Fuel cell system air filter blockage early warning method, system, equipment and medium

Abstract

The invention relates to the technical field of fuel cells, in particular to a fuel cell system air filter blockage early warning method, system and device and a medium, and the method comprises the steps: collecting geographic position information and air filter inner side pressure data in a target area in real time when a vehicle runs; constructing an atmospheric pressure estimation model by utilizing a clustering algorithm, and calculating current atmospheric pressure by utilizing the atmospheric pressure estimation model according to the geographical location information to obtain atmospheric pressure data of the outer side of the air filter; comparing the atmospheric pressure data on the outer side of the air filter with the pressure data on the inner side of the air filter to obtain the pressure difference between the two sides of the air filter; and detecting the air filter according to the pressure difference between the two sides of the air filter and a predetermined self-adaptive early warning threshold value, and generating an air filter blockage early warning signal. According to the method, the geographic position and the pressure data on the inner side of the air filter are collected in real time, the atmospheric pressure estimation model and the self-adaptive early warning threshold value are combined, and accurate early warning of the air filter blockage state is achieved.

Description

Technical Field

[0001] The present invention relates to the field of fuel cell technology, and in particular to a fuel cell system air filter clogging early warning method, system, equipment and medium. Background Art

[0002] As an important part of the new energy field, the performance stability of the fuel cell system is closely related to the cleanliness of the air filter. Air filter blockage will not only affect the system's air intake efficiency, but also cause the system to overheat, thereby affecting the overall performance and life. At present, the existing technology usually determines whether the air filter is blocked by detecting the pressure difference on both sides of the air filter, air quality or system parameter changes. However, these methods have obvious limitations. For example, the existing technology installs pressure sensors on both sides of the air filter and monitors the pressure difference on both sides to determine whether the air filter is blocked. However, in actual applications, if the sensor is only installed on the inside of the air filter, the atmospheric pressure outside the air filter cannot be accurately obtained, resulting in inaccurate pressure difference calculation, and adding an outside sensor will significantly increase the system cost.

[0003] Secondly, the existing technology uses gas sensors to detect changes in air quality before and after the air filter, and evaluates the adsorption capacity and life of the air filter by calculating the mass of pollutants before and after filtration. However, this method is limited by the accuracy and durability of the gas sensor, and is costly, which limits the wide application of this method. In addition, the existing technology also determines the filter element blockage condition by monitoring the combined changes of system parameters such as the back pressure valve opening, the stack flow resistance and the stack power, but this method requires the coupling of multiple system parameters, which increases the calculation complexity and computing power consumption, and the parameter changes under different working conditions interfere with each other, affecting the accuracy of the judgment.

[0004] In summary, the existing technology has many problems in air filter blockage warning, and it is urgent to provide a more accurate, reliable and efficient warning method to solve this technical problem. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a fuel cell system air filter clogging early warning method, system, equipment and medium.

[0006] In a first aspect, the present invention provides a fuel cell system air filter blockage early warning method, the method comprising the following steps:

[0007] Real-time collection of geographic location information and air filter inner pressure data within the target area when the vehicle is running; wherein the geographic location information includes latitude and longitude data and altitude data;

[0008] Using a clustering algorithm to construct an atmospheric pressure estimation model, and according to the geographic location information, using the atmospheric pressure estimation model to calculate the current atmospheric pressure, to obtain atmospheric pressure data outside the air filter;

[0009] Compare the atmospheric pressure data outside the air filter with the pressure data inside the air filter to obtain the pressure difference between the two sides of the air filter;

[0010] The air filter is detected according to the pressure difference between the two sides of the air filter and a predetermined adaptive warning threshold, and an air filter blockage warning signal is generated.

