Study of impacts of parameters identification methods on model-based state estimation for LiFePO4 battery

Model-based methods are widely used for online states estimation of electric vehicles (EVs) due to its accuracy and robustness. Current research mainly focuses on improving estimation filters. However, there is less discussion on the parameters identification methods. In this work, the parameters identification method is divided into two parts: formulation of the regression model and application of identification algorithms. First, two methods of formulation of the regression model are studied, respectively. Then, performances on parameters identification of forgetting factor recursive least squares (FFRLS), optimal bounding ellipsoid (OBE), and linear Kalman filter (LKF) are discussed. Besides, cubature Kalman filter (CKF) is selected for state of charge (SOC) estimation. In order to obtain the experimental data and verify the parameters identification and states estimation accuracy, the Hybrid Pulse Power Characteristic (HPPC) test, urban dynamometer driving schedule (UDDS) test, and new European driving cycle (NEDC) test are carried out. In addition, the maximum absolute error (MAE), mean absolute error (MaE), and root mean square error (RMSE) are calculated for evaluating the SOC estimation accuracy. When the parameters are identified by LKF, the best performance in MAE, MaE, and RMSE of SOC estimation is obtained. Considering the estimated peak power fluctuation and the identified parameters fluctuation, the OBE algorithm is more suitable for co-estimation of SOC and state of power (SOP).