Determination of the level of service and customer crowding for electric charging stations through fuzzy models and simulation techniques

Andrenacci, Natascia; Genovese, Antonino; Ragona, R.

doi:10.1016/j.apenergy.2017.10.053

Cited by 29 publications

(7 citation statements)

References 30 publications

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“…Almost all possible transportation activities are covered. Big data analytics is used to detect real-time traffic flows (Batty, 2013), avoid traffic congestions (Waller and Fawcett, 2013), navigate ocean vessels (Zhang et al , 2018), forecast train delays (Cerreto et al , 2018), adjust ocean vessel speeds (Kim and Lee, 2018), manage infrastructure maintenance (Chow, 2016), optimize truck fillrates (Ilie-Zudor et al , 2015), increase transport safety (Heilig et al , 2015), control carbon emissions (Hsu et al , 2015), enhance collaborative shipping (Mehmood et al , 2017) locate charging stations (Andrenacci et al , 2017), improve parking policies (Liang et al , 2016b), review the effectiveness of relevant traffic laws (Simandl et al , 2016), forecast demand (van der Laan et al , 2016), design crowdsourcing systems (Ta et al , 2018) and to manage supply chains (Kache and Seuring, 2017).…”

Section: Analysis Of the Technologiesmentioning

confidence: 99%

The impact of emerging and disruptive technologies on freight transportation in the digital era: current state and future trends

Dong

Akram

Andersson

et al. 2021

IJLM

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PurposeWith various challenges in the digital era, stakeholders are expressing growing interests in understanding the impact of emerging and disruptive technologies on freight transportation. This paper provides a systematic literature review of the current state of affairs as well as future trends and aims to support stakeholders' decision-making in logistics management in the era of disruptive technologies.Design/methodology/approachSeveral recent and representative articles from academic, industrial and governmental perspectives were investigated to set the scene for this research and to serve as a baseline for electing nine emerging technologies, which were then used to conduct a systematic literature review covering the literature within the area during the past twelve years.Findings3D printing, artificial intelligence, automated robots, autonomous vehicles, big data analytics, blockchain, drones, electric vehicles and the Internet of Things were identified as the emerging technologies. The current state of existing research and potential future opportunities were analyzed.Research limitations/implicationsSince the potential literature body is almost impossible to fully cover, a tradeoff between the number of emerging technologies and the related literature reviewed has been performed. However, the paper provides a novel approach to select the emerging and disruptive technologies and a systematic literature review to fill the identified research gap in the related literature.Practical implicationsThe research support various stakeholders to better capture the current status of and the future opportunities in freight transportation and gain a clearer understanding of the disruptive technologies as well as to guide them in how to deploy these initiatives in future decision-making.Originality/valueBy providing a systematic literature review on the trends, themes and research opportunities in the era of disruptive technologies, the papers bring about broad and comprehensive review on the impact of disruptive technologies on logistics and transportation as well as opportunities to support management decision support in the logistics industry.

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Section: Analysis Of the Technologiesmentioning

confidence: 99%

The impact of emerging and disruptive technologies on freight transportation in the digital era: current state and future trends

Dong

Akram

Andersson

et al. 2021

IJLM

View full text Add to dashboard Cite

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“…[R3] If the clarity of the information is not clear and the ability of the officers is not competent and the availability of facilities and infrastructure is adequate then service satisfaction is not satisfied [10].…”

Section: Implication Functionmentioning

confidence: 99%

Fuzzy Mamdani Model for Assessing the Level of Service Satisfaction for Requirements of Social Welfare Services at the "Prof. Dr. Soeharso" Integrated Center in Surakarta

Charolina,

Fitriyadi

2024

IJCIS

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Abstract-Service quality is one of the competitive advantages, because good service is one of the basic factors that can affect the comfort level of service recipients. Public services by the government apparatus today are still often found to be weak, so that they cannot meet the quality expected by the community. This study is intended to see how much service satisfaction is, and the effect of service levels on satisfaction levels based on Mamdani Model Fuzzy Inference System Logic. There are three input variables used namely clarity of information, ability of officers and availability of facilities and infrastructure to produce service satisfaction output. Based on the stages using Mamdani Model Fuzzy Inference System Logic starting from the formation of fuzzy sets, application of the implementation function, composition of the rules until the confirmation process (defuzzyfication), it can be proved the correlation between input variables so that it can determine the output of service satisfaction. The results of this study are expected to be used by the agency, as a support system for the decision on the results of the assessment given by the community for perceived services. The future development of this research will be re-tested by adding more variables and an interface will be created to facilitate the processing of the results of the quality assessment of public complaints services.

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“…The implementation detail of the output variable is given in Table 4 and shown in Figure 4d. The fuzzy rules represents a set of process that correlates the degree of memberships of a set of inputs to the degree of memberships of the output variable using IF-THEN logical statements [39]. The set of rules is usually designed according to the expert's knowledge of the problem domain [40].…”

Section: Fuzzification Of Crisp Inputs and Their Fuzzy Membership Fun...mentioning

confidence: 99%

“…This work uses overlapping membership functions for input data and consider the COG method to compute the crisp value for the weight variable. To compute the crisp weight value for the ith EV, the standard equations of the COG method can be utilized as given in Equations ( 38) and (39).…”

Section: The N Input Fuzzy Variables and The Sets Amentioning

confidence: 99%

Fuzzy Logic Weight Based Charging Scheme for Optimal Distribution of Charging Power among Electric Vehicles in a Parking Lot

et al. 2020

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Electric vehicles (EVs) parking lots are representing significant charging loads for relatively a long period of time. Therefore, the aggregated charging load of EVs may coincide with the peak demand of the distribution power system and can greatly stress the power grid. The stress on the power grid can be characterized by the additional electricity demand and the introduction of a new peak load that may overwhelm both the substations and transmission systems. In order to avoid the stress on the power grid, the parking lot operators are required to limit the penetration level of EVs and optimally distribute the available power among them. This affects the EV owner’s quality of experience (QoE) and thereby reducing the quality of performance (QoP) for the parking lot operators. The QoE is represents the satisfaction level of EV owners; whereas, the QoP is a measurement representing the ratio of EVs with QoE to the total number of EVs. This study proposes a fuzzy logic weight-based charging scheme (FLWCS) to optimally distribute the charging power among the most appropriate EVs in such a way that maximizes the QoP for the parking lot operators under the operational constraints of the power grid. The developed fuzzy inference mechanism resolves the uncertainties and correlates the independent inputs such as state-of-charge, the remaining parking duration and the available power into weighted values for the EVs in each time slot. Once the weight values for all EVs are known, their charging operations are controlled such that the operational constraints of the power grid are respected in each time slot. The proposed FLWCS is applied to a parking lot with different capacities. The simulation results reveal an improved QoP comparing to the conventional first-come-first-served (FCFS) based scheme.

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Determination of the level of service and customer crowding for electric charging stations through fuzzy models and simulation techniques

Cited by 29 publications

References 30 publications

The impact of emerging and disruptive technologies on freight transportation in the digital era: current state and future trends

The impact of emerging and disruptive technologies on freight transportation in the digital era: current state and future trends

Fuzzy Mamdani Model for Assessing the Level of Service Satisfaction for Requirements of Social Welfare Services at the "Prof. Dr. Soeharso" Integrated Center in Surakarta

Fuzzy Logic Weight Based Charging Scheme for Optimal Distribution of Charging Power among Electric Vehicles in a Parking Lot

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