Simulation and Virtual Product Development of Advanced Automotive Batteries

© 2011 ANSYS, Inc. February 28, 20121

Simulation and Virtual Product Development of Advanced Automotive Batteries

Sandeep Sovani, Ph.D.

Manager, Global Automotive Strategy

ANSYS Inc, Ann Arbor, MI, USA

February 10, 2012


Virtual Product Development

Concept & Design

Physical

Prototype

Production

Simulation-Driven

Product Development

Today’s norm for automotive product development


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Simulation in Automotive Product DevelopmentUsed extensively

Cars and Light

Trucks

Heavy Trucks

And Buses

Off-Highway,

Construction

Motorsports

Two Wheelers

Railways

Other Ground

Transportation


Three key differences

Batteries simulation is very different from traditional automotive simulations

Multi-Scale

Multi-Physics

Multi-Parameter


Current Distribution, EMI/EMC, Abuse

Thermal Mgmt

Durability, NVH,Impact, Abuse

NVH

Tight coupling between multiple fields

Multi-Physics

Battery Image Reference: http://a.img-zemotoring.com/media/news/2010/11/renault-battery-pack.jpg


Multi-PhysicsMultiple physics solvers and seamless interconnections


- Newman & Tidemann (1993); - Gu (1983) ; - Kim et al (2008)*

J

)()( TfUYJ np

Cathode Anode

Current Current

ip= Current Vectors

at Cathode plate in= Current Vectors

at Anode plate

J = Current Density

J (t, x, y, T )

Cathode Anode

Current Current

ip= Current Vectors

at Cathode plate in= Current Vectors

at Anode plate

J = Current Density

J (t, x, y, T )

Transfer current

U and Y are derived from experimentally obtained polarization curve, dependent on Depth of Discharge (DOD) & Temperature

A model based on the work of:

* Reference: U. S. Kim, C. B. Shin , C. S. Kim, “Effect of electrode configuration on the thermal

behavior of a lithium-polymer battery”, Journal of Power Sources 180 (2008) 909–916.

Example 1Multi-Physics


Geometry & Mesh

Temperature Current Density

Example 1Multi-Physics


Temperature DistributionCurrent Density Distribution

Structural Deformation, Fatigue Life

Electro-Thermal-Structural Fatigue of Bus BarsMulti-Physics Example 2


Multi-Scale

Multi-Physics

Multi-Parameter


Phenomena at one level affect those at other levels and need to be simulated in simultaneous co-simulation

ElectrodeLevel

•Electrode layout•Manufacturing process development•Aging

MolecularLevel

•Material innovation•Material selection

Cell Level

•Charging, dischar-ging profiles•Heating •Safety under abuse•Swelling, deformation

Pack Level

•Thermal Mgmt•BMS Logic•Safety •Durability•NVH•EMI/EMC

Powertrain and Vehicle Level

•System Integration

Smal

l Sca

le

Larg

e S

cale

Multi-Scale

Tight inter-coupling between multiple scales


Enabling comprehensive multi-scale simulation:DOE-NREL CAEBAT Project

ElectrodeLevel

MolecularLevel

Cell Level Pack Level Powertrain and Vehicle Level

Smal

l Sca

le

Larg

e S

cale

Multi-Scale

Universities and Research Institutes

Commercial Simulation Software

Companies

ESim

One of the 3 teams in CAEBAT


Multi-Scale

Two key needs for multi-scale simulation:

1. Co-SimulationSimultaneous simulation of a component and a systemE.g. cell and module co-simulation

2. Model Order ReductionRepresenting a component with a simplified model to faster system simulation


Battery Electrical Model

Cell 4

Cell 5

Cell 6

Cell 1

Cell 2

Cell 3

Battery Cooling Flow and Thermal CFD

Model

Heat Dissipated

Temperature

Multi-ScaleElectrical-Thermal-Fluid Co-Simulation

Example 1


Heat Dissipated

Temperature

Heat dissipation

Discharge curve

Temperature contours

Multi-ScaleElectrical-Thermal-Fluid Co-Simulation

Example 1


A sample step

response

Multi-ScaleLTI Model Order Reduction

Example 2

Module Geometry (CAD)

Simulation Model

3D Flow Simulation Result

Thermal step load (heat release) is applied to each cell and the response of the entire cooling flow field is recorded



Example 2

A foster network model is created using the step responses



Example 2

Simulation run time reduced from many hours to few seconds, without loss of accuracy making is possible to simulate very long transient cycles.


• 60 Cells connected in matrix pack

• Packs are connected in matrix to final configuration

5

cells

Multi-ScaleModule Models incorporated into Pack Model

Example 2


Powertrain and

Vehicle Level

System

Integration

Multi-ScalePack Model is integrated into Powertrain Model

Example 2


Multi-Scale

Multi-Physics

Multi-Parameter


Multi-Parameter

• Multitude of design variables in batteries

• Simulation is the only feasible way to handle a large number of variables for –

• Robust design• Optimization


Property

SD/

Mean

Metallic materials, yield 15

Carbon fiber composites 17

Metallic shells, buckling 14

Junction by weld 8

Bonded insert, axial load 12

Honeycomb, tension 16

Launch vehicle , thrust 5

Transient loads 50

Thermal loads 7.5

Deployment shock 10

Acoustic loads 40

Vibration loads 20

Fluid flow 3

FatigueFluids

Structural(thermal)

Geometry

Structural(deformation)

±??%

Potential

Failure?

Multi-ParameterProtection against potential failure

Robust Design


Input with Variations

• Gap Thickness

• Cell Resistance

• Flow Rate

• Six input parameters:– tgap

– tgap

– R

– R

– Frate

– FrateRef: Valhinos et al, “Improving Battery Thermal Management Using Design for Six Sigma Process”, 20th Electric Vehicle

Symposium, Long Beach, CA (November 15-18, 2003)

Multi-Parameter Robust Design Example


Outputs – variation

• Max temperature

• Differential temperature

• Pressure drop

Six output parameters:

• Tmax

• dT

• dP

• Tmax

• dT

• dP

Three Upper Specification Limits (USL)

Ref: Valhinos et al, “Improving Battery Thermal Management Using Design for Six Sigma Process”, 20th Electric Vehicle

Symposium, Long Beach, CA (November 15-18, 2003)

Multi-Parameter Robust Design Example

Potential

Failure


Channel Inlets

Multi-Parameter Optimization Example

Task:Optimize the manifold shape to ensure flow uniformity across channel inlets

Simulation accomplished this task automatically with shape morphing


Initial Geometry

Final Geometry

Multi-Parameter Optimization Example


• Simulation is key to battery development

• Battery simulation poses three challenges

1. Multi-physics

2. Multi-scale

3. Multi-parameter

• Several useful simulation solutions are commercially available today

• Ongoing work is further addressing simulation challenges

Summary

Technology

Simulation and Virtual Product Development of Advanced Automotive Batteries