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Author: Nikhil Ramaswamy
Publisher:
ISBN:
Category : Control thoery
Languages : en
Pages :
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Book Description
This thesis (1) reports a new Dynamic Programming (DP) approach, and (2) reports a Real Time Control strategy to optimize the energy management of a Hybrid Electric Vehicle(HEV). Increasing environmental concerns and rise in fuel prices in recent years has escalated interest in fuel efficient vehicles from government, consumers and car manufacturers. Due to this, Hybrid electric vehicles (HEV) have gained popularity in recent years. HEV's have two degrees of freedom for energy flow controls, and hence the performance of a HEV is strongly dependent on the control of the power split between thermal and electrical power sources. In this thesis backward-looking and forward-looking control strategies for two HEV architectures namely series and parallel HEV are developed. The new DP approach, in which the state variable is not discretized, is first introduced and a theoretical base is established. We then prove that the proposed DP produces globally optimal solution for a class of discrete systems. Then it is applied to optimize the fuel economy of HEV's. Simulations for the parallel and series HEV are then performed for multiple drive cycles and the improved fuel economy obtained by the new DP is compared to existing DP approaches. The results are then studied in detail and further improvements are suggested. A new Real Time Control Strategy (RTCS) based on the concept of preview control for online implementation is also developed in this thesis. It is then compared to an existing Equivalent Cost Minimization Strategy (ECMS) which does not require data to be known apriori. The improved fuel economy results of the RTCS for the series and parallel HEV are obtained for standard drive cycles and compared with the ECMS results.
Author: Nikhil Ramaswamy
Publisher:
ISBN:
Category : Control thoery
Languages : en
Pages :
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Book Description
This thesis (1) reports a new Dynamic Programming (DP) approach, and (2) reports a Real Time Control strategy to optimize the energy management of a Hybrid Electric Vehicle(HEV). Increasing environmental concerns and rise in fuel prices in recent years has escalated interest in fuel efficient vehicles from government, consumers and car manufacturers. Due to this, Hybrid electric vehicles (HEV) have gained popularity in recent years. HEV's have two degrees of freedom for energy flow controls, and hence the performance of a HEV is strongly dependent on the control of the power split between thermal and electrical power sources. In this thesis backward-looking and forward-looking control strategies for two HEV architectures namely series and parallel HEV are developed. The new DP approach, in which the state variable is not discretized, is first introduced and a theoretical base is established. We then prove that the proposed DP produces globally optimal solution for a class of discrete systems. Then it is applied to optimize the fuel economy of HEV's. Simulations for the parallel and series HEV are then performed for multiple drive cycles and the improved fuel economy obtained by the new DP is compared to existing DP approaches. The results are then studied in detail and further improvements are suggested. A new Real Time Control Strategy (RTCS) based on the concept of preview control for online implementation is also developed in this thesis. It is then compared to an existing Equivalent Cost Minimization Strategy (ECMS) which does not require data to be known apriori. The improved fuel economy results of the RTCS for the series and parallel HEV are obtained for standard drive cycles and compared with the ECMS results.
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: 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: Amir Taghavipour
Publisher: Springer
ISBN: 3030003140
Category : Technology & Engineering
Languages : en
Pages : 202
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Book Description
Intelligent Control of Connected Plug-in Hybrid Electric Vehicles presents the development of real-time intelligent control systems for plug-in hybrid electric vehicles, which involves control-oriented modelling, controller design, and performance evaluation. The controllers outlined in the book take advantage of advances in vehicle communications technologies, such as global positioning systems, intelligent transportation systems, geographic information systems, and other on-board sensors, in order to provide look-ahead trip data. The book contains simple and efficient models and fast optimization algorithms for the devised controllers to address the challenge of real-time implementation in the design of complex control systems. Using the look-ahead trip information, the authors of the book propose intelligent optimal model-based control systems to minimize the total energy cost, for both grid-derived electricity and fuel. The multilayer intelligent control system proposed consists of trip planning, an ecological cruise controller, and a route-based energy management system. An algorithm that is designed to take advantage of previewed trip information to optimize battery depletion profiles is presented in the book. Different control strategies are compared and ways in which connecting vehicles via vehicle-to-vehicle communication can improve system performance are detailed. Intelligent Control of Connected Plug-in Hybrid Electric Vehicles is a useful source of information for postgraduate students and researchers in academic institutions participating in automotive research activities. Engineers and designers working in research and development for automotive companies will also find this book of interest. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
Author: Dekun Pei
Publisher:
ISBN:
Category : Electric automobiles
Languages : en
Pages :
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Book Description
This thesis (1) reports the development of simulation tools and control strategies for optimizing hybrid electric vehicle (HEV) energy management, and (2) reports the design and testing of a hydraulic hybrid school bus (HHB) prototype. A hybrid vehicle is one that combines two or more energy sources for use in vehicle propulsion. Hybrid electric vehicles have become popular in the consumer market due to their greatly improved fuel economy over conventional vehicles. The control strategy of an HEV has a paramount effect on its fuel economy performance. In this thesis, backward-looking and forward-looking simulations of three HEV architectures (parallel, power-split and 2-mode power-split) are developed. The Equivalent Cost Minimization Strategy (ECMS), which weights electrical power as an equivalent fuel usage, is then studied in great detail and improvements are suggested. Specifically, the robustness of an ECMS controller is improved by linking the equivalence factor to dynamic programming and then further tailoring its functional form. High-fidelity vehicle simulations over multiple drive-cycles are performed to measure the improved performance of the new ECMS controller, and to show its potential for online application.\r : While HEVs are prominent in the consumer market and studied extensively in current literature, hydraulic hybrid vehicles (HHVs) only exist as heavy utility vehicle prototypes. The second half of this thesis reports design, construction, and testing of a hydraulic hybrid school bus prototype. Design considerations, simulation results, and preliminary testing results are reported, which indicate the strong potential for hydraulic hybrids to improve fuel economy in the school bus vehicle segment.
