Plug-in hybrids (PHEV) have become popular due to zero-emission driving, e.g., in urban areas, and using an internal combustion engine on longer distances. Energy consumption by the PHEV depends on many factors which can be either dependent or independent of the driver. The article examines how the driver can use the vehicle’s capabilities to influence its wear. Determining the optimal driving technique, due to the adopted nature of the timetable, is the basic variable that determines the profitability of using a given drive system. Four driving techniques have been selected to determine which one can offer the largest advantages. A vehicle-dedicated application has recorded the drivetrain performance on a predetermined route through an urban area. The analysis of results has demonstrated which of the driving techniques provides measurable effects in terms of reduced energy consumption and the shortest travelling time. The study shows longitudinal acceleration and torque generated by the electric drive. The information included in the study can help any PHEV user reduce the operating cost by applying an appropriate driving technique. The proposed research introduces the possibilities of assessing the influence of the driving style on energy consumption. The innovative side of this research is the observation of stochastic phenomena that are difficult to detect when using approximation modelling.

The article discusses the causes of failures of trucks based on statistical data obtained from GITD and a transport company. Knowledge of the results of the conducted analyzes will allow to indicate the components or parts of vehicles that are most often damaged. Thanks to such information, it will be possible to prevent vehicle stoppages caused by defects in the future and to minimize the risk posed by a failure.

This paper discusses the matter of fault detection by the diagnosticians of a Regional Vehicle Inspection Station (RVIS) in trucks. The analysis data were obtained directly from RVIS. The produced results were presented as bar graphs indicating the trend change from 2014 to 2018. Thus obtained trends were described by formulae and used to calculate the probability of a fault from the analysed group appearing. The obtained data dependency can be used to calculate the probability of a fault appearing in a truck during a year of operation.