IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i10p7845-d1143996.html
   My bibliography  Save this article

Design of Metaheuristic Optimization with Deep-Learning-Assisted Solar-Operated On-Board Smart Charging Station for Mass Transport Passenger Vehicle

Author

Listed:
  • Shekaina Justin

    (College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia)

  • Wafaa Saleh

    (College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia)

  • Maha M. A. Lashin

    (College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia)

  • Hind Mohammed Albalawi

    (College of Science, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia)

Abstract

Electric vehicles (EVs) have become popular in reducing the negative impact of ICE automobiles on the environment. EVs have been predicted to be an important mode of mass transit around the globe in recent years. Several charging stations in island and remote areas are dependent on off-grid power sources and renewable energy. Solar energy is used in the daytime as it is based on several environmental components. The creation of efficient power trackers is necessary for solar arrays to produce power at their peak efficiency. To deliver energy during emergencies and store it in case there is an excess, energy storage systems are required. It has long been known that reliable battery management technology is essential for maintaining precise battery charge levels and avoiding overcharging. This study suggests an ideal deep-learning-assisted solar-operated off-board smart charging station (ODL-SOOSCS) design method as a result. The development of on-board smart charging for mass transit EVs is the main goal of the ODL-SOOSCS technique that is being described. In the ODL-SOOSCS approach described here, a perovskite solar film serves as the generating module, and the energy it generates is stored in a module with a hybrid ultracapacitor and a lithium-ion battery. Broad bridge converters and solar panels are incorporated into the deep belief network (DBN) controller, which doubles as an EV charging station. An oppositional bird swarm optimization (OBSO) algorithm is used as a hyperparameter optimizer to improve the performance of the DBN model. Moreover, an MPPT device is exploited for monitoring and providing maximal output of the solar panel if the power sources are PV arrays. The proposed system combines the power of metaheuristic optimization algorithms with deep learning techniques to create an efficient and smart charging station for mass transport passenger vehicles. This integration of two powerful technologies is a novel approach toward solving the complex problem of charging electric vehicles in mass transportation systems. The experimental validation of the ODL-SOOSCS technique is tested on distinct converter topologies. A widespread experimental analysis assures the promising performance of the ODL-SOOSCS method over other current methodologies.

