A method and apparatus for predicting the extent of icing of a vehicle crankcase ventilation system

Abstract

The embodiment of the application provides a kind of vehicle crankcase ventilation system icing degree prediction method and device, the method is by collecting the first working condition data of vehicle in preset working condition, and determine the first vehicle running time corresponding to the first working condition data, first working condition data and first vehicle running time are input into preset crankcase ventilation system icing prediction model, the prediction result obtained is used to characterize the icing degree in the crankcase ventilation system. Wherein, the first working condition data includes ambient temperature, ambient humidity, vehicle speed and crankcase blow-by gas amount. User knows the icing degree in the crankcase ventilation system according to the prediction result, so that corresponding processing measures are taken, to avoid the engine failure caused by the serious icing in the crankcase ventilation system.

Description

Technical Field

[0001] This invention relates to the field of vehicle equipment technology, and in particular to a method and apparatus for predicting the degree of icing in a vehicle crankcase ventilation system. Background Technology

[0002] According to relevant regulations, in order to meet increasingly stringent vehicle exhaust emission regulations, gasoline engines are now generally equipped with crankcase ventilation systems to reintroduce unburned fuel vapors from the crankcase into the intake manifold, thereby reducing crankcase pollutant emissions and improving gasoline utilization.

[0003] In cold winter regions, such as Northeast and Northwest my country, temperatures often drop below -30°C. Under these conditions, especially under full load, hot fuel vapor from the crankcase flows through the crankcase ventilation pipe and accumulates at the junction of the air filter intake pipe and the crankcase ventilation pipe. When the cold air in the air filter intake pipe encounters the hot vapor from the crankcase, ice easily forms at the junction, clogging the crankcase ventilation pipe. If the crankcase ventilation pipe is blocked, the fuel vapor cannot escape from the crankcase in time, leading to excessive pressure within the crankcase over time. This can cause oil leaks from the front and rear oil seals, and in severe cases, even oil seal detachment, damaging the engine. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a method for predicting the degree of icing in a vehicle crankcase ventilation system, which overcomes or at least partially solves the above problems, as well as a corresponding device, vehicle, and storage medium for predicting the degree of icing in a vehicle crankcase ventilation system.

[0005] To address the aforementioned problems, this invention discloses, in one aspect, a method for predicting the degree of icing in a vehicle crankcase ventilation system, the method comprising:

[0006] When the vehicle is in a preset operating condition, the first operating condition data of the vehicle is collected, and the first vehicle running time corresponding to the first operating condition data is determined. The first operating condition data includes ambient temperature, ambient humidity, vehicle speed and crankcase blow-by volume.

[0007] The first operating condition data and the first vehicle running time are input into a preset crankcase ventilation system icing prediction model, and a prediction result is obtained based on the crankcase ventilation system icing prediction model. The prediction result is used to characterize the degree of icing in the crankcase ventilation system. The crankcase ventilation system icing prediction model is trained using a neural network model, and the crankcase ventilation system icing prediction model sets several mapping relationships between vehicle operating condition data and the degree of icing in the vehicle's crankcase ventilation system.

[0008] Perform a preset operation based on the prediction result.

[0009] Optionally, the step of inputting the first operating condition data and the first vehicle running time into a preset crankcase ventilation system icing prediction model, and obtaining the prediction result based on the crankcase ventilation system icing prediction model, includes:

[0010] The first operating condition data of the vehicle during the first vehicle's operating time is classified to obtain several categories of target operating condition data. In each category of target operating condition data, the vehicle speed and the crankcase blow-by volume are the same.

[0011] Calculate the target operating time corresponding to the target operating data;

[0012] Several target operating condition data and corresponding target operating condition times are input into a preset crankcase ventilation system icing prediction model, and prediction results are obtained.

[0013] Optionally, the step of inputting several target operating condition data and corresponding target operating condition times into a preset crankcase ventilation system icing prediction model and obtaining prediction results includes:

[0014] Input several target operating condition data and corresponding target operating condition times into a preset crankcase ventilation system icing prediction model to obtain several target icing data corresponding to the target operating condition data respectively;

[0015] The overall icing data is obtained by summing up the icing data of several target icing data points.

