A method for calibrating a hydrogen energy stack partition measurement device

Abstract

The application provides a kind of hydrogen energy electric pile partition measurement device calibration method, the method comprises: operating the electric pile to be measured under the condition of set basic boundary;The voltage, current data of each partition of electric pile is collected by partition measurement device, and the current-voltage curve of each partition is drawn;Check whether the current-voltage curve of each partition is normal, and judge whether the current-voltage consistency of each partition is within the allowable tolerance range, if not satisfied, return to adjust the basic boundary condition or check the hardware connection;If all meet, EIS analysis is carried out, if the device captures the impedance spectrum characteristic change caused by dry and wet state change, then the calibration is completed;If the device cannot distinguish or the data is abnormal, it is determined that the requirement is not met, and the sensor layout or signal processing algorithm is corrected.The application effectively solves the problem that the traditional method cannot locate the internal fault of the electric pile in real time, and ensures the physical authenticity and reliability of the output data of the partition measurement device under complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of battery testing technology, and in particular to a method for calibrating a hydrogen fuel cell stack zonal measurement device. Background Technology

[0002] Hydrogen energy, as a key clean energy carrier for achieving the "dual carbon" goal, has an industrial chain covering the entire process from upstream hydrogen production and midstream storage and transportation to downstream diversified applications. Within this system, hydrogen fuel cell stacks, whether used for water electrolysis in hydrogen production or fuel cell stacks for power generation, are undoubtedly the core technology and equipment of the entire industrial chain. Their performance, lifespan, and reliability directly determine the technological maturity and commercialization progress of hydrogen energy systems. With technological advancements, in pursuit of higher power density, the integration of fuel cell stacks is constantly increasing, with hundreds of cells connected in series and operating voltages rising to the hundreds of volts. This places unprecedentedly stringent requirements on the sealing, insulation, and structural integrity of the fuel cell stack. A direct technological consequence is that once the fuel cell stack is encapsulated, its complex internal physical and electrochemical states become a difficult "black box" to observe, making it difficult for traditional detection methods to achieve in-situ, real-time, and zoned monitoring of its internal state.

[0003] Currently, the traditional evaluation methods relied upon by the industry have significant limitations. The short-reactor testing method used in the early stages of R&D can only conduct small-scale verification and cannot reflect key issues such as local thermal management, water distribution, and current density uniformity within a real high-power long reactor under complex operating conditions. In actual operation, macroscopic parameters such as average temperature, pressure, humidity, and total voltage can typically only be measured at the reactor inlet / outlet or endplate. This holistic parameter measurement method cannot accurately pinpoint internal local faults, such as membrane electrode drying, flooding, or catalyst degradation in specific areas. When performance degradation occurs, destructive offline disassembly and analysis are often the only option. This method is costly, time-consuming, and completely unsuitable for online diagnostics and process control during operation, severely restricting reactor lifespan management, fault early warning, and system optimization.

[0004] To overcome the aforementioned monitoring bottlenecks, partitioned measurement devices based on embedded PCB sensors have emerged in recent years. This technology can simultaneously collect multi-dimensional electrochemical characteristic parameters such as potential, current, impedance, temperature, and humidity of each partition during fuel cell stack operation, offering hope for visualization and accurate diagnosis of internal conditions. However, this new type of measurement system also introduces new uncertainties and challenges. Factors such as sensor position deviation during installation, changes in contact resistance during long-term operation, signal transmission delay, and phase drift of the electrochemical impedance spectroscopy excitation source all directly affect the accuracy of the measurement data. Furthermore, under the complex dynamic conditions actually faced by fuel cell stacks, such as alternating wet and dry conditions and drastic load fluctuations, the sensor's response characteristics may experience nonlinear drift, leading to data distortion. More critically, the industry currently lacks a unified and scientific calibration method and process, resulting in a lack of comparability between data obtained from different devices and laboratories. This severely hinders the engineering application of this technology, the calibration of high-precision models, and the implementation of data-driven intelligent control strategies.

