Co-estimation of state of charge and state of health for lithium-ion batteries based on fractional-order model with multi-innovations unscented Kalman filter method

Abstract

Accurate state of charge (SOC) and state of health (SOH) estimation is very important to ensure safe and efficient operation of electric vehicle battery system. In this study, an improved co-estimation method of SOC and SOH based on a fractional model is proposed. A fractional second-order model is established. The identification of model parameters (including the order of fractional elements) is realized by adaptive genetic algorithm and the SOC is estimated using multi-innovations unscented Kalman filter (MIUKF). At the same time, the unscented Kalman filter (UKF) is used to predict SOH to update the actual capacity of the SOC estimator. The effectiveness of the proposed co-estimation method is validated by experiment data under different test cycles and battery aging degrees. The results show that the root mean square error of SOC at 25 °C is less than 0.38% under different test cycles, and the root mean square error of SOH is less than 0.002%. Compared with UKF, fractional-order unscented Kalman filter and fractional-order MIUKF, the SOC estimation error of the proposed method is the lowest. Under different aging degree, the root mean square error of SOC and SOH at 25 °C is lower than 1.21% and 0.007%, respectively. It indicates that the proposed method has good adaptability and high accuracy.