[0016] The overall icing data is determined as the prediction result.

[0017] Optionally, the vehicle is equipped with an air filter and a turbocharger. Before collecting the first operating condition data of the vehicle, the method further includes:

[0018] Get the vehicle's speed;

[0019] The turbocharger starts when the vehicle speed is greater than the preset vehicle speed;

[0020] After the supercharger is started, the first air pressure of the air filter, the second air pressure of the vehicle crankcase, and the third air pressure of the intake manifold are obtained.

[0021] If the first air pressure is less than the second air pressure, and the second air pressure is less than the third air pressure, the gas in the crankcase mixes with the air in the air filter through the pipe, and the vehicle is determined to be in a preset operating condition.

[0022] Optionally, the process of training the preset crankcase ventilation system icing model includes:

[0023] Acquire vehicle training data, which includes second operating condition data of the vehicle under preset operating conditions and second vehicle running time corresponding to the second operating condition data. The second operating condition data includes ambient temperature, ambient humidity, vehicle speed and crankcase blow-by volume. The preset operating conditions include ambient temperature within a preset temperature range, ambient humidity within a preset humidity range and vehicle speed within a preset vehicle speed range.

[0024] Obtain the actual icing data within the crankcase ventilation system corresponding to the second operating condition data;

[0025] The crankcase ventilation system icing prediction model is trained based on the second operating condition data and the corresponding actual icing data within the crankcase ventilation system.

[0026] Optionally, performing a preset operation based on the prediction result includes:

[0027] Output a prompt message based on the prediction result;

[0028] And / or perform the de-icing function within the crankcase ventilation system based on the predicted results.

[0029] Optionally, the prediction result has a value characterizing the degree of icing within the crankcase ventilation system, and the output of a prompt message based on the prediction result includes:

[0030] If the icing degree value is less than the first icing threshold, no prompt message will be output;

[0031] If the icing degree value is between the first icing threshold and the second icing threshold, the user is reminded to adjust the vehicle's operating conditions, wherein the first icing threshold is less than the second icing threshold;

[0032] If the icing level value is greater than the second icing threshold, the user is alerted to the parking message, and the de-icing function in the vehicle's crankcase ventilation system is executed.

[0033] On the other hand, embodiments of the present invention disclose a device for predicting the degree of icing in a vehicle crankcase ventilation system, comprising:

[0034] The data acquisition module is used to collect the first operating condition data of the vehicle when the vehicle is in a preset operating condition, and to determine the first vehicle running time corresponding to the first operating condition data. The first operating condition data includes ambient temperature, ambient humidity, vehicle speed and crankcase blow-by volume.

[0035] The data processing module is used to input the first operating condition data and the first vehicle running time into a preset crankcase ventilation system icing prediction model, and obtain a prediction result based on the crankcase ventilation system icing prediction model. The prediction result is used to characterize the degree of icing in the crankcase ventilation system. The crankcase ventilation system icing prediction model is trained using a neural network model, and the crankcase ventilation system icing prediction model has several mapping relationships between vehicle operating condition data and the degree of icing in the vehicle's crankcase ventilation system.

[0036] The prediction result execution module is used to perform preset operations based on the prediction results.

[0037] Optionally, the data processing module includes:

[0038] The operating condition data classification submodule is used to classify the first operating condition data of the vehicle during the first vehicle's operating time to obtain several categories of target operating condition data. In each category of target operating condition data, the vehicle speed and the crankcase blow-by volume are the same.

[0039] The operating condition running time statistics submodule is used to count the target operating condition time corresponding to the target operating condition data;

[0040] The operating condition data processing submodule is used to input several target operating condition data and corresponding target operating condition times into a preset crankcase ventilation system icing prediction model and obtain prediction results.

[0041] Optionally, the operating condition data processing submodule includes:

[0042] The target icing data acquisition unit is used to input several target operating condition data and several corresponding target operating condition times into a preset crankcase ventilation system icing prediction model to obtain several target icing data corresponding to the target operating condition data respectively.

