Optimal Locating of Electric Vehicle Charging Stations by Application of Genetic Algorithm

Akbari, Milad; Brenna, Morris; Longo, Michela

doi:10.3390/su10041076

Cited by 49 publications

(30 citation statements)

References 26 publications

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“…Service Area 4 (5), (6), (7), (8), (9), (10), (12), (16) 10 (1), (2), (3), (4), (17), (18), (19), (20), (21)…”

Section: Evcs Numberunclassified

“…Therefore, promoting the development of EVs is critical to deal with climate change challenges and achieve sustainable transport [16,17]. As an important guarantee for the development of EVs, the development level of electric vehicle charging stations (EVCS) has a direct impact on the development speed and quality of the electric vehicle industry [18][19][20]. However, the shortage of EVCS has become one of the important factors hindering the development of the electric vehicle industry [21].…”

Section: Introductionmentioning

confidence: 99%

“…, (6),(7),(8),(9),(10),(12), (16)10(1), (2), (3), (4),(17),(18),(19),(20),(21),(22),(25)13(23),(24),(26),(27),(28),(43),(44),(45),(46),(47),(48),(49),(50)14(11),(12),(14),(15), (29), (30), (31), (32), (34), (35), (36) 17 (33), (37), (38), (39), (40), (41), (42), (51), (52), (53) Int. J. Environ.…”

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confidence: 99%

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Electric Vehicle Charging Station Location towards Sustainable Cities

Luo

Qiu

2020

IJERPH

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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.

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“…Service Area 4 (5), (6), (7), (8), (9), (10), (12), (16) 10 (1), (2), (3), (4), (17), (18), (19), (20), (21)…”

Section: Evcs Numberunclassified

Section: Introductionmentioning

confidence: 99%

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Electric Vehicle Charging Station Location towards Sustainable Cities

Luo

Qiu

2020

IJERPH

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“…The charging infrastructure industry is at a nascent stage and research on the risk identification of charging infrastructure projects is limited [8]. Currently, there are three points focused on charging infrastructure: (i) site selection of charging facilities [9]; (ii) business model for charging infrastructure [10,11], the PPP model [3]; and (iii) incentive industry policies [12][13][14]. Although the PPP model is viewed as an effective means to promote the sustainable development of charging facilities [5], the EVCI-PPP project is in the initial stages in China and faces various uncertain risks.…”

Section: Introductionmentioning

confidence: 99%

Risk Identification and Analysis for PPP Projects of Electric Vehicle Charging Infrastructure Based on 2-Tuple and the DEMATEL Model

Zhang

Zhao

Chai

et al. 2019

WEVJ

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Risk management is critical to the success of electric vehicle charging infrastructure public-private partnership (EVCI-PPP) projects, as risks are present throughout the whole life cycle of projects. However, in EVCI-PPP projects, risk factors are often interdependent and, consequently, the interrelationships among factors affect the risk management, which is ignored in the existing studies. To identify the risk factors of EVCI-PPP projects and analyze their internal influence relations, this paper develops a risk identification and analysis model of EVCI-PPP projects based on the 2-tuple linguistic representation model and the decision-making trial and evaluation laboratory (DEMATEL) model. First, a risk factor set is established including 22 criteria involved in 5 dimensions of political/legal risk, economic/market risk, social/environment risk, project/technical risk, and managing risk. Next, the 2-tuple model is introduced to integrate the decision makers' evaluation information in a linguistic environment, and the direct relation matrix is calculated. Then, the cause-effect relations and a significant degree of risk factors are interpreted using the extended DEMATEL technique. The results show that economic/market risk is the most significant factor of EVCI-PPP projects, and 22 criteria are classified into 14 cause factors and 8 effect factors. Finally, suggestions are provided for decision-makers to ensure the success of EVCI-PPP projects.

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“…There are two ways to solve such a problem. One is to speed up the pace of construction of public charging stations [1,2], which involves consideration of the number and location of public charging stations [2][3][4][5]. The other is to utilize existing public charging stations effectively, guiding EVs to be charged with minimum time cost, avoiding congestion on roads and in stations.…”

Section: Introductionmentioning

confidence: 99%

Congestion Control in Charging Stations Allocation with Q-Learning

Zhang¹,

Gong²,

Xu³

2019

Sustainability

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Navigation systems can help in allocating public charging stations to electric vehicles (EVs) with the aim of minimizing EVs’ charging time by integrating sufficient data. However, the existing systems only consider their travel time and transform the allocation as a routing problem. In this paper, we involve the queuing time in stations as one part of EVs’ charging time, and another part is the travel time on roads. Roads and stations are easily congested resources, and we constructed a joint-resource congestion game to describe the interaction between vehicles and resources. With a finite number of vehicles and resources, there exists a Nash equilibrium. To realize a self-adaptive allocation work, we applied the Q-learning algorithm on systems, defining sets of states and actions in our constructed environment. After being allocated one by one, vehicles concurrently requesting to be charged will be processed properly. We collected urban road network data from Chongqing city and conducted experiments. The results illustrate the proposed method can be used to solve the problem, and its convergence performance was better than the genetic algorithm. The road capacity and the number of EVs affected the initial of Q-value, and not the convergence trends.

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Optimal Locating of Electric Vehicle Charging Stations by Application of Genetic Algorithm

Cited by 49 publications

References 26 publications

Electric Vehicle Charging Station Location towards Sustainable Cities

Electric Vehicle Charging Station Location towards Sustainable Cities

Risk Identification and Analysis for PPP Projects of Electric Vehicle Charging Infrastructure Based on 2-Tuple and the DEMATEL Model

Congestion Control in Charging Stations Allocation with Q-Learning

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