Fuel cell fault diagnosis method and device based on voltage signal and medium

Abstract

The invention discloses a fuel cell fault diagnosis method and device based on a voltage signal and a medium, belongs to the technical field of fuel cells, and aims to solve the problems that in an existing fuel cell fault diagnosis method based on an EIS, interference signals participate in impedance calculation, high information redundancy is caused, the calculation time is increased, and the fault diagnosis efficiency is high. And the fault diagnosis speed of the fuel cell is reduced. The method comprises the following steps: acquiring a voltage response signal after an interference signal is injected into a fuel cell; and inputting the voltage response signal into the classifier model, and determining the fault type of the fuel cell. According to the method, the speed and efficiency of determining the fault type of the fuel cell are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel cells, and in particular to a fuel cell fault diagnosis method, equipment and medium based on voltage signals. Background Art

[0002] Proton exchange membrane fuel cells (PEMFCs) are widely used in the transportation industry for their high efficiency and pollution-free operation. For example, PEMFCs can be used in the power systems of automobiles and aircraft. The next generation of high power density fuel cells is expected to play a greater role in powertrain applications. However, the degradation of fuel cells under typical operating conditions makes it difficult to meet the transient power requirements of automobiles. In addition to the limitations of dynamic response capabilities, fuel cells also face challenges in durability. For example, corrosion currents at startup, temporary relative air shortages under load changes, and water state fluctuations at idle can significantly affect the service life of fuel cells. Therefore, the development of fast and accurate fault diagnosis methods is crucial to extend the service life of fuel cells because it can provide rich and effective status information for subsequent fault recovery measures.

[0003] In the prior art, fuel cell fault diagnosis can be performed by fast electrochemical impedance spectroscopy (EIS). The basic principle is to calculate the impedance based on the relationship between the interference signal and the response signal, and then perform fuel cell fault diagnosis based on the calculated impedance. When EIS is applied to fuel cell fault diagnosis, for fuel cells of different fault types or under normal working conditions, the interference signal is the same, and only the voltage response signal is different. That is, from the perspective of state monitoring and fault diagnosis, only the response signal contains a high amount of unknown information about the working state of the fuel cell, while the interference signal does not contain the above information that can be used for fault diagnosis. Therefore, by calculating the impedance based on the relationship between the interference signal and the response signal, and then performing fuel cell fault diagnosis based on the calculated impedance, the interference signal is involved in the impedance calculation, which will lead to a high degree of information redundancy, increase unnecessary calculation time, and reduce the speed of fuel cell fault diagnosis. Summary of the invention

[0004] The present invention provides a fuel cell fault diagnosis method, device and medium based on voltage signals, which are used to solve the technical problem that in the existing fuel cell fault diagnosis method based on EIS, interference signals participate in impedance calculation, which leads to high information redundancy, increases calculation time and reduces the fuel cell fault diagnosis speed.

[0005] In a first aspect, the present invention provides a fuel cell fault diagnosis method based on a voltage signal, comprising: Acquire a voltage response signal of the fuel cell after the interference signal is injected; The voltage response signal is input into a classifier model to determine the fault type of the fuel cell.

[0006] In a feasible implementation manner, after obtaining the voltage response signal of the fuel cell after the interference signal is injected, the method further includes: Performing time-frequency transformation on the voltage response signal to obtain a time-frequency spectrum of the voltage response signal; A fault type of the fuel cell is determined based on the time-frequency spectrum of the voltage response signal.

[0007] In a feasible implementation manner, performing time-frequency transformation on the voltage response signal to obtain a time-frequency spectrum of the voltage response signal includes: Performing a generalized S transform on the voltage response signal to obtain a generalized S transform time-frequency spectrum of the voltage response signal.

[0008] In a feasible implementation manner, determining the fault type of the fuel cell based on the time-frequency spectrum of the voltage response signal includes: Based on the time-frequency spectrum of the voltage response signal, generating a time-frequency spectrum image of the voltage response signal; The time-frequency spectrum image of the voltage response signal is input into a classifier model to determine the fault type of the fuel cell.

