Temperature and voltage dynamic control of PEMFC Stack using MPC method

Chen, Xi; Ye, Fang; Liu, Qinxiao; He, Lingxuan; Zhao, Yixin; Huang, Taiming; Wan, Zhongmin; Wang, Xiao‐Dong

doi:10.1016/j.egyr.2021.11.271

Cited by 40 publications

(10 citation statements)

References 42 publications

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“…For controlling the PEMFC's voltage, [9] presented a model predictive control system, studied the cell's transient behavior, and checked how important it is that input variables affect the voltage of the cell. Likewise, a PEMFC temperature and voltage dynamic management controller using MPC was proposed in [10]. In particular, the mass flow rate of cooling water and hydrogen gas regulate the operating temperature and the output voltage, respectively.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

2023

IJIES

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“…Similarly, ref. [28] provided a comparative analysis of PID and MPC techniques for controlling the dynamics of voltage and temperature of PEMFC, and [29] worked on the modified whale optimization algorithm to achieve a stable voltage curve. An ES technique was utilized by [30] for PEMFC control with respect to constant current and hydrogen influx.…”

Section: Introductionmentioning

confidence: 99%

Fuel Cell Voltage Regulation Using Dynamic Integral Sliding Mode Control

Yasin

Saqib

et al. 2022

Electronics

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Fuel cells guarantee ecological ways of electricity production by promising zero emissions. Proton exchange membrane fuel cells (PEMFCs) are considered one of the safest methods, with a low operating temperature and maximum conversion efficiency. In order to harness the full potential of PEMFC, it is imperative to ensure the membrane’s safety through appropriate control strategies. However, most of the strategies focus on fuel economy along with viable fuel cell life, but they do not assure constant output voltage characteristics. A comprehensive design to regulate and boost the output voltages of PEMFC under varying load conditions is addressed with dynamic integral sliding mode control (DISMC) by combining the properties of both the dynamic and integral SMC. The proposed system outperforms in robustness against parametric uncertainties and eliminates the reaching phase along with assured stability. A hardware test rig consisting of a portable PEMFC is connected to the power converter using the proposed technique that regulates voltage for varying loads and power conditions. The results are compared with a proportional integral (PI) based system. Both simulation and hardware results are provided to validate the proposed technique. The experimental results show improvements of 35.4%, 34% and 50% in the rise time, settling time and robustness, respectively.

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“…Hierarchical techniques can also be applied to develop a comprehensive energy management strategy consisting of high-level control, which is based on fuzzy logic technique as well as adaptive control loop and low-level control that generates a pulse-width-modulation signal so that appropriate power distribution is achieved and the operating voltage of the powertrain is stabilised [15]. In recent years, applications of MPC algorithms [16][17][18] to PEM fuel cells have been reported. A linear model can be used for MPC prediction [19].…”

Section: Introductionmentioning

confidence: 99%

Fast Model Predictive Control of PEM Fuel Cell System Using the L1 Norm

Nebeluk

Ławryńczuk

2022

Energies

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This work describes the development of a fast Model Predictive Control (MPC) algorithm for a Proton Exchange Membrane (PEM) fuel cell. The MPC cost-function used considers the sum of absolute values of predicted control errors (the L1 norm). Unlike previous approaches to nonlinear MPC-L1, in which quite complicated neural approximators have been used, two analytical approximators of the absolute value function are utilised. An advanced trajectory linearisation is performed on-line. As a result, an easy-to-solve quadratic optimisation task is derived. All implementation details of the discussed algorithm are detailed for two considered approximators. Furthermore, the algorithm is thoroughly compared with the classical MPC-L2 method in which the sum of squared predicted control errors is minimised. A multi-criteria control quality assessment is performed as the MPC-L1 and MPC-L2 algorithms are compared using four control quality indicators. It is shown that the presented MPC-L1 scheme gives better results for the PEM.

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Temperature and voltage dynamic control of PEMFC Stack using MPC method

Cited by 40 publications

References 42 publications

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

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

Fuel Cell Voltage Regulation Using Dynamic Integral Sliding Mode Control

Fast Model Predictive Control of PEM Fuel Cell System Using the L1 Norm

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