The efficient vehicle procurement is an important business segment of different companies with their own vehicle fleet. It has a significant influence on reducing transport and maintenance costs and on increasing the fleet’s energy efficiency. It is indispensable that managers consider various criteria from several aspects when procuring a vehicle. In that sense, we defined 13 relevant criteria and divided them into four multidisciplinary aspects: Construction-technical, financial, operational, and environmental. Decision-Making Trial and Evaluation Laboratory-Based Analytic Network Process (DANP) method was applied to evaluate the significance of defined criteria and aspects and their interdependency. It is established that the three most important criteria for vehicle procurement are vehicle price, vehicle maintenance, and vehicle selling price. The most important aspect is construction technical aspect, while the aspect of the environment is the least important. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used to rank eight different vehicles, which were considered by vehicle fleet manager at the observed company. This model assists fleet managers in the selection of the most suitable vehicle for procurement, while significantly reducing decision-making time and simultaneously observing all necessary criteria and their weights. Moreover, we have considered 10 different scenarios to establish whether and how the rank of the observed alternatives would change.

It is well known that fuzzy logic is a processing tool in circumstances lacking of clear linguistic information, as well as making conclusions based on imprecise assertions and rough data. Eco-driving rules that the drivers should comply with are not always made of concrete values (exact acceleration / deceleration rates, torque or headway / distance kept from the vehicle ahead, etc.), but often linguistically expressed and subjective (e.g. soft acceleration, mid-range engine speed, soft deceleration, sufficient distance, etc.). Therefore, the authors recognized fuzzy logic potentials as an efficient tool to overcome all mentioned barriers and thus to increase vehicle energy eficiency and reduce emissions of harmful gases which are main goals of eco-driving. The primary objective of this paper is to raise the awareness on the potentials and efficiency of fuzzy logic systems' use in eco-driving as a tool for achieving more ecologically & economically sustainable road transport. The rules that drivers should follow in order to achieve and maintain eco-driving goals, as well as the parameters to be monitored to evaluatedriver's behaviour i.e. the compliance with eco-driving rules are presented in the paper. The authors propose a driver rating system based on the fuzzy logic model constructed within MatLab. Within the proposed model the input parameters are actual acceleration/deceleration rates, engine speed and accelerator pedal pressure(APP) and while model output are driver ratings (scores ranging from 0 to 10 points) after completed a driving cycle. A real-world example based on data collected via vehicle OBDII connector by a TEXA logging device in realistic vehicle operation conditions. The consequent actual results of drivers' behaviour rating tool based on the proposed model are presented in the paper.

In this paper, research is done in the influence of different terrain and traffic conditions on road sections on the driver?s driving performances, i.e. on the car energy efficiency and CO2 emission. A methodology aimed at determining to which extent unfavorable traffic and/or terrain conditions on a road section contribute to the driver?s worse driving performances, and also to determine when the driver?s aggressive driving style is responsible for greater fuel consumption and greater CO2 emission is proposed. In order to apply the proposed methodology, a research study was carried out in a cargo transportation company and 12 drives who drove the same vehicle on five different road sections were selected. As many as 284014 of the instances of the data about the defined parameters of the road section and the driver?s driving style were collected, based on which and with the help of machine learning a prediction of the scores for the road section and the scores for the driver?s driving style was performed. The obtained results have shown that the proposed methodology is a useful tool for managers enabling them to simply and quickly determine potential room for increasing the energy efficiency of the vehicle fleet and decreasing CO2 emission.

An estimation model of fuel consumption and exhaust emissions of commercial vehicles in different operating conditions is developed. Depending on vehicle’s average speed, road slope and vehicle’s load/capacity utilization, the model determines the fuel consumption and emissions for the observed vehicle type and category. To validate the model, recordings on actual commercial vehicles were made in real operating conditions. Obtained fuel consumption in real operating conditions and estimated fuel consumption were compared. Deviations of the estimation model from the real consumption values are on average about 5%. After successful fuel consumption validation, the emissions of the harmful gases are estimated.