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EV charging and fuel cell vehicle refuelling with distributed energy resources using hybrid approach

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Abstract

This manuscript proposes a hybrid technique for Electric Vehicle (EV) charging and Fuel Cell vehicle refuelling with distributed energy resources. The proposed hybrid approach, known as the BWO-CCG-DLNN method, combines the Beluga Whale Optimization (BWO) algorithm with the Cascade-Correlation Growing Deep Learning Neural Network (CCG-DLNN). The primary goal of the proposed strategy is to reduce reliance on the utility grid while simultaneously reducing the overall cost of distributed energy resources by using battery storage for peak shaving. The EV charging’s cost is reduced using the proposed BWO approach, and the ideal outcome of the system is predicted using the CCG-DLNN approach. The proposed strategy is implemented into use on the MATLAB platform, and it is contrasted with current strategys, including the Cuckoo Search Algorithm Color Harmony Algorithm, and Particle Swarm Optimization, The proposed method demonstrates the lowest mean (1.0936) and median (1.0158), indicating its effectiveness. The standard deviation (0.1505) suggests relatively consistent results. The proposed method shows better result when compared to other methods. When compared to other existing approaches, the proposed approach has a high efficiency of 98% and a low cost of 200 ($/kW).

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References

  • Amry, Y., Elbouchikhi, E., Le Gall, F., Ghogho, M., & El Hani, S. (2023). Optimal sizing and energy management strategy for EV workplace charging station considering PV and flywheel energy storage system. Journal of Energy Storage, 62, 106937.

    Article  Google Scholar 

  • Çiçek, A. (2022). Optimal operation of an all-in-one EV station with photovoltaic system including charging, battery swapping and hydrogen refueling. International Journal of Hydrogen Energy, 47(76), 32405–32424.

    Article  Google Scholar 

  • Elma, O. (2020). A dynamic charging strategy with hybrid fast charging station for electric vehicles. Energy, 202, 117680.

    Article  Google Scholar 

  • Fang, X., Wang, Y., Dong, W., Yang, Q., & Sun, S. (2023). Optimal energy management of multiple electricity-hydrogen integrated charging stations. Energy, 262, 125624.

    Article  CAS  Google Scholar 

  • Gharibeh, H. F., Yazdankhah, A. S., & Azizian, M. R. (2020). Energy management of fuel cell electric vehicles based on working condition identification of energy storage systems, vehicle driving performance, and dynamic power factor. Journal of Energy Storage, 31, 101760.

    Article  Google Scholar 

  • Hai, T., & Zhou, J. (2023). Optimal planning and design of integrated energy systems in a microgrid incorporating electric vehicles and fuel cell system. Journal of Power Sources, 561, 232694.

    Article  CAS  Google Scholar 

  • Houssein, E. H., & Sayed, A. (2023). Dynamic candidate solution boosted beluga whale optimization algorithm for biomedical classification. Mathematics, 11(3), 707.

    Article  Google Scholar 

  • Lin, K. M., & Wang, F. C. (2021). Optimization of distributed hybrid power systems employing multiple fuel-cell vehicles. International Journal of Hydrogen Energy, 46(40), 21082–21097.

    Article  CAS  Google Scholar 

  • Mo, Y., Qin, Z., Guo, Y., & Zhang, Y. (2022). Day-ahead flexibility enhancement via joint optimization for new energy vehicle fleets and electric vehicle charging/hydrogen refuelling stations. IET Renewable Power Generation, 16(12), 2644–2657.

    Article  Google Scholar 

  • Mohamed, S. A. E. M., Mohamed, M. H., & Farghally, M. F. (2021). A new cascade-correlation growing deep learning neural network algorithm. Algorithms, 14(5), 158.

    Article  Google Scholar 

  • Pham, A. T., Lovdal, L., Zhang, T., & Craig, M. T. (2022). A techno-economic analysis of distributed energy resources versus wholesale electricity purchases for fueling decarbonized heavy duty vehicles. Applied Energy, 322, 119460.