Author: Ying Bai
Publisher: Springer Science & Business Media
ISBN: 1846284694
Category : Technology & Engineering
Languages : en
Pages : 342
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Book Description
This book introduces a dynamic, on-line fuzzy inference system. In this system membership functions and control rules are not determined until the system is applied and each output of its lookup table is calculated based on current inputs. The book describes the real-world uses of new fuzzy techniques to simplify readers’ tuning processes and enhance the performance of their control systems. It further contains application examples.
Author: Lin Lai
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Commercially available Hybrid Electric Vehicles (HEVs) have been around for more than ten years. However, their market share remains small. Focusing only on the improvement of fuel economy, the design tends to reduce the size of the internal combustion engine in the HEV, and uses the electrical drive to compensate for the power gap between the load demand and the engine capacity. Unfortunately, the low power density and the high cost of the combined electric motor drive and battery packs dictate that the HEV has either worse performance or much higher price than the conventional vehicle. In this research, a new design philosophy for parallel HEV is proposed, which uses a full size engine to guarantee the vehicle performance at least as good as the conventional vehicle, and hybridizes with an electrical drive in parallel to improve the fuel economy and performance beyond the conventional cars. By analyzing the HEV fuel economy versus the increasing of the electrical drive power on typical driving conditions, the optimal hybridization electric power capacity is determined. Thus, the full size engine HEV shows significant improvement in fuel economy and performance, with relatively short cost recovery period. A new control strategy, which optimizes the fuel economy of parallel configured charge sustained hybrid electric vehicles, is proposed in the second part of this dissertation. This new approach is a constrained engine on-off strategy, which has been developed from the two extreme control strategies of maximum SOC and engine on-off, by taking their advantages and overcoming their disadvantages. A system optimization program using dynamic programming algorithm has been developed to calibrate the control parameters used in the developed control strategy, so that the control performance can be as close to the optimal solution as possible. In order to determine the sensitivity of the new control strategy to different driving conditions, a passenger car is simulated on different driving cycles. The performances of the vehicle with the new control strategy are compared with the optimal solution obtained on each driving condition with the dynamic programming optimization. The simulation result shows that the new control strategy always keeps its performance close to the optimal one, as the driving condition changes. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/149289
Author: Li Yeuching
Publisher: Springer Nature
ISBN: 3031792068
Category : Technology & Engineering
Languages : en
Pages : 123
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Book Description
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: Wei Liu
Publisher: John Wiley & Sons
ISBN: 1118407393
Category : Transportation
Languages : en
Pages : 428
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Book Description
This is an engineering reference book on hybrid vehicle system analysis and design, an outgrowth of the author's substantial work in research, development and production at the National Research Council Canada, Azure Dynamics and now General Motors. It is an irreplaceable tool for helping engineers develop algorithms and gain a thorough understanding of hybrid vehicle systems. This book covers all the major aspects of hybrid vehicle modeling, control, simulation, performance analysis and preliminary design. It not only systemically provides the basic knowledge of hybrid vehicle system configuration and main components, but also details their characteristics and mathematic models. Provides valuable technical expertise necessary for building hybrid vehicle system and analyzing performance via drivability, fuel economy and emissions Built from the author's industry experience at major vehicle companies including General Motors and Azure Dynamics Inc. Offers algorithm implementations and figures/examples extracted from actual practice systems Suitable for a training course on hybrid vehicle system development with supplemental materials An essential resource enabling hybrid development and design engineers to understand the hybrid vehicle systems necessary for control algorithm design and developments.
Author: Aaron Matthew Sanders
Publisher:
ISBN: 9781267969620
Category :
Languages : en
Pages :
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Book Description
Transportation expends a vast amount of energy in the form of fuels. Environmental concerns and worries of dwindling fuel supplies have led to development of cleaner, more efficient vehicles. These vehicles have opened new avenues of research into novel ways to improve transportation methods using emerging technologies. Hybrid vehicles derive power from two or more power sources to improve performance and efficiency. Fuel cell hybrid vehicles have the potential to emit no pollutants at their end use. Control of hybrid vehicles is inherently more complex than that of conventional vehicles. The instantaneous power split between power sources at a given time is known as a control strategy which determines performance and efficiency characteristics. This study explores design and construction of a fuel cell motorcycle built at the University of California, Davis. The control strategies used and the effects of each strategy are then explored. The strategies explored include proportional battery state of charge (SOC) based control as well as an Equivalent Consumption Minimization Strategy (ECMS). ECMS was found to improve fuel economy by 2.36% in the vehicle tested. ECMS is simply a control change so no hardware modifications are required for similar configuration hybrid vehicles. Fuel cell hybrid vehicles have the potential to provide a reliable and clean transportation system. High costs of fuel cell vehicles and infrastructure competing with low conventional fuel and vehicle costs hinders widespread adoption, yet the control strategies explored could be equally applied to other hybrid systems.
