Battery Data

Battery data analysis focuses on predicting key battery parameters like remaining useful life (RUL), state-of-health (SOH), and state-of-charge (SOC) to improve battery management and safety. Current research heavily utilizes machine learning, employing various architectures such as transformers, recurrent neural networks (like GRUs and LSTMs), and tree-based regression models to analyze high-dimensional, often noisy, datasets encompassing diverse battery chemistries and operating conditions. These advancements enable more accurate predictions, leading to improved battery design, optimized maintenance schedules, and enhanced reliability across various applications, from electric vehicles to renewable energy storage.