Batteries are a necessary part of a low-emission energy system, as they can store renewable electricity and assist the grid. Particularly utility-scale batteries, with capacities of several to hundreds of MWh, are important for e.g. condominiums, local grid nodes and EV charging arrays. But they are expensive and need to be monitored and managed well in order to maintain capacity and reliability. Artificial intelligence is key to this.
This book systematically describes AI-based technologies for battery state estimation and modelling for utility-scale Li-ion batteries. It covers AI methods for circuit modelling, parameter identification, state of charge estimation, state of health evaluation, state of power determination, state of energy calculation, and remaining useful life prediction. The AI methods include machine learning, artificial neural networks, and deep learning. The book provides practical references for the design and application of large-scale lithium-ion battery systems. Examples are given to serve as references.
Fernandez, Carlos: -
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.
Guerrero, Josep M.: -
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.
Liu, Kailong: -
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.
Stroe, Daniel-Ioan: -
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.
Wang, Shunli: -
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.
Wang, Yujie: -
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.