[0011] In a further embodiment, the step of constructing an atmospheric pressure estimation model using a clustering algorithm comprises:

[0012] Collect historical geographic location information of the vehicle passing through the target area during historical operation and ambient atmospheric pressure data collected by the sensor inside the air filter when the air compressor stops working; wherein the historical geographic location information includes historical latitude and longitude data and historical altitude data;

[0013] Taking minimizing the Euclidean distance between each historical geographic location point and the center point of its cluster as the optimization goal, the historical geographic location information in the target area is divided into different clusters using a clustering algorithm.

[0014] The regression algorithm is used to fit all historical altitude data and ambient atmospheric pressure data in each cluster to obtain the atmospheric pressure estimation model.

[0015] In a further embodiment, the optimization objective function of the clustering algorithm is specifically:

[0016]

[0017] Where D ij is the Euclidean distance between the jth historical geographic location point and the center point of the cluster to which it belongs; i is the cluster index; n is the number of clusters; m is the number of historical geographic location points in the i-th cluster; d ij is the true distance between the jth historical geographic location point and the center point of the cluster to which it belongs; is the preset true distance threshold.

[0018] In a further embodiment, the calculation formula of the Euclidean distance is:

[0019]

[0020] Where D ij is the Euclidean distance between the jth historical geographic location point and the center point of the cluster to which it belongs; Long i is the normalized longitude data of the center point of the i-th cluster; Long ′ j is the normalized historical longitude data of the jth historical geographic location point; Lati is the normalized latitude data of the center point of the i-th cluster; Lat ′ j is the normalized historical latitude data of the jth historical geographical location point; H i is the normalized altitude data of the center point of the i-th cluster; H ′ j is the normalized historical altitude data of the jth historical geographic location point.

[0021] In a further embodiment, the step of calculating the current atmospheric pressure using the atmospheric pressure estimation model according to the geographical location information to obtain the atmospheric pressure data outside the air filter comprises:

[0022] Searching for the corresponding cluster according to the geographic location information to obtain the target cluster;

[0023] The current atmospheric pressure is calculated using the atmospheric pressure estimation model of the target cluster to obtain atmospheric pressure data outside the air filter.

[0024] In a further embodiment, the step of detecting the air filter according to the pressure difference between the two sides of the air filter and a predetermined adaptive warning threshold to generate an air filter blockage warning signal includes:

[0025] The pressure difference on both sides of the air filter is compared with a predetermined adaptive warning threshold, and when the pressure difference on both sides of the air filter exceeds the adaptive warning threshold within a preset air filter detection period, the air filter is determined to be blocked and an air filter blockage warning signal is generated.

[0026] In a further embodiment, the process of determining the adaptive warning threshold comprises:

[0027] Collect air flow and pressure difference sample data respectively when the air filter is in normal state and when the air filter is blocked;

[0028] According to the air flow and pressure difference sample data, respectively fitting the air flow-pressure difference characteristic curves in the normal state of the air filter and the blocked state of the air filter;

[0029] A distinction boundary of the air flow-pressure difference characteristic curve is obtained, and an adaptive warning threshold is determined according to the distinction boundary.

[0030] In a second aspect, the present invention provides a fuel cell system air filter blockage warning system, the system comprising:

[0031] A data acquisition module, used to collect in real time geographical location information and air filter inner pressure data within a target area when the vehicle is running; wherein the geographical location information includes latitude and longitude data and altitude data;

[0032] A pressure estimation module, used to construct an atmospheric pressure estimation model using a clustering algorithm, and calculate the current atmospheric pressure using the atmospheric pressure estimation model according to the geographic location information to obtain atmospheric pressure data outside the air filter;

[0033] A pressure analysis module, used for comparing the atmospheric pressure data outside the air filter with the pressure data inside the air filter to obtain the pressure difference between the two sides of the air filter;

[0034] The blockage warning module is used to detect the air filter according to the pressure difference between the two sides of the air filter and a predetermined adaptive warning threshold value, and generate an air filter blockage warning signal.

[0035] In a third aspect, the present invention further provides a computer device, comprising a processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory so that the computer device performs the steps of implementing the above method.

