1. Smart Tire: a pattern based approach using FEM
Prof. Dr.-Ing. Ch. Oertel – Brandenburg University of Applied Sciences
Dipl.-Ing. Jan Hempel – Brandenburg University of Applied Sciences
Overview
basics of the smart tires – sensors and data processing
related questions – where, how many, which properties
pattern bases approach – generating patterns
system design tools – FEM and added features
static and steady state analysis – preparing the pattern data base
applications and – wheel load identification, contact forces
summary - conclusions

2. Smart Tires actions targets educated tire adding sensors
adding knowledge
adding algorithms
targets
inflation pressure
angular velocity
normal force
tangential force
friction estimate
camber
temperature ?
educated tire
signals for control systems
pressure monitoring system
estimate road conditions

3. single sensor approach
Smart Tires – state of the art: measurements
FN = 9000 [N]
FN = 4500 [N]
single sensor approach
hall sensor
accelerometer
strain gauge
location sidewall
location belt
displacement, stress and strain for one revolution from FE analysis (RMOD-K FEM)

4. Smart Tires – state of the art: data processing
processing procedure
data acquisition at specific rate
averaging multiple revolutions
identify characteristic properties
generating related results: R - d
uniqueness ?
patent examples:
DE A1
DE C2
DE A1
US A US A1
WO A1
US A1
US A1
US A1
US A

5. Smart Tires – patterns based approach
processing procedure
data acquisition at specific rate
multiple sensors, different locations
individual pattern at current time measured by ten sensors
pattern data base at different wheel loads
measurement or FE
pattern comparing matching value mv

6. simulation based design using FE model
Smart Tires – related questions
sensor type
distance
acceleration
strain point
strain integrated
others
sensor location
inner tire surface sidewall area
inner tire surface belt area
integrated in sidewall
integrated in belt area
other
number of sensors
cost and energy consumption
uniqueness
fail safeness
real-time pattern search
memory used in pattern base
simulation based design using FE model
sensor type
strain integrated
sensor location
design variable (integrated in sidewall)
design variable (integrated in belt area)
number of sensors
design variable

7. Smart Tires – development tool
sensors description
number of
location
orientation
FE tire model based on cross section mesh
post processing
data collection
pattern matching
DOE: variables and parameters, parameter ranges, number of steps

8. assembly view (example)
Smart Tires – embedded measurement fiber in FE analysis
element view
embedded in rebar layer or measurement layer in isoparametric HEX 8 element
given measurement fiber orientation
fiber reference points
assembly view (example)
curved (polygonal) path of fiber segments building one measurement fiber
fiber parameters: location, length, orientation
preprocessing path thru elements
element 1
element 7
layer
fiber

9. Smart Tires – measurement fibers: some details
initial configuration (without inflation)
calculate length of fibers segment in each element
calculate the total fiber length
calculate fiber electric resistance
deformed configuration (inflated and loaded)
calculate length of fibers segment in each element
calculate the total fiber length
calculate fiber electric resistance changes
arbitrary element deformation
by nodal displacements for matrix and embedded rebar element

10. design parameters support of development tool in FE preprocessor
Smart Tires – measurement fibers: design variation
design parameters support of development tool in FE preprocessor
fiber length in belt in initial configuration
fiber length in carcasses in initial configuration
circumferential distance (number of elements) between two sensor fibers in belt
circumferential distance (number of elements) between two sensor fibers in carcasses
offset measured from belt edge
offset measured from bead
long fibers
integrating strain over the fiber length
measuring the average strain in each fiber
fibers in belt and sidewall as alternatives or integrated system
short fibers
measuring the local strain “at a point”
similar to conventional strain gauges
fibers in belt and sidewall as alternatives or integrated system

11. FE model with 48240 degrees of freedom (RMOD-K FEM)
Smart Tires – getting an initial design guess
tire under inflation and vertical load
strain distribution in the carcass layer from FE model
strain at gaussian points for each layer
identify regions with positive strain in different load cases (camber, longitudinal and lateral slip, different loads, different inflations)
optimize the sensors fiber locations and length wrt. durability
12 sensors used in the examples shown
verify the fiber strain measurement algorithm
FE model with degrees of freedom (RMOD-K FEM)

12. Smart Tires – building patterns
define the pattern data base
variables to be taken into account
number of steps for each variable
range for each variable
define jobs of all required sets (63 in this case)
define incremental variable (yaw angle)
run jobs automatically (DOE-feature)
collect results in a pattern data base
small example data base
inflation and wheel load taken into account
nonlinear model, incremental solver
three jobs for 33 patterns
each point represents one pattern entry in data base
amount of data depends on the number of sensors

