A Cognitive Price-Based Approach for Real-time Management of En-route Electric Vehicles

Hariri, Abla O.; Esfahani, Mohammad Mahmoudian; Mohammed, Osama A.

doi:10.1109/itec.2018.8450178

Cited by 11 publications

(11 citation statements)

References 5 publications

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“…The simulation results showed superior performance by reducing the average waiting time than the random and maximum weight-based scheduling schemes. Re-routing of moving EVs towards an appropriate CS based on multi-agents system for distributing the charging load of EVs among multiple geographically dispersed CSs was proposed in [25]. In this scheme, the EV agent was developed through fuzzy logic controller, which was requesting the other high-level agents to provide reservation services for charging.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The RPD input is based on the operating hours (12 h) of the parking lot, which is normalized into 48 time slots such that each time slot represents 15 min. Considering the dynamic behavior of EV owners, the RPD is modeled with three membership functions and is represented with linguistic terms short duration (SD), average duration (AD) and long duration (LD) [25,32]. The linguistic terms SD and LD are implemented as left and right open shoulder membership functions, whereas the term AD is implemented as trapezoidal membership functions using Equations ( 25)- (27).…”

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

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

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

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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“…The authors in [10] proposed a multi-agent scheme for the rerouting of moving EVs towards the appropriate CSs with the aim to distribute the charging load among the dispersed CSs. The multi-agent scheme was built in a bottom-up hierarchical manner where each agent was modeled with different services.…”

Section: Related Workmentioning

confidence: 99%

Two-Stage Fuzzy Logic Inference Algorithm for Maximizing the Quality of Performance under the Operational Constraints of Power Grid in Electric Vehicle Parking Lots

et al. 2020

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The widespread adoption of electric vehicles (EVs) has entailed the need for the parking lot operators to satisfy the charging and discharging requirements of all the EV owners during their parking duration. Meanwhile, the operational constraints of the power grids limit the amount of simultaneous charging and discharging of all EVs. 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 represents a certain percentage of the EV battery required for its next trip distance; whereas, the QoP refers to the ratio of EVs with satisfied QoE to the total number of EVs during the operational hours of the parking lot. This paper proposes a two-stage fuzzy logic inference based algorithm (TSFLIA) to schedule the charging and discharging operations of EVs in such a way that maximizes the QoP for the parking lot operators under the operational constraints of the power grid. The first stage fuzzy inference system (FIS) of TSFLIA is modeled based on the real-time arrival and departure probability density functions in order to calculate the aggregated charging and discharging energies of EVs according to their next trip distances. The second stage FIS evaluates several dynamic and uncertain input parameters from the electric grid and from EVs to distribute the aggregated energy among the EVs by controlling their charging and discharging operations through preference variables. The feasibility and effectiveness of the proposed algorithm are demonstrated through the IEEE 34-node distribution system.

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“…A multiagent based scheme for rerouting of EVs to the charging station according to the real-time price was presented in Ref. [14]. The main objective was to route and charge EVs at a CS within their power limit.…”

Section: Related Work a Recent Literaturementioning

confidence: 99%

Efficient Power Management Algorithm Based on Fuzzy Logic Inference for Electric Vehicles Parking Lot

2019

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Smart grid is expected to support electric vehicles parking lots with the existing power line infrastructure. In order to support all electric-vehicles (EVs) users to complete their charging needs before leaving the parking lot, the power grid requires that the charging demands of EVs should be within the allowable power limit to avoid the grid overloading. This paper proposes a fuzzy logic inference based algorithm (FLIA) to manage the available power efficiently for EVs in the parking lot. The problem is mathematically formulated and solved by the credibility of the fuzzy inference mechanism to control charging and discharging of the EVs. The key idea is to introduce the fuzzy inference mechanism that evaluates several uncertain input parameters from the electric grid and from EVs to obtain an adequately accurate charging or discharging decision for each of the connected EVs. The proposed scheme is applied to a parking lot with different parking capacities and compared with the conventional-based systems. The simulation results demonstrated the feasibility and effectiveness of the proposed algorithm when dealing with the available power management and satisfying the EV user's requirements. INDEX TERMS Electric vehicles, fast charging stations, fuzzy logic inference, parking lot, power management.

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A Cognitive Price-Based Approach for Real-time Management of En-route Electric Vehicles

Cited by 11 publications

References 5 publications

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

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

Two-Stage Fuzzy Logic Inference Algorithm for Maximizing the Quality of Performance under the Operational Constraints of Power Grid in Electric Vehicle Parking Lots

Efficient Power Management Algorithm Based on Fuzzy Logic Inference for Electric Vehicles Parking Lot

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