Fuzzy logic controller development of a hybrid fuel cell-battery auxiliary power unit for remote applications

Abstract

This paper presents the analysis and design of a hybrid fuel cell battery auxiliary power unit (APU) for remote applications implemented with virtual and rapid prototyping techniques. The fuel cell is the main energy source working in low frequency bandwidth while the battery compensates the transient peak power needs. The control of the fuel cell system using two techniques, the first-order based control where the fuel cell delivers the filtered load current and a fuzzy based control. The simulation show that the last one permits the fuel cell to working most of the time in its optimal working zone as well as maintaining the battery state-of-charge in its optimal windows whatever the initial sate-of-charge.

Full text

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Fuzzy Logic Controller Development of a Hybrid
Fuel Cell-Battery Auxiliary Power Unit for Remote
Applications
Benjamin Blunier∗, Marcelo G. Sim˜ oes†, Abdellatif Miraoui∗
∗Transport and Systems Laboratory (SeT) – EA 3317/UTBM
University of Technology of Belfort-Montb´ eliard, France
Emails: (benjamin.blunier, abdellatif.miraoui)@utbm.fr
†Colorado School of Mines, 1610 Illinois St.Golden CO 80401-1887, U.S.
e-mail: msimoes@mines.edu
Abstract—This paper presents the analysis and design of a
hybrid fuel cell battery auxiliary power unit (APU) for remote
applications implemented with virtual and rapid prototyping
techniques. The fuel cell is the main energy source working
in low frequency bandwidth while the battery compensates the
transient peak power needs. The control of the fuel cell system
using two techniques, the first-order based control where the fuel
cell delivers the filtered load current and a fuzzy based control.
The simulation show that the last one permits the fuel cell to
working most of the time in its optimal working zone as well as
maintaining the battery state-of-charge in its optimal windows
whatever the initial sate-of-charge.
I. INTRODUCTION
This paper introduces the design and simulations of a hybrid
auxiliary power unit (APU) for decentralised energy produc-
tion such as telecom or remote systems applications. Fuel
cells are well adapted for such applications as they provide
a very good efficiency, low noise and high energy density to
provide continuous power during a long time [1]. It is based
on a structure using a 300 watts fuel cell stack connected to
a Ni-MH battery through a DC/DC buck converter. The Ni-
MH battery is a good solution in terms of energy and power
density but it could be replaced in the future by Lithium-
based batteries. Fuel cells are not reversible and have big time
transients which are not fully compatible with any kind of
application, that is why fuel cells are always combined with a
reversible power or energy source (supercapacitors or battery)
which can handle peaks of power.
For a first approach, a rather simple but robust structure was
chosen to validate the relevance of using a fuel cell system
in such a system. The aim is to have the most simple and
less expensive system which does not need any expensive
components and big computation power. In a second step,
structures similar to the ones introduced by the authors of [2]
will be investigated. Wang [3], [4] propose a low-cost quasi-
resonant dc-dc converter for fuel cell that could also be used
to improve the system efficiency compared to a simple buck
converter keeping the costs relatively low.
In the actual structure, the batteries provide the peaks of
power and the fuel cell is sized to provide nearly the average
Fig. 1.Fuel cell battery hybrid Auxiliary Power Unit
power.
The paper is organizedas follows: in a first part, an overview
of the architecture and the components of the APU is given.
The second part introduces the virtual and rapid prototyping
techniques to design the system. The third part presents the
modeling of the system and its control. The last part presents
the simulation and experimentation results.
II. SYSTEM OVERVIEW
The figure 2(a) gives an overview of the APU. The fuel
cell used in this application is a Polymer Electrolyte Fuel
Cell (PEFC). It works at low temperature and at atmospheric
pressure on the cathode side. Its maximum power is about
300 watts and the voltage is between 30V and 58V. A
DC/DC buck converter between the stack and the DC-link
allows the fuel cell current to be controlled. The Ni-MH
battery imposes the DC bus voltage to be around 24 volts
depending on the state of charge of the battery: the system is
designed to allow any batteries with a voltage between 9V and
36V to be connected. The signal conditioning and processing
and the control are done with a microautobox from dSPACE
embedded into the APU (see Fig. 2(b)). This system allows
rapid prototyping and implementation in a embedded real time
system the control laws developed under Matlab-Simulink.

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(a) Electrical architecture overview
fuel cell
stack
300 w
Measurement and
signal processing
Micro-autobox
board
Ni-Mh Battery
Power board
(b) Overview of the real designed and built system
Fig. 2.Architecture including a Ni-MH battery and a 300W fuel cell stack
III. VIRTUAL RAPID PROTOTYPING APPROACH
In order to design the fuel cell system module, the virtual
prototyping method is used. This method consists to validate
all parts of the system, including control and components
design (inductance, battery capacity, power electronics, fuel
cell power, etc.) using simulation tools. The system model and
the control laws (e.g., energy management) are implemented
in Matlab-Simulink. The Matlab-Simulink part, validated in
simulation is directly downloaded into the dSPACE microau-
tobox.
Although the fuel cell system control, including the purges,
the air and the temperature managements, is implemented (see
Fig. 3), this part is not in the scope of this article; for this,
interested reader can refer to the reference [5].
Finally, the virtual prototype permits the energy manage-
ment strategy (see sec.V-B) to be tested under various driving
cycles. This part allows several strategies and set of parameters
to be tuned in order to give the best results and check if the
parameters respect the system constraints (dynamics, currents,
voltage, etc.). In this case, two control strategies for the energy
management are tested:
V
PWM
generator
Air supply
control
+
A
PWM
out
PWM
out
serial com.
+
-
A
Fan control strategy
αfans
Purges management
αtemp
supervision
Temperature
αtot
Purge
on/off
Istack
Vstack
V
α
DC/DC
analog inputs
purge vanne
Schematic view of the real system
Dspace board (compiled from simulink code)
digital
outputs
Tstack
interface
Dspace
board
Load
B.M.S
SoC
analog in.
Iload
Vbatt
Energy management strategy
Battery
SoC
Vstack
Vref
stack
Fuzzy logic
controller
First order
filter
Fig. 3.Rapid prototyping (control implementation on the real system)
1) A first order filter based controller, where the fuel cell
current delivers the load current filtered to reduce the
fuel cell current dynamics;
2) A fuzzy logic controller to manage the fuel cell current
based on the state-of-charge (SoC) of the battery.
IV. SYSTEM MODELING AND CONTROL
A complete model of the electrical part of the system is
built to test virtually the control laws and energy management
strategies before their implementation on the real system.
This section presents the fuel cell stack model. In this
systems, the battery does not need to be modeled accurately
as the real battery has a serial communication interface which
can give in real time the SoC of the battery. For the battery
model, a simple current integration taking into account charge
and discharge efficiencies.
A. Fuel cell model
As the power required is low, the fuel cell works at
atmospheric pressure. The air is supplied with three fans at
a high stoichiometric ratio. The fuel cell is air-cooled using
the same fans than the air supply.
The high stoechiometric ratio ensures that there is nearly no
oxygen depletion and no electrodes flooding. As the fuel cell
system (energy source) is hybridized with a battery considered
here as the peaking power source (PPS) its dynamic does not
need to be high. For this reason, but also to increase the life-
time of the fuel cell, the strategy aims at maintaining the fuel
cell current dynamics as low as possible (see sec.V-B). Ideally,
the fuel cell should deliver a constant power corresponding to
the average load power.
Considering this remarks, it can be assumed that the fuel
cell is almost always in steady state: in this case, a static

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model of the fuel cell can be used. For higher powers, the
static characteristic cannot be used to describe the fuel cell
system: the compressor and the air dynamic (air pressure
inside the channels) [6], [7], as well as the water content of the
membrane which affects the membrane resistance have to be
considered. The authors presented such a model in [8]–[10].
The static characteristic is therefore described as a current
(I)-voltage (V ) function [11]:
Vstack(I) = E0−RIstack−A ln(Istack)+m exp(nIstack) (1)
where E0, R, A, m and n are empirical coefficients to be
determined for the investigated fuel cell stack.
V. FUZZY LOGIC CONTROL
A. Fuzzy Control System
Most commercial fuzzy products are rule-based systems
that receive current information fed back from the device
as it operates and controls the operation of a mechanical
or other device. A fuzzy logic system has four blocks as
shown in Fig. 4. Crisp input information from the device is
converted into fuzzy values for each input fuzzy set by the
fuzzification block. The universe of discourse of the input
variables determines the required scaling for correct per-unit
operation.
Fig. 4. Block diagram of a fuzzy system.
The normalization and scaling are very important because
the fuzzy system can be retrofitted for other devices or ranges
of operation by just changing the scaling of the input and
output. The decision-making logic determines how the fuzzy
logic operations are performed (Sup − Min inference), and
together with the knowledge base determine the outputs of
each fuzzy if − then rule. These are combined and then
converted to crisp values with the defuzzification block.
B. Control strategy
1) Objectives of the control strategy: The objectives of
the control strategy has to take into account the following
components constraints:
1) Limits the fuel cell dynamics and voltage cycling (low
voltages) to increase its lifetime;
2) Make the fuel cell working around the best working
points (where its efficiency is the highest);
3) Maintain the battery State-of-Charge (SoC) in a given
window and limits the cycling;
4) Recover the energy during negative load current.
2) First order filter based controller: The overall system
control has to give a current reference to the fuel cell: this
control has also been successfully demonstrated in a similar
system by the author in [5]. The dynamic of the fuel cell can
be low as it acts as a range extender. When sudden changes
occur in the load power, the fuel cell still works at its previous
working point and the battery provides the difference. The fuel
cell will take time to reach the desired power: until that time,
the battery supplies the load (in the case of a positive current
step) or is charged by the fuel cell (in the case of a negative
current step).
DC
DC
VV
Fuel cell stack
PI
+−
-
+
-
Itot
Istack
Buck converter
Vbatt
Ibatt
Vstack
+
Istack
Load
α
Power balance
Ibus
stack
Istack,ref
Ibus
stack,ref
1
1+τ p
IbattVbat
Vstackηcv
(a) First order filter based controller [5]
DC
DC
VV
Fuel cell stack
PI
+−
B.M.S
-
+
-
Istack
Buck converter
Vbatt
Ibatt
Vstack
+
Istack
Load
α
Battery
SoC
Itot
Fuzzy
Controller
Ibus
stack
Istack,ref
(b) Fuzzy controller
Fig. 5.Control strategies
The time with which the fuel cell takes to reach the desired
power depends on the control strategy. In this case, the load
current (Iload) is filtered through a first-order transfer function
as shown in Fig. 5: the output of this function is the fuel cell
stack current reference on the DC bus (Ibus
stack current reference Istack thanks to the DC/DC converter
power balance: VstackIstackηcv = VbattIbus
converter efficiency. This current Istackreference is obviously
limited to the maximum current the fuel cell can supply. Over
this current, the battery has to supply the remaining current.
stack) which gives the
stack, where ηcv is the
C. Fuzzy logic controller
1) Parameters: The parameters of the fuzzy controller
are tunable by the user and are represented by trapezoidal
functions as shown in Fig. 6.
The parameters of the battery state-of-charge management
and fuel cell current are given in TABLE. I and TABLE. II,
repectively.
2) Rules: The implemented rules are the followings:
1) If (SOC is toolow) then (IFC is toohigh)
2) If (SOC is low) then (IFC is high)
3) If (SOC is good) then (IFC is opt)
4) If (SOC is high) then (IFC is low)

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reached
Never
LowOptimalHigh
Fuel cell current membership function
Ze
Optimal power zone
Interpolation
Experimental
0
50
150
200
250
300
015910
P (W)
I (A)
100
2
34
6
7
8
Fig. 6.Working zones of the fuel cell system
Description
Emergency case
SoC is considered to be low
Notation
SoClimit
SoClowmin
SoClowmax
SoCgoodmin
SoCgoodmax
SoChighmin
SoC Value
0.1
0.2
0.3
0.45
0.7
0.8
SoC is considered to be good (optimal)
SoC is considered to be high (the system
can work in charge depleting mode in this
case : only the battery is working)
SoChighmax
1
TABLE I
BATTERY STATE-OF-CHARGE MEMBERSHIP FUNCTION PARAMETERS (CAN
BE EASILY TUNED BY THE USER)
VI. SIMULATION AND EXPERIMENTAL RESULTS
A. Random load profile generation
The load current profile (see Fig. 7) is randomly generated
(uniform distribution) to give a average current of 7A with
some long current bursts corresponding to a power much more
higher than the fuel cell power. The current profiles can also
contain some zero-current during a long time.
The average power long the total profile is 150W as shown
in Fig. 7(b).
Description
Fuel cell maximum current allowed
Fuel cell current is considered to be low
Notation
FCcurrentmax
FCcurrent lowmin
FCcurrent lowmax
FCcurrent optmin
Current (A)
8
2
3
4Fuel cell current for which the fuel cell
has been designed (corresponding to the
average power of the load)
FCcurrent optmax
FCcurrent highmin
6
7
Fuel cell current is considered to be
high (used in emergency cases when the
battery SoC is too low)
FCcurrent highmax
7.5
TABLE II
FUEL CELL CURRENT MEMBERSHIP FUNCTION PARAMETERS (CAN BE
EASILY TUNED BY THE USER)
012345
4
x 10
−5
0
5
10
15
20
Time (s)
Load current (A)
Bursts of current
Zero load current
(a) Random current load profile generation
012345
4
x 10
60
80
100
120
140
160
180
200
Time (s)
Average power
(b) Average power along the cycle
Fig. 7.Current and average power of the load along the cycle
B. Influence on the battery-converter efficiency
The first order control strategy permits the fuel cell to give
the load average power along the cycle and permits the battery
SoC to be maintained constant (see Fig. 8(a)). However, it
works well if the battery-converter efficiency is 100% but if
this efficiency is below 100% as shown in Fig. 8(b), the SoC
cannot be maintained constant because of the losses between
the converter and the battery.
C. Influence on the initial battery state-of-charge
Fig. 9 shows that the first-order based controller does not
permit the SoC to be recovered if it is low at the beginning of
the cycle as shown in Fig. 9(b) where the initial SoC is very
low.
D. Comparison between the two control strategies on the fuel
cell power distribution
Fig. 10 shows that both of the control strategies permits the
fuel cell to work around the optimal working zone of the fuel
cell. However, using the first order based control (Fig. 10(a)),
the fuel cell shuts are works at full power down very often.
This strategy may decrease the fuel cell lifetime.

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012345
4
x 10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
time (s)
SoC
Too low
Low
Good
High
(a) First order filter based control and 100% battery-converter
efficiency (τ = 30s)
012345
4
x 10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
time (s)
SoC
Too low
Low
Good
High
(b) First order filter based control and 90% battery-converter
efficiency (τ = 30s)
Fig. 8.
the load cycle for the first-order filter based control strategy. The first order
filter control strategy does not permit the state-of-charge to be maintained
along the cycle.
Influence of the system efficiency on the battery SoC profile along
On the contrary, the fuzzy logic controller permits the fuel
cell to work exactly at its optimal power with very few
working point at zero power and high power whatever the
initial state-of-charge.
E. Battery state-of-charge profile with the fuzzy controller
Moreover, as shown in Fig. 11 the fuel cell is shut down
only one time during a long time because the fuel cell power is
not anymore needed: the system can work only on the battery
during this time.
VII. CONCLUSION
A hybrid fuel cell/battery APU has been modeled and
designed. The fuel cell in this system acts as the energy source
allowing this component to have a low dynamic. The control
of the fuel cell system and the control of the overall system
has been presented and validated experimentaly on the system.
Tests and simulation results will be given in the final paper:
they have shown that a very simple control strategy which
does not require a high computation power can give very good
results the energy management of a hybrid battery-fuel cell
012345
4
x 10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
time (s)
SoC
Too low
Low
Good
High
(a) First order filter based control and 90% battery-converter
efficiency,τ = 100s, and initial SoCinit= 1
0200040006000800010000
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
time (s)
SoC
Too low
Low
Good
High
(b) First order filter based control and 90% battery-converter
efficiency,τ = 100s, and initial SoCinit= 0.5
Fig. 9.
along the cycle. The first order filter control strategy does not permit the
state-of-charge to be recovered and maintained along the cycle.
Influence of the initial battery state-of-charge on the SoC profile
system. Fuzzy logic control showed to improve the efficiency
of the system by concentrating the region of operation in the
best performance range and to minimizing the shut-down and
over-current conditions. With Fuzzy Logic it was also possible
to make the system recover the state-of-charge conditions
without any extra state machine decision making procedures.
Therefore, a fuzzy logic based APU system is expected to
have a longer lifetime and best dynamic performance than
traditional APU controllers.
REFERENCES
[1] B. Blunier and A. Miraoui, 20 Questions sur la pile
Technip, 2008, (in French).
[2] A. Khaligh, A. Rahimi, Y. Lee, J. Cia, A. Emadi, and S. Andrews,
“Digital control of an isolated active hybrid fuel cell/li-ion battery power
supply,” IEEE Transactions on Vehicular technology, vol. 56, no. 6,
november 2007.
[3] S. Wang, M. Krishnamurthy, R. Jayabalan, and B. Fahimi, “Quasi-
resonant dc/dc converter for high power fuel cells systems,” in Power
Electronics Specialists Conference, 2006. PESC ’06. 37th IEEE, 18-22
June 2006, pp. 1–7.
[4] ——, “Low-cost quasi-resonant dc-dc converter for fuel cells with
enhanced efficiency,” in Applied Power Electronics Conference and
Exposition, 2006. APEC ’06. Twenty-First Annual IEEE, 19-23 March
2006, p. 6pp.
combustible.