The present work investigates different models of polymer electrolyte membrane fuel cell. More specifically, three models are studied: a nonlinear state-space model, a generic dynamic model integrated into MATLAB/Simulink, and an equivalent RC electrical circuit. A new equivalent electrical RL model is proposed, and the methodology for determining its parameters is also given. An experimental test bench, based on a 1200-W commercial PEMFC, is built to compare the static and dynamic behaviour of the existing models and the proposed RL model with the experimental data. The comparative analysis highlights the advantages and drawbacks of each of these models. The major advantages of the proposed RL model lie in both its simplicity and its ability to provide a similar transitory behaviour compared to the commercially manufactured PEMFC employed in this research.

The main component of the hydrogen production system is the electrolyzer (EL), which is used to convert electrical energy and water into hydrogen and oxygen. The power converter supplies the EL, and the controller is used to ensure the global stability and safety of the overall system. This review aims to investigate and analyze each one of these components: Proton Exchange Membrane Electrolyzer (PEM EL) electrical modeling, DC/DC power converters, and control approaches. To achieve this desired result, a review of the literature survey and an investigation of the PEM EL electrical modeling of the empirical and semi-empirical, including the static and dynamic models, are carried out. In addition, other sub-models used to predict the temperature, gas flow rates (H₂ and O₂), hydrogen pressure, and energy efficiency for PEM EL are covered. DC/DC power converters suitable for PEM EL are discussed in terms of efficiency, current ripple, voltage ratio, and their ability to operate in the case of power switch failure. This review involves analysis and investigation of PEM EL control strategies and approaches previously used to achieve control objectives, robustness, and reliability in studying the DC/DC converter-PEM electrolyzer system. The paper also highlights the online parameter identification of the PEM electrolyzer model and adaptive control issues. Finally, a discussion of the results is developed to emphasize the strengths, weaknesses, and imperfections of the literature on this subject as well as proposing ideas and challenges for future work.

This paper compares the performance of different control techniques applied to a high-performance brushless DC (BLDC) motor. The first controller is a classical proportional integral (PI) controller. In contrast, the second one is based on adaptive neuro-fuzzy inference systems (proportional integral-adaptive neuro-fuzzy inference system (PI-ANFIS) and particle swarm optimization-proportional integral-adaptive neuro-fuzzy inference system (PSO-PI-ANFIS)). The control objective is to regulate the rotor speed to its desired reference value in the presence of load torque disturbance and parameter variations. The proposed controller uses a dSPACE platform (MicroLabBox controller board). The experimental prototype comprises a PEMFC system (the Nexa Ballard FC power generator: 1.2 kW, 52 A) and a brushless DC motor BLDC of 1 kW 1000 rpm. The PSO-PI-ANFIS controller presents better performance than the PI-ANFIS and classical PI controllers due to its ability to optimize the PI-ANFIS controller’s parameters using the particle swarm optimization (PSO) algorithm. This optimization results in improved tracking accuracy and reduced overshoot and settling time.

The proposed work presented in this paper is mainly focused on the control of the active and reactive stator powers generated by a wind energy conversion system (WECS) based on the dual feed induction generator (DFIG). This control is achieved by acting on the rotor side converter (RSC) to extract the maximum power from the wind turbine (WT) while regulating the rotor currents. Furthermore, another control objective is achieved by acting on the grid side converter (GSC), in which the DC bus voltage is maintained constant and a unity power factor is ensured. To do that, a new robust control known as active disturbance rejection control (ADRC) has been proposed and applied to the WECS. This control is based on the extended state observer (ESO), which is the main core of this algorithm; it makes the estimation and cancellation of the total effect of various uncertainties (internal and external disturbances) possible in real time. To validate the effectiveness of the proposed approach, the system was modeled and simulated by using the Matlab/Simulink software. Two tests, namely, tracking and robustness tests, were performed to compare the proposed ADRC technique and classical PI controllers. The obtained results are promising and have shown that the proposed control strategy based on ADRC, especially when varying the mode parameters, is performant and very useful.

Today, hydrogen fuel cells occupy a crucial position in sustainable energy systems. However, a precise model of their performance is needed to improve their efficiency and integrate them into hydrogen electric vehicles. This paper presents a hydrogen fuel cell model based on artificial neural networks (ANNs) to predict its performance characteristics. Using experimental data from a PEMFC NEXA 1200 hydrogen fuel cell in the ISA laboratory, an ANN model optimized by deep learning was developed, integrating advanced training techniques. The model’s performance was evaluated on independent test sets, revealing predictive precision with a low mean squared error (MSE) of 0.0429, a low Mean Absolute Percentage Error (MAPE) of 1.05%, a low Root-Mean-Square Error (RMSE) of 0.2071, and a high coefficient of determination (R²) of 0.9071. The model’s development and evaluation will be reviewed here in order to visualize the training progress and the results of the simulation. The main advantages of the proposed ANN model lie in both its flexible architecture, which can capture complex relationships without the need for explicit physical models, and its predictive and optimization capability.

In this paper, a dc–dc boost converter with continuous input is investigated. In addition to its input current, the converter offers the advantage of using a reduced number of electronic components. The objective is to design an appropriate controller for the nonlinear model taking into account the system nonlinearities, the uncertainty of the resistance load, the constrained fuel cell current, and the saturation of the control input signal (duty ratio).The point is that the converter presents a no minimum phase feature, and the duty ratio must constrain between 0 and 1. Then, a saturated nonlinear controller is elaborated. Detailed analysis and simulation show that the proposed controller meets all control objectives, namely: tight dc-bus voltage regulation and asymptotic stability of the closed-loop system. Experimental results are also given, which show the effectiveness of the controller.