Description:FIELD OF THE INVENTION

[0001] The present invention relates to a control system for a proton exchange membrane fuel cell (PEMFC) that improve PEMFC stability by maintaining consistent performance despite changing conditions, extending its lifespan and efficiency. The system also enhances control accuracy by optimizing tuning parameters, ensuring precise and reliable fuel cell operation.

BACKGROUND OF THE INVENTION

[0002] The system for Proton Exchange Membrane Fuel Cell (PEMFC) ensures optimal performance, efficiency, and durability by regulating key parameters like temperature, pressure, humidity, and fuel flow. It typically involves sensors, actuators, and a controller (e.g., PID or model predictive control) to maintain stable operation under varying loads. The system monitors stack voltage, current, and gas composition to prevent issues like membrane drying, flooding, or fuel starvation. Advanced control strategies, such as adaptive or fuzzy logic control, enhance responsiveness to dynamic conditions. Effective control maximizes power output, minimizes degradation, and ensures safe, reliable operation for applications like vehicles or stationary power.

[0003] Traditional control methods for Proton Exchange Membrane Fuel Cells (PEMFCs), such as PID (Proportional-Integral-Derivative) controllers, rely on fixed parameters to regulate temperature, pressure, and fuel flow. While simple and widely used, they struggle with PEMFC’s nonlinear dynamics and sensitivity to operating conditions. Limitations include slow response to load changes, inability to handle complex interactions (e.g., humidity and gas flow coupling), and poor adaptability to aging or degradation. Manual tuning is often required, which is time-consuming and suboptimal for dynamic environments. These methods may lead to inefficiencies, reduced power output, or membrane damage, necessitating advanced control strategies like model predictive or adaptive control.

[0004] US6551731B1 discloses a invention relates to a fuel cell system comprising an anode chamber and a cathode chamber which are separated from each other by a proton conducting membrane. When the fuel cell system is operated, fuel, in particular H2 or a water/methanol mixture, can be fed to the anode chamber and an oxidant, in particular oxygen, can be fed to the cathode chamber. In standby mode, the cathode chamber does not allow flow through and the oxidant and fuel are present in both the cathode chamber and the anode chamber, respectively. The fuel cell system remains at operating temperature in the standby mode. This enables the fuel cell system to be used as a combined interruption-free power supply unit and backup unit.

[0005] US10507345B2 discloses a Described are inverting systems that may be used on board an aircraft or other passenger transportation vehicle to reduce a risk of fire due to electronic components or to other elements in a compartment and to assist in preventing or extinguishing any fire or hazardous condition that may occur. The systems include a source of inert gas such as oxygen depleted air generated from a fuel cell on board the aircraft. The oxygen depleted air or other inert gas is conveyed through ducts to compartments that house the electronics, thus changing the conditions in the compartment to be less conducive to fire.

[0006] Conventionally, many systems are available in the market for proton exchange but existing PEMFC control systems often struggle with maintaining stability under dynamic operating conditions, leading to inconsistent performance and reduced lifespan. Their fixed tuning parameters limit accuracy and responsiveness to real-time changes. Furthermore, these systems frequently lack in preventing overfitting, compromising diagnostic reliability and overall long-term accuracy.

[0007] In order to overcome the aforementioned drawbacks, there exists a need in the art to develop a system enhances PEMFC stability and accuracy, adapting to dynamic conditions for precise, reliable operation. The system optimizes lifespan and energy output through continuous, control, ensuring consistent performance.

OBJECTS OF THE INVENTION

[0008] The principal object of the present invention is to overcome the disadvantages of the prior art.

[0009] An object of the present invention is to develop a system that is capable of improve the stability of the proton exchange membrane fuel cell by maintaining consistent performance under changing operating conditions, thus extending its lifespan and enhancing its overall efficiency.

[0010] Another object of the present invention is to develop a system that is capable of to enhance the accuracy of the control system by optimizing its tuning parameters for better response to dynamic changes, therefore ensuring precise and reliable fuel cell operation.

[0011] Yet another object of the present invention is to develop a system that is capable of ensure steady-state performance of the fuel cell through continuous monitoring and adjustment based on real-time data, thus maximizing energy output and operational lifespan.

