Journal
JOURNAL OF ENERGY STORAGE
Volume 21, Issue -, Pages 510-518Publisher
ELSEVIER
DOI: 10.1016/j.est.2018.12.011
Keywords
Electric vehicles; Lithium-ion batteries; Elman neural network; Long short-term memory; Remaining useful life
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Funding
- National Natural Science Foundation of China [U1764258]
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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.
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