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Generalizable Fourier Neural Operator for estimation of lithium-ion battery temperature distribution
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DOI：10.1016/j.etran.2026.100596.png)
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Abstract
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• Proposes Fourier neural operator (FNO) and parameter-embedded FNO (PE-FNO) models for Li-ion battery temperature prediction. • Embeds partial differential equation (PDE) parameters via the channel attention mechanism to enhance model generalization ability. • Achieves root mean square error (RMSE) less than 0.07 °C and runtime 5 times faster than classical PDE solvers.
Keywords:
Fourier Neural Operator
Lithium-ion batteries
Temperature distribution
Temperature estimation
Parameter-embedded machine learning
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