Deep Reinforcement Learning Based Energy Management For Hybrid Electric Vehicles

Author : Li Yeuching,He Hongwen
Publisher : Springer Nature
Page : 123 pages
File Size : 45,9 Mb
Release : 2022-06-01
Category : Technology & Engineering
ISBN : 9783031792069

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Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles by Li Yeuching,He Hongwen Pdf

The urgent need for vehicle electrification and improvement in fuel efficiency has gained increasing attention worldwide. Regarding this concern, the solution of hybrid vehicle systems has proven its value from academic research and industry applications, where energy management plays a key role in taking full advantage of hybrid electric vehicles (HEVs). There are many well-established energy management approaches, ranging from rules-based strategies to optimization-based methods, that can provide diverse options to achieve higher fuel economy performance. However, the research scope for energy management is still expanding with the development of intelligent transportation systems and the improvement in onboard sensing and computing resources. Owing to the boom in machine learning, especially deep learning and deep reinforcement learning (DRL), research on learning-based energy management strategies (EMSs) is gradually gaining more momentum. They have shown great promise in not only being capable of dealing with big data, but also in generalizing previously learned rules to new scenarios without complex manually tunning. Focusing on learning-based energy management with DRL as the core, this book begins with an introduction to the background of DRL in HEV energy management. The strengths and limitations of typical DRL-based EMSs are identified according to the types of state space and action space in energy management. Accordingly, value-based, policy gradient-based, and hybrid action space-oriented energy management methods via DRL are discussed, respectively. Finally, a general online integration scheme for DRL-based EMS is described to bridge the gap between strategy learning in the simulator and strategy deployment on the vehicle controller.

Author : Teng Liu
Publisher : Morgan & Claypool Publishers
Page : 99 pages
File Size : 52,7 Mb
Release : 2019-09-03
Category : Technology & Engineering
ISBN : 9781681736198

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Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles by Teng Liu Pdf

Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application. In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.

Author : Teng Liu
Publisher : Synthesis Lectures on Advances
Page : 99 pages
File Size : 49,7 Mb
Release : 2019-09-03
Category : Computers
ISBN : 1681736187

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Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles by Teng Liu Pdf

Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application. In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.

Author : Simona Onori,Lorenzo Serrao,Giorgio Rizzoni
Publisher : Springer
Page : 112 pages
File Size : 44,5 Mb
Release : 2015-12-16
Category : Technology & Engineering
ISBN : 9781447167815

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Hybrid Electric Vehicles by Simona Onori,Lorenzo Serrao,Giorgio Rizzoni Pdf

This SpringerBrief deals with the control and optimization problem in hybrid electric vehicles. Given that there are two (or more) energy sources (i.e., battery and fuel) in hybrid vehicles, it shows the reader how to implement an energy-management strategy that decides how much of the vehicle’s power is provided by each source instant by instant. Hybrid Electric Vehicles: •introduces methods for modeling energy flow in hybrid electric vehicles; •presents a standard mathematical formulation of the optimal control problem; •discusses different optimization and control strategies for energy management, integrating the most recent research results; and •carries out an overall comparison of the different control strategies presented. Chapter by chapter, a case study is thoroughly developed, providing illustrative numerical examples that show the basic principles applied to real-world situations. The brief is intended as a straightforward tool for learning quickly about state-of-the-art energy-management strategies. It is particularly well-suited to the needs of graduate students and engineers already familiar with the basics of hybrid vehicles but who wish to learn more about their control strategies.

Author : Jili Tao,Ridong Zhang,Longhua Ma
Publisher : Elsevier
Page : 348 pages
File Size : 50,5 Mb
Release : 2024-06-07
Category : Science
ISBN : 9780443131905

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Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management by Jili Tao,Ridong Zhang,Longhua Ma Pdf

Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management presents the state-of-the-art in hybrid electric vehicle system modelling and management. With a focus on learning-based energy management strategies, the book provides detailed methods, mathematical models, and strategies designed to optimize the energy management of the energy supply module of a hybrid vehicle.The book first addresses the underlying problems in Hybrid Electric Vehicle (HEV) modeling, and then introduces several artificial intelligence-based energy management strategies of HEV systems, including those based on fuzzy control with driving pattern recognition, multi objective optimization, fuzzy Q-learning and Deep Deterministic Policy Gradient (DDPG) algorithms. To help readers apply these management strategies, the book also introduces State of Charge and State of Health prediction methods and real time driving pattern recognition. For each application, the detailed experimental process, program code, experimental results, and algorithm performance evaluation are provided.Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management is a valuable reference for anyone involved in the modelling and management of hybrid electric vehicles, and will be of interest to graduate students, researchers, and professionals working on HEVs in the fields of energy, electrical, and automotive engineering. Provides a guide to the modeling and simulation methods of hybrid electric vehicle energy systems, including fuel cell systems Describes the fundamental concepts and theory behind CNN, MPC, fuzzy control, multi objective optimization, fuzzy Q-learning and DDPG Explains how to use energy management methods such as parameter estimation, Q-learning, and pattern recognition, including battery State of Health and State of Charge prediction, and vehicle operating conditions

