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

2026-06-18 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Engineering & Applied Sciences, Energy Storage & Grid Technology · Depth: Expert, quick

Summary

This study systematically compares four neural network architectures—MLP, ResNet, U-Net, and FNO—as autoregressive state-transition operators for predicting internal states of lithium-ion batteries. The Doyle-Fuller-Newman (DFN) model offers high-fidelity state resolution but is too computationally intensive for real-time, large-scale applications. This research addresses the limitation of existing machine learning surrogates, which often lack generalizability across operating conditions. By training all models under a unified framework with multi-step unrolling and current-conditioning, the study isolates the impact of spatial inductive bias. Results show the U-Net's multi-scale feature hierarchy achieved a 3% mean final-step nRMSE across all internal state variables after 300-step autoregressive rollouts, delivering a 5.38x speed-up over the numerical solver. These findings emphasize spatial inductive bias as crucial for surrogate performance.

Key takeaway

For Machine Learning Engineers developing battery management systems or digital twins, this research highlights the U-Net architecture as a superior choice for predicting internal battery states. Its multi-scale feature hierarchy delivers a 3% nRMSE over 300-step rollouts and a 5.38x speed-up, making real-time, scalable deployment feasible. You should prioritize architectures with strong spatial inductive bias to achieve high-fidelity, generalizable surrogate models for lithium-ion batteries.

Key insights

Spatial inductive bias is critical for neural surrogates predicting battery internal states.

Principles

• Spatial inductive bias enhances surrogate performance.
• Autoregressive operators predict full DFN internal states.
• Unified training isolates architectural impact.

Method

Neural network architectures (MLP, ResNet, U-Net, FNO) are formulated as autoregressive state-transition operators. They are trained with multi-step unrolling and current-conditioning to predict DFN internal states.

In practice

• Implement U-Net for real-time battery state prediction.
• Develop next-gen battery management systems.
• Create high-fidelity digital twins.

Topics

• Neural Surrogates
• Lithium-ion Batteries
• DFN Model
• U-Net Architecture
• Battery Management Systems
• Digital Twins
• Autoregressive Prediction

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.