[0005] Therefore, as hydrogen fuel cell stacks evolve towards higher power and higher reliability, there is an urgent need to establish a scientific, systematic, and repeatable method for calibrating zoned measurement devices. This method aims to ensure that the measurement system can still output high-precision and high-reliability internal electrochemical state data under complex dynamic operating environments. This will provide a solid and reliable data foundation for fuel cell stack health status assessment, remaining lifetime prediction, and the formulation of optimal control strategies, ultimately supporting the maturation and large-scale commercial application of core equipment in the hydrogen energy industry. Summary of the Invention

[0006] To address the above issues, this invention establishes a closed-loop calibration process covering the entire process from initial condition setting to in-depth analysis under dry and wet conditions. It innovatively uses electrochemical impedance spectroscopy as the core criterion for mechanism verification. Furthermore, it designs specialized testing procedures for membrane dryness / flooding and, combined with a hardware-software collaborative intelligent verification system, constructs a calibration method based on general electrochemical principles. This effectively solves the problem of traditional methods being unable to locate internal faults in the fuel cell stack in real time, ensuring the physical authenticity and reliability of the data output by the zoned measurement device under complex operating conditions. This provides key technical support for accurate diagnosis and lifespan management of the fuel cell stack and has the potential for wider application to electrochemical devices such as electrolytic reactors.

[0007] According to an embodiment of the present invention, a method for calibrating a hydrogen fuel cell stack zonal measurement device is provided.

[0008] In a first aspect of the invention, a method for calibrating a hydrogen fuel cell stack zonal measurement device is provided. The method includes: Step S01: Run the test stack under the set basic boundary conditions; Step S02: Collect voltage and current data of each zone of the fuel cell stack using the zone measurement device, and plot the current-voltage curve of each zone; Step S03: Check whether the current-voltage curves of each partition are normal, and determine whether the current-voltage consistency of each partition is within the allowable tolerance range. If the current-voltage curves are abnormal or the current-voltage consistency of each partition is not within the allowable tolerance range, return to adjust the basic boundary conditions or check the hardware connection. Step S04: If the current-voltage curve is normal and the current-voltage consistency of each zone is within the allowable tolerance range, then perform EIS analysis. If the zone measurement device captures the change in impedance spectrum characteristics caused by the change in dry and wet conditions, then it is determined that the requirements are met and the calibration is completed. If the zone measurement device cannot distinguish or the data is abnormal, then it is determined that the requirements are not met and the sensor layout or signal processing algorithm is corrected.

[0009] Furthermore, the basic boundary conditions described in step S01 include the operating temperature of the fuel cell stack, the stoichiometric ratio of hydrogen to air, and the reaction gas pressures at the anode and cathode.

[0010] Further, in step S02, the partition measurement device includes: The partition detection board assembly consists of a multilayer PCB board located inside the fuel cell stack. The surface of the PCB board is etched with a real flow channel structure consistent with the bipolar plates of the fuel cell stack, and several physical field sensors are embedded in the board. The partition detection board assembly physically divides the fuel cell stack into multiple independent test partitions. Composite excitation source module: It is electrically connected to the partition detection board assembly and is used to apply a composite current signal to the specified partition under test; Multi-channel synchronous acquisition module: It is directly connected to each tested partition and the physical field sensor through circuit traces and probes on the partition detection board assembly, and is used to synchronously acquire the voltage response, current response and physical field data of each partition; Central control and analysis module: It is communicatively connected to the multi-channel synchronous acquisition module, receives the synchronously acquired data, and generates spatial distribution information of the internal state of the fuel cell stack.

[0011] Furthermore, the allowable tolerance range mentioned in step S03 is specifically: the range of voltage measurement values ​​of each partition at the same current point shall not exceed 5% of the average voltage of all partitions.

[0012] Furthermore, the electrochemical impedance spectroscopy test and analysis described in step S04 specifically includes: applying a broadband sinusoidal perturbation signal to the stack and simultaneously acquiring voltage and current responses; converting the time-domain signal into a frequency-domain signal through Fourier transform and calculating the complex impedance; fitting the obtained impedance spectrum data to the equivalent circuit model and extracting characteristic parameters related to the water content of the proton exchange membrane and the gas diffusion process.

[0013] Furthermore, the characteristic parameters include: high-frequency ohmic impedance reflecting proton transport resistance, mid-frequency impedance reflecting charge transfer resistance, and low-frequency impedance reflecting gas diffusion resistance.

[0014] Furthermore, the impedance spectrum characteristics are changed as follows: when simulating a dry film state, the ohmic impedance in the high-frequency region of the impedance spectrum increases significantly; when simulating a flooded state, the diffusion impedance arc in the low-frequency region of the impedance spectrum increases significantly.

[0015] In a second aspect of the invention, a device for calibrating a hydrogen fuel cell stack zoning measurement device is provided. The device includes: Initialization module: used to run the test stack under the set basic boundary conditions; Zone measurement module: used to collect voltage and current data of each zone of the fuel cell stack through the zone measurement device, and to plot the current-voltage curve of each zone; Performance Judgment Module: Used to check whether the current-voltage curves of each partition are normal and to determine whether the current-voltage consistency of each partition is within the allowable tolerance range. If the current-voltage curves are abnormal or the current-voltage consistency of each partition is not within the allowable tolerance range, then return to adjust the basic boundary conditions or check the hardware connection. State Analysis Module: If the current-voltage curve is normal and the current-voltage consistency of each zone is within the allowable tolerance range, EIS analysis is performed. If the zone measurement device captures the change in impedance spectrum characteristics caused by the change in dry and wet conditions, the requirements are met and calibration is completed. If the zone measurement device cannot distinguish or the data is abnormal, the requirements are not met, and the sensor layout or signal processing algorithm is corrected.

[0016] In a third aspect of the invention, an electronic device is provided. The electronic device includes a memory and a processor, the memory storing a computer program, the processor executing the program to implement the method according to a first aspect of the invention.

[0017] In a fourth aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method according to a first aspect of the invention.

[0018] This invention establishes a closed-loop calibration process covering the initial conditions from start to finish, including in-depth analysis under dry and wet conditions. It innovatively uses electrochemical impedance spectroscopy as the core criterion for mechanism verification. Furthermore, it designs specialized testing procedures for membrane dryness / flooding and, combined with a hardware-software collaborative intelligent verification system, constructs a calibration method based on general electrochemical principles. This effectively solves the problem of traditional methods being unable to locate internal faults in the fuel cell stack in real time, ensuring the physical authenticity and reliability of the data output by the zoned measurement device under complex operating conditions. It provides key technical support for accurate diagnosis and lifespan management of fuel cell stack health and has the potential for wider application to electrochemical devices such as electrolytic reactors.

[0019] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of the present invention, nor is it intended to restrict the scope of the invention. Other features of the invention will become readily apparent from the following description.

[0020] 1. A complete closed-loop calibration process covering initial condition setting, system performance testing to in-depth analysis under dry and wet conditions was established, realizing the systematic verification of the reliability of the measuring device under all operating conditions; 2. By using electrochemical impedance spectroscopy analysis as the core criterion, the calibration process is deeply integrated with the electrochemical mechanism. This not only verifies the accuracy of the data but also ensures that it can accurately reflect the real conditions such as membrane dryness and flooding, effectively identifying and eliminating abnormal data that do not conform to physical laws. 3. A dry and wet state verification process was specifically designed to ensure that the measuring device can accurately diagnose critical local water and heat imbalance problems in the operation of hydrogen fuel cell stacks, providing a reliable basis for fault early warning and life management; 4. A hardware and software collaborative verification system was constructed. By combining a dedicated hardware architecture with algorithms such as Fourier transform and equivalent circuit fitting, intelligent evaluation and calibration of the overall performance of the measurement system were realized. 5. The calibration logic is based on general electrochemical principles and has good scalability and versatility. After parameter adjustment, it can be applied to various electrochemical energy storage devices such as water electrolysis hydrogen production reactors, and has broad industrial promotion value. Attached Figure Description

[0021] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent from the accompanying drawings and the following detailed description. Wherein: Figure 1 A flowchart illustrating a method for calibrating a hydrogen fuel cell stack zoning measurement device according to an embodiment of the present invention is shown; Figure 2 A schematic diagram of a partition measurement device according to an embodiment of the present invention is shown; Figure 3 An EIS analysis logic flowchart according to an embodiment of the present invention is shown; Figure 4 A block diagram of a hydrogen fuel cell stack zoning measurement device calibration apparatus according to an embodiment of the present invention is shown; Figure 5 A schematic diagram of the equipment for calibrating a hydrogen fuel cell stack zoning measurement device according to an embodiment of the present invention is shown. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] According to embodiments of the present invention, a method for calibrating a hydrogen fuel cell stack zonal measurement device is proposed. This method establishes a closed-loop calibration process covering initial conditions from setting the scale to in-depth analysis under dry and wet conditions. It innovatively uses electrochemical impedance spectroscopy as the core criterion for mechanism verification. Furthermore, it designs specific testing steps for membrane dryness / flooding and, combined with a hardware-software collaborative intelligent verification system, constructs a calibration method based on general electrochemical principles. This effectively solves the problem of traditional methods being unable to locate internal faults in the fuel cell stack in real time, ensuring the physical authenticity and reliability of the data output by the zonal measurement device under complex operating conditions. This provides key technical support for accurate diagnosis and lifespan management of the fuel cell stack and has potential for application to electrochemical devices such as electrolytic reactors.

[0024] The principles and spirit of the present invention will be explained in detail below with reference to several representative embodiments.

[0025] like Figure 1 As shown below, a specific example will be used to further explain the calibration method of the hydrogen fuel cell stack zonal measurement device.

[0026] Step S01: Run the test stack under the set basic boundary conditions.

[0027] The basic boundary conditions mainly involve the operating parameters of the fuel cell stack, including: temperature: setting the operating temperature point of the stack; stoichiometry: setting the stoichiometric ratio of hydrogen to air; and pressure: setting the reaction gas pressures on the anode and cathode sides. These parameters are primarily empirical values ​​from the development phase, designed to establish a stable baseline for subsequent testing.

[0028] For example, the fuel cell stack operating temperature is set to 70°C, the stoichiometric ratio of hydrogen to air is 1.5 and 2.0 respectively, and the back pressure of both the anode and cathode is set to 150 kPa (gauge pressure). The fuel cell stack is started and operated under these stable conditions.

[0029] Step S02: Collect voltage and current data of each zone of the fuel cell stack using the zone measurement device, and plot the current-voltage curve of each zone.

[0030] This stage primarily focuses on sensitivity testing based on initial conditions, and can be as in-depth as needed. The key indicators examined include: plotting overall and zone-specific IV curves of the fuel cell stack to characterize its performance and efficiency; comparing voltage and current distributions across different zones to assess stack consistency; and recording fundamental macroscopic electrochemical parameters such as open-circuit voltage and operating voltage.

[0031] like Figure 2 As shown, the partition measurement device includes: a partition detection board assembly: the partition detection board assembly is a multilayer PCB board disposed inside the fuel cell stack. The surface of the PCB board is etched with a real flow channel structure consistent with the bipolar plate of the fuel cell stack, and several physical field sensors are embedded in the board; the partition detection board assembly physically divides the fuel cell stack into multiple independent test partitions. Composite excitation source module: It is electrically connected to the partition detection board assembly and is used to apply a composite current signal to the specified partition under test; Multi-channel synchronous acquisition module: It is electrically connected to each tested partition and the physical field sensor through circuit traces and probes on the partition detection board assembly, and is used to synchronously acquire the voltage response, current response and physical field data of each partition; Central control and analysis module: It is communicatively connected to the multi-channel synchronous acquisition module, receives the synchronously acquired data, and generates spatial distribution information of the internal state of the fuel cell stack.

[0032] After the fuel cell stack stabilizes, initial performance testing is conducted using a zoned measurement device. The central control and analysis module controls the composite excitation source module and the multi-channel synchronous acquisition module to simultaneously acquire voltage and current data for the entire stack and each zone. Subsequently, the overall polarization curve of the stack and the current-voltage curve for each zone are plotted. This step focuses on two key indicators: first, the overall performance of the stack (such as open-circuit voltage and rated power point voltage); and second, the consistency between the curves of each zone. Through visual comparison and preliminary calculations, the uniformity of the internal reaction of the stack is assessed. The data at this stage also provides a benchmark for subsequent judgments.

[0033] Step S03: Check whether the current-voltage curves of each partition are normal, and determine whether the current-voltage consistency of each partition is within the allowable tolerance range. If the current-voltage curves are abnormal or the current-voltage consistency of each partition is not within the allowable tolerance range, return to adjust the basic boundary conditions or check the hardware connection.

[0034] Although calibration is primarily for new devices, this step also implicitly confirms the condition of the fuel cell stack. Abnormal test results (such as performance significantly lower than expected) may indicate a decline in the stack's lifespan or a malfunction. In this case, the stack itself must be ruled out before recalibrating the device. If the requirements are not met, return to adjust the initial conditions or check the hardware connections, and retest. If the requirements are met, proceed to the next step of in-depth analysis.

[0035] Specifically, the central control and analysis module automatically checks whether the shape of the plotted IV curves for each zone is normal (e.g., whether there are abnormal shapes such as voltage drops), and quantitatively calculates the voltage value of each zone at a specific current point. It calculates the range of these voltage values ​​(the difference between the maximum and minimum values) and determines whether the range does not exceed 5% of the average voltage of all zones.

[0036] If the result is negative: For example, if the voltage of a certain zone is significantly lower or the curve is abnormal, it may mean that there is a local fault in the fuel cell stack corresponding to that zone, or that the sensor connection of the zone measurement device at that location has failed. In this case, the process returns, and it is necessary to first rule out problems with the fuel cell stack itself, then check and tighten the hardware connection of the zone detection board assembly, or readjust the operating conditions and start testing again from S01.

[0037] If the judgment is yes: it indicates that the fuel cell stack is operating in a basically uniform state under the current conditions, and the basic data acquisition function of the partition measurement device is normal, so it can enter the core deep verification stage.

[0038] Step S04: If the current-voltage curve is normal and the current-voltage consistency of each zone is within the allowable tolerance range, then perform EIS analysis. If the zone measurement device captures the change in impedance spectrum characteristics caused by the change in dry and wet conditions, then it is determined that the requirements are met and the calibration is completed. If the zone measurement device cannot distinguish or the data is abnormal, then it is determined that the requirements are not met and the sensor layout or signal processing algorithm is corrected.

[0039] This is the core step of calibration, designed to verify the device's ability to identify key failure modes within the fuel cell stack (membrane dryness and flooding). Dry / wet conditions are understood as the stability performance of the fuel cell stack or system during operation. This step can also be performed simultaneously during system testing. Under these conditions, EIS (electrochemical impedance spectroscopy) analysis is essential. The process problem is pinpointed to specific electrochemical reaction steps, such as: proton transport resistance (reflecting the degree of membrane wetting, which increases when the membrane is dry); gas diffusion resistance (reflecting the accumulation of liquid water in the flow channel, which increases when flooded); and the ease of redox reactions (reflecting catalyst activity and the state of the three-phase interface). If the device can accurately capture the changes in impedance spectral characteristics caused by changes in dry / wet conditions (e.g., the impedance change trend in the high-frequency or low-frequency region matches theoretical expectations), then the requirements are met, and calibration is complete. If the device cannot distinguish between dry and wet conditions or the data is abnormal, then the requirements are not met, and the sensor layout or signal processing algorithm needs to be modified.

