A genetic ant colony algorithm-based driving cycle generation approach for testing driving range of battery electric vehicle. (January 2020)

• Record Type: Journal Article
• Authors: Shi, Qin; Liu, Bingjiao; Guan, Qingsheng; He, Lin; Qiu, Duoyang
• Is Part Of: Advances in mechanical engineering. Volume 12:Number 1(2020)
• Publication Date: 2020-01
• Subjects: Driving cycle generation -- battery electric vehicle -- driving range -- principal component analysis -- genetic ant colony algorithm; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/1687814019901054
• Languages: English
• ISSNs: 1687-8132

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.