Prognosis and Health Management (PHM) of Solid-State Batteries: Perspectives, Challenges, and Opportunities

Hamed Sadegh Kouhestani, Xiaoping Yi, Guoqing Qi, Xunliang Liu, Ruimin Wang, Yang Gao, Xiao Yu and Lin Liu ()
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Hamed Sadegh Kouhestani: Department of Mechanical Engineering, University of Kansas, 3138 Learned Hall, 1530 W. 15th Street, Lawrence, KS 66045-4709, USA
Xiaoping Yi: School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
Guoqing Qi: School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
Xunliang Liu: School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
Ruimin Wang: Department of Mechanical Engineering, University of Kansas, 3138 Learned Hall, 1530 W. 15th Street, Lawrence, KS 66045-4709, USA
Yang Gao: School of Electromechanic Engineering, North Minzu University, 204th Wenchang North Street, Xixia District, Yinchuan 750030, China
Xiao Yu: School of Electrical and Information Engineering, North Minzu University, 204th Wenchang North Street, Xixia District, Yinchuan 750030, China
Lin Liu: Department of Mechanical Engineering, University of Kansas, 3138 Learned Hall, 1530 W. 15th Street, Lawrence, KS 66045-4709, USA

Energies, 2022, vol. 15, issue 18, 1-26

Abstract: Solid-state batteries (SSBs) have proven to have the potential to be a proper substitute for conventional lithium-ion batteries due to their promising features. In order for the SSBs to be market-ready, the prognostics and health management (PHM) of battery systems plays a critical role in achieving such a goal. PHM ensures the reliability and availability of batteries during their operational time with acceptable safety margin. In the past two decades, much of the focus has been directed towards the PHM of lithium-ion batteries, while little attention has been given to PHM of solid-state batteries. Hence, this report presents a holistic review of the recent advances and current trends in PHM techniques of solid-state batteries and the associated challenges. For this purpose, notable commonly employed physics-based, data-driven, and hybrid methods are discussed in this report. The goal of this study is to bridge the gap between liquid state and SSBs and present the crucial aspects of SSBs that should be considered in order to have an accurate PHM model. The primary focus is given to the ML-based data-driven methods and the requirements that are needed to be included in the models, including anode, cathode, and electrolyte materials.

Keywords: solid-state batteries; prognostics and health management; physics-based approach; data-driven approach (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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