Early warning method for fuel cell failure, fuel cell system and vehicle

Abstract

The application discloses a kind of early warning method of fuel cell failure, fuel cell system and vehicle, it is related to fuel cell technical field.Therein, the early warning method of fuel cell failure includes: in the operation process of fuel cell, data acquisition is carried out to fuel cell, and the current state data of fuel cell is obtained;According to the training model pre-set in cloud, the reference state data interval corresponding to current state data is obtained;When current state data is not in reference state data interval, historical state data stored in cloud is called;According to current state data and historical state data, the prediction time of fuel cell failure occurrence is obtained, and the reminding information is sent to driver through man-machine interaction system, and the reminding information includes prediction time.The application reduces the possibility of fuel cell damage, thereby prolongs the service life of fuel cell, and increases the reliability of fuel cell.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel cells, and in particular to a fuel cell failure early warning method, a fuel cell system and a vehicle. Background Art

[0002] In related technologies, fuel cell system fault detection typically relies on real-time acquisition of status data and comparing this data with preset thresholds to determine whether a fuel cell system fault has occurred. However, current fault detection methods can only confirm a fuel cell system fault after it has actually occurred, resulting in a delay and inability to effectively prevent the occurrence of faults or implement early intervention measures, which can easily damage the fuel cell system. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides a fuel cell failure early warning method that can provide early warning of fuel cell system failure and significantly reduce the risk of damage to the fuel cell system.

[0004] The present invention also provides a vehicle controller, a fuel cell system, a vehicle, an electronic device, and a computer-readable storage medium.

[0005] A fuel cell failure early warning method according to a first embodiment of the present invention is applied to a vehicle having a fuel cell and a human-computer interaction system, the vehicle being communicatively connected to the cloud. The early warning method includes:

[0006] During the operation of the fuel cell, data collection is performed on the fuel cell to obtain current state data of the fuel cell;

[0007] Obtaining a reference state data interval corresponding to the current state data according to a training model preset in the cloud;

[0008] When the current state data is not within the reference state data interval, calling the historical state data stored in the cloud;

[0009] A predicted time of occurrence of the fuel cell failure is obtained based on the current state data and the historical state data, and a reminder message is sent to the driver through the human-computer interaction system, where the reminder message includes the predicted time.

[0010] The fuel cell failure early warning method according to the embodiment of the present invention has at least the following beneficial effects:

[0011] The embodiment of the present invention obtains current status data and compares it with a reference status data interval. When the current status data is not within the reference status data interval, it determines that the current status data of the fuel cell deviates from the normal range; then, by calling the historical status data stored in the cloud, the predicted time of the fuel cell failure is obtained based on the current status data and the historical status data, and a reminder message is sent to the driver through the human-computer interaction system, thereby realizing early warning of fuel cell failure. Compared with confirming that the fuel cell system has failed only after the fuel cell failure actually occurs, by predicting the time when the fuel cell failure will occur, the occurrence of the failure is effectively prevented, and the driver or maintenance personnel can be reminded to take intervention measures in the early stages before and after the failure occurs, thereby reducing the possibility of fuel cell damage, thereby extending the service life of the fuel cell and increasing the reliability of the fuel cell.

[0012] According to some embodiments of the present invention, calling the historical status data stored in the cloud includes:

[0013] Retrieving all the historical status data within a preset time period before the current moment to obtain a historical data set;

[0014] Obtaining a predicted time of occurrence of the fuel cell failure based on the current state data and the historical state data, including:

[0015] Determining the maximum historical state data in the historical data set;

[0016] When the current state data is greater than the maximum historical state data, and the historical state data in the historical data set presents an increasing trend in chronological order, the interval time between the corresponding moment of the maximum historical state data and the current moment is used as the predicted time.

[0017] According to some embodiments of the present invention, the current state data includes multiple sample data types, and the sample data types include at least a current voltage value, a current current value, a current gas concentration value, a current temperature value, and a current humidity value. The early warning method further includes:

[0018] Based on the training model, obtaining a plurality of reference thresholds corresponding one-to-one to a plurality of the sample data types;

[0019] Determining the type of the sample data in which the abnormality occurs as an abnormal parameter based on the current state data and the multiple reference thresholds;

[0020] A faulty module in the fuel cell is identified based on the abnormal parameters.

