Apply & Innovate Conference 2014 Karlsruhe, September 23 rd - 24 th, 2014

Transkript

1 Apply & Innovate Conference 2014 Karlsruhe, September 23 rd - 24 th, 2014 CAE-based Pre-Tuning of ESP -Subfunctions Dipl.-Ing. (BA) M. Vogler, Dipl.-Ing. A. Lutz Robert Bosch GmbH 1 M.Vogler, A. Lutz Robert Bosch GmbH Alle Rechte vorbehalten, auch bzgl. jeder Verfügung, Verwertung, Reproduktion, Bearbeitung, Weitergabe sowie für den Fall von Schutzrechtsanmeldungen.

2 Overview Introduction ESP basics Subfunction: Model based processing (MbP) CAE environment Motivation Why automation of MbP tuning? Benefits of CAE Method Transfer of process from reality to virtuality Structure of the automation Results Overview of automation & tool Summary & Outlook 2

3 Introduction: ESP -basics Target of ESP : Improvement of stability and steerability of a vehicle in critical driving conditions such as oversteer or understeer Approach: Calculate target yaw rate based upon driver steering input Measure current yaw rate Create correcting yaw moment by brake interventions on single wheels based upon deviation between target and current state Potential Problem: Sensor failures lead to mis-interventions and de-stabilise the car Solution: Model based Processing (MbP) monitors sensors for failures 3

4 Introduction: ESP calibration process Project- Targets Performance Robustness NVH Costs, Lifetime, Quality Definition Prepara- tion Basic Application MbP Application parameter tuning Performance Tuning Verification 4

5 Introduction: CAE environment Established within Bosch Software MakeToolChain Generation with each S/W Baseline possible 5

6 Motivation: Yawrate model monitoring Tuneable Yawrate models schematical overview of reference yawrate ω ref, generated in MbP module 6

7 Motivation: MbP tuning in prototype vehicles Preparation of vehicle & measurement equipment Perform tuning manoeuvres n = 1 variant x 1 load condition x 1 type of tyre Tuning on Test Track n1 Calculation of MbP parameter set by MbP-Toolbox Perform verification manoeuvres MbP-Toolbox Verification on Test Track par 1 n1(par 1 ) Check results of verification manoeuvres in MbP-Toolbox Visualization of verification results in MbP-Toolbox MbP-Toolbox MbP-Toolbox QF1 7

8 Motivation: Challenges for MbP tuning Should happen early in the project but needs representative prototypes Early & robust tuning enables robustness checks over project runtime, late tuning is always risky Increasing number of projects & vehicle variants increases need for MbP tuning e.g. different wheelbase, suspension, active sytems each need different settings Availability of prototypes, test tracks, engineers 8

9 Motivation: CAE benefits of virtual MbP tuning Variation of tyres, load conditions & vehicle model is enabled Only infrequently done in real prototypes in current projects Possibility to increase robustness & quality of parameter set Automated tuning Only very little background-knowledge required Quickly repeated after updates in models Always available Test tracks, prototypes, engineers and know-how are covered by automatic process CAE based MbP Tuning can tackle & solve the challenges! 9

10 Motivation: Target for virtual MbP tuning Increase Engineering Efficiency Migrate vehicle tests to the laboratory For early prototypes: 100% MbP tuning in CAE For representative prototypes: MbP tuned in CAE, verified in a few cars Re-Use of current Toolbox algorithms (no re-development) Increase Quality and Robustness of MbP-Parameterset By simulation of variants, tyres and load conditions, that are unavailable in the real world Find out sensitivity of vehicles against different configurations 10

11 Method: Take over current process? Study in CAE has shown that current tuning process is the best approach No change in the manoeuvres required No increase in number or type of manoeuvres necessary Speed range used for manoeuvers up to now is giving very good results Widening of speed range did not increase the quality of the results Widening of range of lateral acceleration conditions for manoeuvres did not increase quality Decision was made to take over current process into CAE directly 11

