Battery Lifetime

Accurately predicting battery lifetime is crucial for optimizing battery usage, managing end-of-life scenarios, and improving battery design. Current research focuses on developing robust machine learning models, including Bayesian neural networks, Gaussian processes, and attention-based architectures, to predict battery health and remaining useful life from early-life data, often incorporating features derived from equivalent circuit models and voltage relaxation curves. These models aim to improve prediction accuracy and quantify uncertainty, addressing practical challenges like data scarcity and variability in battery usage conditions. This work has significant implications for electric vehicle management, grid energy storage, and sustainable battery lifecycle management.