Co-estimation of state-of-charge and state-of-temperature for large-format lithium-ion batteries based on a novel electrothermal model  

作　　者：Chao Yu Jiangong Zhu Wenxue Liu Haifeng Dai Xuezhe Wei 

机构地区：[1]School of Automotive Studies,Tongji University,Shanghai,201804,China [2]Clean Energy Automotive Engineering Center,Tongji University,Shanghai,201804,China [3]College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing,400044,China

出　　处：《Green Energy and Intelligent Transportation》2024年第4期23-38,共16页新能源与智能载运（英文）

基　　金：National Natural Science Foundation of China(NSFC,Grant No.52107230);Fundamental Research Funds for the Central Universities and the Major State Basic Research Development Program of China。

摘　　要：The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge(SOC)and state-of-temperature(SOT)of Lithium-ion(Li-ion)batteries.Given the influence of cross-interference between the two states indicated above,this study establishs a co-estimation framework of battery SOC and SOT.This framwork is based on an innovative electrothermal model and adaptive estimation algorithms.The first-order RC electric model and an innovative thermal model are components of the electrothermal model.Specifically,the thermal model includes two lumped-mass thermal submodels for two tabs and a two-dimensional(2-D)thermal resistance network(TRN)submodel for the main battery body,capable of capturing the detailed thermodynamics of large-format Li-ion batteries.Moreover,the proposed thermal model strikes an acceptable compromise between the estimation fidelity and computational complexity by representing the heat transfer processes by the thermal resistances.Besides,the adaptive estimation algorithms are composed of an adaptive unscented Kalman filter(AUKF)and an adaptive Kalman filter(AKF),which adaptively update the state and noise covariances.Regarding the estimation results,the mean absolute errors(MAEs)of SOC and SOT estimation are controlled within 1%and 0.4°C at two temperatures,indicating that the co-estimation method yields superior prediction performance in a wide temperature range of 5–35°C.

关 键 词：Large-format Li-ion battery Electrothermal model SOT estimation SOC estimation Adaptive algorithm 

分 类 号：TM912[电气工程—电力电子与电力传动]