[0036] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0037] The present invention provides a fuel cell system air filter blockage warning method, system, equipment and medium. The method collects geographical location information and air filter inner pressure data in a target area when a vehicle is running in real time; uses a clustering algorithm to build an atmospheric pressure estimation model, and calculates the current atmospheric pressure using the atmospheric pressure estimation model according to the geographical location information to obtain the atmospheric pressure data outside the air filter; compares the atmospheric pressure data outside the air filter with the pressure data inside the air filter to obtain the pressure difference on both sides of the air filter; detects the air filter according to the pressure difference on both sides of the air filter and a predetermined adaptive warning threshold, and generates an air filter blockage warning signal. Compared with the prior art, the method realizes accurate warning of the air filter blockage state by collecting geographical location and air filter inner pressure data in real time, combining the atmospheric pressure estimation model and the adaptive warning threshold, and avoids the warning error caused by inaccurate atmospheric pressure acquisition or complex system parameter coupling in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the flow chart of the fuel cell system air filter blockage warning method provided by an embodiment of the present invention;

[0039] Figure 2 is a schematic diagram of the flow-pressure difference relationship provided by an embodiment of the present invention;

[0040] Figure 3It is a block diagram of an air filter blockage warning system for a fuel cell system provided by an embodiment of the present invention;

[0041] Figure 4 It is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following specifically illustrates the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and cannot be understood as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0043] refer to Figure 1 , the embodiment of the present invention provides a fuel cell system air filter blockage early warning method, such as Figure 1 As shown, the method comprises the following steps:

[0044] S1. Real-time collection of geographic location information and air filter inner pressure data within a target area when the vehicle is running; wherein the geographic location information includes latitude and longitude data and altitude data.

[0045] Specifically, this embodiment collects the longitude and latitude data and altitude data in the target area when all vehicles are running through the on-board communication terminal Tbox to form geographic location information, wherein the longitude and latitude data and altitude data are collected in real time by the on-board communication terminal Tbox and uploaded to the cloud platform, and the longitude and latitude data and altitude data are normalized to ensure that the data are on the same scale to facilitate the accuracy of the subsequent clustering algorithm.

[0046] S2. constructing an atmospheric pressure estimation model using a clustering algorithm, and calculating the current atmospheric pressure using the atmospheric pressure estimation model according to the geographic location information to obtain atmospheric pressure data outside the air filter.

[0047] In this embodiment, the step of constructing an atmospheric pressure estimation model using a clustering algorithm includes:

[0048] Collect historical geographic location information of the vehicle passing through the target area during historical operation and ambient atmospheric pressure data collected by the sensor inside the air filter when the air compressor stops working; wherein the historical geographic location information includes historical latitude and longitude data and historical altitude data;

[0049] Taking minimizing the Euclidean distance between each historical geographic location point and the center point of its cluster as the optimization goal, the historical geographic location information in the target area is divided into different clusters using a clustering algorithm.

[0050] The regression algorithm is used to fit all historical altitude data and ambient atmospheric pressure data in each cluster to obtain the atmospheric pressure estimation model.

