State of Charge Estimation of Supercapacitor under Different Temperatures Using Particle Filter Algorithm Based on Fractional-Order Model

With the rise of new energy vehicles, supercapacitors (SCs) have been used as energy storage components for new energy vehicles due to their high-power density and good low-temperature performance. Accurate modeling and state of charge estimation of SC can ensure the safe operation of new energy vehicles. In order to explore the low-temperature performance of supercapacitors, this paper proposes a dual ZARC fractional-order circuit model to simulate the dynamic characteristics of SC. Using adaptive genetic algorithm for SC parameter identification, the model terminal voltage error is less than 6.5 mV. In addition, the SOC of SC at different temperatures and working conditions is estimated by using the fractional-order particle filter (FOPF) method and compared with the fractional-order extended Kalman filter (FOEKF). The experimental results show that the FOPF method has high estimation accuracy and robustness. Under the temperature of minus 40 & DEG;C, the maximum mean absolute error and maximum root-mean-square deviation of SOC estimation under different working conditions are less than 2%, showing good low-temperature performance. A fractional-order particle filter algorithm for estimating the SOC of supercapacitor is proposed.Adaptive genetic algorithm is used to identify the model parameters.The impact of low temperature on the accuracy of SOC estimation was studied.The accuracy and robustness of the algorithm are verified using experimental data.