Journal
JOURNAL OF ADVANCED TRANSPORTATION
Volume 2021, Issue -, Pages -Publisher
WILEY-HINDAWI
DOI: 10.1155/2021/5430137
Keywords
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Funding
- National Natural Science Foundation of China [51675530]
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The driving cycle is a speed-to-time curve developed based on firsthand driving data, which goes through a series of processes including data preprocessing, smoothing, interpolation, kinematic fragment extraction, feature parameter evaluation, clustering, and optimization to select the optimal fragments for development. The experiment result indicates that the developed driving cycle accurately represents the kinematic features of the experiment car.
The driving cycle is a speed-to-time curve, a fundamental technique in the automotive industry, and also a basis to set standards for fuel consumption and emissions of vehicles. A driving cycle is developed based on firsthand driving data collected from fieldwork. First, bad data in the original dataset are preprocessed, the time-series standard smoothing algorithm is used to smoothen the data, and Lagrange's interpolation is used to realize data interpolation. Next, the rules for kinematic fragment extraction are set to divide the data into kinematic fragments. Last, an evaluation system of kinematic fragment feature parameters is built. On that basis, the K-means clustering method is used to cluster the dimensionally reduced data, and the adaptive mutation particle swarm optimization (AMPSO) algorithm is employed to select the optimal fragments from candidate fragments to develop a driving cycle. The experiment result shows that the developed driving cycle can represent the kinematic features of the experiment car and provides a basis for the development of a driving cycle for Fuzhou.
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