A Joint Method for Estimation of Lithium Battery SOC and SOH Based on Dual AEKF

To address issues of low accuracy and poor reliability in estimating the state of charge (SOC) and state of health (SOH) of lithium batteries, a joint SOC and SOH estimation method based on dual adaptive extended Kalman filter (AEKF) is proposed. First, the hybrid pulse power characterization (HPPC) test experiment and least squares fitting algorithm are used to achieve high-precision identification of the second-order RC equivalent circuit model parameters of the lithium battery, laying the foundation for the implementation of the AEKF algorithm. Then, based on the traditional Kalman filter algorithm, techniques such as adaptive noise parameter closed-loop optimization and multi-time-scale joint state estimation are employed to enhance the stability and prediction accuracy of the algorithm, thereby achieving precise estimation of the lithium battery"s SOC and SOH. Finally, the proposed algorithm is validated under dynamic stress test (DST) conditions. Experimental results show that the dual AEKF estimation algorithm effectively resolves the issues of real-time SOC tracking and stable SOH estimation, with estimation errors for both SOC and SOH being less than 1.8%, achieving high-precision joint estimation of the lithium battery"s SOC and SOH, and demonstrating significant practical application value.