[0012] The foregoing and other objects, features, and advantages of the present invention will become readily apparent upon further review of the following detailed description of the preferred embodiment as illustrated in the accompanying drawings.

SUMMARY OF THE INVENTION

[0013] The present invention relates to a control system for a proton exchange membrane fuel cell (PEMFC) that enhances the control system's accuracy by optimizing tuning parameters for dynamic changes, ensuring precise fuel cell operation. It also guarantees steady-state performance through continuous real-time data monitoring and adjustments, maximizing energy output and operational lifespan.

[0014] According to an embodiment of the present invention, a control system for a proton exchange membrane fuel cell (PEMFC), comprising a Type-2 fuzzy logic controller connected to the PEMFC, functioning to stabilize the operating point by adjusting control parameters in response to varying operating conditions, a Grey Wolf Optimization module linked to the Type-2 fuzzy logic controller, functioning to optimize tuning parameters for improved control accuracy and stability, a neural network integrated with the Type-2 fuzzy logic controller, functioning to generate optimized control rules, a sensor array coupled to the PEMFC and the Type-2 fuzzy logic controller, functioning to measure operating conditions such as temperature, pressure, and current for input to the control system and a feedback loop connecting the PEMFC output to the Type-2 fuzzy logic controller, functioning to provide real-time data for maintaining steady-state performance.

[0015] While the invention has been described and shown with particular reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0016] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 illustrates a block diagram depicting workflow of a control system for a proton exchange membrane fuel cell (PEMFC).

DETAILED DESCRIPTION OF THE INVENTION

[0017] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

[0018] In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

[0019] As used herein, the singular forms “a,” “an,” and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.

[0020] The present invention relates to a control system for a proton exchange membrane fuel cell (PEMFC) improves PEMFC stability by maintaining consistent performance in varying conditions, extending lifespan and boosting efficiency. The system also ensures steady-state fuel cell performance through continuous real-time data monitoring and adjustments, maximizing energy output and operational lifespan.

[0021] Referring to Figure 1, a block diagram depicting workflow of a control system for a proton exchange membrane fuel cell (PEMFC) is illustrated. The invention is a control system designed to enhance the performance, stability, and efficiency of a proton exchange membrane fuel cell (PEMFC) under varying operating conditions. The system integrates computational techniques and real-time feedback to optimize the PEMFC's operation, ensuring fast response times, minimal power dips, and adaptability to environmental changes such as temperature and load variations. The system comprises five main components: A Type-2 fuzzy logic controller, a Grey Wolf Optimization module, a neural network, a sensor array, and a feedback loop. Additionally, a power management unit is integrated to improve energy efficiency and cost-effectiveness.

[0022] The Type-2 fuzzy logic controller is directly connected to the PEMFC and serves as the primary control unit. It stabilizes the PEMFC's operating point by adjusting control parameters in response to changes in operating conditions, such as temperature, pressure, and load. Unlike traditional Type-1 fuzzy logic controllers, the Type-2 controller uses interval Type-2 fuzzy sets, which are better suited to handle uncertainties in the PEMFC's operation, such as nonlinear behavior and parameter variations. The controller includes a rule base that defines the relationship between input variables (e.g., temperature, pressure, current) and output control actions (e.g., voltage or current adjustments). This rule base is dynamically updated to ensure robust performance.

[0023] The Grey Wolf Optimization (GWO) module is linked to the Type-2 fuzzy logic controller and optimizes its tuning parameters. The GWO protocol, inspired by the hunting behavior of grey wolves, adjusts the membership functions of the Type-2 fuzzy logic controller to improve control accuracy and stability. By iteratively optimizing parameters such as the shape and range of membership functions, the GWO module reduces the response time of the control system under varying load conditions, ensuring the PEMFC operates efficiently and tracks the maximum power point quickly.

[0024] A neural network is integrated with the Type-2 fuzzy logic controller to generate and update control rules in real-time. The neural network processes input data from the sensor array (e.g., temperature, pressure, current) and dynamically adjusts the rule base of the Type-2 fuzzy logic controller. This adaptability enables the system to respond to dynamic environmental changes, such as temperature fluctuations or load variations, ensuring stable and efficient PEMFC operation. The neural network's ability to learn and refine control rules results in a 58.38% faster tracking response compared to traditional control methods.