[0009] In a feasible implementation manner, determining the fault type of the fuel cell based on the time-frequency spectrum of the voltage response signal includes: Based on the time-frequency spectrum of the voltage response signal, generating a time-frequency spectrum image of the voltage response signal; Extracting scale-invariant features of the time-frequency spectrum image through scale-invariant feature transformation; Copying the time-frequency spectrum image to obtain two identical time-frequency spectrum images, and determining whether the scale-invariant features in the two identical time-frequency spectrum images match based on the distance between the scale-invariant features in the two identical time-frequency spectrum images to obtain a set of matching points; Placing the matching point set on a first plane and a second plane respectively to obtain a characteristic landscape, wherein the first plane and the second plane are parallel to each other, and the characteristic landscape is a line segment obtained by connecting the matching points that match each other; Based on the characteristic landscape, a fault type of the fuel cell is determined.

[0010] In a feasible implementation manner, determining the fault type of the fuel cell based on the characteristic landscape includes: Projecting the characteristic landscape onto a third plane to obtain a projection image, wherein the third plane is perpendicular to the first plane; Based on the projection image, a fault type of the fuel cell is determined.

[0011] In a feasible implementation manner, determining the fault type of the fuel cell based on the projection image includes: Based on the number of projections of the corresponding characteristic landscape at each position in the projection image, a barcode image is generated, wherein the barcode image is composed of black rectangles and white rectangles of different widths, including: for the position where the projection of the characteristic landscape exists in the projection image, a black rectangle in the barcode image corresponds, and the width of the black rectangle is proportional to the number of projections of the characteristic landscape in the corresponding position in the projection image; for the area in the projection image where the projection of the characteristic landscape does not exist, a white rectangle in the barcode image corresponds, and the width of the white rectangle is proportional to the width of the area; Based on the barcode image, a fault type of the fuel cell is determined.

[0012] In a feasible implementation manner, determining the fault type of the fuel cell based on the barcode image includes: Calculating the ratio of the area of ​​the black rectangle to the total area in the barcode image, and determining the ratio as the black-bar-space ratio; and / or, Calculating the ratio of the area of ​​the white rectangle to the total area in the barcode image, and determining the ratio as the white-bar-space ratio; Based on the black bar empty ratio and / or the white bar empty ratio, a fault type of the fuel cell is determined.

[0013] In a second aspect, the present invention provides a fuel cell fault diagnosis device based on a voltage signal, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor so that the at least one processor can execute a fuel cell fault diagnosis method based on a voltage signal as described in any of the above embodiments.

[0014] In a third aspect, the present invention provides a non-volatile computer storage medium, which is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program, each of which includes instructions, and when the instructions are executed by a terminal, the terminal executes a fuel cell fault diagnosis method based on a voltage signal as described in any of the above embodiments.

[0015] The fuel cell fault diagnosis method, device and medium based on voltage signal provided by the present invention have the following beneficial technical effects compared with the prior art: (1) In the present invention, for fuel cells with different fault types, the voltage response signal is unknown and contains information related to the working state of the fuel cell; the interference signal is the same for fuel cells with different fault types and is known information; therefore, based on the idea of ​​emphasizing-response and weak disturbance (ERSD), the present invention uses the voltage response signal as the basis for determining the fuel cell fault type, and the interference signal does not participate in the calculation, thereby reducing information redundancy and unnecessary calculation time. On the premise that the fuel cell fault type can be effectively determined, the speed and efficiency of determining the fuel cell fault type are improved.

[0016] (2) After generating the time-frequency spectrum image of the voltage response signal, the present invention copies the time-frequency spectrum image, places the scale-invariant features in the two time-frequency spectrum images on different planes, connects the corresponding scale-invariant features in the two images, and obtains a feature landscape in three-dimensional space. The working state information of the fuel cell in the time-frequency spectrum image of the voltage response signal is obtained from the perspective of three-dimensional space, taking a step towards a higher-dimensional space, obtaining more comprehensive information in the time-frequency spectrum image, overcoming the limitation of observing the state information from a low-dimensional perspective, and improving the accuracy of fuel cell fault diagnosis.

