Energy management of fuel cell electric vehicle with
hydrid tanks
Alexandre Ravey1,3 , Sebastien Faivre1,3 , Charles Higel1,2,3 , Fabien Harel2,3 , and Abdesslem Djerdir1,3
1
2
IRTES, UTBM, 90010 Belfort cedex – France
IFSTTAR / AME / LTE, 25 Avenue François Mitterand, Case24, Cité des mobilités, F-69675 Bron cedex – France
3
FR FCLAB, CNRS 3539 – France
Corresponding author: alexandre.ravey@utbm.fr
Abstract—This paper proposes a novel control strategy for
fuel cell electric vehicle including hydrid tanks using fuzzy logic
controller. The aim of the study is to manage both thermal and
electric energy with the same controller in order to use the fuel
cell system as a range extender by preventing the batteries state
of charge to drop too quickly. The presented controller use both
batteries state of charge and thermal status of hydrid tank to
control the fuel cell power. This work is a part of the Mobypost
project, which aims to build and test a fleet of 10 fuel cell electric
vehicles with their own refuelling hydrogen station based on
hydrogen production by water electrolysis with solar panel.
I.
I NTRODUCTION
Electric mobility is expected to play a key role in the future
of clean energy transportation. Nevertheless, due to autonomy
limitation, Hybrid Electric Vehicle (HEV) appear to be the
best mid-term solution [1]–[3], specially with Fuel Cell Hybrid
Electric Vehicle (FCHEV) which allow zero emission mobility.
The design and control of such powertrain must be carefully
done to determine the size of each energy sources. When
the vehicle is build for specific applications, such as postal
delivery, the conception can be optimize for its use [4], [5].
In order to store the hydrogen into the vehicle, two ways are
commonly used: On one hand, high pressure tanks allow the
vehicle to be quickly filled with hydrogen but requires the
use of a compressor in order to be able to reach the pressure
value (300 bar), degrading the energy cost from the source to
the wheel [6]. On the other hand, metal hydrid tanks allow a
low pressure charge, avoiding the use of compressor but need
to be thermally managed to be able to provide the desired
flow of hydrogen. Several studies about energy management
of FCHEV can be found into the literature [7]–[10], but are
not taking into account the hydrogen storage constraints and
limits. This paper aims to integrate metal hydrid tanks into the
energy management of the powertrain, taking into account its
thermal power needs in order to control the flow of hydrogen
delivered.
In a first part, the Mobypost project within this study has
been done will be explained. Power profile derivated from
specific driving cycles will be then presented, followed by the
components model of the powertrain in order to be able to
simulate the system behavior which will be controlled by a
fuzzy logic controller. Results are finally drawn and discussed
in the conclusion.
k,(((
II.
M OBYPOST P ROJECT
Mobypost is a European project which aims at developing
the concept of electric vehicles powered by fuel cells for
delivery application as well as local hydrogen production
and associated refuelling station. The project is based on a
consortium including 8 partners:
•
University of Technology of Belfort-Montbeliard
(UTBM) - France;
•
DUCATI energia S.p.A. - Italy;
•
Mahytec Sarl - France;
•
MES - Switzerland;
•
European Institute for Energy Research (Eifer) - Germany;
•
H2Nitidor s.r.l. - Italy;
•
SteinBeis-Europa-Zentrum - Germany;
•
La Poste - France.
Fig. 1: Mobypost project concept
Fig. 1 show the concept of the project which is divided in
two parts:
In a first part called infrastructure, the project aims at design
and build a hydrogen production and distribution station. Solar
panel equip the building and which feed an electrolyser in
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order to produce hydrogen which is store on site in a low
pressure tank. No additional gas compression is done, which
allow the system to produce hydrogen with no electricity added
but water.
The second part is focus on the vehicle. The conception of a
lightweight hybrid electric car including fuel cell and batteries
is done by design the powertrain and size the components
taking into account the mobility application: postal delivery
services. Its include a low pressure metal hydrid tank which
is refilled by the hydrogen station. The vehicle is designed
to have an autonomy matching with a postal delivery day of
services.
III.
V EHICLE P OWERTRAIN
A. Powertrain architecture
The vehicle considered in this study is a series FCHEV
based on a Proton Exchange Membrane Fuel Cell (PEMFC)
and Li-iron-phosphate-Magnesium (LiFeMgPO4) batteries
pack with two in-wheels motor (Permanent Magnet Synchronous Machine) and their associated inverters as shown in
Fig. 2 [11], [12]. The PEMFC is connected to the DC bus via
a DC/DC converter whereas the batteries are directly linked
to the bus [13]. Consequently, only the fuel cell power can
be controlled, the batteries are used as a buffer absorbing the
peak power during acceleration phases.
where Fa is the drag force, Fr the rolling friction, and Fg
the force caused by gravity when driving on non-horizontal
roads:
1
ρ A Cx v 2
Fa =
(2)
2
Fr = mv Cr g cos(α)
(3)
(4)
Fg = mv g sin(α)
Variable ρ is the air density, g the standard gravity, v the speed
of the vehicle and α the angle defining the slope of the road.
The vehicle designed regarding postal delivery applications
has the following characteristics:
•
Mass (mv ): 530 kg;
•
Front surface (A): 2.56 m2 ;
•
Drag coefficient (Cx ): 0.8;
•
Rolling coefficient (Cr ): 0.02;
•
Battery capacity: 5 kWh;
•
Fuel cell power: 1 kW.
The power profile can be computed using the driving cycle
shown in Fig. 3 as an input of the model [16]. Results is shown
in Fig. 4.
50
speed (km/h)
40
30
20
10
0
0
Fig. 2: Powertrain architecture and control
2000
4000
6000 8000
Time (s)
10000 12000
Fig. 3: Speed profile
B. Power profile
Simulations are run using a backward approach [14]: The
input of the model is the power profile derivated from the
driving cycle of the vehicle. In order to compute this profile,
a vehicle model is used coupled with a real driving cycle.
2) Vehicle model: The drive power Pmot (t) is derived from
Newton’s second law,as in (1) [15]:
d
Pmot (t) = v mv (t) v(t) + Fa (t) + Fr (t) + Fg (t)
(1)
dt
x 10
1
Power (W)
1) Driving cycle: The driving cycle used for this study
has been recorded from existing Electric Vehicle (EC) used
for French postal delivery. The vehicle designed within the
Mobypost project is willing to replace the existed EV one
(Fig. 3). Knowing this specific use, the design has been focus
around this driving cycle to optimize as best as possible both
the sizing and the control of the powertrain.
4
1.5
0.5
0
−0.5
−1
0
2000
4000
6000 8000
Time (s)
10000 12000
Fig. 4: Power profile
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C. Fuel cell model
Fuel cell system nominal power used in our application
is 1kW . This PEMFC is working at atmospheric pressure
cooled by forced air. Thanks to the powertrain hybrid series
architecture, the fuel cell operates as the main energy converter
(range extender) and the batteries provides the transient currents. In this context the fuel cell is mainly working at specific
operating points that are close to static conditions. Regarding
the previous assumption a static model (5) of the fuel cell is
sufficient and does not require high computation power.
1) electrical model: The voltage of the stack is given by
(5):
Vstack (I) = E0 − RI − Aln(I) − m exp (nI)
(5)
where E0 ,A,m and n are empirical coefficients determined
by a non-linear regression method described in [17].
2) consumption model: The flow of hydrogen by power has
been determined experimentally by the fuel cell manufacturer
for the specified fuel cell used in the powertrain. Results are
shown in Fig. 5.
2) Thermal model: The aim of the thermal model is to
determine the thermal power Pthtank to provide in order to
insure the thermal needs which allows the extraction of a given
hydrogen flow n. It is assumed that the air coming from the
fuel cell is blown uniformly on all the surface of the cylinder
metal hydrid tank. The reaction can be assumed as a thermal
conduction and can be expressed by (8):
Pthtank (t) =
dT (t)2πλL
ln RR21
(8)
Where dT (t) is the difference of temperature of tank
surface assuming no thermal power is given during (t) in K,
λ is the thermal conductivity in W m−1 K −1 which has been
estimated to 1.087 in [18] , L the length of the tank in m,
R2 the external radius of the tank in m and R1 the internal
radius in m.
Fig. 6 shows the different value of dT (t) by hydrogen flow
nl. dT (t) values have been estimated based on measurement
of the temperature at the cylinder’s surface when the fuel cell
is running at different working points. These values are not
SoCH2 dependant. Consequently, this model is not correct for
the whole range of tank’s state of charge since it has been
shown in [18] that the thermal needs increase while SoCH2
decrease.
1
0.9
Delta of temperature (%)
0.8
Fig. 5: Fuel cell flow by power
0.7
0.6
0.5
0.4
0.3
0.2
3) fuel cell thermal model: In thermal power PF Cth which
is produce by the fuel cell is defined by (6):
PF Cth =
1
Vstack Iηb
2
(6)
Where ηb is the blower efficiency, which is determine by
the manufacturer of the fuel cell (70%).
D. Hydrid tank model
1) State of charge determination: Since the State of Charge
of metal hydrid tank (SoCH2 ) is not pressure dependent, its
need to be estimated. A simple flow integration is used (7):
t
1
MH2 n(t)
SoCH2 (t) = SoCH2init −
dt
(7)
mtot 0
60
where mtot is the maximal mass of hydrogen stored in the
tank, MH2 is the hydrogen molar mass and n(t) is the flow
in nl.
0.1
0
0.2
0.4
0.6
0.8
1
h2 flow (%)
Fig. 6: Experimental determination of dT by hydrogen flow n
E. Battery model
The batteries model is a simple current integration to
compute the batteries SoC, in this study Vb is considered as
constant.
SoC(t)
ηb
=
ηb
SoCinit −
Cnom
0.95 if Ib < 0
=
1
if Ib ≥ 0
t
0
Ib (t)dt
(9)
(10)
where Cnom is the batteries nominal capacity given by the
manufacturer and ηb the batteries efficiency.
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F. DC/DC converter model
TABLE I: Controller areas definition for P h
The DC converter is modelled as a basic energy converter
with a given constant efficiency.
Iout = ηconv
Iin Uin
Uout
(11)
Pheater is considered to be high
where Uout is constant because Uout = VBatt and where
Iin = If c , ηconv is constant and equal to 0.85.
Notation
P h fuzzy value
P hlimit
0.9
P hvery high min
0.8
P hvery high max
P hhigh min
P hhigh max
P hgood min
P hgood max
0.7
0.6
0.3
0.1
0
Description
Pheater is considered to be
highly critical
Pheater is considered to be very
high
Pheater is considered to be good
TABLE II: Controller areas definition for SoC
Description
G. Load model
The power split between the fuel cell current IF C and the
batteries current Ib is given by (12).
SoC is considered to be critical
SoC is considered to be low
SoC is considered to be good
SoC is considered to be high
Imot (t) = IF C (t) + Ib (t)
IV.
(12)
Notation
SoC value
SoClimit
SoClow min
SoClow max
SoCgood min
SoCgood max
SoChigh min
SoChigh max
0.2
0.4
0.6
0.7
0.8
0.9
1
TABLE III: Controller areas definition for IF C
E NERGY MANAGEMENT
Description
Since the batteries are directly link to the DC-bus, only the
fuel cell can be controlled. Control strategy will aim to control
the fuel cell running at its best efficiency point while keeping
the state of charge of the batteries in its optimal zone [19], [20].
Moreover, due to technology constraints of the metal hydrid
tank, their temperature need to be kept in a good zone in order
to be able to provide the necessary flow of hydrogen need by
the fuel cell. A fuzzy logic controller is used to determine the
fuel cell current regarding the listed inputs.
Fuel cell is shutdown
Fuel cell run at low power point
Fuel cell runs at its optimal point
Fuel cell runs higher than its optimal
point
Fuel cell runs at maximum power
Notation
IF C value
IF C shutdown
IF C low min
IF C low max
IF C good min
IF C good min
IF C high min
0.1
0.3
0.4
0.5
0.6
0.7
IF C high min
IF C very high
0.8
1
B. Fuzzy logic controller
A. Thermal constraints definition
Based on the model of hydrid tanks described in section
III-D, the hydrogen flow is depending on the thermal power
given by the Heat Transfer System (HTS). In addition of the
fuel cell, a 500 W heater is added to the system in order to
sustain the power needed in case of low fuel cell operation
point. The thermal power given to the tanks can be describe
as (13):
The designed fuzzy logic controller has two inputs: the
state of charge of the batteries SoC and the power needed
by the heater Pheater described respectively in TABLE I and
TABLE II. The ouput fuel cell current IF C is described in
TABLE III.
All datas for each input and output are gather to create the
membership functions of the controller, drawn in Fig. 7, Fig. 8
and Fig. 9
Limit
Where Pthtank is the thermal power given by the HTS to
the tanks, PthF C is the thermal power provided by the fuel
cell, Pheater is the power which need to be provided by an
additional heater.
the controller needs to keep Pheater as low as possible
to avoid additional electrical costs of auxiliary, which will
degrade the efficiency of the whole system. In this way, Pheater
is defined as an input of the controller P h as follow (14):
2Pheater
Ph =
100
Low
Opt
High
1
(13)
Degree of memebership
Pthtank (t) = PthF C (t) + Pheater (t)
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
1
SoC
Fig. 7: Membership function definition for batteries state of
charge
(14)
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Good
High
Very high
cell current, the batteries current and the heater current. The
controller, taking into account the cost of the additional heater,
control the power of the fuel cell in order to keep the thermal
power needed by the tanks as low as possible.
Critical
0.8
0.6
Battery SoC profile
1
0.4
Battery SoC
Degree of memebership
1
0.2
0.5
0
0
0.2
0.4
0.6
0.8
0
1000
2000
3000
4000
5000
6000
4000
5000
6000
4000
5000
6000
time (s)
1
Fuel cell power
Pheater
Fig. 8: Membership function definition for the power needed
by the heater
FC Power (W)
0
1000
500
0
0
1000
2000
3000
time (s)
Shutdown
Low
Opt
High
Driving cycle
Very high
speed (m.s−1)
Degree of memebership
1
0.8
0.6
60
40
20
0
0
1000
2000
3000
time (s)
0.4
Fig. 10: Battery SoC and fuel cell current for the simulated
driving cycle
0.2
0
0.2
0.4
0.6
0.8
1
Fuel cell current
Fig. 9: Membership function definition for the output current
of the fuel cell
Fuel cell power( W)
0
Fuel cell power
1000
500
0
0
1000
2000
3000
4000
5000
6000
4000
5000
6000
4000
5000
6000
The fuzzy logic controller is selecting the ouput regarding
all the state of the input according to the fuzzy rules described
in TABLE IV. These rules has been defined from the study
presented in [8] and experimental test on hydrid tank system
within the vehicle’s architecture presented in [12].
Rate of heat flow (W)
time (s)
Heat power needed by tank
600
400
200
0
0
1000
2000
3000
time (s)
R ESULTS
150
The simulation is run on a postal delivery driving cycle
presented Fig. 3. Fig. 10 shows the output of the fuzzy
controller PF C regarding both batteries state of charge SoC
and vehicle speed v. It can be observed that the SoC is kept in
its good zone (TABLE II) and the fuel cell power is increased
during high power phases causes by high dynamic of the
vehicle.
