Electrically controlled silicone oil fan control method and device, controller and storage medium

Abstract

The application provides an electric control silicone oil fan control method and device, a controller and a storage medium. The method comprises the following steps: obtaining engine water temperatures at different times in a historical preset time period of a vehicle; inputting the engine water temperatures at different times in the historical preset time period into a pre-established cooling system model to output engine predicted water temperatures at different times in a future preset time period through the cooling system model; calculating a target setting rotating speed of an electric control silicone oil fan by using a model predictive control method according to the engine predicted water temperatures; and controlling the rotating speed of the electric control silicone oil fan according to the target setting rotating speed. The application realizes predictive control of the electric control silicone oil fan and reduces engine energy consumption.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobiles, and in particular to a control method and device for an electronically controlled silicon oil fan, a controller and a storage medium. BACKGROUND

[0002] The electronically controlled silicon oil fan is an electronic fan based on temperature control in the engine cooling system, and is used to adjust the temperature during engine operation.

[0003] The control method of the electronically controlled silicon oil fan in the prior art is mainly based on the temperature measured by a water temperature sensor, an intake air temperature sensor and the like, and the set speed of the electronically controlled silicon oil fan is output through a one-variable function of the pre-calibrated temperature and the set speed of the electronically controlled silicon oil fan, so as to control the speed of the electronically controlled silicon oil fan.

[0004] However, the inventors have found that the existing control method is based on the experience of calibration personnel to calibrate the one-variable function, and the set speed of the electronically controlled silicon oil fan is not suitable under some working conditions, and the electronically controlled silicon oil fan is not controlled, which leads to an increase in engine fuel consumption. SUMMARY

[0005] The present application provides a control method and device for an electronically controlled silicon oil fan, a controller and a storage medium to solve the problem of increased engine fuel consumption caused by the uncontrolled electronically controlled silicon oil fan in the prior art.

[0006] In a first aspect, the present application provides a control method for an electronically controlled silicon oil fan, comprising:

[0007] obtaining engine water temperatures at different times in a historical preset time period of a vehicle;

[0008] inputting the engine water temperatures at different times in the historical preset time period into a pre-established cooling system model to output engine predicted water temperatures at different times in a future preset time period through the cooling system model;

[0009] calculating a target set speed of the electronically controlled silicon oil fan according to the engine predicted water temperatures by using a model predictive control method;

[0010] controlling the speed of the electronically controlled silicon oil fan according to the target set speed.

[0011] In a possible design, the establishment process of the cooling system model comprises: obtaining vehicle Internet of Things original data of a vehicle, wherein the vehicle Internet of Things original data includes vehicle operating parameters; preprocessing the vehicle Internet of Things original data to obtain a training data set and a test data set; and training a long short-term memory network model according to the training data set and the test data set to obtain the cooling system model.

[0012] In a possible design, the preprocessing of the original Internet of Vehicles data to obtain the training data set and the test data set includes: performing outlier value cleaning, repeated value cleaning and missing value cleaning on the original Internet of Vehicles data to obtain Internet of Vehicles data; and extracting feature data from the Internet of Vehicles data to form the training data set and the test data set; wherein the feature data includes at least one of the following: an electrically controlled silicon oil fan rotating speed in vehicle operation, a vehicle speed, an engine load rate, an engine water temperature and an ambient temperature.

[0013] In a possible design, in the input layer of the long short-term memory network model, the feature data is input according to different time points.

[0014] In a possible design, the target setting rotating speed of the electrically controlled silicon oil fan is obtained by using a model predictive control method according to the predicted engine water temperature, including: inputting the predicted engine water temperature into an optimization objective function formula and a constraint condition formula to calculate the target setting rotating speed of the electrically controlled silicon oil fan at the current time point.

[0015] The optimization objective function formula is as follows:

[0016]

[0017] In the formula, cost function represents a cost function, min represents a minimum value, predic represents the predicted engine water temperature, and y represents the target control water temperature of the engine.

