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Electric Vehicle Charging System in the Smart Grid Using Different Machine Learning Methods

Author

Listed:
  • Tehseen Mazhar

    (Department of Computer Science, Virtual University of Pakistan, Lahore 54000, Pakistan)

  • Rizwana Naz Asif

    (School of Computer Science, National College of Business Administration & Economics, Lahore 54000, Pakistan)

  • Muhammad Amir Malik

    (Department of Computer Science and Software Engineering, Islamic International University, Islamabad 44000, Pakistan)

  • Muhammad Asgher Nadeem

    (Department of Computer Science, University of Sargodha, Sargodha 40100, Pakistan)

  • Inayatul Haq

    (School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Muhammad Iqbal

    (Department of Computer Science, Virtual University of Pakistan, Lahore 54000, Pakistan)

  • Muhammad Kamran

    (Department of Computer Science, NCBA&E Multan, Multan 60650, Pakistan)

  • Shahzad Ashraf

    (NFC Institute of Engineering and Technology, Multan 60650, Pakistan)

Abstract

Smart cities require the development of information and communication technology to become a reality (ICT). A “smart city” is built on top of a “smart grid”. The implementation of numerous smart systems that are advantageous to the environment and improve the quality of life for the residents is one of the main goals of the new smart cities. In order to improve the reliability and sustainability of the transportation system, changes are being made to the way electric vehicles (EVs) are used. As EV use has increased, several problems have arisen, including the requirement to build a charging infrastructure, and forecast peak loads. Management must consider how challenging the situation is. There have been many original solutions to these problems. These heavily rely on automata models, machine learning, and the Internet of Things. Over time, there have been more EV drivers. Electric vehicle charging at a large scale negatively impacts the power grid. Transformers may face additional voltage fluctuations, power loss, and heat if already operating at full capacity. Without EV management, these challenges cannot be solved. A machine-learning (ML)-based charge management system considers conventional charging, rapid charging, and vehicle-to-grid (V2G) technologies while guiding electric cars (EVs) to charging stations. This operation reduces the expenses associated with charging, high voltages, load fluctuation, and power loss. The effectiveness of various machine learning (ML) approaches is evaluated and compared. These techniques include Deep Neural Networks (DNN), K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) (DNN). According to the results, LSTM might be used to give EV control in certain circumstances. The LSTM model’s peak voltage, power losses, and voltage stability may all be improved by compressing the load curve. In addition, we keep our billing costs to a minimum, as well.

Suggested Citation

  • Tehseen Mazhar & Rizwana Naz Asif & Muhammad Amir Malik & Muhammad Asgher Nadeem & Inayatul Haq & Muhammad Iqbal & Muhammad Kamran & Shahzad Ashraf, 2023. "Electric Vehicle Charging System in the Smart Grid Using Different Machine Learning Methods," Sustainability, MDPI, vol. 15(3), pages 1-26, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2603-:d:1053936
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    References listed on IDEAS

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    Cited by:

    1. Kabir Momoh & Shamsul Aizam Zulkifli & Petr Korba & Felix Rafael Segundo Sevilla & Arif Nur Afandi & Alfredo Velazquez-Ibañez, 2023. "State-of-the-Art Grid Stability Improvement Techniques for Electric Vehicle Fast-Charging Stations for Future Outlooks," Energies, MDPI, vol. 16(9), pages 1-29, May.
    2. Ahmet Aksoz & Burçak Asal & Emre Biçer & Saadin Oyucu & Merve Gençtürk & Saeed Golestan, 2024. "Advancing Electric Vehicle Infrastructure: A Review and Exploration of Battery-Assisted DC Fast Charging Stations," Energies, MDPI, vol. 17(13), pages 1-23, June.
    3. Bahman Ahmadi & Elham Shirazi, 2023. "A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration," Energies, MDPI, vol. 16(19), pages 1-26, October.
    4. Ibrahim Tumay Gulbahar & Muhammed Sutcu & Abedalmuhdi Almomany & Babul Salam KSM Kader Ibrahim, 2023. "Optimizing Electric Vehicle Charging Station Location on Highways: A Decision Model for Meeting Intercity Travel Demand," Sustainability, MDPI, vol. 15(24), pages 1-17, December.
    5. Huilian Liao & Elizabeth Michalenko & Sarat Chandra Vegunta, 2023. "Review of Big Data Analytics for Smart Electrical Energy Systems," Energies, MDPI, vol. 16(8), pages 1-19, April.
    6. Rajeshkumar Ramraj & Ehsan Pashajavid & Sanath Alahakoon & Shantha Jayasinghe, 2023. "Quality of Service and Associated Communication Infrastructure for Electric Vehicles," Energies, MDPI, vol. 16(20), pages 1-28, October.

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