\r\n\tDespite enormous efforts, crop production is compromised due to a number of biotic and abiotic factors hence, the crop demands more attention of breeders, geneticists, biotechnologies, and farmers to improve its sustainable production. Modern approaches like next-generation sequencing, proteogenomics, and genetic engineering provide a wider scope for breeders, geneticists, physiologists, biotechnologists, genomicists, and agronomists for genetic improvement, production, weed, pests, and disease management.
\r\n
\r\n\tThis book will provide a broader platform to discuss issues, advancement in the technologies, and solutions to the problems of the sugarcane industry. Hence, the key features of the book that will broadly be, but not be limited to: \r\n\t * Production Technology and Advancements \r\n\t * Breeding and Molecular Breeding \r\n\t * Tissue Culture and propagation \r\n\t * Pests and Diseases and their Management \r\n\t * Genomics, Genetics, and Biotechnology \r\n\t * Whole-Genome Sequencing and QTL mapping \r\n\t * Genetic transformation and trait development, etc
",isbn:"978-1-83968-936-9",printIsbn:"978-1-83968-935-2",pdfIsbn:"978-1-83968-937-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"a7016e06fe42b55cf41818b7947bf9e5",bookSignature:"Prof. Muhammad Sarwar Khan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10364.jpg",keywords:"Sugarcane, Biotechnology, Production, Breeding, Genotyping, DNA-Based Molecular Markers, Salinity, Drought, Insects, Disease, Biotic and Abiotic Stress Management, DNA Markers",numberOfDownloads:30,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 29th 2020",dateEndSecondStepPublish:"October 27th 2020",dateEndThirdStepPublish:"December 26th 2020",dateEndFourthStepPublish:"March 16th 2021",dateEndFifthStepPublish:"May 15th 2021",remainingDaysToSecondStep:"4 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"A leading plant biotechnologist who innovated expression of oxygen-loving green fluorescent protein (GFP) in plant chloroplasts and pioneered plastid transformation in rice and sugarcane. He earned his Ph.D. from the University of Cambridge, UK and he has published over 100 articles in high impact journals.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"212511",title:"Prof.",name:"Muhammad Sarwar",middleName:null,surname:"Khan",slug:"muhammad-sarwar-khan",fullName:"Muhammad Sarwar Khan",profilePictureURL:"https://mts.intechopen.com/storage/users/212511/images/system/212511.jpg",biography:"Muhammad Sarwar Khan is currently serving as Professor and Director at the Center of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan. He has earned his Bachelor's and Master's degrees in Horticulture with a major thesis on Citrus Tissue Culture. Mr. Khan earned his Ph.D. from the University of Cambridge, UK. Afterward, the Rockefeller foundation fellowship under a Biotechnology Program for Developing Countries to research at the Waksman Institute of Microbiology, Rutgers, USA was awarded to him. His first-of-its-kind research was published in Nature Biotechnology. Dr. Khan has served as the founding group leader of Chloroplast Transformation and Biopharming, and as the head of the Biotech Interdisciplinary Division at NIBGE. Dr. Khan has supervised more than 100 Ph.D., M-Phil students, and researchers. He has published more than 100 articles in high impact journals, including Nature, and is the author of several book chapters and books. Dr. Khan also pioneered plastid transformation in rice and sugarcane, recalcitrant plant species. He has also knocked out several genes from the chloroplast genome of higher plants. His current research interests include the development of edible-marker-carrying transgenics, cost-effective therapeutics, and edible vaccines for animals. Further, his research group is carrying out research projects, in collaboration or alone, on genetic improvement of field and horticultural crops. Furthermore, his research group is working on various projects like whole-genome sequencing, proteogenomics, etc. Dr. Khan Dr. Khan has received prestigious national and international awards and is on the Editorial Boards of several international scientific journals.",institutionString:"University of Agriculture Faisalabad",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Agriculture Faisalabad",institutionURL:null,country:{name:"Pakistan"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"5",title:"Agricultural and Biological Sciences",slug:"agricultural-and-biological-sciences"}],chapters:[{id:"75041",title:"Sugarcane Breeding for Enhanced Fiber and Its Impacts on Industrial Processes",slug:"sugarcane-breeding-for-enhanced-fiber-and-its-impacts-on-industrial-processes",totalDownloads:30,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"301331",firstName:"Mia",lastName:"Vulovic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/301331/images/8498_n.jpg",email:"mia.v@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"50443",title:"Biophysical Properties of the Basal Lamina: A Highly Selective Extracellular Matrix",doi:"10.5772/62519",slug:"biophysical-properties-of-the-basal-lamina-a-highly-selective-extracellular-matrix",body:'\n
\n
1. Molecular composition of the basal lamina
\n
The basal lamina constitutes a thin extracellular matrix, which is located between the connective tissue and the basolateral side of a cell layer. This cellular layer can consist of either endothelial or epithelial cells, and those cell types secrete the different molecular components of the basal lamina. The main components of the basal lamina are laminin, collagen IV, the perlecan complex, and entactin, which are also known as nidogen [1, 2]. Together, those macromolecules form a complex network as illustrated in Figure 1. In addition, the basal lamina may contain several proteases such as matrix metalloproteinase-2 (MMP-2), MMP-9, and growth factors such as transforming growth factor beta (TGF-β), insulin-like growth factor (IGF) and fibroblast growth factor (FGF) [3].
\n
Figure 1.
Schematic illustration of the basal lamina network. Both laminin and collagen IV assemble into a sheet-like network. Those two networks are cross-linked by entactin as well as the perlecan complex.
\n
Laminin is a glycoprotein mainly found in basement membranes such as the basal lamina and is composed of three polypeptide chains: α-chain, β-chain, and γ-chain [4]. These three chains assemble into a cross-like structure, where the short arms of the cross are formed by the N-termini of the three subunits. The long arm of this cross-like structure is formed by all three subunits which assemble into an α-helical coiled-coil structure with a globular end [4, 5]. Laminin self-assembles into a sheet-like structure by binding the short arms of different laminins to each other [6]. The polymerized laminin network is anchored to the underlying cell layer via integrin interactions mediated by the globular end of the cross-like structure [4, 7]. Collagen IV is a collagen variant mostly found in the basal lamina and forms a helix similar to collagen I [8]. Type IV collagen self-assembles via covalent bonding, disulfide cross-linking, and non-covalent side-by-side interactions into a sheet-like structure [2]. Both sheet-like structures, the laminin and collagen IV network, do not interact with each other; however, both laminin and collagen IV can bind to perlecan as well as entactin. As a consequence, the latter two molecules act as cross-linkers between the two sheet-like structures, thus maintaining the complex architecture of the basal lamina [9]. The perlecan complex is a basal lamina-specific proteoglycan. In general, proteoglycans consist of a protein core with glycosaminoglycans covalently attached to the protein. Thus, the perlecan complex consists of perlecan as core protein and 2–15 heparan sulfate (HS) side chains [10]. Also entactin is a glycoprotein and consists of three globular units connected by rod-like structures [11]. Two of the three globular units (G1 and G2) are situated at the N-terminus of entactin. The third globular unit (G3) is found at the C-terminus of the protein. G3 strongly binds to the γ-laminin short arm but can also bind to collagen IV. In contrast, G2 only binds to collagen type IV [11], thus connecting the networks built by laminin and collagen type IV.
\n
In addition to building a complex network and serving as an anchoring matrix for a neighboring cell layer, all of these basal lamina components can directly influence the cell fate: laminin, in combination with collagen IV supports cell attachment, differentiation, migration, and growth [12]. It was suggested that in addition to fibronectin, type IV collagen and laminin are involved in the formation of tight junctions [13]. Laminin and collagen IV are also key players in establishing the mechanical stability of the basal lamina [10]. As mentioned above, the proteoglycan perlecan consists of a core protein to which HS, a heavily charged glycosaminoglycan, is attached. In addition to acting as a cross-linker between laminin and collagen IV, perlecan and, in particular, the highly charged HS chains are responsible for the hydration of the matrix and contribute to the selective filtering properties of the basal lamina [14–16].
\n
Although this highly specific structure–function relationship suggests that the microarchitecture of the basal lamina might be rather static, proteolysis of extracellular matrix (ECM) components and thus matrix remodeling is a process which continuously takes place in vivo. Remodeling of the ECM is, for example, a crucial part of wound healing and cell differentiation [17]. In addition, the degradation of ECM components can be responsible for cell apoptosis but, depending on the ECM component degraded, can also enhance cell viability [18]. In particular, the degradation of laminin is thought to be harmful for cells: In a study conducted in mice, it was suggested that the breakdown of laminin by the MMP-9 induces neuronal apoptosis but can be prevented by the addition of MMP-9 inhibitors [19].
\n
Moreover, the degradation of laminin does not only result in cell apoptosis but also impacts the stability of the basal lamina [20]. Since laminin interacts with the integrins on the cell surface and anchors the cells onto the basal lamina, a breakdown of laminin results in a separation of the basal lamina from the endothelial/epithelial cell layer which in turn induces a loss of cell–matrix communication [21, 22]. It was shown in an in vivo study in a mouse model that when the second structural main component of the basal lamina, collagen IV, is knocked out, embryos develop normal during the first few days, but after 10 days of development lethality occurs [23]. It was suggested that collagen IV is essential for the function and integrity of the basal lamina when mechanical stress increases. However, collagen IV seems to be unimportant in the assembly of the basal lamina at early embryonic states [23]. Similar results were obtained when an enzyme, which catalyzes the assembly of collagen IV, was modified and thus nonfunctional. In these mice, collagen IV was present but did not assemble properly and the mouse embryos died after 10 days [24].
\n
In contrast to those structural main components, loss of the small cross-linking molecule entactin seems to have a weaker influence on basal lamina structure and function. In mice, the inactivation or mutation of the gene encoding entactin results in a normal basal lamina phenotype, and the viability of the mutant mice seems not be strongly impaired by a loss of entactin [25–27]. Exceptions are the lung and the kidney, organs which fulfill important filtering tasks and thus contain a huge amount of basal lamina. Here, a loss of entactin cross-linking function entailed strong alteration of those tissues during embryonal development and ultimately led to death immediately after birth [28]. Of course, alterations in basal lamina properties can also have less severe consequences. For instance, long-term diabetes patients not only often suffer from retinopathies but also show an increased thickness and stiffness in the ocular basal lamina. Here, however, the higher amount of basal lamina proteins is due to the expression of diabetes-specific proteins whereas the production of the normal basal lamina components is not increased [29].
\n
\n
\n
2. Selective permeability of the basal lamina in vivo\n
\n
In the human body, the basal lamina always supports a cell layer of either endothelial or epithelial cells (Figure 2). Together, these two layers form a complex barrier which selectively regulates the entrance and distribution of molecules from or into the connective tissue. Molecules which are selectively transported across the basal lamina include growth factors, nutrients, and hormones. Examples for such basal lamina/cell barriers are found in the skin, the kidney, the blood–brain barrier, and the vascular system [10, 30–34].
\n
Figure 2.
Illustration of complex barriers consisting of a cell layer and an adjacent basal lamina layer. The inner layer of blood vessels is constituted by endothelial cells with a basal lamina layer located on the outer side of the blood vessel. Also epithelial cells are supported with a thin layer of basal lamina. In both examples, selective permeability of the complex cell/biopolymer barrier toward molecules is observed, that is, some molecules can penetrate the barrier whereas others are rejected.
\n
The skin poses one of the largest and, in most cases, the first barrier for foreign compounds. In addition to this protective function, the skin also regulates the uptake of oxygen and prevents the loss of water from the underlying tissue [32]. In kidney tissue, the basal lamina is, in combination with the epithelial cells, responsible for filtering [10], and defects in the basal lamina can result in kidney malfunction [34]. The blood–brain barrier [30] protects the brain tissue from pathogens and neurotoxic molecules, whereas it allows the passage of regulatory molecules such as hormones from the blood stream into the cerebrospinal fluid [35]. A similar structure is present in the vascular system. Here, the first barrier is established by endothelial cells which rest on a thin layer of basal lamina on their basolateral side [10]. Nutrients, growth factors, proteins, hormones, and polysaccharides are prevented from leaking from the blood stream into the connective tissue by tight junctions between the endothelial cells [31, 35–37]. If the integrity of these tight junctions is impaired, the basal lamina becomes directly accessible for blood compounds. Moreover, if the basal lamina layer is damaged, the translocation of solutes from the blood stream into the connective tissue is increased, even if the tight junctions are intact [20]. Of course, for molecules which need to traverse from the connective tissue into the blood stream, the basal lamina is encountered first before the endothelial cells are reached. In this scenario, the basal lamina layer constitutes the primary barrier.
\n
A detailed knowledge of the molecular interactions which determine the selective filtering properties of the basal lamina is especially interesting for the design of new drug carrier vehicles for targeted drug delivery applications. One example for such an application is the specific targeting of tumors. In tumor tissue, the influence of the basal lamina barrier becomes even more important since tumors usually show an increased production of ECM [38]. Drug carriers are often injected intravenously; thus, the vascular system poses the critical barrier which the drug carriers have to pass. Here, the passage of drugs/drug carrier vehicles from the blood stream into the adjoining tissue is primarily regulated by the endothelial cells. However, in most cases, the endothelium around tumors is leaky. This is also known as an “enhanced permeability and retention effect” (EPR). Since the barrier function of the endothelium is impaired by the tumor, the basal lamina becomes directly accessible for compounds from the blood stream. In such a situation, the passage of drug carrier systems and their incorporated drugs is mainly regulated by the basal lamina.