[0051] Specifically, this embodiment uses the vehicle's on-board communication terminal Tbox to collect historical longitude and latitude data of the vehicle passing through the target area during historical operation, and uses the geographic information system (GIS) database to query and record the corresponding historical altitude data according to the longitude and latitude information, and integrates the historical longitude and latitude data and the historical altitude data into historical geographic location information. When the air compressor stops working, the pressure sensor on the inside of the air filter collects the ambient atmospheric pressure data in real time to ensure that the data is not affected by the operating state of the air compressor. The collected historical geographic location information and ambient atmospheric pressure data are cleaned and formatted to ensure the validity and consistency of the data. Then, a clustering algorithm such as the K-means algorithm, the DBSCAN algorithm or the hierarchical clustering algorithm can be selected to divide the historical geographic location information in the target area into different clustering clusters. For ease of understanding, this embodiment takes the K-means algorithm as an example A detailed description is given. For the K-means algorithm, this embodiment sets the number of clusters n, initializes n cluster centers, and iteratively calculates the Euclidean distance between each historical geographic location point and the cluster center point of the cluster cluster to which it belongs according to the optimization objective function of the clustering algorithm, and distributes it to the nearest cluster cluster, and accumulates the Euclidean distances of all historical geographic location points to the cluster center point to which they belong to obtain the total distance sum. Through the iterative process of the K-means algorithm, the total distance sum is continuously reduced until the optimization goal is reached, and finally n cluster clusters are obtained, each cluster containing a group of historical geographic location points. In the clustering algorithm, this embodiment needs to use the maximum and minimum normalization algorithm to normalize the longitude and latitude and altitude data of the historical geographic location points and the cluster center points, and normalize the data in the [0, 1] interval to ensure that the data is on the same scale. The optimization objective function of the clustering algorithm is specifically:

[0052]

[0053]

[0054] Where D ij is the Euclidean distance between the jth historical geographic location point and the center point of the cluster to which it belongs; i is the cluster index; n is the number of clusters; m is the number of historical geographic location points in the i-th cluster; d ij is the true distance (great circle distance) between the jth historical geographic location point and the center point of the cluster to which it belongs; is the preset real distance threshold, and Subject to is the constraint condition. When the real distance between the jth historical geographic location point and the center point of the cluster to which it belongs exceeds a certain range, the temperature and humidity in a cluster vary greatly, and the fitted atmospheric pressure estimation model is not ideal. Therefore, those skilled in the art can adjust the real distance threshold according to the specific implementation situation. For example, Set to 10km, constraining the range of each cluster to not exceed 10km; Long i is the normalized longitude data of the center point of the i-th cluster; Long ′ j is the normalized historical longitude data of the jth historical geographic location point; Lat i is the normalized latitude data of the center point of the i-th cluster; Lat ′ j is the normalized historical latitude data of the jth historical geographical location point; H i is the normalized altitude data of the center point of the i-th cluster; H ′ j is the normalized historical altitude data of the jth historical geographic location point.

[0055] Next, for each cluster, the altitude data therein is extracted as the characteristic value, and the ambient atmospheric pressure data is used as the target value. The regression algorithm is used to fit all the historical altitude data and ambient atmospheric pressure data in each cluster, and the corresponding atmospheric pressure estimation model is obtained for each cluster. In this embodiment, for each cluster, this embodiment can use P = a*(1-b*H) c The atmospheric pressure formula is fitted, the input of the atmospheric pressure formula is the altitude H of all the data of the cluster and the corresponding ambient atmospheric pressure data P, and the output is the fitting parameters a, b and c in the atmospheric pressure formula of the cluster cluster. If there is no historical data for a cluster cluster, the fitting process of the cluster cluster is skipped. The atmospheric pressure estimation model can predict the atmospheric pressure value in the target area according to the geographic location information of the vehicle. The finalized model can be deployed on the vehicle, the geographic location information of the vehicle is collected in real time, and the ambient atmospheric pressure is estimated using the model. In this embodiment, the step of calculating the current atmospheric pressure according to the geographic location information using the atmospheric pressure estimation model to obtain the atmospheric pressure data outside the air filter includes:

[0056] Searching for the corresponding cluster according to the geographic location information to obtain the target cluster;

[0057] The current atmospheric pressure is calculated using the atmospheric pressure estimation model of the target cluster to obtain atmospheric pressure data outside the air filter.

