End-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries  

作　　者：Bin Ma Lisheng Zhang Hanqing Yu Bosong Zou Wentao Wang Cheng Zhang Shichun Yang Xinhua Liu 

机构地区：[1]School of Transportation Science and Engineering,Beihang University,Beijing 102206,China [2]China Software Testing Center,Beijing 100038,China [3]Institute for Clean Growth and Future Mobility,Coventry University,CV15FB,United Kingdom [4]Dyson School of Design Engineering,Imperial College London,London SW72AZ,UK

出　　处：《Journal of Energy Chemistry》2023年第7期1-17,I0001,共18页能源化学（英文版）

基　　金：financially supported by the National Natural Science Foundation of China(No.52102470)。

摘　　要：Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.

关 键 词：State of health Remaining useful life End-cloud collaboration Ensemble learningDifferential thermal voltammetry 

分 类 号：TM912[电气工程—电力电子与电力传动]