Abstract
In this article, a switched reluctance motor (SRM) powered by autonomous stacked proton exchange membrane fuel cells (PEMFC)’s stack with the purpose of optimizing their operating performances is addressed. Three key performance indices are examined that include: (1) torque per ampere ratio, (2) torque smoothness factor and (3) average starting torque. The later mentioned adapted indices characterize the objective functions that can be optimized individually and concurrently using a novel application of multi-objective dragonfly approach (MODA). The MODA is applied to generate the optimal turn (on/off) angles of H-bridge converter and the gains of a proportional-integral speed controller. A Pareto front optimal solutions are made, and the final best compromise solution is carefully chosen. The terminal voltage of the PEMFC is fine controlled by a boost converter, to overcome the noticeable decline of its voltage profile with the increase in loading current. The system under study is demonstrated at various loading conditions with necessary comparisons to other recent competing methods complete with subsequent discussions. The cropped numerical results indicate that PEMFC energy saving, reduction in SRM torque ripples and PEMFC current ripples can be enhanced. In addition, higher average starting torque of the SRM is realized.
Similar content being viewed by others
References
Marks N, Cardenac D, Boulon L, Gustin F, Hissel D (2016) Degraded mode operation of multi-stack fuel cell systems. IET Electr Syst Transp 6(1):3–11. https://doi.org/10.1049/iet-est.2015.0012
Gasbaoui B, Nasri A, Abdelkhalek O, Ghouili J, Ghezouani A (2017) Behavior PEM fuel cell for 4WD electric vehicle under different scenario consideration. Int J Hydrogen Energy 42(1):535–543. https://doi.org/10.1016/j.ijhydene.2016.08.114
Liu Y, Lehnert W, Janben H, Samsun R, Stolten D (2016) A review of high-temperature polymer electrolyte membrane fuel-cell (HT-PEMFC)-based auxiliary power units for diesel-powered road vehicles. J Power Sources 311:91–102. https://doi.org/10.1016/j.jpowsour.2016.02.033
Jo A, Oh K, Lee J, Han D, Kim D, Kim J, Kim B, Kim J, Park D, Kim M, Sohn Y, Kim D, Kim H, Ju H (2017) Modeling and analysis of a 5 kWe HT-PEMFC system for residential heat and power generation. Int J Hydrogen Energy 42(3):1698–1714. https://doi.org/10.1016/j.ijhydene.2016.10.152
Tiar M, Betka A, Drid S, Abdeddaim S, Becherif M, Tabandjat A (2017) Optimal energy control of a PV-fuel cell hybrid system. Int J Hydrogen Energy 42(2):1456–1465. https://doi.org/10.1016/j.ijhydene.2016.06.113
Chen H, Yang C, Deng K, Zhou N, Wu H (2017) Multi-objective optimization of the hybrid wind/solar/fuel cell distributed generation system using Hammersley Sequence Sampling. Int J Hydrogen Energy 42(12):7836–7846. https://doi.org/10.1016/j.ijhydene.2017.01.202
Ettihir K, Boulon L, Agbossou K (2016) Energy management strategy for a fuel cell hybrid vehicle based on maximum efficiency and maximum power identification. IET Electr Syst Transp 6(4):261–268. https://doi.org/10.1049/iet-est.2015.0023
Tahmasbi AA, Hoseini A, Roshandel R (2015) A new approach to multi-objective optimisation method in PEM fuel cell. Int J Sustain Energy 34(5):283–297. https://doi.org/10.1080/14786451.2013.813945
Dokkar B, Settou N, Negrou B, Settou N, Imine O, Chennouf N, Benmhidi A (2013) Optimization of PEM fuel cells for PV-hydrogen power system. Energy Procedia 36:798–807. https://doi.org/10.1016/j.egypro.2013.07.092
Ang SMC, Fraga ES, Brandonc NP, Samsatlid NJ, Brett DJL (2011) Fuel cell systems optimization—methods and strategies. Int J Hydrogen Energy 36(22):14678–14703. https://doi.org/10.1016/j.ijhydene.2011.08.053
Roshandel R, Forough AB (2014) Two strategies for multi-objective optimisation of solid oxide fuel cell stacks. Int J Sustain Energy 33(4):854–868. https://doi.org/10.1080/14786451.2013.777337
Correa JM, Ferret FA, Canha LN, Simoes MG (2004) An electrochemical based fuel cell model suitable for electrical engineering automation approach. IEEE Trans Ind Electron 51(5):1103–1112. https://doi.org/10.1109/TIE.2004.834972
Julio RC, Vector M, Rene V, Luis M, Jonathan M, Pedro G, Romeli B (2016) A novel DC-DC multi-level SEPIC converter for PEMFC systems. Int J Hydrogen Energy 41(48):23401–23408. https://doi.org/10.1016/j.ijhydene.2016.06.042
Jung J, Keyhani A (2008) Fuel cell based distributed generation. In: 12th International middle-east power system conference, Aswan, 12–15 Mar 2008, pp 610–616. https://doi.org/10.1109/mepcon.2008.4562358
Cultura AB, Salameh ZM (2014) Dynamic analysis of a stand-alone operation of PEM fuel cell system. J Power Energy Eng 2(1):1–8. https://doi.org/10.4236/jpee.2014.21001
Garnier J, Pera MC, Hissel D, Harel F, Candusso D, Glandut N, Diard JP, De Bernardinis A, Kauffmann JM, Coquery G (2003) Dynamic PEM fuel cell modeling for automotive applications. In: IEEE 58th vehicular technology conference—VTC2003-Fall, Orlando, FL, USA, 6–9 Oct 2003, pp 3284–3288. https://doi.org/10.1109/vetecf.2003.1286265
Rajabzadeh M, Bathaee SMT, Golkar MA (2016) Dynamic modeling and nonlinear control of fuel cell vehicles with different hybrid power sources. Int J Hydrogen Energy 41(4):3185–3198. https://doi.org/10.1016/j.ijhydene.2015.12.046
Macauley N, Waston M, Lauritzen M, Knights S, Wang G, Kjeang E (2016) Empirical membrane lifetime model for heavy duty fuel cell systems. J Power Sources 336:240–250. https://doi.org/10.1016/j.jpowsour.2016.10.068
Guarnieri M, Negro E, Noto V, Alotto P (2016) A selective hybrid stochastic strategy for fuel-cell multi-parameter identification. J Power Sources 332:249–264. https://doi.org/10.1016/j.jpowsour.2016.09.131
Hamour M, Grandidier J, Ouibrahim A, Martemianov S (2015) Electrical conductivity of PEMFC under loading. J Power Sources 289:160–167. https://doi.org/10.1016/j.jpowsour.2015.04.145
Sari A, Balikci A, Taskin S, Aydin S (2013) A proposed artificial neural network model for PEM fuel cells. In: 8th International conference on electrical and electronics engineering, Bursa, Turkey, 28–30 Nov 2013, pp 205–209. https://doi.org/10.1109/eleco.2013.6713832
Karthik M, Gomathi K (2014) Dynamic neural network based parametric modeling of PEM fuel cell system for electric vehicle applications. In: International conference on advances in electrical engineering (ICAEE), Vellore, India, 9–11 Jan 2014, pp 1–5. https://doi.org/10.1109/icaee.2014.6838559
Su H, Chang-Bock C (2016) Performance prediction and analysis of a PEM fuel cell operating on pure oxygen using data-driven models: a comparison of artificial neural network and support vector machine. Int J Hydrogen Energy 41(24):10202–10211. https://doi.org/10.1016/j.ijhydene.2016.04.247
Roohollah S, Hooshang R, Seyyed H (2014) Modeling of a solid oxide fuel cell power plant using an ensemble of neural networks based on a combination of the adaptive particle swarm optimization and Levenberg–Marquardt algorithms. J Nat Gas Sci Eng 21:1171–1183. https://doi.org/10.1016/j.jngse.2014.07.004
Barrous T, Neto P, Fello P, Moreira A, Ruppert E (2016) Approach for performance optimization of switched reluctance generator in variable-speed wind generation system. Renew Energy 97:114–128. https://doi.org/10.1016/j.renene.2016.05.064
Wang Y, Li P, Ren G (2016) Electric vehicles with in-wheel switched reluctance motors: coupling effects between road excitation and the unbalanced radial force. J Sound Vib 372:69–81. https://doi.org/10.1016/j.jsv.2016.02.040
Tursini M, Villani M, Fabri G, Di Leonardo L (2017) A switched-reluctance motor for aerospace application: design, analysis and results. Electr Power Syst Res 142:74–83. https://doi.org/10.1016/j.epsr.2016.08.044
Huang C, Chen Y (2017) Design of magnetic flywheel control for performance improvement of fuel cells used in vehicles. Energy 118:840–852. https://doi.org/10.1016/j.energy.2016.10.112
Navardi M, Babaghorbani B, Ketabi A (2014) Efficiency improvement and torque ripple minimization of switched reluctance motor using FEM and seeker optimization algorithm. Energy Convers Manag 78:237–244. https://doi.org/10.1016/j.enconman.2013.11.001
Wang J (2016) A common sharing method for current and flux-linkage control of switched reluctance motor. Electr Power Syst Res 131:19–30. https://doi.org/10.1016/j.epsr.2015.09.015
Puranik SV, Keyhani A, Khorrami F (2010) Neural network modeling of proton exchange membrane fuel cell. IEEE Trans Energy Convers 25(2):474–483. https://doi.org/10.1109/TEC.2009.2035691
Luna J, Jemei S, Steiner NY, Hussar A, Serra M, Hissel D (2016) Nonlinear predictive control for durability enhancement and efficiency improvement in a fuel cell power system. J Power Sources 328:250–261. https://doi.org/10.1016/j.jpowsour.2016.08.019
Radenahmad N, Afif A, Petra PI, Rahman SMH, Eriksson SG, Azad AK (2016) Proton-conducting electrolytes for direct methanol and direct urea fuel cells—a state-of-the-art review. Renew Sustain Energy Rev 57:1347–1358. https://doi.org/10.1016/j.rser.2015.12.103
Nehrir MH, Wang C, Shaw SR (2006) Fuel cells: promising devices for distributed generation. IEEE Power Energy Mag 4(1):47–53. https://doi.org/10.1109/MPAE.2006.1578531
Ozen DN, Timurkutluk B, Altinisik K (2016) Effects of operation temperature and reactant gas humidity levels on performance of PEM fuel cells. Renew Sustain Energy Rev 59:1298–1306. https://doi.org/10.1016/j.rser.2016.01.040
Hong P, Li J, Xu L, Ouyang M, Fang C (2016) Modeling and simulation of parallel DC/DC converters for online AC impedance estimation of PEM fuel cell stack. Int J Hydrogen Energy 41(4):3004–3014. https://doi.org/10.1016/j.ijhydene.2015.11.129
Kolli A, Gaillard A, Bernardinis AD, Bethoux O, Hissel D, Khatir Z (2015) A review on DC/DC converter architectures for power fuel cell applications. Energy Convers Manag 105:716–730. https://doi.org/10.1016/j.enconman.2015.07.060
Ahmed OA, Mbleis JA (2015) An overview of DC–DC converter topologies for fuel cell-ultra capacitor hybrid distribution system. Renew Sustain Energy Rev 42:609–626. https://doi.org/10.1016/j.rser.2014.10.067
Yu X, Starke MR, Tolbert LM, Ozpineci B (2007) Fuel cell power conditioning for electric power applications: a summary. IET Electr Power Appl 1(5):643–656. https://doi.org/10.1049/iet-epa:20060386
Navauga A, Navamani JD, Lavanya A, Vijayakumar K (2013) Comparison of high gain topologies of non-isolated DC–DC converters for fuel cell application. In: International green computing, communication and conservation of energy conference (ICGCE), Chennai, India, 12–14 Dec 2013, pp 367–372. https://doi.org/10.1109/icgce.2013.6823462
Yildiz E, Vural B, Akar F (2016) Current ripple minimization of a PEM fuel cell via an interleaved converter to prolong the stack life. In: IEEE 19th international symposium on electrical apparatus and technologies, Siela Bourgas, Bulgaria, 29 May–1 June 2016, pp 1–4. https://doi.org/10.1109/siela.2016.7543066
Nouri A, Salhi I, Elwarraki E, El-Beid S, Essounbouli N (2017) DSP-based implementation of a self-tuning fuzzy controller for three-level boost converter. Electr Power Syst Res 146:286–297. https://doi.org/10.1016/j.epsr.2017.01.036
Marsala G, Ragusa A (2016) Heuristic optimization methods applied to improve the performance of controlled DC–DC high boost converter with coupled inductors. In: 19th IEEE international conference on electrical machines and systems (ICEMS), Chiba, Japan, 13–16 Nov 2016, pp 1–6
Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073. https://doi.org/10.1007/s00521-015-1920-1
Babayigit B (2018) Synthesis of concentric circular antenna arrays using dragonfly algorithm. Int J Electron 105(5):784–793. https://doi.org/10.1080/00207217.2017.1407964
Shilaja C, Ravi K (2017) Optimal power flow using hybrid DA-APSO algorithm in renewable energy resources. Energy Procedia 117:1085–1092. https://doi.org/10.1016/j.egypro.2017.05.232
Bashishtha TK, Srivastava L (2016) Nature inspired meta-heuristic dragonfly algorithms for solving optimal power flow problem. Int J Electr Electr Comput Syst 5(5):111–120
Suresh V, Sreejith S (2017) Generation dispatch of combined solar thermal systems using dragonfly algorithm. Computing 99(1):59–80. https://doi.org/10.1007/s00607-016-0514-9
Abhiraj TK, Aravindhababu P (2017) Dragonfly optimization based reconfiguration for voltage profile enhancement in distribution systems. Int J Comput Appl 158(3):1–4. https://doi.org/10.5120/ijca2017912758
Veeramsetty V, Venkaiah C, Kumar DMV (2017) Hybrid genetic dragonfly algorithm based optimal power flow for computing LMP at DG buses for reliability improvement. Energy Syst. https://doi.org/10.1007/s12667-017-0268-2
Amin AA, Patel RK, Vasavada MR (2017) Optimal placement and sizing of distributed generation in distribution power system using dragonfly algorithm. Int J Innov Res Electr Electron Instrum Control Eng 5(4):197–203. https://doi.org/10.17148/IJIREEICE.2017.5436
Venkatesh M, Sudheer G (2017) Optimal load frequency regulation of micro-grid using dragonfly algorithm. Int Res J Eng Technol 4(8):978–981
Amroune M, Bouktir T, Musirin I (2018) Power system voltage stability assessment using a hybrid approach combining dragonfly optimization algorithm and support vector regression. Arab J Sci Eng. https://doi.org/10.1007/s13369-017-3046-5
The MathWorks. http://www.mathworks.com. Accessed June 2016
Mirjalili S, Jangir P, Saremi S (2017) Multi-objective ant lion optimizer: a multi-objective optimization for solving engineering problems. Appl Intell 46(1):79–95. https://doi.org/10.1007/s10489-016-0825-8
Sarkan S, Ercument K, Kadir Y, Murat A (2016) Finite element modeling and control of high-power SRM for hybrid electric vehicle. Simul Model Pract Theory 62:49–67. https://doi.org/10.1016/j.simpat.2016.01.006
Kumar MK, Murthy GRK (2013) Modeling and simulation of 8/6 pole switched reluctance motor with closed loop speed control. In: IEEE Asia Pacific conference on postgraduate research in microelectronics and electronics (Prime Asia), Visakhapatnam, 19–21 Dec 2013, pp 89–95. https://doi.org/10.1109/primeasia.2013.6731184
Yongqin Z, Qin S, Lanlan L, Hao S (2015) A nonlinear modeling method of switched reluctance motor. In: IEEE seventh international conference on advanced communication and networking (ACN), Kota Kinabalu, 8–11 July 2015, pp 32–35. https://doi.org/10.1109/acn.2015.10
Jebarani S, SureshKumar S, Jayakumar J (2016) Torque modeling of switched reluctance motor using LSSVM-DE. Neurocomputing 211:117–128. https://doi.org/10.1016/j.neucom.2016.02.076
Santos FLM, Anthonis J, Naclerio F, Gyselinck J, Van der Auweraer H, Goe LCS (2014) Multiphysics NVH modeling: simulation of a switched reluctance motor for an electric vehicle. IEEE Trans Ind Electron 61(1):469–476. https://doi.org/10.1109/TIE.2013.2247012
Saadi A, Becherif M, Aboubou A, Ayad MY (2013) Comparison of proton exchange membrane fuel cell static models. Renew Energy 56:64–71. https://doi.org/10.1016/j.renene.2012.10.012
Motapon SN, Tremblay O, Dessaint L (2012) Development of a generic fuel cell model: application to a fuel cell vehicle simulation. Int J Power Electron 4(6):505–522. https://doi.org/10.1504/IJPELEC.2012.052427
http://www.fuelcellmarkets.com/content/images/articles/ps6.pdf. Accessed June 2016
Elhameed MA, El-Fergany AA (2017) Water cycle algorithm-based economic dispatcher for sequential and simultaneous objectives including practical constraints. Appl Soft Comput 58:145–154. https://doi.org/10.1016/j.asoc.2017.04.046
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
In compliance with Springer policy and our ethical obligation as researchers, no potential conflict of interest should be reported. The authors certify that they have no involvement in any organization or entity with any financial interest, or non-financial interest in the subject matter discussed in this manuscript.
Appendices
Appendix A: 60 kW SRM data sheet
Parameter | Value |
---|---|
\({\text{V}}_{n}\) (V) | 240 |
Stator pole arc | 32° |
Rotor pole arc | 45° |
Stator resistance (Ω) | 0.05 |
Unaligned inductance (mH) | 0.67 |
Aligned inductance (mH) | 23.60 |
Maximum flux linkage (V s) | 0.486 |
J (kg m2) | 0.05 |
\(\beta\) (N m s) | 0.02 |
Appendix B: 6 kW PEMFC data sheet
Parameter | Value |
---|---|
\(V_{\text{FC}}\) (nominal) | 45–42 V |
\(R_{\text{ohm}}\) | 0.07833 Ω |
\(P_{\text{fuel}}\)/\(V_{\text{fuel}}\) | 1.5 bar/50.06 lpm |
\(P_{\text{air}}\)/\(V_{\text{air}}\) | 1 bar/300 lpm |
T | 65 °C |
N | 65 |
i 0 | 0.29197 A |
\(x / y\) | 99.95%/21% |
\(T_{d}\) | 1 s |
Rights and permissions
About this article
Cite this article
El-Hay, E.A., El-Hameed, M.A. & El-Fergany, A.A. Improved performance of PEM fuel cells stack feeding switched reluctance motor using multi-objective dragonfly optimizer. Neural Comput & Applic 31, 6909–6924 (2019). https://doi.org/10.1007/s00521-018-3524-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-018-3524-z