State of Health Estimation

State of health (SOH) estimation for lithium-ion batteries focuses on accurately predicting a battery's remaining useful life, crucial for safe and efficient energy management. Recent research emphasizes data-driven approaches, employing various machine learning models such as graph convolutional networks, recurrent neural networks (including GRUs), and large language models, often incorporating techniques like transfer learning and domain adaptation to handle variations in battery chemistry, manufacturer, and operating conditions. These advancements aim to improve the accuracy and robustness of SOH estimation, particularly under challenging scenarios like partial discharge cycles and limited labeled data, leading to better battery health monitoring and improved lifespan predictions in diverse applications. The ultimate goal is to enhance the safety and reliability of battery-powered systems.