A method and system for regulating the moisture content of a fuel cell membrane

Abstract

This application discloses a method and system for regulating the water content of a fuel cell membrane, relating to the field of fuel cell management and control technology. The method includes: acquiring initialization parameters of the fuel cell stack; calculating the current current density and its rate of change in each sampling period based on the initialization parameters; predicting and updating the current current density using an extended Kalman filter algorithm based on the fuel cell state-space model, and outputting the optimal estimate of the current ohmic impedance; obtaining the current membrane water content from the optimal estimate of the current ohmic impedance; determining whether membrane drying or flooding will occur after a predicted time period under the current operating conditions; regulating the current density when membrane drying or flooding occurs; otherwise, allowing the current density to change freely. This application achieves real-time estimation and forward-looking regulation of the membrane water content, effectively avoiding membrane drying and flooding, and improving the adaptability and lifespan of the fuel cell under dynamic operating conditions.

Description

Technical Field

[0001] This application relates to the field of fuel cell management and control technology, and in particular to a method and system for regulating the water content of fuel cell membranes. Background Technology

[0002] Proton exchange membrane fuel cells (PEMFCs), as a highly efficient and clean energy conversion technology, have broad application prospects in fields such as vehicle power. Their performance is highly dependent on the hydration state of their core component—the proton exchange membrane. Appropriate water content is crucial for maintaining high proton conductivity of the membrane, thereby ensuring the overall output performance and operating efficiency of the battery. If the proton exchange membrane dehydrates (i.e., the "membrane dryness"), it will cause a sharp increase in proton conduction resistance, leading to a significant decline in battery performance. Conversely, when liquid water accumulates at the cathode electrode (i.e., the "flooding"), it will block the gas transport paths in the porous medium, making mass transfer of the reactant gases difficult, similarly causing performance degradation and output voltage instability.

[0003] In real-world operating environments, especially in dynamic applications such as vehicles, fuel cell systems often face dynamic conditions with frequent and rapid changes in load current. These conditions can easily disrupt the dynamic balance of water content within the membrane: during the rapid current rise phase, the rapid response of electroosmotic drag and the lag of reverse diffusion may lead to short-term dehydration of the anode membrane (membrane dryness); while during the rapid current fall phase, the inertia of cathode water production and the delay in air purging may jointly induce liquid water accumulation (flooding).

[0004] Currently, monitoring methods for membrane water content mostly rely on offline measurements or complex analytical methods based on electrochemical impedance spectroscopy (EIS), which are insufficient to meet the real-time and embedded requirements of vehicle-mounted systems. Related control strategies are largely based on steady-state models, making it difficult to respond promptly to rapid fluctuations in dynamic operating conditions. Remedial measures are often only taken after membrane dryness or flooding failures occur, resulting in significant control lag and failing to effectively protect the durability of fuel cells. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for regulating the water content of fuel cell membranes. By establishing an intrinsic mapping relationship between ohmic internal resistance and membrane water content, and combining it with real-time analysis of the rate of change of the current density, the method enables early prediction and proactive protection against membrane dryness and flooding.

[0006] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for controlling the water content of a fuel cell membrane, comprising: The initialization parameters of the fuel cell stack are obtained, and the sampling period and prediction time domain length are set. The initialization parameters include the membrane area.

[0007] During each sampling period, dynamic parameters of the fuel cell stack are collected, and the current current density and the rate of change of the current current density are calculated in combination with the initialization parameters. The dynamic parameters include voltage, current, stack temperature, and partial pressure of the anode and cathode reaction gases.

[0008] Based on the fuel cell state-space model, the current current density is used as input, the voltage is used as the observation, and the extended Kalman filter algorithm is used for prediction and updating to obtain the optimal estimate of the current ohmic impedance; the fuel cell state-space model includes the state equation and observation equation of the ohmic impedance.

[0009] Based on the mapping relationship between ohmic impedance and membrane water content, the current membrane water content can be obtained by inverting the optimal estimate of the current ohmic impedance.

[0010] The current operating condition is determined based on the rate of change of the current density; the operating condition includes rapid current increase condition and rapid current decrease condition.

[0011] Under the current operating conditions, based on the rate of change of the current density and the current water content of the membrane, it is determined whether membrane drying or flooding will occur after the predicted time domain length.

[0012] When membrane drying or flooding occurs, the current density is adjusted; when membrane drying or flooding does not occur, the current density is allowed to vary freely.

[0013] In one embodiment, the state equation of the ohmic impedance is: .

[0014] in, for At any given time, the ohmic impedance is relative to The estimated value of the change in ohmic impedance at time t. for At any given time, the ohmic impedance is relative to The change in ohmic impedance at time t. , for The observed value of the ohmic impedance at time t. for The observed value of the ohmic impedance at time t. Time-varying parameters characterizing the decay effect of steady-state operating conditions, Time-varying parameters characterizing the impact of dynamic operating condition changes on degradation, The change in current density , for Current density at time t, for Current density at a given time.

[0015] In one embodiment, the observation equation is: .

[0016] in, for The measured value of the terminal voltage at time [time]. For Nernst potential, , These are empirical parameters. for The anodic activation overpotential at time [time] for The cathode activation overpotential at time [time] for Current density at time t, for The observed value of the ohmic impedance at time t. for The temperature of the fuel cell stack at any given time. for Concentration overpotential at a given moment.

[0017] In one embodiment, the expression for the mapping relationship between the ohmic impedance and the membrane water content is: .

[0018] in, for The estimated water content of the membrane at that time. For membrane area, for The estimated value of the ohmic impedance at time t. For the temperature of the fuel cell stack, for Current density at time t, This represents the thickness of the fuel cell membrane.

[0019] In one embodiment, determining the current operating condition based on the rate of change of the current current density specifically includes: The rate of change of the current density At that time, it was determined to be a rapid current increase condition; among which, To set a threshold.

[0020] The rate of change of the current density At that time, it was determined to be a rapid flow reduction condition.

[0021] In one embodiment, the operating condition further includes a steady-state or slowly changing operating condition, specifically: The rate of change of the current density When the condition is determined to be steady state or slowly changing condition.

[0022] In one embodiment, determining whether membrane drying or flooding will occur after the predicted time domain length, based on the rate of change of the current density and the estimated water content of the membrane under the current operating conditions, specifically includes: Under rapid current rise conditions, based on the rate of change of the current density and the ohmic impedance corresponding to the current membrane water content, the ohmic impedance at the predicted time domain length is obtained using the fuel cell state-space model.

[0023] Based on the mapping relationship between ohmic impedance and membrane water content, the membrane water content at the predicted time domain length can be obtained by inverting the ohmic impedance at the predicted time domain length.

[0024] When the membrane water content is lower than the membrane drying risk threshold at the predicted time domain length, it is determined that membrane drying will occur after the predicted time domain length.

[0025] If the membrane water content at the predicted time domain length is not lower than the membrane dryness risk threshold, it is determined that membrane dryness will not occur after the predicted time domain length.

[0026] Under rapid current reduction conditions, based on the rate of change of the current density and the ohmic impedance corresponding to the current membrane water content, the ohmic impedance at the predicted time domain length is obtained using the fuel cell state-space model.

[0027] Based on the mapping relationship between ohmic impedance and membrane water content, the membrane water content at the predicted time domain length can be obtained by inverting the ohmic impedance at the predicted time domain length.

[0028] When the membrane water content is higher than the flooding risk threshold at the predicted time domain length, it is determined that flooding will occur after the predicted time domain length.

[0029] If the membrane water content is not higher than the flooding risk threshold at the predicted time domain length, it is determined that flooding will not occur after the predicted time domain length.

[0030] In one embodiment, adjusting the current density when membrane drying or flooding occurs specifically includes: When membrane dryness occurs, a control strategy is implemented to limit the rate of increase of current density.

[0031] In the event of flooding, control strategies are implemented to limit the rate of decrease in current density or to perform short-term current boosting.

[0032] Secondly, this application provides a fuel cell membrane water content control system, comprising: a data acquisition component, a current density calculation component, an ohmic impedance calculation component, a membrane water content inversion component, a state determination component, and a control component.

[0033] The data acquisition component is used to acquire the initialization parameters of the fuel cell stack and set the sampling period and prediction time domain length. The initialization parameters include the membrane area.

[0034] The current density calculation component is used to collect dynamic parameters of the fuel cell stack in each sampling period, and calculate the current current density and the rate of change of the current current density in combination with the initialization parameters; the dynamic parameters include voltage, current, stack temperature, and partial pressure of anode and cathode reaction gases.

[0035] The ohmic impedance calculation component is used to predict and update the current ohmic impedance based on the fuel cell state-space model, with the current current density as input and voltage as the observation, and using the extended Kalman filter algorithm to obtain the optimal estimate of the current ohmic impedance; the fuel cell state-space model includes the state equation and observation equation of the ohmic impedance.

[0036] The membrane water content inversion component is used to invert the current membrane water content from the optimal estimate of the current ohmic impedance based on the mapping relationship between ohmic impedance and membrane water content.

[0037] The state determination component is used to determine the current operating state based on the rate of change of the current current density; the operating state includes rapid current increase condition and rapid current decrease condition; under the current operating state, based on the rate of change of the current current density and the current water content of the membrane, it is determined whether membrane drying or flooding will occur after the predicted time domain length.

[0038] The control component is used to regulate the current density when membrane dryness or flooding occurs; when membrane dryness or flooding does not occur, the current density is allowed to vary freely.

[0039] In one embodiment, the state equation of the ohmic impedance is: .

[0040] in, for At time ohmic impedance relative to The estimated value of the change in ohmic impedance at time t. for At time ohmic impedance relative to The change in ohmic impedance at time t. , for The observed value of the ohmic impedance at time t. for The observed value of the ohmic impedance at time t. Time-varying parameters characterizing the decay effect of steady-state operating conditions, Time-varying parameters characterizing the impact of dynamic operating condition changes on degradation, The change in current density , for Current density at time t, for Current density at a given time.

[0041] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method and system for regulating the water content of a fuel cell membrane. By acquiring the initialization parameters of the fuel cell stack and setting the sampling period and prediction time domain length, dynamic parameters of the fuel cell stack are collected within each sampling period. The current current density and its rate of change are calculated based on the initialization parameters. Based on the fuel cell state-space model, an extended Kalman filter algorithm is used for prediction and updating to obtain the optimal estimate of the current ohmic impedance. According to the mapping relationship between ohmic impedance and membrane water content, the current membrane water content is obtained by inverting from the optimal estimate of the current ohmic impedance. Under the current operating conditions, based on the rate of change of the current current density and the current membrane water content, it is determined whether membrane drying or flooding will occur after the predicted time domain length. If membrane drying or flooding occurs, the current density is adjusted; if membrane drying or flooding will not occur, the current density is allowed to change freely. This application achieves real-time estimation and forward-looking regulation of the membrane water content, effectively avoiding membrane drying and flooding, and improving the adaptability and lifespan of the fuel cell under dynamic operating conditions. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A schematic flowchart illustrating a method for controlling the water content of a fuel cell membrane according to an embodiment of this application; Figure 2 A detailed flowchart illustrating a method for controlling the water content of a fuel cell membrane, provided in an embodiment of this application; Figure 3 This is a mapping diagram showing the relationship between ohmic impedance and membrane water content provided in one embodiment of this application; Figure 4This is a schematic diagram of the functional modules of a fuel cell membrane water content control system provided in an embodiment of this application. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] There is an urgent need in this field to develop an intelligent control method and system that can predict the trend of proton exchange membrane water content changes in real time and accurately under dynamic operating conditions, and can implement proactive intervention in advance, so as to improve the adaptability and service life of fuel cells under variable load conditions.

[0047] like Figure 1 As shown, this application provides a method for controlling the water content of a fuel cell membrane, including steps 201 to 207. Wherein: Step 201: Obtain the initialization parameters of the fuel cell stack and set the sampling period and prediction time domain length. The initialization parameters include the membrane area.

[0048] Step 202: In each sampling period, collect the dynamic parameters of the fuel cell stack, and calculate the current current density and the rate of change of the current current density in combination with the initialization parameters; the dynamic parameters include voltage, current, stack temperature, and partial pressure of the anode and cathode reaction gases.

[0049] Step 203: Based on the fuel cell state-space model, using the current current density as input and voltage as the observation, and employing the extended Kalman filter algorithm for prediction and updating, the optimal estimate of the current ohmic impedance is obtained; the fuel cell state-space model includes the state equation and observation equation of the ohmic impedance.

[0050] Step 204: Based on the mapping relationship between ohmic impedance and membrane water content, the current membrane water content is obtained by inverting the optimal estimate of the current ohmic impedance.

[0051] Step 205: Determine the current operating condition based on the rate of change of the current density; the operating condition includes rapid current increase condition and rapid current decrease condition.

[0052] Step 206: Under the current operating conditions, based on the rate of change of the current current density and the current water content of the membrane, determine whether membrane drying or flooding will occur after the predicted time domain length.

[0053] Step 207: When membrane drying or flooding occurs, adjust the current density; when membrane drying or flooding does not occur, allow the current density to change freely.

[0054] By implementing steps 201 to 207 above, the initialization parameters of the fuel cell stack are obtained, and the sampling period and prediction time domain length are set. Within each sampling period, dynamic parameters of the fuel cell stack are collected. The current current density and its rate of change are calculated based on the initialization parameters. Based on the fuel cell state-space model, an extended Kalman filter algorithm is used for prediction and updating to obtain the optimal estimate of the current ohmic impedance. According to the mapping relationship between ohmic impedance and membrane water content, the current membrane water content is obtained by inverting from the optimal estimate of the current ohmic impedance. Under the current operating conditions, based on the rate of change of the current current density and the current membrane water content, it is determined whether membrane drying or flooding will occur after the prediction time domain length. If membrane drying or flooding occurs, the current density is adjusted; if membrane drying or flooding does not occur, the current density is allowed to change freely. This application achieves real-time estimation and forward-looking control of membrane water content, effectively avoiding membrane drying and flooding, and improving the adaptability and lifespan of the fuel cell under dynamic operating conditions.

[0055] In one specific implementation, such as Figure 2 As shown, the method includes the following steps: S1: System Initialization and Parameter Acquisition: Acquire basic parameters of the fuel cell stack (such as membrane area, membrane thickness, channel size, etc.) and relevant empirical parameters, and set the system sampling period. With control frequency ( ), and the predicted time domain length ,in, , ( Take 1, 2, 3, ...).

[0056] S2: Dynamic Data Acquisition: Real-time acquisition of the voltage of the fuel cell stack. Current ,temperature The dynamic parameters of the partial pressure of the anode and cathode reactants are also considered, and the current density is calculated using the membrane area. .

[0057] S3: Based on dynamic parameters and current density, the ohmic impedance of the fuel cell stack is estimated in real time through the constructed dynamic operating condition ohmic impedance calculation model (i.e., fuel cell state-space model). .

[0058] S4: Membrane water content inversion: via a pre-established ohmic resistance. With membrane water content The mapping relationship between them is used to invert the current water content of the membrane (or (Moisture content of the membrane at any given time) .

[0059] S5: Dynamic Condition Judgment and Control Decision: Calculate the rate of change of the current density. and according to The value and sign of the value determine whether the current condition is a rapid current increase or a rapid current decrease.

[0060] Under the current-upflow condition, according to the current Compared with the current water content of the membrane (or (Moisture content of the membrane at any given time) Predicting the length of the prediction time domain Will membrane drying occur within the membrane? If membrane drying is predicted, a control strategy that limits the rate of increase of current density is implemented; if membrane drying is not predicted, the current density is allowed to vary freely.

[0061] Under the current flow reduction condition, based on the current Compared with the current water content of the membrane (or (Moisture content of the membrane at any given time) Predicting the length of the prediction time domain Will there be flooding? If flooding is predicted, a control strategy is implemented to limit the rate of decrease in current density or to perform a short-term current boosting operation; if flooding is not predicted, the current density is allowed to vary freely.

