THESIS
2021
1 online resource (xiv, 103 pages) : illustrations (soem color)
Abstract
Motivated by the increasing concern of environmental pollution and fossil fuel shortage,
transportation electrification, namely the process of integrating a large
fleet of public and
private electric vehicles (EVs) into the transportation system, is conceived to be one of
the promising solutions in future sustainable cities. Enormous efforts have been made
on modeling and optimizing EV networks and their extensions. Here, an EV network is
an integration system of EVs and EV refueling stations, where EVs can get refueled in
stations and drivers/passengers with mobility demand can take EVs to travel. Therefore,
two basic problems are considered in EV networks, namely, i) energy allocation and ii) mobility management problems. First, considering EV networks as energy consumers, a properly...[
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Motivated by the increasing concern of environmental pollution and fossil fuel shortage,
transportation electrification, namely the process of integrating a large
fleet of public and
private electric vehicles (EVs) into the transportation system, is conceived to be one of
the promising solutions in future sustainable cities. Enormous efforts have been made
on modeling and optimizing EV networks and their extensions. Here, an EV network is
an integration system of EVs and EV refueling stations, where EVs can get refueled in
stations and drivers/passengers with mobility demand can take EVs to travel. Therefore,
two basic problems are considered in EV networks, namely, i) energy allocation and ii) mobility management problems. First, considering EV networks as energy consumers, a properly designed refueling strategy is highly necessary in order to relieve the impact of
EVs' refueling load on power grids and meanwhile satisfy EVs' quality-of-service. Second,
considering EV networks as mobility service providers, efficient mobility management is
needed to accommodate temporally and spatially different mobility demand from passengers.
This thesis develops three distinct real-time decision-making models and algorithms
to handle the two basic problems in practical scenarios of EV networks.
In the first technical chapter, we investigate a network of battery swapping stations (BSSs), where battery swapping is considered as a more time-efficient method for EV
refueling compared to plug-in charging. Specifically, in this chapter, we focus on a joint long-term battery inventory planning and real-time vehicle-to-station (V2S) routing problem,
where EVs' refueling demand arrives randomly and sequentially. An online decision-making framework is proposed to model and optimize the operation of BSS networks, and a closed-form performance bound is theoretically guaranteed.
In the second technical chapter, we consider the scenario of electric mobility-on-demand
(EMoD) system (e.g., vehicle-sharing and ride-sharing), where EVs are directly
managed by a system operator to provide mobility services. EVs can be dispatched to
serve passengers' individual mobility demand or reallocated to accommodate unbalanced
demand in different locations. In addition to that, we also make recharging decisions
to refuel EVs and keep their energy levels. An efficient decision policy is derived to
accommodate the real-time demand and maximize the long-term system revenue.
In the third technical chapter, we propose a dynamic pricing mechanism in the EMoD system to incentivize passengers with spatially and temporally unbalanced demand to
make different mobility choices. In this way, passengers' traveling demand can be reshaped
and the vehicle reallocation cost is reduced. We formulate a bi-level optimization problem, with the system revenue maximization and customers' utility maximization as the upper-level
and lower-level problems, respectively. A near-optimal decision policy is derived to
make real-time pricing decisions and maximize the expected long-term system revenue.
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