State of health and remaining useful life prediction for lithiumion batteries based on differential thermal voltammetry and a long and short memory neural network  

作　　者：Bin Ma Han-Qing Yu Wen-Tao Wang Xian-Bin Yang Li-Sheng Zhang Hai-Cheng Xie Cheng Zhang Si-Yan Chen Xin-Hua Liu 

机构地区：[1]College of Automotive Engineering,Jilin University,Changchun 130022,China [2]School of Transportation Science and Engineering,Beihang University,Beijing 100191,China [3]School of Automotive Engineering,Harbin Institute of Technology,Weihai 264209,China [4]Institute for Clean Growth&Future Mobility,Coventry University,Coventry CV15FB,UK

出　　处：《Rare Metals》2023年第3期885-901,共17页稀有金属（英文版）

基　　金：financially supported by the National Natural Science Foundation of China(No.52102470);the Science and Technology Development Project of Jilin province(No.20200501012GX)。

摘　　要：As the lithium-ion battery is widely applied,the reliability of the battery has become a high-profile content in recent years.Accurate estimation and prediction of state of health(SOH)and remaining useful life(RUL)prediction are crucial for battery management systems.In this paper,the core contribution is the construction of a datadriven model with the long short-term memory(LSTM)network applicable to the time-series regression prediction problem with the integration of two methods,data-driven methods and feature signal analysis.The input features of model are extracted from differential thermal voltammetry(DTV)curves,which could characterize the battery degradation characteristics,so that the accurate prediction of battery capacity fade could be accomplished.Firstly,the DTV curve is smoothed by the Savitzky-Golay filter,and six alternate features are selected based on the connection between DTV curves and battery degradation characteristics.Then,a correlation analysis method is used to further filter the input features and three features that are highly associated with capacity fade are selected as input into the data driven model.The LSTM neural network is trained by using the root mean square propagation(RMSprop)technique and the dropout technique.Finally,the data of four batteries with different health levels are deployed for model construction,verification and comparison.The results show that the proposed method has high accuracy in SOH and RUL prediction and the capacity rebound phenomenon can be accurately estimated.This method can greatly reduce the cost and complexity,and increase the practicability,which provides the basis and guidance for battery data collection and the application of cloud technology and digital twin.

关 键 词：Lithium-ion batteries(LIBs) State of health(SOH) Remaining useful life(RUL) Differential thermal voltammetry(DTV) Long short-term memory(LSTM) 

分 类 号：TM912[电气工程—电力电子与电力传动] TP183[自动化与计算机技术—控制理论与控制工程]