Advanced Management and Protection of

Energy storage Devices (AMPED)

NAATBatt 2013 Annual Meeting

Amul D. Tevar, Ph.D., MPH

ARPA-E Fellow

Ilan Gur, Program Director

Russel Ross, Tech SETA

Jan. 18th 2013

ARPA-E Overview

1

Reduce Energy-

Related Emissions Improve Energy

Efficiency

Reduce Energy Imports

To enhance the economic and energy security of the U.S.

To ensure U.S. technological lead in developing and

deploying advanced energy technologies

ARPA-E Overview

2

time

co

st / p

erf

orm

an

ce

existing learning curve

new learning curve

transformational

Transformational & disruptive technologies

that lead to new learning curves

Steam-powered Cugnot (1769)

Benz Motorwagen (1885)

Ford Model T

(1914)

ARPA-E: 14 Focused Programs to Date

3

Stationary Power / Use

Transportation

Electrofuels BEEST

PETRO MOVE AMPED

REACT

SBIR/STTR

HEATS BEETIT GRIDS

ADEPT GENI Solar

ADEPT

Transportation and

Stationary Power / Use

IMPACCT

Stationary Power / Use

Transportation

ARPA-E: 3 Battery Programs to Date

4

Electrofuels BEEST

PETRO MOVE AMPED

REACT

SBIR/STTR

HEATS BEETIT GRIDS

ADEPT GENI Solar

ADEPT

Transportation and

Stationary Power / Use

IMPACCT

State of the Art

5

Battery trends are impressive

1. Anderson, D. Duke University Thesis, 2009.

Why BMS program over new chemistries?

‣ Purpose: Improve safety, performance and return on

investment by optimizing system design & control

– Improve the existing and future breakthroughs of DOE (e.g. VT or

HUBs)

‣ Protection/Safety

‣ Health/Second Life

‣ Performance

‣ Driver/Location change

6

Propulsion

Capacity

Overhead

Capacity

Balance of

System

Capacity needed to propel

vehicle for XX mile range

Additional capacity buffer:

safety/lifetime assurance

Physical protection

Thermal management

Charge balancing

State monitoring

Etc.

State-of-the-Art XEV

Can’t we do better with the chemistries we have today?

Why is BMS so hard?

8 1. Dreyer Nat. Matls et al, 2010

2. Kim et al, IEEE Trans. on Power Elec., vol. 27(1), Jan 2012

3. http://www.thetimes.co.uk/tto/business/industries/engineering/article3655441.ece

Could we improve this for performance and battery valuation?

‣ Change of state is not uniform between particles, cells, packs

What are we protecting against?

9

Cobalt Oxide

Graphite

Ele

ctr

oly

te S

tab

ility

Electrolyte

Oxidation

Po

ten

tia

l (E

) vs

. L

i

Capacity Lost Power Loss

Electrolyte Reduction

(Kinetically limited)

Short Circuit Safety Risk

Lithium Plating

(Dendrites)

Electrode “Breathing”

(Stress/Cracking)

Utilization Constraints

10

Cobalt Oxide

Graphite

Ele

ctr

oly

te S

tab

ility

Electrolyte

Oxidation

Po

ten

tia

l (E

) vs

. L

i

Capacity Lost Power Loss

Electrolyte Reduction

(Kinetically limited)

Short Circuit Safety Risk

Lithium Plating

(Dendrites)

Electrode “Breathing”

(Stress/Cracking)

Utilization Constraints

11

Cobalt Oxide

Graphite

Ele

ctr

oly

te S

tab

ility

Electrolyte

Oxidation

Po

ten

tia

l (E

) vs

. L

i

Capacity Lost Power Loss

Electrolyte Reduction

(Kinetically limited)

Short Circuit Safety Risk

Lithium Plating

(Dendrites)

Electrode “Breathing”

(Stress/Cracking)

Utilization Constraints

12

Cobalt Oxide

Graphite

Ele

ctr

oly

te S

tab

ility

Electrolyte

Oxidation

Po

ten

tia

l (E

) vs

. L

i

Capacity Lost Power Loss

Electrolyte Reduction

(Kinetically limited)

