Method and device for predicting service life of fuel cell, medium and equipment

Abstract

The invention provides a service life prediction method and device of a fuel cell, a medium and equipment. The method comprises the following steps: acquiring an operation state parameter, an operation environment parameter and an operation condition parameter of a target fuel cell under a test condition; determining a first influence factor model, a second influence factor model and a third influence factor model of target fuel cell recession based on the parameter combination; calculating the residual life of the target fuel cell by combining the current operation state parameter, the current operation environment parameter and the current operation condition parameter; according to the method, various operation state parameters, operation environment parameters and operation condition parameter combinations of a target fuel cell are obtained through test measurement, and a plurality of influence factor models of decline of the target fuel cell are determined based on the combinations; and during actual prediction, the residual life of the target fuel cell is predicted in multiple dimensions in combination with the current operation state parameter, the current operation environment parameter and the current operation working condition parameter, so that the prediction accuracy of the residual life of the fuel cell is improved.

Description

Technical Field

[0001] The present application relates to the technical field of fuel cell life prediction, and in particular to a fuel cell life prediction method, device, medium and equipment. Background Art

[0002] Fuel cells have attracted wide attention due to their high efficiency, cleanliness and pollution-free, and have broad development prospects. In fuel cell vehicles, fuel cells are the key power source, and their service life directly affects the operating cost and reliability of the vehicle. In order to extend the service life of fuel cells, it is necessary to be able to accurately estimate and predict their life, which is also a very important aspect of current research.

[0003] The service life of a fuel cell is measured by its output performance. The performance decay rate of a fuel cell will change as it is used, especially in the middle and late stages of a fuel cell's life, where accelerated performance decay may occur. Therefore, the fuel cell life prediction method based on performance decay should also be continuously updated with the actual use of the fuel cell. At present, the fuel cell life prediction method mainly relies on simple parameters such as the fuel cell's working time and the number of cycles to estimate the life decay. However, these methods often ignore the impact of dynamic factors such as ambient temperature and operating conditions on fuel cell performance in actual use, resulting in low accuracy in the prediction of fuel cell life. Therefore, it is necessary to further improve the prediction accuracy of fuel cell life. Summary of the invention

[0004] In order to solve the above technical problems, the present application is proposed. The embodiments of the present application provide a method, device, medium and equipment for predicting the life of a fuel cell.

[0005] According to one aspect of the present application, a method for predicting the life of a fuel cell is provided, comprising: obtaining operating state parameters, operating environment parameters and operating condition parameters of a target fuel cell under test conditions; wherein the operating state parameters include the input voltage, output current and output power of the target fuel cell, the operating environment parameters include the ambient temperature, and the operating condition parameters include the number of starts, the number of shutdowns, the idle time, the load level and the corresponding load duration; based on the operating state parameters, determining a first influencing factor model for the decline of the target fuel cell; based on the operating environment parameters, determining a second influencing factor model for the decline of the target fuel cell; based on the operating condition parameters, determining a third influencing factor model for the decline of the target fuel cell; and based on the current operating state parameters, current operating environment parameters and current operating condition parameters of the target fuel cell, the first influencing factor model, the second influencing factor model and the third influencing factor model, calculating the remaining life of the target fuel cell.

[0006] In one embodiment, the first influencing factor model for determining the degradation of the target fuel cell based on the operating state parameters includes: calculating the cumulative work done by the target fuel cell based on the operating state parameters; fitting a first influencing coefficient of the degradation of the target fuel cell based on the cumulative work done by the target fuel cell; and determining the first influencing factor model based on the first influencing coefficient.

[0007] In one embodiment, the second influencing factor model for determining the target fuel cell degradation based on the operating environment parameters includes: dividing the ambient temperature into multiple temperature levels and accumulating the duration corresponding to each temperature level; fitting the second influencing coefficient of the target fuel cell degradation based on the duration of all temperature levels; and determining the second influencing factor model based on the second influencing coefficient.

[0008] In one embodiment, determining the third influencing factor model of the target fuel cell degradation based on the operating condition parameters includes: fitting a third influencing coefficient of the target fuel cell degradation based on the operating condition parameters; and determining the third influencing factor model based on the third influencing coefficient.

