Road traffic energy consumption quantization method based on motor vehicle running modes

Abstract

Disclosed is a road traffic energy consumption quantization method based on motor vehicle running modes. Necessary preprocessing is conducted on original data, a complete running travel of a motor vehicle is divided into a plurality of running sections as long as 3 minutes, and the running sections less than 3 minutes are eliminated; meso-level running parameters closely related to the energy consumption of the motor vehicle are extracted from different angles with the running sections as a unit, and a feature vector capable of accurately quantizing the running state of the motor vehicle is built; typical motor vehicle running modes are obtained through clustering analysis, each running mode can represent one type of running states with the same energy consumption level, and the running modes reflect the clustering phenomenon of the energy consumption levels of the motor vehicle; running mode distribution rules under different meso-level measurable running parameters are analyzed, and the road traffic energy consumption quantization method based on the traffic parameters is built. According to the road traffic energy consumption quantization method based on motor vehicle running modes, each running parameter can quantize the current running state of the motor vehicle from different angles, and the road traffic energy consumption quantization method is high in accuracy and generalization ability compared with a traditional method only taking one comprehensive parameter in account.

Description

technical field [0001] The invention relates to a method for quantifying road traffic energy consumption based on a motor vehicle driving mode, and belongs to the technical field of motor vehicle energy consumption measurement. Background technique [0002] In recent years, my country's economy has developed rapidly, and the number of motor vehicles has continued to increase rapidly year after year. The resulting traffic energy consumption and environmental pollution have become problems that cannot be ignored. At present, research on road energy consumption in the field of transportation is mostly based on small sample size data, and the law of energy consumption changes is explained through traffic theory. Considering the complexity of urban traffic, it is difficult for small sample size data to contain all information. All energy consumption calculation models have the problems of low accuracy and weak generalization ability. [0003] The MOVES (Motor Vehicle Emission Si...

AI Technical Summary

Problems solved by technology

At present, research on road energy consumption in the field of transportation is mostly based on small sample size data, and the law of energy consumption changes is explained through traffic theory. Considering the complexity of urban traffic, it is difficult for small sample size data to contain all information. All energy consumption calculation models have the problems of low accuracy and weak generalization ability

[0003] The MOVES (Motor Vehicle Emission Simulator) model in the United States is widely used in the calculation of energy consumption and emissions. In the calculation process, the vehicle's operating condition distribution, driving characteristics, weather and fuel type and other information are considered comprehensively. It has been applied in It covers all regions of the United States except California, but its reliance on local information makes it difficult to achieve widespread adoption in other regions

In addition to the environmental protection department, some automobile manufacturers and scientific research institutions have also established a variety of different energy consumption calculation models based on power requirements or regression analysis methods, such as the energy consumption for environmental protection navigation established by Raghu K.Ganti and others at the University of Illinois. Calculation model, in order to obtain higher accuracy, this type of model generally needs the support of detailed road parameters and driving parameters, and the complexity is relatively high

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