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An Automated Design Synthesis System Involving Hardware-In-the- Loop Simulation Steve Hann Wensi Jin Mechanical Simulation Corporation Opal-RT Technologies.

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Presentation on theme: "An Automated Design Synthesis System Involving Hardware-In-the- Loop Simulation Steve Hann Wensi Jin Mechanical Simulation Corporation Opal-RT Technologies."— Presentation transcript:

1 An Automated Design Synthesis System Involving Hardware-In-the- Loop Simulation Steve Hann Wensi Jin Mechanical Simulation Corporation Opal-RT Technologies Mechanical Simulation Corporation Opal-RT Technologies May 2003

2 Introduction Experiment Underlying Technologies Summary Innovation in the Loop Introduction Hardware in-the-loop Why use iSIGHT for HIL? Experiment Automated design synthesis with a development ECU Underlying Technologies HIL platform: RT-LAB (Opal-RT) Real-time simulation: CarSim (Mechanical Simulation) Process integration and design methods: iSIGHT Summary Outline

3 Introduction Experiment Underlying Technologies Summary Innovation in the Loop Real-Time Simulation Simulating at the same speed as real life, not faster/slower Based on fixed time step integration, with time step usually measured in micro- or milli-seconds Hardware-in-the-Loop (HIL) Part of the simulation is the hardware under study/test Requires real-time performance Physical hardware will not wait for the simulation The Hardware Can be a valve, an electronic control module (ECU), an ECU network, a brake system assembly, an engine, a transmission … a full vehicle Introduction Real-Time Simulation & HIL

4 Introduction Experiment Underlying Technologies Summary Innovation in the Loop Hardware-in-the-Loop Widely used in control system development Design – rapid control prototyping Test – in-the-loop testing Allows experimentation with physical parts in a controlled synthetic environment Experiments can be repeated and automated Allows parallel development of mechanical and control systems An important technique to reduce design cycle while improving product quality Introduction Hardware In-the-Loop

5 Introduction Experiment Underlying Technologies Summary Innovation in the Loop Hardware In-the-Loop Example: ECU In-the-Loop RT Simulator (3 x Pentium 3, 1 GHz CPU) ECU under test Host PC (development env.) Allowing controller development while mechanical system is being built Achieving a high degree of test coverage in the lab before driving mechanical system Reducing test effort through automated regression

6 Introduction Experiment Underlying Technologies Summary Innovation in the Loop Hardware-In-the-Loop Automatic Transmission In-the-Loop Moving engineering development from expensive test vehicles to lab Increasing repeatability through controlled environments Accelerating test cycles with minimum operator intervention

7 Introduction Experiment Underlying Technologies Summary Innovation in the Loop HIL systems have evolved rapidly in recent years Latest CPUs and parallel processing Provides computing power for detailed models New hardware technologies Reduces needs for custom hardware New user interface technologies Enhances ease-of-use Increased use of HIL in automotive engineering However, HIL is not used to its fullest potential Although HIL systems have evolved away from custom, one-off designs, their usage has not Why Use iSIGHT for HIL

8 Introduction Experiment Underlying Technologies Summary Innovation in the Loop What is lacking? High fidelity plant models Tools integration Design method integration Process integration We believe these factors are limiting the effectiveness of HIL Solutions have emerged in the offline simulation/CAE world This is the motivation for the feasibility study with Engineous Software using iSIGHT Lets take a look at the experiment in the study Why Use iSIGHT for HIL

9 Introduction Experiment Underlying Technologies Summary Innovation in the Loop Experiment Setup ABS/ECU VEHICLEVEHICLE ECUECU BRAKESBRAKES Solenoid Signals Brake Torques Wheel Speeds Brake Pedal Input

10 Introduction Experiment Underlying Technologies Summary Innovation in the Loop HITL for ECU Evaluation ECUECU Solenoid Signals Wheel Speeds DAQ BoardsDAQ Boards ConditioningConditioning Software Brake Model CarSimCarSim RT–LAB

11 Introduction Experiment Underlying Technologies Summary Innovation in the Loop Use iSIGHT to find value of Mass center of unladen sprung mass that minimizes straight line stopping distance (initial value of 1014 mm) Description of Problem

12 Introduction Experiment Underlying Technologies Summary Innovation in the Loop Choose/Define the vehicle Initial speed of kph Split Mu road (0.2 and 0.5) Driver model set for straight line Step Braking of 15 Mpa (locks brakes) Calculate stopping distance Description Of Simulations

13 Introduction Experiment Underlying Technologies Summary Innovation in the Loop … Firewire Real-Time PC (QNX) TCP/IP Workstation PC Supports CarSim/TruckSim AMEsim GT-Power Matlab/Simulink MATRIXx/SystemBuild HIL Platform: RT-LAB Highlights Intel CPU & PC hardware Open system Scalability through parallel processing Widely connected

14 Introduction Experiment Underlying Technologies Summary Innovation in the Loop RT Vehicle Dynamics Simulation: CarSim

15 Introduction Experiment Underlying Technologies Summary Innovation in the Loop Nominal value of Mass center of unladen sprung mass of 1014 mm yields total stopping distance of m Optimized value of Mass center of unladen sprung mass, mm, yields total stopping distance of m (reducing total stopping distance by 2.38 m) Summary

16 Introduction Experiment Underlying Technologies Summary Innovation in the Loop Summary

17 Introduction Experiment Underlying Technologies Summary Innovation in the Loop Summary Execution Results Summary Total runs: 37 Feasible runs: 37 Infeasible runs: 0 Failed runs: 0 Optimization Plan: NewPlan Executed between RunCounter 1 and 37 (37 runs) Techniques used: Step1: Adaptive Simulated Annealing Step2: Sequential Quadratic Programming - NLPQL Best design: currently previously RunCounter 7 7 ObjectiveAndPenalty Objective Penalty Best design did not improve after executing this Optimization Plan Best design parameter values: LXCG = Distance =


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