CN116882274A

CN116882274A - Training method, device, equipment and storage medium for temperature model

Info

Publication number: CN116882274A
Application number: CN202310787994.5A
Authority: CN
Inventors: 孙丽莉; 钱宗行; 冯志远; 文红举; 杨德
Original assignee: Chongqing Changan Automobile Co Ltd
Current assignee: Chongqing Changan Automobile Co Ltd
Priority date: 2023-06-29
Filing date: 2023-06-29
Publication date: 2023-10-13

Abstract

The application relates to a training method, device and equipment of a temperature model and a storage medium, and relates to the technical field of communication. The method comprises the following steps: and acquiring a plurality of state information of the target vehicle, a plurality of first temperatures and a plurality of second temperatures, wherein the state information is used for indicating the running state of the target vehicle, the first temperatures are temperatures of the clutch in the target vehicle determined by the temperature model based on the state information, the second temperatures are actual temperatures of the clutch in the target vehicle, and one first temperature corresponds to one state information and one second temperature. And calculating the difference value between each first temperature and the corresponding second temperature to obtain a plurality of temperature difference values. If the temperature difference values are larger than the preset temperature difference threshold value, training the temperature model based on the state information until the temperature model converges, and obtaining the trained temperature model. Thus, the training efficiency of the temperature model can be improved.

Description

Training method, device, equipment and storage medium for temperature model

Technical Field

The present application relates to the field of communications technologies, and in particular, to a method, an apparatus, a device, and a storage medium for training a temperature model.

Background

In recent years, with the development of technology, an algorithm model gradually goes into people's life and is widely used in the fields of education, traffic, entertainment, medical treatment, and the like. For example, an automatic transmission control unit (transmission control unit, TCU) in the vehicle manages the temperature of the clutch through a trained temperature model.

Currently, in the process of training a temperature model in a TCU, the TCU may collect state information of a vehicle and calculate a temperature of a clutch by inputting the state information of the vehicle into the temperature model. Then, the management device (such as a terminal, a server, etc.) can compare the temperature calculated by the temperature model with the actual temperature of the clutch, and correct the temperature model according to the comparison result until the temperature model converges, so as to obtain a trained temperature model. However, in the above technical solution, under the condition that the running phase of the vehicle changes, the state information of the vehicle also changes, so as to ensure that the trained temperature model can be applied to different running phases of the vehicle, the management device needs to correct the temperature model for each running phase, thereby increasing the correction times and reducing the training efficiency of the temperature model.

Disclosure of Invention

The application provides a training method, device, equipment and storage medium for a temperature model, which at least solve the technical problem of low training efficiency of the temperature model in the related technology. The technical scheme of the application is as follows:

according to a first aspect of the present application, there is provided a training method of a temperature model, the training method of a temperature model including: a training device (hereinafter referred to as a training device) of a temperature model acquires a plurality of state information of a target vehicle, a plurality of first temperatures and a plurality of second temperatures, wherein the state information is used for indicating the running state of the target vehicle, the first temperatures are temperatures of a clutch in the target vehicle determined by the temperature model based on the state information, the second temperatures are actual temperatures of the clutch in the target vehicle, and one first temperature corresponds to one state information and one second temperature. The training device calculates the difference between each first temperature and the corresponding second temperature to obtain a plurality of temperature differences. If the temperature difference values are larger than the preset temperature difference threshold value, the training device trains the temperature model based on the state information until the temperature model converges, and the trained temperature model is obtained.

According to the technical means, the training device can determine whether the temperature model can be applied to different operation stages of the vehicle by comparing the temperature of the clutch in the vehicle determined by the temperature model based on the state information of the vehicle in different operation stages with the actual temperature of the clutch in the vehicle corresponding to each state information, and can train the temperature model based on the state information of the vehicle in different operation stages under the condition that the temperature model is determined to be not applicable to any operation stage of the vehicle, so that the trained temperature model can be applied to different operation stages of the vehicle. Therefore, the training times of the temperature model can be reduced, and the training efficiency of the temperature model is improved.

In one possible implementation, the status information includes: the method for training the temperature model by the training device based on the plurality of state information until the temperature model converges to obtain a trained temperature model comprises the following steps: the training device obtains a target score of each state index in the plurality of state information, wherein the target score is used for indicating the influence degree of the state index on the temperature of a clutch in a target vehicle. The training device determines a plurality of target indexes from the plurality of state information according to the target scores of each state index, wherein the target scores of the target indexes are larger than a preset score threshold. The training device corrects the reference index of the temperature model according to the target indexes to obtain a corrected temperature model, and the corrected temperature model refers to information corresponding to the target indexes when determining the temperature of the clutch in the target vehicle. The training device trains the corrected temperature model based on the plurality of state information until the corrected temperature model converges, and the trained temperature model is obtained.

According to the technical means, the training device can correct the reference index of the temperature model according to the influence degree of each state index in the plurality of state information on the temperature of the clutch in the vehicle, so that the corrected temperature model can refer to the information corresponding to the state index with larger influence degree on the temperature of the clutch in the vehicle, and train the corrected temperature model until the corrected temperature model converges, and the trained temperature model is obtained. In this way, the adaptability of the temperature model in different operating phases of the vehicle can be improved, and the accuracy of the temperature of the clutch in the vehicle determined by the temperature model in different operating phases of the vehicle can be improved.

In one possible embodiment, the training method of the temperature model further includes: the training device sends an update instruction to the target vehicle, wherein the update instruction is used for indicating that the temperature model in the target vehicle is updated to a trained temperature model.

According to the technical means, the training device can train the temperature model deployed in the vehicle without training the temperature model deployed in the vehicle, and train the temperature model deployed in the vehicle only by sending the trained temperature model to the vehicle and indicating to update the temperature model deployed in the vehicle to the trained temperature model. In this way, the number of corrections to the temperature model deployed in the vehicle can be reduced, and the training efficiency of the temperature model deployed in the vehicle can be improved.

