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In this article, we analyze the interaction between powertrain technology, predictive driving functionalities, and inner-city traffic conditions. A model predictive velocity control algorithm is developed that utilizes dynamic traffic data as well as static route information to optimize the future trajectory of the considered ego-vehicle. This controller is then integrated into a state-of-the-art simulation environment for automated driving functionalities to calculate energy saving potentials for vehicles with a conventional gasoline engine powertrain and a P3-hybrid powertrain configuration as well as for a battery electric vehicle based on real driving measurements. The comparison of these powertrains under various traffic conditions shows that all three technologies profit from predictive driving functionalities. The determined reduction in energy demand ranges from 15% to more than 40%, but it is highly dependent on the boundary conditions and the selected powertrain technology. Specifically, it is shown that electrified powertrains can profit the most when the time-gap to the preceding vehicle is maintained at a high level. For a conventional powertrain, this effect is less pronounced and can be attributed to the efficiency characteristics of gasoline engines. It can be concluded that the development of advanced predictive driving functionalities requires microscopic simulation of inner-city traffic to achieve optimum results with regard to energy consumption.

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An increasing level of driving automation and a successive electrification of modern powertrains enable a higher degree of freedom to improve vehicle fuel efficiency and reduce pollutant emissions. Currently, both domains themselves, driving automation as well as powertrain electrification, face the challenge of a rising development complexity with extensive use of virtual testing environments. However, state-of-the-art virtual testing environments typically strictly focus on just one domain and neglect the other. This paper shows the results of a simulation-based case study considering both domains simultaneously. The influence of energy saving automated functionalities on a conventional, a hybrid, and a pure electric powertrain is investigated for a carefully selected inner-city driving scenario. The vehicle simulation models for the different powertrain configurations are calibrated using test bench results and vehicle measurements. A model predictive acceleration controller is developed for realizing the speed optimization function. By considering traffic conditions such as traffic light schedules and a preceding vehicle as the boundary conditions, unnecessary accelerations and decelerations are avoided to reduce the energy demand. The case study is realized by applying this function to the three powertrains variants. As a final result, a clear difference in energy demand is observed: the hybrid powertrain benefits the most in terms of energy demand reduction in the given use case. The results clearly underscore that in future vehicle development programs, the powertrain and the real-world driving functionalities have to be optimized simultaneously to minimize the energy demand during everyday vehicle operation.