Self-learning method and device for braking friction coefficient, vehicle and storage medium

Abstract

The invention relates to the technical field of vehicle control, in particular to a braking friction coefficient self-learning method and device, a vehicle and a storage medium. The method comprises the following steps: obtaining bench test data of a vehicle, and establishing a preset braking temperature model by using a CAE technology based on the bench test data; actual driving information of the vehicle is obtained, and the current braking friction pair temperature is determined based on a preset braking temperature model and the actual driving information; and determining the current brake friction coefficient according to the current brake friction pair temperature. Therefore, by accurately acquiring the real-time temperature of the braking system before braking and matching the accurate braking friction coefficient according to the temperature, the calculated braking torque can be closer to the actual torque, and the problems that in the braking process, due to temperature rise, the feeling of a brake pedal is different, and deceleration output is affected are solved; therefore, the braking performance is optimized, and the stable and reliable speed reduction effect of the vehicle under various working conditions is ensured.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a self-learning method, device, vehicle and storage medium for a braking friction coefficient. Background Art

[0002] At present, new energy vehicles are generally equipped with an electro-hydraulic coordination system, and their braking systems have been decoupled. In theory, drivers should be able to experience more stable deceleration. However, in the actual braking process, due to long-term or high-intensity braking, the braking system will generate heat due to friction heating, and this heat will cause the friction coefficient of the brake pad to change. The increase or decrease in the friction coefficient will affect the braking effect, which will in turn cause the vehicle's deceleration speed to fluctuate, making the deceleration process less stable than expected.

[0003] In the related art, the braking friction coefficient is usually determined by relying on an empirical formula or a preset friction coefficient table.

[0004] However, this method cannot reflect the actual changes in the friction coefficient in real time, especially under complex and changeable road conditions and ambient temperatures. During braking, the temperature rises, causing differences in the brake pedal feel that affects the deceleration output, which needs to be solved urgently. Summary of the invention

[0005] The present application provides a self-learning method, device, vehicle and storage medium for the braking friction coefficient to solve the problem that the brake pedal feeling varies due to the increase in temperature during braking, affecting the deceleration output, thereby optimizing the braking performance and ensuring that the vehicle can obtain a stable and reliable deceleration effect under various working conditions.

[0006] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a self-learning method for braking friction coefficient, comprising the following steps:

[0007] Acquire bench test data of the vehicle, and establish a preset brake temperature model based on the bench test data using CAE (Computer Aided Engineering) technology;

[0008] Acquiring actual driving information of the vehicle, and determining a current brake friction pair temperature based on the preset brake temperature model and the actual driving information;

[0009] The current brake friction coefficient is determined according to the current brake friction pair temperature.

[0010] According to one embodiment of the present application, the bench test data includes at least one of a friction pair temperature, a friction coefficient, and a vehicle speed.

[0011] According to an embodiment of the present application, the step of establishing a preset brake temperature model based on the bench test data using CAE technology includes:

[0012] Based on the bench test data, fitting the relationship curve between the friction pair temperature and the braking energy using the CAE technology;

[0013] Determining the number of iterations and model parameters, and establishing an initial brake temperature model based on the number of iterations and the model parameters using a relationship curve between the friction plate temperature and the brake energy;

[0014] Comparing the initial brake temperature model with bench test data and actual vehicle test data respectively, and determining whether the accuracy of the initial brake temperature model meets a preset standard;

[0015] If the accuracy of the initial brake temperature model meets the preset standard, the initial brake temperature model is used as the preset brake temperature model; otherwise, the number of iterations and the model parameters are adjusted until the accuracy of the initial brake temperature model meets the preset standard.

[0016] According to an embodiment of the present application, determining the current braking friction coefficient according to the current braking friction pair temperature includes:

[0017] Iteratively determining a current brake friction pair temperature based on the actual driving information;

[0018] A current braking friction coefficient is determined based on the preset braking temperature model and the current braking friction pair temperature.

[0019] According to an embodiment of the present application, after determining the current braking friction coefficient according to the current braking friction pair temperature, the method further includes:

[0020] determining a current hydraulic pressure of a braking system according to the current braking friction coefficient;

[0021] Based on the current hydraulic pressure, the output torque during vehicle braking is controlled to be maintained at a preset constant state.

[0022] According to the self-learning method of the brake friction coefficient proposed in the embodiment of the present application, by obtaining the bench test data of the vehicle, a preset brake temperature model can be established based on the bench test data using CAE technology; the actual driving information of the vehicle is obtained, and the current brake friction pair temperature is determined based on the preset brake temperature model and the actual driving information; the current brake friction coefficient is determined according to the current brake friction pair temperature. Thus, by accurately obtaining the real-time temperature of the brake system before braking and matching the precise brake friction coefficient according to the temperature, the calculated braking torque can be made closer to the actual torque, solving the problem of the difference in brake pedal feeling caused by the temperature increase during braking, which affects the deceleration output, thereby optimizing the braking performance and ensuring that the vehicle can obtain a stable and reliable deceleration effect under various working conditions.

[0023] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a self-learning device for braking friction coefficient, comprising:

[0024] Establishing a module for acquiring bench test data of a vehicle and establishing a preset brake temperature model using CAE technology based on the bench test data;

[0025] A first determination module, used to obtain actual driving information of the vehicle, and determine the current brake friction pair temperature based on the preset brake temperature model and the actual driving information;

[0026] The second determination module is used to determine the current braking friction coefficient according to the current braking friction pair temperature.

[0027] According to one embodiment of the present application, the bench test data includes at least one of a friction pair temperature, a friction coefficient, and a vehicle speed.

[0028] According to one embodiment of the present application, the establishment module is specifically used to:

[0029] Based on the bench test data, fitting the relationship curve between the friction pair temperature and the braking energy using the CAE technology;

[0030] Determining the number of iterations and model parameters, and establishing an initial brake temperature model based on the number of iterations and the model parameters using a relationship curve between the friction plate temperature and the brake energy;

[0031] Comparing the initial brake temperature model with bench test data and actual vehicle test data respectively, and determining whether the accuracy of the initial brake temperature model meets a preset standard;

[0032] If the accuracy of the initial brake temperature model meets the preset standard, the initial brake temperature model is used as the preset brake temperature model; otherwise, the number of iterations and the model parameters are adjusted until the accuracy of the initial brake temperature model meets the preset standard.

[0033] According to an embodiment of the present application, the second determining module is specifically configured to:

[0034] Iteratively determining a current brake friction pair temperature based on the actual driving information;

[0035] A current braking friction coefficient is determined based on the preset braking temperature model and the current braking friction pair temperature.

[0036] According to an embodiment of the present application, after determining the current brake friction coefficient according to the current brake friction pair temperature, the second determination module is further configured to:

[0037] determining a current hydraulic pressure of a braking system according to the current braking friction coefficient;

[0038] Based on the current hydraulic pressure, the output torque during vehicle braking is controlled to be maintained at a preset constant state.

[0039] According to the self-learning device of the brake friction coefficient proposed in the embodiment of the present application, by acquiring the bench test data of the vehicle, a preset brake temperature model can be established based on the bench test data using CAE technology; the actual driving information of the vehicle is acquired, and the current brake friction pair temperature is determined based on the preset brake temperature model and the actual driving information; the current brake friction coefficient is determined according to the current brake friction pair temperature. Thus, by accurately acquiring the real-time temperature of the brake system before braking and matching the precise brake friction coefficient according to the temperature, the calculated braking torque can be made closer to the actual torque, solving the problem of the difference in brake pedal feeling caused by the temperature increase during braking, which affects the deceleration output, thereby optimizing the braking performance and ensuring that the vehicle can obtain a stable and reliable deceleration effect under various working conditions.

[0040] To achieve the above objectives, the third aspect of the present application proposes a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the self-learning method of the braking friction coefficient as described in the above embodiment.

[0041] To achieve the above objectives, the fourth aspect of the present application proposes a computer-readable storage medium on which a computer program is stored. The program is executed by a processor to implement the self-learning method of the braking friction coefficient as described in the above embodiments.

[0042] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0044] Figure 1 A flowchart of a self-learning method for a braking friction coefficient provided according to an embodiment of the present application;

[0045] Figure 2 A flowchart of another self-learning method of brake friction coefficient provided according to an embodiment of the present application;

[0046] Figure 3 A block diagram of a self-learning device for brake friction coefficient provided according to an embodiment of the present application;

[0047] Figure 4 It is a schematic diagram of the structure of a vehicle provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0049] The following describes the self-learning method, device, vehicle and storage medium of the braking friction coefficient proposed in the embodiments of the present application with reference to the accompanying drawings. First, the self-learning of the braking friction coefficient proposed in the embodiments of the present application will be described with reference to the accompanying drawings.

[0050] Figure 1 It is a flow chart of a self-learning method of brake friction coefficient according to an embodiment of the present application.

[0051] For example, Figure 1 As shown, the self-learning method of the braking friction coefficient includes the following steps:

[0052] In step S101 , the bench test data of the vehicle is obtained, and based on the bench test data, a preset brake temperature model is established using CAE technology.

[0053] It can be understood that CAE technology is a technology that uses computer software to simulate product design, analyze and predict product performance.

[0054] Specifically, in order to ensure the performance and safety of the vehicle, obtaining the vehicle's bench test data is a crucial step. Bench testing is an experiment conducted in a controlled environment to evaluate the performance of vehicle components or systems. It is usually carried out on special equipment in a laboratory or test center, which can simulate actual use conditions but is not affected by the external environment. After obtaining this data, a preset brake temperature model can be established using computer-aided engineering (CAE) technology. This model is a simulation model used to predict and analyze the heat and temperature changes generated by the vehicle's braking system during use. It simulates the braking performance of the vehicle under various conditions, which is crucial to ensuring the reliability and safety of the braking system.

[0055] In some embodiments, the bench test data includes at least one of the friction pair temperature, the friction coefficient, and the vehicle speed.

[0056] That is to say, during bench testing, the data collected covers multiple key parameters such as the temperature of the friction pair, the friction coefficient, and the vehicle speed. Among them, the temperature of the friction pair refers to the temperature of the surface of the component under the action of friction; the friction coefficient describes the ratio of the friction force generated when two surfaces contact to the normal pressure; and the vehicle speed refers to the driving speed of the vehicle during the test. These parameters are important indicators for evaluating the performance of vehicle components, especially friction components such as the braking system.

[0057] For ease of understanding, the following describes in detail how to establish a preset brake temperature model using CAE technology based on bench test data.

[0058] As a possible implementation method, in some embodiments, a preset brake temperature model is established based on bench test data using CAE technology, including: based on the bench test data, using CAE technology to fit the relationship curve between the friction pair temperature and the brake energy; determining the number of iterations and model parameters, and based on the number of iterations and model parameters, establishing an initial brake temperature model using the relationship curve between the friction plate temperature and the brake energy; comparing the initial brake temperature model with the bench test data and the actual vehicle test data, respectively, to determine whether the accuracy of the initial brake temperature model meets the preset standard; if the accuracy of the initial brake temperature model meets the preset standard, the initial brake temperature model is used as the preset brake temperature model, otherwise the number of iterations and model parameters are adjusted until the accuracy of the initial brake temperature model meets the preset standard.

[0059] Specifically, the bench test data (including as much data as possible such as friction pair temperature, friction coefficient, vehicle speed, etc.) are input into the CAE system in the form of a database. The braking process is simulated by using these bench test data through the application of CAE technology, and a curve that can reflect the relationship between the friction pair temperature and the braking energy is fitted. Based on the determined number of iterations and model parameters, through multiple iterations and optimizations, an initial brake temperature model is established based on the relationship curve between the friction plate temperature and the braking energy. In order to verify the accuracy and reliability of this initial brake temperature model, the prediction results of the initial brake temperature model can be compared and analyzed with the bench test data and the actual vehicle test data. Through this comparison, it can be evaluated whether the initial brake temperature model can accurately reflect the actual braking process. If the accuracy of the initial brake temperature model meets the preset standard, then this model can be accepted and used as the preset brake temperature model for subsequent research and development. On the contrary, if the accuracy of the model fails to meet the preset standard, the number of iterations and model parameters can be adjusted, and then the model fitting and verification work can be re-performed to obtain a brake temperature model with sufficient accuracy as the preset brake temperature model.

[0060] In step S102, actual driving information of the vehicle is acquired, and the current brake friction pair temperature is determined based on a preset brake temperature model and the actual driving information.

[0061] Specifically, after obtaining the preset brake temperature model, the preset brake temperature model can be input into the vehicle brake control system. The system can determine the current brake friction pair temperature based on the actual driving information of the vehicle during driving, including key information such as the number of brakes, brake deceleration and brake speed, combined with the preset brake temperature model.

[0062] In step S103, the current brake friction coefficient is determined according to the current brake friction pair temperature.

[0063] As a possible implementation method, in some embodiments, the current braking friction coefficient is determined according to the current braking friction pair temperature, including: iteratively determining the current braking friction pair temperature based on actual driving information; and determining the current braking friction coefficient based on a preset braking temperature model and the current braking friction pair temperature.

[0064] Specifically, the braking control system can iterate the current braking friction pair temperature T1 through the preset braking temperature model according to the actual driving information, and at the next braking, the current braking friction pair temperature T2 can be iterated based on the previous braking friction pair temperature T1 through the preset braking temperature model, and so on. After obtaining the current braking friction pair temperature, the braking friction coefficient corresponding to the temperature can be obtained through the preset braking temperature model.

[0065] Furthermore, in some embodiments, after determining the current braking friction coefficient according to the current braking friction pair temperature, it also includes: determining the current hydraulic pressure of the braking system according to the current braking friction coefficient; and based on the current hydraulic pressure, controlling the output torque of the vehicle during braking to remain in a preset constant state.

[0066] In other words, the braking control system can benchmark the braking torque at normal temperature, and calculate the current hydraulic pressure of the braking system based on the current braking friction coefficient and the required braking torque, and then convert the current hydraulic pressure into a more accurate current braking torque through a suitable algorithm (such as multiplication, etc.). Therefore, by adjusting the hydraulic pressure in real time, it can ensure that the output torque of the vehicle during braking is maintained at a preset constant state, thereby improving the braking performance and safety of the vehicle.

[0067] To facilitate those skilled in the art to further understand the self-learning method of the braking friction coefficient proposed in the embodiment of the present application, the following is combined with Figure 2 For further elaboration.

[0068] like Figure 2 As shown, the self-learning method of the braking friction coefficient may also include the following steps:

[0069] Step 1: Obtain bench test data.

[0070] Step 2: Input bench test data into CAE to establish a temperature model.

[0071] Step 3: Input the temperature model into the vehicle brake control system, and obtain the brake friction pair temperature in real time according to the temperature model.

[0072] Step 4: Determine the brake friction coefficient under the current working condition based on the brake friction pair temperature.

[0073] Step 5: Compare the braking torque value corresponding to the braking friction coefficient at normal temperature.

[0074] Step 6: Output stable braking torque through pressure adjustment.

[0075] According to the self-learning method of the brake friction coefficient proposed in the embodiment of the present application, by obtaining the bench test data of the vehicle, a preset brake temperature model can be established based on the bench test data using CAE technology; the actual driving information of the vehicle is obtained, and the current brake friction pair temperature is determined based on the preset brake temperature model and the actual driving information; the current brake friction coefficient is determined according to the current brake friction pair temperature. Thus, by accurately obtaining the real-time temperature of the brake system before braking and matching the precise brake friction coefficient according to the temperature, the calculated braking torque can be made closer to the actual torque, solving the problem of the difference in brake pedal feeling caused by the temperature increase during braking, which affects the deceleration output, thereby optimizing the braking performance and ensuring that the vehicle can obtain a stable and reliable deceleration effect under various working conditions.

[0076] Next, the self-learning device for the braking friction coefficient proposed in the embodiment of the present application is described with reference to the accompanying drawings.

[0077] Figure 3 It is a block diagram of a self-learning device for brake friction coefficient according to an embodiment of the present application.

[0078] like Figure 3 As shown, the self-learning device 10 of the braking friction coefficient includes: an establishing module 100 , a first determining module 200 and a second determining module 300 .

[0079] The module 100 is established to obtain bench test data of the vehicle and establish a preset brake temperature model based on the bench test data by using CAE technology;

[0080] The first determination module 200 is used to obtain actual driving information of the vehicle and determine the current brake friction pair temperature based on a preset brake temperature model and the actual driving information;

[0081] The second determination module 300 is used to determine the current braking friction coefficient according to the current braking friction pair temperature.

[0082] Further, in some embodiments, the bench test data includes at least one of a friction pair temperature, a friction coefficient, and a vehicle speed.

[0083] Furthermore, in some embodiments, the establishment module 100 is specifically used to:

[0084] Based on the bench test data, CAE technology is used to fit the relationship curve between friction pair temperature and braking energy;

[0085] Determine the number of iterations and model parameters, and establish an initial brake temperature model based on the number of iterations and the model parameters using a relationship curve between friction plate temperature and brake energy;

[0086] Compare the initial brake temperature model with bench test data and real vehicle test data respectively to determine whether the accuracy of the initial brake temperature model meets the preset standard;

[0087] If the accuracy of the initial brake temperature model meets the preset standard, the initial brake temperature model is used as the preset brake temperature model, otherwise the number of iterations and model parameters are adjusted until the accuracy of the initial brake temperature model meets the preset standard.

[0088] Further, in some embodiments, the second determining module 300 is specifically configured to:

[0089] Based on actual driving information, iteratively determine the current brake friction pair temperature;

[0090] The current brake friction coefficient is determined based on a preset brake temperature model and the current brake friction pair temperature.

[0091] Further, in some embodiments, after determining the current brake friction coefficient according to the current brake friction pair temperature, the second determination module 300 is further configured to:

[0092] determining a current hydraulic pressure of the brake system according to a current brake friction coefficient;

[0093] Based on the current hydraulic pressure, the output torque during vehicle braking is controlled to remain at a preset constant state.

[0094] It should be noted that the aforementioned explanation of the embodiment of the self-learning method of the braking friction coefficient is also applicable to the self-learning device of the braking friction coefficient of this embodiment, and will not be repeated here.

[0095] According to the self-learning device of the brake friction coefficient proposed in the embodiment of the present application, by acquiring the bench test data of the vehicle, a preset brake temperature model can be established based on the bench test data using CAE technology; the actual driving information of the vehicle is acquired, and the current brake friction pair temperature is determined based on the preset brake temperature model and the actual driving information; the current brake friction coefficient is determined according to the current brake friction pair temperature. Thus, by accurately acquiring the real-time temperature of the brake system before braking and matching the precise brake friction coefficient according to the temperature, the calculated braking torque can be made closer to the actual torque, solving the problem of the difference in brake pedal feeling caused by the temperature increase during braking, which affects the deceleration output, thereby optimizing the braking performance and ensuring that the vehicle can obtain a stable and reliable deceleration effect under various working conditions.

[0096] Figure 4 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

[0097] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0098] When the processor 402 executes the program, the self-learning method of the braking friction coefficient provided in the above embodiment is implemented.

[0099] Furthermore, the vehicle also includes:

[0100] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0101] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0102] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0103] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0104] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0105] The processor 402 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0106] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned self-learning method of the braking friction coefficient.

[0107] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0108] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0109] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A self-learning method for brake friction coefficient, characterized in that: The following steps are involved: Acquire bench test data of the vehicle, and establish a preset brake temperature model using CAE technology based on the bench test data; Acquiring actual driving information of the vehicle, and determining a current brake friction pair temperature based on the preset brake temperature model and the actual driving information; The current brake friction coefficient is determined according to the current brake friction pair temperature.

2. The method according to claim 1, characterized in that The bench test data includes at least one of a friction pair temperature, a friction coefficient, and a vehicle speed.

3. The method according to claim 2, characterized in that The method of establishing a preset brake temperature model based on the bench test data using CAE technology includes: Based on the bench test data, fitting the relationship curve between the friction pair temperature and the braking energy using the CAE technology; Determining the number of iterations and model parameters, and establishing an initial brake temperature model based on the number of iterations and the model parameters using a relationship curve between the friction plate temperature and the brake energy; Comparing the initial brake temperature model with bench test data and actual vehicle test data respectively, and determining whether the accuracy of the initial brake temperature model meets a preset standard; If the accuracy of the initial brake temperature model meets the preset standard, the initial brake temperature model is used as the preset brake temperature model; otherwise, the number of iterations and the model parameters are adjusted until the accuracy of the initial brake temperature model meets the preset standard.

4. The method according to claim 1, characterized in that: Determining the current braking friction coefficient according to the current braking friction pair temperature includes: Iteratively determining a current brake friction pair temperature based on the actual driving information; A current braking friction coefficient is determined based on the preset braking temperature model and the current braking friction pair temperature.

5. The method according to claim 1, characterized in that After determining the current brake friction coefficient according to the current brake friction pair temperature, the method further includes: determining a current hydraulic pressure of a braking system according to the current braking friction coefficient; Based on the current hydraulic pressure, the output torque during vehicle braking is controlled to be maintained at a preset constant state.

6. A self-learning device for brake friction coefficient, characterized in that: include: Establishing a module for acquiring bench test data of a vehicle and establishing a preset brake temperature model using CAE technology based on the bench test data; A first determination module, used to obtain actual driving information of the vehicle, and determine the current brake friction pair temperature based on the preset brake temperature model and the actual driving information; The second determination module is used to determine the current braking friction coefficient according to the current braking friction pair temperature.

7. The device according to claim 6, characterized in that The bench test data includes at least one of a friction pair temperature, a friction coefficient, and a vehicle speed.

8. The device according to claim 7, characterized in that The establishment module is specifically used for: Based on the bench test data, fitting the relationship curve between the friction pair temperature and the braking energy using the CAE technology; Determining the number of iterations and model parameters, and establishing an initial brake temperature model based on the number of iterations and the model parameters using a relationship curve between the friction plate temperature and the brake energy; Comparing the initial brake temperature model with bench test data and actual vehicle test data respectively, and determining whether the accuracy of the initial brake temperature model meets a preset standard; If the accuracy of the initial brake temperature model meets the preset standard, the initial brake temperature model is used as the preset brake temperature model; otherwise, the number of iterations and the model parameters are adjusted until the accuracy of the initial brake temperature model meets the preset standard.

9. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the self-learning method for the braking friction coefficient as described in any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the self-learning method of the braking friction coefficient as described in any one of claims 1-5.

Images