The Experts below are selected from a list of 1794 Experts worldwide ranked by ideXlab platform
Andrew G. Alleyne - One of the best experts on this subject based on the ideXlab platform.
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Integrated Modeling for Battery Electric Vehicle Transcritical Thermal Management System
2018 Annual American Control Conference (ACC), 2018Co-Authors: Sarah G. Garrow, Christopher T. Aksland, Sunny Sharma, Andrew G. AlleyneAbstract:Dynamic modeling approaches are presented for a battery electric vehicle (BEV) transcritical thermal management System. In BEVs thermal management comprises of both temperature regulation of the passenger cabin and the battery pack. This work proposes that a single Vapor Compression System may provide efficient and effective means to manage the temperature constraints on both Systems. However, the transcritical Vapor Compression System, battery pack, and cabin are complex Systems with coupled behavior among electrical and thermal domains. Dynamic and scalable models provide valuable insight to the coupling among Systems, and allow for rapid thermal management architecture and control design. This potential is demonstrated with a simulation comparison of an air-cooled battery pack with recirculated and exhausted return air and examples of parameter variation analysis important for controller robustness.
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ACC - Integrated Modeling for Battery Electric Vehicle Transcritical Thermal Management System
2018 Annual American Control Conference (ACC), 2018Co-Authors: Sarah G. Garrow, Christopher T. Aksland, Sunny Sharma, Andrew G. AlleyneAbstract:Dynamic modeling approaches are presented for a battery electric vehicle (BEV) transcritical thermal management System. In BEVs thermal management comprises of both temperature regulation of the passenger cabin and the battery pack. This work proposes that a single Vapor Compression System may provide efficient and effective means to manage the temperature constraints on both Systems. However, the transcritical Vapor Compression System, battery pack, and cabin are complex Systems with coupled behavior among electrical and thermal domains. Dynamic and scalable models provide valuable insight to the coupling among Systems, and allow for rapid thermal management architecture and control design. This potential is demonstrated with a simulation comparison of an air-cooled battery pack with recirculated and exhausted return air and examples of parameter variation analysis important for controller robustness.
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hvac System modeling and control Vapor Compression System modeling and control
2018Co-Authors: Bryan P. Rasmussen, Christopher R Price, Bryan Keating, Justin P. Koeln, Andrew G. AlleyneAbstract:In this chapter, we delve deeper into understanding modeling and control approaches for one of the important subSystems in an intelligent building, the HVAC System. Specifically, Vapor Compression Systems (VCS) are the primary energy Systems in building air conditioning, heat pump, and refrigeration Systems. We will discuss standard methods for constructing dynamic models of Vapor Compression Systems, and their relative advantages for analysis, design, control design, and fault detection. The principal interests are moving boundary and finite-volume approaches to capture the salient dynamics of two-phase flow heat exchangers. We will present modeling approaches for auxiliary equipment, such as, valves, compressors, fans, dampers, and heating/cooling coils, allowing the reader to understand the construction of typical HVAC System models. We will then highlight limitations of such models and address advanced modeling approaches for challenging transient scenarios. Finally, we give a summary of single-input, single-output control strategies for HVAC System, with simulation and experimental examples to illustrate their effectiveness.
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Switched linear control for refrigerant superheat recovery in Vapor Compression Systems
Control Engineering Practice, 2016Co-Authors: Herschel C Pangborn, Andrew G. AlleyneAbstract:Abstract Extended durations of liquid refrigerant ingestion by the compressor of a Vapor Compression System (VCS) can lead to damage or failure of this component. While this can be prevented by inserting an accumulator between the eVaporator and compressor, this addition of hardware may be undesirable for applications in which the weight or size of the thermal management System is critical. As an alternative, this paper proposes a switched Linear Quadratic Gaussian (LQG) design to quickly recover the presence of a superheated phase at the exit of the eVaporator using feedback control. Stability analysis of the closed-loop switched System is presented, and application of the control approach in both simulation and on an experimental VCS testbed demonstrate the success of the control design.
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Wiener Modeling of a Closed Loop Vapor Compression System for Extremum Seeking Controller Design
Volume 3: Multiagent Network Systems; Natural Gas and Heat Exchangers; Path Planning and Motion Control; Powertrain Systems; Rehab Robotics; Robot Man, 2015Co-Authors: Bryan Keating, Justin P. Koeln, Andrew G. AlleyneAbstract:This paper demonstrates that the dynamic relationship between the power consumption of a Vapor Compression System under closed loop control and its eVaporator and condenser fan inputs is well described about a nominal operating point by a Wiener model, which is useful for extremum seeking controller design. Information about the input dynamics from the Wiener model is used to evaluate the tradeoff between steady-state error and convergence time for single and dual input extremum seeking controllers in simulation.Copyright © 2015 by ASME
J M Belmanflores - One of the best experts on this subject based on the ideXlab platform.
