Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

作     者：Xinghao Du Jinhao Meng Kailong Liu Yingmin Zhang Shunli Wang Jichang Peng Tianqi Liu Xinghao Du;Jinhao Meng;Kailong Liu;Yingmin Zhang;Shunli Wang;Jichang Peng;Tianqi Liu

作者机构：College of Electrical EngineeringSichuan UniversityChengdu610044China School of Electrical EngineeringXi’an Jiaotong University710049Xi’anChina Warwick Manufacturing GroupUniversity of WarwickCoventryUK Southwest University of Science and TechnologyMianyang621010China Nanjing Institute of TechnologyNanjing211103China 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2023年第36卷第1期

页      面：305-314页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0802[工学-机械工程] 

基　　金：National Natural Science Foundation of China(Grant No.52107229) the Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province(Grant No.20KFKT02)。 

主　　题：Li-ion battery Equivalent circuit model Recursive least squares Recursive total least squares 

摘      要：Online parameter identification is essential for the accuracy of the battery equivalent circuit model(ECM).The traditional recursive least squares(RLS)method is easily biased with the noise disturbances from sensors,which degrades the modeling accuracy in practice.Meanwhile,the recursive total least squares(RTLS)method can deal with the noise interferences,but the parameter slowly converges to the reference with initial value uncertainty.To alleviate the above issues,this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM.RLS converges quickly by updating the parameters along the gradient of the cost function.RTLS is applied to attenuate the noise effect once the parameters have converged.Both simulation and experimental results prove that the proposed method has good accuracy,a fast convergence rate,and also robustness against noise corruption.