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A Review on Emerging Communication and Computational Technologies for Increased Use of Plug-In Electric Vehicles

Author

Listed:
  • Vinay Simha Reddy Tappeta

    (School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India)

  • Bhargav Appasani

    (School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India)

  • Suprava Patnaik

    (School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India)

  • Taha Selim Ustun

    (Fukushima Renewable Energy Institute, AIST (FREA), Koriyama 963-0298, Japan)

Abstract

The electric vehicle (EV) industry is quickly growing in the present scenario, and will have more demand in the future. A sharp increase in the sales of EVs by 160% in 2021 represents 26% of new sales in the worldwide automotive market. EVs are deemed to be the transportation of the future, as they offer significant cost savings and reduce carbon emissions. However, their interactions with the power grid, charging stations, and households require new communication and control techniques. EVs show unprecedented behavior during vehicle battery charging, and sending the charge from the vehicle’s battery back to the grid via a charging station during peak hours has an impact on the grid operation. Balancing the load during peak hours, i.e., managing the energy between the grid and vehicle, requires efficient communication protocols, standards, and computational technologies that are essential for improving the performance, efficiency, and security of vehicle-to-vehicle, vehicle-to-grid (V2G), and grid-to-vehicle (G2V) communication. Machine learning and deep learning technologies are being used to manage EV-charging station interactions, estimate the charging behavior, and to use EVs in the load balancing and stability control of smart grids. Internet of Things (IoT) technology can be used for managing EV charging stations and monitoring EV batteries. Recently, much work has been presented in the EV communication and control domain. In order to categorize these efforts in a meaningful manner and highlight their contributions to advancing EV migration, a thorough survey is required. This paper presents existing literature on emerging protocols, standards, communication technologies, and computational technologies for EVs. Frameworks, standards, architectures, and protocols proposed by various authors are discussed in the paper to serve the need of various researchers for implementing the applications in the EV domain. Security plays a vital role in EV authentication and billing activities. Hackers may exploit the hardware, such as sensors and other electronic systems and software of the EV, for various malicious activities. Various authors proposed standards and protocols for mitigating cyber-attacks on security aspects in the complex EV ecosystem.

Suggested Citation

  • Vinay Simha Reddy Tappeta & Bhargav Appasani & Suprava Patnaik & Taha Selim Ustun, 2022. "A Review on Emerging Communication and Computational Technologies for Increased Use of Plug-In Electric Vehicles," Energies, MDPI, vol. 15(18), pages 1-26, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6580-:d:910131
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    References listed on IDEAS

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    1. Prince Waqas Khan & Yung-Cheol Byun, 2021. "Blockchain-Based Peer-to-Peer Energy Trading and Charging Payment System for Electric Vehicles," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    2. Marina Dorokhova & Jérémie Vianin & Jean-Marie Alder & Christophe Ballif & Nicolas Wyrsch & David Wannier, 2021. "A Blockchain-Supported Framework for Charging Management of Electric Vehicles," Energies, MDPI, vol. 14(21), pages 1-32, November.
    3. Bhargav Appasani & Sunil Kumar Mishra & Amitkumar V. Jha & Santosh Kumar Mishra & Florentina Magda Enescu & Ioan Sorin Sorlei & Fernando Georgel Bîrleanu & Noureddine Takorabet & Phatiphat Thounthong , 2022. "Blockchain-Enabled Smart Grid Applications: Architecture, Challenges, and Solutions," Sustainability, MDPI, vol. 14(14), pages 1-33, July.
    4. Furquan Nadeem & Mohd Asim Aftab & S.M. Suhail Hussain & Ikbal Ali & Prashant Kumar Tiwari & Arup Kumar Goswami & Taha Selim Ustun, 2019. "Virtual Power Plant Management in Smart Grids with XMPP Based IEC 61850 Communication," Energies, MDPI, vol. 12(12), pages 1-20, June.
    5. Connor Scott & Mominul Ahsan & Alhussein Albarbar, 2021. "Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings," Sustainability, MDPI, vol. 13(7), pages 1-22, April.
    6. Shihang Wang & Zongmin Li & Yuhong Wang & Qi Zhang, 2019. "Machine Learning Methods to Predict Social Media Disaster Rumor Refuters," IJERPH, MDPI, vol. 16(8), pages 1-16, April.
    7. Yiqi Lu & Yongpan Li & Da Xie & Enwei Wei & Xianlu Bao & Huafeng Chen & Xiancheng Zhong, 2018. "The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load," Energies, MDPI, vol. 11(11), pages 1-16, November.
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    1. Alberto Broatch & Pablo Olmeda & Benjamín Plá & Amin Dreif, 2022. "Novel Energy Management Control Strategy for Improving Efficiency in Hybrid Powertrains," Energies, MDPI, vol. 16(1), pages 1-21, December.

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