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IDIADA Chassis Development
Advanced Target Setting
Jonathan Webb, Guido Tosolin,

Introduction
2011: Metric Based Development
Establish objective relationship between driver’s feeling and dynamic response
Form relationships to subjective “Driver Orientated” parameters
Permits tuning work to be done more “objectively”
Guarantees tuning consistency and protects brand image
Define tuning range of components based on system level target
Permit simulation to give an insight into driver experience
2012: Advanced Target Setting
How to drive target setting in cascade from full vehicle level to component level
In systematic way
Focussing on driver’s feel
Page 2

Introduction
Background
[rad]
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0 1 2 3 4 5 6 7 8 9 10
[m/s 2 0
] 0 1 2 3 4 5 6 7 8 9 10
[rad]
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
[m/s 2 ]
0.5
0.45
0.4
0.35
[deg/s /deg]
0.3
0.25
0.2
0.15
0.1
0.05
0
0.5 1 1.5 2 2.5 3
[Hz]
Passenger vehicles are complex systems whose dynamic performance is related with the
specification of the single components, as well as with the synergic integration of all the
subsystems (suspensions, steering, tyres, etc).
How to objectively quantify the influence of each component specifications on full
vehicle targets?
And how to make sure these characteristics have the desired impact on the
perception of the driver?
Page 3

Introduction
Advanced Target Setting
Advanced Target Setting is a two-steps multi-disciplinary approach aimed at establishing a
qualitative and quantitative link between components specifications and full vehicle targets.
Step one consists of setting objective targets at full vehicle level based on
objective/subjective correlation metrics, in order to ensure targets are actually aimed at
improving drivers’ perception.
In step two, simulation model and design of experiment techniques are used to delineate
how the full vehicle targets are linked in cascade with system level targets and design
variables.
Page 4

Design Process overview
Subjective feel
Objective/Subjective
Correlation
?
v=180 km/h, 0.4 Hz
2.5
2
Measurement
C.OC.DC.3 Modulation
Simulation
1.5
1
1
2
0.5
3
0
4
-0.5
5
-1
6
-1.5
1.33 → 7
-2
8
-2.5
0.91 → 9
-15 -10 -5 0 5 10 15
SWA [deg]
10
Lateral acceleration [m/s 2 ]
Simulation Metrics
Objective performance
(Full Vehicle Level)
Full Vehicle
Modelling
Model Validation
Design of Experiments
Objective performance
(System Level)
System Modelling
Suspension design
Page 5

Design Process overview
Subjective feel
Objective/Subjective
Correlation
?
v=180 km/h, 0.4 Hz
2.5
2
Measurement
C.OC.DC.3 Modulation
Simulation
1.5
1
1
2
0.5
3
0
4
-0.5
5
-1
6
-1.5
1.33 → 7
-2
8
-2.5
0.91 → 9
-15 -10 -5 0 5 10 15
SWA [deg]
10
Lateral acceleration [m/s 2 ]
Simulation Metrics
Objective performance
(Full Vehicle Level)
Full Vehicle
Modelling
Model Validation
Design of Experiments
Objective performance
(System Level)
System Modelling
Suspension design
Page 6

Subjective/Objective correlation: metric development
What is a metric?
A metric is defined as an established mathematical relationship between a subjective
evaluation and objective test parameter
What is the reason?
The motivation behind this is to permit a structured target system to be put in place for
vehicle and system
Such that vehicle development can be controlled precisely and results are not
dependent on individual teams
Can be applied to:
Steering
Ride
Handling
Although primarily established and developed for Vehicle Level parameters, can be
cascaded down to System Level
Page 7

