Integrated human-machine intelligence for EV charging prediction in 5G smart grid

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

doi:10.1186/s13638-020-01752-y

Cited by 26 publications

(15 citation statements)

References 24 publications

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“…This advancement will improve the economic aspects and operating efficiency of V2G to a certain extent [40]. With the current development of 5G technology, high-speed, safe, and efficient communication technology also plays a role in promoting the development of V2G [41]. In terms of software for V2G, actively participating in developing grid frequency/voltage regulation control strategies and orderly charge and discharge control strategies progresses the development of V2G [25,42].…”

Section: Opportunities For Developing Ev-based Energy Storage Technol...mentioning

confidence: 99%

Opportunities, Challenges and Strategies for Developing Electric Vehicle Energy Storage Systems under the Carbon Neutrality Goal

Liu

Zhao

Hao

et al. 2023

WEVJ

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Developing electric vehicle (EV) energy storage technology is a strategic position from which the automotive industry can achieve low-carbon growth, thereby promoting the green transformation of the energy industry in China. This paper will reveal the opportunities, challenges, and strategies in relation to developing EV energy storage. First, this paper clarifies the strategic value and potential of developing EV energy storage under the carbon neutrality goal. Second, this paper demonstrates strategic opportunities and challenges during the development. Third, this paper proposes methods for creating a good market environment and business models. Finally, this paper suggests that relevant policies and regulations should be formulated and charts the course of technology development. The results show that EV energy storage technology has potential in terms of technology, the scale of development, and the user economy. The proposal of the carbon neutrality goal, the increasing market share of EVs, lower-cost and higher-efficiency batteries, etc., have all further accelerated the development of EV energy storage. The EV energy storage field should focus on developing battery technology, make advancements toward delivering longer cycle lives and improving the safety and availability of battery materials, and ramp up the R&D efforts with respect to developing vehicle-to-grid (V2G) management technologies. Simultaneously, it is necessary to create a business ecosystem centered on V2G operating platforms, constituting a process to which various players can contribute and achieve mutually beneficial results. It is also essential to formulate top-level strategic plans across industries and organizations, develop an electricity-trading mechanism as soon as possible, and promote the implementation of technical standards related to EV energy storage.

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Section: Opportunities For Developing Ev-based Energy Storage Technol...mentioning

confidence: 99%

Opportunities, Challenges and Strategies for Developing Electric Vehicle Energy Storage Systems under the Carbon Neutrality Goal

Liu

Zhao

Hao

et al. 2023

WEVJ

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“…August September October changes in the climate across the world. By integrating smart communication, artificial intelligence, sensor and automated control technologies with electricity infrastructures, the smart grid is developing as the next-generation electrical grid to solve these issues Sun et al (2020). In contrast to traditional optimization approaches, using Machine Learning to coordinate EVs is more superficial and takes less time and computational resources Shibl et al, (2020).…”

Section: Oemsmentioning

confidence: 99%

Impact of plug-in electric vehicles on grid integration with distributed energy resources: A review

et al. 2023

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The rise of electric vehicles (EVs) has a massive impact on the electricity grid due to the electrification of vehicles in the transportation sector. As a result, various techniques are needed to minimize the effects of charging on the grid. One of these techniques is having intelligent coordination between the various components of the EV charging network. This ensures that the network has enough electricity to support the charging needs of the vehicles. This article provides an overview of the many aspects of the EV industry and its charging infrastructure. It also provides a step-by-step approach for implementing the Vehicle to Grid (V2G) deployment, the utilization of recordings from the data by the EV battery through Artificial Intelligence and the cost-benefit analysis from effective utilization of the V2G method. The paper also explores the various aspects of the EV market and the role of aggregators and consumers. Finally, it assesses the possibility of expansion of the EV charging and grid integration system and outlines its challenges and solutions.

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“…To accurately identify the target electric vehicles, the authors proposed an artificial intelligence method that is based on the electric vehicle's charging behaviour. In a similar work, Sun et al [171] proposed a novel architecture for 5G enabled smart grid based on edge computing and network slicing for providing provide on-demand services for electric vehicles. This architecture collects the bidirectional information of traffic between the electric vehicles and the smart grids to decrease the cost of the energy providers and improve the charging experience of electric vehicles.…”

Section: ) How Xai Can Helpmentioning

confidence: 99%

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

Wang¹,

Qureshi²,

Miralles-Pechuaán³

et al. 2021

Preprint

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When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and, more importantly, an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made every second. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed data-intensive AI decision-making beyond designers and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This survey paper highlights the need for XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for building 6G systems. This research aligns with goals 9, 11, 16, and 17 of the United Nations Sustainable Development Goals (UN-SDG) 1 , promoting innovation and building infrastructure, sustainable and inclusive human settlement, advancing justice and strong institutions, and fostering partnership at the global level.

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Integrated human-machine intelligence for EV charging prediction in 5G smart grid

Cited by 26 publications

References 24 publications

Opportunities, Challenges and Strategies for Developing Electric Vehicle Energy Storage Systems under the Carbon Neutrality Goal

Opportunities, Challenges and Strategies for Developing Electric Vehicle Energy Storage Systems under the Carbon Neutrality Goal

Impact of plug-in electric vehicles on grid integration with distributed energy resources: A review

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

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