[0062] S6: State Update and Cyclic Execution: After completing the control operation, the system returns to step S2 (i.e., the dynamic data acquisition step), collects a new round of dynamic parameters, and starts the next cycle of membrane state estimation, prediction and control based on the updated data, forming a continuous closed-loop control loop.

[0063] In another exemplary embodiment of this application, the state equation for the ohmic impedance in the fuel cell state-space model is: .

[0064] in, for At time ohmic impedance relative to The estimated value of the change in ohmic impedance at time t. for At time ohmic impedance relative to The change in ohmic impedance at time t. , for The observed value of the ohmic impedance at time t. for The observed value of the ohmic impedance at time t. Time-varying parameters characterizing the decay effect of steady-state operating conditions, Time-varying parameters characterizing the impact of dynamic operating condition changes on degradation, The change in current density , for Current density at time t, for Current density at a given time.

[0065] Specifically, the above ohmic impedance calculation model is based on a fuel cell state-space model under dynamic operating conditions, and its core nonlinear equation expression is as follows: .

[0066] in, for Current density at time t, for The estimated value of the ohmic impedance at time t. for The estimated value of the ohmic impedance at time t.

[0067] By performing a first-order Taylor expansion and discretizing the nonlinear model near the operating point, its linear time-varying form is obtained: .

[0068] in, for At time ohmic impedance relative to The estimated value of the change in ohmic impedance at time t. for At time ohmic impedance relative to The change in ohmic impedance at time t. , for At time ohmic impedance relative to The estimated value of the change in ohmic impedance at time t. for The observed value of the ohmic impedance at time t. for The observed value of the ohmic impedance at time t. Time-varying parameters characterizing the decay effect of steady-state operating conditions, Time-varying parameters characterizing the impact of dynamic operating condition changes on degradation, The change in current density , for Current density at time t, for Current density at a given time.

[0069] In another exemplary embodiment of this application, the observation equation is: .

[0070] in, for The measured value of the terminal voltage at time [time]. For Nernst potential, , These are empirical parameters. for The anodic activation overpotential at time [time] for The cathode activation overpotential at time [time] for Current density at time t, for The observed value of the ohmic impedance at time t. for The temperature of the fuel cell stack at any given time. for Concentration overpotential at a given moment.

[0071] This application combines the state-space model with the equivalent circuit model of a fuel cell (such as a second-order RC model). Its complete state equations and observation equations are used to construct a Kalman filter to achieve control over the ohmic internal resistance. Other state variables (such as activation polarization voltage) Concentration polarization voltage A more accurate joint estimate of the ohmic resistance is obtained, thereby improving the robustness and accuracy of the ohmic resistance estimation, and thus improving the accuracy of the membrane water content inversion.

[0072] In one specific implementation, based on the fuel cell state-space model, using the current current density as input and voltage as the observation, and employing an extended Kalman filter algorithm for prediction and updating, the optimal estimate of the current ohmic impedance is obtained, specifically including: The state update equation (discretized) is: .

[0073] Wherein, the state vector , for At time ohmic impedance relative to The change in ohmic impedance at time t. , for The observed value of the ohmic impedance at time t. for The observed value of the ohmic impedance at time t. for The change in cathode activation overpotential at time t represents Observed value of cathode activation overpotential at time and Observed value of cathode activation overpotential at time The difference, for The change in concentration overpotential at time t is calculated using the same method as the previous two parameters (i.e. Concentration overpotential observations at time and Concentration overpotential observations at time The difference), the control input is . for The state vector of the Kalman filter at time t, for The state vector of the Kalman filter at time t, for Time relative to The change in current density at any given moment; G ( T ) is the state transition matrix, ; H ( T () is the control input matrix. .

[0074] The aforementioned state update equation is essentially the same as the state equation for ohmic impedance in the state-space model of a fuel cell. The state vector of the state update equation includes the change in ohmic impedance, the change in cathode activation overpotential, and the change in concentration overpotential. It aims to perform joint estimation of multiple states through extended Kalman filtering. The state equation for ohmic impedance is a formal expression that focuses only on ohmic impedance.

[0075] matrix , Determined by the linearized continuous-time model: .

[0076] in, , , These represent the time-varying time constants of the dynamic processes of ohmic impedance, cathode activation overpotential, and concentration overpotential, respectively.

[0077] .

[0078] in, , , These represent the gains of the change in current density on the rate of change in ohmic impedance, the rate of change in cathode activation overpotential, and the rate of change in concentration overpotential, respectively.

[0079] The observation equation is: .

[0080] in, for The measured value of the terminal voltage at time [time]. For Nernst potential, , These are empirical parameters. for The anodic activation overpotential at time [time] for The cathode activation overpotential at time [time] for Current density at time t, for The observed value of the ohmic impedance at time t. for The temperature of the fuel cell stack at any given time. for Concentration overpotential at any given moment.

[0081] The prediction and update process of Kalman filtering: Prediction step: based on The optimal estimate of the ohmic impedance at time t. And the state equation, calculate Prior estimate of ohmic impedance at time t .

[0082] Update step: Combine the observed ohmic impedance values ​​obtained from the observation equation. The prior estimation error is adjusted by Kalman gain, and the output is... The optimal estimate of the ohmic impedance at time t. ,Right now .

