Model Predictive Control on Constant Voltage Output of a Proton Exchange  Membrane Fuel Cell

Fan, Liping; Zhang, Jun; Li, Chong

doi:10.25103/jestr.062.24

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…The activation drop which is obtained from the TOFEL equation can be expressed as [8] where I (mA/cm 2 ) is defined as current density and the oxygen concentration on the electrode surface which is calculated as a function of cell temperature can be indicated by [6] Under low current density, the stack losses are indicated in linear mode and can be described as ohmic drop [8] where R c is the contact resistance to electron flow (0.0003 Ω) and R M is the resistance to proton transfer through the membrane (Ω) which can be described as the above expressions. In addition, M is the membrane specific resistivity and l is the membrane thickness in centimeter.…”

Section: System Modeling and Control-oriented Modelmentioning

confidence: 99%

“…specific coefficient based on the fuel-cell membrane type and A is the membrane active area. Under higher current density, because of the limitation of mass transportation, the cell potential is decreased considerably and can be described by [8] where 'B' is a constant depending on the type of fuel cell and in this paper has been selected 0.016. Also, I max is the maximum electrical current passing through the fuel-cell stack in Ampere [8].…”

Section: System Modeling and Control-oriented Modelmentioning

confidence: 99%

“…In terms of power optimization and energy management system with model predictive controller (MPC) [1], in 2013, Fan et al [8], by obtaining the mathematical model of fuelcell powertrain and defining either the inlet hydrogen or oxygen pressures as the controlling inputs and fuel-cell output power as the controlling output, could calculate the linear steady-state equations. Then, by applying the linear model predictive controller they controlled the inlet hydrogen pressure.…”

Section: Introductionmentioning

confidence: 99%

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Real-time nonlinear model predictive energy management system for a fuel-cell hybrid vehicle

Moghadasi

Taghavipour

Shamekhi

2019

J Braz. Soc. Mech. Sci. Eng.

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Proton exchange membrane fuel cell is considered as one of the most efficient sources of renewable energy. Time-varying dynamic and nonlinear equations are two major factors that make control and power optimization of fuel-cell vehicles challenging. In this paper, by using a comprehensive PEMFC vehicle model and nonlinear model predictive controller, a novel energy management method is represented. By considering both of the regenerating brake power and fuel-cell output power as the controller inputs, the steady-state equations have been derived in a nonlinear form. Also, the NMPC contains a constrained quadratic cost function which has been minimized to make the controller inputs completely optimized. Moreover, the two controller references using in this article have been achieved based on experimental FTP drive-cycle data. Finally, the optimized cost-function weighting matrices will be calculated by genetic algorithm, and then, the real-time controller inputs are applied to the vehicle model to get better results on vehicle range during an online procedure, and the online results are compared with the experimental drive-cycle data.

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Section: System Modeling and Control-oriented Modelmentioning

confidence: 99%

Section: System Modeling and Control-oriented Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real-time nonlinear model predictive energy management system for a fuel-cell hybrid vehicle

Moghadasi

Taghavipour

Shamekhi

2019

J Braz. Soc. Mech. Sci. Eng.

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show abstract

“…Nevertheless, these approaches often encounter difficulties in addressing non-linear dynamics and multivariable control, challenges that are frequently encountered in practical applications. Conversely, MPC strategies have demonstrated notable success in numerous industrial processes, showcasing their proficiency in managing complex system dynamics and multivariable control (Zhang and Yu, 2009;Fan et al, 2013;Lechartier et al, 2015;Robin et al, 2016;Chatrattanawet et al, 2017;Ebrahimi et al, 2017). Therefore, this study endeavors to investigate the implementation of dual MPC temperature control strategies in fuel cell systems, with the goal of optimizing their efficiency and performance.…”

Section: Introductionmentioning

confidence: 99%

Enhancing fuel cell performance through a dual MPC strategy for coordinated temperature management

Liu,

Zhang,

Gong

2024

Front. Energy Res.

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Ensuring the optimal operating temperature is imperative for achieving efficient performance in proton exchange membrane fuel cells. Consequently, this study introduces a dual-model predictive control strategy to regulate the water pump and cooling fan in a cooling system. Initially, we establish an electrochemical and thermal model for fuel cell stacks and validate the model’s accuracy through experimental data. The system model is linearized, and the model predictive control (MPC) controller is formulated using the MATLAB/Simulink toolbox. Subsequently, it is collaboratively simulated with the electrochemical model of the fuel cell stack and the temperature model. To evaluate the effectiveness of the MPC controller, we conducted a comparative analysis with the traditional proportional–integral–derivative (PID) control and water pump MPC under step load, uniform load increase, and variable target scenarios. The findings indicate that in contrast to the PID control, the MPC controller significantly decreases the stack temperature difference fluctuation by more than 50%, maintaining the stack temperature within ±0.6 K of the set value. Furthermore, we independently assessed the performance of the MPC controller under varying ambient temperatures. The findings illustrate that the dual MPC method proficiently adapts cooling parameters across different ambient temperature ranges (288.15 K–308.15 K), ensuring the stable performance of the fuel cell. The model is linearized, and the simulation work is explained mainly on the MATLAB/Simulink platform. In order to compare the effectiveness of the MPC controller, the comparison with the MPC controller strategy of the water pump is added, which can better reflect the effectiveness of the proposed collaborative MPC controller strategy.

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“…Several control strategies have been applied to analyze and enhance the system stability of DMFCs [20,21], such as PID control algorithms [22], neural network methods [23], model predictive control schemes [24,25], fuzzy logic [26] and extremum seeking controls [27]. However, most of them were designed to work under the assumption of small disturbance (or little noise).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Operation Strategy for Voltage Stability Enhancement in Active DMFCs

Yang¹,

Hu²,

Lei³

et al. 2018

Preprint

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An adaptive operation strategy for on-demand control of active direct methanol fuel cells (DMFCs) is proposed as an alternative method to enhance the voltage stability. A simplified semi-empirical model is firstly developed to describe I-V relationships based on uniform-designed experiments. It is then embedded into multi-objective optimizations to construct the adaptive operation strategy.Experimental studies are implemented on different DMFC systems to validate the proposed semi-empirical model, control strategy and system response to operational adjustments. Numerical simulations are also performed to investigate the underlying mechanisms of the proposed adaptive operation strategy. The results show that the adaptive operation strategy provides possibilities for voltage stability enhancement without the sacrifice of energy conversion efficiency.The adaptive operations are also found to be able to extend the range of operating current density or to decrease the voltage deviation according to ones

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Model Predictive Control on Constant Voltage Output of a Proton Exchange Membrane Fuel Cell

Cited by 8 publications

References 18 publications

Real-time nonlinear model predictive energy management system for a fuel-cell hybrid vehicle

Real-time nonlinear model predictive energy management system for a fuel-cell hybrid vehicle

Enhancing fuel cell performance through a dual MPC strategy for coordinated temperature management

Adaptive Operation Strategy for Voltage Stability Enhancement in Active DMFCs

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