Stack abnormality type determination method, device, fuel cell system, and storage medium

Abstract

The application discloses a kind of stack abnormal type determination method, device, fuel cell system and storage medium.The method of the stack includes first type detector and end plate, end plate is integrated with bearing including sleeve ring and sleeve rod, second type detector is arranged in sleeve ring, sleeve ring and sleeve rod are connected by rotor, the movement of rotor has any direction, the real-time temperature value and real-time pressure value of the three cavity of stack are detected by first type detector, and the real-time displacement of sleeve rod is detected by second type detector;Real-time temperature value, real-time pressure value and real-time displacement are input into preset abnormal type identification model, and the current abnormal state type of stack is determined.The technical scheme of the application provides a kind of method for diagnosing fault based on new end plate structure to stack, effectively improves stack fault diagnosis efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fuel cell, in particular to a stack abnormal type determination method and device, a fuel cell system and a storage medium. BACKGROUND

[0002] Proton exchange membrane fuel cell is paid more and more attention as zero emission clean energy, and the stack as the core component of proton exchange membrane fuel cell is mainly composed of manifold module, end plate (cathode + anode), current collector plate (cathode + anode), insulating plate (cathode + anode), bipolar plate, membrane electrode, stack packaging structure, etc. The end plate mainly plays the role of supporting the stack components, controlling the contact pressure and building the packaging structure. In the process of using the stack, the state of the fuel cell stack needs to be monitored in real time, and when the stack appears membrane dry, water flooding, oxygen starvation and other faults, timely intervention is needed, so as to prolong the service life of the stack and improve the stability of the stack. When the stack appears membrane dry, water flooding, oxygen starvation and other faults, the temperature and pressure of the three cavity inlets and outlets (air inlet, air outlet, hydrogen inlet, hydrogen outlet, cooling liquid inlet and cooling liquid outlet) of the stack and the length of the whole stack will be affected to different degrees.

[0003] At present, in the aspect of pressure adjustment of proton exchange membrane fuel cell stack, the disc spring superposition method is mostly used to balance the change of the internal pressure of the stack caused by thermal expansion and contraction and working condition change. However, the main problem is that the disc spring can only adjust the displacement in the stacking direction (such as Z direction), and cannot balance the displacement in the other two directions (such as X and Y directions), so that the running condition data of the stack cannot be accurately monitored, resulting in low efficiency of stack fault diagnosis. SUMMARY

[0004] The present application provides a stack abnormal type determination method, device, fuel cell system and storage medium, to provide a method for diagnosing the fault of the stack based on the new end plate structure, and effectively improve the efficiency of stack fault diagnosis.

[0005] According to one aspect of the present application, a stack abnormal type determination method is provided, the stack includes an end plate of a first type of detector, the end plate is integrated with a bearing including a sleeve ring and a sleeve rod, a second type of detector is arranged in the sleeve ring, the sleeve ring and the sleeve rod are connected through a rotor, the rotor movement has any directionality, and the method comprises:

[0006] detecting the real-time temperature value and the real-time pressure value of the three cavity inlets and outlets of the stack through the first type of detector, and detecting the real-time displacement of the sleeve rod through the second type of detector;

[0007] inputting the real-time temperature value, the real-time pressure value and the real-time displacement into a preset abnormal type recognition model to determine the current abnormal state type of the stack.

[0008] According to another aspect of the present application, there is provided a stack abnormal type determination apparatus, the stack comprising a first type detector and an end plate, the end plate integrated with a bearing comprising a sleeve ring and a sleeve rod, a second type detector arranged in the sleeve ring, the sleeve ring and the sleeve rod connected through a rotor, the rotor moving with any directionality, the apparatus comprising:

[0009] a real-time data detection module for detecting real-time temperature values and real-time pressure values of three cavities of the stack through the first type detector, and detecting a real-time displacement of the sleeve rod through the second type detector;

[0010] a current abnormal state type determination module for inputting the real-time temperature values, the real-time pressure values and the real-time displacement into a preset abnormal type recognition model, and determining a current abnormal state type of the stack.