Author : Limin Jia,Yong Qin,Baoming Liu,Zhigang Liu,Lijun Diao,Min An
Publisher : Springer Nature
Page : 869 pages
File Size : 55,8 Mb
Release : 2020-04-07
Category : Technology & Engineering
ISBN : 9789811528620

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Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019 by Limin Jia,Yong Qin,Baoming Liu,Zhigang Liu,Lijun Diao,Min An Pdf

This book reflects the latest research trends, methods and experimental results in the field of electrical and information technologies for rail transportation, which covers abundant state-of-the-art research theories and ideas. As a vital field of research that is highly relevant to current developments in a number of technological domains, the subjects it covered include intelligent computing, information processing, Communication Technology, Automatic Control, etc. The objective of the proceedings is to provide a major interdisciplinary forum for researchers, engineers, academicians as well as industrial professionals to present the most innovative research and development in the field of rail transportation electrical and information technologies. Engineers and researchers in academia, industry, and the government will also explore an insight view of the solutions that combine ideas from multiple disciplines in this field. The volumes serve as an excellent reference work for researchers and graduate students working on rail transportation, electrical and information technologies.

Author : Ferdinando Salata,Iacopo Golasi
Publisher : MDPI
Page : 422 pages
File Size : 46,8 Mb
Release : 2020-12-29
Category : Technology & Engineering
ISBN : 9783039366385

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Advances in Theoretical and Computational Energy Optimization Processes by Ferdinando Salata,Iacopo Golasi Pdf

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes.

Author : Hussein T. Mouftah,Melike Erol-Kantarci,Sameh Sorour
Publisher : CRC Press
Page : 517 pages
File Size : 41,7 Mb
Release : 2020-12-17
Category : Science
ISBN : 9781000258974

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Connected and Autonomous Vehicles in Smart Cities by Hussein T. Mouftah,Melike Erol-Kantarci,Sameh Sorour Pdf

This book presents a comprehensive coverage of the five fundamental yet intertwined pillars paving the road towards the future of connected autonomous electric vehicles and smart cities. The connectivity pillar covers all the latest advancements and various technologies on vehicle-to-everything (V2X) communications/networking and vehicular cloud computing, with special emphasis on their role towards vehicle autonomy and smart cities applications. On the other hand, the autonomy track focuses on the different efforts to improve vehicle spatiotemporal perception of its surroundings using multiple sensors and different perception technologies. Since most of CAVs are expected to run on electric power, studies on their electrification technologies, satisfaction of their charging demands, interactions with the grid, and the reliance of these components on their connectivity and autonomy, is the third pillar that this book covers. On the smart services side, the book highlights the game-changing roles CAV will play in future mobility services and intelligent transportation systems. The book also details the ground-breaking directions exploiting CAVs in broad spectrum of smart cities applications. Example of such revolutionary applications are autonomous mobility on-demand services with integration to public transit, smart homes, and buildings. The fifth and final pillar involves the illustration of security mechanisms, innovative business models, market opportunities, and societal/economic impacts resulting from the soon-to-be-deployed CAVs. This book contains an archival collection of top quality, cutting-edge and multidisciplinary research on connected autonomous electric vehicles and smart cities. The book is an authoritative reference for smart city decision makers, automotive manufacturers, utility operators, smart-mobility service providers, telecom operators, communications engineers, power engineers, vehicle charging providers, university professors, researchers, and students who would like to learn more about the advances in CAEVs connectivity, autonomy, electrification, security, and integration into smart cities and intelligent transportation systems.

Author : Arunesh Kumar Singh,Bhavnesh Kumar,Ibraheem,Asheesh Kumar Singh,Shahida Khatoon
Publisher : CRC Press
Page : 203 pages
File Size : 52,9 Mb
Release : 2023-10-16
Category : Technology & Engineering
ISBN : 9781000963465

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Intelligent Control for Modern Transportation Systems by Arunesh Kumar Singh,Bhavnesh Kumar,Ibraheem,Asheesh Kumar Singh,Shahida Khatoon Pdf

The book comprehensively discusses concepts of artificial intelligence in green transportation systems. It further covers intelligent techniques for precise modeling of complex transportation infrastructure, forecasting and predicting traffic congestion, and intelligent control techniques for maximizing performance and safety. It further provides MATLAB® programs for artificial intelligence techniques. It discusses artificial intelligence-based approaches and technologies in controlling and operating solar photovoltaic systems to generate power for electric vehicles. Highlights how different technological advancements have revolutionized the transportation system. Presents core concepts and principles of soft computing techniques in the control and management of modern transportation systems. Discusses important topics such as speed control, fuel control challenges, transport infrastructure modeling, and safety analysis. Showcases MATLAB® programs for artificial intelligence techniques. Discusses roles, implementation, and approaches of different intelligent techniques in the field of transportation systems. It will serve as an ideal text for professionals, graduate students, and academicians in the fields of electrical engineering, electronics and communication engineering, civil engineering, and computer engineering.

