Ihorizon Enabled Energy Management For Electrified Vehicles
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Ihorizon Enabled Energy Management For Electrified Vehicles
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Author : Clara Marina Martinez
language : en
Publisher: Butterworth-Heinemann
Release Date : 2018-09-11
Ihorizon Enabled Energy Management For Electrified Vehicles written by Clara Marina Martinez and has been published by Butterworth-Heinemann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-11 with Technology & Engineering categories.
iHorizon-Enabled Energy Management for Electrified Vehicles proposes a realistic solution that assumes only scarce information is available prior to the start of a journey and that limited computational capability can be allocated for energy management. This type of framework exploits the available resources and closely emulates optimal results that are generated with an offline global optimal algorithm. In addition, the authors consider the present and future of the automotive industry and the move towards increasing levels of automation. Driver vehicle-infrastructure is integrated to address the high level of interdependence of hybrid powertrains and to comply with connected vehicle infrastructure. This book targets upper-division undergraduate students and graduate students interested in control applied to the automotive sector, including electrified powertrains, ADAS features, and vehicle automation. Addresses the level of integration of electrified powertrains Presents the state-of-the-art of electrified vehicle energy control Offers a novel concept able to perform dynamic speed profile and energy demand prediction
Reinforcement Learning Enabled Intelligent Energy Management For Hybrid Electric Vehicles
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Author : Teng Liu
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2019-09-03
Reinforcement Learning Enabled Intelligent Energy Management For Hybrid Electric Vehicles written by Teng Liu and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-03 with Technology & Engineering categories.
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.
Deep Reinforcement Learning Based Energy Management For Hybrid Electric Vehicles
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Author : Li Yeuching
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Deep Reinforcement Learning Based Energy Management For Hybrid Electric Vehicles written by Li Yeuching and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Technology & Engineering categories.
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.
Vehicle Infrastructure Integration Enabled Plug In Hybrid Electric Vehicles For Energy Management
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Author : Yiming He
language : en
Publisher:
Release Date : 2013
Vehicle Infrastructure Integration Enabled Plug In Hybrid Electric Vehicles For Energy Management written by Yiming He and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Abstract: The U.S. federal government is seeking useful applications of Vehicle-Infrastructure Integration (VII) to encourage a greener and more efficient transportation system; Plug-in Hybrid Electric Vehicles (PHEVs) are considered as one of the most promising automotive technologies for such an application. In this study, the author demonstrates a strategy to improve PHEV energy efficiency via the use of VII. This dissertation, which is composed of three published peer-reviewed journal articles, demonstrates the efficacies of the PHEV-VII system as regards to both the energy use and environmental impact under different scenarios. The first article demonstrates the capabilities of and benefits achievable for a power-split drivetrain PHEV with a VII-based energy optimization strategy. With the consideration of several real-time implementation issues, the results show improvements in fuel consumption with the PHEV-VII system under various driving cycles. In the second article, a forward PHEV model with an energy management system and a cycle optimization algorithm is evaluated for energy efficiency. Prediction cycles are optimized using a cycle optimization strategy, which resulted in 56-86% fuel efficiency improvements for conventional vehicles. When combined with the PHEV power management system, about 115% energy efficiency improvements were achieved. The third article focuses on energy and emission impacts of the PHEV-VII system. At a network level, a benefit-cost analysis is conducted, which indicated that the benefits outweighed costs for PHEV and Hybrid Electric Vehicle (HEV) integrated with a VII system at the fleet penetration rate of 20% and 30%, respectively.
Reinforcement Learning Enabled Intelligent Energy Management For Hybrid Electric Vehicles
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Author : Teng Liu
language : en
Publisher: Synthesis Lectures on Advances
Release Date : 2019-09-03
Reinforcement Learning Enabled Intelligent Energy Management For Hybrid Electric Vehicles written by Teng Liu and has been published by Synthesis Lectures on Advances this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-03 with Computers categories.
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.
Reinforcement Learning Enabled Intelligent Energy Management For Hybrid Electric Vehicles
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Author : Teng Liu
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Reinforcement Learning Enabled Intelligent Energy Management For Hybrid Electric Vehicles written by Teng Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Technology & Engineering categories.
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.
