Optimal charging facility location and capacity for electric vehicles considering route choice and charging time equilibrium

Chen, Rui; Qian, Xinwu; Liu, Miao; Ukkusuri, Satish V.

doi:10.1016/j.cor.2019.104776

Cited by 86 publications

(33 citation statements)

References 42 publications

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“…They formulated EV charging navigation as a Markov Decision Process with an unknown transition probability. Chen et al proposed a bi-level mathematical model to derive optimal design that minimize the joint cost of facility constructions and EV drivers' travel and waiting time over the network [36]. Basso et al proposed a probabilistic energy consumption model with machine learning that can estimate the expected energy and variance for the road links, paths, and routes [37].…”

Section: Related Workmentioning

confidence: 99%

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

Huang¹,

Hu²,

Ho³

et al. 2021

IJCIS

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Traffic congestion in urban areas has become a critical problem that municipal governments cannot overlook. Meanwhile, mixed traffic systems containing both autonomous and human-driven electric vehicles ramp up the challenge for traffic management in urban areas. Although numerous researchers have proposed traffic control heuristics to alleviate traffic congestion problems in the recent literature, scant research has addressed the joint problems of route and charging strategies for electric vehicles along with urban traffic congestion prevention. Accordingly, this work tackles the complex task of traffic management in urban areas during peak periods by using practical congestion prevention strategies that consider the characteristics of mixed traffic flows and the charging demands of electric vehicle users. Notably, we apply support vector regressions to compute the charging time at each charging point and the traverse time of an electric vehicle at each road segment/intersection, based on historical traffic data. The simulation results reveal that the proposed algorithms are feasible because they can avoid possible occurrences of traffic congestion during rush hours and provide the routes and charging options that are chosen by electric vehicle users.

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Section: Related Workmentioning

confidence: 99%

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

Huang¹,

Hu²,

Ho³

et al. 2021

IJCIS

View full text Add to dashboard Cite

show abstract

“…Parvasi et al [45] established a bi-level model to study the efficient transportation system considering the response of students and designed appropriate bus stops and bus routes. Li et al [46,47] used bi-level programming to study the location strategy of charging infrastructure and designed a two-stage heuristic algorithm to solve the problem. To ensure coordination and consider the interests of bus companies and passengers simultaneously, Cheng et al [48] adopted the bi-level programming approach to solve the problem of optimal bus stop locations.…”

Section: Application Of Bi-level Programming In the Lrpmentioning

confidence: 99%

A Bi-Level Programming Approach to the Location-Routing Problem with Cargo Splitting under Low-Carbon Policies

2021

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To identify the impact of low-carbon policies on the location-routing problem (LRP) with cargo splitting (LRPCS), this paper first constructs the bi-level programming model of LRPCS. On this basis, the bi-level programming models of LRPCS under four low-carbon policies are constructed, respectively. The upper-level model takes the engineering construction department as the decision-maker to decide on the distribution center’s location. The lower-level model takes the logistics and distribution department as the decision-maker to make decisions on the vehicle distribution route’s scheme. Secondly, the hybrid algorithm of Ant Colony Optimization and Tabu Search (ACO-TS) is designed, and an example is introduced to verify the model’s and algorithm’s effectiveness. Finally, multiple sets of experiments are designed to explore the impact of various low-carbon policies on the decision-making of the LRPCS. The experimental results show that the influence of the carbon tax policy is the greatest, the carbon trading and carbon offset policy have a certain impact on the decision-making of the LRPCS, and the influence of the emission cap policy is the least. Based on this, we provide the relevant low-carbon policies advice and management implications.

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“…Wang Jingmin [7] proposed a new hybrid model, which is used to determine the location of charging stations, using a mobile refueling location model and a queuing theory model, and a robust optimization algorithm to solve the location model. Chen Rui [8] proposed a two-layer mathematical model to derive the optimal design. This method solves the balance problem between path selection and charging waiting time.…”

Section: Introductionmentioning

confidence: 99%

Location and Capacity Manufacturing Calculation of Electric Vehicle Charging Station Based on Queuing Theory

Mao¹

2021

converter

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With the rapid development of industry and manufacturing, the world vigorously promotes green environmental protection, the construction of charging station is particularly important. It is urgent to study the location and capacity of charging station. In this paper, we will analyze the charging behavior of electric vehicles, and combined with the queuing theory model of electric vehicles, optimize the number of chargers by using the minimum objective function of the comprehensive cost of building charging stations. According to the maximum access power allowed by the distribution network, the service radius and scale of the target charging station are finally determined, and then 0-1 planning is used to finally determine the construction site. At the end, we give a specific example analysis to verify the effectiveness of the research results.

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Optimal charging facility location and capacity for electric vehicles considering route choice and charging time equilibrium

Cited by 86 publications

References 42 publications

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

A Bi-Level Programming Approach to the Location-Routing Problem with Cargo Splitting under Low-Carbon Policies

Location and Capacity Manufacturing Calculation of Electric Vehicle Charging Station Based on Queuing Theory

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