The HY-AgCl resin retained all iAs, which was convenient for direct determination of oAs species in the effluent. The selective bonding of arsenic species on three types of resins, as shown in Figure 4, has been established as the procedure which enables the separation and calculation of all arsenic species in water [2].
EC comprises complex chemical and physical processes involving many surface and interfacial phenomena. Very effective and perspective EC process consists of three processes: electrochemical reactions (simultaneous anodic oxidation and cathodic reduction), flotation and coagulation [9, 20]. The EC process relies on the generation of metal ions from electrodes. The electrodes can be made of iron, aluminum or zinc, depending on the most favorable reactions for arsenic removal. The reaction in reaction chamber starts after the application of direct current. The electrode (metallic anode) dissociates into valent metallic ions. The metallic ions migrate to oppositely charged ions and the precipitation of different insoluble salts occur (different sulfides, oxides, hydroxides, chromates or phosphates, depending on the presence of ions in water). EC has several advantages when compared to other methods. The construction of reaction chamber is compact, control of the process is simple, no additional chemicals are required, and the result is reduced amount of sludge. If the electrode is made of iron, ferric hydroxide is one of the main solid products, as shown in Eq. (1) [9]:
Arsenate co-precipitates or adsorbs to Fe(OH)3(s), as shown in Eq. (2).
The potential of EC as an alternative water treatment technique to remove arsenic from water needs to be realized [8, 9, 20].
Ion-exchange, IE, processes with regeneration capability is a proven, efficient and low-cost treatment method for the exchange of arsenic in the As(V) form [1, 2]. The ion-exchange reaction between As(V) and a bed of chloride-form SBAE resin (designated as R-Cl resin) occurs as presented by Eq. (3):
When the regeneration of resins is needed, both HCl and NaCl can be applied. Still, with HCl solution, more efficient regeneration occurs because the ionic forms of arsenic (anions) transform to molecular form (H3AsO4). Molecular forms do not affect the equilibrium of ion-exchange processes as illustrated by Eq. (4):
Different sorption processes, from adsorption, to chemisorption and ion-exchange, have shown a potential being efficient and cheap (depending on the selected sorbent). With improved, more selective and chemically modified sorbents, the extraction technique can be replaced [17, 18, 19]. What has been specifically used as an advantage for arsenic species separation is different behavior of arsenic species at various pH values [3, 22].
The hybrid resin (HY) that has successfully been applied uses the activity of the hydrated iron oxides (HFO) and anion exchange for selective separation of arsenic [2]. With integrated use of anion exchange and sorption, the separation of As(III) and As(V) species and removal of all species of arsenic can be accomplished. With application of HY resin, two separate things can be accomplished: the collection and preconcentration of low concentrated iAs or the removal of iAs species, if it is interfering the determination.
Membrane separation technologies, such as RO, NF, UF, MF, can be employed in the removal of arsenic from water. Depending on the removal efficiency, RO and NF are more efficient than UF and MF. Operating conditions, membrane material, water quality, temperature, pressure, pH value and chemical compatibility have to be considered during operation of a membrane plant. When MF and UF are applied, less amounts of chemicals are used, and therefore, less sludge is produced. When RO and NF are used, no chemicals are needed and the amount of sludge is neglectable [8].
The comparison and future perspective of different technologies for arsenic removal are presented in Table 3.
Technology for arsenic removal | Advantage | Disadvantage | Some specific feature | Future perspective |
---|---|---|---|---|
Adsorption | Cheap materials, effective and efficient removal | Further treatment for regeneration and consumption of chemicals | Additional filter for removal of fine particles is required | Still attractive as an efficient and cheap technology for As removal. Finding new, environmentally friendly sorbent is still a challenging task |
Chemical coagulation | Effective for industrial wastewater treatment plants and efficient for As(V) removal | Chemical required. pH adjustment needed. Large volumes of sludge that needs further treatment | Arsenic leaching out from sludge | Not attractive as a solution, only if it coupled with electrochemical techniques |
Electrocoagulation | Efficient for arsenic removal. Low maintenance costs. No chemicals or pH adjustment. Low operating costs | Applicable only on batch scale. Passive oxide films for on the electrode. High energy consumption | No generation of secondary pollutants | Attractive for future investigations. Need to overcome the lack of application on a large scale |
Ion exchange | Efficient for As(V) removal. Exchange resins are available; the selective resins for removing arsenic are one of the most important requirements to provide high removal. Together with hybrid solution is an excellent technology | Interference with other ions. Easily blocked. Huge amount of chemicals | Using this kind of technique depends on the pH values of water | Attractive only if selective and sensitive chemical agents are included in ion-exchange process |
Membrane technologies | Efficient in arsenic removal. No chemical reagents. No sludge. Small dimensions for membrane treatment plant. Easy automation and control | Removal of arsenic depends on the pressure, pH value, solute concentration, temperature of feed solution | Arsenic is concentrated in the retentate | Attractive in future perspective. With decrease of investment the MST will prevail in arsenic removal technologies. Different membrane materials and processes need to be evaluated to select the optimum for each situation |
The comparison and future perspective of different technologies for arsenic removal.
