A Novel Method for Estimating State-of-Charge in Power Batteries for Electric Vehicles

Estimation of the state-of- charge (SOC) of power batteries has always been the focus of electric vehicle users’ criticism. Accurate SOC is beneficial for extending the mileage of electric vehicles and the life of the battery pack. The key to improving SOC accuracy is to establish its accurate model and combine it with an appropriate estimation algorithm. Based on characterization experiments related to SOC, this paper describes a second-order charge–discharge resistor–capacitor model that can accurately simulate external characteristics of the battery and identify them online. An improved adaptive unscented Kalman filter algorithm based on Sage–Husa is introduced to estimate SOC. The reliability of the algorithm is verified by building a MATLAB/Simulink simulation model. The results show that the improved algorithm displays increased robustness and can quickly converge to the true value; the steady-state error is also within a small range.