Pearson-ShuffleDarkNet37-SE-Fully Connected-Net for Fault Classification of the Electric System of Electric Vehicles
As the core components of electric vehicles, the safety of the electric system, including motors, batteries, and electronic control systems, has always been of great concern. To provide early warning of electric-system failure and troubleshoot the problem in time, this study proposes a novel energy-...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/24/13141 |
| _version_ | 1797382166994223104 |
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| author | Quan Lu Shan Chen Linfei Yin Lu Ding |
| author_facet | Quan Lu Shan Chen Linfei Yin Lu Ding |
| author_sort | Quan Lu |
| collection | DOAJ |
| description | As the core components of electric vehicles, the safety of the electric system, including motors, batteries, and electronic control systems, has always been of great concern. To provide early warning of electric-system failure and troubleshoot the problem in time, this study proposes a novel energy-vehicle electric-system failure-classification method, which is named Pearson-ShuffleDarkNet37-SE-Fully Connected-Net (PSDSEF). Firstly, the raw data were preprocessed and dimensionality reduction was performed after the Pearson correlation coefficient; then, data features were extracted utilizing ShuffleNet and an improved DarkNet37-SE network based on DarkNet53; secondly, the inserted squeeze-and-excitation networks (SE-Net) channel attention were able to obtain more fault-related target information; finally, the prediction results of the ShuffleNet and DarkNet37-SE networks were aggregated with a fully connected neural network to output the classification results. The experimental results showed that the proposed PSDSEF-based electric vehicles electric-system fault-classification method achieved an accuracy of 97.22%, which is better than other classical convolutional neural networks with the highest accuracy of 92.19% (ResNet101); the training time is faster than the average training time of the comparative networks. The proposed PSDSEF has the advantage of high classification accuracy and small number of parameters. |
| first_indexed | 2024-03-08T21:01:30Z |
| format | Article |
| id | doaj.art-558913e97ab346499f1ccceb55a2712a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T21:01:30Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-558913e97ab346499f1ccceb55a2712a2023-12-22T13:51:36ZengMDPI AGApplied Sciences2076-34172023-12-0113241314110.3390/app132413141Pearson-ShuffleDarkNet37-SE-Fully Connected-Net for Fault Classification of the Electric System of Electric VehiclesQuan Lu0Shan Chen1Linfei Yin2Lu Ding3Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, ChinaGuangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, ChinaGuangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, ChinaGuangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, ChinaAs the core components of electric vehicles, the safety of the electric system, including motors, batteries, and electronic control systems, has always been of great concern. To provide early warning of electric-system failure and troubleshoot the problem in time, this study proposes a novel energy-vehicle electric-system failure-classification method, which is named Pearson-ShuffleDarkNet37-SE-Fully Connected-Net (PSDSEF). Firstly, the raw data were preprocessed and dimensionality reduction was performed after the Pearson correlation coefficient; then, data features were extracted utilizing ShuffleNet and an improved DarkNet37-SE network based on DarkNet53; secondly, the inserted squeeze-and-excitation networks (SE-Net) channel attention were able to obtain more fault-related target information; finally, the prediction results of the ShuffleNet and DarkNet37-SE networks were aggregated with a fully connected neural network to output the classification results. The experimental results showed that the proposed PSDSEF-based electric vehicles electric-system fault-classification method achieved an accuracy of 97.22%, which is better than other classical convolutional neural networks with the highest accuracy of 92.19% (ResNet101); the training time is faster than the average training time of the comparative networks. The proposed PSDSEF has the advantage of high classification accuracy and small number of parameters.https://www.mdpi.com/2076-3417/13/24/13141DarkNet53ShuffleNetPearson correlation coefficientfault classificationelectric systems |
| spellingShingle | Quan Lu Shan Chen Linfei Yin Lu Ding Pearson-ShuffleDarkNet37-SE-Fully Connected-Net for Fault Classification of the Electric System of Electric Vehicles Applied Sciences DarkNet53 ShuffleNet Pearson correlation coefficient fault classification electric systems |
| title | Pearson-ShuffleDarkNet37-SE-Fully Connected-Net for Fault Classification of the Electric System of Electric Vehicles |
| title_full | Pearson-ShuffleDarkNet37-SE-Fully Connected-Net for Fault Classification of the Electric System of Electric Vehicles |
| title_fullStr | Pearson-ShuffleDarkNet37-SE-Fully Connected-Net for Fault Classification of the Electric System of Electric Vehicles |
| title_full_unstemmed | Pearson-ShuffleDarkNet37-SE-Fully Connected-Net for Fault Classification of the Electric System of Electric Vehicles |
| title_short | Pearson-ShuffleDarkNet37-SE-Fully Connected-Net for Fault Classification of the Electric System of Electric Vehicles |
| title_sort | pearson shuffledarknet37 se fully connected net for fault classification of the electric system of electric vehicles |
| topic | DarkNet53 ShuffleNet Pearson correlation coefficient fault classification electric systems |
| url | https://www.mdpi.com/2076-3417/13/24/13141 |
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