关键词: 行驶工况, 影响因子, 特征权重, 加权K-means聚类

Abstract: In order to solve the problem that the initial center of traditional clustering algorithm is easy to fall into local optimum and time-consuming. An improved K-means clustering algorithm is proposed. In this algorithm, the maximum minimum distance and weighted Euclidean distance are introduced to avoid the influence of outliers and edge data. The weight method is used to improve the principal component, and the feature influence factor is used as the initial feature weight to construct a weight- ed Euclidean distance measure. According to the influence factors of feature contribution rate on cluster-  ing, a clustering method of feature weight influence factors is proposed, which selects representative feature factors to highlight clustering effect, and finally synthesizes driving cycle and analyzes instantaneous fuel consumption. The results show that: the difference value of speed acceleration joint distribution of the proposed method is only 1.05%, which saves 44.2% of the time, compared with the traditional K-means clustering. The driving cycle fitting degree is high, which can reflect the actual vehicle operation characteristics and fuel consumption.

Key words: driving cycle, influence factor, feature weight, weighted K-means clustering