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Author: Kshitij Shah
Publisher:
ISBN:
Category : Hybrid electric vehicles
Languages : en
Pages : 95
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Book Description
The fuel economy prediction in an automobile is a significant and complex issue. There are numerous variables involved in a vehicle’s daily usage that influence its fuel economy. This problem is even more complex for a hybrid electric vehicle (HEV), due to the presence of the supervisory controller overseeing the energy management strategy. The control strategies implemented in production vehicles involve the use of hundreds of calibration parameters in the form of Lookup Tables (LUTs). The work described in this document aims to lay the groundwork in resolving this complex issue of fuel economy prediction in an HEV using a model based optimization approach. There are two distinct aspects of the approach utilized here: 1) Calibration and Validation of the Vehicle Models, 2) Optimization of the Supervisory Controller. An Open Loop Vehicle Model is utilized for the calibration and validation aspect. Experimental data corresponding to a driving distance of ~36,000 km collected over the span of 2 years is made available. The vehicle models used for the research represent the same vehicle on which this data was obtained. The calibration, validation and optimization tasks need to consider different weather patterns across the year to aid in accurately estimating the fuel economy. The primary reason for the use of an open loop model for the calibration and validation aspect is to eliminate the effects of the vehicle controller so that an accurate representation of the 'Vehicle Plant' is available. This thesis details the methodology undertaken for validating the open loop model. A novel technique of converting a look-up table into a surface fit to calibrate the same is implemented and the results are discussed. Once validated, the model truly represents the actual vehicle behavior and the results obtained from the optimization performed on it are reliable. The optimization techniques used through the work described here and in further research, are termed as "Derivative-free Simulation based Optimization". There is an absence of a definitive analytical function to describe the control variables as a function of the objective, and there is a vehicle simulator/model in this loop, that ultimately warrants the use of such methods. Finally, implementation of this validated plant in the closed loop model is illustrated using commonly available derivative-free optimization methods.
Author: Kshitij Shah
Publisher:
ISBN:
Category : Hybrid electric vehicles
Languages : en
Pages : 95
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Book Description
The fuel economy prediction in an automobile is a significant and complex issue. There are numerous variables involved in a vehicle’s daily usage that influence its fuel economy. This problem is even more complex for a hybrid electric vehicle (HEV), due to the presence of the supervisory controller overseeing the energy management strategy. The control strategies implemented in production vehicles involve the use of hundreds of calibration parameters in the form of Lookup Tables (LUTs). The work described in this document aims to lay the groundwork in resolving this complex issue of fuel economy prediction in an HEV using a model based optimization approach. There are two distinct aspects of the approach utilized here: 1) Calibration and Validation of the Vehicle Models, 2) Optimization of the Supervisory Controller. An Open Loop Vehicle Model is utilized for the calibration and validation aspect. Experimental data corresponding to a driving distance of ~36,000 km collected over the span of 2 years is made available. The vehicle models used for the research represent the same vehicle on which this data was obtained. The calibration, validation and optimization tasks need to consider different weather patterns across the year to aid in accurately estimating the fuel economy. The primary reason for the use of an open loop model for the calibration and validation aspect is to eliminate the effects of the vehicle controller so that an accurate representation of the 'Vehicle Plant' is available. This thesis details the methodology undertaken for validating the open loop model. A novel technique of converting a look-up table into a surface fit to calibrate the same is implemented and the results are discussed. Once validated, the model truly represents the actual vehicle behavior and the results obtained from the optimization performed on it are reliable. The optimization techniques used through the work described here and in further research, are termed as "Derivative-free Simulation based Optimization". There is an absence of a definitive analytical function to describe the control variables as a function of the objective, and there is a vehicle simulator/model in this loop, that ultimately warrants the use of such methods. Finally, implementation of this validated plant in the closed loop model is illustrated using commonly available derivative-free optimization methods.
