Operational efficiency of cargo vehicles in terms of energy and environmental criteria
Abstract
Abstract Rising fuel costs, stricter decarbonisation requirements for freight transport, and the increasing use of telematics monitoring call for a shift from assessing freight vehicles solely based on average fuel consumption to a mode-specific assessment of their performance on specific routes. The purpose of the study was to substantiate and formalise the model of operational efficiency of vehicles of category N3, which combines fuel, environmental, route, regime, and information indicators. The source base was formed from peer-reviewed publications from 2020-2025, selected based on direct relevance to heavy trucks, real route conditions, telematics or on-board data, eco-management, and emissions. System, comparative, and structural and functional analysis, formalisation of indicators, and scenario modelling were applied. The information of the smart tachograph, Global Positioning System-telematics, Controller Area Network/Fleet Management System/On-Board Diagnostics and fuel sensors was differentiated; equations for fuel consumption per 100 km and tonne-kilometre, weight CO₂, idle and Eco-Roll fractions, average acceleration, speed stability, and integral index were proposed. An analysis of the factors showed that the route and the weight of the road train determine the base load, whilst the most readily controllable factors are the duration of idling, the frequency of acceleration, speed stability, the use of coasting, and the carrier’s organisational decisions. Practical operation of the model was demonstrated on a conditional 100-kilometre route of a road train with a gross weight of 36 tonnes with a load of 20 tonnes. Reducing idle speed from 24 to 12 minutes, forming an Eco-Roll share of 6.9% of driving time, and speed stabilisation reduced estimated fuel consumption from 32.4 to 31.1 litres, and direct CO₂ emissions from 85.54 to 82.10 kg; the integral index increased from 0.804 to 0.933. The above scenario is a demonstration scenario and requires further verification based on actual route data. The practical value lies in the ability to use the model to compare trips, identify the causes of cost overruns, and provide recommendations to the driver and dispatcher under specific route and operational conditions Keywords: