Design of a Mobile Charging Service for Electric Vehicles in an Urban Environment

Huang, Shisheng; He, Liang; Gü, Yü; Wood, Kristin L.; Benjaafar, Saif

doi:10.1109/tits.2014.2341695

Cited by 78 publications

(58 citation statements)

References 23 publications

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“…The subset F 0 = {γ + βα + 1, · · · , γ + (β + 1)α } of F is the dummy nodes of depot. In Figure 2, β = 4 and α = 2, so F = {11, 12,13,14,15,16,17,18,19, 20}, F = {11, 13, 15, 17, 0}, and F 0 = {19, 20}, which is not marked in Figure 2.…”

Section: Problem Descriptionmentioning

confidence: 98%

“…Compared with the traditional gasoline vehicles, it is not easy to refill the power of BEVs. Therefore, Huang et al [17] presented a new charging service method where, according to the charging service request sent by the BEV, the mobile charging vehicle (MCV) arrives at the appointed place and provides a charging service for the car. The charging service requests can be sent via apps on smartphones, like Eyuechongdian.…”

Section: Motivationmentioning

confidence: 99%

“…To the extent of our knowledge, until now, there has only been one study focused on the mobile charging service for BEVs, which was conducted by Huang et al [17]. In the study, a queuing based analytical approach with a strategy called the 'nearest job next' was provided.…”

Section: Contribution Of This Papermentioning

confidence: 99%

“…S1 (11) S4 (17) C10 (10) C4 (4) C7 (7) C8 (8) C9 (9) S3 (16) C6 (6) C1 (1 For the convenience of model building, the definition of some sets is stated here. For the depot, we set both 0 and + 1 to denote it, which is the same node.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Because, in the early assumptions, all of the vehicles are full when they leave the depot {0}, therefore Constraint (16) and Constraint (17) are different from the power when leaving the node where the electricity leaving node is equal to the sum of the remaining charge level, y i , and the recharging amount, F i , in Constraint (16), while equaling the capacity of battery Q in Constraint (17). Constraint (18) suggests that the MCVs can only recharge at the nodes with a charging station.…”

Section: Optimization Frameworkmentioning

confidence: 99%

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Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles

2018

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Abstract:Increasing attention is being paid to the technology of battery electric vehicles (BEVs) because of their environmental friendliness. However, their short range, extended recharging times, and insufficient charging facilities hinder the improvement in the market share of BEVs. As a remedy, this paper presents a novel approach to providing a service for the battery charge replenishment of BEVs. Instead of using traditional alternative methods by only providing a charging service in a fixed location, such as battery-swapping and charging lanes, the novel charge replenishment is provided by mobile charging vehicles (MCVs), which could offer a charging service at any time and at location requested. To consider the limited running range and the opportunity to recharge from MCVs, as well as to determine the location strategy of multiple types of plug-in charging facility locations and the routing plan of the MCVs simultaneously, the location routing problem (LRP) that can integrate two decision levels, with a strategic level (location) and tactical level (routing), is applied. Then, we present the multiple types of plug-in charging facilities' location-routing problem with time windows for mobile charging vehicles (MTPCF-LRPwTW-MCVs), and formulate the MTPCF-LRPwTW-MCVs as a mixed integer linear program for the convenience of solving. To demonstrate the model, test instances are designed and computational results are presented. Furthermore, sensitivity analyses on battery capacity, recharging rate, and so on, are also examined. The results show that with the increase of the battery capacity or the improvement of the charging rate of the charging facilities, the service efficiency of the MCVs can reasonably be improved. Therefore, the proposed method could be used in real world problems.

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Section: Problem Descriptionmentioning

confidence: 98%

Section: Motivationmentioning

confidence: 99%

Section: Contribution Of This Papermentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Optimization Frameworkmentioning

confidence: 99%

See 3 more Smart Citations

Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles

2018

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Mobile battery‐integrated charging station for reducing electric vehicles charging queue and cost via renewable energy curtailment recovery

Saboori

Jadid

2021

Intl J of Energy Research

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Summary Today, charging electric vehicles (EVs) from the utility grid has resulted in high electricity costs besides enhanced emissions. On the other hand, increased renewable energy penetration has caused curtailing a considerable share of the produced energy. This paper aims at bridging the gap between these challenges utilizing mobile charging stations (MCS). Integrated with battery energy storage, the MCS shifts the curtailed renewable energy spatially and temporally for EV charging. To this end, a novel model is proposed for joint MCS spatio‐temporal status and battery power‐energy scheduling in the presence of fixed charging stations (FCSs). Besides, a new paradigm is introduced for EV charging management and queuing in both MCS and FCS. The excess curtailed renewable energy is stored in the MCS battery for EV charging later and at a different location. The numerical testing of the model denotes a 9.63% reduction in the daily operation cost. Besides, wind and solar energy curtailment are reduced by 15.60% and 54.60%, respectively. Additionally, the EV charging queue represents a 90% reduction while FCS empty sockets increase by 80%. The benefits for the grid include peak shaving, improved voltages, and increased renewable penetration.

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Optimal Deployment and Scheduling of a Mobile Charging Station in the Internet of Electric Vehicles

Wang

et al. 2022

Wireless Algorithms, Systems, and Applications

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Design of a Mobile Charging Service for Electric Vehicles in an Urban Environment

Cited by 78 publications

References 23 publications

Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles

Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles

Mobile battery‐integrated charging station for reducing electric vehicles charging queue and cost via renewable energy curtailment recovery

Optimal Deployment and Scheduling of a Mobile Charging Station in the Internet of Electric Vehicles

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