应用智能轮胎车辆集成之动态摩擦预估技术於防抱死系统控制

1Anti-LockBrakeSystemControlUsingAnInnovativeIntelligentTire-VehicleIntegratedDynamicFrictionEstimationTechniqueKanwarBharatSingh,GraduateStudentSaiedTaheri,AssociateProfessorMechanicalEngineeringDepartmentCenterforVehicleSystemsandSafetyIntelligentTransportationLaboratoryVirginiaTech2Stateoftheart-ModernDayChassisControlSystemsIMU(6axis)WheelSpeedSensorsSteeringWheelAngleSensorVehicleWithOn-boardSensorsVehicleStateEstimatorIntegratedChassisControllerControlInputsDriverInputEstimatedStatesABSCMDAFSCMDCDCCMDEstimatedtireforcesandtire–roadfrictioncoefficientControllerOptimizesTheTireUsageOn-lineMeasurementsOfTheState-of-theVehicleKnowledgeOfCurrentTireForceUtilizationLevelAndHandlingLimitsCriticalInputForTheControllerMasterpieceOfBothTechnologicalInnovationAndImpeccableDesignRideandHandlingCharacteristicsAdvancedChassisControlSystemIncreasedVehicleSafetyIncreasedComfortBetterHandlingPerformance

What?How?Why?HostofTechnologicalInnovationsOptimizingtheinteractionbetweenthesubsystemsofavehicle3NonlinearTireandVehicleModelVehicleStateEstimatorArchitectureSteeringWheelAngle

VehicleStateEstimatorDigitalSignalProcessing(DSP)ChipActualVehicleActualSensorOutputEstimatedSensorOutputVehicleCANBusEstimatedStatesVehicleController+-FeedbackCorrectionActualYawRateEstimatedYawRateFx,Fy,FzEstimateOfTheTireForcesInputToTheControllerVirtualSensorExcitedWithDriverInputModelBasedOutputsAndActualSensorsMeasurementsToMakeEstimatesOfUnknownMeasurementsImplementedANonlinearStateEstimatorUsingAHighFidelityVehicleDynamicsModel4Tire-ForceEstimatorPerformanceTireForceEstimatesVehicleCANBusTireForceEstimatorArchitecture4WhatAboutThePerformanceUnderExtremeManeuvers?Situationsinwhichthecontrollersshouldintervenetoavoidamajormishap5PerformanceUnderExtremeConditionsTireForceEstimatesWhatarethemainsourcesoferror?

SignificantErrorInTheTire-ForceEstimatesCouldBeDetrimentalToThePerformanceOfVehicleStabilityControlAlgorithms!!VEHICLEUNSTABLE6TireModelVehicleModelVehicleStateEstimatorInputsVariablesForATypicalTireModelHowexactlydoweestimatethesevariables?Slip-ratioFrictionCoefficientSensorInfoVehicleobserverInfoIndirectEstimationTechniqueCurrentTire-ForceEstimationMethodologyVehicleCANbusVehicleStateEstimatorLoadSlipangleSlip-ratioFrictionCoefficient7effectsofpayloadparametricvariationsontheLWVstatesUncertaintiesOfEachSensorAndStateEstimatorsUsedInTheEstimationOfTheseVariablesReducesTheAccuracyAndReliabilityOfTheTireForceEstimatesIncorrectlydetectedlargebankingangleswhennoneexistede.g.whendrivingandsideslippingonafrozenlake.Modelingerror:Dynamicsoftherollmotionaredifferentduringnormaloperation(allwheelsontheground)andinrolloverphase(intwowheelliftoffcondition).ChallengeistodifferentiatethebiasinducedbyroadbankdisturbancesfromactualeffectofvehiclelateraldynamicsincurrentmeasurementsEffectsofpayloadparametricvariationsonthevehiclemodelstatesIndirectEstimationTechniquesHaveSeveralInherentWeaknessesDrawback…8TheWayForwardDevelopaDirectParameterEstimationTechniqueRobustAndPromptInformationAboutTheContactDynamicsMeasuredDataWouldBeDirectlyAvailableWithoutAnyUncertainty-addingProceduresDirectEstimationTechniqueMethodologyAttachSensorModulesToTheInnerlinerIntelligentTireSystemAdd“Intelligence〞ToTheModernDayPassiveTire9LowGripOn-boardVehicleControllerDriverAssistSystemTheTireofTheFuture(ImprovetheperformanceofcurrentcontrolsystemslikeABS/VSC)VehicleEquippedWithIntelligentTiresTireForceFeedbackBasedAdvancedChassisControlSystemsforVehicleHandlingandActiveSafety“Tire-In-TheLoop(TIL)System〞(Driverscanadjusttheirdrivingstyle)FeedbackFromTheTire10ProjectRoadmap-PathsofDevelopmentPath1TireInstrumentation&TestingPath2SensorSignalProcessing&AlgorithmDevelopmentPath3VehicleIntegration(SensorFusion)Path4DevelopmentOfChassisControlSystemAlgorithmsSensorGluedToTheInnerLinerIn-houseTireTestTrailerBasedTestingOutdoorVehicleBasedTestingFx,Fy,FzRawSignalProcessingAlgorithmFx,Fy,Fz,µEstimateAdditionalVehicleStatesRequiredForDevelopingIntegratedChassisControlAlgorithmsVehicleCANbusVehicleEquippedWithIntelligentTiresAlgorithmForEstimatingTireForcesAndTire-roadFrictionCoefficientCANBusABS/VSC/EBD/AFS/DYCCommandControllerNon–linearVehicleModelTireForceDistributionAlgorithmTireSensorSignalIdentitySensorPlatformsForTireApplicationsConvertToValuableInformationEstimateAdditionalStatesPerformanceImprovement11TireInstrumentationandTestingExtensiveOutdoorTestingHighSpeedTestingWetTestingOutdoorVehicleBasedTestingAsphalt/ConcreteTestingGravelTestingTri-axialaccelerometerSensorplacedinthecrownregionSensorLocationMounting:AdhesiveEvaluatethesystemperformanceinrealworldconditionsGoal:ExamineSensorPerformanceImplementDesignOptimizePath1