[0058] This embodiment can search for the cluster to which the location belongs in the pre-trained clustering results based on the real-time acquired geographic location information (latitude and longitude, altitude). The pre-trained clustering results include the coordinates of the center point of each cluster and the boundary information of the cluster. This embodiment can use nearest neighbor search or spatial indexing methods to quickly find the cluster to which the real-time geographic location information belongs. If the geographic location information is located near the boundary of multiple clusters, a decision can be made based on factors such as distance and area overlap to select the most appropriate cluster, obtain the atmospheric pressure estimation model corresponding to the found cluster, input the real-time acquired geographic location information into the atmospheric pressure estimation model of the target cluster to calculate the current atmospheric pressure value, and obtain the atmospheric pressure data outside the air filter; if the cluster to which the location belongs is not found, calculate the great circle distance from the current geographic location information to the center of all known clusters, find the nearest cluster containing the atmospheric pressure estimation model, and use the atmospheric pressure estimation model of the cluster to calculate the atmospheric pressure as the atmospheric pressure value outside the air filter.

[0059] S3. Compare the atmospheric pressure data outside the air filter with the pressure data inside the air filter to obtain the pressure difference between the two sides of the air filter.

[0060] S4. Detect the air filter according to the pressure difference between the two sides of the air filter and a predetermined adaptive warning threshold, and generate an air filter blockage warning signal.

[0061] Specifically, this embodiment makes a difference between the pressure data Pin on the inside of the air filter and the atmospheric pressure data Pout on the outside of the air filter, and calculates the pressure difference ΔP on both sides of the air filter, wherein ΔP=Pin-Pout. Since the pressure on the outside of the air filter (atmospheric side) is usually greater than or equal to the pressure on the inside of the air filter (intake side) when the air compressor is working, ΔP is usually a negative number. Then, this embodiment compares the pressure difference on both sides of the air filter with a predetermined adaptive warning threshold, and when the pressure difference on both sides of the air filter exceeds the adaptive warning threshold within a preset air filter detection period, it is determined that the air filter is blocked, and an air filter blockage warning signal is generated, wherein the process of determining the adaptive warning threshold includes:

[0062] Collect air flow and pressure difference sample data respectively when the air filter is in normal state and when the air filter is blocked;

[0063] According to the air flow and pressure difference sample data, respectively fitting the air flow-pressure difference characteristic curves in the normal state of the air filter and the blocked state of the air filter;

[0064] A distinction boundary of the air flow-pressure difference characteristic curve is obtained, and an adaptive warning threshold is determined according to the distinction boundary.

[0065] When the vehicle is operating normally and the air filter is not blocked, the pressure difference data on both sides of the air filter under different air flow rates are collected. The air flow-pressure difference characteristic curves under the normal state and the blocked state of the air filter are fitted according to the collected sample data. Here, a one-dimensional quadratic mathematical model can be used for fitting, that is, P = a*Q 2 +b*n+c, where Q is the air flow, a, b, and c are fitting coefficients. On the two characteristic curves obtained by fitting, a boundary point or boundary line that can distinguish the normal state and the blocked state of the air filter is found. For example, in this embodiment, the boundary point or boundary line that can distinguish the normal state and the blocked state of the air filter can be found by calculating the minimum distance, the maximum difference, and the like between the two curves. According to the distinguishing boundary, one or more adaptive warning thresholds are determined. These adaptive warning thresholds should be able to accurately reflect the transition of the air filter from the normal state to the blocked state. During the operation of the vehicle, the pressure difference data on both sides of the air filter is collected in real time, and the actual measured pressure difference data is used as the pressure difference data. The measured pressure difference is compared with a preset adaptive warning threshold. If within a preset air filter detection period (such as t ≥ 5 seconds), the actually measured pressure difference continues to exceed the adaptive warning threshold, it is determined that the air filter is clogged. When it is determined that the air filter is clogged, the system generates an air filter clogged warning signal to prompt the driver to check and replace the air filter in time. This signal can be conveyed to the driver through instrument panel display, sound alarm, etc. In summary, this embodiment can accurately and timely detect the air filter blockage by real-time monitoring of the pressure difference on both sides of the air filter and combining it with the adaptive warning threshold for judgment, thereby improving the operating safety and reliability of the vehicle.

