## 4 Conclusions  
Accurately and quickly classifying the electric-system failure of EVs before the EVs fail is essential to troubleshooting the failure promptly and reducing the loss of the owner of the vehicle and the personal threat. To reduce the uncertainty and error generated by the manual feature extraction, this study utilizes multiple CNNs with deep characterization capability to extract and model the features of the data under the premise of big data. An EV electric-system fault-classification method (i.e., PSDSEF) is proposed in this study; the features of the method can be summarized as follows.  
1. The accuracy, Macro-Precision, Macro-Recall, and Macro-F1 score of the PSDSEF for classifying electric-system failures in EVs are higher than all comparison networks.
2. In this study, the selected dataset is firstly correlated by the Pearson correlation coefficient method, which in turn reduces the dimensionality of the data; then, the maximum-minimum normalization is employed to eliminate the difference in magnitude that exists between the dimensional data and to simplify the parameters of the data, which allows the features to be extracted more efficiently.
3. PSDSEF integrates DarkNet37-SE and the lightweight network ShuffleNet in order to automatically extract features utilizing convolution and pooling operations, and then the classification results of the two networks are aggregated by the fully connected neural network, which draws on the advantages of the two networks to obtain higher classification results.  
In future work, the PSDSEF method could be generalized to other brands and models of vehicles. In addition, if enough sample data are collected from the other fault codes, the dataset could be supplemented to increase the categories of the fault codes in the dataset. Moreover, better data dimensionality reduction methods could be adopted to further reduce the data dimensions; then, data lightening could greatly reduce the training time. Furthermore, the structure of PSDSEF could be optimized by appropriately adjusting the number of convolutional layers, and the spatial attention mechanism could be added to improve the model performances in conjunction with the channel attention mechanism. Meanwhile, the information relating to time dimension could be introduced for the EV electric-system fault classification; the safety of the EV electric system could be associated with the usage time of the EV electric system as well as the previous state of the EV electric system.  
Q.L.: project administration, supervision. S.C.: data curation, formal analysis, investigation, visualization, writing--original draft. L.Y.: conceptualization, funding acquisition, project administration, supervision, methodology, resources, writing--review and editing. L.D.: validation, software. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant. 52107081, the Natural Science Foundation of Guangxi Province (China) under Grant. AA22068071, and the Key Laboratory of AI and Information Processing (Hechi University) of Education Department of Guangxi Zhuang Autonomous Region under Grant. 2022 GXZDSY006.  
Not applicable.  
Data Availability Statement: 2022 Digital Vehicle Competition of the National Big Data Alliance of New Energy Vehicles, [[http://www.ncbdc.top/](http://www.ncbdc.top/)]([http://www.ncbdc.top/](http://www.ncbdc.top/)) (accessed on 14 November 2023).  
The authors declare no conflict of interest.