    Article  Google Scholar 

  • Pitchai, M. K., Narayanan, P., Rajendiran, E., & Venkataramani, V. (2024). IoT-enabled EMS for grid-connected solar PV-fed DC residential buildings with hybrid HBA-DCGNN approach. Energy Conversion and Management, 308, 118361.

    Article  Google Scholar 

  • Schröder, M., Abdin, Z., & Mérida, W. (2020). Optimization of distributed energy resources for electric vehicle charging and fuel cell vehicle refueling. Applied Energy, 277, 115562.

    Article  Google Scholar 

  • Sharma, P., Mishra, A., Saxena, A., & Shankar, R. (2021). A novel hybridized fuzzy PI-LADRC based improved frequency regulation for restructured power system integrating renewable energy and electric vehicles. IEEE Access, 9, 7597–7617.

    Article  Google Scholar 

  • Shoja, Z. M., Mirzaei, M. A., Seyedi, H., & Zare, K. (2022). Sustainable energy supply of electric vehicle charging parks and hydrogen refueling stations integrated in local energy systems under a risk-averse optimization strategy. Journal of Energy Storage, 55, 105633.

    Article  Google Scholar 

  • Tao, Y., Qiu, J., Lai, S., Zhang, X., & Wang, G. (2020). Collaborative planning for electricity distribution network and transportation system considering hydrogen fuel cell vehicles. IEEE Transactions on Transportation Electrification, 6(3), 1211–1225.

    Article  Google Scholar 

  • Wang, S., Lu, L., Han, X., Ouyang, M., & Feng, X. (2020). Virtual-battery based droop control and energy storage system size optimization of a DC microgrid for electric vehicle fast charging station. Applied Energy, 259, 114146.

    Article  Google Scholar 

  • Yuan, H. B., Zou, W. J., Jung, S., & Kim, Y. B. (2022). A real-time rule-based energy management strategy with multi-objective optimization for a fuel cell hybrid electric vehicle. IEEE Access, 10, 102618–102628.

    Article  Google Scholar 

  • Zhao, D., Zhou, M., Wang, J., Zhang, T., Li, G., & Zhang, H. (2021). Dispatching fuel-cell hybrid electric vehicles toward transportation and energy systems integration. CSEE Journal of Power and Energy Systems, 9(4), 1540–50.

    CAS  Google Scholar 

  • Zhou, S., Han, Y., Mahmoud, K., Darwish, M. M., Lehtonen, M., Yang, P., & Zalhaf, A. S. (2023). A novel unified planning model for distributed generation and electric vehicle charging station considering multi-uncertainties and battery degradation. Applied Energy, 348, 121566.

    Article  Google Scholar 

  • Zhou, Y., Ravey, A., & Péra, M. C. (2020). Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer. Applied Energy, 258, 114057.

    Article  Google Scholar 

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, PSG Institute of Technology and Applied Research, Coimbatore, Tamilnadu, 641062, India

    M. Senthilkumar

  2. Symbiosis Institute of Digital and Telecom Management (SIDTM), Symbiosis International (Deemed University), Pune, India

    Sandeep Prabhu

  3. Department of Electrical and Electronics Engineering, Faculty of Engineering, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore, 641021, , Tamil Nadu, India

    U. Arun Kumar

  4. Department of Electrical and Electronics Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu, 641022, India

    R. Krishnakumar

Authors
  1. M. Senthilkumar
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  2. Sandeep Prabhu
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  4. R. Krishnakumar
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Contributions

Dr. M. Senthilkumar—Conceptualization Methodology, Original draft preparation, Dr. Sandeep Prabhu—Supervision, Dr. U. Arun Kumar—Supervision, Dr. R. Krishnakumar—Supervision.

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Correspondence to M. Senthilkumar.

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Senthilkumar, M., Prabhu, S., Arun Kumar, U. et al. EV charging and fuel cell vehicle refuelling with distributed energy resources using hybrid approach. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-05138-8

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