Optimum parameters extraction of proton exchange membrane fuel cells using Fractional‐Order Whale Optimization Algorithm

This study proposes a novel metaheuristic‐based technique for optimum parameters' estimation of the proton exchange membrane fuel cells (PEMFCs). To provide better results with more reliability and accuracy, a Fractional Order‐based design of the Whale Optimizer Algorithm (FO‐WOA) is designed. A validation test showed that the proposed method provides a good trade‐off between accuracy and convergence speed. Performing the algorithm for multiple independent runs also shows that the proposed method delivers reliable results toward some other comparative metaheuristic algorithms. This algorithm is then used for the minimization of the sum of square deviation between the experimental voltage‐current polarization and the optimal achieved results by the model based on FO‐WOA. The method is validated by considering two practical case studies, which are the Nexa PEMFC and 250 W PEM system, and its achievements are put in comparison with some approaches to indicate the higher effectiveness of the proposed method toward the others. The experimental results on the Nexa PEMFC indicate that the proposed FO‐WOA‐based method with 12 sum square errors (SSE) provided the minimum error toward the other. Also, the experiments on the 250 W PEM Stack indicated that for 250 W PEM Stack with 3/5 bar and 80°C, the proposed method with 0.01 SSE provides the fittest profile with the experimental data, and finally, the experiments on the same stack with 3/5 bar and 80°C showed the higher accuracy of the proposed method with the least SSE value (0.16) toward the others.

[1]  Behnam Sobhani,et al.  Application of the improved chaotic grey wolf optimization algorithm as a novel and efficient method for parameter estimation of solid oxide fuel cells model , 2021, International journal of hydrogen energy.

[2]  Noradin Ghadimi,et al.  Robust multi-objective optimal design of islanded hybrid system with renewable and diesel sources/stationary and mobile energy storage systems , 2021 .

[3]  Hany M. Hasanien,et al.  Precise modeling of PEM fuel cell using improved chaotic MayFly optimization algorithm , 2021, International Journal of Energy Research.

[4]  Jingzheng Ren,et al.  Conventional and advanced exergy analyses of a vehicular proton exchange membrane fuel cell power system , 2021 .

[5]  A. Gougui,et al.  The Whale Optimization Algorithm for efficient PEM fuel cells modeling , 2021 .

[6]  A. Macias,et al.  Fuel cell-supercapacitor topologies benchmark for a three-wheel electric vehicle powertrain , 2021 .

[7]  Mohamed Elhoseny,et al.  An efficient heap-based optimization algorithm for parameters identification of proton exchange membrane fuel cells model: Analysis and case studies , 2021 .

[8]  Farschad Torabi,et al.  Precise PEM fuel cell parameter extraction based on a self-consistent model and SCCSA optimization algorithm , 2021 .

[9]  Mahdi Mir,et al.  Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction , 2019, Evolving Systems.

[10]  Kittisak Jermsittiparsert,et al.  An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm , 2020 .

[11]  Zhi Yuan,et al.  Developed Coyote Optimization Algorithm and its application to optimal parameters estimation of PEMFC model , 2020 .

[12]  Ahmed A. Zaki Diab,et al.  Parameter estimation of PEMFC model based on Harris Hawks’ optimization and atom search optimization algorithms , 2020, Neural Computing and Applications.

[13]  Salah Kamel,et al.  Developing the coyote optimization algorithm for extracting parameters of proton-exchange membrane fuel cell models , 2020 .

[14]  Mohamed I. Mosaad,et al.  Optimal economic study of hybrid PV-wind-fuel cell system integrated to unreliable electric utility using hybrid search optimization technique , 2020 .

[15]  R. M. Rizk-Allah,et al.  Artificial ecosystem optimizer for parameters identification of proton exchange membrane fuel cells model , 2020 .

[16]  Weiqing Wang,et al.  Probabilistic decomposition‐based security constrained transmission expansion planning incorporating distributed series reactor , 2020 .

[17]  Jun Liu,et al.  An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles , 2020 .

[18]  Karzan Wakil,et al.  Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach , 2019 .

[19]  Yuanping Zhou,et al.  Contrast enhancement of medical images using a new version of the World Cup Optimization algorithm. , 2019, Quantitative imaging in medicine and surgery.

[20]  Dmitriy Gavrilov,et al.  AI Recognition in Skin Pathologies Detection , 2019, 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI).

[21]  Hadi Zayandehroodi,et al.  A New Formulation to Reduce the Number of Variables and Constraints to Expedite SCUC in Bulky Power Systems , 2019 .

[22]  Aboul Ella Hassanien,et al.  Enhanced Elephant Herding Optimization for Global Optimization , 2019, IEEE Access.

[23]  Noradin Ghadimi,et al.  Robust optimization based optimal chiller loading under cooling demand uncertainty , 2019, Applied Thermal Engineering.

[24]  Sankalap Arora,et al.  Chaotic whale optimization algorithm , 2018, J. Comput. Des. Eng..

[25]  Wei Wang,et al.  Electricity load forecasting by an improved forecast engine for building level consumers , 2017 .

[26]  Majdi M. Mafarja,et al.  Hybrid Whale Optimization Algorithm with simulated annealing for feature selection , 2017, Neurocomputing.

[27]  Ali Kaveh,et al.  Enhanced whale optimization algorithm for sizing optimization of skeletal structures , 2017 .

[28]  Dr. Dinakara Prasad Reddy P,et al.  Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems , 2017 .

[29]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[30]  S. Deb,et al.  Elephant Herding Optimization , 2015, 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI).

[31]  Tine Konjedic,et al.  Identification of a Proton-Exchange Membrane Fuel Cell’s Model Parameters by Means of an Evolution Strategy , 2015, IEEE Transactions on Industrial Informatics.

[32]  Ahmed M. Nassef,et al.  An enhanced Archimedes optimization algorithm based on Local escaping operator and Orthogonal learning for PEM fuel cell parameter identification , 2021, Eng. Appl. Artif. Intell..

[33]  Ahmed Djafour,et al.  Accurate PEM Fuel Cell Parameters Identification Using Whale Optimization Algorithm , 2020 .

[34]  Fariborz Jolai,et al.  Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm , 2016, J. Comput. Des. Eng..