[0083] in, for The optimal estimate of the ohmic impedance at time t. for The prior estimate of the ohmic impedance at time t. For Kalman gain, for The measured value of the terminal voltage at time [time]. The voltage prediction is based on prior estimates.

[0084] In another exemplary embodiment of this application, the expression for the mapping relationship between the ohmic impedance and the membrane water content is: .

[0085] in, for The estimated water content of the membrane at that time. For membrane area, for The estimated value of the ohmic impedance at time t. For the temperature of the fuel cell stack, for Current density at time t, This represents the thickness of the fuel cell membrane.

[0086] In another exemplary embodiment of this application, determining the current operating condition based on the rate of change of the current current density specifically includes: The rate of change of the current density At that time, it was determined to be a rapid current increase condition; among which, To set a threshold.

[0087] The rate of change of the current density At that time, it was determined to be a rapid flow reduction condition.

[0088] In another exemplary embodiment of this application, the operating condition further includes a steady-state or slowly changing operating condition, specifically: The rate of change of the current density When the condition is determined to be steady state or slowly changing condition.

[0089] In another exemplary embodiment of this application, the step of determining whether membrane drying or flooding will occur after the predicted time domain length, based on the rate of change of the current density and the current water content of the membrane under the current operating conditions, specifically includes: Under rapid current rise conditions, based on the rate of change of the current density and the ohmic impedance corresponding to the current membrane water content, the ohmic impedance at the predicted time domain length is obtained using the fuel cell state-space model.

[0090] Based on the mapping relationship between ohmic impedance and membrane water content, the membrane water content at the predicted time domain length can be obtained by inverting the ohmic impedance at the predicted time domain length.

[0091] When the membrane water content is lower than the membrane drying risk threshold at the predicted time domain length, it is determined that membrane drying will occur after the predicted time domain length.

[0092] If the membrane water content at the predicted time domain length is not lower than the membrane dryness risk threshold, it is determined that membrane dryness will not occur after the predicted time domain length.

[0093] Under rapid current reduction conditions, based on the rate of change of the current density and the ohmic impedance corresponding to the current membrane water content, the ohmic impedance at the predicted time domain length is obtained using the fuel cell state-space model.

[0094] Based on the mapping relationship between ohmic impedance and membrane water content, the membrane water content at the predicted time domain length can be obtained by inverting the ohmic impedance at the predicted time domain length.

[0095] When the membrane water content is higher than the flooding risk threshold at the predicted time domain length, it is determined that flooding will occur after the predicted time domain length.

[0096] If the membrane water content is not higher than the flooding risk threshold at the predicted time domain length, it is determined that flooding will not occur after the predicted time domain length.

[0097] In another exemplary embodiment of this application, adjusting the current density when membrane drying or flooding occurs specifically includes: When membrane dryness occurs, a control strategy is implemented to limit the rate of increase of current density.

[0098] In the event of flooding, control strategies are implemented to limit the rate of decrease in current density or to perform short-term current boosting.

[0099] The following example illustrates this application using the membrane water content control of a commercial vehicle equipped with a PEMFC system during operation. The method for controlling the water content of a fuel cell membrane under dynamic operating conditions based on the rate of change of current density includes the following steps: Step 1: When the vehicle starts, the fuel cell controller powers on. The controller reads the basic parameters (such as membrane area, membrane thickness, channel size, etc.) and empirical parameters (such as the internal resistance-water content mapping table, etc.) of the fuel cell stack model from its memory. , (Initial values, etc.). Simultaneously, set the controller's operating frequency. and the predicted time domain length ( Take 1, 2, 3, ...).

[0100] Step 2: In each control cycle The controller collects the total voltage of the fuel cell stack through sensors. Total current fuel cell stack temperature Dynamic parameters of the partial pressures of the reacting gases at the anode and cathode. Current density is calculated from the effective membrane area of ​​the proton exchange membrane in the fuel cell stack. .

[0101] Step 3: Based on dynamic parameters and current density, the ohmic impedance of the fuel cell stack is estimated in real time using the constructed dynamic operating condition ohmic impedance calculation model. .

[0102] The ohmic impedance calculation model is based on a state-space model of a fuel cell capable of characterizing the effects of dynamic operating conditions. The core of this model is a nonlinear equation describing the dynamic changes in ohmic internal resistance, expressed as: .

[0103] in, for Current density at time t, for The estimated value of the ohmic impedance at time t. for The estimated value of the ohmic impedance at time t.

[0104] By performing a first-order Taylor expansion and discretizing the nonlinear model near the operating point, its linear time-varying form is obtained: in, for At time ohmic impedance relative to The estimated value of the change in ohmic impedance at time t. for At time ohmic impedance relative to The change in ohmic impedance at time t. , for At time ohmic impedance relative to The estimated value of the change in ohmic impedance at time t. for The observed value of the ohmic impedance at time t. for The observed value of the ohmic impedance at time t. Time-varying parameters characterizing the decay effect of steady-state operating conditions, Time-varying parameters characterizing the impact of dynamic operating condition changes (rate of change of current density) on degradation. The change in current density , for Current density at time t, for Current density at a given time.

[0105] The core processing module runs the built-in Extended Kalman Filter (EKF). Its observation equation is as follows: .

[0106] in, for The measured value of the terminal voltage at time [time]. For Nernst potential, , These are empirical parameters. for The anodic activation overpotential at time [time] for The cathode activation overpotential at time [time] for Current density at time t, for The observed value of the ohmic impedance at time t. for The temperature of the fuel cell stack at any given time. for Concentration overpotential at a given moment.

