Clutch temperature prediction method and device, electronic equipment and storage medium

Abstract

The application provides a clutch temperature prediction method and device, electronic equipment and a storage medium. The method calculates a clutch temperature prediction value according to a cooling parameter through a pre-constructed temperature prediction model. The cooling parameter is continuously updated based on the least square method according to the clutch temperature prediction value and a measured clutch temperature. Until the error sum of squares of the clutch temperature prediction value and the measured clutch temperature is less than a set value, or the current iteration number is greater than a set iteration number, the target cooling parameter is output. The target cooling parameter is input into the temperature prediction model to predict the clutch temperature, and the prediction accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of automotive clutch technology, and more particularly to a clutch temperature prediction method, device, electronic device, and storage medium. Background Technology

[0002] Clutches are widely used in automobiles, and clutch temperature prediction is of great importance to automobiles. However, some current clutch temperature prediction methods are not very accurate, which affects the performance of automobiles. Summary of the Invention

[0003] The main objective of this application is to provide a clutch temperature prediction method, device, electronic device, and storage medium. The aim is to continuously update cooling parameters based on the predicted and measured clutch temperatures using the least squares method to obtain target cooling parameters, which are then input into a temperature prediction model to predict the clutch temperature. This improves prediction accuracy and enables the prediction of clutch temperatures under different operating conditions.

[0004] To achieve the above objectives, a first aspect of this application proposes a clutch temperature prediction method, the method comprising:

[0005] Based on the clutch cooling parameters, the predicted clutch temperature is calculated using a pre-built temperature prediction model.

[0006] Based on the predicted clutch temperature and the measured clutch temperature, the cooling parameters are updated using the least squares method.

[0007] Return to the step of calculating the predicted value of clutch temperature based on the cooling parameters of the clutch using a pre-built temperature prediction model, until the termination condition is met, and output the updated target cooling parameters. The termination condition includes a first termination condition or a second termination condition. The first termination condition is that the current iteration number is greater than the set iteration number, and the second termination condition is that the sum of the squares of the errors between the predicted value of clutch temperature and the measured value of clutch temperature is less than a set value.

[0008] The clutch temperature is predicted using the temperature prediction model based on the target cooling parameters.

[0009] In some embodiments, the step of calculating the predicted clutch temperature based on the clutch's cooling parameters using a pre-built temperature prediction model includes:

[0010] The convective heat transfer coefficient is calculated based on the cooling parameters of the clutch using a pre-built temperature prediction model.

[0011] The predicted clutch temperature is calculated based on the convective heat transfer coefficient.

[0012] In some embodiments, updating the cooling parameters based on the least squares method according to the predicted clutch temperature and the measured clutch temperature includes:

[0013] Based on the predicted clutch temperature and the measured clutch temperature, an objective function is constructed.

[0014] The objective function is solved using the least squares method to update the cooling parameters.

[0015] In some embodiments, the clutch temperature includes the temperature of the clutch steel plates and the temperature of the friction plates. The step of predicting the clutch temperature using the temperature prediction model based on the target cooling parameters includes:

[0016] The convective heat transfer coefficient of the steel sheet and the convective heat transfer coefficient of the friction plate are calculated by the temperature prediction model based on the target cooling parameters.

[0017] The temperature of the clutch steel sheet is calculated based on the convective heat transfer coefficient of the steel sheet.

[0018] The friction plate temperature of the clutch is calculated based on the convective heat transfer coefficient of the friction plate.

[0019] In some embodiments, the calculation of the convective heat transfer coefficients of the steel sheet and the friction plate based on the target cooling parameters using the temperature prediction model is performed by the following formula:

[0020] h s =k s ·f(Q,Δw,T r )·g(T oil ,u oil );

[0021] h f =k f ·f(Q,Δw,T r )·g(T oil ,u oil );

[0022] in,

[0023] In the formula, h s k represents the convective heat transfer coefficient of the steel sheet. s Let f(Q,Δw,T) represent the steel sheet constant. r The target cooling parameters are the lubricating oil volumetric flow rate Q, the friction pair speed difference Δw, and the friction pair transmitted torque T. r The relevant function, g(T) oil ,u oil) represents a function related to lubricating oil temperature and lubricating oil flow rate, h f k represents the convective heat transfer coefficient of the friction plate. f T represents the friction constant. r This indicates the torque transmitted by the friction pair; N represents the number of friction pairs; μ represents the equivalent friction coefficient of the friction pair; p i R represents the equivalent pressure of the i-th friction pair; R represents the equivalent friction radius of the friction pair, r1 represents the inner radius, and r2 represents the outer radius.

[0024] In some embodiments, the calculation of the clutch steel plate temperature based on the convective heat transfer coefficient of the steel plate is performed using the following formula:

[0025]

[0026] in,

[0027] In the formula, T S (k+1) represents the temperature of the steel sheet at time k+1, T S (k) represents the temperature of the steel sheet at time k, ε s The heat distribution coefficient of the steel sheet is represented by Δw, and the velocity difference is represented by m. s c represents the mass of the steel sheet. s h represents the specific heat capacity of the steel sheet. s A represents the convective heat transfer coefficient of the steel sheet. s T represents the heat exchange area of ​​the steel sheet. oil (k) represents the temperature of the lubricating oil at time k, λ s ρ represents the thermal conductivity of the steel sheet. s c represents the density of the steel sheet. f λ represents the specific heat capacity of the friction plate. f ρ represents the thermal conductivity of the friction plate. f This indicates the density of the friction plate.

[0028] In some embodiments, the calculation of the clutch friction plate temperature based on the convective heat transfer coefficient of the friction plate is performed by the following formula:

[0029]

[0030] in,

[0031] In the formula, T f (k+1) represents the temperature of the friction plate at time k+1, T f (k) represents the temperature of the friction plate at time k, ε f The friction plate's heat distribution coefficient is represented by Δw, and the velocity difference is represented by m. f c represents the mass of the friction plate.f h represents the specific heat capacity of the friction plate. f A represents the convective heat transfer coefficient of the friction plate. f T represents the heat transfer area of ​​the friction plate. oil (k) represents the temperature of the lubricating oil at time k, λ s ρ represents the thermal conductivity of the steel sheet. s c represents the density of the steel sheet. s λ represents the specific heat capacity of the steel sheet. f ρ represents the thermal conductivity of the friction plate. f This indicates the density of the friction plate.

[0032] To achieve the above objectives, a second aspect of this application provides a clutch temperature prediction device, the device comprising:

[0033] The calculation module is used to calculate the predicted clutch temperature based on the clutch cooling parameters using a pre-built temperature prediction model.

[0034] The update module is used to update the cooling parameters based on the predicted clutch temperature and the measured clutch temperature using the least squares method.

[0035] The output module is used to return the steps of calculating the predicted value of clutch temperature based on the cooling parameters of clutch using a pre-built temperature prediction model, until the termination condition is met, and output the updated target cooling parameters. The termination condition includes a first termination condition or a second termination condition. The first termination condition is that the current iteration number is greater than the set iteration number, and the second termination condition is that the sum of the squares of the errors between the predicted value of clutch temperature and the measured value of clutch temperature is less than the set value.

[0036] The prediction module is used to predict the clutch temperature based on the target cooling parameters using the temperature prediction model.

[0037] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0038] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0039] This application proposes a clutch temperature prediction method, device, electronic device, and storage medium. The method calculates a predicted clutch temperature based on the clutch's cooling parameters using a pre-built temperature prediction model. It then updates the cooling parameters using the least squares method based on the predicted and measured clutch temperatures. The process returns to the step of calculating the predicted clutch temperature using the pre-built temperature prediction model based on the clutch's cooling parameters, until a termination condition is met. The updated target cooling parameters are then output. The termination condition includes a first termination condition or a second termination condition. The first termination condition is that the current iteration count is greater than a set iteration count. The second termination condition is that the sum of the squares of the errors between the predicted and measured clutch temperatures is less than a set value. The clutch temperature is predicted using the temperature prediction model based on the target cooling parameters. This method continuously updates the cooling parameters based on the predicted and measured clutch temperatures using the least squares method to obtain the target cooling parameters, which are then input into the temperature prediction model to predict the clutch temperature, thus improving prediction accuracy. Attached Figure Description

[0040] Figure 1 This is a flowchart of the clutch temperature prediction method provided in the embodiments of this application;

[0041] Figure 2 This is a schematic diagram illustrating the segmentation of the clutch friction pair according to an embodiment of this application;

[0042] Figure 3 This is a flowchart of the updated cooling parameters provided in an embodiment of this application;

[0043] Figure 4 This is a schematic diagram of all updated cooling parameter maps provided in the embodiments of this application;

[0044] Figure 5 This is a schematic diagram of the clutch temperature prediction device provided in the embodiments of this application;

[0045] Figure 6 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0049] This application proposes a clutch temperature prediction method. Based on the predicted clutch temperature and the measured clutch temperature, the cooling parameters are continuously updated using the least squares method to obtain the target cooling parameters. The obtained target cooling parameters are then input into the temperature prediction model to predict the clutch temperature, which can improve the prediction accuracy.

[0050] Reference Figure 1 , Figure 1 This is a flowchart of the clutch temperature prediction method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0051] Step S101: The clutch temperature prediction value is calculated based on the clutch cooling parameters using a pre-built temperature prediction model.

[0052] Step S102: Update the cooling parameters based on the predicted clutch temperature and the measured clutch temperature using the least squares method.

[0053] Step S103: Return to the step of calculating the predicted value of clutch temperature based on the cooling parameters of clutch using the pre-built temperature prediction model until the termination condition is met, and output the updated target cooling parameters. The termination condition includes a first termination condition or a second termination condition. The first termination condition is that the current iteration number is greater than the set iteration number, and the second termination condition is that the sum of the squares of the errors between the predicted clutch temperature value and the measured clutch temperature value is less than the set value.

[0054] Step S104: The clutch temperature is predicted using a temperature prediction model based on the target cooling parameters.

[0055] In the embodiments of this application, reference is made to Figure 2The clutch friction pairs are segmented, and the transmitted torque and heat distribution coefficient are calculated based on these segments. Then, the clutch temperature can be calculated using the transmitted torque, heat distribution, and convective heat transfer coefficient. This allows for the construction of a clutch temperature prediction model.

[0056] For example, the friction pair is divided into 5 equal parts, where each friction pair includes a steel plate and a friction plate, and the temperature is uniformly distributed on each steel plate and each friction plate segment. The transmitted torque of the clutch can then be calculated using Equation 1:

[0057]

[0058] in,

[0059] In Equation 1, T r This indicates the torque transmitted by the friction pair; N represents the number of friction pairs; μ represents the equivalent friction coefficient of the friction pair; p i R represents the equivalent pressure of the i-th friction pair; R represents the equivalent friction radius of the friction pair, r1 represents the inner radius, and r2 represents the outer radius.

[0060] Then, the heat distribution coefficient of the steel sheet can be calculated according to Equation 2, and the heat distribution coefficient of the friction plate can be calculated according to Equation 3. Where:

[0061]

[0062] In Equation 2, λ s c represents the thermal conductivity of the steel sheet. s ρ represents the specific heat capacity of a steel sheet. s λ represents the density of the steel sheet. f c represents the thermal conductivity of the friction plate. f ρ represents the specific heat capacity of the friction plate. f This indicates the density of the friction plate.

[0063]

[0064] In Equation 3, λ f c represents the thermal conductivity of the friction plate. f ρ represents the specific heat capacity of the friction plate. f λ represents the density of the friction plate. s c represents the thermal conductivity of the steel sheet. s ρ represents the specific heat capacity of a steel sheet. s This indicates the density of the steel sheet.

[0065] Based on the heat distribution coefficients of the steel plate and friction plate, the heat distributed to the friction plate and steel plate can be calculated accordingly. Then, based on the heat distributed to the friction plate and steel plate, the clutch outlet oil temperature can be calculated using Equation 4. Where:

[0066]

[0067] Among them, Q covf (k)=ε f ·T r ·Δw, Q covs (k)=ε s ·T r ·Δw;

[0068] In the formula, T i-oilout (k+1) represents the outlet oil temperature at time k+1, T i-oilout (k) represents the outlet oil temperature at time k, Q covf (k) represents the heat Q generated by the sliding friction work at time k and distributed to the friction plate. covs (k) represents the heat distributed to the steel sheet by the sliding friction work at time k, m oil Indicates the quality of the lubricating oil, c oil ε represents the specific heat capacity of lubricating oil. f T represents the heat distribution coefficient of the friction plate. r The friction pair represents the torque transmitted, Δw represents the speed difference, and ε represents the torque transmitted by the friction pair. s This represents the heat distribution coefficient of the steel sheet.

[0069] It is assumed that all the frictional work generated is converted into heat. Based on the principle of thermal balance and Newton's law of cooling, and introducing the convective heat transfer coefficient, the temperature of the steel sheet at the current moment can be calculated using Equation 5. Similarly, the temperature of the friction plate at the current moment can be calculated using Equation 5. Where:

[0070]

[0071] In Equation 5, T S (k+1) represents the temperature of the steel sheet at time k+1, T S (k) represents the temperature of the steel sheet at time k, ε s The heat distribution coefficient of the steel sheet is represented by Δw, and the velocity difference is represented by m. s c represents the mass of the steel sheet. s h represents the specific heat capacity of the steel sheet. s A represents the convective heat transfer coefficient of the steel sheet. s T represents the heat exchange area of ​​the steel sheet. oil (k) represents the temperature of the lubricating oil at time k.

[0072]

[0073] In Equation 6, T f (k+1) represents the temperature of the friction plate at time k+1, T f (k) represents the temperature of the friction plate at time k, εf The friction plate's heat distribution coefficient is represented by Δw, and the velocity difference is represented by m. f c represents the mass of the friction plate. f h represents the specific heat capacity of the friction plate. f A represents the convective heat transfer coefficient of the friction plate. f T represents the heat transfer area of ​​the friction plate. oil (k) represents the temperature of the lubricating oil at time k.

[0074] In equations 5 and 6, due to the convective heat transfer coefficient h of the steel sheet s and the convective heat transfer coefficient h of the friction plate f These are not physical properties of the clutch, and therefore cannot be obtained; the convective heat transfer coefficient h of the steel sheet is used instead. s For example, the convective heat transfer coefficient h s It can be calculated using Equation 7:

[0075]

[0076] in,

[0077] In the formula, h s λ represents the convective heat transfer coefficient of the steel sheet. oil λ represents the thermal conductivity of lubricating oil. s Represents the characteristic length of the steel sheet; Re s p is the Reynolds number. r Let u be the Prandtl number. s υ is the relative velocity between the lubricating oil and the steel sheet. oil ρ is the dynamic viscosity of the lubricating oil. oil c is the density of the lubricating oil. oil This refers to the specific heat capacity of the lubricating oil.

[0078] Among them, Prandtl number p r This parameter is related to the physical properties of the lubricating oil, and its value is related to the lubricating oil temperature. It can be calculated by looking up a table using the clutch oil temperature. Reynolds number Re s The coefficient of heat transfer of the steel sheet is related to the flow rate and temperature of the lubricating oil, but it is difficult to measure directly, thus affecting the convective heat transfer coefficient h. s It also cannot be directly calculated from the measured values. Similarly, the convective heat transfer coefficient h of the friction plate... f It cannot be directly calculated from the measured values.

[0079] Based on this, the embodiments of this application require first adjusting the convective heat transfer coefficient h of the steel sheet. s and the convective heat transfer coefficient h of the friction plate f The conversion is carried out, with the convective heat transfer coefficient h of the steel sheet also being considered. s For example, the convective heat transfer coefficient h of steel sheets sThe transformation formula is Equation 8:

[0080] h s =k s ·f(Q,Δw,T r )·g(T oil ,u oil (Equation 8);

[0081] In the formula, h s k represents the convective heat transfer coefficient of the steel sheet. s Let f(Q,Δw,T) represent the steel sheet constant. r The target cooling parameters are the lubricating oil volumetric flow rate Q, the friction pair speed difference Δw, and the friction pair transmitted torque T. r The relevant function, g(T) oil ,u oil ) represents a function related to lubricating oil temperature and lubricating oil flow rate.

[0082] Among them, the function g(T) oil ,u oil The target cooling parameter f(Q,Δw,T) can be calculated from the lubricating oil's physical properties and the oil temperature data obtained from a table. r By doing so, the convective heat transfer coefficient h of the steel sheet can be calculated. s Once the convective heat transfer coefficient of the steel sheet is calculated, the temperature of the steel sheet can be further calculated.

[0083] Understandably, similarly, the convective heat transfer coefficient h of the friction plate... f The transformation can also be performed in the same way to calculate the target cooling parameters f(Q,Δw,T). r By doing so, the convective heat transfer coefficient h of the friction plate can be calculated. f Once the convective heat transfer coefficient of the friction plate is calculated, the temperature of the friction plate can be further calculated.

[0084] In this embodiment of the application, in order to obtain the target cooling parameter f(Q,Δw,T) r First, initial values ​​are assigned to the parameters in the temperature prediction model, and then the model is run. The predicted clutch temperature is calculated based on the initial cooling parameters using the temperature prediction model. Then, the measured clutch temperature under the current operating conditions is obtained. The measured clutch temperature can be obtained through the following experimental measurements:

[0085] The actual clutch temperature can be measured using a clutch plate temperature telemetry device. This device includes multiple temperature sensor signal lines, which are connected to temperature sensors installed at each of the clutch plates to measure their temperature. For example, temperature sensors are embedded in the clutch plates, where the inner radius of the clutch friction pair is r1 = 64 mm and the outer radius is r2 = 90 mm. The device can collect the highest radial temperature during slippage. With the clutch cooling lubricating oil flow rate set to 12.5 L / min, and the transmitted torques set to 50 Nm, 100 Nm, and 150 Nm, and the friction pair speed differences set to 750 rpm, 1500 rpm, and 2500 rpm (friction plate speed is 0), the temperature change curves of the clutch under the conditions of a transmitted torque of 50 Nm and friction pair speed differences of 750 rpm, 1500 rpm, and 2500 rpm can be measured. Simultaneously, the temperature change curves of the clutch under operating conditions with a transmitted torque of 100 Nm and friction pair speed differences of 750 rpm, 1500 rpm, and 2500 rpm can also be measured. Furthermore, the temperature change curves of the clutch under operating conditions with a transmitted torque of 150 Nm and friction pair speed differences of 750 rpm, 1500 rpm, and 2500 rpm can also be measured.

[0086] Specifically, the measurement and testing steps are as follows: First, set the speeds of the drive motor and load motor, for example, by setting the speeds of the drive motor and load motor to achieve a speed difference of 750 rpm for the friction pair. Then, set the cooling flow rate. Next, gradually increase the clutch pressure until the target transmitted torque is reached, for example, 50 Nm. After a period of slippage, release the clutch pressure to enter the clutch cooling stage. Finally, adjust the motor speed to 0 to complete one test. Then, set the speeds of the drive motor and load motor to achieve a speed difference of 1500 rpm for the friction pair. Again, set the cooling flow rate. Then, gradually increase the clutch pressure until the target transmitted torque is reached, for example, 50 Nm. After a period of slippage, release the clutch pressure to enter the clutch cooling stage. Finally, adjust the motor speed to 0 to complete one test. All tests are completed in this manner.

[0087] It is understandable that the temperature change of the clutch under different operating conditions can be measured through experiments. Therefore, after the temperature prediction value of the clutch is calculated by the temperature prediction model, the actual measured value of the clutch temperature under the same operating conditions can be obtained through experiments.

[0088] After obtaining the predicted and measured clutch temperatures, an objective function is constructed, and then the least squares method is used to find the optimal solution for the objective function to update the cooling coefficient. Specifically, the constructed objective function is shown in Equation 9:

[0089]

[0090] In Equation 9, F(x) represents the objective function, f(x i ) is the predicted temperature value of the i-th clutch, y i is the measured temperature value of the i-th clutch, m is the number of sampling points, and x is the cooling parameter. The objective function means solving for a target cooling parameter that minimizes the sum of squares of the errors between the predicted clutch temperature value and the measured clutch temperature value.

[0091] In the embodiments of the present application, a nonlinear least squares method based on a trust region algorithm is used to optimally solve the objective function. According to the definition of the trust region algorithm, at each feasible point x k , under the given trust region radius r k , the search direction d is solved by Equation 10:

[0092]

[0093] where s.t.||d||≤r k .

[0094] After obtaining the search direction d according to Equation 10, the trust region radius r k is corrected, and the calculation is terminated by continuous iteration until the convergence condition is satisfied or the maximum number of iterations is reached.

[0095] Herein, Equation 11 is used to judge whether the search direction d is reasonable according to the magnitude of the coefficient ρ k .

[0096]

[0097] In the k-th iteration calculation, u is the lower bound of the trust region, η is the upper bound of the trust region, where η=(r-r1) / (r2-r1), and r is the position of the sensor measuring point. Specifically, by comparing the calculated ρ k with the lower trust region bound u and the upper trust region bound η, it can be determined whether the search region is appropriate, and corresponding adjustment can be performed according to the judgment result.

[0098] If ρ k <u, it is considered that the search region is too large. At this time, it is necessary to reduce the trust region radius, and set the value of the search center point x k unchanged;

[0099] If u<ρ k <η, it is considered that the search region is moderate. At this time, the value of the trust region radius r k is not adjusted, the x k value of the search center point is adjusted, and x is set k+1 =x k +d;

[0100] If ρ k If the value is greater than η, then the search region is considered too small, and the trust region radius is expanded. Adjust center point x k Let x be the value of x. k+1 =x k +d.

[0101] In this embodiment of the application, the iterative convergence and termination conditions, as well as the upper and lower bounds of the trust region, are set as shown in Table 1.

[0102] Table 1. Parameter settings for least squares method

[0103] 0.001 100 0.25 0.75

[0104] Reference Figure 3 , Figure 3 This is a flowchart of updating cooling parameters provided in the embodiments of this application, including but not limited to steps S301 to S307.

[0105] Step S301: Initialize the temperature prediction model parameters;

[0106] Step S302: The temperature prediction model calculates the predicted value of the clutch temperature;

[0107] Step S303: Construct an objective function based on the predicted clutch temperature and the measured clutch temperature.

[0108] Step S304: Solve the objective function using the least squares method to update the cooling parameters;

[0109] Step S305: Determine whether the current number of iterations is greater than the set maximum number of iterations; if the current number of iterations is greater than the set maximum number of iterations, output the target cooling parameter;

[0110] Step S306: If the current iteration number is not greater than the set maximum iteration number, determine whether the sum of squares of the errors between the predicted clutch temperature and the measured clutch temperature is less than the set value.

[0111] Step S307: If the sum of squares of the errors between the predicted clutch temperature and the measured clutch temperature is less than the set value, output the target cooling parameter; if the sum of squares of the errors between the predicted clutch temperature and the measured clutch temperature is not less than the set value, return to step S302.

[0112] Reference Figure 4 , Figure 4 This is a schematic diagram of all updated cooling parameter maps provided in the embodiments of this application. Figure 4The two local maxima are the maximum cooling parameter numbers corresponding to speed differences of 1500 rpm and 2600 rpm, respectively. The local maxima indicate that for the same speed difference, there exists an optimal cooling parameter under different clutch pressure conditions that minimizes the friction pair temperature. At this time, the cooling parameter is only related to the lubricating oil volume flow rate Q. Therefore, there exists an optimal lubricating oil volume flow rate Q that maximizes the cooling parameter. This method can be applied to optimize the cooling parameter under the same speed difference but different pressure conditions.

[0113] In this embodiment, the cooling parameters are continuously updated using the least squares method to obtain the optimal target cooling parameters, ensuring that the clutch temperature predicted by the temperature prediction model is as close as possible to the actual clutch temperature. The updated target cooling parameters are then input into the temperature prediction model, enabling it to predict the clutch temperature, which includes the temperature of the clutch plates and friction plates. By inputting the updated target cooling parameters, the prediction accuracy of the temperature prediction model can be improved.

[0114] Please see Figure 5 This application embodiment also provides a clutch temperature prediction device 50, which can implement the above-described clutch temperature prediction method. The device includes:

[0115] The calculation module 501 is used to calculate the predicted value of the clutch temperature based on the clutch cooling parameters using a pre-built temperature prediction model.

[0116] The update module 502 is used to update the cooling parameters based on the predicted clutch temperature and the measured clutch temperature using the least squares method.

[0117] The output module 503 is used to return the steps of calculating the predicted value of clutch temperature based on the cooling parameters of clutch using a pre-built temperature prediction model until the termination condition is met, and output the updated target cooling parameters. The termination condition includes a first termination condition or a second termination condition. The first termination condition is that the current iteration number is greater than the set iteration number, and the second termination condition is that the sum of the squares of the errors between the predicted clutch temperature value and the measured clutch temperature value is less than the set value.

[0118] The prediction module 504 is used to predict the clutch temperature based on the target cooling parameters using a temperature prediction model.

[0119] The specific implementation of the clutch temperature prediction device is basically the same as the specific embodiment of the clutch temperature prediction method described above, and will not be repeated here.

[0120] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described clutch temperature prediction method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0121] Please see Figure 6 , Figure 6 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0122] The processor 601 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0123] The memory 602 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 602 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 602 and called by the processor 601 to execute the clutch temperature prediction method of the embodiments of this application.

[0124] The input / output interface 603 is used to implement information input and output;

[0125] The communication interface 604 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0126] Bus 605 transmits information between various components of the device (e.g., processor 601, memory 602, input / output interface 603, and communication interface 604);

[0127] The processor 601, memory 602, input / output interface 603, and communication interface 604 are connected to each other within the device via bus 605.

[0128] This application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the above-described clutch temperature prediction method.

[0129] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0130] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0131] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0132] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0134] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0135] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0137] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0138] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0139] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0140] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for predicting clutch temperature, characterized in that, The method includes: Based on the clutch cooling parameters, the predicted clutch temperature is calculated using a pre-built temperature prediction model. Based on the predicted clutch temperature and the measured clutch temperature, the cooling parameters are updated using the least squares method. Return to the step of calculating the predicted value of clutch temperature based on the cooling parameters of the clutch using a pre-built temperature prediction model, until the termination condition is met, and output the updated target cooling parameters. The termination condition includes a first termination condition or a second termination condition. The first termination condition is that the current iteration number is greater than the set iteration number, and the second termination condition is that the sum of the squares of the errors between the predicted value of clutch temperature and the measured value of clutch temperature is less than a set value. The clutch temperature is predicted using the temperature prediction model based on the target cooling parameters. The clutch temperature includes the temperature of the clutch steel plates and the temperature of the friction plates. The step of predicting the clutch temperature using the temperature prediction model based on the target cooling parameters includes: The convective heat transfer coefficient of the steel sheet and the convective heat transfer coefficient of the friction plate are calculated by the temperature prediction model based on the target cooling parameters. The temperature of the clutch steel sheet is calculated based on the convective heat transfer coefficient of the steel sheet. The friction plate temperature of the clutch is calculated based on the convective heat transfer coefficient of the friction plate. The convective heat transfer coefficients of the steel sheet and the friction plate are calculated using the temperature prediction model based on the target cooling parameters, and this is done through the following formula: ； ； in, ; In the formula, Indicates the convective heat transfer coefficient of the steel sheet. Represents the steel sheet constant. Target cooling parameters, wherein the target cooling parameters are related to the lubricating oil volumetric flow rate. Friction pair speed difference Torque transmission between friction pairs Related functions, This represents a function related to lubricating oil temperature and lubricating oil flow rate. This represents the convective heat transfer coefficient of the friction plate. Represents the friction constant. This indicates that the friction pair transmits torque. Indicates the number of friction pairs; This represents the equivalent friction coefficient of the friction pair. Indicates the first The equivalent pressure of each friction pair; This represents the equivalent friction radius of the friction pair. Indicates the inner radius. Indicates the outer radius.

2. The method according to claim 1, characterized in that, The method of calculating the predicted clutch temperature based on the clutch cooling parameters using a pre-built temperature prediction model includes: The convective heat transfer coefficient is calculated based on the cooling parameters of the clutch using a pre-built temperature prediction model. The predicted clutch temperature is calculated based on the convective heat transfer coefficient.

3. The method according to claim 1, characterized in that, The step of updating the cooling parameters based on the predicted clutch temperature and the measured clutch temperature using the least squares method includes: Based on the predicted clutch temperature and the measured clutch temperature, an objective function is constructed. The objective function is solved using the least squares method to update the cooling parameters.

4. The method according to claim 1, characterized in that, The clutch steel plate temperature is calculated based on the convective heat transfer coefficient of the steel plate using the following formula: ； in, ; In the formula, Indicates that the steel sheet is in Temperature at any moment Indicates that the steel sheet is in Temperature at any moment This represents the heat distribution coefficient of the steel sheet. Indicates speed difference, Indicates the quality of the steel sheet. This indicates the specific heat capacity of the steel sheet. Indicates the convective heat transfer coefficient of the steel sheet. This indicates the heat exchange area of ​​the steel sheet. Indicates that the lubricating oil is in Temperature at any moment This indicates the thermal conductivity of the steel sheet. This indicates the density of the steel sheet. This indicates the specific heat capacity of the friction plate. This indicates the thermal conductivity of the friction plate. This indicates the density of the friction plate.

5. The method according to claim 1, characterized in that, The friction plate temperature of the clutch is calculated based on the convective heat transfer coefficient of the friction plate using the following formula: ； in, ; In the formula, Indicates the friction plate at Temperature at any moment Indicates the friction plate at Temperature at any moment This represents the heat distribution coefficient of the friction plate. Indicates speed difference, Indicates the mass of the friction plate. This indicates the specific heat capacity of the friction plate. This represents the convective heat transfer coefficient of the friction plate. This indicates the heat transfer area of ​​the friction plate. Indicates that the lubricating oil is in Temperature at any moment This indicates the thermal conductivity of the steel sheet. This indicates the density of the steel sheet. This indicates the specific heat capacity of the steel sheet. This indicates the thermal conductivity of the friction plate. This indicates the density of the friction plate.

6. A clutch temperature prediction device, characterized in that, The device includes: The calculation module is used to calculate the predicted clutch temperature based on the clutch cooling parameters using a pre-built temperature prediction model. The update module is used to update the cooling parameters based on the predicted clutch temperature and the measured clutch temperature using the least squares method. The output module is used to return the steps of calculating the predicted value of clutch temperature based on the cooling parameters of clutch using a pre-built temperature prediction model, until the termination condition is met, and output the updated target cooling parameters. The termination condition includes a first termination condition or a second termination condition. The first termination condition is that the current iteration number is greater than the set iteration number, and the second termination condition is that the sum of the squares of the errors between the predicted value of clutch temperature and the measured value of clutch temperature is less than the set value. The prediction module is used to predict the clutch temperature based on the target cooling parameters using the temperature prediction model. The clutch temperature includes the temperature of the clutch steel plates and the temperature of the friction plates. The prediction module is also used for: The convective heat transfer coefficient of the steel sheet and the convective heat transfer coefficient of the friction plate are calculated by the temperature prediction model based on the target cooling parameters. The temperature of the clutch steel sheet is calculated based on the convective heat transfer coefficient of the steel sheet. The friction plate temperature of the clutch is calculated based on the convective heat transfer coefficient of the friction plate. The calculation of the convective heat transfer coefficients of the steel sheet and the friction plate based on the target cooling parameters using the temperature prediction model is performed using the following formula: ； ； in, ; In the formula, Indicates the convective heat transfer coefficient of the steel sheet. Represents the steel sheet constant. Target cooling parameters, wherein the target cooling parameters are related to the lubricating oil volumetric flow rate. Friction pair speed difference Torque transmission between friction pairs Related functions, This represents a function related to lubricating oil temperature and lubricating oil flow rate. This represents the convective heat transfer coefficient of the friction plate. Represents the friction constant. This indicates that the friction pair transmits torque. Indicates the number of friction pairs; This represents the equivalent friction coefficient of the friction pair. Indicates the first The equivalent pressure of each friction pair; This represents the equivalent friction radius of the friction pair. Indicates the inner radius. Indicates the outer radius.

7. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

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