Prediction of journey parameters for the intelligent control of a hybrid electric vehicle by Christopher Patrick Quigley

Cover of: Prediction of journey parameters for the intelligent control of a hybrid electric vehicle | Christopher Patrick Quigley

Published by typescript in [s.l.] .

Written in English

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Edition Notes

Thesis (M.Sc.) - University of Warwick, 1997.

Book details

StatementChristopher Patrick Quigley.
The Physical Object
Paginationxvii,223p.
Number of Pages223
ID Numbers
Open LibraryOL17511050M

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A hybrid electric vehicle is one which utilises both an internal combustion engine and electric motor for propulsion. This combination of power sources make its optimal control difficult. To enable optimised control of a hybrid electric powertrain it is desirable to have a priori knowledge of the characteristics for a given : C.P.

Quigley, R.J. Ball. Predicting journey parameters for the intelligent control of a hybrid electric vehicle Abstract: The results from an ongoing project whose aim is to design an intelligent controller for hybrid electric vehicles is presented.

Predicting journey parameters for the intelligent control of a hybrid electric vehicle. By HASH(0x55dd99e9bc10) Topics: QA76, TL, TK. Publisher: I E E E. OAI identifier: oai: Author: HASH(0x55dd99e9bc10). Quigley, C.P.; “Prediction of Journey Parameters for the Intelligent Control of a Hybrid Electric Vehicle”, Thesis, Dept.

of Engineering, University of Warwick, Coventry, United Kingdom, Cited by: 2. As an artificial intelligent-based control strategy, reinforcement learning (RL) is applied to an energy management strategy of a super-mild hybrid electric vehicle.

Since the intelligent vehicle longitudinal dynamics poses a challenging hybrid control problem, thus to model and control the vehicle longitudinal dynamics effectively, the special modeling approach for hybrid systems, i.e. the MLD framework, and the corresponding MPC technique are adopted in this paper.

Control of hybrid electric vehicles Abstract: Global optimization techniques, such as dynamic programming, serve mainly to evaluate the potential fuel economy of a given powertrain configuration.

Unless the future driving conditions can be predicted during real-time operation but the results obtained using this noncausal approach establish a.

Malasiotis, E. N.; " Prediction of the Journey Energy Requirements for the Optimised Control of a Hybrid Electric Vehicle ", Master of Science Thesis, University of Warwick, Coventry CV4 7AL, UK. The latest developments in the field of hybrid electric vehicles.

Hybrid Electric Vehicles provides an introduction to hybrid vehicles, which include purely electric, hybrid electric, hybrid hydraulic, fuel cell vehicles, plug-in hybrid electric, and off-road hybrid vehicular systems.

It focuses on the power and propulsion systems for these vehicles, including issues related to power and. C.P. Quigley, R.J. Ball, A.M. Vinsome, R.P.

JonesPredicting journey parameters for the intelligent control of a hybrid electric vehicle Proceedings of the IEEE International Symposium on Intelligent Control, IEEE International Symposium on Intelligent Control, IEEE, New York, USA (), pp. Inhybrid electric vehicles experienced a renewed interest from competing manufacturers, owing to its potential for fuel and emissions reduction.

As a result, several variations to the hybrid electric vehicle technology, as explained below, were developed: micro HEVs, mild HEVs, full HEVs and plug-in HEVs.

The vehicle driving cycle performance analyses is performed using a database server and then the optimum data is transformed to update the control parameter of hybrid electric vehicle control unit. Yu, M. Mukai, and T. Kawabe, “A battery management system using nonlinear model predictive control for a hybrid electric vehicle,” in Proceedings of the 7th IFAC Symposium on Advances in Automotive Control, pp.

–, Tokyo, Japan, September Electric vehicles/hybrid electric vehicles (EV/HEV) commercialization is still a challenge in industries in terms of performance and cost. The performance along with cost reduction are two tradeoffs which need to be researched to arrive at an optimal solution.

