In this paper, particular attention is paid to an advanced variant of the plug-in hybrid engine, known as PHEV, which combines two functionalities: the vehicle's internal combustion engine (ICEV) and the electric engine (BEV). The paper also presents the assumptions of the DEVS model, which allows for the estimation of vehicle’s energy consumption. The study described herein also presents the influence of factors such as ambient temperature and constant speed on the PHEV’s energy consumption. It has been shown that the vehicle’s range estimated based on its electronic control module (ECU) is about 20% shorter per annum on average for its year-round operation in every-day driving conditions. When analyzing the energy consumption based on the vehicle’s unit energy consumption model, attention was paid to values that are strongly correlated with the traffic volume and weather conditions affecting the moving vehicle. In addition, the authors emphasized that the estimated total energy consumption of an electric (BEV) or hybrid (PHEV) vehicle, relative to the normative values arising from the type approval test cycle, deviate from the actual values arising from real driving conditions and often vary substantially. In extreme cases, the actual range of a hybrid vehicle is 75% shorter.

The process involved in preparing driving simulator tests is a labor intensive one, and the nature of this complexity depends on the sophistication of the tests to be carried out, as well as the number of participants. Which has an impact on the duration of the research itself. The very solution of using driving simulators is a research tool that has been in use for many years and with great success. They are used in a very wide range of areas of human functioning. They replicate in a very realistic way the conditions that can occur in the external environment, offer the possibility of repetition of particular conditions and insight into certain behaviors that could be very difficult to initiate in real life. This article also describes the dis- ease of using simulators to conduct research activities, along with the workflow from assumptions to finalizing the research. An algorithm is also presented for the study of individual participants, with examples for separate research scenarios concerning driver behavior in the vicinity of pedestrian crossings.