[0009] In one embodiment, the remaining life of the target fuel cell is calculated based on the current operating state parameters, current operating environment parameters and current operating condition parameters of the target fuel cell, the first influencing factor model, the second influencing factor model and the third influencing factor model, including: calculating the first decay coefficient of the target fuel cell based on the current operating state parameters and the first influencing factor model; calculating the second decay coefficient of the target fuel cell based on the current operating environment parameters and the second influencing factor model; calculating the third decay coefficient of the target fuel cell based on the current operating condition parameters and the third influencing factor model; and calculating the remaining life of the target fuel cell based on the first decay coefficient, the second decay coefficient and the third decay coefficient.

[0010] In one embodiment, the remaining life of the target fuel cell is calculated based on the first decay coefficient, the second decay coefficient and the third decay coefficient, including: calculating the decay ratio of the target fuel cell based on the first decay coefficient, the second decay coefficient and the third decay coefficient; calculating the remaining life of the target fuel cell based on the decay ratio of the target fuel cell.

[0011] In one embodiment, the fuel cell life prediction method further includes: calculating the power generation efficiency of the target fuel cell based on the operating state parameters; and determining the remaining life of the target fuel cell based on the power generation efficiency of the target fuel cell.

[0012] According to another aspect of the present application, a fuel cell life prediction device is provided, including: a test parameter acquisition module, used to obtain the operating state parameters, operating environment parameters and operating condition parameters of the target fuel cell under test conditions; wherein the operating state parameters include the input voltage, output current and output power of the target fuel cell, the operating environment parameters include the ambient temperature, and the operating condition parameters include the number of starts, the number of shutdowns, the idle time, the load level and the corresponding load duration; a first model determination module, used to determine the first influencing factor model of the decline of the target fuel cell based on the operating state parameters; a second model determination module, used to determine the second influencing factor model of the decline of the target fuel cell based on the operating environment parameters; a third model determination module, used to determine the third influencing factor model of the decline of the target fuel cell based on the operating condition parameters; a battery life prediction module, used to calculate the remaining life of the target fuel cell based on the current operating state parameters, current operating environment parameters and current operating condition parameters of the target fuel cell, the first influencing factor model, the second influencing factor model and the third influencing factor model.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute any of the above methods.

[0014] According to another aspect of the present application, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; and the processor is used to execute any of the above-described methods.

[0015] The present application provides a fuel cell life prediction method, device, medium and equipment, which obtains the operating state parameters, operating environment parameters and operating condition parameters of the target fuel cell under test conditions; wherein the operating state parameters include the input voltage, output current and output power of the target fuel cell, the operating environment parameters include the ambient temperature, and the operating condition parameters include the number of starts, the number of shutdowns, the idling time, the load level and the corresponding load duration; based on the operating state parameters, a first influencing factor model of the target fuel cell decline is determined; based on the operating environment parameters, a second influencing factor model of the target fuel cell decline is determined; based on the operating condition parameters, a third influencing factor model of the target fuel cell decline is determined; based on the operating condition parameters, a third influencing factor model of the target fuel cell decline is determined; The remaining life of the target fuel cell is calculated based on the current operating state parameters, current operating environment parameters and current operating condition parameters of the target fuel cell, a first influencing factor model, a second influencing factor model and a third influencing factor model; that is, various operating state parameters, operating environment parameters and operating condition parameter combinations of the target fuel cell are obtained through experimental measurements, and multiple influencing factor models of the target fuel cell degradation are determined based on the operating state parameters, operating environment parameters and operating condition parameters, and the remaining life of the target fuel cell is predicted by considering multiple dimensions such as the current operating state parameters, the current operating environment parameters and the current operating condition parameters in actual prediction, so as to improve the prediction accuracy of the remaining life of the fuel cell. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0017] Figure 1 It is a flow chart of a fuel cell life prediction method provided by an exemplary embodiment of the present application.

