计算机科学
人工神经网络
可靠性(半导体)
卷积神经网络
电池(电)
人工智能
均方误差
深度学习
可靠性工程
机器学习
工程类
统计
数学
量子力学
物理
功率(物理)
作者
Brahim Zraibi,Chafik Okar,Hicham Chaoui,Mohamed Mansouri
出处
期刊:IEEE Transactions on Vehicular Technology
[Institute of Electrical and Electronics Engineers]
日期:2021-05-01
卷期号:70 (5): 4252-4261
被引量:103
标识
DOI:10.1109/tvt.2021.3071622
摘要
The prediction of a Lithium-ion battery's lifetime is very important for ensuring safety and reliability. In addition, it is utilized as an early warning system to prevent the battery's failure. Recent advance in Machine Learning (ML) is an enabler for new data-driven estimation approaches. In this paper, we suggest a hybrid method, named the CNN-LSTM-DNN, which is a combination of Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Deep Neural Networks (DNN), for the estimation of the battery's remaining useful life (RUL) and improving prediction accuracy with acceptable execution time. A comparison against various ML estimation algorithms is carried out to show the superiority of the proposed hybrid estimation approach. For that, three statistical indicators, i.e., the MAE, R 2 , and RMSE, are selected to assess numerically the prediction results. Experimental validation is performed using two datasets of different lithium-ion batteries from NASA and CALCE. Thus, results reveal that hybrid methods perform better than the single ones, also the effectiveness of the suggested method in reducing the prediction error and in achieving better RUL prediction performance compared to the other methods.
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