Intelligent Energy Management Strategy Based on an Improved Reinforcement Learning Algorithm With Exploration Factor for a Plug-in PHEV

Lin, Xinyou; Zhou, Kuncheng; Mo, Li‐Ping; Li, Hailin

doi:10.1109/tits.2021.3085710

Cited by 22 publications

(7 citation statements)

References 39 publications

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“…ChatGPT, as a dialogue text generation tool, adopts dialogue teaching method to help students get inspiration and learning in conversation, so that students can communicate and learn with "private learning consultants" more intuitively and conveniently, and can give full play to their autonomy. In personalized learning, ChatGPT can provide support for students in the following aspects: first, provide personalized learning programs and learning materials according to students' different learning levels; Second, assist reading and writing to help students better understand and master the teaching content; Third, generate feedback and evaluation results according to the different characteristics of students; Fourth, according to students' feedback in the learning process, we can understand students' learning situation and learning bottlenecks, so as to provide students with more accurate learning suggestions and guidance [6]. ChatGPT, if used properly, can realize the individualization and autonomy of students' learning and accelerate the shift to learner-centered learning [7].…”

Section: Promote Personalized Autonomous Learningmentioning

confidence: 99%

Research on the Intelligentization of Education Management Based on ChatGPT Model

Qin

2023

Atlantis Highlights in Computer Sciences

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In order to innovate teachers' teaching process, this paper puts forward the research of intelligent education management based on ChatGPT model. Based on the current situation of education management in senior high schools and the application of Internet technology in education, this paper studies the ways and means to realize the intelligent management mode of senior high school education. ChatGPT, as a language processing tool, can not only answer users' questions, but also complete some tasks set by users, and even continuously optimize tasks. ChatGPT has powerful functions, but at the same time, it is accompanied by shortcomings that need to be improved urgently: insufficient accuracy in answering questions, data pollution, ethics and safety issues and the risk of knowledge plagiarism. There are both opportunities and challenges in applying ChatGPT in the process of promoting school education reform. The appearance of ChatGPT gives teachers the opportunity to reflect on the value of human-teacher existence, and puts forward higher requirements for teachers.

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Section: Promote Personalized Autonomous Learningmentioning

confidence: 99%

Research on the Intelligentization of Education Management Based on ChatGPT Model

Qin

2023

Atlantis Highlights in Computer Sciences

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“…Compared with HEVs, the PHEV has an energy storage system as well as an internal combustion engine. The PHEVs are equipped with large battery powers and can be charged as needed, thus further reducing the fuel consumptions [152]. Different from HEVs and PHEVs, BEVs only use batteries for energy resources and are considered as the future development trend of new energy vehicles.…”

Section: Green Energy Management For Evsmentioning

confidence: 99%

“…RL is appealing to design model-free adaptive energy management strategy by predicting the related information of HEVs/PHEVs based on historical driving data, such as the vehicles' velocities, driving routes, power demands, driver behaviours, battery lifetimes, etc. [152], [159], [162], [163]. For example, in [156], the authors adopt the fuzzy encoding and nearest neighbor approaches to predict the vehicle velocity, and exploit a finite-state Markov chain model to learn the transition probabilities of power demand.…”

Section: Ev Energy Efficiency Optimizationmentioning

confidence: 99%

“…Considering the challenges of energy optimization for PHEVs due to system complexity and physical and operational constraints, C. Liu et al [159] propose an optimal power management algorithm for PHEVs based on Q-learning and neuro-dynamic programming (NDP), where the evaluated future trip information is incorporated into NDP to make in-vehicle learning possible and effective. Similarly, the authors in [152] also propose an energy-efficient RL-based power management algorithm for PHEVs, which outputs the engine working power based on the SoC of battery, engine speed, and power demand. Different from the above work which only focus on the…”

Section: Ev Energy Efficiency Optimizationmentioning

confidence: 99%

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Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

Wang¹,

Zhu²,

Hossain³

2021

Preprint

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As one of the most promising applications in future Internet of Things, Internet of Vehicles (IoV) has been acknowledged as a fundamental technology for developing the Intelligent Transportation Systems in smart cities. With the emergence of the sixth generation (6G) communications technologies, massive network infrastructures will be densely deployed and the number of network nodes will increase exponentially, leading to extremely high energy consumption. There has been an upsurge of interest to develop the green IoV towards sustainable vehicular communication and networking in the 6G era. However, as a special mobile ad-hoc network, the energy cost in an IoV system involves the communication and computation energy in addition to the fuel consumption and the electricity cost of moving vehicles. Moreover, the energy harvesting technology, which is likely to be adopted widely in 6G systems, will complicate the optimization of energy efficiency in the entire system. Current studies focus only on part of the energy issues in IoV systems without a comprehensive discussion of the state-of-the-art energy-efficient approaches and the influence of the development of 6G networks on green IoV. In this paper, we present the main considerations for green IoV from five different scenarios, including the communication, computation, traffic, Electric Vehicles (EVs), and energy harvesting management. The literatures relevant to each of the scenarios are compared from the perspective of energy optimization (e.g., with respect to resource allocation, workload scheduling, routing design, traffic control, charging management, energy harvesting and sharing, etc.) and the related factors affecting energy efficiency (e.g., resource limitation, channel state, network topology, traffic condition, etc.). In addition, we introduce the potential challenges and the emerging technologies in 6G for developing green IoV systems. Finally, we discuss the research trends in designing energy-efficient IoV systems.

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“…Highway [19][20][21][22]24,25,27,28,30,[32][33][34][35]38,44,45,49,[53][54][55]57,60,64,66,69,70,73,84,86,[88][89][90][91][93][94][95][96][97][98][99]101,102,105,107,108,112,113,[115][116][117][118][119]121,…”

mentioning

confidence: 99%

Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey

2023

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Electric vehicles are growing in popularity as a form of transportation, but are still underused for several reasons, such as their relatively low range and the high costs associated with manufacturing and maintaining batteries. Many studies using several approaches have been conducted on electric vehicles. Among all studied subjects, here we are interested in the use of machine learning to efficiently manage the energy consumption of electric vehicles, in order to develop intelligent electric vehicles that make quick unprogrammed decisions based on observed data allowing minimal electricity consumption. Our interest is motivated by the adequate results obtained using machine learning in many fields and the increasing but still insufficient use of machine learning to efficiently manage the energy consumption of electric vehicles. From this standpoint, we have built this comprehensive survey covering a broad variety of scientific papers in the field published over the last few years. According to the findings, we identified the current trend and revealed future perspectives.

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Intelligent Energy Management Strategy Based on an Improved Reinforcement Learning Algorithm With Exploration Factor for a Plug-in PHEV

Cited by 22 publications

References 39 publications

Research on the Intelligentization of Education Management Based on ChatGPT Model

Research on the Intelligentization of Education Management Based on ChatGPT Model

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey

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