Mission profile based parameter estimation of supercapacitors for reliability improvement in energy storage systems

Accurate prediction of supercapacitor's performance under different operating condition is important for its reliable and optimized application. In order to predict the characteristic parameters of supercapacitor under various working condition with small number of experiments, the paper presents a prediction method with less dependency on experimental data. Firstly, a set of experiments have been carried out to get mission profile data. Then, a comparative study of parameter estimation using Support vector machine (SVM) and traditional multiple linear regression (MLP) has been carried out. It's concluded that SVM can achieve same fitting and prediction ability with small sized data. Furthermore, the available energy of supercapacitor energy storage system has been derived based on the prediction results, and the effectiveness of the proposed method is verified by experiments.