[0018] Figure 2 It is a schematic diagram of the structure of a fuel cell life prediction device provided by an exemplary embodiment of the present application.

[0019] Figure 3 It is a structural diagram of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0020] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0021] Figure 1 FIG. 1 is a flow chart of a fuel cell life prediction method provided by an exemplary embodiment of the present application. Figure 1 As shown, the fuel cell life prediction method includes the following steps: Step 110: Obtain operating state parameters, operating environment parameters, and operating condition parameters of the target fuel cell under test conditions.

[0022] Among them, the operating state parameters include the input voltage, output current and output power of the target fuel cell, the operating environment parameters include the ambient temperature (which may include high temperature environment, normal temperature environment and low temperature environment), and the operating condition parameters include the number of starts, the number of shutdowns, the idling time, the load level (which may include high load, medium load and low load) and the corresponding load duration. Specifically, the present application can fix the fuel cell vehicle equipped with the target fuel cell on a chassis dynamometer with an environmental chamber for testing to collect the operating state parameters, operating environment parameters and operating condition parameters to simulate the operating parameters of the target fuel cell under different working conditions and environments.

[0023] Step 120: Determine a first influencing factor model of degradation of the target fuel cell based on the operating state parameters.

[0024] The present application uses the operating status parameters collected in the experiment to determine the first influencing factor model of the target fuel cell degradation. Specifically, the present application calculates the parameters of the first influencing factor model based on the operating status parameters, thereby determining the first influencing factor model of the target fuel cell degradation.

[0025] Step 130: Determine a second influencing factor model of degradation of the target fuel cell based on the operating environment parameters.

[0026] The present application uses the operating environment parameters collected in the experiment to determine the second influencing factor model of the target fuel cell degradation. Specifically, the present application calculates the parameters of the second influencing factor model according to the operating environment parameters, thereby determining the second influencing factor model of the target fuel cell degradation.

[0027] Step 140: Determine a third influencing factor model of degradation of the target fuel cell based on the operating condition parameters.

[0028] The present application uses the operating condition parameters collected in the experiment to determine the third influencing factor model of the target fuel cell degradation. Specifically, the present application calculates the parameters of the third influencing factor model based on the operating condition parameters, thereby determining the third influencing factor model of the target fuel cell degradation.

[0029] Step 150: Calculate the remaining life of the target fuel cell based on the current operating state parameters, current operating environment parameters, current operating condition parameters, the first influencing factor model, the second influencing factor model, and the third influencing factor model of the target fuel cell.

[0030] After determining the first influencing factor model, the second influencing factor model and the third influencing factor model that affect the degradation of the target fuel cell, the present application inputs the current operating state parameters, the current operating environment parameters and the current operating condition parameters into the corresponding influencing factor model based on the first influencing factor model, the second influencing factor model and the third influencing factor model to obtain the corresponding influencing factor coefficients, thereby calculating the remaining life of the target fuel cell.

[0031] The present application provides a fuel cell life prediction method, which obtains the operating state parameters, operating environment parameters and operating condition parameters of the target fuel cell under test conditions; wherein the operating state parameters include the input voltage, output current and output power of the target fuel cell, the operating environment parameters include the ambient temperature, and the operating condition parameters include the number of starts, the number of stops, the idling time, the load level and the corresponding load duration; based on the operating state parameters, a first influencing factor model of the target fuel cell degradation is determined; based on the operating environment parameters, a second influencing factor model of the target fuel cell degradation is determined; based on the operating condition parameters, a third influencing factor model of the target fuel cell degradation is determined; based on the operating condition parameters, a third influencing factor model of the target fuel cell degradation is determined; based on the target fuel cell degradation .... The remaining life of the target fuel cell is calculated based on the current operating state parameters, current operating environment parameters and current operating condition parameters of the material battery, the first influencing factor model, the second influencing factor model and the third influencing factor model; that is, various operating state parameters, operating environment parameters and operating condition parameter combinations of the target fuel cell are obtained through experimental measurement, and multiple influencing factor models of the target fuel cell degradation are determined based on the operating state parameters, operating environment parameters and operating condition parameters, and the remaining life of the target fuel cell is predicted by considering multiple dimensions such as the current operating state parameters, the current operating environment parameters and the current operating condition parameters in actual prediction, so as to improve the prediction accuracy of the remaining life of the fuel cell.

