Fast and generalisable parameter-embedded neural operators for lithium-ion battery simulation

Energy and AI, vol. 22, pp. 100647

Abstract

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-speed and high-fidelity electrochemical digital twins.

Authors 6

1. Amir Ali Panahi corresponding Aachen Center for Ageing Institute for Power Electronics and Electrical Drives (ISEA)

   Imperial College London · RWTH Aachen University

   Affiliation as printed

   Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany

   Department of Mechanical Engineering, Imperial College London, London, SW7 2AZ, United Kingdom

   Dyson School of Design Engineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom

   Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany

2. Daniel Luder Aachen Center for Ageing Institute for Power Electronics and Electrical Drives (ISEA)

   RWTH Aachen University

   Affiliation as printed

   Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany

   Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany

3. Billy Wu

   The Faraday Institution · Imperial College London

   Affiliation as printed

   Dyson School of Design Engineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom

   The Faraday Institution, Harwell Science and Innovation Campus, Didcot, OX11 0RA, United Kingdom

4. Gregory James Offer

   The Faraday Institution · Imperial College London

   Affiliation as printed

   Department of Mechanical Engineering, Imperial College London, London, SW7 2AZ, United Kingdom

   The Faraday Institution, Harwell Science and Innovation Campus, Didcot, OX11 0RA, United Kingdom

   Vehicle Futures Hub, Imperial College London, London, SW7 2AZ, United Kingdom

5. Dirk Uwe Sauer Aachen Center for Ageing Institute for Power Electronics and Electrical Drives (ISEA)

   RWTH Aachen University

   Affiliation as printed

   Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany

   Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany

6. Weihan Li corresponding Aachen Center for Ageing Institute for Power Electronics and Electrical Drives (ISEA)

   RWTH Aachen University

   Affiliation as printed

   Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany

   Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany