Ion Battery

Ion battery research centers on accurately predicting battery health and remaining useful life (RUL) to optimize performance and safety in diverse applications. Current research heavily utilizes machine learning, employing architectures like transformers, graph neural networks, and recurrent neural networks, often enhanced with techniques such as attention mechanisms and Bayesian methods to improve prediction accuracy and robustness across varying operating conditions and data scarcity. These advancements are crucial for improving battery management systems (BMS), enabling more efficient electric vehicles, and facilitating the wider adoption of battery-powered technologies. Furthermore, federated learning approaches are being explored to address data privacy and scalability concerns in large-scale battery deployments.