[0018] The constraint condition formula is as follows:

[0019]

[0020] In the formula, predic represents the predicted engine water temperature at the next time point of the current time point, and y t represents the target control water temperature of the engine at the current time point, and u t represents the target setting rotating speed of the electrically controlled silicon oil fan at the current time point.

[0021] In a possible design, the rotating speed of the electrically controlled silicon oil fan is controlled according to the target setting rotating speed, including: based on the target setting rotating speed, the actual rotating speed of the electrically controlled silicon oil fan is controlled by using a proportional-integral-derivative control method.

[0022] In a second aspect, the present application provides an electrically controlled silicon oil fan control device, including:

[0023] The acquisition module is configured to acquire engine water temperatures at different time points in a historical preset time period of a vehicle.

[0024] The output module is used to input the engine coolant temperature at different times within the historical preset time period into a pre-established cooling system model, so as to output the predicted engine coolant temperature at different times within the future preset time period through the cooling system model.

[0025] The calculation module is used to calculate the target set speed of the electronically controlled silicone oil fan based on the predicted engine water temperature using a model predictive control method.

[0026] The control module is used to control the speed of the electronically controlled silicone oil fan according to the target set speed.

[0027] Thirdly, this application provides a vehicle controller, comprising: at least one processor and a memory; the memory storing computer-executable instructions; the at least one processor executing the computer-executable instructions stored in the memory, causing the at least one processor to execute the electronically controlled silicone oil fan control method of the first aspect and any possible design of the first aspect.

[0028] Fourthly, this application provides a computer-readable storage medium storing a computer program / instruction that, when executed by a processor, implements the electronically controlled silicone oil fan control method as described in the first aspect and any possible design of the first aspect.

[0029] The electronically controlled silicone oil fan control method, device, controller, and storage medium provided in this application acquire the engine coolant temperature at different times within a historical preset time period of the vehicle and input it into a pre-established cooling system model. The cooling system model then outputs the predicted engine coolant temperature at different times within a future preset time period. Based on the predicted engine coolant temperature, the target set speed of the electronically controlled silicone oil fan is calculated, thereby controlling the electronically controlled silicone oil fan to operate at the target set speed, achieving predictive control of the electronically controlled silicone oil fan, and reducing engine energy consumption. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic diagram of the system architecture for controlling an electrically controlled silicone oil fan, provided in an embodiment of this application.

[0032] Figure 2 The flowchart of the electronically controlled silicone oil fan control method provided in the embodiments of this application Figure 1 ;

[0033] Figure 3 The flow of the electric control silicone oil fan control method provided for the embodiments of the present application Figure 2 ;

[0034] Figure 4 The schematic diagram of the establishment method of the cooling system model provided for the embodiments of the present application

[0035] Figure 5 The structural schematic diagram of the electric control silicone oil fan control device provided for the embodiments of the present application

[0036] Figure 6 The hardware structure schematic diagram of the vehicle controller provided for the embodiments of the present application DETAILED DESCRIPTION

[0037] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without any creative work fall within the protection scope of the present application.

[0038] Term explanation

[0039] Electric control silicone oil fan: composed of an electric control silicone oil clutch and a fan, using silicone oil as medium, the engagement and disengagement of the fan are realized by adjusting the amount of silicone oil in the silicone oil clutch through the control of the electromagnetic valve. Precise control of the fan rotating speed can be realized.

[0040] Vehicle networking raw data: data such as engine speed, engine torque, vehicle speed, engine water temperature, and environmental temperature collected by vehicle Internet of Things devices.

[0041] Data-driven method: based on artificial intelligence model, a large amount of data is used to establish the physical model of the controlled object.

[0042] Model predictive control (MPC): using existing model to predict the future state of the system, and optimizing the performance within a certain future time period.

[0043] The electrically controlled silicone oil fan is an electronic fan based on temperature control in the cooling system of the engine, which is used to adjust the temperature when the engine is working. The control method of the electrically controlled silicone oil fan in the prior art is mainly based on the temperature measured by the water temperature sensor, the intake air temperature sensor and the like, and the set speed of the electrically controlled silicone oil fan is output through a one-variable function of the temperature and the set speed of the electrically controlled silicone oil fan, and then the speed control of the electrically controlled silicone oil fan is performed. However, the existing control method is based on the experience of the calibration personnel to calibrate the one-variable function, and the set speed of the electrically controlled silicone oil fan is not suitable under some working conditions, and the electrically controlled silicone oil fan is not controlled, which leads to the problem that the fan cannot be quickly disengaged after full engagement, and the engine oil consumption is increased.

[0044] To solve the above problems, the application provides an electrically controlled silicone oil fan control method, which predicts the engine predicted water temperature in a future preset time period according to the historical engine water temperature through a cooling system model, and obtains the target set speed of the electrically controlled silicone oil fan according to the engine predicted water temperature, so as to control the electrically controlled silicone oil fan to work at the target set speed, realize predictive control of the electrically controlled silicone oil fan, and reduce the energy consumption of the engine.

[0045] The technical solutions of the application will be described in detail in the following specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in some embodiments.

[0046] Figure 1 The system architecture diagram of the electrically controlled silicone oil fan control provided by the embodiments of the application is shown in FIG. 1. Figure 1 As shown in the figure, the system provided by the embodiments of the application includes a vehicle controller 101, an engine 102 and an electrically controlled silicone oil fan 103. The electrically controlled silicone oil fan 103 is a device in the cooling system of the engine 102, which can adjust the water temperature of the engine 102. The vehicle controller 101 is in communication connection with the engine 102 and the electrically controlled silicone oil fan 103, and the vehicle controller 101 can control the engine 102 and the electrically controlled silicone oil fan 103.

[0047] Figure 2 The flowchart of the electrically controlled silicone oil fan control method provided by the embodiments of the application is shown in FIG. 2. Figure 1 The execution subject of the method of the embodiments of the application can be a vehicle controller. As shown in the figure, the method of the embodiments of the application can include the following steps: Figure 2

[0048] S201, obtaining the engine water temperature at different times in a historical preset time period of a vehicle.

[0049] ​In this embodiment, the historical preset time period can be set according to actual conditions, for example, the historical preset time period can be set to 300 seconds. The engine water temperature at different times within 300 seconds can include the engine water temperature at the 1st second, the engine water temperature at the 2nd second,..., and the engine water temperature at the 300th second. That is, the engine water temperature at different times within the historical preset time period is a sequence of engine water temperatures corresponding to different times.

[0050] S202, input the engine water temperature at different times within the historical preset time period into the pre-established cooling system model to output the engine predicted water temperature at different times within the future preset time period through the cooling system model.

[0051] In this embodiment, the cooling system model can be established by using a data-driven method, which can include statistical analysis method, machine learning method and natural language processing method. Specifically, in this embodiment, a recurrent neural network (RNN) or a long short-term memory (LSTM) network model is used for modeling.

[0052] The pre-established cooling system model can predict the engine water temperature at different times within a future period of time by using the engine water temperature at different times within the historical preset time period.

[0053] S203, the target set speed of the electrically controlled silicon oil fan is calculated by using a model predictive control method according to the engine predicted water temperature.

[0054] In this embodiment, the method for calculating the target set speed of the electrically controlled silicon oil fan is as follows:

[0055] The engine predicted water temperature is input into the optimization objective function formula and the constraint condition formula to calculate the target set speed of the electrically controlled silicon oil fan at the current time;

[0056] The optimization objective function formula is as follows:

[0057]

[0058] In the formula, cost function represents the cost function, min represents the minimum value, y represents the engine target control water temperature.

[0059] The constraint condition formula is as follows:

[0060]

[0061] In the formula, y represents the engine predicted water temperature at the next time of the current time, y t y represents the engine target control water temperature at the current time, u tA target setting rotating speed of the electrically controlled silicone oil fan at the current time.

