Observations on the Parameter Estimation Problem of Polymer Electrolyte Membrane Fuel Cell Polarization Curves

Ohenoja, Markku; Leiviskä, Kauko

doi:10.1002/fuce.201900155

Cited by 20 publications

(5 citation statements)

References 74 publications

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“…These methods have different variants and have been used for parameter estimation. For more information about these parameter estimation problems, you can refer to the comprehensive review conducted by M. Ohenoja et al [59].…”

Section: B Summary Of Semi-empirical Degradation Modelsmentioning

confidence: 99%

A Comprehensive Review of Degradation Prediction Methods for an Automotive Proton Exchange Membrane Fuel Cell

Nguyen

Lee

2023

Energies

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Proton exchange membrane fuel cells (PEMFCs) are an alternative power source for automobiles that are capable of being cleaner and emission-free. As of yet, long-term durability is a core issue to be resolved for the mass production of hydrogen fuel cell vehicles that requires varied research in the range from sustainable materials to the optimal operating strategy. The capacity to accurately estimate performance degradation is critical for developing reliable and durable PEMFCs. This review investigates various PEMFC performance degradation modeling techniques, such as model-based, data-driven, and hybrid models. The pros and cons of each approach are explored, as well as the challenges in adequately predicting performance degradation. Physics-based models are capable of simulating the physical and electrochemical processes which occur in fuel cell components. However, these models tend to be computationally demanding and can vary in terms of parameters between different studies. On the other hand, data-driven models provide rapid and accurate predictions based on historical data, but they may struggle to generalize effectively to new operating conditions or scenarios. Hybrid prediction approaches combine the strengths of both types of models, offering improved accuracy but also introducing increased computational complexity to the calculations. The review closes with recommendations for future research in this area, highlighting the need for more extensive and accurate prediction models to increase the reliability and durability of PEMFCs for fuel cell electric vehicles.

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Section: B Summary Of Semi-empirical Degradation Modelsmentioning

confidence: 99%

A Comprehensive Review of Degradation Prediction Methods for an Automotive Proton Exchange Membrane Fuel Cell

Nguyen

Lee

2023

Energies

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“…Therefore, MHAs have been adopted to get the optimum solutions for several engineering problems such as power systems problems, as presented in [95][96][97][98][99][100][101][102][103][104]. Besides, depending on the no freelunch theory [105], many researchers have utilized various algorithms for accurate and effective investigation of the polarization characteristics of the PEMFC, as reported in [5,7,16,57]. It's worth saying that this section highlights the most recent works regarding presenting a new MHA to identify the undefined parameters of the PEMFC Extract the optimal values of the unknown parameters…”

Section: Mhas For Pemfc's Model Parameters Identificationmentioning

confidence: 99%

“…The cuckoos implement a haphazard strategy to pick the host nest. Furthermore, the basic construction of CSO depends on three concepts, relying on which each cuckoo produces next solutions referring to (16) [118][119][120].…”

Section: Cuckoo Search Optimizer (Cso)mentioning

confidence: 99%

“…PEMFCs have distinguished by their flexibility, high power density, short startup time, fast response for load changes, low operating temperatures and pressures and no safety issues. Thus, they have been involved in the widest range of applications, mainly in transportation applications [1,2,[14][15][16][17][18]. Nevertheless, the expensive cost of PEMFCs hinders their competitive penetration in the market [1,2].…”

Section: Introductionmentioning

confidence: 99%

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Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Ashraf

