ce58 023 prt eis seminar tyre modelling 1vf-no...

Title: CE58_023_PRT_EIS_Seminar_Tyre_Modelling_1v1

Record Owner: JPRINS

Classification: CONFIDENTIAL

Record Type: TRANSIENT

Template Last Modified: 10/01/2018

Template Effective Date: 10/01/2018

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TYRE MODELLING IN JAGUAR LAND ROVER

Jan PrinsTechnical Specialist Tyre Modelling & CAEChassis Steering Wheel & Tyres22/11/18

©2018

Marco Furlan TasaraProject Engineer Tyre Modelling & CAE

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Objective

Provide an overview of and introduction to:

• Vehicle Development and CAE

• Testing of tyres for purpose of modelling

• Modelling of tyres

• Challenges and Vision

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Objective






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Vehicle DevelopmentPhysical versus Virtual Testing

Physical Testing

• Prototype vehicles (unique)

− ≈ £250,000 each

− ≈ 6 month lead time

• Ship vehicle to test site

− ≈ £15,000 (airfreight)

− ≈ 10 days each way

• Travel costs

− ≈ £1500 per ticket

• Poor repeatability and reproducibility

(changeable conditions and inconsistencies

between engineers)

Virtual Testing (CAE)

• Software (universal) licence costs (purchase

≈ £20,000 each & annual maintenance ≈

30%)

• All tests can be done anywhere at any time

• Some tests can be automated

• Extremely good repeatability and

reproducibility

• Accuracy of results depend on quality of

models and ability to simulate real world

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Virtual EngineeringJaguar Land Rover ‘2020 Vision’

‘More Great Products Faster’ (to market):

By the year 2020 …

There will be ‘virtual test’ (=Computer Aided Engineering =CAE) capability to match each

and every physical test

Vehicle development and attribute balancing will be done ‘virtually’

All vehicle requirements will be signed off ‘virtually’ (at FDJ)

‘Virtual capability’ will be such that …

The first prototype vehicles built will be within ‘tuneable range’ of final target (i.e. within 5% of

target)

Prototype vehicles will only be used for fine tuning and validation of ‘virtual’ predictions

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1. Models from testing tyres (‘business as usual’)

2. CAE Driven Tyre Development (‘Virtual Submissions’)

Both work streams are being advanced in parallel in line with agreed strategy

There is overlap between streams 1 and 2, and current practices are converging towards

‘CAE Driven Tyre Development’ and ‘Virtual Submissions’ capabilities

• Timing

• Tyres (logistics)

• Testing

• Parameterisation

• Collaboration

• Requirement definition

• Jaguar Land Rover ↔ Tyre OEM

interaction

Jaguar Land Rover Tyre Modelling Work Streams

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Facilitate realisation of Jaguar Land Rover ‘2020 Vision’ for Virtual Capability

Provide dynamic tyre, rim and surface friction models to allow FDJ sign off through virtual testing only, eliminating any need for whole vehicle physical testing, other than for validation

ALL whole vehicle dynamic simulations needs tyre models. Tyre models are critical to the design process!

Tyre models are enabler for virtual development for nearly all CAE areas:

Pre-development and sign-off using CAE

Reducing prototypes, reducing physical testing, and overall reducing development time

DNA

Vehicle Development and CAE ‘Mission Statement’

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Objective






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Tyre modelling processes

We want to design / optimise a tyre in CAE, then make it (‘print at tyre’)

Until then all tyre models require test data to create models from the data

Jaguar Land Rover sources its own data / models

• Ensures comparable results

• Economies of scale

Current Process Desired Process

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Flat track testing

Cons

• Artificial surface (sand paper) not representative of real road surface

• Capability for cleat testing t.b.c.

Pros

• Repeatability

• Flat surface © Calspan Inc.

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Drum testing

External Drum

Internal Drum

Image KIT

Cons

• Curved contact patch

• Artificial surface (sand paper) not representative of real road surface (external drum)

Pros

• Cleat test capability

• Realistic road surface (internal drum)

Image FKA

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Cons

• Real world surface variability (weather etc.)

• (Currently) requires large open space (VDA)

• Test repeatability

• Difficult to minimise unwanted ‘transient’ behaviour

Vehicle based objective tyre testing (VBOTT)

Dedicated vehicle fully instrumented with wheel force transducers and other instrumentation to gather tyre data at the wheel hub. This project is industry leading!

Pros

• Real world surface (any that can take car)

• Testing 4 tyres at once

• Fully inhouse

• A ‘self-propelled’ tyre test rig (fully instrumented LR vehicle)

for use on real world surfaces

• World’s most advanced project using on-vehicle data

acquisition!

• Measures tyre forces and moments on any real world surface (dry/wet asphalt, snow/ice, grass, etc.)