[0021] According to some embodiments of the present invention, the early warning method further includes:

[0022] Calculating a difference between the abnormal parameter and the reference threshold value corresponding to the abnormal parameter;

[0023] When the difference is less than or equal to a preset difference threshold, determining that the failure risk level of the fuel cell is low risk;

[0024] When the difference is greater than the preset difference threshold, the fault risk level is determined to be high risk.

[0025] According to some embodiments of the present invention, sending a reminder message to the driver through the human-computer interaction system includes:

[0026] When the fault risk level is low risk, a warning animation is displayed through the human-computer interaction system;

[0027] When the fault risk level is high, an alarm animation is displayed and an audible alarm is played through the human-computer interaction system.

[0028] According to some embodiments of the present invention, the early warning method further includes:

[0029] At intervals of a first preset time, clearing all the historical status data stored in the cloud;

[0030] At intervals of a second preset time, sample status data of the fuel cell is obtained and sent to the cloud for storage as the historical status data.

[0031] A vehicle controller according to a second embodiment of the present invention is applied to a vehicle having a fuel cell and a human-computer interaction system, wherein the vehicle is communicatively connected to a cloud, and the vehicle controller includes:

[0032] an acquisition module configured to acquire data from the fuel cell during operation of the fuel cell and obtain current state data of the fuel cell;

[0033] an acquisition module configured to acquire a reference state data interval corresponding to the current state data according to a training model preset in the cloud;

[0034] a calling module configured to call the historical state data stored in the cloud when the current state data is not within the reference state data interval;

[0035] The execution module is configured to obtain a predicted time of occurrence of the fuel cell failure based on the current state data and the historical state data, and send a reminder message to the driver through the human-computer interaction system, wherein the reminder message includes the predicted time.

[0036] Since the vehicle controller adopts all the technical solutions of the fuel cell failure early warning method of the first embodiment, it has at least all the beneficial effects brought by the technical solutions of the above embodiments, which will not be repeated here.

[0037] The fuel cell system according to the third embodiment of the present invention applies the fuel cell failure early warning method as described in the first embodiment.

[0038] The fuel cell system according to the embodiment of the present invention has at least the following beneficial effects:

[0039] The fuel cell system applies the fuel cell failure early warning method of the first embodiment, realizes the early warning of fuel cell system failure, effectively reduces the risk of fuel cell system failure, increases the service life of the fuel cell system, and increases the reliability and stability of the fuel cell system.

[0040] A vehicle according to an embodiment of the fourth aspect of the present invention includes the fuel cell system described in the embodiment of the third aspect.

[0041] The vehicle according to the embodiment of the present invention has at least the following beneficial effects:

[0042] The vehicle adopts the fuel cell system of the third aspect of the embodiment, and realizes early warning of fuel cell system failure by adopting a fuel cell failure early warning method, effectively reducing the risk of fuel cell system failure, reducing the possibility of fuel cell damage, and increasing the service life of the fuel cell system, thereby reducing the maintenance cost of the vehicle and increasing the reliability and stability of the vehicle.

[0043] According to an embodiment of the fifth aspect of the present invention, an electronic device includes: at least one processor and at least one memory, wherein the at least one memory is used to store at least one program; when the at least one program is executed by the at least one processor, the at least one processor implements the early warning method for fuel cell failure disclosed in the first aspect.

[0044] According to the computer-readable storage medium of the sixth aspect embodiment of the present invention, processor-executable instructions are stored therein, and the processor-executable instructions, when executed by the processor, are used to execute the fuel cell failure early warning method disclosed in the first aspect.

[0045] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0047] Figure 1 A flowchart of a method for early warning of fuel cell failure according to an embodiment of the present invention;

[0048] Figure 2 Flowchart of steps S103 and S104 in a fuel cell failure early warning method according to an embodiment of the present invention;

[0049] Figure 3 A flowchart of a fuel cell failure warning method according to another embodiment of the present invention;

[0050] Figure 4 A flowchart of a fuel cell failure warning method according to another embodiment of the present invention;

[0051] Figure 5 This is a flowchart of step S104 in the fuel cell failure early warning method according to one embodiment of the present invention;

[0052] Figure 6 FIG. 1 is a structural diagram of a vehicle controller according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0054] In the description of the present invention, it should be understood that descriptions involving orientation, such as the orientation or positional relationship indicated by up, down, etc., are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0055] In the description of the present invention, "a plurality" refers to more than two. The use of "first" or "second" is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of the indicated technical features, or implicitly indicating the order of the indicated technical features.

