Research on Multi-State Estimation Strategy for Lithium-Ion Batteries Considering Temperature Bias

Accurate state estimation is a key technology for improving battery utilization and ensuring operational safety in electric vehicles. The joint estimation of the state of charge (SOC) and the state of power (SOP) over a wide temperature range is therefore essential for intelligent battery management systems. To address modeling uncertainties and estimation accuracy degradation induced by ambient temperature variations, a dual-polarization equivalent circuit thermal model incorporating temperature bias is proposed, and online parameter updating is achieved using the forgetting factor recursive least squares (FFRLS) algorithm. Furthermore, an unscented particle filter (UPF) is constructed by employing the unscented Kalman filter (UKF) as the proposal density function of the particle filter, thereby improving the estimation accuracy and convergence speed of SOC under wide temperature conditions. Based on the coupling relationship between SOC and SOP, a stepwise progressive strategy is then developed to predict the peak power state under multiple constraints, enhancing the robustness of SOP estimation. Simulation and experimental results demonstrate that the proposed method can accurately estimate SOC and SOP under complex operating conditions over a wide temperature range from −5 °C to 45 °C, exhibiting favorable convergence performance and estimation accuracy, which contributes to the safe operation and performance optimization of electric vehicle battery systems.