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Energies 2017, 10(11), 1796; doi:10.3390/en10111796

Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles

1,2,* , 1,2,* , 1,2
,
1,2
and
1,2
1
National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
2
Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Suleiman M Sharkh
Received: 15 October 2017 / Revised: 28 October 2017 / Accepted: 2 November 2017 / Published: 8 November 2017
(This article belongs to the Special Issue The International Symposium on Electric Vehicles (ISEV2017))
View Full-Text   |   Download PDF [8257 KB, uploaded 23 November 2017]   |  

Abstract

This paper presents a freeway driving cycle (FDC) construction method based on traffic information. A float car collected different type of roads in California and we built a velocity fragment database. We selected a real freeway driving cycle (RFDC) and established the corresponding time traffic information tensor model by using the data in California Department of Transportation performance measure system (PeMS). The correlation of road velocity in the time dimension and spatial dimension are analyzed. According to the average velocity of road sections at different times, the kinematic fragments are stochastically selected in the velocity fragment database to construct a real-time FDC of each section. The comparison between construction freeway driving cycle (CFDC) and real freeway driving cycle (RFDC) show that the CFDC well reflects the RFDC characteristic parameters. Compared to its application in plug-in electric hybrid vehicle (PHEV) optimal energy management based on a dynamic programming (DP) algorithm, CFDC and RFDC fuel consumption are similar within approximately 5.09% error, and non-rush hour fuel economy is better than rush hour 3.51 (L/100 km) at non-rush hour, 4.29 (L/km) at rush hour)). Moreover, the fuel consumption ratio can be up to 13.17% in the same CFDC at non-rush hour. View Full-Text
Keywords: driving cycle construction; traffic information; tensor model; PHEV; energy management; dynamic programming algorithm driving cycle construction; traffic information; tensor model; PHEV; energy management; dynamic programming algorithm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

He, H.; Guo, J.; Zhou, N.; Sun, C.; Peng, J. Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles. Energies 2017, 10, 1796.

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