Open AccessResearch on the influence factors of real driving cycle with statistical analysis and dynamic time warping
Author(s) -
Yu Shu,
Lü Lin
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5275
Subject(s) - dynamic time warping , set (abstract data type) , selection (genetic algorithm) , cluster (spacecraft) , data mining , driving cycle , process (computing) , computer science , engineering , simulation , statistics , mathematics , artificial intelligence , physics , power (physics) , electric vehicle , quantum mechanics , programming language , operating system
The real driving cycle (RDC), which aims to reflect the real driving behaviour of vehicles, plays an important role in evaluating the performance or pollution of vehicles. At present, the related researches most focus on developing RDCs of different functions or regions, while the influence factors and the rules of RDC construction are not involved. In this study, through statistical analysis and theoretical analysis of RDC construction process, the influence factors of candidate RDC are explored. These factors include statistical characteristics, common factors number, cluster number, number selection principle, and order principle. A different value of factors means a different candidate RDC and different proximity of RDCs to the real driving data. Through the dynamic time warping index, the proximity of candidate RDCs is calculated, then the factors rules and the optimum RDC are obtained. When the slope is added as one statistical characteristic, the common factors number is set as 5, the cluster number is set as 6, the number selection principle is set as ratio principle, and order principle chooses positive sequence, the candidate RDC is the optimal cycle which is closest to the real vehicles driving conditions.