Wheel radius self-learning method, storage medium and vehicle

Abstract

The invention provides a wheel radius self-learning method, a storage medium and a vehicle, and belongs to the technical field of vehicles. According to the embodiment of the invention, under the condition that a target wheel meets a self-learning enabling condition, the wheel radius measurement value of the target wheel is determined, and then the self-learning enabling condition of the target wheel is determined based on the wheel radius measurement value and a wheel radius reference value; according to the method, the initial radius ratio coefficient is determined, and the initial radius ratio coefficient is filtered, so that the influence of the change of the vehicle driving state on the wheel self-learning can be effectively reduced, the accuracy of the target radius ratio coefficient is improved, and meanwhile, the stability of the target radius ratio coefficient is detected, so that the reliability of the target radius ratio coefficient can be ensured; and based on the target radius ratio coefficient, accurate self-learning of the wheel radius learning value can be realized in the self-learning period of the wheel, so that the accuracy and safety of a vehicle control system are ensured.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a wheel radius self-learning method, a storage medium and a vehicle. Background Art

[0002] Wheel radius is an important parameter in vehicle control systems, and plays a key role in functions such as braking systems, traction control, and vehicle speed measurement. During vehicle use, accurate calculation of wheel radius ensures the accuracy and safety of the vehicle control system.

[0003] At present, the wheel radius is usually calculated by combining the wheel speed signal collected by the wheel speed sensor with the rotation speed of the power source. However, this method is affected by the detection accuracy and the changes in the vehicle's driving state. The calculated wheel radius often cannot represent the actual wheel radius, resulting in inaccurate wheel radius, which affects the accuracy and safety of the vehicle control system. Summary of the invention

[0004] The present application provides a wheel radius self-learning method, a storage medium and a vehicle to solve the current problem that it is difficult to accurately self-learn the wheel radius during vehicle use.

[0005] In order to solve the above problems, this application adopts the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a wheel radius self-learning method, the method comprising:

[0007] When the target wheel satisfies the self-learning enabling condition, determining a wheel radius measurement value of the target wheel;

[0008] Determining an initial radius ratio coefficient based on the wheel radius measured value and the wheel radius reference value;

[0009] Filtering the initial radius ratio coefficient to obtain a target radius ratio coefficient;

[0010] When it is determined that the target radius ratio coefficient satisfies the stability condition, the wheel radius learning value of the target wheel in the current self-learning cycle is determined based on the target radius ratio coefficient and the wheel radius reference value.

[0011] In an embodiment of the present application, the method further includes:

[0012] When the drive axle where the target wheel is located is in gear and the vehicle is in a target driving mode, it is determined that the target wheel meets the self-learning enabling condition.

[0013] In one embodiment of the present application, the step of determining the wheel radius measurement value of the target wheel includes:

[0014] Acquire the motor speed of the target motor, the gear ratio of the target transmission, and the average wheel speed of the target wheel; wherein the target motor is connected to the target wheel through the target transmission;

[0015] A wheel radius measurement value of a target wheel is determined based on the motor speed, the speed ratio and the average wheel speed.

[0016] In one embodiment of the present application, the step of filtering the initial radius ratio coefficient to obtain the target radius ratio coefficient includes:

[0017] Obtaining a historical initial radius ratio coefficient corresponding to a historical self-learning cycle; the historical self-learning cycle is at least one self-learning cycle before the current self-learning cycle;

[0018] Based on the historical initial radius ratio coefficient, the initial radius ratio coefficient is filtered to obtain the target radius ratio coefficient.

[0019] In an embodiment of the present application, the method further includes:

[0020] determining a stability factor based on a ratio between the target radius ratio coefficient and the initial radius ratio coefficient;

[0021] When the stability factor is within a preset interval and the shaft speed of the drive shaft where the target wheel is located is greater than a shaft speed threshold, triggering timing for the duration of the first state;

[0022] When the duration of the first state is greater than a first duration threshold, it is determined that the target radius ratio coefficient satisfies the stability condition.

[0023] In one embodiment of the present application, after the step of determining the stability factor based on the ratio between the target radius ratio coefficient and the initial radius ratio coefficient, the method further includes:

[0024] When the stability factor is not within the preset interval and the shaft speed is greater than the shaft speed threshold, triggering timing of the duration of the second state;

[0025] When the duration of the second state is greater than a second duration threshold, it is determined that the target radius ratio coefficient does not satisfy the stability condition.

[0026] In an embodiment of the present application, the method further includes:

[0027] When the target wheel does not satisfy the self-learning enabling condition, or when it is determined that the target radius ratio coefficient does not satisfy the stability condition, the target radius ratio coefficient is determined as a preset coefficient.