[0056] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0057] Understandably, current fault detection methods often only confirm a fuel cell system failure after it has actually occurred. This lag means that when the fuel cell system is about to reach critical failure point, the driver may not be notified in a timely manner and may continue to drive the vehicle, which can lead to the escalation of the fault's impact, damage to the fuel cell system, increased maintenance costs, and even safety hazards.

[0058] To this end, some embodiments of the present invention provide a fuel cell failure early warning method, which is applied to a vehicle equipped with a fuel cell and a human-machine interface system. The vehicle is connected to a cloud computing platform. In one example, the vehicle is connected to the cloud computing platform via a network. The cloud computing platform may also be referred to as a cloud platform or a cloud server, and may be implemented as a standalone server or a server cluster consisting of multiple servers.

[0059] Reference Figure 1 As shown, in an embodiment of the present invention, the early warning method for fuel cell failure includes:

[0060] Step S101 : During the operation of the fuel cell, data of the fuel cell is collected to obtain current state data of the fuel cell.

[0061] It is understood that in the embodiments of the present invention, when the fuel cell is operating, various types of operating data are generated, such as temperature, current, voltage, etc. This embodiment can obtain current state data by collecting this data in real time, and the current state data is used to represent the current state of the fuel cell.

[0062] Specifically, in an embodiment of the present invention, the vehicle is equipped with at least a temperature sensor for detecting the temperature of the fuel cell, a pressure sensor for detecting the gas pressure of the fuel cell, a flow sensor for detecting the gas flow of the fuel cell, a humidity sensor for detecting the ambient humidity of the fuel cell installation location, a concentration sensor for detecting the gas concentration of the fuel cell, etc.

[0063] Step S102: Obtain a reference state data interval corresponding to the current state data according to a training model preset in the cloud.

[0064] It should be noted that in this embodiment of the present invention, the status data collected by various sensors installed on the vehicle is uploaded to the cloud. A pre-set mathematical model is learned based on the status data stored in the cloud, thereby generating a training model. In this embodiment, the choice of mathematical model can be determined based on actual circumstances and is not limited in this embodiment.

[0065] Specifically, in an embodiment of the present invention, the fuel cell failure early warning method further includes:

[0066] Step S301: clear all historical status data stored in the cloud at intervals of a first preset time.

[0067] It is understandable that in order to prevent the cloud storage space from being full and unable to store the latest historical status data, in this embodiment, after a first preset time interval, the system can clear all historical status data stored in the cloud, thereby avoiding saturation of the cloud and ensuring that the cloud can continue to effectively save the latest historical status data. For example, the first preset time is set to 20 hours.

[0068] In an embodiment of the present invention, the fuel cell failure early warning method further includes:

[0069] In step S302 , sample status data of the fuel cell is obtained at intervals of a second preset time, and the sample status data is sent to the cloud for storage as historical status data.

[0070] In an embodiment of the present invention, during the operation of the fuel cell, data of the fuel cell is periodically collected at intervals of a second preset time to obtain sample status data. The sample status data stored in the cloud becomes the basis for subsequent training model learning.

[0071] It will be appreciated that in embodiments of the present invention, based on a preset training model, the system can obtain reference state data intervals corresponding to various parameters. This reference state data interval represents the range of various indicators that should be maintained during normal fuel cell operation. Taking temperature data as an example, based on a preset training model, the system can obtain a reference state data interval corresponding to temperature data of 50°C (Celsius) to 100°C (Celsius). Therefore, when the current temperature value in the current state data falls within this range, it indicates that the fuel cell is operating stably and has no obvious potential for failure.

[0072] Step S103: When the current state data is not within the reference state data interval, the historical state data stored in the cloud is called.

[0073] In an embodiment of the present invention, when the current state data is not within the reference state data interval, it indicates that the current state data of the fuel cell has deviated from the normal range, and thus it can be determined that the fuel cell is abnormal. It should be noted that the occurrence of an abnormality in the fuel cell does not mean that the fuel cell has failed; it only indicates that the fuel cell has a potential failure. Based on this, this embodiment uses historical state data stored in the cloud to perform a retrospective analysis of the fuel cell state at various time points before the current moment.

