Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm
This paper proposes a scientific and systematic methodology for the development of a representative electric vehicle (EV) urban driving cycle. The methodology mainly includes three tasks: test route selection and data collection, data processing, and driving cycle construction. A test route is desig...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/1890753 |
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| _version_ | 1832568143096053760 |
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| author | Xuan Zhao Qiang Yu Jian Ma Yan Wu Man Yu Yiming Ye |
| author_facet | Xuan Zhao Qiang Yu Jian Ma Yan Wu Man Yu Yiming Ye |
| author_sort | Xuan Zhao |
| collection | DOAJ |
| description | This paper proposes a scientific and systematic methodology for the development of a representative electric vehicle (EV) urban driving cycle. The methodology mainly includes three tasks: test route selection and data collection, data processing, and driving cycle construction. A test route is designed according to the overall topological structure of the urban roads and traffic flow survey results. The driving pattern data are collected using a hybrid method of on-board measurement method and chase car method. Principal component analysis (PCA) is used to reduce the dimensionality of the characteristic parameters. The driving segments are classified using a hybrid k-means and support vector machine (SVM) clustering algorithm. Scientific assessment criteria are studied to select the most representative driving cycle from multiple candidate driving cycles. Finally, the characteristic parameters of the Xi’an EV urban driving cycle, international standard driving cycles, and other city driving cycles are compared and analyzed. The results indicate that the Xi’an EV urban driving cycle reflects more aggressive driving characteristics than the other cycles. |
| format | Article |
| id | doaj-art-50042003e07e4d8f9de6388f45388762 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-50042003e07e4d8f9de6388f453887622025-02-03T00:59:40ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/18907531890753Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering AlgorithmXuan Zhao0Qiang Yu1Jian Ma2Yan Wu3Man Yu4Yiming Ye5School of Automobile, Chang’an University, Xi’an, ChinaSchool of Automobile, Chang’an University, Xi’an, ChinaSchool of Automobile, Chang’an University, Xi’an, ChinaSchool of Automobile, Chang’an University, Xi’an, ChinaSchool of Automobile, Chang’an University, Xi’an, ChinaSchool of Automobile, Chang’an University, Xi’an, ChinaThis paper proposes a scientific and systematic methodology for the development of a representative electric vehicle (EV) urban driving cycle. The methodology mainly includes three tasks: test route selection and data collection, data processing, and driving cycle construction. A test route is designed according to the overall topological structure of the urban roads and traffic flow survey results. The driving pattern data are collected using a hybrid method of on-board measurement method and chase car method. Principal component analysis (PCA) is used to reduce the dimensionality of the characteristic parameters. The driving segments are classified using a hybrid k-means and support vector machine (SVM) clustering algorithm. Scientific assessment criteria are studied to select the most representative driving cycle from multiple candidate driving cycles. Finally, the characteristic parameters of the Xi’an EV urban driving cycle, international standard driving cycles, and other city driving cycles are compared and analyzed. The results indicate that the Xi’an EV urban driving cycle reflects more aggressive driving characteristics than the other cycles.http://dx.doi.org/10.1155/2018/1890753 |
| spellingShingle | Xuan Zhao Qiang Yu Jian Ma Yan Wu Man Yu Yiming Ye Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm Journal of Advanced Transportation |
| title | Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm |
| title_full | Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm |
| title_fullStr | Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm |
| title_full_unstemmed | Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm |
| title_short | Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm |
| title_sort | development of a representative ev urban driving cycle based on a k means and svm hybrid clustering algorithm |
| url | http://dx.doi.org/10.1155/2018/1890753 |
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