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

Abstract

The present application relates to a self-learning method and apparatus for a braking friction coefficient, and a vehicle and a storage medium. The method comprises: acquiring bench test data of a vehicle, and on the basis of the bench test data, use CAE technology to establish a preset braking temperature model; acquiring actual travelling information of the vehicle, and on the basis of the preset braking temperature model and the actual travelling information, determining the current temperature of a braking friction pair; and on the basis of the current temperature of the braking friction pair, determining the current braking friction coefficient.

Description

Self-learning methods, devices, vehicles, and storage media for braking friction coefficient

[0001] This application claims priority to Chinese Patent Application No. 202510005099.2, filed on January 2, 2025, entitled "Self-learning method, apparatus, vehicle and storage medium for braking friction coefficient", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of vehicle control technology, and in particular to a self-learning method, device, vehicle, and storage medium for braking friction coefficient. Background Technology

[0003] Currently, most new energy vehicles are equipped with electro-hydraulic coordination systems, which decouple the braking system, theoretically allowing drivers to experience more stable deceleration. However, in actual braking processes, prolonged or high-intensity braking causes the braking system to generate heat due to friction. This heat causes changes in the coefficient of friction of the brake pads. Increases or decreases in the coefficient of friction affect braking performance, leading to fluctuations in vehicle deceleration and making the deceleration process less stable than expected.

[0004] In related technologies, the braking friction coefficient is usually determined by empirical formulas or a pre-set friction coefficient table.

[0005] However, this method cannot reflect the actual changes in the friction coefficient in real time. Especially under complex and variable road conditions and ambient temperatures, the temperature rise during braking causes differences in the feel of the brake pedal, affecting the deceleration output, which urgently needs to be addressed. Summary of the Invention

[0006] This application provides a self-learning method, device, vehicle, and storage medium for the braking friction coefficient to solve the problem of differences in brake pedal feel caused by temperature rise during braking, which affects deceleration output, thereby optimizing braking performance and ensuring that the vehicle can obtain stable and reliable deceleration effect under various operating conditions.

[0007] To achieve the above objectives, the first aspect of this application proposes a self-learning method for braking friction coefficient, comprising the following steps:

[0008] Obtain vehicle bench test data, and based on the bench test data, establish a preset brake temperature model using CAE (Computer Aided Engineering) technology;

[0009] Obtain the vehicle's actual driving information, and determine the current brake friction pair temperature based on the preset brake temperature model and the actual driving information;

[0010] The current braking friction coefficient is determined based on the current temperature of the braking friction pair.

[0011] According to one embodiment of this application, the bench test data includes at least one of friction pair temperature, friction coefficient, and vehicle speed.

[0012] According to one embodiment of this application, the step of establishing a preset brake temperature model using CAE technology based on the bench test data includes:

[0013] Based on the bench test data, the CAE technology is used to fit the relationship curve between the friction pair temperature and braking energy.

[0014] Determine the number of iterations and model parameters, and based on the number of iterations and model parameters, establish an initial braking temperature model using the relationship curve between the friction pair temperature and braking energy;

[0015] The initial braking temperature model is compared with bench test data and real vehicle test data to determine whether the accuracy of the initial braking temperature model meets the preset standard.

[0016] If the accuracy of the initial braking temperature model meets the preset standard, then the initial braking temperature model is used as the preset braking temperature model; otherwise, the number of iterations and the model parameters are adjusted until the accuracy of the initial braking temperature model meets the preset standard.

[0017] According to one embodiment of this application, determining the current braking friction coefficient based on the current braking friction pair temperature includes:

[0018] Based on the actual driving information, the current temperature of the brake friction pair is determined iteratively.

[0019] Based on the preset braking temperature model and the current braking friction pair temperature, the current braking friction coefficient is determined.

[0020] According to one embodiment of this application, after determining the current braking friction coefficient based on the current braking friction pair temperature, the method further includes:

[0021] The current hydraulic pressure of the braking system is determined based on the current braking friction coefficient.

[0022] Based on the current hydraulic pressure, the output torque of the vehicle during braking is kept at a preset constant state.

