This paper analyses transport energy consumption of conventional and electric vehicles in mountainous roads. A standard round trip in Andorra has been modelled in order to characterise vehicle dynamics in hilly regions. Two conventional diesel vehicles and their electric-equivalent models have been simulated and their performances have been compared. Six scenarios have been simulated to study the effects of factors such as orography, traffic congestion and driving style. The European fuel consumption and emissions test and Artemis urban driving cycles, representative of European driving cycles, have also been included in the comparative analysis. The results show that road grade has a major impact on fuel economy, although it affects consumption in different levels depending on the technology analysed. Electric vehicles are less affected by this factor as opposed to conventional vehicles, increasing the potential energy savings in a hypothetical electrification of the car fleet. However, electric vehicle range in mountainous terrains is lower compared to that estimated by manufacturers, a fact that could adversely affect a massive adoption of electric cars in the short term.

This paper builds a model to estimate current car fleet energy consumption in Andorra and forecasts such consumption as a reference scenario. It shows how a useful modelling tool can be developed and applied in the absence of significant data. The base-year model is built through a bottom-up methodology using vehicle registration and technical inspection data. The model forecasts energy consumption up to 2050, taking into account the fleet structure, the car survival profile, trends in activity of the various car categories, and the fuel price and income elasticities that affect car stock and total fleet activity. It provides an initial estimate of private car energy demand in Andorra and charts a baseline scenario that describes a hypothetical future based on historical trends. A local sensitivity analysis is conducted to determine the most sensitive input parameters and study the effect of its variability. In addition, four scenarios are built to represent the largest expected variability in the results with respect to the reference scenario and provide a broad estimate of potential energy savings related to different policy strategies.

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