长序电压序列预测用于内部短路早期检测离子电池
Binghan Cui1, Han Wang1, Renlong Li1
1MIIT 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 (New York, N.Y.)
|July 6, 2023
概括
本研究介绍了一种深度学习方法,用于早期检测离子电池 (LiB) 中的内部短路 (ISC). 该方法准确预测电池性能,使可靠的ISC识别和提高电池安全.
科学领域:
- 材料科学与工程 材料科学与工程
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 内部短路 (ISC) 对离子电池 (LiB) 的安全性和可靠性构成重大风险.
- 建立可靠的标准来识别ISC仍然是电池管理的关键挑战.
研究的目的:
- 开发一种准确和快速的方法来检测离子电池 (LiB) 中的内部短路 (ISC).
- 通过先进的深度学习技术建立可靠的ISC检测标准.
主要方法:
- 在编码器-解码器架构中开发了一种深度学习模型,采用多头注意力机制和多规模层次学习策略.
- 该模型准确预测电压和功率序列,以建立健康电池性能的基线.
- 通过将实际电压数据与模型预测的电压进行比较来实现ISC检测,识别出ISC标志性的偏差.
主要成果:
- 开发的深度学习方法在检测ISC时平均达到86%的准确性.
- 该方法在包括多种电池和不同ISC电阻 (1,000 Ω到10 Ω) 的数据集中证明了有效性.
结论:
- 拟议的深度学习方法为早期检测LiBs内部短路提供了强大而准确的解决方案.
- 这种方法通过及时识别关键故障,提高了电池的安全性和可靠性.
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