[0074] Step S104 , obtaining a predicted time of occurrence of a fuel cell failure based on the current state data and the historical state data, and sending a reminder message to the driver through the human-computer interaction system, the reminder message including the predicted time.

[0075] In this embodiment of the present invention, by retrospectively analyzing the fuel cell status at various points prior to the current moment and combining it with current status data, it is possible to determine the evolutionary trends of the fuel cell status data over time and possible future development trends. Based on the predicted development trends, the time point at which the fuel cell is likely to fail can be predicted, generating a predicted time.

[0076] In an embodiment of the present invention, the human-computer interaction system may be a central control screen, an instrument panel, or a head-up display device, and this embodiment is not limited thereto. It is understood that when the system detects a potential fuel cell failure and generates a time point at which the failure may occur, the system may send a reminder message to the driver via the human-computer interaction system, thereby promptly notifying the driver of the current operating status of the fuel cell, prompting the driver to stop driving the vehicle, and promptly perform inspections and repairs to avoid damage to the fuel cell.

[0077] The embodiment of the present invention obtains current status data and compares it with a reference status data interval. When the current status data is not within the reference status data interval, it determines that the current status data of the fuel cell deviates from the normal range; then, by calling the historical status data stored in the cloud, the predicted time of the fuel cell failure is obtained based on the current status data and the historical status data, and a reminder message is sent to the driver through the human-computer interaction system, thereby realizing early warning of fuel cell failure. Compared with confirming that the fuel cell system has failed after the fuel cell failure actually occurs, by predicting the time of the fuel cell failure, the occurrence of the failure is effectively prevented, and the driver or maintenance personnel can also be reminded to take intervention measures in the early stage before and after the failure occurs, thereby reducing the possibility of fuel cell damage, thereby extending the service life of the fuel cell, increasing the reliability of the fuel cell, and reducing the maintenance cost of the fuel cell.

[0078] Reference Figure 2 As shown, in this embodiment of the present invention, step S103 includes:

[0079] Step S1031: call all historical status data within a preset time period before the current moment to obtain a historical data set.

[0080] It is understood that in order to predict the future development trend of status data by analyzing the changing trends of status data within a preset time period before the current moment, in embodiments of the present invention, the system can retrieve all historical status data within the preset time period before the current moment from the cloud. In one example, the preset time period is 1 minute, and the system can retrieve all historical status data stored in the cloud within the past 1 minute from the cloud to form a historical data set.

[0081] In this embodiment, step S104 includes:

[0082] Step S1041: determine the maximum historical status data in the historical data set.

[0083] Step S1042: When the current state data is greater than the maximum historical state data, and the historical state data in the historical data set increases in chronological order, the interval between the corresponding moment of the maximum historical state data and the current moment is used as the prediction time.

[0084] It should be noted that the maximum historical status data is the one with the largest value among all the historical status data. Among them, when the historical status data in the historical data set shows an increasing trend in chronological order, the maximum historical status data is the historical status data obtained closest to the current moment. For the convenience of description, temperature data is used as an example. In one example, the preset duration is 1 minute, and the cloud stores historical status data of 80°C for the past 1 minute, historical status data of 90°C for the past 40 seconds, and historical status data of 100°C for the past 20 seconds. Among them, the historical status data in the historical data set shows an increasing trend, and the maximum historical status data is 100°C.

[0085] At this point, the current temperature data detected is 110°C, exceeding the reference temperature range of 50°C to 100°C and exceeding the maximum historical temperature of 100°C. This indicates that the fuel cell temperature is continuing to rise and is approaching a state of uncontrolled growth, posing a serious risk of failure due to excessive temperature. Understandably, given that the current temperature data has exceeded the maximum historical temperature data and considering the previous upward trend, the system can reasonably infer that the fuel cell temperature is likely to continue this upward trend.

[0086] Based on this logic, the system can use the time difference (20 seconds) between the collection time of the maximum historical status data (i.e., 100°C) and the current time as a reference for the prediction time, thereby predicting that within the next 20 seconds, the fuel cell may fail due to excessive temperature.

[0087] Reference Figure 3 As shown, in this embodiment of the present invention, the early warning method further includes:

[0088] Step S201: Based on the training model, a plurality of reference thresholds corresponding to a plurality of sample data types are obtained.