[0062] Specifically, the engine predicted water temperature and the engine target control water temperature y in the optimization target function formula are known quantities, the engine predicted water temperature and the engine target control water temperature y at the next time of the current time in the constraint formula are known quantities, and the target setting rotating speed u of the electrically controlled silicone oil fan at the current time can be calculated by substituting the known quantities into the above formula. t . t .

[0063] In the embodiment, the model predictive control (MPC) method is a special control method, which can predict the future state of the system by using the existing model and optimize the performance in a certain future time period.

[0064] S204. Control the rotating speed of the electrically controlled silicone oil fan according to the target setting rotating speed.

[0065] In the embodiment, the specific method of controlling the rotating speed of the electrically controlled silicone oil fan according to the target setting rotating speed is that the actual rotating speed of the electrically controlled silicone oil fan is controlled by using a proportion integration differentiation (PID) control method based on the target setting rotating speed.

[0066] Specifically, the proportion integration differentiation (PID) control method is a common stable control algorithm.

[0067] To sum up, the electrically controlled silicone oil fan control method provided in the embodiment can obtain the engine water temperature at different times in a historical preset time period of the vehicle, input the engine water temperature into a pre-established cooling system model, and output the engine predicted water temperature at different times in a future preset time period by the cooling system model. The target setting rotating speed of the electrically controlled silicone oil fan is calculated according to the engine predicted water temperature, so that the electrically controlled silicone oil fan can work according to the target setting rotating speed, the predictive control of the electrically controlled silicone oil fan is realized, and the engine energy consumption is reduced.

[0068] Figure 3 The flow of the electrically controlled silicone oil fan control method provided in the embodiment Figure 2 . Figure 3 Based on the example embodiment, Figure 3 the establishment process of the cooling system model in step S202 is explained in detail, as shown in the following table. Figure 4 The method of the embodiment can include the following steps.

[0069] S301, acquire the vehicle Internet of Things original data of the vehicle, wherein the vehicle Internet of Things original data comprises vehicle operation parameters.

[0070] In this embodiment, the vehicle Internet of Things device can store the collected engine speed, vehicle speed, engine water temperature, ambient temperature and other data to the remote terminal. The vehicle Internet of Things original data of the vehicle can be obtained from the remote terminal.

[0071] Specifically, the vehicle operation parameters can include: electrically controlled silicon oil fan speed, vehicle speed, engine load rate, engine water temperature and ambient temperature.

[0072] S302, pre-process the vehicle Internet of Things original data to obtain a training data set and a test data set.

[0073] In this embodiment, the pre-processing of the vehicle Internet of Things original data comprises the following steps:

[0074] a1: cleaning the vehicle Internet of Things original data of abnormal values, repeated values and missing values to obtain vehicle Internet of Things data, so as to eliminate abnormal data existing in the original data due to data distortion, transmission errors and other problems.

[0075] Specifically, the abnormal value cleaning includes: (1) abnormal value cleaning of ambient atmospheric pressure: the minimum value of ambient atmospheric pressure is 400 hPa, and the maximum value of ambient atmospheric pressure is 1050 hPa. (2) Abnormal value cleaning of vehicle speed: the minimum value of vehicle speed is 0 km / h, and the maximum value of vehicle speed is 150 km / h. (3) Abnormal value cleaning of engine water temperature: the minimum value of engine water temperature is-50℃, and the maximum value of engine water temperature is 150℃. (4) Abnormal value cleaning of electrically controlled silicon oil fan speed: the minimum value of fan speed is 0 rpm, and the maximum value of fan speed is 3000 rpm.

[0076] The repeated value cleaning includes: based on the original data timestamp, removing the repeated data, that is, there is only one data record at the same time for the same vehicle.

[0077] The missing value cleaning includes: if the water temperature data is missing at a certain time, the missing value is filled with the mean value of the previous and next time.

[0078] a2: extract feature data from the vehicle Internet of Things data to form a training data set and a test data set; wherein the feature data comprises at least one of: electrically controlled silicon oil fan speed, vehicle speed, engine load rate, engine water temperature and ambient temperature in vehicle operation.

[0079] S303, training a long short-term memory network model according to the training data set and the test data set to obtain a cooling system model.

