EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation

TL;DR

This paper introduces EVBattery, a large-scale public dataset of real-world EV battery data, enabling improved battery health and capacity estimation through deep learning and customized algorithms.

Contribution

The paper provides the first extensive real-world EV battery dataset with labels for health and capacity estimation, and develops a novel algorithm exploiting battery data structure.

Findings

Deep learning models can be effectively applied to battery estimation tasks.

Customized algorithms exploiting data structure outperform generic models.

The dataset supports research on EV battery aging and safety.

Abstract

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.