What is it about?
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 predicted set as the input of the neural network. The root mean square error of the predicted result is about 0.0261 and 0.0276. 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.
Featured Image
Photo by Hudson Hintze on Unsplash
Why is it important?
A model to predict the lifetime of TE devices under thermal cycling is proposed. The damage growth of leg-electrode interface is derived. Shorter leg length leads to reduced damage growth rate. The expressions of TE performance degradation under thermal cycling are derived.
Perspectives
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 predicted set as the input of the neural network. The root mean square error of the predicted result is about 0.0261% and 0.0276%. 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.
Professor Kai Wang
Read the Original
This page is a summary of: Remaining useful life prediction for supercapacitor based on long short-term memory neural network, Journal of Power Sources, November 2019, Elsevier,
DOI: 10.1016/j.jpowsour.2019.227149.
You can read the full text:
Contributors
The following have contributed to this page
