Fusion Technology-Based CNN-LSTM-ASAN for RUL Estimation of Lithium-Ion Batteries

Yanming Li (), Xiaojuan Qin, Furong Ma, Haoran Wu, Min Chai, Fujing Zhang, Fenghe Jiang and Xu Lei
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Yanming Li: School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
Xiaojuan Qin: School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
Furong Ma: School of Electrical and Electronic Engineering, Lanzhou Petrochemical College of Vocational Technology, Lanzhou 730060, China
Haoran Wu: School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
Min Chai: School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
Fujing Zhang: School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
Fenghe Jiang: School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
Xu Lei: School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China

Sustainability, 2024, vol. 16, issue 21, 1-22

Abstract: Accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) not only prevents battery system failure but also promotes the sustainable development of the energy storage industry and solves the pressing problems of industrial and energy crises. Because of the capacity regeneration phenomenon and random interference during the operation of lithium-ion batteries, the prediction precision and generalization performance of a single model can be poor. This article proposes a novel RUL prediction based on data pre-processing methods and the CNN-LSTM-ASAN framework. The model is based on a fusion technique for optimizing the tandem fusion of the Convolutional Neural Network (CNN) and the Long Short-Term Memory Network (LSTM). Firstly, the improved adaptive noise fully integrates empirical mode decomposition (ICEEMDAN) and the Pearson correlation coefficient (PCC), which are used to estimate the global deterioration tendency component and the local capacity restoration component, to reconstruct the dataset and eliminate the noise. Then, the Adaptive Sparse Attention Network (ASAN) is added in the model construction stage to improve the training efficiency of the model. The reconstructed degraded data are features extracted by the CNN-LSTM-ASAN model. Finally, the proposed method is validated against models such as DCLA, using the NASA public datasets, the CALCE public datasets, and the self-use datasets. And the results show that the root mean square error (RMSE) of the model is below 1.5%.

Keywords: lithium-ion battery; remaining useful life; CNN; LSTM; ASAN (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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