model based calibration optimization using machine ... learning (genetic algorithm) for optimization...
TRANSCRIPT
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Model Based Calibration Optimization Using MachineLearning Algorithms
MathWorks Automotive Conference - 2018Plymouth, MIMay 02, 2018
Shehan Haputhanthri and Shuzhen Liu
May 1, 2018
Electrified Powertrain Engineering (EPE)Ford Motor Company
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Outline
Part I : IntroductionPart II : Infrastructure and Process DevelopmentPart III : Example Study
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Outline
Part I : Introduction
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Calibration Optimization Efficiency
Manual powertrain calibration optimizationRequires hardware prototypesSubstantial costEngineering (design validation) time
Design Validation (DV) efficiency improvementUse high fidelity CAE powertrain models (Simulink) for initial calibrationOptimized combination of design variablesReduce prototype vehicles and engineering timeExplore designs which would otherwise not be possible
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Calibration Optimization of Electrified Powertrains
Electrified vehiclesComplex hardware architecture and software controls1000s of design (calibration) variables
Finding the optimum designMillions of design combinationsHigher computation time for high fidelity Simulink SIL modelsSubstantial amount of computing resourcesCluster/ parallel computing for simulationMachine learning (Genetic Algorithm) for optimization100s (even 1000s) of designs to run in parallel
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Parallalization, Licenses and Scalability
Parallel computingRunning 100s/1000s of designs in parallelNot cost effective with standalone licenses
Parallel/distributed computing licenses for MATLAB & SimulinkParallel Computing Toolbox (PCT)MATLAB Distributed Computing Server (MDCS)
Scalable parallel/distributed computingFinal solution test in workstations (before deployed to HPC)HPC utilization for computation
With varying number of workersScalable from sequential to 100s CPUs in parallel
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Outline
Part II : Infrastructure and Process Development
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Distributed Computing Capability
Distributed Computing
HPCFord Internal
Online ClustersPay per Use
Local Cluster (Unused CPUs)MATLAB DCS/Other SW
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Cost Effective Parallel/Distributed Computing Licenses
Model Based Calibration Optimization (MBCO) teamPurchased 1000 MDCS licensesAdded to existing PCT/MDCS license poolDedicated ”Project Group”For license management
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Machine Learning Algorithms for OptimizationNeed of machine learning algorithms
All design possibilities can’t be evaluatedWith in the simulation budget and available time
Change calibration variablesOptimize for multi-objectivesSubjected to multi-constraints
Alternatives evaluating (in-progress)No ”all-in-one” solution algorithmMATLAB Global Optimization ToolboxmodeFrontierSeveral other commercial optimization SW packages
Currently modeFrontier is used as the optimization toolCompatibility with non MathWorks SW toolsOff the shelf optimization algorithms
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Optimization SW packages and Large Scale Parallelization
”Off the shelf” optimization SW packages - IssuesParallel jobs are launched using internal schedulersIndividual job submitted to clusterUses standalone licenseDo not use PCT and MDCS for MATLAB & SimulinkNot suitable for large scale parallel computing
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Final Requirements and Solution
Combining modeFrontier and MATLABMATLAB - Capable of parallelization to any scalemodeFrontier - License issueSimulations to run in HPC
Must use distributed computing licensesPCT and MDCSJobs submitted to HPC from a remote MATLAB Client
New in-house tool and a process developedMATLAB GUI - ModefrontierSCVSPCopyrightedCombines modeFrontier and MATLAB & SimulinkMeets above requirements
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Challenge - 4 Unconnected Dots
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Connecting Dots via a MATLAB GUI - ModefrontierSCVSP
1. Genetic algorithm2. Model + cal file2.5 License mgt.3. Job submission4. Results collection5. Results reporting
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ModefrontierSCVSP - Standardized Modular Solution
MATLAB GUI - Glue CodeCombines HW, SW and Licenses seamlesslyAcross both Windows and Linux platformsUses PCT/MDCSManages the large scale Simulink simulations
Modular (runs simulations in)In workstation with PCT (limited by physical cores)HPC with MDCS (limited by MDCS licenses)
User friendly GUINo coding/programming requiredEasy to train, learn and apply
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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ModefrontierSCVSP - Standardized Modular SolutionSelect to run in local workstation or HPC
Select the number of cores to run
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Possibilities With the New Tool
Scalabale parallel/distributed computingSequential computing(1 CPU) to parallel in HPC (1000 CPUs)Click of a button12000 Simulink simulations (4000-6000 hours)Completed in less than 15 hours (approx.)Using 512 simulations in parallel
modeFrontier combined with MATLAB & SimulinkMachine learning algorithmsDesign of Experiments (DoE)Scheduling (Genetic Algorithms)Multi-objectives (+ multi-constraints) optimizationSelected 12000 out of 1000000+ design combinations
Deployed and used by the MBCO teamShehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Outline
Part III : Example Study
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Optimizing Vehicle Attributes with Battery Power TableBattery power tableOptimum power - different conditionsf(Driver Pwr, Bat. SOC)Linked attributesFuel Economy/EmissionsPerformance/Regeneration/NVH etc.modeFrontier - machine learningOptimize battery power table*Using Genetic Algorithms (MOGA-II)Evolutionary (generation) algorithmSimulation in HPC512 in parallelPopulation size
*Indirect method used - Non Uniform Rational Basis Spline (NURBS) based*Developed by Ford Model Based Systems Engineering (MBSE) team (@ RIC)
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Optimizing Vehicle Attributes with Battery Power Table*
Cost function - maximize vehicle attribute(s) mentioned aboveSingle objective optimization**
Design Iteration0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
% In
crea
se o
f Cos
t Fun
ctio
n V
alue
-20
-15
-10
-5
0
5 "Evolution" of the Cost Function Value
*Exact details not shared due to confidentiality*Not validated with prototypes yet**Unfeasible/error designs excluded
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Acknowledgments
FordPoyu TsouTengko LienDoug BellCiro SotoGary SkiminAlex AkkermanBahij El FadlDennis Resso
EstecoZhendan XueDaniel SchmidtApurva Ghokal
MathWorksBill MaherNathan CrostyWill Wilson
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms
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Thank You!!!
Shehan Haputhanthri and Shuzhen Liu Model Based Calibration Optimization Using Machine Learning Algorithms