Becausc of growing concern about the impact of emissions from the transport sector on global climate change, vehicle energy consumption is a factor of great interest to network planners. In addition, drivers are interested In reducing energy consumption and, thus, fuel costs. However, traditional models of vehicle energy consumption have neglected an important factor: road grade. This assumption has traditionally been supported by the Idea that the energy consumed because of the road grade would be reflected In changes in speed and acceleration, but a demonstration of this on an aggregate network in a city of a realistic size has been difficult to show. This work demonstrated the impact of road grade on networkwide vehicle energy consumption by the integration of energy consumption equations based on road load equations, elevation data available from the Google Elevation advanced programming Interface, and a dynamic traflic assignment model to capture the effect of user route choice. This work quantified thc impact of the energy consumed because of road grades on two city networks, and the results indicate that the effects of grades should not he excluded from evaluations of vehicle energy consumption. In addition, the effects of ecorouting, in which drivers choose the shortest path that consumes the least amount of energy, were explored. The results for the city networks indicate that if drivers do not account for grades, they might choose a route that actually increases vehicle energy consumption. The proposed modeling tool is scalable and easily adaptable to different cities.