math enabled innovation · math enabled innovation dr. paul e. krajewski director, vehicle systems...
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![Page 1: Math Enabled Innovation · Math Enabled Innovation Dr. Paul E. Krajewski Director, Vehicle Systems Research Lab GM Global Research & Development J. T. Wang, Bahram Khalighi, Yilu](https://reader036.vdocuments.us/reader036/viewer/2022062923/5f0b5c487e708231d4302268/html5/thumbnails/1.jpg)
Math Enabled Innovation
Dr. Paul E. KrajewskiDirector, Vehicle Systems Research Lab
GM Global Research & Development
J. T. Wang, Bahram Khalighi, Yilu Zhang
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Connected Living
Fuel Economy CO2
– Aero
– Electric Vehicles
– Lightweight Materials
Personal Security
Future / Summary
AGENDA
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Mobile-cellular subscriptions approaching 7B, the number of people on the earth
3B use the Internet
Facebook has over 1.4 billion active users globally; 85% are mobile users
90% of the world’s data has been created in the last two years –2.5 quintillion bytes of data created every day!
CONNECTED LIVING
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CUSTOMER EXPECTATIONS
Bring their digital life into their vehicle Bring their vehicle into their digital life
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Vertical Implementation ICS BinArm Rest
Phone overheating in vehicle cabins using inductive wireless charging led to
unacceptable phone charging performance
WIRELESS CHARGING
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Air gap for convective cooling
Phone
POWER Module
• Passive cooling incorporates an air-gap to facilitate the dissipation of trapped heat
from the interface between the phone and charging surface
• CFD tools like Fluent were used to design bumps and spacing
Armrest
PASSIVE COOLING
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Eva
po
rat
or
ICS Bin
ICS Bin
2 mm air gap
Phone temperature
Air flow to ICS bin
ACTIVE THERMAL MANAGEMENT
Air from the HVAC module routed to the phone to maintain it within operational temperatures.
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• Significant improvement in phone charge times
during extreme hot and cold.
• Increased customer acceptance and satisfaction
by not compromising charging performance.
• Enabled GM to be the first OEM to provide an
integrated dual protocol (WPC and PMA-
Powermat) wireless charging system (inductive) in
vehicles.
Phone Temperature
RESULTS
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IHS/CSM
Segment
NHTSA
Segment
Representative
Model
Tailpipe
CO2
(g/KM)
Fuel
Economy
(mpg)
B-segment Car Compact Car Chevrolet Sonic 78 61.1
D-segment Car Mid-Size Car Buick Regal 88 54.9
E-segment Car Full-Size Car Cadillac XTS 102 48.0
C-segment Truck Small SUV Chevrolet Equinox 102 47.5
D-segment Truck Mid-Size CUV Cadillac SRX 112 43.4
FSFF Truck Large PU Chevy Silverado 150 33.0
2025 CO2 AND FUEL ECONOMY TARGETS
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ELECTRIFICATION
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11
Chevy Volt
Battery pack
BATTERY THERMAL MANAGEMENT
• Temperature impacts battery performance and life.
• Li-Ion batteries in hybrid and EV vehicles undergo constant charging and
discharging.
• Active battery cooling is necessary to maintain the cell temperatures within
allowable temperature limits (25oC ~ 35oC)
• Peak durability and reliability requires DT<5oC within the cell and across the pack
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BATTERY PACK DESIGNElectrochemistry
Heat Transfer
Fluid Flow
Material Properties
Design
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Curr
ent C
olle
cto
r (C
u)
+-
Curr
ent C
olle
cto
r (A
l)
sep
Negative
Ele
ctr
ode
Separa
tor
Positiv
e
Ele
ctr
ode
L
Li+
e-
rLi
xC
6
rLi
yCoO
2
e-
Electrolyte
x
cs,e
ce
cs(r)c
s(r)
cs,e
Computer-aided software design tools for hybrid/electric and
electric vehicle (HEV/EV) batteries to reduce cost, improve
performance and increase life.
• GM : End user requirements,
verification/validation, project management
• ANSYS : Software dev. and commercialization
• ESim : Cell level sub models, life model
• NREL : Technical monitor
Partners
Funding provided by DOE Vehicle Technologies Program .
CAEBAT
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• Cooling design implemented on 2016 Chevy Volt
• Enabled more uniform temperature
• Lower power requirements for cooling
RESULTS
Total pressure drop
= 28.4 kPa
2011 Chevy Volt
Total pressure drop
= 12.4 kPa
2016 Chevy Volt
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AERODYNAMICS
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Parametric sensitivity
•Approximate a vehicle shape with many
design parameters, such as angle, length,
curvatures…
•Calculate the sensitivity for each design
variables
N design parameters
N+1 CFD Flow solutions
DRAG REDUCTION - CONVENTIONAL METHOD
• Incremental.
• Takes too long.
• Doesn’t account for multiple
interactions
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Precept (Cd=0.19)
DRAG REDUCTION - ADJOINT METHOD
• CFD provides diagnostic information for drag reduction.
• CFD is not prescriptive on shape sensitivities nor direction of the shape change.
• Adjoint optimization method provides aerodynamic design information for both shape
sensitivity and direction for shape improvements.
