lightweight design for bev body using modular-based multi...

Lightweight Design for BEV Body Using Modular-based Multi-material Space Frame (M3-SF) Technology

Dr. Lei Shi

Ph.D., ASME/SAE Member

Vice President, R&D of LVCHI Auto

Nov. 28, 2017, Shanghai

OUTLINE

• Overview

• M3-SF Technology

• Platform Development for Lightweight Design

• Case Study: Lightweight Design for BEV Body

• Summary

LVCHI R&D Center

Overview – Who we are?

LVCHI Auto, founded in 2016, is an international enterprise focusing on new energy automobile R&D, production and sales, it is a scientific and technological innovation enterprise of the new energy company.

USA (Detroit)

Italy (Turin)

China (Shanghai)

Parts and systems development, vehicle design and other high and new technologies

Styling, vehicle integration, high-end vehicle R & D (luxury brand), European core suppliers

Vehicle integration, product planning, supplier integration, plant construction, etc.

USA (Silicon Valley)

Intelligent driving, E&E, core suppliers and other high and new technologies, etc.

R&D Branch: Wuxi, Beijing and Chengdu

LVCHI R&D Center

Overview

Global Safety Requirements

Best Package Space

Design Leadership

Lightweight

Battery Package & Protection

Fuel economy

• Today’s automobile challenge

P/T Integration

LVCHI R&D Center

Overview

• Roadmap for vehicle lightweight design

Co

st

WeightWeight Target

Co

st T

arge

t

Opt. Vehicle Performance

A

B

C

D

Conventional Material & ProcessAdvanced Steel & Process & More Light Material

Ideal Line

Optimum Design Process with multiple iteration of CAE

Maximum use of HSS & Process

Lightweight Design Cost & Weight saving Design

Trad. Line

CAE iteration

LVCHI R&D Center

Overview

• Roadmap for vehicle lightweight design

DesignBrief

GATE Technology

Selection

GATE Design Freeze

GATE Tooling

SimulationAcceptance

Tooling tryoutManufacturing

plant

GATE Implementation

Readiness

GATE PrototypeSelection

GATE Design & Process

Sign-off

Tooling tryout at tooling

manufacturerSOP

Time-line for complete concept to Start of Production (SOP) projects

LVCHI R&D Center

Max. Conf.

Min. Conf.

M3-SF Technology

• Challenges for future architecture development – Key criteria of architecture designs

Increase variability, flexibility and reusability

Reduce the effort in development

Reduce the effort in validation and testing

Save production effort on parts w/ common interfaces

Cost reduction

Time saved

Max. Conf.

Min. Conf.

Vehicle platform

绿驰汽车LVCHI AUTO

Max. Conf.

Min. Conf.

Max. Conf.

Min. Conf.

A B C

Re-development of same functions on different platform

Time-to-market

Costs

Complexity

Variability

Flexibility

Resuability

A B C

Vehicle platform

LVCHI R&D Center

绿驰汽车LVCHI AUTO

M3-SF Technology

• Challenges for future architecture development – Key criteria of architecture designs

De

sign

PlatformAdaptive New

Ad

apti

veN

ew

LVCHI R&D Center

M3-SF Technology

• LVCHI architecture – Modular-based multi-material space frame (M3-SF)

M3-SF Structure Efficiency: Al extrusions

• Mostly straight & tunable cross sections that underload impact load

• Very flexible for family product extension with different wheel base etc.

• Process: reduce spot welds, joints etc.

• Highly integrated assembly without many parts compared with traditional sheet metal

• Easily achieved highly lightweight design with the better attributes, such as stiffness, normal mode etc.

- Energy absorbing Al 7 Series

LVCHI R&D Center

Platform Development for Lightweight Design

• Vehicle attributes

Vehicle Attributes

Safety NVH Durability Vehicle dynamicsCFD & Thermal Management

Front impact- FMVSS 208- NCAP- IIHS Offset

Side impact- FMVSS214- LINCAP

Rear impact- 35mph RMB- 50mph C/C Inline- 50mph C/C Side

Roof crash Head impact ……

Idle Tactile Idle Acoustic Driveline unbalance tactile / sound Rough road tactile Brake roughness tactile Impact Harshness tactile / acoustic Exhaust NVH Wind Noise TB principal modes TB static stiffness BIP principal modes Point mobility NTF/VTF/IPI ……

Body durability Dash/cowl fatigue Chassis durability

- Front suspension- Rear suspension- Frame and

mounting system Sheet metal fatigue Spot weld durability……

Vehicle dynamics- Steering- Handling- Ride- Braking

Chassis systems- General vehicle- Front suspension- Rear suspension- Steering

……

Aerodynamics CFD Heat management Coolant flow simulations Vehicle level climate control