[0017] (3) The present invention generates a barcode image with high information density based on the projection of the characteristic landscape in the three-dimensional space onto the third plane. The barcode image can accurately and quickly track the working state of the fuel cell and enhance the distinction between various fault types, including water flooding fault, membrane dry fault, hydrogen starvation fault and oxygen starvation fault, thereby reducing the error rate of fault type determination. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A flow chart of a fuel cell fault diagnosis method based on a voltage signal provided by the present invention; Figure 2 A schematic diagram of a time-frequency spectrum image of a voltage response signal provided by the present invention; Figure 3 A schematic diagram of a characteristic landscape provided by the present invention; Figure 4A schematic diagram of a barcode image corresponding to a fuel cell under different fault types provided by the present invention; Figure 5 A schematic diagram of the black bar empty ratio corresponding to a fuel cell under different water fault types provided by the present invention; Figure 6 A schematic diagram of the black bar space ratio corresponding to a fuel cell under different gas fault types provided by the present invention; Figure 7 A schematic structural diagram of a fuel cell fault diagnosis device based on voltage signals provided by the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] The technical solution proposed by the present invention is described in detail below with reference to the accompanying drawings.

[0021] Figure 1 The present invention provides a flow chart of a fuel cell fault diagnosis method based on voltage signals. Figure 1 As shown, the method includes the following execution steps: S101, obtaining a voltage response signal of a fuel cell after an interference signal is injected into the fuel cell.

[0022] In the present invention, the voltage signal of the fuel cell includes a normal voltage signal component and a fault signal component. The fault signal component in the voltage response signal obtained after the interference signal is injected is amplified.

[0023] S102: Input the voltage response signal into a classifier model to determine the fault type of the fuel cell.

[0024] In the present invention, for fuel cells under different fault types, the voltage response signal is unknown and contains information related to the working state of the fuel cell; the interference signal is the same for fuel cells under different fault types and is known information; therefore, based on the idea of ​​ERSD, the present invention uses the voltage response signal as the basis for determining the fuel cell fault type, and the interference signal does not participate in the calculation, thereby reducing information redundancy and unnecessary calculation time, and improving the speed and efficiency of determining the fuel cell fault type while effectively determining the fuel cell fault type.

[0025] As a possible implementation, after obtaining the voltage response signal of the fuel cell after the interference signal is injected, the method also includes: performing time-frequency transformation on the voltage response signal to obtain the time-frequency spectrum of the voltage response signal; and determining the fault type of the fuel cell based on the time-frequency spectrum of the voltage response signal.

[0026] The time-frequency spectrum of the voltage response signal in the present invention contains relevant information about the working state of the fuel cell. The time-frequency spectrum corresponding to fuel cells under the same fault type is similar, and the time-frequency spectrum corresponding to fuel cells under different fault types is different. Therefore, the fault type of the fuel cell can be determined through the time-frequency spectrum of the voltage response signal. For example, the time-frequency spectrum of the voltage response signal is directly input into the classifier model, and the fault type of the fuel cell is determined according to the output result of the classifier model.

[0027] As a possible implementation, performing time-frequency transformation on the voltage response signal to obtain the time-frequency spectrum of the voltage response signal includes: performing generalized S transformation on the voltage response signal to obtain the generalized S transform time-frequency spectrum of the voltage response signal.

[0028] In some embodiments, the product of the Fast Fourier Transform (FFT) of the voltage response signal and the FFT of the Gaussian window function is calculated to obtain V ST ( k ), as shown in the following formula.

[0029]

[0030] in, k Indicates frequency, V ( k ) represents the FFT of the voltage response signal, G ( k ; σ ( k )) is the FFT of the Gaussian window function, σ ( k ) represents the scale parameter associated with frequency k.

[0031] Then, by multiplying the above product V ST ( k ) to perform an inverse fast Fourier transform (IFFT) to obtain the generalized S-transform time-frequency spectrum of the voltage response signal V ST ( n , k ), as shown below.

[0032]

[0033] in, n Indicates time.

