Electric vehicle charging station locations: Elastic demand, station congestion, and network equilibrium

Huang, Yantao; Kockelman, Kara M.

doi:10.1016/j.trd.2019.11.008

Cited by 156 publications

(57 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The construction of charging stations in the urban area of Wenjiang to provide enough electric power for EVs requires a reasonable selection of some necessary parameters. The parameters were set based on previous research [54,55,60], and the specific values are shown in Table 3. Most charging events start with a 40%-50% SOC [52].…”

Section: Resultsmentioning

confidence: 99%

Electric Vehicle Charging Station Location towards Sustainable Cities

Luo

Qiu

2020

IJERPH

View full text Add to dashboard Cite

Electric vehicles, a significant part of sustainable transport, are attracting increasing attention with the development of sustainable cities. However, as supporting facilities of electric vehicles, public charging stations are of great significance to the promotion of electric vehicles. This paper proposes an electric vehicle charging station location model to improve the resource utilization of electric vehicles for sustainable cities. In this model, reservation services, idle rates during off-peak periods, and waiting time during peak periods are considered. Finally, a case from Chengdu, China, is used to examine the effectiveness of the proposed model. Then, further analyses of reservation ratios and penetration rates are conducted. The results show that the introduction of a reservation service has a positive effect on reducing the total cost, which would provide further support for sustainable cities and have an even greater impact on healthier lives.

show abstract

Section: Resultsmentioning

confidence: 99%

Electric Vehicle Charging Station Location towards Sustainable Cities

Luo

Qiu

2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…The required capacity C of the charging station located by applying ECM to CS placement is equal to C = KT (15) where K = [k 1 , k 2 , ..., k ns ] and T = [T 1 , T 2 , ..., T ns ] and n s is the number of nodes in the graph. Further, K is given by (13) and P is the solution of the following Riccati equation…”

Section: Proposed Cs Sizing Formulation and Solutionmentioning

confidence: 99%

“…The anticipated challenges associated with the increasing number of EVs are long waiting times at public CSs with impacts on actual road traffic patterns and the electricity demand from utility networks. To resolve these challenges, researchers have been investigating various aspects of EV charging including PEV load shifting to address the so called duck curve challenges associated with the rapid increase in demand at sunset ( [12]- [13]).…”

Section: Introductionmentioning

confidence: 99%

A Graph Automorphic Approach for Placement and Sizing of Charging Stations in EV Network Considering Traffic

Parastvand

Moghaddam

Bass

et al. 2020

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

This paper proposes a novel graph-based approach with automorphic grouping for the modelling, synthesis, and analysis of electric vehicle (EV) networks with charging stations (CSs) that considers the impacts of traffic. The EV charge demands are modeled by a graph where nodes are positioned at potential locations for CSs, and edges represent traffic flow between the nodes. A synchronization protocol is assumed for the network where the system states correspond to the waiting time at each node. These models are then utilized for the placement and sizing of CSs in order to limit vehicle waiting times at all stations below a desirable threshold level. The main idea is to reformulate the CS placement and sizing problems in a control framework. Moreover, a strategy for the deployment of portable charging stations (PCSs) in selected areas is introduced to further improve the quality of solutions by reducing the overshooting of waiting times during peak traffic hours. Further, the inherent symmetry of the graph, described by graph automorphisms, are leveraged to investigate the number and positions of CSs. Detailed simulations are performed for the EV network of Perth Metropolitan in Western Australia to verify the effectiveness of the proposed approach.

show abstract

“…In a 2018 user survey, a larger share of users stated that they were stressed by charge queues, than those that said they had range anxiety [5]. There is separate strand of research on charge queues in general [27][28][29][30][31], and more specific areas such as how users react or adapt to charge queues [5,29], on how to build out fast chargers to avoid queues [27,[29][30][31], and on measures to reduce queues [27,30]. There is also research on what users do while fast charging, and in Norway users say the use facilities at the location, they read e-mails and news, use social media, or take a stroll [5].…”

Section: Bmw I3mentioning

confidence: 99%

“…Climatic Effects, i.e., the influence on the vehicles energy consumption SOC and battery temperature of the ambient temperature and the use of winter tires and cabin heating or cooling [3][4][5]7,[16][17][18][19][20][21]24,25]; 4. Network Effects, i.e., how the total charging network offers and the number of other users influence the charge process, and how users need for charging co-varies over time with the risk of charge queues building up, and how charger location and availability of services at the location influence charger use and charge session results [3][4][5][6]10,[13][14][15][27][28][29][30][31]. These four parameters influence the user perception of the charging process, and thus the quality of life with a BEV in general [3][4][5].…”

Section: Framework Of Analysismentioning

confidence: 99%

Battery Electric Vehicle Fast Charging–Evidence from the Norwegian Market

Figenbaum

2020

WEVJ

View full text Add to dashboard Cite

Norway is the largest Battery Electric Vehicle (BEV) market in the world per capita. The share of the passenger vehicle fleet passed 9.4% at the end of 2019, and users have access to 1500 Combined Charging System (CCS)/Chademo standard fast chargers located in more than 500 different locations. This paper analyses the usage pattern of these fast chargers using a dataset from two large operators covering most of their charging events between Q1 2016 and Q1 2018. The target of the analysis was to understand the fundamental factors that drive the demand for fast charging and influences the user experience, so that they can be taken into account when dimensioning charge facilities, and when designing vehicles. The data displays clear variations in charge power, charge time and charged energy between winter and summer, and a large spread of results due to the BEV models different technical characteristics. The charge power is clearly reduced in the winter compared to the summer, while the charge time is longer. Some charge events have a particularly low charge power which may be due to users fast charging a cold battery at a high State of Charge (SOC) in a vehicle with passive battery thermal management.

show abstract

Electric vehicle charging station locations: Elastic demand, station congestion, and network equilibrium

Cited by 156 publications

References 39 publications

Electric Vehicle Charging Station Location towards Sustainable Cities

Electric Vehicle Charging Station Location towards Sustainable Cities

A Graph Automorphic Approach for Placement and Sizing of Charging Stations in EV Network Considering Traffic

Battery Electric Vehicle Fast Charging–Evidence from the Norwegian Market

Contact Info

Product

Resources

About