[0107] The state vector of the filter is ,in, , for At time ohmic impedance relative to The change in ohmic impedance at time t. , for The observed value of the ohmic impedance at time t. for The observed value of the ohmic impedance at time t. for The change in cathode activation overpotential at time t represents Observed value of cathode activation overpotential at time and Observed value of cathode activation overpotential at time The difference, for The change in concentration overpotential at time t is calculated using the same method as the previous two parameters (i.e. Concentration overpotential observations at time and Concentration overpotential observations at time The difference), the control input is The filter estimates the state from the previous time step and the measured value (voltage) at the current time step. Current density Based on the discretized state equation and observation equation, recursive calculations are performed to output the optimal ohmic internal resistance estimate in real time. The discretized state equation (or state update equation) is as follows: .

[0108] in, for The state vector of the Kalman filter at time t, for The state vector of the Kalman filter at time t, for Time relative to The change in current density at any given moment; G ( T ) is the state transition matrix, ; H ( T () is the control input matrix. .

[0109] matrix , Determined by the linearized continuous-time model: .

[0110] in, , , These represent the time-varying time constants of the dynamic processes of ohmic impedance, cathode activation overpotential, and concentration overpotential, respectively.

[0111] .

[0112] in, , , These represent the gains of the change in current density on the rate of change in ohmic impedance, the rate of change in cathode activation overpotential, and the rate of change in concentration overpotential, respectively.

[0113] Step 4: The controller calculates the internal resistance in ohms based on the value estimated in Step 3. The estimated water content of the membrane can be obtained by inversion calculation using the following formula. : .

[0114] in, for The estimated water content of the membrane at that time. For membrane area, for The estimated value of the ohmic impedance at time t. For the temperature of the fuel cell stack, for Current density at time t, The thickness of the fuel cell membrane. The mapping relationship between ohmic impedance and membrane water content is as follows: Figure 3 As shown.

[0115] Step 5: The controller calculates the rate of change of current density in the current cycle (i.e., the rate of change of current density in the current period): Set a threshold. This threshold is a pre-set critical value for the rate of change of current, based on the safety and durability of the fuel cell, used to distinguish dynamic operating conditions. Its specific value is influenced by the material properties of the fuel cell stack, the system response speed, and the hydrothermal management requirements. If the current rate of change... If the rate of change of the current density is..., it is determined to be a "rapid current increase condition"; if the rate of change of the current density is... If the rate of change of the current density is..., it is determined to be a "rapid current reduction condition"; if the rate of change of the current density is... If so, it is determined to be a "steady-state or slowly changing operating condition".

[0116] For rapid current increase conditions: The prediction module predicts the rate of change of the current current density. Value, estimated current water content of the membrane And the embedded dynamic model of membrane water content (i.e., the fuel cell state-space model and the mapping relationship between ohmic impedance and membrane water content) predicts the prediction time domain length. ( Take the membrane water content of 1, 2, 3... .

[0117] If the predicted membrane water content Below the membrane dryness risk threshold (Its value is usually 4), then it is determined that " "After a period of time, the membrane will dry out." The control module immediately sends a command to the vehicle control unit (VCU) or load management system to limit the rate of increase of the requested current, for example, by limiting the maximum allowable current. From 0.5 A / (cm) 2 •s) decreased to 0.2 A / (cm 2 ·s), thereby slowing down the loss of membrane moisture.

[0118] If the prediction is risk-free, no intervention will be taken.

[0119] For rapid descent conditions: The prediction module predicts the rate of change of the current current density. Value, estimated current water content of the membrane And the embedded dynamic model of membrane water content (i.e., the fuel cell state-space model and the mapping relationship between ohmic impedance and membrane water content) predicts the prediction time domain length. ( Take the membrane water content of 1, 2, 3... .

[0120] If the predicted membrane water content Above the flood risk threshold (Its value is usually 14), then it is determined that " "Flooding will occur after a certain time." Simultaneously, the control module will execute one of the following strategies: Limit the rate of decrease of current density.

[0121] Implementing a "short-time upflow" operation involves briefly applying a small load pulse to promote the evaporation and discharge of liquid water within the membrane by increasing current and generating heat.

[0122] If the prediction is risk-free, no intervention will be taken.

[0123] Step Six: Feed back the adjusted stack state parameters to the system's data acquisition and processing unit in real time, so that Steps Two to Five can be continuously cycled, thereby forming a closed-loop control system for real-time estimation, forward prediction and dynamic control of membrane water content.

[0124] Based on the same inventive concept, this application also provides a fuel cell membrane water content control system for implementing the above-described fuel cell membrane water content control method. The solution provided by this system is similar to the solution described in the above method; therefore, the specific limitations in one or more fuel cell membrane water content control system embodiments provided below can be found in the limitations of the fuel cell membrane water content control method described above, and will not be repeated here.

[0125] In one exemplary embodiment, a fuel cell membrane water content control system is provided, comprising: a data acquisition component, a current density calculation component, an ohmic impedance calculation component, a membrane water content inversion component, a state determination component, and a control component.

[0126] The data acquisition component is used to acquire the initialization parameters of the fuel cell stack and set the sampling period and prediction time domain length. The initialization parameters include the membrane area.