[0034] In addition to the generalized S transform, other time-frequency transform methods can also be used, for example, short-time Fourier transform, continuous wavelet transform, real wavelet transform, wavelet synchronous compression transform, wavelet synchronous extraction transform, vertical second-order synchronous compression transform, multi-scale synchronous compression transform, local maximum synchronous compression transform, transient extraction transform, Hilbert transform and other time-frequency transform methods.

[0035] As a possible implementation method, the fault type of the fuel cell is determined based on the time-frequency spectrum of the voltage response signal, including: S201 : Generate a time-frequency spectrum image of the voltage response signal based on the time-frequency spectrum of the voltage response signal.

[0036] Exemplarily, a generalized S transform is performed on the voltage response signal to obtain a generalized S transform time-frequency spectrum of the voltage response signal, and a time-frequency spectrum image of the voltage response signal is generated, such as Figure 2 Shown is a schematic diagram of a time-frequency spectrum image of a voltage response signal provided by the present invention.

[0037] S202: Input the time-frequency spectrum image of the voltage response signal into a classifier model to determine the fault type of the fuel cell.

[0038] The time-frequency spectrum image of the voltage response signal in the present invention contains relevant information about the working state of the fuel cell. The time-frequency spectrum images corresponding to fuel cells under the same fault type are similar, and the time-frequency spectrum images corresponding to fuel cells under different fault types are different. Therefore, the fault type of the fuel cell can be determined through the time-frequency spectrum image of the voltage response signal. For example, the time-frequency spectrum image of the voltage response signal is directly input into the classifier model, and the fault type of the fuel cell is determined according to the output result of the classifier model.

[0039] In the prior art, fault diagnosis of fuel cells is generally based on one-dimensional signals or two-dimensional images. That is, the prior art is limited to two-dimensional space and does not explore high-dimensional space. Due to the inherent limitations of low-dimensional space, the amount of information observed in low-dimensional space is limited. It is difficult to obtain more comprehensive and reasonable information about the status of the fuel cell through one-dimensional signals or two-dimensional images, making it difficult to improve the accuracy of fault diagnosis.

[0040] As a possible implementation method, the fault type of the fuel cell is determined based on the time-frequency spectrum of the voltage response signal, including: Based on the time-frequency spectrum of the voltage response signal, a time-frequency spectrum image of the voltage response signal is generated; through scale-invariant feature transformation, the scale-invariant features of the time-frequency spectrum image are extracted; the time-frequency spectrum image is copied to obtain two identical time-frequency spectrum images, and based on the distance between the scale-invariant features in the two identical time-frequency spectrum images, it is determined whether the scale-invariant features in the two identical time-frequency spectrum images match, and a matching point set is obtained; the matching point set is placed on a first plane and a second plane respectively to obtain a feature landscape, the first plane and the second plane are parallel to each other, and the feature landscape is a line segment obtained by connecting the matching points that match each other; based on the feature landscape, the fault type of the fuel cell is determined.

[0041] In some embodiments, D 1 and D 2 represent the scale-invariant features in two identical time-frequency spectrogram images, and the Euclidean distance between the scale-invariant features of the two images is calculated. d ( D 1 ,D 2), as shown in the following formula.

[0042]

[0043] in, d 1i yes D 1 in i The value of the dimension, d 2i yes D 2 in i The value of the dimension.

[0044] For one of the images, the scale-invariant features D 1. Find the scale-invariant feature closest to the scale-invariant feature in another image D nn and the second closest scale-invariant feature D nnn .

[0045] Then, the closest scale-invariant feature D nn and the second closest scale-invariant feature D nnn Apply a ratio test between them to compute the scale-invariant features of one of the images D 1 and the closest scale-invariant feature in the other image D nn The first distance between, and one of the scale-invariant features of the image D 1 and the second closest scale-invariant feature in the other image D nnnIf the ratio of the first distance to the second distance is less than a preset ratio threshold, the match is determined to be valid, and the scale-invariant feature of one of the images is converted into D 1 and scale-invariant features in another image D nn Determined as a set of matching points.

