Improved Double-Layer Adaptive Extended Kalman Filter Method for Synergistic Prediction of SOC and SOE in Lithium-Ion Batteries

To address the issue of insufficient accuracy in estimating parameters and states for lithium batteries, which restricts the performance evaluation and optimization application of batteries, this paper proposes a collaborative prediction strategy for the state of charge (SOC) and state of energy (SOE) based on the Double-layer adaptive extended Kalman filter (DAEKF) algorithm. The core principle is to adopt a bidirectional feedback mechanism to achieve real-time correction of SOC and SOE, effectively improving the accuracy and robustness of the estimation. Furthermore, by establishing a partnership for a new generation of vehicle (PNGV) equivalent circuit model, this paper effectively characterizes the charging and discharging kinetic characteristics of the battery. The experimental results show that under the three working conditions of Hybrid Pulse Power Characteristic (HPPC), Dynamic Stress Test (DST), and Urban Dynamometer Driving Schedule (UDDS), compared with the estimation accuracy of the Extended Kalman filter (EKF) and Adaptive extended Kalman filter (AEKF) algorithms, the proposed DAEKF algorithm has a significant improvement, with its estimation error controlled within 5%. This is of great significance for the subsequent evaluation and optimization of battery performance.