[0043] The overall icing data acquisition unit is used to sum up several target icing data points to obtain overall icing data.

[0044] The prediction result confirmation unit is used to determine the overall icing data as the prediction result.

[0045] Optionally, the vehicle is equipped with an air filter and a turbocharger. Before collecting the first operating condition data of the vehicle, the device further includes:

[0046] The vehicle speed acquisition module is used to acquire the vehicle speed.

[0047] A turbocharger control module is used to activate the turbocharger when the vehicle speed exceeds a preset vehicle speed.

[0048] The air pressure acquisition module is used to acquire the first air pressure of the air filter, the second air pressure of the vehicle crankcase, and the third air pressure of the intake manifold after the turbocharger is started.

[0049] The vehicle operating condition judgment module is used to determine that the vehicle is in a preset operating condition if the first air pressure is less than the second air pressure and the second air pressure is less than the third air pressure, and the gas in the crankcase mixes with the air in the air filter through a pipeline.

[0050] Optionally, the preset crankcase ventilation system icing model is trained using the following modules:

[0051] The training data acquisition module is used to acquire training data of the vehicle. The training data includes second operating condition data of the vehicle under preset operating conditions and second vehicle running time corresponding to the second operating condition data. The second operating condition data includes ambient temperature, ambient humidity, vehicle speed and crankcase blow-by volume. The preset operating conditions include ambient temperature within a preset temperature range, ambient humidity within a preset humidity range and vehicle speed within a preset vehicle speed range.

[0052] The training data processing module is used to acquire the actual icing data in the crankcase ventilation system corresponding to the second operating condition data;

[0053] The model training module is used to train an icing prediction model for the crankcase ventilation system based on the second operating condition data and the corresponding actual icing data within the crankcase ventilation system.

[0054] Optionally, the prediction result execution module includes:

[0055] The first execution submodule is used to output prompt information based on the prediction result;

[0056] The second execution submodule is used to execute the de-icing function in the crankcase ventilation system based on the prediction result.

[0057] Optionally, the prediction result has a value characterizing the degree of icing within the crankcase ventilation system, and the first execution submodule includes:

[0058] The first execution unit is configured not to output a prompt message if the icing degree value is less than the first icing threshold.

[0059] The second execution unit is used to remind the user to adjust the vehicle's operating conditions if the icing degree value is between the first icing threshold and the second icing threshold, wherein the first icing threshold is less than the second icing threshold.

[0060] The third execution unit is used to remind the user to pay attention to the parking message and execute the de-icing function in the vehicle crankcase ventilation system if the icing degree value is greater than the second icing threshold.

[0061] On the other hand, embodiments of the present invention also provide a vehicle including a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the steps of a method for predicting the degree of icing in the crankcase ventilation system of the vehicle.

[0062] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a method for predicting the degree of icing in a vehicle crankcase ventilation system.

[0063] This invention collects first operating condition data of a vehicle under preset operating conditions and determines the first vehicle running time corresponding to the first operating condition data. The first operating condition data and the first vehicle running time are then input into a preset crankcase ventilation system icing prediction model. The resulting prediction is used to characterize the degree of icing within the crankcase ventilation system. Users can understand the degree of icing within the crankcase ventilation system based on the prediction results and take appropriate measures to avoid engine failure caused by severe icing in the crankcase ventilation system. Attached Figure Description

[0064] Figure 1 A flowchart illustrating the steps of a method for predicting the degree of icing in a vehicle crankcase ventilation system, provided in an embodiment of the present invention;

[0065] Figure 2 This is a partial structural schematic diagram of a vehicle crankcase ventilation system provided in an embodiment of the present invention;

[0066] Figure 3 This is a structural block diagram of a device for predicting the degree of icing in a vehicle crankcase ventilation system, provided in an embodiment of the present invention. Detailed Implementation

[0067] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0068] In cold winter regions, such as Northeast and Northwest my country, the main cause of crankcase ventilation system icing in vehicles is that during prolonged high-speed driving under heavy load in low-temperature and high-humidity environments, engine crankcase blow-by gas (high-temperature, high-humidity gas) and fresh air create hot and cold air convection at the air filter, leading to sublimation and icing. The degree of crankcase icing worsens with the duration of this condition, ultimately damaging the crankcase and engine. To prevent crankcase ventilation ducts from becoming clogged due to icing, some automakers add electric heating devices to the crankcase ventilation system. This auxiliary heating effectively prevents icing. However, current technology lacks methods for detecting or predicting the degree of crankcase icing.