[0028] In one embodiment of the present application, after the step of determining the wheel radius learning value of the target wheel in the current self-learning cycle based on the target radius ratio coefficient and the wheel radius reference value, the method further includes:

[0029] When it is detected that the shaft speed is less than or equal to the shaft speed threshold, obtaining a historical target radius ratio coefficient corresponding to a previous self-learning cycle of the current self-learning cycle;

[0030] The historical target radius ratio coefficient is determined as the target radius ratio coefficient of the current self-learning cycle.

[0031] In a second aspect, based on the same inventive concept, an embodiment of the present application provides a wheel radius self-learning device, the device comprising:

[0032] A radius calculation module, used to determine a wheel radius measurement value of the target wheel when the target wheel meets the self-learning enabling condition;

[0033] A coefficient determination module, used for determining an initial radius ratio coefficient based on the wheel radius measurement value and the wheel radius reference value;

[0034] A coefficient filtering module, used for filtering the initial radius ratio coefficient to obtain a target radius ratio coefficient;

[0035] The radius determination module is used to determine the wheel radius learning value of the target wheel in the current self-learning cycle based on the target radius ratio coefficient and the wheel radius reference value when it is determined that the target radius ratio coefficient meets the stability condition.

[0036] In one embodiment of the present application, the wheel radius self-learning device further includes:

[0037] The enabling condition determination module is used to determine that the target wheel meets the self-learning enabling condition when the drive axle where the target wheel is located is in gear and the vehicle is in a target driving mode.

[0038] In one embodiment of the present application, the radius calculation module includes:

[0039] An information acquisition submodule, used to acquire the motor speed of the target motor, the gear ratio of the target transmission and the average wheel speed of the target wheel; wherein the target motor is connected to the target wheel through the target transmission;

[0040] The radius calculation submodule is used to determine the wheel radius measurement value of the target wheel based on the motor speed, the speed ratio and the average wheel speed.

[0041] In one embodiment of the present application, the coefficient filtering module includes:

[0042] A historical coefficient acquisition submodule, used to acquire a historical initial radius ratio coefficient corresponding to a historical self-learning cycle; the historical self-learning cycle is at least one self-learning cycle before the current self-learning cycle;

[0043] The coefficient filtering submodule is used to filter the initial radius ratio coefficient based on the historical initial radius ratio coefficient to obtain the target radius ratio coefficient.

[0044] In one embodiment of the present application, the wheel radius self-learning device further includes:

[0045] a stability factor determination module, configured to determine a stability factor based on a ratio between the target radius ratio coefficient and the initial radius ratio coefficient;

[0046] A first timing module, configured to trigger timing for a duration of a first state when the stability factor is within a preset interval and the shaft speed of the drive shaft where the target wheel is located is greater than a shaft speed threshold;

[0047] The first condition determination module is used to determine that the target radius ratio coefficient satisfies the stability condition when the duration of the first state is greater than a first duration threshold.

[0048] In one embodiment of the present application, the wheel radius self-learning device further includes:

[0049] A second timing module, configured to trigger timing of a duration of the second state when the stability factor is not within the preset interval and the shaft speed is greater than the shaft speed threshold;

[0050] The second condition determination module is used to determine that the target radius ratio coefficient does not meet the stability condition when the duration of the second state is greater than a second duration threshold.

[0051] In one embodiment of the present application, the wheel radius self-learning device further includes:

[0052] The first coefficient determination module is used to determine the target radius ratio coefficient as a preset coefficient when the target wheel does not meet the self-learning enabling condition or when it is determined that the target radius ratio coefficient does not meet the stability condition.

[0053] In one embodiment of the present application, the wheel radius self-learning device further includes:

[0054] A coefficient acquisition module, for acquiring a historical target radius ratio coefficient corresponding to a previous self-learning cycle of the current self-learning cycle when it is detected that the shaft speed is less than or equal to the shaft speed threshold;

[0055] The second coefficient determination module is used to determine the historical target radius ratio coefficient as the target radius ratio coefficient of the current self-learning cycle.

[0056] In the third aspect, based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium having an executable program stored thereon, which, when executed by a processor, implements the wheel radius self-learning method proposed in the first aspect of the present application.

[0057] In a fourth aspect, based on the same inventive concept, an embodiment of the present application provides a vehicle, including:

[0058] A memory for storing an executable program;

[0059] processor;

[0060] When the executable program is executed by the processor, the wheel radius self-learning method proposed in the first aspect of the present application is implemented.

