Multi-time scale identification of key kinetic processes for lithium-ion batteries considering variable characteristic frequency

作     者：Haotian Shi Shunli Wang Jianhong Liang Paul Takyi-Aninakwa Xiao Yang Carlos Fernandez Liping Wang Haotian Shi;Shunli Wang;Jianhong Liang;Paul Takyi-Aninakwa;Xiao Yang;Carlos Fernandez;Liping Wang

作者机构：School of Information EngineeringSouthwest University of Science and TechnologyMianyang 621010SichuanChina Department of Mechanical EngineeringTsinghua UniversityBeijing 100084China School of Pharmacy and Life SciencesRobert Gordon UniversityAberdeen AB10-7GJUK 

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

年 卷 期：2023年第82卷第7期

页      面：521-536,I0011页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：supported by the National Natural Science Foundation of China,China(Grant Nos.62173281,51975319,61801407) the State Key Laboratory of Tribology and Institute of Manufacturing Engineering at Tsinghua University 

主　　题：Lithium-ion battery Kinetic parameters Entropy evaluation Parameter identification Frequency characteristic 

摘      要：The electrification of vehicles puts forward higher requirements for the power management efficiency of integrated battery management systems as the primary or sole energy *** this paper,an efficient adaptive multi-time scale identification strategy is proposed to achieve high-fidelity modeling of complex kinetic processes inside the *** specifically,a second-order equivalent circuit model network considering variable characteristic frequency is constructed based on the high-frequency,medium-high-frequency,and low-frequency characteristics of the key kinetic ***,two coupled sub-filters are developed based on forgetting factor recursive least squares and extended Kalman filtering methods and decoupled by the corresponding time-scale *** coupled iterative calculation of the two sub-filter modules at different time scales is realized by the voltage response of the kinetic diffusion *** addition,the driver of the low-frequency subalgorithm with the state of charge variation amount as the kernel is designed to realize the adaptive identification of the kinetic diffusion process ***,the concept of dynamical parameter entropy is introduced and advocated to verify the physical meaning of the kinetic *** experimental results under three operating conditions show that the mean absolute error and root-mean-square error metrics of the proposed strategy for voltage tracking can be limited to 13 and 16 mV,***,from the entropy calculation results,the proposed method can reduce the dispersion of parameter identification results by a maximum of 40.72%and 70.05%,respectively,compared with the traditional fixed characteristic frequency *** proposed method paves the way for the subsequent development of adaptive state estimators and efficient embedded applications.