[0066] In order to facilitate the explanation of the process of obtaining the adaptive warning threshold, this embodiment uses several vehicles marked as having air filter blockage as examples. This embodiment extracts the operating data of these vehicles 5 days after the air filter is replaced from the vehicle operation database as a normal state data set; at the same time, this embodiment also obtains the data of each vehicle 5 days before maintenance as a blockage state data set. To ensure the accuracy of the data, this embodiment requires that the maximum flow rate in the daily data must be greater than the preset gas flow rate threshold. For the normal state and blockage state data sets, this embodiment establishes a univariate quadratic model for them respectively. In the normal state model, the pressure difference (ΔP) is used as the dependent variable, the air flow rate (Q) is used as the independent variable, and a, b, and c are the parameters of the model; similarly, in the blockage state model, this embodiment also uses the same variables and different parameters a ′ 、b ′ 、c ′To express, next, this embodiment uses the least squares method to fit the parameters of the two models according to the collected data to determine the distinguishing boundary between the normal state and the blocked state, that is, the warning threshold. In this embodiment, considering that in the low air flow interval, the normal state curve and the blocked state curve are close, and are greatly affected by the pressure disturbance, if the boundary is set too close to the normal state curve, it will cause false triggering of the warning, and in the high air flow interval, the fuel cell system is more sensitive to air supply, so it is very important to capture the blockage information in advance. If the boundary is set too close to the blocked state curve, it will cause missed warnings. To solve this problem, this embodiment designs an adaptive boundary characteristic curve associated with the air flow and the normal / blocked state curve. The curve is closer to the blocked state curve in the low air flow interval and closer to the normal state curve in the high air flow interval. The accuracy of the warning threshold is ensured by smoothly and adaptively adjusting the parameters. The specific formula is as follows:

[0067]

[0068] Where a(Q), b(Q), and c(Q) are the coefficients of the warning threshold curve when the flow rate is Q; w0 is the weight of the low flow interval; w1 is the weight of the high flow interval, where w0 and w1 are both greater than or equal to 0 and less than or equal to 1, and w0 ≥ w1; Q max is the maximum air flow rate of this model of air compressor; Q is the current air flow value.

[0069] In practical applications, this embodiment can calculate the warning threshold in real time based on any air flow rate, such as Figure 2 As shown, by drawing the blockage sample fitting curve, the normal sample fitting curve and the warning threshold curve (taking the flow rate of 200g / s as an example), the relationship between them can be intuitively seen.

[0070] The embodiment of the present invention provides a fuel cell system air filter blockage warning method, the method collects the geographical location information and the air filter inner pressure data in the target area when the vehicle is running in real time; uses the clustering algorithm to build an atmospheric pressure estimation model, and calculates the current atmospheric pressure based on the geographical location information using the atmospheric pressure estimation model to obtain the atmospheric pressure data outside the air filter; compares the atmospheric pressure data outside the air filter with the air filter inner pressure data to obtain the pressure difference on both sides of the air filter; detects the air filter based on the pressure difference on both sides of the air filter and a predetermined adaptive warning threshold, and generates an air filter blockage warning signal. Compared with the prior art, this method realizes accurate warning of the air filter blockage state by collecting the geographical location and the air filter inner pressure data in real time, combining the atmospheric pressure estimation model and the adaptive warning threshold.

[0071] It should be noted that the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0072] In one embodiment, Figure 3 As shown, an embodiment of the present invention provides an air filter blockage warning system for a fuel cell system, the system comprising:

[0073] The data acquisition module 101 is used to collect the geographical location information and the pressure data inside the air filter in real time when the vehicle is running; wherein the geographical location information includes the latitude and longitude data and the altitude data;

[0074] The pressure estimation module 102 is used to construct an atmospheric pressure estimation model using a clustering algorithm, and calculate the current atmospheric pressure using the atmospheric pressure estimation model according to the geographical location information to obtain atmospheric pressure data outside the air filter;

[0075] The pressure analysis module 103 is used to compare the atmospheric pressure data outside the air filter with the pressure data inside the air filter to obtain the pressure difference between the two sides of the air filter;

[0076] The clogging warning module 104 is used to detect the air filter according to the pressure difference between the two sides of the air filter and a predetermined adaptive warning threshold value, and generate an air filter clogging warning signal.

