Yujie Wang – författare

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

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.Contains technical references for system design and applicationAddresses battery equivalent modeling, including electrical circuit modeling and parameter identification theoryIncludes coverage of battery state estimation methods such as state of charge estimation, state of health estimation, and state-of-charge and state-of-health co-estimation

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.

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.

Packing problems, which are concerned with optimal arrangements of objects in space, are cross-disciplinary in nature and are encountered in mathematics, physics, chemistry, biology, engineering, and architecture. Such problems form a subject of interest in its own right, providing intriguing intellectual challenges, but are also at the heart of many material properties of condensed matter. In view of this, a series of international conferences on packing problems was launched in 2012 to provide a platform for soft-matter researchers to disseminate their findings. To continue the spirit of this conference series, this international community of researchers has also been invited to contribute reviews of their research to this book. Covering topics on models of ordered and disordered packings, mechanical behaviour of packings, and applications in soft matter and biology, this book provides a broad and authoritative overview of current research.

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.

The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management.