Suggested Citation

  • Shekaina Justin & Wafaa Saleh & Maha M. A. Lashin & Hind Mohammed Albalawi, 2023. "Design of Metaheuristic Optimization with Deep-Learning-Assisted Solar-Operated On-Board Smart Charging Station for Mass Transport Passenger Vehicle," Sustainability, MDPI, vol. 15(10), pages 1-16, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7845-:d:1143996
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/10/7845/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/10/7845/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Muhammad Rizalul Wahid & Bentang Arief Budiman & Endra Joelianto & Muhammad Aziz, 2021. "A Review on Drive Train Technologies for Passenger Electric Vehicles," Energies, MDPI, vol. 14(20), pages 1-24, October.
    2. Jean-Michel Clairand & Paulo Guerra-Terán & Xavier Serrano-Guerrero & Mario González-Rodríguez & Guillermo Escrivá-Escrivá, 2019. "Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies," Energies, MDPI, vol. 12(16), pages 1-22, August.
    3. Suresh Chavhan & Subhi R. M. Zeebaree & Ahmed Alkhayyat & Sachin Kumar, 2022. "Design of Space Efficient Electric Vehicle Charging Infrastructure Integration Impact on Power Grid Network," Mathematics, MDPI, vol. 10(19), pages 1-20, September.
    4. Kuruva Lakshmanna & Neelakandan Subramani & Youseef Alotaibi & Saleh Alghamdi & Osamah Ibrahim Khalafand & Ashok Kumar Nanda, 2022. "Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    5. Mostafa Shibl & Loay Ismail & Ahmed Massoud, 2021. "Electric Vehicles Charging Management Using Machine Learning Considering Fast Charging and Vehicle-to-Grid Operation," Energies, MDPI, vol. 14(19), pages 1-22, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Angel Recalde & Ricardo Cajo & Washington Velasquez & Manuel S. Alvarez-Alvarado, 2024. "Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review," Energies, MDPI, vol. 17(13), pages 1-39, June.
    3. Youssef Amry & Elhoussin Elbouchikhi & Franck Le Gall & Mounir Ghogho & Soumia El Hani, 2022. "Electric Vehicle Traction Drives and Charging Station Power Electronics: Current Status and Challenges," Energies, MDPI, vol. 15(16), pages 1-30, August.
    4. Wenz, Klaus-Peter & Serrano-Guerrero, Xavier & Barragán-Escandón, Antonio & González, L.G. & Clairand, Jean-Michel, 2021. "Route prioritization of urban public transportation from conventional to electric buses: A new methodology and a study of case in an intermediate city of Ecuador," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    5. Arkadiusz Małek & Jacek Caban & Agnieszka Dudziak & Andrzej Marciniak & Piotr Ignaciuk, 2023. "A Method of Assessing the Selection of Carport Power for an Electric Vehicle Using the Metalog Probability Distribution Family," Energies, MDPI, vol. 16(13), pages 1-16, June.
    6. Jacek Caban & Arkadiusz Małek & Branislav Šarkan, 2024. "Strategic Model for Charging a Fleet of Electric Vehicles with Energy from Renewable Energy Sources," Energies, MDPI, vol. 17(5), pages 1-17, March.
    7. Yao Pei & Yann Le Bihan & Mohamed Bensetti & Lionel Pichon, 2021. "Comparison of Coupling Coils for Static Inductive Power-Transfer Systems Taking into Account Sources of Uncertainty," Sustainability, MDPI, vol. 13(11), pages 1-13, June.
    8. Aoqi Xu & Mehdi Darbandi & Danial Javaheri & Nima Jafari Navimipour & Senay Yalcin & Anas A. Salameh, 2023. "The Management of IoT-Based Organizational and Industrial Digitalization Using Machine Learning Methods," Sustainability, MDPI, vol. 15(7), pages 1-28, March.
    9. Rob Shipman & Rebecca Roberts & Julie Waldron & Chris Rimmer & Lucelia Rodrigues & Mark Gillott, 2021. "Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic," Energies, MDPI, vol. 14(21), pages 1-16, November.
    10. Sharma, Reema Bera & Majumdar, Bandhan Bandhu & Maitra, Bhargab, 2024. "Commuter and non-commuter preferences for plug-in hybrid electric vehicle: A case study of Delhi and Kolkata, India," Research in Transportation Economics, Elsevier, vol. 103(C).
    11. Robby Dwianto Widyantara & Muhammad Adnan Naufal & Poetro Lebdo Sambegoro & Ignatius Pulung Nurprasetio & Farid Triawan & Djati Wibowo Djamari & Asep Bayu Dani Nandiyanto & Bentang Arief Budiman & Muh, 2021. "Low-Cost Air-Cooling System Optimization on Battery Pack of Electric Vehicle," Energies, MDPI, vol. 14(23), pages 1-14, November.
    12. Shantanu Pardhi & Sajib Chakraborty & Dai-Duong Tran & Mohamed El Baghdadi & Steven Wilkins & Omar Hegazy, 2022. "A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions," Energies, MDPI, vol. 15(24), pages 1-55, December.
    13. Teresa Pamuła & Wiesław Pamuła, 2020. "Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning," Energies, MDPI, vol. 13(9), pages 1-17, May.
    14. Abd Ur Rehman & Minsung Kim & Jin-Woo Jung, 2023. "State-Plane Trajectory-Based Duty Control of a Resonant Bidirectional DC/DC Converter with Balanced Capacitors Stress," Mathematics, MDPI, vol. 11(14), pages 1-17, July.
    15. Sebastian Angermeier & Jonas Ketterer & Christian Karcher, 2020. "Liquid-Based Battery Temperature Control of Electric Buses," Energies, MDPI, vol. 13(19), pages 1-20, September.
    16. Bong-Gi Choi & Byeong-Chan Oh & Sungyun Choi & Sung-Yul Kim, 2020. "Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method," Energies, MDPI, vol. 13(7), pages 1-20, April.
    17. Xingxing Wang & Peilin Ye & Yujie Zhang & Hongjun Ni & Yelin Deng & Shuaishuai Lv & Yinnan Yuan & Yu Zhu, 2022. "Parameter Optimization Method for Power System of Medium-Sized Bus Based on Orthogonal Test," Energies, MDPI, vol. 15(19), pages 1-26, October.
    18. Gao, Bingzhao & Meng, Dele & Shi, Wentong & Cai, Wenqi & Dong, Shiying & Zhang, Yuanjian & Chen, Hong, 2022. "Topology optimization and the evolution trends of two-speed transmission of EVs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    19. Carola Leone & Laura Sturaro & Giacomo Geroli & Michela Longo & Wahiba Yaici, 2021. "Design and Implementation of an Electric Skibus Line in North Italy," Energies, MDPI, vol. 14(23), pages 1-22, November.
    20. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7845-:d:1143996. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.