Authors: Aadya Arora | Siya Patil | Pallavi Bhardwaj
Source: IEEE Transactions on Industry Applications (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

TEMPEST: A Machine Learning Framework for Battery Thermal Prediction and Chemistry Selection Lithium-ion batteries are the cornerstone of modern portable electronics and electric vehicles. However, cell temperature profoundly influences their operational efficiency, safety, and lifetime. We present TEMPEST: Thermal Estimation and Chemistry Selection Toolkit, a data-driven framework that couples machine learning-based temperature prediction with chemistry recommendation. Our method trains long-short-term-memory (LSTM) models on three lithium-ion battery chemistries: nickel-cobalt-aluminum (NCA), nickel-manganese-cobalt (NMC), and lithium-iron-phosphate (LFP). These models are used to estimate surface temperature from voltage and current time series under controlled ambient conditions. TEMPEST consistently achieves high fidelity under varying operational load and ambient conditions, with mean absolute error below <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.5^{\circ }\mathrm{C}$</tex-math></inline-formula> for the same chemistry. The proposed approach is also extended to cross-chemistry transfer, providing stable generalization at room temperatures, with typical errors up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2.0^{\circ }\mathrm{C}$</tex-math></inline-formula>, enabling accurate thermal monitoring without intrusive sensors. At extreme temperature conditions, the mean absolute error can rise up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$5^{\circ }\mathrm{C}$</tex-math></inline-formula>, giving higher errors for larger electrochemical mismatch between chemistries. Further, to experimentally validate the proposed method, in-lab data is collected using a standard electro-thermal battery cycler, where thermal prediction errors as low as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.4^{\circ }\mathrm{C}$</tex-math></inline-formula> are achieved, demonstrating strong adaptation capability under limited data. When benchmarked against popular methods, our model achieves a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10\%$</tex-math></inline-formula> accuracy improvement while requiring approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20\%$</tex-math></inline-formula> less compute. We also introduce a battery selection algorithm that, given ambient temperature, recommends the battery chemistry expected to operate at the lowest temperature. Our results reveal that NMC and NCA cells are preferable at sub-ambient conditions, whereas LFP chemistries offer thermal stability at elevated temperatures. Selection (genetic algorithm) ; Battery (electricity) ; Thermal ; Feature selection ; Artificial neural network ; Support vector machine Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Phase Equilibria and Thermodynamics