Access Type

Open Access Dissertation

Date of Award

January 2019

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Electrical and Computer Engineering

First Advisor

Hao . Ying

Abstract

Real-world second-by-second vehicle driving cycle data is very important for research and development of the traditional fuel-powered vehicles, the emerging electric vehicles, and the hybrid vehicles. A project solely dedicated to generating such information would be extremely costly and time-consuming. Alternatively, we introduce a method to develop such a database by utilizing two publicly available passenger vehicle travel surveys; the 2004-2006 Puget Sound Regional Commission (PSRC) Travel Survey and the 2011 Atlanta Regional Commission (ARC) Travel Survey. The two surveys complement each other – the former is in low time resolution but covers vehicle driving and non-driving operation for over one year whereas the latter is in high time resolution but represents only one-week long driving operation. After analyzing the PSRC survey, we chose 382 vehicles, each of which continuously operated for one year, and then match their trips to all the ARC trips after generating ARC sub-trips from the original ARC trips. The matching is carried out based on trip distance first, then on average speed, and finally on duration. Of the total 509,158 trips made by the 382 PSRC vehicles, 496,276 trips (97.47%) are successfully matched by single original ARC trips. The remaining trips are matched by either ARC sub-trips or combined ARC trips. The resulting high-resolution year-long database can be used by drive cycle analysis tools such as the advanced vehicle simulator ADVISOR™ to investigate fuel economy, battery life, and vehicle emissions under various driving and climate conditions. Our approach can be employed to produce other realistic databases from other publicly available vehicle travel surveys.

Utility and performance of Battery Electric Vehicles (BEVs) are affected by important factors such as battery recharging strategy, ambient temperature, and driving pattern. None of the studies in the literature covers the performance or utility of BEV for a full year using second-by-second vehicle driving and non-driving activities. Furthermore, most used recharging strategies do not relate to trip actual destination. I study these same factors but employ year-long, second-by-second activities of 376 passenger vehicles from the dataset I generated in the first part of this dissertation along with their trip destinations. I use ADVISOR software with the 2018 Nissan Leaf as a representative BEV. Los Angeles, Atlanta, Phoenix, Seattle, New York, and Minneapolis are chosen to create diverse ambient temperature profiles from the Typical Meteorological temperature dataset and all the 376 vehicles are assumed to operate in each of these cities. The battery is recharged with a Level-2 charger immediately after driver reaches home if the BEV will not be used for at least 30 minutes. Charging may continue until next trip starts. Our simulation shows that this recharging strategy can cover all activities of 15% of the vehicles. It Also covers 94.82% of the driving days in the year performed by an average vehicle in the remaining vehicle pool. The average fuel economy of the simulated BEV in the six cities is 112.33 MPGe while the average range is 148.5 miles. The BEV requires, on average, 2.31 MW of electrical energy to cover year-long activities of a vehicle in a mild-climate city (i.e. Los Angeles) while in a cold-climate city (i.e. Minneapolis) the average increases to 3.18 MW. Our findings reveal BEV performance under more realistic driving and non-driving conditions. Such study can be extended and employed to explore and analyze other types of BEVs.

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