UNIVERSITY OF SALERNO Department of Mechanical Engineering

Development of a Model-Based Diagnostics Tool for Solid Oxide Fuel Cells

Marco Sorrentino, Cesare Pianese eProLab

(Energy and Propulsion Laboratory)

msorrentino@unisa.it –

www.eprolab.unisa.it

Presentation Outline

•

Motivation, goals, approach

•

Design/Control/Diagnosis oriented model for APU

•

Application of the model

–

Design and Control

–

Simulation of a typical automotive auxiliary load profile

–

Diagnostics

•

Conclusions

2

Motivations•

Developing SOFC technology to face environmental-

and energy-related

issues.•

High SOFC potential for both residential

and mobile

applications (high

efficiency, cogeneration, modularity, fuel flexibility, low emissions and noise).

•

Specific short-term target: enhance the development of highly-efficient SOFC-APUs

destined to a wide application area (ground transportation,

marine and airplane APUs).

Goals•

Development of

model-based tools for

Fast Design, Control and

Diagnosis

of SOFC systems.•

Adopt lumped modeling approach

to meet the trade-off

between

computational burden, experimental efforts and model accuracy.

Motivations and goals

3

Objectives•

Prevent SOFCs

from highly damaging system failures•

Monitoring SOFC operations throughout its lifetime•

Detect and manage faults

Type of faults•

BoP level–

Sensors (pressure, temperature) & Actuators (electric motors, valves)–

Auxiliaries (blower/compressor, heat exchangers, reformer)–

Components (pipes, manifolds)•

Stack level–

Material degradation–

Electrodes poisoning–

Etc.Design and real time operation of diagnostics tools

On-Field Diagnosis of SOFC Systems

4Symptom (s2 ) Symptom (s3 )

Symptom (s1 )

Fault (f1 )

Event (e2 )

Fault (f2 )

Symptom (s4 ) Symptom (s5 )

Event (e3 )

DE

SIG

N

FAU

LT D

IAG

NO

SIS

Diagnostic process

5

Residual or Fault/Degradation Index

BB/ph model-based

Fault IsolationRule-based

Fault/Degradation Detection

Preprocessing

Real System

status

SOFC

U

Sub-model 1

Y

F

ˆ Y

Features evaluationSub-model i

Sub-model n

Features generation

FAULT Y/N

Inference

Diagnosis

Model-based approach

It requires suited SOFC+BoP models

Modeling Approach

6

task 1

1-D steady-state model

Control & Diagnostics Strategies

Verification of Control and Diagnostics

Tools

SOFC SOFC APU1. Real system

2. Physical modeling

3. Control-oriented modeling

task 2

task 3

task 4

task 5

task 6

Control-orienteddynamic model

SOFC-APU model

[IMECE2005_82359]

[IMECE2004_60927]

[FuelCell2007-25056 ]

Mod

els’

Hie

rarc

hyA hierarchical structure combining several models was developed to achieve accurate and fast SOFC APU dynamic model

SOFC APU Block Diagram

Planar co-flow SOFC:•

Highly efficient.

•

Easy monitoring of temperature gradients.

•

No hot spots.

7

CH4

Air

AC power

Battery pack

Fuel pre-reformer

Air pre-heater

Power conditioner

DC

AC

SOFCStack

Air blower

APU

Thermalpower

recovery

Exhausts

H2

OvapBoiler

Reformate fuel

Pos

t-bu

rner

Heat exchanger

0.30.45

H2

O

Hybridized with battery forstart-up; peak power; transients; energy storage

Modeling scheme

Outer APU scheme

Inner SOFC system scheme

Net stack power

Cell voltage

Post burner

Net Power demand SOFC

stack

Steampre-reformer

Air pre-heater

SOC

Pbatt

PnetDC

DC APU P AC APU P

requestedPbatt

requestedPnet

POWER CONDITIONER

SOFC SYSTEM

Power Demand

Fuzzy Logic Controller-VMU

1

BATTERY PACK

8

Modeling assumptions

•

Spatial variations are not considered, i.e. lumped modeling approach.

•

Thermal dynamics is predominant; mass transfer and electrochemistry were assumed instantaneous.

•

Lumped

heat transfer coefficients were assumed to model heat exchangers.

•

Adiabatic

components.

•

Water gas shift reaction is considered at equilibrium.

9

iNiO nn ,2,2 ,

oCOoCOoOHoH nnnn ,2,,2,2 ,,,

iNoO nn ,2,2 ,

isT , osT ,

IVP sel

iCOiCOiCHiOHiH nnnnn ,2,,4,2,2 ,,,,

SOFC dynamic model

SOFC stack

isT , osT ,

sososisisos

s VIQTETEdt

dTK

,,,,,1°

principle

Q

0.5 0.6 0.7 0.8 0.9 10.5

0.6

0.7

0.8

0.9

1

Reference data [V]

Mod

el d

ata

[V]

identificationvalidation test

, , ,, 0.1844 0.0819 1.2352 0.0041 0.8594 0.71531000 1000 1000

s out s out s ins s f

T T TV f x u V U J J

Identified via hierarchical approach from the 1-D model of SOFC performance [IMECE2005_82359]

10

The outlet temperature is the state variable

The entire model (stack+BoP) is

≈ 100 time faster th

an real-time !!!

11

Model-based design

Configuration PlanarMaterial CeramicElectroactive area 100 cm2

Anode thickness 600 mElectrolyte thickness 50 mCathode thickness 50 mInterconnect thickness 500 mHeat capacity 8234 J/KPressure 1 barTemperature in 700 °CTemperature out 825 °CNumber of cells 150Max DC Gross Power @ 0.8 A/cm2 7.5 kWMax AC Net Power @ 0.8 A/cm2 5 kWFuel utilization 0.7xfuel - reformate xH2 = 0.273

xH2O = 0.483xCH4 = 0.171xCO = 0.019xCO2 = 0.054

SOFC stackType Printed plate

Material Ceramic

Heat transfer coefficient 200 W/m2/K

Heat Transfer Area 0.56 m2

Heat capacity 588 J/K

Air pre-heater

Type Steam

Material Ceramic

CH4 conversion efficiency 0.3

S/C 2.5

Heat transfer coefficient 200 W/m2/K

Heat Transfer Area 0.04 m2

Heat capacity 44 J/K

Pre-reformer

Battery-packType Lead-acid

Number of modules 15

Open circuit voltage 12 V

Capacity 25 Ah

H2O = 1.12 kg/h

V1

V2700 °C

700 °C 825°C

825°C

V3

SOFC system – Block diagram

SOFC stack

Air- Compressor

Mass flowEnergy flow

By-pass flow

Pre-reformer

Post

-bur

ner

Air pre-heater

Heat recovery

AC DC

Water tank

Net AC power

2.88 kW

0.31 kW

1057 °C

855 °C

392 °C

150 °C

2.29 kW

CH4 = 0.40 kg/h

AIR = 23.13 kg/h

24.65 kg/hExhaust

2.31 kW

Useful heat

V4

I = 25 A -

net,AC = 0.38

69 °C25 °C

25 °C

12

APU control scheme

Supervisory Control

Look up table

SOFCSystem

Battery

PI

Ts,out,des

Look up tableV3

Look up tableV4

0 10 20 30 400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 10 20 30 400.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

0 10 20 30 400.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

+-

+Pnet, DC Pbatt

AC DC

AC power demand

Pdemand,AC

Ts,out

J

Accessories load

00.2

0.40.6

0.81

0

0.5

1-1

-0.5

0

0.5

1

Normalized power [-]State of charge [-]

Spl

ittin

g in

dex

[-]

SOC

PSOFC, req Pbatt, req

Low-level control

Low-level control aims at ensuring proper thermal management of the SOFC with:•2 PI controllers (cold start; warmed-up)

Fuzzy-logic based map designed to:•

satisfy power demand; •

maximize global efficiency;•

guarantee a charge sustaining strategy for the batteries.

Rate Limiter

13

3 look-up tables (J; V3; V4)

0 10 20 30 400

20

40

60

80

100

120

Time [min]

[V]

Voltage trajectory

0 10 20 30 400

200

400

600

800

1000

Time [min]

[°C]

Temperature trajectories

Tcat,in

TSOFC,out

SOFC - Cold-start control

Ts,out,des =

Tair,in -100

SOFCSystem

PI+

-

Ts,out

Tair,in (t)

14

SOFC are air-cooled then

is control variable

Batteries only

•

The PI acts

on the excess of air to limit the difference between Tair,in and Ts,out below 100 °C.

•

20 minutes Start-up

time was obtained.

-5 0 5 10 150.64

0.66

0.68

0.7

0.72

0.74

Time [s]

V s [V]

controlled

-10 0 10 20 30 400.64

0.66

0.68

0.7

0.72

0.74

Time [min]

V s [V]

uncontrolledcontrolled

-10 0 10 20 30 40820

825

830

835

840

845

850

855

Time [min]

T s,ou

t [°C

]

uncontrolledcontrolled

-5 0 5 10 155.5

6

6.5

7

7.5

Time [s]

[/]

Tair,in (t)

SOFC - Warmed-up control

Ts,out,des =

Tair,in +125

SOFCSystem

PI+

-

Ts,out

J(t)

15

Simulated power profile•

Simulation-based testing of the SOFC-APU model.

•

The reference profile, generated randomly, is a typical auxiliary

load

profile for commercial trucks

in parked idling

phase.

•

Two scenarios were analyzed:

0 20 40 60 80 100 1200

1

2

3

4

5

6

Time [min]

P [k

W]

Auxiliaries AC power demand

Pav

= 2 kW Pmax

= 6 kW

Case 1 with SOFC cold-startCase 2 warmed-up SOFC

16

22 37 52 67 82-6

-4

-2

0

2

4

6

8

10

Time [min]

P [k

W]

DC powers - Case 1

Paux

Pnet

Pbatt

0 20 40 60 80 100 1200.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Time [min]

SO

C [/

]

Case 1, Nb = 15Case 2, Nb = 12Case 2, Nb = 15

22 37 52 67 82-6

-4

-2

0

2

4

6

8

10

Time [min]

P [k

W]

DC powers - Case 2

Paux

Pnet

Pbatt

Simulation results•

The selected number of modules allows a more conservative operation of the battery pack.

•

Cold-start influences SOFC power requests (case 1).

17

Case 1 warm-up period

Simulation results

0 20 40 60 800

0.1

0.2

0.3

0.4

0.5

0.6

I [A]

[/]

net,AC

Case 1 distributionCase 2 distribution

•

Cold-start operation impacts on fuel consumption.

•

Low-level control guarantees a proper thermal management.

mCH4

[kg]mDiesel,eq

[kg]mDiesel,c

[kg]Fuel savings[% of mDiesel,c ]

Case 1 1.54 1.80 3.19 44

Case 2 0.84 0.98 3.19 70

30 40 50 60 70 80200

400

600

800

1000

1200

Time [min]

T [°

C]

Temperature trajectories, Case 2

Ts,cat,in

Ts,an,in

Ts,out

Tpb,out

Th,HE,in

Th,HE,out

18

Diagnostics Application

19

SOFC stack

Post

-bur

nerCH4

AIR

Exhaust

Air leakage degradation

Fault

Symptom

→

Pcp

Pgross

= Pnet

→

Pcp

Pgross Pnet

Diagnostics scheme

, ,

, ,

, ,

1

2

3

net p net m

gross p gross m

cp p cp m

R P P

R P P

R P P

Detection of air leakage (i) and stack degradation (ii) with Parity-equation based diagnostics method:

Fault R1 R2 R3i) Air leakage 1 0 1ii) Increased Ohmic losses 1 1 1

Fault [yes/no]

Plant

Diagnosis

u y

Fault schemeModel y^ Fault [yes/no]

Plant

Diagnosis

u y

Fault schemeModel y^

Inference from symptoms

redundancy

Fault detectioni) Stepwise Air leakage

0 5 10 15 20-1

-0.5

0

0.5

1

1.5

2

Time [min]

[/]

Residual trajectories

R1R2R3

0 5 10 15 200

1

2

3

4

5

Time [min]

Power trajectories

[kW

]

Pnet

Pgross

Pcp

ii) Stepwise Stack degradation

0 5 10 15 20-1

-0.5

0

0.5

1

1.5

2

Time [min]

[/]

Residual trajectories

R1R2R3

0 5 10 15 200

1

2

3

4

5

Time [min]

Power trajectories

[kW

]

Pnet

Pgross

Pcp

21

Conclusions•

Modeling methodologies were proposed to develop control-

/diagnostic-oriented

models of SOFC cells/stacks.

•

Model-based design of suited control strategies to ensure optimal energy and thermal management.

•

The developed model and related control strategies were tested via simulation of a typical auxiliary power demand profile for commercial heavy-duty trucks.

•

Model suitability

for developing appropriate diagnostics strategies/architecture.

•

Future work will focus on diagnosing the whole SOFC system and perform experimental testing

of control and diagnostic

strategies.

22