\n
In all of these examples, the selective barrier properties of the basal lamina are key for regulating complex biological processes. To possess such a high selectivity toward molecules or drug carrier particles, that is, deciding which of them are allowed to pass and which are rejected, an advanced molecular filter system based on various interactions is needed. Understanding the physical interactions between drug carriers and the complex multicomponent, basal lamina is crucial to efficiently adjust the surface parameters of drug carriers in such a way either that they are able to easily penetrate the basal lamina barrier or that they accumulate at the basal lamina interface. Studying the mechanistic principles which govern the selective permeability properties of the basal lamina layer in vivo is, however, very difficult: On the one hand, the basal lamina has a thickness of only a few hundred nanometers which would require optical experiments with a supreme spatial resolution such as PALM/STORM or STED microscopy [39]. On the other hand, the presence of a plethora of molecules, dynamic alterations in the basal lamina composition by enzymatic processes, or generation of new basal lamina components by the adjacent cell layer further complicates the interpretation of in vivo permeability studies and the correlation of the experimental results with physicochemical principles. Thus, a detailed investigation of the selective permeability properties of a complex biopolymer barrier such as the basal lamina requires a reliable in vitro model system, which is available in quantities large enough to conduct systematic tests while reproducing the behavior of the in vivo basal lamina layer.\n
\n
\n
\n
3. Basal lamina model systems
\n
A suitable source for the purification of an extracellular matrix that mimics the basal lamina is the Engelbreth–Holm–Swarm sarcoma of mice. This tumor produces, in contrast to healthy tissue, large amounts of ECM with laminin and collagen IV being the main components [3]. Depending on the question asked, individual macromolecular components of the basal lamina may be sufficient to take over the role of the complex biopolymer mixture. For instance, adhesion of cells to solid substrates is promoted similarly well by laminin coatings as by coatings with the multicomponent ECM [40, 41]. For other basal lamina properties such as viscoelasticity and selective permeability, it is crucial that the biological complexity of the system is maintained so that the full spectrum of basal lamina function is obtained.
\n
The high abundance of ECM in Engelbreth–Holm–Swarm tumor tissue makes it possible to purify reasonable amounts of this multicomponent matrix as required for systematic in vitro experiments. A first purification protocol for this ECM was established by Kleinman et al. [42, 43] in the 1980s. The extract is liquid at temperatures between 4°C and approx. 15°C and forms a gel at higher temperatures. In its gel form, the matrix was tested for its biological activity, and it was shown in several studies that the purified ECM successfully promotes the differentiation of various cell types [44–47]. Cells can be either plated on top of the gel, thus simulating a two-dimensional (2D) environment, or they can be embedded into a 3D ECM matrix. Which configuration is chosen depends on the detailed experimental setup, the cell type used and the biological question. For instance, cell migration experiments can be conducted both on flat surfaces which have been coated by ECM components and in 3-dimensional basal lamina gels [48, 49].
\n
The purification protocol of Kleinman et al. is used by several companies for the commercial production of ECM. Although these commercial ECM variants are extracted according to the same purification protocol, significant differences in the behavior of cells embedded into those gels have recently been described [50]: The migration behavior of leukocyte-like dHL-60 cells in four different commercially available ECM gel variants differed strongly even though the gels were prepared at matching total protein concentrations. Moreover, in one of the ECM gels, life–dead stains demonstrated a significantly increased percentage of nonviable cells. At the same time, for this gel variant, there was an additional band visible when the gel was analyzed by SDS-PAGE. Mass spectrometry showed that this additional band contained laminin fragments which indeed are suspected to be harmful for cells. This result demonstrates the dilemma a researcher is exposed to when working with commercial model systems: On the one hand, the relatively easy availability of the material in reasonable quantities allows for conducting in vitro experiments which otherwise would not be possible. On the other hand, comparability to results from other researchers is often difficult if different vendor sources for the biopolymer mixture are used: The data obtained need to be interpreted with great care and ideally should be double-checked with a second, independent ECM preparation. SDS-PAGE analysis also suggested that the commercial ECM variants differed in terms of the relative concentration of basal lamina components. Whereas, in all ECM preparations, the bands corresponding to collagen IV and laminin were clearly most pronounced, the strongest variability occurred in a band around 50 kDa which matches the molecular weight of entactin. Since this molecule acts as a cross-linker between laminin and collagen IV, it is reasonable to assume that variations in its relative concentration will also affect the structure and permeability properties of the ECM gels and, ultimately, the migration behavior of cells in those gels. However, biochemical techniques are not able to predict those gel parameters, which is why physical methods are required to further characterize the different basal lamina model systems.
\n
\n
\n
4. Physical properties of basal lamina gels in vitro\n
\n
In addition to the biochemical structure of its constituents, the following three physical parameters dictate the behavior of molecules, nanoparticles, or cells within the basal lamina: the microstructure, the mechanical properties, and the permeability of the hydrogel. In biopolymer networks, the microstructure of the system has direct implications on both the viscoelastic properties of the network [51] and its permeability properties [31]. Thus, imaging methods for visualizing biopolymer networks such as the basal lamina are discussed first.
\n
\n
4.1. Microstructure of ECM gels
\n
There are various methods to evaluate the structure of a material, and those methods can be subdivided into the following categories: surface imaging techniques, near-field/contact-based techniques and far-field imaging. Which method is used to resolve the structure depends on the material and on the experimental question: Do I require information on the surface topology or on the inner structure of the material? For biological samples, a fixation is needed for most of the imaging techniques so that the structure does not change over time or during the sample preparation process. One technique which is often used to image biological samples is fluorescence confocal microscopy as this method can visualize the 3D structure of a biopolymer network. However, most biological samples are not fluorescent by themselves and thus a fluorescent dye has to be used to stain the structure of interest. For many target proteins, commercial antibodies are available to which a fluorescent dye is attached. Before such a staining with antibodies is performed, the samples are typically fixed to ensure that the structure of the biopolymer network is not altered by the antibody application and the following washing step.
\n
A suitable technique for imaging the surface of a biopolymer material is scanning electron microscopy (SEM). For this technique, the sample surface needs to be electrically conductive. Since this is typically not the case for biological samples, the application of a thin conductive layer, for example gold, is necessary. Depending on the type of SEM used for imaging, the samples are also exposed to a vacuum for imaging; this requires sample fixation and subsequent dehydration as typical additional preparation steps for this imaging method. It is clear that sample preparation steps necessary for both imaging techniques may introduce artifacts, that is, alterations of the microstructure of the biopolymer network. However, as the preparation steps for both techniques are different, the obtained pictures are reliable if both imaging methods return comparable structures.
Example images of basal lamina model systems are shown in Figure 3, where the microstructure of four different ECM gel variants is compared. For the fluorescent confocal image, the ECM component laminin was stained with a fluorescent antibody and an optical slice with a thickness of 0.9 μm was acquired inside the 3-dimensional gel. Here, the ECM variant in which leukocyte migration was slowed down most (ECM2) shows the lowest porosity. The same difference in the microarchitecture of the ECM gels is obtained when SEM is used for imaging: ECM2 shows the highest density, whereas the other gel variants exhibit a comparable network structure. As the four gel variants have all been reconstituted at identical total protein concentrations, the observed structural difference is most likely due to the higher content of the cross-linking molecule entactin in this ECM variant as detected by SDS-PAGE.
\n
\n
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4.2. Viscoelastic properties of ECM gels
\n
Especially for cell differentiation, the mechanical properties of the ECM play an important role. Using artificial hydrogels such as cross-linked polyacrylamide gels [52], it was shown that cell differentiation can be directed by the stiffness of the substrate. The ECM is a viscoelastic material, that is, its mechanical behavior combines both viscous as well as elastic properties. Those viscoelastic properties can be probed macroscopically with a shear rheometer as illustrated in Figure 4 as well as microscopically with single-particle tracking microrheology or AFM indentation.
\n
In macroscopic shear rheology, the sample is placed between two plates, a stationary bottom plate and a rotating top plate for shear stress application. The bottom plate can be heated or cooled depending on the desired temperature conditions. The top plates are available in various diameters and shapes and are chosen according to the sample properties and the quantity to be measured. For determining viscoelastic properties, the top plate is typically oscillated at different frequencies, either at a fixed strain or at a fixed torque. Small torques during such a measurement ensure that the material response is quantified in the linear response regime, where Hooke’s law holds, that is, where the ratio of stress and strain is independent from the amplitude of the applied force.
\n
Figure 4.
The viscoelastic behavior of ECM gels can be quantified by shear rheology. The sample is placed between a stationary bottom plate and a measuring plate, and then, an oscillating shear stress is induced. The temporal delay (phase shift) of the material response is measured. For a purely elastic material, the phase shift is 0°; in contrast, a phase shift of 90° is obtained for a purely viscous substance. For a viscoelastic material, the phase shift can assume any value between 0° and 90° (adapted from an illustration by Stefan Grumbein). A typical gelation curve for a basal lamina gel at a concentration of 8.3 mg/mL is shown at the bottom right and was acquired using a plate–plate geometry oscillating at a frequency of 1 Hz. For the first 200 s, the measurement was performed at 4°C. At this temperature, the ECM is in its liquid state, thus the loss modulus (open circles) dominates over the storage modulus (full circles). When the temperature is increased to 37°C, gelation is initiated and the storage modulus dominates. After a few minutes, a plateau value is reached. A typical frequency spectrum after gelation is shown as inset. The storage modulus dominates over the loss modulus over four decades of frequencies.
\n
The viscoelastic properties of the ECM can then be described by the storage modulus G’ and the loss modulus G”. Here, G’ is a measure for the elastic properties and G” for the viscous properties of the ECM gel. At low temperatures around 4°C, the ECM is in a liquid state. Here, its viscous properties dominate and the loss modulus is larger than the storage modulus. When the ECM gel is heated to room temperature or above, a gelation process is initiated which results in an increased storage modulus: Within a few minutes after the temperature increase is applied, the storage modulus starts to dominate over the loss modulus and then increases further until it reaches a plateau value (see Figure 4). In general, there are several parameters determining the absolute value of the plateau elasticity: The higher the concentration of proteins/polymers the higher is typically the storage modulus [53]. In addition to the concentration of protein, also the type of polymer/polymer interaction plays a role. The storage modulus of an entangled solution is usually lower than for a cross-linked network. In the study conducted in [50], it was shown that the amount of the cross-linking molecule entactin influences the network stiffness: The higher the concentration of entactin the higher the storage modulus and thus the elasticity of the formed matrix. Typical values for the elastic modulus obtained for ECM gels at a total protein concentration of 3.5 mg/mL are in the range of 1-10 Pa which is very soft and lies in the range of moduli reported to induce neuron-like differentiation of stem cells [52].
\n
In general, the viscoelastic properties of a biopolymer network may depend strongly on the probing frequency [51], especially if the network constituents are only entangled. For cross-linked systems, however, a pronounced plateau in the frequency-dependent shear moduli is expected, and exactly such behavior is also observed for ECM gels (Figure 4).
\n
The absolute values of the viscoelastic parameters obtained with macrorheology may not necessarily reflect the local stiffness of a biopolymer network. Thus, microrheological techniques such as bead microrheology [54] or AFM nanoindentation [55] have been introduced and already applied to other biopolymer systems such as cytoskeletal networks [56–58] or cartilage [59]. With those nano-/microscopic techniques, it is also possible to spatially map the mechanical properties of native basement membranes [60, 61], which might give insights important for cellular processes such as differentiation or migration.
\n
\n
\n
4.3. Permeability of ECM gels
\n
One of the major tasks of the basal lamina is to act as a molecular filter. Here, the exclusion of particles or molecules according to their size is one of the simplest mechanisms for establishing permeability: A mesh size smaller or in the order of the particle diameter will prevent the entrance of particles into the network; conversely, if particles have already entered the network, they will be efficiently trapped within the biopolymer matrix. However, this filter mechanism is not very sophisticated as it cannot differentiate between objects of the same size. Thus, a second filter mechanism based on binding interactions between diffusing particles/molecules and the basal lamina constituents has been put forward to contribute to the selective permeability properties of biopolymer hydrogels such as the basal lamina [31]. With the ECM model system discussed above, the physicochemical principles governing the high selectivity of basal lamina gels can be studied systematically.
\n
To probe the interactions between particles and the ECM, single-particle tracking (SPT) can be employed. In contrast to SPT used for microrheology [62], the diameter of the particles embedded into the ECM should be small compared to the mesh size of the gel. Only then one can be sure that the particle motion is not geometrically restricted by the network microarchitecture—which demonstrates the importance of obtaining structural information on the system prior to commencing SPT experiments. In SPT measurements, the diffusive movement of particles within the gel is recorded via light microscopy and every single particle is evaluated separately. The trajectory of motion of each particle, in particular the x- and y-position, is extracted from recorded movies for every frame of the movie—typically over a time course of several seconds up to a minute (depending on the temporal resolution of the image acquisition process, Figure 5). These data are then used to calculate the mean squared displacement (MSD) of every particle according to the following
Here, N denotes the total number of recorded frames, \n\n\nr\n⃑\n\n\nt\n\n\n is the position of the particle at time t, and τ denotes the time interval between two particle positions within a given trajectory. For diffusive processes, the MSD typically grows with time as a power law τα, with the exponent α characterizing the type of diffusive motion: One can distinguish sub-diffusive (α<1), normal diffusive (α=1), or superdiffusive behavior (α>1), the latter of which is typically linked to active transport phenomena or liquid flow.
