Realistic fault detection of li-ion battery via dynamical deep learning.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 37741826.
- Also identified by DOI 10.1038/s41467-023-41226-5 and PMC identifier 10517941.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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 develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems and configured by social and financial factors. We test our detection algorithm on released datasets comprising over 690,000 LiB charging snippets from 347 EVs. Our model overcomes the limitations of state-of-the-art fault detection models, including deep learning ones. Moreover, it reduces the expected direct EV battery fault and inspection costs. Our work highlights the potential of deep learning in improving LiB safety and the significance of social and financial information in designing deep learning models.