Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks

• Published in: Journal of energy storage Vol. 21; pp. 510 - 518
• Main Authors: Li, Xiaoyu, Zhang, Lei, Wang, Zhenpo, Dong, Peng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2019
• Subjects: Electric vehicles; Elman neural network; Lithium-ion batteries; Long short-term memory; Remaining useful life; Lithium-ion batteries; Long short-term memory; Electric vehicles; Remaining useful life; Elman neural network
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2018.12.011

This paper presents a novel hybrid Elman-LSTM method for battery remaining useful life prediction by combining the empirical model decomposition algorithm and long short-term memory and Elman neural networks. The empirical model decomposition algorithm is employed to decompose the recorded battery capacity verse cycle number data into several sub-layers. The recurrent long short-term memory and Elman neural networks are then established to predict high- and low-frequency sub-layers, respectively. Comprehensive battery test datasets have been collected and used for model parameterization and performance evaluation. The comparison results indicate that the proposed hybrid Elman-LSTM model yields superior performance relative to the other counterparts and can predict the battery remaining useful life with high accuracy. The relative prediction errors are 3.3% and 3.21% based on two unseen datasets, respectively.