Modeling and State Estimation of Automotive Lithium-Ion Batteries PDF Download

Author: Shunli Wang
Publisher: CRC Press
ISBN: 1040046754
Category : Science
Languages : en
Pages : 145

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Book Description
This book aims to evaluate and improve the state of charge (SOC) and state of health (SOH) of automotive lithium-ion batteries. The authors first introduce the basic working principle and dynamic test characteristics of lithium-ion batteries. They present the dynamic transfer model, compare it with the traditional second-order reserve capacity (RC) model, and demonstrate the advantages of the proposed new model. In addition, they propose the chaotic firefly optimization algorithm and demonstrate its effectiveness in improving the accuracy of SOC and SOH estimation through theoretical and experimental analysis. The book will benefit researchers and engineers in the new energy industry and provide students of science and engineering with some innovative aspects of battery modeling.

Author: Shunli Wang
Publisher:
ISBN: 9781032781150
Category : Lithium ion batteries
Languages : en
Pages : 0

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Book Description
"This book aims to evaluate and improve the state of charge (SOC) and state of health (SOH) of automotive lithium-ion batteries. The authors first introduce the basic working principle and dynamic test characteristics of lithium-ion batteries. They present the dynamic transfer model, compare it with the traditional second-order reserve capacity (RC) model, and demonstrate the advantages of the proposed new model. In addition, they propose the chaotic firefly optimization algorithm and demonstrate its effectiveness in improving the accuracy of SOC and SOH estimation through theoretical and experimental analysis. The book will benefit researchers and engineers in the new energy industry, and provide students of science and engineering with some innovative aspects of battery modeling"--

Author: Shunli Wang
Publisher: Elsevier
ISBN: 0443161615
Category : Business & Economics
Languages : en
Pages : 377

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Book Description
State Estimation Strategies in Lithium-ion Battery Management Systems presents key technologies and methodologies in modeling and monitoring charge, energy, power and health of lithium-ion batteries. Sections introduce core state parameters of the lithium-ion battery, reviewing existing research and the significance of the prediction of core state parameters of the lithium-ion battery and analyzing the advantages and disadvantages of prediction methods of core state parameters. Characteristic analysis and aging characteristics are then discussed. Subsequent chapters elaborate, in detail, on modeling and parameter identification methods and advanced estimation techniques in different application scenarios. Offering a systematic approach supported by examples, process diagrams, flowcharts, algorithms, and other visual elements, this book is of interest to researchers, advanced students and scientists in energy storage, control, automation, electrical engineering, power systems, materials science and chemical engineering, as well as to engineers, R&D professionals, and other industry personnel. Introduces lithium-ion batteries, characteristics and core state parameters Examines battery equivalent modeling and provides advanced methods for battery state estimation Analyzes current technology and future opportunities

Author: Shunli Wang
Publisher: Elsevier
ISBN: 0323904335
Category : Science
Languages : en
Pages : 356

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Book Description
Battery System Modeling provides advances on the modeling of lithium-ion batteries. Offering step-by-step explanations, the book systematically guides the reader through the modeling of state of charge estimation, energy prediction, power evaluation, health estimation, and active control strategies. Using applications alongside practical case studies, each chapter shows the reader how to use the modeling tools provided. Moreover, the chemistry and characteristics are described in detail, with algorithms provided in every chapter. Providing a technical reference on the design and application of Li-ion battery management systems, this book is an ideal reference for researchers involved in batteries and energy storage. Moreover, the step-by-step guidance and comprehensive introduction to the topic makes it accessible to audiences of all levels, from experienced engineers to graduates. Explains how to model battery systems, including equivalent, electrical circuit and electrochemical nernst modeling Includes comprehensive coverage of battery state estimation methods, including state of charge estimation, energy prediction, power evaluation and health estimation Provides a dedicated chapter on active control strategies

Author: Jiuchun Jiang
Publisher: John Wiley & Sons
ISBN: 1118414810
Category : Technology & Engineering
Languages : en
Pages : 296

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Book Description
A theoretical and technical guide to the electric vehicle lithium-ion battery management system Covers the timely topic of battery management systems for lithium batteries. After introducing the problem and basic background theory, it discusses battery modeling and state estimation. In addition to theoretical modeling it also contains practical information on charging and discharging control technology, cell equalisation and application to electric vehicles, and a discussion of the key technologies and research methods of the lithium-ion power battery management system. The author systematically expounds the theory knowledge included in the lithium-ion battery management systems and its practical application in electric vehicles, describing the theoretical connotation and practical application of the battery management systems. Selected graphics in the book are directly derived from the real vehicle tests. Through comparative analysis of the different system structures and different graphic symbols, related concepts are clear and the understanding of the battery management systems is enhanced. Contents include: key technologies and the difficulty point of vehicle power battery management system; lithium-ion battery performance modeling and simulation; the estimation theory and methods of the lithium-ion battery state of charge, state of energy, state of health and peak power; lithium-ion battery charge and discharge control technology; consistent evaluation and equalization techniques of the battery pack; battery management system design and application in electric vehicles. A theoretical and technical guide to the electric vehicle lithium-ion battery management system Using simulation technology, schematic diagrams and case studies, the basic concepts are described clearly and offer detailed analysis of battery charge and discharge control principles Equips the reader with the understanding and concept of the power battery, providing a clear cognition of the application and management of lithium ion batteries in electric vehicles Arms audiences with lots of case studies Essential reading for Researchers and professionals working in energy technologies, utility planners and system engineers.

