Unscented Particle Filter Based State of Energy Estimation for LiFePO 4 Batteries Using an Online Updated Model

Range issue has become the concern focus in the field of electric vehicles. In contrast to the generally used State-of-Charge (SoC), battery State-of-Energy (SoE) is regarded more appropriate in representing the remnant driving range by taking account of the voltage decline across the discharging process. In this paper, a SoE estimator is constructed using a pseudo power definition upon battery open-circuit-voltage (OCV) to exclude the energy loss on internal resistance; simultaneously, by combining with an equivalent circuit model (ECM), the unscented particle filter (UPF) is exploited to deal with problems of model nonlinearities, internal interferences, sensor noises and accumulated errors. Further, to adapt to battery time-variant features, the ECM parameters are on-line identified resorting to the recursive least square with forgetting factor algorithm. Finally, SoE estimation experiments using the proposed estimator on a LiFePO 4 battery show superior performance regarding robustness and accuracy against high-dynamic loads and various temperatures.