[0069] Figure 1 A flowchart illustrating the steps of a method for predicting the degree of icing in a vehicle crankcase ventilation system, provided in an embodiment of the present invention, includes:

[0070] Step 101: When the vehicle is in a preset operating condition, collect the first operating condition data of the vehicle and determine the first vehicle running time corresponding to the first operating condition data. The first operating condition data includes ambient temperature, ambient humidity, vehicle speed and crankcase blow-by volume.

[0071] Figure 2 This is a partial structural diagram of a vehicle crankcase ventilation system provided in an embodiment of this application. The vehicle is equipped with an air filter and a turbocharger (not shown in the figure). When the engine is running under low load, crankcase gas enters the intake manifold through the partial load oil-gas separator. When the engine is running under high load, crankcase gas enters the intake manifold after mixing with the gas in the air filter through the full load oil-gas separator. Since the air filter does not freeze when the engine is running under low load, this embodiment considers the high load operating condition where the turbocharger starts working, connects to the intake manifold, and generates a large positive pressure. Furthermore, the engine leakage is relatively large during high load operation. After the turbocharger starts, the first air pressure P1 of the air filter, the second air pressure P2 of the vehicle crankcase, and the third air pressure P3 of the intake manifold are obtained. At this time, the pressure distribution is: P3 > P2 > P1. Gas leaking from the crankcase, as shown by the arrow, is guided through the oil-gas separator and vents to the rear end of the air filter, and finally into the intake manifold.

[0072] Before collecting the first operating condition data of the vehicle, the vehicle speed is acquired. When the vehicle speed is greater than the preset speed, the turbocharger starts. After the turbocharger starts, the first air pressure of the air filter, the second air pressure of the vehicle crankcase, and the third air pressure of the intake manifold are acquired. If the first air pressure is less than the second air pressure, and the second air pressure is less than the third air pressure, the gas in the crankcase mixes with the air in the air filter through the pipeline, indicating that the vehicle is in the preset operating condition. For example, when the vehicle is in the preset operating condition, the ambient temperature is generally below -20 degrees Celsius, the ambient humidity is generally between 60% and 80%, and the vehicle speed is generally greater than 100 km / h.

[0073] Step 102: Input the first operating condition data and the first vehicle running time into the preset crankcase ventilation system icing prediction model, and obtain the prediction result according to the crankcase ventilation system icing prediction model. The prediction result is used to characterize the degree of icing in the crankcase ventilation system.

[0074] The degree of icing in the crankcase ventilation system is related to ambient temperature, ambient humidity, vehicle speed, intake manifold pressure, crankcase pressure, piston blow-by volume, vehicle operating time, and the piping layout of the crankcase ventilation system. For vehicle models using the same engine, the piping structure is identical and is considered a non-variable factor. Engine compartment thermal management and crankcase pressure do not change significantly and are therefore considered non-variable factors in this embodiment. Therefore, the first operating condition data collected includes ambient temperature, ambient humidity, vehicle speed, and crankcase blow-by volume.

