Evaluation on the Fuel Economy of Automated Vehicles with Data-Driven Simulation Method

Abstract

With more commercialized automated vehicles (AVs) shortly entering the market, evaluating their fuel economy has become an important topic. The traditional fixed-profile test methods only indicate the effect of powertrain efficiency on fuel economy and cannot reflect the influences of control algorithms or driving behaviors on fuel consumption under real-world traffic conditions. Therefore, a data-driven simulation method for evaluating the real driving fuel economy of automated vehicles is developed. It utilizes naturalistic driving data to reconstruct test cycles. This method can inspect the performance of automated vehicle control algorithms under realistic traffic conditions in terms of fuel economy. The naturalistic driving data collected on urban expressways and freeways were used to model the longitudinal driving scenarios. Then, the fuel consumption of automated and human-driven vehicle was evaluated under the simulated scenarios via the Monte Carlo integration approach. It was found that the fuel consumption rate of the automated vehicle was 11.944 L/100 km under the car-following scenarios. The human-driven vehicle had a fuel consumption rate of 10.124 L/100 km under the same traffic conditions. The tested automated vehicle control algorithm is tuned to achieve better performance in terms of safety and travel efficiency and hence it tends to maintain a relatively steady time headway to the leading vehicle. It applies more frequent accelerating/braking cycles and higher average speed compared to typical human drivers. These characteristics lead to a higher fuel consumption rate of the automated vehicle. The presented method provides an accurate and efficient way to analyze the fuel economy performance of automated vehicles under practical conditions. This method can easily be scaled for large-scale traffic flow analyses. It can also be used to study the effects of human driving styles on fuel economy.