[0040] Electrochemical inertial analysis (EIS) is a core tool for understanding the internal electrochemical reactions of hydrogen fuel cell stacks and diagnosing membrane dryness and flooding faults. In the calibration method of this invention, EIS analysis follows a strict logical chain, such as… Figure 3 As shown.

[0041] Matching the appropriate signal: Before conducting EIS testing, the system must meet three basic conditions: Causality: The response signal must be generated by the excitation signal. Stability: The system remains in a steady state during the test, without drastic fluctuations. Linearity: The amplitude of the excitation signal is small enough to ensure that the system response is within the linear range. Minimal interference: Environmental noise and power frequency interference are controlled to the lowest possible level.

[0042] Signal Application and Acquisition: Matching a Suitable Signal: Select the optimal excitation frequency range (wideband) and amplitude (constant value) based on the stack characteristics. Applying the Excitation Signal: Output a sinusoidal disturbance through the EIS load excitation source, while simultaneously locking the phase angle (constant value). Acquiring the Signal: Simultaneously record the voltage and current responses using a data acquisition instrument. During this process, time-domain-frequency-domain Fourier transform technology is employed to effectively eliminate interference signals and confirm the correct signal frequency components.

[0043] Impedance calculation and equivalent circuit analysis: Based on the collected voltage and current data, the complex impedance value is calculated using Euler's formula. The real and imaginary parts of the impedance are obtained. Equivalent circuit modeling and analysis: The calculated impedance spectrum is fitted to an equivalent circuit model (such as the Randles equivalent circuit). The equivalent circuit is then modified according to its actual physical meaning to make it closer to the actual physical structure of the fuel cell stack.

[0044] Relationship mapping: Establishing the relationship between the equivalent circuit and electrochemistry. High-frequency impedance: Primarily corresponds to ohmic impedance ( This is directly related to the water content of the proton exchange membrane. Membrane dryness can lead to… Significantly increased. Mid-frequency impedance: corresponding to charge transfer resistance ( The impedance in the low-frequency region reflects the electrochemical reaction rate. Low-frequency impedance corresponds to concentration polarization impedance (Warburg impedance), which is related to gas diffusion and water management. Flooding hinders gas diffusion, increasing the impedance arc in the low-frequency region.

[0045] This step is crucial for verifying whether the zone measurement device can diagnose internal hydrothermal imbalances in the fuel cell stack. While maintaining stack operation, the hydrothermal state of the stack is artificially altered through a testing system. For example, the inlet humidity is first reduced to simulate a "film drying" tendency, and then the humidity is increased or the temperature is decreased to simulate a "flooding" tendency.

[0046] Under each steady-state condition, electrochemical impedance spectroscopy (EIS) testing is performed: the composite excitation source module applies a wideband sinusoidal current disturbance signal (frequency range, for example, 10 kHz to 0.1 Hz) of appropriate amplitude (typically 2-5% of the rated current to ensure linear response) to the fuel cell stack. The multi-channel synchronous acquisition module simultaneously acquires the voltage response signals of all zones.

[0047] The acquired time-domain signal is converted into a frequency-domain signal via Fourier transform, and the central control and analysis module calculates the independent complex impedance spectrum for each region. Subsequently, the impedance spectrum data is fitted to an equivalent circuit model (e.g., a Randle-type circuit including ohmic impedance, charge transfer impedance, and diffusion impedance) to extract key feature parameters, such as the high-frequency intercept (representing ohmic impedance). This mainly reflects the proton exchange membrane impedance, as well as the characteristic parameters of the low-frequency arc (reflecting gas diffusion impedance).

