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Research Progress on State of Charge Estimation Methods for Power Batteries in New Energy Intelligent Connected Vehicles

Author

Listed:
  • Hongzhao Li

    (School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China)

  • Hongsheng Jia

    (School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China)

  • Ping Xiao

    (School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China)

  • Haojie Jiang

    (School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China)

  • Yang Chen

    (National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

Accurately estimating the State of Charge (SOC) of power batteries is crucial for the Battery Management Systems (BMS) in new energy intelligent connected vehicles. It directly influences vehicle range, energy management efficiency, and the safety and lifespan of the battery. However, SOC cannot be measured directly with instruments; it needs to be estimated using external parameters such as current, voltage, and internal resistance. Moreover, power batteries represent complex nonlinear time-varying systems, and various uncertainties—like battery aging, fluctuations in ambient temperature, and self-discharge effects—complicate the accuracy of these estimations. This significantly increases the complexity of the estimation process and limits industrial applications. To address these challenges, this study systematically classifies existing SOC estimation algorithms, performs comparative analyses of their computational complexity and accuracy, and identifies the inherent limitations within each category. Additionally, a comprehensive review of SOC estimation technologies utilized in BMS by automotive OEMs globally is conducted. The analysis concludes that advancing multi-fusion estimation frameworks, which offer enhanced universality, robustness, and hard real-time capabilities, represents the primary research trajectory in this field.

Suggested Citation

  • Hongzhao Li & Hongsheng Jia & Ping Xiao & Haojie Jiang & Yang Chen, 2025. "Research Progress on State of Charge Estimation Methods for Power Batteries in New Energy Intelligent Connected Vehicles," Energies, MDPI, vol. 18(9), pages 1-30, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2144-:d:1639508
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    References listed on IDEAS

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    1. Xiong, Rui & Li, Zhengyang & Li, Hailong & Wang, Jun & Liu, Guofang, 2025. "A novel method for state of charge estimation of lithium-ion batteries at low-temperatures," Applied Energy, Elsevier, vol. 377(PB).
    2. Ramesh Kumar Chidambaram & Dipankar Chatterjee & Barnali Barman & Partha Pratim Das & Dawid Taler & Jan Taler & Tomasz Sobota, 2023. "Effect of Regenerative Braking on Battery Life," Energies, MDPI, vol. 16(14), pages 1-24, July.
    3. Wang, Luxiao & Duan, Jiandong & Fan, Shaogui & Zhao, Ke, 2024. "An estimated value compensation method for state of charge estimation of lithium battery based on open circuit voltage change rate," Energy, Elsevier, vol. 313(C).
    4. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    5. Yun Bao & Wenbin Dong & Dian Wang, 2018. "Online Internal Resistance Measurement Application in Lithium Ion Battery Capacity and State of Charge Estimation," Energies, MDPI, vol. 11(5), pages 1-11, April.
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