Author: Alexander Thaler
Publisher: Springer Science & Business Media
ISBN: 3319025236
Category : Technology & Engineering
Languages : en
Pages : 135

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Book Description
The use of electrochemical energy storage systems in automotive applications also involves new requirements for modeling these systems, especially in terms of model depth and model quality. Currently, mainly simple application-oriented models are used to describe the physical behavior of batteries. This book provides a step beyond of state-of-the-art modeling showing various different approaches covering following aspects: system safety, misuse behavior (crash, thermal runaway), battery state estimation and electrochemical modeling with the needed analysis (pre/post mortem). All this different approaches are developed to support the overall integration process from a multidisciplinary point-of-view and depict their further enhancements to this process.

Author: Shunli Wang
Publisher: CRC Press
ISBN: 1000799565
Category : Technology & Engineering
Languages : en
Pages : 355

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Book Description
Multidimensional Lithium-Ion Battery Status Monitoring focuses on equivalent circuit modeling, parameter identification, and state estimation in lithium-ion battery power applications. It explores the requirements of high-power lithium-ion batteries for new energy vehicles and systematically describes the key technologies in core state estimation based on battery equivalent modeling and parameter identification methods of lithium-ion batteries, providing a technical reference for the design and application of power lithium-ion battery management systems. Reviews Li-ion battery characteristics and applications. Covers battery equivalent modeling, including electrical circuit modeling and parameter identification theory Discusses battery state estimation methods, including state of charge estimation, state of energy prediction, state of power evaluation, state of health estimation, and cycle life estimation Introduces equivalent modeling and state estimation algorithms that can be applied to new energy measurement and control in large-scale energy storage Includes a large number of examples and case studies This book has been developed as a reference for researchers and advanced students in energy and electrical engineering.

Author: Jiuchun Jiang
Publisher:
ISBN:
Category :
Languages : en
Pages : 300

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Book Description
A theoretical and technical guide to the electric vehicle lithium-ion battery management system Covers the timely topic of battery management systems for lithium batteries. After introducing the problem and basic background theory, it discusses battery modeling and state estimation. In addition to theoretical modeling it also contains practical information on charging and discharging control technology, cell equalisation and application to electric vehicles, and a discussion of the key technologies and research methods of the lithium-ion power battery management system. The author systematically expounds the theory knowledge included in the lithium-ion battery management systems and its practical application in electric vehicles, describing the theoretical connotation and practical application of the battery management systems. Selected graphics in the book are directly derived from the real vehicle tests. Through comparative analysis of the different system structures and different graphic symbols, related concepts are clear and the understanding of the battery management systems is enhanced. Contents include : key technologies and the difficulty point of vehicle power battery management system; lithium-ion battery performance modeling and simulation; the estimation theory and methods of the lithium-ion battery state of charge, state of energy, state of health and peak power; lithium-ion battery charge and discharge control technology; consistent evaluation and equalization techniques of the battery pack; battery management system design and application in electric vehicles. A theoretical and technical guide to the electric vehicle lithium-ion battery management system Using simulation technology, schematic diagrams and case studies, the basic concepts are described clearly and offer detailed analysis of battery charge and discharge control principles Equips the reader with the understanding and concept of the power battery, providing a clear cognition of the application and management of lithium ion batteries in electric vehicles Arms audiences with lots of case studies Essential reading for Researchers and professionals working in energy technologies, utility planners and system engineers.