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application of artificial neural networks for generation of energetic maps of a variable speed Compression System working with r1234yf
Applied Thermal Engineering, 2014Co-Authors: Sergio Ledesma, J M BelmanfloresAbstract:Abstract This paper proposes a new tool that uses artificial neural networks to build energetic maps for a Vapor Compression System working with R1234yf. From these energetic maps, it is possible to visualize and identify the zones with the best performance. Additionally, it was concluded that the temperature of the condensing agent and the brine has a greater influence on the COP than their volumetric flows. Several computer simulations were performed to analyze the impact of changing the configuration of the artificial neural network. A hybrid method to train the artificial neural network was used; this method was a combination of: simulated annealing, regression, and the conjugate gradient in multi-dimensions. The results show that artificial neural networks can be used to predict the COP of a Vapor Compression System, and to analyze how several input parameters of the System may affect its energetic performance. Additionally and for energetic comparison purposes, one artificial neural network was trained using data from a Compression System operating with R134a, and another artificial neural network was trained with R1234yf.
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analysis of a variable speed Vapor Compression System using artificial neural networks
Expert Systems With Applications, 2013Co-Authors: J M Belmanflores, Sergio Ledesma, M G Garcia, J Ruiz, J L RodriguezmunozAbstract:An artificial neural network (ANN) is a mathematical model that is inspired by the operation of biological neural networks. However, this is typically considered a computational model. An ANN can easily adapt to multiple situations and extract information that is apparently hidden in a System. An ANN can be used in three basic configurations: mapping, auto-association and classification. An ANN in a mapping configuration can be used to model a two port System with inputs and outputs. Therefore, a Vapor Compression System can be modeled using an ANN in a mapping configuration. That is, some parameters from the Compression System can be used for input while other parameters can be used as output. The simulation experiments include the design, training and validation of a set of ANNs to find the best architecture while minimizing over-fitting. This paper presents a new method to model a variable speed Vapor Compression System. This method accurately estimates the number of neurons in the hidden layer of an ANN. The analysis and the experimental results provide new insights to understand the dependency between the input and output parameters in a Vapor Compression System, concluding that the model can predict the energetic performance of a variable speed Vapor Compression System. Additionally, the simulation results indicate that an ANN can extract, from the data sets, information that is implicit in the configuration of the Vapor Compression System.
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experimental analysis of r1234yf as a drop in replacement for r134a in a Vapor Compression System
International Journal of Refrigeration-revue Internationale Du Froid, 2013Co-Authors: Joaquin Navarroesbri, J M Mendozamiranda, Adrian Motababiloni, Angel Barragancervera, J M BelmanfloresAbstract:This paper presents an experimental analysis of a Vapor Compression System using R1234yf as a drop-in replacement for R134a. In this work, we compare the energy performance of both refrigerants, R134a and R1234yf, in a monitored Vapor Compression System under a wide range of working conditions. So, the experimental tests are carried out varying the condensing temperature, the eVaporating temperature, the superheating degree, the compressor speed, and the internal heat exchanger use. Comparisons are made taking refrigerant R134a as baseline, and the results show that the cooling capacity obtained with R1234yf in a R134a Vapor Compression System is about 9% lower than that obtained with R134a in the studied range. Also, when using R1234yf, the System shows values of COP about 19% lower than those obtained using R134a, being the minor difference for higher condensing temperatures. Finally, using an internal heat exchanger these differences in the energy performance are significantly reduced.
Daniel J. Burns - One of the best experts on this subject based on the ideXlab platform.
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Model Predictive Control of Multi-zone Vapor Compression Systems
Intelligent Building Control Systems, 2018Co-Authors: Daniel J. Burns, Christopher R. Laughman, Claus Danielson, Stefano Di Cairano, Scott A. BortoffAbstract:While the previous chapter presented modeling and control strategies for Vapor Compression Systems in general, in this chapter, a model predictive controller is designed for a multi-zone Vapor Compression System. Controller requirements representing desired performance of production-scale equipment are provided and include baseline requirements common in control literature (constraint enforcement, reference tracking, disturbance rejection) and also extended requirements necessary for commercial application (selectively deactivating zones, implementable on embedded processors with limitedComputation memory/computation, compatibility with demand response events.). A controller architecture is presented based on model predictive control to meet the requirements. Experiments are presented validating constraint enforcement and automatic deactivation of zones.
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Reconfigurable Model Predictive Control for MultieVaporator Vapor Compression Systems
IEEE Transactions on Control Systems Technology, 2018Co-Authors: Daniel J. Burns, Claus Danielson, Junqiang Zhou, Stefano Di CairanoAbstract:This paper considers the control of a multieVaporator Vapor Compression System (ME-VCS) where individual eVaporators are permitted to turn ON or OFF. We present a model predictive controller (MPC) that can be easily reconfigured for different ON/OFF configurations of the System. In this approach, only the cost function of the constrained finite-time optimal control problem is updated depending on the System configuration. Exploiting the structure of the System dynamics, the cost function is modified by zeroing elements of the state, input, and terminal cost matrices. The advantage of this approach is that cost matrices for each configuration of the ME-VCS do not need to be stored or computed online. This reduces the effort required to tune and calibrate the controller and the amount of memory required to store the controller parameters in a microprocessor. The reconfigurable MPC is compared with a conventional approach in which individual model predictive controllers are independently designed for each ON/OFF configuration. The simulations show that the reconfigurable MPC method provides a similar closed-loop performance in terms of reference tracking and constraint satisfaction to the set of individual model predictive controllers. Further, we show that our controller requires substantially less memory than the alternative approaches. Experiments on a residential two-zone Vapor Compression System further validate the reconfigurable MPC method.
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Proportional-Integral Extremum Seeking for Optimizing Power of Vapor Compression Systems
2016Co-Authors: Daniel J. Burns, Christopher R. Laughman, Martin GuayAbstract:While traditional perturbation-based extremum seeking controllers (ESC) for Vapor Compression Systems have proven effective at optimizing power without requiring a process model, the algorithm’s requirement for multiple distinct timescales has limited the applicability of this method to laboratory tests where boundary conditions can be carefully controlled, or simulation studies with unrealistic convergence times. In this paper, we optimize power consumption through the application of a newly-developed proportional–integral extremum seeking controller (PI-ESC) that converges at the same timescale as the process. This method uses an improved gradient estimation routine previously developed by the authors but also modifies the control law part of the algorithm to include terms proportional to the estimated gradient. PI-ESC is applied to the problem of compressor discharge temperature selection for a Vapor Compression System so that power consumption is minimized. We test the performance of this method using a custom-developed model of a Vapor Compression System written in the Modelica object-oriented modeling language. We compare the convergence times of PI-ESC to our previously developed time-varying ESC method and the conventional perturbation-based ESC method. For the conditions tested, PI-ESC is shown to converge to the optimum in about 15 minutes, whereas TV-ESC converges in 45 minutes and perturbation ESC requires more than 7,000 minutes due to its ine_cient estimation of the gradient. Because of the improved convergence properties of PI-ESC, self-optimization algorithms for HVAC equipment can be deployed into situations where previous methods have failed.
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Proportional-integral extremum seeking for Vapor Compression Systems
2016 American Control Conference (ACC), 2016Co-Authors: Daniel J. Burns, Christopher R. Laughman, Martin GuayAbstract:In this paper, we optimize Vapor Compression System power consumption through the application of a newly-developed proportional-integral extremum seeking controller (PI-ESC) that converges at the same timescale as the process. This method modifies the control law to include terms proportional to the estimated gradient, but this modification of the control law requires a more sophisticated gradient estimator in order to avoid bias. We develop a PI-ESC for which this bias is eliminated. PI-ESC is applied to the problem of compressor discharge temperature setpoint selection for a Vapor Compression System where setpoints are automatically determined so that power consumption is minimized. The Vapor Compression System operates with a regulating feedback controller configured to drive the compressor discharge temperature to setpoints selected by the PI-ESC, and we use a physics-based simulation model to demonstrate that power consumption is minimized dramatically faster than by traditional perturbation-based methods.