[0032] In one embodiment, the specific implementation method of the above step 120 can be: based on the operating state parameters, calculate the cumulative work done by the target fuel cell; based on the cumulative work done by the target fuel cell, fit the first influence coefficient of the target fuel cell degradation; based on the first influence coefficient, determine the first influence factor model.

[0033] The present application calculates the cumulative work done by the target fuel cell based on the operating state parameters of the target fuel cell obtained by experimental measurement, and based on the cumulative work done by the target fuel cell, fits the first influence coefficient of the degradation of the target fuel cell, and then obtains the first influence factor model. Specifically, the calculation formula for the cumulative work done by the target fuel cell is as follows: ; in, is the cumulative work done, for t The power generation efficiency of the battery stack at the moment; for t The stack voltage at the moment; for t The stack current at the moment; for t The energy recovery power at all times.

[0034] The present application calculates the corresponding cumulative work done based on different operating status parameters under different working conditions and environmental conditions to simulate the cumulative work done under various states in the actual operation process, and fits the relationship between the cumulative work done and the first influence coefficient based on the cumulative work done under various states (for example, various states in high temperature environment or high load condition) and a combination of various states (for example, states in high temperature environment and high load condition), so that the first influence coefficient corresponding to the current operating status parameter (corresponding to the current cumulative work done) can be obtained based on the relationship.

[0035] In one embodiment, the specific implementation method of the above step 130 may be: dividing the ambient temperature into multiple temperature levels and accumulating the duration corresponding to each temperature level; fitting the second influence coefficient of the target fuel cell degradation based on the duration of all temperature levels; and determining the second influence factor model based on the second influence coefficient.

[0036] The present application divides the ambient temperature into multiple temperature levels and accumulates the duration corresponding to each temperature level to obtain the continuous operating time and the corresponding proportion of each temperature level, and based on the continuous operating time of each temperature level and its proportion in the overall operating time, the relationship between the ambient temperature and the second influence coefficient is fitted, so that the second influence coefficient corresponding to the current operating environment parameters (corresponding to the continuous operating time and the corresponding proportion of the current ambient temperature) can be known based on this relationship.

[0037] In one embodiment, the specific implementation of the above step 140 may be: based on the operating condition parameters, fitting to obtain a third influencing coefficient of the degradation of the target fuel cell; based on the third influencing coefficient, determining a third influencing factor model.

[0038] Based on different operating condition parameters (including total number of starts, total number of stops, idle time, high load duration, and low load duration), the present application fits the relationship between different operating condition parameters and the third influence coefficient, so that the third influence coefficient corresponding to the current operating condition parameters (corresponding to a combination of multiple parameters such as total number of starts, total number of stops, idle time, high load duration, and low load duration) can be obtained based on this relationship.

[0039] In one embodiment, the specific implementation method of the above step 150 may be: based on the current operating state parameters and the first influencing factor model, calculate the first decay coefficient of the target fuel cell; based on the current operating environment parameters and the second influencing factor model, calculate the second decay coefficient of the target fuel cell; based on the current operating condition parameters and the third influencing factor model, calculate the third decay coefficient of the target fuel cell; based on the first decay coefficient, the second decay coefficient and the third decay coefficient, calculate the remaining life of the target fuel cell.

[0040] During actual prediction, the present application collects the current operating state parameters, the current operating environment parameters and the current operating condition parameters, and inputs the current operating state parameters, the current operating environment parameters and the current operating condition parameters into the corresponding influencing factor model, respectively, to obtain the corresponding first decay coefficient, second decay coefficient and third decay coefficient, and calculates the remaining life of the target fuel cell based on the first decay coefficient, the second decay coefficient and the third decay coefficient.

