This paper presents a simple method of using Voronoi partitions for estimating vehicle fuel economy from a limited set of engine operating conditions. While one of the overarching goals of engine research is to continually improve vehicle fuel economy, evaluating the impact of a change in engine operating efficiency on the resulting fuel economy is a non-trivial task and typically requires drive cycle simulations with experimental data or engine model predictions and a full suite of engine controllers over a wide range of engine speeds and loads. To avoid the cost of collecting such extensive data, proprietary methods exist to estimate fuel economy from a limited set of engine operating conditions. This study demonstrates the use of Voronoi partitions to cluster and quantize the fuel consumed along a complex trajectory in speed and load to generate fuel consumption estimates based on limited simulation or experimental results. Detailed vehicle drive cycle simulations were conducted for the FTP, HWY, and US06 cycles using vehicle configurations corresponding to a passenger sedan, crossover, and pickup truck. Several engine maps representing naturally aspirated and downsized turbocharged designs with varying efficiency were considered for each vehicle configuration. The predicted speed and torque visitation points were then used to select a small set of Voronoi anchor points and determine weighting factors to estimate vehicle fuel economy. Predictions with the estimation method replicated detailed drive cycle results within 0.3 MPG under most configurations tested, using the engine performance at as few as 10 anchor points as input. The engine operating conditions of greatest impact on vehicle fuel economy were in the mid-speed and mid-load region and corresponded to 5 anchor points capturing > 85% of the vehicle fuel consumption on the FTP and HWY drive cycles.