This book focuses on the convergence of various technologies involved in EV/HEV. The book brings together the research that is being. The additional variables and parameters are the fol- lowing: R defined the tire rolling resistance coefficient, M car is the mass of the vehicle, M d is the mass of the people in the vehicle (only.

Hybrid Electric Vehicles (HEVs) are projected as one of the solutions to the world's need for cleaner and more fuel-efficient vehicles. The efficacy of a Hybrid Electric Vehicle lies in its control strategy. The diligent use of the two power sources, namely the internal combustion engine (ICE) and t.

Jungme Park, et igent vehicle power control based on machine learning of optimal control parameters and prediction of road type and traffic congestion IEEE Trans. Veh. Technol., (), pp. Quigley, C.P., Ball, R.J., Vinsome, A.M., Jones, R.P.: ‘ Predicting journey parameters for the intelligent control of a hybrid electric vehicle ’.

Proc. IEEE Int. Symp. Intelligent Control, Dearborn,pp. – The Use of Vehicle Navigation Information and Prediction of Journey Characteristics for the Optimal Control of Hybrid and Electric Vehicles With the current concerns over vehicle emissions, oil availability and pricing there is a lot of interest in environmentally friendly vehicles such as electric and hybrid electric as ways of.

Intelligent Components for Autonomous Navigation. Guidance of autonomous vehicles by means of structured light (J.L. Lázaro Galilea et al.). Automotive Intelligent Components. Prediction of journey characteristics for the intelligent control of a hybrid electric vehicle (C.P.

Quigley, R.J. Ball). Position Estimation. Obstacle Avoidance. To enable maximized use of electrical energy during the control of a hybrid electric power train, it is desirable to have a priori knowledge of the characteristics for a given journey.

The invention relates to a method for prediction and identification of journey situations. A method for prediction of journey situations has the following steps: defining a plurality of standard journey situations, with each standard journey situation being characterized by a group of driving state parameters; defining a plurality of route state parameters in order to describe the state of a.

Due to the complexity of the real time control for a parallel hybrid electric vehicle it is necessary to integrate all the elements in a high speed CAN communication network (1Mbps) to assure the distributed control of all resources [CANopen, ], [Chacko, ].

The experimental model uses a CANopen network with four slave nodes and one. pline of advanced vehicle technologies in both undergraduate and graduate programs. Inthe principal author of this book shared his first lecture on “Advanced Vehicle Technologies — Design Methodology of Electric and Hybrid Electric Vehicles” with graduate students in mechanical and electri-cal engineering at Texas A&M University.

System and method for vehicle drive cycle determination and energy management is provided. Based on a number of inputs, the system can determine the type of road that the vehicle is likely to drive on as well as the level of traffic congestion that the vehicle is likely to experience. Using these determinations, setpoints for various degrees of freedom, such as engine speed and battery power.

The battery management system (BMS) is a critical component of electric and hybrid electric vehicles. The purpose of the BMS is to guarantee safe and reliable battery operation. To maintain the safety and reliability of the battery, state monitoring and evaluation, charge control, and cell balancing are functionalities that have been implemented in BMS.

Lifetime cost of plug-in hybrid electric vehicles 4. Lifetime cost of fuel-cell electric vehicles 5. Discussion Acknowledgments References 3.

Relative Fuel Economy Potential of Intelligent, Hybrid and Intelligent–Hybrid Passenger Vehicles 1. Introduction 2.

Vehicle models for simulation studies 3. Velocity scheduling using traffic preview 4. History of Electrical Vehicle. Historical Journey of Hybrids and Electric Vehicle ; Economic and Environmental Impact of Electric Hybrid Vehicle; Dynamics of Electric and Hybrid vehicles.

Motion and Dynamic equations for vehicles; Architecture of Hybrid and Electric Vehicles. Vehicle Power Plant and Transmission Characteristics.

Wang Y., Wang Z., Zhang L., Liu M. and Zhu J., Lateral stability enhancement based on a novel sliding mode prediction control for a four-wheel-independently actuated electric vehicle, IET Intelligent Transport Systems 13(1) (), – [30].

