Prognostics Comparison of Lithium-Ion Battery Based on the Shallow and Deep Neural Networks Model

Bing Long, Xiangnan Li, Xiaoyu Gao and Zhen Liu
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Bing Long: School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
Xiangnan Li: School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
Xiaoyu Gao: School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
Zhen Liu: School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China

Energies, 2019, vol. 12, issue 17, 1-13

Abstract: Prognostics of the remaining useful life (RUL) of lithium-ion batteries is a crucial role in the battery management systems (BMS). An artificial neural network (ANN) does not require much knowledge from the lithium-ion battery systems, thus it is a prospective data-driven prognostic method of lithium-ion batteries. Though the ANN has been applied in prognostics of lithium-ion batteries in some references, no one has compared the prognostics of the lithium-ion batteries based on different ANN. The ANN generally can be classified to two categories: the shallow ANN, such as the back propagation (BP) ANN and the nonlinear autoregressive (NAR) ANN, and the deep ANN, such as the long short-term memory (LSTM) NN. An improved LSTM NN is proposed in order to achieve higher prediction accuracy and make the construction of the model simpler. According to the lithium-ion data from the NASA Ames, the prognostics comparison of lithium-ion battery based on the BP ANN, the NAR ANN, and the LSTM ANN was studied in detail. The experimental results show: (1) The improved LSTM ANN has the best prognostic accuracy and is more suitable for the prediction of the RUL of lithium-ion batteries compared to the BP ANN and the NAR ANN; (2) the NAR ANN has better prognostic accuracy compared to the BP ANN.

Keywords: lithium-ion battery; prognostics; remaining useful life (RUL); nonlinear autoregressive (NAR); long-short term memory (LSTM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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