Comparative Study of two Kalman Algorithms for Estimating the State of Charge of Lithium-Ion Cells at Ambient Temperature

Fuwu Yan, Hubei Key Laboratory of Advanced Technology for Automotive Components (Wuhan University of Technology), Wuhan 430070, China
2018 International Journal of Electrochemical Science  
Kalman filters (KFs) are effective tools for estimating online state of charge (SOC), and a great variety of studies about different kinds of KFs have been published. However, problems remain in this field. First, the impact of ambient temperature on the internal parameters of equivalent circuit models (ECM) are seldom discussed. Second, comparative studies about different KFs are not fully validated under different conditions. To solve these problems, a modified equivalent circuit model was
more » ... eloped. The model was proposed to serve in ambient temperature and the usage of the total available capacity. Two typical nonlinear KFs, namely, the extended and unscented KFs were applied in SOC estimation. The model parameters were identified by hybrid pulse power characterization tests at 0, 15, 30, 45, and 55 ℃. Meanwhile, the algorithms were validated under self-designed federal urban driving schedule sequence profiles at 0, 10, 20, 30, 40, and 50 ℃ with the same tuning setups. The robustness of the algorithm was also investigated in terms of voltage sensor uncertainty and the initial SOC offset. Results indicated that the proposed model can achieve the minimum mean absolute error and root mean squared error with the unscented KF at all test conditions.
doi:10.20964/2018.12.65 fatcat:6b6747mghrf3znaksk3qyyhfce