Document Type

Article

Publication Date

2026

DOI

10.3390/wevj17010002

Publication Title

World Electric Vehicle Journal

Volume

17

Issue

1

Pages

2 (1-40)

Abstract

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally segmented temperature sequences. Among all evaluated predictors, the Bidirectional Long Short-Term Memory (BiLSTM) network achieved the highest prediction fidelity, yielding an RMSE of 0.018 °C, a 35.7% improvement over the conventional Long Short-Term Memory (LSTM) (RMSE = 0.028 °C) due to its ability to simultaneously encode forward and backward temporal dependencies inherent in cyclic electrochemical operation. While CatBoost exhibited the strongest performance among classical regressors (RMSE = 0.022 °C), outperforming Random Forest, Gradient Boosting, Support Vector Regression, XGBoost, and LightGBM, it remained inferior to BiLSTM because it lacks the capacity to represent bidirectional electrothermal dynamics. This performance hierarchy confirms that LIB thermal evolution is not dictated solely by historical load sequences; it also depends on forthcoming cycling patterns and entropic interactions, which unidirectional and memoryless models cannot capture. The resulting hybrid physics-data-driven framework provides a reliable surrogate for real-time LIB thermal estimation and can be directly embedded within BMS to enable proactive intervention strategies such as predictive cooling activation, current derating, and early detection of hazardous thermal conditions. By coupling physics-based decomposition with deep sequential learning, this study establishes a validated foundation for next-generation LIB thermal-management platforms and identifies a clear trajectory for future work extending the methodology to module- and pack-level systems suitable for industrial deployment.

Rights

© 2025 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The data used in this study were obtained from the publicly available battery dataset reported by Severson et al., Data-Driven Prediction of Battery Cycle Life before Capacity Degradation, Nature Energy, 2019 [DOI: 10.1038/s41560-019-0356-8] [44]. The dataset is openly accessible and was used without modification beyond standard preprocessing for analysis and modeling. No new experimental data were generated in this work."

Original Publication Citation

Madani, S. S., Shabeer, Y., Fowler, M., Panchal, S., Ziebert, C., Chaoui, H., & Allard, F. (2026). Physics-informed temperature prediction of lithium-ion batteries using decomposition-enhanced LSTM and BiLSTM models. World Electric Vehicle Journal, 17(1), 1-40, Article 2. https://doi.org/10.3390/wevj17010002

ORCID

0000-0001-8728-3653 (Chaoui)

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