[0011] According to another aspect of the present application, there is provided a fuel cell system, the fuel cell system comprising:

[0012] at least one processor; and

[0013] a memory in communication connection with the at least one processor; wherein,

[0014] the memory stores a computer program executable by the at least one processor, the computer program executed by the at least one processor to enable the at least one processor to execute the stack abnormal type determination method according to any one of the embodiments of the present application.

[0015] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for enabling a processor to implement the stack abnormal type determination method according to any one of the embodiments of the present application when executed by the processor.

[0016] The technical scheme of the embodiments of the present application, the stack comprising a first type detector and an end plate, the end plate integrated with a bearing comprising a sleeve ring and a sleeve rod, a second type detector arranged in the sleeve ring, the sleeve ring and the sleeve rod connected through a rotor, the rotor moving with any directionality, the real-time temperature values and the real-time pressure values of three cavities of the stack detected through the first type detector, and the real-time displacement of the sleeve rod detected through the second type detector; the real-time temperature values, the real-time pressure values and the real-time displacement inputted into a preset abnormal type recognition model, and the current abnormal state type of the stack determined, the technical means solving the problem that the current Belleville spring can only improve the floating of the stack in a certain direction, leading to the problem that the stack operating condition data cannot be accurately monitored, and the problem of low efficiency of stack fault diagnosis, providing a method for fault diagnosis of the stack based on a new end plate structure, and effectively improving the efficiency of stack fault diagnosis.

[0017] It should be understood that the matters described in this section are not intended to identify key or essential features of the embodiments of the application, nor are they used to limit the scope of the application. Other features of the present application will be apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative effort based on these drawings.

[0019] Figure 1a A flowchart of a stack abnormal type determination method provided for the first embodiment of the present application;

[0020] Figure 1b A schematic diagram of an end plate packaged stack shell provided for the first embodiment of the present application;

[0021] Figure 1c A schematic diagram of an end plate structure integrated with a floating bearing provided for the first embodiment of the present application;

[0022] Figure 1d A schematic diagram of a sleeve ring structure provided for the first embodiment of the present application;

[0023] Figure 1e A schematic diagram of a sleeve rod structure provided for the first embodiment of the present application;

[0024] Figure 2a A flowchart of another stack abnormal type determination method provided for the second embodiment of the present application;

[0025] Figure 2b A flowchart of an example of obtaining a preset abnormal type identification model provided for the second embodiment of the present application;

[0026] Figure 2c An application flowchart of an example of a preset abnormal type identification model provided for the second embodiment of the present application;

[0027] Figure 3 A structural schematic diagram of a stack abnormal type determination device provided for the third embodiment of the present application;

[0028] Figure 4 A structural schematic diagram of a fuel cell system for implementing the stack abnormal type determination method of the embodiments of the present application. DETAILED DESCRIPTION

[0029] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of embodiments of the present application, rather than all embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work should fall within the protection scope of the present application.

[0030] It should be noted that the terms "first", "second" and the like in the description and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in other than the order illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a list of steps or units need not be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to such processes, methods, products or devices.

[0031] Embodiment one

[0032] Figure 1a A flow chart of a stack abnormal type determination method provided by the first embodiment of the present application, the present embodiment can be applied to the case of fault detection on the operating condition of a fuel cell stack, and the method can be executed by a stack abnormal type determination device, which can be realized in the form of hardware and / or software, and can be configured in a controller of a fuel cell management system. As shown in Figure 1a , the method comprises:

[0033] S110, detecting the real-time temperature value and the real-time pressure value of the three cavities of the stack by the first type detector, and detecting the real-time displacement of the sleeve rod by the second type detector.

