An Improved Transformer Model for Remaining Useful Life Prediction of Lithium-Ion Batteries under Random Charging and Discharging

With the development of artificial intelligence and deep learning, deep neural networks have become an important method for predicting the remaining useful life (RUL) of lithium-ion batteries. In this paper, drawing inspiration from the transformer sequence-to-sequence task’s transformation capabili...

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Bibliographic Details
Published in:Electronics (Basel) Vol. 13; no. 8; p. 1423
Main Authors: Zhang, Wenwen, Jia, Jianfang, Pang, Xiaoqiong, Wen, Jie, Shi, Yuanhao, Zeng, Jianchao
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.04.2024
Subjects:
Accuracy
Algorithms
Analysis
Artificial intelligence
Artificial neural networks
Datasets
Deep learning
Discharge
Electric transformers
Electric vehicles
Electrolytes
Feature extraction
Integrated approach
Life prediction
Lithium
Lithium-ion batteries
Machine learning
Neural networks
Noise reduction
Prediction models
Principal components analysis
Rechargeable batteries
Time series
Transformers
Trends
Wavelet transforms
ISSN:2079-9292, 2079-9292
Online Access:Get full text
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Summary:With the development of artificial intelligence and deep learning, deep neural networks have become an important method for predicting the remaining useful life (RUL) of lithium-ion batteries. In this paper, drawing inspiration from the transformer sequence-to-sequence task’s transformation capability, we propose a fusion model that integrates the functions of the stacked denoising autoencoder (SDAE) and the Transformer model in order to improve the performance of RUL prediction. Firstly, the health factors under three different conditions are extracted from the measurement data as model inputs. These conditions include constant current and voltage, random discharge, and the application of principal component analysis (PCA) for dimensionality reduction. Then, SDAE is responsible for denoising and feature extraction, and the Transformer model is utilized for sequence modeling and RUL prediction of the processed data. Finally, accurate prediction of the RUL of the four battery cells is achieved through cross-validation and four sets of comparison experiments. Three evaluation metrics, MAE, RMSE, and MAPE, are selected, and the values of these metrics are 0.170, 0.202, and 19.611%, respectively. The results demonstrate that the proposed method outperforms other prediction models in terms of prediction accuracy, robustness, and generalizability. This provides a new solution direction for the daily life prediction research of lithium-ion batteries.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13081423