Output Voltage Of Fuel Cell Research Articles

An enhanced maximum power point tracking and voltage control for proton exchange membrane fuel cell using predictive model control techniques

Proton Exchange Membrane Fuel Cells serve as sustainable devices for converting renewable energy sources into electricity, offering advantages such as rapid startup, high power density, and operation at low temperatures. They are highly efficient and environmentally friendly energy sources, yet their non-linear output characteristics under variable conditions present challenges in maximizing power output. Furthermore, they generates direct current, while many electrical devices rely on alternating current. This research addresses the necessity of enhancing the performance and efficiency through the development of an advanced maximum power point tracking technique and voltage control strategy. A modified finite-control-set model predictive control technique is proposed for both maximum power point tracking and voltage control. Specifically, a finite-control-set model predictive control technique approach is employed to modulate the switching signal for both the DC-DC boost converter and the DC-AC inverter. The DC-DC boost converter step up the fuel cell output voltage to the desired level, ensuring it reaches the maximum power point, and a DC-AC inverter to convert the direct current voltage to a pure sinusoidal alternating waveform. The investigation demonstrates the effectiveness of the proposed method in achieving its objectives. The proposed maximum power point tracking technique accuracy achieved the maximum power with a rate of 97 % with excellent respond time within 7 ms. For alternating current power, only less than 1.5 % of total harmonic distortion is recorded. The study evaluates the control scheme under robust operating conditions, demonstrating its effectiveness in optimizing PEMFC output and providing high-quality AC voltage.

Enhancing voltage stability through wavelet-fuzzy control of hydrogen flow in OC-PEM fuel cell

Open cathode proton exchange membrane fuel cells (OC-PEM fuel cells) serve as electricity generators, utilizing hydrogen as an input source. While effective for fixed loads like residential applications, challenges arise in dealing with output voltage fluctuations caused by rapid load changes. These fluctuations not only impact fuel cell performance but also introduce instability in the supplied power. To solve this issue, the study proposes an innovative hydrogen flow control system employing a feedforward wavelet- fuzzy method. The primary goal of this control system is to enhance fuzzy control performance using wavelets, mitigating signal fluctuations and achieving optimal stability in fuel cell output voltage under constant load conditions. Wavelet functions act as filters on the fuzzy control input, minimizing fluctuations and refining the entire process. Additionally, a feedforward system is incorporated to maintain hydrogen flow at the set point value. The proposed control system is implemented on a validated model using experimental data. Performance analysis reveals that the proposed method effectively stabilizes voltage by accelerating the recovery time from disturbances.

Composite predictive trajectory control of HS-PMSM with LCL filter for hydrogen electric vehicle at low fuel condition

Limitations in current fuel cell technology lead to instability in output voltage for hydrogen fuel cells, particularly under the influence of temperature variations and fuel flow fluctuations, thereby affecting the normal operation of hydrogen-powered vehicles. Improving the motor drive control system and optimizing energy management are crucial for enhancing overall vehicle performance. To improve dynamic response speed while suppressing harmonics, this paper proposes a predictive control strategy for high-speed permanent magnet synchronous motors with LCL filter in hydrogen fuel cell electric vehicles. The proposed control strategy suppresses high-frequency current harmonics by sampling only the current on the motor side, enabling fast predictive control of the high-order system motor-side current. Additionally, to mitigate the issue of unstable torque output of the electric motor at low fuel levels, this paper proposes a new composite predictive torque trajectory control strategy based on the previous control strategy. This strategy ensures rapid and accurate tracking of motor output torque, particularly during decreases in fuel cell output voltage. Experimental results validate the feasibility and effectiveness of the proposed control strategies in this paper.