[0077] For the specific definition of a fuel cell system air filter blockage warning system, please refer to the above-mentioned definition of a fuel cell system air filter blockage warning method, which will not be repeated here. A person of ordinary skill in the art will appreciate that the various modules and steps described in conjunction with the embodiments disclosed in this application can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0078] The embodiment of the present invention provides an air filter blockage warning system for a fuel cell system. The system collects geographic location information and air filter inner pressure data in real time in the target area when the vehicle is running through a data acquisition module; the pressure estimation module uses a clustering algorithm to construct an atmospheric pressure estimation model, and calculates the current atmospheric pressure based on the geographic location information using the atmospheric pressure estimation model to obtain the atmospheric pressure data outside the air filter; the pressure analysis module compares the atmospheric pressure data outside the air filter with the pressure data inside the air filter to obtain the pressure difference on both sides of the air filter; the blockage warning module detects the air filter based on the pressure difference on both sides of the air filter and a pre-determined adaptive warning threshold, and generates an air filter blockage warning signal. Compared with the prior art, the system realizes accurate warning of the air filter blockage state by collecting geographic location and air filter inner pressure data in real time, combining the atmospheric pressure estimation model and the adaptive warning threshold.

[0079] Figure 4 A computer device provided by an embodiment of the present invention includes a memory, a processor and a transceiver, which are connected via a bus; the memory is used to store a set of computer program instructions and data, and can transmit the stored data to the processor, and the processor can execute the program instructions stored in the memory to perform the steps of the above method.

[0080] The memory may include a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories; the processor may be a central processing unit, a microprocessor, an application-specific integrated circuit, a programmable logic device, or a combination thereof. By way of example but not limitation, the programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0081] Additionally, the memory may be a physically separate unit or may be integrated with the processor.

[0082] It can be understood by those skilled in the art that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0083] In one embodiment, the present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0084] An embodiment of the present invention provides a fuel cell system air filter clogging warning method, system, device and medium. The fuel cell system air filter clogging warning method ensures the timeliness and accuracy of the data by collecting geographic location information and air filter inner pressure data in real time, and dynamically calculates the atmospheric pressure using an atmospheric pressure estimation model based on the geographic location information, thereby avoiding errors caused by changes in atmospheric pressure in traditional methods and achieving accurate monitoring and warning of the air filter clogging status.

[0085] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., an SSD), etc.

[0086] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0087] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A fuel cell system air filter blockage early warning method, characterized in that: The following steps are involved: Real-time collection of geographic location information and air filter inner pressure data within the target area when the vehicle is running; wherein the geographic location information includes latitude and longitude data and altitude data; Using a clustering algorithm to construct an atmospheric pressure estimation model, and according to the geographic location information, using the atmospheric pressure estimation model to calculate the current atmospheric pressure, to obtain atmospheric pressure data outside the air filter; Compare the atmospheric pressure data outside the air filter with the pressure data inside the air filter to obtain the pressure difference between the two sides of the air filter; The air filter is detected according to the pressure difference between the two sides of the air filter and a predetermined adaptive warning threshold, and an air filter blockage warning signal is generated.

