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State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Based on Regional Capacity and LGBM

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  • Zhaosheng Zhang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center for Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Shuo Wang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center for Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Ni Lin

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center for Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Zhenpo Wang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center for Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Peng Liu

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center for Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Battery state of health (SOH) estimation is a prerequisite for battery health management and is vital for second-life utilization. Existing techniques implemented in well-controlled experimental conditions fail to reflect complex working conditions during actual vehicular operation. In this article, a novel SOH estimation method for battery systems in real-world electric vehicles (EVs) is presented by combing results of regional capacity calculation and a light gradient boosting machine (LGBM) model. The LGBM model is used to capture the relationship between battery degeneration and influential factors based on datasets from real-world EVs. The regional capacity, which is calculated through incremental capacity analysis with a Gaussian smoothing filter, is utilized to reflect the battery degradation level while ensuring high flexibility and applicability. Accumulated mileage, average charging current, average charging temperature, and start and end of SOC values are chosen as influential factors for model establishment. The effectiveness, complexity, superiority, and robustness of the proposed method are verified using data from real-world EVs. Results indicate accurate SOH estimation can be achieved with an average absolute error of only 0.89 Ah, where the MAPE and RMSE of the test vehicles are 2.049% and 1.153%, respectively.

Suggested Citation

  • Zhaosheng Zhang & Shuo Wang & Ni Lin & Zhenpo Wang & Peng Liu, 2023. "State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Based on Regional Capacity and LGBM," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2052-:d:1043211
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    References listed on IDEAS

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    2. Yongquan Sun & Xinkun Qin & Lin Li & Youmei Zhang & Jiahai Zhang & Jia Qi, 2024. "The Impact of Temperature on the Performance and Reliability of Li/SOCl 2 Batteries," Energies, MDPI, vol. 17(13), pages 1-14, June.
    3. Jiakun An & Wei Guo & Tingyan Lv & Ziheng Zhao & Chunguang He & Hongshan Zhao, 2023. "Joint Prediction of the State of Charge and the State of Health of Lithium-Ion Batteries Based on the PSO-XGBoost Algorithm," Energies, MDPI, vol. 16(10), pages 1-14, May.
    4. Sun, Jinlei & Liu, Xinwei & Li, Xin & Chen, Siwen & Xing, Shiyou & Guo, Yilong, 2025. "State of health estimation of lithium-ion battery based on constant current charging time feature extraction and internal resistance compensation," Energy, Elsevier, vol. 315(C).
    5. Hojin Cheon & Jihun Jeon & Byungil Jung & Hongseok Kim, 2025. "Battery Health Diagnosis via Neural Surrogate Model: From Lab to Field," Energies, MDPI, vol. 18(9), pages 1-15, May.
    6. Wang, Siwei & Xiao, Xinping & Ding, Qi, 2024. "A novel fractional system grey prediction model with dynamic delay effect for evaluating the state of health of lithium battery," Energy, Elsevier, vol. 290(C).
    7. Giovane Ronei Sylvestrin & Joylan Nunes Maciel & Marcio Luís Munhoz Amorim & João Paulo Carmo & José A. Afonso & Sérgio F. Lopes & Oswaldo Hideo Ando Junior, 2025. "State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review," Energies, MDPI, vol. 18(3), pages 1-77, February.
    8. Yu He & Norasage Pattanadech & Kasiean Sukemoke & Minling Pan & Lin Chen, 2025. "The State of Health Estimation of Retired Lithium-Ion Batteries Using a Multi-Input Metabolic Gated Recurrent Unit," Energies, MDPI, vol. 18(5), pages 1-21, February.
    9. Peng, Simin & Zhu, Junchao & Wu, Tiezhou & Tang, Aihua & Kan, Jiarong & Pecht, Michael, 2024. "SOH early prediction of lithium-ion batteries based on voltage interval selection and features fusion," Energy, Elsevier, vol. 308(C).

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