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•All the surface mesh (x,y,z)
locations are the design
parameters
•Large number of design
parameters 0.5 to 1 million
N design parameters
1 CFD Flow solution
1 Adjoint solution
CFD Flow Solver
Adjoint Solver
Calculate
Shape Sensitivity
Shape
Change
ADJOINT METHODOLOGY
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Reduce the stagnation region
Recessing the A-pillar to reduce the transverse pressure gradient
Lowering the roofline to reduce the frontal area
Smooth transition near the front fascia Corner rounding along
the sharp corner
Boat-tailing near the rear fender for a larger pressure recovery
Raising the cowl area to provide a smooth and continuous flow acceleration
The modified shape reduced the drag by 4%
Red: push outward
Blue: push inward
ADJOINT EXAMPLE RESULTS
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ADJOINT SUMMARY
• Currently using to give guidance to engineers and designers
• Seeing reduction in windtunnel and CFD iterations
• Future integration with other MDO approaches
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LIGHTWEIGHT MATERIALS
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Philosophy nm mm mm m
log(Length scale)
Engineering
Materials
Science
Chemistry
Physics
Competency
Mg flowMg flow
Electronic
Atomistic
Microstructural
Continuum
$
MULTI-SCALE MODELING OF MATERIALS
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Med. Mn (10wt%)
Q&P
ICME: 3RD GENERATION STEELSIntegrated Computational Materials Engineering
MathWorks tools used to measure
retained austenite volume fraction with
strain and parse data
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PERSONAL SECURITY
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CRASH TEST DUMMIES
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INDIRECT INJURY
MEASURES:
• Head injury criterion
• Neck forces
• Chest deflection
• Femur load
• Tibia index
Crash
Simulations
Dummy Response
Crash Model
CRASH DUMMY BASED SIMULATION
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HUMAN-LIKE CRASH TEST/SIMULATION TOOLS
Reduced Biofidelity
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GLOBAL HUMAN BODY MODELS CONSORTIUMGHBMC
• Co-founded by GM in 2006, GHBMC is an international consortium of automakers & suppliers working with research institutes and government agencies to advance human body modeling technologies for crash simulations.
• OBJECTIVE: To consolidate world-wide HBM R&D effort into a single global efforts
• MISSION: To develop and maintain high fidelity FE human body models for crash simulations
MEMBERS
SPONSOR PARTICIPANTS
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GHBMC DEVELOPMENT STATUS
• Developed 10 models
• Three more detailed pedestrian models (M50-P,
F05-P, and M95-P) to be delivered in 2017
F05-OSM50-O M50-OSM95-O M95-OSF05-O F05-PS M50-PS M95-PS6YO-PS
Detailed Simplified
BELTED
PASSENGER
UNBELTED
PASSENGER
MathWorks tools used for crash
sensing algorithm development
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TISSUE LEVEL INJURY ASSESSMENT:
• Skull fracture
• Brain Injury
• Vertebra fracture
• Spinal cord injury
• Rib fracture
• Major artery injury
• Lung injury
• Liver injury
• Pelvis fracture
• Femur fracture
• ………
Human Body Response
Crash
Simulations
Crash Model
FUTURE: A HUMAN BODY MODEL BASED VEHICLE DEVELOPMENT PROCESS
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FUTURE: INFORM FIRST RESPONDERS AND ER
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VEHICLE PROGNOSIS
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MOTIVATIONEverything wears out over time
Customer’s life is disrupted, when his/her vehicle needs repair unexpectedly
OnStarTM Proactive Alert – A new customer care service
– Alert before failure happens
– Transform an emergency repair to planned maintenance
– Enhance ownership experience - a delight to customers
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PROGNOSTIC ALGORITHM DEVELOPMENT
34
Physical-model based algorithm generation:
• Study failure modes - FMEA
• Model physics of failure
• Generate fault signatures and failure
precursors
• Develop prognostics algorithm
• Validate concept on benches and test
vehicles
Lead Acid Battery (Plate Surface Scanning Electron Microscopy)
Electric Motor
MathWorks tools used for algorithm
development and data analysis
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PROGNOSTIC ALGORITHM DEVELOPMENT
35
Big-data based algorithm validation:
• Collect data from >1M vehicles
• Analyze warranty return parts
• Correlate algorithm outputs with
engineering assessment
• Calibrate algorithm parameters
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.04
0.08
0.12
0.16
0.2
0.24
Crank Time To 200 RPM (s)
Perc
enta
ge
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.2
0.4
0.6
0.8
1
1.2
Mem
bers
hip
Gra
de
Fuzzy Set Long
3
6
12/17 01/06 01/26 02/15 03/07 03/27 04/16 05/06 05/26 06/150.2
0.25
0.3
0.35
0.4
0.45
0.5
Date
Cra
nkT
imeF
orC
onventionalV
ehic
les (
Second)
12/17 01/06 01/26 02/15 03/07 03/27 04/16 05/06 05/26 12/17
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
Date
Cra
nk
Ratio (
)
Statistical analysis
Time series analysis
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VALIDATION
36
06/15 08/04 09/23 11/12 01/01 02/20 04/110
20
40
60
80
Date
Rho
c ()
GY
R
G
G
Y
R
G
Replacement Date
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LIDAR DATA
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ARTIFICIAL INTELLIGENCE AND LEARNING
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SUMMARY
• New mathematical models and methodologies are driving automotive
innovation in a variety of areas.
• These approaches have reduced the time to bring innovative solutions to
the market.
• Themes
• Model Integration
• Collaboration
• The future of mobility will rely on continued breakthroughs in
• Model integration
• Big data
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