Short Circuit Safety Risk

Lithium Plating

(Dendrites)

Electrode “Breathing”

(Stress/Cracking)

Utilization Constraints

13

Cobalt Oxide

Graphite

Ele

ctr

oly

te S

tab

ility

Electrolyte

Oxidation

Po

ten

tia

l (E

) vs

. L

i

Capacity Lost Power Loss

Electrolyte Reduction

(Kinetically limited)

Short Circuit Safety Risk

Lithium Plating

(Dendrites)

Electrode “Breathing”

(Stress/Cracking)

Situational Awareness – Removing the Blinders

14

Electrolyte Oxidation

Lithium Plating

(Dendrites)

Internal Cell Defects

What we are protecting against What we currently monitor

Temperature

Voltage

Current

Every

Cell

G

rou

ps o

f cells

State

Sensing

Advanced Models &

Adaptive Controls

System Design

Many areas for BMS innovation

15

Active

System

Design

How did AMPED evolve?

16

Timeline: 6-8 Months from Program Conception to Execution

Envision

Establish

Engage

Evaluate

Contract Negotiation

and Awards

Program Conception

(Idea / Vision)

Workshop

Further Refinement

& FOA DevelopmentFOA Announced

Concept Paper

Review

Award

Announcements

Technical

Deep Dive

Project

Selection

Full Proposal

Panel ReviewInternal Debate

Proposal

Rebuttal Stage

Program

Execution

Metrics created for AMPED Program

‣Safety

– Fault detection, prevention of thermal runaway

‣Performance

– Reduce cost (Mobile), increase revenue (Grid)

‣Prognostics

– Prediction of remaining life

‣No chemistry or additive changes

– Projects should be chemistry independent

‣System-level context for projects

17

AMPED Project Portfolio

18

1. Sensing – Monitor internal cell temperature in real time?

– Monitor intercalation strain for SOC/SOH estimation?

– Track physical/chemical states with optical sensing?

– Track gas signatures of various degradation modes?

2. Modeling & controls – Employ real-time physical state and degradation

models to optimize utilization and balancing control?

3. Systems – Implement cost effective cell-level power management?

– Utilize flexible power architectures for diff’l diagnostics?

– Wireless communications and control

– Design intra-cell thermal management systems?

ALSO: Diagnostics & prognostics – Identify degradation/failure modes quickly with non-

destructive acoustic inspection?

– Measure high-precision columbic efficiency on

production cells and practical drive cycles?

Takeaways

‣ AMPED aims for system level innovation & safety

– Exploit current and future chemistry innovation

‣ Can we improve valuation, utilization or cost?

– Lifetime meter

‣ Highly adaptable approaches

– Stochastic systems need flexible controls

19

I am intrigued, now what?

‣ Get to the ARPA-E Summit in February!

‣ Get involved!

‣ Follow-Up with Me, Ilan, or Russ

– Projects, PIs, Contact Info on Website

20

Questions?

Contact Info:

Amul D. Tevar, ARPA-E Fellow

amul.tevar@hq.doe.gov

Ilan Gur, Program Director

ilan.gur@hq.doe.gov

Russel Ross, Tech SETA

russel.ross@hq.doe.gov

AMPED Information:

http://arpa-e.energy.gov/

21

Technology

• Ford, Arbin & Sandia National Lab will develop a

commercially viable battery testers with measurement

precision ten times more precise than SOA

High Precision Tester for Automotive

and Stationary Batteries

Ford Motor Company

Mr. Alvaro Masias, Research Engineer

313.418.9606 | amasias@ford.com

Variable Tester Precision

Present Target

Columbic

Efficiency ppm 349 50

Voltage ppm 200 25

Current ppm 200 50

Impact of Improved Precision on Prediction

Technology

• Washington University will create 2D thermal-

electrochemical coupled models with capacity fade

mechanisms integrated into BMS

Optimal Operation and Management of

Energy Storage Systems Based on Real

time Predictive Modeling and Adaptive

Battery Management Techniques

Dr. Venkat Subramanian

314-935-4622

vsubramanian@seas.wustl.edu

Efficient reformulation

Improved

SafetyPrecise

SOC

Accurate

SOH

Time

RemainingBetter Cell-

Balancing

Advanced

BMS

Different capacity fade mechanisms

2D thermal-electro-chemical coupled physics

based model

Metrics State of the Art Proposed Metric

1D EC model ~1 min ~30 ms

Pseudo 2D EC model 1-2 min ~100 ms

2D Thermal EC coupled ~15 min < 5 s

Models for BMSCircuit based/

Empirical

Detailed 2D,

thermal-EC model

with capacity fade

Technology

• GE will develop an ultrathin sensor array capable of

measuring strain and temperature

• U-M will use multiphysics models for selecting the

critical sensor locations

• Ford will be implementing and testing the sensors

• The program objective is to

demonstrate that the sensor and

controls implemented lead to

increased cell utilization by 20%

Control Enabling Solutions with

Ultrathin Strain and Temperature Sensor

System for Reduced Battery Life Cycle

Cost

General Electric, U. of Michigan, Ford

Aaron J. Knobloch, Senior Scientist

518-387-7355 knobloch@research.ge.com

Performance Targets

Fault Sensing in Operating Batteries

• Battelle and its team member, the University of Akron,

will modify the internal structure of a battery cell to

function as an optical waveguide.

• The operating cell will be monitored continuously for

impending faults, providing sufficient early warning to the

control system.

Technology

Battelle

James Saunders, Research Leader

614 424-3271; jhs@battelle.org

Performance Targets

Metric Battelle Target

•Measurement of fault

signatures in a special

fixture.

•Measurement in a

visual cell

•Measurement in a Li-

ion battery

Determine early warning

time of incipient, local

faults before they

become a global event.

Demonstrate calibrated

signal above background

in a test fixture, a visual

cell, and a battery.

cathode

anode

Fiber #2

separator

dendrite

Fiber #1

Optical output altered by dendrite

Technology • LLNL and Yardney are partnering to demonstrate BMS that

utilizes distributed addressable wireless sensors

• Can serve as early indicators of the onset of thermal runaway

and be used to control their operation.

Metrics State-of-Art LLNL-YTP System

Failure detection speed 1x ≥9x

Sensors per cell 0.3 ≥3

Wire-caused failures 1x 0

Sensor comm. protocol wired wireless

Battery Management System with

Distributed Wireless Sensors

Lawrence Livermore National Lab

Dr. Todd M. Bandhauer

bandhauer1@llnl.gov

Performance Targets

Please contact regarding: R&D Collaboration, Funding

Technology

Heat Conduction in a Li-ion Cell

• The ORNL and Farasis Energy team is developing a

new cell design and temperature control technology for

large format Li-ion cells.

• In-plane cooling and temperature control will be 20-30

times more efficient.

Temperature Self-regulation for Large

Format Li-ion Cells

Oak Ridge National Laboratory

Dr. Hsin Wang, Senior R&D Staff

Tel: (865)576-5074, E-mail:wangh2@@ornl.gov

Performance Targets

Please contact Shaun Gleason (gleasonss@ornl.gov) regarding: r&d collaboration, funding, press

Cu

Al

Al

Cu

Measured Thermal Conductivity:

Through thickness ~ 1.5 W/mK

In-plane ~ 30W/mK

A factor of 20 improvement!

Accessing high thermal

conductivity Cu and Al

is the key! Metrics State-of-Art ORNL/Farasis

Maximum Cell Temperature ~45ºC <30ºC (25Ahr 5C Discharge)

Cell cycle life (10% drop) ~1000 cycles ~1250 cycles

Pass safety pinch test ~85-90% SOC 100% SOC

(ORNL/UL)

BACKUP SLIDES

28

29

Metrics

30

Metrics Task #1 Task #2 Task #3

Safety Fault Detection Prevent Runaway

Performance 25% Cost (EV) 2x Revenue 2x Charge Rate

Prognostics Remaining Life

Example – Residual Value

31

Battery Experts Controls Experts

System Design

Diverse Community Needed

32

Po

wer E

xp

erts

D

iag

no

sti

c E

xp

ert

s