[0061] Compared with the prior art, this application has the following advantages:

[0062] A wheel radius self-learning method provided in an embodiment of the present application first determines the wheel radius measurement value of the target wheel when the target wheel meets the self-learning enabling condition; then determines the initial radius ratio coefficient based on the wheel radius measurement value and the wheel radius reference value, and filters the initial radius ratio coefficient to obtain the target radius ratio coefficient; finally, when it is determined that the target radius ratio coefficient meets the stability condition, determines the wheel radius learning value of the target wheel in this round of self-learning cycle based on the target radius ratio coefficient and the wheel radius reference value. By filtering the initial radius ratio coefficient, the embodiment of the present application can effectively reduce the impact of changes in the vehicle's driving state on the wheel self-learning, improve the accuracy of the target radius ratio coefficient, and at the same time perform stability detection on the target radius ratio coefficient to ensure the reliability of the target radius ratio coefficient, and then based on the target radius ratio coefficient, can achieve accurate self-learning of the wheel radius learning value in this round of self-learning cycle, thereby ensuring the accuracy and safety of the vehicle control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 This is a flowchart of the steps of a wheel radius self-learning method in one embodiment of the present application;

[0065] Figure 2 This is a schematic diagram of the functional modules of a wheel radius self-learning device in one embodiment of the present application;

[0066] Figure 3 It is a structural schematic diagram of a vehicle in one embodiment of the present application. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] It should be noted that various vehicle control systems require accurate wheel radius information to function properly, for example:

[0069] Anti-lock Braking System (ABS): ABS relies on the speed signal of each wheel to detect whether a wheel is in a locked state. The accuracy of the wheel radius directly affects the accuracy of ABS's judgment of vehicle speed, which in turn affects the distribution of braking force.

[0070] Traction Control System (TCS): TCS needs to determine whether a wheel is spinning and slipping based on the wheel speed, so as to adjust the torque distribution. Incorrect wheel radius will lead to misjudgment and affect system performance.

[0071] Adaptive Cruise Control (ACC): ACC estimates the distance to the vehicle ahead through radar distance measurement and calculates the vehicle's own speed through wheel speed, thereby controlling the distance between vehicles. Changes in wheel radius will directly affect the calculation accuracy of distance and speed.

[0072] Electronic Stability Program (ESP): ESP monitors the deviation between the actual direction of the vehicle and the driving intention, and corrects the deviation by distributing the braking force and driving force. The error of the wheel radius will affect ESP's assessment of the actual movement state of the vehicle.

[0073] For these and many other vehicle control and driver assistance systems, accurate wheel radius self-learning is critical to the proper and efficient operation of the system.

[0074] At present, the wheel radius is usually calculated by combining the wheel speed signal collected by the wheel speed sensor with the rotation speed of the power source. However, this method is affected by the detection accuracy and the changes in the vehicle's driving state. The calculated wheel radius often cannot represent the actual wheel radius, resulting in inaccurate wheel radius, which affects the accuracy and safety of the vehicle control system.

[0075] In response to the current problem that it is difficult to accurately self-learn the wheel radius during vehicle use, the present application aims to provide a wheel radius self-learning method, which determines an initial radius ratio coefficient based on the measured wheel radius value of the target wheel in combination with a wheel radius reference value, and then filters the initial radius ratio coefficient. This can effectively reduce the impact of changes in the vehicle's driving state on the wheel self-learning, improve the accuracy of the target radius ratio coefficient, and simultaneously perform stability detection on the target radius ratio coefficient to ensure the reliability of the target radius ratio coefficient. Based on the target radius ratio coefficient, accurate self-learning of the wheel radius learning value can be achieved within the current self-learning cycle, thereby ensuring the accuracy and safety of the vehicle control system.

[0076] Reference Figure 1 , shows a wheel radius self-learning method of the present application, which may include the following steps:

[0077] S101: When the target wheel meets the self-learning enabling condition, determine the wheel radius measurement value of the target wheel.

[0078] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, or an electronic device with the above functions such as a driving computer, a vehicle-mounted computer, etc., such as an ECU (Electronic Control Unit), a VCU (Vehicle Control Unit), etc. This implementation will be described using the VCU as the execution subject, and this implementation does not make specific restrictions on the type of the execution subject.

[0079] In this embodiment, the VCU will collect the vehicle's status information in real time during the vehicle's driving process, and then determine whether the target wheel meets the self-learning enabling conditions based on the status information.

[0080] It should be noted that the target wheel represents the wheel that needs to perform radius self-learning, for example, it can be a front axle wheel or a rear axle wheel. The VCU can perform self-learning for the front axle wheel and the rear axle wheel at the same time to obtain the wheel radius learning value corresponding to the front axle wheel and the wheel radius learning value corresponding to the rear axle wheel respectively.

