The limited possibilities to evaluate the energy efficiency of driving algorithms for connected and autonomous vehicles (CAVs) make it very difficult for policymakers to decide on the potential of autonomous driving. This study is introducing a method to analyze the energy performance of a driving algorithm under various simulated traffic conditions using the microscopic traffic simulator SUMO. The method can also be used to optimize driving algorithm parameters for chosen traffic scenarios. Therefore, a tool-chain is developed that can simulate a CAV under many traffic scenarios in SUMO systematically. In those scenarios, one or more vehicles are controlled by the implemented driving algorithm. The resulting driving cycles are then analyzed by a forward-facing energy model to calculate the consumed energy. To validate the model, three measurement cycles under real urban traffic conditions were taken and the speed and state of charge (SOC) data of the test vehicle, a 2017 Tesla Model S 75D, were collected. The energy model was shown to be highly accurate and the simulated road network and traffic, which were chosen to represent the same urban traffic scenario as the measured cycles, were shown to result in similar statistics as the measurements. A simple driving algorithm that is already implemented in SUMO's Kraus car-following model was chosen to verify the model's applicability. For different values of the algorithm parameters acceleration and deceleration, a range of random driving cycles was simulated. In the simulations and the measurements, the effect of higher and lower use of auxiliary systems was also analyzed. The results show that the analyzed driving algorithm achieves similar results for the energy consumption as the human driver in the measurements with the best performing parameters. Also, the significance of auxiliary system usage on the energy consumption and its effect on a driving algorithm's parameter to remain energy efficient due to the higher impact of the trip duration is pointed out.