Experimental investigation of the two-phase flow distribution and pressure drop characteristic within the cathode bending type channel of fuel cell

The accurate prediction of the two-phase flow distribution within the channel is of great significance for enhancing fuel cell water management and improving output efficiency. The current study constructs a novel transparent fuel cell visualization test system and experimentally investigates the characteristics of two-phase flow pattern distribution, pressure drop, and output voltage within the bending type channel. Furthermore, a focused analysis of slug flow which may induce fuel cell flooding is conducted. The results show that the two-phase flow pattern maps of both the bent and straight channels are highly consistent. The force balance analysis is performed to explain the internal mechanism that the bending structure has less influence on the two-phase flow pattern. Additionally, the bending structure has a significant impact on the channel pressure drop magnitude and time-domain characteristics. The two-phase pressure drop can be used as an empirical tool to judge the flow pattern and occurrence position. The fuel cell output voltage jump phenomenon occurs under slug flow pattern, and there is a strong positive correlation between the voltage jump amplitude and current density, with a Pearson correlation coefficient of 0.924. The research results provide theoretical guidance for two-phase flow prediction within the channel and flood prevention of the fuel cell.

Optimal dual-model controller of solid oxide fuel cell output voltage using imitation distributed deep reinforcement learning

Solid oxide fuel cell (SOFC) is disadvantaged by significant nonlinearity, which makes it difficult to control output voltage of SOFC and satisfy the constraints of fuel utilization simultaneously. In order to solve this problem, a dual-model control framework (DMCF) is proposed. In particular, there are two controllers deployed under this framework, with an PID controller and a supplementary dynamic controller to track the SOFC output voltage. The supplementary dynamic controller is conducive to the stabilization of tracking by adapting to the uncertainties, considering the constraint on fuel utilization. In addition, an imitation distributed deep deterministic policy gradient (ID3PG) algorithm, which integrates imitation learning and distributed deep reinforcement learning to enhance the robustness and adaptive capacity of this framework, is proposed for the supplementary dynamic controller. The simulation results obtained in this work have demonstrated that the proposed framework is effective in imposing control on SOFC output voltage and preventing constraint violations of fuel utilization.

Performance study and structure optimization of new type of radial flow field proton exchange membrane fuel cell

ABSTRACT On the basis of a circular bipolar plate (BP) of the proton exchange membrane fuel cell (PEMFC), a new rim-type radial flow field (FF) structure was designed inspired by the rim style of the vehicle. In this type of FF structure, the key variables are the number of layers of annular flow FFs, the number of branched FFs, and the amplitude of the waveform of branched FFs. To compare the effects of these variables on FF (especially the FF of cathode), the PEMFC single-cell model with different variables was developed by COMSOL Multiphysics software and then simulated. The reasonable value of these variables in enhancing the PEMFC performance was finally determined by comparing the oxygen distribution, water distribution, pressure drop on the cathode side, and the fuel cell output voltage and power of different schemes. This study achieves the highest ultimate current density and maximum power density among all schemes when the number of branches is 8. A four-layer structure is the most appropriate for selecting the number of FF layers. For the selection of the curvature of the branch, 15° is the most appropriate.

Study on the coupling properties of hydrogen PEMFC and air source heat pump

Abstract A steady-state model for the coupling of a proton exchange membrane fuel cell and an air source heat pump (PEMFC–ASHP) is established in this paper. The influence of reaction pressure and relative humidity on the polarization curve and operating characteristics of PEMFC, as well as the influence of ambient temperature and compressor speed on the system performance are investigated. The results show that the increase of reaction pressure and relative humidity is conducive to the improvement of fuel cell output voltage and efficiency, and the influence of reactant gas relative humidity is more obvious. As the increase of ambient temperature and compressor speed, the heat pump efficiency and system efficiency decrease. The research results can provide theoretical guidance for the design and operation of the PEMFC–ASHP system.

