Automotive Air Conditioning Optimization Control and Diagnosis Quansheng Zhang

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Automotive Air Conditioning Optimization Control and Diagnosis Quansheng Zhang

Preface

Many engineering applications are based on vapor compression cycle, a complex
thermodynamic process that cannot be directly described by low-order differential
equations (ODEs). Such systems have been studied extensively from the viewpoint
of numerical simulation. However, the optimization, control, and fault diagnosis of
such systems is a relatively new subject, which has been developing steadily over the
last decades, inspired partially by research advances in the modeling methodology
of moving-boundary method.
This book presents, in a unified framework, recent results on the output tracking,
energy optimization, and fault diagnosis for the air conditioning system used on onroad
vehicles. The intent is not to include all of the developments on this subject
but, through a focused exposition, to introduce the reader to the tools and methods
that we can employ to improve the current control strategies on product system.
A second objective is to document the occurrence and significance of model-based
optimization and control in automotive air conditioning system, a large class of
applications that have received limited attention in the existing literature, in contrast
to building heating, ventilation, and air conditioning (HVAC) system.
The book is intended primarily as a reference for engineers interested in
optimization and control of thermofluid system and the mathematical modeling of
engineering applications.
More specifically, the book focuses on typical layout of automotive air conditioning
system. The book is organized into four sections. Part I focuses on
control-oriented model development. Chapter 1 introduces the traditional modeling
approach of the thermodynamics of heat exchangers in a passenger compartment.
Chapter 2 exemplifies the model development process of an industrial project for
automotive air conditioning system in heavy-duty trucks. Chapter 3 details the
model order reduction method used in building HVAC system that might shed light
on the difficulty of deriving low-order control-oriented models. Part II focuses on
control design for output tracking of cooling capacity and superheat temperature,
two critical requirements on system performance. Chapter 4 presents the recent
development of robust control of parameter-varying model, a promising framework
that could be used to describe the air conditioning system dynamics at different
cooling loads. Chapter 5 utilizes the H infinity synthesis technique to design local
controller ensuring the trajectories of the two outputs tracked. Chapter 6 utilizes the
mu synthesis technique to improve the tracking performance when both parameter
and system uncertainties exist. Chapter 7 details the theory of mean-field control
that is proved to improve building HVAC efficiency significantly. Chapter 8 details
a specific optimal control theory for constrained nonlinear systems. Both theories
have promising applications in the problem of output tracking in automotive air
conditioning system. Part III focuses on the problem of electrified vehicle energy
management when the air conditioning load is considered. Chapter 9 presents the
recent development of energy management strategy for hybrid electric vehicles
when multiple-objective conflict and trade-off are required. Chapter 10 utilizes
embedded method to design optimal operation sequence for mechanical clutch
connecting the crankshaft and compressor in vehicles with conventional powertrain.
Chapter 11 utilizes hybrid minimum principle to design the optimal operation
sequence when phase change material is stored in an evaporator. Chapter 12 details
controllers for cruising control of hybridized powertrain. Part IV focuses on the fault
diagnosis of automotive air conditioning system. Chapter 13 presents the recent
development of fault detection and isolation methods, as well as their applications
to vehicle systems. Chapter 14 utilizes H infinity filter to detect and isolate a variety
of fault types, such as actuator fault, sensor fault, and parameter fault. Chapter
15 evaluates the performance of automated fault detection and diagnosis tools
developed for building HVAC system.
I am grateful to Marcello Canova, my advisor in the Department of Mechanical
and Aerospace Engineering at the Ohio State University, for having created a
stimulating atmosphere of academic excellence, within which the research that led
to this book was performed over my graduate study. I am also indebted to John
Kessels from DAF Trucks, Professor P.P.J. van den Bosch from Eindhoven University
of Technology, Professor Chang Duan from Prairie View A&M University,
Professor Fen Wu from North Carolina State University, Professor Simona Onori
from Clemson University, Professor Pierluigi Pisu from Clemson University, and
Professor David Yuill from the University of Nebraska.
I would like to express my gratitude to my parents Hechuan Zhang and Xiuying
Zhang for their affection and unquestioning support. The presence of my wife
Marina Neklepaeva beside me made the completion of this book all the more

Contents

Part I Model Development
1 CFD-Based Modeling of Heat Transfer in a Passenger
Compartment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Tiezhi Sun, Qian Jiang, and PengchuanWang
2 Model Development for Air Conditioning Systemin Heavy
Duty Trucks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
J.T.B.A. Kessels and P.P.J. van den Bosch
3 Aggregation-Based Thermal Model Reduction . . . . . . . . . . . . . . . . . . . . . . . . . 29
Kun Deng, Shengbo Eben Li, Sisi Li, and Zhaojian Li
Part II Control
4 RobustH1 Switching Control of Polytopic
Parameter-Varying Systems via Dynamic Output Feedback . . . . . . . . . . 53
Chengzhi Yuan, Chang Duan, and Fen Wu
5 Output Feedback Control of Automotive Air Conditioning
System Using H1 Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Quansheng Zhang and Marcello Canova
6 Improving Tracking Performance of Automotive Air
Conditioning System via  Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Quansheng Zhang and Marcello Canova
7 Mean-Field Control for Improving Energy Efficiency . . . . . . . . . . . . . . . . . 125
Sisi Li, Shengbo Eben Li, and Kun Deng
8 Pseudospectral Optimal Control of Constrained
Nonlinear Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
Shengbo Eben Li, Kun Deng, Xiaoxue Zhang,
and Quansheng Zhang
Part III Optimization
9 Multi-Objective Supervisory Controller for Hybrid
Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
Stefano Marelli and Simona Onori
10 Energy-Optimal Control of an Automotive
Air Conditioning System for Ancillary Load Reduction .. . . . . . . . . . . . . . 217
Quansheng Zhang, Stephanie Stockar, and Marcello Canova
11 Modeling Air Conditioning System with Storage
Evaporator for Vehicle Energy Management . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
Quansheng Zhang and Marcello Canova
12 Cruising Control of Hybridized Powertrain forMinimized
Fuel Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
Shengbo Eben Li, Shaobing Xu, Kun Deng,
and Quansheng Zhang
Part IV Fault Diagnosis
13 Fault Detection and Isolation with Applications to Vehicle Systems . . 293
Pierluigi Pisu
14 Fault Detection and Isolation of Automotive
Air Conditioning Systems using First Principle Models . . . . . . . . . . . . . . . 323
Quansheng Zhang and Marcello Canova
15 Evaluating the Performance of Automated Fault Detection
and Diagnosis Tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343
David Yuill
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359