In one possible implementation, the status information includes at least one of: vehicle weight, frontal area, engine speed, engine torque, road grade, ambient temperature, cooling oil input temperature, cooling oil flow.

According to the technical means, the training device can provide valuable references for determining the temperature of the clutch in the vehicle in different operation stages of the vehicle for the temperature model by acquiring the information corresponding to different state indexes of the vehicle in each operation stage, and the accuracy of the temperature of the clutch in the vehicle determined by the temperature model is improved.

According to a second aspect of the present application, there is provided a training apparatus for a temperature model, the training apparatus for a temperature model including: an acquisition module and a processing module.

The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a plurality of state information of a target vehicle, a plurality of first temperatures and a plurality of second temperatures, the state information is used for indicating the running state of the target vehicle, the first temperatures are temperatures of clutches in the target vehicle, which are determined by a temperature model based on the state information, the second temperatures are actual temperatures of the clutches in the target vehicle, and one first temperature corresponds to one state information and one second temperature. And the processing module is used for calculating the difference value between each first temperature and the corresponding second temperature to obtain a plurality of temperature difference values. And the processing module is also used for training the temperature model based on the state information if the temperature difference values have values larger than the preset temperature difference threshold value, until the temperature model converges, and obtaining the trained temperature model.

In one possible implementation, the status information includes: a plurality of status indicators. The acquisition module is specifically configured to acquire a target score of each state index in the plurality of state information, where the target score is used to indicate a degree of influence of the state index on a temperature of a clutch in the target vehicle. The processing module is further used for determining a plurality of target indexes from the plurality of state information according to the target score of each state index, wherein the target score of the target index is larger than a preset score threshold value. The processing module is further used for correcting the reference index of the temperature model according to the target indexes to obtain a corrected temperature model, and the corrected temperature model refers to information corresponding to the target indexes when determining the temperature of the clutch in the target vehicle. The processing module is further used for training the corrected temperature model based on the plurality of state information until the corrected temperature model converges to obtain the trained temperature model.

In one possible embodiment, the target vehicle has a temperature model deployed therein, the apparatus further comprising: and a transmitting module. And the sending module is used for sending an updating instruction to the target vehicle, wherein the updating instruction is used for indicating to update the temperature model in the target vehicle into the trained temperature model.

According to a third aspect of the present application, there is provided an electronic apparatus comprising: a processor. A memory for storing processor-executable instructions. Wherein the processor is configured to execute instructions to implement the method of the first aspect and any of its possible embodiments described above.

According to a fourth aspect of the present application there is provided a computer readable storage medium, which when executed by a processor of an electronic device, enables the electronic device to perform the method of the first aspect and any of its possible embodiments.

According to a fifth aspect of the present application there is provided a computer program product comprising computer instructions which, when run on an electronic device, cause the electronic device to perform the method of the first aspect and any of its possible embodiments.

Therefore, the technical characteristics of the application have the following beneficial effects:

(1) The training device can determine whether the temperature model can be applied to different operation stages of the vehicle by comparing the temperature of the clutch in the vehicle determined by the temperature model based on the state information of the vehicle in different operation stages with the actual temperature of the clutch in the vehicle corresponding to each state information, and can train the temperature model based on the state information of the vehicle in different operation stages under the condition that the temperature model is determined not to be applied to any operation stage of the vehicle, so that the trained temperature model can be applied to different operation stages of the vehicle. Therefore, the training times of the temperature model can be reduced, and the training efficiency of the temperature model is improved.

(2) The training device can correct the reference index of the temperature model according to the influence degree of each state index in the plurality of state information on the temperature of the clutch in the vehicle, so that the corrected temperature model can refer to the information corresponding to the state index with larger influence degree on the temperature of the clutch in the vehicle, and train the corrected temperature model until the corrected temperature model converges, and the trained temperature model is obtained. In this way, the adaptability of the temperature model in different operating phases of the vehicle can be improved, and the accuracy of the temperature of the clutch in the vehicle determined by the temperature model in different operating phases of the vehicle can be improved.

(3) The training device can realize the training of the temperature model deployed in the vehicle by sending the trained temperature model to the vehicle and indicating to update the temperature model deployed in the vehicle to the trained temperature model without training the temperature model deployed in the vehicle. In this way, the number of corrections to the temperature model deployed in the vehicle can be reduced, and the training efficiency of the temperature model deployed in the vehicle can be improved.

(4) The training device can provide valuable references for determining the temperature of the clutch in the vehicle in different operation phases of the vehicle for the temperature model by acquiring information corresponding to different state indexes of the vehicle in each operation phase, and improves the accuracy of the temperature of the clutch in the vehicle determined by the temperature model.

It should be noted that, the technical effects caused by any implementation manner of the second aspect to the fifth aspect may refer to the technical effects caused by the corresponding implementation manner in the first aspect, which are not described herein.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application and do not constitute a undue limitation on the application.

FIG. 1 is a schematic diagram illustrating an example relationship between temperature of a clutch and performance and service life of the clutch, according to an exemplary embodiment;

FIG. 2 is a schematic diagram of a communication system shown in accordance with an exemplary embodiment;

FIG. 3 is a flowchart illustrating a method of training a temperature model, according to an exemplary embodiment;

FIG. 4 is a flowchart illustrating yet another temperature model training method, according to an exemplary embodiment;

FIG. 5 is a flowchart illustrating yet another temperature model training method, according to an exemplary embodiment;

FIG. 6 is a flowchart illustrating yet another temperature model training method, according to an exemplary embodiment;

FIG. 7 is a schematic diagram illustrating an example of a change in simulated temperature at different phases of operation of a vehicle, according to an exemplary embodiment;

FIG. 8 is a block diagram of a training apparatus for a temperature model, according to an exemplary embodiment;

fig. 9 is a block diagram of an electronic device, according to an example embodiment.

Detailed Description

Further advantages and effects of the present invention will become readily apparent to those skilled in the art from the disclosure herein, by referring to the accompanying drawings and the preferred embodiments. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be understood that the preferred embodiments are presented by way of illustration only and not by way of limitation.

It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present application by way of illustration, and only the components related to the present application are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.

Before describing the training method of the temperature model provided by the embodiment of the application in detail, the implementation environment and application field Jing Jinhang of the embodiment of the application are described.

First, an application scenario of the embodiment of the present application is described.

As shown in fig. 1, the clutch (e.g., a wet dual clutch) is a core component of a transmission (e.g., a wet dual clutch transmission), and has an important effect on durability of the transmission, smoothness of gear shifting of a vehicle, operational comfort of a user, transmission efficiency and fuel economy, and temperature of the clutch affects performance, service life and reliability of the clutch.

Specifically, the clutch transmits engine torque through friction torque, and a slide mill in the friction process generates a large amount of heat, which causes serious temperature rise problems. The temperature model has the function of calculating the clutch temperature through collecting vehicle data, providing basis for the temperature control module to realize clutch temperature control, and the accurate temperature model is critical to the development of the clutch and provides basic data for a protection strategy set by the friction surface of the clutch not damaged by high temperature.

Currently, in the process of training a temperature model in a TCU, the TCU may collect state information of a vehicle and calculate a temperature of a clutch by inputting the state information of the vehicle into the temperature model. Then, the management device (such as a terminal, a server, etc.) can compare the temperature calculated by the temperature model with the actual temperature of the clutch, and correct the temperature model according to the comparison result until the temperature model converges, so as to obtain a trained temperature model.

However, in the above technical solution, under the condition that the running phase of the vehicle changes, the state information of the vehicle also changes, so as to ensure that the trained temperature model can be applied to different running phases of the vehicle, the management device needs to correct the temperature model for each running phase, thereby increasing the correction times and reducing the training efficiency of the temperature model.

That is, the prior art has the problems of long time consumption, complex resource preparation, easy deviation of collected data, huge workload of data analysis and long clutch development period and high cost in the training of the temperature model in the clutch development process.

In order to solve the above problems, an embodiment of the present application provides a training method for a temperature model, where the training method for a temperature model provided by the embodiment of the present application is applied to a scene for training a temperature model. The training device can acquire state information of the vehicle in different operation stages, the temperature of the clutch in the vehicle determined by the temperature model based on each state information and the actual temperature of the clutch in the vehicle corresponding to each state information. Then, the training device may calculate a difference between the temperature of the clutch in each of the vehicles determined by the temperature model and the actual temperature of the clutch in the corresponding vehicle, obtain a plurality of temperature differences, and determine whether a value greater than a preset temperature difference threshold exists in the plurality of temperature differences. If the training device determines that the temperature difference values are larger than the preset temperature difference threshold value, training the temperature model based on state information of the vehicle in different operation stages until the temperature model converges, and obtaining the trained temperature model. That is, the training device may determine whether the temperature model may be applied to different operation stages of the vehicle by comparing the temperature of the clutch in the vehicle determined by the temperature model based on the state information of the vehicle at the different operation stages with the actual temperature of the clutch in the vehicle corresponding to each state information, and in the case where it is determined that the temperature model may not be applied to any operation stage of the vehicle, the training device may train the temperature model based on the state information of the vehicle at the different operation stages, so that the trained temperature model may be applied to the different operation stages of the vehicle. Therefore, the training times of the temperature model can be reduced, and the training efficiency of the temperature model is improved.

The following describes an implementation environment of an embodiment of the present application.

As shown in fig. 2, a communication system according to an embodiment of the present application includes: training device 201 and target vehicle 202. Wherein the training device 201 may be in wired/wireless communication with the target vehicle 202.

Specifically, the target vehicle 202 may collect state information of the target vehicle 202 and an actual temperature of a clutch in the target vehicle 202 and input the state information of the target vehicle 202 into a temperature model deployed in the target vehicle 202 to obtain a calculated temperature of the clutch in the target vehicle 202. Next, the training device 201 may form a Data (DTA) file of the status information, the calculated temperature, and the actual temperature, and store the data in the TCU. Thereafter, the training device 201 may send the DTA file stored by the TCU to the target vehicle 202. After that, the training device 201 may analyze the DTA file by using computer software, read the calculated temperature and the actual temperature in the DTA file, compare the calculated temperature with the actual temperature, and determine whether to manage the temperature model deployed in the target vehicle 202 according to the comparison result.

For example, if the training device 201 determines that the difference between the calculated temperature and the actual temperature is greater than the preset temperature difference threshold, the training device 201 simulates a temperature model and reads the status information in the DTA file. The training device 201 may then train the simulated temperature model based on the state information until the temperature model converges, resulting in a trained temperature model. Thereafter, the training device 201 may send the trained temperature model to the target vehicle 202 and instruct the target vehicle 202 to update the temperature model deployed in the target vehicle 202 to the trained temperature model.

It should be noted that, the embodiment of the present application is not limited to computer software. For example, the computer software may be MATLAB. For another example, the computer software may be Maple. For another example, the computer software may be Julia. For another example, the computer software may be GNU Octave. For another example, the computer software may be NumPy. For another example, the computer software may be Scilab. For another example, the computer software may be SageMath.

In one implementation, a thermocouple sensor may be deployed in the target vehicle 202, and the target vehicle 202 may collect the actual temperature of the clutch in the target vehicle 202 via the thermocouple sensor during the process of the target vehicle 202 collecting the actual temperature of the clutch in the target vehicle 202.

In one design, during the process of the target vehicle 202 collecting the actual temperature of the clutch in the target vehicle 202 through the thermocouple sensor, the target vehicle 202 may collect the temperature through the thermocouple sensor, and determine the actual temperature of the clutch in the target vehicle 202 according to the installation position, progress level, range, sensitivity, linearity, and other parameters of the thermocouple sensor, as well as parameters affecting the valve body precision, current precision, delay, fluid performance, and oil circuit design of the cooling oil.

In one design, the thermocouple sensors deployed in the target vehicle 202 may be multiple, with each of the clutch plates of the clutch in the target vehicle 202 deployed with a thermocouple sensor. The target vehicle 202 may determine the temperature of each clutch plate of the clutch in the target vehicle 202 via a plurality of thermocouple sensors and take the highest temperature as the temperature of the clutch in the target vehicle 202.

