Battery Datasets

Battery datasets are crucial for developing accurate models to predict battery health, remaining useful life, and optimal charging strategies, ultimately improving battery performance and lifespan. Current research focuses on leveraging machine learning, particularly deep learning architectures like convolutional neural networks, recurrent neural networks (including GRUs), and graph neural networks, to analyze battery data and predict key parameters such as state-of-health, state-of-charge, and remaining useful life. This work also addresses challenges like data scarcity through techniques such as transfer learning and generative AI to create synthetic datasets. The resulting advancements have significant implications for battery management systems, electric vehicle applications, and sustainable battery recycling practices.