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Volume 10 Issue 7
Jul.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  >  10(7): 1530-1543
G. J. Ma, Z. D. Wang, W. B. Liu, J. Z. Fang, Y. Zhang, H. Ding, and  Y. Yuan,  “Estimating the state of health for lithium-ion batteries: A particle swarm optimization-assisted deep domain adaptation approach,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1530–1543, Jul. 2023. doi: 10.1109/JAS.2023.123531
Citation: G. J. Ma, Z. D. Wang, W. B. Liu, J. Z. Fang, Y. Zhang, H. Ding, and  Y. Yuan,  “Estimating the state of health for lithium-ion batteries: A particle swarm optimization-assisted deep domain adaptation approach,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1530–1543, Jul. 2023. doi: 10.1109/JAS.2023.123531
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Estimating the State of Health for Lithium-ion Batteries: A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

doi: 10.1109/JAS.2023.123531
Funds:  This work was supported in part by the National Natural Science Foundation of China (92167201, 62273264, 61933007)
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    Abstract

  • The state of health (SOH) is a critical factor in evaluating the performance of the lithium-ion batteries (LIBs). Due to various end-user behaviors, the LIBs exhibit different degradation modes, which makes it challenging to estimate the SOHs in a personalized way. In this article, we present a novel particle swarm optimization-assisted deep domain adaptation (PSO-DDA) method to estimate the SOH of LIBs in a personalized manner, where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy. The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method. The proposed PSO-DDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials, ambient temperatures and charge-discharge configurations. Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method. The PyTorch implementation of the proposed PSO-DDA method is available at

    https://github.com/mxt0607/PSO-DDA

    .

     

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    Highlights

    • A novel deep domain adaptation method is developed for personalized LIB SOH estimation
    • The domain loss of labels is considered in the developed deep domain adaptation method
    • The conditioning strategy of the CGAN is employed to analyze the conditional distribution
    • The PSO algorithm is used to automatically optimize the chosen hyperparameters

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