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Comparative analysis on parametric estimation of a PEM fuel cell using metaheuristics algorithms

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  • Wilberforce, Tabbi
  • Rezk, Hegazy
  • Olabi, A.G.
  • Epelle, Emmanuel I.
  • Abdelkareem, Mohammad Ali

Abstract

One of the primary issues in the modelling of fuel cell is the determination of specific boundary conditions often deduced from the manufacturer of the fuel cell. Realistically, not all data is available from the manufacturer's data sheet; hence, to improve the accuracy as well as predict the performance of the cell, all these information need to be determined. This investigation however advanced the concept of using five different algorithms (Grey Wolf Optimization(GWO), Particle Swarm Optimization(PSO), Slime Mould Algorithm(SMA), Harris Hawk Optimiser (HHO), artificial ecosystem-based algorithm(AEO)) to ascertaining seven (ξ1,ξ2,ξ3,ξ4,R,B,λ) unknow parameters that affect the mathematical modelling of the cell. The unknown parameters were used as the modelling variables. A minimum fitness function implied a good correlation between the measured/experimental data and the predicted/modelled data. The study had to rank the performance of the algorithms from the best value to the worse value, average and standard deviation. The artificial ecosystem-based algorithm showed the best results compared to the PSO, SMA, GWO and HHO algorithms.

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  • Wilberforce, Tabbi & Rezk, Hegazy & Olabi, A.G. & Epelle, Emmanuel I. & Abdelkareem, Mohammad Ali, 2023. "Comparative analysis on parametric estimation of a PEM fuel cell using metaheuristics algorithms," Energy, Elsevier, vol. 262(PB).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pb:s0360544222024124
    DOI: 10.1016/j.energy.2022.125530
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    Cited by:

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    2. Hassan Ali, Hossam & Fathy, Ahmed, 2024. "Reliable exponential distribution optimizer-based methodology for modeling proton exchange membrane fuel cells at different conditions," Energy, Elsevier, vol. 292(C).
    3. Abdel-Basset, Mohamed & Mohamed, Reda & Abouhawwash, Mohamed, 2023. "On the facile and accurate determination of the highly accurate recent methods to optimize the parameters of different fuel cells: Simulations and analysis," Energy, Elsevier, vol. 272(C).
    4. Huang, Kai & Yao, Kaixin & Guo, Yongfang & Lv, Ziteng, 2023. "State of health estimation of lithium-ion batteries based on fine-tuning or rebuilding transfer learning strategies combined with new features mining," Energy, Elsevier, vol. 282(C).
    5. Chen, Zhijie & Zuo, Wei & Zhou, Kun & Li, Qingqing & Huang, Yuhan & E, Jiaqiang, 2023. "Multi-factor impact mechanism on the performance of high temperature proton exchange membrane fuel cell," Energy, Elsevier, vol. 278(PB).
    6. Chen, Zhijie & Zuo, Wei & Zhou, Kun & Li, Qingqing & Yi, Zhengming & Huang, Yuhan, 2024. "Numerical investigation on the performance enhancement of PEMFC with gradient sinusoidal-wave fins in cathode channel," Energy, Elsevier, vol. 288(C).
    7. Li, Dexin & Zuo, Wei & Li, Qingqing & Zhang, Guangde & Zhou, Kun & E, Jiaqiang, 2023. "Effects of pulsating flow on the performance of multi-channel cold plate for thermal management of lithium-ion battery pack," Energy, Elsevier, vol. 273(C).
    8. Fathy, Ahmed & Rezk, Hegazy & Alharbi, Abdullah G. & Yousri, Dalia, 2023. "Proton exchange membrane fuel cell model parameters identification using Chaotically based-bonobo optimizer," Energy, Elsevier, vol. 268(C).
    9. Atak, Nisa Nur & Dogan, Battal & Yesilyurt, Murat Kadir, 2023. "Investigation of the performance parameters for a PEMFC by thermodynamic analyses: Effects of operating temperature and pressure," Energy, Elsevier, vol. 282(C).

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