Energy Management In Hybrid Electric Vehicles
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Author : Siba Prasada Panigrahi
language : en
Publisher: Butterworth-Heinemann
Release Date : 2016-09-01
Energy Management In Hybrid Electric Vehicles written by Siba Prasada Panigrahi and has been published by Butterworth-Heinemann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-01 with Technology & Engineering categories.
Energy Management in Hybrid Electric Vehicles provides the basics of energy management, powertrain configuration, and optimization in hybrid electric vehicles (HEVs), beginning with an introduction to industry challenges and the state-of-the-art in electric, hybrid, and fuel cell vehicles. It then considers, in detail, critical topics such as HEV architecture, battery technology, and regenerative braking, also providing guidance on different control and simulation models alongside the latest advances in rule-based and optimization-based approaches to energy management. Users will find a rare, practical overview of the knowledge needed to work in this fast-moving area. Provides an overview of the theory and practical examples needed for engineers to confidently analyze hybrid configurations and control strategies Ideal reference for those interested in energy management, hybrid electric vehicles, powertrain configuration, fuel cell vehicles, HEV architecture, battery technology, and regenerative braking Brings together, in a single resource, cutting-edge knowledge from the different fields involved in the development of hybrid electric vehicle technology Offers guidance on different control, simulation, and optimization approaches, enabling the selection of appropriate energy management solutions for particular applications
Hybrid Electric Vehicles
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Author : Simona Onori
language : en
Publisher: Springer
Release Date : 2015-12-28
Hybrid Electric Vehicles written by Simona Onori and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-28 with Technology & Engineering categories.
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.
An Optimal Energy Management Strategy For Hybrid Electric Vehicles
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Author :
language : en
Publisher:
Release Date : 2017
An Optimal Energy Management Strategy For Hybrid Electric Vehicles written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Abstract : Hybrid Electric Vehicles (HEVs) are used to overcome the short-range and long charging time problems of purely electric vehicles. HEVs have at least two power sources. Therefore, the Energy Management (EM) strategy for dividing the driver requested power between the available power sources plays an important role in achieving good HEV performance. This work, proposes a novel real-time EM strategy for HEVs which is named ECMS-CESO. ECMS-CESO is based on the Equivalent Consumption Minimization Strategy (ECMS) and is designed to Catch Energy Saving Opportunities (CESO) while operating the vehicle. ECMS-CESO is an instantaneous optimal controller, i. e., it does not require prediction of the future demanded power by the driver. Therefore, ECMS-CESO is tractable for real-time operation. Under certain conditions ECMS achieves the maximum fuel economy. The main challenge in employing ECMS is the estimation of the optimal equivalence factor L*. Unfortunately, L* is drive-cycle dependent, i. e., it changes from driver to driver and/or route to route. The lack of knowledge about L* has been a motivation for studying a new class of EM strategies known as Adaptive ECMS (A-ECMS). A-ECMS yields a causal controller that calculates L(t) at each moment t as an estimate of L*. Existing A-ECMS algorithms estimate L*, by heuristic approaches. Here, instead of direct estimation of L*, analytic bounds on L* are determined which are independent of the drive-cycle. Knowledge about the range of L*, can be used to adaptively set L(t) as performed by the ECMS-CESO algorithm. ECMS-CESO also defines soft constraints on the battery state of charge (SOC) and a penalty for exceeding the soft constraints. ECMS-CESO is allowed to exceed a SOC soft constraint when an energy saving opportunity is available. ECMS-CESO is efficient since there is no need for prediction and the intensive calculations for finding the optimal control over the predicted horizon are not required. Simulation results for 3 different HEVs are used to confirm the expected performance of ECMS-CESO. This work also investigates the performance of the model predictive control with respect to the predicated horizon length.
Comprehensive Energy Management Eco Routing Velocity Profiles
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Author : Daniel Watzenig
language : en
Publisher: Springer
Release Date : 2017-05-17
Comprehensive Energy Management Eco Routing Velocity Profiles written by Daniel Watzenig and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-17 with Technology & Engineering categories.
The book discusses the emerging topic of comprehensive energy management in electric vehicles from the viewpoint of academia and from the industrial perspective. It provides a seamless coverage of all relevant systems and control algorithms for comprehensive energy management, their integration on a multi-core system and their reliability assurance (validation and test). Relevant European projects contributing to the evolvement of comprehensive energy management in fully electric vehicles are also included.
