Neue Applikationen erfordern ein Umdenken im E/E · PDF fileEric Sax • Leiter des...

KIT – Universität des Landes Baden-Württemberg und nationales Forschungszentrum in der Helmholtz-Gemeinschaft

Institut für Technik der Informationsverarbeitung (ITIV)

www.kit.edu

Institut für Technik der Informationsverarbeitung

„ Neue Applikationen erfordern ein Umdenken im E/E-Lebenszyklus Stuttgart, 2016-11-29 Prof. Dr.-Ing. Eric Sax • Leiter des Instituts für Technik der Informationsverarbeitung (ITIV) am KIT • Direktor im Bereich (ESS) am FZI Forschungszentrum Informatik am KIT • Programm-Direktor ESEM an der Hector-School [email protected]

, fahr schon mal den vor“ Harry Wagen

Institute for Information Processing Technology (ITIV) 2

Prof. Dr.-Ing. Eric Sax Stuttgart, 2016-11-29 © 2016

Content

Introduction

State of the Art („Enabler“)

Trends, Show Cases and new Applications

Challenges

Processes, Methods and Tools

Summary



Berühmte Persönlichkeiten aus Karlsruhe „Die Liebe zum Erfinden höret niemals auf“ Carl Benz

Heinrich Hertz 1886/88 der Nachweis der elektromagnetischen Wellen in Karlsruhe, die Basis für alle heutigen „over-the-air“ Kommunikationen.

Carl Benz Geboren und studiert in Karlsruhe; Sein Benz Patent-Motorwagen Nummer 1 von 1885 gilt als erstes praxistaugliches Automobil.

Emile Skoda Studierte in Karlsruhe; Namengeber des Automobilherstellers „Škoda Auto“.

Karl Steinbuch Gilt als Begründer der Informatik; war von 1958-1980 Professor an der Fakultät. Die Zukunft neuer Automotive-Anwendungen steckt in der Informationsverarbeitung und Digitalisierung.

Dieter Zetsche Vorstandsvorsitzender der Daimler AG seit 2006 ist. Von 1971 bis 1976 studierte er an der Fridericiana Elektrotechnik. Seit 1. Oktober 2009 gehört er dem Aufsichtsrat des KIT an.

Die Region Karlsruhe ist traditionsreich in den großen, aktuellen Themen der Mobilität (connected, E-mobility, autonomous-driving) seit über 125 Jahren.



ITIV: Inhalte der Wissenschaft und Lehre

1958 Gründung des Instituts Leitung Prof. K. Steinbuch „Geburtsort“ der Informatik in Deutschland

Institut für Technik der Informationsverarbeitung (ITIV)

Eines der 13 Institute der Fakultät für Elektrotechnik und Informationstechnik www.kit.itiv.edu

Aktuell 50 Wissenschaftliche Mitarbeiter Inklusive Gruppen am Forschungszentrum Informatik (www.fzi.de)

Forschungsschwerpunkt Systems Engineering Eingebettete elektronische Systeme Mikrosystemtechnik und Optik



Content

Introduction



Challenges


Summary



1941: Z3, first computer world wide (relais technology)

Semi-Conductors Key to automotive innovations

1947: controllable voltage opens the digital world with semi-conductors

1985: continuation of integration1) enables for micro-electronics applications

1960: silicon enables assembly line production of transistors

1) the number of chips on an integrated circuit is 4 times bigger than 3 years ago



Semi-Conductors Key to automotive innovations

Seit 2000: In Mikro-Prozessoren/-Controllern integrierte Halbleiter-Technik bzw. darauf ausführbare Software sind der Schlüssel zu mechatronischen Systemen

1947: controllable voltage opens the digital world with semi-conductors

1985: continuation of integration1) enables for micro-electronics applications

1960: silicon enables assembly line production of transistors

1) the number of chips on an integrated circuit is 4 times bigger than 3 years ago

1941: Z3, first computer world wide (relais technology)



Enabler: Sensors



Enabler: Car2X Communication

Vehicles become part of the „Internet of things“ (everything)

Quelle: Vector Informatik



Enabler: Electric Drives

HVAC Front

Li-Ion Battery

Battery cooling

Inverters front drive, Fusebox

HVAC Back

High / Low Temperature Cooling System

Inverters rear drive

Articulated link

Electrical drive axles (Wheelhub motors)

Ancillaries

Quelle Daimler AG



Content

Introduction



Challenges


Summary



Increasing Functionality (Automotive)

* Source Vector Informatik

Semi-Conductor (Technology) improvement is the front runner Applications follow

• Electric fuel injection • Cruise control

• Gearbox control • Traction control • ABS • Electric fuel injection • Cruise control

• Airbags • ESP • Active body control • Adaptive gearbox contr. • Adaptive cruise control • Emergency call • Gearbox control • Traction control • ABS • Electric fuel injection • Cruise control

• Adaptive low beam • Active steering • Turn warning signal • Stop and go • Lane assistant • Automatic parking • Collision avoidance • Hybridization • Telediagnosis • Vehicle-to-vehicle

communication • Alternative Drivetrains • Airbags • ESP • Active body control • Adaptive gearbox control • Adaptive cruise control • Emergency call • Gearbox control • Traction control • ABS • Electric fuel injection • Cruise control



