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Remaining discharge energy estimation of lithium-ion batteries based on average working condition prediction and multi-parameter updating

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Abstract

The remaining discharge energy (RDE) estimation of lithium-ion batteries heavily depends on the battery’s future working conditions. However, the traditional time series-based method for predicting future working conditions is too burdensome to be applied online. In this study, an RDE estimation method based on average working condition prediction and multi-parameter updating is proposed. First, the ohmic resistance of batteries is identified online, the temperature-aging factor is introduced against battery aging and temperature changes, and the OCV-SOC is estimated by curve scaling. Then, the future working conditions of the battery are predicted based on the average working condition prediction with less calculation. Finally, the RDE is estimated under complex working conditions. The experimental results show that the RDE estimation error of the battery is less than 3% during battery aging and temperature changes under complex working conditions. Moreover, in addition, the computational burden of the proposed method is only 1% of that of traditional methods, making it very suitable for online applications.

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Funding

This work is supported by the National Natural Science Foundation of China (Grant Nos. 51977131 and 52277223).

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Authors and Affiliations

  1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Xin Lai, Jiahui Weng, Yunfeng Huang, Ming Yuan, Yi Yao & Yuejiu Zheng

  2. Science and Technology On Ship Integrated Power System Technology Laboratory, Wuhan Institute of Marine Electric Propulsion, Wuhan Hubei Province, 430064, China

    Yipeng Yang & Changqing Qiu

Authors
  1. Xin Lai
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  2. Jiahui Weng
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  3. Yipeng Yang
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  4. Changqing Qiu
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  5. Yunfeng Huang
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  6. Ming Yuan
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  7. Yi Yao
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  8. Yuejiu Zheng
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Correspondence to Xin Lai or Yuejiu Zheng.

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Highlights

• An RDE estimation method is proposed based on average working condition prediction and multi-parameter updating.

• Battery model parameters are updated using the FFRLS algorithm.

• SOC-OCV is estimated by curve scaling.

• A simplified method for predicting the future operating conditions of batteries is proposed.

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Lai, X., Weng, J., Yang, Y. et al. Remaining discharge energy estimation of lithium-ion batteries based on average working condition prediction and multi-parameter updating. J Solid State Electrochem 28, 229–242 (2024). https://doi.org/10.1007/s10008-023-05683-8

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  • DOI: https://doi.org/10.1007/s10008-023-05683-8

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