Integrated Human-Machine Intelligence for EV Charging Prediction in 5G Smart Grid

Sun, Donglei; Ou, Qinghai; Yao, Xianjiong; Gao, Songji; Wang, Zhiqiang; Ma, Wenhui; Li, Wenjing

doi:10.21203/rs.3.rs-18565/v1

Cited by 3 publications

(5 citation statements)

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“…The results from References 20‐24 show us the main advantages of implementing vehicle ‐ to ‐ grid connection using 5G in the electric vehicles system (cost effectiveness, quality of services, provides a database for the future research, auxiliary electrical services, upgrades systems stability, power utilization and network lifetime, provides secure and flexible resource sharing).…”

Section: Methodsmentioning

confidence: 99%

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Intelligent charging station in 5G environments: Challenges and perspectives

2021

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Summary The popularity of the electric vehicles has been growing due to the concern of increase in environmental pollution. In order to overcome the environmental issues, electric vehicles have been introduced all over the globe. As the use and number of electric vehicles is growing, there is a great demand to identify the sustainable solutions for intelligent charging station dedicated to electric vehicles. The objective of this paper is to explore the possibilities for intelligent charging station using 5G services. This paper is a state‐of‐the‐art concept to identify the current challenges and issues of intelligent charging station. It should be noted that in the future, the use of the smart charging station will dramatically improve the efficiency of current charging stations. It is identified based on several existing techniques that the biggest challenges are the connectivity between the vehicles and among different charging stations. This manuscript could be useful for researchers working on intelligent charging station worldwide and on their integration with the recent communication technologies like 5G. The features of intelligent charging stations combined with the fifth‐generation network represent a new path for humanity and introduce a safer and more advanced environment. From data privacy to human safety, the network provides enough resources to rise driving quality, time and fuel economy and human lives saving. The results of this research can be well applied in the industry or in the field of academic research.

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Section: Methodsmentioning

confidence: 99%

“…Sun et al 21 propose a 5G smart grid, which exchanges information and provides a database for the future research. They pursue scheduling efficiency of electric vehicles.…”

Section: Methodsmentioning

confidence: 99%

Intelligent charging station in 5G environments: Challenges and perspectives

2021

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“…The different approaches consider charging EV batteries and using AI-based algorithms that minimize costs and increase battery life while avoiding users from being stranded. Sun et al (2020) propose an EV charging behavior prediction design based on hybrid AI to identify targeted EVs and predict the charging behavior. Simulation results confirm that the proposed prediction scheme outperforms the existing EV charging behavior prediction methods in forecasting accuracy and scheduling efficiency.…”

Section: Integrated Intelligent Hardware and Sensor Fusion Functionsmentioning

confidence: 99%

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

Vermesan

John

Pype

et al. 2021

Front. Future Transp.

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The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.

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“…The blade angle of twist is the angle at which blades are set to accumulate the maximum air pressure for effective generation, where N is the rotational speed of the rotor given in revolutions per minute(rpm) [35], which can be calculated through the formula in equation (11). The force acting on the fan to operate is the difference of two forces acting on it.…”

Section: Rpms and Angle Of Twistmentioning

confidence: 99%

“…The constantly increasing carbon emissions caused by the conventional gas powered vehicles have led the research efforts toward the inception of Electric Vehicles (EV) [11][12][13], being one of the greatest inventions of recent decades. The concept started to took an implementation form in the early 21st century.…”

Section: Introductionmentioning

confidence: 99%

Designing Wind Energy Harvester for Connected Vehicles in Green Cities

Khan¹,

Sherazi²,

Ali³

et al. 2021

Preprint

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The recent advancements in the field of communication have led data sharing to become an integral part of today's smart cities with the evolution of concepts such as the internet of vehicles (IoV) paradigm. As a part of IoV, Electric Vehicles (EVs) have recently gained momentum as authorities have started expanding their Low Emission Zones (LEZ) in an effort to build green cities with low carbon footprints. Energy is one of the key requirements of EVs not only to support the smooth and sustainable operation of EV itself but to also ensure connectivity between the vehicles and infrastructure with controlling devices like sensors and actuators installed within an EV. In this context, renewable energy sources (such as wind energy) dramatically play their parts in the automobile sector towards designing the energy harvesting electric vehicles (EH-EV) to pare the energy reliance on the national grid. In this article, a novel approach is presented to achieve electric generation due to vehicle mobility to support the communication primitives in electric vehicles which enables plenty of IoV use cases in the presence of surplus energy at hand. A small-scale wind turbine is designed to harness wind power for converting it into mechanical power. This power is then fed to the onboard DC generator to produce electrical energy. Furthermore, the acquired power is processed through a regulation circuitry to consequently achieve the desired power supply for the end load, i.e. the batteries installed. The suitable orientation for efficient power generation is proposed on ANSYS-based aerodynamics analysis. The voltages induced by DC generator at No-Load condition are 35V while at Full-Load 25V are generated at rated current of 6.9A, along with the generation of power at around 100W (at constant voltage) at the rated speed of 90 mph for nominal battery charging. Moreover, the acquired data can be monitored via an android application interface by using a Bluetooth module.

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Integrated Human-Machine Intelligence for EV Charging Prediction in 5G Smart Grid

Cited by 3 publications

References 0 publications

Intelligent charging station in 5G environments: Challenges and perspectives

Intelligent charging station in 5G environments: Challenges and perspectives

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

Designing Wind Energy Harvester for Connected Vehicles in Green Cities

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