Shunli Wang

Smart Safety Management of Energy Storage Batteries addresses battery management in new power systems which is an important component of the new generation of information technology and power systems. This book covers the application of this new type of power storage as well as power system identification modeling, intelligent energy storage battery status evaluation, and key technologies in intelligent management monitoring. Written for researchers, engineers, and students studying related areas, this book supports research in control science and control, automation, and electrical engineering, and serves as a technical reference for the application of new electric energy storage battery science and technology.

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.

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.

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.

Batteries are a necessary part of a low-emission energy system, as they can store renewable electricity and assist the grid. Utility-scale batteries, with capacities of several to hundreds of MWh, are particularly important for condominiums, local grid nodes, and EV charging arrays. However, such batteries are expensive and need to be monitored and managed well to maintain capacity and reliability. Artificial intelligence offers a solution for effective monitoring and management of utility-scale batteries. This book systematically describes AI-based technologies for battery state estimation and modeling for utility-scale Li-ion batteries. Chapters cover utility-scale lithium-ion battery system characteristics, AI-based equivalent modeling, parameter identification, state of charge estimation, battery parameter estimation, offer samples and case studies for utility-scale battery operation, and conclude with a summary and prospect for AI-based battery status monitoring. The book provides practical references for the design and application of large-scale lithium-ion battery systems. AI for Status Monitoring of Utility-Scale Batteries is an invaluable resource for researchers in battery R&D, including battery management systems and related power electronics, battery manufacturers, and advanced students.

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.

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.

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.

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.