Remaining useful life prediction for supercapacitor based on long short-term memory neural network

Abstract The remaining useful life prediction of supercapacitor is an important part of the supercapacitor management system. In order to improve the reliability of the entire supercapacitor bank, this paper proposes a life prediction method based on long short-term memory neural network. It is used to learn the long-term dependence of degraded capacity of supercapacitor. The Dropout algorithm is used to prevent overfitting and the neural network is optimized by the Adam algorithm. The supercapacitor data measured under different working conditions is divided into training set and predictive set as the input of the neural network. The root mean square error of the predicted result is about 0.0261. At the same time, in order to verify the applicability of the algorithm, it is also used for the life prediction of offline data, and the root mean square error is about 0.0338. The overall results show that long short-term memory neural network exhibits excellent performance for remaining useful life prediction of supercapacitor.