This document discusses optimizing the structural compliance and safety of electric vehicle battery packs against crush loads. It summarizes using finite element analysis to simulate crushing of a battery pack according to industry standards. The analysis found the initial design met safety requirements and with topology optimization, an improved design reduced mass by 0.5kg while maintaining stiffness. Ongoing work looks to use machine learning to make simulations more efficient. The company provides these services and invites contact to discuss modeling and simulation needs.

1. Ensuring Structural Compliance of
Electric Vehicle Battery Pack Against
Crush Load & Optimization of
Structure
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The benefit of electric vehicles is the contribution that they can make towards improving air quality in towns and cities.
With no tailpipe, pure electric cars produce no carbon dioxide emissions when driving. This reduces air pollution
considerably.
The global EV market size was USD 246.70 billion in 2020 and is projected to reach USD 1,318.22 billion by 2028.
Faulty battery cells, and modules likely to cause e-vehicle fire, initial probe finds Lithium-ion batteries, whether used in
electric vehicles or electronic devices, can catch fire if they have been improperly manufactured or physically damaged, or if
the software that operates the battery is not designed correctly.
Concerns over safety jeopardize consumer confidence and could derail the growth of a sector that is key to the
country's carbon reduction goals.
Excitements & Challenges

6. Battery Pack for Modern Vehicle
• Lithium-ion batteries are currently used in EVs due to their fast-charging rate enabled by
high energy density and cell voltage.
• The high energy stored in EV battery packs translates to a higher probability of fire in the
battery compartment due to an automotive crash.
• Battery catches fire due to its thermal runaway, where an exothermic reaction between
the electrolyte causes high temperature, often leads to a destructive result and this may
be fatal to the EV occupant.
• OEMs and industry stakeholders continue to evolve requirements and best practices to
help mitigate the risk of fire and electrical hazards and enhance battery and system
safety. Some of these requirements include:
•UL 2580 Batteries for Use In Electric Vehicles
•UL 2271 Batteries for Use In Light Electric Vehicle (LEV) Applications
•ISO 26262 Road vehicles
•UN ECE R100 and UN ECE R136 for European market access
6

7. Objective
Finite Element Analysis to simulate
crushing of an automotive battery
pack as per ISO 12405-
3:2014 standard and evaluate the
crashworthiness performance of the
battery pack & optimize the
structure.
7

8. Battery Pack Crush Simulation Methodology
8
• The present work describes a methodology to simulate the crushing of an
EV battery pack as per ISO 12405-3:2014 standard and evaluate the
crashworthiness performance of the battery pack.
• Nonlinear code like SIMULIA Abaqus helps to accelerate the battery
development process. A complete battery pack with enclosure is
considered for the FEA. Explicit dynamic analysis is performed to simulate
the crushing of the battery pack and to improve the design/optimize
performance.
CAD Abaqus/CAE
Explicit
Dynamics
Solve Validation
Contacts,
Load & BC
Meshing
Material

9. 9
Problem Statement and Assumptions
• The battery pack to be crushed between a rigid plate and a crush plate as described with a force of at least 100 kN,
but not exceeding 105 kN, with an onset time less than 3 minutes and a hold time of at least 100 ms but not
exceeding 10 s.
• Battery pack dimension is approximately 904mm x 360mm x 220mm.
Impactor
Enclosure
Crush Wall

10. 10
Geometry & Material
Cross Members
Top & Bottom
Cover
Enclosure
Battery Case
• Converted very thin solid components to shell objects retaining the original
thickness in section definition.
• Crush wall is modelled as an analytical rigid surface.
• Crush plate is modelled as a discrete rigid part with infinite stiffness.
• Multiple solid sections are defined with the appropriate material type.
• Most of the load-carrying members in the crush test are made of Aluminum Alloy.
Johnson-Cook material model is used to describe the plastic deformation of this
material.