- Front end air flow- Front end

openings

System level climate controls

- A/C performance- Heater

performance……

Complex and Multi-Physics

LVCHI R&D Center

System Functions

Requirement

SubsystemFunctions

Components

System

Subsystems

Components

Voice of Customer

HighFidelity

LowFidelity

User experience

System Acceptance

IntegrationTest

Subsystem test

Requirement Conceptual Preliminary Detailed Validation (Test) Manufacture In Use


• Multidisciplinary decisions throughout product lifecycle

MDO

Design Domain: Discipline A

Design Domain: Discipline B

Feasible Design Domain

Design Variables

Pe

rfo

rman

ce

Baseline Design

Conventional Trails

Multidisciplinary Optimal Design

SuboptimalDesign

Discipline A Optimum

Discipline B Optimum

MDO provides and identifies the most weight/cost effective balanced design across multiple disciplinary

Multidisciplinary Design Optimization (MDO) is a methodology for improving design of engineering systems, e.g., automobile, aircraft, or spacecraft, in which everything influences everything else.

- By Dr. J. Sobieski from NASA Langley

LVCHI R&D Center


• Key techniques enable the innovative design to be reality in automotive industry

Effective approximated model: computation intensive for high fidelity models

Design space identification: Large number of design variables and constraints

Efficient RBDO technique: Variabilities of design variables, data uncertainty

Manfacturing Loading

Material Others

Simulation of

Discipline ‘1’

Simulation of

Discipline ‘n’

Variation of Product

x

y

z Original design space

Reduced design space

Unfeasible designs

Feasible designsParent node

Parent node Reduced design space 2

Reduced design space1 Unfeasible space

Rule 1

Rule 2

x

y

/2ˆ ˆ( ) diag MSE ( )e ez y x d y x d

Low-fidelity surrogate model

Tests / High-fidelity CAE model

Prediction mean

×100% PI (prediction interval)

ˆ ( )ey x d

2

2 21

22 2

( ( , ))ˆ( )(2 )

ln ln exp( )2

(2 ) det

nm

i i

i

n

y A xprob

Q

aa | A,I

K

LVCHI R&D Center


• Effective approximated model: computation intensive for high-fidelity models

Motivation

• Most surrogate or approximated models are built without considering data uncertainty in simulation

• Conventional evaluation criterion, i.e., root mean square error, prefers the model with largest number of model parameters, which may lead to over-fitting

• In the literature, selection of surrogate model and the size of DOE is often arbitrary

Source of data uncertainty

• Parameter Uncertainty

Fixed but unknown parameters of the computer model

Example: Damage or fracture coefficients, local free-fall acceleration

• Model Discrepancy

Representation of unknown reality

Even without unknown parameters, discrepancy

still exists between simulations and experiments

• Numerical / Algorithmic Uncertainty

Numerical implementation of the computer model

Example: Numerical integration, infinite sum truncation

• Interpolation Uncertainty / Lack of Data

Fewer data, more uncertainty

• Experimental Variability

Experiments have different results with the same inputs

LVCHI R&D Center


• Effective approximated model: computation intensive for high-fidelity models

A Bayesian metric (lnQ) is developed considering data uncertainty

2

2 21

22 2

( ( , ))ˆ( )(2 )

ln ln exp( )2

(2 ) det

nm

i i

i

n

y A xprob

Q

aa | A,I

K

• lnQ is a measure of likelihood rather than a probability.• Surrogate model with larger value of lnQ means that

the model is the most probable model in view of the same data and all known prior information

4 20.25 1.25 1 , [0,2]y x x x 2(0,0.1 ) : ~N Random Noise

Data uncertainty 0.1

Table 1 Summary of one-dimensional approximation

Surrogate model Root mean square error (Rank)

Bayesian metric lnQ (Rank)

y1=-0.79x+0.93 0.057 (6) -81.95 (6)

y2=0.51x2-1.81x+1.25 0.044 (4) -59.43 (4)

y3=0.25x4-1.23x

2+0.96 0.019 (2) -26.73 (1)

y4=2.03e(-0.86x)

-0.84 0.048 (5) -67.01 (5)

y5=-0.71sin(-2.32x-7.5)+0.21 0.024 (3) -38.71 (2)

y6=-0.62x5+3.69x

4-6.73x

3+4.29x

2-1.66x+1.04 0.017 (1) -43.35 (3)

• lnQ can identify the best surrogate model (i.e., y3).• RMSE favors model y6 with more model parameters.• lnQ is more effective than traditional metric under data uncertainty

Mathematical Example

01

2-1

-0.50

0.51

1.5

x

y

std=

0.1

01

2-1

-0.50

0.51

1.52

x

y

std=

0.2

01

2-1

.5-1

-0.50

0.51

1.52

x

y

std=

0.3

01

2-1

.5-1

-0.50

0.51

1.52

x

y

std=

0.4

01

2-1

.5-1

-0.50

0.51

1.52

x

y

std=

0.5

LVCHI R&D Center

-5 -3 -1 1 3 5-5

-3

-1

1

3

5

x1

x2

AB C


• Design space identification: large number of design variables and constraints

Motivation

• Identifying Pareto set requires many analysis

• Reduced design space may alleviate computation burden

• FE simulation in automobile industry is computation intensive

Classification and Regression Tree (CART)

PrPl

Parent node

(tp)

Left child node(tl)

Right child node(tr)

Splitting scheme of CART.