Simulation of PEM fuel cell integrated DVR using SRF-ANFIS controller for power quality enhancement

Voltage related Power Quality (PQ) problems are attracting the eyes of researchers, as these cause huge loss in productivity and profitability for both utilities and consumers. Dynamic Voltage Restorer (DVR) is a well-known custom power device that provides an economical solution for the alleviation of power quality problems. Generally, battery energy storage is used as an input for DVR, but batteries are still bulky and costly and must be disposed once their chemicals are used, which limits the compensation capability of DVR. Nowadays, Fuel Cell (FC) technologies have attracted much attention owing to their high efficiencies and low emissions. This project aims to model and simulate Proton Exchange Membrane Fuel Cell (PEMFC) and is used as a DC input source for DVR. The suitable boost converter is designed and modelled to lift up the fuel cell output voltage suitable as DC link voltage for DVR. Adaptive Neuro Fuzzy Inference System (ANFIS) controller uses Synchronous Reference Frame Theory (SRF) algorithm to generate d and q axis components of reference signal for Voltage Source Converter (VSC) of DVR. This PEMFC DVR is verified for balanced and unbalanced voltage sag, swell, and harmonics using MATLAB/SIMULINK. A simulation result proves that this PEMFC DVR is capable of providing a technically advanced and economic solution for balanced and unbalanced voltage sag and swell. During compensation, the DVR DC input voltage is also preserved as constant, which enhances the compensation capability of DVR.

A Hybrid Prognostic Method for PEMFC With Aging Parameter Prediction

Prognostic of the proton-exchange membrane fuel cell can effectively extend the fuel cell lifespan, which can contribute to its large-scale commercialization. In this article, a hybrid prognostic approach is proposed to predict the fuel cell output voltage and other aging parameters that can reflect the stack’s internal degradation. During the training stage, the prognostic parameters are obtained by using the extended Kalman filter (EKF). Besides, the fuel cell output voltage is used to train the long short-term memory (LSTM) recurrent neural network. During the prediction stage, the hybrid EKF and LSTM method will predict the output voltage and aging parameters, and the degradation can be predicted under dynamic conditions. The proposed method is validated by experimental tests under static, quasi-dynamic, and dynamic conditions. Results indicate that the hybrid method can accurately predict the degradation trend of fuel cell voltage and aging parameters. The RMSE of the method is less than 0.0110, 0.0262, and 0.0317 under static, quasi-dynamic, and dynamic conditions, respectively, which are smaller than the conventional model-based methods or data-driven methods. Furthermore, the hybrid method can provide more detailed information for prognostic decision-making and better prolong the fuel cell lifespan.

Mathematical Modeling of an Electrotechnical Complex of a Power Unit Based on Hydrogen Fuel Cells for Unmanned Aerial Vehicles

The issues of mathematical and numerical simulation of an electrical complex of a power plant based on hydrogen fuel cells with a voltage step-down converter were considered. The work was aimed at developing a mathematical model that would provide for determining the most loaded operation mode of the complex components. The existing mathematical models do not consider the effect of such processes as the charge and discharge of the battery backup power supply on the power plant components. They often do not consider the nonlinearity of the fuel cell output voltage. This paper offers a mathematical model of an electrical complex based on the circuit analysis. The model combines a well-known physical model of a fuel cell based on a potential difference and a model of a step-down converter with a battery backup power supply developed by the authors. A method of configuring a fuel cell model based on the experimental current–voltage characteristic by the least-squares method has been proposed. The developed model provides for determining currents and voltages in all components of the power plant both in the nominal operating mode and in the mode of limiting the power consumed from the fuel cell when the battery backup power supply is being charged. The correctness of the calculated ratios and the mathematical model has been confirmed experimentally. Using the proposed model, a 1300 W power plant with a specific power of 529.3 W∙h/kg was developed and tested.

