Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles
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 in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/10/11/1796 |
| _version_ | 1811307488470892544 |
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| author | Hongwen He Jinquan Guo Nana Zhou Chao Sun Jiankun Peng |
| author_facet | Hongwen He Jinquan Guo Nana Zhou Chao Sun Jiankun Peng |
| author_sort | Hongwen He |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T09:05:14Z |
| format | Article |
| id | doaj.art-21f66ad01c8a4d3880f2c2f439af79b4 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T09:05:14Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-21f66ad01c8a4d3880f2c2f439af79b42022-12-22T02:53:01ZengMDPI AGEnergies1996-10732017-11-011011179610.3390/en10111796en10111796Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric VehiclesHongwen He0Jinquan Guo1Nana Zhou2Chao Sun3Jiankun Peng4National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, ChinaNational Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, ChinaNational Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, ChinaNational Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, ChinaNational Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, ChinaThis 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.https://www.mdpi.com/1996-1073/10/11/1796driving cycle constructiontraffic informationtensor modelPHEVenergy managementdynamic programming algorithm |
| spellingShingle | Hongwen He Jinquan Guo Nana Zhou Chao Sun Jiankun Peng Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles Energies driving cycle construction traffic information tensor model PHEV energy management dynamic programming algorithm |
| title | Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles |
| title_full | Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles |
| title_fullStr | Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles |
| title_full_unstemmed | Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles |
| title_short | Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles |
| title_sort | freeway driving cycle construction based on real time traffic information and global optimal energy management for plug in hybrid electric vehicles |
| topic | driving cycle construction traffic information tensor model PHEV energy management dynamic programming algorithm |
| url | https://www.mdpi.com/1996-1073/10/11/1796 |
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