Integrated Architectures Management, Behavior models, Controls and Software

Integrated Architectures Management, Behavior models, Controls and Software Realize innovation.

Engineering challenges Bringing everything together Fuel efficiency Emissions Acceleration Performance Energy management Drivability NVH Active safety Functional performance Subsystem and system Controls functions Page 2

Engineering challenges Dealing with many variants Engine (5 types) Transmission (2 types AT/MT) Vehicle (2 platforms) Model accuracy Concept models Measured data 2160 configurations to be evaluated Detailed models Battery (3 types) Tires AC motor Load cases EU. (2x2 each) Asia US These models could be represented through 1 architecture and 48 component models Page 3

Engineering challenges Vehicle integration Real product Assembly of systems interacting in complex ways Complexity (number of interaction, variants) Design and modeling responsibility shared between several actors/departments Virtual product Assembly of models representing each parts Different tools are used for each models No standard covering all application domains LMS Amesim Other simulation tools Need for interfacing heterogeneous models 3B MBS C-code CFD Page 4

Architecture-driven simulation with LMS System Synthesis Tool neutral architecture consisting of templates defining the interface contract between subsystem behavioral models LMS Amesim LMS Amesim Easy integration of subsystem models from different departments Driver Execution of heterogenous simulation architectures VCU Vehicle LMS Amesim Battery Electric motor Gearbox LMS Amesim Page 5

Architecture-driven simulation with LMS System Synthesis Tool neutral architecture consisting of templates defining the interface contract between subsystem behavioral models Easy integration of subsystem models from different departments Driver Execution of heterogenous simulation architectures VCU Vehicle Enable scenario and variant management Battery Electric motor Gearbox Page 6

Architecture-driven design LMS System Synthesis Tool-neutral architecture consisting of templates defining the interface contract between subsystem behavioral models Base architecture Attributes Integration of subsystem models from different departments Performance Comfort Execution of heterogenous simulation architectures Enable scenario and variant management Perform multi-attribute balancing within one project Performance simulation architecture Comfort simulation architecture Page 7

Demo overview Architecture and template definition Scenario s Attributes Model instrumentation NEDC WOT Loaded Architecture 150 km 0,12 kwh/km 140 km/h Range Efficiency Performance slope Driver 50 s 80 km/h Peak Power Continuous Power Model assembly VCU Vehicle Study and run execution Variants Battery Electric motor Gearbox Variant evaluation Variant evaluation Battery #1 Battery #2 Battery #3 Page 8 EM #1 EM #2

Evaluating electric vehicle variants with LMS System Synthesis Architecture and template definition Model instrumentation Model assembly Architecture Define subsystems Define the connections / relations between subsystems Templates Defined for each subsystem Interface contract (i.e. Input/output definition) Parameters and variables Architecture Study and run execution SOC Battery i V Variant evaluation Templates Page 9

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Evaluating electric vehicle variants with LMS System Synthesis Architecture and template definition Increase control and collaboration Model instrumentation Model assembly Study and run execution Variant evaluation Page 12

Evaluating electric vehicle variants with LMS System Synthesis Architecture and template definition Model instrumentation Instrumented model Combination of behavioral model and interface contract Instrumentation process Map ports, parameters and variables Model assembly Study and run execution Battery Controls Scenario SOC Battery i V EM Gearbox Vehicle Variant evaluation Behavioral models Templates Page 13

Evaluating electric vehicle variants with LMS System Synthesis Architecture and template definition Model instrumentation Behavioral model connections Complex interactions involve multiple wires between subsystems No interface contract defined Limited re-use conditions Model assembly Study and run execution Instrumented model connections Interface contract definition Subsystems are connected using a single connector Increased re-use of subsystems Variant evaluation Page 14

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Evaluating electric vehicle variants with LMS System Synthesis Architecture and template definition Increase control and collaboration Model instrumentation Reduce rework by reusable assets Model assembly Study and run execution Variant evaluation Page 16

