文献类型：期刊文献

英文题名：Interpretable deep learning for accelerated fading recognition of lithium-ion batteries

作者：Wang, Chang[1];Chen, Ying[1];Luan, Weiling[1];Li, Songyang[1];Yao, Yiming[1];Chen, Haofeng[1,2]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Key Lab Adv Battery Syst & Safety CPCIF, Shanghai 200237, Peoples R China;[2]Univ Strathclyde, Dept Mech & Aerosp Engn, Glasgow G1 1XJ, Scotland

年份：2023

卷号：18

外文期刊名：ETRANSPORTATION

收录：;EI(收录号：20233814735833);WOS:【SCI-EXPANDED(收录号:WOS:001165465000001)】；

基金：The authors gratefully acknowledge the support from the National Natural Science Foundation of China (52375144, 52150710540 and 52205153) , China Postdoctoral Science Foundation (2022M721138 and2023T160216) , the East China University of Science and Technology, and the University of Strathclyde during the course of this work.r 2023T160216) , the East China University of Science and Technology, and the University of Strathclyde during the course of this work.

语种：英文

外文关键词：Interpretability; Deep learning; Accelerated fading; Knee-point; Lithium-ion battery

摘要：Data-driven approaches have gained increasing attention in the field of battery life-related prediction, as building a comprehensive mechanistic degradation model remains a challenge. Deep learning has emerged as a powerful data-driven fitting method for battery-related applications. However, interpretability remains an issue in this field, hindering the practical utilization of deep learning methods. With the development of interpretable techniques, deep learning methods not only can be conducted as black box tools for fitting, but also for exploring the relationship between external battery data and internal electrochemical changes. In this paper, an interpretable deep learning procedure is proposed and exemplified by accelerated fading point (knee-point) recognition based on an open battery dataset. The Gradient-weighted Class Activation Mapping (Grad-CAM) is conducted to explain the link between the input and output of the trained convolutional neural networks (CNN) model. The trained CNN model possesses deep insight into battery degradation, giving the very first warning when accelerated fading occurs. Through interpretability analysis, it is confirmed that the well-trained model can spontaneously focus on features associated with internal battery degradation and identify some additional features beyond existing human experience. The proposed method can be used to discover the relationship between battery data and degradation mechanism by artificial intelligence in the electric vehicles (EVs) field.