# Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach
## 4 Experimental Results and Discussion
### Collision Classification Results under Different Conditions
To assess the collision classification performance of the proposed method in the presence of relative angular displacement between the charger and the charging port, we employed C1 as the training set and selected datasets with relative angular deviations of \(1^{\circ},2^{\circ}\), and \(3^{\circ}\) (C4, C5, and C6 as the test sets). For a comparative analysis, we also tested SVM, KNN, FNN, and the fusion of these three models with EMD. Figure 11 illustrates the collision classification results with varying levels of noise and different relative angular displacements. Similar to the scenarios without relative angular displacement, as the noise level increased, the collision classification accuracy of all methods notably decreased. Across various noise levels, EMD-LightGBM and LightGBM consistently demonstrated higher average collision classification accuracy compared to the other methods. Their respective highest prediction accuracies reached 91.66% and 90.09%. In scenarios with low noise levels, the presence of relative angular displacement led to significantly lower collision classification accuracy for all methods compared to the scenarios without relative  
Figure 9: The average collision classification accuracy results with varying levels of noise. angular displacement. For instance, at SNR=9, the maximum collision classification accuracies for EMD-LightGBM, EMD-SVM, EMD-KNN, and EMD-FNN were 91.66%, 86.04%, 86.46%, and 85.05%, respectively. In comparison to the scenarios without relative angular displacement, these four methods experienced reductions of 5.87%, 6.72%, 8.85%, and 11%, respectively. However, this performance gap diminished noticeably in conditions with more noise. This indicates that the presence of relative angular displacement influenced collision classification, with its impact being more pronounced in conditions with less noise. As noise increased, obscuring the original features of the signal, the influence of relative angular displacement on prediction confusion diminished. Moreover, as the relative angular displacement increased, EMD-LightGBM and EMD-KNN exhibited a declining trend in collision classification accuracy with low noise levels. Therefore, for both of these methods, enhancing the predictive performance of collision classification can be achieved by collecting a more extensive dataset that includes a diverse range of relative angular displacements. It is worth noting that introducing EMD for noise reduction is not universally effective across different methods. For instance, EMD-FNN demonstrated significantly lower collision classification accuracy in high-intensity noise scenarios compared to using FNN alone. This indicates that when the introduction of EMD results in significant changes to features, the classification model needs to exhibit good adaptability to such features to achieve effective noise suppression through integration with EMD. Figure 10: Comparison of collision classification accuracy on C2 and C3 test sets: (**a**) EMD-LightGBM, (**b**) EMD-KNN, (**c**) EMD-SVM, (**d**) EMD-FNN.