Improvement of Temperature and Humidity Control of Proton Exchange Membrane Fuel Cells

Temperature and humidity are two important interconnected factors in the performance of PEMFCs (Proton Exchange Membrane Fuel Cells). The fuel and oxidant humidity and stack temperature in a fuel cell were analyzed in this study. There are many factors that affect the temperature and humidity of the stack. We adopt the fuzzy control method of multi-input and multi-output to control the temperature and humidity of the stack. A model including a driver, vehicle, transmission motor, air feeding, electrical network, stack, hydrogen supply and cooling system was established to study the fuel cell performance. A fuzzy controller is proven to be better in improving the output power of fuel cells. The three control objectives are the fan speed control for regulating temperature, the solenoid valve on/off control of the bubble humidifier for humidity variation and the speed of the pump for regulating temperature difference. In addition, the results from the PID controller stack model and the fuzzy controller stack model are compared in this research. The fuel cell bench test has been built to validate the effectiveness of the proposed fuzzy control. The maximum temperature of the stack can be reduced by 5 °C with the fuzzy control in this paper, so the fuel cell output voltage (power) increases by an average of approximately 5.8%.

An improved differential evolution optimization controller for enhancing the performance of PEM fuel cell powered electric vehicle system

At present, the fuel cell is playing major role in the current automotive industry application. Also, the fuel cell have the features of good reliability, high flexibility, reduced demand for foreign oil, and enhanced environment quality. However, for each and every functioning temperature variation, the Fuel Cell (FC) gives different working peak power points on it nonlinear voltage versus current curves. In this article, an Improved Differential Evolutionary Optimization (DEO) method is included in the Fuzzy Logic Controller (FLC) to enhance the maximum power delivered by the fuel cell stack. Here, the main objective of the DEO technique is to optimize the membership functions of the fuel cell stack input and output variables. Due to the proper selection of membership functions, the proposed MPPT controller gives fast tracing speed, good accuracy, and high sustainability. Also, a high voltage gain, nonisolated dc-dc converter is proposed in this work to enhance the fuel cell output voltage and reduce the current flowing through the consumer loads. The properties of converter are less potential pressure across the switches, extensive output voltage functioning, low supply current ripples, and high supply voltage withstand capability.

Model identification of Solid Oxide Fuel Cell using hybrid Elman Neural Network/Quantum Pathfinder algorithm

In this research, a new efficient method is introduced for model assessment of Solid Oxide Fuel Cell (SOFC) model using a new hybrid Elman Neural Network (ENN). The main purpose of this research is to minimize the Mean Squared Error (MSE) between empirical data and modeling data of the fuel cell output voltage using the suggested hybrid ENN. The designed ENN is indeed a combination of this network with an improved metaheuristic, called Quantum Pathfinder (QPF) algorithm to give an optimal model. The proposed QPF-ENN model is then performed in a SOFC case study to show its efficiency. The results of the suggested method are validated by the reference voltage and also two other methods to show the higher minimum value of the Mean Squared Error (MSE) toward the others. Simulation results are analyzed the mean squared error value of the methods for 5000 samples, where, the voltage is limited between 320 V and 361 V. The results show that the mean square error for the QPF-Elman method, GWO-RHNN method, and PF-Elman method are 0.0014, 0.0017, and 0.0018, respectively. This indicates that the proposed QPF-Elman delivers the minimum value of the mean square error.

Nonlinear control of a PEM fuel cell integrated system with water electrolyzer

In this study, a multi-input multi-output (MIMO) sliding mode control (SMC) scheme combined with a couple of low-order nonlinear observers is proposed for a proton exchange membrane fuel cell (PEMFC) integrated with water electrolyzer system. First, the PEMFC is developed coupling with a few auxiliary systems, namely air compressor, air cooler, internal humidifier, and primary, supply and return manifolds. With this, to recycle the unused hydrogen fuel and maintaining the fuel cell system temperature, the model of an ejector and cooling system, respectively, are developed. Subsequently, as a source of hydrogen fuel for the PEMFC integrated system, a proton exchange membrane (PEM) water electrolyzer is modeled. Next, a MIMO sliding mode scheme is proposed to control the fuel cell output voltage, compressor airflow and fuel cell system temperature for the PEMFC, and hydrogen production rate for the electrolyzer. To predict the unmeasured state information required for the SMC, a couple of nonlinear observers, namely adaptive state observer (ASO), sliding mode observer (SMO) and extended Kalman filter (EKF) are constructed using the low-order process model. After investigating the satisfactory state response, these three observers are coupled separately with the SMC. Finally, the comparative control performance of the hybrid SMC-ASO, SMC-SMO and SMC-EKF are evaluated against a conventional proportional integral (PI) controller.

