Neural Adaptive Control Strategy for Hybrid Electric Vehicles with Parallel Powertrain PDF Download
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Author: Yusuf Gurkaynak
Publisher:
ISBN:
Category :
Languages : en
Pages : 268
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Book Description
Author: Yusuf Gurkaynak
Publisher:
ISBN:
Category :
Languages : en
Pages : 268
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Book Description
Author: Yong Chen
Publisher: Springer Nature
ISBN: 9811995664
Category : Technology & Engineering
Languages : en
Pages : 461
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Book Description
This book focuses on transmission systems for pure electric and hybrid vehicles. It first discusses system development and optimization technologies, comprehensively and systematically describing the development trends, structures and technical characteristics, as well as the related technologies and methods. It highlights the principles, implementation process and energy management of the power transmission system based on the pure electric and hybrid mode management method, and examines the reliability and NVH characteristic tests and optimization technologies. Combining research theory and engineering practice, the book is a valuable reference resource for engineering and technical professionals in the field of automobile and related power transmission machinery as well as undergraduate and graduate students.
Author: Bernd Michael Baumann
Publisher:
ISBN:
Category :
Languages : en
Pages : 196
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Book Description
Author: Frederick G. Harmon
Publisher:
ISBN:
Category :
Languages : en
Pages : 566
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Book Description
Parallel hybrid-electric propulsion systems would be beneficial for small unmanned aerial vehicles (UAVs) used for military, homeland security, and disaster monitoring missions involving intelligence, surveillance, or reconnaissance (ISR). The benefits include increased time-on-station and range than electric-powered UAVs and stealth modes not available with gasoline-powered UAVs. A conceptual design of a small UAV with a parallel hybrid-electric propulsion system, an optimization routine for the energy use, the application of a neural network to approximate the optimization results, and simulation results are provided. The two-point conceptual design includes an internal combustion engine sized for cruise and an electric motor and lithium-ion battery pack sized for endurance speed. The flexible optimization routine allows relative importance to be assigned between the use of gasoline, electricity, and recharging. The Cerebellar Model Arithmetic Computer (CMAC) neural network approximates the optimization results and is applied to the control of the parallel hybrid-electric propulsion system. The CMAC controller saves on the required memory compared to a large look-up table by two orders of magnitude. The energy use for the hybrid-electric UAV with the CMAC controller during a one-hour and a three-hour ISR mission is 58% and 27% less, respectively, than for a gasoline-powered UAV.
Author: Simona Onori
Publisher: Springer
ISBN: 1447167813
Category : Technology & Engineering
Languages : en
Pages : 121
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Book Description
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: Bram de Jager
Publisher: Springer Science & Business Media
ISBN: 1447150767
Category : Technology & Engineering
Languages : en
Pages : 159
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Book Description
Optimal Control of Hybrid Vehicles provides a description of power train control for hybrid vehicles. The background, environmental motivation and control challenges associated with hybrid vehicles are introduced. The text includes mathematical models for all relevant components in the hybrid power train. The power split problem in hybrid power trains is formally described and several numerical solutions detailed, including dynamic programming and a novel solution for state-constrained optimal control problems based on the maximum principle. Real-time-implementable strategies that can approximate the optimal solution closely are dealt with in depth. Several approaches are discussed and compared, including a state-of-the-art strategy which is adaptive for vehicle conditions like velocity and mass. Three case studies are included in the book: • a control strategy for a micro-hybrid power train; • experimental results obtained with a real-time strategy implemented in a hybrid electric truck; and • an analysis of the optimal component sizes for a hybrid power train. Optimal Control of Hybrid Vehicles will appeal to academic researchers and graduate students interested in hybrid vehicle control or in the applications of optimal control. Practitioners working in the design of control systems for the automotive industry will also find the ideas propounded in this book of interest.
Author:
Publisher: DIANE Publishing
ISBN: 142896049X
Category :
Languages : en
Pages : 50
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Book Description
Author: Thomas J. Böhme
Publisher: Springer
ISBN: 3319513176
Category : Technology & Engineering
Languages : en
Pages : 549
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Book Description
This book assembles new methods showing the automotive engineer for the first time how hybrid vehicle configurations can be modeled as systems with discrete and continuous controls. These hybrid systems describe naturally and compactly the networks of embedded systems which use elements such as integrators, hysteresis, state-machines and logical rules to describe the evolution of continuous and discrete dynamics and arise inevitably when modeling hybrid electric vehicles. They can throw light on systems which may otherwise be too complex or recondite. Hybrid Systems, Optimal Control and Hybrid Vehicles shows the reader how to formulate and solve control problems which satisfy multiple objectives which may be arbitrary and complex with contradictory influences on fuel consumption, emissions and drivability. The text introduces industrial engineers, postgraduates and researchers to the theory of hybrid optimal control problems. A series of novel algorithmic developments provides tools for solving engineering problems of growing complexity in the field of hybrid vehicles. Important topics of real relevance rarely found in text books and research publications—switching costs, sensitivity of discrete decisions and there impact on fuel savings, etc.—are discussed and supported with practical applications. These demonstrate the contribution of optimal hybrid control in predictive energy management, advanced powertrain calibration, and the optimization of vehicle configuration with respect to fuel economy, lowest emissions and smoothest drivability. Numerical issues such as computing resources, simplifications and stability are treated to enable readers to assess such complex systems. To help industrial engineers and managers with project decision-making, solutions for many important problems in hybrid vehicle control are provided in terms of requirements, benefits and risks.
Author: Teng Liu
Publisher: Morgan & Claypool Publishers
ISBN: 1681736195
Category : Technology & Engineering
Languages : en
Pages : 99
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Book Description
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: Matthew Tyler Young
Publisher:
ISBN:
Category : Energy storage
Languages : en
Pages :
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Book Description
As requirements related to vehicle fuel economy and emissions continue to increase, automakers are developing complex hybrid powertrain control systems to meet these requirements. With the increase in powertrain complexity and performance requirements of a hybrid vehicle, embedded control systems have become an integral part of these vehicles. A hybrid's ability to recapture energy normally lost as heat during braking situations can account for an increase in efficiency of up to 28 percent. This study explores the use of a grade adaptive regeneration strategy for improving a hybrid vehicle's energy recapture capability. The concept of the grade adaptive regeneration strategy was developed using a computer aided simulation model and then implemented on the Mississippi State University Challenge X hybrid vehicle. The real-time performance of the system was evaluated through chassis dynamometer and on-road tests. Substantial improvements over the native hybrid control strategy, including fuel-economy and energy recapture, have been achieved.
