Model predictive control of a shared autonomous electric vehicles system with charge scheduling and electricity price response

Shared autonomous electric vehicles (SAEV s), also known as autonomous mobility on demand systems, are expected to soon be commercially available. This work proposes a methodology for the optimization of SAEV charging taking into account optimized vehicles routing and rebalancing. The methodology presented is based on previous work expanded to include charge scheduling optimization. Our model deals with the different time frames at which transport service and charging have to be optimized with a model-predictive control optimization routine which is run in parallel at two different time scales. Vehicle charging is optimized over longer time scales to minimize waiting times for passengers and electricity costs. Routing and rebalancing is optimized at shorter time-scales to minimize waiting times for passengers, taking as charging constraints the results of the long-time-scale optimization. This approach allows the efficient optimization of both aspects of SAEV operation. The problem is solved as a mixed-integer linear program. A case study using real transport data for Tokyo is used to test the model, showing that the system can substantially cut charging costs while keeping passenger wait times low