关键词: 三元锂离子电池；SOC 估计；一阶 Thevenin 模型；AEKF

Abstract: Accurate and reliable estimate of the state of charge has a pivotal impact on the performance of electric vehicles, thus becoming an important research direction for scholars at home and abroad. This paper chooses ternary lithium-ion battery as the research object, and then introduces several estimation methods of charge state. Model based method is chosen to estimate charge state, and first-order Thevenin model is constructed. Then the function correspondence of open circuit voltage- state of charge (OCV-SOC) is constructed by using the data of charge and discharge experiment and polynomial fitting tool in MATLAB. The parameter values in the model can be identified offline by using the least square method, and then the adaptive extended Kalman filter (AEKF) algorithm is used to estimate the state of charge of lithium-ion batteries. Then the dynamic stress test (DST) experiment is carried out, and the extended Kalman filter (EKF) algorithm and AEKF algorithm are compared and the error is analyzed, so as to check the accuracy of AEKF algorithm. Finally, through the experimental data, it can be concluded that the AEKF algorithm introduced in this paper can ensure a satisfactory accuracy in the estimation of the DST experimental conditions, and this algorithm is not very complicated and has strong operability.

Key words: Ternary lithium-ion battery; SOC estimation; First-order Thevenin model; AEKF