NDLI: Characterizing naturalistic driving patterns for Plug-in Hybrid Electric Vehicle analysis

Content Provider	IEEE Xplore Digital Library
Author	Adornato, B. Patil, R. Filipi, Z. Baraket, Z. Gordon, T.
Copyright Year	2009
Description	Author affiliation: University of Michigan, Department of Mechanical Engineering, Ann Arbor, United States of America (Adornato, B.; Patil, R.; Filipi, Z.) \|\| University of Michigan, University of Michigan Transportation Research Institute, Ann Arbor, United States of America (Baraket, Z.; Gordon, T.)
Abstract	While much of the previous research relies on Federal Driving Schedules originally developed for emission certification tests of conventional vehicles, consumer acceptance and market penetration will depend on PHEV performance under realistic driving conditions. Therefore, characterizing the actual driving is essential for PHEV design and control studies, and for establishing realistic forecasts pertaining to vehicle energy consumption and charging requirements. To achieve this goal, we analyze naturalistic driving data generated in Field Operational Tests (FOT) of passenger vehicles in Southeast Michigan. The FOT were originally conceived for evaluating driver interaction with advanced safety systems, but the databases are rich with information pertaining to vehicle energy. After the initial statistical analysis of the vehicle speed histories, the naturalistic driving schedules are used as input to the PHEV computer simulation to predict energy usage as a function of trip length. The highest specific energy, i.e. energy per mile, is critical for battery and motor sizing. As an illustration of the impact of actual driving, the low-energy and high-energy driving patterns would require PHEV20 battery sizes of 6.12 kWh and 13.6 kWh, respectively. This is determined assuming that the minimum state of charge (SOC) is 40%. In addition, the naturalistic driving databases are mined for information about vehicle resting time, i.e. time spent at typical locations during the 24-hour period. The locations include “home”, “work”, “large-business” such as a large retail store, and “small business”, such as a gas station, and finally “residential” other than home. The characterization of vehicle daily missions supports analysis of charging schedules, as it indicates times spent at given locations as well as the likely battery SOC at the time of arrival.
Starting Page	655
Ending Page	660
File Size	455924
Page Count	6
File Format	PDF
ISBN	9781424426003
DOI	10.1109/VPPC.2009.5289786
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hybrid electric vehicles Pattern analysis Vehicle driving Batteries Testing Vehicle safety Databases Certification Load forecasting Economic forecasting Specific Energy Drive Cycle Plug-in Hybrid
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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