The State-of-Charge Estimation of Supercapacitor With Kalman Filtering Algorithm

Supercapacitor has been considered one of the most promising energy storage devices and has been widely used in new energy generation, electric vehicles, pulse power supply and other fields in these years. Because supercapacitor can charge and discharge at large current, it has been employed to output and absorb peak power in energy storage systems. In those applications, the state-of-charge(SOC) of supercapacitor is usually calculated in Amper-Hour integral (AHI) measurement. Though the nonlinearity of supercapacitor working process isn't as intense as that of lithium battery, the SOC estimation error of AHI for supercapacitor can't be ignored. In this paper, a SOC estimation method of supercapacitor with Kalman filtering algorithm is proposed. Firstly, the equivalent circuit model of supercapacitor is established, and the function relationship between its open circuit voltage and SOC is obtained by theoretical analysis and experimental test. Then parameters of the equivalent circuit model are updated with Forgetting Factor Least Square method. Finally Kalman filter operator is designed by using the state equation of charge and discharge of supercapacitor. The experiment result shows the estimation error is ranging from −0.51 % to 0.07% and RMSE is 0.0023, which indicates the accuracy of the SOC estimation algorithm.