[0023] According to one embodiment of this application, the actual driving information includes the number of braking operations, braking deceleration, and braking speed within a preset time period. The step of iteratively determining the current brake friction pair temperature based on the actual driving information includes:

[0024] If the number of braking events within the preset time period exceeds a preset braking event threshold, the previous braking friction pair temperature is obtained, and the current braking friction pair temperature is obtained based on the actual driving information and the previous braking friction pair temperature.

[0025] According to one embodiment of this application, determining the current braking friction coefficient based on the current braking friction pair temperature includes:

[0026] Obtain the preset table of the relationship between the temperature of the braking friction pair and the friction coefficient;

[0027] Look up the current braking friction coefficient corresponding to the current braking friction pair temperature in the table of the relationship between braking friction pair temperature and friction coefficient.

[0028] According to the self-learning method for braking friction coefficient proposed in this application, by acquiring vehicle bench test data, a preset braking 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 braking friction pair temperature is determined based on the preset braking temperature model and the actual driving information; the current braking friction coefficient is determined based on the current braking friction pair temperature. Therefore, by accurately acquiring the real-time temperature of the braking system before braking, and matching a precise braking friction coefficient based on the temperature, the calculated braking torque can be closer to the actual torque, solving the problem of discrepancies in brake pedal feel caused by temperature rise during braking, which affects deceleration output. This optimizes braking performance and ensures that the vehicle achieves stable and reliable deceleration under various operating conditions.

[0029] To achieve the above objectives, a second aspect of this application provides a self-learning device for braking friction coefficient, comprising:

[0030] A module is established to acquire bench test data of the vehicle and, based on the bench test data, to establish a preset brake temperature model using CAE technology.

[0031] The first determining module is used to acquire the actual driving information of the vehicle and determine the current temperature of the brake friction pair based on the preset brake temperature model and the actual driving information.

[0032] The second determining module is used to determine the current braking friction coefficient based on the current braking friction pair temperature.

[0033] According to one embodiment of this application, the bench test data includes at least one of friction pair temperature, friction coefficient, and vehicle speed.

[0034] According to one embodiment of this application, the establishment module is specifically used for:

[0035] Based on the bench test data, the CAE technology is used to fit the relationship curve between the friction pair temperature and braking energy.

[0036] Determine the number of iterations and model parameters, and based on the number of iterations and model parameters, establish an initial braking temperature model using the relationship curve between the friction pair temperature and braking energy;

[0037] The initial braking temperature model is compared with bench test data and real vehicle test data to determine whether the accuracy of the initial braking temperature model meets the preset standard.

[0038] If the accuracy of the initial braking temperature model meets the preset standard, then the initial braking temperature model is used as the preset braking temperature model; otherwise, the number of iterations and the model parameters are adjusted until the accuracy of the initial braking temperature model meets the preset standard.

[0039] According to one embodiment of this application, the second determining module is specifically used for:

[0040] Based on the actual driving information, the current temperature of the brake friction pair is determined iteratively.

[0041] Based on the preset braking temperature model and the current braking friction pair temperature, the current braking friction coefficient is determined.

[0042] According to one embodiment of this application, after determining the current braking friction coefficient based on the current braking friction pair temperature, the second determining module is further configured to:

[0043] The current hydraulic pressure of the braking system is determined based on the current braking friction coefficient.

[0044] Based on the current hydraulic pressure, the output torque of the vehicle during braking is kept at a preset constant state.

[0045] According to one embodiment of this application, the second determining module is specifically used for:

[0046] Based on the actual driving information, the current temperature of the brake friction pair is determined iteratively.

[0047] Based on the preset braking temperature model and the current braking friction pair temperature, the current braking friction coefficient is determined.

[0048] According to one embodiment of this application, after determining the current braking friction coefficient based on the current braking friction pair temperature, the second determining module is further configured to:

[0049] The current hydraulic pressure of the braking system is determined based on the current braking friction coefficient.

[0050] Based on the current hydraulic pressure, the output torque of the vehicle during braking is kept at a preset constant state.

[0051] According to one embodiment of this application, the second determining module is specifically used for:

[0052] Obtain the preset table of the relationship between the temperature of the braking friction pair and the friction coefficient;

[0053] Look up the current braking friction coefficient corresponding to the current braking friction pair temperature in the table of the relationship between braking friction pair temperature and friction coefficient.

[0054] The self-learning device for braking friction coefficient proposed in this application acquires vehicle bench test data and establishes a preset braking temperature model based on the bench test data using CAE technology; it acquires actual vehicle driving information and determines the current braking friction pair temperature based on the preset braking temperature model and the actual driving information; and it determines the current braking friction coefficient based on the current braking friction pair temperature. Therefore, by accurately acquiring the real-time temperature of the braking system before braking and matching a precise braking friction coefficient based on the temperature, the calculated braking torque can be closer to the actual torque. This solves the problem of discrepancies in brake pedal feel caused by temperature increases during braking, which affects deceleration output, thereby optimizing braking performance and ensuring stable and reliable deceleration under various operating conditions.

[0055] To achieve the above objectives, a third aspect of this application provides 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 for the braking friction coefficient as described in the above embodiments.

[0056] To achieve the above objectives, a fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the self-learning method for the braking friction coefficient as described in the above embodiments.

[0057] To achieve the above objectives, a fifth aspect of this application provides a computer program product that, when run on a computer, causes the computer to execute the self-learning method for braking friction coefficient as described above.

[0058] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0059] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0060] Figure 1 is a flowchart of a self-learning method for braking friction coefficient provided according to an embodiment of this application;

[0061] Figure 2 is a flowchart of another self-learning method for braking friction coefficient provided according to an embodiment of this application;

[0062] Figure 3 is a block diagram of a self-learning device for braking friction coefficient provided according to an embodiment of this application;

[0063] Figure 4 is a structural schematic diagram of a vehicle provided according to an embodiment of this application. Detailed Implementation

[0064] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0065] The following describes, with reference to the accompanying drawings, a self-learning method, apparatus, vehicle, and storage medium for the braking friction coefficient according to embodiments of this application. First, the self-learning method for the braking friction coefficient according to embodiments of this application will be described with reference to the accompanying drawings.

[0066] Figure 1 is a flowchart of a self-learning method for braking friction coefficient according to an embodiment of this application.

[0067] For example, as shown in Figure 1, the self-learning method for the braking friction coefficient includes the following steps:

[0068] In step S101, bench test data of the vehicle is acquired, and a preset brake temperature model is established based on the bench test data using CAE technology.

[0069] Understandably, CAE technology is a technique that uses computer software to simulate product design, analyze, and predict product performance.

[0070] Specifically, obtaining bench test data is a crucial step in ensuring vehicle performance and safety. Bench testing is an experiment conducted in a controlled environment to evaluate the performance of vehicle components or systems. It is typically performed on specialized equipment in laboratories or testing centers, simulating real-world usage conditions but unaffected by external environmental factors. After acquiring this data, Computer-Aided Engineering (CAE) technology can be used to build a pre-defined brake temperature model. This brake temperature model is a simulation model used to predict and analyze the heat and temperature changes generated by the vehicle's braking system during use, simulating the vehicle's braking performance under various conditions. This is essential for ensuring the reliability and safety of the braking system.

[0071] In some embodiments, the bench test data includes at least one of the following: friction pair temperature, friction coefficient, and vehicle speed.

[0072] In other words, the data collected during bench testing covers several key parameters, including the temperature of the friction pair, the coefficient of friction, and vehicle speed. The friction pair temperature refers to the surface temperature of the components under friction; the coefficient of friction describes the ratio of the frictional force generated when two surfaces are in contact to the normal force; and vehicle speed refers to the vehicle's speed during the test. These parameters are all important indicators for evaluating the performance of vehicle components, especially friction components such as braking systems.

[0073] To facilitate understanding, the following details how to establish a preset brake temperature model based on bench test data using CAE technology.

[0074] As one possible implementation, in some embodiments, a preset braking temperature model is established using CAE technology based on bench test data. This includes: fitting a curve showing the relationship between friction pair temperature and braking energy using CAE technology based on the bench test data; determining the number of iterations and model parameters; establishing an initial braking temperature model based on the number of iterations and model parameters using the curve showing the relationship between friction pair temperature and braking energy; comparing the initial braking temperature model with both bench test data and real vehicle test data to determine whether the accuracy of the initial braking temperature model meets a preset standard; if the accuracy of the initial braking temperature model meets the preset standard, then the initial braking temperature model is used as the preset braking temperature model; otherwise, the number of iterations and model parameters are adjusted until the accuracy of the initial braking temperature model meets the preset standard.

[0075] Vehicle speed is a sequence of vehicle speeds, which is used to characterize the speed of a vehicle at different points in time, so that vehicle deceleration can be simulated based on the vehicle speed sequence.

[0076] Braking energy is determined based on vehicle speed; for example, braking energy is determined based on a sequence of vehicle speeds.

[0077] The friction pair temperature is the temperature of the target surface in the friction pad component. The target surface is the two surfaces in the friction pad component that can come into contact with each other to generate friction and use the friction to achieve vehicle braking.

[0078] Specifically, bench test data (including as much data as possible such as friction pair temperature, friction coefficient, and vehicle speed) is input into the CAE system in the form of a database. CAE technology is then used to simulate the braking process using this bench test data, fitting a curve that reflects the relationship between friction pair temperature and braking energy. Based on a determined number of iterations and model parameters, through multiple iterations and optimizations, an initial braking temperature model is established based on the relationship curve between friction pad temperature and braking energy.

[0079] In other words, the CAE system uses bench test data to simulate the braking process and fits a curve that reflects the relationship between the friction pair temperature and braking energy. Then, based on the number of iterations and model parameters, the CAE system iterates and optimizes the relationship curve between the friction pair temperature and braking energy multiple times, and outputs an initial braking temperature model. Here, the CAE system is the system that executes CAE technology.

[0080] To verify the accuracy and reliability of this initial braking temperature model, its predictions can be compared with bench test data and real-vehicle test data. This comparison allows for an assessment of whether the initial braking temperature model accurately reflects the actual braking process. If the accuracy of the initial braking temperature model meets the preset standard, then the model can be accepted and used as the preset braking temperature model for subsequent research and development. Conversely, if the model's accuracy fails to meet the preset standard, the number of iterations and model parameters can be adjusted, and the model fitting and verification process can be repeated to obtain a braking temperature model with sufficiently high accuracy, which can then be used as the preset braking temperature model.

[0081] In step S102, the actual driving information of the vehicle is obtained, and the current temperature of the brake friction pair is determined based on the preset brake temperature model and the actual driving information.

[0082] Specifically, after obtaining the preset brake temperature model, it can be input into the vehicle brake control system. This system can then determine the current brake friction pair temperature based on the vehicle's actual driving information, including key information such as braking frequency, braking deceleration, and braking speed, combined with the preset brake temperature model. In other words, the self-learning of the brake friction coefficient in this application refers to the brake temperature model determining the current brake friction pair temperature based on the vehicle's actual driving information, thereby automatically adapting to changes in actual driving information and autonomously iterating to obtain a current brake friction pair temperature that matches the actual driving information.

[0083] For example, actual driving information is input into a preset braking temperature model executed by the vehicle braking control system, and the current braking friction pair temperature can be iteratively calculated from the preset braking temperature model.

[0084] For example, actual driving information includes: the number of braking operations, braking deceleration, and vehicle speed during braking within a preset time period.

[0085] In step S103, the current braking friction coefficient is determined based on the current braking friction pair temperature.

[0086] As one possible approach, in some embodiments, the current braking friction coefficient is determined based on 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.

[0087] Specifically, the vehicle braking control system can iteratively calculate the current brake friction pair temperature T1 based on actual driving information and a preset brake temperature model. During the next braking operation, it can use the previous brake friction pair temperature T1 as a basis to iteratively calculate the next current brake friction pair temperature T2 using the preset brake temperature model, and so on. After obtaining the current brake friction pair temperature T1, the corresponding brake friction coefficient can be obtained through the preset brake temperature model.

[0088] In other words, in this embodiment, the actual driving information and the previous brake friction pair temperature are input into a preset brake temperature model executed by the vehicle brake control system, and the preset brake temperature model outputs the current brake friction pair temperature and the brake friction coefficient corresponding to the current brake friction pair temperature.

[0089] Specifically, considering that heat accumulates on the brake friction pair when the time interval between two braking actions is relatively short, the vehicle braking control system obtains the number of braking actions within a preset time period and determines whether the number of braking actions within the preset time period is greater than a preset braking action threshold. If the number of braking actions within the preset time period is greater than the preset braking action threshold, the system obtains the temperature of the brake friction pair in the previous braking action and obtains the current temperature of the brake friction pair based on the temperature of the brake friction pair in the previous braking action and the braking deceleration and braking speed in the actual driving information.

[0090] For example, the end time of the preset time period is the time when the user currently presses the brake pedal. That is, if the brake pedal is pressed at the current time, the number of braking actions within the preset time period before the current time is obtained. If the number of braking actions within the preset time period is greater than a preset braking action threshold, the current brake friction pair temperature and the brake friction coefficient corresponding to the current time are determined based on the brake friction pair temperature used during the last braking action and the real-time vehicle information corresponding to the current time.

[0091] It is understandable that if the number of braking events within a preset time period is less than or equal to a preset braking event threshold, the temperature of the previous braking friction pair is a preset value. For example, if the number of braking events within a preset time period is less than or equal to a preset braking event threshold, the temperature of the previous braking friction pair is the ambient temperature, which is obtained through a temperature sensor.

[0092] As one possible approach, in some embodiments, determining the current braking friction coefficient based on the current temperature of the braking friction pair includes:

[0093] Obtain the preset table of relationship between brake friction pair temperature and friction coefficient; query the current brake friction coefficient corresponding to the current brake friction pair temperature in the table of relationship between brake friction pair temperature and friction coefficient.

[0094] Furthermore, in some embodiments, after determining the current braking friction coefficient based on the current braking friction pair temperature, the method further includes: determining the current hydraulic pressure of the braking system based on the current braking friction coefficient; and controlling the output torque of the vehicle during braking to remain in a preset constant state based on the current hydraulic pressure.

[0095] In other words, the vehicle braking control system can be benchmarked against the normal temperature braking torque and calculate the current hydraulic pressure of the braking system based on the current braking friction coefficient and the required braking torque. Then, through a suitable algorithm (such as multiplication), the current hydraulic pressure is converted into a more accurate current braking torque. Thus, by adjusting the hydraulic pressure in real time, the output torque of the vehicle during braking can be kept at a preset constant state, thereby improving the vehicle's braking performance and safety.

[0096] In other words, when a user presses the brake pedal, the vehicle's braking control system acquires the pedal opening at that moment. Based on this opening, it determines the required braking torque. Then, according to the current braking friction coefficient and the required braking torque, it calculates the current required hydraulic pressure and controls the braking system to apply that pressure, thus obtaining the braking torque corresponding to the pedal opening. Specifically, the product of the current braking friction coefficient and the required braking torque equals the product of the current required hydraulic pressure and a preset coefficient.

[0097] Therefore, even if the braking friction coefficient changes due to temperature, the hydraulic pressure of the braking system can be adjusted. When braking based on the adjusted hydraulic pressure, the braking torque corresponding to the opening of the brake pedal can be obtained. This realizes the real-time adjustment of hydraulic pressure according to the current braking friction coefficient and the required braking torque, so that the output torque of the vehicle when braking under extreme temperature conditions is the same as or close to the output torque of the vehicle when braking under normal temperature conditions, thereby improving the braking performance and safety of the vehicle.

[0098] To facilitate those skilled in the art to further understand the self-learning method for braking friction coefficient proposed in the embodiments of this application, further explanation is provided below with reference to Figure 2.

[0099] As shown in Figure 2, the self-learning method for the braking friction coefficient may also include the following steps:

[0100] Step 1: Obtain bench test data.

[0101] Step 1 can be referred to in the description of step S101, and will not be repeated here.

[0102] Step 2: Input the bench test data into CAE to establish a temperature model.

[0103] Step 2 can be referred to the description of step S101, and will not be repeated here.

[0104] Step 3: Input the temperature model into the vehicle braking control system, and obtain the temperature of the brake friction pair in real time based on the temperature model.

[0105] In other words, in step 3, the temperature model will be input into the vehicle braking control system so that the vehicle braking control system can execute the temperature model to process the actual driving information and obtain the current temperature of the brake friction pair.

[0106] The specific process of step 3 can be found in the description of step S102, and will not be repeated here.

[0107] Step 4: Determine the braking friction coefficient under the current operating conditions based on the temperature of the braking friction pair.

[0108] The specific process of step 4 can be found in the description of step S103, and will not be repeated here.

[0109] Step 5: Compare the braking torque value corresponding to the braking friction coefficient at room temperature.

[0110] The specific process of step 5 can be found in the description of step S103, and will not be repeated here.

[0111] Step 6: Adjust the pressure to output a stable braking torque.

[0112] The specific process of step 6 can be found in the description of step S103, and will not be repeated here.

[0113] According to the self-learning method for braking friction coefficient proposed in this application, by acquiring vehicle bench test data, a preset braking 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 braking friction pair temperature is determined based on the preset braking temperature model and the actual driving information; the current braking friction coefficient is determined based on the current braking friction pair temperature. Therefore, by accurately acquiring the real-time temperature of the braking system before braking, and matching a precise braking friction coefficient based on the temperature, the calculated braking torque can be closer to the actual torque, solving the problem of discrepancies in brake pedal feel caused by temperature rise during braking, which affects deceleration output. This optimizes braking performance and ensures that the vehicle achieves stable and reliable deceleration under various operating conditions.

[0114] Next, referring to the accompanying drawings, a self-learning device for braking friction coefficient proposed according to an embodiment of this application is described.

[0115] Figure 3 is a block diagram of a self-learning device for braking friction coefficient according to an embodiment of this application.

[0116] As shown in Figure 3, the self-learning device 10 for braking friction coefficient includes: a setup module 100, a first determination module 200, and a second determination module 300.

[0117] The module 100 is used to acquire bench test data of the vehicle and, based on the bench test data, to establish a preset brake temperature model using CAE technology.

[0118] The first determining module 200 is used to acquire the actual driving information of the vehicle and determine the current temperature of the brake friction pair based on the preset brake temperature model and the actual driving information.

[0119] The second determining module 300 is used to determine the current braking friction coefficient based on the current temperature of the braking friction pair.

[0120] Furthermore, in some embodiments, the bench test data includes at least one of the following: friction pair temperature, friction coefficient, and vehicle speed.

[0121] Furthermore, in some embodiments, the establishment module 100 is specifically used for:

[0122] Based on bench test data, CAE technology was used to fit the relationship curve between friction pair temperature and braking energy.

[0123] Determine the number of iterations and model parameters, and based on the number of iterations and model parameters, establish an initial braking temperature model using the relationship curve between friction pair temperature and braking energy;

[0124] The initial braking temperature model was compared with bench test data and real vehicle test data to determine whether the accuracy of the initial braking temperature model met the preset standard.

[0125] If the accuracy of the initial braking temperature model meets the preset standard, then the initial braking temperature model is used as the preset braking temperature model; otherwise, the number of iterations and model parameters are adjusted until the accuracy of the initial braking temperature model meets the preset standard.

[0126] Furthermore, in some embodiments, the second determining module 300 is specifically used for:

[0127] Based on actual driving information, the current temperature of the brake friction pair is determined iteratively.

[0128] The current braking friction coefficient is determined based on the preset braking temperature model and the current braking friction pair temperature.

[0129] Furthermore, in some embodiments, after determining the current braking friction coefficient based on the current braking friction pair temperature, the second determining module 300 is further configured to:

[0130] Determine the current hydraulic pressure of the braking system based on the current coefficient of friction.

[0131] Based on the current hydraulic pressure, the output torque during vehicle braking is kept at a preset constant level.

[0132] Furthermore, in some embodiments, the actual driving information includes the number of braking operations within a preset time period, and the second determining module is specifically used for:

[0133] If the number of braking events exceeds a preset threshold within a preset time period, the temperature of the previous braking friction pair is obtained, and the current braking friction pair temperature is obtained based on the actual driving information and the previous braking friction pair temperature.

[0134] Furthermore, in some embodiments, the second determining module 300 is specifically used for:

[0135] Obtain the preset table of the relationship between the temperature of the braking friction pair and the friction coefficient;

[0136] Look up the current braking friction coefficient corresponding to the current braking friction pair temperature in the table of relationship between braking friction pair temperature and friction coefficient.

[0137] It should be noted that the explanation of the aforementioned self-learning method embodiment for braking friction coefficient also applies to the self-learning device for braking friction coefficient in this embodiment, and will not be repeated here.

[0138] The self-learning device for braking friction coefficient proposed in this application acquires vehicle bench test data and establishes a preset braking temperature model based on the bench test data using CAE technology; it acquires actual vehicle driving information and determines the current braking friction pair temperature based on the preset braking temperature model and the actual driving information; and it determines the current braking friction coefficient based on the current braking friction pair temperature. Therefore, by accurately acquiring the real-time temperature of the braking system before braking and matching a precise braking friction coefficient based on the temperature, the calculated braking torque can be closer to the actual torque. This solves the problem of discrepancies in brake pedal feel caused by temperature increases during braking, which affects deceleration output, thereby optimizing braking performance and ensuring stable and reliable deceleration under various operating conditions.

[0139] Figure 4 is a structural schematic diagram of a vehicle provided in an embodiment of this application. The vehicle may include:

[0140] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0141] When the processor 402 executes the program, it implements the self-learning method for the braking friction coefficient provided in the above embodiments.

[0142] Furthermore, the vehicle also includes:

[0143] Communication interface 403 is used for communication between memory 401 and processor 402.

[0144] The memory 401 is used to store computer programs that can run on the processor 402.

[0145] The memory 401 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0146] If the memory 401, processor 402, and communication interface 403 are implemented independently, they can be interconnected via a bus to 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. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in Figure 4, but this does not imply that there is only one bus or one type of bus.

[0147] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0148] Processor 402 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of this application.

[0149] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the self-learning method for the braking friction coefficient as described above.

[0150] This application also provides a computer program product containing instructions. When the computer program product is run on a computer or processor, it causes the computer or processor to execute the self-learning method for braking friction coefficient as described above.

[0151] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0152] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0153] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A self-learning method for braking friction coefficient, wherein, The method includes: Obtain vehicle bench test data, and based on the bench test data, establish a preset brake temperature model using CAE technology; Obtain the vehicle's actual driving information, and determine the current brake friction pair temperature based on the preset brake temperature model and the actual driving information; The current braking friction coefficient is determined based on the current temperature of the braking friction pair.

2. The method according to claim 1, wherein, The bench test data includes at least one of the following: friction pair temperature, friction coefficient, and vehicle speed.

3. The method according to claim 2, wherein, The step of establishing a preset brake temperature model based on the bench test data using CAE technology includes: Based on the bench test data, the CAE technology is used to fit the relationship curve between the friction pair temperature and braking energy. Determine the number of iterations and model parameters, and based on the number of iterations and model parameters, establish an initial braking temperature model using the relationship curve between the friction pair temperature and braking energy; The initial braking temperature model is compared with the bench test data and the actual vehicle test data to determine whether the accuracy of the initial braking temperature model meets the preset standard. If the accuracy of the initial braking temperature model meets the preset standard, then the initial braking temperature model is used as the preset braking temperature model; otherwise, the number of iterations and the model parameters are adjusted until the accuracy of the initial braking temperature model meets the preset standard.

4. The method according to claim 1, wherein, The step of determining the current braking friction coefficient based on the current braking friction pair temperature includes: Based on the actual driving information, the current temperature of the brake friction pair is determined iteratively. Based on the preset braking temperature model and the current braking friction pair temperature, the current braking friction coefficient is determined.

5. The method according to claim 1, wherein, After determining the current braking friction coefficient based on the current braking friction pair temperature, the method further includes: The current hydraulic pressure of the braking system is determined based on the current braking friction coefficient. Based on the current hydraulic pressure, the output torque of the vehicle during braking is kept at a preset constant state.

6. The method according to claim 1, wherein, The actual driving information includes the number of braking operations, braking deceleration, and braking speed within a preset time period. The iterative determination of the current brake friction pair temperature based on the actual driving information includes: If the number of braking events within the preset time period exceeds a preset braking event threshold, the previous braking friction pair temperature is obtained, and the current braking friction pair temperature is obtained based on the actual driving information and the previous braking friction pair temperature.

7. The method according to claim 1, wherein, Determining the current braking friction coefficient based on the current braking friction pair temperature includes: Obtain the preset table of the relationship between the temperature of the braking friction pair and the friction coefficient; Look up the current braking friction coefficient corresponding to the current braking friction pair temperature in the table of the relationship between braking friction pair temperature and friction coefficient.

8. A self-learning device for braking friction coefficient, wherein, include: A module is established to acquire bench test data of the vehicle and, based on the bench test data, to establish a preset brake temperature model using CAE technology. The first determining module is used to acquire the actual driving information of the vehicle and determine the current temperature of the brake friction pair based on the preset brake temperature model and the actual driving information. The second determining module is used to determine the current braking friction coefficient based on the current braking friction pair temperature.

9. The apparatus according to claim 8, wherein, The bench test data includes at least one of the following: friction pair temperature, friction coefficient, and vehicle speed.

10. The apparatus according to claim 8, wherein, The establishment module is specifically used for: Based on the bench test data, the CAE technology is used to fit the relationship curve between the friction pair temperature and braking energy. Determine the number of iterations and model parameters, and based on the number of iterations and model parameters, establish an initial braking temperature model using the relationship curve between the friction pair temperature and braking energy; The initial braking temperature model is compared with bench test data and real vehicle test data to determine whether the accuracy of the initial braking temperature model meets the preset standard. If the accuracy of the initial braking temperature model meets the preset standard, then the initial braking temperature model is used as the preset braking temperature model; otherwise, the number of iterations and the model parameters are adjusted until the accuracy of the initial braking temperature model meets the preset standard.

11. The apparatus according to claim 8, wherein, The second determining module is specifically used for: Based on the actual driving information, the current temperature of the brake friction pair is determined iteratively. Based on the preset braking temperature model and the current braking friction pair temperature, the current braking friction coefficient is determined.

12. The apparatus according to claim 8, wherein, After determining the current braking friction coefficient based on the current braking friction pair temperature, the second determining module is further configured to: The current hydraulic pressure of the braking system is determined based on the current braking friction coefficient. Based on the current hydraulic pressure, the output torque of the vehicle during braking is kept at a preset constant state.

13. As claimed in claim 8, it is used for: If the number of braking events within the preset time period exceeds a preset braking event threshold, the previous braking friction pair temperature is obtained, and the current braking friction pair temperature is obtained based on the actual driving information and the previous braking friction pair temperature.

14. The apparatus according to claim 8, wherein, The second determining module is specifically used for: Obtain the preset table of the relationship between the temperature of the braking friction pair and the friction coefficient; Look up the current braking friction coefficient corresponding to the current braking friction pair temperature in the table of the relationship between braking friction pair temperature and friction coefficient.

15. A vehicle, wherein, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the self-learning method for the braking friction coefficient as described in any one of claims 1-7.

16. A computer-readable storage medium having a computer program stored thereon, wherein, The program is executed by the processor to implement the self-learning method for the braking friction coefficient as described in any one of claims 1-7.

17. A computer program product, wherein, When the computer program product is run on a computer, the computer performs the self-learning method for braking friction coefficient as described in any one of claims 1 to 7.

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