1© 2016 The MathWorks, Inc.

Addressing Challenges in Designing Powertrain

System using Model-Based Design

Vijayalayan R

Manager

Control Design Application Engineering

MathWorks India Pvt Ltd

Design

with

Simulatio

n

Executable

Specification

s

Continuous

Test and

Verification

Automatic

Code Generation

Models

2

Emission Reduction

World-wide trend

India: phasing in BS VI and new

drive cycle requirement

China: fuel injection on all road vehicles,

phasing in National 5 and beyond

Off-road: EU/US looking beyond Tier-4

Closing the gap between real-world

emission and lab testing

Change is the essence of auto industry

Algorithms and software are vital part of the solution

Rising design complexity

3

Automotive Companies Moving to Model-Based

Design to Increase “Capacity for Complexity”

INTEGRATION

IMPLEMENTATION

DESIGN

Environment Models

Physical Components

Algorithms

CAD toolsVHDL,

VerilogC, C++

TE

ST

& V

ER

IFIC

AT

ION

RESEARCH REQUIREMENTS

Other

hardwareMCU DSP FPGA ASIC

4

Design

with

Simulation

Executable

Specifications

Continuous

Test and

Verification

Automatic

Code Generation

Models

Virtual Engine

Calibration

Optimization

Model-Based System

Engineering

Frontloading

Embedded Software

Development

Engineering Data

Analytics

Current Trends in Model-Based Design

5

Design

with

Simulation

Executable

Specifications

Continuous

Test and

Verification

Automatic

Code Generation

Models

Virtual Engine

Calibration

Optimization

Model-Based System

Engineering

Frontloading

Embedded Software

Development

Engineering Data

Analytics

Current Trends in Model-Based Design

6

Iterate

Build Test

Optimize Engine

Calibration: Days

Engine Development Process

Model and Design

: 4 Years

Problem: late assessment of engine design changesIdea: front-load calibrationChallenge: computing power, unproven processMathWorks proposal: Virtual Engine Calibration Optimization Process

7

Enablers: engine CAE models, parallel computing, virtual dynamometer, cloud

15 Engine Speeds

X

15 Engine Torques

X

4 Variables

900 Calibration ValuesThrottle Position

Turbo

Wastegate AreaIgnition Timing

Air/Fuel Ratio2

31

4

8

Build TestModel, Calibration,

and Evaluate

Result

20 minutes on

225 PC Cores

Calibrate via Numerical Optimization

9

Mercedes-AMG

ChallengeOptimize engine calibration for high-performing and

environmentally-friendly powertrains

SolutionUse MathWorks tools to develop a calibration tool

that enables simultaneous testing of multiple

variables

Results Calibration process streamlined

Euro 6 compliance goals achieved

Faster and more fuel-efficient cars developed

“We developed a custom engine

calibration tool using MathWorks tools

that enables engineers at all levels of

expertise to extract the highest

possible performance from AMG

powertrains. The tool supports the

entire calibration process, from Design

of Experiments to optimization.”

Hasan Uzun

Mercedes-AMG GmbH

Link to article

AMG calibration tool main menu.

10

Design

with

Simulation

Executable

Specifications

Continuous

Test and

Verification

Automatic

Code Generation

Models

Virtual Engine

Calibration

Optimization

Model-Based System

Engineering

Frontloading

Embedded Software

Development

Engineering Data

Analytics

Current Trends in Model-Based Design

11

MODEL-BASED DESIGN ADOPTION TRENDS IN AUTOMOTIVE

Code Generation Has Become Mainstream for

Production Programs

Using Hand-Code in Models Production Code Generation from Models

Hand Code Modeled Elements None Some Most All

(Translated)

12

Subsystem

Design

Subsystem

Integration and Test

System

Integration and Test

Complete

Integration and Test

System-Level

Design

Requirements

Subsystem

Implementation

Stakeholder NeedsHIL Testing

TRENDS OF MODEL-BASED DESIGN

Frontloading Embedded Software Development

Automotive industry has

embraced HILS

Current focus: systematic

verification of software functions

in model, before HILS

Benefit: reducing manpower and

time for embedded software

Source: Continental presentation at MathWorks Automotive Conference 2008, Stuttgart, Germany

13

Subsystem

Design

Subsystem

Integration and Test

System

Integration and Test

Complete

Integration and Test

System-Level

Design

Requirements

Subsystem

Implementation

Stakeholder Needs

Optimize Design

Evaluate Design

Alternatives

Analyze System

Behavior

Analyze interaction between subsystems

Trade-off design options

Assess requirement feasibility

Size key components

Before building prototypes

System Level Simulation

14

ChallengeAccelerate the development of complex engine control

system software

SolutionDevelop a comprehensive engine model and combine it

with SIL+M testing to frontload the development process

Results Comprehensive engine model developed

Designs verified early in development

Difficult-to-test conditions simulated

Toyota Front-Loads Development of Engine

Control Systems Using Comprehensive Engine

Models and SIL+M

A Toyota engine.

Link to user story

“Simscape enables us to create a

comprehensive model of the

engine appropriate for our design

tasks that is easily understood by

all teams. Closed-loop simulations

of the ECU and engine performed

in Simulink, completed as early as

possible, are essential to our front-

loaded development process.”

Dr. Hisahiro Ito

Toyota Motor Corporation

15

Design

with

Simulation

Executable

Specifications

Continuous

Test and

Verification

Automatic

Code Generation

Models

Virtual Engine

Calibration

Optimization

Model-Based System

Engineering

Frontloading

Embedded Software

Development

Engineering Data

Analytics

Current Trends in Model-Based Design

16

Trend: Data Economy

“Information is the oil of the 21st

century, and analytics is the combustion engine”

Peter Sondergaard, Gartner Research

Develop

Predictive

Models

Access and

Explore DataPreprocess Data

Integrate Analytics

with Systems

17

Safran Engine Health Monitoring Solution

http://www.mathworks.com/company/events/conferences/matlab-virtual-conference/

Beyond Virtual Sensors: Prognostics

18

Safran Engine Health Monitoring Solution

Monitor Systems

– Detect failure indicators

– Predict time to maintenance

– Identify components

Improve Aircraft Availability

– On time departures and arrivals

– Plan and optimize maintenance

– Reduce engine out-of-service time

Reduce Maintenance Costs

– Troubleshooting assistance (isolate faulty element)

– Limit secondary damage

Performance- Modular analysis- Thermodynamic cycle

Oil System

- Smart filter- Debris

- Consumption

Liftoff- Tracking- Monitoring ignition

Control System- Sensors- Actuators- Troubleshooting assistance- Errors and Warnings

Fuel System- Smart filter- Fuel pump

General- Anomaly detection- Decision support- Fleet monitoring

- Imbalance- Vibration- Transient events

Mechanical Health

Enterprise

Integration

• Real-time analytics

• Integrated with

maintenance and service

systems

• Ad-hoc data analysis

• Analytics to predict failure

• Suite of MATLAB Analytics

• Shared with other teams

• Proof of readiness

DesktopCompiled

Shared

19

Design

with

Simulation

Executable

Specifications

Continuous

Test and

Verification

Automatic

Code Generation

Models

Virtual Engine

Calibration

Optimization

Model-Based System

Engineering

Frontloading

Embedded Software

Development

Engineering Data

Analytics

Current Trends in Model-Based Design

20

Thank You