[0127] The current density calculation component is used to collect dynamic parameters of the fuel cell stack in each sampling period, and calculate the current current density and the rate of change of the current current density in combination with the initialization parameters; the dynamic parameters include voltage, current, stack temperature, and partial pressure of anode and cathode reaction gases.

[0128] The ohmic impedance calculation component is used to predict and update the current ohmic impedance based on the fuel cell state-space model, with the current current density as input and voltage as the observation, and using the extended Kalman filter algorithm to obtain the optimal estimate of the current ohmic impedance; the fuel cell state-space model includes the state equation and observation equation of the ohmic impedance.

[0129] The membrane water content inversion component is used to invert the current membrane water content from the optimal estimate of the current ohmic impedance based on the mapping relationship between ohmic impedance and membrane water content.

[0130] The state determination component is used to determine the current operating state based on the rate of change of the current current density; the operating state includes rapid current increase condition and rapid current decrease condition; under the current operating state, based on the rate of change of the current current density and the current water content of the membrane, it is determined whether membrane drying or flooding will occur after the predicted time domain length.

[0131] The control component is used to regulate the current density when membrane dryness or flooding occurs; when membrane dryness or flooding does not occur, the current density is allowed to vary freely.

[0132] In another specific implementation, such as Figure 4 As shown, the system includes: Data acquisition module: Used to acquire the voltage of the fuel cell stack in real time. Current ,temperature Partial pressure parameters of the reacting gases at the cathode and anode.

[0133] Ohmic impedance estimation and membrane water content inversion module: This module is used to perform the ohmic impedance calculation and membrane water content inversion. It includes components for estimating ohmic impedance. Ohmic impedance estimation unit and used for inverting membrane water content Membrane water content inversion unit.

[0134] Operating Condition Analysis and Prediction Module: This module performs the operating condition judgment and membrane dry / flood prediction. It includes functions for calculating the rate of change of current density. The current density change rate calculation unit (i.e., the rate of change of the current density) and the membrane state prediction unit for predicting the membrane water content state.

[0135] Control execution module: Based on the decision result (or prediction result) of the prediction module, it outputs control signals to the load or auxiliary system of the fuel cell system to perform corresponding current density change rate limiting or short-term current boosting operation.

[0136] In another exemplary embodiment of this application, the state equation of the ohmic impedance is: .

[0137] in, for At time ohmic impedance relative to The estimated value of the change in ohmic impedance at time t. for At time ohmic impedance relative to The change in ohmic impedance at time t. , for The observed value of the ohmic impedance at time t. for The observed value of the ohmic impedance at time t. Time-varying parameters characterizing the decay effect of steady-state operating conditions, Time-varying parameters characterizing the impact of dynamic operating condition changes on degradation, The change in current density , for Current density at time t, for Current density at a given time.

[0138] The ohmic impedance estimation unit in this application uses an extended Kalman filter based on a state-space model. Real-time estimation.

[0139] In addition, the control execution module communicates with the vehicle controller or load management system to coordinate the execution of limiting operations on the rate of change of current density.

[0140] The beneficial effects of this application are as follows: (1) By analyzing the rate of change of current density It can predict membrane state risks within future time windows, realizing the transformation from "passive response" to "active protection".

[0141] (2) Based on easily obtainable online voltage, current and other signals and equivalent circuit models and state estimation algorithms suitable for online calculation, it is suitable for real-time operation of vehicle-mounted embedded systems.

[0142] (3) The dynamic model adopted not only considers the degradation of steady-state conditions, but also innovatively introduces the current density variation term. This allows for a precise description of the rapid impact of dynamic operating conditions on the internal state of a fuel cell, particularly ohmic resistance and membrane water content.

[0143] (4) By limiting the harmful rate of change of current in advance, the two main fuel cell degradation modes of membrane dryness and water flooding are effectively avoided, which helps to significantly extend the service life of fuel cells.

[0144] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0145] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for controlling the water content of a fuel cell membrane, characterized in that, include: The initialization parameters of the fuel cell stack are obtained, and the sampling period and prediction time domain length are set. The initialization parameters include the membrane area. During each sampling period, dynamic parameters of the fuel cell stack are collected, and the current current density and the rate of change of the current current density are calculated in combination with the initialization parameters; the dynamic parameters include voltage, current, stack temperature, and partial pressure of the anode and cathode reaction gases; Based on the fuel cell state-space model, the current current density is used as input, the voltage is used as the observation, and the extended Kalman filter algorithm is used for prediction and updating to obtain the optimal estimate of the current ohmic impedance; the fuel cell state-space model includes the state equation and observation equation of the ohmic impedance. Based on the mapping relationship between ohmic impedance and membrane water content, the current membrane water content is obtained by inverting the optimal estimate of the current ohmic impedance. The current operating condition is determined based on the rate of change of the current density; the operating condition includes rapid current increase condition and rapid current decrease condition. Under the current operating conditions, based on the rate of change of the current density and the current water content of the membrane, it is determined whether membrane drying or flooding will occur after the predicted time domain length. When membrane dryness or flooding occurs, adjust the current density; The current density can be freely varied as long as there is no risk of membrane drying or flooding.

2. The method for controlling the water content of a fuel cell membrane according to claim 1, characterized in that, The state equation for the ohmic impedance is: ； in, for At time ohmic impedance relative to The estimated value of the change in ohmic impedance at time t. for At time ohmic impedance relative to The change in ohmic impedance at time t. , for The observed value of the ohmic impedance at time t. for The observed value of the ohmic impedance at time t. Time-varying parameters characterizing the decay effect of steady-state operating conditions, Time-varying parameters characterizing the impact of dynamic operating condition changes on degradation, The change in current density , for Current density at time t, for Current density at a given time.