[0080] In this embodiment, a long short-term memory network model LSTM is used for modeling. The LSTM model is a time recurrent neural network suitable for processing time series prediction problems and suitable for predicting future water temperature based on historical engine water temperature data in this application. The process of modeling the cooling system model using LSTM is shown in Figure 4 Figure 4 The cooling system model provided in this embodiment is shown in the following figure.

[0081] As shown in Figure 5 LSTM is used as a black box model, and the input and output of the LSTM model are defined. In the input layer of the LSTM model, the feature data is input according to different time points. Among them, X(t) is the model input feature vector at t time, and the input features are the current time [ECU silicon oil fan speed, vehicle speed, engine load rate, engine water temperature and ambient temperature]. X(t-n) is the model input feature vector at t-n time, and the input features are the t-n time [ECU silicon oil fan speed, vehicle speed, engine load rate, engine water temperature and ambient temperature]. c(t-1) and h(t-1) are the internal outputs of the LSTM hidden layer, and y(t) is the predicted next time engine water temperature at t time.

[0082] The loss function of the LSTM model is defined as shown in the following formula:

[0083]

[0084] In the formula, cost function represents the loss function, y(t) represents the predicted engine water temperature at t time, and y t y(t+1) represents the target control water temperature of the engine at t time.

[0085] The loss function is used to train the model with the training data set and the test data set. The training target is to minimize the loss function.

[0086] In summary, the ECU silicon oil fan control method provided in this embodiment obtains the original data of the vehicle Internet of Things, pre-processes the original data of the vehicle Internet of Things, and obtains a training data set and a test data set. The long short-term memory network model is trained according to the training data set and the test data set, and a cooling system model is obtained. The cooling system model can predict the engine water temperature in the future time period based on the historical engine water temperature data, and provides a basis for obtaining the target speed of the ECU silicon oil fan and reducing the engine energy consumption.

[0087] Figure 5 The structure diagram of the ECU silicon oil fan control device provided in this embodiment is shown in the following figure. Figure 6 ​As shown, the electric control silicone oil fan control device of the embodiment is used to realize the operation corresponding to the vehicle controller in any method embodiment described above, and the electric control silicone oil fan control device of the embodiment comprises an acquisition module 501, an output module 502, a calculation module 503 and a control module 504.

[0088] The acquisition module 501 is configured to acquire engine water temperatures at different times in a historical preset time period of the vehicle.

[0089] The output module 502 is configured to input the engine water temperatures at different times in the historical preset time period into a pre-established cooling system model to output engine predicted water temperatures at different times in a future preset time period through the cooling system model.

[0090] The calculation module 503 is configured to calculate a target set rotating speed of the electric control silicone oil fan according to the engine predicted water temperatures by using a model predictive control method.

[0091] The control module 504 is configured to control the rotating speed of the electric control silicone oil fan according to the target set rotating speed.

[0092] In a possible implementation, the electric control silicone oil fan control device further comprises an establishment module 505 configured to acquire vehicle Internet of Things original data of the vehicle, wherein the vehicle Internet of Things original data comprises vehicle operating parameters; pre-process the vehicle Internet of Things original data to obtain a training data set and a test data set; and train a long short-term memory network model according to the training data set and the test data set to obtain the cooling system model.

[0093] In a possible implementation, the establishment module 505 is further configured to perform outlier cleaning, repeated value cleaning and missing value cleaning on the vehicle Internet of Things original data to obtain vehicle Internet of Things data; extract feature data from the vehicle Internet of Things data to form the training data set and the test data set; and the feature data comprises at least one of the following: the rotating speed of the electric control silicone oil fan, the vehicle speed, the engine load rate, the engine water temperature and the ambient temperature during the operation of the vehicle.

[0094] In a possible implementation, in the input layer of the long short-term memory network model in the establishment module 505, the feature data is input according to different times.

