Abstract
Proper estimation of vehicle operating conditions is of great importance to ordinary drivers as well as for public transport companies. It can yield profits in lower energy (fuel) consumption and reduced exhaust emissions. Car operating conditions are identified in this work by the energy consumed per distance covered and the vehicle mass (specific energy consumption), which contains impact of the traffic conditions as well as the style of driving. The factors mentioned above have influence on the amount of mechanical energy transferred to the driving wheels. The traffic and the style of driving can finally be described with a probability density function of the parameter: specific energy consumption. This function can be determined from periodical recording of the car’s basic operating parameters, among others: engine rotational speed and torque (e.g.: on the grounds of the propulsion system model). Examples of vehicle operating conditions identification have been presented. Records, in a regular traffic in the city center (Gdansk), using sensors installed on board (speed, acceleration, fuel consumption, etc.) have been registered. From the examples given in this paper it appears that the increase of traffic intensity in case of calm style of driving causes the increase of the value of the average specific energy consumption. This is a result of a bigger number of start phases that are characterized by great specific energy consumption (high energy transmitted by the engine corresponds to relatively short distance covered by a car). A drop in the share of the distance covered by the car in the engine propulsion phase as compared to the total distance is observed at the same time. It is caused by an increase of the number of braking phases thus shortening the distance, when the vehicle is propelled by the engine. Consequently, dynamic style of driving causes that the effects mentioned above are intensified. Propelling phases are short but intensive. Distribution of the specific energy consumption is strongly related to the fuel consumption (obligatory with CO2 emission), which has been presented in this paper. The parameters proposed can be then used to forecast the fuel consumption with high accuracy. Creation of an individual map of operating conditions for a chosen agglomeration would enable to optimize choice of vehicle or fleet for an intended place of operation. Examples of utilization of such operating conditions map may be: to determine reference fuel consumption for assumed operation area or optimal route of drive from the point of view of minimizing fuel consumption, energy or CO2 emission to the atmosphere.
KEYWORDS vehicle operating conditions, traffic conditions, driving style, fuel consumption, CO2 emission