Maximization problem:(1)

indicates the maximum reduction of impurity of the tree due to the best split

(2)

, 1,...,

argmax [ ( ) - ( ) - ( )]R

j j

p l l r rx x j N

i t Pi t Pi t

, 1,...,R

j jx x j N

2 2 2

, 1,..., 1 1 1

argmax - ( | ) ( | ) ( | )R

j j

K K K

p l l r rx x j N k k k

p k t P p k t P p k t

Breiman et al. 1984

x

y

z Original design space

Reduced design space

Unfeasible designs

Feasible designsParent node

Parent node Reduced design space 2

Reduced design space1 Unfeasible space

Rule 1

Rule 2

LVCHI R&D Center


• Design space identification: large number of design variables and constraints

Motivation

• Identifying Pareto set requires many analysis

• Reduced design space may alleviate computation burden

• FE simulation in automobile industry is computation intensive

Yes

No

Yes

Formulate MOO problem

Generate DOE metrics in the

original design domain

CART analysis

Select initial designs in

reduced design domain

Stop

Obtain enough

feasible designs [20, 80] ?

Relax constraints

Identify non-dominated sub-domains

Solve MOO problem

(e.g., SQP, NSGA-II)

Start

Multi-objective?

No

Vehicle design

Attributes Full Frontal 40% Offset NVH

Elements 1,064,616 1,128,735 466,108

Nodes 936,234 1,007,337 374,032

CAE solver LS-DYNA LS-DYNA MSC.NASTRAN

ResponseChest G

Crush distance

Intrusion

(Footrest, etc)

Torsion

Vertical bending

Finite

element

model

Converging to real solution faster than the original design domain

LVCHI R&D Center


• Efficient RBDO technique: variabilities of design variables, data uncertainty

Design Variables (d)Noise Variables (W)

W

p(W

)

Input Variables Probabilistic Response

Computer Simulator

( , )

( , )i

Y

g

d W

d W

min ( , ) , s.t. Pr ( , ) 0 %, 1, ,i iE y g i k d W d W

Y

X

Z

d

Y(d

,W)

W

Corrected Model

Model RefinementUncertainty Quantification

due to both input variabilityand model uncertainty

Reliability-Based Design Optimization (RBDO)Obtain an improved while reliable design, regardless of input variable variations

LVCHI R&D Center


• Efficient RBDO technique: variabilities of design variables, data uncertainty

L Ufor

( )

1, , ,

( ) 0,j

j n

G

c

X

X

X

X X X

Min

i

imiz

s det

e Cost

Subject

erministic

va

to

whe

ria

re

ble.

DDO Formulation

X2

Failure Surfaceg1(X)=0

Failure Surfaceg2(X)=0

Initial Design

X10

RBDO Optimum Design with Desired Reliability

Input Joint PDF fX(X) Contour

Deterministic Optimum Design – 50% Reliability

RBDO Formulation

T r

L U

a

( )

for 1, , ,

P( ( ) 0) ( ),jj F t

t

G P

j nc

X

X

d

d

d

d d

Minimize Cost

Subject to

iwhere of the

is the target reliability index.

s the mean value random

variable

and

Risks of DDO Active Constraints – uncertainty, variation leads to failed designs Sensitive “peak” solutions – small changes in inputs results in significant loss of performance

Benefits of RBDO

Search for reliable designs:

- feasible w.r.t deterministic constraints

- meet a desired minimum level of reliability

- do not exceed a maximum probability of Failure

Has the effect of pulling deterministic optimization solutions away from the constraints

Deterministic design optimization (DDO) vs. Reliability-based design optimization (RBDO)

LVCHI R&D Center


• Framework of a MDO-based platform - attributes integration and lightweighting

MDO Integrator

HPC Cluster

Solver: Ls-dyna, Nastran etc.Input files Output files

NVH Model Library Safety Model LibraryNVH Model Library Safety Model Library

Pre-processing Post-processing

Parameter model synchronization

In-house code:1. Surrogate model2. Design space identification3. RBDO tech

LVCHI R&D Center

Case Study: Lightweight Design for BEV Body

• Strategy for body design

Find the lightest & efficient structure that is able to meet design targets under a given set of conditions & loads

Rightdesigndetails

Rightmaterial

Righttopology

Right size

Predecessor architecture

New vehicle

LVCHI R&D Center


• Strategy for body design

Define Engineering Problem

Investigate Potential Process and

Materials for Body Design

under Cost Target

Build up Initial Simulation Model to

Perform CAE Runs

Conduct Design Sensitivity Analysis to

Identify the Critical Design Variables

Build up Parameter Model to Setup

Morph Parameter

Define DOESubmit DOE

Jobs

Extract CAE

Results

Construct Metamodel

Perform Optimization

Achieve Targets

?