Fig. 11 shows respectively the power of the fuel cell PF C ,
of the tank Pthtank and by the additional heater Pheater . It
can be observed that during the first half of the simulation,
when the driving cycle has high dynamics, the fuel cell power
is set at a higher point than optimal value (TABLE III) in
order to maintain the state of charge of the batteries in the
good zone. Consequently, the power needed by the additional
heater is high and the range extender system’s efficiency is not
optimal. During the second phases, the power of the fuel cell
is controlled in order to avoid the use of the additional heater.
Fig. 12 focus on this part by presenting respectively the fuel
Pheater (W)
V.
Additional Heat power
100
50
0
0
1000
2000
3000
time (s)
Fig. 11: Metal hydrid tanks results
VI.
C ONCLUSION
A control strategy of a fuel cell electric vehicle with hydrid
tanks has been presented. The fuzzy controller include the
thermal management of the powertrain including heat transfer
between the fuel cell and the metal hydrid tank in order to
control the fuel cell power. A simulation of a realistic driving
cycle for postal delivery application has been run, showing the
good behavior of the controller. Future works aims to embed
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TABLE IV: Fuzzy logic rules
SoClimit
SoClow
SoCgood
SoChigh
P hlimit
IF C min
IF C shutdown
IF C shutdown
IF C shutdown
P hvery high
IF C high
IF C good
IF C min
IF C min
P hhigh
IF C high
IF C good
IF C good
IF C min
P hgood
IF C very high
IF C high
IF C good
IF C shutdown
FC current (A)
Fuel cell current
35
30
25
20
15
4000
4500
5000
5500
6000
5500
6000
5500
6000
Battery current (A)
time (s)
Battery current
200
100
0
−100
4000
4500
5000
Heater current (A)
time (s)
Additional Heat current
2
1.5
1
0.5
0
4000
4500
5000
time (s)
Fig. 12: Focus on the impact of the additional heat on the
control
the control inside the electronic control unit of each of the ten
vehicles which will be build for the project.
ACKNOWLEDGMENT
This research work is carried out within the framework
of European project MobyPost which aims at developing the
concept of electric vehicles powered by fuel cells for delivery
application as well as local hydrogen production and associated
refueling station and hydrogen production apparatus from photovoltaic generators. MobyPost is a project funded under the
Grant Agreement no. 256834 by the European Union’s seventh
Framework programme (FP7/2007-2013) for the Fuel Cell
and Hydrogen Joint Technology Initiative (http://mobypostproject.eu/).
R EFERENCES
[1] A. Emadi and S. Williamson, “Fuel cell vehicles: opportunities and
challenges,” in Power Engineering Society General Meeting, 2004.
IEEE. IEEE, 2004, pp. 1640–1645.
[2] B. Blunier, D. Bouquain, and A. Miraoui, Alternative Propulsion
Systems for Automobiles. expert verlag, 2008, no. 2, ch. Fuel cells,
Energy Management using Fuel Cells and Supercapacitors, pp. 97–116,
iSBN-13: 978-3-8169-2835-5.
[4] A. Ravey, N. Watrin, B. Blunier, D. Bouquain, and A. Miraoui,
“Energy-source-sizing methodology for hybrid fuel cell vehicles based
on statistical description of driving cycles,” Vehicular Technology, IEEE
Transactions on, vol. 60, no. 9, pp. 4164–4174, 2011.
[5] A. Ravey, R. Roche, B. Blunier, and A. Miraoui, “Combined optimal
sizing and energy management of hybrid electric vehicles,” pp. 1–6,
2012.
