Table Of ContentCyber-Physical Vehicle Systems
Methodology and Applications
Synthesis Lectures on
Advances in Automotive
Technologies
Editor
AmirKhajepour,UniversityofWaterloo
Theautomotiveindustryhasenteredatransformationalperiodthatwillseeanunprecedented
evolutioninthetechnologicalcapabilitiesofvehicles.Significantadvancesinnewmanufacturing
techniques,low-costsensors,highprocessingpower,andubiquitousreal-timeaccesstoinformation
meanthatvehiclesarerapidlychangingandgrowingincomplexity.Thesenew
technologies—includingtheinevitableevolutiontowardautonomousvehicles—willultimately
deliversubstantialbenefitstodrivers,passengers,andtheenvironment.SynthesisLectureson
AdvancesinAutomotiveTechnologySeriesisintendedtointroducesuchnewtransformational
technologiesintheautomotiveindustrytoitsreaders.
Cyber-PhysicalVehicleSystems:MethodologyandApplications
ChenLv,YangXing,JunzhiZhang,andDongpuCao
2020
ReinforcementLearning-EnabledIntelligentEnergyManagementforHybridElectric
Vehicles
TengLiu
2019
DeepLearningforAutonomousVehicleControl:Algorithms,State-of-the-Art,and
FutureProspects
SampoKuuti,SaberFallah,RichardBowden,andPhilBarber
2019
NarrowTiltingVehicles:Mechanism,Dynamics,andControl
ChenTangandAmirKhajepour
2019
DynamicStabilityandControlofTrippedandUntrippedVehicleRollover
ZhilinJin,BinLi,andJungxuanLi
2019
iii
Real-TimeRoadProfileIdentificationandMonitoring:TheoryandApplication
YechenQin,HongWang,YanjunHuang,andXiaolinTang
2018
NoiseandTorsionalVibrationAnalysisofHybridVehicles
XiaolinTang,YanjunHuang,HongWang,andYechenQin
2018
SmartChargingandAnti-IdlingSystems
YanjunHuang,SoheilMohagheghiFard,MiladKhazraee,HongWang,andAmirKhajepour
2018
DesignandAvancedRobustChassisDynamicsControlforX-by-WireUnmanned
GroundVehicle
JunNi,JibinHu,andChangleXiang
2018
ElectrificationofHeavy-DutyConstructionVehicles
HongWang,YanjunHuang,AmirKhajepour,andChuanHu
2017
VehicleSuspensionSystemTechnologyandDesign
AvestaGoodarziandAmirKhajepour
2017
Copyright©2020byMorgan&Claypool
Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmittedin
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Cyber-PhysicalVehicleSystems:MethodologyandApplications
ChenLv,YangXing,JunzhiZhang,andDongpuCao
www.morganclaypool.com
ISBN:9781681737317 paperback
ISBN:9781681737324 ebook
ISBN:9781681737331 hardcover
DOI10.2200/S00969ED1V01Y201912AAT010
APublicationintheMorgan&ClaypoolPublishersseries
SYNTHESISLECTURESONADVANCESINAUTOMOTIVETECHNOLOGIES
Lecture#10
SeriesEditor:AmirKhajepour,UniversityofWaterloo
SeriesISSN
Print2576-8107 Electronic2576-8131
Cyber-Physical Vehicle Systems
Methodology and Applications
Chen Lv
NanyangTechnologicalUniversity,Singapore
Yang Xing
NanyangTechnologicalUniversity,Singapore
Junzhi Zhang
TsinghuaUniversity,P.R.China
Dongpu Cao
UniversityofWaterloo
SYNTHESISLECTURESONADVANCESINAUTOMOTIVE
TECHNOLOGIES#10
M
&C Morgan &cLaypool publishers
ABSTRACT
Thisbookstudiesthedesignoptimization,stateestimation,andadvancedcontrolmethodsfor
cyber-physicalvehiclesystems(CPVS)andtheirapplicationsinreal-worldautomotivesystems.
First,inChapter1,keychallengesandstate-of-the-artofvehicledesignandcontrolinthecon-
textofcyber-physicalsystemsareintroduced.InChapter2,acyber-physicalsystem(CPS)based
frameworkisproposedforhigh-levelco-designoptimizationoftheplantandcontrollerparam-
eters for CPVS, in view of vehicle’s dynamic performance, drivability, and energy along with
differentdrivingstyles.Systemdescription,requirements,constraints,optimizationobjectives,
and methodology are investigated. In Chapter 3, an Artificial-Neural-Network-based estima-
tion method is studied for accurate state estimation of CPVS. In Chapter 4, a high-precision
controllerisdesignedforasafety-criticalCPVS.Thedetailedcontrolsynthesisandexperimen-
talvalidationarepresented.Theapplicationresultspresentedthroughoutthebookvalidatethe
feasibilityandeffectivenessoftheproposedtheoreticalmethodsofdesign,estimation,control,
andoptimizationforcyber-physicalvehiclesystems.
