DocumentCode
1337498
Title
Synthesis of Real-World Driving Cycles and Their Use for Estimating PHEV Energy Consumption and Charging Opportunities: Case Study for Midwest/U.S.
Author
Lee, Tae-Kyung ; Adornato, Brian ; Filipi, Zoran S.
Author_Institution
Dept. of Mech. Eng., Univ. of Michigan, Ann Arbor, MI, USA
Volume
60
Issue
9
fYear
2011
Firstpage
4153
Lastpage
4163
Abstract
This paper analyzes plug-in hybrid electric vehicle (PHEV) behavior, its impact on the electric grid, and possible charging opportunities using representative synthetic cycles with the consideration of daily driving schedules. The representative naturalistic cycles are synthesized through a stochastic process utilizing transition probability matrices extracted from naturalistic driving data collected in the Midwest region of the United States. The representativeness of the cycles is achieved through the subsequent statistical analysis. The distributions of the departure/arrival time and the rest time, analyzed from the real-world data at the key locations, complete the picture to analyze vehicle daily missions and the PHEV impact on the grid. PHEV simulation is used to determine the battery state of charge (SOC) distribution upon arrival. The results for typical locations such as residential, work, large business, and small business allow the assessment of the PHEV impact on the grid and possible charging opportunities during daily missions.
Keywords
hybrid electric vehicles; matrix algebra; power grids; probability; statistical analysis; stochastic processes; Midwest region; PHEV energy consumption estimation; SOC distribution; United States; battery state of charge; electric grid; plug-in hybrid electric vehicle; real-world driving cycle synthesis; representative synthetic cycles; statistical analysis; stochastic process; transition probability matrices; Batteries; Electric vehicles; Hybrid electric vehicles; Markov processes; Stochastic systems; System-on-a-chip; Markov chain; naturalistic driving cycle; plug-in hybrid electric vehicle (PHEV); statistical methodology; stochastic process; synthesis; validation;
fLanguage
English
Journal_Title
Vehicular Technology, IEEE Transactions on
Publisher
ieee
ISSN
0018-9545
Type
jour
DOI
10.1109/TVT.2011.2168251
Filename
6032118
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