NDLI: A Compact Error Management Algorithm to Minimize False-Alarm Rate of Motor/Generator Faults in (Hybrid) Electric Vehicles

Content Provider	IEEE Xplore Digital Library
Author	Seung-Deog Choi Akin, B. Sangshin Kwak Toliyat, H.A.
Copyright Year	2013
Abstract	This paper addresses an emerging reliability and safety requirement for more electric transportation systems by proposing an on-board diagnostics plug-in tool. For this purpose, a compact diagnostic algorithm is presented using an advanced speed feedback error management technique for traction motor and generator faults which is one of the most critical parts of the vehicle powertrain. The speed measurement accuracy can be degraded for various reasons such as: sensor issues, vehicle vibration, hardware tolerances, and environmental impacts like temperature, moisture, and electromagnetic interferences. Although the speed feedback errors within an acceptable level can be tolerated by most of the drive systems, the accuracy of speed measurement is critical for the reliability of fault diagnosis. The major reason behind this fact is the frequency of fault signature in the spectrum is primarily determined using the motor speed. It is shown how the reliability of a motor fault diagnosis decision can significantly be improved under erroneous speed feedback conditions by using the developed effective error localization and coarse-to-fine detection technique. The proposed scheme is implemented through effective strategies to minimize computational complexity and memory occupancy in every step of signal processing according to manufacturers' strict cost limitation.
Starting Page	618
Ending Page	626
Page Count	9
File Size	1292449
File Format	PDF
ISSN	21686777
Volume Number	2
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Traction motors Induction motors Estimation Vehicles Rotors Monitoring Noise motor drive Condition monitoring electric vehicle fault diagnosis hybrid vehicle
Content Type	Text
Resource Type	Article
Subject	Energy Engineering and Power Technology Electrical and Electronic Engineering

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