[0048] The calibration decision logic is as follows: When the state changes from normal to "membrane dry", the data from each zone should consistently show the high-frequency ohmic impedance. A significant increase.

[0049] As the state changes towards "flooded", the data from each partition should consistently show a significant increase in low-frequency diffusion impedance.

[0050] If the impedance spectrum data collected and analyzed by the partition measurement device can clearly and consistently capture the characteristic change trend that conforms to the electrochemical theory, it proves that the device has a reliable internal state diagnosis capability and is deemed to have passed the calibration.

[0051] If the data output by the device is chaotic, the trends of change in each zone are inconsistent or seriously inconsistent with theoretical expectations, and the dry and wet states cannot be distinguished, then the calibration is deemed unqualified. In this case, it is necessary to correct the sensor layout (such as adjusting the position or contact method of the sensors on the zone detection board assembly) or optimize the signal processing algorithm (such as filtering parameters and equivalent circuit models) based on data analysis, and then re-perform the calibration process.

[0052] Based on the same inventive concept, this invention also proposes a device for calibrating a hydrogen fuel cell stack zonal measurement device. The implementation of this device can be found in the implementation of the method described above; repeated details will not be repeated. Figure 4 As shown, the device 100 includes: Initialization module 101: Used to run the test stack under the set basic boundary conditions; Zone measurement module 102: used to collect voltage and current data of each zone of the fuel cell stack through the zone measurement device, and to plot the current-voltage curve of each zone; Performance judgment module 103: Used to check whether the current-voltage curve of each partition is normal and to determine whether the current-voltage consistency of each partition is within the allowable tolerance range. If the current-voltage curve is abnormal or the current-voltage consistency of each partition is not within the allowable tolerance range, then return to adjust the basic boundary conditions or check the hardware connection. State Analysis Module 104: If the current-voltage curve is normal and the current-voltage consistency of each zone is within the allowable tolerance range, then EIS analysis is performed. If the zone measurement device captures the change in impedance spectrum characteristics caused by the change in dry and wet conditions, then the requirements are met and calibration is completed. If the zone measurement device cannot distinguish or the data is abnormal, then the requirements are not met and the sensor layout or signal processing algorithm is corrected.

[0053] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0054] like Figure 5 As shown, the device includes a central processing unit (CPU), which can perform various appropriate actions and processes based on computer program instructions stored in read-only memory (ROM) or loaded from storage units into random access memory (RAM). The RAM can also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0055] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0056] The processing unit executes the various methods and processes described above, such as method steps S01 to S04. For example, in some embodiments, method steps S01 to S04 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of method steps S01 to S04 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute method steps S01 to S04 by any other suitable means (e.g., by means of firmware).

[0057] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0058] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0059] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0060] Furthermore, although the operations are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0061] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for calibrating a hydrogen fuel cell stack zoning measurement device, characterized in that, The method includes: Step S01: Run the test stack under the set basic boundary conditions; Step S02: Collect voltage and current data of each zone of the fuel cell stack using the zone measurement device, and plot the current-voltage curve of each zone; Step S03: Check whether the current-voltage curves of each partition are normal, and determine whether the current-voltage consistency of each partition is within the allowable tolerance range. If the current-voltage curves are abnormal or the current-voltage consistency of each partition is not within the allowable tolerance range, return to adjust the basic boundary conditions or check the hardware connection. Step S04: If the current-voltage curve is normal and the current-voltage consistency of each zone is within the allowable tolerance range, then perform EIS analysis. If the zone measurement device captures the change in impedance spectrum characteristics caused by the change in dry and wet conditions, then it is determined that the requirements are met and the calibration is completed. If the zone measurement device cannot distinguish or the data is abnormal, then it is determined that the requirements are not met and the sensor layout or signal processing algorithm is corrected.