End

Trade-off Study

Yes

No

Finite Element Validation

Selection of critical design variables:

- Topology-based method for shape

- Traditional design sensitivity analysis approach (Nastran 200) for gauge

LVCHI R&D Center


• Problem formulation

Minimize: Weight

Subject to:

P( Safety attributes ≤ Design Targets ) ≥ Reliability

P( NVH attributes ≤ Design Targets ) ≥ Reliability

with respect to:

L.B. ≤ DV (Gage) ≤ U.B.

L.B. ≤ DV (Shape) ≤ U.B.

No. of design variables = 47

No. of attributes = 14

Reliability targets = 90%

NSGA-II (Non-dominated GA) plus SQP

- Multi-objective Exploratory Technique

- Well-suited for highly non-linear and discontinuous design space

- 40 Population size and 250 Generations

- SQP is used to search a final design

LVCHI R&D Center


• DOE and post-process process

Design Variables

DOE Matrix Parameter template

Batch scripts

DOE Process:

Post-Processing Process:

• 120 jobs completed • 0.6 GB data for each job• Responses: Mass, Intrusion, Stiffness, Velocities

Normal mode etc.

• Batch scripts developed with in-house code for results extraction• High-performance computing system environment stability

LVCHI R&D Center


• Optimization study

ObjectiveConstraint

Design Variable

Approximated model

Optimization results w/ 10000 designs

Items Mass ChestG CrashD BrkPel FootRt LftTop CntTop RigTop LftIP RigIP BpilS-V RoofLd BedStif TorStif Nm1St

Base 257.6 81.2 53.5 32.2 35.5 21.4 21.1 29.6 62.2 77.9 99.9 73.5 42.2 27.9 62.1

Opt. 247.6 91.2 91.5 92.0 100 100 100 100 96.2 96.2 100 85.2 97.7 100 100

FE Vali. 236.1 92.0 88.0 100 100 100 100 100 82.0 86.0 100 88.0 96.0 96.0 100

LVCHI R&D Center


• Optimization study

0

20

40

60

80

100

ChestG CrashD BrkPel FootRt LftTop CntTop RigTop LftIP RigIP BpilS-V RoofLd BedStif TorStif Nm1St

81.2

53.5

32.235.5

21.4 21.1

29.6

62.2

77.9

99.9

73.5

42.2

27.9

62.1

91.2 91.5 92

100 100 100 10096.2 96.2

100

85.2

97.7 100 100

9288

100 100 100 100 100

8286

100

88

96 96100

Reliability: Baseline vs. Optimum vs. FE Validation Base Opt. FE Vali.Target line: 90% Reliability

0

20

40

60

80

100

Base Opt. FE Vali.

Average Reliability

94.9

51.4

96.4 Mass (Kg)Lightweight Index*

2deg/ mmkNmm

kg

AK

mL

T

BIW

* Lightweight index

257.6

236.1

2.7

2.3

Achieve almost 20 Kg weight reduction and 40% reliability improvement

8.3

0%

14

.8%

LVCHI R&D Center

Summary

M3-SF (Modular-based Multi-material Space Frame) technology is well developed in LVCHI auto and

extended to other vehicle size, e.g., A-class, B-class etc.

A high-fidelity MDO-based platform is successfully implemented by using advanced optimization

methodology, parameter model synchronization and pre/post processing etc., and provide several major

elements:

- Facilitate collaboration of discipline experts/engineers that in turn facilitate steering of the design and enhance efficiency

of product development.

- Offer well-founded decision-making or trade-off tool for conflicting design target during product development process.

- Provide the CAE or design engineer the design direction or space to validate the multiple attribute requirements when the

design targets are not met.

- Achieve a superior design meeting design target faster through the modification and enhancement of design model from

the engineering judgment and expert inputs.

A case study for BEV lightweight design is well demonstrated through the proposed platform with M3-SF

technology and achieved over 20 Kg weight saving as well as 40% reliability improvement.

Thank you!

Dr. Lei Shi

Ph.D., ASME/SAE Member

Vice President, R&D of LVCHI Auto

E-mail: [email protected]

Tel: +86 152 1671 2970

mailto:[email protected]

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