[0025] The sensor array is coupled to both the PEMFC and the Type-2 fuzzy logic controller. It includes temperature and pressure sensors, as well as other relevant sensors (e.g., current sensors), to measure the PEMFC's operating conditions in real-time. The sensor array provides precise data on parameters such as cell temperature, gas pressure, and output current, which are critical for accurate control decisions. These measurements are fed into the Type-2 fuzzy logic controller and the neural network, enabling real-time adjustments to maintain optimal performance.

[0026] A feedback loop connects the PEMFC's output to the Type-2 fuzzy logic controller, providing real-time data on the fuel cell's performance (e.g., output voltage, current, or power). This feedback enables the controller to monitor the PEMFC's steady-state performance and make necessary adjustments to maintain stability. The feedback loop ensures that deviations from the desired operating condition are quickly corrected, minimizing power dips and improving overall efficiency.

[0027] The present invention work best in the manner, where the invention is the sophisticated control system designed to optimize the performance, stability, and efficiency of the proton exchange membrane fuel cell (PEMFC) under varying conditions. The system integrates the Type-2 fuzzy logic controller, the Grey Wolf Optimization (GWO) module, the neural network, the sensor array, and the feedback loop, alongside the power management unit for enhanced energy efficiency. The Type-2 fuzzy logic controller, directly connected to the PEMFC, stabilizes its operating point by adjusting parameters like voltage and current in response to environmental changes, using interval Type-2 fuzzy sets to handle uncertainties effectively. The GWO module optimizes the controller’s membership functions, inspired by grey wolf hunting behavior, ensuring rapid response and maximum power point tracking. The neural network dynamically updates the controller’s rule base using real-time data from the sensor array, which includes temperature, pressure, and current sensors, achieving 58.38% faster tracking than traditional methods. The sensor array delivers precise operational data to the controller and neural network, enabling adaptive responses to fluctuations. The feedback loop monitors the PEMFC’s output (voltage, current, power) and facilitates real-time adjustments to minimize power dips and maintain stability. The power management unit enhances cost-effectiveness by optimizing energy use. Together, these components ensure the PEMFC operates efficiently, adapts to dynamic conditions, and delivers stable, high-performance output.

[0028] Although the field of the invention has been described herein with limited reference to specific embodiments, this description is not meant to be construed in a limiting sense. Various modifications of the disclosed embodiments, as well as alternate embodiments of the invention, will become apparent to persons skilled in the art upon reference to the description of the invention. , Claims:1) A control system for a proton exchange membrane fuel cell (PEMFC), comprising:

i) a Type-2 fuzzy logic controller connected to the PEMFC, functioning to stabilize the operating point by adjusting control parameters in response to varying operating conditions;
ii) a Grey Wolf Optimization module linked to the Type-2 fuzzy logic controller, functioning to optimize tuning parameters for improved control accuracy and stability;
iii) a neural network integrated with the Type-2 fuzzy logic controller, functioning to generate optimized control rules for real-time adaptation to dynamic environmental changes;
iv) a sensor array coupled to the PEMFC and the Type-2 fuzzy logic controller, functioning to measure operating conditions such as temperature, pressure, and current for input to the control system; and
v) a feedback loop connecting the PEMFC output to the Type-2 fuzzy logic controller, functioning to provide real-time data for maintaining steady-state performance.

2) The system as claimed in claim 1, wherein the system achieves better results than traditional methods, reaching the maximum power point quickly, reducing dips, and adapting to different temperatures while staying efficient.

3) The system as claimed in claim 1, wherein the Type-2 fuzzy logic controller includes a rule base updated by the neural network, functioning to handle uncertainties in PEMFC operation for a 58.38% faster tracking response.

4) The system as claimed in claim 1, wherein the Grey Wolf Optimization module adjusts the membership functions of the Type-2 fuzzy logic controller, functioning to reduce response time under varying load conditions.

5) The system as claimed in claim 1, wherein the neural network dynamically updates control rules based on sensor data, functioning to improve steady-state performance of the PEMFC.

6) The system as claimed in claim 1, wherein the sensor network includes temperature and pressure sensors, functioning to provide precise data for real-time control adjustments.

7) The system as claimed in claim 1, wherein the power management unit integrates with the feedback system, functioning to minimize energy losses and enhance cost-effectiveness.