Enabler: Driver Assistent Systems vs. „good old times“

• No pedestrian protection

• No brake assist

• No speed limiter

• No lane departure warning

• No hill holder

• No automated parking



Project-References for Auto-Driving



Big Data Management („Telediagnosis“)

Vehicle is part of Internet of things (everything) Communication with a vehicle enables to check the current status online Typical Use Cases:

Telediagnosis in case of breakdown (24h Service) Error recognition and diagnosis Online repair support Optimized preparation and logistics of spare parts Updates „over the air“ …

Consequently: Prediction less breakdown Reduced time in the workshop Business case

Quelle: Vector Informatik



Business Cases Commercial Vehicles

driver 66,5%

price 12,0%

tires maintenance

utilities

fuel 13,7%

insurance

Workshop- Management

Diver Duty

Predictive Energy Management

Automated Bus Stop Approach

Avoid Damages

Quelle: Daimler



More and more HMI in a modern car



Content

Introduction



Challenges


Summary



Emission Standards 2012

Euro V

Euro IV

Euro III

Euro II

Euro IV for 15 metropolitan

areas

Alternativ: EPA 07

Alternativ: EPA 98

Hong Kong Euro V

Alternativ: EPA 10

Alternativ: EPA 04

Singapore: EURO IV

Alternative: EPA98, Japan 98

Legislation for China III-V stages are based on EURO

III-V

JP 09

EPA 10

Alternative: EPA 04

Alternativ: EPA 04

Alternativ: EPA 94




Euro V

Euro IV

Euro III

Euro II

Alternativ: EPA 07

Alternativ: EPA 04

Hong Kong Euro V

Alternativ: EPA 10 EURO V

Q4, 2014 Alternativ:

EPA 07

Singapore: EURO IV



III-V

JP 09

GHG14

Alternative: EPA 10

Alternativ: EPA 07

Alternativ: EPA 94

Euro VI




Euro V

Euro IV

Euro III

Euro II

Alternativ: EPA 10

Alternativ: EPA 04

Hong Kong Euro V

Alternativ: EPA 10 Alternativ:

EPA 07

Singapore: EURO V



III-V

JP 09

GHG17

Alternative: EPA 10

Alternativ: EPA 07

Alternativ: EPA 94

Euro VI




Euro V

Euro IV

Euro III

Euro II

Alternativ: EPA 10

Alternativ: EPA 04

Hong Kong Euro V

Alternativ: EPA 10 Alternativ:

EPA 07

Singapore: EURO V



III-V

JP 09

GHG17

Alternative: EPA 10

Alternativ: EPA 07

Alternativ: EPA 94

Euro VI



Different Markets (Mega-Cities) Be

völk

erun

g in

Mill

iard

en

Bangalore Metropolitan Transport Corporation

• ca. 8,5 Mill. citizens.

• ca. 4,35 Millionen passengers/day

• Over 6.000 Buses

• 36 Depots

• More than 32.000 employees



Mega-Cities Be

völk

erun

g in

Mill

iard

en

Bangalore Metropolitan Transport Corporation

• ca. 8,5 Mill. citizens.

• ca. 4,35 Millionen passengers/day

• Over 6.000 Buses

• 36 Depots

• More than 32.000 employees



Product Variants in parallel



Characteristics and Categories

Consumer/PC-Electronics Life cycle

2 years for one innovation cycle (Enabler: technological improvement of semi- conductors) 1 year for warranty

Further examples Software-Updates of Windows Memory capacity of USB sticks

Re-boot- and throw away mentality

Transportation/automotive Life cycle Reaction in real time in any case

50-100.000km p.a., Warranty of more than 30 years (trucks/buses, aircrafts) Stable value

development: production: áfter-sales: Pass.: 3 6 15 Truck: 5 10 15

Bus: 5 15 15

?

http://de.123rf.com/photo_9457779_handys-von-drei-generationen-sehr-alt-mittlerer-alte-und-neue-smartphone.html



Example: Decision Making for auto. Driving

v3 = 180 km/h

v2 = 120 km/h

v1 = 80 km/h

v0 = 80 km/h



Example

v3 = 180 km/h

v2 = 120 km/h

v1 = 80 km/h

v0 = 80 km/h



Alternatives in scenario

Initial situation Blue truck and orange car at 80 km/h orange car changes lane

∆s2; ∆v2 ∆s1; ∆v1

• ∆v from 0 km/h to 120 km/h in 10 km/h steps → 12 variants • ∆s from 0 m to 100 m in 10 m steps → 10 variants

• Two pairs → 12*12*10*10* = 14,400 variants for initial situation



Challenge: Sensors



Challenge: Sensors



Challenge: Sensors



Challenge: Sensors



Further Challenges

BIG DATA SAFETY

Liability Ethic questions Standards Sociological aspects …



Content

Introduction



Challenges


Summary



Processes, Methods and Tools (Automotive)

* Source Vector Informatik

Semi-Conductor (Technology) improvement is the front runner Applications follow Processes, methods and tools lack behind

