MICDE-TARDEC Faculty Workshop

Overview of research

Wei Lu

Mechanical Engineering

University of Michigan, Ann Arbor

weilu@umich.edu

September 15, 2017

1

Overall of Research Areas

2

• Joining of dissimilar materials, modeling of intermetallics formation

(Fe/Al) with effect of temperature history, interfacial conditions, and

strain states, FSW processing.

• Dynamic impact and fretting wear of structures and materials

Calculated wear map shows wear rate as a function of the grid-to-rod gap size and the frequency of the excitation force. rod

grid

Dynamic impact and fretting wear

Overall of Research Areas

3

• Coupled wear and multi-mechanics modeling

Approach to couple fretting wear and creep

simulation, addressing the drastic different time

scales of vibration (short time scale) and creep

(long time scale).

Coupled wear and oxide growth.

Oxide growth + wear Wear profile

Oxide layer

Overall of Research Areas

4

• Coupled wear, creep and fracture

Modeling of hydride formation

• Modelling and experiments of wear of fabrics

Design of wear resistant fibers and coating

• Multi-scale simulation

Hyrdride formation

In-situ wear

observation

Battery Research Areas

emphasis on

battery

optimization,

degradation

analysis and

management

Multi-scale/Multi-physics

Modeling and Simulation

macroscale (finite element

methods, phase field

models)

microscale (ab initio,

classical and reactive

molecular dynamics)

Material Characterization,

Cell Fabrication, and Testing

material and parameter

characterization (TEM, SEM,

AFM, XPS, XRD etc.)

cell fabrication and diverse

performance testing (cycling,

EIS, thermal, dissolution,

degradation, etc.)

New material development

(e.g. Li metal, Black

Phosphorus, Si/CNT)

Smart Battery Management System and Control

optimization for battery system operation as well

as design

5

Grain boundary dramatically improves capacity utilization !

6

ca

pa

city u

tiliz

ation (

%)

C-rate normalized sgb/vp

R=5 μm

Example: Modeling of Grain Structures and

Diffusion

Coupled Electrochemical-Mechanical

Degradation

7

• Developed tools that can predict mechanical

and electrochemical behaviors at the

particle, cell, and pack level, so that battery

performance can be predicted accurately.

• Developed tools to predict mechanical

failure such as fracture and delamination: it

can be used in battery design for more

robust cells as well as guiding the cell

applications to reduce related failures.

• Developed module-level stress analysis tool

that considers cell expansion and face

pressure, which can be used in pack

design.

rad

ius

(10

-6m

)

Fracture Map

current density (A/m2)

65% SOC

Multi-scale Analysis

8

opening

binder

graphite

no

rmal

tra

ctio

n (

GPa

)

separation (Å)

30

chains

40

chains

50% SOC

65% SOC

Atomistic Scale Continuum Scale

1. damage initiation: maximum stress (Tmax)

normal : 300 Mpa, shear : 50 Mpa

2. damage evolution: fracture energy (Gc)

normal : 0.45 J/m2 shear : 0.175 J/m2

Machine Learning in Material Modeling and

Discovery

9

Ragone plots from neural network calculations and FEM simulations. Each FEM dot represents a finite element simulation. Five of the design variables are kept constant while the C-rate changes from 0.5 C to 3 C.

Cell & Pack Capacity, Reliability and Safety Optimizations

10

interior

view

Bitrode

Controller PC

Instron

Controller PC

electrical and test leads

• constant

thickness/pressure

• HPPC protocol

• Identified source of performance gain through

experimentally directed modeling.

• Optimized pressure on cells for maximum

performance boost.

• Face pressure analysis has provided packing design criteria for better

battery performance. An appropriate applied pressure can reduce capacity

degradation relative to no pressure.

measurement simulation

11

• Thermal analysis provided better management strategy of large format cells

considering environmental, cycling, and C-rate effects. Revealed unique local

characteristics from each heat source. Identified the effect of temperature and

its gradient on cell/pack performance and degradation.

simulation measurement IR camera

4 5 6

1 2 3

7 8 9

12 10 11

15 13 14

RTD sensors

validation

Electro-Thermal Model

RTD/Thermal

Camera

Measurement

C-rate

Ambient Temperature

Cycling effect

ambient temperature: 20C, 5C

voltage

ambient temperature

T(

T-T 0

) (

C)

time (s)

volt

age

(V)

Cell & Pack Capacity, Reliability and Safety Optimizations

A Comprehensive Degradation Model

Anode Cathode

𝐸𝐶 + 𝑒−(𝑔𝑟𝑎𝑝ℎ𝑖𝑡𝑒) → 𝐸𝐶−

𝐸𝐶− + 𝐸𝐶− → 𝑂2𝐶𝑂 − 𝐶𝐻2 2 𝑂2𝐶𝑂 − + 𝐶2𝐻4 ↑

𝐸𝐶− + 𝐸𝐶 + 𝑒−(𝑔𝑟𝑎𝑝ℎ𝑖𝑡𝑒) → 𝑂2𝐶𝑂 − 𝐶𝐻2 2 𝑂2𝐶𝑂 − + 𝐶2𝐻4 ↑

𝑂2𝐶𝑂 − 𝐶𝐻2 2 𝑂2𝐶𝑂 − + 2𝐿𝑖+ → 𝐿𝑖+ 𝑂2𝐶𝑂 − 𝐶𝐻2 2 𝑂2𝐶𝑂 −𝐿𝑖+

𝑆𝑜𝑙𝑣𝑒𝑛𝑡𝑜𝑥𝑖𝑑𝑎𝑡𝑖𝑜𝑛

𝑆𝐿𝑜 + 𝐻+(𝑜𝑟 𝑆𝐿+) + 𝑒−

12

Side reaction rates limited by the SEI layer: * side

side sidei e i

Coupling of electrochemical, chemical, mechanical, thermal, and transport processes

X. Lin, J. Park, L. Liu, Y. Lee, A.M. Sastry and W. Lu, “A comprehensive

capacity fade model and analysis for Li-ion batteries,” Journal of the

Electrochemical Society, 160, A1701-A1710, 2013.

Application: SOC Swing Window

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• SOC swing window has provided useful information on battery health state,

and suggested optimal charge/discharge strategy evolving with aging of the

battery.

Application: Battery Health Optimization

• Set energy density requirement

• Keep the power density requirement

• Conduct battery health optimization

1. power density requirement: 2200 W/kg

2. energy density requirement : 86 W ∙ hr/kg

20%

60%

Parameters Symbol average optimized for health

Cathode particle radius

rp_pos [mm] 0.39 0.95

Cathode thickness

L_pos [mm] 100.34 61.12

Cathode porosity

Epsl_pos 0.18 0.17

Cathode conductivity

Ks_pos [S/m]

6.42 5.18

Anode particle radius

rp_neg [mm] 1.50 7.08

Anode thickness L_neg [mm] 105.26 60.00

Anode porosity epsl_neg 0.33 0.31

Mass ratio mass_ratio 2.04 2.14

degradation 60% 20%

14

After optimization,

battery degradation is

reduced 3 times.

• Balanced health and energy density design can significantly reduce the

reserved capacity, and thereby reduce the battery cost and weight.

Application: Smart Battery Management System

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• Heat generation/flow, lithium plating,

manganese dissolution, gas generation,

SEI layer growth

• Reduced-order high-

fidelity versions of these models,

suitable for controls

Current control system Future control system

Physics-based model

electrochemical, chemical,

mechanical, thermal, and

transport processes

equivalent circuit-based mimic