A Total Li-Ion Battery Simulation Solution - Ozen … · A Total Li-Ion Battery Simulation Solution...

© 2015 ANSYS, Inc. Collins / Convergence Apr 2015 1

A Total Li-Ion Battery Simulation Solution

Lewis Collins, Director of Software Development ANSYS Convergence Regional Conference Santa Clara, CA April 21, 2015


Advantages

Industry Solutions Value-Added Services Global Support

Unequalled Depth Unparalleled Breadth Comprehensive Multiphysics Engineered Scalability Adaptive Architecture

SDPD Vision Company Strength Independence

Engaged in electrochemical systems R&D for >15 years

• Primarily batteries and fuel cells

Confidence from usage in dozens of industries, hundreds of applications, thousands of organizations


Ongoing Collaborations

Scale-Bridging Models for Electrochemical Power Sources • U.S. Office of Naval Research, 2005-

• ANSYS, NRL, universities

• Metrology and resolved modeling of electrodes

• Upscaling methods for cell, pack, and system

Computer-Aided Engineering of Electric Drive Vehicle Batteries (CAEBAT)

• U.S. DoE Vehicle Technologies Office, 2011-

• ANSYS, GM, ESim, NREL, universities

• Improve CAE tools for battery cells and packs (usability, validation, interoperability)

http://www.onr.navy.mil/default.asp


In mobile devices, battery is connected with key design issues

Battery Considerations in Electronics

Power management (battery-to-chip)

Pressure on noise margins

Signal and power integrity

Electromagnetic interference (EMI)

Thermal

Hardware/software integration


Battery has become a limiting factor

Driving ultra-low power design methodologies

Demand Trend for Mobile Power

(for a specified form factor)


EV Everywhere Grand Challenge goal: make plug-in electric vehicles as affordable and convenient as today’s gasoline-powered vehicles by 2022

Battery is Key to Vehicle Electrification

Image credit: “FY 2013 Annual Progress Report”, Energy Storage R&D, Vehicle Technologies Office, U.S. Department of Energy


Cost

Performance (power and energy density)

Durability and service life (in disparate environments)

Safety (tolerance to abusive conditions)

Complex multi-scale, multi-physics system

Rapidly evolving materials and design concepts

Existing software tools not “tuned” for batteries

Engineering Challenges

Thermal

Elec-trical

Chemical

Fluid

Molecular Particle Electrode Cell Pack System

Need to account for interconnection


Methods Toolkit

Field simulation Electrode cell module scales

e.g. CFD: fluid, thermal, chemical, electrical

System simulation Module pack vehicle scales

Lumped-parameter models, controls

Reduced-order models (ROM) Small number of (linear or nonlinear) state eqns

Multidisciplinary design optimization

Cosimulation

Extraction

Expansion

Instantiation

Electrochemistry sub-models Homogenization (over particles, electrode layers)

DuCxy

BuAxdt

dx


Model-Based Systems Engineering

System Validation

Sub-System Integ. & Verification

Component Integration

& Verification

Requirements and Specifications

Sub-System Design

System Functional & Architectural Design

Mechanical Electrical Software

Detailed Design & Optimization

Functional Allocations

Detailed Architecture Architecture

Simplorer

Maxwell

Fluent

Mechanical


Balancing Speed and Resolution New techniques can reduce cost while preserving a sufficiently

accurate approximation of the responses of interest

“Selective use” of CFD, tailored to the unique objectives

fidelity

cost (log scale)

System Simulation

Field Simulation (e.g., CFD)

Spreadsheet/ Handbook Calcs

(orders of magnitude)

With ROM, cosimulation


Geometry: built-in parametric templates • Inputs available in Workbench Parameter Manager and DesignXplorer

Meshing: also templated, based on best practices

Cell Model: Field Simulation


Traditional approach supports maximum design creativity • SpaceClaim, DesignModeler, or CAD Interfaces

• Assumption: lithium transport is perpendicular to local electrode plane

Custom Geometry is Easily Handled

Image credits: www.apple.com/macbook/design


Electro-Chemical-Thermal Simulation

Battery Module a standard feature of Fluent

Single or multiple cells


Φ- Φ+

q, j

Φ- , Φ+ , T

No need to resolve individual electrode layers with the mesh

User-defined scalars represent electrical potentials F+ , F-

Chemical species are not explicit solution variables • Sub-grid model may track lithium ion concentration

Multiscale Approach

Ref: G-H Kim et al, “Multi-Domain Modeling of Lithium-Ion Batteries Encompassing Multi-Physics in Varied length Scales” J. Electrochem. Soc. 158(8) A955-A969 (2011).

