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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hamed Sadegh Kouhestani & Xiaoping Yi & Guoqing Qi & Xunliang Liu & Ruimin Wang & Yang Gao & Xiao Yu & Lin Liu, 2022. "Prognosis and Health Management (PHM) of Solid-State Batteries: Perspectives, Challenges, and Opportunities," Energies, MDPI, vol. 15(18), pages 1-26, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6599-:d:910983
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    References listed on IDEAS

    as
    1. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    3. Shao, Yu-qiang & Liu, Huan-ling & Shao, Xiao-dong & Sang, Lin & Chen, Zeng-tao, 2022. "An all coupled electrochemical-mechanical model for all-solid-state Li-ion batteries considering the effect of contact area loss and compressive pressure," Energy, Elsevier, vol. 239(PA).
    4. Held, Marcel & Tuchschmid, Martin & Zennegg, Markus & Figi, Renato & Schreiner, Claudia & Mellert, Lars Derek & Welte, Urs & Kompatscher, Michael & Hermann, Michael & Nachef, Léa, 2022. "Thermal runaway and fire of electric vehicle lithium-ion battery and contamination of infrastructure facility," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    5. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    6. Omar, Noshin & Monem, Mohamed Abdel & Firouz, Yousef & Salminen, Justin & Smekens, Jelle & Hegazy, Omar & Gaulous, Hamid & Mulder, Grietus & Van den Bossche, Peter & Coosemans, Thierry & Van Mierlo, J, 2014. "Lithium iron phosphate based battery – Assessment of the aging parameters and development of cycle life model," Applied Energy, Elsevier, vol. 113(C), pages 1575-1585.
    7. Hu, Xiaosong & Li, Shengbo Eben & Jia, Zhenzhong & Egardt, Bo, 2014. "Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles," Energy, Elsevier, vol. 64(C), pages 953-960.
    8. Prabhakar V. Varde & Michael G. Pecht, 2018. "Prognostics and Health Management," Springer Series in Reliability Engineering, in: Risk-Based Engineering, chapter 0, pages 447-507, Springer.
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