Author : China Society of Automotive Engineers
Publisher : Springer Nature
Page : 1670 pages
File Size : 43,8 Mb
Release : 2022-01-13
Category : Technology & Engineering
ISBN : 9789811620904

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Proceedings of China SAE Congress 2020: Selected Papers by China Society of Automotive Engineers Pdf

These proceedings gather outstanding papers presented at the China SAE Congress 2020, held on Oct. 27-29, Shanghai, China. Featuring contributions mainly from China, the biggest carmaker as well as most dynamic car market in the world, the book covers a wide range of automotive-related topics and the latest technical advances in the industry. Many of the approaches in the book will help technicians to solve practical problems that affect their daily work. In addition, the book offers valuable technical support to engineers, researchers and postgraduate students in the field of automotive engineering.

Author : China Society of Automotive Engineers
Publisher : Springer Nature
Page : 1601 pages
File Size : 53,6 Mb
Release : 2024-06-02
Category : Electronic
ISBN : 9789819702527

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Proceedings of China SAE Congress 2023: Selected Papers by China Society of Automotive Engineers Pdf

Author : Maude Josée Blondin,João Pedro Fernandes Trovão,Hicham Chaoui,Panos M. Pardalos
Publisher : Springer Nature
Page : 434 pages
File Size : 44,8 Mb
Release : 2022-05-28
Category : Science
ISBN : 9783030844745

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Intelligent Control and Smart Energy Management by Maude Josée Blondin,João Pedro Fernandes Trovão,Hicham Chaoui,Panos M. Pardalos Pdf

This volume aims to provide a state-of-the-art and the latest advancements in the field of intelligent control and smart energy management. Techniques, combined with technological advances, have enabled the deployment of new operating systems in many engineering applications, especially in the domain of transport and renewable resources. The control and energy management of transportation and renewable resources are shifting towards autonomous reasoning, learning, planning and operating. As a result, these techniques, also referred to as autonomous control and energy management, will become practically ubiquitous soon. The discussions include methods, based on neural control (and others) as well as distributed and intelligent optimization. While the theoretical concepts are detailed and explained, the techniques presented are tailored to transport and renewable resources applications, such as smart grids and automated vehicles. The reader will grasp the most important theoretical concepts as well as to fathom the challenges and needs related to timely practical applications. Additional content includes research perspectives and future direction as well as insight into the devising of techniques that will meet tomorrow’s scientific needs. This contributed volume is for researchers, graduate students, engineers and practitioners in the domains of control, energy, and transportation.

Author : Neeraj Priyadarshi,Sanjeevikumar Padmanaban,Kamal Kant Hiran,Jens Bo Holm-Nielson,Ramesh C. Bansal
Publisher : Walter de Gruyter GmbH & Co KG
Page : 395 pages
File Size : 41,6 Mb
Release : 2021-11-22
Category : Computers
ISBN : 9783110714159

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Artificial Intelligence and Internet of Things for Renewable Energy Systems by Neeraj Priyadarshi,Sanjeevikumar Padmanaban,Kamal Kant Hiran,Jens Bo Holm-Nielson,Ramesh C. Bansal Pdf

This book explains the application of Artificial Intelligence and Internet of Things on green energy systems. The design of smart grids and intelligent networks enhances energy efficiency, while the collection of environmental data through sensors and their prediction through machine learning models improve the reliability of green energy systems. --

Author : Yingmin Jia,Weicun Zhang,Yongling Fu,Shoujun Zhao
Publisher : Springer Nature
Page : 958 pages
File Size : 42,7 Mb
Release : 2022-09-24
Category : Technology & Engineering
ISBN : 9789811962264

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Proceedings of 2022 Chinese Intelligent Systems Conference by Yingmin Jia,Weicun Zhang,Yongling Fu,Shoujun Zhao Pdf

This book constitutes the proceedings of the 18th Chinese Intelligent Systems Conference, CISC 2022, which was held during October 15–16, 2022, in Beijing, China. The 178 papers in these proceedings were carefully reviewed and selected from 185 submissions. The papers deal with various topics in the field of intelligent systems and control, such as multi-agent systems, complex networks, intelligent robots, complex system theory and swarm behavior, event-triggered control and data-driven control, robust and adaptive control, big data and brain science, process control, intelligent sensor and detection technology, deep learning and learning control guidance, navigation and control of aerial vehicles.

Author : Hao Dong,Zihan Ding,Shanghang Zhang
Publisher : Springer Nature
Page : 526 pages
File Size : 55,8 Mb
Release : 2020-06-29
Category : Computers
ISBN : 9789811540950

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Deep Reinforcement Learning by Hao Dong,Zihan Ding,Shanghang Zhang Pdf

Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.