# Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach
## 4 Experimental Results and Discussion
### Feature Selection Results
It can be observed that when the total number of retained features was greater than 14, the removed features had little impact on the classification effect. However, as the features were removed, the classification accuracy began to decrease significantly. When the total number of features was 10, compared to the process without feature removal, the classification accuracy dropped by 9.56%. To ensure the effectiveness of the training of the collision classification model while minimizing the number of features, this study utilized a set of 14 selected features in the subsequent experiments. Specifically, these features consisted of the first six time domain features (T6, T7, \(\ldots\), T5 in Table 4) and the first eight frequency domain features (F8, F7, \(\ldots\), F10 in Table 4). In the context of collision processing problems, the sequence lengths of collision signals significantly determine the richness of the information embedded in the signals. The appropriate sequence length is crucial because sequences that are too short may lead to insufficient feature information, severely impacting classification performance. Conversely, excessively long sequences may decrease computational efficiency and make the classification method more reliant on sustained collision information post-collision. To determine the suitable information sequence length, we employed a multimodel fusion approach to evaluate the sequence information, using LightGBM and comparison methods. The dataset used for the evaluation was from the C1 dataset, and the feature representation was based on the feature selection results in Table 4. Additionally, the effective period was set from 50 sample points to 350 sample points, with testing conducted at intervals of 30 sample points. The collision classification accuracy results with different effective periods are illustrated in Figure 6. It was seen that as the effective period length increased, the collision classification accuracy of the different classifiers showed an upward trend. When the effective period length reached 290 sample points, further increasing the sequence length resulted in minimal fluctuations in classification accuracy. For the FNN method, there was a decline in classification accuracy. This suggests that when the effective period \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ranking** & **Time Domain Indicator** & **Frequency Domain Indicator** \\ \hline
1 & T6 & F8 \\
2 & T7 & F7 \\
3 & T8 & F6 \\
4 & T9 & F4 \\
5 & T4 & F9 \\
6 & T5 & F2 \\
7 & T1 & F3 \\
8 & T10 & F10 \\
9 & T3 & F5 \\
10 & T2 & F11 \\
11 & T11 & F1 \\
12 & - & F14 \\
13 & - & F13 \\
14 & - & F12 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Importance ranking of time domain and frequency domain features. length reached 290 sample points, the information content was sufficiently rich. Therefore, for subsequent analyses, we set the effective period length of the collision vibration signal to 290 sample points. Figure 5: The average collision classification accuracy values of different classification models with varying feature representation quantities. Figure 6: Collision classification accuracy of different models with different effective periods.