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<p>Battery management systems (BMSs) are essential for accessing and managing battery performance information, with state of health (SOH) estimation providing insights into the battery’s life expectancy. Electrochemical impedance spectroscopy (EIS) is a non-destructive method for SOH assessment. However, collecting EIS data across diverse operating conditions and battery types is both time-intensive and costly, presenting challenges related to data distribution and heterogeneity. This work investigates a lightweight gradient-based fusion strategy to enable collective learning across independently trained models without sharing raw data. Specifically, the collective inference via gradient aggregation (CIGAR) algorithm is applied to multiple convolutional neural network–bidirectional long short-term memory models trained on disjoint EIS datasets. The approach is evaluated on a real-world SOH prediction task, demonstrating that gradient-based collective learning can facilitate knowledge exchange among models under compatible conditions. The results highlight both the potential and the limitations of CIGAR in heterogeneous battery scenarios, indicating that further optimisation and validation on larger and more diverse datasets are required to improve robustness and generalisation.</p>

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Application of CIGAR for collective learning between CNN–BiLSTM models in lithium-ion battery state of health prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月27日

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  • Sylwia Olbrych,
  • Zi Xuan Tung,
  • Sehriban Celik,
  • Hans Aoyang Zhou,
  • Anas Abdelrazeq,
  • Dirk Uwe Sauer,
  • Robert H. Schmitt

摘要

Battery management systems (BMSs) are essential for accessing and managing battery performance information, with state of health (SOH) estimation providing insights into the battery’s life expectancy. Electrochemical impedance spectroscopy (EIS) is a non-destructive method for SOH assessment. However, collecting EIS data across diverse operating conditions and battery types is both time-intensive and costly, presenting challenges related to data distribution and heterogeneity. This work investigates a lightweight gradient-based fusion strategy to enable collective learning across independently trained models without sharing raw data. Specifically, the collective inference via gradient aggregation (CIGAR) algorithm is applied to multiple convolutional neural network–bidirectional long short-term memory models trained on disjoint EIS datasets. The approach is evaluated on a real-world SOH prediction task, demonstrating that gradient-based collective learning can facilitate knowledge exchange among models under compatible conditions. The results highlight both the potential and the limitations of CIGAR in heterogeneous battery scenarios, indicating that further optimisation and validation on larger and more diverse datasets are required to improve robustness and generalisation.