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A hybrid data-driven method optimized by physical rules for online state collaborative estimation of lithium-ion batteries

Author

Listed:
  • Zhang, Ying
  • Gu, Pingwei
  • Duan, Bin
  • Zhang, Chenghui

Abstract

Accurate online estimation of state of charge (SOC) and state of health (SOH) is crucial for safe operation and reasonable planning of battery management system (BMS). However, battery internal states are unmeasurable directly and coupled with each other, which results in serious difficulties on multi-states accurate estimation. Considering SOC estimation is deeply influenced by SOH, this paper proposes a collaborative estimation method combined machine learning algorithm with simple physical rule. Integrated the merits of the above methods, it can not only improve the estimating accuracy but also low computational burden to a large extent. Firstly, SOH acts as input of SOC in collaboration, which means SOH should have been already known before SOC estimation operation. Thus, SOH is creatively predicted based on gate recurrent unit network in ahead of SOC estimation. Subsequently, the well-trained SOC data-driven model is combined with the ampere hour (Ah) integration, which act as observation and state equation respectively in the particle filtering algorithm to realize the final estimation of SOC. This way, the sole data-driven model is well improved under physics restriction without any other computational procedure, such as online parameter identification. Simultaneously, the predicted SOH is used to fill the Ah integration expression to realize the collaboration between SOC and SOH. What's more, the SOH is further corrected by the incremental capacity peak at the checkpoint, thereby further reducing the estimation deviation. The experimental result on battery shows that the SOH estimated error is below 0.3Ah (the rated capacity is 32.5Ah) and SOC error is less than 1.5 %. What's more, the generalization capacity of the proposed method is verified on different battery types based on public datasets. Obviously, the proposed method has fairly satisfactory property and performance and can totally support its promotion into state online evaluation of battery energy storage.

Suggested Citation

  • Zhang, Ying & Gu, Pingwei & Duan, Bin & Zhang, Chenghui, 2024. "A hybrid data-driven method optimized by physical rules for online state collaborative estimation of lithium-ion batteries," Energy, Elsevier, vol. 301(C).
    • Handle: RePEc:eee:energy:v:301:y:2024:i:c:s036054422401483x
    DOI: 10.1016/j.energy.2024.131710
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    References listed on IDEAS

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