[0075] After collecting the first operating condition data, the first operating condition data of the vehicle during the first vehicle's operating time is classified to obtain several categories of target operating condition data, in which the vehicle speed is the same. For example, when the collected first operating condition data is that the vehicle travels on the highway for 20 minutes, the time interval is 15:16-15:36, the ambient temperature is -30 degrees Celsius, the ambient humidity is 65%, the vehicle speed is 105 km / h for 6 minutes, the vehicle speed is 110 km / h for 2 minutes, the vehicle speed is 112 km / h for 8 minutes, and the vehicle speed is 115 km / h for 4 minutes, the first operating condition data can be classified according to the vehicle speed to obtain four sets of target operating condition data with vehicle speeds of 105 km / h, 110 km / h, 112 km / h, and 115 km / h respectively. After obtaining the four sets of target operating condition data, if the crankcase blow-by volume is different at the same vehicle speed, the four sets of target operating condition data can be further classified according to the crankcase blow-by volume. When the first vehicle runs for a long time corresponding to the first operating condition data, such as driving on the highway for 2 hours, the environment in which the vehicle is located may change significantly, such as the temperature being between -20 degrees and -32 degrees and the ambient humidity being between 60% and 70%. When classifying the first operating condition data, it is necessary to further classify it according to the ambient temperature and ambient humidity. This is to facilitate the establishment of a correspondence between different operating condition data and the degree of icing in the crankcase ventilation system, so as to make the prediction results more accurate.

[0076] After obtaining target operating condition data for several categories, the target operating condition time corresponding to the target operating condition data is statistically analyzed. The target operating condition data and corresponding target operating condition times are input into a preset crankcase ventilation system icing prediction model to obtain several target icing data points corresponding to the target operating condition data. These target icing data points are then summed to obtain the overall icing data, which serves as the detection result. It should be noted that neural network models applied to data classification are a relatively mature technology. The crankcase ventilation system icing prediction model of this application can be developed based on neural network models applied to data classification, establishing the correspondence between ambient temperature, ambient humidity, vehicle speed, crankcase blow-by volume, and the degree of icing in the vehicle's axle box ventilation system. This allows for real-time prediction of the icing condition within the vehicle's axle box ventilation system after obtaining the first operating condition data.

[0077] For example, based on the collected first working condition data, after classifying it, the specific value I of icing per unit time under multiple target working conditions and the corresponding vehicle running time t are calculated, and then the data are summed:

[0078] Overall icing level = I1*t1 + I2*t2 + ... + In*tn. After obtaining the overall icing level data within the vehicle's axle box ventilation system, preset operations are executed based on the overall icing level to prevent engine malfunctions caused by excessive icing within the axle box ventilation system, thereby improving the user experience.

[0079] Step 103: Perform preset operations based on the prediction results.

[0080] Performing preset operations based on the prediction results includes: outputting prompts based on the prediction results, and / or performing the de-icing function in the crankcase ventilation system based on the prediction results. The prompts can be displayed on the in-vehicle display in text or image form to show the degree of icing in the crankcase ventilation system, or they can be delivered via voice prompts. When the icing level in the vehicle's crankcase ventilation system reaches a preset threshold, the driver is alerted to reduce speed, adjust engine operating conditions, or choose a suitable location to stop and rest.

[0081] This invention collects first operating condition data of a vehicle under preset operating conditions and determines the first vehicle running time corresponding to the first operating condition data. The first operating condition data and the first vehicle running time are then input into a preset crankcase ventilation system icing prediction model. The resulting prediction is used to characterize the degree of icing within the crankcase ventilation system. Users can understand the degree of icing within the crankcase ventilation system based on the prediction results and take appropriate measures to avoid engine failure caused by severe icing in the crankcase ventilation system.

[0082] In this embodiment of the invention, the process of training a preset crankcase ventilation system icing model includes: acquiring vehicle training data, which includes second operating condition data of the vehicle under preset operating conditions and the corresponding second vehicle running time. The second operating condition data includes ambient temperature, ambient humidity, vehicle speed, and crankcase blow-by volume. The preset operating conditions include ambient temperature within a preset temperature range, ambient humidity within a preset humidity range, and vehicle speed within a preset speed range. The process also involves acquiring actual icing data within the crankcase ventilation system corresponding to the second operating condition data, establishing correspondences between ambient temperature, ambient humidity, vehicle speed, and crankcase blow-by volume and the actual icing data within the vehicle's crankcase ventilation system, and training a crankcase ventilation system icing prediction model based on these correspondences. The trained crankcase ventilation system icing prediction model can predict the icing status within the vehicle's crankcase ventilation system based on the operating condition data during vehicle operation.