11. 11
Mesh, Contacts, & Constraints
Total number of nodes: 225029
Total number of elements: 149983
73062 linear hexahedral elements of type C3D8R
17073 quadratic tetrahedral elements of type C3D10
45272 linear quadrilateral elements of type S4R
592 linear triangular elements of type S3
13977 linear quadrilateral elements of type R3D4
7 linear triangular elements of type R3D3
• Mesh sensitivity study for the complete model was performed.
• The general contact algorithm in Abaqus/Explicit is used.
• Closely spaced the surfaces are tied together by using tie constraint.
• Penalty method with a friction coefficient of 0.2

12. 12
Loads, Boundary Conditions, & Solver Type
Rigid Wall
Impactor
• The rigid wall is assumed to be fixed in all directions and the crush plate is constrained in all directions except the Y-
direction.
• A variable mass scaling is applied to the whole model so that the stable time increment of 1e-6 is maintained
throughout the analysis.
• Total step time: 0.25s. Displacement of 20 mm is applied to
establish the load of 100KN - 105KN.
• Abaqus/Explicit solver is used which is particularly well-suited to
simulate brief transient dynamic events such as consumer
electronics drop testing, automotive crashworthiness, and
ballistic impact. The ability of Abaqus/Explicit to effectively
handle severely nonlinear behavior such as contact makes it
very attractive for the simulation of many quasi-static events.
Disp Y Dir – 20mm
All DOF Fixed

13. 13
Iterations – Design Candidates
Initial Design
Final Design

14. 14
Results

15. 15
Results

16. 16
Results – Damage based Failure
ABAQUS offers a general capability for modeling progressive damage
and failure in engineering structures.
– Material failure refers to the complete loss of load-carrying capacity that
results from progressive degradation of the material stiffness.
– Stiffness degradation is modeled using damage mechanics
Without Damage

17. 17
SIMULIA for BATTERY ENGINEERING
Dassault Systemes provides battery solutions for all of these scales. Our
BIOVIA brand provides chemistry modeling capabilities to optimally
design battery materials for aging. Our CATIA brand provides battery
libraries to efficiently use 1D simulation for cells, modules, and packs. In
this system-level representation, the aging, thermal, and electrical
behaviors of each cell are combined to understand how an entire module
of cells behaves.
With the molecular level modeling characteristics, the mechanical,
thermal, diffusion, and electrical behavior of the individual cell can then
be simulated in 3D.
SIMULIA capabilities are extensively used on cell and full battery modules
to improve strength, stiffness, and safety in abuse test scenarios. Finally,
battery packs integrated into full vehicle models can be simulated for
realistic test conditions.

18. Integrated Simulation Solution
Power of
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Portfolio
Abaqus,
CST,
XFlow
fe safe
Isight
Tosca
Non-Parametric
Optimization
FEA
Durability / Fatigue
Process Automation &
Design Exploration
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SIMULIA’s Power of the Portfolio

19. 20
Design space
model
Optimization
max. stiffness
s.t. V ≤ 45% V0
Force
Relative material
density
Design result
0
1
Objective function
Minimize/maximize
quantity
Constraints
Physical bounds
DV constraints
Manufacturing, symmetry
Process
Result
Change density of elements within design space (“design variables /
DV”) considering:
Best material distribution for given optimization problem
Topology Optimization

20. 21
Topology Optimization
Mass properties:
Volume: 121283.73
Mass: 3.27e-04 (0.327Kg)
Mass properties:
Volume: 184622.77
Mass: 4.98e-04 (0.498Kg)
Total Mass saving : 0.513 Kg

21. 22
• Displacement of 20mm was applied on the discrete rigid impactor and the corresponding stress values for
reaction force at 100kN to 105kN were studied.
• At 100kN, the enclosure panel doesn’t come in contact with the battery case. Thus, the enclosure design is
satisfactory and meets the ISO 12405-3:2014 standards.
• Topology optimization on the vertical supports was set up and has contributed to a reduction in mass by 0.5Kg
with the same stiffness.
Conclusion

22. Ongoing Work – Efficient Simulation using AI-
ML
23
RAW DATA
(INPUT)
DATA
STRUCTURING TRAINING
ML, ANN
PREDICTIONS Output of
Interest (stress,
deformation,
pressure,..)
ML, ANN,
autoencoder
CNN, RNN

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