The BatteryLife dataset is a battery life prediction dataset released by the research team of Hong Kong University of Science and Technology and CALB Zhongxinhang in 2025. The related paper results are "BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction". This dataset was originally created to support research on battery life prediction. It integrates 16 different datasets and provides more than 90,000 samples from 998 batteries, all with life labels. The scale of the BatteryLife dataset is 2.4 times that of the previous largest battery life resource, BatteryML. This dataset is not only large, but also very diverse. It contains 8 different battery formats, 80 chemistries, 12 operating temperatures, and 646 different charge/discharge protocols. Compared to the previous largest battery life resource, BatteryLife provides 4x, 16x, 2.4x, and 3.4x more diversity in formats, chemistries, operating temperatures, and charge/discharge protocols, respectively. BatteryLife also provides a comprehensive benchmark for battery life prediction. It provides open source code for 18 benchmark methods in the code repository, which include popular battery life prediction methods, popular baselines in time series analysis, and a series of baselines proposed in this work.

Citation

@inproceedings{10.1145/3711896.3737372,
author = {Tan, Ruifeng and Hong, Weixiang and Tang, Jiayue and Lu, Xibin and Ma, Ruijun and Zheng, Xiang and Li, Jia and Huang, Jiaqiang and Zhang, Tong-Yi},
title = {BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction},
year = {2025},
isbn = {9798400714542},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3711896.3737372},
doi = {10.1145/3711896.3737372},
booktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2},
pages = {5789–5800},
numpages = {12},
location = {Toronto ON, Canada},
series = {KDD '25}
}