In some embodiments, the target vehicle 202 may collect state information of the target vehicle 202 and an actual temperature of a clutch in the target vehicle 202 and send the state information and the actual temperature to the training device 201. Thereafter, the training device 201 may simulate the temperature model deployed in the target vehicle 202 and input the state information into the simulated temperature model to obtain a calculated temperature of the clutch in the target vehicle 202. Next, the training device 201 may compare the calculated temperature with the actual temperature, and determine whether to manage the temperature model deployed by the target vehicle 202 according to the comparison result.

It should be noted that the training device 201 is not limited in the embodiment of the present application. For example, the training device 201 may be a TCU deployed in the target vehicle 202. For another example, the training device 201 may be an onboard host deployed in the target vehicle 202. For another example, the training device 201 may be a terminal deployed in a backend system. For another example, the exercise device 201 may be a server deployed in a backend system.

The server may be a single physical server, or may be a server cluster formed by a plurality of servers. Alternatively, the server cluster may also be a distributed cluster. Alternatively, the server may be a cloud server. The embodiment of the application does not limit the specific implementation mode of the server.

The terminal can be a mobile phone, a tablet computer, a desktop computer, a laptop computer, a handheld computer, a notebook computer, an Ultra-mobile Personal Computer (UMPC), a netbook and other devices with a receiving and transmitting function, and the specific form of the terminal is not particularly limited. The system can perform man-machine interaction with a user through one or more modes of a keyboard, a touch pad, a touch screen, a remote controller, voice interaction or handwriting equipment and the like.

For easy understanding, the training method of the temperature model provided by the application is specifically described below with reference to the accompanying drawings.

FIG. 3 is a flowchart illustrating a method of training a temperature model, as shown in FIG. 3, according to an exemplary embodiment, which may include the steps of: S301-S305.

S301, the training device acquires a plurality of state information, a plurality of first temperatures and a plurality of second temperatures of the target vehicle.

Wherein the status information is used to indicate the operational status of the target vehicle.

In one possible design, the plurality of state information is used to indicate an operation state of the vehicle in different operation phases, one state information corresponds to each operation phase, and the operation phases may include: the state information may include: whole vehicle parameters, engine dynamic characteristics, clutch operating conditions, transmission characteristics, vehicle operating environment, vehicle operating parameters, driver operating information and the like.

The parameters of the whole vehicle can include: vehicle weight, vehicle speed, speed ratio, rolling radius, throttle size, tire parameters, windward area, braking amount, air resistance, temperature of the sensor itself, cooling oil temperature rise delay, cooling oil temperature rise accumulation and the like, and engine dynamic characteristics can include: engine speed, engine torque, flywheel inertia, engine characteristics, etc., the clutch operating state may include: the clutch cooling oil pressure, clutch cooling oil temperature received from the transmission, clutch cooling oil flow errors, etc., transmission characteristics may include: the vehicle operating environment may include: road grade, obstacles, road ice, road desertification, ambient temperature, altitude/polar, etc., vehicle operating parameters may include: vehicle load, gear, etc., the driver's operation information may include: collision obstacle, sudden stepping on accelerator, sudden stepping on brake, frequent start and stop, etc.

In the embodiment of the application, the state information may include information corresponding to any one of a vehicle parameter, an engine dynamic characteristic, a clutch operating state, a transmission characteristic, a vehicle operating environment, a vehicle operating parameter, and operation information of a driver.

That is, the training device may provide a valuable reference for the temperature model to determine the temperature of the clutch in the vehicle at different operating phases of the vehicle by acquiring information corresponding to different state indexes of the vehicle at each operating phase, and improve the accuracy of the temperature model determination of the temperature of the clutch in the vehicle.

In the embodiment of the application, the first temperature is the temperature of the clutch in the target vehicle determined by the temperature model based on the state information, and the second temperature is the actual temperature of the clutch in the target vehicle, and one first temperature corresponds to one state information and one second temperature.

In one possible implementation, the training device may receive a plurality of status information, a plurality of first temperatures, and a plurality of second temperatures from the target vehicle. The first temperature is the temperature of a clutch in the target vehicle, which is obtained by inputting state information into a temperature model deployed in the target vehicle by the target vehicle.

In another possible implementation, the training device may receive a plurality of status information and a plurality of second temperatures from the target vehicle. Then, the training device may simulate a temperature model deployed in the target vehicle, and input a plurality of state information into the simulated temperature model to obtain a plurality of first temperatures.

S302, the training device calculates the difference value between each first temperature and the corresponding second temperature to obtain a plurality of temperature difference values.

S303, the training device determines whether a value larger than a preset temperature difference threshold exists in the temperature difference values.

In some embodiments, if the training device determines that there are no values of the plurality of temperature differences that are greater than the preset temperature difference threshold, the training device does not manage a temperature model deployed in the target vehicle.

That is, in the case where the training device determines that the difference between the calculated temperature and the actual temperature of the temperature model is less than or equal to the preset temperature difference threshold, the training device may determine that the temperature model deployed in the target vehicle does not have an abnormal condition, and does not need to train and update the temperature model deployed in the target vehicle.

In other embodiments, if the training device determines that there is a value greater than the preset temperature difference threshold value in the plurality of temperature difference values, the training device performs S304.

S304, the training device trains the temperature model based on the plurality of state information.

In one possible implementation, the state information may include: the plurality of state indexes and information corresponding to each state index, the temperature model may include: the system comprises a plurality of reference indexes and weight values corresponding to the reference indexes. The training device can screen out information corresponding to the state index identical to the reference index from the plurality of state information to obtain a first training set. Then, the training device can simulate a temperature model deployed in the target vehicle in the training device by writing software, and modify a weight value corresponding to each reference index in the simulated temperature model. Then, the training device can input the first training set into the temperature model with the weight value modified to obtain a plurality of third temperatures, wherein one third temperature corresponds to one second temperature. Then, the training device can calculate the difference value between each third temperature and the corresponding second temperature to obtain a plurality of first temperature difference values, and determine whether a value larger than a preset temperature difference threshold exists in the plurality of first temperature difference values.