[0083] In one optional embodiment, the icing degree value in the crankcase ventilation system represented by the prediction result has a first icing threshold and a second icing threshold, and the first icing threshold is less than the second icing threshold. For example, the first icing threshold can be 10%, and the second icing threshold can be 20%. Those skilled in the art can set the first icing threshold and the second icing threshold according to actual needs, and this application embodiment does not specifically limit this. In actual use, if the icing degree value is less than the first icing threshold, no prompt message is output; if the icing degree value is between the first icing threshold and the second icing threshold, the user is reminded to adjust the vehicle's operating conditions; if the icing degree value is greater than the second icing threshold, the user is reminded to pay attention to the parking message, and the de-icing function in the vehicle's crankcase ventilation system is activated. By setting a threshold for the icing degree in the crankcase ventilation system represented by the prediction result, it is possible to effectively assist users in understanding the icing degree in the crankcase ventilation system, thereby taking corresponding measures to avoid engine failure caused by severe icing in the crankcase ventilation system.

[0084] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0085] To implement the aforementioned method for predicting the degree of icing in vehicle crankcase ventilation systems, Figure 3 An embodiment of the present invention provides a device for predicting the degree of icing in a vehicle crankcase ventilation system. The device includes:

[0086] The data acquisition module 301 is used to collect first operating condition data of the vehicle when the vehicle is in a preset operating condition, and to determine the first vehicle running time corresponding to the first operating condition data. The first operating condition data includes ambient temperature, ambient humidity, vehicle speed and crankcase blow-by volume.

[0087] The data processing module 302 is used to input the first operating condition data and the first vehicle running time into a preset crankcase ventilation system icing prediction model and obtain a prediction result. The prediction result is used to characterize the degree of icing in the crankcase ventilation system. The crankcase ventilation system icing prediction model is trained using a neural network model and has several mapping relationships between vehicle operating condition data and the degree of icing in the vehicle's crankcase ventilation system.

[0088] The prediction result execution module 303 is used to perform preset operations based on the prediction result.

[0089] In an optional embodiment, the data processing module 302 may include:

[0090] The operating condition data classification submodule is used to classify the first operating condition data of the vehicle during the first vehicle's operating time to obtain several categories of target operating condition data. In each category of target operating condition data, the vehicle speed and the crankcase blow-by volume are the same.

[0091] The operating condition running time statistics submodule is used to count the target operating condition time corresponding to the target operating condition data;

[0092] The operating condition data processing submodule is used to input several target operating condition data and corresponding target operating condition times into a preset crankcase ventilation system icing prediction model and obtain prediction results.

[0093] In one optional embodiment, the operating condition data processing submodule may include:

[0094] The target icing data acquisition unit is used to input several target operating condition data and several corresponding target operating condition times into a preset crankcase ventilation system icing prediction model to obtain several target icing data corresponding to the target operating condition data respectively.

[0095] The overall icing data acquisition unit is used to sum up several target icing data points to obtain overall icing data.

[0096] The prediction result confirmation unit is used to confirm that the overall icing data is the detection result.

[0097] In an optional embodiment, the vehicle is equipped with an air filter and a turbocharger, and the device may further include:

[0098] The vehicle speed acquisition module is used to acquire the vehicle speed.

[0099] A turbocharger control module is used to activate the turbocharger when the vehicle speed exceeds a preset vehicle speed.

[0100] The air pressure acquisition module is used to acquire the first air pressure of the air filter, the second air pressure of the vehicle crankcase, and the third air pressure of the intake manifold after the turbocharger is started.

[0101] The vehicle operating condition judgment module is used to determine that the vehicle is in a preset operating condition if the first air pressure is less than the second air pressure and the second air pressure is less than the third air pressure, and the gas in the crankcase mixes with the air in the air filter through a pipeline.

