A novel multi-criteria decision-making method for selecting the site of an electric-vehicle charging station from a sustainable perspective

Feng, Jianghong; Xu, Su Xiu; Li, Ming

doi:10.1016/j.scs.2020.102623

Cited by 66 publications

(19 citation statements)

References 48 publications

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“…Moreover, the ranking orders of the four EVCS alternatives obtained by the developed framework are slightly different with those computed by the SVN-TOPSIS method. Similarly, we have also compared the developed methodology with extant approaches given by Guo and Zhao, 7 Ju et al 1 and Feng et al 5 In comparison with Guo and Zhao, 7 Ju et al 1 and Feng et al, 5 the SVN-ARAS framework applied in this study is more comprehensive in handling the EVCS selection problems with uncertain, inconsistent and indeterminate information. Here, we present the advantages of the proposed SVN-ARAS framework, given as (i) The proposed approach develops the methodology using the SVNSs, unlike Guo and Zhao, 7 Feng et al, 5 wherein the FSs have been employed, a particular case of the SVNSs and in Ju et al, 1 the PiFSs have been employed, also a particular case of the SVNSs.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…Thus, the present method is more appropriate for handling the uncertain, indeterminacy, and inconsistent information. (ii) In our approach, the weights of DMEs are obtained using developed formula resulting in more realistic outcomes unlike randomly chosen weights in Guo and Zhao, 7 Nancy and Garg, 67 Ju et al, 1 Feng et al 5 (iii) The attribute weights in our methodology are obtained through the SVN-SOWIA, which can overcome the inconsistency occur either in objective weighting process or subjective weighting process, therefore, the obtained results are more accurate and optimal, whereas in Guo and Zhao, 7 the objective criteria weights were estimated by linguistic fuzzy aggregated operator on Triangular fuzzy numbers, while in Ju et al, 1 the subjective weights of criteria were computed by AHP process, while in Nancy and Garg, 67 the objective weights of attributes were obtained by divergence measure-based procedure, in Feng et al, 5 the objective weights of attributes were obtained by linguistic entropy weighting procedure. (iv) The developed SVN-ARAS method only finds the ideal solution, while the SVN-TOPSIS, 67 fuzzy TOPSIS, 7 and picture fuzzy GRA techniques need to determine the ideal and the anti-ideal solutions.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…Thus, it is very important to develop a suitable technique to assess and choose the appropriate EVCS site. 5 Sustainability is defined as long-term development which integrates social, economical, and environmental dimensions. 6 The social dimension of EVCS site selection includes the management of charging station with the augmentation of city road network, traffic expediency, service capacity, and consequence on community's life.…”

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confidence: 99%

“…Further, Yang et al 12 designed a decision support system for determining the desirable candidate locations for EV fast charging stations. Feng et al 5 constructed a hybrid MCDM technique for the evaluation of EVCS sites from sustainable perspectives.…”

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confidence: 99%

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Single‐valued neutrosophic similarity measure‐based additive ratio assessment framework for optimal site selection of electric vehicle charging station

Mishra¹,

Rani

Saha

2021

Int J Intell Syst

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Sustainable site selection for electric vehicle charging station (EVCS) is a significant process in the promotion of electric vehicle system development. The assessment and selection of suitable EVCS site is a very critical decision, involving complexity due to the presence of several associated criteria. Furthermore, uncertainty is an inevitable component of the information in the decision-making procedure and its significance in the selection process is relatively high and needs to be cautiously measured. Single-valued neutrosophic set (SVNS) is one of the valuable and flexible tools for handling such type of uncertain information arising in multi-criteria decision-making (MCDM) applications. Thus, the objective of this study is to introduce novel single-valued neutrosophic information-based additive ratio assessment (ARAS) approach for evaluating and prioritizing the sustainable EVCS sites. In this method, novel single-valued subjective and objective weighted integrated approach (SVN-SOWIA) is developed to compute the criteria by aggregating the objective weights resulted from a similarity measure-based procedure and the subjective weights given by the experts. For this purpose, an innovative similarity measure is proposed for SVNSs.To display the performance of the present methodology, a computational study of EVCS sites evaluation is conferred under single-valued neutrosophic environment. Comparative and sensitivity analyses are further performed to verify the strength of the developed approach. The outcome illustrates EVCS site EvUrjaa-Electric Vehicle Charging Station is the most optimal EVCS site in Indore region, India. Also, the environmental (0.324) and social (0.273) criteria are more important than technological (0.236) and economical (0.167) criteria in assessing the EVCS sites. The sensitivity analysis outcomes signify the EVCS option EvUrjaa-Electric Vehicle Charging Station always acquires its highest ranking in spite of how sub-criteria weights fluctuate. The outcome of this study indicates that the developed approach can suggest more realistic performance under uncertain environment and therefore, provides a wide range of applications. K E Y W O R D Sadditive ratio assessment, electric vehicle charging station, multi-criteria decision-making, similarity measure, single-valued neutrosophic set | INTRODUCTIONDue to the rapid urbanization and depletion of natural resources, environmental degradation is a vital issue in today's world. 1 Rapid growth in population, urban development, modernization of agriculture, rapid industrialization, multiplicity of the means of transport, and deforestation are main causes of the environmental degradation. Energy is currently a crucial constraint on transport and transport is a foremost determinant of energy demand. To reduce air pollution and protect the environment, several countries have implemented different strategies to promote electric vehicle (EV) production. 2 EV is regarded as an innovative eco-friendly means of transportation which encourages the secu...

