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Optimum impedance spectroscopy circuit model identification using deep learning algorithms
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DOI：10.1016/j.jelechem.2022.116854.png)
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摘要
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In this work, a deep learning based optimum impedance spectroscopy model finding algorithm is proposed. The technique uses convolutional neural networks with the long short-term memory to identify the circuit model that is most suitable for fitting measured spectral impedance data. A modified two-stage optimization technique is also proposed to find the optimum circuit model parameters. Experimentally measured battery impedance data at different points on its current-voltage characteristic curve are used to validate the proposed algorithm. The techniques was also applied to experimentally measured cherry tomato bio-impedance data. The procedure can be used in applications requiring high throughput screening and fast diagnoses of large numbers of samples such as in commercial battery testing, and in vegetable/fruit freshness monitors using bio-impedance.
Keyword:
Deep Learning
Electrical Impedance Spectroscopy
Circuit Identification
Convolutional Neural Networks
Recurrent Neural Networks
期刊
IF:
4.1
论文数:
1.7W
被引数:
4.0W
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