电动汽车锂离子电池基于等效电路模型的荷电状态估计

The State-of-Charge (SoC) of Lithium-Ion Batteries (LIBs) is a crucial parameter for Battery Management Systems (BMSs) used in Electric Vehicles (EVs). This paper presents a comprehensive study on the SoC estimation of LIBs using advanced model-based methods. The practical implications of this research are significant, as they provide a reliable and efficient approach to SoC estimation, enhancing the performance and lifespan of LIBs in real-world applications, particularly EVs. A third-order equivalent circuit model is employed for the LIB based on electrochemical impedance spectra test results, with model parameters identified using a particle swarm optimization algorithm. Two real-time model-based estimation algorithms, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), are compared for SoC estimation. A hybrid approach based on UKF and EKF is presented. The results demonstrate that the UKF outperforms the EKF in SoC estimation, with the root mean squared error (RMSE) and maximum error for SoC estimation being 1.06% and 1.15%, respectively. The hybrid EKF-UKF approach provides the best performance for SoC estimation, achieving the lowest root mean squared error (RMSE) of 0.2% and a maximum error of 0.5% for SoC estimation. This approach leverages the strengths of EKF and UKF, offering superior accuracy and robustness in real-time battery monitoring in EV applications.