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Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles

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  • Li, Renzheng
  • Hong, Jichao
  • Zhang, Huaqin
  • Chen, Xinbo

Abstract

State of health (SOH) estimation is critical to the safety of battery systems in real-world electric vehicles. Accurate battery health status is difficult to be measured during dynamic and robust vehicular operation conditions. This paper proposes a novel SOH estimation model based on Catboost and interval capacity during the charging process. A year-long operation dataset of an electric taxi is derived with all charging segments separated to construct the research dataset. The charging patterns are analyzed, and the segments with rich aging information are extracted, then a general aging feature of interval capacity is extracted by incremental capacity analysis. Furthermore, comparison with the other six machine learning methods is conducted, and five inputs are determined through Pearson correlation analysis, including start charging state of charge (SOC), end charging SOC, mileage, temperature of probe, and current. The results show the Catboost-based model achieves the best accuracy, with the mean absolute percentage error and root mean squared error limited within 2.74% and 1.12%, respectively. More importantly, a battery aging evaluation strategy and its further research plan is proposed for the application in real-world electric vehicles.

Suggested Citation

  • Li, Renzheng & Hong, Jichao & Zhang, Huaqin & Chen, Xinbo, 2022. "Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles," Energy, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:energy:v:257:y:2022:i:c:s0360544222016747
    DOI: 10.1016/j.energy.2022.124771
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    References listed on IDEAS

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    Cited by:

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    3. Zhao, Guangcai & Kang, Yongzhe & Huang, Peng & Duan, Bin & Zhang, Chenghui, 2023. "Battery health prognostic using efficient and robust aging trajectory matching with ensemble deep transfer learning," Energy, Elsevier, vol. 282(C).
    4. Xue, Jingsong & Ma, Wentao & Feng, Xiaoyang & Guo, Peng & Guo, Yaosong & Hu, Xianzhi & Chen, Badong, 2023. "Stacking integrated learning model via ELM and GRU with mixture correntropy loss for robust state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 284(C).
    5. Wen, Shuang & Lin, Ni & Huang, Shengxu & Wang, Zhenpo & Zhang, Zhaosheng, 2023. "Lithium battery health state assessment based on vehicle-to-grid (V2G) real-world data and natural gradient boosting model," Energy, Elsevier, vol. 284(C).
    6. Yang, Jufeng & Li, Xin & Sun, Xiaodong & Cai, Yingfeng & Mi, Chris, 2023. "An efficient and robust method for lithium-ion battery capacity estimation using constant-voltage charging time," Energy, Elsevier, vol. 263(PB).
    7. Dapai Shi & Jingyuan Zhao & Chika Eze & Zhenghong Wang & Junbin Wang & Yubo Lian & Andrew F. Burke, 2023. "Cloud-Based Artificial Intelligence Framework for Battery Management System," Energies, MDPI, vol. 16(11), pages 1-21, May.
    8. Wu, Muyao & Wang, Li & Wu, Ji, 2023. "State of health estimation of the LiFePO4 power battery based on the forgetting factor recursive Total Least Squares and the temperature correction," Energy, Elsevier, vol. 282(C).

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