Research on Multifractal Characteristics of Vehicle Driving Cycles
Vehicle driving cycles have complex characteristics, but there are few publicly reported methods for their quantitative characterization. This paper innovatively investigates their multifractal characteristics using the fractal theory to characterize their complex properties, laying the foundation f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/11/4/423 |
| _version_ | 1827744680896364544 |
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| author | Mengting Yuan Wenguang Luo Hongli Lan Yongxin Qin |
| author_facet | Mengting Yuan Wenguang Luo Hongli Lan Yongxin Qin |
| author_sort | Mengting Yuan |
| collection | DOAJ |
| description | Vehicle driving cycles have complex characteristics, but there are few publicly reported methods for their quantitative characterization. This paper innovatively investigates their multifractal characteristics using the fractal theory to characterize their complex properties, laying the foundation for applications such as vehicle driving cycle feature identification, vehicle energy management strategies (EMS), and so on. To explore the scale-invariance of the vehicle driving cycles, the four vehicle driving cycles were analyzed using the Multifractal Detrended Fluctuation Analysis (MF-DFA) method, three of which are standard vehicle test cycles: the New European Driving Cycle (NEDC), the World-wide harmonized Light-duty Test Cycle (WLTC) and the China Light-duty Vehicle Test Cycle for Passenger Car (CLTC-P), and the other is the Urban Road Real Driving Cycle (URRDC), which was obtained by analyzing and processing vehicle driving data collected in actual urban driving conditions. The fluctuation functions, the generalized Hurst exponents, the mass exponent spectra, the multifractal singularity spectra, and the multifractal characteristic parameters were calculated to verify the multifractal characteristics, and to quantify the fluctuation singularities of different driving cycles as the time series. The results show that the fluctuations of all four driving cycles have long-range anticorrelations and exhibit significant multifractal characteristics. The results can provide a basis for the analysis of the complexity of the vehicle driving cycles. |
| first_indexed | 2024-03-11T04:49:36Z |
| format | Article |
| id | doaj.art-f42f147ea85a4f56aa4298a5adbddf1a |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-11T04:49:36Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-f42f147ea85a4f56aa4298a5adbddf1a2023-11-17T20:08:23ZengMDPI AGMachines2075-17022023-03-0111442310.3390/machines11040423Research on Multifractal Characteristics of Vehicle Driving CyclesMengting Yuan0Wenguang Luo1Hongli Lan2Yongxin Qin3School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, ChinaSchool of Automation, Guangxi University of Science and Technology, Liuzhou 545006, ChinaSchool of Electronic Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaSchool of Automation, Guangxi University of Science and Technology, Liuzhou 545006, ChinaVehicle driving cycles have complex characteristics, but there are few publicly reported methods for their quantitative characterization. This paper innovatively investigates their multifractal characteristics using the fractal theory to characterize their complex properties, laying the foundation for applications such as vehicle driving cycle feature identification, vehicle energy management strategies (EMS), and so on. To explore the scale-invariance of the vehicle driving cycles, the four vehicle driving cycles were analyzed using the Multifractal Detrended Fluctuation Analysis (MF-DFA) method, three of which are standard vehicle test cycles: the New European Driving Cycle (NEDC), the World-wide harmonized Light-duty Test Cycle (WLTC) and the China Light-duty Vehicle Test Cycle for Passenger Car (CLTC-P), and the other is the Urban Road Real Driving Cycle (URRDC), which was obtained by analyzing and processing vehicle driving data collected in actual urban driving conditions. The fluctuation functions, the generalized Hurst exponents, the mass exponent spectra, the multifractal singularity spectra, and the multifractal characteristic parameters were calculated to verify the multifractal characteristics, and to quantify the fluctuation singularities of different driving cycles as the time series. The results show that the fluctuations of all four driving cycles have long-range anticorrelations and exhibit significant multifractal characteristics. The results can provide a basis for the analysis of the complexity of the vehicle driving cycles.https://www.mdpi.com/2075-1702/11/4/423vehicle driving cyclescomplexitymultifractaldetrended fluctuation analysis |
| spellingShingle | Mengting Yuan Wenguang Luo Hongli Lan Yongxin Qin Research on Multifractal Characteristics of Vehicle Driving Cycles Machines vehicle driving cycles complexity multifractal detrended fluctuation analysis |
| title | Research on Multifractal Characteristics of Vehicle Driving Cycles |
| title_full | Research on Multifractal Characteristics of Vehicle Driving Cycles |
| title_fullStr | Research on Multifractal Characteristics of Vehicle Driving Cycles |
| title_full_unstemmed | Research on Multifractal Characteristics of Vehicle Driving Cycles |
| title_short | Research on Multifractal Characteristics of Vehicle Driving Cycles |
| title_sort | research on multifractal characteristics of vehicle driving cycles |
| topic | vehicle driving cycles complexity multifractal detrended fluctuation analysis |
| url | https://www.mdpi.com/2075-1702/11/4/423 |
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