Two-stage charging strategy of plug-in electric vehicles based on fuzzy control

Bandpey, M. Fattahi; Firouzjah, Khalil Gorgani

doi:10.1016/j.cor.2017.07.014

Cited by 24 publications

(19 citation statements)

References 23 publications

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“…The objective of controlling the charging and discharging rates is to provide the exact power to the grid respecting voltage, load constraints, and battery characteristics. Uncoordinated charging of batteries threatens the distribution system and it can lead to power outage and undesirable voltage sag [19]. Hence, the real time smart control charging strategy maximizes customer satisfaction and it adjusts both the energy that is delivered by the grid to the battery and the energy that is injected on the grid from the battery, with fair power allocation.…”

Section: T C O ( € ) V D ( % )mentioning

confidence: 99%

“…Furthermore, controlling the charging and discharging of batteries is required to maintain the voltage level and stability of the power system. In this regard, Bandpey et al [19] used a Fuzzy Logic Controller (FLC) to smooth the load profile and obtain an optimum charging strategy when considering the State of Charge (SoC) and the Urgency Level (UL) as inputs, which refers to battery charging urgency, and as output the Preference Factor (PF), which indicates the batteries charging/discharging priority. Moreover, Suresh et al [20] developed a battery capacity fade minimization model by introducing the model predictive control (MPC) framework, which has also been developed in [21], for the identification and realization of optimal charge-discharge cycles for Lithium-ion batteries.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

et al. 2019

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This paper proposes an optimal design for hybrid grid-connected Photovoltaic (PV) Battery Energy Storage Systems (BESSs). A smart grid consisting of PV generation units, stationary Energy Storage Systems (ESSs), and domestic loads develops a multi-objective optimization algorithm. The optimization aims at minimizing the Total Cost of Ownership (TCO) and the Voltage Deviation (VD) while considering the direct and indirect costs for the prosumer, and the system stability with regard to intermittent PV generation. The optimal solution for the optimization of the PV-battery system sizing with regard to economic viability and the stability of operation is found while using the Genetic Algorithm (GA) with the Pareto front. In addition, a fuzzy logic-based controller is developed to schedule the charging and discharging of batteries while considering the technical and economic aspects, such as battery State of Charge (SoC), voltage profile, and on/off-peak times to shave the consumption peaks. Thus, a hybrid approach that combines a Fuzzy Logic Controller (FLC) and the GA is developed for the optimal sizing of the combined Renewable Energy Sources (RESs) and ESSs, resulting in reductions of approximately 4% and 17% for the TCO and the VD, respectively. Furthermore, a sensitivity cost-effectiveness analysis of the complete system is conducted to highlight and assess the profitability and the high dependency of the optimal system configuration on battery prices.

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Section: T C O ( € ) V D ( % )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

et al. 2019

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“…The invention of the first model of an electric vehicle has been attributed to different people. In 1828, Ányos István Jedlik invented the first model of a electric motor and designed a small electric vehicle using his new motor [6]. In 1834, Thomas Davenport who was a blacksmith from Vermont invented a similar device that worked on a short electric route.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In 1834, Thomas Davenport who was a blacksmith from Vermont invented a similar device that worked on a short electric route. In 1835, Professor Sibrandus Stratingh and his assistant developed a small electric machine powered by primary cells [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis and Designing of a Wireless Charging System for Electric Vehicles Using the Topology of Double Sided llC Compensator

Moghaddam¹,

Khademi²,

Baygi³

et al. 2019

Adv. Sci. Technol. Res. J.

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The purpose of the present study was to simulate the equivalent circuit in the MATLAB software in order to implement the desired relationships for both continuous-conduction mode (CCM) and discontinuous-conduction mode (DCM), and to obtain the power and efficiency values at different frequencies. Then, it was necessary to optimize the effective values on the power by Particle Swarm Optimization (PSO) algorithm. After optimization, the optimization and pre-optimization results were compared and, if post-optimization results were not desirable, effective parameters should be reviewed before the optimization stage and the tunable parameters should be changed to achieve the desired results. This process will continue to obtain the optimization results. The results show that the highest efficiency is 98% in the DCM mode and 95% in the CCM mode. In both methods, we achieved more favorable results than the with the PSO method. However, the DCM mode provides an improvement which is about 2.6% higher than CCM.

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“…In addition, fuzzy-based algorithms were used in subjects such as energy cost, consumption and peak load reduction of smart houses [9]. There were also studies using fuzzy logic models for EV charging management [10][11][12]. The main goal of this study is to develop a fuzzy-based smart charging management system to enable more economic benefit to the EV PL from the roof-top PV system.…”

Section: Introductionmentioning

confidence: 99%

The Fuzzy-Based Smart Charging Management System for an Electric Vehicle Parking Lot Including a Roof-Top Pv System

Güner

Kir

2020

Mugla Journal of Science and Technology

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Electric vehicles (EVs) and photovoltaic (PV) systems are one of the most appropriate coupled facilities with each other in smart grids. The goal of this study is to develop a fuzzy-based smart charging management system (SCMS) in order to enable more economic benefits to the EV parking lot from the roof-top PV system under market conditions with dynamic pricing. To achieve this, PV model, parking lot model and energy market model are generated and they are considered as the inputs of the SCMS. The SCMS decides whether the energy generated by the PV system is injected into the grid or used for charging electric vehicles. All models are generated using real system data in this study. Furthermore, using bi-facial PV panels in the PV system simulation is one of the novel approaches of this study. Charging EVs in the PLs from renewable energy resources is very important as it will facilitate the management of this mobile load in the near future. The results of this study will both help to manage the EV load and increase the profit of the EV PL.

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Two-stage charging strategy of plug-in electric vehicles based on fuzzy control

Cited by 24 publications

References 23 publications

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

Analysis and Designing of a Wireless Charging System for Electric Vehicles Using the Topology of Double Sided llC Compensator

The Fuzzy-Based Smart Charging Management System for an Electric Vehicle Parking Lot Including a Roof-Top Pv System

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