[0034] In the present embodiment, one end of the shell of the stack can be packaged by an end plate, and the packaging outer side surface of the end plate can be integrated with a bearing including a sleeve ring and a sleeve rod, as shown in Figure 1b , the right end of the stack shell is packaged by an end plate, and the floating bearing is integrated on the packaging outer side surface of the end plate.

[0035] The inner surface of the collar can be uniformly provided with a target number of grooves, each groove is internally provided with a fixed elastic device to connect the rotor, and the rotor can be moved along the axis of the fixed elastic device. The surface of the sleeve rod can be provided with a corresponding hemispherical hole for fixing the rotor to connect the collar. For example, the end plate structure integrated with the floating bearing can be as shown in Figure 1c The end plate and the floating bearing are designed as one, including an end plate body, a collar, a displacement sensor integrated in the collar, and a sleeve rod. For example, the collar structure diagram can be as shown in Figure 1d The middle layer of the collar is provided with eight cylindrical grooves at intervals of 45 degrees (i.e. eight cylindrical grooves are uniformly arranged in the middle layer of the collar for 360 degrees), and each groove is internally connected to a spherical rotor through a fixed spring, and the rotor can move along the spring axis for 360 degrees. For example, the sleeve rod structure diagram can be as shown in Figure 1e Eight hemispherical holes are opened at positions corresponding to the collar rotors for fixing the rotor position, and the connection part with the shell adopts IP sealing with sealing glue.

[0036] When the stack has faults such as thermal expansion and contraction, membrane drying, flooding, oxygen starvation, etc., the temperature and pressure inside the stack will change to a certain extent. Due to the change of the internal pressure of the core, the distance between the bipolar plate and the membrane electrode changes, thereby affecting the change of the core length. The relative cooperation of the end plate of the stack with the rotor through the floating bearing sleeve rod will also displace a certain amount to balance the change of the internal pressure of the stack, so that the pressure returns to the normal value, ensuring the stability of the stack. At this time, the displacement sensor integrated in the floating bearing collar can collect the real-time displacement of the sleeve rod.

[0037] In this embodiment, the stack of the fuel cell can be provided with a first type of detector, and the collar can be provided with a second type of detector. The first type of detector can be a temperature and pressure sensor arranged at the three cavities of the stack, that is, the real-time temperature and pressure values of the three cavities of the stack are collected through the temperature and pressure sensor. The second type of detector can be a displacement sensor integrated in the collar, that is, the real-time displacement of the sleeve rod is collected through the displacement sensor.

[0038] Optionally, detecting the real-time temperature and pressure values of the three cavities of the stack through the first type of detector can include: obtaining the air inlet real-time temperature and pressure values of the air port of the stack through the first type of detector; obtaining the hydrogen inlet real-time temperature and pressure values of the hydrogen port of the stack through the first type of detector; and obtaining the cooling liquid inlet real-time temperature and pressure values of the cooling liquid port of the stack through the first type of detector.

[0039] S120, inputting the real-time temperature, real-time pressure and real-time displacement into a preset abnormal type recognition model to determine the current abnormal state type of the stack.

[0040] The abnormal state type of the stack can refer to a fault type in the operation of the stack, for example, can include fault types such as membrane dry, water flooding and oxygen starvation.

[0041] In the embodiment, the real-time air inlet temperature value, the real-time air inlet pressure value, the real-time hydrogen inlet temperature value, the real-time hydrogen inlet pressure value, the real-time cooling liquid inlet temperature value, the real-time cooling liquid inlet pressure value and the real-time displacement of the sleeve rod are input to the pre-trained abnormal type identification model to output the current abnormal state type of the stack.

