A driving cycle is a time series of a vehicle’s speed, reflecting its movement in real road conditions. In addition to certification and comparative research, driving cycles are used in the virtual design of drive systems and embedded control algorithms, traffic management and intelligent road transport (traffic engineering). This study aimed to develop an adaptive driving cycle for a known route to optimize the energy consumption of an electric vehicle and improve the driving range. A novel distance-based adaptive driving cycle method was developed. The proposed algorithm uses the segmentation and iterative synthesis procedures of Markov chains. Energy consumption during driving is monitored on an ongoing basis using Gaussian process regression, and speed and acceleration are corrected adaptively to maintain the planned energy consumption. This paper presents the results of studies of simulated driving cycles and the performance of the algorithm when applied to the real recorded driving cycles of an electric vehicle.

This study looks at the impact on selected toxic exhaust gas components in a compression ignition engine supplied with diesel-fuel emulsion. During the empirical research, a turbocharged PERKINS 1104C-E44T diesel engine was supplied with water-fuel emulsion where water content in the mixture was 15% by volume and diesel fuel. The tests were performed with load characteristics at two speeds of the engine crankshaft at maximum torque speed of 1,400 rev/min and maximum engine power speed of 2,200 rev/min. At each operating point of the engine concentrations of toxic exhaust gases we recorded carbon monoxide CO, hydrocarbons HC, nitrogen oxides NO_x, and particulate matter PM. On the basis of results and calculations, specific emissions of toxic substances in accordance with ISO 8178 were designated.

Time signals recorded by the on-board diagnostic system (OBD), describing the manner of vehicle’s movement in actual road conditions show non-stationarity and non-linearity, as well as statistical multiscalarity. In practice, it means that the analysis of registered time series requires modelling of non-linear phenomena. The aim of this study was to examine the nature of the vehicle speed profile in actual road conditions with the method of multifractal analysis. A number of studies indicates that the driving tests applied for many years have not been representative for the actual operating conditions of vehicles. For both the new Worldwide Harmonised Light duty Test Cycle (WLTC), a worldwide harmonised procedure of light vehicle testing, as well as in actual urban driving conditions along the measuring route, being subject to empirical research, confirmation of strong multifractal properties of the recorded vehicle speed time series have been obtained.