Battery health prediction under data scarcity: A cross-domain physics-informed 5-shot framework with GRU-Transformer

Xiaobo Nie, Yongjun Pan, Yongzhi Zhang, Zhenning Luo and Shuxin Wang

Applied Energy, 2026, vol. 402, issue PB, No S0306261925017428

Abstract: Battery health prediction faces multi-factor challenges. Significant differences exist in degradation patterns across material systems and usage scenarios. Traditional models relying on homogeneous data struggle with complex operating conditions. This study innovatively proposes a 5-shot meta-learning framework to accurately predict battery health under data scarcity. Experiments employ dual-threshold truncation for multi-source data processing, eliminating sensor noise interference. By analyzing capacity decay dynamics, we design a physics-informed loss function that embeds electrochemical mechanisms into a gated recurrent unit-Transformer hybrid architecture. Combined with cross-domain transfer strategies, the model achieves high-precision, robust predictions through physical law constraints and meta-learning fast adaptation. The constructed multi-physics synergistic model integrates diffusion-reaction-bias dynamics, while dynamic masking enhances small-sample adaptability. 349,443 cross-material query tests demonstrate model MAPE as low as 0.45 % with R2 reaching 0.9634, validating the effectiveness of physical constraints and meta-transfer mechanisms.

Keywords: Battery health prediction; Meta-learning framework; Data scarcity; Physics-informed fusion; Cross-domain transfer (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1016/j.apenergy.2025.127012

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