cmc india - etas paper

1 Public| ETAS India| 2014-05-29 | © ETAS GmbH 2014. All rights reserved, also regarding any disposal, exploitation, reproduction,

editing, distribution, as well as in the event of applications for industrial property rights.

Frontloading through Virtualization Stay Real, Start Virtual

Ms. Shruthi Ananthachar

Mr. Nithin Nath



1975 1985 1995 2005

Electronic fuel injection

Cruise control

Gearbox control

Traction control

Anti lock brakes


Cruise control

Airbags

Electronic stability control

Active body control

Adaptive gearbox control

Adaptive cruise control

Emergency call

Gearbox control

Traction control

Anti lock brakes


Cruise control

Tele-diagnostics

Internet browser

Apps

Car-2-car communication

Road trains

Software updates

Adaptive headlights

Active steering

Curve warning

Stop and Go

Lane keeping assistance

Automated parking

Collision mitigation

Hybrid powertrain

Emergency call

Airbags

Electronic stability control

Active body control

Adaptive gearbox control

Adaptive cruise control

Gearbox control

Traction control

Anti lock brakes


Cruise control

− Pressure to differentiate through innovative functions

− Many innovations are either safety-relevant and/or involve connectivity to the outside world

− Restrictive legal and standard requirements

− Cost and time-to-market pressures, high penalties for

poor quality

2015

Frontloading through Virtualization

Increase in Control System Complexity




Increase in Calibration Complexity

Increased inter-domain connectivity

Battery Management

Transmission Management

Engine Management

Brake Management

Vehicle Motion Management

ECU Hardware

Microcontroller Abstraction

Basic Software Module (BSM)

AUTOSAR-RTE

SW- Component 1

AUTOSAR Interface

SW- Component 2 AUTOSAR Interface

SW- Component 3 AUTOSAR Interface …

New layered standard architecture

New functions and variants

Engine control complexity

1997 2002 2004 2007

40

00

90

00

12

00

0

16

00

0

Effort Number of calibrations

100%

0%

25

00

0

2010

40

00

0+

2020




…leads to significant cost reduction

Benches Vehicles

Effo

rt

Today – 5%

2015 – 20%

Simulation

Today – 70%

Today – 25%

2015 – 40% 2015 – 40%

Reduce Cost for Function Development and Calibration



Edit-Integrate-Validate-Update

Validate the function in Virtual or real environment

Change specification,

Debug SW,

Change parameter

Integrate Functions with base SW,

Simulation Environment

Develop Concept,

Implement Concept,

Calibration,

Fine tuning

Edit Integrate

Validate Update

Validation Feedback Loop is a key towards efficiency




Motivation





Compressing the V-cycle




Motivation



Validating new vehicle functions today…

Reserve a test vehicle

Flash software or configure hardware

for prototyping

Validate against requirements

Failed Make required

adjustments Passed


Function development Frontloading



Compile

Validating new vehicle functions in the future…

Virtual Prototype Validate against requirements

Failed Make required

adjustments Passed





F

F

New developed functions, e.g. in ASCET or Simulink®

4

7

Solution

− Create a complete virtual prototype to allow detailed validation

− Execute virtual prototype on a standard windows PC

− Experiment and calibrate with existing tools e.g. INCA

− Better quality models streamlines collaboration ETAS INCA

+ Automotive Production OS

+ Measure Data Files (Stimuli) + Plant Model + Calibration Infrastructure

Production Function Componentss (FCs)

+

F

F

OS

MDF Stimuli

Plant Model

ECU FCs




Co-simulation during modeling



Rea-time-PC MCAL

OS

Rea-time-PC MCAL

OS

Rea-time-PC MCAL

OS

ECU MCAL

OS

Basic SW

RTE

Application Software

Basic SW

RTE


Basic SW

RTE


Basic SW

RTE


Virtual PC MCAL

OS

Real-time PC MCAL

OS

Real-time-PC MCAL

OS

Taking Benefit of AUTOSAR: same SW under test in all environment


Software development Frontloading





Virtual ECU




Testing Frontloading




ETAS approach




Calibration Frontloading




Conventional Calibration Approach

Calibrator Workstation

Vehicle Environment

I don’t believe in virtualization… I don’t think it even works!

I prefer to work on a “real” target



Users of Conventional Approach expect…

• Very high accuracy – Best trade-off between model-error & no. of training data.

• Global engine models for calibration possible.

• Best model is built automatically.

• Easy and descriptive rating of model quality – Only basic DoE and statistic knowledge needed

• Many optimization features for calibration, e.g: multi-criteria (pareto) or fast cycle-optimization

• Easy & flexible data import & export: xls, txt, csv, dcm …

• Open Matlab interface for scripts, extensions or test bench connection. Model export e.g. to C, Matlab, Simulink, Excel, …


Conventional Calibration Approach



Model Based Calibration with

ASCMO ®

Injection

Ignition

P-rail

EGR

Outlet-cam

Inlet-cam

AFR

Injection

Ignition

P-rail

EGR

Outlet-cam

Inlet-cam

AFR

T-exhaust

Soot

Roughness

Torque

NOx

HC

Consumption

T-exhaust

Soot

Roughness

Torque

NOx

HC

Consumption

Guided Calibration with

INCA-FLOW ®


Model & Automation based Calibration Approach

Integrated solution for the calibrator

Advanced Simulation for Calibration Measurement Optimization




Calibration Frontloading – model based approach





Po

we

r

Engine Speed

250 230 200 180 160 130 100

Scalability & re-use of global data • Varied Torque • Payload • Fuel Economy target • ….

Pictures courtesy Ashok Leyland



25

Principle:

Search in a complete function space:

Automatic determination of the most likely function

Advantages High flexibility without assumptions or expertise

Gives local confidence interval (model variance)

Robust against outliers

Principle:

Search in a given class of functions (polynomial, neuronal net, ...)

Fit the model parameter by experts and validation measurements

Disadvantages: Limited flexibility & danger of over-fitting

High expertise and assumptions necessary

Training Data

Model Prediction

Training Data & Model Prediction

Modelvariance & Validity

Modeling a complex 1-D signal with classical DoE-Models („Advanced Polynomials“)

Modeling a complex 1-D signal with new statistical machine learning methods

Polynomials or Neuronal Nets Statistical machine learning methods





ECU software

Hex + A2L+

ES592

Engine Speed

To

rqu

e

Driver Demand

Full-Load Curve

Smoke Limit Curve

Smoke Limitation Optimization on a Diesel platform Visual Optimization Method – Partial Online


Calibration Frontloading Use-Case



ECU software

Hex + A2L+

ES592 INCA FLOW®

Sequence

INCA On-line Environment







DoE-Plan

Optimising outputs

based on the model System-

inputs

System-

outputs

Model training using measured data from DoE-Plan

150

200

250

300

350

20

40

60

80

100

145

150

155

160

165

CO2 Ordnung 3, Minimum bei (170Nm,30kW)

Mathematical Model



Model based & Guided Calibration reduces calibration effort by roughly 70% for this use case!






Summary – Advantages




Stay Real, Start Virtual

cmc india - etas paper

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