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Prognosability regularized generative adversarial network for battery state of health estimation with limited samples

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  • Ye, Zhuang
  • Chang, Jiantao
  • Yu, Jianbo

Abstract

State of health (SOH) estimation is essential to improve reliability of battery. However, it is a challenging issue to develop an effective health prognostics model due to the lack of run-to-failure data in the industrial scenarios. In this paper, a prognosability regularized generative adversarial network (ProGAN) is proposed to implement SOH estimation of battery. Firstly, a prognosability regulator is proposed in ProGAN to generate the samples with good trendability over time. Secondly, a multi-level wavelet decomposition long short-term memory (MwLSTM) is proposed to generate samples from low and high frequency components. Finally, an SOH estimator is constructed based on the synthetic samples. Compared to existing generative models, ProGAN introduces a novel prognosability regulator that enhances the trendability of generated samples. Additionally, a MwLSTM is developed by considering both low and high frequency components. Three battery SOH estimation cases are adopted to verify the effectiveness of ProGAN. The experimental results indicate that ProGAN can not only generate the samples with high consistency in space (i.e., data distribution), but also have good trendability over time (i.e., prognosability). Moreover, ProGAN has a better performance in both data augmentation and SOH estimation than other generative models.

Suggested Citation

  • Ye, Zhuang & Chang, Jiantao & Yu, Jianbo, 2025. "Prognosability regularized generative adversarial network for battery state of health estimation with limited samples," Energy, Elsevier, vol. 325(C).
    • Handle: RePEc:eee:energy:v:325:y:2025:i:c:s0360544225015646
    DOI: 10.1016/j.energy.2025.135922
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