[0102] In one optional embodiment, the preset crankcase ventilation system icing model is trained using the following modules:

[0103] The training data acquisition module is used to acquire training data of the vehicle. The training data includes second operating condition data of the vehicle under preset operating conditions and second vehicle running time corresponding to the second operating condition data. The second operating condition data includes ambient temperature, ambient humidity, vehicle speed and crankcase blow-by volume. The preset operating conditions include ambient temperature within a preset temperature range, ambient humidity within a preset humidity range and vehicle speed within a preset vehicle speed range.

[0104] The training data processing module is used to acquire the actual icing data in the crankcase ventilation system corresponding to the second operating condition data;

[0105] The model training module is used to train an icing prediction model for the crankcase ventilation system based on the second operating condition data and the corresponding actual icing data within the crankcase ventilation system.

[0106] In an optional embodiment, the prediction result execution module 303 may include:

[0107] The first execution submodule is used to output prompt information based on the prediction result;

[0108] The second execution submodule is used to execute the de-icing function in the crankcase ventilation system based on the prediction result.

[0109] In an optional embodiment, the prediction result has a value characterizing the degree of icing within the crankcase ventilation system, and the first execution submodule includes:

[0110] The first execution unit is configured not to output a prompt message if the icing degree value is less than the first icing threshold.

[0111] The second execution unit is used to remind the user to adjust the vehicle's operating conditions if the icing degree value is between the first icing threshold and the second icing threshold, wherein the first icing threshold is less than the second icing threshold.

[0112] The third execution unit is used to remind the user to pay attention to the parking message and execute the de-icing function in the vehicle crankcase ventilation system if the icing degree value is greater than the second icing threshold.

[0113] This invention collects first operating condition data of a vehicle under preset operating conditions and determines the first vehicle running time corresponding to the first operating condition data. The first operating condition data and the first vehicle running time are input into a preset crankcase ventilation system icing prediction model. The prediction results are used to characterize the degree of icing in the crankcase ventilation system. Users can know the degree of icing in the crankcase ventilation system based on the prediction results, and thus take corresponding measures to avoid engine failure caused by severe icing in the crankcase ventilation system.

[0114] On the other hand, embodiments of the present invention also provide a vehicle including a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the steps of a method for predicting the degree of icing in the crankcase ventilation system of the vehicle.

[0115] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a method for predicting the degree of icing in a vehicle crankcase ventilation system.

[0116] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0117] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0119] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0121] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0122] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0123] The above provides a detailed description of the method and device for predicting the degree of icing in a vehicle crankcase ventilation system provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for predicting the degree of icing in a vehicle crankcase ventilation system, characterized in that, include: When the vehicle is in a preset operating condition, the first operating condition data of the vehicle is collected, and the first vehicle running time corresponding to the first operating condition data is determined. The first operating condition data includes ambient temperature, ambient humidity, vehicle speed and crankcase blow-by volume. The first operating condition data and the first vehicle running time are input into a preset crankcase ventilation system icing prediction model, and a prediction result is obtained based on the crankcase ventilation system icing prediction model. The prediction result is used to characterize the degree of icing in the crankcase ventilation system. The crankcase ventilation system icing prediction model is trained using a neural network model, and the crankcase ventilation system icing prediction model sets several mapping relationships between vehicle operating condition data and the degree of icing in the vehicle's crankcase ventilation system. Perform a preset operation based on the prediction result; The vehicle is equipped with an air filter and a turbocharger. Before collecting the first operating condition data of the vehicle, the method further includes: Get the vehicle's speed; When the vehicle speed is greater than the preset vehicle speed, the turbocharger is activated; After the supercharger is started, the first air pressure of the air filter, the second air pressure of the vehicle crankcase, and the third air pressure of the intake manifold are obtained. If the first air pressure is less than the second air pressure, and the second air pressure is less than the third air pressure, the gas in the crankcase mixes with the air in the air filter through the pipe, and the vehicle is determined to be in a preset operating condition.

