battery system technology

© Fraunhofer ISE

Battery system technology

Dr. Matthias Vetter

Fraunhofer Institute

for Solar Energy Systems

Workshop - Renewable energy concepts for SKA and its pathfinders

Berlin, 7th of April 2011

© Fraunhofer ISE

PV off-grid solutions and battery system technology

Team “Autonomous systems and mini-grids”

Team “Battery modules and systems”

Team “Solar driven water supply and storage systems”

© Fraunhofer ISE

Agenda

Introduction “battery system technology”

Overview of battery technologies

Lead acid batteries

Lithium-ion batteries

Vanadium redox-flow batteries

Conclusions

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Battery system technology

Battery testing

Development of battery modules and systems

Battery monitoring

State of charge determination

State of health determination (capacity)

Charging and operating control strategies

Development of charge controllers and battery management systems

Modeling and simulation

Technical and economical system analyses (e.g. life cycle cost)

© Fraunhofer ISE

1 x 250 kW, 1 kV, 600 A (Pack tester)

1 x 500 V, 100 A (Pack tester)

3 x 300 V, 5 A

32 x 6 V, 3 A (with reference electrode)

32 x 5 V, 5 A

18 x 12 V, 200 mA-10 A

8 x 18 V, 5A

32 x 5 V, 30 A (parallel switchable)

1 x 20V, 300 A

3 x 18 V, 100 A

12 x 70 V, 50 A

3 x 18 V, 100 A

4 channels impedance spectroscopy1μHz – 4,5kHz

9 Climate and temperature chambers

146 test circuits

Battery laboratory at Fraunhofer ISE

© Fraunhofer ISE

Batteries: Ragone plot

© Fraunhofer ISE

Source: www.saftbatteries.com

Source: www.ngk.co.jpSource: B.L. Norris

NiMhV-redox-flow

NaSZinc-bromine

Lithium

Storage solutions – batteries

Lead-acid

© Fraunhofer ISE



NiMh

Zinc-bromineLead-acid

Storage solutions – batterieskW / kWh

MW / MWhV-redox-flow Lithium

kW / kWh

MW

NaSMW / MWh

kW / kWh

© Fraunhofer ISE



NiMh

Zinc-bromine

Storage solutions – batteries for MW PV power plantsMW / MWhV-redox-flow Lithium

MW

NaSMW / MWh Lead-acid

© Fraunhofer ISE

Advantages:

Market leading battery type

Available in large quantities

Available in a variety of sizes and designs

Relatively high efficiency

Low specific costs

Lead-acid batteries

Disadvantages:

Low life cycle

Limited energy density

Hydrogen evolution in some designs

High maintenance costs

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Schematics of different charging regimes

Standard: constant current / constant voltage charge cc-cvSolar: constant current / constant voltage charge with two end-of-charge voltage limits cc-cv-cvIntensive: constant current / constant voltage charge followed by a limited constant current phase cc-cv-cc

time

Standard Solar Intensive

volt

age

/ cu

rren

t

voltage

current

time

volt

age

/ cu

rren

t

voltage

current

time

volt

age

/ cu

rren

t voltagecurrent

© Fraunhofer ISE

Capacity gain by intensive charging

VRLA gel battery operatesin a hybrid PV system with solar charging regime. Each half year capacity test plus intensive charging (I80 up to total charge of 112% Cnom)

Capacity after solar charging: 80 % CnomCapacity after intensive charging: 100 % Cnom

cell

volt

age

[V]

capacity [% Cnom]0 20 40 60 80 100

2.2

2.1

2.0

1.9

1.8

after „intensive” charging

after „solar” charging

battery 1battery 2

© Fraunhofer ISE

2006: without BMS 2007: with BMS

Battery management system –Results of field trial with lead acid batteries

© Fraunhofer ISE

Advantages:

High energy density

High power to capacity ratio

Little or no maintenance

Low self discharge

High energy efficiency

Long calendar life times

Large number of cycles

Disadvantages:

Safety – need for protective circuit

High initial costs

Thermal runaway possible when overcharged or crushed

Lithium-ion batteries

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From cell to module to system

231x179x289 mm

Lithium battery systems

BMS

EMS

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16

Battery module – Connection methods

Cell interconnectorCooling plate

Cell interconnectorsLaser weldingUltrasonic weldingSpot weldingGluing

Mechanical stability of cells within a battery moduleThermal connection of cells (e.g. via cooling plates)

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Energy and battery management – Architecture

Energy management system as central control unit

Decentralized battery management system for each single battery module

Determination of state of charge and state of health of each single cell possible

BMS(Microcontroller, 12

Kalman filter,LTC6802 Voltage

Measurement and Cell Balancing) B

atte

ry M

odul

e(1

2 C

ells

in s

erie

s)

...

EMS(Energy Management

System)Embedded System

BMS+

Battery Module

BMS+

Battery Module

BMS+

Battery Module

. . .

. .

BMS+

Battery Module

BMS+

Battery Module

BMS+

Battery Module

BMS+

Battery Module

. . .

. .

BMS+

Battery Module

BMS+

Battery Module

BMS+

Battery Module

BMS+

Battery Module

. . .

. .

One Cell Temperature Monitoring

Strin

g 1

Strin

g 2

Strin

g 3

VehicleControl Device

Battery Charger

Com

mun

icat

ion

Bus

Saf

ety

Shu

tdow

n

Pumping System Cooling Circuit

Com

mun

icat

ion

Bus

Redundancy:2x EMS2x Communication

GND_BAT

GND_BAT

Current Measurement

© Fraunhofer ISE

18


Ah counter: Integration of measurement errors

Most conventional approaches:

Use of some kind of OCV correction in combination with Ah counting

Recalibration of the SOC value via OCV consideration needs resting phases

Flat OCV characteristic with hysteresis for LiFePO4

© Fraunhofer ISE

19


Approach: Kalman Filter

More insensitive against measurement errors

No resting phases necessary for recalibration of SOC

Fast identification of starting values

Improved performance for aged batteries

Recursive state estimator

Optimal estimator for processes with Gaussian noises

Suitable only for linear systems

For non-linear systems: Extended or Unscented Kalman Filter

B’ H’

A’

+ +

wk vk+1

B

H

A

+

-K

Process

Model(Kalman Filter)

Output:Estimation

Measurement:System Output

Measurement:System Input

Z -1

Measurement ModelProcess Model

1ˆ +kx−+1ˆ kx

1~

+kz

kx̂

1ˆ +kz

1+kx

kx

1+kzku

+

Z -1

© Fraunhofer ISE

20


Approach: Extended Kalman Filter (EKF)

Extension of Kalman Filter approach for non-linear systems:

Linearization within the operating point using first order Taylor series approximation

LiFePO4

© Fraunhofer ISE

State of health determination

Principle of Dual Extended Kalman Filter

Two decoupled parallel Kalman Filters

Exchange of computed states of state filter (state of charge) and of weight filter (state of health)

Compute a priori estimation (Prediction)

Compute a posteriori value

(Correction)

Sta

te F

ilter

Initi

aliz

atio

nCompute a priori

estimation (Prediction)

Compute a posteriori value

(Correction)

Wei

ght F

ilter

Initi

aliz

atio

n

New measured value

Θ−ˆ

x̂−

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22

State of health determination

Aged battery: 80 % SOH

Cathode: NMC

Anode: Carbon

2.45 Ah, 3.6 V

DEKF: Dual Extended Kalman Filter; MA: Mean Average

© Fraunhofer ISE

Redox-flow batteries

Flow batteries:

Different redox couples possible

Research focuses on Vanadium

Power and capacity decoupled

Oxygen evolution

-1.0 -0.5 0.0 +0.5 +1.0 +1.5 +2.0 (vs. NHE)Standard Potential [V]

V (2/3) V (4/5)

Fe (2/3)Cr (2/3)V (3/4)

Ti (3/4)

Cu (1/2) Cr (3/6) Mn (2/3) Ni (2/4)Co (2/3)Mn (4/7)

OCV

Hydrogen evolution

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Advantages:

Decoupling of power and capacity

Modularity

Only two manufacture worldwide (!?)

