Fuzzy Sliding Mode Control with Constant Power Control for a Proton Exchange Membrane Fuel Cell

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1 Reearch Journal of Applied Science, Engineering and Technology 5(3): , 013 ISSN: ; E-ISSN: Maxwell Scientific Organization, 013 Submitted: June 9, 01 Accepted: July 4, 01 Publihed: January 1, 013 Fuzzy Sliding Mode Control with Contant Power Control for a Proton Exchange Membrane Fuel Cell Minxiu Yan and Liping Fan College of Information, Shenyang Univerity of Chemical Technology, Shenyang 11014, China Abtract: Proton exchange membrane fuel cell have been cauing attention becaue of their many advantage. In order to maintain good working condition good controller i required. In thi tudy, the mathematical model for proton exchange membrane fuel cell i developed. Then combining the characteritic of fuzzy control and liding mode control fuzzy liding mode controller i deigned. The controller can realize contant power output and improve tability by fuzzy reaoning to control the amount of output variation that effectively reduce chattering. Simulation reult how that the propoed controller can obtain better control effect compared with fuzzy controller. Keyword: Contant power, fuel cell, fuzzy control, liding mode control INTRODUCTION Fuel cell i a device capable of converting chemical energy from a fuel into electrical energy through a chemical reaction with oxygen or another oxidizing agent. In recent year, a one of the mot popular kind of fuel cell the Proton Exchange Membrane Fuel Cell (PEMFC) ha many application uch a emergency power upply and mall mobile power upply for outdoor power upply and high reliable and high table power upply (Logan et al., 00; Hatziadoniu et al., 00; Padulle et al., 000). Unlike conventional fuel cell, PEMFC ha many advantage including low temperature, low electrolyte corroion, high efficiency (Pukruhpan et al., 004). The PEMFC need to be controlled rapidly and efficiently in correct operating condition. One of the ignificant challenge in control algorithm i that many parameter uch a operating temperature, preure and flow rate of fuel and oxidant gae and o on affect the performance of PEMFC (Riaco and Pereira, 010). Many reearch tudie uch a many linear controller have been carried out on proton exchange membrane fuel cell technology. However, the complex and nonlinear dynamic of PEMFC make it hard to maintaining a fuel cell ytem in correct operating condition when ubjected to fat load change (Kaytakoglu and Akyalm, 007). ariable tructure control with liding mode a one of the effective nonlinear robut control approache wa firt introduced in the It can yield a cloed loop ytem with an invariance property to uncertaintie when the ytem tate are on the liding mode (Edward and Spurgeon, 1998; Kaynak et al., 001). Sliding mode controller ha been ued in wide range area. However, everal diadvantage exit for pure liding mode controller. One i chattering problem which caued the high frequency ocillation in the controller output. Another i that controller deign depend on the dynamic equation. Fuzzy logic can expre the amount of ambiguity in human thinking and poe everal advantage uch a robutne, model-free, univeral approximation theorem (Pouramad and Montazeri, 007; Nhivekar et al., 011; Corcau and Stoenecu, 007; Horiuchi and Kihimoto, 00). In the pat everal decade, fuzzy control ha been ued in many application. However, the huge amount of fuzzy rule for a high-order ytem make the analyi complex. At the ame time, the fuzzy controller parameter mut go through repeated attempt to determine and be the lack of the tability analyi. In many cae contant power ource are needed. Therefore keeping a PEMFC output a contant power during work proce hould be ometime neceary. The mathematical model for a typical proton exchange membrane fuel cell i decribed in thi tudy and a fuzzy liding mode controller for PEMFC i deigned to maintain a contant power of PEMFC under load diturbance. MODEL OF PEM FUEL CELL PEM fuel cell tranform chemical energy on the anode ide into electric and thermal energy on the cathode ide. Figure 1 how the baic tructure of a ingle cell. In PEM fuel cell the following chemical reaction exit: Correponding Author: Minxiu Yan College of Information, Shenyang Univerity of Chemical Technology, Shenyang 11014, China 1059

2 H +1/O H O+heat+electrical energy (1) In thi electrochemical proce hydrogen molecule are carried by flow plate channel on the anode ide. Anode catalyt divide hydrogen on proton H + and electron e -. At the cathode hydrogen proton H + and electron e - combine with oxygen to form water and heat. The emi reaction on both electrode can be expreed by the following equation (Carne et al., 005; Moreira and Silva, 009; Rezazadeh et al., 010; Youef et al., 010): + - H H +e anode () 1 O + - +H +e H O Cathode (3) Modeling of fuel cell i needed a powerful fuel cell tack are getting available and have to be integrated into power ytem. An adequate model can etimate overall performance of a fuel cell according to operating condition. According to Mammar and Chaker (009), the output voltage of a ingle cell can be defined a the following expreion: Here R c (Ω) i the reitance to electron flow and R m (Ω) i the reitance to proton tranfer through the membrane. con i the voltage drop due to the ma tranport which can be decribed by the following expreion: con J = Bln(1 ) J max (9) where, B() i a parametric coefficient depending on the cell, J(A/cm ) i the actual current denity of the cell (Rezazadeh et al., 011). The output power of the ingle fuel cell can be written a: PFC = FCI (10) tack Fuel cell dynamic model can be et up baed on the above decribed mathematical model (Fan, 01; Correa et al., 004). An accepted dynamic model of the PEM fuel cell i hown in Fig.. q O i the input molar flow of hydrogen, q H i the input molar flow of oxygen, K H i the hydrogen valve molar contant and K O i oxygen valve molar contant. FC = ENernt act ohmic con (4) H/HO Anode Membrane Cathode Air/HO where, E Nernt i the thermodynamic potential of the cell and repreent it reverible voltage: E Nernt ( T 98.15) = T[ln( ph) + ln( po)] (5) In which p H and p O are the partial preure of hydrogen and oxygen, repectively and variable T denote the operating temperature. act i the voltage drop due to the activation of the anode and cathode: = T T c act ln( O) Tln( Itack ) (6) H/HO Fig.1: A typical PEM fuel cell Active Layer Air/HO where, l tack (A) i the electrical current and c O (mol/cm) repreent the oxygen concentration in the catalytic interface of the cathode, determined by: c = p O O 6 498/ T e (7) ohmic i the ohmic voltage drop reult from the conduction of proton through the olid electrolyte and electron through the internal electronic reitance: = I ( R + R ) (8) ohmic tack m c q O i q H K r K r nernt Fig. : PEMFC dynamic model P H = E 1/ 1+τ K H 1 P O act 1/ 1+τ K O ohmic con P 1060

3 P du dt c Sliding urface du dt Fuzzy controller u PEMFC P Fig. 3: The cloed-loop fuzzy control ytem DESIGN OF FUZZY SLIDING MODE CONTROLLE In order to overcome the parameter uncertainty and external diturbance, a fuzzy liding mode control i preented. The algorithm adjut the control of variable ize by fuzzy control rule baed on the previou experience. The fuzzy control in the algorithm can leen the chattering problem of liding mode control. The configuration of a cloed-loop fuzzy liding mode controller i hown in Fig. 3. The propoed controller can make the PEM fuel cell keep contant power output P. The error e (k), the change in error de (k) are given * a follow: * ek ( ) = P P (11) ek ( ) ek ( 1) de( k) = T (1) A witching function k ( ) i deigned a following: k ( ) = cek ( ) + dek ( ) c> 0 (13) dk ( ) = k ( ) k ( 1) (14) Here we ue the proportion witching control method to deign controller which meet the condition for the exitence of liding mode. Controller i deigned a: u = ( α e + βe )gn( ) (15) Uing a two-dimenional fuzzy controller, liding mode control u i deigned by fuzzy control rule directly. The fuzzy controller input and repectively denote fuzzy variable of k ( ) and d( k ). The fuzzy controller output U i fuzzy variable of u. According to fuzzy control theory, the fuzzy et are hown a following: Table 1: Fuzzy control rule Δ U PB PS ZO NS NB PB PB PB PB PM ZO PM PB PB PM PS NS PS PB PM PS ZO NM ZO PB PS ZO NS NB NS PM ZO NS NM NB NM PS NS NM NB NB NB ZO NM NB NB NB / Fig. 4: Output voltage controlled by adjuting oxygen flow P/ Fig. 5: Output power controlled by adjuting oxygen flow U = Time () Time () { NB,NM,NS,ZO,PS,PM,PB} = { NB,NS,ZO,PS,PB} = { NB,NM,NS,ZO,PS,PM,PB} 1061 The fuzzy domain for, and U i [-1, 1]. The triangular type memberhip function i choen for the above fuzzy variable. Fuzzy control rule bae i hown in Table 1.

4 SIMULATION RESULTS In order to verify the validity of the propoed fuzzy liding mode controller, imulation reult have been carried out. Output power i controlled by adjuting the oxygen flow. The reference output power of the fuel cell i 0.5 W. There i a change in the load from 5 Ω to 6 Ω at the time of 5. Simulation reult are hown in Fig. 4 and 5. In the following figure the olid line repreent the fuzzy liding mode control and the dotted line repreent the fuzzy control propoed in Fan (01). It can be een from Fig. 4: when the load i 5, fuzzy control with a rie time of 11.8 reache 1.58v and fuzzy liding mode control with the 6 reache 1.58v. When the load change into 6, fuzzy control with a rie time of 19 reache 1.7v and fuzzy liding mode control with the 14.4 reache 1.73v. In Fig. 5, output power of fuzzy control i about 0.5 W in 11 and output power of fuzzy liding mode control reache 0.5 W in about 5.1. When the load change, output power i about 9 to teady tate under fuzzy control while it i about 4 to teady tate uing fuzzy liding mode control. At the ame time uing fuzzy mode control there i a maller overhoot and ytem reache final value more quickly than that uing the fuzzy control. Obviouly, imulation validate that the fuzzy liding mode controller i characterized by a fater time repone and higher preciion compared to the fuzzy controller. CONCLUSION Fuel cell need contant power output when load change. The fuzzy liding mode control propoed in thi tudy can not only have fat repone characteritic, but alo have good teady-tate behavior and trong robutne compared with fuzzy control, Simulation reult indicate that the fuzzy liding mode controller i very effective to realize contant power output. ACKNOWLEDGMENT Thi tudy i upported by the National Natural Science Foundation of China (No ) and the National Science and Technology Support Project of China (No. 01BAF09B01). REFERENCES Carne, B. and N. Djilal, 005. Sytematic parameter etimation for PEM fuel cell model. J. Power Sour., 144(1): Corcau, J.I. and E. Stoenecu, 007. Fuzzy logic controller a a power ytem tabilizer. Int. J. Circuit Syt. Signal Proce., 3(1): Correa, J.M., F.A. Farret, L.N. Canha and M.G. Simoe, 004. An electrochemical-baed fuel-cell model uitable for electrical engineering automation approach. IEEE Tran. Indut. Electr., 51(5): Edward, C. and S.K. Spurgeon, Sliding Mode Control: Theory and Application. Taylor and Franci, London, UK. Fan, L.P., 01. Simulation tudy on the influence factor of generated output of proton exchange membrane fuel cell. Appl. Mech. Mater., 11-16: Hatziadoniu, C.J., A.A. Lobo and F.M.P. Danehdoot, 00. A implified dynamic model of gridconnected fuel-cell generator. IEEE Tran. Power Deliv., 17(): Horiuchi, J.I. and M. Kihimoto, 00. Application of fuzzy control to indutrial bioprocee in Japan. Fuzzy Set. Syt., 18(1): Kaynak, O., K. Erbatur and M. Ertugrul, 001. The fuion of computationally intelligent methodologic and liding-mode control-a urvey. IEEE Trun. Inti. Electron, 48: Kaytakoglu, S. and L. Akyalm, 007. Optimization of parametric performance of a PEMFC. Int. J. Hydrogen Energ., 3: Logan, B.E., R.. Kulkarni and D. Stroud, 00. Microbial fuel cell and other bioelectrochemical ytem. Appl. Microbiol. Biotechnol., 85(6): Mammar, K. and A. Chaker, 009. Fuzzy logic control of fuel cell ytem for reidential power generation. J. Electr. Eng., 60(6): Moreira, M.. and G.E.D. Silva, 009. A practical model for evaluating the performance of proton exchange membrane fuel cell. Renew. Energ., 34(7): Nhivekar, G.S., S.S. Nirmale and R.R. Mudholker, 011. Implementation of fuzzy logic control algorithm in embedded microcomputer for dedicated application. Int. J. Eng., 3(4): Padulle, J., G.W. Ault and J.R. McDonald, 000. An integrated SOFC plant dynamic model for power ytem imulation. J. Power Sour., 86(1-): Pouramad, A. and M. Montazeri, 007. Deign of genetic-fuzzy control trategy for parallel hybrid electric vehicle. Control Eng. Pract., 16: Pukruhpan, J., A. Stefanopoulou and H. Peng, 004. Control of fuel cell breathing. IEEE Control Syt., 4():

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