Intelligent energy management scheme‐based coordinated control for reducing peak load in grid‐connected photovoltaic‐powered electric vehicle charging stations

Amir, Mohammad; Din, Zaheer ud; Haque, Ahteshamul; Bakhsh, Farhad Ilahi; Kurukuru, Varaha Satya Bharath; Sedighizadeh, Mostafa

doi:10.1049/gtd2.12772

Cited by 26 publications

(1 citation statement)

References 54 publications

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“…The hardware controller is more convenient, which has a faster speed. The disadvantage is that it requires variable processing (Amir et al, 2023;Zhang G. et al, 2023b;Chobe et al, 2023). Therefore, for the fuzzy algorithm of optimal efficiency point system control, the process is shown in Figure 5.…”

Section: Frontiers In Mechanical Engineeringmentioning

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: Frontiers In Mechanical Engineeringmentioning

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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Optimizing Cost and Emission Reduction in Photovoltaic–Battery‐Energy‐Storage‐System‐Integrated Electric Vehicle Charging Stations: An Efficient Hybrid Approach

Kirubadevi,

Sathesh Kumar,

et al. 2024

Energy Tech

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In this article, an optimal photovoltaic (PV) and battery energy storage system with hybrid approach design for electric vehicle charging stations (EVCS) is proposed. The hybrid approach combines the use of polar transformer networks (PTNs) and the puzzle optimization algorithm (POA); hence it is called as POA–PTN approach. The main objective is to lessen the charging station cost and pollutant emissions. The proposed method is minimizing the pollutant emissions and the annual cost of PV with EVCS, which is done by POA method and predicts the optimal solution is done by PTN approach. The proposed method is executed in the MATLAB platform, and compared with existing methods, including heap‐based optimization, particle swarm optimization, and wild horse optimizer approaches. The proposed technique demonstrates superior performance, achieving the lowest net present cost of €1.45 × 106 and emissions of 2.25 × 108 g year−1 with a shorter computing time of 13.04 s compared to the other methods.

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Comparative Study of Machine Learning for Managing EV Energy Storage with Battery-Hydrogen Tank

Elabbassi,

Elyanboiy,

Khala

et al. 2024

Advances in Electrical Systems and Innovative Renewable Energy Techniques

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Intelligent energy management scheme‐based coordinated control for reducing peak load in grid‐connected photovoltaic‐powered electric vehicle charging stations

Cited by 26 publications

References 54 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

Optimizing Cost and Emission Reduction in Photovoltaic–Battery‐Energy‐Storage‐System‐Integrated Electric Vehicle Charging Stations: An Efficient Hybrid Approach

Comparative Study of Machine Learning for Managing EV Energy Storage with Battery-Hydrogen Tank

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