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Modeling and Optimization of Electric Vehicle Networks in Future Sustainable Cities.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling and Optimization of Electric Vehicle Networks in Future Sustainable Cities./
Author:	Ni, Liang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	118 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
Subject:	Motivation. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29003126
ISBN:	9798209784616

Modeling and Optimization of Electric Vehicle Networks in Future Sustainable Cities.
Ni, Liang.

Modeling and Optimization of Electric Vehicle Networks in Future Sustainable Cities. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 118 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2021.

This item must not be sold to any third party vendors.

Motivated by the increasing concern of environmental pollution and fossil fuel shortage,transportation electrification, namely the process of integrating a large fleet of public andprivate electric vehicles (EVs) into the transportation system, is conceived to be one ofthe promising solutions in future sustainable cities. Enormous efforts have been madeon modeling and optimizing EV networks and their extensions. Here, an EV network isan integration system of EVs and EV refueling stations, where EVs can get refueled instations 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 ofEVs' refueling load on power grids and meanwhile satisfy EVs' quality-of-service. Second,considering EV networks as mobility service providers, efficient mobility management isneeded to accommodate temporally and spatially different mobility demand from passengers.This thesis develops three distinct real-time decision-making models and algorithmsto 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 EVrefueling 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 directlymanaged by a system operator to provide mobility services. EVs can be dispatched toserve passengers' individual mobility demand or reallocated to accommodate unbalanceddemand in different locations. In addition to that, we also make recharging decisionsto refuel EVs and keep their energy levels. An efficient decision policy is derived toaccommodate 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 tomake different mobility choices. In this way, passengers' traveling demand can be reshapedand 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-leveland lower-level problems, respectively. A near-optimal decision policy is derived tomake real-time pricing decisions and maximize the expected long-term system revenue.

ISBN: 9798209784616Subjects--Topical Terms:

532704
Motivation.

Modeling and Optimization of Electric Vehicle Networks in Future Sustainable Cities.
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245 1 0 $a Modeling and Optimization of Electric Vehicle Networks in Future Sustainable Cities.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 118 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
500 $a Advisor: Tsang, Danny H. K. .
502 $a Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Motivated by the increasing concern of environmental pollution and fossil fuel shortage,transportation electrification, namely the process of integrating a large fleet of public andprivate electric vehicles (EVs) into the transportation system, is conceived to be one ofthe promising solutions in future sustainable cities. Enormous efforts have been madeon modeling and optimizing EV networks and their extensions. Here, an EV network isan integration system of EVs and EV refueling stations, where EVs can get refueled instations 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 ofEVs' refueling load on power grids and meanwhile satisfy EVs' quality-of-service. Second,considering EV networks as mobility service providers, efficient mobility management isneeded to accommodate temporally and spatially different mobility demand from passengers.This thesis develops three distinct real-time decision-making models and algorithmsto 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 EVrefueling 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 directlymanaged by a system operator to provide mobility services. EVs can be dispatched toserve passengers' individual mobility demand or reallocated to accommodate unbalanceddemand in different locations. In addition to that, we also make recharging decisionsto refuel EVs and keep their energy levels. An efficient decision policy is derived toaccommodate 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 tomake different mobility choices. In this way, passengers' traveling demand can be reshapedand 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-leveland lower-level problems, respectively. A near-optimal decision policy is derived tomake real-time pricing decisions and maximize the expected long-term system revenue.
590 $a School code: 1223.
650 4 $a Motivation. $3 532704
650 4 $a Electric vehicles. $3 1613392
650 4 $a Optimization. $3 891104
650 4 $a Decision making. $3 517204
650 4 $a Automobiles. $3 560291
650 4 $a Design. $3 518875
650 4 $a Travel. $3 585743
650 4 $a Energy. $3 876794
650 4 $a Algorithms. $3 536374
650 4 $a Computer science. $3 523869
650 4 $a Recreation. $3 535376
650 4 $a Transportation. $3 555912
690 $a 0791
690 $a 0389
690 $a 0984
690 $a 0814
690 $a 0709
710 2 $a Hong Kong University of Science and Technology (Hong Kong). $3 1022235
773 0 $t Dissertations Abstracts International $g 83-09B.
790 $a 1223
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29003126