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From Empirical Measurements to AI Fusion—A Holistic Review of SOH Estimation Techniques for Lithium-Ion Batteries in Electric and Hybrid Vehicles

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  • Runzhe Shan

    (State Key Laboratory of Space Power-Sources, MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, MOE Engineering Research Center for Electrochemical Energy Storage and Carbon Neutrality in Cold Regions, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Yaxuan Wang

    (State Key Laboratory of Space Power-Sources, MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, MOE Engineering Research Center for Electrochemical Energy Storage and Carbon Neutrality in Cold Regions, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Shilong Guo

    (State Key Laboratory of Space Power-Sources, MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, MOE Engineering Research Center for Electrochemical Energy Storage and Carbon Neutrality in Cold Regions, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Yue Cui

    (State Key Laboratory of Space Power-Sources, MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, MOE Engineering Research Center for Electrochemical Energy Storage and Carbon Neutrality in Cold Regions, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Lei Zhao

    (State Key Laboratory of Space Power-Sources, MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, MOE Engineering Research Center for Electrochemical Energy Storage and Carbon Neutrality in Cold Regions, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Junfu Li

    (School of Automotive Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China)

  • Zhenbo Wang

    (State Key Laboratory of Space Power-Sources, MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, MOE Engineering Research Center for Electrochemical Energy Storage and Carbon Neutrality in Cold Regions, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, China)

Abstract

Accurate assessment of lithium-ion battery state of health (SOH) represents a cross-disciplinary challenge that is critical for the reliability, safety, and total cost of ownership of electric vehicles (EVs) and hybrid electric vehicles (HEVs). This review systematically examines the evolutionary trajectory of SOH estimation methods, ranging from conventional experimental measurement approaches to cutting-edge data-driven techniques. We analyze how these techniques address critical challenges in battery aging and performance evaluation, while discussing their respective advantages across different application scenarios. The paper highlights emerging trends in artificial intelligence-integrated advanced technologies for SOH estimation, along with practical implementation considerations. Special emphasis is placed on key challenges of SOH estimation in EVs/HEVs applications with proposed alternative solutions. By synthesizing current research directions and identifying critical knowledge gaps, this work provides valuable insights for fundamental research and industrial applications in battery health management.

Suggested Citation

  • Runzhe Shan & Yaxuan Wang & Shilong Guo & Yue Cui & Lei Zhao & Junfu Li & Zhenbo Wang, 2025. "From Empirical Measurements to AI Fusion—A Holistic Review of SOH Estimation Techniques for Lithium-Ion Batteries in Electric and Hybrid Vehicles," Energies, MDPI, vol. 18(13), pages 1-42, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3542-:d:1694909
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