Method for predicting mesoscopic fuel consumption on basis of RBFNN (radial basis function neural networks)

Abstract

The invention relates to a method for predicting mesoscopic fuel consumption on the basis of RBFNN (radial basis function neural networks). The method includes determining road energy consumption influence factors; dividing vehicle travel tracks into travel fragments; computing average energy consumption of vehicles in each form fragment; analyzing average energy consumption distribution laws of road sections and computing average energy consumption of the road sections; determining setting of parameters such as the road energy consumption influence factors; utilizing obtained data sets as training sets for neural networks and carrying out model learning; inputting test data sets and acquiring road fuel consumption prediction results by means of computing. The method has the advantages that large quantities of observation samples of input parameters and road energy consumption output parameters in regard to road section types, average speeds of the vehicles and the like can be accumulated under the support of large data volumes of energy consumption track data sets and can be trained, laws of correlations between the road energy consumption influence factors and average energy consumption of the roads can be mastered, accordingly, the energy consumption can be predicted for other road sections, with insufficient quantities of energy consumption track samples, in road networks, energy consumption laws can be extensively popularized, and the method is high in precision in the aspect of monitoring granularity.

Description

technical field [0001] The invention relates to a mesoscopic fuel consumption prediction method based on RBFNN (RBF neural network, also called Radial Basis Function) for application in energy-saving and emission-reducing intelligent transportation systems. Background technique [0002] The mesoscopic fuel consumption prediction model is to measure and calculate the average energy consumption of vehicles driving through designated road sections. With the rapid development of urban traffic today, people pay more and more attention to the intelligent application of energy saving and emission reduction. The accuracy of measurement is guaranteed on the basis of a finer measurement granularity. [0003] The traditional mesoscopic fuel consumption prediction method initially uses the driving cycle to describe the characteristics of traffic flow. Many researchers no longer use the driving cycle but VSP (Vehicle Specific Power, motor vehicle specific power) to achieve more fine-gra...

AI Technical Summary

Problems solved by technology

[0005] However, the currently implemented models based on VSP distribution (including other similar models) can only measure energy consumption and emissions according to road grades, and have not yet realized fine-grained monitoring of road microstructure.

At the same time, the existing mesoscopic model uses intermediate quantities such as driving mode and VSP distribution as a bridge to communicate mesoscopic parameters (road grade, average speed, etc.) Multiple parameter c

Images