Lithium ion battery internal short circuit detection method based on impedance spectrum and Elman neural network
The invention discloses a lithium ion battery internal short circuit detection method based on an impedance spectrum and an Elman neural network, and the method comprises the steps: collecting the impedance spectrum data of a lithium ion battery, and extracting eight internal short circuit features...
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Format | Patent |
Language | Chinese English |
Published |
03.11.2023
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Abstract | The invention discloses a lithium ion battery internal short circuit detection method based on an impedance spectrum and an Elman neural network, and the method comprises the steps: collecting the impedance spectrum data of a lithium ion battery, and extracting eight internal short circuit features based on the impedance spectrum; calculating a Pearson correlation coefficient of the internal short circuit feature, and retaining four features with relatively strong correlation; performing feature fusion on the internal short-circuit features by using canonical correlation analysis to obtain a one-dimensional comprehensive variable, and constructing an internal short-circuit feature data set based on the comprehensive variable; optimizing the Elman neural network through a particle swarm optimization algorithm; training an Elman neural network based on the internal short circuit feature data set; measuring the impedance spectrum of the to-be-detected battery, performing feature extraction and fusion, and inputt |
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AbstractList | The invention discloses a lithium ion battery internal short circuit detection method based on an impedance spectrum and an Elman neural network, and the method comprises the steps: collecting the impedance spectrum data of a lithium ion battery, and extracting eight internal short circuit features based on the impedance spectrum; calculating a Pearson correlation coefficient of the internal short circuit feature, and retaining four features with relatively strong correlation; performing feature fusion on the internal short-circuit features by using canonical correlation analysis to obtain a one-dimensional comprehensive variable, and constructing an internal short-circuit feature data set based on the comprehensive variable; optimizing the Elman neural network through a particle swarm optimization algorithm; training an Elman neural network based on the internal short circuit feature data set; measuring the impedance spectrum of the to-be-detected battery, performing feature extraction and fusion, and inputt |
Author | LUO ZHANG NIE WEI ZHANG HAILONG WANG YI YOO KI-YEOL CHEN WENLI |
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DocumentTitleAlternate | 一种基于阻抗谱和Elman神经网络的锂离子电池内短路检测方法 |
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Snippet | The invention discloses a lithium ion battery internal short circuit detection method based on an impedance spectrum and an Elman neural network, and the... |
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Title | Lithium ion battery internal short circuit detection method based on impedance spectrum and Elman neural network |
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