This paper presents an algorithmic framework to optimize the operation of an Autonomous Mobility-on-Demand system whereby a centrally controlled fleet of electric self-driving vehicles provides on-demand mobility. In particular, we first present a mixed-integer linear program that captures the joint vehicle coordination and charge scheduling problem, accounting for the battery level of the single vehicles and the energy availability in the power grid. Second, we devise a heuristic algorithm to compute near-optimal solutions in polynomial time. Finally, we apply our algorithm to realistic case studies for Newport Beach, CA. Our results validate the near optimality of our method with respect to the global optimum, whilst suggesting that through vehicle-to-grid operation we can enable a 100% penetration of renewable energy sources and still provide a high-quality mobility service.
|Title of host publication||2020 American Control Conference, ACC 2020|
|Publisher||Institute of Electrical and Electronics Engineers|
|Number of pages||8|
|Publication status||Published - Jul 2020|
|Event||2020 American Control Conference (ACC2020) - Denver, United States|
Duration: 1 Jul 2020 → 3 Jul 2020
|Conference||2020 American Control Conference (ACC2020)|
|Period||1/07/20 → 3/07/20|
Bibliographical noteFunding Information:
This research was supported by the National Science Foundation under CAREER Award CMMI-1454737 and CPS Award-1837135, and the Toyota Research Institute (TRI). This article solely reflects the opinions and conclusions of its authors and not NSF, TRI, or any other entity.