温度和电压因素对超级电容性能衰退的影响机制及其老化建模研究

Supercapacitor with excellent high specific power characteristics is popular in electric transport equipment. It is important to analyze and model the performance degradation mechanism for its high efficiency and reliability. In order to obtain comprehensive aging data and improve the adaptability of model, two types of supercapacitors were selected for accelerated aging experiments under different temperatures and cut-off voltages. The results show that both temperature and cut-off voltage can affect their degradation performance. The increase of cut-off voltage will significantly accelerate the growth of internal resistance. To describe the capacity degradation characteristics and internal resistance variation law, Box-Cox transformation was used to convert the capacity degradation data into a linear decay trajectory and build a linear aging model. Then, the data-driven degradation prediction model of supercapacitor capacity and internal resistance was established with Arrhenius law. Under different aging states and cut-off voltages, the proportional coefficient functions of the full life cycle were constructed to solve the problem of supercapacitors’ capacity degradation difference. The test and simulation results show that the prediction error of capacity degradation trajectory is less than 5%, and the prediction error of the internal resistance change trajectory is less than 10%.