A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehiclesopen access
- Authors
- Ji, Yonghyeok; Jeong, Seongyong; Cho, Yeongjin; Seo, Howon; Bang, Jaesung; Kim, Jihwan; Lee, Hyeongcheol
- Issue Date
- Nov-2021
- Publisher
- MDPI
- Keywords
- fault detection; anomaly detection; hybrid electric vehicle; transmission mounted electric drive; engine clutch engagement/disengagement; machine learning; multi-layer perceptron (MLP); long short-term memory (LSTM); convolutional neural network (CNN); one-class SVM
- Citation
- APPLIED SCIENCES-BASEL, v.11, no.21, pp.1 - 21
- Indexed
- SCIE
SCOPUS
- Journal Title
- APPLIED SCIENCES-BASEL
- Volume
- 11
- Number
- 21
- Start Page
- 1
- End Page
- 21
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140529
- DOI
- 10.3390/app112110187
- Abstract
- Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV & LRARR;HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly. Therefore, it is required to verify whether engine clutch engagement/disengagement operates normally in the vehicle development process. This paper studied machine learning-based methods for detecting anomalies in the engine clutch engagement/disengagement process. We trained the various models based on multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and one-class support vector machine (one-class SVM) with the actual vehicle test data and compared their results. The test results showed the one-class SVM-based models have the highest anomaly detection performance. Additionally, we found that configuring the training architecture to determine normal/anomaly by data instance and conducting one-class classification is proper for detecting anomalies in the target data.
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