[0042] The technical scheme of the embodiment of the application, the stack includes a first type of detector and an end plate, the end plate is integrated with a bearing including a sleeve ring and a sleeve rod, the second type of detector is arranged in the sleeve ring, the sleeve ring and the sleeve rod are connected through a rotor, movement of the rotor has any directionality, the real-time temperature value and the real-time pressure value of the three cavities of the stack are detected through the first type of detector, and the real-time displacement of the sleeve rod is detected through the second type of detector; the real-time temperature value, the real-time pressure value and the real-time displacement are input to a pre-set abnormal type identification model to determine the current abnormal state type of the stack, which solves the problem that the disc spring can only improve the floating of the position of the stack in one direction, so that the running condition data of the stack cannot be accurately monitored, and the efficiency of fault diagnosis of the stack is low, provides a method for fault diagnosis of the stack based on a new end plate structure, and effectively improves the efficiency of fault diagnosis of the stack.

[0043] Embodiment two

[0044] Figure 2a The flowchart of another method for determining the abnormal type of the stack provided in the embodiment two, the embodiment further increases operations after determining the current abnormal state type of the stack on the basis of the above-mentioned embodiment. As shown in the figure, Figure 2a The method comprises the following steps.

[0045] S210, detecting the real-time temperature value and the real-time pressure value of the three cavities of the stack through the first type of detector, and detecting the real-time displacement of the sleeve rod through the second type of detector.

[0046] S220, inputting the real-time temperature value, the real-time pressure value and the real-time displacement into a pre-set abnormal type identification model to determine the current abnormal state type of the stack.

[0047] In the embodiment, the pre-set abnormal type identification model can be obtained by the following method: obtaining at least one group of sample data; using the at least one group of sample data to train the BP neural network classifier for at least one round to obtain the pre-set abnormal type identification model.

[0048] Optionally, the obtaining the at least one set of sample data can comprise: obtaining real-time abnormal temperature values, real-time abnormal pressure values and real-time abnormal displacements of the sleeve rod of the stack under each abnormality degree of the target abnormality type; and obtaining the at least one set of sample data according to the real-time abnormal temperature values, the real-time abnormal pressure values and the real-time abnormal displacements corresponding to each abnormality degree of the target abnormality type.

[0049] The target abnormality type can be one or more of film dryness, water flooding and oxygen starvation, and the various degrees of the target abnormality type can be, for example, slight film dryness, general film dryness and severe film dryness, and can also be slight water flooding, general water flooding and severe water flooding, and can also be slight oxygen starvation, general oxygen starvation and severe oxygen starvation.

[0050] For example, a slight film dryness fault can be input during the operation of the stack, and in this case, the real-time abnormal temperature values, the real-time abnormal pressure values and the real-time abnormal displacements of the sleeve rod of the stack are obtained, and the slight film dryness and the real-time abnormal temperature values, the real-time abnormal pressure values and the real-time abnormal displacements of the sleeve rod corresponding to the slight film dryness are taken as a set of sample data.

[0051] S230, determining a correction strategy matched with the stack according to the current abnormality state type, to instruct the stack to operate according to the correction strategy.

[0052] In this embodiment, the specific fault problem can be intervened in time, so that the stack can be restored to a normal state as soon as possible, the stability of the stack is ensured, and the service life of the stack is prolonged.

[0053] In order for those skilled in the art to better understand the stack abnormality type determination method of the embodiments of the present application, Figure 2b A flowchart for obtaining a preset abnormality type identification model is provided.

[0054] First, film dryness, water flooding, oxygen starvation and the like can be injected into the stack during the operation of the stack, the temperature and pressure sensors of the stack three-cavity port can collect real-time temperature and pressure signals, the displacement sensor of the sleeve ring can collect the sleeve rod displacement signal, that is, the air inlet temperature, the air inlet pressure, the hydrogen inlet temperature, the hydrogen inlet pressure, the cooling liquid inlet temperature, the cooling liquid inlet pressure and the floating bearing displacement under various faults, and these signals can be transmitted to the BP system, a usable BP neural network classifier can be established by accumulating a large amount of data and using software, so as to obtain a preset abnormality type identification model.

