Characterizing naturalistic driving patterns for plugin hybrid electric vehicle analysis

Adornato, B. and Patil, R. and Filipi, Z. and Baraket, Z. and Gordon, T. (2009) Characterizing naturalistic driving patterns for plugin hybrid electric vehicle analysis. In: Vehicle Power and Propulsion Conference, 2009. VPPC '09. IEEE, 7 - 10 September 2009, Dearborn, MI.

Full content URL: http://dx.doi.org/10.1109/VPPC.2009.5289786

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

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 locate ons as well as the likely battery SOC at the time of arrival. ©2009 IEEE.

Additional Information:Article number 5289786 Conference Code:78620
Keywords:Battery size, Battery SOC, Certification tests, Consumer acceptance, Design and control, Drive cycles, Driver interaction, Driving conditions, Driving database, Driving pattern, Energy usage, Gas stations, High energy, In-field, Low energies, Market penetration, Michigan, Operational test, Passenger vehicles, Plug-In Hybrid Electric Vehicle, Plug-ins, Safety system, Small business, Specific energy, State of charge, Statistical analysis, Time of Arrival, Time spent, Vehicle energy, Vehicle speed, Aerospace vehicles, Automobiles, Charging (batteries), Computer simulation, Electric vehicles, Programmable logic controllers, Propulsion, Retail stores, Electric load forecasting
Subjects:H Engineering > H331 Road Vehicle Engineering
Divisions:College of Science > School of Engineering
ID Code:11657
Deposited On:13 Feb 2014 09:27

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