12 Method: Automation process Engineer defines n vehicle configurations Simulation of tuning manoeuvres n = vehicle model x load condition x tyre n1 n2 n Individual calculation of parameter sets par 1 par 2 par n par init Simulation of verification manoeuvres n1(par 1 ) n2(par 1 ) n(par 1 ) n1(par 2 ) n2(par 2 ) n(par 2 ) Evaluation of verification QF(par 1 ) QF(par 2 ) QF(par n ) QF(par init ) Visualization Visualization of Results w/ proposal Weighting of best fit parameterset n1 = 70 % par 1 = 85 % n2 = 25 % par 2 = 24 % 12

13 Result: Guided Tool w/ GUI 1) 1a) 1b) 1c 1d 7) 2) 4) 1) Major application-test cases 1a) GPS-based 3-dimensional tracks 1b) Simulation based on measured signals 3 1c) Maneuvers for the App-Process Fahrmanöverkatalog CatX 1d) Pre-Tuning MbP Tuning and Verification 2) Specification of vehicle, tires, trailer 3) Loading condition 4) Subsets of major application-test cases 5) 6) 5) Simulation specific settings 6) Check&Start of test scenario 7) Parameter variation 13

14 Result: Guided Tool w/ GUI, basic configuration Basic Informationen ESP -Controller Project specific information Initial MbP-Parameterset Needed for optimization Are verified & evaluated, too Configuration of Variations Vehicle model Tyre model front & rear Loading condition 14

15 Result: step-by-step automation (1) 1. Definition of Basic Configurations (as before) 2. Check of Signals for Postprocessing Each vehicle configuration is simulated once to check basic functionality Availability of necessary measurement signals is checked 3. Simulation of manoeuvres Performing all tuning manoeuvres with each of n vehicle configuration Calculation & optimization of each vehicle config specific parameter set, par n, by MbP- Toolbox Performing all verification manoeuvres with each vehicle config & each parameter set, n x par n Calculation of Quality Factors for n(par n )-configuration by MbP-Toolbox Tool is designed very user-orientated 15

16 Result: step-by-step automation (2) 4. Visualization of Results (1) Overview of results created in HTML file, contains: Basic Information regarding tuning Description of vehicle configurations All calculated & optimized MbP-Parametersets Statistical Evaluation Overview table with one-view color-code (green, yellow, red) giving direct feedback regarding quality of tuning Example: 16

17 Result: step-by-step automation (3) 5. Visualization of Results (2) Mean value of model quality, MATLAB based tool Mean value for parameter set j for each yawrate model calculated for each vehicle configuration n Engineer dictates weighting factor for each vehicle configuration weight i Engineer gets feedback of best fit based upon weighting Engineer gets spider plot visualization, of model-individual-best-fit and overall-best-fit, limited to best six parameter-sets 17

18 Results: Further Features Single measurement evaluation of simulation results is possible with tool support (i.e. same as in vehicle) Tool enables a crosscheck of vehicle-tuned parameters versus a big number of variations in CAE (i.e. using initial MbP-parameter set) Tool gives an estimate for simulation duration; depending on number of variations, less than 1h up to multiple days For basic setup (2 x 3 x 2 = 12 variations) an over-lunch-run is possible on fast computers; for larger sets over-night or overweekend is necessary 18

19 Result: Verification versus real prototypes Verification of method project #1 Parameter set could be used as initial parameter set for first prototypes and was only slightly refined for final prototypes Verification of method project #2 Parameter set could be used as initial parameter set for first prototypes Verification of method project #3 Parameter set could be used as series parameter set after verification in final prototypes (target achieved) 19

20 Summary Automated MbP-Tuning Tool calculates sets of parameters for an ESP subfunction by means of vehicle dynamic simulation Possible for single or multiple vehicle configurations to increase robustness of the results Evaluation of results is supported and simplified by a tool-integrated visualization Statistical evaluation of verification manoeuvres enables tool to propose a best-fit parameter set Significantly reduced time consumption compared to effort in vehicle 20

21 Outlook Further projects have been using this tool since first introduction Dependency on availability & quality of OEM-vehicle-model is still a major road block for wider roll-out within Bosch Good results show capability of vehicle dynamics simulation w.r.t pre-tuning of basic parameters & robustness checks 21

22 Thank You for Your Attention Questions? Remarks, Comments, Proposals? 22