Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering
作者机构:Department of EnergyAalborg UniversityAalborg 9220Denmark Department of Mathematical SciencesAalborg UniversityAalborg 9220Denmark Walker Department of Mechanical EngineeringThe University of Texas at AustinAustinTX 78712USA
出 版 物:《Journal of Energy Chemistry》 (能源化学(英文版))
年 卷 期:2024年第92卷第5期
页 面:591-604页
核心收录:
学科分类:0820[工学-石油与天然气工程] 0808[工学-电气工程] 0817[工学-化学工程与技术] 08[工学] 0703[理学-化学]
基 金:funded by China Scholarship Council.The fund number is 202108320111 and 202208320055
主 题:Feature engineering Dynamic forklift aging profile State of health comparison Machine learning Lithium-ion batteries
摘 要:State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and *** of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real *** work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is *** features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding ***,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset *** linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation *** results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR *** was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher *** work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.