[0041] The specific calculation formulas for the first recession coefficient, the second recession coefficient and the third recession coefficient are as follows: , , ; in, , and are the first recession coefficient, the second recession coefficient and the third recession coefficient respectively. α is the first influence coefficient, is the second influence coefficient, is the third influence coefficient, T is the current operating environment parameter, C It is the current operating condition parameter.

[0042] In one embodiment, the specific implementation method of the above step 150 can be: based on the first decay coefficient, the second decay coefficient and the third decay coefficient, calculate the decay ratio of the target fuel cell; based on the decay ratio of the target fuel cell, calculate the remaining life of the target fuel cell.

[0043] Specifically, after calculating the first decay coefficient, the second decay coefficient and the third decay coefficient, the present application calculates the decay ratio of the target fuel cell based on the first decay coefficient, the second decay coefficient and the third decay coefficient, and estimates the remaining life of the target fuel cell based on the decay ratio of the target fuel cell. The calculation formula of the decay ratio of the target fuel cell is as follows: ; in, is the degradation ratio of the target fuel cell.

[0044] The present application can calculate the output power of the target fuel cell based on the operating state parameters. If the output power drops to 80% of the initial power, the target fuel cell is determined to be failed (i.e., the end of the life of the target fuel cell), i.e. ;in, is the actual output power of the target fuel cell at the current moment; is the initial power of the target fuel cell.

[0045] In one embodiment, the above-mentioned fuel cell life prediction method may further include: calculating the power generation efficiency of the target fuel cell based on the operating state parameters; and determining the remaining life of the target fuel cell based on the power generation efficiency of the target fuel cell.

[0046] The present application can also calculate the power generation efficiency of the target fuel cell based on the operating state parameters. If the power generation efficiency of the target fuel cell is lower than a preset threshold, the target fuel cell is determined to be failed, and the remaining life of the target fuel cell is predicted accordingly. Preferably, the present application can use the power generation efficiency to predict the remaining life of the target fuel cell to verify the remaining life predicted based on the decay ratio, thereby further improving the accuracy of the remaining life prediction of the target fuel cell.

[0047] Figure 2 FIG. 1 is a schematic diagram of a fuel cell life prediction device provided by an exemplary embodiment of the present application. Figure 2As shown, the life prediction device 20 of the fuel cell includes: a test parameter acquisition module 21, which is used to obtain the operating state parameters, operating environment parameters and operating condition parameters of the target fuel cell under test conditions; wherein the operating state parameters include the input voltage, output current and output power of the target fuel cell, the operating environment parameters include the ambient temperature, and the operating condition parameters include the number of starts, the number of shutdowns, the idling time, the load level and the corresponding load duration; a first model determination module 22, which is used to determine the first influencing factor model of the decline of the target fuel cell based on the operating state parameters; a second model determination module 23, which is used to determine the second influencing factor model of the decline of the target fuel cell based on the operating environment parameters; a third model determination module 24, which is used to determine the third influencing factor model of the decline of the target fuel cell based on the operating condition parameters; a battery life prediction module 25, which is used to calculate the remaining life of the target fuel cell based on the current operating state parameters, current operating environment parameters and current operating condition parameters of the target fuel cell, the first influencing factor model, the second influencing factor model and the third influencing factor model.

[0048] The present application provides a fuel cell life prediction device, which obtains the operating state parameters, operating environment parameters and operating condition parameters of the target fuel cell under test conditions through a test parameter acquisition module 21; wherein the operating state parameters include the input voltage, output current and output power of the target fuel cell, the operating environment parameters include the ambient temperature, and the operating condition parameters include the number of starts, the number of shutdowns, the idling time, the load level and the corresponding load duration; the first model determination module 22 determines the first influencing factor model of the target fuel cell degradation based on the operating state parameters; the second model determination module 23 determines the second influencing factor model of the target fuel cell degradation based on the operating environment parameters; the third model determination module 24 determines the target fuel cell degradation based on the operating condition parameters. The third influencing factor model; the battery life prediction module 25 calculates the remaining life of the target fuel cell based on the current operating state parameters, current operating environment parameters and current operating condition parameters of the target fuel cell, the first influencing factor model, the second influencing factor model and the third influencing factor model; that is, various operating state parameters, operating environment parameters and operating condition parameter combinations of the target fuel cell are obtained through experimental measurements, and multiple influencing factor models of the target fuel cell degradation are determined based on the operating state parameters, operating environment parameters and operating condition parameters, and the remaining life of the target fuel cell is predicted by considering multiple dimensions such as the current operating state parameters, the current operating environment parameters and the current operating condition parameters in actual prediction, so as to improve the prediction accuracy of the remaining life of the fuel cell.

