Relevance-Based Reconstruction Using an Empirical Mode Decomposition Informer for Lithium-Ion Battery Surface-Temperature Prediction

Chao Li, Yigang Kong (), Changjiang Wang, Xueliang Wang, Min Wang and Yulong Wang
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Chao Li: School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
Yigang Kong: School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
Changjiang Wang: Shanxi Provincial New Energy Aviation Intelligent Support Equipment Technology Innovation Center, Changzhi 046000, China
Xueliang Wang: School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
Min Wang: Shanxi Provincial New Energy Aviation Intelligent Support Equipment Technology Innovation Center, Changzhi 046000, China
Yulong Wang: School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China

Energies, 2024, vol. 17, issue 19, 1-16

Abstract: Accurate monitoring of lithium-ion battery temperature is essential to ensure these batteries’ efficient and safe operation. This paper proposes a relevance-based reconstruction-oriented EMD-Informer machine learning model, which combines empirical mode decomposition (EMD) and the Informer framework to estimate the surface temperature of 18,650 lithium-ion batteries during charging and discharging processes under complex operating conditions. Initially, based on 9000 data points from the U.S. NASA Prognostics Center of Excellence’s random battery-usage dataset, where each data point includes three features: temperature, voltage, and current, EMD is used to decompose the temperature data into intrinsic mode functions (IMFs). Subsequently, the IMFs are reconstructed into low-, medium-, and high-correlation components based on their correlation with the original data. These components, along with voltage and current data, are fed into sub-models. Finally, the model captures the long-term dependencies among temperature, voltage, and current. The experimental results show that, in single-step prediction, the mean squared error, mean absolute error, and maximum absolute error of the model’s predictions are 0.00095, 0.02114, and 0.32164 °C; these metrics indicate the accurate prediction of the surface temperature of lithium-ion batteries. In multi-step predictions, when the prediction horizon is set to 12 steps, the model achieves a hit rate of 93.57% where the maximum absolute error is within 0.5 °C; under these conditions, the model combines high predictive accuracy with a broad predictive range, which is conducive to the effective prevention of thermal runaway in lithium-ion batteries.

Keywords: lithium-ion batteries; informer; empirical mode decomposition; relevance reconstruction; thermal runaway; temperature prediction (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: 2024
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