• Overall model costs comparable to Calspan

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Vehicle Based Objective Tyre Testing (VBOTT)

Sample Results:

Benefits:

• Better correlation; CAE community needs tyre models for real world surfaces, in all sizes

• More realistic driving feel in the Jaguar Land Rover simulator

Way Forward:

• Routine VBOTT model delivery (~20 models in ‘18), based on current vehicle rigs

• Commission and deploy bespoke VBOTT vehicle rig (2nd half of ‘19)

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Objective






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All tyre models try and predict the behaviour of a tyre through calculations based on inputs from the vehicle model and give outputs to the vehicle model:

Inputs to the tyre model:

• Vertical load

• Camber

• Slip Angle

• Slip Ratio

• Speed (rotational)

• Inflation Pressure

• Temperature(s)

Outputs from the tyre model:

• Forces & Moments

• Longitudinal (X)

• Lateral (Y)

• Vertical (Z)

• Inflation Pressure

• Temperature(s)Not included in

all tyre models

Tyre model calculation from inputs to outputs

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Tyre models used currently by Jaguar Land Rover

Magic Formula (MF) used for ‘handling’ (mainly horizontal plane)

Developed by Prof. Hans Pacejka (Delft, Netherlands), circa ‘90

FTire for ride & durability/loads (mainly vertical plane)

Developed by Prof. Michael Gipser (Munich, Germany), circa ‘98

Alternative / additional models are being tried (subject to strategic review / benchmark), in particular enhancements of MF (‘+ thermal’) and CDTire from Fraunhofer ITWM (i.l.o. FTire).

Image TUD

Image Cosin

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Lateral force (Fy) vs Slip Angle (SA)

Tyre (265/45R21 104 Y) versus data from Calspan flat track

Actual MF example graphs

Pros

• Relatively cheap to produce (£5k)

• Open source (public model)

• Fast computation

• Limited ‘synthesising’ possible

Cons

• Limitations with regards to high frequency transients (e.g. ABS)

• No thermal effects included (in basic form)

• Only for longitudinal and lateral behaviour, not good for vertical

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With scaling factors, the effect of changing friction coefficient, cornering stiffness, camber stiffness, etc, can be quickly investigated in a qualitative way.

Magic Formula scaling factors

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FTire

FTire (Flexible ring Tire) models the tyre as a coarse surface mesh, dividing the surface of the tyre into great many square cells, attached to each other by very many spring-dampers.

Model ‘fitting’ principle is same as for MF (in a range of conditions vary model parameters until model output matches measurements).

Models are purchased externally together with the test data used to produce them.

Pros

• Good for ride comfort and durability application (loads)

• Deals well with transient (changing) conditions

• Useful animations

• Option to approximate wheel rim effects (‘Flexrim’)

Cons

• Expensive (~2x as much as MF, which is needed as base)

• Long computation time

• Not suitable for handling and lateral characteristics

• Difficult to ‘synthesise’ modelsImage Cosin

21Low Complexity

High Complexity

Mag

ic F

orm

ula

FTire / CDTire

Misuse

Low Slip Handling

Primary Ride

Handling on

‘Real’ Surface

Handling on Rough

Road

ABS Stop

Transient Steering

Hot/Cold Weather

Secondary Ride

Lap Time Simulation

Limited SCS Tune

Parking

Power on Oversteer

Extreme Manoeuvres

Capability Gap

Handling Ride / Loads

NVH

Off-Road

Rolling Resitance

‘Other’ Attributes

Capability Gap

Tyre ModellingStrategic Vision

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Objective






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MFeval Toolbox

• In-house Magic Formula coded in MATLAB and Simulink

• C-code compatible

• STI and CPI interfaces for MBS integration

• Baseline for future enhancements:

• Thermal

• Friction

• Parking

• Advance transients

• Wear

• Basic MF6.1 formulation is free and open source:

https://mfeval.wordpress.com/

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Friction Research and Modelling

Why:

• We need tyre models for real surfaces, in order to improve

correlation with physical test

• If we can model friction then can test on one surface (sand

paper) and convert model to be representative of real surface

(e.g. asphalt)

What:

• VBOTT work ongoing on asphalt and snow/ice

• Self funded PhD Marco Furlan (started Feb. ’17)

• Jaguar Land Rover Chassis funded EngD Alex O’Neill

(started Oct. ‘17)

• Jaguar Land Rover VE funded PhD Gian-Matteo Bianchi

(started ‘15)

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Jaguar Land Rover Tyre Modelling – Conclusions

• The Jaguar Land Rover Tyre CAE Team is putting a lot of effort into understanding the

tyre/road runway interaction:

• Vehicle based objective testing (VBOTT) on asphalt (and other surfaces)

• Research (multiple PhDs sponsored and/or supported)

• The ultimate aim is being able to test on a rig using a known repeatable surface (i.e.

sandpaper) and then convert data/models to be applicable to real world surfaces (e.g.

dry asphalt, etc.).

• This will enable Jaguar Land Rover’s Tyre Modelling and CAE capabilities to be

advanced, facilitating increased virtual development capabilities, to deliver even better

vehicles in ever shorter times.

26Questions …

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Jaguar Land RoverW/1/26 Abbey Road, WhitleyCoventry CV3 4LF, UK

jaguarlandrover.com

THANK YOU Jan PrinsTechnical SpecialistM +44(0)7771 965252

[email protected]

Presented on behalf of the Jaguar Land Rover Tyre CAE Team;

Javier Garcia, Marco Furlan, Carlos Rayo, Jorge Guitierrez

Mateo Gladstone, Tako Houtzager & Gian-Matteo Bianchi

[email protected]

The many people supporting us at Jaguar Land Rover

The Teams at Calspan, Cosin, FKA, Fraunhofer ITWM, Mathworks, OptimumG, Sova Motion, Stackpole Engineering and many more.

Dr. Dan O’Boy, Dr. Georgios Mavros, (Loughborough University)

Prof. John Watts, Dr. Patrick Gruber, Alex O’Neill (Surrey University)

Prof. Mike Blundell, Dr. Gary Wood (Coventry University)

To name but a few..!

AKNOWLEDGEMENTS

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