\n
Such SPT experiments revealed that both positively and negatively charged microparticles were efficiently immobilized in the ECM gel, whereas PEGylated (and thus only weakly charged) polystyrene particles of identical size were able to diffuse almost freely within the gel [63]. Equivalent results were obtained with liposome particles and suggested that free diffusion within the ECM matrix is only possible as long as the particle surface charge (as quantified by the zeta potential) lies within a window ranging from intermediate negative charge to low positive charge. Enzymatic digestion of the ECM component HS entailed a mobilization of positively charged particles. This finding suggested that the polyanionic HS chains present in the perlecan complex critically contribute to the selective properties of the ECM gel—likely through trapping of positively charged objects by means of electrostatic binding.
The notion that electrostatic binding interactions contribute to particle trapping in ECM gels was confirmed by experiments conducted at elevated ionic strength of the hydrogel buffer. Increased salt concentrations lead to charge screening effects by the formation of a layer of counter ions around the surface of charged objects such as particles or hydrogel polymers. As a consequence, the strength of electrostatic interactions at a given separation distance between two objects is reduced—a process which is described by the Debye–Hückel theory [64]. At physiological concentrations of KCl, both positively and negatively charged polystyrene microparticles are immobilized in ECM gels. However, when the KCl concentration is increased, a fraction of the particles becomes mobile [63, 65]. This mobilization does not have to be permanent as individual particles can dynamically switch between a freely diffusing and bound state over time, and—while in the bound state—also between a weakly and strongly bound configuration. As shown in Figure 5, the degree of particle mobilization depends both on the ion concentration and valency which is consistent with the Debye–Hückel theory. However, particle mobilization efficiency seems also to depend on the particular ion species as identical concentrations of the divalent ions Mg2+ and Ca2+ lead to different experimental outcome [65]. This ion-specific effect suggests that, in addition to electrostatic forces, also hydrophobic interactions are likely to contribute to the selective filtering properties of the basal lamina.
Systematic permeability studies with artificial particles were very helpful to unravel the physical mechanisms which are responsible for the trapping of solutes in the basal lamina. However, most compounds which encounter the basal lamina layer under physiological conditions are small molecules rather than microparticles. To investigate the selective properties of the basal lamina toward small molecules, a microfluidic setup (Figure 6) was recently introduced [66]. Here, customized peptides with tailored amino acid sequences and thus different net charges were used as diffusion probes. To ensure optimal comparability, the molecular weight of those oligopeptides was kept constant. The penetration behavior of those peptides into an ECM gel was visualized by fluorescent microscopy, and similar to the SPT experiments discussed above also the behavior of those molecules critically depended on their charge. Positively charged peptides accumulated at the gel/buffer interface, whereas negatively charged peptides did not. Moreover, when the net charge of the positively charged peptides was increased, the accumulation propensity of the molecules at the gel interface was increased as well. Of course, such an artificial microfluidic setup does not reproduce the complex situation of the basal lamina interface found in vivo. However, peptide injection tests in the connective tissue of living mice demonstrated a similar charge-selective accumulation behavior at the basal lamina layer of blood vessels as observed on-chip with the simplified ECM/buffer interface. This underscores the great potential basal lamina model systems and biophysical characterization methods hold for gaining a better insight into the mechanistic principles that establish the complex properties of the basal lamina.
\n
\n
\n
\n
5. Outlook
\n
Here, we have summarized selected aspects of our current understanding how the biochemical composition of the basal lamina is mirrored in the complex microarchitecture as well as the multi-facetted material properties of the biopolymer network. Deciphering the physicochemical principles which dictate the microstructure, viscoelastic properties, and selective permeability of the basal lamina layer are not only interesting for cell biology studies [67, 68], drug delivery questions and tumor treatment [69–72], but might also have strong implications for tattoo removal applications: Here, ink nanoparticles trapped in the skin tissue have to be mobilized, for example, by soaking the tissue in salt solutions, so that they can be washed out from the skin rather than removed by painful and scar-inducing laser treatment. The lessons learned from systematically unraveling the physical and chemical mechanisms, which give rise to the complex properties of the basal lamina, may also help in the rational design of artificial hydrogel systems for tissue engineering approaches: The synthesis of complex macromolecules with well-defined chemical properties may allow for constructing hydrogels with both tailored mechanical properties and selective permeability behavior.
\n
\n
Acknowledgments
\n
We thank Iris König-Decker for providing graphics. We thank Kathrin Boettcher, Benjamin Käsdorf and Corinna Lieleg for critical reading of the manuscript. Financial support from the Deutsche Forschungsgemeinschaft (DFG) through project B7 within the framework of the SFB 1032 is gratefully acknowledged.
\n
\n',keywords:"laminin, collagen, entactin, microstructure, viscoelastic properties, permeability",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/50443.pdf",chapterXML:"https://mts.intechopen.com/source/xml/50443.xml",downloadPdfUrl:"/chapter/pdf-download/50443",previewPdfUrl:"/chapter/pdf-preview/50443",totalDownloads:1562,totalViews:1065,totalCrossrefCites:4,totalDimensionsCites:4,hasAltmetrics:0,dateSubmitted:"October 6th 2015",dateReviewed:"February 15th 2016",datePrePublished:null,datePublished:"June 15th 2016",dateFinished:null,readingETA:"0",abstract:"In this chapter, we discuss a specialized version of the extracellular matrix, the basal lamina. We focus on biophysical approaches which helped in identifying the mechanistic principles that allow the basal lamina to act as a selective permeability barrier. We discuss the physicochemical interactions that entail binding of molecules or nanoparticles to the basal lamina matrix and outline physiological scenarios where altered selective permeability properties of the basal lamina might contribute to physiological (mal) function.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/50443",risUrl:"/chapter/ris/50443",book:{slug:"composition-and-function-of-the-extracellular-matrix-in-the-human-body"},signatures:"Fabienna Arends and Oliver Lieleg",authors:[{id:"178691",title:"Prof.",name:"Oliver",middleName:null,surname:"Lieleg",fullName:"Oliver Lieleg",slug:"oliver-lieleg",email:"oliver.lieleg@tum.de",position:null,institution:null},{id:"185383",title:"MSc.",name:"Fabienna",middleName:null,surname:"Arends",fullName:"Fabienna Arends",slug:"fabienna-arends",email:"fabienna.arends@tum.de",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Molecular composition of the basal lamina",level:"1"},{id:"sec_2",title:"2. Selective permeability of the basal lamina in vivo\n",level:"1"},{id:"sec_3",title:"3. Basal lamina model systems",level:"1"},{id:"sec_4",title:"4. Physical properties of basal lamina gels in vitro\n",level:"1"},{id:"sec_4_2",title:"4.1. Microstructure of ECM gels",level:"2"},{id:"sec_5_2",title:"4.2. Viscoelastic properties of ECM gels",level:"2"},{id:"sec_6_2",title:"4.3. Permeability of ECM gels",level:"2"},{id:"sec_8",title:"5. Outlook",level:"1"},{id:"sec_9",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'\nTimpl R. Macromolecular organization of basement membranes. Current Opinion in Cell Biology. 1996;8(5):618–24.\n'},{id:"B2",body:'\nYurchenco PD, Orear JJ. Basal lamina assembly. Current Opinion in Cell Biology. 1994;6(5):674–81.\n'},{id:"B3",body:'\nKleinman HK, Martin GR. Matrigel. Basement membrane matrix with biological activity. Seminars in Cancer Biology. 2005;15(5):378–86.\n'},{id:"B4",body:'\nAumailley M. The laminin family. Cell Adhesion & Migration. 2013;7(1):48–55.\n'},{id:"B5",body:'\nPaulsson M, Deutzmann R, Timpl R, Dalzoppo D, Odermatt E, Engel J. Evidence for coiled-coil alpha-helical regions in the long arm of laminin. The Embo Journal. 1985;4(2):309–16.\n'},{id:"B6",body:'\nCheng YS, Champliaud MF, Burgeson RE, Marinkovich MP, Yurchenco PD. Self-assembly of laminin isoforms. Journal of Biological Chemistry. 1997;272(50):31525–32.\n'},{id:"B7",body:'\nAumailley M, Pesch M, Tunggal L, Gaill F, Fassler R. Altered synthesis of laminin 1 and absence of basement membrane component deposition in beta 1 integrin-deficient embryoid bodies. Journal of Cell Science. 2000;113(2):259–68.\n'},{id:"B8",body:'\nKhoshnoodi J, Pedchenko V, Hudson BG. Mammalian collagen IV. Microscopy Research and Technique. 2008;71(5):357–70.\n'},{id:"B9",body:'\nMouw JK, Ou G, Weaver VM. Extracellular matrix assembly: a multiscale deconstruction. Nature Reviews Molecular Cell Biology. 2014;15(12):771–85.\n'},{id:"B10",body:'\n\nAlberts B, et al. Molecular biology of the cell. 4th ed. Garland Science, New York; 2002.\n'},{id:"B11",body:'\nYurchenco PD, Patton BL. Developmental and pathogenic mechanisms of basement membrane assembly. Current Pharmaceutical Design. 2009;15(12):1277–94.\n'},{id:"B12",body:'\nKalluri R. Basement membranes: structure, assembly and role in tumour angiogenesis. Nature Reviews Cancer. 2003;3(6):422–33.\n'},{id:"B13",body:'\nTilling T, Korte D, Hoheisel D, Galla HJ. Basement membrane proteins influence brain capillary endothelial barrier function in vitro. Journal of Neurochemistry. 1998;71(3):1151–7.\n'},{id:"B14",body:'\nIozzo RV. Matrix proteoglycans: from molecular design to cellular function. Annual Review of Biochemistry. 1998;67:609–52.\n'},{id:"B15",body:'\nKresse H, Schonherr E. Proteoglycans of the extracellular matrix and growth control. Journal of Cell Physiology. 2001;189(3):266–74.\n'},{id:"B16",body:'\nYanagishita M. Function of proteoglycans in the extracellular-matrix. Acta Pathology Japan. 1993;43(6):283–93.\n'},{id:"B17",body:'\nStreuli C. Extracellular matrix remodelling and cellular differentiation. Current Opinion in Cell Biology. 1999;11(5):634–40.\n'},{id:"B18",body:'\nWerb Z. ECM and cell surface proteolysis: regulating cellular ecology. Cell. 1997;91(4):439–42.\n'},{id:"B19",body:'\nGu ZZ, Cui J, Brown S, Fridman R, Mobashery S, Strongin AY, et al. A highly specific inhibitor of matrix metalloproteinase-9 rescues laminin from proteolysis and neurons from apoptosis in transient focal cerebral ischemia. Journal of Neuroscience. 2005;25(27):6401–8.\n'},{id:"B20",body:'\nWang CX, Shuaib A. Critical role of microvasculature basal lamina in ischemic brain injury. Progress in Neurobiology. 2007;83(3):140–8.\n'},{id:"B21",body:'\nKitajewski J. Endothelial laminins underlie the tip cell microenvironment. Embo Reports. 2011;12(11):1087–8.\n'},{id:"B22",body:'\nTimpl R, Brown JC. The laminins. Matrix Biology. 1994;14(4):275–81.\n'},{id:"B23",body:'\nPoschl E, Schlotzer-Schrehardt U, Brachvogel B, Saito K, Ninomiya Y, Mayer U. Collagen IV is essential for basement membrane stability but dispensable for initiation of its assembly during early development. Development. 2004;131(7):1619–28.\n'},{id:"B24",body:'\nHolster T, Pakkanen O, Soininen R, Sormunen R, Nokelainen M, Kivirikko KI, et al. Loss of assembly of the main basement membrane collagen, type IV, but not fibril-forming collagens and embryonic death in collagen prolyl 4-hydroxylase I null mice. The Journal of Biological Chemistry. 2007;282(4):2512–9.\n'},{id:"B25",body:'\nKang SH, Kramer JM. Nidogen is nonessential and not required for normal type IV collagen localization in Caenorhabditis elegans. Molecular Biology of the Cell. 2000;11(11):3911–23.\n'},{id:"B26",body:'\nMurshed M, Smyth N, Miosge N, Karolat J, Krieg T, Paulsson M, et al. The absence of nidogen 1 does not affect murine basement membrane formation. Molecular and Cellular Biology. 2000;20(18):7007–12.\n'},{id:"B27",body:'\nSchymeinsky J, Nedbal S, Miosge N, Poschl E, Rao C, Beier DR, et al. Gene structure and functional analysis of the mouse nidogen-2 gene: nidogen-2 is not essential for basement membrane formation in mice. Molecular and Cellular Biology. 2002;22(19):6820–30.