[0046] It can be understood that it is necessary to ensure that each scale-invariant feature is matched only once to obtain a set of matching points of two identical time-frequency spectrum images.

[0047] For example, Figure 3 A schematic diagram of a characteristic landscape provided by the present invention, such as Figure 3 As shown, the matching point set of one of the two identical time-frequency spectrum images is placed on a first plane, and the matching point set of the other image is placed on a second plane. The first plane and the second plane are different and are both parallel to the XY plane. The line segment obtained by connecting the matching points that match each other is the characteristic landscape.

[0048] The present invention constructs a characteristic landscape in three-dimensional space based on a two-dimensional time-frequency spectrum image in the above manner, obtains the working status information of the fuel cell in the time-frequency spectrum image of the voltage response signal from the perspective of three-dimensional space, takes a step towards a higher-dimensional space, obtains more comprehensive information in the time-frequency spectrum image, overcomes the limitations of observing status information from a low-dimensional perspective, and improves the accuracy of fuel cell fault diagnosis.

[0049] As a possible implementation method, the fault type of the fuel cell is determined based on the characteristic landscape, including: as a possible implementation method, the characteristic landscape is projected onto a third plane to obtain a projection image, and the third plane is perpendicular to the first plane; based on the projection image, the fault type of the fuel cell is determined.

[0050] As described in the above example, the first plane and the second plane are planes parallel to the XY plane, and the third plane may be a plane parallel to the XZ plane, or a plane parallel to the YZ plane.

[0051] As a possible implementation method, a barcode image is generated based on the number of projections of the corresponding characteristic landscape at each position in the projection image, and the barcode image is composed of black rectangles and white rectangles of different widths, including: for the position where the projection of the characteristic landscape exists in the projection image, it corresponds to a black rectangle in the barcode image, and the width of the black rectangle is proportional to the number of projections of the characteristic landscape in the corresponding position in the projection image; for the area in the projection image where the projection of the characteristic landscape does not exist, it corresponds to a white rectangle in the barcode image, and the width of the white rectangle is proportional to the width of the area; based on the barcode image, the fault type of the fuel cell is determined.

[0052] In this way, based on the projection of the characteristic landscape in the three-dimensional space onto the third plane, a barcode image with high information density is generated. The barcode image can accurately and quickly track the working status of the fuel cell and enhance the distinction between various fault types, including water flooding fault, membrane dry fault, hydrogen starvation fault and oxygen starvation fault, thereby reducing the error rate of fault type determination.

[0053] For example, Figure 4 A schematic diagram of a barcode image corresponding to a fuel cell under different fault types provided by the present invention, Figure 4 The barcode images corresponding to the fuel cells under water flooding fault, membrane dry fault, hydrogen starvation fault, and oxygen starvation fault are shown. It can be seen that the barcode images corresponding to the fuel cells under the same fault type are similar, and the barcode images corresponding to the fuel cells under different fault types are different. In other words, the fuel cell fault type can be determined through the barcode image.

[0054] As a possible implementation method, based on the barcode image, determining the fault type of the fuel cell includes: calculating the ratio of the area of ​​the black rectangle in the barcode image to the total area, and determining the ratio as the black bar-space ratio; and / or calculating the ratio of the area of ​​the white rectangle in the barcode image to the total area, and determining the ratio as the white bar-space ratio; based on the black bar-space ratio and / or the white bar-space ratio, determining the fault type of the fuel cell.

[0055] For example, Figure 5 A schematic diagram of the black bar empty ratio corresponding to a fuel cell under different water fault types provided by the present invention, Figure 5 The black bar to space ratio of the fuel cell under the flooding fault and the membrane dry fault is shown. It can be seen that the black bar to space ratio corresponding to the flooding fault is significantly different from the black bar to space ratio corresponding to the membrane dry fault, that is, the flooding fault and the membrane dry fault can be effectively distinguished by the black bar to space ratio; Figure 6 A schematic diagram of the black bar space ratio corresponding to a fuel cell under different gas fault types provided by the present invention, Figure 6 The black bar space ratio of the fuel cell under hydrogen starvation fault and oxygen starvation fault is shown. It can be seen that the black bar space ratio corresponding to the hydrogen starvation fault and the black bar space ratio corresponding to the oxygen starvation fault are significantly different, that is, the hydrogen starvation fault and the oxygen starvation fault can be effectively distinguished by the black bar space ratio.

