Co-Simulation for hybrid vehicle control software development Marcus Boumans, Sebastian Wansleben Robert Bosch GmbH GT User Conference 21. October 2013 Frankfurt 1
Contents Scope & Motivation Use cases & Requirements Simulation Environments Parameterization & Validation Summary and Conclusion 2
Scope of (Co)Simulation Scope & Motivation Support the development of innovative energy management control strategies for passenger cars Domains under investigation: Combined control strategy Thermal system control strategy HEV Powertrain control strategy 3
Motivation for (Co)Simulation Scope & Motivation Good reproducibility and fast repeatability of experiments Required especially for traffic simulation & warm-up experiments Investigation of real world systems with limited availability Not all HEV / thermal topologies are available as real systems Time & costs savings by Virtual Controller Prototyping Run control strategy model on a rapid prototyping system in a vehicle Using (Co)Simulation makes us faster and saves money! 4
Contents Usecases & Requirements Scope & Motivation Use cases & Requirements Simulation Environments Parameterization & Validation Summary and Conclusion 5
Development: Thermal System Control Strategy Function: Optimized thermal management with prioritization iti of comfort and fuel consumption Considered information: Engine load and speed Cooling/Exhaust system temperatures/flowrates Influence on powertrain control Usecases & Requirements 6 EM E-Machine TC Turbo Charger HTC High Temperature Cooler OC Oil Cooler Requirements for the Simulation: Engine model with thermal behavior Detailed cooling/ exhaust system model Powertrain model including friction Model of control strategy software
Development: Map-based ecoacc (HEV) Navigation HMI Map-based ecoacc ACC Usecases & Requirements Function: Vehicle increases, decreases and holds speed autonomously Considered information: Desired vehicle behavior from HMI Track information from navigation Traffic information from radar Brake Control Powertrain Control CO 2 -reduction Cross domain function Requirements for the Simulation: Radar system model, traffic model Navigation system model Powertrain model Model of control strategy software 7 Department 06/09/2013 Robert Bosch GmbH 2013. All rights reserved, also regarding any disposal, exploitation, reproduction, editing,
Contents Simulation Environments Scope & Motivation Use cases & Requirements Simulation Environments Parameterization & Validation Summary and Conclusion 8
Simulation Environments Optimization Thermal System Control Strategy (HEV) load, speed Control Software Model Plant Model Thermal system Lubrication Exhaust System actuator sensor make Rapid-Prototyping-HW temperatures, fuel consumption Tasks: Optimization of the thermal system (HW & SW) Testing of optimized SW version in a concept car 9
Combined HEV & Thermal Strategy Optimization Driving cycle Simulation Environments Control Software Model (operation strategy thermal) actuator sensor Exhaust System Plant Model Thermal system Lubrication Control Software Model (operation strategy hev) DLL Import Vehicle temperatures, fuel consumption, SOC Task: Investigate t CO2 benefit of combined thermal and HEV strategy t optimization i 10
Simulation Environment for Map-based ecoacc Simulation Environments Maneuver, Road,Traffic ADAS RP ehorizon Control Software Model actuator, sensor Plant Model Surrounding sensor info Powertrain Plant Model GPS Position torque, speed fuel consumption, SOC Tasks: Optimize & develop predictive powertrain control strategy 11
Contents Parameterization & Validation Scope & Motivation Use cases & Requirements Used Simulation Environments Parameterization & Validation Summary and Conclusion 12
Parameterization Parameterization & Validation Use data from calibration departments Perform special vehicle measurements Use catalogue data geometric data EngineDyno XLS MotorDyno XLS Vehicle.dat Script Accel. Pedal Pos. [%] 100 90 80 70 60 50 40 shift12 30 shift23 shift34 20 shift45 shift56 10 shift67 shift78 0 0 50 100 150 200 250 300 Vehicle Speed [km/h] Shift strategy 13
Validation: Fuel Consumption Parameterization & Validation el consumption [ml/s] 18 16 14 12 10 8 6 Rel. Error MEAN 2.61 % SDEV 10.94 % Fuelconsumption NEDC Measurement Simulation Fu 4 2 0 0 100 200 300 400 500 600 700 800 900 1000 Accumulated fuel co onsumption [ml] 1800 1600 1400 1200 1000 800 600 400 200 Rel. Error MEAN 1.71 % SDEV 1.69 % 0 0 100 200 300 400 500 600 700 800 900 1000 time [s] 14
Validation: Exhaust Temperature Parameterization & Validation 1000 Rel. Error MEAN - 1.89 % 900 SDEV 10.31 % Temperature downstream Catalyst Measurement Simulation 800 700 600 Temperature [ C] 500 400 300 200 100 0 0 200 400 600 800 1000 1200 1400 time [s] 15
Contents Scope & Motivation Use cases & Requirements Simulation Environments Parameterization & Validation Summary and Conclusion 16
Summary Summary & Conclusion There are use case specific simulation environments for Thermal Strategy Combined Strategy Predictive Strategy GTSuite is applied for plant models CarMaker is applied for radar sensor models & maneuver, traffic simulation ASCET & Simulink are applied for control software models 17
Conclusion GT Suite fulfills our requirements for a scalable simulation/modeling environment for plant models To cover all use cases several tools have to be integrated to one useful simulation application Requirement: Standardized interfaces for tool interoperability Reuse of subsystem models coming from different tools is essential Requirement: Standardized ex-/import features for model exchange Continue development in tool interoperability & model exchange features 18
Thank you for your attention! ti 19