AI for Status Monitoring of Utility Scale Batteries
Institution of Engineering and Technology (Verlag)
978-1-83953-738-7 (ISBN)
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.
Shunli Wang is a professor at Southwest University of Science and Technology, Sichuan, China. He is an expert in the field of new energy research. He is the head of NELab, conducting modeling and state estimation strategy research for lithium-ion batteries. He has undertaken over 40 projects and 30 patents, published over 100 research papers, and won 20 awards such as the Young Scholar, and Science & Technology Progress Awards. Kailong Liu is an assistant professor at the University of Warwick, UK. His research experience lies at the intersection of AI and electrochemical energy storage applications, especially data science in battery management. His current research is focusing on the development of AI strategies for battery applications. Yujie Wang is an associate professor with the Department of Automation, University of Science and Technology of China. He received his PhD degree in control science and engineering from the University of Science and Technology of China in 2017. He has co-authored over 60 SCI journal papers in battery-related topics. His research interests include energy saving and new energy vehicle technology, complex system modelling, simulation and control, fuel cell system management and optimal control. Daniel-Ioan Stroe is an associate professor with AAU Energy, Aalborg University, Denmark and the leader of the Batteries research group. He received his PhD degree in lifetime modeling of Lithium-ion batteries from Aalborg University in 2010. He has co-authored one book and over 150 scientific peer-review publications on battery performance, modeling and state estimation. His research interests include energy storage systems for grid and e-mobility, lithium-based batteries' testing, modeling, lifetime estimation, and their diagnostics. Carlos Fernandez is a is a senior lecturer at Robert Gordon University, Scotland. He received his PhD in electrocatalytic reactions from The University of Hull, and then worked as a consultant technologist in Hull and in a post-doctoral position in Manchester. His research interests include analytical chemistry, sensors and materials and renewable energy. Josep M. Guerrero is a full professor with AAU Energy, Aalborg University, Denmark. He is the director for the Center for Research on Microgrids (CROM). He has published more than 800 journal articles in the fields of microgrids and renewable energy systems, which have been cited more than 80,000 times. His research interests focus on different microgrid aspects, including hierarchical and cooperative control, and energy management systems.
Chapter 1: Introduction
Chapter 2: Utility-scale lithium-ion battery system characteristics
Chapter 3: AI-based equivalent modeling and parameter identification
Chapter 4: Use of artificial intelligence for utility-scale battery systems
Chapter 5: AI-based state-of-charge estimation
Chapter 6: AI-based battery parameter estimation
Chapter 7: Examples and case studies for utility-scale battery operation
Chapter 8: Summary and prospect for AI-based battery status monitoring
| Erscheinungsdatum | 06.01.2023 |
|---|---|
| Reihe/Serie | Energy Engineering |
| Verlagsort | Stevenage |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Technik ► Elektrotechnik / Energietechnik |
| Technik ► Fahrzeugbau / Schiffbau | |
| ISBN-10 | 1-83953-738-8 / 1839537388 |
| ISBN-13 | 978-1-83953-738-7 / 9781839537387 |
| Zustand | Neuware |
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