[0056] Exemplarily, a fuel cell fault diagnosis method based on voltage signals provided by the present invention can be implemented by three core components: component 1 is used to obtain the voltage response signal of the fuel cell after the interference signal is injected, and based on the idea of ​​ERSD, the voltage response signal is subjected to a generalized S transform to obtain a time-frequency spectrum image of the voltage response signal; component 2 is used to obtain a characteristic landscape based on the time-frequency spectrum image of the voltage response signal and generate a barcode image; component 3 uses a barcode reader to calculate the black bar-space ratio or the white bar-space ratio of the barcode image, and determines the fault type of the fuel cell based on the black bar-hole ratio or the white bar-space ratio.

[0057] Specifically, component 1 is responsible for injecting interference signals, and then using a rolling window to combine the voltage response signals collected by the sensor at a fixed frequency. Each new data point forms a window with the previous 19 data points, which rolls forward and slides point by point. It takes 0.845 seconds to slide a data point. For each data window, a generalized S transform is performed to obtain a time-frequency spectrum image, such as Figure 2 Compared with the traditional architecture that calculates the electrochemical impedance spectrum based on the interference signal and the response signal, component 1 determines the working state and fault type of the fuel cell based on the electrochemical impedance spectrum, reduces the information redundancy in the data processing process, and only takes 0.007 seconds to calculate the time-frequency spectrum image of each window. It can be seen that the above method greatly reduces the data processing time.

[0058] Component 2 is responsible for extracting scale-invariant features from the time-frequency spectrum image obtained by component 1 and generating a feature landscape in three-dimensional space, as shown in 3. Component 2 also includes a barcode generator, which obtains a barcode image with high-dimensional features and high information density based on the projection of the feature landscape on the third plane, as shown in Figure 4 As shown. Compared with the prior art method of fuel cell fault diagnosis based on one-dimensional signals or two-dimensional images, component 2 overcomes the inherent limitations of low-dimensional features. It only takes 1.06 seconds for component 2 to generate a barcode image based on a time-frequency spectrum image.

[0059] The barcode reader of component 3 is responsible for using virtual photoelectric recognition to scan the barcode image obtained by component 2, determine the black bar hole ratio or white bar space ratio of the barcode image, and determine the fault type of the fuel cell through a machine learning algorithm. The time for component 3 to determine the fuel cell fault type based on the barcode image is only 0.017 seconds.

[0060] Corresponding to the above-mentioned embodiment, the present invention further provides a fuel cell fault diagnosis device based on a voltage signal. Figure 7A structural schematic diagram of a fuel cell fault diagnosis device based on a voltage signal is provided for an embodiment of the present invention, and the device may include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor so that the at least one processor can execute a fuel cell fault diagnosis method based on a voltage signal as described in the above embodiment.

[0061] In a possible implementation of the present invention, the aforementioned at least one processor is capable of executing, obtaining a voltage response signal of the fuel cell after the interference signal is injected into the fuel cell; inputting the voltage response signal into a classifier model to determine the fault type of the fuel cell.

[0062] In a specific implementation, the present invention can also be provided as a non-volatile computer storage medium, wherein the storage medium is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program, each program includes instructions, and when the instructions are executed by the terminal, the terminal executes a fuel cell fault diagnosis method based on a voltage signal as described in the above embodiment.

[0063] In a possible implementation of the present invention, the terminal obtains a voltage response signal of the fuel cell after the interference signal is injected; and inputs the voltage response signal into a classifier model to determine the fault type of the fuel cell.

[0064] The various embodiments of the present invention are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0065] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0066] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A fuel cell fault diagnosis method based on voltage signal, characterized in that: The method comprises: Acquire a voltage response signal of the fuel cell after the interference signal is injected; The voltage response signal is input into a classifier model to determine the fault type of the fuel cell.

