Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries PDF Download
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Author: Remus Teodorescu
Publisher:
ISBN: 9783036598758
Category : Science
Languages : en
Pages : 0
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Book Description
This reprint aims to showcase manuscripts presenting efficient SOH estimation methods using AI which exhibit good performance such as high accuracy, high robustness against the changes in working conditions, and good generalization, etc. Lithium-ion batteries have a wide range of applications, but one of their biggest problems is their limited lifetime due to performance degradation during usage. It is, therefore, essential to determine the battery's state of health (SOH) so that the battery management system can control the battery, enabling it to run in the best state and thus prolonging its lifetime. Artificial intelligence (AI) technologies possess immense potential in inferring battery SOH and can extract aging information (i.e., SOH features) from measurements and relate them to battery performance parameters, avoiding a complex battery modeling process.
Author: Remus Teodorescu
Publisher:
ISBN: 9783036598758
Category : Science
Languages : en
Pages : 0
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Book Description
This reprint aims to showcase manuscripts presenting efficient SOH estimation methods using AI which exhibit good performance such as high accuracy, high robustness against the changes in working conditions, and good generalization, etc. Lithium-ion batteries have a wide range of applications, but one of their biggest problems is their limited lifetime due to performance degradation during usage. It is, therefore, essential to determine the battery's state of health (SOH) so that the battery management system can control the battery, enabling it to run in the best state and thus prolonging its lifetime. Artificial intelligence (AI) technologies possess immense potential in inferring battery SOH and can extract aging information (i.e., SOH features) from measurements and relate them to battery performance parameters, avoiding a complex battery modeling process.
Author: Qi Huang
Publisher: Cambridge Scholars Publishing
ISBN: 1527552187
Category : Technology & Engineering
Languages : en
Pages : 164
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Book Description
To deal with environmental deterioration and energy crises, developing clean and sustainable energy resources has become the strategic goal of the majority of countries in the global community. Lithium-ion batteries are the modes of power and energy storage in the new energy industry, and are also the main power source of new energy vehicles. State-of-charge (SOC) and state-of-health (SOH) are important indicators to measure whether a battery management system (BMS) is safe and effective. Therefore, this book focuses on the co-estimation strategies of SOC and SOH for power lithium-ion batteries. The book describes the key technologies of lithium-ion batteries in SOC and SOH monitoring and proposes a collaborative optimization estimation strategy based on neural networks (NN), which provide technical references for the design and application of a lithium-ion battery power management system. The theoretical methods in this book will be of interest to scholars and engineers engaged in the field of battery management system research.
Author: IEEE Staff
Publisher:
ISBN: 9781665430999
Category :
Languages : en
Pages :
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Book Description
ICEEMT 2021 will bring together top professionals from industry, government, and academia from around the world ICEEMT 2021 includes invited talks, oral presentations and poster presentations of refereed papers We invite submissions of papers and abstracts on all topics related to Electrical Engineering and Mechatronics Technology The conference will provide networking opportunities for participants to share ideas, designs, and experiences on the state of the art and future direction of the field ICEEMT 2021 will feature a high quality technical & experiential program dealing with a mix of traditional and contemporary hot topics in paper presentations and high profile keynotes
Author: Johan A. K. Suykens
Publisher: Springer Science & Business Media
ISBN: 9780792381952
Category : Language Arts & Disciplines
Languages : en
Pages : 284
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Book Description
This collection of eight contributions presents advanced black-box techniques for nonlinear modeling. The methods discussed include neural nets and related model structures for nonlinear system identification, enhanced multi-stream Kalman filter training for recurrent networks, the support vector method of function estimation, parametric density estimation for the classification of acoustic feature vectors in speech recognition, wavelet based modeling of nonlinear systems, nonlinear identification based on fuzzy models, statistical learning in control and matrix theory, and nonlinear time- series analysis. The volume concludes with the results of a time- series prediction competition held at a July 1998 workshop in Belgium. Annotation copyrighted by Book News, Inc., Portland, OR.