\n'},{id:"B28",body:'\nWillem M, Miosge N, Halfter W, Smyth N, Jannetti I, Burghart E, et al. Specific ablation of the nidogen-binding site in the laminin gamma1 chain interferes with kidney and lung development. Development. 2002;129(11):2711–22.\n'},{id:"B29",body:'\nTo M, Goz A, Camenzind L, Oertle P, Candiello J, Sullivan M, et al. Diabetes-induced morphological, biomechanical, and compositional changes in ocular basement membranes. Experimental Eye Research. 2013;116:298–307.\n'},{id:"B30",body:'\nCecchelli R, Berezowski V, Lundquist S, Culot M, Renftel M, Dehouck MP, et al. Modelling of the blood–brain barrier in drug discovery and development. Nature Reviews Drug Discovery. 2007;6(8):650–61.\n'},{id:"B31",body:'\nLieleg O, Ribbeck K. Biological hydrogels as selective diffusion barriers. Trends in Cell Biology. 2011;21(9):543–51.\n'},{id:"B32",body:'\nProksch E, Brandner JM, Jensen JM. The skin: an indispensable barrier. Experimental Dermatology. 2008;17(12):1063–72.\n'},{id:"B33",body:'\nSebinger DDR, Ofenbauer A, Gruber P, Malik S, Werner C. ECM modulated early kidney development in embryonic organ culture. Biomaterials. 2013;34(28):6670–82.\n'},{id:"B34",body:'\nMiner JH. Renal basement membrane components. Kidney International. 1999;56(6):2016–24.\n'},{id:"B35",body:'\nBallabh P, Braun A, Nedergaard M. The blood–brain barrier: an overview—structure, regulation, and clinical implications. Neurobiology of Disease. 2004;16(1):1–13.\n'},{id:"B36",body:'\nGabe SM. Gut barrier function and bacterial translocation in humans. Clinical Nutrition. 2001;20:107–12.\n'},{id:"B37",body:'\nTsang KY, Cheung MC, Chan D, Cheah KS. The developmental roles of the extracellular matrix: beyond structure to regulation. Cell Tissue Research. 2010;339(1):93–110.\n'},{id:"B38",body:'\nBenton G, Kleinman HK, George J, Arnaoutova I. Multiple uses of basement membrane-like matrix BME/Matrigel) in vitro and in vivo with cancer cells. International Journal of Cancer. 2011;128(8):1751–7.\n'},{id:"B39",body:'\nGodin AG, Lounis B, Cognet L. Super-resolution microscopy approaches for live cell imaging. Biophysical Journal. 2014;107(8):1777–84.\n'},{id:"B40",body:'\nKoh HS, Yong T, Chan CK, Ramakrishna S. Enhancement of neurite outgrowth using nano-structured scaffolds coupled with laminin. Biomaterials. 2008;29(26):3574–82.\n'},{id:"B41",body:'\nHidalgo-Bastida LA, Barry JJ, Everitt NM, Rose FR, Buttery LD, Hall IP, et al. Cell adhesion and mechanical properties of a flexible scaffold for cardiac tissue engineering. Acta Biomaterialia. 2007;3(4):457–62.\n'},{id:"B42",body:'\nKleinman HK, Mcgarvey ML, Hassell JR, Star VL, Cannon FB, Laurie GW, et al. Basement-membrane complexes with biological-activity. Biochemistry. 1986;25(2):312–8.\n'},{id:"B43",body:'\nKleinman HK, Mcgarvey ML, Liotta LA, Robey PG, Tryggvason K, Martin GR. isolation and characterization of type-iv procollagen, laminin, and heparan-sulfate proteoglycan from the EHS sarcoma. Biochemistry. 1982;21(24):6188–93.\n'},{id:"B44",body:'\nVukicevic S, Luyten FP, Kleinman HK, Reddi AH. Differentiation of canalicular cell processes in bone-cells by basement-membrane matrix components—regulation by discrete domains of laminin. Cell. 1990;63(2):437–45.\n'},{id:"B45",body:'\nLi ML, Aggeler J, Farson DA, Hatier C, Hassell J, Bissell MJ. Influence of a reconstituted basement-membrane and its components on casein gene-expression and secretion in mouse mammary epithelial-cells. Proceedings of the National Academy of Sciences of the United States of America. 1987;84(1):136–40.\n'},{id:"B46",body:'\nKibbey MC, Royce LS, Dym M, Baum BJ, Kleinman HK. Glandular-like morphogenesis of the human submandibular tumor cell line A253 on basement membrane components. Experimental Cell Research. 1992;198(2):343–51.\n'},{id:"B47",body:'\nHadley MA, Byers SW, Suarezquian CA, Kleinman HK, Dym M. Extracellular-matrix regulates Sertoli-cell differentiation, testicular cord formation, and germ-cell development in vitro. Journal of Cell Biology. 1985;101(4):1511–22.\n'},{id:"B48",body:'\nZaman MH, Trapani LM, Siemeski A, MacKellar D, Gong H, Kamm RD, et al. Migration of tumor cells in 3D matrices is governed by matrix stiffness along with cell-matrix adhesion and proteolysis (vol. 103, p. 10889, 2006). Proceedings of the National Academy of Sciences of the United States of America. 2006;103(37):13897.\n'},{id:"B49",body:'\nValster A, Tran NL, Nakada M, Berens ME, Chan AY, Symons M. Cell migration and invasion assays. Methods. 2005;37(2):208–15.\n'},{id:"B50",body:'\nArends F, Nowald C, Pflieger K, Boettcher K, Zahler S, Lieleg O (2015) The Biophysical Properties of Basal Lamina Gels Depend on the Biochemical Composition of the Gel. PloS ONE 10(2): e0118090. doi:10.1371/journal.pone.0118090\n'},{id:"B51",body:'\nLieleg O, Claessens MMAE, Bausch AR. Structure and dynamics of cross-linked actin networks. Soft Matter. 2010;6(2):218–25.\n'},{id:"B52",body:'\nEngler AJ, Sen S, Sweeney HL, Discher DE. Matrix elasticity directs stem cell lineage specification. Cell. 2006;126(4):677–89.\n'},{id:"B53",body:'\nMassensini AR, Ghuman H, Saldin LT, Medberry CJ, Keane TJ, Nicholls FJ, et al. Concentration-dependent rheological properties of ECM hydrogel for intracerebral delivery to a stroke cavity. Acta Biomaterialia. 2015;27:116–30.\n'},{id:"B54",body:'\nChen DTN, Wen Q, Janmey PA, Crocker JC, Yodh AG. Rheology of soft materials. Annu. Rev. Condens. Matter Phys. 2010;1:301–22.\n'},{id:"B55",body:'\nTakeyasu K. Atomic force microscopy in nanobiology, Pan Stanford Publishing Pte. Ltd., Singapore, 2014\n'},{id:"B56",body:'\nLuan Y, Lieleg O, Wagner B, Bausch AR. Micro- and macrorheological properties of isotropically cross-linked actin networks. Biophysics Journal. 2008;94(2):688–93.\n'},{id:"B57",body:'\nLu L, Oswald SJ, Ngu H, Yin FCP. Mechanical properties of actin stress fibers in living cells. Biophysical Journal. 2008;95(12):6060–71.\n'},{id:"B58",body:'\nKirmizis D, Logothetidis S. Atomic force microscopy probing in the measurement of cell mechanics. International Journal of Nanomedicine. 2010;5:137–45.\n'},{id:"B59",body:'\nNia HT, Bozchalooi IS, Li Y, Han L, Hung HH, Frank E, et al. High-bandwidth AFM-based rheology reveals that cartilage is most sensitive to high loading rates at early stages of impairment. Biophysical Journal. 2013;104(7):1529–37.\n'},{id:"B60",body:'\nCandiello J, Balasubramani M, Schreiber EM, Cole GJ, Mayer U, Halfter W, et al. Biomechanical properties of native basement membranes. The FEBS Journal. 2007;274(11):2897–908.\n'},{id:"B61",body:'\nLast JA, Liliensiek SJ, Nealey PF, Murphy CJ. Determining the mechanical properties of human corneal basement membranes with atomic force microscopy. Journal of Structural Biology. 2009;167(1):19–24.\n'},{id:"B62",body:'\nValentine MT, Perlman ZE, Gardel ML, Shin JH, Matsudaira P, Mitchison TJ, et al. Colloid surface chemistry critically affects multiple particle tracking measurements of biomaterials. Biophysical Journal. 2004;86(6):4004–14.\n'},{id:"B63",body:'\nLieleg O, Baumgärtel RM, Bausch AR. Selective filtering of particles by the extracellular matrix: an electrostatic bandpass. Biophysical Journal. 2009;97(6):1569–77.\n'},{id:"B64",body:'\nHunter RJ. Zeta potential in colloid science: principles and applications. Academic Press, London; 1989.\n'},{id:"B65",body:'\nArends F, Baumgartel R, Lieleg O. Ion-specific effects modulate the diffusive mobility of colloids in an extracellular matrix gel. Langmuir: the ACS Journal of Surfaces and Colloids. 2013;29(51):15965–73.\n'},{id:"B66",body:'\nArends F, Sellner S, Seifert P, Gerland U, Rehberg M, Lieleg O. A microfluidics approach to study the accumulation of molecules at basal lamina interfaces. Lab Chip, 2015,15, 3326–3334.\n'},{id:"B67",body:'\nKaemmerer E, Melchels FP, Holzapfel BM, Meckel T, Hutmacher DW, Loessner D. Gelatine methacrylamide-based hydrogels: an alternative three-dimensional cancer cell culture system. Acta Biomaterialia. 2014;10(6):2551–62.\n'},{id:"B68",body:'\nSeto SP, Casas ME, Temenoff JS. Differentiation of mesenchymal stem cells in heparin-containing hydrogels via coculture with osteoblasts. Cell Tissue Research. 2012;347(3):589–601.\n'},{id:"B69",body:'\nChauhan VP, Stylianopoulos T, Boucher Y, Jain RK. Delivery of molecular and nanoscale medicine to tumors: transport barriers and strategies. Annual Review of Chemical and Biomolecular. 2011;2:281–98.\n'},{id:"B70",body:'\nHolback H, Yeo Y. Intratumoral drug delivery with nanoparticulate carriers. Pharmaceutical Research. 2011;28(8):1819–30.\n'},{id:"B71",body:'\nErnsting MJ, Murakami M, Roy A, Li SD. Factors controlling the pharmacokinetics, biodistribution and intratumoral penetration of nanoparticles. Journal Controlled Release. 2013;172(3):782–94.\n'},{id:"B72",body:'\nBertrand N, Wu J, Xu X, Kamaly N, Farokhzad OC. Cancer nanotechnology: the impact of passive and active targeting in the era of modern cancer biology. Advanced Drug Delivery Reviews. 2014;66:2–25.\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Fabienna Arends",address:null,affiliation:'
Institute of Medical Engineering IMETUM, Technical University of Munich, Boltzmannstrasse 11, Garching, Germany
Department of Mechanical Engineering, Technical University of Munich, Boltzmannstrasse 15, Garching, Germany
Institute of Medical Engineering IMETUM, Technical University of Munich, Boltzmannstrasse 11, Garching, Germany
Department of Mechanical Engineering, Technical University of Munich, Boltzmannstrasse 15, Garching, Germany
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1. Introduction
We are currently seeing a significant increase in global environmental pollution, with immediate repercussions on air, water and soil quality. More precisely, especially in the developed countries around the world the environmental pollution has reached scary limits. Related to this it is worth to mention the presence of a significant amount of hydrocarbons pollutants (benzene, toluene and xylene) in the emissions of the vehicles equipped with gasoline or diesel engines that are characterized by a variable toxicity depending on the chemical composition of the exhaust gases. Furthermore, these toxic substances are propagated through the air from one region of the world to another one and surrounds countries and continents becoming a global phenomenon, consisting of irreversible pollution of water, air and soil at the planetary scale.
Therefore, the need to conceive and implement new environmental conservation strategies at the global scale is required. Also, a changing in the thinking of the people about a significant reduction in energy consumption without sacrificing the comfort is crucial. In these circumstances there is a real hope that with the current technology available could stop the global destruction of the environment. Moreover, the new strategies based on electrical energy consumption assure a sustainable development of each community are critical to achieve a clean and efficient urban or rural transportation. As a viable solution to the global energy shortage and growing environmental pollution is the use of the electric vehicles (EVs) [1]. Nowadays, the electric vehicles (EVs) including hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and pure battery electric vehicles (BEVs) are gaining popularity in automotive industry and will dominate soon the clean vehicle market [2]. Related, in [2] is mentioned also that by 2020, it is expected that “more than half of new vehicle sales will likely be EV models”, with the batteries playing “the key and the enabling technology to this revolutionary change”. They are conceived “to handle high power (up to a hundred kW) and high energy capacity (up to tens of kWh) within a limited space and weight and at an affordable price” [2]. The most advanced and promising battery technologies existing in EVs manufacturing automotive industry are the nickel-metal hydride (NiMH), lithium-ion (Li-Ion) and nickel-cadmium (NiCad) batteries considered as the most suitable for HEVs/PHEVs/EVs all over the world.
They have a great potential to reduce greenhouse and other exhaust gas emissions, and require extensive research efforts and huge investments [2].
Nevertheless, amongst them the most promising power source with a great potential to be developed and to get a wide application in the future on the EVs market is the Li-Ion battery recommended by its light weight, high energy density, tiny memory effect, and relatively low self-discharge compared to its strong competitors, namely Ni-Cad and Ni-MH batteries, as is mentioned in [1, 2].
Additionally, the newest Li-Ion batteries are safer and less toxic than the same batteries in competition. Due to the diversity and the complexity of EVs field we limit our case study only to HEVs applications since we have got some research experience in modeling, control and the estimation strategies related to this field.