[0055] Figure 2cAn application flowchart of a preset abnormal type identification model is provided. The temperature, pressure and sleeve rod displacement signals of the three cavities of the stack can be collected in real time, that is, the air inlet temperature, air inlet pressure, hydrogen inlet temperature, hydrogen inlet pressure, cooling liquid inlet temperature, cooling liquid inlet pressure and floating bearing displacement in the actual running state of the stack, and these signals are transmitted as inputs to the preset abnormal type identification model to analyze and judge the stack failure state through the preset abnormal type identification model.

[0056] Further, the model output result can be fed back to the PCU, and the stack is intervened in time for specific failure states, so that the stack can be restored to normal state as soon as possible, the stability of the stack is ensured, and the service life of the stack is prolonged. For example, membrane dryness can be intervened by increasing the humidity of the gas, water flooding can be intervened by increasing the gas inlet pressure, and oxygen starvation can be intervened by increasing the cathode gas flow.

[0057] The technical scheme of the embodiment of the application, the stack includes a first type of detector and an end plate, the end plate is integrated with a bearing including a sleeve ring and a sleeve rod, a second type of detector is arranged in the sleeve ring, the sleeve ring and the sleeve rod are connected through a rotor, the movement of the rotor has any directionality, the real-time temperature value and the real-time pressure value of the three cavities of the stack are detected through the first type of detector, and the real-time displacement of the sleeve rod is detected through the second type of detector; the real-time temperature value, the real-time pressure value and the real-time displacement are input into a preset abnormal type identification model to determine the current abnormal state type of the stack; and a correction strategy matched with the stack is determined according to the current abnormal state type to indicate a technical means for the stack to run according to the correction strategy, which solves the problem that the current disc spring can only improve the floating of the stack in a certain direction position, which leads to the inability to effectively control the pressure change in the running process of the stack, and the inability to accurately monitor the running state of the stack, thereby resulting in low efficiency of stack fault diagnosis, and thus affecting the stability of the stack. By accurately monitoring the running state data of the stack, the efficiency of stack fault diagnosis is effectively improved, the stack fault is corrected, the stability of the stack is improved, and the service life of the stack is prolonged.

[0058] Embodiment three

[0059] Figure 3 A structure diagram of a stack abnormal type determination device provided for the third embodiment of the application. The stack includes a first type of detector and an end plate, the end plate is integrated with a bearing including a sleeve ring and a sleeve rod, a second type of detector is arranged in the sleeve ring, the sleeve ring and the sleeve rod are connected through a rotor, the movement of the rotor has any directionality, as shown in Figure 3 The device includes a real-time data detection module 310 and a current abnormal state type determination module 320. Wherein:

[0060] The real-time data detection module 310 is configured to detect the real-time temperature value and the real-time pressure value of the three cavities of the electric pile through the first type of detector, and detect the real-time displacement of the sleeve rod through the second type of detector.

[0061] The current abnormal state type determination module 320 is configured to input the real-time temperature value, the real-time pressure value and the real-time displacement into a preset abnormal type identification model, and determine the current abnormal state type of the electric pile.

[0062] The technical scheme of the embodiment of the present application, the electric pile comprises a first type of detector and an end plate, the end plate is integrated with a bearing comprising a sleeve ring and a sleeve rod, the second type of detector is arranged in the sleeve ring, the sleeve ring and the sleeve rod are connected through a rotor, the movement of the rotor has any directionality, the real-time temperature value and the real-time pressure value of the three cavities of the electric pile are detected through the first type of detector, and the real-time displacement of the sleeve rod is detected through the second type of detector; the real-time temperature value, the real-time pressure value and the real-time displacement are input into a preset abnormal type identification model, and the current abnormal state type of the electric pile is determined, which solves the problem that the current disc spring can only improve the floating of the electric pile in a certain direction, so that the running condition data of the electric pile cannot be accurately monitored, and the efficiency of electric pile fault diagnosis is low, provides a method for diagnosing the fault of the electric pile based on a new end plate structure, and effectively improves the efficiency of electric pile fault diagnosis.

