Lithium Ion

Lithium-ion battery research intensely focuses on improving diagnostics, prognostics, and safety. Current efforts leverage machine learning, particularly physics-informed neural networks (PINNs) and transformer architectures, to create accurate and computationally efficient surrogate models for battery behavior, enabling faster parameter inference and state-of-health estimations. These advancements are crucial for optimizing battery performance, predicting remaining useful life, and mitigating safety risks associated with manufacturing defects and counterfeit batteries, ultimately impacting the widespread adoption of this critical technology.