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影响因子：10.7

DOI码：10.1016/j.est.2026.125187

发表刊物：Journal of Energy Storage

关键字：Lithium-ion battery; State of health; Domain adaptation; SOC dependence; Transformer

摘要：Accurate State of Health (SOH) estimation is essential for ensuring the safety, reliability, and long-term performance of electric vehicle batteries. However, the generalization performance of Artificial Intelligence methods depends on the quality and coverage of training data. Acquiring high-quality labeled data with sufficient coverage remains a challenge. To address this, this paper first comprehensively compares and analyzes the State of Charge (SOC) dependence of the mechanical, electrical, and thermal features of batteries under different initial SOC. Features with weak SOC dependence are selected as model inputs, enabling robust SOH estimation in partial charge–discharge scenarios using only fully completed charge–discharge data. This effectively reduces the model’s demand for broad data coverage. Then, this paper proposes a domain adaptation method based on Maximum Mean Discrepancy (MMD), which integrates the Transformer and Kolmogorov–Arnold Network (KAN). Experimental results show that adopting features with weak SOC dependence reduces the relative Mean Absolute Percentage Error (MAPE) by 85.48% for slow charging and 64.33% for fast charging. Moreover, the proposed domain adaptation method achieves a 47.69% relative reduction in MAPE.

备注：中科院2区Top

合写作者：Changpeng Tan,Ji Wu,Ning Chen

第一作者：Li Wang

论文类型：期刊论文

通讯作者：Muyao Wu

论文编号：125187

学科门类：工学

文献类型：J

卷号：183

ISSN号：2352-152X

是否译文：否

发表时间：2026-10-22

收录刊物：SCI、EI

发布期刊链接：https://www.sciencedirect.com/science/article/pii/S2352152X26048516?ref=pdf_download&fr=RR-2&rr=a4eca46c3aa3fd1b