2. The method for calibrating a hydrogen fuel cell stack zoning measurement device according to claim 1, characterized in that, The basic boundary conditions mentioned in step S01 include the operating temperature of the fuel cell stack, the stoichiometric ratio of hydrogen to air, and the reaction gas pressures at the anode and cathode.

3. The method for calibrating a hydrogen fuel cell stack zoning measurement device according to claim 1, characterized in that, In step S02, the partition measurement device includes: The partition detection board assembly consists of a multilayer PCB board located inside the fuel cell stack. The surface of the PCB board is etched with a real flow channel structure consistent with the bipolar plates of the fuel cell stack, and several physical field sensors are embedded in the board. The partition detection board assembly physically divides the fuel cell stack into multiple independent test partitions. Composite excitation source module: It is electrically connected to the partition detection board assembly and is used to apply a composite current signal to the specified partition under test; Multi-channel synchronous acquisition module: It is directly connected to each tested partition and the physical field sensor through circuit traces and probes on the partition detection board assembly, and is used to synchronously acquire the voltage response, current response and physical field data of each partition; Central control and analysis module: It is communicatively connected to the multi-channel synchronous acquisition module, receives the synchronously acquired data, and generates spatial distribution information of the internal state of the fuel cell stack.

4. The method for calibrating a hydrogen fuel cell stack zoning measurement device according to claim 1, characterized in that, The allowable tolerance range mentioned in step S03 is as follows: the range of voltage measurements of each partition at the same current point shall not exceed 5% of the average voltage of all partitions.

5. The method for calibrating a hydrogen fuel cell stack zoning measurement device according to claim 1, characterized in that, The electrochemical impedance spectroscopy test and analysis described in step S04 specifically includes: applying a broadband sinusoidal perturbation signal to the stack and simultaneously acquiring voltage and current responses; converting the time-domain signal into a frequency-domain signal through Fourier transform and calculating the complex impedance; fitting the obtained impedance spectrum data to the equivalent circuit model and extracting characteristic parameters related to the water content of the proton exchange membrane and the gas diffusion process.

6. The method for calibrating a hydrogen fuel cell stack zoning measurement device according to claim 5, characterized in that, The characteristic parameters include: high-frequency ohmic impedance reflecting proton transport resistance, mid-frequency impedance reflecting charge transfer resistance, and low-frequency impedance reflecting gas diffusion resistance.

7. The method for calibrating a hydrogen fuel cell stack zoning measurement device according to claim 5, characterized in that, The impedance spectrum characteristics change as follows: when simulating a dry film state, the ohmic impedance in the high-frequency region of the impedance spectrum increases significantly; when simulating a flooded state, the diffusion impedance arc in the low-frequency region of the impedance spectrum increases significantly.

8. A device for calibrating a hydrogen fuel cell stack zoning measurement device, characterized in that, The device implements the method as described in any one of claims 1 to 7, comprising: Initialization module: used to run the test stack under the set basic boundary conditions; Zone measurement module: used to collect voltage and current data of each zone of the fuel cell stack through the zone measurement device, and to plot the current-voltage curve of each zone; Performance Judgment Module: Used to check whether the current-voltage curves of each partition are normal and to determine whether the current-voltage consistency of each partition is within the allowable tolerance range. If the current-voltage curves are abnormal or the current-voltage consistency of each partition is not within the allowable tolerance range, then return to adjust the basic boundary conditions or check the hardware connection. State Analysis Module: If the current-voltage curve is normal and the current-voltage consistency of each zone is within the allowable tolerance range, EIS analysis is performed. If the zone measurement device captures the change in impedance spectrum characteristics caused by the change in dry and wet conditions, the requirements are met and calibration is completed. If the zone measurement device cannot distinguish or the data is abnormal, the requirements are not met, and the sensor layout or signal processing algorithm is corrected.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

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