Ain Shams Engineering Journal (2014) 5, 75–84
Ain Shams University
Ain Shams Engineering Journal www.elsevier.com/locate/asej www.sciencedirect.com
ELECTRICAL ENGINEERING
Effect of process parameters on the dynamic behavior of polymer electrolyte membrane fuel cells for electric vehicle applications A.A. Abd El Monem a, Ahmed M. Azmy a b c
b,*
, S.A. Mahmoud
c
Khalda Petroleum Company, Road 290, New Maadi, Cairo, Egypt Electrical Power and Machines Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt Electrical Engineering Department, Faculty of Engineering, Menoufiya University, Shebin El-Kom, Egypt
Received 20 July 2012; revised 9 February 2013; accepted 3 May 2013 Available online 13 June 2013
KEYWORDS Dynamic behavior; Electric vehicle; Fuel cells; Parametric study
Abstract This paper presents a dynamic mathematical model for Polymer Electrolyte Membrane ‘‘PEM’’ fuel cell systems to be used for electric vehicle applications. The performance of the fuel cell, depending on the developed model and taking the double layer charging effect into account, is investigated with different process parameters to evaluate their effect on the unit behavior. Thus, it will be easy to develop suitable controllers to regulate the unit operation, which encourages the use of fuel cells especially with electric vehicles applications. The steady-state performance of the fuel cell is verified using a comparison with datasheet data and curves provided by the manufacturer. The results and conclusions introduced in this paper provide a base for further investigation of fuel cells-driven dc motors for electric vehicle. Ó 2013 Production and hosting by Elsevier B.V. on behalf of Ain Shams University.
1. Introduction The increasing demand for enhanced environmental performance is placing greater demands for the development of clean * Corresponding author. Tel.: +20 (0)122 9715040; fax: +20 (0) 4033158603. E-mail addresses:
[email protected] (A.A. Abd El Monem),
[email protected] (A.M. Azmy), engineering@menofia.edu.eg (S.A. Mahmoud). Peer review under responsibility of Ain Shams University.
Production and hosting by Elsevier
energy sources. Furthermore, there are increasing demands to reduce the global warming and ozone depletion processes related to the use of fossil fuel [1]. Moreover, oil reserves are depleting worldwide, while the demand on energy is increasing in large scales [2]. Therefore, it becomes a necessity to replace traditional fuels with new energy sources that depend on nonconventional fuels. Hydrogen is a good candidate to substitute conventional fuels by employing fuel cells to produce electricity from hydrogen with high efficiency and considerably lower environmental impact. A fuel cell system is characterized by low emission of nitrogen oxide and sulfur oxide, high generation efficiency, very low noise, fuel flexibility, and possibility of cogeneration [3]. Fuel cells are devices that utilize electrochemical processes to convert fuel into electrical energy, which can be used for
2090-4479 Ó 2013 Production and hosting by Elsevier B.V. on behalf of Ain Shams University. http://dx.doi.org/10.1016/j.asej.2013.05.001
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A.A. Abd El Monem et al.
Nomenclature Symbol A CB Etheor Elosses Eotheor h ifc K n Pfuel PO2 UfH2 Vlpm(air) Vcell Vconc Vout x% z DG DH CS
description Tafel slope bulk concentration reference potential (V) loss voltage (V) ideal standard potential at 298 K (V) planck constant (J s) fuel cell current (A) Boltzmann constant (J/K) number of electrons participating in the reaction absolute supply pressure of fuel (atm) oxygen partial pressure inside the stack rate of conversion (utilization) of hydrogen (%) air flow rate (l/min) cell voltage (V) concentration voltage (V) output voltage of fuel cell stack (V) percentage of hydrogen in the fuel (%) cell number of moving electrons change in Gibbs free energy (J/mole) enthalpy change surface concentration
vehicles applications, portable-power applications, and stationary power generation [4]. Fuel cells are preferred compared to batteries and conventional heat engines. Fuel cells convert fuel into electrical power without storage devices within its structure, unlike batteries, which store energy. The operation of fuel cells is thus restricted only by the existence of fuel and it is capable of generating power as long as fuel is supplied [5]. On the other hand, battery operation depends on its size and stored energy [5]. Moreover, the absence of intermediate conversion compared to mechanical and combustion processes gives many advantages to fuel cell technology [6]. Fuel cell vehicles are now spreading all over the world in real utilizations not only as research or prototypes models [6]. There are many types of fuel cells that are generally defined according to their electrolyte [7]. Alkaline fuel cell (AFC) has an electrolyte of a liquid solution of potassium hydroxide [8]. AFCs cannot use normal outside air to extract the required oxygen since they are very intolerant of carbon dioxide. In addition, they use a corrosive electrolyte, which erodes its parts and contributes to shorten its operating life [8]. Phosphoric acid fuel cells (PAFCs) utilize a liquid phosphoric acid as an electrolyte. The high operating temperature requires a warmup period and the relatively low current and power densities increase its overall size [8]. Solid oxide fuel cells (SOFC) are high temperature units that use an electrolyte of solid ceramic materials. The high operating temperature causes a high efficiency but speeds up the breakdown of cell components [8]. Molten carbonate fuel cells (MCFCs) operate at high temperature and thus they require large start-up time. The main application of these units is high-rating power generation. Recently, membraneless fuel cells showed many advantages that make them promising units [9]. The elimination of membrane reduces the cost and simplifies the structure. This structure, however, weakens the func-
E ideal fuel cell potential (V) Eotheor:actual actual theoretical voltage (V) En nernst voltage (V) F faraday constant (coulombs/mole) io exchange current (A) Ilimit limitation current (A) Kr modeling constant Pair absolute supply pressure of air (atm) PH2 partial pressure of hydrogen inside the stack R gas constant (J/mol K) UfO2 rate of conversion (utilization) of oxygen (%) Vlpm(fuel) fuel flow rate (l/min) Vact activation voltage (V) Vohm ohmic voltage (V) Wel maximum electrical work of fuel cell (J) y% percentage of oxygen in the air (%) a charge transfer coefficient DG0 gibbs free energy change of reaction at standard conditions DS entropy change
tion of separating the two streams [9]. It is also not ensured that the unwanted mixing with impact of diffusional or convectional interfacial transport is prevented [9]. The polymer electrolyte membrane fuel cell ‘‘proton exchange membrane’’ (PEMFC) employs a solid polymer as the electrolyte. The main advantages of the PEMFC are its high power density, long life, lower corrosion, and lower operating temperature in addition to the use of a solid electrolyte. Thus, PEMFC has a quick start and compact size, which are very important for vehicle applications [1,2,7,9,10]. In addition, PEMFCs operate at low temperatures (50–100 °C), which allows for fast start-up. This characteristics make PEMFCs a strong candidate in transportation activities that require rapid start-up and fast dynamic response over transient times (start, stop, acceleration and deceleration) [3,4]. For real utilization of fuel cells for vehicle applications, many features should be considered, such as performance, reliability, durability, cost, and fuel availability. Since FC systems are large, complex and expensive, it becomes a must to have accurate models prior going through the process of design and building new prototypes. Also, system behavior has to be analyzed at the design stage under different operating conditions to ensure its suitability for the application. In addition, the effect of different operating parameters on the performance has to be investigated. Several mathematical models of PEM fuel cells have been presented [2,10,11–13]. The majority of them succeeded to simulate the steady-state behavior [10,11], while in practical applications, the fuel cell output power undergoes large variations especially during acceleration and deceleration. During such processes, simple and steady-state models will not be enough to represent the transient dynamics and therefore the analysis under dynamic conditions cannot be carried out. Some dynamic models [2,12] are characterized by their high complexity
Effect of process parameters on the dynamic behavior of polymer electrolyte membrane fuel cells for electric with several partial differential equations to be considered. Dynamic models reported in [12,13] did not take the double layer charging effect into account. The regions, where mass transfer limitations occur, have not been considered in the dynamic models presented in [13]. The effect of varying the process variables did not included in the investigations reported in [11–13]. This paper introduces a study of the effect of varying process variables on the performance of the fuel cell. A simplified and accurate mathematical model of PEM fuel cell system is used to accomplish this study. Both the double layer charging effect and the thermodynamic characteristic inside the fuel cell are included in the model. The model is implemented using MATLAB/SIMULINK package. The simulation results are validated by comparing them with datasheet data and curves provided by the manufacturer [14]. This model will be useful for the optimal design and real-time control of PEM fuel cell systems. 2. Dynamic modeling of PEMFCS A simplified dynamic model of PEMFC, based on physical– chemical knowledge of the phenomena occurring inside the unit, is presented in this section. To simplify the analysis, the following assumptions are made [15]. (1) One-dimensional treatment is supposed, i.e., all quantities vary only in the direction orthogonal to anode and cathode surfaces. (2) The gases are ideal and uniformly distributed. (3) The pressures in the fuel cell gas flow channels are constant. (4) Humidified hydrogen and air are used as fuel and oxidant, respectively. (5) The operating stack temperature is 100 °C, and the reaction product at the cathode is in a liquid phase. (6) Thermodynamic properties are evaluated at an average stack temperature of 100 °C, temperature variations across the stack are neglected, and the overall specific heat capacity of the stack is assumed to be constant (7) Parameters for individual cells can be lumped together to represent the stack.
where ‘‘DH’’ is the enthalpy change and ‘‘DS’’ is the entropy change. ‘‘DH’’ represents the total thermal energy, while the enthalpy change minus the quantity ‘‘TDS’’ represents the available free energy. ‘‘TDS’’ is the thermodynamic irreversible loss, with ‘‘T’’ is the operating temperature in Kelvin. The electrochemical oxidation and reduction reactions in a hydrogen/oxygen (H2/O2) fuel cell are given by following reactions: Anode reaction : H2 ! 2Hþ þ 2e
ð3Þ
1 Cathode reaction : O2 þ 2Hþ þ 2e ! H2 O 2
ð4Þ
1 The overall reaction : H2 þ O2 ! H2 O 2
ð5Þ
For every mole of hydrogen consumption, the cell consumes 0.5 mol of oxygen and produces one mole of water. In addition, two moles of electrons are produced. The Gibbs free energy change of reaction is given by the following equation [16,13]. DG ¼ DG0 RT lnðPH2 ÞðPO2 Þ1=2
ð6Þ
0
where ‘‘DG ’’ is the Gibbs free energy change of reaction under standard conditions (pressure = 1 atm and temperature = 298 K), ‘‘R’’ is the gas constant, 8.3145 J/(mol K), ‘‘PH2’’ is the partial pressure of hydrogen inside the stack, and ‘‘PO2’’ is the partial pressure of oxygen inside the stack. The theoretical potential ‘‘E0theor ’’ for the H2 =O2 cell reaction is derived from the change in the Gibbs free energy (DG0) as follows [6]. 0 DG 0 ð7Þ Etheor ¼ zF where ‘‘z’’ is the cell number of moving electrons, z = 2. From Eqs. (1), (6), and (7), the general form of the Nernst equation is obtained as follows [8,13]. En ¼ Etheor ¼ E0theor:actual þ
RT lnðPH2 ÞðPO2 Þ1=2 zF
ð8Þ
where ‘‘E0theor:actual ’’ is a function of temperature and can be expressed as: E0theor:actual ¼ E0theor KE ðT 298Þ
For understanding the operation of fuel cells, the ideal performance is firstly defined. Then, losses arising from non-ideal behavior can be estimated and then deducted from the ideal performance to investigate the actual operation.
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where ‘‘KE’’ is the empirical constant for calculating (volt per Kelvin)
ð9Þ E0theor:actual
2.2. Ideal performance
2.1. The gibbs free energy and nernst potential When the fuel cell operates at constant temperature and pressure, the maximum electrical work (Wel) that can be obtained is given by the change in Gibbs free energy (DG) of the electrochemical reaction [16,12,13]. Wel ¼ DG ¼ nFEtheor
ð1Þ
where ‘‘n’’ is the number of electrons participating in the reaction, ‘‘F’’ is the Faraday constant (96,485 coulombs per mol), and ‘‘Etheor’’ is the reference potential (in volts). The Gibbs free energy change is calculated as follows [6]. DG ¼ DH TDS
ð2Þ
In Eq. (8), Etheor gives the ideal open-circuit cell potential. This potential defines the maximum performance that can be obtained from a fuel cell. The Nernst equation is used to relate the ideal standard potential (Eotheor ) and the ideal equilibrium potential (Etheor) at other partial pressures of reactants. Generally, the cell potential increases with the increase in partial pressure (concentration) of reactants. Thus, the ideal potential can be increased by increasing the reactant pressures at a given temperature and hence higher pressures cause improvements in fuel cell performance [17]. The ideal potential (Eotheor ) at 298 K and with pure hydrogen and oxygen is 1.229 V with liquid water product and 1.18 V with gaseous water product. This value is sometimes referred
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Figure 1 fuel cell.
A.A. Abd El Monem et al.
Ideal and actual voltage/current characteristic of PEM
to as the oxidation potential of hydrogen [18]. The difference between the 2 V, i.e., 1.229 V and 1.18 V, is attributed to the Gibbs free energy change of water vaporization under standard conditions [6]. The reactant concentrations affect open-circuit voltage of a fuel cell, where maximum ideal potential takes place when the reactants at the anode and cathode are pure. For fuel cells that utilize air and/or impure dry hydrogen, the potential will be decreased. In addition, the decrease in reactants concentration at the exit of the cell compared to that at the entrance causes a Nernst correction that reduces the open-circuit voltage. To obtain the best performance for low-temperature fuel cells, such as PEMFC, a noble metal electro-catalyst, such as platinum (Pt), has to be used [6,17]. 2.3. Actual performance 2.3.1. Polarization characteristics of PEMFC The electrochemical process within fuel cells is associated with many losses as shown in Fig. 1 [19]. The causes of losses are activation polarization, ohmic polarization, and concentration
Figure 2
Figure 3 The block diagram of MATLAB/SIMULINK model of PEMFCs.
polarization. Due to these losses, the cell voltage (V) is less than the ideal value ‘‘E’’ as follows: V ¼ E Elosses
ð10Þ
The activation polarization loss is more significant, compared to other losses, at low currents, where there is a need to overcome the electronic barriers before current and ion flow. Ohmic polarization loss, on the other hand, is almost proportional to current and thus it increases with the current since cell resistance is almost constant. Finally, the gas transport losses take place commonly at high limiting currents. The reason is the difficulty of providing sufficient reactants flow to the cell reaction sites. However, this kind of losses occurs also over the entire range of current, but with lower effect [19]. 2.3.1.1. Activation loss. The main reason of activation loss is the slowness of the reactions at the electrode surfaces. To force
The general diagram model of PEMFCs.
Effect of process parameters on the dynamic behavior of polymer electrolyte membrane fuel cells for electric Table 1
79
Parameters of the fuel cell.
Type
Net rated electrical peak power
7 kW (DC)
Performance
Net rated electrical nominal power Output voltage Operating current range Efficiency – LHV Time from 10% to full power Expected life Operational ambient temperature
5 kW (AC) 32–60 V (DC) 0–225 A (DC) 55% (stack) / 50% (system) Approx. 10 s 20.000 h (stack) Up to 40 °C
Fuel
Supply pressure Stack operating pressure Maximum Consumption
0.5–5 bar Ambient 12.5 slpm/kW
Air delivery system
Flow rate Supply pressure
Max. 500 l/min Ambient
Cooling system requirements
Maximum ambient temperature Cooling method
45 °C Radiator fan
Voltage (volt)
70
Data sheet Model
60
50
Vact ¼ A ln ifc
ð11Þ
RT aF Z
ð12Þ
A¼
40
30
rived. Thus, the activation voltage loss for the fuel cell can be obtained as follows [8].
0
50
100
150
200
250
Current (Amp)
Figure 4 Polarization curves of the fuel cell according to the proposed model and the datasheet.
the electrons to the electrodes through the chemical reaction, a portion of the potential is lost. The Tafel equation is commonly used to account for these losses [16]. Using this equation, a relation between the overvoltage at the electrode surface and the natural logarithm of current density can be de-
where ‘‘ifc’’ is the fuel cell current in Ampere, ‘‘A’’ is the Tafel slope and ‘‘a’’ is the charge transfer coefficient, which depends on the type of electrodes and catalysts used. If it is required to investigate the performance at low currents, the Bulter–Volmer equation can be used instead of the Tafel equation since it is more accurate, while the Tafel equation can be invalid for very low currents. 2.3.1.2. Ohmic loss. The ohmic resistance of a PEM fuel cell consists of three terms: the resistance of polymer membrane, the contact resistance between the membrane and electrodes, and that of electrodes. The total ohmic voltage drop can be expressed as follows [8]. Vohm ¼ Vohm;a þ Vohm;membrane þ Vohm;c ¼ Ifc Rohm
ð13Þ
2.3.1.3. Concentration loss. As a result of mass diffusions from the flow channels to the reaction sites, concentration gradients can be formed during the reaction process. The reason of concentration voltage drop at high current densities is the slow transportation of reactants to the reaction sites. The concentration over potential can be written as [8]. Vconc ¼
RT CS ln zF CB
ð14Þ
where ‘‘CS’’ and ‘‘CB’’ are the surface and bulk concentrations, respectively. The above equation can be rewritten as [8]. RT Ifc ln 1 ð15Þ Vconc ¼ zF Ilimit Figure 5 Power–current curves according to the model and datasheet.
where ‘‘Ilim-it’’ is the limitation current (A). Finally, the equivalent concentration resistance is:
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A.A. Abd El Monem et al. certain time and hence the voltage will not immediately follow the current changes unlike the ohmic voltage drop. This results in an instant voltage change after any current change owing to the internal resistance and then the voltage changes gradually to its final value. Considering the effect of the double layer while building the PEMFC dynamic model will give the model more accuracy when describing the dynamic performance. Thus, it is quite reasonable to use a capacitor to model the capacitance effect resulting from the charge double layer [15].
Figure 6
Effect of temperature change on fuel cell performance.
2.3.2.2. Flow rate and thermodynamics characteristics. There are delays between the change in the load current and flow rates of fuel and air, which is represented in this model by using an inductance [19,20]. The series inductor is inserted to take into account the time constant associated with the current. When load varies while the input fuel is maintained constant, the current will not change immediately; rather, it will take a certain time delay [19,20]. Without this inductor, the current from the model will change instantaneously. Another delay is used to represent the thermodynamic time constant inside the fuel cell. Thus, the fuel and oxidant flow delays, the thermodynamic characteristics, and the double layer effects will dominate the transient responses of the fuel cell model. The time constants in the model are defined according to the actual delay action recorded by the datasheet curves. More details about the dynamic model of the PEMFC are given in [21]. 2.3.3. Cell voltage The open-circuit voltage can be calculated as follows [22,23].
Figure 7
Effect of fuel pressure on fuel cell performance.
Eoc ¼ NEn
ð17Þ
where ‘‘Eoc’’ is actually the open-circuit voltage of the fuel cell. However, under normal operating conditions, the fuel cell output voltage is less than Eoc. Taking into consideration the activation loss, ohmic resistance voltage drop, and concentration over potential, the cell voltage is given as [23]. Vcell ¼ EocðcellÞ VactðcellÞ VohmðcellÞ VconcðcellÞ
ð18Þ
To represent a fuel cell stack, the parameters of individual cell are lumped and the output voltage of the fuel cell is obtained as [8,16]. Vout ¼ Ncell Vcell ¼ Eoc Vact Vohm Vconc
ð19Þ
2.3.4. Reactant utilization
Figure 8
Rconc
Effect of fuel flow rate on fuel cell performance.
Vconc RT Ifc ¼ ¼ ln 1 zFIfc Ifc Ilimit
Reactant utilization has a major impact on fuel cell performance. A utilization factor ‘‘Uf’’ is defined to represent the ratio of the amount of hydrogen that reacts with the oxygen to the amount of hydrogen entering the anode. The rate of conversion (utilization) of hydrogen ‘‘UfH2’’ and oxygen ‘‘UfO2’’ are determined as follows [24].
ð16Þ
2.3.2. Dynamics of PEMFC 2.3.2.1. Double layer charging effect. Any collection of charges, e.g., hydrogen ions (in the electrolyte) and electrons (in the electrodes) will generate an electrical voltage. When this layer of charges is formed at the surface of electrode and electrolyte, it will represent a store of electrical charges similar to a capacitor. With the current changes, the charge will change during a
UfH2 ¼
Kr RTNifc ; zFPfuel VlpmðfuelÞ x%
0 6 UfH2 6 1;
ð20Þ
UfO2 ¼
Kr RTNifc ; 2z FPair VlpmðairÞ y%
0 6 UfO2 6 1;
ð21Þ
where ‘‘Kr’’ is the modeling constant, ‘‘N’’ is the number of cells in the stack, ‘‘Pfuel’’ is the absolute supply pressure of fuel (atm), ‘‘Vlpm(fuel)’’ is the fuel flow rate (l/min), ‘‘x%’’ is the percentage of hydrogen in the fuel (%), ‘‘Pair ’’ is the absolute sup-
Effect of process parameters on the dynamic behavior of polymer electrolyte membrane fuel cells for electric
81
where ‘‘K’’ is the Boltzmann constant, 1:38 1023 J/K and ‘‘h’’ is the Planck constant, 6:626 1034 J s The complete model of the fuel cell is built based on the abovementioned equations as shown in Fig. 2. The figure indicates the logical connections of the different blocks and shows the different inputs and outputs. The model is simulated using Matlab/Simulink using the available data sheet of NedStack PS6 fuel cell, 6 kW [14]. For electric vehicle applications, the fuel cell model should be rescaled to be suitable for the electric vehicle power demand. Fig. 3 shows the block diagram of Matlab/Simulink model, which has been developed for the PEMFC and Table 1 summarizes the data and operating parameters of the simulated fuel cell. 3. Model validation Figure 9
Effect of air pressure on fuel cell performance.
3.1. Comparing simulation results and datasheet data and curves
PH2 ¼ PFuel x%ð1 UfH2 Þ
ð22Þ
The results for base case operating conditions were verified by comparing them with datasheet data and curves provided by the manufacturer [14]. The comparison is given in Fig. 4. The polarization curve of the cell potential versus cell current for the model is in a good agreement with the datasheet polarization curve in the middle load region. However, there are minor differences between the model and datasheet data and curves in low load region and mass transport limitation region. The difference in the low load region or the activation region is due to the estimation of some parameters affecting the activation regions, which are not available in the datasheet. For the difference in the mass transport limitation region, the curve obtained from the model is shifted upward compared to datasheet data and curves. This difference is acceptable taking into account neglecting some physical process such as water flooding at the cathode and anode drying. Secondly, the model and datasheet power curves are compared, which is shown in Fig. 5. Similar to the polarization curve, the model power curve is very close to the datasheet curve in the load region below the mass transport region. At high loading condition, the predicted values are slightly higher than the datasheet values for the same abovementioned reasons. From these comparisons, it is clear that the developed model has a high accuracy for steady-state behavior and can be used to simulate the performance of the fuel cell unit in this mode.
PO2 ¼ PAir y%ð1 UfO2 Þ
ð23Þ
3.2. Parametric study
Figure 10
Effect of air flow rate on fuel cell performance.
ply pressure of air (atm), ‘‘ Vlpm(air) ’’ is the air flow rate (l/min), and ‘‘y%’’ is the percentage oxygen in the air (%). The partial pressures PH2 and PO2 are determined as follows [24].
The exchange current is given as follows [25,26]. zFkðPH2 þ PO2 Þ DG io ¼ eRT Rh
Table 2
ð24Þ
The proposed model can be used for studying the effects of different operating parameters on fuel cell performance. The performance characteristics of the fuel cell based on a certain
Effect of different operating parameters on fuel cell performance.
Operating parameter
Change
Change in the polarization curve
Normalized percentage change in the polarization curve (for 10% parameter change)
Temperature Fuel pressure Fuel flow rate Air pressure Air flow rate
332–342 K 1.4–3 bar 100–400 lpm 0.6–1.2 bar 300–700 lpm
0.61% results in 1.4% 18.2% results in 0.5% 33.3% results in 0.61% 19% results in 0.11% 23% results in 0.07%
11.71 0.275 0.183 0.058 0.0304
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A.A. Abd El Monem et al.
Figure 12
Figure 11
Effect of process parameters on the output voltage.
parameter can be obtained by varying that parameter, while all other parameters are kept constant. Results obtained from these studies will allow identifying the critical parameters for fuel cell performance as well as the sensitivity of the model to these parameters. The fuel cell performance at various operating conditions is studied according to the polarization and voltage curves. Operating parameters are set during the operation of the fuel cell to give the desired output for a given application. The most important operating parameters are the following: temperature, pressure, and flow rates. The effects of these parameters on fuel cell performance are discussed in the following sections. 3.2.1. Effect of process parameters on the polarization characteristics 3.2.1.1. Effect of temperature. Temperature variations affect all transport phenomena and electrochemical kinetics inside the fuel cell. In this study, the temperature is varied from 332 K to 342 K, where the polarization curve is shown in Fig. 6. The polarization curves of the cell at different operating temperatures show that the fuel cell performance is improved with increasing temperature. This is in agreement with experimental parametric study, which indicated that the polarization curves of the fuel cell at different operating temperatures showed improved performance with increasing temperatures [27]. Generally, the performance is better in all regions along the polarization curve. From the results, a 0.6% increase in the temperature results in 1.4% average increase in the voltage at the same current and a 1.18% decrease in the temperature results in 0.8% average decrease in the voltage at the same current. This represents a significant effect on the performance. Therefore, the operating temperature is an important design factor that affects the performance of fuel cells. 3.2.1.2. Effect of fuel pressure. Fuel pressure is another operating parameter that can affect fuel cell performance. To evaluate this effect, fuel pressure is varied from 1.4 bar to 3 bar and the fuel cell performance is studied as shown in Fig. 7. Generally, the increase in the pressure increases the cell potential at the same current, i.e., shifts the curve up. From the results, an 18.2% increase in the fuel pressure results in 0.5% average increase in the voltage at the same current. The performance
Effect of double layer charging.
gain is lower in the region from 2.2 bar to 3 bar (0.375%) compared with the region from 1.4 bar to 2.2 bar (0.66%). It is notable that the direct effect of pressure is not similar to the temperature since it results in a minor change in the performance. 3.2.1.3. Effect of fuel flow rate. Fuel flow rate can also affect the fuel cell performance. The effect of changing the fuel flow rate from 100 lpm to 400 lpm is shown in Fig. 8, where the fuel cell performance is slightly changed. From the results, a 33.3% increase in the fuel flow rate results in 0.61% average increase in the voltage at the same current. 3.2.1.4. Effect of air pressure. Air pressure affects the performance of the fuel cell, but the influence is insignificant. However, it is important to define the degree to which it can affect the cell operation. The performance of the fuel cell is studied under different air pressures, and some results are illustrated in Fig. 9. The air pressure is varied from 0.6 bar to 1.2 bar, which shifts the performance curve of the fuel cell upward. From the results, a 19% increase in the air pressure results in 0.11% average increase in the voltage at the same current. 3.2.1.5. Effect of air flow rate. The performance of the fuel cell is also affected by air flow rate. To study this effect, air flow rate is varied from 300 lpm to 700 lpm and the fuel cell performance is investigated as shown in Fig. 10. From the results, a 23.5% increase in the air flow rate results in 0.07% average increase in the voltage at the same current, which represents a minor influence. The effect of different operating parameters on the fuel cell performance is summarized in Table 2. 3.2.2. Effect of process parameters on the PEMFC output voltage To study the effect of different process parameters on the output voltage of PEMFC, a step change is applied for each of the process parameters (temperature, fuel pressure, fuel flow rate, air pressure, and air flow rate). The value of each step change is 10% of the initial value of each process parameter. Only a step change of 1% is applied for temperature because of its large influence on the voltage. Fig. 11 shows the fuel cell output voltage after applying sequential step changes in process parameters. Varying these parameters requires certain time to appear in the output voltage as a result of both double layer capacitance and the thermodynamic time constant inside the
Effect of process parameters on the dynamic behavior of polymer electrolyte membrane fuel cells for electric
83
performance had been studied, where it is found that the temperature has a great effect on the fuel cell performance. In addition, fuel pressure and fuel flow rate can affect the performance but with lower degrees for the direct action. Air pressure and air flow rate have insignificant effect and cannot be used to control the unit operation. This study enables the prediction of PEMFC dynamic behavior under different operating conditions, which is considered as a foundation for optimization and control development. References
Figure 13 controller.
PEMFC output voltage with and without PI
fuel cell. The curve confirms the results shown in Table 2 regarding the effect weight of different process parameters. To evaluate the effect of the double layer charging, Fig. 12 shows a comparison between the fuel cell model with and without double layer charging effect. Two sequential disturbances are applied, where the first is a step load increase after 6 s from 1950 W to 2250 W and the second is a step load decrease after 26 s from 2250 W to 1800 W. It is clear that the voltage with double layer charging effect has more delay compared to the model without double layer charging effect. This delay has to be considered when dealing with the vehicle system. 4. Closed loop operation of PEMFC From the previous results, it is clear that the fuel pressure and fuel flow rate are the most effective process parameters after the temperature. In this model, the temperature will be kept constant since it affects the life time of the unit. To design a PI voltage controller, the two most effective process parameters (fuel pressure and fuel flow rate) are to be regulated. Since the study aims at studying the effect of process parameters on the dynamic behavior, it is important to study the effect of varying some parameters on the performance. At this stage, the controller is built to evaluate the effect of varying a certain parameter on the output of the fuel cell since the model is not completed, where the dc motor and the vehicle system are not connected yet. Fig. 13 shows the output voltage without controller and with PI controller. The PEMFC starts with 4000 W, and at time 7.5 s, an additional load of 1000 W is added, which caused an instantaneous voltage decrease. The load is modeled as a constant power load using the three-phase load block that can be set by defining the required power in the SIMULINK. The reference voltage of the PI controller is 51.5 V, which is the nominal output voltage. The controller succeeded to maintain the voltage at the desired value with acceptable time delay and overshoots. 5. Conclusion This paper describes a dynamic model of PEMFC, which can be used in different dynamic studies especially in vehicle systems. The effects of different operating parameters on fuel cell
[1] Hwang JJ, Chen YJ, Kuo JK. The study on the power management system in a fuel cell hybrid vehicle. Int J Hydrogen Energy 2012;37(5):4476–89. [2] Pollet BG, Staffell I, Shang JL. Current status of hybrid, battery and fuel cell electric vehicles: from electrochemistry to market prospects. Electrochim Acta 2012;84(1):235–49. [3] El-Sharkh MY, Rahman A, Alam MS, Byrne PC, Sakla AA, Thomas T. A dynamic model for a stand-alone PEM fuel cell power plant for residential applications. J Power Sources 2004;138:199–204. [4] Doddathimmaiah A, Andrews J. Theory, modelling and performance measurement of unitised regenerative fuel cells. Int J Hydrogen Energy 2009;34(19):8157–70. [5] Garcia P, Fernandez LM, Garcia CA, Jurado F. Energy management system of fuel-cell-battery hybrid tramway. Ind Electr, IEEE Trans 2010;57(12):4013–23. [6] Mikkola M. Experimental studies on polymer electrolyte membrane fuel cell stacks. Helsinki Univ Technol 2001. [7] Henao N, Kelouwani S, Agbossou K, Dube´ Y. Proton exchange membrane fuel cells cold startup global strategy for fuel cell plugin hybrid electric vehicle. J Power Sources 2012;220:31–41. [8] Andres Gabriel Christou, Hydrogen fuel cell power system performance of plug power gen core 5B48 Unit, thesis of Master of science, University of Strathclyde, Engineering; 2010. [9] Xuan J, Leung MKH, Leung DYC, Wang H. Towards orientation-independent performance of membraneless microfluidic fuel cell: understanding the gravity effects. Appl Energy 2012;90(1):80–6. [10] Fisher P, Jostins J, Hilmansen S, Kendall K. Electronic integration of fuel cell and battery system in novel hybrid vehicle. J Power Sources 2012;220(15):114–21. [11] Panos C, Kouramas KI, Georgiadis MC, Pistikopoulos EN. Modelling and explicit model predictive control for PEM fuel cell systems. Chem Eng Sci 2012;67(1):15–25. [12] M Sharifi S, Rowshan Zamir S, Eikani MH. Modelling and simulation of the steady-state and dynamic behaviour of a PEM fuel cell. Energy 2010;35(4):1633–46. [13] Pasricha S, Shaw SR. A dynamic PEM fuel cell model. IEEE Trans Energy Convers 2006;21(2). [14] NedStack PS6 Product Data, Ned Stack fuel cell technology; 2011. . [15] Wang C, Nehrir MH. Dynamic models and model validation for PEM fuel cells using electrical circuits. Energy Convers IEEE Trans 2005;20(2):442–51. [16] Quan Peng. A study of PEM fuel cell modelling. Thesis of Master of Engineering. Ryerson University; 2003. [17] Wasburn WE. International critical tables of numerical data, physics, chemistry and technology. 1st Electronic Edition. Knovel: Norwich, NY; 2003. [18] Spiegel C. PEM fuel cell modelling and simulation using MATLAB. Oxford UK: Elsevier Academic Press; 2008. [19] Azmy Ahmed M, Erlich I. Impact of distributed generation on the stability of electrical power systems. In the 2005 IEEE
84
[20]
[21]
[22]
[23]
[24] [25]
[26]
[27]
A.A. Abd El Monem et al. PES General Meeting, San Francisco, California; 12–16 June 2005. Azmy Ahmed M, Erlich I. Dynamic simulation of fuel cells and micro-turbines integrated with a multi-machine network. In: Power tech conference proceedings, 2003 IEEE Bologna, vol. 2. Italy; June 23–26, 2003. p. 550–5. Abd El Monem AA, Azmy Ahmed M. Mahmoud SA. Dynamic modelling of proton exchange membrane fuel cells for electric vehicle applications engineering research journal (ERJ), vol. 35(3). Minoufiya University, Faculty of Eng., Shebin El-Kom, Egypt; July 2012. p. 205–14. Wei L, Xin-jian Z, Guang-yi C. Modeling and control of a small solar fuel cell hybrid energy system. J Zhejiang Univ Sci A 2007;8(5):734–40. Choe SY, Ahn JW, Lee JG, Baek SH. Dynamic simulator for a PEM Fuel cell system with a PWM DC/DC converter. IEEE Trans Energy Convers 2008;23(2). ; 2010. Yuan Wei, Tang Yong, Pan Minqiang, Li Zongtao, Tang Biao. Model prediction of effects of operating parameters on proton exchange membrane fuel cell performance. Renew Energy 2010;35(3):656–66. Mann RF, Amphlett JC, Peppley BA, Thurgood CP. Henry’s Law and the solubilities of reactant gases in the modelling of PEM fuel cells. J Power Sources 2006;161:768–74. Nguyen PT. A three dimensional computational model of pem fuel cell with serpentine gas channels. University of Western Ontario 2001.
A. A. Abd El Monem was born in El-Gharbia, Egypt. He received the B.Sc. degree in electrical engineering with honor from the Tanta University, Egypt in 2005. He is now pursuing M. Sc. in ‘‘Study of Dynamic Behaviour of Fuel Cells-Driven DC Motors for Electric Vehicle’’ with 1 journal publication and 1 conference accepted paper related to his topic in publication. Currently, he is an electric senior engineer at Khalda Petroleum Com-
pany, Egypt. His research interests include renewable energy sources and power electronics.
Ahmed M. Azmy was born in El-Menoufya, Egypt. He received the B.Sc. and M.Sc. degrees in electrical engineering from the ElMenoufya University, Egypt in 1991 and 1996, respectively. He received the Ph.D. degrees in electrical engineering from the university Duisburg-Essen, Germany in 2005. He is the head of department of Electrical Power and Machines Engineering- Tanta University, director of the automated library project in Tanta University- director of the quality assurance unit and executive director of the continuous improvement and qualification for accreditation program. His research topics are directed to the intelligent techniques, dynamic simulation, smart grids, distributed generating units and renewable energy resources. Dr. Azmy has been awarded Tanta University Incentive Awards (2010) and Prizes for international publishing in 2010, 2012 and 2013.
Sabry Abd EL-Latif Mahmoud was born in Dakahlia, Egypt in 1945. He received his B.Sc. degree in Electrical Engineering from ELMansoura University in 1969. He awarded the D.E.A.Degree and the Ph.D. Degree in Power Electronics from I.N.P.L., Nancy, France, in 1977 and 1979 respectively. He promoted to associate professor in June 1983 and to full professor in Nov 1987 at the same Department. He was the Dean of the faculty of Engineering, Menoufiya University, from Apr.2001 to Jan. 2003. He was the Vice President of the Menoufiya University for Community and Environmental Development, From Jan. 2003 to July 2005. His current interests are power electronics, control of electrical machines and analysis of machine dynamics.
