Abstract:

A nonlinear unscented Kalman filter (UKF) estimation algorithm was proposed for real-time monitoring lithium-ion power battery’s internal temperature to improve the vehicle power battery’s performances. An equivalent variable parameter thermal model was established for a 2.6 Ah ternary lithium ion battery; The correlation between the internal and external temperature of the battery is established and discretized by the state equation analysis method; The recursive least square (RLS) method is used to identify the four thermal parameters of time, surface temperature, ambient temperature and input current in the thermal model, update the parameter matrix of system state and observation equation in real time, and realize the estimation of battery internal temperature combined with UKF algorithm. The simulation model is built by MATLAB, and the accuracy of the algorithm is verified by three working conditions: hybrid impulse capability characteristic (HPPC), dynamic stress test (DST) and constant current. The results show that the UKF algorithm can estimate the internal temperature of the battery within 1℃ for these three working conditions.

Key words: electric cars, power batteries, internal temperature of battery, monitoring in real time, recursive least squares (RLS), unscented Kalman filter (UKF) algorithm