)(tI

)(tV

1R2R

1C 2C

sR

)( socVocv

)(tI


“Single-point” (0-D) analysis option, from the same interface

Auto-merge separately-created cell and adjacent-structure models

Auto-fit properties from test data file • e.g. from calorimeter testing

Cell Model: Productivity Aids


Logical step for system simulation • Verification of material properties,

electrochemistry models, stoichiometry assumptions, etc.

Representative results from CAEBAT project:

Cell Model Validation

Images on this slide courtesy of General Motors LLC

HPPC @ 25 degC, 30% DOD


Goal: automate workflow while preserving tool generality

Exploit periodicity or symmetry when possible

System Simulation Templates and Scripts

Unit-Model Template

• Reusable building-block

• Define based on thermal

and electrical connectivity

• One-time manual creation

using standard Simplorer

components

• Store to User Library

Script

• Reusable procedure for a

specific pack architecture

• Automate connections, layout,

post-solution statistics

• One-time optional programming

task using highly-accessible

Python language


Automotive Examples

Image credits Left: General Motors LLC Right: Ricardo Strategic Consulting

Application PHEV BEV

Cell type 15 Ah pouch 3 Ah cylindrical

# cells 288 7104

Electrical configuration 8 x (12S3P) 16 x (6S74P)

Cooling configuration (cells/channel)

Totally parallel, 2

Series within module, 444

Unit definition (# cells) 6 2


Applicable to both rectangular and cylindrical types • Form-factor effects incorporated into lumped coefficients

Electrical • Equivalent-circuit model

• Coulomb-counter (SOC = state of charge)

Thermal • Includes adjacent part

of fluid cooling channel

System Hierarchy Starts with a Single Cell

Two-way Coupling

Drag-and-drop components


Created once and stored to Simplorer library file

(Shapes and layout for reference, not part of model)

Unit Model (Domain Decomposition)

f f f f c1

c2

c1 c2 c3 c4 c5 c6

– Hydraulic – Electrical

–

+ – +

– Thermal


Exploit periodicity, instancing templated models from library

Module Model

2S3P

Unit

2S3P

Unit

2S3P

Unit

2S3P

Unit

. . . (6x)

2P

Unit

2P

Unit

2P

Unit

2P

Unit . . . (37x)

Group (row)

– Electrical – Thermal – Hydraulic


Ready for coupling with BMS or full-vehicle models

Pack Model

12S3P

Module

12S3P

Module

12S3P

Module

12S3P

Module

. . . (8x)

LOAD

Outer

Case

Thermal

Mgmt System

6S74P

Module

6S74P

Module

6S74P

Module

6S74P

Module

. . . (16x)

LOAD

Outer

Case

Thermal

Mgmt System

– Electrical – Thermal – Hydraulic

AMBIENT AMBIENT


Script-generated form • Enter 3 integers

• Select unit from library

Process Automation

Click on any instance To drill into hierarchy


GM prototype 24-cell module • LG 24-Ah pouch cells (LMO/NCA cathode)

• Steady-state liquid cooling

• 32 thermocouples

Progressive model comparison: • “Brute force” CFD model

• Lumped-parameter system model

– Some inputs guided by CFD results

• CFD-derived ROM

Progressive case complexity: • Symmetric pulse charge/discharge

• Simulated drive-cycle load profile

Validation


Top View

Measurement CFD Prediction

No

dat

a

No

dat

a

Coolant in

Coolant out

Typical Comparison: Middle Cell

High-frequency ±3.5C pulse charge/discharge @50% SOC

Maximum difference between simulation and measurement < 1 C

Cooling fin Foam spacer


Typical Comparison: System Model

Volume-average cell temperature versus time

1 C

– Simplorer prediction – averaged measurements (other colors = individual measured locations on cell)

