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Deep Learning for State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles: A Systematic Review

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  • Chenyuan Liu

    (Xi’an Institute of Optics and Precision Mechanics of CAS, University of Chinese Academy of Sciences, Xi’an 710119, China
    School of Electronic Information, Central South University, Changsha 410004, China
    These authors contributed equally to this work.)

  • Heng Li

    (School of Electronic Information, Central South University, Changsha 410004, China
    These authors contributed equally to this work.)

  • Kexin Li

    (School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yue Wu

    (School of Electronic Information, Central South University, Changsha 410004, China)

  • Baogang Lv

    (Xi’an Institute of Optics and Precision Mechanics of CAS, University of Chinese Academy of Sciences, Xi’an 710119, China)

Abstract

Electric vehicles (EVs) play a crucial role in addressing the energy crisis and mitigating the greenhouse effect. Lithium-ion batteries are the primary energy storage medium for EVs due to their numerous advantages. State of health (SOH) is a critical parameter for managing the health of lithium-ion batteries, and accurate SOH estimation forms the foundation of battery management systems (BMS), ensuring the safe operation of EVs. Data-driven deep learning techniques are attracting significant attention because of their strong ability to model complex nonlinear relationships, which makes them highly suitable for SOH estimation in lithium-ion batteries. This paper provides a comprehensive introduction to the common deep learning techniques used for SOH estimation of lithium-ion batteries, with a focus on model architectures. It systematically reviews the application of various deep learning algorithms in SOH estimation in recent years. Building on this, the paper offers a detailed comparison of these deep learning methods and discusses the current challenges and future directions in this field, with the aim of providing an extensive review of the role of deep learning in SOH estimation.

Suggested Citation

  • Chenyuan Liu & Heng Li & Kexin Li & Yue Wu & Baogang Lv, 2025. "Deep Learning for State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles: A Systematic Review," Energies, MDPI, vol. 18(6), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1463-:d:1613862
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    References listed on IDEAS

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