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.

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.