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Flexible method for estimating the state of health of lithium-ion batteries using partial charging segments

Author

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  • Zhang, Chaolong
  • Luo, Laijin
  • Yang, Zhong
  • Du, Bolun
  • Zhou, Ziheng
  • Wu, Ji
  • Chen, Liping

Abstract

Batteries are crucial components of electric vehicles (EVs), necessitating the accurate estimation of their state of health (SOH). Despite numerous works focusing on SOH estimation, most assume complete battery charging data, which seldom aligns with real-world scenarios where charging rarely initiates from 0% state of charge (SOC). This study introduces a novel battery SOH estimation method tailored for partial charging segments. The proposed methodology involves introducing an Incremental Energy per SOC (IES) curve to analyze battery aging characteristics. This curve is derived by dividing incremental energy by SOC during the charging phase. Key battery health indicators (HIs), namely the maximum and standard deviation of the IES curve, are then extracted from charging data with varying initial SOCs. Subsequently, we present a Bidirectional Long Short-Term Memory with Reduction Mechanism (LSTM-reduction). This model integrates forward and reverse LSTM structures, featuring a reduction gate within the LSTM architecture. This gate filters unnecessary data, preserving valuable information during processing. The bidirectional LSTM-reduction combines both LSTM structures, effectively incorporating historical and future information for improved time series modeling. This comprehensive approach enhances sequence modeling efficiency, thereby elevating reliability. To demonstrate the effectiveness and robustness of the proposed SOH estimation method, we utilize randomly generated partial charging segments from a battery aging dataset with four distinct charging rates. In the experimental phase, the Root Mean Square Error (RMSE) of estimated SOH consistently remains below 0.8%. Moreover, a significant majority of the Coefficient of Determination (R2) values exceed 0.9, across varying initial battery SOC charging processes. These results affirm the feasibility and robustness of the proposed SOH estimation method for partial charging segments with different charging rates.

Suggested Citation

  • Zhang, Chaolong & Luo, Laijin & Yang, Zhong & Du, Bolun & Zhou, Ziheng & Wu, Ji & Chen, Liping, 2024. "Flexible method for estimating the state of health of lithium-ion batteries using partial charging segments," Energy, Elsevier, vol. 295(C).
    • Handle: RePEc:eee:energy:v:295:y:2024:i:c:s0360544224007813
    DOI: 10.1016/j.energy.2024.131009
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    References listed on IDEAS

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    1. Wen, Jianping & Chen, Xing & Li, Xianghe & Li, Yikun, 2022. "SOH prediction of lithium battery based on IC curve feature and BP neural network," Energy, Elsevier, vol. 261(PA).
    2. Guo, Shanshan & Ma, Liang, 2023. "A comparative study of different deep learning algorithms for lithium-ion batteries on state-of-charge estimation," Energy, Elsevier, vol. 263(PC).
    3. Xiong, Rui & Li, Linlin & Li, Zhirun & Yu, Quanqing & Mu, Hao, 2018. "An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 264-275.
    4. Hanlei Sun & Jianrui Sun & Kun Zhao & Licheng Wang & Kai Wang & Mohammad Yaghoub Abdollahzadeh Jamalabadi, 2022. "Data-Driven ICA-Bi-LSTM-Combined Lithium Battery SOH Estimation," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, March.
    5. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo, 2020. "State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression," Energy, Elsevier, vol. 203(C).
    6. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
    7. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2022. "A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction," Energy, Elsevier, vol. 251(C).
    8. Zheng, Yuejiu & Cui, Yifan & Han, Xuebing & Ouyang, Minggao, 2021. "A capacity prediction framework for lithium-ion batteries using fusion prediction of empirical model and data-driven method," Energy, Elsevier, vol. 237(C).
    9. Wang, Jinyu & Zhang, Caiping & Zhang, Linjing & Su, Xiaojia & Zhang, Weige & Li, Xu & Du, Jingcai, 2023. "A novel aging characteristics-based feature engineering for battery state of health estimation," Energy, Elsevier, vol. 273(C).
    10. Xu, Huanwei & Wu, Lingfeng & Xiong, Shizhe & Li, Wei & Garg, Akhil & Gao, Liang, 2023. "An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries," Energy, Elsevier, vol. 276(C).
    11. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2022. "Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model," Energy, Elsevier, vol. 240(C).
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