Deep neural network-enabled battery open-circuit voltage estimation based on partial charging data

作     者：Ziyou Zhou Yonggang Liu Chengming Zhang Weixiang Shen Rui Xiong Ziyou Zhou;Yonggang Liu;Chengming Zhang;Weixiang Shen;Rui Xiong

作者机构：State Key Laboratory of Mechanical Transmission for Advanced Equipment&College of Mechanical and Vehicle EngineeringChongqing UniversityChongqing 400000China Department of Vehicle EngineeringSchool of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Department of Electrical EngineeringHarbin Institute of TechnologyHarbin 150000HeilongjiangChina School of ScienceComputing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia 

出 版 物：《Journal of Energy Chemistry》 (能源化学（英文版）)

年 卷 期：2024年第90卷第3期

页      面：120-132,I0005页

核心收录：

学科分类：0820[工学-石油与天然气工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 0817[工学-化学工程与技术] 081104[工学-模式识别与智能系统] 08[工学] 0703[理学-化学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the National Key R&D Program of China(2021YFB2402002) the Beijing Natural Science Foundation(L223013) the Chongqing Automobile Collaborative Innovation Centre(No.2022CDJDX-004) 

主　　题：Lithium-ion battery Open-circuit voltage Health diagnosis Deep learning 

摘      要：Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion *** main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation *** addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging *** incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV *** analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional *** validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 *** this level of precision for OCV estimation requires only around 50 s collection of partial charging *** validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed *** cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV *** method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.