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.
Highlights
Vehicle road driving cycles provide data support for the inspection of vehicle emissions levels and their dynamic matching parameter design [1,2,3]
A novel freeway driving cycle (FDC) construction method and global optimal energy management is proposed in this paper to improve the fuel economy of PHEVs
The validity of the FDC construction method is proved by comparing the Construction freeway driving cycle (CFDC) and RFDC
Summary
Vehicle road driving cycles provide data support for the inspection of vehicle emissions levels and their dynamic matching parameter design [1,2,3]. Energies 2017, 10, 1796 proposed a practical driving cycle construction method using data collection, route selection process to construct a Hong Kong driving cycle [9] These driving cycles can only be used in the offline condition due to the fact that they do not consider real traffic information from the start location to the destination. In terms of the dynamic programming (DP) algorithm used in energy management control strategy, it is well known as the one of the best control strategies and usually as the fuel benchmark compared with other control strategies [16] This algorithm is not widely used in online energy control strategy mainly for two reasons: the lack of driving cycle from the start to the destination, and the calculation time cannot achieve online use standards. We proposed a novel method called the economic driving system (EDC) in this paper to realize online DP optimal energy management control strategy for the first time. A comparison of the results and discussion are in Section 5, and Section 6 is the conclusion of this paper
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