A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model

Xinyu Gu, K.W. See, Penghua Li, Kangheng Shan, Yunpeng Wang, Liang Zhao, Kai Chin Lim and Neng Zhang

Energy, 2023, vol. 262, issue PB

Abstract: State-of-health (SOH) estimation of lithium-ion batteries is crucial for ensuring the reliability and safety of battery operation while keeping maintenance and service costs down in the long run. This study suggests a novel SOH estimation based on data pre-processing methods and a convolutional neural network (CNN)-Transformer framework. In data pre-processing, highly related features are selected by the Pearson correlation coefficient (PCC). Principal correlation analysis (PCA) is also employed to minimize the computational burden of the estimation model by eliminating redundant feature information. Then, all the features are normalized by the min-max feature scaling method, which will speed up the training process to reach the minimum cost function. After pre-processing, all the features are fed into the CNN-Transformer model. The dataset of the battery from the NASA is employed as a training and testing dataset to build the proposed model. The simulations indicate that the proposed performance, proven by absolute estimation errors for each dataset, is within 1%. The estimation performance index is proven by mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) are held within 0.55%. These show that the proposed model can estimate the battery SOH with high accuracy and stability.

Keywords: Lithium-ion battery; State-of-health (SOH); Convolution neural network (CNN); Long short-term memory (LSTM); Transformer (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222023830
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:262:y:2023:i:pb:s0360544222023830

DOI: 10.1016/j.energy.2022.125501

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().