Development Method for the Driving Cycle of Electric Vehicles

Zhecheng Jing (), Tianxiao Wang, Shupei Zhang and Guolin Wang
Additional contact information
Zhecheng Jing: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Tianxiao Wang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Shupei Zhang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Guolin Wang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China

Energies, 2022, vol. 15, issue 22, 1-12

Abstract: With the development of electric vehicles, more attention has been paid to the role of the driving cycle in vehicle performance testing. At present, the K-means algorithm is often used in the development of driving cycles. However, it is sensitive to the outlier points and also difficult to determine the K value. To solve this problem, the hierarchical cluster method is applied in this study. First, the real-world driving data are collected and denoised through wavelet domain denoising. Then, the data are divided into micro-trips and the characteristic parameters are extracted. The hierarchical cluster method is adopted to classify the micro-trips into different categories. An appropriate number of micro-trips are selected from each group in proportion to each category to assemble the driving cycle. Finally, both the economic simulation and the statistical analysis prove the accuracy of the generated driving cycle and the feasibility of the development method proposed in this paper.

Keywords: electric vehicle; driving cycle; hierarchical cluster method; principal component analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/22/8715/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/22/8715/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:22:p:8715-:d:978358

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().