Dynamics and control of a turbocharged solid oxide fuel cell system

Mantelli, Luca; Ferrari, Mario L.; Traverso, Alberto

doi:10.1016/j.applthermaleng.2021.116862

Cited by 20 publications

(14 citation statements)

References 52 publications

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“…Proportional reduces the error, integral eliminates the accumulated deviation, and differential predicts the error trend. [ 21,22 ] The control law is as follows

u false(t false) = K_{normalp} [e false(t false) + 1 / K_{i} \int_{0}^{t} e false(t false) d t + K_{normald} d e false(t false) / d t]

…”

Section: Establishment and Verification Of The Fuel Cell Thermal Mana...mentioning

confidence: 99%

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Backpropagation Neural Network‐Based Prediction Model for Fuel Cell Thermal Management System

Xin

Zhang

et al. 2023

Energy Tech

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The temperature prediction of fuel cell thermal management systems (FCTMS) has been the focus of research in fuel cell vehicle. Herein, a prediction model of FCTMS based on the backpropagation neural network is proposed. Predictive models are applied to fuel cell TMSs for predicting temperature changes within the system. First, the fuel cell TMS is established based on Amesim software and verified by using experimental data. Then a prediction model is established based on the simulation data of the system model. After the validation calculation, the highest accuracy of the stack temperature prediction was found, with a relative error of 0.75%. The heat sink outlet temperature prediction accuracy is the worst, with a relative error of 4.3%. The mean square error of the overall output of the prediction model is 0.043, and the mean absolute percentage error of the three results is 0.23%, 0.48%, and 0.16%. Both are below 5%. Therefore, the prediction model has more precise prediction performance, which helps the parameter study and control decision setting of FCTMS.

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“…Proportional reduces the error, integral eliminates the accumulated deviation, and differential predicts the error trend. [ 21,22 ] The control law is as follows

u false(t false) = K_{normalp} [e false(t false) + 1 / K_{i} \int_{0}^{t} e false(t false) d t + K_{normald} d e false(t false) / d t]

…”

Section: Establishment and Verification Of The Fuel Cell Thermal Mana...mentioning

confidence: 99%

“…Proportional reduces the error, integral eliminates the accumulated deviation, and differential predicts the error trend. [21,22] The control law is as follows…”

Section: Control Strategymentioning

confidence: 99%

Backpropagation Neural Network‐Based Prediction Model for Fuel Cell Thermal Management System

Xin

Zhang

et al. 2023

Energy Tech

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“…The solid oxide FC (SOFC) 11‐13 is the most promising and appealing. SOFCs' advantages, such as fuel flexibility, low emissions, and high electrical efficiency, make them promising candidates in a variety of industries, including heat, and electricity cogeneration 14,15 . The research on improving the performance of SOFCs leads to the development of realistic models and the usage of various materials.…”

Section: Introductionmentioning

confidence: 99%

“…SOFCs' advantages, such as fuel flexibility, low emissions, and high electrical efficiency, make them promising candidates in a variety of industries, including heat, and electricity cogeneration. 14,15 The research on improving the performance of SOFCs leads to the development of realistic models and the usage of various materials. Because the voltage of the SOFC is closely reliant on the parameters that describe the internal processes, 16,17 the more accurate the model parameters are, the more efficient the performance enhancement because the SOFC is a multivariable coupled system, optimizing model parameters is a critical issue.…”

Section: Introductionmentioning

confidence: 99%

Optimal parameter estimation of solid oxide fuel cells model using bald eagle search optimizer

Abaza

El‐Sehiemy

El‐Fergany

et al. 2022

Intl J of Energy Research

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Summary In this article, a new nature‐inspired optimizer based on bald eagles' fish hunting behavior is proposed for accurate parameters estimation of the solid oxide fuel cells stack's model. The optimal estimated values of the seven control parameters are generated by the bald eagle search (BES) optimizer. These parameters have optimized the model of the solid oxide fuel cells stack and formulated it as an optimization problem with associated constraints. The employed BES optimizer is a flexible algorithm with a relatively large population size which effectively improves the optimization process and has the ability to be improved. The BES optimizer is smooth, rapid, and steady in the convergence process. The BES optimizer is a very simple algorithm that consists of three main stages which are selecting hunting space, searching in the hunting area, and swooping stage. The model of the stack is tested at four operating conditions at 3 bar and varied temperatures of 923/973/1023/1073 K. The BES optimizer leads to more accurate parameters with good convergences. The insignificant values of reported worst mean square errors of something around 4.7e‐6 V2 points out the full conformity and coincidence between actual and estimated dataset voltage points. In addition to that, the efficacy of the BES optimizer is assured by comparing the results to parameters estimated from some recent techniques. The cropped estimated polarization curves, V‐I and P‐I, are very close to the measured datasets at various operating conditions. Statistics metrics indicate the robustness and the viability of the BES compared to other recent challenging optimizers.

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“…The introduction of LNG to replace fuel oil in internal combustion engines (ICE) will help to reduce CO2 emissions, but it is not sufficient to reach the target: thus, different more sustainable solutions are under analysis. Recent studies in literature highlighted the possibility of employing fuel cell systems, fed by methanol [5,6], biogas [7,8] or hydrogen [9,10], while other authors developed algorithms to compare different fuels by adopting multi-criteria approaches [11,12], considering technical, environmental and economic aspects [13,14]. Among the different choices, Proton Exchange Membrane Fuel Cells (PEMFC), fed by pure hydrogen, present several advantages, such as low noise and vibrations, zero pollutant and CO2 emissions, fast response to load variations and high compactness.…”

Section: Introductionmentioning

confidence: 99%

Thermal integration of PEM Fuel Cells and metal hydrides storage system for Zero Emission Ultimate Ship (ZEUS)

et al. 2022

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The ZEUS (Zero Emission Ultimate Ship), developed in the framework of the national research project TecBia conducted by Fincantieri and co-founded by Italian Ministry of Economic Development, is a 25m length vessel characterized by a zero-emissions propulsion system. The on-board power generation is provided by 4 PEM Fuel Cell modules (140 kW power installation) fed by hydrogen stored into 48 Metal Hydride tanks (MH). PEMFC and MH thermal systems are coupled to recover the heat produced by PEMFC and to feed the endothermic dissociation reactions of hydrogen from MHs. This paper provides a Matlab-Simulink model to simulate the dynamic behaviour of the PEMFC power generation system and the thermal coupling with MH racks installed onboard. Three typical operative profiles are simulated to verify the thermal management control system and the impact of transient conditions on the propulsion plant. Furthermore, the effects of the major exogenous parameters are investigated. Results verify that thermal coupling between the two systems is guaranteed; however, an excessive load increase can lead the stacks to operate under non-optimal conditions for significant periods of time. The effect of exogenous parameters has been verified to be negligible and does not significantly affect the control system.

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Dynamics and control of a turbocharged solid oxide fuel cell system

Cited by 20 publications

References 52 publications

Backpropagation Neural Network‐Based Prediction Model for Fuel Cell Thermal Management System

Backpropagation Neural Network‐Based Prediction Model for Fuel Cell Thermal Management System

Optimal parameter estimation of solid oxide fuel cells model using bald eagle search optimizer

Thermal integration of PEM Fuel Cells and metal hydrides storage system for Zero Emission Ultimate Ship (ZEUS)

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