A genetic ant colony algorithm-based driving cycle generation approach for testing driving range of battery electric vehicle

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 para...

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Vydané v:Advances in mechanical engineering Ročník 12; číslo 1
Hlavní autori: Shi, Qin, Liu, Bingjiao, Guan, Qingsheng, He, Lin, Qiu, Duoyang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London, England SAGE Publications 01.01.2020
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ISSN:1687-8132, 1687-8140
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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.
AbstractList 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.
Author Qiu, Duoyang
Liu, Bingjiao
Shi, Qin
He, Lin
Guan, Qingsheng
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Issue 1
Keywords driving range
Driving cycle generation
genetic ant colony algorithm
principal component analysis
battery electric vehicle
Language English
License This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
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SubjectTerms Algorithms
Ant colony optimization
Battery cycles
Electric vehicles
Parameters
Principal components analysis
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