Abdellatif

Elkholy

et al. 2022

Arch Computat Methods Eng

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For the sake of precise simulation, and proper controlling of the performance of the proton exchange membrane fuel cells (PEMFCs) generating systems, robust and neat mathematical modelling is crucially needed. Principally, the robustness and precision of modelling strategy depend on the accurate identification of PEMFC’s uncertain parameters. Hence, in the last decade, with the noteworthy computational development, plenty of meta-heuristic algorithms (MHAs) are applied to tackle such problem, which have attained very positive results. Thus, this review paper aims at announcing novel inclusive survey of the most up-to-date MHAs that are utilized for PEMFCs stack’s parameter identifications. More specifically, these MHAs are categorized into swarm-based, nature-based, physics-based and evolutionary-based. In which, more than 350 articles are allocated to attain the same goal and among them only 167 papers are addressed in this effort. Definitely, 15 swarm-based, 7 nature-based, 6 physics-based, 2 evolutionary-based and 4 others-based approaches are touched with comprehensive illustrations. Wherein, an overall summary is undertaken to methodically guide the reader to comprehend the main features of these algorithms. Therefore, the reader can systematically utilize these techniques to investigate PEMFCs’ parameter estimation. In addition, various categories of PEMFC’s models, several assessment criteria and many PEMFC commercial types are also thoroughly covered. In addition to that, 27 models are gathered and summarized in an attractive manner. Eventually, some insights and suggestions are presented in the conclusion for future research and for further room of improvements and investigations.

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“…Recently, metaheuristic optimization methods have been used by many researchers to derive the PEMFC’s model required parameters owing to the major improvement of artificial intelligent-based methodologies. Metaheuristic techniques are the most effective and reliable method to use when estimating PEMFCs parameters as treated as one of optimization problems 4 , 12 , 13 . Regarding the optimization techniques used in this context, shark smell technique 14 , coyote optimization algorithm 15 , 16 , grey wolf algorithm (GWA) 17 , whale optimization algorithm (WOA) 18 , grasshopper optimization algorithm (GOA) 19 bald eagle search optimizer 20 and bonobo algorithm 21 .…”

Section: Introductionmentioning

confidence: 99%

Fuel-cell parameter estimation based on improved gorilla troops technique

Shaheen

El‐Sehiemy

El‐Fergany

et al. 2023

Sci Rep

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The parameter extraction of the proton exchange membrane fuel cells (PEMFCs) is an active study area over the past few years to achieve accurate current/voltage (I/V) curves. This work proposes an advanced version of an improved gorilla troops technique (IGTT) to precisely estimate the PEMFC’s model parameters. The GTT's dual implementation of the migration approach enables boosting the exploitation phase and preventing becoming trapped in the local minima. Besides, a Tangent Flight Strategy (TFS) is incorporated with the exploitation stage for efficiently searching the search space. Using two common PEMFCs stacks of BCS 500W, and Modular SR-12, the developed IGTT is effectively applied. Furthermore, the two models are evaluated under varied partial temperature and pressure. In addition to this, different new recently inspired optimizers are employed for comparative validations namely supply demand optimization (SDO), flying foxes optimizer (FFO) and red fox optimizer (RFO). Also, a comparative assessment of the developed IGTT and the original GTT are tested to ten unconstrained benchmark functions following to the Congress on Evolutionary Computation (CEC) 2017. The proposed IGTT outperforms the standard GTT, grey wolf algorithm (GWA) and Particle swarm optimizer (PSO) in 92.5%, 87.5% and 92.5% of the statistical indices. Moreover, the viability of the IGTT is proved in comparison to various previously published frameworks-based parameter's identification of PEMFCs stacks. The obtained sum of squared errors (SSE) and the standard deviations (STD) are among the difficult approaches in this context and are quite competitive. For the PEMFCs stacks being studied, the developed IGTT achieves exceedingly small SSE values of 0.0117 and 0.000142 for BCS 500 and SR-12, respectively. Added to that, the IGTT gives superior performance compared to GTT, SDO, FFO and RFO obtaining the smallest SSE objective with the least STD ever.

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Observations on the Parameter Estimation Problem of Polymer Electrolyte Membrane Fuel Cell Polarization Curves

Cited by 20 publications

References 74 publications

A Comprehensive Review of Degradation Prediction Methods for an Automotive Proton Exchange Membrane Fuel Cell

A Comprehensive Review of Degradation Prediction Methods for an Automotive Proton Exchange Membrane Fuel Cell

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Fuel-cell parameter estimation based on improved gorilla troops technique

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