Evaluating electric vehicle variants with LMS System Synthesis Architecture and template definition Model instrumentation Model assembly Select for each template an instrumented model Plug & play connection of models thanks to interface contract Model assembly Study and run execution Controls Scenario SOC Battery Battery i V EM Gearbox Vehicle Variant evaluation Behavioral models Templates Page 17

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Evaluating electric vehicle variants with LMS System Synthesis Architecture and template definition Increase control and collaboration Model instrumentation Reduce rework by reusable assets Model assembly Save time and focus on engineering Study and run execution Variant evaluation Page 19

Evaluating electric vehicle variants with LMS System Synthesis Architecture and template definition Model instrumentation Run Defines a single simulation scenario (example: NEDC cycle) Study Groups several runs Model assembly 150 km 0,12 kwh/km 140 km/h Study and run execution NEDC Range Efficiency Performance 50 s 80 km/h Variant evaluation WOT Loaded Scenario s slope Peak power Attributes Continuous power Page 20

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Study Running multiple scenario s to score on different attributes NEDC WOT Loaded slope Page 23

Evaluating electric vehicle variants with LMS System Synthesis Architecture and template definition Increase control and collaboration Model instrumentation Reduce rework by reusable assets Model assembly Save time and focus on engineering Study and run execution Analyze more virtually Variant evaluation Page 24

Evaluating electric vehicle variants with LMS System Synthesis Architecture and template definition Model instrumentation Run Defines a single simulation scenario (NEDC cycle) Study Defined for each variant Groups all scenario s for this variant Model assembly Study and run execution Battery #1 Battery #2 Battery #3 Variant evaluation EM #1 EM #2 Variants Page 25

Variant evaluation Scenario s Architecture Attributes NEDC WOT Loaded Driver 150 km 0,12 kwh/km 140 km/h Range Efficiency Performance VCU Vehicle 50 s 80 km/h slope Peak Power Continuous Power Variants Battery Electric motor Gearbox Variant evaluation Battery #1 Battery #2 Battery #3 EM #1 EM #2 Page 26

Variant evaluation EM Behavioral models impemented as FMU s for co-simulation (FMI 2.0) Battery behavioral models impemented as seperate LMS Amesim supercomponents EM #1 EM #2 Battery #1 Battery #2 Battery #3 Max Torque = 208 Nm Efficient Power Loss Map: Max Torque = 215 Nm Less efficient Power Loss Map: Energy = 16.5 kwh Voltage = 386 V Mass = 118 kg Energy = 21.8 kwh Voltage = 336 V Mass = 156 kg Energy = 24 kwh Voltage = 360 V Mass = 171 kg Page 27

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Variant evaluation Attributes Page 29

Variant evaluation Configurations and evaluation Bat #1 Bat #2 Bat #3 Best range = 206 km Efficiency Energy EM #1 Best balanced Config_1_1 Config_1_2 Config_1_3 Best efficiency = 0,1125kW/ km EM #2 Config_2_1 Config_2_2 Config_2_3 Torque Best power & performance ratings Page 30

Evaluating electric vehicle variants with LMS System Synthesis Architecture and template definition Increase control and collaboration Model instrumentation Reduce rework by reusable assets Model assembly Save time and focus on engineering Study and run execution Analyze more virtually Variant evaluation Capitalize knowledge to improve design decisions Page 31

Summary Architecture and template definition Increase control and collaboration Model instrumentation Reduce rework by reusable assets Model assembly Save time and focus on engineering Study and run execution Analyze more virtually Variant evaluation Capitalize knowledge to improve design decisions Page 32

Application case Japanese A-OEM Architectural choices for 2020 vehicles Don t leave this slide to the customers Pains Large diversity of vehicle architectures to explore Large choices of technologies Huge number of variants Manage Model quality for trade-off balancing Application of LMS Sysdm and System Synthesis Close to 1000 variants have been evaluated for 6 different architectures (colors) Balancing fuel economy against acceleration performance Connecting 3 user communities Architect Modelers End user Page 33

David ALMER Systems Simulation Manager EMEA Center of Excellence david.almer@siemens.com Realize innovation.