Cui Binghan, Wang Han, Li Renlong, Xiang Lizhi, Du Jiannan, Zhao Huaian, Li Sai, Zhao Xinyue, Yin Geping, Cheng Xinqun, Ma Yulin, Huo Hua, Zuo Pengjian, Han Guokang, Du Chunyu
MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, China.
Patterns (N Y). 2023 Apr 18;4(6):100732. doi: 10.1016/j.patter.2023.100732. eCollection 2023 Jun 9.
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.
准确早期检测内部短路（ISC）对于锂离子电池（LiB）的安全可靠应用至关重要。然而，主要挑战在于找到一个可靠的标准来判断电池是否遭受ISC。在这项工作中，开发了一种基于编码器-解码器架构的具有多头注意力和多尺度分层学习机制的深度学习方法，以准确预测电压和功率序列。通过将无ISC时预测的电压作为标准，并检测采集的和预测的电压序列的一致性，我们开发了一种快速准确检测ISC的方法。通过这种方式，我们在包含不同电池以及等效ISC电阻从1000Ω到10Ω的数据集上实现了86%的平均准确率，表明ISC检测方法的成功应用。