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ACC - Proportional-integral extremum seeking for Vapor Compression Systems
2016 American Control Conference (ACC), 2016Co-Authors: Daniel J. Burns, Christopher R. Laughman, Martin GuayAbstract:In this paper, we optimize Vapor Compression System power consumption through the application of a newly-developed proportional-integral extremum seeking controller (PI-ESC) that converges at the same timescale as the process. This method modifies the control law to include terms proportional to the estimated gradient, but this modification of the control law requires a more sophisticated gradient estimator in order to avoid bias. We develop a PI-ESC for which this bias is eliminated. PI-ESC is applied to the problem of compressor discharge temperature setpoint selection for a Vapor Compression System where setpoints are automatically determined so that power consumption is minimized. The Vapor Compression System operates with a regulating feedback controller configured to drive the compressor discharge temperature to setpoints selected by the PI-ESC, and we use a physics-based simulation model to demonstrate that power consumption is minimized dramatically faster than by traditional perturbation-based methods.
J L Rodriguezmunoz - One of the best experts on this subject based on the ideXlab platform.
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analysis of a variable speed Vapor Compression System using artificial neural networks
Expert Systems With Applications, 2013Co-Authors: J M Belmanflores, Sergio Ledesma, M G Garcia, J Ruiz, J L RodriguezmunozAbstract:An artificial neural network (ANN) is a mathematical model that is inspired by the operation of biological neural networks. However, this is typically considered a computational model. An ANN can easily adapt to multiple situations and extract information that is apparently hidden in a System. An ANN can be used in three basic configurations: mapping, auto-association and classification. An ANN in a mapping configuration can be used to model a two port System with inputs and outputs. Therefore, a Vapor Compression System can be modeled using an ANN in a mapping configuration. That is, some parameters from the Compression System can be used for input while other parameters can be used as output. The simulation experiments include the design, training and validation of a set of ANNs to find the best architecture while minimizing over-fitting. This paper presents a new method to model a variable speed Vapor Compression System. This method accurately estimates the number of neurons in the hidden layer of an ANN. The analysis and the experimental results provide new insights to understand the dependency between the input and output parameters in a Vapor Compression System, concluding that the model can predict the energetic performance of a variable speed Vapor Compression System. Additionally, the simulation results indicate that an ANN can extract, from the data sets, information that is implicit in the configuration of the Vapor Compression System.
Sergio Ledesma - One of the best experts on this subject based on the ideXlab platform.
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application of artificial neural networks for generation of energetic maps of a variable speed Compression System working with r1234yf
Applied Thermal Engineering, 2014Co-Authors: Sergio Ledesma, J M BelmanfloresAbstract:Abstract This paper proposes a new tool that uses artificial neural networks to build energetic maps for a Vapor Compression System working with R1234yf. From these energetic maps, it is possible to visualize and identify the zones with the best performance. Additionally, it was concluded that the temperature of the condensing agent and the brine has a greater influence on the COP than their volumetric flows. Several computer simulations were performed to analyze the impact of changing the configuration of the artificial neural network. A hybrid method to train the artificial neural network was used; this method was a combination of: simulated annealing, regression, and the conjugate gradient in multi-dimensions. The results show that artificial neural networks can be used to predict the COP of a Vapor Compression System, and to analyze how several input parameters of the System may affect its energetic performance. Additionally and for energetic comparison purposes, one artificial neural network was trained using data from a Compression System operating with R134a, and another artificial neural network was trained with R1234yf.
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analysis of a variable speed Vapor Compression System using artificial neural networks
Expert Systems With Applications, 2013Co-Authors: J M Belmanflores, Sergio Ledesma, M G Garcia, J Ruiz, J L RodriguezmunozAbstract:An artificial neural network (ANN) is a mathematical model that is inspired by the operation of biological neural networks. However, this is typically considered a computational model. An ANN can easily adapt to multiple situations and extract information that is apparently hidden in a System. An ANN can be used in three basic configurations: mapping, auto-association and classification. An ANN in a mapping configuration can be used to model a two port System with inputs and outputs. Therefore, a Vapor Compression System can be modeled using an ANN in a mapping configuration. That is, some parameters from the Compression System can be used for input while other parameters can be used as output. The simulation experiments include the design, training and validation of a set of ANNs to find the best architecture while minimizing over-fitting. This paper presents a new method to model a variable speed Vapor Compression System. This method accurately estimates the number of neurons in the hidden layer of an ANN. The analysis and the experimental results provide new insights to understand the dependency between the input and output parameters in a Vapor Compression System, concluding that the model can predict the energetic performance of a variable speed Vapor Compression System. Additionally, the simulation results indicate that an ANN can extract, from the data sets, information that is implicit in the configuration of the Vapor Compression System.
