## 3 Case Study
### Discussions  
In this study, the effect of the improved modules was verified by ablation experiments, and the outcomes of the ablation experiments are shown in Table 6.  
Table 6 shows that (1) from Improvement 1, DarkNet37 reduces the training time by 26.38% compared to DarkNet53 with a loss of 3.07% in accuracy; and DarkNet37 is more lightweight; (2) Improvement 2 adds multiple SENet attention mechanisms to DarkNet37; the accuracy of DarkNet37-SE is 2.19% higher than that of DarkNet37 without the attention network; but the training time is only 187 s more than that of DarkNet37, which is an increase of only 1.78%; meanwhile, compared with the training of the DarkNet53 results, the accuracy is only reduced by 0.88%; the training time is reduced by 3568 s, which is 25.07%; (3) Improvement 3 aims to aggregate the experimental results of ShuffleNet and DarkNet37-SE through multiple fully connected layer networks before outputting the classification results, which achieves an accuracy of 97.22%, with a training duration of 10,680 s; the training accuracy is higher than that of DarkNet53, ShuffleNet, and DarkNet37-SE by 5.79%, 10.73%, and 6.67%, respectively; the time is only 14 s more than DarkNet37-SE. Meanwhile, aggregating the outputs of DarkNet37 and ShuffleNet without the added attention network (i.e., the ShuffleDarkNet37 columns in the table), the accuracy of the network is 95.73%, which is higher than that of the PSDSEF by 1.49%; the training time is 10,499 s, which is only 181 s less than that of PSDSEF, with a decrease percentage of 1.72%; (4) the testing time of the networks does not differ much owing to the offline training approach, and it can be seen that both the original and the improved networks take only a few seconds to classify the fault categories. From the ablation experiments, after the introduction of the three improvements, the number of operations is less than the original DarkNet53 algorithm; the operation speed is faster; the accuracy is greatly improved; the performance of the improved algorithm is superior to the original algorithm.  
It is a competitive task to utilize the original data to classify the faults of the EV electric system because the original data are nonlinear, which leads to the difficulty of data feature extraction. To achieve the EV electric-system fault classification accurately and efficiently, a novel method based on PSDSEF is proposed in this study. The PSDSEF reduces the error generated by manually extracting data features and does not need to learn the relevant physical prior knowledge. Experiments show that the proposed PSDSEF performs well in the EV electric-system fault classification, with improved prediction accuracy and speed, which validates the feasibility of the PSDSEF. Compared with other networks, this PSDSEF can predict the specific faults generated by the EV electric system in advance for early  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Models** & **Accuracy (\%)** & **Training Time (s)** & **Testing Time (s)** \\ \hline DarkNet53 & 91.43 & 14,234 & 2.45 \\ ShuffleNet & 86.49 & 4010 & 1.61 \\ DarkNet37 (Improvement 1) & 88.36 & 10,479 & 1.58 \\ ShuffleDarkNet37 & 95.73 & 10,499 & 1.59 \\ DarkNet37-SE (Improvement 2) & 90.55 & 10,666 & 1.73 \\ PSDSEF (Improvement 3) & 97.22 & 10,680 & 1.62 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Outcomes of ablation experiments.  
warning, which greatly reduces the economic losses and personal safety damage caused by EV electric-system failures.  
The proposed PSDSEF is effective but still has some limitations.  
1. The samples in this study are the same fault code in 40 rows constituting an input sample; there may be a large span between rows and rows of data, which leads to a large difference in the data generating a certain degree of error.
2. The number of samples corresponding to some of the fault codes contained in the dataset are too few to model the extraction of their features well, such as the fault codes \"2112\" and \"2128\", both of which have only one fault sample.