Physics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with Electrolyte
arXiv:2606.28220v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving nonlinear partial differential equations (PDEs), including battery electrochemical models. They typically en-force conservation laws within the loss function to ensure physically consistent solutions. Tradi-tional numerical methods such as finite difference, finite volume, and finite element techniques, re-ly on discretization and can be computationally expensive for nonlinear systems. To address this challenge, PINNs offer improved scalability, particularly for
The increasing demand for efficient and safe battery management, coupled with advancements in AI and physics-informed modeling, drives the need for better state estimation methods.
This development offers a computationally efficient way to monitor and manage lithium-ion batteries, crucial for extending their lifespan, improving performance, and ensuring safety across numerous applications.
The adoption of PINNs with transfer learning provides a more scalable and accurate method for battery state estimation, potentially reducing reliance on traditional, computationally intensive numerical simulations.
- · Battery manufacturers
- · Electric vehicle industry
- · Renewable energy storage providers
- · AI/ML model developers
- · Traditional numerical simulation software providers for battery modeling
- · Less efficient battery management system developers
More accurate and real-time battery state-of-health and state-of-charge predictions become feasible.
Improved battery longevity and safety could accelerate the adoption of electric vehicles and grid-scale energy storage.
Reduced computational overhead for battery design and optimization could lead to faster innovation cycles and cost reductions across many sectors dependent on battery technology.
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Read at arXiv cs.LG