State-of-charge estimation tolerant of battery aging based on a physics-based model and an adaptive cubature Kalman filter

Abstract

Battery performance declines with aging. This phenomenon makes it difficult to estimate the state-of-charge (SOC) of the battery. Because physics-based battery models (PBMs) can predict the performance decline caused by battery aging with high accuracy and robustness, a high-fidelity and reduced-order PBM is developed for battery SOC estimation according to the requirements of electric vehicle applications. The key model parameters are calibrated primarily according to low C-rate battery charging data. Based on the relationship between the lithium insertion ratios of the electrodes and the battery SOC, an SOC observer is designed. An adaptive cubature Kalman filter (ACKF) is combined with the reduced-order PBM to achieve adaptive tracking of the battery SOC. Three battery cells with different aging states are tested to verify the effectiveness of the proposed method. In addition, cycle aging experiments are conducted on a fresh battery for more than 1300 cycles. The experimental results reveal that the maximum error of SOC estimation is within 1.6% and the root mean square error is within 0.4% for both fresh and aged batteries.