Monitoring of Hydrogen Fuel Cell Modeling with Boost Converter

Energy plays a very important role in human life. Every year, the demand for energy needs continues to increase and the majority of energy generation uses fossil fuels. So we need an energy source that is environmentally friendly. One of the environmentally friendly energy sources is a fuel cell. Fuel cells can produce electrical energy at a lower cost than the electrical energy generated by conventional power grids. In making a fuel cell system, a modeling is needed so that the fuel cell system can work properly and in accordance with the desired specifications. One method for modeling a fuel cell system is to use MATLAB. The use of a DC-DC boost converter with a properly designed closed loop PID controller has a very important role in regulating the PWM of the DC-DC boost converter switch and plays a very important role in controlling power regulation. In this research, a modeling analysis of NEXATM 1.2 kW hydrogen fuel cell with a DC-DC boost converter controlled by a PID controller was carried out for a compact Power Conditioning Unit (PCU) design. The purpose of this research is to model and analyze the characteristics of the performance of the hydrogen fuel cell system and the performance of the PID controller in regulating the DC-DC boost converter output voltage on the hydrogen fuel cell. The results showed that the performance of the hydrogen fuel cell was influenced by the pressure of oxygen gas, hydrogen, and temperature. The greater the value of oxygen gas pressure, hydrogen gas pressure, and temperature on the fuel cell, the greater the voltage and current output of the fuel cell. The simulation results show the fuel cell output voltage is 47.89 V with an error percentage of 4.22%, and the fuel cell output current is 23.94 A with an error percentage of 0.25%, and the fuel cell output power is 1147 W with an error percentage of 4.12%. The performance of the PID controller with the DC-DC boost converter in regulating the fuel cell output voltage is very good. This is indicated by the results of the response curve for the fuel cell output current, namely the value of rise time (tr) of 4 seconds, delay time (td) of 0.2 seconds, peak time (tp) of 4 seconds, settling time (ts) of 4 seconds, and a maximum overshoot (Mp) of 0%. For output voltage, the value of rise time (tr) is 4 seconds, delay time (td) is 0.2 seconds, peak time (tp) is 4 seconds, settling time (ts) is 4 seconds, and maximum overshoot (Mp) is 0% with the parameter value Proportional (P) of 0.001, Integral (I) of 10, and Derivative (D) of 0.

A High Gain Step-Up DC-DC Converter Driven BLDC Motor for Fuel Cell Vehicles

The paper deals with the simulation for closed loop control of a high gain step-up DC-DC converter (HGSUC) driven Brush Less Direct Current (BLDC) motor for Fuel Cell Vehicles (FCV). The simplest control strategy using hall-effect sensors are employed to control the BLDC motor. The output voltage of fuel cells (FC) are very low as compared to the voltage of DC bus in case of a fuel cell vehicle. With increase in output current output voltage reduces significantly. A HGSUC with a wide input span is crucial to match the output voltage of FC and the DC bus voltage. And also it should have high conversion efficiency and the power semiconductor devices should exhibit low voltage stress. The simulation of high gain converter with 1kW/310V and a BLDC motor of 1hp/310V has been done which validates the correctness of its theoretical analysis and suitability of the high gain converter as a power interface.

