点击次数：

影响因子：7.9

DOI码：10.1016/j.jpowsour.2026.240209

发表刊物：Journal of Power Sources

关键字：Lithium-ion battery; State of Health; Mixture of Experts; Transformer; Incremental learning

摘要：Accurate State of Health (SOH) estimation is vital for lithium-ion battery safety and optimization. Traditional data-driven methods often lack generalization across diverse operational scenarios like slow-charging and fastcharging. This study proposes an incremental learning framework using a Transformer-integrated Mixture of Experts (MoE) architecture. The model integrates multi-source data (expansion and voltage) with lightweight expert networks, enhancing accuracy while reducing memory consumption. For new scenarios, it activates only a sparse subset of experts, enabling efficient knowledge updates without full retraining. Experimental results show the Transformer-MoE model achieves a Mean Absolute Percentage Error (MAPE) of 1.52%, a Root Mean Square Percentage Error (RMSPE) of 2.24%, and a Maximum Absolute Error (MAX-AE) of 6.74%, with only 0.4578 MB memory usage—a 72.31% reduction versus baseline. The proposed incremental learning framework maintains excellent performance on both fast and slow-charging test sets, achieving an overall MAPE of 2.53% and an RMSPE of 3.25%, outperforming traditional data-driven models in most scenarios.

备注：中科院2区

合写作者：Changpeng Tan,Ji Wu

第一作者：Li Wang

论文类型：期刊论文

通讯作者：Muyao Wu

论文编号：240209

学科门类：工学

文献类型：J

卷号：679

ISSN号：0378-7753

是否译文：否

发表时间：2026-04-26

收录刊物：SCI、EI