A Node-Charge Graph-Based Online Carshare Rebalancing Policy with Capacitated Electric Charging

Abstract

Viability of electric car-sharing operations depends on rebalancing algorithms. Earlier methods in the literature suggest a trend toward nonmyopic algorithms using queueing principles. We propose a new rebalancing policy using cost function approximation. The cost function is modeled as a p-median relocation problem with minimum cost flow conservation and path-based charging station capacities on a static node-charge graph structure. The cost function is NP complete, so a heuristic is proposed that ensures feasible solutions that can be solved in an online system. The algorithm is validated in a case study of electric carshare in Brooklyn, New York, with demand data shared from BMW ReachNow operations in September 2017 (262 vehicle fleet, 231 pickups per day, and 303 traffic analysis zones) and charging station location data (18 charging stations with four-port capacities). The proposed nonmyopic rebalancing heuristic reduces the cost increase compared with myopic rebalancing by 38%. Other managerial insights are further discussed.

Cooperative Learning for Smart Charging of Shared Autonomous Vehicle Fleets

“…Another common unrealistic core assumption of the existing research is an unlimited (i.e., charging stations (CSs) are available anytime) and/or homogeneous (i.e., the same capacity and power rate) charging network. At the same time, Guillet et al (2022), Pantelidis et al (2022), and Froger et al (2022) demonstrate the significant impact of capacitated CSs on the charging scheduling of electric fleets. Moreover, most researchers (Iacobucci, McLellan, and Tezuka et al 2019;He et al 2021) manage the charging behavior of shared EVs to meet mobility demands or minimize energy costs but fail to treat both goals simultaneously.…”

“…The objective is to maximize service quality (the rate of served requests) and profitability (profits from serving trips minus energy cost). We design a realistic comprehensive package of operations but zoom in on smart charging, which substantially challenges the fleet managers of shared electric vehicles due to the technological challenges of EVs (e.g., long charging time and infrastructure scarcity) to which their non-EV counterparts are immune (Pantelidis et al 2022).…”

AST-GIN: Attribute-Augmented Spatiotemporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting

“…The other direction analyzes modeling and predicting the charging occupancy profile at the chargers, which is quite similar to the parking availability prediction problem [ 27 , 28 ]. The purpose is to design the scheduling algorithm to allocate EVs among eligible chargers to realize the global or local optimal charging waiting plan [ 29 , 30 ].…”

Survey of charging management and infrastructure planning for electrified demand-responsive transport systems: Methodologies and recent developments

“…Keskin and Çatay [21] point out that the full charging policy is not realis- tic, in particular when the charging operation is close to the end of service time and vehicles do not need to be fully recharged to return to their depots. The partial charging policy reduces charging times and costs to meet customer demand [21,22], and another approach involves battery-swapping technology that allows EVs to exchange their depleted batteries for fully charged ones to lower EV charging times [23]. c. Charging cost Most studies consider charging cost to be a linear function of the charged amount of energy, with a constant electricity price [24].…”

“…However, this approach does not optimize the charging levels of EVs. Different from previous studies, recent studies consider the joint optimization of EV repositioning and partial recharge for online car-share rebalancing policy design [22,49]. The problem is modeled as p-median relocation based on a node-charge graph to jointly optimize EV repositioning and partial recharge decisions so as to minimize overall operational costs while satisfying customer demand.…”