[6] B. Blunier and A. Miraoui, “Proton exchange membrane fuel cell air
management in automotive applications,” Journal of Fuel Cell Science
and Technology, vol. 7, no. 4, p. 041007, 2010. [Online]. Available:
http://link.aip.org/link/?FCT/7/041007/1
[7] M. Zandi, A. Payman, J. Martin, S. Pierfederici, B. Davat,
and F. Meibody-Tabar, “Energy management of a fuel
cell/supercapacitor/battery power source for electric vehicular
applications,” Vehicular Technology, IEEE Transactions on, vol. 60,
no. 2, pp. 433–443, 2011.
[8] A. Ravey, B. Blunier, and A. Miraoui, “Control strategies for fuel-cellbased hybrid electric vehicles: From offline to online and experimental
results,” Vehicular Technology, IEEE Transactions on, vol. 61, no. 6,
pp. 2452–2457, 2012.
[9] J. T. Pukrushpan, A. G. Stefanopoulou, and H. Peng, Control of Fuel
Cell Power Systems : Principle, Modeling Analysis and Feedback
Design. Advances in Industrial Control, 2004, iSBN : 1852338164.
[10] A. Ravey, B. Blunier, S. Lukic, and A. Miraoui, “Control strategy of
fuel cell hybrid electric vehicle based on driving cycle recognition,” pp.
1–5, 2012.
[11] C. Higel, F. Harel, D. Candusso, S. Faivre, A. Ravey, D. Guilbert,
A. Ndiaye, D. Bouquain, A. Djerdir, A. Gaillard et al., “Part 1:
Mobypost vehicles powertrain modeling, simulation and sizing,” 2013.
[12] S. Faivre, A. Ravey, D. Guilbert, A. Ndiaye, A. Gaillard, D. Bouquain,
A. Djerdir, C. Higel, F. Harel, D. Candusso et al., “Part 2-mobypost
vehicles powertrain design and experimental validation,” 2013.
[13] S. Lukic, J. Cao, R. Bansal, F. Rodriguez, and A. Emadi, “Energy
storage systems for automotive applications,” Industrial Electronics,
IEEE Transactions on, vol. 55, no. 6, pp. 2258–2267, 2008.
[14] A. Ravey, N. Watrin, B. Blunier, and A. Miraoui, “Energy sources
sizing for hybrid fuel cell vehicles based on statistical description of
driving cycles,” in Vehicle Power and Propulsion Conference, 2010.
VPPC ’2010. IEEE, 2010.
[15] L. Guzzella and A. Sciarretta, Vehicle Propulsion Systems, Introdution
to modeling and optimization, 1st ed. Springer, 2005.
[16] J. Liu and H. Peng, “Modeling and control of a power-split hybrid
vehicle,” Control Systems Technology, IEEE Transactions on, vol. 16,
no. 6, pp. 1242–1251, 2008.
[17] B. Blunier and A. Miraoui, Piles à combustible, Principe, modélisation
et applications avec exercices et problèmes corrigés, ser. Technosup,
Ellipses, Ed., 2007.
[18] A. Jemni, S. B. Nasrallah, and J. Lamloumi, “Experimental and
theoretical study of ametal–hydrogen reactor,” International Journal of
Hydrogen Energy, vol. 24, no. 7, pp. 631–644, 1999.
[19] A. Ravey, B. Blunier, and A. Miraoui, “Control strategies for fuel cell
based hybrid electric vehicles: From offline to online,” in Vehicle Power
and Propulsion Conference (VPPC), 2011 IEEE. IEEE, 2011, pp. 1–4.
[20] M. Hajimiri and F. Salmasi, “A fuzzy energy management strategy
for series hybrid electric vehicle with predictive control and durability
extension of the battery,” pp. 1–5, 2006.
[3] S. Barrett, “Fcvs will be a key element in european vehicle powertrains
portfolio to achieve 2050 goals,” Fuel Cells Bulletin, vol. 2011, no. 1,
pp. 12–15, 2011.
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