Process modeling of an automotive pem fuel cell system

Akroot, Abdulrazzak; Ekici, Özgür; Köksal, Murat

doi:10.1080/15435075.2019.1641105

Cited by 23 publications

(3 citation statements)

References 21 publications

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“…A is flow following the current control strategy in the figure, B is a neural network active disturbance rejection control strategy, and C is flow following current and power control strategy simultaneously. Control strategy A is the same as the experimental control strategy [8]. Under the condition of the step change of reactor current, the variation trend of the simulation value of cooling water flow is consistent with the experimental results, and the relative error is within 3%.…”

Section: Verification and Simulation Of Control Model Of The Thermal Management Systemsupporting

confidence: 59%

Optimization of fuel cell thermal management system based on back propagation neural network

Wei

2021

THERM SCI

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Two thermal management control strategies, namely flow following current and power mode and back propagation neural network auto-disturbance rejection method, were proposed to solve significant temperature fluctuation problems, long regulation time, and slow response speed in fuel cell thermal management system variable load. The results show that the flow following current and power control strategy can effectively weaken the coupling effect between pump and radiator fan and significantly reduce the overshoot and adjustment time of inlet and outlet cooling water temperature and temperature difference reactor. Although the control effect of the neural network and strategy is insufficient under maximum power, the overall control effect is better than that of the flow following the current control strategy.

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Section: Verification and Simulation Of Control Model Of The Thermal Management Systemsupporting

confidence: 59%

Optimization of fuel cell thermal management system based on back propagation neural network

Wei

2021

THERM SCI

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“…Therefore, a control-oriented fuel cell performance model with high prediction accuracy and low computing cost is urgently needed in industry to implement model predictive control strategies and to avoid harmful operating conditions. The most prevalent models in the model-based control of fuel cells are 0D analytical and empirical models [18][19][20][21][22]. For instance, Tirnovan and Giurgea [20] developed an empirical fuel cell model based on the first law of thermodynamics, taking the pressure drop in pipes and channels into account.…”

Section: Introductionmentioning

confidence: 99%

A computationally efficient and high-fidelity 1D steady-state performance model for PEM fuel cells

Zhao

Li²,

Shum³

et al. 2023

J. Phys. Energy

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The performance of a proton exchange membrane (PEM) fuel cell is determined by many factors, including operating conditions, component specifications, and system design, making it challenging to predict its performance over a wide range of operating conditions. Existing fuel cell models can be complex and computationally demanding or may be over-simplified by neglecting many transport phenomena. Therefore, a high-fidelity and computationally efficient model is urgently needed for the model-based control of fuel cells. In this study, semi-implicit multi-physics numerical models have been established, taking the mass, momentum, reactants, liquid water, membrane water, electrons, ions, and energy in all fuel cell components into account. The developed 1D model is of high fidelity by incorporating the two-phase flow, non-isothermal effect, and convection, and is still computationally efficient. These models are validated against data from an auto manufacturer with good agreements, and the computing efficiency is evaluated on a modest laptop computer. The modeling results suggest that the two-phase flow model exhibits better prediction accuracy than the single-phase flow model when reactants are fully humidified, while under low humidity conditions, the two models present equivalent performance as liquid water does not exist in the fuel cell components. The results also suggest that the maximum convective/diffusive ratio of H2, O2, and vapor mass fluxes can be 12%, 5.3%, and 35%, respectively, which are ignored in most diffusion-dominant models. The developed models are computationally efficient, requiring only 0.56 s and 0.26 s to simulate a steady-state operation of fuel cells for the two- and single-phase flow models, respectively. This implies that the developed models are suitable for the control of PEM fuel cells.

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“…Fuel cells, hydrogen, and oxidant supply systems that feed the cells and the cooling system used due to the heat generated may remain under harsh operating conditions in practical applications [7]. A literature survey shows that most of the research on fuel cells has focused on modeling and parametric studies [8][9][10][11][12]. Le and Zhou [8] formed a general PEM fuel cell to calculate the fluid flow, heat, and mass transfers as well as, electrochemical reactions and current.…”

Section: Introductionmentioning

confidence: 99%

Response of a proton exchange membrane fuel cell to step changes in mass flow rates

2021

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Transient regime effects are particularly important in fuel cells designed for vehicles. Three-dimensional modeling of a proton exchange membrane fuel cell with a serpentine channel is presented, and the response of the fuel cell to a step-change in the mass flow rates is analyzed by using the computational fluid dynamics techniques. After a validation study of the mathematical and numerical model, step increases of 20% in mass flow rates are applied to the inlet boundary conditions, and time dependent power and current density responses of the fuel cell are analyzed. Polarization curves are generated for the assessment of the fuel cell performance, and their variations in time are presented. The results show that current and power densities increase with time at low cell voltage values due to concentration losses; however, increases in power and current are negligible at high voltages.

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Process modeling of an automotive pem fuel cell system

Cited by 23 publications

References 21 publications

Optimization of fuel cell thermal management system based on back propagation neural network

Optimization of fuel cell thermal management system based on back propagation neural network

A computationally efficient and high-fidelity 1D steady-state performance model for PEM fuel cells

Response of a proton exchange membrane fuel cell to step changes in mass flow rates

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