Author: Abdelilah Hammou
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Lithium-ion batteries are dominating the EV market due to their higher performance in comparison with other energy storage systems. However, as any electrochemical system the lithium-ion batteries suffer from several side reactions that lead to the fade of their performance and increase the risks of the system failure. Therefore, the state-of-health monitoring of these energy storage systems is necessary for an efficient and safe operation of the battery system. This PhD thesis contributes to this topic: where it exploits the ageing data extracted from battery cycling tests to derive diagnostic and prognostic approaches.The first part of this study focuses on the experimental investigation of the sensitivity the equivalent circuit model's parameters to the discharge current rate. The results show that the polarization capacitance and resistance vary with the variation of the current rate, while the series resistance is the less parameter sensitive to the current rate. The second part aims to study the ageing effect on the performances of the cells and generate experimental data of battery cycling. During these tests, the cells are cycled using a dynamic current profile extracted from the Worldwide Harmonized Light Vehicle Test Cycle (WLTC). The obtained results show that the cells' capacity and energy decrease with the increasing of the performed cycles. The investigation on the effect of the SoC cycling window showed that reducing the SoC window from 100%-0% to 80%-20% contributes to the extension of the battery lifetime for NMC cells. In contrast, for LFP cells, the SoC window reduction has an inverse effect on their lifetime.The experimental data generated are used then to design and evaluate the diagnosis of the prognosis approaches for the lithium-ion cells. Concerning the diagnosis approaches, the derived methods aim to estimate the health indicators from the available measurements such as current, voltage and temperature. This work presented and evaluated three diagnosis methods: The first method, combines the auto-encoders with the LSTM neural networks for the estimation of the cell's capacity and energy. The second method focuses on the investigation of the statistical features of the voltage measurement. The results show that the Kullback-Leibler Divergence is strongly correlated with the battery state of health. The third diagnosis model is based on an analytical model to estimate the battery energy and capacity from energy-based features. These features are extracted from a short segment of battery discharge, which makes it the most suitable for vehicular applications.Concerning the prognosis, four models are developed to predict battery capacity: LSTM, GRU, ESN and GPR. These prognostic models are tested and evaluated considering two scenarios: the first one uses measured capacities, while the second uses estimated capacities as training data. The prognostic models are evaluated by considering three percentages of training data corresponding to three stages of cycle ageing: premature, medium and advanced. The results showed that ESN provides accurate predictions of cell capacity and requires less training time, making it suitable for vehicular applications.
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: Qi Huang
Publisher: Springer Nature
ISBN: 9819953448
Category : Technology & Engineering
Languages : en
Pages : 101
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Book Description
This book investigates in detail long-term health state estimation technology of energy storage systems, assessing its potential use to replace common filtering methods that constructs by equivalent circuit model with a data-driven method combined with electrochemical modeling, which can reflect the battery internal characteristics, the battery degradation modes, and the battery pack health state. Studies on long-term health state estimation have attracted engineers and scientists from various disciplines, such as electrical engineering, materials, automation, energy, and chemical engineering. Pursuing a holistic approach, the book establishes a fundamental framework for this topic, while emphasizing the importance of extraction for health indicators and the significant influence of electrochemical modeling and data-driven issues in the design and optimization of health state estimation in energy storage systems. The book is intended for undergraduate and graduate students who are interested in new energy measurement and control technology, researchers investigating energy storage systems, and structure/circuit design engineers working on energy storage cell and pack.
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: CRC Press
ISBN: 1000799603
Category : Technology & Engineering
Languages : en
Pages : 333
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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: Radek Silhavy
Publisher: Springer Nature
ISBN: 3030633195
Category : Technology & Engineering
Languages : en
Pages : 954
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Book Description
This book constitutes the refereed proceedings of the 4th Computational Methods in Systems and Software 2020 (CoMeSySo 2020) proceedings. Software engineering, computer science and artificial intelligence are crucial topics for the research within an intelligent systems problem domain. The CoMeSySo 2020 conference is breaking the barriers, being held online. CoMeSySo 2020 intends to provide an international forum for the discussion of the latest high-quality research results.
