Shunli Li's Books

Modeling and State Estimation of Automotive Lithium-Ion Batteries

By: Shunli Wang,Fan Wu,Jialu Qiao,Carlos Fernandez,Josep M Guerrero

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Modeling and State Estimation of Automotive Lithium-Ion Batteries

By: Shunli Wang,Fan Wu,Jialu Qiao,Carlos Fernandez,Josep M Guerrero

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.

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Page Count

145

Publisher

CRC Press

Published Date

ISBN 10

1040046754

ISBN 13

9781040046753

State Estimation Strategies in Lithium-ion Battery Management Systems

By: Kailong Liu,Yujie Wang,Daniel-Ioan Stroe,Carlos Fernandez,Josep M. Guerrero,Shunli Wang

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State Estimation Strategies in Lithium-ion Battery Management Systems

By: Kailong Liu,Yujie Wang,Daniel-Ioan Stroe,Carlos Fernandez,Josep M. Guerrero,Shunli Wang

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

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Page Count

377

Publisher

Elsevier

Published Date

ISBN 10

0443161615

ISBN 13

9780443161612

Multidimensional Lithium-Ion Battery Status Monitoring

By: Shunli Wang,Kailong Liu,Yujie Wang,Daniel-Ioan Stroe,Carlos Fernandez,Josep M Guerrero

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Multidimensional Lithium-Ion Battery Status Monitoring

By: Shunli Wang,Kailong Liu,Yujie Wang,Daniel-Ioan Stroe,Carlos Fernandez,Josep M Guerrero

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.

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Page Count

355

Publisher

CRC Press

Published Date

ISBN 10

1000799565

ISBN 13

9781000799569

Neural Network-Based State-of-Charge and State-of-Health Estimation

By: Qi Huang,Shunli Wang,Yujie Wang,Chao Wang,Carlos Fernandez,Josep M. Guerrero

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Neural Network-Based State-of-Charge and State-of-Health Estimation

By: Qi Huang,Shunli Wang,Yujie Wang,Chao Wang,Carlos Fernandez,Josep M. Guerrero

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.

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Page Count

164

Publisher

Cambridge Scholars Publishing

Published Date

ISBN 10

1527552187

ISBN 13

9781527552180

Battery System Modeling

By: Shunli Wang,Carlos Fernandez,Yu Chunmei,Yongcun Fan,Cao Wen,Daniel-Ioan Stroe,Zonghai Chen

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Battery System Modeling

By: Shunli Wang,Carlos Fernandez,Yu Chunmei,Yongcun Fan,Cao Wen,Daniel-Ioan Stroe,Zonghai Chen

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

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Page Count

356

Publisher

Elsevier

Published Date

ISBN 10

0323904335

ISBN 13

9780323904339

Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs

By: Qi Huang,Shunli Wang,Zonghai Chen,Ran Xiong,Carlos Fernandez,Daniel-I. Stroe

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Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs

By: Qi Huang,Shunli Wang,Zonghai Chen,Ran Xiong,Carlos Fernandez,Daniel-I. Stroe

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.

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Page Count

101

Publisher

Springer Nature

Published Date

ISBN 10

9819953448

ISBN 13

9789819953448

Fuzzy Filter-Based State of Energy Estimation for Lithium-Ion Batteries

By: Shunli Wang,Yujie Wang,Xiao Yang,Carlos Fernandez,Josep M. Guerrero

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Fuzzy Filter-Based State of Energy Estimation for Lithium-Ion Batteries

By: Shunli Wang,Yujie Wang,Xiao Yang,Carlos Fernandez,Josep M. Guerrero

Awareness of the safety issues of lithium-ion batteries is crucial in the development of new energy technologies, and real-time and high-precision State of Energy (SOE) estimation is not only a prerequisite for battery safety, but also serves as the basis for predicting the remaining driving range of electric vehicles and aircrafts. In order to achieve real-time and accurate estimation of the energy state of lithium-ion batteries, this book improves the calculation method of the open-circuit voltage in the traditional second-order RC equivalent circuit model. It also combines a fuzzy controller and a dual-weighted multi-innovation algorithm to optimize the traditional Centralized Kalman Filter (CKF) algorithm in terms of the aspects of convergence speed, estimation accuracy, and algorithm robustness. This enables the precise estimation of SOE and the maximum available energy. The content of this book provides theoretical support for the development of new energy initiatives.