2. The method according to claim 1, characterized in that, The step of inputting the first operating condition data and the first vehicle running time into a preset crankcase ventilation system icing prediction model, and obtaining the prediction result based on the crankcase ventilation system icing prediction model, includes: The first operating condition data of the vehicle during the first vehicle's operating time is classified to obtain several categories of target operating condition data. In each category of target operating condition data, the vehicle speed and the crankcase blow-by volume are the same. Calculate the target operating time corresponding to the target operating data; Several target operating condition data and corresponding target operating condition times are input into a preset crankcase ventilation system icing prediction model, and prediction results are obtained.

3. The method according to claim 2, characterized in that, The step involves inputting several target operating condition data points and corresponding target operating condition times into a preset crankcase ventilation system icing prediction model, and obtaining prediction results, including: Input several target operating condition data and corresponding target operating condition times into a preset crankcase ventilation system icing prediction model to obtain several target icing data corresponding to the target operating condition data respectively; The overall icing data is obtained by summing up the icing data of several target icing data points. The overall icing data is determined as the predicted result.

4. The method according to claim 1, characterized in that, The process of training the preset crankcase ventilation system icing model includes: Acquire vehicle training data, which includes second operating condition data of the vehicle under preset operating conditions and second vehicle running time corresponding to the second operating condition data. The second operating condition data includes ambient temperature, ambient humidity, vehicle speed and crankcase blow-by volume. The preset operating conditions include ambient temperature within a preset temperature range, ambient humidity within a preset humidity range and vehicle speed within a preset vehicle speed range. Obtain the actual icing data within the crankcase ventilation system corresponding to the second operating condition data; The crankcase ventilation system icing prediction model is trained based on the second operating condition data and the corresponding actual icing data within the crankcase ventilation system.

5. The method according to claim 1, characterized in that, The step of performing a preset operation based on the prediction result includes: Output a prompt message based on the prediction result; And / or perform the de-icing function within the crankcase ventilation system based on the predicted results.

6. The method according to claim 5, characterized in that, The prediction result has a value characterizing the degree of icing within the crankcase ventilation system, and the output of prompt information based on the prediction result includes: If the icing degree value is less than the first icing threshold, no prompt message will be output; If the icing degree value is between the first icing threshold and the second icing threshold, the user is reminded to adjust the vehicle's operating conditions, wherein the first icing threshold is less than the second icing threshold. If the icing level value is greater than the second icing threshold, the user is alerted to the parking message, and the de-icing function in the vehicle's crankcase ventilation system is executed.

7. A device for predicting the degree of icing in a vehicle crankcase ventilation system, characterized in that, include: The data acquisition module is used to collect the first operating condition data of the vehicle when the vehicle is in a preset operating condition, and to determine the first vehicle running time corresponding to the first operating condition data. The first operating condition data includes ambient temperature, ambient humidity, vehicle speed and crankcase blow-by volume. The data processing module is used to input the first operating condition data and the first vehicle running time into a preset crankcase ventilation system icing prediction model, and obtain a prediction result based on the crankcase ventilation system icing prediction model. The prediction result is used to characterize the degree of icing in the crankcase ventilation system. The crankcase ventilation system icing prediction model is trained using a neural network model, and the crankcase ventilation system icing prediction model has several mapping relationships between vehicle operating condition data and the degree of icing in the vehicle's crankcase ventilation system. The prediction result execution module is used to perform preset operations based on the prediction result; The vehicle is equipped with an air filter and a turbocharger. Before collecting the first operating condition data of the vehicle, the device also includes: The vehicle speed acquisition module is used to acquire the vehicle speed. A turbocharger control module is used to activate the turbocharger when the vehicle speed exceeds a preset vehicle speed. The air pressure acquisition module is used to acquire the first air pressure of the air filter, the second air pressure of the vehicle crankcase, and the third air pressure of the intake manifold after the turbocharger is started. The vehicle operating condition judgment module is used to determine that the vehicle is in a preset operating condition if the first air pressure is less than the second air pressure and the second air pressure is less than the third air pressure, and the gas in the crankcase mixes with the air in the air filter through a pipeline.

8. A vehicle, characterized in that, include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the method as described in any one of claims 1-6.

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