Battery life prediction has been a critical subject for energy storage field, and the incorporation of machine learning in recent years has substantially accelerated its advancements. However, Battery life prediction presents a high technical barrier as a multidisciplinary issue, posing challenges for researchers in both battery and machine learning fields. Machine learning researchers often lack essential knowledge about batteries, and understanding various battery types and related information requires significant time and effort. For battery researchers, unique models are implemented on specific datasets, and the complexity of these models obstructs their adaptation to individual battery data. To address these challenges, we introduce BatteryML, a one-stop open-source platform that streamlines the process, covering data preprocessing, feature extraction, and the application of both classical and cutting-edge models. This efficient approach enables practical applications for researchers. Currently, unified standards for battery life prediction are lacking, encompassing data format and evaluation criteria for predictions. Through BatteryML, we aim to establish these standards, allowing researchers from diverse fields to contribute to battery research and cultivating a collaborative platform for experts across both disciplines.
BatteryML: An Open-source Platform for Machine Learning on Battery Degradation
Han Zhang Xiaofan Gui Shun Zheng Ziheng Lu Yuqi Li Jiang Bian