Realistic fault detection of Li-ion battery via dynamical deep learning approach

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 ".