New mobility concepts such as shared, autonomous, electric vehicle (SAEV) fleets raise questions to the vehicles’ technical design. Compared to privately owned human driven cars, SAEVs are expected to exhibit different load profiles that entail the need for newly dimensioned powertrain and battery components. Since vehicle architecture is very sensitive to operating characteristics, detailed SAEV driving cycles are crucial for requirement engineering. As real world measurements reach their limit with new mobility concepts, this contribution seeks to evaluate three different traffic simulation approaches in their ability to model detailed SAEV driving profiles. (i) The mesoscopic traffic simulation framework MATSim is analyzed as it is predestined for large-scale fleet simulation and allows the tracking of individual vehicles. (ii) To improve driving dynamics, MATSim’s simplified velocity profiles are enhanced with real-world driving cycles. (iii) A sequential tool-coupling of MATSim with the microscopic traffic simulation tool SUMO is pursued. All three approaches are compared and evaluated by means of a comprehensive test case study. The simulation results are compared in terms of driving dynamics and energy related key performance indicators (KPI) and then benchmarked against real driving cycles. The sequential tool-coupling approach shows the greatest potential to generate reliable SAEV driving profiles.

Usage profiles of shared autonomous fleets will considerably differ from present-day privately owned vehicles. Thus, requirements on powertrain and other vehicle components are expected to change significantly. While there are still no real-world data available, automotive requirement engineering strongly depends on synthetic driving profiles, for example, forwarded by traffic simulation. These simulations, however, are quite challenging as they need to combine multi-modal, large-scale fleet simulations with microscopic traffic modeling to simultaneously produce realistic usage profiles and detailed driving cycles. We aim to combine the two open-source tools MATSim and SUMO to achieve this goal. As an important step in this endeavor, we analyze the consistency of both MATSim and SUMO with regard to traffic dynamics by means of three experiments with an increasing level of complexity: (i) analytically on a homogeneous road segment in the steady-state; (ii) numerically on a homogeneous road segment in the non-stationary state for a synthetic test case; and (iii) numerically for a highly non-linear medium-sized real-world test case in Berlin. We analyze the simulation results with respect to macroscopic flow–density–speed relations. In addition, we also study network impedances for the Berlin test case. We show that the traffic dynamics of MATSim and SUMO behave differently for the various test cases and discuss the implications on our tool-coupling efforts.

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