2. A fuel cell system air filter blockage early warning method as claimed in claim 1, characterized in that: The step of constructing the atmospheric pressure estimation model using a clustering algorithm comprises: Collect historical geographic location information of the vehicle passing through the target area during historical operation and ambient atmospheric pressure data collected by the sensor inside the air filter when the air compressor stops working; wherein the historical geographic location information includes historical latitude and longitude data and historical altitude data; Taking minimizing the Euclidean distance between each historical geographic location point and the center point of its cluster as the optimization goal, the historical geographic location information in the target area is divided into different clusters using a clustering algorithm. The regression algorithm is used to fit all historical altitude data and ambient atmospheric pressure data in each cluster to obtain the atmospheric pressure estimation model.

3. A fuel cell system air filter blockage early warning method as claimed in claim 2, characterized in that: The optimization objective function of the clustering algorithm is specifically: Where D ij is the Euclidean distance between the jth historical geographic location point and the center point of the cluster to which it belongs; i is the cluster index; n is the number of clusters; m is the number of historical geographic location points in the i-th cluster; d ij is the true distance between the jth historical geographic location point and the center point of the cluster to which it belongs; is the preset true distance threshold.

4. A fuel cell system air filter blockage early warning method as claimed in claim 3, characterized in that: The calculation formula of the Euclidean distance is: Where D ij is the Euclidean distance between the jth historical geographic location point and the center point of the cluster to which it belongs; Long i is the normalized longitude data of the center point of the i-th cluster; Long ′ j is the normalized historical longitude data of the jth historical geographic location point; Lat i is the normalized latitude data of the center point of the i-th cluster; Lat ′ j is the normalized historical latitude data of the jth historical geographical location point; H i is the normalized altitude data of the center point of the i-th cluster; H ′ j is the normalized historical altitude data of the jth historical geographic location point.

5. The fuel cell system air filter blockage early warning method according to claim 1, characterized in that: The step of calculating the current atmospheric pressure using the atmospheric pressure estimation model according to the geographical location information to obtain atmospheric pressure data outside the air filter comprises: Searching for the corresponding cluster according to the geographic location information to obtain the target cluster; The current atmospheric pressure is calculated using the atmospheric pressure estimation model of the target cluster to obtain atmospheric pressure data outside the air filter.

6. A fuel cell system air filter blockage early warning method as claimed in claim 1, characterized in that: The step of detecting the air filter according to the pressure difference between the two sides of the air filter and a predetermined adaptive warning threshold to generate an air filter blockage warning signal comprises: The pressure difference on both sides of the air filter is compared with a predetermined adaptive warning threshold, and when the pressure difference on both sides of the air filter exceeds the adaptive warning threshold within a preset air filter detection period, the air filter is determined to be blocked and an air filter blockage warning signal is generated.

7. A fuel cell system air filter blockage early warning method as claimed in claim 1, characterized in that: The process of determining the adaptive warning threshold includes: Collect air flow and pressure difference sample data respectively when the air filter is in normal state and when the air filter is blocked; According to the air flow and pressure difference sample data, respectively fitting the air flow-pressure difference characteristic curves in the normal state of the air filter and the blocked state of the air filter; A distinction boundary of the air flow-pressure difference characteristic curve is obtained, and an adaptive warning threshold is determined according to the distinction boundary.

8. A fuel cell system air filter blockage warning system, characterized in that: The system comprises: A data acquisition module, used to collect in real time geographical location information and air filter inner pressure data within a target area when the vehicle is running; wherein the geographical location information includes latitude and longitude data and altitude data; A pressure estimation module, used to construct an atmospheric pressure estimation model using a clustering algorithm, and calculate the current atmospheric pressure using the atmospheric pressure estimation model according to the geographic location information to obtain atmospheric pressure data outside the air filter; A pressure analysis module, used for comparing the atmospheric pressure data outside the air filter with the pressure data inside the air filter to obtain the pressure difference between the two sides of the air filter; The blockage warning module is used to detect the air filter according to the pressure difference between the two sides of the air filter and a predetermined adaptive warning threshold value, and generate an air filter blockage warning signal.

9. A computer device, characterized in that: The computer device comprises a processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the computer device executes the method according to any one of claims 1 to 7.

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

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