Battery Model

Battery modeling aims to accurately predict battery behavior, crucial for optimizing charging strategies, managing battery health (State of Health, or SOH), and designing efficient energy systems. Current research emphasizes developing accurate yet computationally efficient models, employing diverse approaches such as physics-informed neural networks (PINNs), large language models (LLMs), and graph neural networks (GNNs) to improve parameter inference and SOH estimation. These advancements are driven by the need for improved battery management systems (BMS) in electric vehicles and grid-scale energy storage, impacting both the efficiency and safety of these applications. The development of hybrid models that combine data-driven and physics-based approaches is a significant trend, aiming to balance accuracy and computational speed.