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Page Count

141

Publisher

Cambridge Scholars Publishing

Published Date

ISBN 10

1527570673

ISBN 13

9781527570672

Visual Object Tracking from Correlation Filter to Deep Learning

By: Weiwei Xing,Weibin Liu,Jun Wang,Shunli Zhang,Lihui Wang,Yuxiang Yang,Bowen Song

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Visual Object Tracking from Correlation Filter to Deep Learning

By: Weiwei Xing,Weibin Liu,Jun Wang,Shunli Zhang,Lihui Wang,Yuxiang Yang,Bowen Song

The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.

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Page Count

202

Publisher

Springer Nature

Published Date

ISBN 10

9811662428

ISBN 13

9789811662423

Clastic Hydrocarbon Reservoir Sedimentology

By: Xinghe Yu,Shengli Li,Shunli Li

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Clastic Hydrocarbon Reservoir Sedimentology

By: Xinghe Yu,Shengli Li,Shunli Li

This book presents a comprehensive assessment of clastic sedimentology and its application to reservoir geology. It covers the theoretical foundations of the topic and its use for scientists as well as professionals in the field. Further, it addresses all aspects of reservoir sedimentology, clastic sequence stratigraphy, sedimentation, reservoir diagenesis and heterogeneity, as well as depositional systems (alluvial, fluvial, lacustrine, delta, sandy coast, neritic, deep-water) in detail. The research team responsible for this book has been investigating clastic sedimentology for more than three decades and consists of highly published and cited authors. The Chinese edition of this book has been a great success, and is popular among sedimentologists and petroleum geologists alike.

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Page Count

720

Publisher

Springer

Published Date

ISBN 10

3319703358

ISBN 13

9783319703350

Clastic Hydrocarbon Reservoir Sedimentology

By: Xinghe Yu,Shengli Li,Shunli Li

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Clastic Hydrocarbon Reservoir Sedimentology

By: Xinghe Yu,Shengli Li,Shunli Li

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720

Publisher

Springer

Published Date

ISBN 10

3319703358

ISBN 13

9783319703350

Multidimensional Lithium-Ion Battery Status Monitoring

By: Shunli Wang,Kailong Liu,Yujie Wang,Daniel-Ioan Stroe,Carlos Fernandez,Josep M Guerrero

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Multidimensional Lithium-Ion Battery Status Monitoring

By: Shunli Wang,Kailong Liu,Yujie Wang,Daniel-Ioan Stroe,Carlos Fernandez,Josep M Guerrero

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Page Count

333

Publisher

CRC Press

Published Date

ISBN 10

1000799603

ISBN 13

9781000799606

Battery System Modeling

By: Shunli Wang,Carlos Fernandez,Yu Chunmei,Yongcun Fan,Cao Wen,Daniel-Ioan Stroe,Zonghai Chen

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Battery System Modeling

By: Shunli Wang,Carlos Fernandez,Yu Chunmei,Yongcun Fan,Cao Wen,Daniel-Ioan Stroe,Zonghai Chen

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Page Count

356

Publisher

Elsevier

Published Date

ISBN 10

0323904335

ISBN 13

9780323904339

Book's by Shunli Li

Modeling and State Estimation of Automotive Lithium-Ion Batteries

Modeling and State Estimation of Automotive Lithium-Ion Batteries

State Estimation Strategies in Lithium-ion Battery Management Systems

State Estimation Strategies in Lithium-ion Battery Management Systems

Multidimensional Lithium-Ion Battery Status Monitoring

Multidimensional Lithium-Ion Battery Status Monitoring

Neural Network-Based State-of-Charge and State-of-Health Estimation

Neural Network-Based State-of-Charge and State-of-Health Estimation

Battery System Modeling

Battery System Modeling

Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs

Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs

Fuzzy Filter-Based State of Energy Estimation for Lithium-Ion Batteries

Fuzzy Filter-Based State of Energy Estimation for Lithium-Ion Batteries

Visual Object Tracking from Correlation Filter to Deep Learning

Visual Object Tracking from Correlation Filter to Deep Learning

Clastic Hydrocarbon Reservoir Sedimentology

Clastic Hydrocarbon Reservoir Sedimentology

Clastic Hydrocarbon Reservoir Sedimentology

Clastic Hydrocarbon Reservoir Sedimentology

Multidimensional Lithium-Ion Battery Status Monitoring

Multidimensional Lithium-Ion Battery Status Monitoring

Battery System Modeling

Battery System Modeling

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