EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation
This provides a valuable resource for researchers, policymakers, and industry professionals working on EV battery aging, though it is incremental as it builds on existing deep learning methods with a new dataset.
The authors tackled the problem of electric vehicle battery safety by introducing EVBattery, a large-scale public dataset of real-world charging records from hundreds of EVs, and developed a customized algorithm that improves performance for battery health and capacity estimation tasks.
Electric vehicles (EVs) play an important role in reducing carbon emissions. As EV adoption accelerates, safety issues caused by EV batteries have become an important research topic. In order to benchmark and develop data-driven methods for this task, we introduce a large and comprehensive dataset of EV batteries. Our dataset includes charging records collected from hundreds of EVs from three manufacturers over several years. Our dataset is the first large-scale public dataset on real-world battery data, as existing data either include only several vehicles or is collected in the lab environment. Meanwhile, our dataset features two types of labels, corresponding to two key tasks - battery health estimation and battery capacity estimation. In addition to demonstrating how existing deep learning algorithms can be applied to this task, we further develop an algorithm that exploits the data structure of battery systems. Our algorithm achieves better results and shows that a customized method can improve model performances. We hope that this public dataset provides valuable resources for researchers, policymakers, and industry professionals to better understand the dynamics of EV battery aging and support the transition toward a sustainable transportation system.