Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies. Despite the recent progress in artificial intelligence, anomaly detection methods are not customized for or validated in realistic battery settings due to the complex failure mechanisms and the lack of real-world testing frameworks with large-scale datasets. Here, we release three datasets comprising over 690,000 LiB charging snippets from 347 EVs.
The dataset is released as part of our paper " Realistic fault detection of Li-ion battery via dynamical deep learning approach ".
Funding
IIIS young scholar fellowship
the National Natural Science Foundation of China under grant number 62103220
the National Natural Science Foundation of China under grant number 52177217
the National Natural Science Foundation of China under grant number 72271008
the National Natural Science Foundation of China under grant number 62273197
Beijing Natural Science Foundation under grant number 3212031
International Science & Technology Cooperation Program of China under grant number 2019YFE0100200