Author: Adithya Jayakumar
Publisher:
ISBN:
Category : Calibration
Languages : en
Pages :
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Book Description
The particular simulator and application addressed here is the optimization of fuel economy in hybrid electric vehicles (HEVs). Accurately estimating the energy consumption of hybrid electric vehicles is complicated by the fact that these vehicles have multiple power sources and complex control strategies. As a starting point of this research, to ensure that available vehicle simulators can be validated, a thorough literature review of energy consumption in HEVs was done both on a component and an overall level. This then allowed model validation to be performed. New methods of model validation for the case of vehicle simulators were also developed and are discussed in this dissertation. Also in this document, the optimization framework developed to robustly minimize fuel economy in a hybrid electric vehicle simulator is discussed. Since the vehicle simulator is a hybrid system using LUTs, the methodology developed here will be applicable in many simulation optimization environments.
Author: Siyu Jiang
Publisher:
ISBN:
Category : Hybrid electric vehicles
Languages : en
Pages : 105
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Book Description
The research also investigates methods to reduce number of parameters to optimize, the initialization of the optimization set and ways to generate representative drive cycles based on real-world driving data. The important thing is that these methods are not vehicle-specific and therefore can be migrated to calibration of other HEVs easily.
Author: Kevin Lloyd Snyder
Publisher:
ISBN:
Category : Automobiles
Languages : en
Pages : 138
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Book Description
% Charge Depletion Range o 43% --> 24% Charge Sustaining Fuel Consumption o 47% --> 27% UF-Weighted Fuel Energy Consumption o 9% --> 1% UF-Weighted AC Electric Energy consumption o 38% --> 21% UF-Weighted Total Energy Consumption o 45% --> 26% UF-Weighted Well To Wheel Petroleum Energy Use o 43% --> 31% UF-Weighted Well To Wheel GHG Emissions However, significant error (more than 10%) still exists and more work is needed in: * 1 of 3 Dynamic Performance metrics * 6 of 8 Emissions & Energy Consumption metrics Future work includes adding a torque converter plant model between the engine plant model and the transmission plant model on the front wheel drive powertrain, implementing identified advancements into the engine and transmission plant models, and additional analysis for validation of the engine and transmission plant models. The vehicle plant model now provides higher confidence and higher accuracy (in most cases) for the simulation results, making the vehicle plant model significantly more useful for evaluating fuel economy, dynamic performance, and emissions improvement results when testing the team's controls code changes for optimization.
Author: Lino Guzzella
Publisher: Springer Science & Business Media
ISBN: 3540746927
Category : Technology & Engineering
Languages : en
Pages : 345
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Book Description
The authors of this text have written a comprehensive introduction to the modeling and optimization problems encountered when designing new propulsion systems for passenger cars. It is intended for persons interested in the analysis and optimization of vehicle propulsion systems. Its focus is on the control-oriented mathematical description of the physical processes and on the model-based optimization of the system structure and of the supervisory control algorithms.
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: Ke Fang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
With the dramatic development of the automotive industry and global economy, the motor vehicle has become an indispensable part of daily life. Because of the intensive competition, vehicle manufacturers are investing a large amount of money and time on research in improving the vehicle performance, reducing fuel consumption and meeting the legislative requirement of environmental protection. Engine calibration is a fundamental process of determining the vehicle performance in diverse working conditions. Control maps are developed in the calibration process which must be conducted across the entire operating region before being implemented in the engine control unit to regulate engine parameters at the different operating points. The traditional calibration method is based on steady-state (pseudo-static) experiments on the engine. The primary challenge for the process is the testing and optimisation time that each increases exponentially with additional calibration parameters and control objectives. This thesis presents a basic dynamic black-box model-based calibration method for multivariable control and the method is applied experimentally on a gasoline turbocharged direct injection (GTDI) 2.0L virtual engine. Firstly the engine is characterized by dynamic models. A constrained numerical optimization of fuel consumption is conducted on the models and the optimal data is thus obtained and validated on the virtual system to ensure the accuracy of the models. A dynamic optimization is presented in which the entire data sequence is divided into segments then optimized separately in order to enhance the computational efficiency. A dynamic map is identified using the inverse optimal behaviour. The map is shown to be capable of providing a minimized fuel consumption and generally meeting the demands of engine torque and air-fuel-ratio. The control performance of this feedforward map is further improved by the