TY - JOUR

T1 - Distributed Scheduling of Charging for On-Demand Electric Vehicle Fleets

AU - Bistritz, Ilai

AU - Klein, Moshe

AU - Bambos, Nicholas

AU - Maimon, Oded

AU - Rajagopal, Ram

N1 - Publisher Copyright:
© 2020 Elsevier B.V.. All rights reserved.

PY - 2020

Y1 - 2020

N2 - Consider a fleet of N autonomous electric vehicles (EVs), where EV n has a battery state ßn. Customers arrive at rate ? and each customer requests a trip that costs some battery charge b. The goal is to coordinate the pick-ups and charging of the fleet in order to minimize the discounted number of unpicked customers over an infinite time horizon. Solving the dynamic program for this scheduling problem is infeasible since the state space has an exponential size in N. We propose a distributed approach instead where each EV models the fleet as a "field" that picks the customer i.i.d. at random with some pick-up probability a (b). Based on this model, each EV computes its optimal charging and pick-up strategy, and reports which customer they are available to pick-up (as a function of b) or if they decided to charge. The server computes a maximal matching between customers and EVs, taking into account their reported availability. The process continues iteratively as each EV recomputes its optimal response to the new empirical a (b). We prove that the optimal local strategy is a threshold policy with respect to ßn, where the threshold depends on a (b) and b. We also prove that the empirical pick-up probability a (b) converges to the true stationary probability, which explains why our algorithm converges to an equilibrium. We then study our algorithm in simulations, that show that at equilibrium, its performance is significantly better than that of an uncoordinated charging strategy. Therefore, our solution has low-complexity but is still sophisticated enough to coordinate the charging of the fleet.

AB - Consider a fleet of N autonomous electric vehicles (EVs), where EV n has a battery state ßn. Customers arrive at rate ? and each customer requests a trip that costs some battery charge b. The goal is to coordinate the pick-ups and charging of the fleet in order to minimize the discounted number of unpicked customers over an infinite time horizon. Solving the dynamic program for this scheduling problem is infeasible since the state space has an exponential size in N. We propose a distributed approach instead where each EV models the fleet as a "field" that picks the customer i.i.d. at random with some pick-up probability a (b). Based on this model, each EV computes its optimal charging and pick-up strategy, and reports which customer they are available to pick-up (as a function of b) or if they decided to charge. The server computes a maximal matching between customers and EVs, taking into account their reported availability. The process continues iteratively as each EV recomputes its optimal response to the new empirical a (b). We prove that the optimal local strategy is a threshold policy with respect to ßn, where the threshold depends on a (b) and b. We also prove that the empirical pick-up probability a (b) converges to the true stationary probability, which explains why our algorithm converges to an equilibrium. We then study our algorithm in simulations, that show that at equilibrium, its performance is significantly better than that of an uncoordinated charging strategy. Therefore, our solution has low-complexity but is still sophisticated enough to coordinate the charging of the fleet.

KW - Distributed control

KW - Electric vehicles

KW - Game theory

KW - Scheduling algorithms

KW - Transportation control

UR - http://www.scopus.com/inward/record.url?scp=85106601186&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2021.04.043

DO - 10.1016/j.ifacol.2021.04.043

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AN - SCOPUS:85106601186

SN - 2405-8963

VL - 53

SP - 472

EP - 477

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 4

T2 - 15th IFAC Workshop on Discrete Event Systems, WODES 2020

Y2 - 11 November 2020 through 13 November 2020

ER -