Battery Health

Battery health research focuses on accurately predicting and monitoring battery performance and lifespan, crucial for optimizing electric vehicle operation and safety, as well as for efficient battery recycling and second-life applications. Current research employs machine learning models, including transformer networks, Bayesian neural networks, and Gaussian processes, to analyze diverse datasets encompassing voltage, current, temperature, and other relevant parameters, often incorporating data augmentation techniques to improve model robustness. These advancements enable more precise estimations of state-of-health, remaining useful life, and capacity, leading to improved battery management systems and more sustainable energy solutions.