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Section: Comparison and Discussionmentioning

confidence: 99%

Section: Comparison and Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Single‐valued neutrosophic similarity measure‐based additive ratio assessment framework for optimal site selection of electric vehicle charging station

Mishra¹,

Rani

Saha

2021

Int J Intell Syst

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“…For example, Javid et al [52] found that PHEV adoption rate is positively correlated with per capita charging infrastructure. Other studies assumed the benefits of a widespread or properly distributed charging station network and proposed innovative approaches in management to optimize cost and the charging experience [53][54][55][56][57], which is hypothesized to retain and attract EV drivers. Respondents with pro-environmental self-identification are more inclined to have a positive perception of EVs [50,58].…”

Section: Factors Influencing New Energy Vehicle Purchasementioning

confidence: 99%

Determining the Factors That Influence Electric Vehicle Adoption: A Stated Preference Survey Study in Beijing, China

Ling

Cherry

2021

Sustainability

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The transition from conventional vehicles (CVs) to electric vehicles (EVs) could be promising in tackling environmental challenges in China. Using a sample of 1216 respondents in Beijing, China, our study intends to understand the underlying factors that drive the decision to purchase an EV among potential Chinese vehicle purchasers. We built two choice models to estimate vehicle purchase behavior and fuel choice. We found that males and having higher household income are associated with greater intention to purchase EVs (both plug-in and battery electric vehicles). However, a previous inclination to choose CV negatively impacted willingness to buy EVs. Between specific EV types, we found that Plug-in Hybrid EV (PHEV) purchase was negatively associated with plans to obtain a driver’s license within three years and longer durations of having owned a motorized vehicle first. Yet, the number of electric bicycles in the household was positively associated with PHEV-purchase likelihood. For Battery EVs (BEV), we found that respondents who had previous experience with an EV (either as a driver or passenger) were more likely to purchase a BEV while existing ownership of a driver’s license and a higher purchase budget reduced such possibility. Based on our findings, we recommend authorities continue to, or increasingly, provide direct monetary incentives to purchase EVs, and to provide EV driving and riding experience to customers, especially who are in the middle- and low-income vehicle purchasing groups, to improve the Chinese EV market relative to CVs.

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Recent advances of optimal sizing and location of charging stations: A critical overview

Yang

Shu

et al. 2022

Intl J of Energy Research

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Summary With the rapid development of the global economy and technology, the demand for fossil fuels in transportation and power industries is ever‐growing, which inevitably leads to lower fossil fuel storage and higher prices. Electric vehicles (EVs) are widely used as an effective solution to energy shortage and environmental pollution. However, there exist numerous obstacles, such as unmatchable capacity and unsuitable location. Therefore, an appropriate size and location of the charging station (CS) are very crucial. Based on a thorough investigation of 138 literature, this paper comprehensively summarizes the past from three aspects including charging technology, CS model, and methodologies. Besides, a comprehensive summary of the charging way and its level is proposed for a more overall analysis with enriched technologies diversity. Furthermore, two different CS models are discussed detailly in the aspects of the objective function, factors, advantages and disadvantages, applicability, and complexity. Additionally, a detailed summary of three methodologies, including meta‐heuristic, multi‐criteria decision making (MCDM), and analytical methods, are proposed and a comprehensive evaluation and their advantages/disadvantages, applicability, and complexity are summarized and assessed thoroughly. Finally, 11 valuable and insightful findings are drawn and two promising challenges, two opportunities, and three future directions are proposed for further research. Highlights Key technical difficulties and future challenges of sizing and placement of charging stations (CSs) are discussed. Most representative papers are selected and presented in table form for readers to compare more conveniently. Two different kinds of models and evaluation indexes are introduced in detail and each model is compared and evaluated fairly from different angles. Thirty‐three methods of sizing and location determination are summarized and compared strictly, and the advantages and disadvantages, applicability, and complexity of each method are dedicated to being described. Eleven main findings and seven constructive suggestions are put forward because of the development status of electric vehicles.

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A novel multi-criteria decision-making method for selecting the site of an electric-vehicle charging station from a sustainable perspective

Cited by 66 publications

References 48 publications

Single‐valued neutrosophic similarity measure‐based additive ratio assessment framework for optimal site selection of electric vehicle charging station

Single‐valued neutrosophic similarity measure‐based additive ratio assessment framework for optimal site selection of electric vehicle charging station

Determining the Factors That Influence Electric Vehicle Adoption: A Stated Preference Survey Study in Beijing, China

Recent advances of optimal sizing and location of charging stations: A critical overview

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