## 6 Discussion  
In this paper, we studied the methods for detecting anomalies in the engine clutch engagement/disengagement process required for EV\(\leftrightarrow\)HEV mode transitions of TMEMED type HEVs. We used machine learning-based methods such as MLP, LSTM, CNN, and one-class SVM and trained various models according to different parameters like the number of hidden layers and hidden units, outlier fraction, etc. For data verification at an actual vehicle level, we used the data acquired through actual vehicle tests for model training and testing. The training results showed that the models based on MLP, LSTM, and CNN have low TNRs, whereas one-class SVM-m3 though m5, the models based on one-class SVM, have high TPRs and TNRs. Through these results, we could obtain the following conclusions.  
* For engine clutch engagement/disengagement data, constructing training architecture to determine normal/anomaly by data instance and performing one-class classification are advantageous for anomaly detection.
* The structure of determining normal/anomaly per pattern cannot learn characteristics of engine clutch engagement/disengagement data properly.  
For the second item, the various durations of a vehicle state, such as the duration of clutch engaged, may be one of the reasons why determining normal/anomaly per the pattern is not adequate. We expected the training architectures to learn normal/anomaly for pattern regardless of the duration of the vehicle state by learning the relationship between the data at the previous time and the data at the current time. But it is presumed that the structure of determining normal/anomaly per pattern cannot learn these characteristics well.  
Since most of the data acquired through real vehicle tests will have similar characteristics, one-class classification by data instance is expected to be effective for other vehicle test data. Therefore, future work should examine whether a one-class classification by data instance is also effective in detecting anomalies in other HEV powertrain control functions.  
Finally, we also anticipate that real-time detection is possible because the time to detect anomalies for given data is short if there is an already trained model. But if there is no trained model, it is difficult to perform this part in real-time because training time is very long.  
**Author Contributions:** Formal analysis, Y.J., S.J. and Y.C.; project administration, Y.J., Y.C. and H.L.; resources, Y.C., H.S., J.K. and J.B.; software, Y.J. and S.J.; validation, Y.J., S.J., Y.C. and H.S.; writing--original draft preparation, Y.J.; writing--review and editing, Y.J. and H.L.; supervision, H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This work was supported by the Industrial Strategic Technology Development Program (20010132, Development of the systematization technology of e-powertrain core parts development platform for expending the industry of xEV parts) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea). This paper is the result of research carried out by a research fund and technical support from IMC.  
**Conflicts of Interest:** The authors declare no conflict of interest.