Fuel Cell Voltage Regulation Using Dynamic Integral Sliding Mode Control

Yasin, Amina; Yasin, Abdul Rehman; Saqib, Muhammad Bilal; Zia, Saba; Riaz, Mudassar; Nazir, Robina; Abdalla, Ridab Adlan Elamin; Bajwa, Shaherbano

doi:10.3390/electronics11182922

Cited by 7 publications

(4 citation statements)

References 65 publications

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“…Further research is needed in this direction in the future. Additionally, the research did not consider the specific impact and role of the identified parameters on the cell itself [36][37][38].…”

Section: Discussionmentioning

confidence: 99%

Generalized Regression Neural Network Based Meta-Heuristic Algorithms for Parameter Identification of Proton Exchange Membrane Fuel Cell

He¹,

Zhou²,

Liu³

et al. 2023

Energies

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An accurate parameter extraction of the proton exchange membrane fuel cell (PEMFC) is crucial for establishing a reliable cell model, which is also of great significance for subsequent research on the PEMFC. However, because the parameter identification of the PEMFC is a nonlinear optimization problem with multiple variables, peaks, and a strong coupling, it is difficult to solve this problem using traditional numerical methods. Furthermore, because of insufficient current and voltage data measured by the PEMFC, the precision rate of cell parameter extraction is also very low. The study proposes a parameter extraction method using a generalized regression neural network (GRNN) and meta-heuristic algorithms (MhAs). First of all, a GRNN is used to de-noise and predict the data to solve the problems in the field of PEMFC, which include insufficient data and excessive noise data of the measured data. After that, six typical algorithms are used to extract the parameters of the PEMFC under three operating conditions, namely high temperature and low pressure (HTLP), medium temperature and medium pressure (MTMP), and low temperature and high pressure (LTHP). The last results demonstrate that the application of GRNN can prominently decrease the influence of data noise on parameter identification, and after data prediction, it can greatly enhance the precision rate and reliability of MhAs parameter identification, specifically, under HTLP conditions, the V-I fitting accuracy achieved 99.39%, the fitting accuracy was 99.07% on MTMP, and the fitting accuracy was 98.70%.

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Section: Discussionmentioning

confidence: 99%

Generalized Regression Neural Network Based Meta-Heuristic Algorithms for Parameter Identification of Proton Exchange Membrane Fuel Cell

He¹,

Zhou²,

Liu³

et al. 2023

Energies

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“…The authors in [11] have designed a dynamic integral sliding mode control (DISMC) strategy to boost and regulate the output voltage of proton exchange membrane fuel cell (PEMFC) system operating under varying loads. The voltage control problem is solved using a 2-loop control strategy, with a PI controller in the outer loop for voltage regulation and the inner current control loop employs the DISMC algorithm.…”

Section: Review Of Published Papersmentioning

confidence: 99%

Sliding Mode Control in Dynamic Systems

2023

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“…Microbial fuel cells are emerging as multipurpose renewable energy systems [ 15 ]. This is mainly due to the multidimensional usages of this environmentally friendly system, such as electricity generation, hydrogen production, and wastewater/waste treatment [ 16 , 17 ]. The ability to generate electricity makes the MFC the preferred bioelectrochemical system over the MEC.…”

Section: Introductionmentioning

confidence: 99%

Application of Magnetite-Nanoparticles and Microbial Fuel Cell on Anaerobic Digestion: Influence of External Resistance

et al. 2023

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In this paper, the application of magnetite-nanoparticles and a microbial fuel cell (MFC) was studied on the anaerobic digestion (AD) of sewage sludge. The experimental set-up included six 1 L biochemical methane potential (BMP) tests with different external resistors: (a) 100 Ω, (b) 300 Ω, (c) 500 Ω, (d) 800 Ω, (e) 1000 Ω, and (f) a control with no external resistor. The BMP tests were carried out using digesters with a working volume of 0.8 L fed with 0.5 L substrate, 0.3 L inoculum, and 0.53 g magnetite-nanoparticles. The results suggested that the ultimate biogas generation reached 692.7 mL/g VSfed in the 500 Ω digester, which was substantially greater than the 102.6 mL/g VSfed of the control. The electrochemical efficiency analysis also demonstrated higher coulombic efficiency (81.2%) and maximum power density (30.17 mW/ m2) for the 500 Ω digester. The digester also revealed a higher maximum voltage generation of 0.431 V, which was approximately 12.7 times the 0.034 V of the lowest-performing MFC (100 Ω digester). In terms of contaminants removed, the best-performing digester was the digester with 500 Ω, which reduced contaminants by more than 89% on COD, TS, VS, TSS and color. In terms of cost-benefit analysis, this digester produced the highest annual energy profit (48.22 ZAR/kWh or 3.45 USD/kWh). This infers the application of magnetite-nanoparticles and MFC on the AD of sewage sludge is very promising for biogas production. The digester with an external resistor of 500 Ω showed a high potential for use in bioelectrochemical biogas generation and contaminant removal for sewage sludge.

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Fuel Cell Voltage Regulation Using Dynamic Integral Sliding Mode Control

Cited by 7 publications

References 65 publications

Generalized Regression Neural Network Based Meta-Heuristic Algorithms for Parameter Identification of Proton Exchange Membrane Fuel Cell

Generalized Regression Neural Network Based Meta-Heuristic Algorithms for Parameter Identification of Proton Exchange Membrane Fuel Cell

Sliding Mode Control in Dynamic Systems

Application of Magnetite-Nanoparticles and Microbial Fuel Cell on Anaerobic Digestion: Influence of External Resistance

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