Subjective/Objective correlation: metric development
User level
Sporty,
Fun to drive,
Easy
Expert driver level
Linear, Neutral,
Progressive,
Balanced, …
Objective Test Driver
Understeer
Gradient, Yaw
overshoot, Roll
damping, …
Correlation Matrix &
Driver Metrics
Frequency Response 0.3g
x x x x
100 km/h Difference DeltaT gain SS - @ 1Hz (Nm/º)
YawR delay @ 1Hz (s) x x x
YawR vs DeltaT gain (º/s/Nm)
Difference F-R Slip Peak delays @ 1Hz (s) x
Ay delay @ 1Hz (s)
x
Step Steer 0.6g
x x x x x x x
100 km/h Ratio Ay response time1/2 (-) x x x
Ratio YawR response time1/2 (-)
x
YawR response Peak variance with Ay (º/s)
x
100 km/h, 0.6g Ratio YawR Peak/OS (-) x
Ay OS (m/s2)
x
YawR Peak delay (s) x x
Difference F-R Slip Peak delays (s)
x
Ratio F/R Slip Peaks (-)
x
Phase Front / Rear
Progressiveness
Yaw Response
Yaw Damping
Response Level & Delay
Trajectory tracing @ High speed
Balance Front / Rear
25
20
15
10
5
0
Frequency response test: ay-yawR
GAIN
Hyundai JM
0.2
0.1
0
-0.1
-0.2
-0.3
Delay (sec)
-0.4
180
120
60
0
-60
-120
Delay (deg)
-180
1
0.5
Coherence
0.5 1 1.5 2 2.5 3 0
0.5 1 1.5 2 2.5 3
ay-yawR
Frequency [Hz]
Roll-steer, Roll
stiffness, Lateral
compliance, …
Suspension Engineer
Suspension
Development
Page 8

Metrics validation and continuous improvement
For a complete validation of the objective/subjective metrics it is necessary to
extend the correlation process to a large number of vehicles
Vehicle 3
Subjective
rating
The foundation of these
metrics is key in being
able to use objective test
data to draw a relation to
driver subjective feedback
Vehicle 2
Vehicle 1
Vehicle 1
Vehicle 3
Objective
parameter
IDIADA is developing and
using metrics with a
variety of manufacturers
as a means to establish
chassis development
principles
Vehicle 2
Page 9

Metrics validation and continuous improvement
Regression can have different aspects, depending on the nature of the metric
Building up databases for metrics improves understanding of the relative
character/feel of different vehicles
Page 10

Design Process overview
Subjective feel
Objective/Subjective
Correlation
?
v=180 km/h, 0.4 Hz
2.5
2
Measurement
C.OC.DC.3 Modulation
Simulation
1.5
1
1
2
0.5
3
0
4
-0.5
5
-1
6
-1.5
1.33 → 7
-2
8
-2.5
0.91 → 9
-15 -10 -5 0 5 10 15
SWA [deg]
10
Lateral acceleration [m/s 2 ]
Simulation Metrics
Objective performance
(Full Vehicle Level)
Full Vehicle
Modelling
Model Validation
Design of Experiments
Objective performance
(System Level)
System Modelling
Suspension design
Page 11

Design of Experiments (DOE)
DOE in the virtual development process:
In the virtual development process, it allows to create a link between the suspension
performance objectives (Design Objectives) and the suspension Design Parameters (such as
hardpoints position, bushings, etc).
Subjective feeling
Therefore it is possible to understand which design
features impact on suspension performance.
Then, suspension performance impacts on the vehicle
response, which is finally linked to the subjective
feeling of the driver (objective/subjective metrics).
Objective measure
25
20
15
10
5
GAIN
Frequency response test: ay-yawR
Hyundai JM
0.2
0.1
0
-0.1
-0.2
-0.3
Delay (sec)
-0.4
180
120
60
0
-60
-120
Delay (deg)
-180
1
0
0.5
Coherence
0
0.5 1 1.5 2 2.5 3
0.5 1 1.5 2 2.5 3
ay-yawR
Frequency [Hz]
Suspension performance Design of Experiments Suspension design
Page 12

Design of Experiments (DOE)
Phase 1:
Definition of design parameters
(factors) and of design
objectives (responses)
0.5
[deg/s /deg]
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
1 1.5 2 2.5 3
[Hz]
Phase 2:
Simulation of parameters effect
and evaluation of design
objectives within ADAMS/insight
simulation environment
Phase 3:
Analysis of results with inhouse
processing tools
(response surfaces, bar
diagrams, etc)
Page 13

Design of Experiments (DOE)
To ensure the high quality of the analysis the process is performed in two steps:
Step 1: DOE Screening
Two-level analysis
High number of factors included
Selection of the most important factors to be included in step 2
Step 2: DOE Response surface (RSM)
Executed with a selection of factors
High number of runs
Check of fitness statistics (goodness of fit, residuals, etc)
Data refinement (remove outliers, etc)
Page 14