• Electric fuel injection • Cruise control

• Gearbox control • Traction control • ABS • Electric fuel injection • Cruise control

• Airbags • ESP • Active body control • Adaptive gearbox contr. • Adaptive cruise control • Emergency call • Gearbox control • Traction control • ABS • Electric fuel injection • Cruise control

• Adaptive low beam • Active steering • Turn warning signal • Stop and go • Lane assistant • Automatic parking • Collision avoidance • Hybridization • Telediagnosis • Vehicle-to-vehicle

communication • Alternative Drivetrains • Airbags • ESP • Active body control • Adaptive gearbox control • Adaptive cruise control • Emergency call • Gearbox control • Traction control • ABS • Electric fuel injection • Cruise control



Realisation of functions

EE-Architektur (n ECUs)

ECU (Electronic Control Unit)

PCB (Printed Circuit Board)

Micro-Controller

Software

Source: Daimler



Questions

Creativity falls by the way side



How it all started … (Things used to be much better …)

Chance of working on

demand

Small, manageable teams

No electronics No software

Great depth of own production

Small quantities Few variants

No Big Data



Back to Start!



Make the right Choice



System Requirements Analysis

System Design

SW / HW Requirements Analysis

Detailed Software Design

Software Implementation

Software Integration

System Integration

Transition to Utilization

anal

ysis

de

sign

test

im

plem

enta

tion

Verification

Validation

Acceptance test


System Integration


Preliminary Software Design

Seamless Design Flow (HW/SW)

Test

Preliminary Hardware

Detailed H

ware Design

Hardware Implementation

Hardware Integration




Detailed H

ware Design




System Design





System Integration


Verification

Validation

Acceptance test


System Integration



Seamless Design Flow (RCP)

Test

anal

ysis

de

sign

test

im

plem

enta

tion




Detailed H

ware Design




System Design





System Integration


Verification

Validation

Acceptance test


System Integration



Seamless Design Flow (RCP)

Test

anal

ysis

de

sign

test

im

plem

enta

tion







System Design





System Integration


Verification

Validation

Acceptance test


System Integration



Seamless testing

MiL

SiL K-HiL

I-HiL

Tet

anal

ysis

de

sign

test

im

plem

enta

tion







System Design





System Integration


Verification

Validation

Acceptance test


System Integration



Test as part of the Life Cycle Model (V-Model as an Example)

MiL

SiL K-HiL

I-HiL







System Design





System Integration


analysis

design

test

implementation

Verification

Validation

Acceptance test


System Integration



Seamless testing

MiL

SiL K-HiL

I-HiL

Tet







System Design





System Integration


test

implementation

Verification

Validation

Acceptance test


System Integration



Seamless testing

MiL

SiL K-HiL

I-HiL

Test







System Design





System Integration


Verification

Validation

Acceptance test


System Integration



Seamless testing

MiL

SiL K-HiL

I-HiL

Test ViL

Use complete car simulators Use robots Use objects Use Vehicle-in-the-Loop

Virtualize real world Find abnormalities Set prioritities Training / learning techniques Swarm intelligence







System Design





System Integration


analysis

design

test

implementation

Verification

Validation

Acceptance test


System Integration



Seamless testing

MiL

SiL K-HiL

I-HiL

Test ViL

Use complete car simulators Use robots Use objects Use Vehicle-in-the-Loop

Virtualize real world Find abnormalities Set prioritities Training / learning techniques Swarm intelligence



Testfeld in Karlsruhe!



Test Case Conflicts

http://www.autobild.de/bilder/autonomes-fahren-mit-der-s-klasse

„… it was surprising that in Bad Mingolsheim suddenly "Bertha" ran into a deadlock. When a few pedestrians were waving at a zebra crossing without the intention to cross the road, „Bertha“ neglected to drive on.“ How can we find the critical situations?



New goal: find the needle in the haystack

Find abnormalities use science of „anomaly“ Use them reverse from the field to prioritize test scenarios the abnormal test responses will guide us to the relevant stimuli



New goal: find the needle in the haystack

Anomalous Subsequence

Find abnormalities use science of „anomaly“ Use them reverse from the field to prioritize test scenarios the abnormal test responses will guide us to the relevant stimuli Probabilistic instead of constructive Methods will more and more dominate

http://www.iomniscient.com/products_iq140.htm



Data Management

„Wer es fertig bringt 2 Halme wachsen zu lassen, wo bisher nur 1 wächst, der ist größer als der größte

Feldherr.“ (Friedrich der Große)

„Wer es fertig bringt die großen Datenmengen, die im Fahrzeug

künftig anfallen, wertschöpfend zu interpretieren, der ist größer als…“

• Telediagnosis • Fleetmanagement • Environmental Conditions • Swarm-Knowledge • …



Decision Making based on „big“ data

Knowledge

• Telediagnosis • Fleetmanagement • Environmental Conditions • Swarm-Knowledge • …

Physical Environment

Sensors

Filtered Data

Pattern

Selection

Interpretation Evaluation

Selected Data

Normed Data

Transformation

Data-Mining

Pre- Processing



Content

Introduction



Challenges


Summary



Main Areas of Research



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