摘要：AbstractCarsharing operators could benefit from vehicle automation even before full vehicle automation is available city-wide: so-called Park- & Charge Lanes (PCL) installed in closed environments can increase customer convenience and reduce costs for parking space and charging operations. The concept introduced in this study comprises the stacking of vehicles in several lanes of optimized width, the division of lanes into charging and parking areas, and control strategies for efficient operation. Compared to conventional parking lots with two lanes and two perpendicularly arranged parking spaces, the stacking of vehicles allows for space reductions of up to 43 %. Additional cost savings can be achieved, since it is not necessary to equip every parking space with an inductive charging plate. Splitting each lane into a parking and a charging area makes the optimal control problem non-trivial: In order to provide the vehicles with a battery level that is high enough to serve customer requests, the PCL has to be controlled in a smart way. Both rule-based and model-predictive control policies are developed for assigning arriving vehicles to lanes and selecting vehicles for customer requests. An event-based simulation framework is created in order to test the performance of the introduced policies for the resulting dynamic and stochastic PCL problem. The best of the four described rule-based policies performs nearly as good as the implemented model-predictive control approach in the numerical experiment. The model-predictive control policy outperforms the random lane selection by 27 %, which clearly refects the benefit of using advanced control strategies.
关键词：KeywordsAutonomous vehiclesElectricsolar vehiclesAutomatic controloptimizationreal-time operations in transportationModelingsimulation of transportation systemsCharging infrastructureParkingCarsharing
