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Battery pack consistency modeling based on generative adversarial networks

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  • Fan, Xinyuan
  • Zhang, Weige
  • Sun, Bingxiang
  • Zhang, Junwei
  • He, Xitian

Abstract

In working condition of battery packs, the battery pack consistency has a great impact on the overall performance of the battery pack. In order to build an accurate battery pack model, we need to build a battery pack consistency model. Firstly, we used a Gaussian mixture model to fit the statistical characteristics of a single parameter. This method can accurately fit the skewness in the parameter distribution and fit the multi-peak characteristics that may appear. Secondly, we constructed a nonparametric battery pack consistency model using a Generative Adversarial Networks (GAN). Our consistency model can accurately describe the statistical characteristics of a single parameter and fits the correlation coefficient between parameters. The battery pack model substituted into the GAN-generated battery parameters exhibits a very high similarity to the experimental data. The relative errors of the simulation results are less than 0.6 % for the terminal voltage and less than 0.3 % for the energy utilization efficiency (EUE), proving the advantages of the GAN consistency model in fitting the distribution of the battery parameters. Finally, we implemented the GAN consistency model in an embedded system with limited computing resources, which proves that our proposed model has the ability to run normally on existing BMS.

Suggested Citation

  • Fan, Xinyuan & Zhang, Weige & Sun, Bingxiang & Zhang, Junwei & He, Xitian, 2022. "Battery pack consistency modeling based on generative adversarial networks," Energy, Elsevier, vol. 239(PE).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pe:s0360544221026682
    DOI: 10.1016/j.energy.2021.122419
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    References listed on IDEAS

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    Cited by:

    1. Fan, Xinyuan & Qi, Hongfeng & Zhang, Weige & Zhang, Yanru, 2024. "Experiment-free physical hybrid neural network approach for battery pack inconsistency estimation," Applied Energy, Elsevier, vol. 358(C).
    2. An, Fulai & Zhang, Weige & Sun, Bingxiang & Jiang, Jiuchun & Fan, Xinyuan, 2023. "A novel battery pack inconsistency model and influence degree analysis of inconsistency on output energy," Energy, Elsevier, vol. 271(C).
    3. Xu, Maoshu & Zhang, E. & Wang, Sheng & Shen, Yi & Zou, Binchen & Li, Haomiao & Wan, Yiming & Wang, Kangli & Jiang, Kai, 2024. "Dynamic ultrasonic response modeling and accurate state of charge estimation for lithium ion batteries under various load profiles and temperatures," Applied Energy, Elsevier, vol. 355(C).
    4. Ma, Chen & Chang, Long & Cui, Naxin & Duan, Bin & Zhang, Yulong & Yu, Zhihao, 2022. "Statistical relationships between numerous retired lithium-ion cells and packs with random sampling for echelon utilization," Energy, Elsevier, vol. 257(C).
    5. Tian, Jiaqiang & Fan, Yuan & Pan, Tianhong & Zhang, Xu & Yin, Jianning & Zhang, Qingping, 2024. "A critical review on inconsistency mechanism, evaluation methods and improvement measures for lithium-ion battery energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    6. Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).

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