Abstract. Aiming at a hybrid electric vehicle with compound power supply, firstly a fuzzy control strategy of engine and motor torque distribution is designed by taking the difference between engine target torque and current torque and SOC of storage battery as inputs and the actual output torque coefficient of engine as output.

Intelligent Components for Autonomous Navigation. Guidance of autonomous vehicles by means of structured light (J.L. Lazaro Galilea et al.). Automotive Intelligent Components.

Prediction of journey characteristics for the intelligent control of a hybrid electric vehicle (C.P. Quigley, R.J. Ball). Position Estimation. Obstacle Avoidance. 1 Abstract—This is the second paper in a series of two that describe our research in intelligent energy management in a hybrid electric vehicle (HEV).

Energy management in Hybrid Electric Vehicles (HEV) has been actively studied recently because of its potential to significantly improve fuel economy and emission control.

Vehicle Services Appointment Book: Hourly x 11 Booking Diary for Car and Truck Services: Mechanic, Valeting, Servicing and Car Wash Providers - 52 Week Book Jayne Carley Planners out of. Abstract—The reliability prediction of hybrid vehicles is of paramount importance for planning, design, control and opera- availability of pure electric vehicle, hybrid electric vehicle, and The parameters of the vehicle used in this paper are obtained from the commer-cial vehicles and literature [11], and have been tabulated in.

Vehicle trajectory prediction and energy management; This paper proposes a combination method of longitudinal control and fuel management for an intelligent Hybrid Electric Vehicle (HEV) fleet. the driver model of the piloting vehicle and the following vehicle was built by using an intelligent fuzzy control method.

Finally, the. The objective of this work is to develop an optimal management strategy to improve the energetic efficiency of a hybrid electric vehicle. The strategy is built based on an extensive experimental study of mobility in order to allow trips recognition and prediction.

For this experimental study, a dedicated autonomous acquisition system was developed. Speed Control of Hybrid Electric Vehicle Using Artificial Intelligence Techniques Japjeet Kaur, Prerna Gaur, Piyush Saxena and Vikas Kumar Department of Instrumentation of Control Engineering, Netaji Subhas Institute of Technology, New Delhi, India E-mail: [email protected], [email protected], [email protected] [email protected] The latest developments in the field of hybrid electric vehicles.

Hybrid Electric Vehicles provides an introduction to hybrid vehicles, which include purely electric, hybrid electric, hybrid hydraulic, fuel cell vehicles, plug-in hybrid electric, and off-road hybrid vehicular systems. It focuses on the power and propulsion systems for these vehicles, including issues related to power and Reviews: 2.

Hybrid Electrical Vehicles Introduction A hybrid electric vehicle (HEV) has two types of energy storage units, electricity and fuel. Electricity means that a battery (sometimes assisted by ultracaps) is used to store the energy, and that an electromotor (from now on called motor) will be used as traction motor.

The maps could be used in an intelligent hybrid electric vehicle in the following way. At any interval of 60 seconds, the predicted speed profile is obtained from the prediction block; The corresponding power request profile over 60s is calculated according to the vehicle and road specification (es.

grade) with VPR. The paper addresses evolution of fuzzy systems for core applications of automotive engineering. The presented study is based on the analysis of bibliography dedicated to fuzzy sets and fuzzy control for ground vehicles.

A special attention is given to fuzzy approaches used in the following domains of automotive engineering: vehicle dynamics control systems, driver and driving .At the same time, shorter braking distance and better control of slip ratio verify the performance of MPC compared with a logical threshold-based control.

Therefore, this study may offer a useful theoretical reference to the choice of braking system and braking control strategy design in hybrid electric vehicle. The electric powertrain is a nonlinear dynamic system, when electric vehicles (EVs) drive under torque control mode, unexpected oscillation of direct-axis current i d and quadrature-axis currents i q of PMSM may occur in case of an unreasonable control parameter of PI regulator is set.

Thus it influences effective and stable output torque of electric powertrain.

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