[0095] In a possible implementation, the calculation module 503 is specifically configured to input the engine predicted water temperatures into an optimization objective function formula and a constraint condition formula to calculate the target set rotating speed of the electric control silicone oil fan at the current time; and the optimization objective function formula is as follows:

[0096]

[0097] In the formula, cost function represents a cost function, and min represents a minimum value. y represents the engine's predicted coolant temperature, and y represents the engine's target control coolant temperature.

[0098] The constraint formula is:

[0099]

[0100] In the formula, y represents the engine's predicted coolant temperature for the next moment, from the current moment. t This indicates the target control coolant temperature of the engine at the current moment, u t This indicates the target set speed of the electronically controlled silicone oil fan at the current moment.

[0101] In one possible implementation, the control module 504 is specifically used to control the actual speed of the electronically controlled silicone oil fan based on the target set speed using a proportional-integral-derivative control method.

[0102] The apparatus provided in this application embodiment can execute the above method embodiment. Its specific implementation principle and technical effect can be found in the above method embodiment, and will not be repeated here.

[0103] Figure 6 This is a schematic diagram of the hardware structure of the vehicle controller provided in an embodiment of this application. ​ As shown, the vehicle controller includes a memory 601 and at least one processor 602. The memory 601 stores instructions to be executed by the computer. The memory 601 may include high-speed random access memory (RAM) or non-volatile memory (NVM), such as at least one disk drive, and may also be a USB flash drive, external hard drive, read-only memory, disk, or optical disc, etc.

[0104] At least one processor 602 is used to execute computer execution instructions stored in memory to implement the vehicle ramp shifting control method in the above embodiments. For details, please refer to the relevant descriptions in the foregoing method embodiments. The processor 602 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0105] Optionally, the memory 601 can be independent or integrated with the processor 602.

[0106] When the memory 601 is a device independent of the processor 602, the vehicle controller can further include a bus 603. The bus 603 is used to connect the memory 601 and the processor 602. The bus 603 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0107] The vehicle controller provided by the embodiment can be used to execute the electrically controlled silicone oil fan control method described above, and the implementation manner and technical effects are similar, which will not be described here again.

[0108] The present application also provides a computer readable storage medium, which stores computer programs / instructions, and when the processor executes the computer programs / instructions, the electrically controlled silicone oil fan control method provided by the various embodiments described above is realized.

[0109] The computer readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transfer of computer programs from one place to another. The computer storage medium can be any available medium that can be accessed by a general or special purpose computer. For example, the computer readable storage medium is coupled to the processor, so that the processor can read information from the computer readable storage medium and write information to the computer readable storage medium. Of course, the computer readable storage medium can also be a component of the processor. The processor and the computer readable storage medium can be located in an application specific integrated circuit (ASIC). In addition, the ASIC can be located in the user equipment. Of course, the processor and the computer readable storage medium can also exist as discrete components in the communication device.

[0110] In particular, the computer readable storage media can be realized by any type of volatile or non-volatile storage devices or a combination thereof, such as a Static Random-Access Memory (SRAM), an Electrically-Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read Only Memory (EPROM), a Programmable read-only memory (PROM), a Read-Only Memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk. The storage media can be any available media that can be accessed by a general or special purpose computer.

[0111] The present application also provides a computer program product, which includes computer programs / instructions stored in a computer readable storage medium. At least one processor of the device can read the computer programs / instructions from the computer readable storage medium, and the at least one processor executes the computer programs / instructions to enable the device to implement the method provided by the various embodiments described above.

[0112] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the described apparatus embodiments are merely schematic. The division of the modules is merely a logical function division. There can be another division manner for the actual implementation. For example, a plurality of modules or a functional apparatus can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, apparatuses or modules, and can be electrical, mechanical or in other forms.

[0113] The various modules can be physically separated, for example, installed in different positions of one device, or installed on different devices, or distributed on a plurality of network units, or distributed on a plurality of processors. The various modules can also be integrated together, for example, installed in the same device, or integrated in a set of codes. The various modules can exist in the form of hardware, or can exist in the form of software, or can be realized in the form of software plus hardware. The present application can select some or all of the modules to achieve the purpose of the embodiments.