Therefore, one of the main objectives of this research work is to disseminate the most relevant results obtained until now in this area and to share some interesting ideas with our readers. The Li-Ion battery is a main component integrated in the battery management system (BMS) of a HEV that is responsible for “improving the battery performance, prolonging battery life, and ensuring its safety”, as is mentioned in [1, 2]. This desideratum is achieved by the BMS through recording continuously the main parameters of the Li-Ion battery and performing an accurate estimation of its state-of-charge (SOC). An accurate SOC estimation is a vital operation to be performed by the BMS of the HEV in order to prevent the dangerous situations when the battery is over-charged or over-discharged, and to improve considerably the battery performance [1]. More precisely, the battery SOC is an inner state of a battery that can be defined as the available capacity of a battery, as a percentage of its rated capacity [1, 2, 3]. Its estimation is an essential operational condition parameter for battery management system (BMS) but it cannot be measured directly [1]. The estimation of Li-Ion battery SOC value is based on the measurable data set of the battery parameters, mainly the current, voltage, and temperature by using several estimation strategies, implemented in real-time MATLAB/SIMULINK platform that includes many real-time features [3, 4, 5, 6, 7, 8, 9, 10, 11]. All SOC estimation strategies are model-based, and can be grouped in Kalman Filter (standard, extended, unscented, particles filter), as those developed and implemented in real-time in [3, 4, 5, 6, 7, 8, 9, 10, 11], linear, nonlinear and sliding mode observers estimators, including also fuzzy improved versions, well documented in [1, 8, 9, 10]. The environmental impact is a key issue on the enhancing the battery technologies, as is mentioned in [12].
Definitely, the selection criteria of the specific chemistry battery to be integrated in BMS structure of a HEV are the cost, the specific power and energy, cycle life, and the presence of poisonous heavy metals [2, 12]. A complete literature review on life cycle assessment (LCA) of HEVs since 1998 until 2013 has been conducted in [12]. In our research we are only focused on the technical aspects, such as battery modeling and developing the most suitable estimation techniques of battery SOC.
The remainder of this chapter is organized as follows. In Section 2, the widely-used 2RC-series cells Li-Ion battery equivalent model circuit (EMC) is introduced and the state space equations are derived. In Section 3, is proposed for design and implementation in real-time three nonlinear estimators, namely an Unscented Kalman Filter (UKF), Particle Filter (PF), and a nonlinear observer estimator (NOE).
The simulation results and the performance analysis of the proposed estimators are presented in Section 4. Section 5 concludes the book chapter.
2. The continuous and the discrete time state-space Li-Ion battery model representation
In this section we introduce a generic model capable to describe accurately the dynamics of Li-Ion battery, based on the same set of first-order differential equations in a state-space representation. For simulation purpose, a specific Li-Ion battery model is considered to prove the effectiveness of the proposed SOC estimation strategies. This model can be obtained from the generic model by changing only the values of the model parameters in state-space equations. For our case study we choose the widely-used 2RC - series cells Li-Ion battery equivalent model circuit (EMC) as model-based support, well documented in [1, 8, 11].
2.1. Li-Ion battery terminology
In this subsection we introduce briefly the same terminology as in [2] to introduce the most used terms in this chapter that characterize the Li-Ion battery architecture and its performance.
A single battery cell is a complete battery with two current leads and separate compartment holding electrodes (positive (+) and negative (−)), separator, and electrolyte. A few cells are connected in series, parallel or in series - parallel combinations, either by physical attachment or by welding in between cells to form a battery module. Similar, several modules are connected to form a battery pack placed in a single compartment for thermal management. A HEV may have more than one batteries packs placed in a different location of the car.
2.1.2. Li-Ion battery performance terminology
A battery cell is fully charged when its terminal voltage reaches the maximum voltage limit value after being charged at infinitesimal current levels, for example a maximum limit voltage value of 4.2 V at room temperature (25°C). A battery terminal voltage value greater than this limit corresponds to a dangerous over-charging operating condition. Similar, a battery cell is fully discharged when its terminal voltage reaches the minimum voltage limit value after being drained at infinitesimal current levels, for example a minimum limit voltage value of 3.0 V at room temperature. A battery terminal voltage value smaller than this limit corresponds to a dangerous over-discharging operating condition. The capacity of a battery is measured in Ampere-hours (Ah) defined as the total charge that can be discharged from a fully charged battery under specified conditions. The rated Ah capacity is the nominal capacity of a fully charged new battery under the conditions predefined by the catalog specifications of the battery, e.g. the nominal condition could be defined as room temperature 25°C and battery discharging is at 1/25 C-rate. The C-rate is used to represent a charge or discharge rate equal to the capacity of a battery in 1 hour, e.g. for a 6 Ah battery, 1C-rate is equal to charge or discharge the battery at a constant current value of 6 A; in the same way, 0.1C-rate is equivalent to 0.6 A, and 2C-rate for charging or discharging the battery at a constant current value of 12 A. The power density of the battery is an important criterion of battery selection that is defined as the peak power per unit volume of a battery (W/l). In the battery model dynamics, the effect of its internal resistance is significant. It is defined as the overall equivalent resistance within the battery. Also, it is worth to mention that its value varies during the charging and discharging battery cycles, and moreover may vary as the operating condition changes. The peak power according to the U.S. Advanced Battery Consortium (USABC)‘s definition is given by:
P=2VOC29RE1
where VOC is the open-circuit voltage (OCV) and R is the battery internal resistance.
The peak power is defined at the condition when the terminal voltage is 2/3 of its OCV. The SOC of the battery provides an important feedback about the state of health (SOH) of the battery and its safe operation. SOC is defined as battery available capacity expressed as a percentage of its rated capacity. More precisely, the SOC can be defined as the remaining capacity of a battery and it is affected by its operating conditions such as load current and temperature:
SOC=Remaining capacityRated capacityE2
The SOC for a fully charged battery is 100% and for an empty battery is 0%, defined for a discharging cycle, when discharging battery current is positive, as:
SOCt=1001−ηAhnom∫0tiτdτ%,iτ≥0E3
where η is the coulombic efficiency of the charging or discharging battery cycle, Ahnom represents the nominal battery capacity, iτ is the instantaneous value of charging (it≤0) or discharging (it≥0) battery current.
The relation (3) can be also written as a first order differential equation that further it will be used for SOC state estimation:
ddtSOCt=−100η×itAhnom,it≥0E4
The SOC is a critical condition parameter for battery management system (BMS), often affected by its operating conditions such as load current and temperature; consequently, an accurate estimation of SOC is very important, since it is the key issue for the healthy and safe operation of batteries. The depth of discharge (DOD) is used to indicate the percentage of the total battery capacity that has been discharged at time t, defined as:
DODt=1001−SOCt%E5
The SOH of the battery is defined as the ratio of the maximum charge capacity of an aged battery to the maximum charge capacity when this battery was new. Its life cycle (number of cycles) is given by the number of discharging-charging cycles that the battery can withstand at a specific DOD (normally 80%) before it fails to meet the desired performance criteria. The actual operating life of the battery is affected by the charging and discharging rates, DOD, and by the temperature. The higher the DOD is the shorter will be the cycle life. To attain a higher life cycle, a larger battery is required to be used for a lower DOD during normal operating conditions.
The Battery Management System is an integrated battery structure consisting of measurement sensors, controllers, serial communication, and computation hardware with software algorithms such as Proportion Integral Derivative (PID) and adaptive control laws, Kalman filters estimators, adaptive or sliding mode observers designed to decide the maximum charging/discharging cycles current and the duration from the estimation of SOC and SOH of the battery pack, as is shown in [11, 13, 14, 15].
A BMS as an important connector between the battery and the HEV plays a vital role in improving battery performance and optimizing vehicle operation in a safe and reliable manner, as is mentioned in [13]. Nowadays, the trend of the BMSs is a rapid growth of the EV and HEV market, thus it is essential for the automotive industry to develop a comprehensive and mature BMSs. As is stated in [13]”the U.S. Council for Automotive Research (USCAR) and the U.S. Advanced Battery Consortium (USABC) have set minimum goals for battery characteristics for the long-term commercialization of advanced batteries in EVs and hybrid electric vehicles (HEVs)”. Furthermore, to increase the market segment of EVs and HEVs automotive industry, the main concerns such as reliability and safety remain constantly for both of them, battery technology (BT) and BMS.
The BMS hardware and software components, the safety circuitry incorporated within the battery packs play an important role to monitor, control, compute and to show continually the safety state, the SOC, SOH, as well as the longevity of the battery.
Moreover, in [13] is emphasized one of the most dangerous situations such as the ignition of a Li-Ion battery during overcharging operating conditions, due to the volatility, flammability and entropy changes. Also, after repeated over-discharging cycles, the battery cell capacity is reduced significantly, due to irreversible chemical reactions, and thus the BMS needs to monitor and control constantly the Li-Ion battery state. Whenever any abnormal conditions happen, such as self-discharge leakage current through the insulation resistance of the battery, well-known as ground insulation resistances of the negative and positive bus of BMS Rp, and Rn respectively, over-voltage or overheating operating conditions are identified, and in a very short time the BMS should notify the user and to execute the preset correction procedures [13, 16]. In addition to these foremost functions, the BMS also monitors the system temperature to provide a better power consumption scheme, and communicates with individual components and operators [13]. Technically, a comprehensive BMS is equipped with the most suitable hardware and software components of the newest generation [14, 15], integrated in the HEVs and EVs structure to accomplish the main following functions: (1) real-time monitoring of battery states by a performing data acquisition system of external signals (i.e. voltage, current, cell temperature etc.); (2) ensure user safety protection, and extend the battery life; (3) using performing and intelligent algorithms (as for example, genetic, fuzzy logic, neural networks and expert systems based on artificial intelligence) has the ability to estimate and monitor the battery internal parameters and states (i.e. the DC resistance, insulation resistances Rp, and Rn, polarization voltage, maximum available capacity, SOC, SOH, etc.); (4) the ability to prevent over-charge or over-discharge of the battery; (5)efficient battery energy utilization, thermal management and SOC cell balancing; (6) delivery of battery status and authentication to a user interface; (7) the ability to communicate with vehicle controller and all other components [13, 16]. In order to achieve these objectives, researchers focus on battery modeling, SOC estimation, consistency evaluation and equalization, such as stated in [16].
Furthermore, to increase the effectiveness of BMS in [13] is proposed a new BMS with the following categories of components:
A. Hardware with the following components:
Safety circuitry; Sensor system; Data acquisition; Charge and discharge control; Communication; Thermal management.
The sensor system block integrates different sensors capable to monitor and measure battery parameters including cell voltage, battery temperature, and battery current. The new proposed BMS in [13] it seems to have a lot of improvements in terms of current safety circuitry designs that can be easily implemented, amongst them is worth to mention the addition of accurate alarms and controls to prevent overcharge, over-discharge, and overheating. Therefore, the external signals such as the current, voltage, and temperature must be measured to improve the capability of state tracking in real life applications [13]. Data acquisition (DAQ) block in conjunction with the data storage block are critical parts for the software in the BMS to analyze and build a database for system modeling and estimation algorithms development.
The charge and discharge control block is integrated in hardware architecture of the BMS to implement the charge-discharge protocol. A variable resistor may be necessary to help balance cells or perform internal resistance measurements. In [13] is mentioned also that the “cell balancing control is still a critical design feature with room for improvement in order to equalize the battery pack and estimate the battery status in an efficient way”. Most subsystems in a BMS are stand-alone modules, and hence, data transfer throughout the BMS is required. Communication through a CAN Bus is a major way to transfer data between the stand-alone subsystems within the BMS [13].
Furthermore, the recent development of smart batteries within the context of innovative electronics and artificial intelligence, more data can be collected to communicate with the user and the charger through the microchips incorporated within the battery [13]. In addition, “wireless and telecommunication techniques are gradually being incorporated into charging systems that facilitate communication between the battery and the charger”. Finally, the last thermal management hardware module proved during the time that its integration in a BMS is critical to be integrated since the temperature variations between the cells have a great impact on cell imbalance, reliability and performance [13].
B. Software with the following components:
SOC estimation and monitoring; SOH estimation and monitoring; Cell balancing; Fault Detection and Isolation; User interface.
The software compartment integrated in BMS structure is in reality such as an artificial “brain” that controls all hardware operations and examines all sensors data for making decisions and to implement in real-time linear, nonlinear and intelligent SOC, SOH estimators, and also FDI techniques. As is mention in [13], “the switch control, sample rate monitoring in the sensor system block, cell balancing control, and even dynamic safety circuit design should be handled by the software of a BMS”. In addition, the software components assure online data processing, and also reliable and robust automated data analysis by controlling battery functions, representing an essential factor to perform successfully the SOC, SOH estimation and fault detection and isolation algorithms. The robust SOC and SOH estimators are design to integrate also a capability assessment of the life status of the battery and to “sets the operating limits according to state-of-the-art algorithms, such as fuzzy logic, neural networks, state-space-based models, and so on “[13]. Most of the soft battery faults are detected through online data processing by means of an intelligent data analysis that provides information about the occurrence of the faults and their persistence by fault warnings, and also indicates abnormal limits of operating conditions. The history of the past measurements data set is recorded and provides the pre-alarm condition before the possible battery faults. In [17, 18] is proposing a real-time diagnosis approach that detects, isolates, and estimate specific sensor faults, such as battery voltage, current temperature, and fan motor. The sensor fault estimation can provide great benefits for enhancing the reliability of BMSs. Also, the sensor fault estimation can provide fault-tolerance capability to the BMS by allowing it to continue in degraded and in safe mode even after the sensor faults occur, as is mentioned in [17]. Failure in Li-Ion batteries can be also attributed to a combination of manufacturing defects, safety component failure, or human operating errors, as is specified in [18]. Furthermore, in [18] are developed some methods for realistic fault injection. Two types of sensor faults are considered in [18], namely an intermittent signal loss due to faulty wiring connections and sensor bias resulting from time or temperature drift. For the fan motor, the only fault considered is a total motor failure, and thus no longer provide cooling to the battery. The occurrence of the realistic battery faults are established based on the preset thresholds. These thresholds are computed as a minimum values that assure the prevention of the false alarms, based on the minimization of the residual error probability when occurs a fault, by studying the probability density functions (PDF)) of the healthy and faulty signals, such is shown in [18]. The PDfs are obtained through extensive experimental data collection or simulations on the system.
