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Theoretical and Experimental Analysis of a New Intelligent Charging Controller for Off-Board Electric Vehicles Using PV Standalone System Represented by a Small-Scale Lithium-Ion Battery

Author

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  • Peter Makeen

    (Electrical Engineering Department, Faculty of Engineering, The British University of Egypt (BUE), El Sherouk 11837, Egypt
    Electrical and Electronic Engineering Division, School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK)

  • Hani A. Ghali

    (Electrical Engineering Department, Faculty of Engineering, The British University of Egypt (BUE), El Sherouk 11837, Egypt)

  • Saim Memon

    (Electrical and Electronic Engineering Division, School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK
    Department of Engineering and Technology, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DR, UK)

Abstract

Electric vehicles are rapidly infiltrating the power grid worldwide, initiating an immediate need for a smart charging technique to maintain the stability and robustness of the charging process despite the generation type. Renewable energy sources (RESs), especially photovoltaic (PV), are becoming the essential source for electric vehicle charging points. The stochastic behavior of the PV output power affects the power conversion for regulating the battery charger voltage levels, which influences the battery to overheat and degrade. This study presents a PV standalone smart charging process for off-board plug-in electric vehicles, represented by a small-scale lithium-ion battery based on the multistage charging currents (MSCC) protocol. The charger comprises a DC–DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory recurrent neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. Additionally, it was used as an alarm flag for any possible PV output shortage during the charging process in the long- and short-term prediction to be supported by any other electricity source. The NNPC–LSTM controller was compared with the fuzzy logic and the conventional PID controllers while varying the input voltage and implementing the MSCC protocol. The proposed charging controller perfectly ensured that the minimum battery terminal voltage ripple and charging current ripple reached 1 mV and 1 mA, respectively, with a very high-speed response of 1 ms in reaching the predetermined charging current stages. The present simulated and experimental results are in good agreement with the previous related work in the literature survey.

Suggested Citation

  • Peter Makeen & Hani A. Ghali & Saim Memon, 2022. "Theoretical and Experimental Analysis of a New Intelligent Charging Controller for Off-Board Electric Vehicles Using PV Standalone System Represented by a Small-Scale Lithium-Ion Battery," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7396-:d:840725
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    References listed on IDEAS

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    1. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    2. İnci, Mustafa & Büyük, Mehmet & Demir, Mehmet Hakan & İlbey, Göktürk, 2021. "A review and research on fuel cell electric vehicles: Topologies, power electronic converters, energy management methods, technical challenges, marketing and future aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    3. Yilmaz, Unal & Kircay, Ali & Borekci, Selim, 2018. "PV system fuzzy logic MPPT method and PI control as a charge controller," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 994-1001.
    4. Peter M. Attia & Aditya Grover & Norman Jin & Kristen A. Severson & Todor M. Markov & Yang-Hung Liao & Michael H. Chen & Bryan Cheong & Nicholas Perkins & Zi Yang & Patrick K. Herring & Muratahan Ayko, 2020. "Closed-loop optimization of fast-charging protocols for batteries with machine learning," Nature, Nature, vol. 578(7795), pages 397-402, February.
    5. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
    6. Mostafa Shibl & Loay Ismail & Ahmed Massoud, 2020. "Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers," Energies, MDPI, vol. 13(20), pages 1-24, October.
    7. Uddin, Kotub & Moore, Andrew D. & Barai, Anup & Marco, James, 2016. "The effects of high frequency current ripple on electric vehicle battery performance," Applied Energy, Elsevier, vol. 178(C), pages 142-154.
    8. Li, Yunjian & Li, Kuining & Xie, Yi & Liu, Jiangyan & Fu, Chunyun & Liu, Bin, 2020. "Optimized charging of lithium-ion battery for electric vehicles: Adaptive multistage constant current–constant voltage charging strategy," Renewable Energy, Elsevier, vol. 146(C), pages 2688-2699.
    9. Mahdi Bayati & Mehrdad Abedi & Maryam Farahmandrad & Gevork B. Gharehpetian & Kambiz Tehrani, 2021. "Important Technical Considerations in Design of Battery Chargers of Electric Vehicles," Energies, MDPI, vol. 14(18), pages 1-20, September.
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