Abstract
In this article, an approach of driving cycle generation for battery electric vehicle is proposed based on genetic ant colony algorithm. The real-world traffic information is utilized to build up a local driving cycle database, in which definitions of the short trip and kinematic characteristic parameters are discussed to describe the driving cycle. A method of principal component analysis is taken as a preprocessor for reducing the dimension of driving cycle data. And then, genetic ant colony algorithm is used to classify the type of short trips and generate the driving cycle. The experimental results on board indicate that, compared with the Economic Commission for Europe driving cycle, the error of driving range and characteristic parameters tested by genetic ant colony driving cycle are reduced by 18.1% and 18.3%, respectively. Therefore, genetic ant colony driving cycle is a good candidate to test driving range of battery electric vehicle.
Highlights
The problem that battery electric vehicle (BEV) cannot reach the nominal driving range has been discovered by consumers
Calculate the correlation coefficient s between each short trip and its kinematic cluster by equation (15)
Where value G is the characteristic parameter of each kinematic cluster, value H is the characteristic parameter of every short trip
Summary
The problem that battery electric vehicle (BEV) cannot reach the nominal driving range has been discovered by consumers. Keywords Driving cycle generation, battery electric vehicle, driving range, principal component analysis, genetic ant colony algorithm
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