Assessing battery electric vehicle energy consumption performance: the effects of driving style, road infrastructure, weather and traffic intensity

Battery Electric Vehicles (BEVs) are expected to reduce negative externalities of mobility. The market penetration of BEVs is still small partially due to range anxiety. Understanding in the energy consumption of BEVs is necessary to reduce range anxiety in the short term. In the long term, it could lead to new sustainability strategies and business models. However, a gap exists in the understanding of the energy consumption of BEVs, including the microscopic influence of the built environment, weather, and human behavior. To fill this research gap, a microscopic traffic model and an energy prediction model have been combined to calculate the influence of built environment variables, weather conditions, traffic intensities and driving styles on the energy consumption. They have been researched individually first. Afterwards, these elements have been combined in a case study in Nieuwegein. By using a VISSIM model, various scenarios have been tested to measure the influence of traffic intensity, weather conditions and driving style strategies. This resulted in a qualitative insight into the energy consumption and travel time. The results have been validated by performing 30 driving tests and by using dynamometer data from Argonne National Laboratory. The results indicate that built environment variables have a large effect on the energy consumption of EVs. An increasing traffic intensity has been found to not always increase the energy consumption due to lower aerodynamic drag forces. Therefore we conclude that the choice of the most energy efficient route is dependent on weather conditions and personal preferences of drivers.Battery Electric Vehicles (BEVs) are expected to reduce negative externalities of mobility. The market penetration of BEVs is still small partially due to range anxiety. Understanding in the energy consumption of BEVs is necessary to reduce range anxiety in the short term. In the long term, it could lead to new sustainability strategies and business models. However, a gap exists in the understanding of the energy consumption of BEVs, including the microscopic influence of the built environment, weather, and human behavior. To fill this research gap, a microscopic traffic model and an energy prediction model have been combined to calculate the influence of built environment variables, weather conditions, traffic intensities and driving styles on the energy consumption. They have been researched individually first. Afterwards, these elements have been combined in a case study in Nieuwegein. By using a VISSIM model, various scenarios have been tested to measure the influence of traffic intensity, weather conditions and driving style strategies. This resulted in a qualitative insight into the energy consumption and travel time. The results have been validated by performing 30 driving tests and by using dynamometer data from Argonne National Laboratory. The results indicate that built environment variables have a large effect on the energy consumption of EVs. An increasing traffic intensity has been found to not always increase the energy consumption due to lower aerodynamic drag forces. Therefore we conclude that the choice of the most energy efficient route is dependent on weather conditions and personal preferences of drivers