Physics-Informed Neural Network Boosts Battery Diagnostics with Rapid Health Predictions

NREL researchers have developed a physics-informed neural network (PINN) model to diagnose battery health. The PINN model can predict battery health nearly 1,000 times faster than traditional models and can quantify physical degradation mechanisms. This AI approach can enhance battery health diagnostics and pave the way for more efficient and scalable approaches to managing battery aging.

National Renewable Energy Laboratory (NREL) researchers have developed a groundbreaking physics-informed neural network (PINN) model to diagnose battery health, significantly enhancing the efficiency and accuracy of battery diagnostics. The PINN model can predict battery health nearly 1,000 times faster than traditional methods, offering a substantial leap forward in the field of energy storage.

Enhancing Battery Diagnostics

The traditional methods for diagnosing battery health, such as the Single-Particle Model (SPM) and the Pseudo-2D Model (P2D), are resource-intensive and time-consuming. These models require massive computational resources and are limited in their ability to offer rapid diagnostics. The PINN model, developed by NREL, addresses these limitations by combining the predictive power of artificial intelligence with the rigor of physics-based modeling.

The PINN model is designed to understand and follow physical laws, embedding them directly into its training procedure. This allows it to predict battery parameters with a level of scientific rigor previously achievable only by complex, time-intensive models. By doing so, the PINN surrogate model drastically reduces the computational time and resources required, enabling researchers to quickly diagnose battery degradation and provide real-time feedback on battery health.

Implications for Battery Management

The success of the NREL-developed PINN surrogate model has wide-ranging implications for battery management. For instance, it can provide rapid state-of-health predictions, allowing for faster decision-making across battery applications. By drastically lowering the computational barriers to battery diagnostics, the PINN surrogate model paves the way for widespread, scalable, and efficient energy storage management.

This development is particularly revolutionary in the context of lithium-ion batteries, which are experiencing rapid market growth due to their advantageous qualities such as low self-discharge rates, high power densities, and long lifetimes [1]. However, continuous usage of lithium-ion batteries inevitably leads to degradation over time due to irreversible side reactions and changes in material morphology. The PINN model can help in understanding and managing these degradation mechanisms, ensuring that batteries remain efficient and safe throughout their service life.

Future Directions

Future research will focus on refining the PINN model to handle highly dimensional problems, allowing it to predict a broader array of internal battery parameters with increased precision. This means creating models that can both respond to diverse current loads and scale effectively to future battery designs and usage patterns.

Currently, researchers are working to transition the PINN surrogate from controlled simulations to real-work data validation, using batteries cycled within NREL's laboratories. By bridging this gap, researchers hope to deploy PINN-based diagnostics across a wide range of battery systems, enhancing battery performance monitoring and extending lifespans.

References

[1] https://techxplore.com/news/2025-06-physics-neural-network-significantly-boosts.html
[2] https://www.sciencedirect.com/science/article/abs/pii/S2590116825000396