Smart Grid Communication Networks for Electric Vehicles Empowering Distributed Energy Generation: Constraints, Challenges, and Recommendations

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Abstract

Modern communication networks and digital control techniques are used in a smart grid. The first step is to classify the features of several communication networks and conduct a comparative investigation of the communication networks applicable to the smart grid. The integration of distributed generation has significantly increased as the global energy demand rises, and sustainable energy for electric vehicles and renewable energies worldwide are being pursued. Additional explanations for this surge include environmental concerns, the reforming of the power sector, and the advancing of small-scale electricity generation technologies. Smart monitoring and control of interconnected systems are required to successfully integrate distributed generation into an existing conventional power system. Electric-vehicles-based smart grid technologies are capable of playing this part. Smart grids are crucial to avoid becoming locked in an obsolete energy infrastructure and to draw in new investment sources and build an effective and adaptable grid system. To achieve reliability and high-quality power systems, it is also necessary to apply intelligent grid technologies at the bulk power generation and transmission levels. This paper presents smart grid applicable communication networks and electric vehicles empowering distributed generation systems. Additionally, we address some constraints and challenges and make recommendations that will give proper guidelines for academicians and researchers to resolve the current issues.

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A Comprehensive Review on Supercapacitor Applications and Developments

Mustafa Şahin, Frede Blaabjerg, Ariya Sangwongwanich (2022)

The storage of enormous energies is a significant challenge for electrical generation. Researchers have studied energy storage methods and increased efficiency for many years. In recent years, researchers have been exploring new materials and techniques to store more significant amounts of energy more efficiently. In particular, renewable energy sources and electric vehicle technologies are triggering these scientific studies. Scientists and manufacturers recently proposed the supercapacitor (SC) as an alternating or hybrid storage device. This paper aims to provide a comprehensive review of SC applications and their developments. Accordingly, a detailed literature review was first carried out. The historical results of SCs are revealed in this paper. The structure, working principle, and materials of SC are given in detail to be analysed more effectively. The advantages and disadvantages, market profile, and new technologies with manufacturer corporations are investigated to produce a techno-economic analysis of SCs. The electric vehicle, power systems, hybrid energy storage systems with integration of renewable energy sources, and other applications of SCs are investigated in this paper. Additionally, SC modelling design principles with charge and discharge tests are explored. Other components and their price to produce a compact module for high power density are also investigated.

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Review of electric vehicle energy storage and management system: Standards, issues, and challenges

Mohammad Kamrul Hasan, Md. Mahmud, A.K.M. Ahasan Habib … (2021)

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Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Elmustafa Sayed Ali, Mohammad Kamrul Hasan, Rosilah Hassan … (2021)

Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.