# Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach
## 4 Experimental Results and Discussion
### Collision Classification Results under Different Conditions  
Furthermore, we conducted an in-depth analysis of collision classification in scenarios where there was no relative deflection angle between the charger and the charging port. To observe the influence of different joint configurations on the results and mitigate potential information leakage issues, we designated the C1 dataset as the training set, while the C2 and C3 datasets served as the test sets. In the experiment, for a detailed examination of the impact of noise on classification results, we introduced noise signals with varying SNRs, ranging from \(-9\) dB to \(9\) dB, with intervals of \(2\) dB. Through this experimental design, we gained comprehensive insights into the effect of noise intensity on collision classification performance. Furthermore, we conducted an analysis of the impact of introducing EMD and reconstruction on noise suppression. For comparison, we also employed collision classification methods without the EMD process in the experiment. The average collision classification accuracy results are illustrated in Figure 9.  
It can be observed that, as the noise intensity decreased, the classification accuracy of various methods showed a gradual upward trend. In the comprehensive comparison, EMD-LightGBM stood out among the different noise levels, exhibiting a higher overall classification accuracy compared to the other methods. Its maximum average classification accuracy reached an impressive \(97.3\%\). In comparison to the LightGBM method without the introduction of the EMD process, incorporating EMD significantly enhanced the advantage of the collision classification method, particularly in the presence of stronger noise, resulting in a maximum accuracy improvement of up to \(3.64\%\). However, it is noteworthy that among the other classifiers, the introduction of the EMD process did not result in a significant advantage in handling noisy signals. This phenomenon indicates, to some extent, that these classifiers exhibited poor adaptability to signal features after undergoing EMD processing. Figure 8: The collision classification accuracy under the exclusion of different IMF components. Taking all factors into consideration, we believe that in the practical noise suppression process, apart from using EMD combined with reconstruction for noise suppression, the choice of a suitable classifier is also crucial. The results in Figure 10 display the collision classification accuracy values of various collision classification methods with the inclusion of the EMD step with different joint configurations. It can be observed that in the case of collisions with no relative angular bias between the charger and the charging port, overall, the accuracy with C2 as the test set was higher than that with C3 as the test set. This phenomenon indicates that the joint configuration had a certain impact on the accuracy results, and when there was significant variation in the joint configuration, the final classification accuracy was lower. The classification accuracy differences between C1 and C2 using different methods were as follows: the maximum accuracy difference for EMD-LightGBM was 0.61%; EMD-KNN's maximum accuracy difference was 0.56%; EMD-SVM's maximum accuracy difference was 0.7%; and EMD-FNN's maximum accuracy difference was 0.52%. Through a comprehensive analysis of these data, we can conclude that although there was an overall trend suggesting that changes in joint configurations had an impact on the collision classification results, this impact did not lead to a significant performance decline. Within the scope of our study, the fluctuation in classification accuracy caused by changes in joint configurations was relatively mild, and it did not trigger significant instability.