High cycle stability

Low self discharge

Redox-flow batteries

Disadvantages:

Low energy density

Complex control strategies

Flow battery Risk of leakages

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Cost analyses

Case study„Rappenecker Hof“:

5 kW / 57 kWh VRFB

0

500

1000

1500

2000

0 10 20 30 40 50 60Autonomy time [h]

Spec

. inv

estm

ent c

ost [

€/kW

h]

VRFB 5 kW

Lead acid

Electrolyte

Investment cost

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Cost analyses

Case study„Rappenecker Hof“:

5 kW / 57 kWh VRFB

ALCC including:

Investment

Maintenance

Replacement

0

2000

4000

6000

8000

10000

12000

14000

0 5 10 15 20 25 30

Autonomy time [h]

ALL

C [€

]

Lead acid( 6 years)

Lead acid (4 years)

VRFB (4.8 years)

Annualized life cycle cost

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Efficiencies

At different current densities

Complete charging / discharging

CE: Coulombic Efficiency

EE: Energy Efficiency

0

0,2

0,4

0,6

0,8

1

1 2 3 4 5Current density [mA/cm²]

Effic

ienc

y [-]

CE EE

10 20 40 60 80

5-cell stack à 250 cm²

Mea

n c

ell v

olt

age

[V]

SOC [ - ]

Current density

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Development of a “Smart Redox-flow Control“

AC-Grid VRB + PeripheryInverter

Pum

p co

ntro

l

Cur

rent

AC

Set

Mea

sure

men

t va

lues

Actual values

SOC-forecast

Power AC Demand

Smart Redox flow Control

EnergieManagementSystem (EMS)

Pump control

Charge/ Discharge controller

SOC forecast

SOC Determination

Smart Redox-flow Control:

Control loops for devices of redox-flow battery

Determination of set points (e.g. inverter, pumps)

Optimization of the process cycle

energy efficiency

Interface with energy management system (e.g. UESP)

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Smart Redox-flow Control: SOC forecast

SOC forecast:

Simplified model of the VRFB system

SOC calculation according to power demand

Interface with energy management system

Energy management system determines mode of operation for the VRFB

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Batteries in PV applicationsClassification of operating conditions

class 1 class 2 class 3 class 4 typical

application parking meter

residential house mountain hut or village supply

small big solar

fraction 100 % 70 - 90 % about 50 % < 50 %

storage size

> 10 days 3 -5 days 1 -3 days about 1 day

charac-teristics

for battery

• small currents • few cycles (mainly one yearly cycle)

• small currents • large number of partial cycles (at different states of charge)

• medium currents • large number of partial cycles (at good states of charge)

• high currents • deep cycles (0.5 to 1 cycles per day)

© Fraunhofer ISE

40

50

60

70

80

90

100

0 200 400 600 800 1000 1200 1400 1600 1800

Partial Cycles (17 – 40 % SOC )

C /

C(m

ax)

[%]

Pb_solar

Pb_best

ThunderSky

Lecla_10Ah

Lecla_20Ah

Gaia

A123

LionTec

Sony

liTec

Lead acid

[Source ZSW]

Batteries in PV applicationsPartial cycling at low states of charge

© Fraunhofer ISE

Conclusions

Storages are the key component for 100 % renewables

In off-grid applications

And in on-grid applications

Batteries have a huge potential to fulfil this task:

Modular design

Usable as decentralised and centralised storage systems

For different purposes a variety of storages is available:

Lithium-ion for short term storages and residential use

Redox-flow for long term storages and in bigger stationary applications

Lead-acid battery as state of the art in today’s off-grid applications and UPS

Hybridisation of (battery) storages Optimized system solutions

Presentation on energy concept for ASKAP / SKA

battery system technology

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