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Journal of Asian Electric Vehicles
Article . 2004 . Peer-reviewed
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Fuzzy Logic Based Driving Pattern Recognition for Driving Cycle Analysis

Authors: Liaw, Bor Yann;

Fuzzy Logic Based Driving Pattern Recognition for Driving Cycle Analysis

Abstract

Conducting driving cycle analysis (DCA) using trip data collected from vehicles operated in the field is very difficult. In fact, no comprehensive approach has been conceived to date, except those using standard driving cycles. A successful DCA could significantly enhance our understanding of vehicle performance and readily relate it to real-life driving. In the past few years, we have been developing tools for vehicle performance analysis (VPA). In particular, we were able to collect data from a fleet of 15 Hyundai Santa Fe electric sports utility vehicles (e-SUVs) operated on Oahu, Hawaii, from July 2001 to June 2003. A fuzzy logic-based driving pattern recognition (FL-DPR) technique was used to perform DCA. This technique was successfully applied to create a compositional driving histogram, called "trip driving pattern composition (TDPC)," for each vehicle, which enables us to analyze vehicle performance in great details.

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Keywords

driving pattern recognition, fuzzy logic, driving cycle analysis, BEV, vehicle performance analysis

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
22
Top 10%
Top 10%
Top 10%
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