Vehicle Real Driving Cycle Construction Based on Improved Fuzzy C-means Clustering Algorithm

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Vehicle Real Driving Cycle Construction Based on Improved Fuzzy C-means Clustering Algorithm

Journal Title: 河南科技大学学报（自然科学版） - Year 2017, Vol 38, Issue 6

Abstract

Since the performance of Fuzzy C-means ( FCM) clustering algorithm depended on the initial clustering center, the iterative was inclined to fall into the local extremum and could not ensure that FCM converges to an optimal solution. Therefore, the initial cluster center of FCM was determined by optimization algorithm, and combination of multi-island genetic algorithm ( MIGA) and sequential quadratic programming ( SQP) method in order to get closer to the global optimal clustering. Dimension reduction treatment was performed on the characteristic parameters of kinematics sequences by principal component analysis and improved FCM clustering technique. Thus, a driving cycle based on large sample and traffic characteristics of Zhengzhou was constructed. The driving cycle was compared with the measured data. The relative average error of characteristic parameters between the synthetic cycle of passenger cars and measured data is 2. 097%, and the joint difference of velocity-acceleration ( SAFDdiff) is only 1.74%. The clustering technique has higher accuracy in driving cycle fitting and can more comprehensively reflect the real situation of Zhengzhou.

Authors and Affiliations

Jianping GAO, Xiaojie GAO

Keywords

FCM clustering; cluster center; principal component analysis; driving cycle;

EP ID EP478131
DOI -
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