Design of Experiments (DOE)
The output of the RSM method is processed with an in-house tool which is able to:
generate 2D and 3D diagrams where relative importance of each factor for each objective is defined
perform real-time what-if analysis with:
Real-time update of the target book
Real-time update of the most significant suspension diagrams
perform multi-objective optimization studies (IDIADA routine with Matlab Optimization Toolbox)
Page 15

Design of Experiments (DOE)
Tolerance (± mm)
0.4
static_scrub_radius [ 21.8 mm ]
lca_outer_z
1.2%
static_caster_trail [ 19.1 mm ] 0.4%
bump_steer [ -4.3 deg/m ] 13.5%
bump_camber [ -3.99 deg/m ] 9.1%
bump_caster [ -1.8 deg/m ] 0.1%
bump_wc_y [ -10.5 mm/m ] 12.1%
bump_wc_x [ -10.9 mm/m ] 8.6%
roll_steer [ -0.06 deg/deg ] 14.3%
roll_camber [ -0.065 deg/deg ] 8.3%
toe_compl_brake [ 0.04 deg/kN ] 0.9%
Page 16

- - - - Linearisation
Design optimization
The optimization has to be a human-driven process that evolves in several loops: at each step
the tool supports decision making of the vehicle dynamics engineer, who is able to control the
evolution of the process by:
setting the scale of priorities for the design objectives (responses)
assign target values for each design objective
setting the design parameters (factors) that have to be involved in the optimization loop
For the success of the optimization, the target setting is of crucial importance.
Target setting process needs to be strongly based on the objective/subjective correlation work.
This ensures that the optimization process has a direct link with driver’s feel.
Subjective feel
Evaluation of design
objectives (responses)
Optimization
loops
Selection of design
parameters (factors)
RIGHT
LEFT
Front
REF
Rear
Slip angle [º]
14
12
10
8
6
4
2
0
-2
-4
-6
-8
-10
-12
-14
-10 -8 -6 -4 -2 0 2 4 6 8 10
Lateral acceleration [m/s²]
Objective/Subjective
Correlation
Sensitivity Analysis
C.OC.DC.3
Modulation
1
2
3
4
5
6
1.33 → 7
8
0.91 → 9
10

Design optimization
Examples of typical factors and responses that are included in the optimization process
Design Parameters
(FACTORS)
Design Objectives
(RESPONSES)
Bushing characteristics
Hardpoints
Dampers
…
Toe variation in roll
Camber variation in roll
Ackermann variation
…
Understeer gradient
Yaw rate gain
Lateral acceleration gain
Weave deadbands
…
System level
Full vehicle
level
5
4.5
0.5
4
0.45
3.5
0.4
3
0.35
[deg]
2.5
2
1.5
[deg/s /deg]
0.3
0.25
0.2
1
0.15
0.5
0
0 1 2 3 4 5 6 7 8
[m/s2]
0.1
0.05
0
0.5 1 1.5 2 2.5 3
[Hz]
Page 18

Conclusions
Advanced Target Setting allows to set coherent targets at various levels
Joint use of objective/subjective metrics and of experimental design is able to
generate consistent links between full vehicle level targets, system level targets
and design variables in a robust and systematic way
Advanced target setting relies on good consolidated repository of metrics as
well as on well validated vehicle model
The tools involved in the process are commercial software (e.g. MBS simulation
packages) and also in-house tools.
Page 19

IDIADA Fahrzeugtechnik GmbH
T +49 (0) 841 8 85 38 0 (Ingolstadt)
T +49 (0) 893 0 90 56 0 (München)
e-mail: idiada_germany@idiada.com
CTAG IDIADA Safety Technology SL
T +34 986 900 300
e-mail: ctag_idiada@idiada.com
IDIADA CZ a. s.
T +420 493 654 811
e-mail: info@idiada.cz
For further information:
Jonathan Webb
Product Manager, Chassis Development
L’Albornar – PO Box 20
E-43710 Santa Oliva (Tarragona) Spain
T +34 977 166 000
F +34 977 166 007
e-mail: idiada@idiada.com
www.idiada.com
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