The paper presents an analysis of energy consumption in a Plug-in Hybrid Electric Vehicle (PHEV) used in actual road conditions. Therefore, the paper features a comparison of the consumption of energy obtained from fuel and from energy taken from the vehicle’s batteries for each travel with a total distance of 5000 km. The instantaneous energy consumption per travelling kilometre in actual operating conditions for a combustion engine mode are within the range of 233 to 1170 Wh/km and for an electric motor mode are within the range of 135 to 420 Wh/km. The average values amount to 894 Wh/km for the combustion engine and 208 Wh/km for the electric motor. The experimental data was used to develop curves for the total energy consumption per 100km of road section travelled divided into particular engine types (combustion/electric), demonstrating a close correlation to actual operating conditions. These values were referred to the tested passenger vehicle’s approval data in a WLTP test, with the average values of 303 Wh/km and CO2 emission of 23 g/km.

This paper presents the concept of an expert system supporting the decision-making process of rational energy consumption by an electric car. The driving style and the average speed achieved are very important in terms of the driving range. In addition, the influence of ambient temperature on the efficiency of the electric vehicle and ultimately on energy consumption is very important. The proposed system, based on the method of multi-valued logic trees, allows minimizing the objective function, which is aimed at minimizing the energy consumption of an electric car at different ambient temperatures. The generated decisions, directed to the energy management system, can be processed in a variety of temporal and situational dimensions. The application of the system can also provide prompts to the driver on how to adjust the driving style by operating the accelerator and brake pedal. The expert system is an open system, allowing for further generalizations and modifications.

In this paper, particular attention is paid to an advanced variant of the plug-in hybrid electric vehicle, known as PHEV, which combines two functionalities: the vehicle’s internal combustion engine (ICE) and the electric motor. The study described herein also presents the influence of factors such as the ambient temperature, vehicle speed and traffic distance 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 everyday driving conditions. When analyzing the energy consumption based on the vehicle’s unitary energy consumption model, attention was paid to values that are strongly correlated with traffic and weather conditions. In addition, the authors emphasized that the estimated total energy consumption of a battery electric vehicle (BEV) or hybrid vehicle (PHEV), 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. As shown in this paper, the energy consumption intensity of a vehicle is significantly influenced not only by its speed but also by weather conditions, including ambient temperature. In extreme cases, energy consumption intensity can increase by up to 68% relative to a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) cycle.

This paper presents a method of identification of non-homogeneity or static unbalance of the structure of a car wheel based on a simple road test. In particular a method the detection of single wheel unbalance is proposed which applies an acceleration sensor fixed on windscreen. It measures accelerations cause by wheel unbalance among other parameters. The location of the sensor is convenient for handling an autonomous device used for diagnostic purposes. Unfortunately, its mounting point is located away from wheels. Moreover, the unbalance forces created by wheels spin are dumped by suspension elements as well as the chassis itself. It indicates that unbalance acceleration will be weak in comparison to other signals coming from engine vibrations, road roughness and environmental effects. Therefore, the static unbalance detection in the standard way is considered problematic and difficult. The goal of the undertaken research is to select appropriate transformations and procedures in order to determine wheel unbalance in these conditions. In this investigation regular and short time Fourier transform were used as well as wavelet transform. It was found that the use of Fourier transforms is appropriate for static condition (constant velocity) but the results proves that the wavelet transform is more suitable for diagnostic purposes because of its ability of producing clearer output even if car is in the state of acceleration or deceleration. Moreover it was proved that in the acceleration spectrum of acceleration measured on the windscreen a significant peak can be found when car runs with an unbalanced wheel. Moreover its frequency depends on wheel rotational frequency. For that reason the diagnostic of single wheel unbalance can be made by applying this method.

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.