# Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach
## 4 Experimental Results and Discussion
### Feature Selection Results
Since extracting a single statistical feature using sliding windows generated multiple feature points, feature selection helped reduce the feature dimensions, decreased the number of features, and improved the computational efficiency of the model. During the feature selection process, the ReliefF method was employed, which is an effective technique for assessing feature importance. The samples used for feature selection still came from the C1 dataset. The ranking results are presented in Table 4. A lower ranking in the importance order indicates a more crucial feature. Due to significant differences in the descriptive aspects of the time domain and frequency domain features, independent rankings were performed for these two feature types during the importance sorting process. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Type** & **Name of Feature** & **Feature Definition** \\ \hline F8 & Crest factor & \(CF=\frac{S(f^{*})}{E_{d}}\) \\ \hline F9 & Spectral crest factor & \(SCF=\frac{S(f^{*})}{S\left(\overline{f}\right)}\) \\ \hline F10 & Relative energy in the first frequency band & \(E_{b1}=\frac{\int_{0}^{B_{f}}S(f)df}{\int_{0}^{f*}S(f)df}\) \\ \hline F11 & Relative energy in the second frequency band & \(E_{b2}=\frac{\int_{B_{f}}^{2B_{f}}S(f)df}{\int_{0}^{f*}S(f)df}\) \\ \hline F12 & Relative energy in the third frequency band & \(E_{b3}=\frac{\int_{2B_{f}}^{3B_{f}}S(f)df}{\int_{0}^{f*}S(f)df}\) \\ \hline F13 & Relative energy in the fourth frequency band & \(E_{b4}=\frac{\int_{3B_{f}}^{4B_{f}}S(f)df}{\int_{0}^{f*}S(f)df}\) \\ \hline F14 & Relative energy in the fifth frequency band & \(E_{b5}=\frac{\int_{4B_{f}}^{5B_{f}}S(f)df}{\int_{0}^{f*}S(f)df}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Name of Hyperparameter** & **Searching Range** &
\begin{tabular}{c} **Optimal** \\ **Hyperparameter** \\ \end{tabular} \\ \hline \multirow{4}{*}{LightGBM} & Boosting type & “GBDT”, “Dart”, “Goss” & GBDT \\  & Learning\_rate & 0.0001, 0.001, 0.01 & 0.01 \\  & Num\_leaves & 2, 2\({}^{2}\),..., 2\({}^{10}\) & 2\({}^{6}\) \\  & Max\_depth & 1, 2,..., 10 & 8 \\  & Feature\_fraction & 0.05, 0.1,..., 1 & 0.35 \\  & Bagging\_fraction & 0.05, 0.1,..., 1 & 0.25 \\  & N\_estimator & 100, 150,..., 1500 & 450 \\ \hline \multirow{2}{*}{KNN} & K value & \(2,3,\ldots,50\) & 7 \\  & Distance indicator & “chebyshev”, “euclidean”, “manhattan”, “minkowski” & “manhattan” \\ \hline \multirow{2}{*}{SVM} & C value & 1, 10, 100, 1000 & 100 \\  & Kernel function & “linear”, “rbf”, “sigmoid” & “rbf” \\ \hline \multirow{2}{*}{FNN} & Number of hidden units in the first layer & \(2^{6}\), \(2^{7}\), \(2^{8}\), \(2^{9}\) & \(2^{8}\) \\  & Number of hidden units in the second layer & \(2^{5}\), \(2^{6}\), \(2^{7}\) & \(2^{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of hyperparameter optimization for the models. Building upon the feature representation ranking using the ReliefF method, we further combined LightGBM, k-NN, SVM, and FNN to eliminate redundant features for collision classification in the C1 dataset. Specifically, we followed the importance ranking of the features and sequentially removed less significant time domain and frequency domain features. The removal alternated between time domain and frequency domain features, starting with the removal of frequency domain features. To assess the effectiveness of feature selection, we first shuffled the C1 dataset and then evaluated the selected features using a five-fold cross-validation approach. The classification models used the hyperparameters in Table 3. The final evaluation results are presented in Figure 5. The figure displays the average classification accuracy values of the classification models after removing different features. A higher accuracy indicates a smaller impact on the classification results when the corresponding features are removed.