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Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
GT CONFERENCE 2014 Oct. 20th - Frankfurt
Dipl. Ing. Thorsten Krenek Ass. Prof. Dr. Thomas Lauer
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 2
Overview
Introduction
Comprehensive vehicle modelling
Development of the simulation model
Powertrain optimization
Problemdefinition and constraints
Applied optimization software
Experimental results
Conclusions and outlook
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 3
Introduction Motivation
Electric hybrid powertrains are a promising approach in terms of CO2
emissions
Higher electrificication higher complexity higher degree of freedom
Operation strategy
Component dimensioning
Thermal management
Development and application of numerical simulation models and
optimization software
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 4
Overview
Introduction
Comprehensive vehicle modelling
Development of the simulation model
Powertrain optimization
Problemdefinition and constraints
Applied optimization software
Experimental results
Conclusions and outlook
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 5
Numerical Vehicle Simulation Model Overview
The longitudinal vehicle model and all sub-models were built in GT-SUITE
Thermal- management
ICE
Operation Strategy
Battery
Aux. Driving Cycle
Brake Control E-Maschine
Generator E-Maschine Motor
Clutch- control
PGS
Vehicle
Ambient
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 6
Numerical Vehicle Simulation Model Internal Combustion Engine
Vibe Combustion
Inlet ports Exhaust ports Inlet ports Exhaust ports
Vibe parameters are calculated using neural networks Mean cylinder
Calculation of the influencing variables by neural networks
Detailed ICE and mean value ICE
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 7
Numerical Vehicle Simulation Model Internal Combustion Engine
Why using a detailed instead of map-based ICE model ?
Geometry, Cylindernumber and ICE parameters can be varied
But much higher computational effort
Mean Value Models
Realtime capability
ICE parameters still variable
Variations must be included in
neural network training
Lower accuracy
Simplification necessary for optimization purposes
Fast Running Models (FRM)
Simplified geometries
Still Good accuracy
Lower computational effort
But to slow for optimization
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 8
Coolant Cyle with and without using ICE Waste Heat
Numerical Vehicle Simulation Model Thermal management - coolant circle
ICE waste-heat
Coolant Cabin-air Heat-exchange
Electric heater
Using Waste-Heat
Without waste-heat usage
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 9
Coolant Cyle with and without using ICE Waste Heat
Numerical Vehicle Simulation Model Thermal management - coolant circle
ICE waste-heat
Coolant Cabin-air Heat-exchange
Electric heater
Using Waste-Heat
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 10
Overview
Introduction
Comprehensive vehicle modelling
Development of the simulation model
Powertrain optimization
Problemdefinition and constraints
Applied optimization software
Experimental results
Conclusions and outlook
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 11
Optimization Goal
Minimized fuel consumption for specified driving cycles
Constraints
Battery state-of-charge (SOC) at the end of the driving cycle must be equal to the
reference solutions, otherwise penalty consumption will be added
Maximum deviation of the driving-cycle of +/- 1 km/hr or +/- 1 second
At cold ambient conditions (-10C) a specific cabin temperature must be achieved
after five minutes
Powertrain Optimization Problemdefinition and constraints
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 12
Why using numerical optimization?
higher degrees of freedom at electro-hybrid powertrains
High computational effort for the objective
function calculation
Development of numerical optimization
software for continuous parameter variations
Cooperation with the workgroup for algorithms
and data structures at the Vienna University of
Technology
Combination of metaheuristics (Particle Swarm,
Genetic Algorithms, Evolutionary Algorithms)
Model approximation by neural networks
Powertrain Optimization Optimization Software
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 13
For the investigations two different driving cycles were considered
Ambient Temperatures -10C, 20C, 40C
Min and max SOC at cycle start
Powertrain Optimization Experimental Results
Vehi
cle
Spe
ed [k
m/h
]
Time [s]
NEFZ US06
33 km/h Average speed Low dynamic
77 km/h Average speed High dynamic
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 14
Optimization and parameter analyse using Self-organizing-maps (SOMs)
Powertrain Optimization Experimental results example: US06 at cold ambient conditions
Amount of calculated solutions Fuel consumption
low
high high
low
low
high
Maximum used generator power
Valu
es
Valu
es
Max. e-heat power
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 15
ICE operation points distribution
High powerdemand ICE at high load / speed lambda < 1 necessary
Limitation of the max.
generator power
most of the operation time is
at lower speed with higher
efficiency
Powertrain Optimization Experimental results example: US06 at cold ambient conditions
Low efficiency High efficiency
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 16
Electrical heat demand
Electrical heating deactivated
at optimized solutions
higher usage of the coolant
thermal energy
Disadvantage: lower coolant
temperature higher friction
Nearly the same thermal
comfort during heat up
Powertrain Optimization Experimental results example: US06 at cold ambient conditions
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 17
Overview
Introduction
Comprehensive vehicle modelling
Development of the simulation model
Powertrain optimization
Problemdefinition and constraints
Applied optimization software
Experimental results
Conclusions and outlook
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 18
Conclusions
For investigations of an electro-hybrid powertrain a comprehensive
longitudinal model was developed with GT-SUITE and calibrated with
measurements on the engine test bench and dynamometer
Using numerical optimization software the operating strategy and
powertrain components were optimized under different ambient
conditions and driving cycles
For the specific driving cycles optimized operating strategies could be
developed with respect to fuel consumption using numerical optimization
methods
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GT Conference 2014 Optimization of a Hybrid Powertrain using a Comprehensive Vehicle Model
20.10.2014 | Frankfurt | T. Krenek | Folie 19
Outlook
In-Car GPS Coupling derivation of
the power demand
Results of optimized strategies with
similar power demand could be used
Optimization of real and longer
driving-cycles
Segmentation of the driving
cycles
Simplification and
approximation of the models
Use of the simulation model and the optimized strategies for real driving
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Thank you for your attention!
Dipl. Ing. Thorsten Krenek [email protected]
Optimization of a Hybrid Powertrain using aComprehensive Vehicle ModelOverviewIntroductionMotivationOverviewNumerical Vehicle Simulation ModelOverviewNumerical Vehicle Simulation ModelInternal Combustion EngineNumerical Vehicle Simulation ModelInternal Combustion EngineNumerical Vehicle Simulation ModelThermal management - coolant circleNumerical Vehicle Simulation ModelThermal management - coolant circleOverviewPowertrain OptimizationProblemdefinition and constraintsPowertrain OptimizationOptimization SoftwarePowertrain OptimizationExperimental ResultsPowertrain OptimizationExperimental results example: US06 at cold ambient conditionsPowertrain OptimizationExperimental results example: US06 at cold ambient conditionsPowertrain OptimizationExperimental results example: US06 at cold ambient conditionsOverviewConclusionsOutlookFoliennummer 20