[0049] In one embodiment, the first model determination module 22 can be further configured as follows: based on the operating state parameters, calculating the cumulative work done by the target fuel cell; based on the cumulative work done by the target fuel cell, fitting the first influence coefficient of the degradation of the target fuel cell; based on the first influence coefficient, determining the first influence factor model.

[0050] In one embodiment, the second model determination module 23 can be further configured as follows: dividing the ambient temperature into multiple temperature levels and accumulating the duration corresponding to each temperature level; fitting a second influence coefficient of the target fuel cell degradation based on the duration of all temperature levels; and determining a second influence factor model based on the second influence coefficient.

[0051] In one embodiment, the third model determination module 24 may be further configured to: obtain a third influencing coefficient of the degradation of the target fuel cell by fitting based on the operating condition parameters; and determine a third influencing factor model based on the third influencing coefficient.

[0052] In one embodiment, the battery life prediction module 25 can be further configured as follows: based on the current operating state parameters and the first influencing factor model, calculating the first degradation coefficient of the target fuel cell; based on the current operating environment parameters and the second influencing factor model, calculating the second degradation coefficient of the target fuel cell; based on the current operating condition parameters and the third influencing factor model, calculating the third degradation coefficient of the target fuel cell; based on the first degradation coefficient, the second degradation coefficient and the third degradation coefficient, calculating the remaining life of the target fuel cell.

[0053] In one embodiment, the battery life prediction module 25 can be further configured to: calculate the decay ratio of the target fuel cell based on the first decay coefficient, the second decay coefficient and the third decay coefficient; calculate the remaining life of the target fuel cell based on the decay ratio of the target fuel cell.

[0054] In one embodiment, the fuel cell life prediction device 20 may be further configured to: calculate the power generation efficiency of the target fuel cell based on the operating state parameters; and determine the remaining life of the target fuel cell based on the power generation efficiency of the target fuel cell.

[0055] Below, reference Figure 3 The electronic device according to the embodiment of the present application is described. The electronic device may be any one or both of the first device and the second device, or a stand-alone device independent of them, and the stand-alone device may communicate with the first device and the second device to receive the collected input signal from them.

[0056] Figure 3 A block diagram of an electronic device according to an embodiment of the present application is illustrated.

[0057] like Figure 3 As shown, the electronic device 10 includes one or more processors 11 and a memory 12 .

[0058] The processor 11 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0059] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may run the program instructions to implement the methods of the various embodiments of the present application described above and / or other desired functions. Various contents such as input signals, signal components, noise components, etc. may also be stored in the computer-readable storage medium.

[0060] In one example, the electronic device 10 may further include: an input device 13 and an output device 14 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0061] When the electronic device is a stand-alone device, the input device 13 may be a communication network connector, which is used to receive the collected input signals from the first device and the second device.

[0062] In addition, the input device 13 may also include, for example, a keyboard, a mouse, and the like.

[0063] The output device 14 can output various information to the outside, including the determined distance information, direction information, etc. The output device 14 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0064] Of course, to simplify, Figure 3 Only some of the components related to the present application in the electronic device 10 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device 10 may also include any other appropriate components.

[0065] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.