Roughly, for many faults scenarios to compute the magnitude of the thresholds we can use also the statistics of the residual errors for healthy system between the measured values data set and its estimated values, such as the mean μX and the standard deviation (σ) calculated in steady state. The threshold can be calculated by addition to the mean μX of residual error approximatively two times the standard deviation 2σ. The decision for occurrence of the fault is given by comparison of the residual error and the computed threshold. The tuning of the threshold to avoid the false alarms can be done by well-known trials and error method. The user interface block must display the essential information of the BMS to the users.
2.2. Li-Ion battery EMC description
The equivalent model circuits (EMCs) are simple electric circuits consisting of voltage sources, resistors and capacitors networks, commonly-used to simulate the dynamic behavior of the Li-Ion batteries [1, 8, 11]. The role of the resistor-capacitor (RC) series networks integrated in EMCs is to improve models’ accuracy, and also to increase the structural complexity of the models [1, 8]. In this research paper we chose for Li-Ion battery model design and implementation stages an equivalent second order model RC circuit. It consists of a resistor R, two series RC cells (R1C1, R2C2), and an open-circuit voltage (OCV) source dependent of SOC, i.e. OCV (SOC), as is shown in Figure 1(a), in NI Multisim 14.1 editor. In EMC architecture, R denotes the internal ohmic resistance of the Li-Ion battery, the parallel cells (R1, C1), (R2, C2) connected in series denote the electrochemical polarization resistances and capacitances, for which T1 = R1C1, T2 = R2C2 can imitate the fast polarization time constant, and the slow polarization time constant for the voltage recovery of the Li-Ion battery respectively {14]. Also, it is the battery instantaneous value of the direct current (DC) flowing through the OCV source, and vt represents the measured battery instantaneous value DC voltage. The main benefit of the RC second order EMC architecture selection is its simplicity and the ability to be implemented in real-time applications with acceptable range of performance [1, 8].
Figure 1.
The second order RC equivalent model circuit (EMC) 1(a), and the insulation resistances Rn and Rp 1(b) of Li-Ion battery, represented in NI Multisim 14.1 editor (see for details [16]).
Also “this choice is due to the early popularity of BMS for portable electronics, where the approximation of the battery model with the proposed EMC is appropriate”, as is mentioned in [1, 8]. Related, this approach has been extended easily to Li-Ion batteries for automotive industry and for many other similar energy storage applications [11]. Since the EMC is used in following sections to design and implement in real-time all three proposed nonlinear state estimators, now it is essential to precise the main factors that affects the battery parameters, especially the internal and insulation resistances and their impact on the battery dynamics in a realistic operating conditions environment. The internal resistance of the battery is affected by the following factors: conductor resistance, electrolyte resistance, ionic mobility, separator efficiency, reactive rates at the electrodes, and concentration polarization, temperature effects and changes in SOC. When a battery fails, it is typically since it has built up enough internal resistance that it can no longer supply a useful amount of power to an external load, according to the maximum power transfer between the source and the load. The insulation positive and negative resistances of the battery are denoted by Rn, and Rp respectively, and are shown in the diagram from Figure 1(b). The main objective is to get a good insight on the impact of the insulation resistances on the battery dynamics, as is described in detail in [16]. Also, in [16] the HEV is considered as a “complex production of mechanical-electrical integration”, for which the power supply typically being in the range 100–500 V is obtained by means of several series battery packs. Amongst the BMS hardware devices consisting of high voltage components, the traction battery, electrical motor, energy recycle device, the battery charger and its auxiliary device deal with a large current and insulation [16], thus insulation issue must be under consideration since the design stage. As is stated in [16], the poor working conditions, such as shaking, corrosion, changes in temperature and humidity, “could cause fast aging of the power cable and insulation materials, or even brake the insulation, which would decrease the insulation strength and endanger personnel”. Thus, needs to ensure safety operating conditions for personnel are required to evaluate the insulation conditions for entire HEV’s BMS.
The National Standard (NS) 18384.3-2001, stipulates several safety requirements for HEVs, especially for insulation resistance state, measurement method [16]. According to NS, the insulation state of an EV is evaluated according to the ground insulation resistance of the DC positive and negative bus, Vp, and Vn respectively, as is shown in Figure 1(b) [16]. The definition of traction battery insulation resistance in NS “is the relative resistance to maximum leakage current (in the worst condition) where there is a short between the traction battery and ground (electric chassis)” [16]. Also, “under the conditions that the maximum AC voltage is less than 660 V, the maximum DC voltage is less than 1000 V and the car weight is less than 3500 Kg, the requirements of the high voltage security are as follows: (1) personnel’s security voltage is less than 35 V, or the product of the contact current with a person and the duration of time is less than 30 mA s; insulation resistance divided by the battery rated voltage should be more than 100ΩV−1, and preferably more 500ΩV−1”, as is formulated in [16]. Thus, to ensure the insulation security of on-board BMS, it is necessary to detect the insulation resistance and raise an alarm in time. Currently, the insulation measurement methods, such as passive ground detection and active ground detection, include the AC voltage insulation measurement method, and the DC voltage insulation measurement method [16]. The passive ground detection insulation measurement method principle is presented in Figure 1(b) where the presence of leakage current is detected by discovering the resultant magnetic field generated by AC voltage around the mutual inductor.
2.3. Li-Ion battery continuous time state-space representation
By simple manipulations of Ohm’s, current and voltage Kirchhoff’s laws applied to the proposed Li-Ion battery’s EMC can be written the following first order differential equations that are most suitable to capture the entire dynamics of EMC in time domain:
By defining the following state variables x1=vC1t,x2=vC2t, and x3=SOCt, representing the polarization voltages of the two RC cells, and considering as battery input u the DC instantaneous current that flows through battery, u=it, and as a battery output y, designating the terminal battery DC instantaneous voltage, i.e. y=vt, the Eq. (6) can be written in a state-space representation, as follows.
where T1=R1C1s,T2=R2C2s represent the time constants of the both polarization RC cells, and the OCV combines three additional well-known models, as defined and used in [4, 8], given by:
OCVx3t=hx3t=K0−K11x3t−K2x3t+K3lnx3t+K4ln1−x3tE8
For simulation purpose, in order to validate the proposed battery model, as well as to prove the effectiveness of the SOC estimation strategies developed in Section 3, we consider as the most suitable nominal values for Li-Ion battery EMC parameters and OCV coefficients the same values that are carefully chosen for model validation in [8] as follows
Li-Ion battery ECM parameters:
The battery internal ohmic resistance (slightly different for charging and discharging cycles), R = 0.0022 [Ω] (ohms), the first RC cell polarization resistance and capacitance, R1 = 0.00077 [Ω], C1 = 14475.24 [F], and the second RC cell polarization resistance and capacitance, R2 = 0.0011 [Ω], C2 = 98246.01 [F] (Farads).
Li-Ion battery characteristics:
The value of the battery capacity, Q = 6 Ah (Amperes hours), the voltage nominal value of the battery, Vnom = 3.6 V (Volts), and the coulombic efficiency, η = 1, for charging cycle, and η = 0.85, for discharging cycle
The OCVx3t is a nonlinear function of SOC, i.e.x3, similar as in [1, 3, 4, 8, 9, 10, 11], increasing the accuracy of the Li-Ion battery EMC model, known as the EMC combined model, one of the most accurate formulation by combining Shepherd, Unnewehr universal, and Nernst models introduced in [4, 8]. The tuning values of model parameters K0K1K2K3K4 are chosen to fit the model to the manufacture’s data by using a least squares curve fitting identification method OCV = h (SOC), as is shown in [3, 4, 8], where the OCV curve is assumed to be the average of the charge and discharge curves taken at low currents rates from fully charged to fully discharged battery [3, 4, 6, 8]. Also, by using low charging and discharging DC currents can be minimized the battery cell dynamics. A simple offline (batch) processing method for parameters calculation is carried out in [3, 4, 6, 8].
2.4. Li-Ion battery discrete time state-space representation
We introduce now the following new notations:
x1k=x1kTs,x2k=x2kTs,x3k=x3kTs, for state variables, uk=ukTs,yk=ykTs for input current profile, and output battery terminal DC samples voltage respectively, commonly used for discrete time description with the sampling time Tss, similar as in [1, 3, 4, 6, 8, 9, 10, 11].
With these notations, a discrete time state space representation of the combined EMC Li-Ion battery model is obtained::
where the nonlinear function Ψx3k can be further linearized around an operating point to get a linear Li-Ion battery combined EMC, easily to be implemented in real-time. To analyze the behavior of the proposed Li-Ion battery EMC for different driving conditions such as urban, suburban and highway, some different current profiles tests will be introduced in the next subsection.
2.5. Li-Ion battery equivalent model in ADVISORY MATLAB platform—case study
For EMC validation purpose we compare the results of the tests using a NREL with two capacitors already integrated in an Advanced Vehicle Simulator (ADVISOR) MATLAB platform, developed by US National Renewable Energy Laboratory (NREL), as is shown in [9, 10]. The NREL Li-Ion battery model approximates with high accuracy the Li-Ion battery model 6 Ah and nominal voltage of 3.6 V, manufactured by the company SAFT America, as is mentioned in [9, 10]. Also, for simulation purpose and comparison of the tests results, the EMC battery model is incorporated in a BMS’ HEV, and its performance is compared to those obtained by a particular Japanese Toyota Prius, selected as an input vehicle in ADVISOR MATLAB platform, under standard initial conditions and the setup shown in Figure 2.
Figure 2.
The setup of the Japanese Toyota Prius HEV’ car in ADVISOR MATLAB platform under standard initial conditions (initial value of SOC of 70%).
Among different driving speed cycles for a large collection of cars provided by the ADVISOR US Environmental Protection Agency (EPA), in our case study for Toyota Prius HEV car, the speed profile is selected as an Urban Dynamometer Driving Schedule (UDDS), as is shown in Figures 3 and 4, respectively.
Figure 3.
The UDDS cycle profile for Toyota Prius car speed test represented on the ADVISOR MATLAB platform.
Figure 4.
The corresponding car speed, current profile, exhaust gases emission, and SOC curves to UDDS cycle tests in ADVISOR MATLAB platform.
Both, the driving UDDS cycle for car speed (mph) profile and its corresponding Li-Ion battery UDDS current profile are represented separately in the same ADVISOR MATLAB platform, as is shown in the first two corresponding top graphs from Figure 4.
Also, the exhaust gases emission and SOC curves, as a result to the same UDDS cycle test on the ADVISOR MATLAB platform, are shown in the last two bottom graphs of Figure 4, accompanied by details in the next section.
The Li-Ion battery EMC and the ADVISOR SOCs, for the same initial condition, SOC = 70% and the same UDDS cycle profile input current test, are implemented in a real-time MATLAB environment, as is shown on the same graph at the top of Figure 5. The SOC simulations reveal a great accuracy between Li-Ion battery EMC and its corresponding ADVISOR NREL model represented on ADVISOR MATLAB platform, as is shown in Figure 4, thus an expected confirmation of the EMC validation results.
Figure 5.
The SOC curves for Li-Ion battery EMC and NREL/6 Ah SAFT models in MATLAB R17a (the top graphs), and its corresponding EMC battery output terminal voltage, Vbatt (the second bottom graph).
The second graph from the bottom in the same Figure 5 depicts the EMC battery output terminal DC instantaneous voltage y=vt, as a response to the UDDS cycle current input profile test.
The simulation results of Li-Ion battery EMC output voltage show a stabilization of the battery output voltage value at the end of UDDS cycle, i.e. after 1370 seconds.
Likewise, the Li-Ion battery OCV for a discharging cycle at 1C-rate (i.e. 6 Ah × 1/h = 6A constant input discharging current) is shown in the top of Figure 6, and its corresponding SOC during the same discharging cycle is revealed on the bottom graph of the same Figure 6 respectively.
Figure 6.
The Li-Ion battery EMC OCV curve during a complete discharging cycle at 1C-rate (top) and the corresponding battery SOC (bottom).
3. Development and implementation in real-time of SOC Li-Ion battery estimators on MATLAB/SIMULINK platform
In this section we propose for Li-Ion battery EMC SOC estimation three nonlinear on-board real-time estimators integrated in BMS of HEV, based on Kalman Filter (KF) technique, specifically a nonlinear Gaussian Unscented Kalman Filter (UKF), a non-Gaussian nonlinear Particle Filter (PF), and a nonlinear observer estimator (NOE). The simulations results and a comprehensive performance analysis for each proposed SOC estimator are presented in the following subsections of this section.
