Joint estimation of SOC and SOH for lithium-ion batteries based on FOAMIUHF-UKF model

Accurate and rapid estimation of power battery state of charge (SOC) and state of health (SOH) is crucial for ensuring the safe and reliable operation of electric vehicles. A joint SOC and SOH estimation method proposed in the study based on a fractional-order model incorporates an adaptive multi-innovations unscented H-infinity filter (FOAMIUHF) to address challenges such as insufficient model accuracy and algorithm robustness in SOC and SOH co-estimation for lithium-ion batteries. First, a fractional-order second-order equivalent circuit model (FOM) is established, then the sparrow search algorithm (SSA) is employed in order to identify model parameters and fractional orders. By integrating the FOAMIUHF algorithm, dynamic adjustments of fading and weighting factors is introduced to suppress noise and enhance the accuracy of the SOC estimation process. Meanwhile, the unscented Kalman filter (UKF) is employed for SOH prediction and battery capacity updates, enabling joint SOC/SOH estimation. Experimental results demonstrate that under dynamic stress test (DST), highway fuel economy test (HWFET), and Japan working conditions, the proposed algorithm achieves a maximum mean absolute error (MAE) of 0.53% and a maximum root mean square error (RMSE) of 0.61% for SOC estimation, outperforming existing methods such as FOMIUKF-UKF and FOMIUKF. Additionally, under various initial SOC (50%-90%) and aging levels (capacity decay to 72.2%), the estimation error of SOC remains below 1%, validating the algorithm's high accuracy and robustness. The proposed method offers a reliable and effective solution battery state estimation under complex operating conditions and aging scenarios.