[0089] It will be appreciated that in embodiments of the present invention, current state data includes multiple sample data types, and multiple reference thresholds can be obtained based on the training model, wherein the multiple reference thresholds correspond one-to-one to the multiple sample data types. In this embodiment, the sample data types include at least current voltage value, current current value, current gas concentration value, current temperature value, and current humidity value. Based on this, the reference thresholds include at least current voltage threshold, current current threshold, current gas concentration threshold, current temperature threshold, and current humidity threshold.

[0090] Step S202: Determine the type of abnormal sample data as an abnormal parameter based on the current state data and multiple reference thresholds.

[0091] In an embodiment of the present invention, when sample data is greater than a corresponding reference threshold, the sample data type is determined to be abnormal, and its sample parameter is an abnormal parameter. In one example, the current temperature is 130°C, and the current temperature threshold is 120°C. Therefore, the current temperature value is an abnormal sample data type, and the abnormal parameter is 130°C.

[0092] Step S203: confirming the faulty module in the fuel cell according to the abnormal parameters.

[0093] It should be noted that a fuel cell, as a complex system, integrates multiple functional modules, including but not limited to an energy submodule, a circuit submodule, and a monitoring module, etc. The operating status of the multiple functional modules can be reflected through sample data of various sample data types.

[0094] For example, hydrogen concentration is directly linked to hydrogen management in the energy submodule, and voltage is directly linked to hydrogen management in the energy submodule. When the system detects an anomaly in a specific type of sample data, it can quickly locate the corresponding functional module, accurately tracing the source of the potential fault. This simplifies the diagnostic process for maintenance personnel, eliminating the need for tedious troubleshooting, reduces maintenance difficulty for maintenance personnel, and improves fuel cell maintenance efficiency.

[0095] Reference Figure 4 As shown, in this embodiment of the present invention, the early warning method further includes:

[0096] Step S204: Calculate the difference between the abnormal parameter and the reference threshold value corresponding to the abnormal parameter.

[0097] Step S205 : When the difference is less than or equal to the preset difference threshold, it is determined that the failure risk level of the fuel cell is low risk.

[0098] Step S206: When the difference is greater than the preset difference threshold, the fault risk level is determined to be high risk.

[0099] For ease of description, let's use temperature data as an example. In one example, assume the detected abnormal parameter is 130°C, and the reference threshold corresponding to this temperature parameter is set to 120°C. Calculation reveals that the difference between the two is 10°C. In this embodiment, the preset difference threshold is set to 20°C. The difference between the abnormal parameter and the reference threshold does not exceed the preset difference threshold, so the system can determine that the fuel cell's failure risk level is low.

[0100] In another example, assume the detected abnormal parameter is 150°C, and the reference threshold corresponding to this temperature parameter is set at 120°C. Calculation shows that the difference between the two is 30°C. In this embodiment, the preset difference threshold is set at 20°C. The difference between the abnormal parameter and the reference threshold exceeds the preset difference threshold, so the system can determine that the fuel cell failure risk level is high.

[0101] Reference Figure 5 As shown, in this embodiment of the present invention, step S104 further includes:

[0102] Step S1043: When the fault risk level is low risk, a warning animation is displayed through the human-computer interaction system.

[0103] Step S1044: When the fault risk level is high risk, an alarm animation is displayed and a sound alarm is played through the human-computer interaction system.

[0104] It is understood that in embodiments of the present invention, when the failure risk level is low, this means that while the fuel cell is experiencing an abnormality and there is a possibility of failure within a period of time, the urgency of the failure is relatively low. Therefore, when the failure risk level is low, this embodiment only displays a warning animation through the human-computer interaction system to intuitively convey to the driver the potential for fuel cell failure within a period of time, allowing the driver sufficient time to react and take necessary preventive measures within a certain period of time. In one example, the human-computer interaction system is a display screen on the instrument panel or central control system, and the warning animation can be a flashing indicator light or a text pop-up warning window, which is not limited in this embodiment.