Path2Path3Path412XYZSensorSignalforOneTireRotationSensorSignalDYNAMICPHENMENONLinkedtoLeadingEdgeTrailingEdgeTireEngineeringDimensions&CharacteristicsAlgorithmDevelopmentProcessFeatureExtractionAlgorithmRA

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SIGNALTestDataFromExtensiveOutdoorTestingPath1Path2Path3Path4Goal:DeriveacorrelationbetweenthesignalandphysicalphenomenonunderinvestigationRawSignalValuableInformation13Path1Path2Path3Path4SignalProcessingandFeatureExtractionRawSignalPeakDetectionDigitalIntegratorSignalAmplitudeSlopeEstimationPowerSpectralDensityWaveletTransformContactPatchLengthSignalSlopeSignalPower(DomainExtracted)MultiresolutionSignalDecomposition(SignalEnergyContent)LocusOfDeformationVibrationRatioDevelopEstimationAlgorithmsToEstimateVariablesOfInterestSummaryofSignalFeatureExtractionAlgorithms14Load(Fz)EstimationAlgorithmInputsOutputArtificialNeuralNetwork(ANN)BasedParameterEstimationAlgorithmFzFeatures:FootprintlengthRadialDeformationCanwecapturetheloadtransfereffectsusingasinglepointsensor?LongitudinalLoadTransferLateralLoadTransferAccelerationBraking

SteadyStateAxleLoadVariationsOscillationsAtBodyBounceAndWheelHopFrequenciesCriticalforanyvehicledynamicsapplicationPath1Path2Path3Path4Limitation:WorkingWithASinglePointSensor15DynamicTireLoadEstimationAlgorithmCANBUSVehicleEquippedWithIntelligentTiresLoadTransferRatio(LTR)RollAngleEstimate(bankanglecompensated)ParameterAdaptationInformationfromanintelligenttireKalmanFilter(Observer)RollangleRollrateDynamicTireLoadEstimationAlgorithmStaticnormalloadAdaptiveLoadTransferRatio(ALTR)Estimation(adaptiveparameterestimation)Path1Path2Path3Path4DevelopedASensorFusionApproachIntelligenttire

+VehicleCANBus15ExperimentalValidationPath1Path2Path3Path4Extensiveoutdoortestsunderseverehandlingmaneuvers16Path1Path2Path3Path4LeadingEdgeTrailingEdgeLocusOfDeformationMultiresolutionDecompositionHelpustorecognizeslidingconditionsDirecttireslipangleestimationfromthetiresensormeasurementsTireSlip-angleEstimationAlgorithmLateralDisplacementOfTheContactPatchSaturationEffectAtHigherSlipAnglesIdentifyfrequencybandswherevibrationsriseduetoslidingStrainWillSaturate17DynamicTireSlip-angleEstimationAlgorithmTireSlipAngleObserverSingle-trackmodelDynamicsofslipangleVehicleCANBus?Path1Path2Path3Path4(notavailable)availableDevelopedanonlinenonlinearaxle-forceestimatorHighlights:ObserverusessensorinformationalreadyavailableinmoderncarsequippedwithVSCNopriorknowledgeoftirecharacteristics,suchasaPacejkamodel,isrequiredtoimplementtheobserver.Tire-AxleForceEstimatorPerformance

VehicleCANBusNonlinearObserver–Tire-AxleForceEstimatorLongitudinalForceEstimator–PerWheel18EstimationResultsVehicleequippedwithVSCcontroller*EvaluatedperformanceusingthecommercialsoftwareCARSIM18ImprovedPerformanceSpeciallyInTheNonlinearRegionOfHandlingDynamicTireSlip-angleEstimationAlgorithmValidationResultsFeedbackTermIntelligentTirePath1Path2Path3Path4LowFrequencyVehicleCANbusVehicleStateEstimatorHighFrequencyHighFrequency19DynamicTireSlip-ratioEstimationAlgorithmPath1Path2Path3Path4ABSModuleHighFrequencyVibrationsAppearInTheAccelerationDataInTheRadialDirectionOfTheTire.Slip-ratioestimatorSlipstateinthecontactpatchABSslip-ratioestimator–Duringahardbrakingevent-significanterrorinourestimatesofslip-ratio.Getameasureoftheslipstateofthetirebyidentifyinghighfrequencyvibrationsintheaccelerationdata-Feedbackforourslip-ratioestimator.Combinationofslip-ratio+tireslipstateestimator20DynamicTireForceEstimationIntelligentTireVehicleCANBusVehicle&TireModelSENSORFUSIONPath1Path2

