@inproceedings{29f97ef3d0ff44e699fa14fb886c9291,
title = "Cycle life prediction of lithium ion battery based on DE-BP neural network",
abstract = "Aiming at the low prediction accuracy of current lithium-ion battery cycle, this paper proposes a model based on differential evolution algorithm (DE) and BP neural network fusion. BP neural network is used to predict the cycle life of lithium-ion battery. The DE algorithm is used to optimize the initial weight and threshold of BP neural network, which reduces the number of iterations of neural network and accelerates the convergence speed. The prediction results show that the prediction model has higher prediction accuracy, effectively improves the convergence speed of BP neural network, and meets the characteristics of battery operation, which is of great significance for improving the timeliness and accuracy of battery life assessment.",
keywords = "BP neural network, Cycle life prediction, Differential evolution, Lithium-ion battery",
author = "Zhao Yao and Shun Lu and Yingshun Li and Xiaojian Yi",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019 ; Conference date: 15-08-2019 Through 17-08-2019",
year = "2019",
month = aug,
doi = "10.1109/SDPC.2019.00033",
language = "English",
series = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "137--141",
editor = "Chuan Li and Shaohui Zhang and Jianyu Long and Diego Cabrera and Ping Ding",
booktitle = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
address = "United States",
}
