A Novel System-Level Model for a Fuel Cell in a Strategic Context

Rubio, A.; Agila, W.

doi:10.1109/icrera.2018.8566819

Cited by 10 publications

(3 citation statements)

References 11 publications

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“…The flowchart of the information in the agent PERCEIVE ALERT is depicted in Figure 9. Where "N" stands for the total restrictions that each variable must meet and "n" is the restriction to analyze; "n" takes values from Table 3 [1][2][3][4]. The time of the operation cycle of the PERCEIVE ALERT agent is determined by the frequency of the readings provided by the sensors.…”

Section: Alert Typementioning

confidence: 99%

“…The most severe problems facing humankind, particularly the issue of energy sustainability, must be tackled by the engineering sciences, taking the "bull by the horns". For this, the hydrogen fuel cell, in the context of the "hydrogen economy", constitutes a clean energy source with great potential for the present and the future [1,2]. The PEMFC is an electrochemical device that converts chemical energy from fuel (H 2 ) into electrical energy in a single step and produces water and heat as by-products.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Intelligence in Autonomous PEM Fuel Cell Control

Rubio,

Agila,

González

et al. 2023

Energies

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A combination of perceptive and deliberative processes is necessary to ensure the efficient and autonomous control of proton exchange membrane fuel cells (PEMFCs) under optimal humidification conditions. These processes enable monitoring and control tasks across various application scenarios and operating conditions. Consequently, it becomes crucial to adjust parameter values corresponding to different states of the PEMFC during its operation. In this context, this work presents the design and development of an architecture for the control and management of a PEMFC with a maximum power output of 500 [W] based on intelligent agents operating under optimal conditions (membrane humidification). The proposed architecture integrates perception and action algorithms that leverage sensory and contextual information using heuristic algorithms. It adopts a hierarchical structure with distinct layers, each featuring varying time windows and levels of abstraction. Notably, this architecture demonstrates its effectiveness in achieving the desired energy efficiency objective, as evidenced by successful validation tests conducted with different electrical power values delivered by the fuel cell, encompassing three distinct operating states (dry, normal, and flooded). An exemplary application of this scheme is the dynamic control of the humidification of the polymeric membrane, which further highlights the capabilities of this architecture.

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Section: Alert Typementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Intelligence in Autonomous PEM Fuel Cell Control

Rubio,

Agila,

González

et al. 2023

Energies

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“…The great problems of today's society such as economic inequality, climate change, decreasing of the ozone layer, lack of resources, toxic pollution, diminishing biodiversity, human health, coastal settlements; must be confronted by the academy and decidedly by the engineering sciences. The hydrogen-powered fuel cell stack is the most promising energy source of the future [2], as it would help eliminate the world's serious problems [3,4]. The hydrogen economy-where hydrogen and fuel cells are essential partners-is estimated to grow from USD 122 billion in 2018 to USD 155 billion in 2022 [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Model to Manage Water in Polymer Electrolyte Membrane Fuel Cells

Rubio

Agila

2021

Processes

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In this paper, a fuzzy model is presented to determine in real-time the degree of dehydration or flooding of a proton exchange membrane of a fuel cell, to optimize its electrical response, and, consequently, its autonomous operation. By applying load, current, and flux variations in the dry, normal, and flooded states of the membrane, it was determined that the temporal evolution of the fuel cell voltage is characterized by changes in slope and by its voltage oscillations. The results were validated using electrochemical impedance spectroscopy and show slope changes from 0.435 to 0.52 and oscillations from 3.6 to 5.2 mV in the dry state, and slope changes from 0.2 to 0.3 and oscillations from 1 to 2 mV in the flooded state. The use of fuzzy logic is a novelty and constitutes a step towards the progressive automation of the supervision, perception, and intelligent control of fuel cells, allowing them to reduce their risks and increase their economic benefits.

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