Comparative Study of Neural Surrogate Architectures for Autoregressive Prediction of Internal Battery States
arXiv:2606.20053v1 Announce Type: new 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 st
Rapid advancements in AI and machine learning are enabling the creation of surrogate models that can overcome the computational limitations of complex scientific simulations, such as those for battery dynamics.
This development allows for real-time monitoring and prediction of critical internal states in batteries, which is essential for optimizing performance, extending lifespan, and ensuring safety in scalable applications.
The ability to deploy real-time, high-fidelity battery models moves beyond lab-based simulations, enabling dynamic management of battery packs and fleets for electric vehicles, grid storage, and other large-scale systems.
- · Battery manufacturers
- · Electric vehicle industry
- · Grid energy storage providers
- · AI/ML model developers
- · Traditional battery management system providers (if slow to adapt)
- · Developers of computationally intensive simulation software
Improved battery performance and longevity across numerous applications due to more precise internal state management.
Accelerated adoption of electric vehicles and renewable energy storage solutions as battery reliability and efficiency increase.
New competitive landscapes emerge for battery technology and energy systems, favoring those who integrate advanced AI for predictive maintenance and optimization.
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