Digital PID Control Law Design for Fuel Cell Model Based on FPGA Emulator System

This paper proposes an off-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. The aim of this research is to obtain the best hydrogen partial pressure (PH2) value using FPGA emulator to design and implement a digital PID controller that track the fuel cell output voltage during a variable load current applied. The off-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID controller parameters that improve the dynamic behavior of the closed loop digital control fuel cell system and to achieve the stability of the desired output voltage of fuel cell. The numerical simulation results (MATLAB) package and FPGA emulator experimental work show the performance of the proposed FPGA-PID controller in terms of voltage error reduction and generating optimal value of the (PH2) control action without oscillation in the output and no saturation state when these results are compared with other control methodology.

Genetic algorithm assisted fixed frequency sliding mode controller for quadratic boost converter in fuel cell vehicle

This study presents the quadratic boost converter (QBC) as a power electronic interface between the fuel cell (FC) stack and the DC bus of the FC vehicle (FCV). QBC provides the high voltage gain to interface the low voltage, high current and non-linear FC stack to higher voltage DC bus of the proposed architecture. The output voltage of FC is variable and is a function of cell chemistry, load variations and atmospheric conditions. Therefore, a genetic algorithm assisted fixed frequency sliding mode controller (SMC) based two-loop control strategy is proposed and developed in this study. The accuracy of the control signal generated by the SMC depends on the accuracy of reference current generation. Therefore, genetic algorithm tuned proportional–integral controller is proposed to generate the ripple-free reference current generation which follows the load-side or source-side variations. Further, a control algorithm is integrated with SMC which uses the design constants such that the chattering amplitude is suppressed and generates the control signal with fixed frequency. Consequently, the dynamic performance of the proposed controller is compared in terms of the tracking error, control effort and sliding surface error. The extensive studies are carried out under different dynamic conditions to analyse the robustness of the proposed controller for QBC-based FCV.

Pq Open-Loop Control of a Grid-Tied Inverter Interfacing a Large-Scale Fuel Cell Stack

A fuel cell output voltage varies significantly with the variation of the output current. To maintain a stable voltage of a grid-connected fuel cell stack and converts its output voltage into AC, an inverter is required. Generally, the power quality, and grid stability and performance can be significantly affected bythe extensive use of large-scale power converters. Proper control settings of these power converters are essential to eliminate such issues and ensure that the system complies with the grid voltage, frequency,and phase. This paper deals with the control of a grid-connected diode clamped three-phase inverter interfacing a1.2 MW fuel cell stack. The aim is to model and to simulate a grid-connected fuel cell inverter. The adopted control strategy is based on active and reactive power open-loop control scheme. The frequency and phase of the inverter are synchronised with the grid frequency using a phase-locked-loop. An LCL filter serving as an interface between the inverter and the grid is used to mitigate the effect of harmonics. The modelling and simulation are carried out using Matlab/Simulink environment. Results show that the grid-connected fuel cell inverter complies with the grid voltage, frequency, and phase with both total harmonic distortions of voltage and current around 0.09%.

PEM Fuel Cell Voltage Neural Control Based on Hydrogen Pressure Regulation

Fuel cells are promising devices to transform chemical energy into electricity; their behavior is described by principles of electrochemistry and thermodynamics, which are often difficult to model mathematically. One alternative to overcome this issue is the use of modeling methods based on artificial intelligence techniques. In this paper is proposed a hybrid scheme to model and control fuel cell systems using neural networks. Several feature selection algorithms were tested for dimensionality reduction, aiming to eliminate non-significant variables with respect to the control objective. Principal component analysis (PCA) obtained better results than other algorithms. Based on these variables, an inverse neural network model was developed to emulate and control the fuel cell output voltage under transient conditions. The results showed that fuel cell performance does not only depend on the supply of the reactants. A single neuro-proportional–integral–derivative (neuro-PID) controller is not able to stabilize the output voltage without the support of an inverse model control that includes the impact of the other variables on the fuel cell performance. This practical data-driven approach is reliably able to reduce the cost of the control system by the elimination of non-significant measures.