[0105] In an embodiment of the present invention, when the fault risk level is high, it means that the fuel cell is experiencing an abnormality and there is a possibility of imminent failure, that is, the urgency of the failure is relatively high. Therefore, when the fault risk level is high, this embodiment displays an alarm animation through the human-computer interaction system while playing an audible alarm, thereby intuitively conveying to the driver that the fuel cell is about to fail, allowing the driver to immediately stop driving to avoid further damage to the fuel cell. It should be noted that in this embodiment, the alarm animation is a more intense and eye-catching reminder screen than the early warning animation. For example, the alarm animation can be a high-frequency flashing of the entire instrument panel screen. In this embodiment, the human-computer interaction system can also be an audio system, and the audible alarm can be a periodic beeping sound.

[0106] This embodiment provides both visual and audible reminders, forming a dual visual and auditory warning, ensuring that the driver can immediately perceive the information that the fuel cell is about to fail and take necessary preventive measures immediately, while effectively distinguishing between high and low risk levels of reminders.

[0107] Reference Figure 6 As shown, an embodiment of the present invention further provides a vehicle controller 600, which is applied to a vehicle having a fuel cell and a human-computer interaction system, and the vehicle is communicatively connected to the cloud. The vehicle controller 600 includes:

[0108] The acquisition module 601 is configured to acquire data from the fuel cell during its operation and obtain current state data of the fuel cell.

[0109] The acquisition module 602 is configured to acquire a reference state data interval corresponding to the current state data according to a training model preset in the cloud.

[0110] The calling module 603 is configured to call the historical state data stored in the cloud when the current state data is not within the reference state data interval.

[0111] The execution module 604 is configured to obtain a predicted time of occurrence of a fuel cell failure based on the current state data and the historical state data, and send a reminder message to the driver through the human-computer interaction system, where the reminder message includes the predicted time.

[0112] Since the vehicle controller 600 adopts all the technical solutions of the fuel cell failure early warning method of the above embodiment, it has at least all the beneficial effects brought by the technical solutions of the above embodiment, which will not be repeated here.

[0113] It should be noted that, in an embodiment of the present invention, the acquisition module 601 can also execute steps S301 to S302 in the above embodiment; the calling module 603 can also execute step S1031 in the above embodiment; the execution module 604 can also execute steps S1041 to S1042, steps S201 to S203, steps S204 to S206 and steps S1043 to S1044 in the above embodiment.

[0114] An embodiment of the present invention also provides a fuel cell system that utilizes the fuel cell failure early warning method of the above-described embodiment, suitable for use in a vehicle. It will be appreciated that this embodiment provides early warning of fuel cell system failure, effectively reducing the risk of fuel cell system failure, extending the service life of the fuel cell system, and increasing the reliability and stability of the fuel cell system.

[0115] An embodiment of the present invention further provides a vehicle comprising the fuel cell system of the above embodiment. Specifically, in this embodiment of the present invention, the vehicle can be a private vehicle, such as a sedan, SUV, MPV, or pickup truck. The vehicle can also be a commercial vehicle, such as a van, bus, small truck, or large trailer.

[0116] It can be understood that the vehicle of this embodiment achieves early warning of fuel cell system failure by adopting a fuel cell failure early warning method, effectively reducing the risk of fuel cell system failure, reducing the possibility of fuel cell damage, and increasing the service life of the fuel cell system, thereby reducing the maintenance cost of the vehicle and increasing the reliability and stability of the vehicle.

[0117] An embodiment of the present invention further provides an electronic device, comprising:

[0118] at least one processor;

[0119] at least one memory for storing at least one program;

[0120] When at least one program is executed by at least one processor, the above-mentioned fuel cell failure early warning method is implemented.

[0121] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program executable by a processor. When the computer program executable by the processor is executed by the processor, it is used to implement the above-mentioned fuel cell failure early warning method.

[0122] The terms "first," "second," "third," "fourth," and the like (if any) in the description of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present invention described herein can, for example, be implemented in orders other than those illustrated or described herein. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0123] It should be understood that in the present invention, "at least one (item)" refers to one or more, and "plurality" refers to two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can represent: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0124] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0125] In the embodiments of the present invention, the term "module" or "unit" refers to a computer program or portion of a computer program that has a predetermined function and works together with other related components to achieve a predetermined goal. The term "module" or "unit" refers to a computer program or portion of a computer program that has a predetermined function and works together with other related components to achieve a predetermined goal. The term "module" or "unit" may be implemented in whole or in part using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a single processor (or multiple processors or memories) may be used to implement one or more modules or units. Furthermore, each module or unit may be part of an overall module or unit that incorporates the functionality of that module or unit.