[0081] In a specific implementation, the state information may include the gear information of the drive axle where the target wheel is located and the driving mode information of the vehicle. If the VCU detects that the drive axle where the target wheel is located is in gear and the vehicle is in the target driving mode, it is determined that the target wheel meets the self-learning enabling condition.

[0082] It should be noted that if the drive axle where the target wheel is located is in gear, it means that the target wheel is in a state of driving the vehicle. At this time, it is meaningful to perform radius self-learning on the target wheel; the target driving mode indicates a driving mode that needs to control the vehicle based on the wheel radius, so that after completing the wheel radius self-learning, when the vehicle is in the target driving mode, it can achieve precise and stable control of the vehicle based on the learned wheel radius learning value.

[0083] In this embodiment, by triggering the calculation of the wheel radius measurement value when it is detected that the target wheel meets the self-learning enabling conditions, the accuracy and reliability of the wheel radius measurement value can be effectively ensured.

[0084] S102: Determine an initial radius ratio coefficient based on the wheel radius measurement value and the wheel radius reference value.

[0085] It should be noted that the wheel radius reference value represents a fixed value set for the target wheel. Different models of target wheels correspond to different wheel radius reference values.

[0086] In a specific implementation, the ratio between the wheel radius measurement value and the wheel radius reference value may be determined as an initial radius ratio coefficient, which may characterize the deviation between the current radius measurement value of the target wheel and the radius reference value.

[0087] S103: Filter the initial radius ratio coefficient to obtain a target radius ratio coefficient.

[0088] In this embodiment, considering the different driving states of the vehicle, the initial radius ratio coefficient may change greatly. For example, when the road condition changes or the vehicle is driving on a bumpy road, the vibration and impact of the whole vehicle will cause the vehicle speed and wheel state to change significantly. Therefore, in order to reduce the adverse effects caused by the change of the vehicle driving state, after calculating the initial radius ratio coefficient, the VCU will filter the initial radius ratio coefficient to obtain a more accurate target radius ratio coefficient.

[0089] In this embodiment, the filtering process may specifically include low-pass filtering and smoothing filtering, wherein the low-pass filtering is used to filter the obviously abnormal initial radius ratio coefficient; and the smoothing filtering is used to smooth the initial radius ratio coefficient to reduce the variation range of the initial radius ratio coefficient.

[0090] In this embodiment, in order to improve the filtering effect, low-pass filtering and smoothing filtering can be combined, that is, the initial radius ratio coefficient is first low-pass filtered, and then the initial radius ratio coefficient after low-pass filtering is smoothed to obtain the target radius ratio coefficient.

[0091] In a specific implementation, the initial radius ratio coefficient can be low-pass filtered based on a preset coefficient filtering interval, which is a closed interval consisting of a maximum coefficient value and a minimum coefficient value. Specifically, if the initial radius ratio coefficient is within the coefficient filtering interval, the initial radius ratio coefficient is considered valid; if the initial radius ratio coefficient is greater than the maximum coefficient value, the maximum coefficient value is determined as the initial radius ratio coefficient; if the initial radius ratio coefficient is less than the minimum coefficient value, the minimum coefficient value is determined as the initial radius ratio coefficient. In this way, the initial radius ratio coefficient that is obviously too large or too small can be effectively filtered.

[0092] S104: When it is determined that the target radius ratio coefficient satisfies the stability condition, a wheel radius learning value of the target wheel in the current self-learning cycle is determined based on the target radius ratio coefficient and the wheel radius reference value.

[0093] In this embodiment, after the VCU calculates the target radius ratio coefficient, it will detect the change of the target radius ratio coefficient to determine whether the target radius ratio coefficient meets the stability condition. If it is detected that the target radius ratio coefficient meets the stability condition, it means that the target radius ratio coefficient is in a stable state within a certain period of time, and then the target radius ratio coefficient is determined to be valid, and the self-learning result for the target wheel in this round of self-learning cycle is considered to be successful.

[0094] It should be noted that if it is determined that the target radius ratio coefficient does not meet the stability condition, it means that under the current driving condition, the target radius ratio coefficient fluctuates greatly, and then the target radius ratio coefficient is determined to be invalid, and the self-learning result for the target wheel in this round of self-learning cycle is considered to be a failure.