Arsenic contamination of water has been reported as a critical issue in many articles, which reflects the latest state-of-the-art understanding of the behavior and toxicity of various arsenic species. Many water sources in the world contain low concentration of arsenic (mostly traces of arsenic, level of μg L−1 or less). If the concentration of arsenic in drinking water is higher than 10 μg L−1, which is the WHO provisional guideline value for arsenic, it causes various health problems. All arsenic compounds dissolved in water are toxic. In natural waters, arsenic appears most often in inorganic forms and to a lesser extent in organic form. Inorganic species, arsenic acids (H3AsO3 and H3AsO4) and their ions are more toxic than organic forms. In addition, As(III) species are more toxic than As(V) ones. The valence (+III and +V), the type of arsenic species, ionic or molecular forms are dependent on the oxidation–reduction condition and pH of the water. Arsenic in water occurs in both inorganic and organic forms, but inorganic species are predominant in natural waters. In neutral conditions, As(V) species are completely in ionic form (H2AsO4− and HAsO42−), while As(III) is in molecular form (H3AsO3 or HAsO2).
Arsenic compounds are colorless and odorless, and testing water for arsenic is an important strategy for the health and well-being of people. Working with a water professional to monitor and maintain the quality of the well and water supply is an important responsibility.
In this work, methods for arsenic and arsenic speciation separation, determination and removal were reviewed. There are numerous methods for separation and determination of arsenic species in water. It is very important to recognize easy, simple and inexpensive methods to estimate the very low concentrations of arsenic.
The total concentration of arsenic in drinking water can be detected by simple Gutzeit method, and some similar colorimetric methods of comparing stains produced on treated paper strips. Although its minimum detectable concentration is 1.0·μ L−1, these tests should be used when only a qualitative or semiqualitative detection is needed.
For precise, and reliable determination of arsenic in water, only sophisticated analytical techniques as ICP-MS, GF-AAS and HG-AAS can be applied. These methods are approved by US EPA. The features of these methods are high sensitivity, high accuracy, minimal sample volume; no sample pretreatment and short measurement time with minimum detectable concentration of 0.1 μ L−1. They are expensive, need lot of knowledge for operating and interpretation of data.
For As speciation analysis, well-established methods that involve the coupling of separation techniques, such as HPLC with a sensitive detection system, that is, ICP-MS, are recommended, and they are mostly used. Through the limits, it is possible to define the smallest concentration of analyte that can be reliably detected and quantified. Limit of detection for the HPLC-ICP-MS system is 0.001 μ L−1. This system is also expensive and needs lot of knowledge for operating and interpretation of data.
In all works, a special attention is paid to the preservation of arsenic species in environmental water samples for reliable speciation analysis. An appropriate procedure for the extraction of arsenic species from water should be accomplished without changing any original state of arsenic. This is still a challenging topic for research. The proposed system showed themselves to be accurate, precise and time efficient, as just a very simple sample treatment is required. Successful application of all methods required considerable practice.
Sorption processes (ion exchange, adsorption, chemisorption) with regeneration capability are proven as efficient and low-cost treatment methods for the removal of arsenic species from water. Separation of arsenic species using these new selective and chemically active sorbents recognize as a cost- and time-saving alternative to the traditional extraction techniques. The major drawback of all these techniques is that they are unable to remove As(III) efficiently.
Membrane separation technologies, such as RO, NF, UF, MF, are recommended for the removal of arsenic from water in water treatment plants.
Although there are numerous research papers focused on extraction techniques, yet it is not possible to set universal extraction procedures. These procedures depend on the presence of different species as well as on the type of matrices. For arsenic speciation, the choice of the most appropriate method is of great importance for obtaining reliable and accurate results.
The authors are grateful to the Ministry of Education and Science of the Republic of Serbia which supported our scientific work (projects no. TR37009, TR37010 and III43009).
As | arsenic |
iAs | inorganic arsenic |
oAs | organic arsenic |
As(III) | arsenite ion |
As(V) | arsenate ions |
MMA | monomethylarsenic acid |
DMA | dimethylarsenic acid |
TMAO | trimethylarsine oxide |
TETRA | tetramethylarsonium ion |
AsB | arsenobetaine |
AsC | arsenocholine |
IC | ion chromatography |
HPLC | high-performance liquid chromatography |
MS | mass spectrometry |
AES | atomic emission spectrometry |
ICP-MS | inductively coupled plasma-mass spectrometry |
ASV | anodic stripping voltammetry |
CSV | cathodic stripping voltammetry |
DPCSV | differential pulse cathodic stripping voltammetry |
GF-AAS | graphite furnace absorption spectrometry |
HG-AAS | hydride generation atomic absorption spectrometry |
HPLC-HG-AAS | high-performance liquid chromatography-hydride generation-atomic absorption spectrometry |
HPLC-HG-AFS | high-performance liquid chromatography or solid-phase cartridge separation combined with hydride generation-atomic fluorescence spectrometry |
HPLC-ICP-MS | high-performance liquid chromatography-inductively coupled plasma-mass spectrometry |
SPE | solid phase extraction |
IE | ion exchange |
SBAE | strong base anion exchange resin |
HY | hybrid resin |
EC | electrocoagulation |
RO | reverse osmosis |
NF | nanofiltration |
UF | ultrafiltration |
MF | microfiltration |
MST | membrane separation technologies |
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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