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Energy Management and Control of Plug-In Hybrid Electric Vehicle Charging Stations in a Grid-Connected Hybrid Power System

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  • Sidra Mumtaz

    (Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan)

  • Saima Ali

    (Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan)

  • Saghir Ahmad

    (Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan)

  • Laiq Khan

    (Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan)

  • Syed Zulqadar Hassan

    (Department of Power System and Its Automation, Chongqing University, Chongqing 400044, China)

  • Tariq Kamal

    (Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, Serdivan/Sakarya 54050, Turkey)

Abstract

The charging infrastructure plays a key role in the healthy and rapid development of the electric vehicle industry. This paper presents an energy management and control system of an electric vehicle charging station. The charging station ( CS ) is integrated to a grid-connected hybrid power system having a wind turbine maximum power point tracking ( MPPT ) controlled subsystem, photovoltaic ( PV ) MPPT controlled subsystem and a controlled solid oxide fuel cell with electrolyzer subsystem which are characterized as renewable energy sources. In this article, an energy management system is designed for charging and discharging of five different plug-in hybrid electric vehicles ( PHEVs ) simultaneously to fulfil the grid-to-vehicle ( G2V ), vehicle-to-grid ( V2G ), grid-to-battery storage system ( G2BSS ), battery storage system-to-grid ( BSS2G ), battery storage system-to-vehicle ( BSS2V ), vehicle-to-battery storage system ( V2BSS ) and vehicle-to-vehicle ( V2V ) charging and discharging requirements of the charging station. A simulation test-bed in Matlab/Simulink is developed to evaluate and control adaptively the AC-DC-AC converter of non-renewable energy source, DC-DC converters of the storage system, DC-AC grid side inverter and the converters of the CS using adaptive proportional-integral-derivate ( AdapPID ) control paradigm. The effectiveness of the AdapPID control strategy is validated through simulation results by comparing with conventional PID control scheme.

Suggested Citation

  • Sidra Mumtaz & Saima Ali & Saghir Ahmad & Laiq Khan & Syed Zulqadar Hassan & Tariq Kamal, 2017. "Energy Management and Control of Plug-In Hybrid Electric Vehicle Charging Stations in a Grid-Connected Hybrid Power System," Energies, MDPI, vol. 10(11), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1923-:d:119842
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    References listed on IDEAS

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    2. Syed Zulqadar Hassan & Hui Li & Tariq Kamal & Uğur Arifoğlu & Sidra Mumtaz & Laiq Khan, 2017. "Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 10(3), pages 1-16, March.
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    5. Goli, P. & Shireen, W., 2014. "PV powered smart charging station for PHEVs," Renewable Energy, Elsevier, vol. 66(C), pages 280-287.
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    Citations

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    Cited by:

    1. Zhenpo Wang & Changhui Qu & Lei Zhang & Jin Zhang & Wen Yu, 2018. "Integrated Sizing and Energy Management for Four-Wheel-Independently-Actuated Electric Vehicles Considering Realistic Constructed Driving Cycles," Energies, MDPI, vol. 11(7), pages 1-22, July.
    2. Eunice Espe & Vidyasagar Potdar & Elizabeth Chang, 2018. "Prosumer Communities and Relationships in Smart Grids: A Literature Review, Evolution and Future Directions," Energies, MDPI, vol. 11(10), pages 1-24, September.
    3. Nicu Bizon & Mihai Oproescu, 2018. "Experimental Comparison of Three Real-Time Optimization Strategies Applied to Renewable/FC-Based Hybrid Power Systems Based on Load-Following Control," Energies, MDPI, vol. 11(12), pages 1-32, December.
    4. Petronilla Fragiacomo & Giuseppe De Lorenzo & Orlando Corigliano, 2018. "Performance Analysis of an Intermediate Temperature Solid Oxide Electrolyzer Test Bench under a CO 2 -H 2 O Feed Stream," Energies, MDPI, vol. 11(9), pages 1-17, August.
    5. Muhammad Awais & Laiq Khan & Said Ghani Khan & Qasim Awais & Mohsin Jamil, 2023. "Adaptive Neural Network Q-Learning-Based Full Recurrent Adaptive NeuroFuzzy Nonlinear Control Paradigms for Bidirectional-Interlinking Converter in a Grid-Connected Hybrid AC-DC Microgrid," Energies, MDPI, vol. 16(4), pages 1-40, February.
    6. Yajing Gao & Shixiao Guo & Jiafeng Ren & Zheng Zhao & Ali Ehsan & Yanan Zheng, 2018. "An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors," Energies, MDPI, vol. 11(8), pages 1-17, August.
    7. Sidra Mumtaz & Saghir Ahmad & Laiq Khan & Saima Ali & Tariq Kamal & Syed Zulqadar Hassan, 2018. "Adaptive Feedback Linearization Based NeuroFuzzy Maximum Power Point Tracking for a Photovoltaic System," Energies, MDPI, vol. 11(3), pages 1-15, March.
    8. Damien Guilbert & Gianpaolo Vitale, 2019. "Dynamic Emulation of a PEM Electrolyzer by Time Constant Based Exponential Model," Energies, MDPI, vol. 12(4), pages 1-17, February.
    9. Nicu Bizon & Valentin Alexandru Stan & Angel Ciprian Cormos, 2019. "Optimization of the Fuel Cell Renewable Hybrid Power System Using the Control Mode of the Required Load Power on the DC Bus," Energies, MDPI, vol. 12(10), pages 1-15, May.
    10. Goffé, Jonathan & Ferrasse, Jean-Henry, 2019. "Stoichiometry impact on the optimum efficiency of biomass conversion to biofuels," Energy, Elsevier, vol. 170(C), pages 438-458.
    11. Jorge García Álvarez & Miguel Ángel González & Camino Rodríguez Vela & Ramiro Varela, 2018. "Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm," Energies, MDPI, vol. 11(10), pages 1-19, October.

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