State of charge estimation under different temperatures using unscented Kalman filter algorithm based on fractional-order model with multi-innovation

In this study, a battery test platform is built to study and analyze the battery model. A fractional-order model (FOM) of battery is developed by combining the second-order equivalent circuit model with fractional calculus theory. By combining the multi-innovation with the unscented Kalman filter algorithm based on FOM, a fractional-order multi innovation unscented Kalman filter algorithm is proposed. Under different temperatures and dynamic conditions, the tracking of terminal voltage and the estimation results of SOC using the proposed algorithm are compared and analyzed. In order to verify the accuracy and robustness of the proposed algorithm, the tracking effect of the terminal voltage and the estimation results of SOC under different initial SOC values are investigated. The experimental results indicate that the FOM is more accurate than the integer-order model in reflecting the battery characteristics. Under different temperatures, dynamic conditions, and initial SOC values, the values of mean absolute error (MAE) and root mean squared error (RMSE) of the proposed algorithm are the lowest, which verifies the effectiveness of the proposed algorithm. The maximum values of MAE and RMSE of SOC estimation results of the proposed algorithm are 1.04 % and 0.9122 %, respectively, indicating that the proposed algorithm has high accuracy and good robustness. • A fractional-order model of batteries is developed. • A multi-innovation unscented Kalman filter method is proposed. • The estimation of the state of charge under different temperatures is investigated. • The effectiveness of the proposed algorithm is validated by the experimental data.