A vehicle coordination and charge scheduling algorithm for electric Autonomous Mobility-on-Demand Systems

Felix Boewing, Maximilian Schiffer, Mauro Salazar, Marco Pavone

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publication2020 American Control Conference, ACC 2020
PublisherInstitute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781538682661
Publication statusPublished - Jul 2020
Event2020 American Control Conference (ACC2020) - Denver, United States
Duration: 1 Jul 20203 Jul 2020


Conference2020 American Control Conference (ACC2020)
Abbreviated titleACC2020
CountryUnited States
Internet address

Bibliographical note

Funding 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.

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