Operation and control of multiple electric vehicle load profiles in bipolar microgrid with photovoltaic and battery energy systems

Nisha, K. S.; Gaonkar, D. N.; Jayalakshmi, N. S.

doi:10.1016/j.est.2022.106261

Cited by 12 publications

(6 citation statements)

References 35 publications

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“…If-Then rules to define control actions (Mahmud et al, 2023) Rules that describe the charging behavior at different SoCs (Nisha et al, 2023) Inference Mechanism Process to deduce the control actions from the rules Mamdani, Sugeno, or Tsukamoto…”

Section: Fuzzy Rule Basementioning

confidence: 99%

Optimal efficiency point membership incorporating fuzzy logic and automatic control of various charging stages for electric vehicles

Wang,

2024

Front. Mech. Eng.

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Introduction: The rapid development of electric vehicle technology in the field of renewable energy has brought significant challenges to wireless charging systems. The efficiency of these systems is crucial for improving availability and sustainability. The main focus of the research is to develop an intelligent charging strategy that utilizes fuzzy logic to optimize the efficiency of wireless charging systems for electric vehicles.Method: Introduce a model that combines fuzzy logic algorithm with automatic control system to improve the wireless charging process of electric vehicles. The model adopts dynamic tracking and adaptive control methods by analyzing the characteristics of static wireless charging systems. Utilizing primary phase shift control and secondary controllable rectifier regulation, combined with optimized fuzzy control algorithm.Result and discussion: The experimental results show that when the secondary coil is stable, the model maintains a stable duty cycle of about 75.6% and a stable current of 5A. It was observed that when the mutual inductance values were set to 10, 15, and 20 uH, the efficiency of the primary coil before applying control decreased with increasing resistance.Conclusion: The proposed system has shown great potential for application in real-world electric vehicle charging systems, demonstrating good applicability and feasibility in controlling the charging process and tracking the optimal efficiency point. The integration of fuzzy logic enhances the system’s ability to adapt to different operating conditions, which may lead to wider implementation and improved operational efficiency.

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Section: Fuzzy Rule Basementioning

confidence: 99%

Optimal efficiency point membership incorporating fuzzy logic and automatic control of various charging stages for electric vehicles

Wang,

2024

Front. Mech. Eng.

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“…A decentralized predictive control method was devised by Nisha et al [17]. In this study, EV charging load profiles were designed by taking the power demand, typical driving cycles, and SOCs of many vehicles into account for studying the impact of uncertain changing EV loads in bipolar DC microgrids.…”

Section: Related Workmentioning

confidence: 99%

Design of Metaheuristic Optimization with Deep-Learning-Assisted Solar-Operated On-Board Smart Charging Station for Mass Transport Passenger Vehicle

et al. 2023

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Electric vehicles (EVs) have become popular in reducing the negative impact of ICE automobiles on the environment. EVs have been predicted to be an important mode of mass transit around the globe in recent years. Several charging stations in island and remote areas are dependent on off-grid power sources and renewable energy. Solar energy is used in the daytime as it is based on several environmental components. The creation of efficient power trackers is necessary for solar arrays to produce power at their peak efficiency. To deliver energy during emergencies and store it in case there is an excess, energy storage systems are required. It has long been known that reliable battery management technology is essential for maintaining precise battery charge levels and avoiding overcharging. This study suggests an ideal deep-learning-assisted solar-operated off-board smart charging station (ODL-SOOSCS) design method as a result. The development of on-board smart charging for mass transit EVs is the main goal of the ODL-SOOSCS technique that is being described. In the ODL-SOOSCS approach described here, a perovskite solar film serves as the generating module, and the energy it generates is stored in a module with a hybrid ultracapacitor and a lithium-ion battery. Broad bridge converters and solar panels are incorporated into the deep belief network (DBN) controller, which doubles as an EV charging station. An oppositional bird swarm optimization (OBSO) algorithm is used as a hyperparameter optimizer to improve the performance of the DBN model. Moreover, an MPPT device is exploited for monitoring and providing maximal output of the solar panel if the power sources are PV arrays. The proposed system combines the power of metaheuristic optimization algorithms with deep learning techniques to create an efficient and smart charging station for mass transport passenger vehicles. This integration of two powerful technologies is a novel approach toward solving the complex problem of charging electric vehicles in mass transportation systems. The experimental validation of the ODL-SOOSCS technique is tested on distinct converter topologies. A widespread experimental analysis assures the promising performance of the ODL-SOOSCS method over other current methodologies.

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“…The control system also controls charging/discharging of EVs to reduce power purchases from the utility grid or to take advantage of selling prices to the utility grid. The energy management system proposed in [26] represents decentralized control techniques used for the bipolar DC microgrid. The proposed system controls power sharing between individual parts of the microgrid and utility grid according to the load demand.…”

Section: Power Flow Control Of Microgridsmentioning

confidence: 99%

A Review of Advanced Control Strategies of Microgrids with Charging Stations

Tkac,

Kajanova,

Bracinik

2023

Energies

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In the context of the global drive towards sustainability and rapid integration of renewables, electric vehicles, and charging infrastructure, the need arises for advanced operational strategies that support the grid while managing the intermittent nature of these resources. Microgrids emerge as a solution, operating independently or alongside the main grid to facilitate power flow management among interconnected sources and different loads locally. This review paper aims to offer a comprehensive overview of the different control strategies proposed in the literature to control microgrids with electric vehicle charging stations. The surveyed research is primarily categorized according to the employed control algorithms, although distinctions are also made based on defined microgrid architecture, utilization of specific power sources, and charging stations configurations. Additionally, this paper identifies research gaps in the current research. These gaps encompass the use of oversimplified models for charging stations and/or renewable sources operation, limited simulation time periods, or lack of experimental testing of proposed approaches. In the light of these identified shortcomings, this manuscript presents recommendations for guiding future research.

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Operation and control of multiple electric vehicle load profiles in bipolar microgrid with photovoltaic and battery energy systems

Cited by 12 publications

References 35 publications

Optimal efficiency point membership incorporating fuzzy logic and automatic control of various charging stages for electric vehicles

Optimal efficiency point membership incorporating fuzzy logic and automatic control of various charging stages for electric vehicles

Design of Metaheuristic Optimization with Deep-Learning-Assisted Solar-Operated On-Board Smart Charging Station for Mass Transport Passenger Vehicle

A Review of Advanced Control Strategies of Microgrids with Charging Stations

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