addition of a closed loop controller. An open loop compensator for torque control and a Smith predictor for air-fuel-ratio control are designed and shown to solve the issues of practical implementation on production engines. A basic pseudo-static engine-based calibration is generated for comparative purposes and the resulting static map is implemented in order to compare the fuel consumption and torque and air-fuel-ratio control with that of the proposed dynamic calibration method. Methods of optimal test signal design and parameter estimation for polynomial models are particularly detailed and studied in this thesis since polynomial models are frequently used in the process of dynamic calibration and control. Because of their ease of implementation, the input designs with different objective functions and optimization algorithms are discussed. Novel design criteria which lead to an improved parameter estimation and output prediction method are presented and verified using identified models of a 1.6L Zetec engine developed from test data obtained on the Liverpool University Powertrain Laboratory. Practical amplitude and rate constraints in engine experiments are considered in the optimization and optimal inputs are further validated to be effective in the black box modelling of the virtual engine. An additional experiment of input design for a MIMO model is presented based on a weighted optimization method. Besides the prediction error based estimation method, a simulation error based estimation method is proposed. This novel method is based on an unconstrained numerical optimization and any output fitness criterion can be used as the objective function. The effectiveness is also evaluated in a black box engine modelling and parameter estimations with a better output fitness of a simulation model are provided.
Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 756
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Book Description
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309268524
Category : Science
Languages : en
Pages : 395
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Book Description
For a century, almost all light-duty vehicles (LDVs) have been powered by internal combustion engines operating on petroleum fuels. Energy security concerns about petroleum imports and the effect of greenhouse gas (GHG) emissions on global climate are driving interest in alternatives. Transitions to Alternative Vehicles and Fuels assesses the potential for reducing petroleum consumption and GHG emissions by 80 percent across the U.S. LDV fleet by 2050, relative to 2005. This report examines the current capability and estimated future performance and costs for each vehicle type and non-petroleum-based fuel technology as options that could significantly contribute to these goals. By analyzing scenarios that combine various fuel and vehicle pathways, the report also identifies barriers to implementation of these technologies and suggests policies to achieve the desired reductions. Several scenarios are promising, but strong, and effective policies such as research and development, subsidies, energy taxes, or regulations will be necessary to overcome barriers, such as cost and consumer choice.
Author: Jeremy Wise
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
In this paper, the need to develop a control strategy and test apparatus for nextgeneration hybrid vehicles was realized. The complexity of today's and future hybridvehicles necessitates the need for an equally advanced method of control that can extractthe optimal fuel economy from the system as a whole. A review of existing hybrid vehicle control strategies was performed. Overall, muchresearch has been done on the optimization of series and parallel type vehicles, butvirtually no information was found on the optimal use of advanced powersplit drivetrains. However, the control strategy concepts explored in the literature are useful, and can beextended to complex architectures like the General Motors Two-Mode design. Theequivalent consumption minimization strategy (ECMS) method developed by Rizzoni etal at the Ohio State University has proven to be a well developed control strategy that hasseen much progress over the last decade. Although it has been only demonstrated onparallel-type vehicles, it was chosen as the basis for the control strategy methodology. An in-depth analysis on the Two-Mode transmission operation was performed. Thefundamental equations for each of its range states were derived for future use indeveloping a plant model, and for use in control strategy development. The torque andspeed capabilities of each of its modes and gears were analysed. A detailed plant model was created to form a virtual test bed for control strategy development purposes. The models use empirical data provided by manufactures, which ensures a reasonable level of accuracy in portraying component constraints and efficiencies. Building on the ECMS, a similar hybrid vehicle control strategy was developed forTwo-Mode transmission based vehicles. It was modified to handle two degrees offreedom as required by the system. Its objective is to constantly minimize the totalequivalent power use in the system which is defined as the sum of the chemical power inthe fuel and the power used by the battery multiplied by an equivalency factor. Overall, the control strategy provides a strong basis for the optimal control of nextgenerationhybrid vehicles incorporating powersplit transmissions. It is suggested thatfurther research be explored in combining rule-based control methods with the developedoptimization based method since rule-based methods can add the stability required forenhanced drivability.
