Feedforward based transient control in solid oxide fuel cells

Madani, Omid; Das, Tuhin

doi:10.1016/j.conengprac.2016.08.008

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…In contrast to the work by Carré [75], many control strategies such as in [2,3,9,30,[60][61][62][64][65][66]73] are also limited to application on a simulation model and a respective verification of the proposed control strategy. Application on a simulation is advantageous and gives the ability to perform many runs without any risk to improve the control strategy until a final robust version.…”

Section: Sofc-system With Aegrmentioning

confidence: 99%

“…Han et al [58] present a MPC-strategy, where an adaption to model deviations is performed with a specific algorithm to counteract the nonlinearities and disturbances with little computational effort. Das and Weisman [30], as well as Madani and Das [61] use an analytical formulation of stack-FU at stationary conditions for its control at load changes.…”

Section: Review On Control Strategiesmentioning

confidence: 99%

“…Volume flow measurements, which are needed for accurate determination of the recirculation ratio in SOFC-systems with AEGR, are subject to deviations or measurement errors. Therefore, an exact measurement and control of the recirculation flow with its high temperature and the supplied natural gas flow cannot be achieved as it is assumed in the studies [30,57,61] with fixed or known recirculation ratio.…”

Section: Review On Control Strategiesmentioning

confidence: 99%

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Control of oxygen-to-carbon ratio and fuel utilization with regard to solid oxide fuel cell systems with anode-offgas recirculation: A review

Schaefer¹

2021

Preprint

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One of the possible SOFC system-configurations providing the highest potential of electrical DC-efficiency of up to 65% is a SOFC-system with anode exhaust gas recirculation (AEGR), where part of the depleted anode exhaust gas is recirculated and mixed with fresh natural gas upstream of the reformer. For safe and durable operation of a SOFC-system, the oxygen-to-carbon-ratio and the fuel utilization as characteristic parameters must not exceed stack- and reformer-specific thresholds. The determination and control of the characteristic parameters are therefore of crucial importance. However, this poses especially for SOFC-systems with AEGR due to enhanced system complexity a challenging task. In this paper, the authors present an overview on representative control strategies as well as different approaches to determine or diagnose characteristic parameters with emphasis on SOFC-systems with AEGR. Some conclusions are discussed based on the provided overview and outlines recommendations for future research work.

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Section: Sofc-system With Aegrmentioning

confidence: 99%

Section: Review On Control Strategiesmentioning

confidence: 99%

Section: Review On Control Strategiesmentioning

confidence: 99%

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Control of oxygen-to-carbon ratio and fuel utilization with regard to solid oxide fuel cell systems with anode-offgas recirculation: A review

Schaefer¹

2021

Preprint

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“…SOFC operations are often subjected to transient conditions, and, as a result, the fuel cell dynamics have been increasingly considered in modeling activities [19]. Several published works have concentrated on the dynamic modeling and the control of solid oxide fuel cells [20,21]. Li and Choi [22] studied the control of the power output of an SOFC by applying proportional-integral (PI) controllers to maintaining fuel utilization and voltage as the current of the stack changed.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of the Off-Line Robust Model Predictive Control for Solid Oxide Fuel Cells

et al. 2019

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An off-line robust linear model predictive control (MPC) using an ellipsoidal invariant set is synthesized based on an uncertain polytopic approach and then implemented to control the temperature and fuel in a direct internal reforming solid oxide fuel cell (SOFC). The state feedback control is derived by minimizing an upper bound on the worst-case performance cost. The simulation results indicate that the synthesized robust MPC algorithm can control and guarantee the stability of the SOFC; although there are uncertainties in some model parameters, it can keep both the temperature and fuel at their setpoints.

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“…SOFCs fast and reliable simulation is of interest of a bunch of different applications. First, at component level, they are called in 3D-CFD models, to improve significantly computational times and to approach the problem of transient simulation [25]. Second, at system level, a consistent simulation tool is necessary to forecast the SOFC performance when the system asks for operating conditions modification, to further optimize its working point and to provide a prompt regulation.…”

Section: Introductionmentioning

confidence: 99%

Regarding Solid Oxide Fuel Cells Simulation through Artificial Intelligence: A Neural Networks Application

et al. 2018

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Because of their fuel flexibility, Solid Oxide Fuel Cells (SOFCs) are promising candidates to coach the energy transition. Yet, SOFC performance are markedly affected by fuel composition and operative parameters. In order to optimize SOFC operation and to provide a prompt regulation, reliable performance simulation tools are required. Given the high variability ascribed to the fuel in the wide range of SOFC applications and the high non-linearity of electrochemical systems, the implementation of artificial intelligence techniques, like Artificial Neural Networks (ANNs), is sound. In this paper, several network architectures based on a feedforward-backpropagation algorithm are proposed and trained on experimental data-set issued from tests on commercial NiYSZ/8YSZ/LSCF anode supported planar button cells. The best simulator obtained is a 3-hidden layer ANN (25/22/18 neurons per layer, hyperbolic tangent sigmoid as transfer function, obtained with a gradient descent with adaptive learning rate backpropagation). This shows high accuracy (RMS = 0.67% in the testing phase) and successful application in the forecast of SOFC polarization behaviour in two additional experiments (RMS in the order of 3% is scored, yet it is reduced to about 2% if only the typical operating current density range of real application is considered, from 300 to 500 mA·cm−2). Therefore, the neural tool is suitable for system simulation codes/software whether SOFC operating parameters agree with the input ranges (anode feeding composition 0–48%vol H2, 0–38%vol CO, 0–45%vol CH4, 9–32%vol CO2, 0–54%vol N2, specific equivalent hydrogen flow-rate per unit cell active area 10.8–23.6 mL·min−1·cm−2, current density 0–1300 mA·cm−2 and temperature 700–800 °C).

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Feedforward based transient control in solid oxide fuel cells

Cited by 13 publications

References 17 publications

Control of oxygen-to-carbon ratio and fuel utilization with regard to solid oxide fuel cell systems with anode-offgas recirculation: A review

Control of oxygen-to-carbon ratio and fuel utilization with regard to solid oxide fuel cell systems with anode-offgas recirculation: A review

Design and Implementation of the Off-Line Robust Model Predictive Control for Solid Oxide Fuel Cells

Regarding Solid Oxide Fuel Cells Simulation through Artificial Intelligence: A Neural Networks Application

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