[0066] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0067] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the method according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0068] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0069] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0070] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0071] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0072] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0073] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for predicting the life of a fuel cell, characterized in that: include: Obtaining operating state parameters, operating environment parameters and operating condition parameters of the target fuel cell under test conditions; wherein the operating state parameters include the input voltage, output current and output power of the target fuel cell, the operating environment parameters include the ambient temperature, and the operating condition parameters include the number of starts, the number of stops, the idling time, the load level and the corresponding load duration; Determining a first influencing factor model of the target fuel cell degradation based on the operating state parameter; Determining a second influencing factor model of the target fuel cell degradation based on the operating environment parameters; Determining a third influencing factor model of the target fuel cell degradation based on the operating condition parameters; The remaining life of the target fuel cell is calculated based on the current operating state parameters, current operating environment parameters and current operating condition parameters of the target fuel cell, the first influencing factor model, the second influencing factor model and the third influencing factor model.

2. The fuel cell life prediction method according to claim 1, characterized in that: The first influencing factor model for determining the degradation of the target fuel cell based on the operating state parameter includes: Calculating the cumulative work done by the target fuel cell based on the operating state parameter; Based on the accumulated work done by the target fuel cell, a first influence coefficient of the degradation of the target fuel cell is obtained by fitting; Based on the first influence coefficient, the first influence factor model is determined.

3. The fuel cell life prediction method according to claim 1, characterized in that: The second influencing factor model for determining the degradation of the target fuel cell based on the operating environment parameter includes: Dividing the ambient temperature into a plurality of temperature levels and accumulating the duration corresponding to each temperature level; Based on the duration of all temperature levels, a second influence coefficient of the degradation of the target fuel cell is obtained by fitting; Based on the second impact coefficient, the second impact factor model is determined.

4. The fuel cell life prediction method according to claim 1, characterized in that: The determining of the third influencing factor model of the target fuel cell degradation based on the operating condition parameters includes: Based on the operating condition parameters, fitting to obtain a third influence coefficient of the degradation of the target fuel cell; Based on the third influence coefficient, the third influence factor model is determined.

5. The fuel cell life prediction method according to claim 1, characterized in that: The calculating the remaining life of the target fuel cell based on the current operating state parameter, the current operating environment parameter and the current operating condition parameter of the target fuel cell, the first influencing factor model, the second influencing factor model and the third influencing factor model comprises: Calculating a first degradation coefficient of the target fuel cell based on the current operating state parameter and the first influencing factor model; Calculating a second degradation coefficient of the target fuel cell based on current operating environment parameters and the second influencing factor model; Calculating a third degradation coefficient of the target fuel cell based on the current operating condition parameters and the third influencing factor model; The remaining life of the target fuel cell is calculated based on the first decay coefficient, the second decay coefficient, and the third decay coefficient.

6. The fuel cell life prediction method according to claim 5, characterized in that: The calculating the remaining life of the target fuel cell based on the first decay coefficient, the second decay coefficient and the third decay coefficient comprises: Based on the first decay coefficient, the second decay coefficient and the third decay coefficient, calculating a decay ratio of the target fuel cell; Based on the degradation ratio of the target fuel cell, the remaining life of the target fuel cell is calculated.

7. The fuel cell life prediction method according to claim 1, characterized in that: The fuel cell life prediction method further includes: Calculating the power generation efficiency of the target fuel cell based on the operating state parameter; Based on the power generation efficiency of the target fuel cell, a remaining life of the target fuel cell is determined.

8. A fuel cell life prediction device, characterized in that: include: A test parameter acquisition module, used to acquire the operating state parameters, operating environment parameters and operating condition parameters of the target fuel cell under test conditions; wherein the operating state parameters include the input voltage, output current and output power of the target fuel cell, the operating environment parameters include the ambient temperature, and the operating condition parameters include the number of starts, the number of stops, the idling time, the load level and the corresponding load duration; A first model determination module, used for determining a first influencing factor model of the degradation of the target fuel cell based on the operating state parameter; A second model determination module, configured to determine a second influencing factor model of the target fuel cell degradation based on the operating environment parameters; A third model determination module, configured to determine a third influencing factor model of the target fuel cell degradation based on the operating condition parameters; A battery life prediction module is used to calculate the remaining life of the target fuel cell based on the current operating state parameters, current operating environment parameters and current operating condition parameters of the target fuel cell, the first influencing factor model, the second influencing factor model and the third influencing factor model.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is used to execute the method described in any one of claims 1 to 7.

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