3.1. Unscented Kalman filter real-time estimator design and robustness analysis
The main aim of this subsection is to build a nonlinear UKF SOC estimator, following the same design procedure described rigorously in [5]. We are motivated by some preliminary results obtained in our research, as you can see in [6, 7]. Technically, UKF estimator is based on the principle that one set of discrete sampled points parameterizes easily the mean and the covariance of a Gaussian random variable, as is stated in [5]. Moreover, the nonlinear estimator UKF yields an equivalent performance compared to a linear extended Kalman filter (EKF), well documented in [3, 4, 8], excluding the linearization steps required by EKF. In addition, the results of UKF real-time implementation for the majority of similar applications are encouraging, and it seems that the anticipated performance of this approach is slightly superior compared to EKF [3, 4, 8]. Furthermore, the nonlinear UKF SOC estimator can be extended to the applications where the distributions of the process and measurements noises are not Gaussian [19]. Concluding, the implementation simplicity and a great estimation accuracy of the proposed UKF SOC estimator recommend it as the most suitable estimator to be used in almost all similar applications. Explicitly, the nonlinear UKF estimator is an algorithm of “predictor–corrector” type applied to a nonlinear discrete - time systems, such as those described in [3, 4, 5, 6, 7]:
xk+1=fxkukk+wkE13
yk=gxkukk+νkE14
where f., g. are two nonlinear functions of system states and inputs; wk, vk are zero-mean, uncorrelated process, and measurement Gaussian noise respectively. Since the noise injected in the state and output equations are randomly, also the system state vector and the output become random variables, having the mean and covariance matrices as a statistics. The nonlinear UKF SOC estimator has a “predictor-corrector” structure that propagates the mean and the covariance matrix of a Gaussian distribution for the random state variable xk in a recursively way, in both prediction and correction phases, as is stated in [3, 4, 5, 6, 7]. The propagation of these first two moments is performed by using an unscented transformation (UT) to calculate the statistics of any random variable that undergoes a nonlinear transformation [5, 6, 7]. As is stated in the original and fundamental work [5], by means of this UT transformation “a set of points, the so-called sigma points, are chosen such that their sample mean, and sample covariance matrix arex¯andPxx, respectively”. Also, “the nonlinear functiong.is applied to each sigma points generating a “cloud” of transformed points with the meany¯and the covariance matrixPyy”. Furthermore, since statistical convergence is not an issue, only a very small number of sample points is enough to capture high order information about the random state variable distribution. The different selection strategies of the sigma points and the UKF algorithm steps in a “predictor-corrector” structure, as well as its tuning parameters are well documented in the literature, and for details we recommend the fundamental work [5]. Since we follow the same design procedure steps for building a nonlinear UKF SOC estimator as in [5, 6, 7], we are focused only to the implementation aspects.
The simulation results of the real-time implementation of proposed UKF SOC nonlinear estimator in MATLAB R17a environment are shown in Figures 7 and 8. In Figure 7 are presented the simulation results for EMC SOC true value versus EMC SOC-UKF and ADVISOR MATLAB platform estimated values.
Figure 7.
Li-Ion EMC SOC and output terminal battery DC voltage versus Li-Ion EMC-UKF and ADVISOR estimated values for an UDDS cycle current profile test.
Figure 8.
Li-Ion EMC polarization DC voltages versus Li-Ion EMC-UKF estimated values for an UDDS cycle current profile test.
Also, at the bottom of Figure 7 is shown the EMC battery output terminal true value DC voltage versus EMC-UKF battery output terminal estimated DC voltage. The simulation results reveal an accurate SOC estimation values, and also a very good robustness of UKF estimator to the changes in initial SOC value (guess value, SOCinit = 20%). In Figure 8 are shown the Li-Ion EMC polarization voltages versus Li-Ion EMC -UKF estimates, and in Figure 9 is represented the robustness of EMC-UKF SOC nonlinear estimator to a gradual increase in the internal resistance by 1.5 until 2 times of its initial value. The simulation results reveal a significant decrease in Li-Ion EMC UKF SOC estimator performance to an increase in internal resistance, but still remains convergent to EMC measurements after a long transient.
Figure 9.
The robustness of Li-Ion EMC-UKF estimator to the changes in internal battery resistance for an UDDS cycle current profile test.
3.2. Particle filter real-time estimator design and robustness analysis
In this subsection we propose a real-time PF SOC nonlinear estimator with a similar” prediction-corrector” structure found to the nonlinear UKF SOC estimator described in the previous subsection 3.1. Consequently, is expected that the proposed nonlinear PF SOC estimator to update recursively an estimate of the state and to find the innovations driving a stochastic process given a sequence of observations, as is shown in detail in the original work [19]. In [19] is stated that the PF SOC estimator accomplishes this objective by a sequential Monte Carlo method (bootstrap filtering), a technique for implementing a recursive Bayesian filter by Monte Carlo simulations.
The process state estimates are used to predict and smooth the stochastic process, and with the innovations can be estimated the parameters of the linear or nonlinear dynamic model [19]. The basic idea of PF SOC estimator is that any probability distribution function (pdf) of a random variable can be represented as a set of samples (particles) as is described in [19], similar thru sigma points UKF SOC estimator technique developed in subsection 3.1 [5]. Each particle has one set of values for each process state variable. The novelty of this method is its ability to represent any arbitrary distribution, even if for non-Gaussian or multi-modal pdfs [5].
Compared to nonlinear UKF SOC estimator design, the nonlinear PF SOC estimator has almost a similar approach that does not require any local linearization technique, i.e. Jacobean matrices, or any rough functional approximation. Also, the PF can adjust the number of particles to match available computational resources, so a tradeoff between accuracy of estimate and required computation. Furthermore, it is computationally compliant even with complex, non-linear, non-Gaussian models, as a tradeoff between approximate solutions to complex nonlinear dynamic model versus exact solution to approximate dynamic model [6, 19]. In the Bayesian approach to dynamic state estimation the PF estimator attempts to construct the posterior probability function (pdf) of a random state variable based on available information, including the set of received measurements. Since the pdf represents all available statistical information, it can be considered as the complete solution to the optimal estimation problem. More information useful for design and implementation in real-time of a nonlinear PF estimator can be found in the fundamental work [19]. Since we follow the same design procedure steps to build and implement in real-time a nonlinear PF estimator, as is developed in [19], we are focused only on the implementation aspects. The simulation results in real-time MATLAB R17a environment are shown in Figures 10 and 11.
Figure 10.
EMC SOC and output terminal voltage versus EMC-PF estimated values during UDDS cycle current profile test.
Figure 11.
Li-Ion EMC polarization voltages versus Li-Ion EMC-PF estimated values during UDDS cycle current profile test.
In Figure 10 are shown the simulation results for EMC SOC true value versus EMC SOC-PF and ADVISOR MATLAB platform estimated values. The number of filter particles is set to 1000, a very influent tuning parameter for an accurate SOC estimation performance. At the bottom of Figure 10 are shown the Li-Ion EMC battery output terminal true values of DC instantaneous voltage versus Li-Ion EMC battery output terminal DC voltage estimated by the nonlinear PF estimator. In Figure 11 are shown the Li-Ion EMC polarization DC voltages versus EMC PF estimates. In Figure 12 is shown the robustness test of Li-Ion EMC-PF SOC nonlinear estimator to a gradual increase in the internal battery resistance by same values considered for Li-Ion EMC-UKF SOC estimator.
Figure 12.
The robustness of EMC-PF estimator to the changes in internal battery resistance for an UDDS cycle current profile test.
The simulation results reveal a good robustness and convergence of Li-Ion EMC-PF estimator, but with a lot variance in the estimated values. Overall, the simulation results reveal a fast PF estimator convergence, a good SOC filtering, an accurate SOC estimation value, and also a very good robustness of PF estimator to big changes in the initial SOC value (guess value, SOCinit = 20%), and slightly slow behavior to an increase in internal resistance of the Li-Ion battery.
3.3. Nonlinear observer real-time estimator
In this subsection, a nonlinear observer SOC estimator (NOE) is under consideration. It is proposed to have more flexibility for a suitable choice of the best Li-Ion battery SOC estimator amongst UKF, PF, and NOE SOC estimators. We follow the same design procedure steps for its design and implementation in a real-time MATLAB R2017a simulation environment as in [1]. The estimator design is based on an important information provided by the linear structure of the matrix Eq. (9).
According to this structure all three state variables x1k,x2k,x3k=SOCk change independently. Precisely, the nonlinear, linear and sliding mode observers are most applied in state estimation problems to eliminate state estimation error using deviation feedback, as is mentioned in [1]. Furthermore, for Li-Ion battery SOC estimation, most of existing observers are model based using for structure design the difference between the estimated value of battery output terminal DC instantaneous voltage and its corresponding measured DC voltage value multiplied by the observer gains to correct the dynamics of all estimated states, as follows:
where the matrices A and B are the same as in Eq. (9), x̂k is the estimate of the EMC states vector, and Lk is the observer gains vector (similar to the extended Luenberger observer for nonlinear systems). The particular structure of Li-Ion battery EMC reveals that the output estimation error ey is mainly caused by an inaccurate SOC estimated value, as is stated also in [1]. Consequently, only the SOC state estimate from third discrete state Eq. (15) will be affected, i.e. the observer gains vector becomes:
l1k=0,l2k=0,l3k≠0E16
This outcome improves significant the NOE SOC estimation accuracy and simplifies the structural complexity of the proposed nonlinear observer estimator. The dynamics of the nonlinear observer estimation errors can be described by the following differential equations [1]:
In [1] is proved that all three states estimation errors described by the system of Eq. (17) converge asymptotically to zero in steady-state, and the observer gain for the new simplified structure is approximated by an adaptive law:
l3k=l30+αeβey,l30>0,α<0,β<0E18
that allows the value of l3k to change dynamically according to the deviation between the measured battery output DC voltage and battery EMC output DC voltage. In Eq. (18), l30,α and β are tuning parameters designed to adjust the adaptive property of l3k. Amongst them, l30 determines the convergence rate of the proposed NOE at first “inaccurate” stage, the coefficients α and β are used to adjust observer gain l3k when the SOC state estimation also reaches “accurate” stage, as is stated in [1].
Furthermore, three main assumptions are formulated in [1] to tune the values of EMC-NOE parameters l30,α and β: (a) l3k≥0 to ensure the stability of the proposed NOE; (b) if SOC state estimation error is large, the value of l3k should be big enough to ensure a fast convergence rate; (c) if the voltage estimation error is small, the value of l3k should be small enough to avoid SOC estimation “jitter” effect. By extensive simulations performed in a real-time MATLAB R2017a simulation environment the requirements (a), (b), (c) are met if the NOE parameters l30,α and β are tuned for the following values: l30 = 0.3, α = −0.01, and β = − 1. The simulation results on the estimation performance of Li-Ion EMC-NOE are shown in Figures 13 and 14.
Figure 13.
Li-Ion EMC SOC and output terminal DC voltage versus Li-Ion EMC-NOE estimated values during UDDS cycle current profile test.
Figure 14.
Li-Ion EMC-NOE polarization DC voltages versus Li-Ion EMC-NOE estimated values during UDDS cycle current profile test.
In Figure 13 are shown the simulation results for Li-Ion EMC SOC true value versus Li-Ion EMC SOC-NOE and ADVISOR MATLAB platform estimated values. At the bottom of Figure 13 are presented the Li-Ion EMC battery output terminal true values DC voltage versus Li-Ion EMC battery output terminal DC voltage estimated by the proposed Li-Ion EMC NOE SOC estimator. In Figure 14 are displayed the Li-Ion EMC polarization DC voltages versus Li-Ion battery EMC-NOE estimates.
In Figure 15 is depicted the robustness of Li-Ion EMC-NOE SOC estimator to an increase in internal battery Li-Ion resistance with the same values as used for the previous nonlinear estimators, EMC UKF and EMC PF respectively.
Figure 15.
The robustness of Li-Ion EMC-NOE estimator to the changes in internal battery resistance for an UDDS cycle current profile test.
The simulation results in Figure 15 reveal slightly slow robustness to an increase in internal resistance of Li-Ion battery compared to simulation results from Figure 13, but still the convergence is reached in a long transient with much variation in the SOC estimates. Overall, the simulation results from this section reveal a fast NOE estimator convergence, a good SOC filtering, an accurate SOC estimation value, and also a very good robustness of PF estimator to big changes in the initial SOC value (the same guess value as for UKF and PF, SOCinit = 20%), compared to a gradual degrade in SOC estimation performance to an increase in internal battery resistance.
4. Real-time implementation of the Li-Ion battery SOC estimators on MATLAB/SIMULINK platform: Results comparison
For comparison purpose, we represent on the same graph the SOC estimates of all three real-time nonlinear estimators (UKF, PF, and NOE) versus EMC SOC true value and ADVISOR MATLAB SOC estimate, as are revealed in Figures 7, 10 and 13. The simulation results in Figure 16 disclose a precious information about the convergence of all three proposed estimators to Li-Ion EMC SOC true values for the proposed Li-Ion battery EMC. Furthermore, a useful benchmark is built in terms of the statistics errors between the states estimates and the corresponding model states values, such as root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE), defined in [6]. They are very easy to be computed in MATLAB R2017a, and the results are shown in Table 1. The MSE is a measure of how close the estimates values fit to model “true” values. The squaring is done so negative values do not cancel positive values. The smaller the MSE, the closer the fit of the estimates values is to the model “true” values. The RMSE is just the square root of the MSE [6].
