Development of Hybrid Strategies with Optimization and Engine in the-loop Testing
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1 Development of Hybrid Strategies with Optimization and Engine in the-loop Testing Michael Planer, Thorsten Krenek, Thomas Lauer, Bernhard Geringer, Markus Zuschrott
2 Overview 1. Introduction Motivation - Background 2. Hybrid Electric Vehicle (HEV) Modeling Model Setup Numerical Optimization Results of Simulation 3. Test and Verification Test-Bed Setup Simulation vs. Test-Bed 4. Outlook and Next Steps 5. Summary Frankfurt am Main Michael Planer Slide 2
3 1. Introduction Motivation for HEV Major benefits of HEVs: significant improvement of fuel consumption. Therefore, it is a promising concept to contribute to the future CO 2 -targets. On the other hand, powertrain complexity is increased dramatically for HEV s. Beside the dimensioning of the internal combustion engine and the electric components, the operating strategy of the hybrid powertrain is of particular importance to optimize the vehicles fuel consumption. For the best possible solution elaborate numerical optimization strategies must be employed in dependency of numerous vehicle parameters. Objective of the following investigations is the minimization of the vehicle s fuel consumption in the New European Driving Cycle (NEDC) Frankfurt am Main Michael Planer Slide 3
4 Overview 1. Introduction Motivation - Background 2. Hybrid Electric Vehicle (HEV) Modeling Model Setup Numerical Optimization Results of Simulation 3. Test and Verification Test-Bed Setup Simulation vs. Test-Bed Outlook and Next Steps 4. Summary Frankfurt am Main Michael Planer Slide 4
5 2. Model Setup and Numerical Optimization Model Setup Capability of Hybrid Electric Vehicles: Recuperative Braking Boosting Load Point Shifting Start/Stop Electric Driving ICE Topology of the powertrain: BAT MG ATM starter C ICE Internal Combustion Engine C Clutch MG Electric Motor- Generator ATM Automatic Transmission BAT Battery Frankfurt am Main Michael Planer Slide 5
6 2. Model Setup and Numerical Optimization Model Setup A numerical simulation model of a vehicle with mild hybridization was set up in GT-Suite. The 6-cylinder SI engine, the electric components and the vehicle were modeled in accordance with a close-to-series powertrain Frankfurt am Main Michael Planer Slide 6
7 2. Model Setup and Numerical Optimization Model Setup The battery, the electric motor and the SI engine were characterized with maps to provide fast simulation times. mechanical output map em-efficiency map Operating strategies were defined with the GT-EventManager. Operating Strategy El. Drive Load Point Shift Boost Recup. Braking Start/Stop Frankfurt am Main Michael Planer Slide 7
8 2. Model Setup and Numerical Optimization Numerical Optimization Background: Target: One simulation of the complete NEDC-cycle takes approx. 5 minutes Best possible strategy in reasonable time range Approach Metaheuristics Example Particle-Swarm-Optimization a) Definition of a search space b) Equally distributed solution approach c) Particles are moving in the direction of the best solution - No guarantee to find the best solution but + Reasonable calculation time ranges with numerous parameters Frankfurt am Main Michael Planer Slide 8
9 2. Model Setup and Numerical Optimization Numerical Optimization Combination of 4 methods: Monte-Carlo initial solutions Exchange of all solutions Particle-Swarm-Optimization High diversity, large search space no yes Improvements? Surface-Fitting Try to improve best known solution Exchange of solutions which are next to the best solution Genetic Algorithm Recombination of solutions and randomized parameter modifying Downhill-Simplex no Improvements? yes High specialization, small search space Frankfurt am Main Michael Planer Slide 9
10 2. Model Setup and Numerical Optimization Numerical Optimization An interface to GT-Suite was created to control the simulations and parameters automatically Parallel calculations are automatically started by the optimization tool Parameter ranges can be defined Frankfurt am Main Michael Planer Slide 10