[0063] Optionally, the inner surface of the sleeve ring is uniformly provided with a target number of grooves, and each groove is internally provided with a fixed elastic device to connect the rotor.

[0064] Optionally, the surface of the sleeve rod is provided with a hemisphere hole corresponding to each groove, and the hemisphere hole is used to fix the rotor to connect the sleeve ring.

[0065] Optionally, the real-time data detection module 310 can be specifically used for:

[0066] The first type of detector is used to obtain the real-time temperature value and the real-time pressure value of the air inlet of the air port of the electric pile;

[0067] The first type of detector is used to obtain the real-time temperature value and the real-time pressure value of the hydrogen inlet of the hydrogen port of the electric pile;

[0068] The first type of detector is used to obtain the real-time temperature value and the real-time pressure value of the cooling liquid inlet of the cooling liquid port of the electric pile.

[0069] Optionally, the electric pile abnormal type determination device further comprises a preset abnormal type identification model acquisition module, which comprises:

[0070] The sample data acquisition unit is configured to acquire at least one group of sample data;

[0071] The preset anomaly type identification model acquisition unit is used to obtain the preset anomaly type identification model by training the BP neural network classifier for at least one round using the at least one set of sample data.

[0072] Optional, the sample data acquisition unit can be used for:

[0073] The real-time abnormal temperature value, real-time abnormal pressure value, and real-time abnormal displacement of the sleeve rod are obtained at the three cavities of the fuel cell stack under the target abnormality type of each abnormality level.

[0074] Based on the real-time abnormal temperature value, the real-time abnormal pressure value, and the real-time abnormal displacement corresponding to the target abnormality type of each abnormality level, at least one set of sample data is obtained.

[0075] Optionally, the stack anomaly type determination device further includes a correction strategy determination module, used to: after determining the current anomaly state type of the stack:

[0076] Based on the current abnormal state type, a correction strategy matching the stack is determined to instruct the stack to operate according to the correction strategy.

[0077] The fuel cell anomaly type determination device provided in the embodiments of the present invention can execute the fuel cell anomaly type determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0078] Example 4

[0079] Figure 4 A schematic diagram of a fuel cell system 400 that can be used to implement embodiments of the present invention is shown. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the invention described and / or claimed herein.

[0080] like Figure 4 As shown, the fuel cell system 400 includes at least one processor 401 and a memory, such as a read-only memory (ROM) 402 and a random access memory (RAM) 403, communicatively connected to the at least one processor 401. The memory stores computer programs executable by the at least one processor. The processor 401 can perform various appropriate actions and processes based on the computer program stored in the ROM 402 or loaded into the RAM 403 from the storage unit 408. The RAM 403 can also store various programs and data required for the operation of the fuel cell system 400. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0081] A plurality of components in the fuel cell system 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the fuel cell system 400 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0082] The processor 401 can be various general and / or special purpose processing components having processing and computing capabilities. Some examples of the processor 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 401 performs various methods and processes described above, such as the stack abnormality type determination method.

[0083] In some embodiments, the stack abnormality type determination method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed onto the fuel cell system 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded onto the RAM 403 and executed by the processor 401, one or more steps of the stack abnormality type determination method described above can be performed. Alternatively, in other embodiments, the processor 401 can be configured to perform the stack abnormality type determination method by any other appropriate means, such as by means of firmware.

[0084] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0085] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be implemented on general purpose computers, special purpose computers, or other programmable data processing apparatus to produce the functions / acts specified in the flow diagrams and / or block diagrams. Computer programs can be applied to input data to perform the functions of the present application and to generate output information. The output information can be applied to one or more output devices such as a display screen, printer, storage, etc. These functions / acts performed by the computer programs are referred to as instructions.

[0086] In the context of the present application, a computer-readable storage medium can be any tangible medium that can contain or store program instructions for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0087] To provide for interaction with a user, the systems and techniques described here can be implemented on a fuel cell system having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the fuel cell system. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0088] The systems and techniques described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0089] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0090] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in a different order, as long as the desired results of the technical solutions of the present disclosure are achieved, and the present disclosure is not limited herein.

