In this section
@article{Panahi:2025:10.1016/j.egyai.2025.100647,
author = {Panahi, AA and Luder, D and Wu, B and Offer, G and Sauer, DU and Li, W},
doi = {10.1016/j.egyai.2025.100647},
journal = {Energy and AI},
title = {Fast and generalisable parameter-embedded neural operators for lithium-ion battery simulation},
url = {http://dx.doi.org/10.1016/j.egyai.2025.100647},
volume = {22},
year = {2025}
}
TY - JOUR
AB - Digital twins of lithium-ion batteries are increasingly used to enable predictive monitoring, control, and design at system scale. Increasing their capabilities involves improving their physical fidelity while maintaining sub-millisecond computational speed. In this work, we introduce machine learning surrogates that learn physical dynamics. Specifically, we benchmark three operator-learning surrogates for the Single Particle Model (SPM): Deep Operator Networks (DeepONets), Fourier Neural Operators (FNOs) and a newly proposed parameter-embedded Fourier Neural Operator (PE-FNO), which conditions each spectral layer on particle radius and solid-phase diffusivity. We extend the comparison to classical machine-learning baselines by including U-Nets. Models are trained on simulated trajectories spanning four current families (constant, triangular, pulse-train, and Gaussian-random-field) and a full range of State-of-Charge (SOC) (0 % to 100 %). DeepONet accurately replicates constant-current behaviour but struggles with more dynamic loads. The basic FNO maintains mesh invariance and keeps concentration errors below 1 %, with voltage mean-absolute errors under 1.7 mV across all load types. Introducing parameter embedding marginally increases error but enables generalisation to varying radii and diffusivities. PE-FNO executes approximately 200 times faster than a 16-thread SPM solver. Consequently, PE-FNO's capabilities in inverse tasks are explored in a parameter estimation task with Bayesian optimisation, recovering anode and cathode diffusivities with 1.14 % and 8.4 % mean absolute percentage error, respectively, and 0.5918 percentage points higher error in comparison with classical methods. These results pave the way for neural operators to meet the accuracy, speed and parametric flexibility demands of real-time battery management, design-of-experiments and large-scale inference. PE-FNO outperforms conventional neural surrogates, offering a practical path towards high-s
AU - Panahi,AA
AU - Luder,D
AU - Wu,B
AU - Offer,G
AU - Sauer,DU
AU - Li,W
DO - 10.1016/j.egyai.2025.100647
PY - 2025///
TI - Fast and generalisable parameter-embedded neural operators for lithium-ion battery simulation
T2 - Energy and AI
UR - http://dx.doi.org/10.1016/j.egyai.2025.100647
VL - 22
ER -
Dyson School of Design Engineering
Imperial College London
25 Exhibition Road
South Kensington
London
SW7 2DB
design.engineering@imperial.ac.uk
Tel: +44 (0) 20 7594 8888