[0126] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0127] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0128] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media that can store program codes.

[0129] The step numbers in the above method embodiment are only provided for the convenience of explanation and description, and do not limit the order of the steps. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

Claims

1. A fuel cell failure early warning method, characterized in that: Applied to a vehicle having a fuel cell and a human-machine interaction system, the vehicle being communicatively connected to the cloud, the early warning method includes: During the operation of the fuel cell, data collection is performed on the fuel cell to obtain current state data of the fuel cell; Obtaining a reference state data interval corresponding to the current state data according to a training model preset in the cloud; When the current state data is not within the reference state data interval, calling the historical state data stored in the cloud; Obtaining a predicted time of occurrence of the fuel cell failure based on the current state data and the historical state data, and sending a reminder message to the driver through the human-computer interaction system, the reminder message including the predicted time; The calling of the historical status data stored in the cloud includes: Retrieving all the historical status data within a preset time period before the current moment to obtain a historical data set; Obtaining a predicted time of occurrence of the fuel cell failure based on the current state data and the historical state data, including: Determining the maximum historical state data in the historical data set; When the current state data is greater than the maximum historical state data, and the historical state data in the historical data set presents an increasing trend in chronological order, the interval time between the corresponding moment of the maximum historical state data and the current moment is used as the predicted time.

2. The fuel cell failure early warning method according to claim 1, characterized in that: The current state data includes multiple sample data types, and the sample data types include at least a current voltage value, a current current value, a current gas concentration value, a current temperature value, and a current humidity value. The early warning method further includes: Based on the training model, obtaining a plurality of reference thresholds corresponding one-to-one to a plurality of the sample data types; Determining the type of the sample data in which the abnormality occurs as an abnormal parameter based on the current state data and the multiple reference thresholds; A faulty module in the fuel cell is identified based on the abnormal parameters.

3. The fuel cell failure early warning method according to claim 2, characterized in that: The early warning method further includes: Calculating a difference between the abnormal parameter and the reference threshold value corresponding to the abnormal parameter; When the difference is less than or equal to a preset difference threshold, determining that the failure risk level of the fuel cell is low risk; When the difference is greater than the preset difference threshold, the fault risk level is determined to be high risk.

4. The fuel cell failure early warning method according to claim 3, characterized in that: The sending of reminder information to the driver through the human-computer interaction system includes: When the fault risk level is low risk, a warning animation is displayed through the human-computer interaction system; When the fault risk level is high, an alarm animation is displayed and an audible alarm is played through the human-computer interaction system.

5. The fuel cell failure early warning method according to claim 1, characterized in that: The early warning method further includes: At intervals of a first preset time, clearing all the historical status data stored in the cloud; At intervals of a second preset time, sample status data of the fuel cell is obtained and sent to the cloud for storage as the historical status data.

6. A vehicle controller, characterized in that: Applicable to a vehicle with a fuel cell and a human-machine interaction system, wherein the vehicle is connected to the cloud for communication, and the vehicle controller includes: an acquisition module configured to acquire data from the fuel cell during operation of the fuel cell and obtain current state data of the fuel cell; an acquisition module configured to acquire a reference state data interval corresponding to the current state data according to a training model preset in the cloud; a calling module configured to call the historical state data stored in the cloud when the current state data is not within the reference state data interval; the calling module is further configured to call all the historical state data within a preset time period before the current moment to obtain a historical data set; An execution module is configured to obtain a predicted time of occurrence of the fuel cell failure based on the current state data and the historical state data, and to send a reminder message to the driver through the human-computer interaction system, wherein the reminder message includes the predicted time; the execution module is further configured to determine the maximum historical state data in the historical data set; when the current state data is greater than the maximum historical state data, and the historical state data in the historical data set show an increasing trend in chronological order, the interval time between the corresponding moment of the maximum historical state data and the current moment is used as the predicted time.

7. A fuel cell system, characterized in that: The fuel cell failure early warning method according to any one of claims 1 to 5 is applied.

8. A vehicle, characterized in that: Comprising a fuel cell system as claimed in claim 7.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the fuel cell failure early warning method according to any one of claims 1 to 5.

10. A computer-readable storage medium storing instructions executable by a processor, characterized in that: The processor-executable instructions are used to execute the fuel cell failure early warning method according to any one of claims 1 to 5 when executed by the processor.

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