Comparative Study of Neural Surrogate Architectures for Autoregressive Prediction of Internal Battery States

• URL: http://arxiv.org/abs/2606.20053v1
• Date: Thu, 18 Jun 2026 10:27:36 GMT
• Title: Comparative Study of Neural Surrogate Architectures for Autoregressive Prediction of Internal Battery States
• Abstract summary: Doyle-Fuller-Newman (DFN) model resolves internal electrochemical states in lithium-ion batteries with high fidelity.<n>The numerical solution of its governing equations is computationally prohibitive for real-time deployment.<n>This work presents a systematic comparison of four neural network architectures (MLP, ResNet, U-Net, FNO)<n>Results demonstrate that the U-Net's multi-scale feature hierarchy achieves a mean final-step nRMSE of 3% averaged across all internal state variables after 300-step autoregressive rollouts.
• Score: 1.37013665345905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Doyle-Fuller-Newman (DFN) model resolves internal electrochemical states in lithium-ion batteries with high fidelity. However, the numerical solution of its governing equations is computationally prohibitive for real-time deployment, limiting scalability from individual cells to pack and fleet-scale applications. While machine learning surrogates can substantially reduce inference latency through GPU acceleration, most existing approaches learn solution approximations tied to specific operating conditions rather than learning generalizable state-evolution dynamics. This work presents a systematic comparison of four neural network architectures (MLP, ResNet, U-Net, FNO) formulated as autoregressive state-transition operators that predict full DFN internal states across a wide range of operating conditions. To ensure a controlled architectural comparison, all models are trained under a unified framework using multi-step unrolling and current-conditioning, isolating the impact of spatial inductive bias. Results demonstrate that the U-Net's multi-scale feature hierarchy achieves a mean final-step nRMSE of 3% averaged across all internal state variables after 300-step autoregressive rollouts, while providing a 5.38x speed-up over the numerical solver. These findings highlight spatial inductive bias as a critical determinant of surrogate performance, advancing the development of surrogates for internal state observability for next-generation battery management systems and digital twins.

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