11 2. Model Setup and Numerical Optimization Numerical Optimization First approach of optimization: One parameter for Load Point Shifting in separated areas within the NEDC One parameter for all Gear-Shift Speeds One parameter for max. Electric Driving Velocity Specific fuel consumption NEDC [%] - 17,8 % - 20,2 % -33 % 3 Parameters 3 Parameters Conv. DOE Optimizer Optimizer Frankfurt am Main Michael Planer Slide 11 better solutions with heuristic methods in same time range as results with included DOE 18 Parameters
12 2. Model Setup and Numerical Optimization Numerical Optimization Different parameters for each section of the NEDC: Load Point Shifting Gear-Shift Speed Maximum Electric Driving Velocity Specific fuel consumption NEDC [%] - 17,8 % - 20,2 % -33 % Parameters 3 Parameters Conv. DOE Optimizer Optimizer Frankfurt am Main Michael Planer Slide Parameters
13 2. Model Setup and Numerical Optimization Results of Simulation: Load-Spectrum Tendency of operating strategy: Long phases of electric driving combined with aggressive load point shifting to balance the battery s state of charge Load Spectrum: Conv. Powertrain Brake Specific Fuel Consumption Load Spectrum: hybr. Powertrain Brake Specific Fuel Consumption Incidence: points of operation ICE-Torque [Nm] 0 Nm ICE-Torque [Nm] 0 Nm Idle-Speed ICE-Speed [rpm] Idle-Speed ICE-Speed [rpm] Frankfurt am Main Michael Planer Slide 13
14 Overview 1. Introduction Motivation - Background 2. Hybrid Electric Vehicle (HEV) Modeling Model Setup Numerical Optimization Results of Simulation 3. Test and Verification Test-Bed Setup Simulation vs. Test-Bed 4. Outlook and Next Steps 5. Summary Frankfurt am Main Michael Planer Slide 14
15 3. Test and Verification Test-Bed Set-Up Engine in the Loop Test-Bed Engine Load: asynchronous machine Sensors & Actuators Test bench equipment from ETAS Frankfurt am Main Michael Planer Slide 15
16 3. Test and Verification Test -Bed Set-Up Matlab Simulink is used to combine the GT-RealTime HEV model with the ETAS Experiment Environment. ETAS Experiment Environment Matlab Simulink Frankfurt am Main Michael Planer Slide 16
17 3. Test and Verification Simulation vs. Test-Bed 100 specific fuel consumption (NEDC) [%]: Conv. Start-Stop Hybrid Simulation Test Bench Comparison: Good correlation between simulation and test bench results Frankfurt am Main Michael Planer Slide 17
18 Overview 1. Introduction Motivation - Background 2. Hybrid Electric Vehicle (HEV) Modeling Model Setup Numerical Optimization Results of Simulation 3. Test and Verification Test-Bed Setup Simulation vs. Test-Bed 4. Outlook and Next Steps 5. Summary Frankfurt am Main Michael Planer Slide 18
19 4. Outlook and Next Steps Next Steps Further enhancements of the model The engine s mean friction pressure must be considered in dependency of the oil temperature. A thermal model of the engine will be developed and verified with the test bench results. Light-off behavior of the exhaust system to control the emissions. Numerical optimizer will be enhanced with additional optimization methods for integrating a larger amount of parameters Frankfurt am Main Michael Planer Slide 19
20 5. Summary A numerical simulation model of a vehicle with mild hybridization was set up in GT-Suite. The components were characterized with maps to provide real-time capability. In addition, a numerical optimizer based on several heuristic methods was developed to minimize the vehicle s fuel consumption for the NEDC. Optimized operating strategies were found that improved the fuel consumption significantly. The most promising concepts were verified on a engine-in-the-loop test-bed Frankfurt am Main Michael Planer Slide 20
21 Thank you for your attention! Michael Planer, Thorsten Krenek, Thomas Lauer, Bernhard Geringer, Markus Zuschrott
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