Efficient Voltage Control in Proton Exchange Membrane Fuel Cell: An Approach based on Intelligent Algorithms

Hamedi, Himan; Khezri, Rahmat; Golshannavaz, Sajjad; Ershadifard, Behrouz

doi:10.1080/03772063.2016.1257375

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…In [70], a fault-tolerant strategy based on sliding mode control for thermal management system of PEMFC was proposed, they tracked the temperature by building balance surface and switching rate based on sine function. Moreover in [71], BFA method was used to optimize the voltage controller online, according to the voltage gain, controller parameters are corrected to minimize the voltage deviation.…”

Section: Adaptive Model Based On Neural Networkmentioning

confidence: 99%

A review of fault diagnosis and fault-tolerant control of vehicular polymer electrolyte membrane fuel cell power system

Pan

Zhang

et al. 2022

J. Phys.: Conf. Ser.

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Proton exchange membrane fuel cell (PEMFC) is a promising vehicle power because of its high energy conversion efficiency and pollution-free reactant. However, the complex structure of multi-system coordination makes the correlation between parameters strong, the fault rate is high and the control is difficult. Timely and accurate fault diagnosis and effective fault-tolerant control are of great significance to the stable output and durability of PEMFC system. Firstly, this paper classifies different faults and describes the applicable scenarios of each classification method. Then, the fault diagnosis methods based on experiment, model and data are summarized, and their advantages and disadvantages are compared. Then, the methods and characteristics of hardware fault tolerance and software fault tolerance are analyzed from the point of redundancy, and the application of fault tolerance control in maintaining the stability of PEMFC system is summarized. Finally, the improvement direction and development prospect of fault diagnosis related technology of vehicle PEMFC system are proposed.

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Section: Adaptive Model Based On Neural Networkmentioning

confidence: 99%

A review of fault diagnosis and fault-tolerant control of vehicular polymer electrolyte membrane fuel cell power system

Pan

Zhang

et al. 2022

J. Phys.: Conf. Ser.

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“…Additionally, these algorithms take a long time to regulate and stabilize, making it practically impossible to attain ideal control performance. Furthermore, the researchers in [8] proposed an NNbased model predictive control (MPC) system. In this system, the rate of hydrogen was controlled by the proposed controller, which was learned by the presented NN to achieve the desired efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 8. The maximum power and the output voltage against load current with variable hydrogen partial pressure…”

mentioning

confidence: 99%

Development of Predictive Voltage Controller Design for PEM Fuel Cell System based on Identifier Model

2023

IJIES

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This paper presents a new development of a predictive voltage neural controller to control the stack terminal output voltage of a nonlinear proton exchange membrane fuel cell (PEMFC) system based on a neural network technique and a back-propagation learning algorithm. The main objective of this paper is to precisely and quickly identify the best control action of the hydrogen partial pressure to enhance the nonlinear performance of the fuel cell output voltage under a variable load current. This optimal control action prevents damage to the fuel cell membrane, thereby prolonging the fuel cell's lifetime. The proposed predictive voltage controller consists of three sub-controllers. The first one is the numerical feed-forward controller (NFFC), which is used to decide the steady-state hydrogen partial pressure (PH2) control action depending on the desired voltage. The second sub-controller is a feedback neural controller that uses a multi-layer perceptron (MLP) and a back-propagation learning algorithm to generate the hydrogen partial pressure feedback control action to track the desired output voltage of the fuel cell during transient conditions. The third sub-controller is the predictive control law equation, which is based on the modified Elman recurrent neural network (MERNN) as an identifier for the PEMFC model and the multi-objective performance index. From the simulation results, the proposed controller, which is composed of the three sub-controllers, has the capability to generate a precisely and quickly timed response to the hydrogen partial pressure control action in order to minimize the tracking voltage error and eliminate oscillation in the output voltage of the fuel cell. Finally, the suggested predictive voltage control strategy's numerical simulation results are then verified by comparison with those of other types of controllers in terms of the minimum number of steps ahead prediction (reducing from 10 to 1 step ahead prediction) and enhancement of the tracking voltage error by 81.8% when comparing with a predictive neural controller and improvement of the tracking voltage error by 87.5% when comparing with an inverse neural controller. Moreover, the oscillation effect in the output voltage is completely eliminated, resulting in a response without any overshoot.

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A novel intelligent-based method to control the output voltage of Proton Exchange Membrane Fuel Cell

Nejad

Farshad

Gholamalizadeh

et al. 2019

Energy Conversion and Management

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Efficient Voltage Control in Proton Exchange Membrane Fuel Cell: An Approach based on Intelligent Algorithms

Cited by 5 publications

References 17 publications

A review of fault diagnosis and fault-tolerant control of vehicular polymer electrolyte membrane fuel cell power system

A review of fault diagnosis and fault-tolerant control of vehicular polymer electrolyte membrane fuel cell power system

Development of Predictive Voltage Controller Design for PEM Fuel Cell System based on Identifier Model

A novel intelligent-based method to control the output voltage of Proton Exchange Membrane Fuel Cell

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