[0114] When the integrated modules are implemented in the form of software functional modules, the integrated modules can be stored in a computer readable storage medium. The above software functional modules are stored in a storage medium, and include a plurality of instructions for causing a computer device (which can be a personal computer, a vehicle controller, or a network device, etc.) or a processor to perform part of the steps of the method of each embodiment of the present application.

[0115] It should be understood that, although each step in the flowchart in the above embodiments is shown in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other sequences. Moreover, at least part of the steps in the figure can include a plurality of sub-steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps.

[0116] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. An electrically controlled silicone oil fan control method characterized by, The method comprises: obtaining engine water temperatures at different times in a historical preset time period of a vehicle; inputting the engine water temperatures at different times in the historical preset time period into a pre-established cooling system model to output engine predicted water temperatures at different times in a future preset time period through the cooling system model; calculating a target set speed of an electrically controlled silicon oil fan according to the engine predicted water temperatures by using a model predictive control method; controlling the speed of the electrically controlled silicon oil fan according to the target set speed; wherein the process of establishing the cooling system model comprises: obtaining vehicle Internet of Vehicles original data, wherein the vehicle Internet of Vehicles original data comprises vehicle operating parameters; preprocessing the vehicle Internet of Vehicles original data to obtain a training data set and a test data set; training a long short-term memory network model according to the training data set and the test data set to obtain the cooling system model.

2. The method of claim 1, wherein, The preprocessing of the vehicle Internet of Vehicles original data to obtain the training data set and the test data set comprises: performing outlier cleaning, repeated value cleaning and missing value cleaning on the vehicle Internet of Vehicles data to obtain feature data, wherein the feature data comprises at least one of the following: the speed of the electrically controlled silicon oil fan, the vehicle speed, the engine load rate, the engine water temperature and the ambient temperature. The feature data is inputted into the input layer of the long short-term memory network model according to different times.

3. The method of claim 2, wherein, The calculation of the target set speed of the electrically controlled silicon oil fan according to the engine predicted water temperatures by using the model predictive control method comprises:

4. The method of claim 1, wherein, inputting the engine predicted water temperatures into an optimization objective function formula and a constraint condition formula to calculate the target set speed of the electrically controlled silicon oil fan at the current time; wherein the optimization objective function formula is: and the constraint condition formula is: wherein represents a cost function, represents a minimum value, represents an engine predicted water temperature, y represents an engine target control water temperature; The control of the speed of the electrically controlled silicon oil fan according to the target set speed comprises: In the formula, represents the engine predicted water temperature at the next time from the present time, represents the engine target control water temperature at the present time, represents the target set rotation speed of the electrically controlled silicone oil fan at the present time.

5. The method according to any one of claims 1 to 4, characterized in that, controlling the actual speed of the electrically controlled silicon oil fan by using a proportional-integral-derivative control method based on the target set speed. The method comprises:

6. An electrically controlled silicone oil fan control device characterized by comprising: an obtaining module configured to obtain engine water temperatures at different times in a historical preset time period of a vehicle; an outputting module configured to input the engine water temperatures at different times in the historical preset time period into a pre-established cooling system model to output engine predicted water temperatures at different times in a future preset time period through the cooling system model; a calculating module configured to calculate a target set speed of an electrically controlled silicon oil fan according to the engine predicted water temperatures by using a model predictive control method; a control module configured to control the speed of the electrically controlled silicon oil fan according to the target set speed; an establishing module configured to obtain vehicle Internet of Vehicles original data, wherein the vehicle Internet of Vehicles original data comprises vehicle operating parameters; preprocess the vehicle Internet of Vehicles original data to obtain a training data set and a test data set; and train a long short-term memory network model according to the training data set and the test data set to obtain the cooling system model. The method comprises:

7. A vehicle controller characterized by comprising: at least one processor and a memory; ​ The memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the electrically-controlled silicone oil fan control method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer programs / instructions, and when the processor executes the computer programs / instructions, the electrically-controlled silicone oil fan control method according to any one of claims 1 to 5 is implemented.

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