It should be noted that, the embodiment of the present application is not limited to writing software. For example, the written software may be MATLAB. For another example, the written software may be Maple. For another example, the written software may be Julia. For another example, the written software may be Maxima. For another example, the written software may be GNU actave. For another example, the written software may be SageMath. For another example, the written software may be NumPy. For another example, the written software may be Scilab.

That is, the training device may simulate the temperature model deployed in the target vehicle to obtain a simulated temperature model, and depending on the computing resource of the training device, train the simulated temperature model through a plurality of state information of the target vehicle. Therefore, the speed of training the temperature model can be improved, and the time of training the temperature model can be reduced.

In the embodiment of the present application, after the training apparatus simulates the temperature model deployed in the target vehicle, the training apparatus may send a message for indicating whether the logic for verifying the simulated temperature model is correct to the management device. If the training device receives a message from the management equipment, wherein the message is used for indicating that the logic verification of the simulated temperature model is successful, the training device modifies the weight value corresponding to each reference index in the simulated temperature model.

Optionally, if the training device receives a message from the management apparatus indicating that the logical verification of the simulated temperature model fails, the training device re-simulates the temperature model deployed in the target vehicle.

In one possible design, the temperature model may be constructed by equation one.

Wherein T is used for indicating the temperature of a clutch in a target vehicle, T is used for indicating time, M is used for Indicating the clutch thermal conductivity equivalent mass, C is used for indicating the clutch thermal conductivity equivalent specific heat capacity, P ₁ For indicating clutch slip power, P ₂ For indicating clutch surface temperature rise.

In the embodiment of the application, the clutch skid power can be determined by the corresponding information and weight values of state indexes such as vehicle weight, speed ratio, rolling radius, throttle size, windward area, vehicle load, road ramp, barrier, plateau, collision barrier, sudden stepping brake, frequent start and stop, engine torque, engine rotating speed and the like.

The clutch surface temperature rise can be determined by information and weight values corresponding to state indexes such as cooling oil flow rate in the transmission, cooling oil cooling characteristic in the transmission, a transmission valve body, cooling oil temperature in the transmission, cooling oil temperature rise characteristic in the transmission and the like.

The equivalent heat conduction quality of the clutch can be determined by the weight of the friction plate, the weight of the steel plate, the weight of the adjusting steel plate, the weight of the inner/outer support of the inner clutch, the weight of the inner/outer support of the outer clutch, the weight of the cooling liquid and other corresponding information and weight values.

Similarly, the clutch thermal conductivity equivalent specific heat capacity can be determined by the corresponding information and weight values of state indexes such as the specific heat capacity of the friction plate, the specific heat capacity of the steel sheet, the specific heat capacity of the adjusting steel sheet, the specific heat capacity of the inner/outer support of the inner clutch, the specific heat capacity of the inner/outer support of the outer clutch, the specific heat capacity of the cooling liquid and the like.

In some embodiments, if the training device determines that a value greater than the preset temperature difference threshold exists in the plurality of first temperature differences, the training device determines that the temperature model is not converged and re-modifies the weight value corresponding to each reference index in the temperature model.

In other embodiments, if the training device determines that there are no values greater than the preset temperature difference threshold among the plurality of first temperature differences (i.e., the plurality of first temperature differences are all greater than the preset temperature difference threshold), the training device determines that the temperature model converges, and performs S305.

305. The training device obtains a trained temperature model.

The technical scheme provided by the embodiment at least brings the following beneficial effects: the training device may acquire a plurality of state information of the target vehicle, a plurality of first temperatures, and a plurality of second temperatures, where the state information is used to indicate an operation state of the target vehicle, the first temperatures are temperatures of a clutch in the target vehicle determined by the temperature model based on the state information, and the second temperatures are actual temperatures of the clutch in the target vehicle, and one first temperature corresponds to one state information and one second temperature. Then, the training device can calculate the difference value between each first temperature and each second temperature to obtain a plurality of temperature difference values, and determine whether a value larger than a preset temperature difference threshold exists in the plurality of temperature difference values. If the training device determines that a value larger than the preset temperature difference threshold exists in the temperature difference values, the training device can train the temperature model based on the state information until the temperature model converges, and a trained temperature model is obtained. That is, the training device may determine whether the temperature model may be applied to different operation stages of the vehicle by comparing the temperature of the clutch in the vehicle determined by the temperature model based on the state information of the vehicle at the different operation stages with the actual temperature of the clutch in the vehicle corresponding to each state information, and in the case where it is determined that the temperature model may not be applied to any operation stage of the vehicle, the training device may train the temperature model based on the state information of the vehicle at the different operation stages, so that the trained temperature model may be applied to the different operation stages of the vehicle. Therefore, the training times of the temperature model can be reduced, and the training efficiency of the temperature model is improved.

In some embodiments, in order to improve accuracy of the output result of the temperature model, as shown in fig. 4, in the training method of the temperature model provided by the embodiment of the present application, S304 may include the following steps: S401-S404.

S401, the training device acquires the target score of each state index in the plurality of state information.

Wherein the target score is used to indicate the extent to which the status indicator affects the temperature of the clutch in the target vehicle.

In one possible implementation, the training device may present a plurality of status information to the user and receive a target score for each status indicator entered by the user.

It should be noted that, the correspondence between the target score and the influence degree is not limited in the embodiment of the present application. For example, the target score is positively correlated with the degree of influence, and the greater the target score, the greater the degree of influence. For another example, the target score is inversely related to the degree of influence, and the smaller the target score, the greater the degree of influence.

The process of training the temperature model by the training device will be described below by taking the example that the target score and the influence degree are positively correlated.

S402, the training device determines a plurality of target indexes from a plurality of state information according to the target scores of each state index.

Wherein the target score of the target index is greater than a preset score threshold.

