锂电池状态跨域估计算法综述OA北大核心CSTPCD

Accurate state estimation and prediction of lithium-ion battery are crucial for ensuring operational performance and safety.Data-driven state estimation algorithms are prone to the distribution shift between training data and testing data,limiting their generalization capabilities.Transfer-learning-based cross-domain state estimation algorithms are proposed to address these issues.This paper discusses around three common application scenarios:state of charge estimation,state of health estimation,and remaining useful life estimation.While comparing the differences between methods across various scenarios,the review also reveals their commonalities.From a technical perspective,this paper categorizes commonly used transfer methods into three types:finetuning-based transfer,metric-based transfer,and adversarial training-based transfer.Based on these technical approaches,this paper provides a comprehensive and clear summary of recent cross-domain lithium-ion battery state estimation methods.