Path3Path4HighFrequency(reliable)LowFrequencySelfAligningTorqueObserverInputsteeringwheelangleObserverPerformanceElectricpowersteering(EPS)isbecomingcommoninmoderndaycars..

Lineardisturbanceobserverenablesustoextractselfaligningtorquefromsteeringtorquemeasurements.Observer‘‘Effect-basedApproach”MeasureTheEffectsThatFrictionHasOnTheTiresDuringDriving.AttemptToExtrapolateWhatTheLimitFrictionWillBeBasedOnThisData21FrictionCoefficient(µ)Estimation–PureSlipConditionsTireModelUsed:BrushModelEstimationAlgorithm:NLLSTireModelUsed:BrushModelEstimationAlgorithm:NLLSEstimationAlgorithm:NLLSTireModelUsed:BrushModelTireModelUsed:LinearModelEstimationAlgorithm:RLSTireModelUsed:BrushModelEstimationAlgorithm:RLSFyv/sSlipangleMzv/sSlipangleFyv/sMzFxv/sSlipratioFxv/sSlipratio“Force-SlipMethod〞“Moment-SlipMethod〞“Force-MomentMethod〞“Force-SlipMethod〞“Force-SlipMethod〞ExcitationEstimatorUnderlyingPrincipleLargeLateralExcitation(80-100%)MediumLateralExcitation(50-80%)

SmallLateralExcitation(30-50%)SmallLongitudinalExcitation(0-2%)LargeLongitudinalExcitation(30-100%)Lookedatanumberofdifferentalgorithmsanddidaparametricanalysistostudytheperformanceofeachofthesemethodsunderdifferentlevelsofexcitation22LeftTurnCoverageOfThePresentedEstimationMethodInTheFrictionCircleRightTurnAccelerationDecelerationFrictionLimit

--LateralDynamicsBased--LongitudinalDynamicsBasedLargeExcitationMediumExcitationSmallExcitationLargeExcitationSmallExcitationTypically,duringaseverehandlingmaneuver,vehicleexperiencescombinedslipconditions!!WayForward:DevelopAFrictionEstimatorWithIncreasedCoveragePureslipmethodscoveralmostalloftherangeofpureexcitationAllthesemethodsbasedonpure-slipassumptionmightnothandlecombinedslipconditions.23IncreaseCoverageModelBasedµEstimationNonlinearLeastSquaresParameterEstimationTherequiredparametersfortheestimationalgorithminclude:L.H.SR.H.STireload(Fz)Slipratio(λ)Slipangle()(Unknownparametersbeingestimated)24IntegratedFrictionEstimationAlgorithm–FlowDiagramPureLongitudinalSlipPureLateralSlipCombinedSlipµForce-SlipMethodSmallSlip-ratioMethodForce-SlipMethodLargeSlip-ratioMethodYesYesYesHoldForce-MomentMethodNoYesMoment-Slip

MethodNoForce-Slip

MethodYesYesNoHoldNoCombined-slipTireModelBasedNonlinearLeastSquareParameterEstimationAlgorithmNoNoNoYesIntelligentTirePath1Path2Path3Path425Path1Path2Path3

Path4FrontLeftRearLeftFrontRightRearRightMotivationtoDevelopAdvancedChassisControlSystemsforVehicleHandlingandActiveSafety26Anti-LockBrakeSystem(ABS)Path1Path2Path3

Path4ThecontroltargetofABS:Keepthewheelsfromlocking,thusguaranteeinggoodcontrollabilityofthevehicleandexploitingmaximallythecoefficientoffrictionbetweenthetireandtheroadTargetSlipToMaximizeTheBrakeForceIsDependentOnRoadSurfaceCondition!!BackgroundBrakingForceMagnitudesDependOnTheTireLoadABSModule(OptimalSlipControl)(OptimalBrakeForceDistribution)27PresentABSControlStrategyPath1Path2Path3

Path4Firstpartofthemaneuver(about1.5s)isusedbythecontrolsystemtoadjustbrakingpressureaccordingtotire–roadadherenceconditions.PayloadUnladenLadenRoadSurfaceConditionBasedTargetSlipSelectionTireLoadBasedOptimalForceDistributionInitialinstantsofabrakingmaneuverareoftenusedbytheABScontrollertodetectweightdistribution.ReducesEffectivenessOfTheControllervv28AnIntelligentTireBasedAdaptiveABSAlgorithmPath1Path2