State of Health

Accurately estimating a system's "state of health" (SOH) is crucial for predictive maintenance and optimizing performance, particularly in applications like battery management and industrial equipment monitoring. Current research heavily utilizes machine learning, employing various architectures such as graph neural networks, transformers, and recurrent neural networks (like GRUs) to predict SOH from sensor data, often incorporating techniques like transfer learning and data synchronization to improve accuracy and generalizability across different systems and operating conditions. These advancements enable more precise real-time estimations, leading to improved efficiency, reduced downtime, and enhanced safety in diverse fields ranging from electric vehicles to industrial processes. The development of robust and transferable SOH estimation methods is a significant area of ongoing research with substantial practical implications.