Construction of Driving Cycle Based on Big Data and Markov Chain

doi:10.12068/j.issn.1005-3026.2019.01.015

Abstract

Abstract: In order to improve the accuracy of typical driving cycle， the constructing algorithm for typical driving cycle was studied. 10 passenger cars in Shenyang City were selected to collect the driving data by autonomous driving， and the big sample database was established. Firstly， the raw data was filtered for noise reduction by using Fourier transform method. Secondly， the modified Kneser-Ney smoothing method was applied to compute the state transfer probability matrix， and the driving cycle constructing algorithm based on Markov chain was proposed. Finally， the typical driving cycle for passenger cars in Shenyang City was constructed and compared with the overall characteristics of the database. The results showed that the average deviation between the constructed cycle and the database population is 2.46%， the deviation values of all the characteristic parameters are within 10%， and the validness of the proposed algorithm is thus verified.

Key words: driving cycle, passenger car, big sample, Markov chain, algorithm

CLC Number:

U491.2

CAO Qian， LI Jun， LIU Yu， QU Da-wei. Construction of Driving Cycle Based on Big Data and Markov Chain[J]. Journal of Northeastern University Natural Science, 2019, 40(1): 77-81.

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