2. A fuel cell fault diagnosis method based on voltage signal according to claim 1, characterized in that: After obtaining the voltage response signal of the fuel cell after the interference signal is injected, the method further includes: Performing time-frequency transformation on the voltage response signal to obtain a time-frequency spectrum of the voltage response signal; A fault type of the fuel cell is determined based on the time-frequency spectrum of the voltage response signal.

3. A fuel cell fault diagnosis method based on voltage signal according to claim 2, characterized in that: Performing a time-frequency transformation on the voltage response signal to obtain a time-frequency spectrum of the voltage response signal includes: Performing a generalized S transform on the voltage response signal to obtain a generalized S transform time-frequency spectrum of the voltage response signal.

4. A fuel cell fault diagnosis method based on voltage signal according to claim 2, characterized in that: Determining the fault type of the fuel cell based on the time-frequency spectrum of the voltage response signal includes: Based on the time-frequency spectrum of the voltage response signal, generating a time-frequency spectrum image of the voltage response signal; The time-frequency spectrum image of the voltage response signal is input into a classifier model to determine the fault type of the fuel cell.

5. A fuel cell fault diagnosis method based on voltage signal according to claim 2, characterized in that: Determining the fault type of the fuel cell based on the time-frequency spectrum of the voltage response signal includes: Based on the time-frequency spectrum of the voltage response signal, generating a time-frequency spectrum image of the voltage response signal; Extracting scale-invariant features of the time-frequency spectrum image through scale-invariant feature transformation; Copying the time-frequency spectrum image to obtain two identical time-frequency spectrum images, and determining whether the scale-invariant features in the two identical time-frequency spectrum images match based on the distance between the scale-invariant features in the two identical time-frequency spectrum images to obtain a set of matching points; Placing the matching point set on a first plane and a second plane respectively to obtain a characteristic landscape, wherein the first plane and the second plane are parallel to each other, and the characteristic landscape is a line segment obtained by connecting the matching points that match each other; Based on the characteristic landscape, a fault type of the fuel cell is determined.

6. A fuel cell fault diagnosis method based on voltage signal according to claim 5, characterized in that: Determining the fault type of the fuel cell based on the characteristic landscape includes: Projecting the characteristic landscape onto a third plane to obtain a projection image, wherein the third plane is perpendicular to the first plane; Based on the projection image, a fault type of the fuel cell is determined.

7. A fuel cell fault diagnosis method based on voltage signal according to claim 6, characterized in that: Determining a fault type of the fuel cell based on the projection image includes: Based on the number of projections of the corresponding characteristic landscape at each position in the projection image, a barcode image is generated, wherein the barcode image is composed of black rectangles and white rectangles of different widths, including: for the position where the projection of the characteristic landscape exists in the projection image, a black rectangle in the barcode image corresponds, and the width of the black rectangle is proportional to the number of projections of the characteristic landscape in the corresponding position in the projection image; for the area in the projection image where the projection of the characteristic landscape does not exist, a white rectangle in the barcode image corresponds, and the width of the white rectangle is proportional to the width of the area; Based on the barcode image, a fault type of the fuel cell is determined.

8. A fuel cell fault diagnosis method based on voltage signal according to claim 7, characterized in that: Determining a fault type of the fuel cell based on the barcode image includes: Calculating the ratio of the area of ​​the black rectangle to the total area in the barcode image, and determining the ratio as the black-bar-space ratio; and / or, Calculating the ratio of the area of ​​the white rectangle to the total area in the barcode image, and determining the ratio as the white-bar-space ratio; Based on the black bar empty ratio and / or the white bar empty ratio, a fault type of the fuel cell is determined.

9. A fuel cell fault diagnosis device based on voltage signal, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute a fuel cell fault diagnosis method based on a voltage signal according to any one of claims 1-8.

10. A non-volatile computer storage medium, characterized in that: The storage medium is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program, each of which includes instructions, and when the instructions are executed by the terminal, the terminal executes a fuel cell fault diagnosis method based on a voltage signal according to any one of claims 1-8.

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