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SOH prediction of lithium battery based on IC curve feature and BP neural network

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  • Wen, Jianping
  • Chen, Xing
  • Li, Xianghe
  • Li, Yikun

Abstract

Precise battery SOH (state of health) prediction and monitoring are of extreme importance for the future intelligent battery management system (BMS). In this paper, battery discharge experiments at different temperatures were carried out. A battery SOH prediction model based on incremental capacity analysis and BP neural network is proposed to predict battery SOH at different ambient temperatures. By analyzing the correlation between the characteristics of IC curve and SOH, the mapping relationship between temperature and IC curve characteristics is established by using the least square method, and the SOH prediction model at different temperatures is obtained. At the same time, combined with ICA, an online real-time correction prediction model is established, and the characteristic data is continuously updated to ensure the SOH prediction accuracy under different aging states. Finally, the feasibility of the prediction method proposed in this paper is verified by comparing the model test results and experimental results, the average error of the model prediction results is 1.16%. Thus, by establishing the relationship between temperature and IC curve characteristics, the battery SOH at different temperatures can be predicted.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:261:y:2022:i:pa:s0360544222021223
    DOI: 10.1016/j.energy.2022.125234
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    17. Nanlan Wang & Xiangyang Xia & Xiaoyong Zeng, 2023. "State of charge and state of health estimation strategies for lithium-ion batteries," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 18, pages 443-448.
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