Long-sequence voltage series forecasting for internal short circuit early detection of lithium-ion batteries.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 37409054.
- Also identified by DOI 10.1016/j.patter.2023.100732 and PMC identifier 10318363.
- Licence recorded as CC BY-NC-ND.
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Abstract
Accurate early detection of internal short circuits (ISCs) is indispensable for safe and reliable application of lithium-ion batteries (LiBs). However, the major challenge is finding a reliable standard to judge whether the battery suffers from ISCs. In this work, a deep learning approach with multi-head attention and a multi-scale hierarchical learning mechanism based on encoder-decoder architecture is developed to accurately forecast voltage and power series. By using the predicted voltage without ISCs as the standard and detecting the consistency of the collected and predicted voltage series, we develop a method to detect ISCs quickly and accurately. In this way, we achieve an average percentage accuracy of 86% on the dataset, including different batteries and the equivalent ISC resistance from 1,000 Ω to 10 Ω, indicating successful application of the ISC detection method.