335,233 research outputs found

    Improving the Accuracy and Scope of Control-Oriented Vapor Compression Cycle System Models

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    The benefits of applying advanced control techniques to vapor compression cycle systems are well know. The main advantages are improved performance and efficiency, the achievement of which brings both economic and environmental gains. One of the most significant hurdles to the practical application of advanced control techniques is the development of a dynamic system level model that is both accurate and mathematically tractable. Previous efforts in control-oriented modeling have produced a class of heat exchanger models known as moving-boundary models. When combined with mass flow device models, these moving-boundary models provide an excellent framework for both dynamic analysis and control design. This thesis contains the results of research carried out to increase both the accuracy and scope of these system level models. The improvements to the existing vapor compression cycle models are carried out through the application of various modeling techniques, some static and some dynamic, some data-based and some physics-based. Semiempirical static modeling techniques are used to increase the accuracy of both heat exchangers and mass flow devices over a wide range of operating conditions. Dynamic modeling techniques are used both to derive new component models that are essential to the simulation of very common vapor compression cycle systems and to improve the accuracy of the existing compressor model. A new heat exchanger model that accounts for the effects of moisture in the air is presented. All of these model improvements and additions are unified to create a simple but accurate system level model with a wide range of application. Extensive model validation results are presented, providing both qualitative and quantitative evaluation of the new models and model improvements.Air Conditioning and Refrigeration Project 17

    Overview of methods to analyse dynamic data

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    This book gives an overview of existing data analysis methods to analyse the dynamic data obtained from full scale testing, with their advantages and drawbacks. The overview of full scale testing and dynamic data analysis is limited to energy performance characterization of either building components or whole buildings. The methods range from averaging and regression methods to dynamic approaches based on system identification techniques. These methods are discussed in relation to their application in following in situ measurements: -measurement of thermal transmittance of building components based on heat flux meters; -measurement of thermal and solar transmittance of building components tested in outdoor calorimetric test cells; -measurement of heat transfer coefficient and solar aperture of whole buildings based on co-heating or transient heating tests; -characterisation of the energy performance of whole buildings based on energy use monitoring

    Modeling and control of a plastic film manufacturing web process

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    This paper is concerned with the modelling of aplastic film manufacturing process and the development and implementation of a model-based Cross-Directional (CD) controller. The model is derived from first-principles and some empirical relationships. The final validated nonlinear model could provide a useful off-line platform for developing control and monitoring algorithms.A new controller is designed which has a similar structureto that of Internal Model Control (IMC) with the addition ofan observer whose gain is designed to minimise process andmodel mis-match. The observer gain is obtained by solving amulti-objective optimisation problem through the application of a genetic algorithm. The controller is applied to the nonlinear model and simulation results are presented demonstrating improvements that can be achieved by the proposed controller over two existing CD controllers

    Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles

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    © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input- Multiple output systems. This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi- domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.Accepted versio
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