60 minutes (5 x US06 drive cycle + cooled rest, SOC 0.9-0.2)


Excellent thermal replication of cells in ordinary operation

ROM execution several orders of magnitude faster than CFD

Typical Comparison: ROM versus CFD

Ref: X. Hu and S. Stanton, “A Complete Li-Ion Battery Simulation Model,“ SAE 2014-01-1842


Temperatures transfer to Mechanical for analysis at any scale

Thermal Stress


Plug-and-play concept: several interfaces to support native, in-house, and third-party tool integration

• E.g., user-defined function (UDF) for custom electrochemistry models

• Other Tools can include micromechanics, cell-design apps, system simulators, battery cost models, …

Plug-In Hybrid Software

ANSYS

Workbench

CAD

Simplorer

UDF Fluent

Battery Model

Other Tools

Mechanical


Maintained by the non-profit Modelica Association

Supports simulation model exchange and tool coupling, including hardware-in-the-loop (HiL)

Functional Mockup Unit (FMU) = portable package containing • Interface description (XML schema)

• Model functionality (C code – source or binary)

Supported by ANSYS Simplorer, SCADE (and >60 other tools)

Functional Mockup Interface (FMI)

Ref: T. Blochwitz, et.al., “The Functional Mockup Interface for Tool independent Exchange of Simulation Models”, Modelica Association, Proceedings of the 2011 Modelica Conference, modelica.org

Tool

Solver

FMU

Model

FMI Wrapper

Master

Slave

Model

Solver


ANSYS is working with experts on simulation of abuse scenarios

Improving Battery Safety

Damage translation

Extensions for abuse kinetics

Common geometry

Structural Simulation

Quarter-symmetric indentation (image courtesy NREL)

Current and temperature after internal short

Electro-Chemical-Thermal

(image courtesy MIT)


Using Workbench Parameter Manager and DesignXplorer, parametric studies can explore battery trade-offs

• Robust design, service life and safety improvements, …

Design Optimization


Battery requires complex control system for • Cell SOC balancing (maximize lithium utilization)

• Charging protocols and dynamic power-limiting (maximize battery life)

• Safety isolation and cell protection

• Integration with power electronics and other systems

A model-based development environment for embedded software

Battery Management Systems

Image courtesy of General Motors LLC


Next frontier for simulation: efficient coupling with materials-science and -processing (ICME)

• Closing the loop on the real-world chain of dependencies

• Example issues:

– Optimal particle size, morphology, binder/conductor mixture, etc.

– New cathode / anode / electrolyte materials, beyond lithium-ion, etc.

• ANSYS collaborates with molecular-modeling software leaders

Supporting Materials Innovation

Atomistic Particles Electrodes Cell Pack Vehicle


Reconstructing Microstructure Geometry

To create a representative volume element (RVE) model


RVE considers more-fundamental physics while limiting cost

Resolved Electrode Approach

(Image courtesy R. Kee, Colorado School of Mines)


Advanced batteries present a challenging application for the ANSYS vision of Simulation-Driven Product Development

Key gaps involve domain interfaces and transitions

• Multiscale methods for electrode battery system

• Cyber-physical (software hardware) system optimization

• Materials & manufacturing process in-service performance

ANSYS is addressing these challenges using several technologies, to support battery innovation

Summary

Chevrolet Bolt - image courtesy of General Motors LLC


ANSYS contributors: Erik Ferguson, Xiao Hu, Genong Li, Shaoping Li, Sandeep Sovani, Dimitri Tselepidakis

Acknowledgments

This material is based in part upon work supported by the Alliance for Sustainable Energy LLC, Management and Operating Contractor for the National Renewable Energy Laboratory, under Award Number ZCI-1-40497-01, and by the Office of Naval Research, under Award Number N00014-05-1-0339.

This presentation includes an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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