scholarly journals Simulation of Electric Drive System Faults in Hybrid Electric Vehicles

2009 ◽  
Vol 3 (3) ◽  
pp. 664-669
Author(s):  
Dietmar Winkler ◽  
Clemens Gühmann
Keyword(s):  
Electric Vehicles ◽  
Electric Drive ◽  
Hybrid Electric Vehicles ◽  
Drive System ◽  
Electric Drive System ◽  
Hybrid Electric

scholarly journals A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

2021 ◽  
Vol 11 (21) ◽  
pp. 10187
Author(s):  
Yonghyeok Ji ◽  
Seongyong Jeong ◽  
Yeongjin Cho ◽  
Howon Seo ◽  
Jaesung Bang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Electric Vehicles ◽  
Electric Drive ◽  
Hybrid Electric Vehicles ◽  
Short Term Memory ◽  
Support Vector ◽  
Clutch Engagement ◽  
Hybrid Electric ◽  
Engine Clutch

Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔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.

Recycling of NdFeB Magnets from Electric Drive Motors of (Hybrid) Electric Vehicles

2016 ◽  
Vol 3 (1) ◽  
pp. 108-121 ◽  
Author(s):  
Tobias Elwert ◽  
Daniel Goldmann ◽  
Felix Roemer ◽  
Sabrina Schwarz
Keyword(s):  
Electric Vehicles ◽  
Electric Drive ◽  
Hybrid Electric Vehicles ◽  
Ndfeb Magnets ◽  
Hybrid Electric

Optimal hybrid drive system architecture exploration considering performance index of 48V mild HEV

2019 ◽  
Author(s):  
Yonghyeok Ji ◽  
Taeho Park ◽  
Hyeongcheol Lee
Keyword(s):  
Hybrid System ◽  
Electric Drive ◽  
System Architecture ◽  
Performance Index ◽  
Drive System ◽  
Hybrid Drive ◽  
Electric Supercharger ◽  
Electric Drive System ◽  
Parallel Hybrid ◽  
Hybrid Electric

The 48V hybrid system has mostly adopted parallel hybrid system architecture. In the parallel hybrid system, various architecture can be derived depending on the location of the motor. In this paper, we explored a hybrid system architecture considering one or two motors and 48V electric supercharger and derived the optimal architecture by comparing the performance of each architecture. Performance of the hybrid system is mostly evaluated as fuel economy. However, since the hybrid system has increasingly been applied to various types of vehicles with different purpose of the operation, another performance index for evaluating a hybrid system is needed. Therefore, in this paper, we introduced an additional performance index to evaluate the hybrid electric drive system and used it to derive the optimal architecture of the hybrid electric drive system. We used Dynamic programming (DP) to evaluate each architecture and DP simulation was performed in the Matlab environment.

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