State of Charge Estimation for Lithium-Ion Battery Based on Hybrid Compensation Modeling and Adaptive H-Infinity Filter

Accurate estimation of state of charge (SOC) is crucial for operation performance promotion of lithium-ion batteries. However, the variations of temperature and loading current directly impact the estimation accuracy of SOC. To fully account for these influences, this study proposes a hybrid compensation model and exploits an advanced algorithm for high-performance SOC estimation. First, a fractional-order model (FOM) is constructed to delineate the electrochemical behaviors of batteries with higher accuracy, compared with traditional integral-order model (IOM). Then, the relationship among discharge rate, temperature, and available capacity is explored, and a capacity compensation model is established via the random forest (RF) algorithm. Based on the trustworthy parameter identification and capacity recognition, the SOC is estimated by the adaptive H-infinity filter (AHIF) to fully cope with the model and operation condition variations raised by different temperatures and loading currents. By this manner, the presented method enhances the robustness to parameter uncertainty and modeling errors and promotes the estimation accuracy of SOC in wide temperature range. The experimental results highlight that compared with the traditional IOM and adaptive extended Kalman filter (AEKF), the proposed method can highly boost the temperature adaptability, convergence speed, and estimation accuracy of SOC.