State-of-Charge and State-of-Health Estimation for Lithium-Ion Batteries Based on Dual Fractional-Order Extended Kalman Filter and Online Parameter Identification

Abstract

Accurate state-of-charge (SOC) and state-of-health (SOH) estimations of batteries are of great significance for electric vehicles. A combined SOC and SOH estimation method for lithium-ion batteries based on a dual extended Kalman filter (EKF) and fractional-order model (FOM) is proposed. A fractional second-order RC model is established and model parameters are identified offline by an adaptive genetic algorithm (AGA). One of the dual filters is used to jointly estimate the SOC and SOH (ohmic internal resistance and capacity), and another is employed to update the model parameters online. Compared with single filter with fixed parameters, the dual filters can obtain more accurate SOC estimation and model voltage prediction. The SOC root-mean square errors (RMSEs) decrease from 6.87%, 8.50% and 7.32% to 0.48%, 0.63% and 0.86% under the Federal Urban Driving Schedule (FUDS), the Dynamic Stress Test (DST) and the US06 Highway Driving Schedule tests, respectively, and the model voltage RMSEs decrease from 88.6 mV, 79.3 mV and 68.4 mV to 4.9 mV, 5.7 mV and 3.8 mV, respectively at room temperature. The accuracy of the SOH estimation is also verified under these three tests. The convergence and robustness of the proposed method are discussed and verified by using the wrong initial state value and noise analysis.