Utilizing Principal Component Analysis and Hierarchical Clustering to Develop Driving Cycles: A Case Study in Zhenjiang

Tianxiao Wang, Zhecheng Jing (), Shupei Zhang and Chengqun Qiu
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Tianxiao Wang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Zhecheng Jing: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Shupei Zhang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Chengqun Qiu: Jiangsu Province Intelligent Optoelectronic Devices and Measurement-Control Engineering Research Center, Yancheng Teachers University, Yancheng 224007, China

Sustainability, 2023, vol. 15, issue 6, 1-13

Abstract: Accurate driving cycles are key for effectively evaluating electric vehicle performance. The K-means algorithm is widely used to construct driving cycles; however, this algorithm is sensitive to outliers, and determining the K value is difficult. In this paper, a novel driving cycle construction method based on principal component analysis and hierarchical clustering is proposed. Real road vehicle data were collected, denoised, and divided into vehicle microtrip data. The eigenvalues of the microtrips were extracted, and their dimensions were reduced through principal component analysis. Hierarchical clustering was then performed to classify the microtrips, and a representative set of microtrips was randomly selected to construct the driving cycle. The constructed driving cycle was verified and compared with a driving cycle constructed using K-means clustering and the New European Driving Cycle. The average relative eigenvalue error, maximum speed acceleration probability distribution difference rate, average cycle error, and simulated relative power consumption error per 100 km between the hierarchical driving cycle and the real road data were superior to those of the K-means driving cycle, which indicated the effectiveness of the proposed method. Though the methodology proposed in this paper has not been verified in other regions, it provided a certain reference value for other research of the developing driving cycle.

Keywords: principal component analysis; hierarchical cluster; electric vehicle; driving cycle; driving cycle construction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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