Virtual Integration of Engine, Vehicle and AT Systems: Approaches, Challenges & Benefits

Transcription

1 Virtual of Engine, Vehicle and AT Systems: es, & Benefits Wen Wang, Andy Leonard, Syed Wahiduzzaman, Christian Armbruster and Christopher Contag Gamma Technologies Inc. Enrico Pautasso POWERTECH Engineering S.r.l. International Symposium on of Exhaust Gas After-Treatment 14th 15th September, 2009

2 MV NN Exhaust aftertreatment modeling must be collaborative effort among Performance engineers: cycle simulation tools Aftertreatment domain experts / chemists: Standalone models Control / calibration engineers: Real time capable tools 2

3 MV NN Need to couple various sub-models such that data exchange is dynamic between: AT model Engine model Control model Vehicle model 3

4 Submodels can have disparate timescales and varying levels of numerical stiffness MV NN levels required by domain experts are not always the same Accuracy Numerical efficiency A domain expert may want to treat models outside his domain as black boxes 4

5 MV NN Construct sub-models of varying levels of accuracies and numerical efficiencies Mean value Model Neural Network (NN) model model Making these sub-models suitable for mix and match strategy Making data exchange/coupling transparent 5

6 Engine MV NN engine model In-cylinder modeling, e.g. combustion models Manifold dynamics Engine control Mean-value engine model Lumped flow components Mean-value modeling of in-cylinder process 6

7 Engine Model MV NN 7

8 Mean Value Models MV NN Cylinder Neural Network 8

9 Results, DI Example MV NN 9

10 Reduction of DOC and SCR Models MV NN 10

11 Reduction of DOC and SCR Models MV NN DOC Reaction Set: R1: CO + 0.5O 2 CO 2 R2: C 3 H O 2 3CO 2 + 3H 2 O R3: C 3 H 8 + 5O 2 3CO 2 + 4H 2 O R4: CO + NO CO N 2 R5: H O 2 H 2 O R6: NO + 0.5O 2 NO 2 R7: NO 2 NO + 0.5O 2 SCR Reaction Set: R8: NH 3 + S NH 3 (S) R9: NH 3 (S) NH 3 + S R10: 4NH 3 + 3O 2 2N 2 + 6H 2 O R11: 4NH 3 + 4NO +O 2 4N 2 + 6H 2 O R12: 8NH 3 (S) + 6NO 2 7N H 2 O + 8S R13: 4NH 3 (S) +2NO +2NO 2 4N 2 + 6H 2 O + 4S 11

12 SIL/HIL Model Generation Consist of Neural Networks or physical models No detailed chemistry or flow is modeled MV NN 12

13 SIL/HIL Enabled NN-AT Model DOC Results MV NN 13

14 SIL/HIL Enabled NN-AT Model DOC Results MV NN 14

15 SIL/HIL Enabled NN-AT Model SCR Results CO NO 2 MV NN 15

16 SIL/HIL Enabled NN-AT Model SCR Results MV NN 16

17 Computational Speed (NEDC 1180s) MV NN Model Type Solver Standalone Kinetic AT QS 90s Standalone Neural Network AT Explicit 2s Engine + Kinetic AT Explicit 60h MV Engine + Kinetic AT Explicit 39h MV Engine + Neural Network AT Explicit 625s 17

18 of Hi-Fidelity Submodels RT-enabled models are excellent tool for control system development MV NN Integrated models may not be sufficient to issue feed backs from the sub-models A new NN model has to be created every time the AT system is altered An alternate form of coupling is needed computationally efficient preserves fidelities of the individual submodels 18

19 A New Way of Submodel Circuit Splitting MV NN Model #1 Model #2 19

20 Advantages of Virtual integration via Circuit Splitting (CS) Quasi-steady (QS) solver is not suitable when there is flow branching MV NN Conventionally integrated time step of the entire model is determined by the stiffest submodel In the new approach (CS) each sub-model uses a solver that is optimal for the subsystem 20

21 Integrated Model: Vehicle + Engine + AT Engine Submodel MV NN Aftertreatment Submodel Vehicle Submodel 21

22 Integrated Model of Engine Circuit + AT Circuit Engine + Vehicle Submodel Circuit Splitter Aftertreatment Submodel MV NN Circuit Splitter 22

23 Comparative Results Single Circuit vs. Circuit Split MV NN 23

24 Comparative Results Single Circuit vs. Circuit Split MV NN 24

25 s MV NN Calculation time for a NEDC cycle with Explicit solver for all models: Engine & Kinetic AT: 63 h MV Engine & Kinetic AT: 39 h Calculation time with Circuit Split (Engine with Explicit, AT with Quasi Steady solver) Engine & Kinetic AT: 7.5 h MV Engine & Kinetic AT: 20 min MV Engine, Kinetic AT & Vehicle Model: 29 min 25

26 s Standalone AT models can be made realtime (RT) compatible for control system development MV NN Feasibility of SIL/HIL compatible NN model generation was demonstrated using detailed kinetic AT model NN model was improved using a hybrid methodology that improved the fidelity of the system model 26

27 s with engine/vehicle model can introduce numerical inefficiency or loss of model fidelity MV NN NN models may have to be retrained if the catalyst chemistry and/or geometry is altered A new approach was demonstrated that preserves submodel fidelity and near RT modeling efficiency 27

28 s Quasi-steady (QS) solver is not suitable when there are flow branchings MV NN Models can be broken into as many pieces as needed by use of circuit splitters and solved simultaneously Separate solving technologies (implicit, explicit and QS) can be combined to produce a highly integrated, efficient solution from within a single environment 28

29 s MV NN It is today possible for engine performance & vehicle departments to reuse and integrate the know-how of chemist specialists, AT control developers & catalyst suppliers for transient & driving cycle analysis. and vice versa This numerical solution opens new virtual analysis which will help the community to understand dynamic phenomenon and to enhance communication & encrypted model exchange between departments & partners. 29