Estimating battery state of health with 10-min relaxation voltage across various charging states of charge
作者机构：College of Mechanical and Vehicle EngineeringChongqing UniversityChongqing 400030China Department of Vehicle EngineeringSchool of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China School of Vehicle EngineeringChongqing University of TechnologyChongqing 400054China
出 版 物：《iEnergy》 (电力能源汇刊（英文）)
年 卷 期：2023年第2卷第4期
页 面：308-313页
核心收录：
基 金：supported by the National Natural Science Foundation of China(No.52307234) Beijing Natural Science Foundation(Grant No.L223013)
主 题：Battery state of health 10-min relaxation voltage varying charging states physical features machine learning
摘 要：Battery capacity assessment is a crucial research direction in the field of lithium-ion battery *** the previous research,a novel data-driven state of health(SOH)estimation method based on the voltage relaxation curve at full charging is *** experimental results have shown the evidence of the superiority of accurate battery SOH estimation based on physical features derived from equivalent circuit models(ECMs).However,the earlier research has limitations in estimating battery capacity with a diversity of battery charging states of *** study represents an extension of the previous work,aiming to investigate the feasibility of this technology for battery degradation evaluation under various charging states so that the application capability in practice is *** this study,six ECM features are extracted from 10-min voltage relaxation data across varying charging states to characterize the battery degradation *** process regression(GPR)is employed to learn the relationship between the physical features and battery *** results under 10 different state of charge(SOC)ranges show that the developed methodology predicts accurate battery SOH,with a root mean square error being 0.9%.
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