[0091] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method of stack abnormal type determination, characterized by, The electric pile comprises a first type detector and an end plate, the end plate is integrated with a bearing comprising a sleeve ring and a sleeve rod, a second type detector is arranged in the sleeve ring, the sleeve ring and the sleeve rod are connected through a rotor, movement of the rotor has any directionality, the method comprises: detecting real-time temperature values and real-time pressure values of three cavities of the electric pile through the first type detector, and detecting real-time displacement of the sleeve rod through the second type detector; detecting real-time temperature values and real-time pressure values of three cavities of the electric pile through the first type detector, comprising: acquiring real-time air inlet temperature values and real-time air inlet pressure values of an air inlet of the electric pile through the first type detector; acquiring real-time hydrogen inlet temperature values and real-time hydrogen inlet pressure values of a hydrogen inlet of the electric pile through the first type detector; and acquiring real-time cooling liquid inlet temperature values and real-time cooling liquid inlet pressure values of a cooling liquid inlet of the electric pile through the first type detector; inputting the real-time temperature values, the real-time pressure values and the real-time displacement into a preset abnormal type identification model to determine a current abnormal state type of the electric pile.

2. The method of claim 1, wherein, The inner surface of the sleeve ring is uniformly provided with a target number of grooves, and each groove is internally provided with a fixed elastic device to connect the rotor.

3. The method of claim 2, wherein, The surface of the sleeve rod is provided with a hemisphere hole corresponding to each groove, and the hemisphere hole is used to fix the rotor to connect the sleeve ring.

4. The method of claim 1, wherein, The preset abnormal type identification model is obtained by the following way: acquiring at least one group of sample data; training a BP neural network classifier for at least one round by using the at least one group of sample data to obtain the preset abnormal type identification model.

5. The method of claim 4, wherein, Acquiring at least one group of sample data, comprising: acquiring real-time abnormal temperature values, real-time abnormal pressure values of three cavities of the electric pile under each abnormal type of target abnormal degree, and real-time abnormal displacement of the sleeve rod; acquiring at least one group of sample data according to the real-time abnormal temperature values, the real-time abnormal pressure values and the real-time abnormal displacement corresponding to each abnormal type of target abnormal degree.

6. The method of claim 1, wherein, After determining the current abnormal state type of the electric pile, further comprising: determining a correction strategy matched with the electric pile according to the current abnormal state type to instruct the electric pile to operate according to the correction strategy.

7. A stack abnormality type determination device characterized by comprising: The electric pile comprises a first type detector and an end plate, the end plate is integrated with a bearing comprising a sleeve ring and a sleeve rod, a second type detector is arranged in the sleeve ring, the sleeve ring and the sleeve rod are connected through a rotor, movement of the rotor has any directionality, the method comprises: The real-time data detection module is configured to detect real-time temperature values and real-time pressure values of the three cavities of the stack through the first type of detector and detect a real-time displacement of the sleeve rod through the second type of detector. The detection of the real-time temperature values and the real-time pressure values of the three cavities of the stack through the first type of detector includes: obtaining, by the first type of detector, real-time air inlet temperature values and real-time air inlet pressure values of the air cavity of the stack; obtaining, by the first type of detector, real-time hydrogen inlet temperature values and real-time hydrogen inlet pressure values of the hydrogen cavity of the stack; and obtaining, by the first type of detector, real-time coolant inlet temperature values and real-time coolant inlet pressure values of the coolant cavity of the stack. The current abnormal state type determination module is configured to input the real-time temperature values, the real-time pressure values and the real-time displacement into a preset abnormal type identification model to determine a current abnormal state type of the stack.

8. A fuel cell system characterized by comprising: The fuel cell system comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the stack abnormal type determination method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to execute the stack abnormal type determination method according to any one of claims 1-6 when executed.

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