Search Results

Driving cycles are typically used for estimation of vehicle fuel/energy consumption and CO2 emissions. In most of applications only the vehicle velocity vs. time profile is considered as a driving cycle, while a road slope is typically omitted. Since the road slope significantly impacts the fuel consumption, it should be included into realistic driving cycles for hilly roads. As a part of wider research of multidimensional driving cycle synthesis, this paper focuses on analysis of a broad city bus driving cycle dataset recorded in the city of Dubrovnik. The analysis is aimed at revealing the impact of road slope on velocity and acceleration distributions, and clustering the recorded data into several groups reflecting various driving and traffic congestion characteristics. Finally, the Markov chain method is employed to synthesize 3D driving cycles for the selected data clusters, where the Markov chain states include vehicle velocity, vehicle acceleration, and road slope.

Driving cycles are usually defined by vehicle speed as a function of time and they are typically used to estimate fuel consumption and pollutant emissions. Currently, certification driving cycles are mainly used for this purpose. Since they are artificially generated, the resulting estimates and analyzes can generally be biased. In order to address these shortcomings, recent research efforts have been directed towards development of statistically representative synthetic driving cycles derived from recorded real-world data. To this end, this paper focuses on synthesis of multidimensional driving cycles using the Markov chain-based method and particularly on their validation. The synthesis is based on Markov chain of fourth order, where the road slope is accounted, as well. The corresponding transition probability matrix is implemented in the form of a sparse matrix parameterized with a rich set of recorded city bus driving cycles.