Author: Manoj Mathew
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
As lithium-ion (Li-Ion) battery packs grow in popularity, so do the concerns of its safety, reliability, and cost. An efficient and robust battery management system (BMS) can help ease these concerns. By measuring the voltage, temperature, and current for each cell, the BMS can balance the battery pack, and ensure it is operating within the safety limits. In addition, these measurements can be used to estimate the remaining charge in the battery (state-of-charge (SOC)) and determine the health of the battery (state-of-health (SOH)). Accurate estimation of these battery and system variables can help improve the safety and reliability of the energy storage system (ESS). This research aims to develop high-fidelity battery models and robust SOC and SOH algorithms that have low computational cost and require minimal training data. More specifically, this work will focus on SOC and SOH estimation at the pack-level, as well as modeling and simulation of a battery pack. An accurate and computationally efficient Li-Ion battery model can be highly beneficial when developing SOC and SOH algorithms on the BMS. These models allow for software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testing, where the battery pack is simulated in software. However, development of these battery models can be time-consuming, especially when trying to model the effect of temperature and SOC on the equivalent circuit model (ECM) parameters. Estimation of this relationship is often accomplished by carrying out a large number of experiments, which can be too costly for many BMS manufacturers. Therefore, the first contribution of this research is to develop a comprehensive battery model, where the ECM parameter surface is generated using a set of carefully designed experiments. This technique is compared with existing approaches from literature, and it is shown that by using the proposed method, the same degree of accuracy can be obtained while requiring significantly less experimental runs. This can be advantageous for BMS manufacturers that require a high-fidelity model but cannot afford to carry out a large number of experiments. Once a comprehensive model has been developed for SIL and HIL testing, research was carried out in advancing SOH and SOC algorithms. With respect to SOH, research was conducted in developing a steady and reliable SOH metric that can be determined at the cell level and is stable at different battery operating conditions. To meet these requirements, a moving window direct resistance estimation (DRE) algorithm is utilized, where the resistance is estimated only when the battery experiences rapid current transients. The DRE approach is then compared with more advanced resistance estimation techniques such as extended Kalman filter (EKF) and recursive least squares (RLS). It is shown that by using the proposed algorithm, the same degree of accuracy can be achieved as the more advanced methods. The DRE algorithm does, however, have a much lower computational complexity and therefore, can be implemented on a battery pack composed of hundreds of cells. Research has also been conducted in converting these raw resistance values into a stable SOH metric. First, an outlier removal technique is proposed for removing any outliers in the resistance estimates; specifically, outliers that are an artifact of the sampling rate. The technique involves using an adaptive control chart, where the bounds on the control chart change as the internal resistance of the battery varies during operation. An exponentially weighted moving average (EWMA) is then applied to filter out the noise present in the raw estimates. Finally, the resistance values are filtered once more based on temperature and battery SOC. This additional filtering ensures that the SOH value is independent of the battery operating conditions. The proposed SOH framework was validated over a 27-day period for a lithium iron phosphate (LFP) battery. The results show an accurate estimation of battery resistance over time with a mean error of 1.1% as well as a stable SOH metric. The findings are significant for BMS developers who have limited computational resources but still require a robust and reliable SOH algorithm. Concerning SOC, most publications in literature examine SOC estimation at the cell level. Determining the SOC for a battery pack can be challenging, especially an estimate that behaves logically to the battery user. This work proposes a three-level approach, where the final output from the algorithm is a well-behaved pack SOC estimate. The first level utilizes an EKF for estimating SOC while an RLS approach is used to adapt the model parameters. To reduce computational time, both algorithms will be executed on two specific cells: the first cell to charge to full and the first cell to discharge to empty. The second level consists of using the SOC estimates from these two cells and estimating a pack SOC value. Finally, a novel adaptive coulomb counting approach is proposed to ensure the pack SOC estimate behaves logically. The accuracy of the algorithm is tested using a 40 Ah Li-Ion battery. The results show that the algorithm produces accurate and stable SOC estimates. Finally, this work extends the developed comprehensive battery model to examine the effect of replacing damaged cells in a battery pack with new ones. The cells within the battery pack vary stochastically, and the performance of the entire pack is evaluated under different conditions. The results show that by changing out cells in the battery pack, the SOH of the pack can be maintained indefinitely above a specific threshold value. In situations where the cells are checked for replacement at discrete intervals, referred to as maintenance event intervals, it is found that the length of the interval is dependent on the mean time to failure of the individual cells. The simulation framework, as well as the results from this paper, can be utilized to better optimize Li-ion battery pack design in electric vehicles (EVs) and make long-term deployment of EVs more economically feasible.

Author: Haifeng Dai
Publisher: Elsevier
ISBN: 0443155445
Category : Technology & Engineering
Languages : en
Pages : 326

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Book Description
Advances in Lithium-Ion Batteries for Electric Vehicles: Degradation Mechanism, Health Estimation, and Lifetime Prediction examines the electrochemical nature of lithium-ion batteries, including battery degradation mechanisms and how to manage the battery state of health (SOH) to meet the demand for sustainable development of electric vehicles. With extensive case studies, methods and applications, the book provides practical, step-by-step guidance on battery tests, degradation mechanisms, and modeling and management strategies. The book begins with an overview of Li-ion battery aging and battery aging tests before discussing battery degradation mechanisms and methods for analysis. Further methods are then presented for battery state of health estimation and battery lifetime prediction, providing a range of case studies and techniques. The book concludes with a thorough examination of lifetime management strategies for electric vehicles, making it an essential resource for students, researchers, and engineers needing a range of approaches to tackle battery degradation in electric vehicles. Evaluates the cause of battery degradation from the material level to the cell level Explains key battery basic lifetime test methods and strategies Presents advanced technologies of battery state of health estimation