Optimizing PEMFC model parameters using ant lion optimizer and dragonfly algorithm: A comparative study

Isa, Zainuddin Mat; Nayan, Norkharziana Mohd; Arshad, Mohd Hafiz; Kajaan, Nor Ashbahani Mohamad

doi:10.11591/ijece.v9i6.pp5295-5303

Cited by 11 publications

(10 citation statements)

References 26 publications

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“…Neglecting the concentration voltage drop for simplification, the E Nernst can be calculated as Equation (3). The further details can be found in [30]:…”

Section: Modeling Of Fuel Cellmentioning

confidence: 99%

Fuel Cell Hybrid Locomotive with Modified Fuzzy Logic Based Energy Management System

Kaleybar

Brenna

et al. 2022

Sustainability

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As one of the most environmentally friendly energy sources today, fuel cells have become the focus of research in countries around the world, especially in the electric transportation field. This paper mainly studies the modeling of fuel cell hybrid locomotives (FCHL) including fuel cells, batteries, motors, and energy management systems. To increase the operating efficiency and improve the performance of FCHL, a modified fuzzy logic-based energy management system (MFL-EMS) is proposed and compared with the traditional power flow energy management system (PF-EMS). Meanwhile, a modified fuel cell hybrid power system model for locomotives is proposed, taking into account the traction motor features that, compared with a simplified controlled source load, can directly reflect the status of the locomotive running speed and the output power of the traction motor load. The proposed system parameters and configurations are determined by combining the characteristics of power and energy density, response characteristics, and charging/discharging characteristics of fuel cells and batteries. The precise simulation results revealed that adopting the proposed MFL-EMS in comparison to the traditional PF-EMS, reduced the hydrogen consumption by 2.943%. Comparing the battery output voltage, it is confirmed that with MFL-EMS it tends to be steeper than the one with PF-EMS, showing the proposed strategy’s robustness. Overall, the obtained results revealed an improved performance in terms of power distribution as well as SOC, which means less hydrogen consumption and therefore a more economical solution.

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“…Neglecting the concentration voltage drop for simplification, the E Nernst can be calculated as Equation (3). The further details can be found in [30]:…”

Section: Modeling Of Fuel Cellmentioning

confidence: 99%

Fuel Cell Hybrid Locomotive with Modified Fuzzy Logic Based Energy Management System

Kaleybar

Brenna

et al. 2022

Sustainability

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“…So far, meta-heuristic algorithms have been roughly divided into four categories, which are based on biology, physics, sociology, and mathematics. In addition, many meta-heuristic algorithms have been applied to PEMFC parameter extraction, for example, antlion optimization algorithm (ALO) (Isa et al, 2019), particle swarm optimization algorithm (PSO) (Ye et al, 2009), biogeographybased optimization (BBO) (Gong and Cai, 2014), improved beetle antennae search (IBAS) (Sun et al, 2020), hybrid artificial bee colony algorithm (ABC) (Oliva et al, 2014), vortex search algorithm (VSA) (Fathy et al, 2020), differential evolution (DE) (Chakraborty et al, 2012), month flame optimizer algorithm (MFO) (Messaoud et al, 2020), multi-verse optimizer (MVO) (Zhao et al, 2016), gray wolf optimizer (GWO) (Yang et al, 2017), genetic algorithm (GA) (Ohenoja and Leiviskä, 2010), flower pollination algorithm (FPA) (Priya and Rajasekar, 2019), and equilibrium optimizer (EO) (Seleem et al, 2021).…”

Section: Parameter Extraction Using Only Meta-heuristic Algorithmsmentioning

confidence: 99%

A Critical Note of Major Parameter Extraction Methods for Proton Exchange Membrane Fuel Cell (PEMFC)

Yang

Han

2022

Front. Energy Res.

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“…The applied methods may be divided into two groups; the first is based on only one algorithm, while the second is based on combining two techniques or more [8,17,26,27]. The first group of single algorithms such as adaptive differential evolution algorithm (ADE) [25,26], particle swarm optimisation, seeker optimisation algorithm (SOA) and genetic algorithm (GA) [18,21,27], Grey Wolf Optimisation (GWO) [8], Antlion Optimiser (ALO) [33] and Dragonfly Algorithm (DA) [34], Grasshopper Optimisation Algorithm (GOH) [16], Slap Swarm Optimiser (SSO) [17], Shark Smell Optimiser (SHSO) [35], JAYA algorithm [24], and Cuckoo Search (CS) [13] have been proposed to resolve the issue of parameter estimation of PEMFCs. The main objective of applying the reported methods is to obtain an accurate model of the PEMFC.…”

Section: Literature Review and Research Gapmentioning

confidence: 99%

An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

Diab

Abdul-Ghaffar²,

Ahmed

et al. 2021

IET Renewable Power Gen

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This paper introduces the application of the variants of the Grey Wolf Optimisation algorithm for the sake of assessing unknown parameters of Proton Exchange Membrane Fuel Cells models. Three versions of Grey Wolf Optimisation algorithm are applied: Conventional Grey Wolf Optimisation, Improved Grey Wolf Optimisation based on dimension learning-based hunting, and Selective Opposition-based Grey Wolf Optimisation. Moreover, Optimisation algorithms of Ant Lion Optimiser, Atom search optimisation, Dragonfly algorithm, and Multi-Verse Optimiser have been applied to estimate the parameters of Proton Exchange Membrane Fuel Cells models for the validation purpose. These algorithms are applied to three commercial Proton Exchange Membrane Fuel Cells stacks: BCS 500W-PEM, 500W-SR-12PEM and 250W-stack. The analyses are executed considering several operational circumstances. Sum of square errors value of the results based on parameters estimation and those experimentally tested are calculated. The objective function is chosen as sum of square errors value. The results are compared with those obtained using well-known methods in the literature to validate the effectiveness of the proposed methods. It is noticeable that the simulated I-V curves momentously match the datasheet curves for all the studied cases. In addition, considering the accuracy of the solution and the convergence speed, the Proton Exchange Membrane Fuel Cells model based on the Improved Grey Wolf Optimisation algorithm excels all other algorithms. Based on the simulation results, the Improved Grey Wolf Optimisation algorithm can improve the optimisation efficiency to 99.97 for the 250 W stack while the efficiency with Grey Wolf Optimisation and Selective Opposition-based Grey Wolf Optimisation are 98.83 and 98.84, respectively, for the 250 W stack case study.

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Optimizing PEMFC model parameters using ant lion optimizer and dragonfly algorithm: A comparative study

Cited by 11 publications

References 26 publications

Fuel Cell Hybrid Locomotive with Modified Fuzzy Logic Based Energy Management System

Fuel Cell Hybrid Locomotive with Modified Fuzzy Logic Based Energy Management System

A Critical Note of Major Parameter Extraction Methods for Proton Exchange Membrane Fuel Cell (PEMFC)

An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

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