KEYWORDS
cyber-physicalvehiclesystems,co-designoptimization,dynamicmodeling,design
spaceexploration,parameteroptimization,stateestimation,neuralnetworks,con-
trollersynthesis,simulationvalidation,experimentaltesting
vii
Contents
Preface ........................................................... ix
1 Introductions ......................................................1
2 Co-DesignOptimizationforCyber-PhysicalVehicleSystem ...............5
2.1 ProblemFormulation .............................................. 5
2.1.1 HierarchicalOptimizationMethodology ....................... 5
2.1.2 SystemDescription......................................... 5
2.1.3 DrivingEvent ............................................. 7
2.1.4 DrivingStyleRecognition ................................... 7
2.1.5 RequirementsfortheDesignandOptimizationofCPVS .......... 9
2.1.6 ConstraintsforVehicleDesignandOptimization ............... 10
2.2 SystemModelingandValidation.................................... 11
2.2.1 ElectricPowertrainsystem .................................. 11
2.2.2 BlendedBrakeSystem ..................................... 12
2.2.3 DynamicModeloftheVehicleandTyre....................... 12
2.2.4 ExperimentalValidation .................................... 13
2.3 ControllerDesignforDifferentDrivingStyles ........................ 13
2.3.1 High-LevelControllerArchitecture .......................... 13
2.3.2 Low-LevelControllerforDifferentDrivingStyles .............. 14
2.4 Driving-Style-BasedPerformanceExplorationandParameter
Optimization ................................................... 16
2.4.1 DesignSpaceExploration .................................. 16
2.4.2 PerformanceExplorationMethodology........................ 16
2.4.3 Driving-Style-OrientedMulti-ObjectiveOptimization........... 16
2.5 OptimizationResultsandAnalysis .................................. 18
2.5.1 OptimizationResultsfortheAggressiveDrivingStyle............ 19
2.5.2 OptimizationResultsoftheModerateDrivingStyle ............. 19
2.5.3 OptimizationResultsoftheConservativeDrivingStyle .......... 21
2.5.4 ComparisonandDiscussion................................. 21
viii
3 StateEstimationofCyber-PhysicalVehicleSystems .....................23
3.1 MultilayerArtificialNeuralNetworksArchitecture ..................... 25
3.1.1 SystemArchitecture ....................................... 25
3.1.2 MultilayerFeed-ForwardNeuralNetwork ..................... 25
3.2 StandardBackpropagationAlgorithm ............................... 27
3.3 Levenberg–MarquardtBackpropagation ............................. 30
3.4 ExperimentalTestingandDataCollection............................ 33
3.4.1 TestingVehicleandScenario ................................ 33
3.4.2 DataCollectionandProcessing .............................. 35
3.4.3 FeatureSelectionandModelTraining......................... 35
3.5 ExperimentResultsandDiscussions................................. 38
3.5.1 ResultsoftheANN-BasedBrakingPressureEstimation .......... 38
3.5.2 ImportanceAnalysisoftheSelectedFeatures ................... 40
3.5.3 ComparisonofEstimationResultswithDifferentLearning
Methods ................................................ 40
4 ControllerDesignofCyber-PhysicalVehicleSystems ....................43
4.1 DescriptionoftheNewlyProposedBBWSystem ...................... 45
4.2 ControlAlgorithmDesignforHydraulicPump-BasedPressure
Modulation..................................................... 47
4.3 ControlAlgorithmDesignforClosed-Loop
Pressure-Difference-LimitingModulation ............................ 49
4.3.1 LinearModulationofOn/OffValve .......................... 49
4.3.2 Closed-LoopPressure-Difference-LimitingControl ............. 53
4.4 Hardware-in-the-LoopTestResults................................. 54
4.4.1 ComparisonofHPBPMandCLPDLControl ................. 56
4.4.2 BrakeBlendingAlgorithmBasedonCLPDLModulation ........ 59
5 Conclusions ......................................................61
References ........................................................63
Authors’Biographies ...............................................73
ix
Preface
Thisbookstudiesthedesignoptimization,stateestimation,andadvancedcontrolmethodsfor
Cyber-Physical Vehicle Systems (CPVS) and their applications in real-world automotive sys-
tems.
InChapter1,keychallengesandstate-of-the-artofvehicledesignandcontrolinthecon-
text of cyber-physical systems are introduced. In Chapter 2, a Cyber-Physical System (CPS)-
basedframeworkforco-designoptimizationofanautomatedelectricvehiclewithdifferentdriv-
ingstyleswasproposed.Themulti-objectiveoptimizationproblemwasformulated.Thedriving
style recognition algorithm was developed using unsupervised machine learning and validated
viavehicletesting.Thesystemmodellingandexperimentalverificationwerecarriedout.Vehicle
control algorithms were synthesized for three typical driving styles with different protocol se-
lections.Theperformanceexplorationmethodologyandalgorithmswereproposed.Testresults
show that the overall performances of the vehicle were significantly improved by the proposed
co-designoptimizationapproach.Futureworkwillbefocusedonrealvehicleapplicationofthe
proposedmethodsandCPSdesignmethodologyimprovement.
InChapter3,anovelprobabilisticestimationmethodofbrakepressureisdevelopedfor
asafetycriticalCPVSbasedonmultilayerArtificialNeuralNetwork(ANN)withLevenberg-
Marquardt Backpropagation Traning (LMBP) training algorithm. The high-level architecture
of the proposed multilayer ANN for brake pressure estimation is illustrated at first. Then, an
efficient algorithm of LMBP method is developed for model training. The real vehicle testing
is carried out on a chassis dynamometer under New European Drive Cycle (NEDC) driving
cycles.Theexperimentalresultsshowthatthedevelopedmodelcanaccuratelyestimatethebrake
pressure,anditsperformanceisadvantageousoverotherlearning-basedmethodswithrespectto
estimationaccuracy,demonstratingthefeasibilityandeffectivenessoftheproposedalgorithm.
In Chapter 4, a typical safety-critical CPVS, i.e., the Brake-By Wire (BBW) system,
wasintroduced.ComparedtotheexistingBBWsystem,thenewlydevelopedsystemenjoysthe
advantage of a simple structure and low cost because only conventional valves and sensors are
addedtotheusualhydrauliclayouts.Twopressuremodulationmethods,namely,theHydraulic
Pump-BasedPressureModulation(HPBPM)andClosed-LoopPressure-Difference-Limiting
(CLPDL) modulation, were proposed to improve the modulation precision of hydraulic brake