3. The method for controlling the water content of a fuel cell membrane according to claim 1, characterized in that, The observation equation is: ； in, for The measured value of the terminal voltage at time [time]. For Nernst potential, , These are empirical parameters. for The anodic activation overpotential at time [time] for The cathode activation overpotential at time [time] for Current density at time t, for The observed value of the ohmic impedance at time t. for The temperature of the fuel cell stack at any given time. for Concentration overpotential at a given moment.

4. The method for controlling the water content of a fuel cell membrane according to claim 1, characterized in that, The expression for the mapping relationship between the ohmic impedance and the membrane water content is as follows: ； in, for The estimated water content of the membrane at that time. For membrane area, for The estimated value of the ohmic impedance at time t. For the temperature of the fuel cell stack, for Current density at time t, This represents the thickness of the fuel cell membrane.

5. The method for controlling the water content of a fuel cell membrane according to claim 1, characterized in that, The determination of the current operating condition based on the rate of change of the current current density specifically includes: The rate of change of the current density At that time, it was determined to be a rapid current increase condition; among which, To set a threshold; The rate of change of the current density At that time, it was determined to be a rapid flow reduction condition.

6. The method for controlling the water content of a fuel cell membrane according to claim 1, characterized in that, The operating conditions also include steady-state or slowly changing operating conditions, specifically: The rate of change of the current density When the condition is determined to be steady state or slowly changing condition.

7. The method for controlling the water content of a fuel cell membrane according to claim 1, characterized in that, Under the current operating conditions, based on the rate of change of the current density and the current water content of the membrane, it is determined whether membrane drying or flooding will occur after the predicted time domain length. Specifically, this includes: Under rapid current rise conditions, based on the rate of change of the current density and the ohmic impedance corresponding to the current water content of the membrane, the ohmic impedance at the predicted time domain length is obtained based on the fuel cell state space model. Based on the mapping relationship between ohmic impedance and membrane water content, the membrane water content at the predicted time domain length is obtained by inverting the ohmic impedance at the predicted time domain length. When the membrane water content is lower than the membrane drying risk threshold at the predicted time domain length, it is determined that membrane drying will occur after the predicted time domain length. If the membrane water content at the predicted time domain length is not lower than the membrane dryness risk threshold, it is determined that membrane dryness will not occur after the predicted time domain length. Under rapid current reduction conditions, based on the rate of change of the current density and the ohmic impedance corresponding to the current membrane water content, the ohmic impedance at the predicted time domain length is obtained using the fuel cell state-space model. Based on the mapping relationship between ohmic impedance and membrane water content, the membrane water content at the predicted time domain length is obtained by inverting the ohmic impedance at the predicted time domain length. When the membrane water content is higher than the flooding risk threshold at the predicted time domain length, it is determined that flooding will occur after the predicted time domain length. If the membrane water content is not higher than the flooding risk threshold at the predicted time domain length, it is determined that flooding will not occur after the predicted time domain length.

8. The method for controlling the water content of a fuel cell membrane according to claim 1, characterized in that, The method of adjusting the current density when membrane drying or flooding occurs specifically includes: When membrane dryness occurs, a control strategy is implemented to limit the rate of increase of current density. In the event of flooding, control strategies are implemented to limit the rate of decrease in current density or to perform short-term current boosting.

9. A fuel cell membrane water content control system, characterized in that, include: A data acquisition component is used to acquire the initialization parameters of the fuel cell stack and set the sampling period and prediction time domain length. The initialization parameters include the membrane area. The current density calculation component is used to collect dynamic parameters of the fuel cell stack in each sampling period, and calculate the current current density and the rate of change of the current current density in combination with the initialization parameters; the dynamic parameters include voltage, current, stack temperature, and partial pressure of the anode and cathode reaction gases; The ohmic impedance calculation component is used to predict and update the current ohmic impedance based on the fuel cell state-space model, with the current current density as input and voltage as the observation, and using the extended Kalman filter algorithm to obtain the optimal estimate of the current ohmic impedance; the fuel cell state-space model includes the state equation and observation equation of the ohmic impedance. The membrane water content inversion component is used to invert the current membrane water content from the optimal estimate of the current ohmic impedance based on the mapping relationship between ohmic impedance and membrane water content. The state determination component is used to determine the current operating state based on the rate of change of the current current density; the operating state includes rapid current increase condition and rapid current decrease condition; under the current operating state, based on the rate of change of the current current density and the current water content of the membrane, it is determined whether membrane drying or flooding will occur after the predicted time domain length. The control component is used to regulate the current density when membrane dryness or flooding occurs. The current density can be freely varied as long as there is no risk of membrane drying or flooding.

10. The fuel cell membrane water content control system according to claim 9, characterized in that, The state equation for the ohmic impedance is: ； in, for At time ohmic impedance relative to The estimated value of the change in ohmic impedance at time t. for At time ohmic impedance relative to The change in ohmic impedance at time t. , for The observed value of the ohmic impedance at time t. for The observed value of the ohmic impedance at time t. Time-varying parameters characterizing the decay effect of steady-state operating conditions, Time-varying parameters characterizing the impact of dynamic operating condition changes on degradation, The change in current density , for Current density at time t, for Current density at a given time.

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