Life Prediction of Hybrid Supercapacitor Based on Improved Model-Extreme Learning Machine

Hybrid supercapacitors not only have the characteristics of high current fast charging and discharging of traditional capacitors, but also have the energy storage characteristics of batteries. They are widely used in various fields and are hotspots in energy technology research. In this paper, the hybrid supercapacitor with antimony pentoxide/cerium oxide composite electrode is studied, and the influence mechanism of temperature, charge and discharge voltage, current, depth of discharge and vibration on its degradation is studied in depth. A new model of performance degradation mechanism of hybrid supercapacitors is proposed. The Model-Extreme Learning Machine (MELM) is used to identify the above models online. The specific steps are as follows: the above model is integrated into the hidden layer in the extreme learning machine, and then the weight of the hidden layer in the extreme learning machine is identified by using the recursive least squares method, and then the online parameter identification of the above model can be completed. The aging degree of the supercapacitor is monitored by ESR and C to accurately estimate the remaining service life. The experimental results show that the model has higher recognition accuracy and better prediction effect.