Figure 16.
EMC SOCs and output terminal voltages versus EMC, EMC-UKF, EMC-PF, and EMC-NOE estimated values during UDDS cycle current profile test.
UKF estimator
PF estimator
NOE
MAE
MSE
RMSE
MAE
MSE
RMSE
MAE
MSE
RMSE
0.0915
26.1030
0.1380
0.0572
4.9724
0.0602
0.0247
2.4584
0.0424
Table 1.
Statistics on SOC estimation performance of the proposed nonlinear estimators UKF, PF and NOE - Benchmark.
RMSE=∑i=1i=Nxesti−xmodeli2N=MSE The RMSE is probably the most easily interpreted statistic, since it has the same units as the model states. Similar as MSE, lower RMSE the state estimates fit better the model states, i.e. battery SOC and the battery polarization voltages. The MAE statistic is helpful to determine the accuracy of the Li-Ion battery EMC-UKF, EMC-PF and EMC-NOE SOC estimates with respect to the “true” model values. It is usually similar in magnitude to RMSE, but slightly smaller, and has the same units as the model states data set. MAE=∑i=1i=Nxesti−xmodeliN The statistics of errors from benchmark reveal that EMC nonlinear observer estimator (EMC-NOE) outperforms in terms of all statistics (RMSE, MSE, MAE) the UKF and PF estimators. Consequently, for this kind of applications the EMC-NOE SOC estimator is the most suitable estimator compare to UKF and PF estimators.
5. Conclusions
The main contribution of this research paper is the design and implementation in real time of a three robust nonlinear estimators, namely UKF, PF and NOE, capable to estimate with high accuracy and robustness the Li-Ion battery SOC based on a simple battery 2R EMC without disturbance uncertainties. The simulation results obtained in a real-time MATLAB simulation environment reveal that amongst all three proposed nonlinear SOC estimators the EMC NOE is a most suitable alternative to SOC UKF and PF estimators for this kind of application. The number of tuning parameters for SOC EMC NOE is much smaller than for UKF and PF estimators. The proposed SOC EMC NOE proves its effectiveness in terms of implementation simplicity, Li-Ion battery SOC estimation accuracy and robustness. Therefore, it can be considered as one of the most suitable nonlinear estimator, and a feasible alternative to UKF and PF estimators.
Conflict of interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
\n',keywords:"Li-Ion battery state-of-charge, state estimation, unscented Kalman filter estimator, particle filter estimator, nonlinear observer estimator, battery management system",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/62089.pdf",chapterXML:"https://mts.intechopen.com/source/xml/62089.xml",downloadPdfUrl:"/chapter/pdf-download/62089",previewPdfUrl:"/chapter/pdf-preview/62089",totalDownloads:1140,totalViews:588,totalCrossrefCites:1,dateSubmitted:"December 11th 2017",dateReviewed:"March 6th 2018",datePrePublished:"November 5th 2018",datePublished:"January 30th 2019",dateFinished:null,readingETA:"0",abstract:"The battery state-of-charge estimation is essential in automotive industry for a successful marketing of both electric and hybrid electric vehicles. Furthermore, the state-of-charge of a battery is a critical condition parameter for battery management system. In this research work we share from the experience accumulated in control systems applications field some preliminary results, especially in modeling and state estimation techniques, very useful for state-of-charge estimation of the rechargeable batteries with different chemistries. We investigate the design and the effectiveness of three nonlinear state-of-charge estimators implemented in a real-time MATLAB environment for a particular Li-Ion battery, such as an Unscented Kalman Filter, Particle filter, and a nonlinear observer. Finally, the target to be accomplished is to find the most suitable estimator in terms of performance accuracy and robustness.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/62089",risUrl:"/chapter/ris/62089",signatures:"Roxana-Elena Tudoroiu, Mohammed Zaheeruddin, Sorin-Mihai\nRadu and Nicolae Tudoroiuv",book:{id:"6767",title:"New Trends in Electrical Vehicle Powertrains",subtitle:null,fullTitle:"New Trends in Electrical Vehicle Powertrains",slug:"new-trends-in-electrical-vehicle-powertrains",publishedDate:"January 30th 2019",bookSignature:"Luis Romeral Martínez and Miguel Delgado Prieto",coverURL:"https://cdn.intechopen.com/books/images_new/6767.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"86501",title:"Dr.",name:"Luis",middleName:null,surname:"Romeral Martinez",slug:"luis-romeral-martinez",fullName:"Luis Romeral Martinez"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"239295",title:"Dr.Ing.",name:"Nicolae",middleName:null,surname:"Tudoroiu",fullName:"Nicolae Tudoroiu",slug:"nicolae-tudoroiu",email:"ntudoroiu@gmail.com",position:null,institution:{name:"John Abbott College",institutionURL:null,country:{name:"Canada"}}},{id:"239610",title:"Dr.",name:"Elena-Roxana",middleName:null,surname:"Tudoroiu",fullName:"Elena-Roxana Tudoroiu",slug:"elena-roxana-tudoroiu",email:"tudelena80@gmail.com",position:null,institution:null},{id:"239611",title:"Dr.",name:"Sorin-Mihai",middleName:null,surname:"Radu",fullName:"Sorin-Mihai Radu",slug:"sorin-mihai-radu",email:"sorin_mihai_radu@yahoo.com",position:null,institution:null},{id:"243050",title:"Dr.",name:"Mohamed",middleName:null,surname:"Zaheeruddin",fullName:"Mohamed Zaheeruddin",slug:"mohamed-zaheeruddin",email:"zaheer@encs.concordia.ca",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. The continuous and the discrete time state-space Li-Ion battery model representation",level:"1"},{id:"sec_2_2",title:"2.1. Li-Ion battery terminology",level:"2"},{id:"sec_2_3",title:"2.1.1. Li-Ion battery characterization architecture terminology",level:"3"},{id:"sec_3_3",title:"2.1.2. Li-Ion battery performance terminology",level:"3"},{id:"sec_4_3",title:"2.1.3. Battery management systems: architecture, trends, functions, monitoring, faults detection, hardware and software components",level:"3"},{id:"sec_6_2",title:"2.2. Li-Ion battery EMC description",level:"2"},{id:"sec_7_2",title:"2.3. Li-Ion battery continuous time state-space representation",level:"2"},{id:"sec_8_2",title:"2.4. Li-Ion battery discrete time state-space representation",level:"2"},{id:"sec_9_2",title:"2.5. Li-Ion battery equivalent model in ADVISORY MATLAB platform—case study",level:"2"},{id:"sec_11",title:"3. Development and implementation in real-time of SOC Li-Ion battery estimators on MATLAB/SIMULINK platform",level:"1"},{id:"sec_11_2",title:"3.1. Unscented Kalman filter real-time estimator design and robustness analysis",level:"2"},{id:"sec_12_2",title:"3.2. Particle filter real-time estimator design and robustness analysis",level:"2"},{id:"sec_13_2",title:"3.3. Nonlinear observer real-time estimator",level:"2"},{id:"sec_15",title:"4. Real-time implementation of the Li-Ion battery SOC estimators on MATLAB/SIMULINK platform: Results comparison",level:"1"},{id:"sec_16",title:"5. Conclusions",level:"1"},{id:"sec_20",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Xia B, Zheng W, Zhang R, Lao Z, Sun Z. Mint: A novel observer for Lithium-ion battery state of charge estimation in electric vehicles based on a second-order equivalent circuit model. Energies. 2017;10(8):1150. DOI: 10.3390/en10081150. Available from: http://www.mdpi.com/1996-1073/10/8/1150/htm. [Accessed: 2017-01-21]'},{id:"B2",body:'Young K, Wang C, Wang LY, Strunz K. Electric vehicle battery technologies–chapter 2. In: Garcia-Valle R, Lopes JAP, editors. Electric Vehicle Integration into Modern Power Networks. 1st, 9, and 325 ed. New-York, USA: Springer Link: Springer-Verlag; 2013. pp. 15-26. DOI: 10.1007/978-1-4614-0134-6.ch.2'},{id:"B3",body:'Plett GL. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs - part 1. In: Modeling and Identification, Power Sources. Vol. 134. Amsterdam, Netherlands: Elsevier B.V.; 2004. pp. 252-261. DOI: 10.1016/j.jpowsour.2004.02.031'},{id:"B4",body:'Plett GL. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs - part 2. In: Modeling and Identification, Power Sources. Vol. 134. Amsterdam, Netherlands: Elsevier B.V.; 2004. pp. 262-276. DOI: 10.1016/j.jpowsour.2004.02.032'},{id:"B5",body:'Simon JJ, Uhlmann JK. A new extension of the Kalman filter to nonlinear systems. [Internet]. In: Process of AeroSense, 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls; 1997. Available from: https://people.eecs.berkeley.edu/∼pabbeel/cs287-fa09/readings/JulierUhlmann-UKF.pdf. [Accessed: 2017-01-21]'},{id:"B6",body:'Tudoroiu N, Radu SM, Tudoroiu E-R. Improving Nonlinear State Estimation Techniques by Hybrid Structures. 1st ed. Saarbrucken, Germany: LAMBERT Academic Publishing; 2017. p. 56 ISBN: 978-3-330-04418-0'},{id:"B7",body:'Tudoroiu N, Zaheeruddin M, Cretu V, Tudoroiu E-R. IMM-UKF versus frequency analysis [past and present]. IEEE Industrial Electronics Magazine. 2010;4(3):7-18. DOI: 10.1109/MIE.2010.937937'},{id:"B8",body:'Farag M. Lithium-ion batteries. In: Modeling and state of charge estimation (Thesis). Ontario, Canada: McMaster University of Hamilton; 2013. p. 169'},{id:"B9",body:'Johnson VH. Battery performance models in ADVISOR. Journal of Power Sources, Elsevier Science B.V. Publishing. 2001;110:321-329'},{id:"B10",body:'Tremblay O, Dessaint L-A. A generic battery model for the dynamic simulation of hybrid electric vehicles. IEEE Xplore. 2007:284-289. DOI: 10.1109/VPPC.2007.4544139'},{id:"B11",body:'Xu J, Cao B. Battery management system for electric drive vehicles-modeling state estimation and balancing - chapter 4. In: Chomat M, editor. New Applications of Electric Drives. Croatia: Intech; 2015. pp. 87-113. DOI: 10.5772/61609'},{id:"B12",body:'Nordelof A, Messagie M, Tilmann A-M, Soderman ML, Mierlo JV. Environmental impacts of hybrid, plug-in hybrid, and battery electric vehicles. Journal of Life Cycle assessment. 2014;19(11):1866-1890. DOI: 10.1007/s11367-014-0788-0'},{id:"B13",body:'Xing Y, Ma EWM, Tsui KL, Pecht M. Battery management systems in electric and hybrid vehicles. Energies. 2011;4:1840-1857. DOI: 10.3390/en4111840'},{id:"B14",body:'Battery Management System. [Internet]. Available from the web site: https://www.nasa.gov/centers/johnson/techtransfer/technology/MSC-24466-1_Batt-Mgmt-Sys.html. [Accessed: 2018-02-10]'},{id:"B15",body:'Prophet G. 12-Cell Li-Ion Battery Monitor for EV/HEV Protection. 2016. [Internet]. Available from the web site: http://www.eenewspower.com/news/12-cell-li-ion-battery-monitor-evhev-protection. [Accessed: 2018-02-10]'},{id:"B16",body:'Jiang J, Zhang C. Fundamentals and Applications of Lithium-Ion Batteries in Electric Drive Vehicles. 1st ed. Singapore: John Wiley & Sons Singapore Pvt Ltd. p. 300; ISBN: 978-1-118-41478-1'},{id:"B17",body:'Dey S, Mohon S, Pisu P, Ayalew B. Sensor detection, isolation, and estimation in Lithium-ion batteries. IEEE Transactions on Control Systems Technology. 2016;24(6):2141-2149. DOI: 10.1109/TCST.2016.2538200'},{id:"B18",body:'Singh A, Izadian A, Anwar S. Nonlinear model based fault detection of Lithium ion battery using multiple model adaptive estimation. Proceedings of IFAC, Cape Town. 2014;47(3):8546-8551. DOI: https://doi.org/10.3182/20140824-6-ZA-1003.00711'},{id:"B19",body:'Arulampalam MS, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing. 2002;50(2):174-188. DOI: 10.1109/78.978374'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Roxana-Elena Tudoroiu",address:null,affiliation:'
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The Open Access Publishing Fee (OAPF) is payable only after your full chapter, monograph or Compacts monograph is accepted for publication.
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OAPF Publishing Options
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4,000 GBP Compacts Monograph - Short Form
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*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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Services included are:
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What isn't covered by the Open Access Publishing Fee?
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Your Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
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Open Access Funding
\n\n
To explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at oapf@intechopen.com for further details or assistance.
\n\n
For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
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Added Value of Publishing with IntechOpen
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Indexing and listing across major repositories, see details ...
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Proven world leader in Open Access book publishing with over 10 years experience
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