13. Smart Tires – find a matching pattern
pi = 2.0 [bar]
pi = 2.5 [bar]
pi = 3.0[bar]
test case outside the data base
no correct tire state predictable
significant difference between both inflations, estimate of wheel load may be taken from minimum matching value
possible matching procedures
compute all matching values
find most close point or
interpolate between most close points or
fit surface and find minimum or
search in prior used area
matching procedure based on interpolation possible, very good match of test case parameters inflation and wheel load

14. Smart Tires – example 1 static deformation
variations of sensor properties
reduce the number of sensors
total of 48 sensors
total of 40 sensors
total of 24 sensors
FE static deformation analysis
variation of inflation pressure (DOE)
variation of wheel load by load increments
number of steps inflation: number of jobs
number of steps wheel load: number of increments per job
post processing
collecting data from each job (defined in batch file from DOE)
build the data base
comparing with current tire state (load step j of single analysis)
compare different pattern matching algorithms

15. Smart Tires – example 1 matching: all sensors
total of 48 sensors
nearest pi=2.20 [bar], =17.60 [mm]
test case pi=2.30 [bar], =18.20 [mm]
resolution pi=0.40 [bar], = [mm]
total of 24 sensors
nearest pi=2.20 [bar], =17.00 [mm]
test case pi=2.30 [bar], =18.20 [mm]
resolution pi=0.40 [bar], = [mm]

16. Smart Tires – example 1 matching: selected sensors
total of 6 sensors in left belt area
nearest pi=2.20 [bar], =17.60 [mm]
test case pi=2.30 [bar], =18.20 [mm]
resolution pi=0.40 [bar], = [mm]
total of 6 sensors in left sidewall
nearest pi=2.20 [bar], =17.60 [mm]
test case pi=2.30 [bar], =18.20 [mm]
resolution pi=0.40 [bar], = [mm]

17. combined slip situation at a certain wheel load
Smart Tires – example 2 steady state rolling: combined slip
combined slip situation at a certain wheel load
DOE variable 1 wheel load (vertical displacement)
DOE variable 2 angular rim velocity
incremental variable yaw angle
pattern base
DOE variable 1 7 steps: resolution = 3.33 [mm]
DOE variable 2 7 steps: resolution = [rad/s]
incremental variable 50 increments = 0.3 [deg]
49 steady state jobs
2450 pattern base states
4 alternative sensor locations with 6 sensors per location
DOE variable 1
DOE variable 2
incremental variable

18. ALE – approach for steady state analysis
Smart Tires – example 2 building pattern base
ALE – approach for steady state analysis
rigid body motion and elastic deformation
velocity field in the contact area, normal load
steady state tangential contact forces
iterative method to solve coupled PDE-system
mesh remains non-rolling
steady state analysis
given wheel load
given longitudinal slip
given camber angle
yaw angle as incremental variation

19. Smart Tires – example 2 matching: all sensors
nearest = [mm] = 7.50 [deg] = [rad/s]
test case = [mm] = 7.50 [deg] = [rad/s]
resolution = [mm] = 0.30 [deg] = [rad/s]
test results

20. Smart Tires – example 2 matching: selected sensors
right carcass 6 sensors
= [mm] = [deg] = [rad/s]
point number 1176
left carcass 6 sensors
= [mm] = [deg] = [rad/s]
point number 1175
left belt 6 sensors
= [mm] = [deg] = [rad/s]
point number 1176
test case = [mm] = 7.50 [deg] = [rad/s]
resolution = [mm] = 0.30 [deg] = [rad/s]

21. Smart Tires – further development
subject topics
rolling tire rotating the measured pattern for pattern matching
reducing the data base from double to integer
reducing the data base optimal number of patterns
compare different matching methods optimal balance between data base size and real time matching algorithm
road noise disturbance filters and averaging procedures (depending on system sample rate)
data transfer use conventional pressure monitoring radio transmission
building a prototype testing strain measurement fibers (smart textiles)
courtesy of SFE

22. contact: christian.oertel@fh-brandenburg.de
Smart Tires - summary
pattern base identification of tire state proven for static and steady state cases
information about inflation, normal and tangential force, angular velocity, friction … at real time available
simulation based design of the sensor system supported by FEM tool RMOD-K FEM
test pattern data base by FEM tool RMOD-K FEM
further development concentrate on rolling tires
first application could be race tires
contact: