A novel self-tuning type-2 fuzzy maximum power point tracking technique for efficiency enhancement of fuel cell based battery chargers

Abstract Given the uncertainties associated with proton-exchange membrane fuel cell systems and relatively low efficiency of the fuel cell stacks for low-power applications, designing a high-efficiency maximum power point tracking (MPPT) controller for the fuel cell electric vehicles is an important and also technically challenging issue. For this purpose, in this article, aiming to develop a high-efficiency and low cost battery charger, a novel self-tuning type-2 fuzzy MPPT controller is presented. The main task of the controller is to provide the better performance and regulate the switching duty cycle of the used power converter under the system's uncertainty conditions in order to dynamically extract the maximum power from the fuel cell system and maintain the battery at its highest possible state of charge while protecting it from overcharging. For the sake of computational efficiency, an improved invasive weed optimization algorithm, called elitist invasive weed optimization (EIWO), is also presented to tune the type-2 fuzzy set parameters, whose improvement is demanding due to the limited human experience and knowledge. All data processing and simulations are conducted in the MATLAB software. Finally, the performance of the proposed MPPT controller is examined through using experimental tests with a prototype device.

[1]  Caro Lucas,et al.  A novel numerical optimization algorithm inspired from weed colonization , 2006, Ecol. Informatics.

[2]  Nicu Bizon,et al.  FC energy harvesting using the MPP tracking based on advanced extremum seeking control , 2013 .

[3]  Haitham Saad Mohamed Ramadan,et al.  Optimal fractional order PI control applicability for enhanced dynamic behavior of on-grid solar PV systems , 2017 .

[4]  Abdelghani Harrag,et al.  How fuzzy logic can improve PEM fuel cell MPPT performances , 2018 .

[5]  Mahdi Yaghoobi,et al.  Improved invasive weed optimization algorithm (IWO) based on chaos theory for optimal design of PID controller , 2019, J. Comput. Des. Eng..

[6]  D. Guilbert,et al.  Design and experimental validation of a high voltage ratio DC/DC converter for proton exchange membrane electrolyzer applications , 2019, International Journal of Hydrogen Energy.

[7]  Nazih Moubayed,et al.  Comparative study between P&O and incremental conductance for fuel cell MPPT , 2014, International Conference on Renewable Energies for Developing Countries 2014.

[8]  Nader Nariman-Zadeh,et al.  Pareto optimization of a five-degree of freedom vehicle vibration model using a multi-objective uniform-diversity genetic algorithm (MUGA) , 2010, Eng. Appl. Artif. Intell..

[9]  Mohammad Bagher Menhaj,et al.  Fuzzy logic controller vs. PI controller for MPPT of three-phase grid-connected PV system considering different irradiation conditions , 2016, J. Intell. Fuzzy Syst..

[10]  Abdullah Caliskan,et al.  Performance enhancement of energy extraction capability for fuel cell implementations with improved Cuckoo search algorithm , 2020 .

[11]  Xiongwen Zhang,et al.  Nonlinear model predictive control based on the moving horizon state estimation for the solid oxide fuel cell , 2008 .

[12]  Hitesh C. Boghani,et al.  Maximum Power Point Tracking to Increase the Power Production and Treatment Efficiency of a Continuously Operated Flat‐Plate Microbial Fuel Cell , 2016 .

[13]  Kodjo Agbossou,et al.  Design of an adaptive EMS for fuel cell vehicles , 2017 .

[14]  Ö. Fatih Kececioglu.  Robust control of high gain DC-DC converter using Type-2 fuzzy neural network controller for MPPT , 2019, J. Intell. Fuzzy Syst..

[15]  Mohamed Chaabane,et al.  T-S fuzzy control for MPPT of photovoltaic pumping system , 2018, J. Intell. Fuzzy Syst..

[16]  Yuan Ren,et al.  Adaptive maximum power point tracking control of fuel cell power plants , 2008 .

[17]  Julio C. Rosas-Caro,et al.  Quadratic buck–boost converter with positive output voltage and continuous input current for PEMFC systems , 2017 .

[18]  Abdelghani Harrag,et al.  Variable Step Size IC MPPT Controller for PEMFC Power System Improving Static and Dynamic Performances , 2017 .

[19]  Sh. Abdi,et al.  Maximum power point tracking of a proton exchange membrane fuel cell system using PSO-PID controller , 2017 .

[20]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[21]  Nicu Bizon,et al.  Improving the PEMFC energy efficiency by optimizing the fueling rates based on extremum seeking algorithm , 2014 .

[22]  Saeid Nahavandi,et al.  Load Forecasting Using Interval Type-2 Fuzzy Logic Systems: Optimal Type Reduction , 2014, IEEE Transactions on Industrial Informatics.

[23]  Subhojit Ghosh,et al.  Fractional order modeling and two loop control of PEM fuel cell for voltage regulation considering both source and load perturbations , 2018 .

[24]  Lakhdar Khochemane,et al.  An adaptive fuzzy logic controller (AFLC) for PEMFC fuel cell , 2015 .

[25]  Ali Jamali,et al.  Optimum Pareto design of non-linear predictive control with multi-design variables for PEM fuel cell , 2012 .

[26]  Rui Chen,et al.  Powerpath controller for fuel cell & battery hybridisation , 2016 .

[27]  Hongwen He,et al.  Proton exchange membrane fuel cell-powered bidirectional DC motor control based on adaptive sliding-mode technique with neural network estimation , 2020 .

[28]  Xiaowei Fu,et al.  Development of solid oxide fuel cell and battery hybrid power generation system , 2020 .

[29]  Jerry M. Mendel,et al.  Interval type-2 fuzzy logic systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[30]  K. Shanti Swarup,et al.  Application of improved invasive weed optimization technique for optimally setting directional overcurrent relays in power systems , 2019, Appl. Soft Comput..

[31]  N. Sudhakar,et al.  High Voltage Gain Interleaved Boost Converter With Neural Network Based MPPT Controller for Fuel Cell Based Electric Vehicle Applications , 2018, IEEE Access.

[32]  Chih-Hong Lin,et al.  Design of a range extension strategy for power decentralized fuel cell/battery electric vehicles , 2015 .

[33]  G. Scott Samuelsen,et al.  Plug-in fuel cell electric vehicles: A California case study , 2017 .

[34]  Her-Terng Yau,et al.  A novel variable step size fractional order incremental conductance algorithm to maximize power tracking of fuel cells , 2017 .

[35]  Sousso Kelouwani,et al.  Improving a free air breathing proton exchange membrane fuel cell through the Maximum Efficiency Point Tracking method , 2017 .

[37]  A. Sanli,et al.  A borohydride/peroxide fuel cell-Li-ion battery hybrid system controlled with a maximum peak power tracking algorithm , 2015 .

[38]  Hamed Beirami,et al.  Optimal PID plus fuzzy controller design for a PEM fuel cell air feed system using the self-adaptive differential evolution algorithm , 2015 .

[39]  Zhi-xin Zheng,et al.  An improved invasive weed optimization algorithm for solving dynamic economic dispatch problems with valve-point effects , 2019, J. Exp. Theor. Artif. Intell..

[40]  Abdelghani Harrag,et al.  Novel neural network IC-based variable step size fuel cell MPPT controller: Performance, efficiency and lifetime improvement , 2017 .

[41]  Fariborz Jolai,et al.  AN ENHANCED INVASIVE WEED OPTIMIZATION FOR MAKESPAN MINIMIZATION IN A EXIBLE OWSHOP SCHEDULING PROBLEM , 2014 .

[42]  Daniel Hissel,et al.  MPPT of a PEMFC based on air supply control of the motocompressor group , 2010 .

[43]  D. Srinivasan,et al.  Interval Type-2 Fuzzy Logic Systems for Load Forecasting: A Comparative Study , 2012, IEEE Transactions on Power Systems.

[44]  Sousso Kelouwani,et al.  Free air breathing proton exchange membrane fuel cell: Thermal behavior characterization near freezing temperature , 2014 .