Vehicle driving condition establishment method combining principal component analysis with fuzzy c-mean clustering

Abstract

The invention discloses a vehicle driving condition establishment method combining principal component analysis with fuzzy c-mean clustering. The method comprises the steps of extracting satellite positioning data of each road traffic condition in a vehicle dynamic monitoring system of a road transport enterprise, and calculating and dividing the data into small sections of micro-strokes; and performing calculation of characteristic parameters such as an average speed, an idle time proportion and the like for each micro-stroke, obtaining a matrix of a sample quantity (row) X the characteristic parameters (column), adopting mean normalization and principal component analysis for matrix data, selecting preorder principal components which meet the conditions that the cumulative contribution rate of characteristic values of the principal components is greater than 85% and the principal components can comprehensively reflect all the characteristic parameters, performing fuzzy c-mean clustering analysis on scores of the principal components, and clustering the micro-strokes into different groups, namely, screening sub-conditions. An initial synthesis condition is smoothed by adopting a filter with a double-weighted smoothing kernel function. According to the method, existing satellite positioning data is fully utilized; compliance testing is easily carried out on a dynamometer; relatively high universality is achieved; and the research cost of vehicle driving condition establishment is reduced.

Description

technical field [0001] The invention relates to a method for formulating vehicle driving conditions, in particular to a method for formulating vehicle driving conditions combining principal component analysis and fuzzy c-means clustering. Background technique [0002] For large cities, air pollution is a long-standing major problem, and vehicles are one of the most important sources of pollution. On the other hand, vehicle fuel consumption accounts for a large proportion in energy consumption. Vehicle fuel consumption and exhaust emissions are influenced by the driving patterns developed in different traffic situations. A vehicle driving profile is a speed-time curve representing driving patterns in a region or city. It is used to simulate vehicle driving conditions on a laboratory chassis dynamometer for evaluating fuel consumption and exhaust emissions. [0003] Currently, there are two types of methods for formulating operating conditions. In the first type of method, ...

AI Technical Summary

Problems solved by technology

[0005] There are deficiencies in the existing public methods for formulating some driving conditions: 1) The method of collecting original driving data mostly uses a single test vehicle and a fixed route, and the collection period is only a few days to about one month, resulting in the original driving conditions used to construct The extensiveness and representativeness of driving data sources are not ideal; 2) The V-A matrix method is often used in the formulation of existing working conditions

Therefore, this method requires more computing time and computing resources; 3) The operating conditions formulated by the existing methods are mostly actual driving conditions, and the directly synthesized operating condition curves have not been filtered and smoothed, and there are many peak points, making This kind of working condition is difficult to directly follow the test on the dynamometer, and the operability is poor

Images