Artificial Intelligence Models Improve Efficiency of Battery Diagnostics - CleanTechnica

The National Renewable Energy Laboratory (NREL) has developed an innovative physics-informed neural network (PINN) model that significantly enhances the efficiency and accuracy of diagnosing lithium-ion battery health. Traditional battery diagnostic models, such as the Single-Particle Model (SPM) and the Pseudo-2D Model (P2D), provide detailed insights into battery degradation mechanisms but are computationally intensive and slow, limiting their practical use for real-time diagnostics. NREL’s PINN surrogate model integrates artificial intelligence with physics-based modeling to analyze complex battery data, enabling battery health predictions nearly 1,000 times faster than conventional methods. This breakthrough allows researchers and manufacturers to non-destructively monitor internal battery states, such as electrode and lithium-ion inventory changes, under various operating conditions. By training the PINN surrogate on data generated from established physics models, NREL has created a scalable tool that can quickly estimate battery aging and lifetime performance across different scenarios. This advancement promises to improve battery management, optimize design, and extend the operational lifespan of energy storage systems, which are critical for resilient and sustainable energy infrastructures.