S403, the training device corrects the reference index of the temperature model according to the target indexes to obtain a corrected temperature model.

Wherein the corrected temperature model references information corresponding to a plurality of target indices when determining the temperature of the clutch in the target vehicle.

Exemplary, reference indicators of the temperature model before correction include: vehicle weight, speed ratio, rolling radius, cooling oil temperature in the transmission, cooling oil temperature rise characteristic in the transmission, weight and specific heat capacity of the coolant, weight and specific heat capacity of the steel sheet. If the plurality of target indexes include: the reference indicators of the corrected temperature model may include: vehicle weight, rolling radius, engine torque, engine speed, transmission cooling oil flow, transmission cooling oil cooling characteristics, transmission cooling oil temperature rise characteristics, inner/outer carrier weight and specific heat capacity of the inner clutch, and inner/outer carrier weight and specific heat capacity of the outer clutch.

S404, the training device trains the corrected temperature model based on the plurality of state information.

In one possible implementation manner, the training device may screen out information corresponding to the same state index as the target index from the plurality of state information, to obtain the second training set. Then, the training device can modify the weight value corresponding to each target index in the corrected temperature model, and input the second training set into the corrected temperature model to obtain a plurality of fourth temperatures, wherein one fourth temperature corresponds to one second temperature. Then, the training device can calculate the difference value between each fourth temperature and the corresponding second temperature to obtain a plurality of second temperature difference values, and determine whether a value larger than a preset temperature difference threshold exists in the second temperature difference values.

In some embodiments, if the training device determines that there is a value greater than the preset temperature difference threshold in the plurality of second temperature differences, the training device determines that the temperature model is not converged and re-modifies the weight value corresponding to each reference index in the temperature model.

In other embodiments, if the training device determines that no value greater than the preset temperature difference threshold exists in the plurality of second temperature differences, the training device determines that the temperature model converges and performs S305.

It will be appreciated that the training apparatus may determine the extent to which each of the plurality of state information affects the temperature of the clutch in the target vehicle by obtaining a target score for each of the plurality of state information. Then, the training device may determine a plurality of target indexes from the plurality of state information according to the target score of each state index, where the target score of the target index is greater than a preset score threshold. And then, the training device can correct the reference index of the temperature model according to the target indexes to obtain a corrected temperature model, and the corrected temperature model refers to information corresponding to the target indexes when determining the temperature of the clutch in the target vehicle. Then, the training device may train the corrected temperature model based on the plurality of state information until the corrected temperature model converges, to obtain a trained temperature model. That is, the training device may correct the reference index of the temperature model according to the influence degree of each state index in the plurality of state information on the temperature of the clutch in the vehicle, so that the corrected temperature model may refer to the information corresponding to the state index having a larger influence degree on the temperature of the clutch in the vehicle, and train the corrected temperature model until the corrected temperature model converges, thereby obtaining the trained temperature model. In this way, the adaptability of the temperature model in different operating phases of the vehicle can be improved, and the accuracy of the temperature of the clutch in the vehicle determined by the temperature model in different operating phases of the vehicle can be improved.

In some embodiments, in order to ensure that the temperature model deployed in the target vehicle is a trained temperature model, as shown in fig. 5, after the training device obtains the trained temperature model, that is, S305), the training method of the temperature model provided by the embodiment of the present application may further include the following steps: s501.

S501, the training device sends an update instruction to the target vehicle.

The updating instruction is used for indicating that the temperature model deployed in the target vehicle is updated to a trained temperature model.

In the embodiment of the application, the target vehicle can receive the update instruction from the training device and respond to the update instruction to manage the temperature model deployed in the target vehicle.

It is understood that the training device may instruct the target vehicle to update the temperature model deployed in the target vehicle to the trained temperature model by sending an update instruction to the target vehicle after obtaining the trained temperature model. That is, the training device may implement training of the temperature model deployed in the vehicle by transmitting the trained temperature model to the vehicle and instructing to update the temperature model deployed in the vehicle to the trained temperature model without training the temperature model deployed in the vehicle. In this way, the number of corrections to the temperature model deployed in the vehicle can be reduced, and the training efficiency of the temperature model deployed in the vehicle can be improved.

The training method of the temperature model provided by the application is described below with reference to specific examples. As shown in fig. 6, the training method of the temperature model provided by the present application may include:

s601, an automatic gearbox control unit collects state information of a vehicle.

S602, the automatic gearbox control unit inputs state information of the vehicle into a temperature model deployed in the vehicle to obtain a calculated temperature (namely a first temperature) of a clutch in the vehicle.

In some embodiments, the automatic transmission control unit may store the calculated temperature of the clutch in the vehicle and the state information of the vehicle in the DTA file after obtaining the calculated temperature of the clutch in the vehicle, and synchronize the DTA file to the computer (i.e., the training device).

S603, the thermocouple sensor tests the actual temperature of the clutch in the vehicle (i.e., the second temperature).

In some embodiments, the thermocouple sensor may store the actual temperature of the clutch in the vehicle in the DTA file after testing the actual temperature of the clutch in the vehicle and synchronize the DTA file to the computer.

Note that the order of S602 and S603 is not limited in the embodiment of the present application. For example, the automatic transmission control unit may perform S602 first, and the thermocouple sensor performs S603 second. For another example, the thermocouple sensor may perform S603 first, and the automatic transmission control unit may perform S602 second. For another example, the automatic transmission control unit execution S602 and the thermocouple sensor execution S603 may be performed simultaneously.

S604, the computer acquires the DTA file (namely S301).

S605, the computer analyzes and calculates the DTA file through software to determine a first temperature difference between the calculated temperature and the actual temperature (namely S302).

S606, the computer determines whether the first temperature difference is greater than a design value (S303).

In some embodiments, if the computer determines that the first temperature difference is less than or equal to the design value, the computer determines that the temperature model deployed in the vehicle is in a normal operating state without managing the temperature model deployed in the vehicle.

In some embodiments, if the computer determines that the first temperature difference is greater than the design value, the computer performs S607.

S607, the computer determines the influencing factors (namely S401-S402).

S608, the computer analyzes the influence coefficient of each influence factor on the temperature of the clutch in the vehicle (namely, the training device can modify the weight value corresponding to each target index in the corrected temperature model).

S609, the computer optimizes the structure of the temperature model (namely, corrects the reference index of the temperature model) according to each influence factor.

And S610, the computer simulates the temperature model according to the structure of the optimized temperature model and the influence coefficient of each influence factor on the temperature of the clutch in the vehicle, and the simulated temperature model (namely, the temperature model after modification of the weight value).

S611, the computer confirms whether the logic of the simulated temperature model is correct (i.e. the training apparatus may send a message to the management device indicating whether the logic of the simulated temperature model is correct).

In some embodiments, if the computer confirms that the logic of the simulated temperature model is incorrect, the computer re-executes S609.

In other embodiments, if the computer confirms that the logic of the simulated temperature model is correct, the computer performs S612.

S612, the computer inputs the state information of the vehicle into the simulated temperature model to obtain the simulated temperature (namely, the fourth temperature) of the clutch in the vehicle.

Exemplary, as shown in fig. 7, the simulated temperature model is based on the state information of the vehicle, and the variation of the simulated temperature is obtained at different operation stages of the vehicle.

S613, the computer determines a second temperature difference (namely a second temperature difference value) between the simulated temperature and the actual temperature.

S614, the computer determines whether the second temperature difference is greater than a design value (i.e. the training device determines whether a value greater than a preset temperature difference threshold exists among the plurality of second temperature difference values).

In some embodiments, if the computer determines that the second temperature difference is greater than the design value, the computer re-executes S607 (i.e., if the training device determines that there is a value greater than the preset temperature difference threshold value in the plurality of second temperature difference values, the training device determines that the temperature model is not converged and re-modifies the weight value corresponding to each reference index in the temperature model).

In other embodiments, if the computer determines that the second temperature difference is less than or equal to the design value, the computer executes S615.

S615, the computer determines that the simulated temperature model is a trained temperature model (i.e. if the training device determines that a value greater than a preset temperature difference threshold does not exist in the plurality of second temperature difference values, the training device determines that the temperature model converges, and executes S305).

In some embodiments, after the computer determines the trained temperature model, the computer may deploy the trained temperature model in the vehicle.

The foregoing description of the solution provided by the embodiments of the present application has been mainly presented in terms of a method. In order to achieve the above functions, the training device or the electronic device of the temperature model includes a hardware structure and/or a software module for executing the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

According to the method, the training device or the electronic device of the temperature model can be divided into functional modules, for example, the training device or the electronic device of the temperature model can comprise each functional module corresponding to each functional division, and two or more functions can be integrated into one processing module. The integrated modules may be implemented in hardware or in software functional modules. It should be noted that, in the embodiment of the present application, the division of the modules is schematic, which is merely a logic function division, and other division manners may be implemented in actual implementation.

FIG. 8 is a block diagram of a training apparatus for a temperature model, according to an example embodiment. Referring to fig. 8, the training apparatus 800 of the temperature model includes: an acquisition module 801 and a processing module 802.

The obtaining module 801 is configured to obtain a plurality of state information of the target vehicle, a plurality of first temperatures, and a plurality of second temperatures, where the state information is used to indicate an operation state of the target vehicle, the first temperatures are temperatures of a clutch in the target vehicle determined by a temperature model based on the state information, and the second temperatures are actual temperatures of the clutch in the target vehicle, and one first temperature corresponds to one state information and one second temperature. The processing module 802 is configured to calculate a difference between each first temperature and the corresponding second temperature, so as to obtain a plurality of temperature differences. The processing module 802 is further configured to train the temperature model based on the plurality of state information if there is a value greater than the preset temperature difference threshold value in the plurality of temperature difference values until the temperature model converges, and obtain a trained temperature model.

In one possible implementation, the status information includes: a plurality of status indicators. The obtaining module 801 is specifically configured to obtain a target score of each of the plurality of status information, where the target score is used to indicate a degree of influence of the status index on a temperature of a clutch in the target vehicle. The processing module 802 is further configured to determine a plurality of target indexes from the plurality of state information according to the target score of each state index, where the target score of the target index is greater than a preset score threshold. The processing module 802 is further configured to modify a reference index of the temperature model according to the plurality of target indexes, and obtain a modified temperature model, where the modified temperature model refers to information corresponding to the plurality of target indexes when determining a temperature of a clutch in the target vehicle. The processing module 802 is further configured to train the corrected temperature model based on the plurality of state information until the corrected temperature model converges, and obtain a trained temperature model.

In one possible embodiment, the target vehicle has a temperature model deployed therein, and the training apparatus 800 for temperature model may further include: a transmitting module 803. A sending module 803, configured to send an update instruction to the target vehicle, where the update instruction is used to instruct to update the temperature model in the target vehicle to the trained temperature model.

The specific manner in which the individual units perform the operations in relation to the apparatus of the above embodiments has been described in detail in relation to the embodiments of the method and will not be described in detail here.

Fig. 9 is a block diagram of an electronic device, according to an example embodiment. As shown in fig. 9, electronic device 900 includes, but is not limited to: a processor 901 and a memory 902.

The memory 902 is configured to store executable instructions of the processor 901. It will be appreciated that the processor 901 is configured to execute instructions to implement the temperature model training method of the above embodiment.

It should be noted that the electronic device structure shown in fig. 9 is not limited to the electronic device, and the electronic device may include more or less components than those shown in fig. 9, or may combine some components, or may have different arrangements of components, as will be appreciated by those skilled in the art.

The processor 901 is a control center of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, and performs various functions of the electronic device and processes data by running or executing software programs and/or modules stored in the memory 902 and calling data stored in the memory 902, thereby performing overall monitoring of the electronic device. The processor 901 may include one or more processing units. Alternatively, the processor 901 may integrate an application processor that primarily handles operating systems, user interfaces, applications, etc., with a modem processor that primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 901.

The memory 902 may be used to store software programs as well as various data. The memory 902 may primarily include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs (such as a processing unit) required for at least one functional module, and the like. In addition, the memory 902 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.

In an exemplary embodiment, a computer readable storage medium is also provided, e.g., a memory 902, comprising instructions executable by the processor 901 of the electronic device 900 to implement the method of training the temperature model in the above-described embodiments.

In actual implementation, the functions of the acquisition module 801, the processing module 802, and the transmission module 803 in fig. 8 may be implemented by the processor 901 in fig. 9 calling a computer program stored in the memory 902. For specific execution, reference may be made to the description of the training method portion of the temperature model in the above embodiment, and details are not repeated here.

Alternatively, the computer readable storage medium may be a non-transitory computer readable storage medium, for example, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

In an exemplary embodiment, the present application also provides a vehicle including the processor 901 shown in fig. 9. The vehicle may be used to perform the training method of the temperature model in the above-described embodiment.

In an exemplary embodiment, embodiments of the application also provide a computer program product comprising one or more instructions executable by a processor of an electronic device to perform the method of training a temperature model in the above-described embodiments.

It should be noted that, when the instructions in the computer readable storage medium or one or more instructions in the computer program product are executed by the processor of the electronic device, the respective processes of the foregoing embodiment of the training method of the temperature model are implemented, and the technical effects that are the same as those of the foregoing embodiment of the training method of the temperature model can be achieved, and for avoiding repetition, a detailed description is omitted here.

From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to implement all or part of the functions described above.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another apparatus, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and the parts shown as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed in a plurality of different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application may be essentially or a part contributing to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, including several instructions for causing a device (may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.

The present application is not limited to the above embodiments, and any changes or substitutions within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.

Claims

1. A method of training a temperature model, the method comprising:

Acquiring a plurality of state information of a target vehicle, a plurality of first temperatures and a plurality of second temperatures, wherein the state information is used for indicating the running state of the target vehicle, the first temperatures are temperatures of a clutch in the target vehicle, which are determined by a temperature model based on the state information, and the second temperatures are actual temperatures of the clutch in the target vehicle, and one first temperature corresponds to one state information and one second temperature;

calculating the difference value between each first temperature and the corresponding second temperature to obtain a plurality of temperature difference values;

and if the temperature difference values are larger than a preset temperature difference threshold value, training the temperature model based on the state information until the temperature model converges, and obtaining the trained temperature model.

2. The method of claim 1, wherein the status information comprises: and training the temperature model based on the plurality of state indexes until the temperature model converges to obtain the trained temperature model, wherein the training comprises the following steps of:

obtaining a target score of each state index in the plurality of state information, wherein the target score is used for indicating the influence degree of the state index on the temperature of a clutch in the target vehicle;

Determining a plurality of target indexes from the plurality of state information according to the target score of each state index, wherein the target score of the target index is larger than a preset score threshold;

correcting the reference index of the temperature model according to the target indexes to obtain a corrected temperature model, wherein the corrected temperature model refers to information corresponding to the target indexes when determining the temperature of a clutch in the target vehicle;

and training the corrected temperature model based on the plurality of state information until the corrected temperature model converges, so as to obtain the trained temperature model.

3. The method of claim 2, wherein the temperature model is deployed in the target vehicle, and wherein after obtaining the trained temperature model, the method further comprises:

and sending an update instruction to the target vehicle, wherein the update instruction is used for indicating that the temperature model in the target vehicle is updated to the trained temperature model.

4. A method according to any of claims 1-3, characterized in that the status information comprises at least one of: vehicle weight, frontal area, engine speed, engine torque, road grade, ambient temperature, cooling oil input temperature, cooling oil flow.

5. A training device for a temperature model, the device comprising:

an obtaining module, configured to obtain a plurality of state information of a target vehicle, a plurality of first temperatures, and a plurality of second temperatures, where the state information is used to indicate an operation state of the target vehicle, the first temperatures are temperatures of a clutch in the target vehicle determined by a temperature model based on the state information, and the second temperatures are actual temperatures of the clutch in the target vehicle, and one of the first temperatures corresponds to one of the state information and one of the second temperatures;

the processing module is used for calculating the difference value between each first temperature and the corresponding second temperature to obtain a plurality of temperature difference values;

and the processing module is further used for training the temperature model based on the state information if the temperature difference values are larger than a preset temperature difference threshold value, until the temperature model converges, and obtaining the trained temperature model.

6. The apparatus of claim 5, wherein the status information comprises: a plurality of status indicators;

the acquisition module is specifically configured to acquire a target score of each of the state indexes in the plurality of state information, where the target score is used to indicate a degree of influence of the state index on a temperature of a clutch in the target vehicle;

The processing module is further configured to determine a plurality of target indexes from the plurality of state information according to the target score of each state index, where the target score of the target index is greater than a preset score threshold;

the processing module is further configured to modify a reference index of the temperature model according to the plurality of target indexes, so as to obtain a modified temperature model, where the modified temperature model refers to information corresponding to the plurality of target indexes when determining a temperature of a clutch in the target vehicle;

the processing module is further configured to train the corrected temperature model based on the plurality of state information until the corrected temperature model converges, to obtain the trained temperature model.

7. The apparatus of claim 6, wherein the temperature model is deployed in the target vehicle, the apparatus further comprising: a transmitting module;

the sending module is used for sending an updating instruction to the target vehicle, wherein the updating instruction is used for indicating that the temperature model in the target vehicle is updated to the trained temperature model.

8. The apparatus according to any of claims 5-7, wherein the status information comprises at least one of: vehicle weight, frontal area, engine speed, engine torque, road grade, ambient temperature, cooling oil input temperature, cooling oil flow.

9. An electronic device, comprising: a processor; a memory for storing the processor-executable instructions; wherein the processor is configured to execute the instructions to implement the method of any one of claims 1 to 4.

10. A computer readable storage medium, characterized in that, when computer-executable instructions stored in the computer readable storage medium are executed by a processor of an electronic device, the electronic device is capable of performing the method of any one of claims 1 to 4.