State of charge estimation for electric vehicle batteries using unscented kalman filtering

Due to the increasing concern over global warming and fossil fuel depletion, it is expected that electric vehicles powered by lithium batteries will become more common over the next decade. However, there are still some unresolved challenges, the most notable being state of charge estimation, which alerts drivers of their vehicle’s range capability. We developed a model to simulate battery terminal voltage as a function of state of charge under dynamic loading conditions. The parameters of the model were tailored on-line in order to estimate uncertainty arising from unit-to-unit variations and loading condition changes. We used an unscented Kalman filtering-based method to self-adjust the model parameters and provide state of charge estimation. The performance of the method was demonstrated using data collected from LiFePO4 batteries cycled according to the federal driving schedule and dynamic stress testing.