[0095] In this embodiment, in order to ensure that the wheel radius used by the target vehicle control system is continuously valid during vehicle operation, the VCU will perform radius self-learning on the target wheel according to a preset self-learning cycle during vehicle driving. Once the VCU determines that the self-learning result of this self-learning cycle is successful, it will send the wheel radius learning value of this self-learning cycle to the target vehicle control system, so that the target vehicle control system controls the vehicle based on the wheel radius learning value. The target vehicle control system may include, but is not limited to, ABS, ACC, ESP, TCS and other systems.

[0096] In this embodiment, by determining the initial radius ratio coefficient based on the measured value of the wheel radius of the target wheel in combination with the wheel radius reference value, and then filtering the initial radius ratio coefficient, the impact of changes in the vehicle's driving state on the wheel self-learning can be effectively reduced, and the accuracy of the target radius ratio coefficient can be improved. At the same time, the target radius ratio coefficient is tested for stability to ensure the reliability of the target radius ratio coefficient. Based on the target radius ratio coefficient, accurate self-learning of the wheel radius learning value can be achieved within this round of self-learning cycle, thereby ensuring the accuracy and safety of the relevant vehicle control system.

[0097] In a feasible implementation manner, the step of determining the calculated value of the wheel radius of the target wheel in S101 may specifically include the following sub-steps:

[0098] S101 - 1 : Obtain the motor speed of the target motor, the gear ratio of the target transmission, and the average wheel speed of the target wheels.

[0099] In this embodiment, the target motor is connected to the target wheel via a target transmission.

[0100] It should be noted that the average wheel speed of the target wheel represents the average wheel speed of the left wheel and the right wheel in the target wheel. For example, when the target wheel is a front axle wheel, the average value of the wheel speeds of the left front wheel and the right front wheel can be determined as the average wheel speed of the target wheel.

[0101] S101-2: Determine the wheel radius measurement value of the target wheel based on the motor speed, gear ratio and average wheel speed.

[0102] In this embodiment, considering that the target motor and the target wheel are rotationally connected, accurate calculation of the wheel radius measurement value can be achieved by comprehensively considering the gear ratio of the target transmission based on the motor speed and the average wheel speed.

[0103] It should be noted that the above is a calculation method when the power source of the vehicle is a motor. When the power source of the vehicle is an engine, the wheel radius measurement value of the target wheel can also be calculated based on the engine speed, the gear ratio of the target transmission and the average wheel speed of the target wheel. In other words, this solution is applicable to both fuel vehicles and new energy vehicles. Since the motor speed is usually more stable than the engine speed, the wheel radius measurement value for new energy vehicles is usually more accurate.

[0104] In this embodiment, in order to achieve effective filtering of the wheel radius measurement value, a radius filtering interval can be set for the wheel radius measurement value, and the radius filtering interval is a closed interval consisting of a maximum radius value and a minimum radius value. In other words, if the wheel radius measurement value is within the radius filtering interval, the wheel radius measurement value is considered valid; if the wheel radius measurement value is greater than the maximum radius value, the maximum radius value is determined as the wheel radius measurement value; if the wheel radius measurement value is less than the minimum radius value, the minimum radius value is determined as the wheel radius measurement value. In this way, the wheel radius measurement value that is obviously too large or too small can be effectively filtered.

[0105] In a feasible implementation, S103 may specifically include the following sub-steps:

[0106] S103-1: Obtain a historical initial radius ratio coefficient corresponding to a historical self-learning cycle; a historical self-learning cycle is at least one self-learning cycle before the current self-learning cycle.

[0107] In this embodiment, after the VCU completes self-learning of the target wheel in a self-learning cycle, it will store the initial radius ratio coefficient calculated in the self-learning cycle, and then obtain the historical initial radius ratio coefficient. In this way, the VCU can retrieve the required data from the stored historical initial radius ratio coefficient according to demand in the current self-learning cycle.

[0108] S103-2: Based on the historical initial radius ratio coefficient, the initial radius ratio coefficient is filtered to obtain the target radius ratio coefficient.

[0109] In this embodiment, the VCU can obtain the historical initial radius ratio coefficients corresponding to one or more self-learning cycles before the current self-learning cycle according to a preset filtering algorithm, and filter the initial radius ratio coefficient of the current self-learning cycle.

[0110] In a specific implementation, a moving average filtering algorithm may be used to smooth the initial radius ratio coefficient using the average value of the historical initial radius ratio coefficients within a preset window size to obtain the target radius ratio coefficient.

[0111] In the specific implementation, the exponential moving average algorithm (EMA) can also be used. By assigning different weights to the historical initial radius ratio coefficients, the weight of the closest data point is larger, so as to more accurately reflect the changing trend of the initial radius ratio coefficient, thereby realizing the filtering of the initial radius ratio coefficient.

[0112] In this embodiment, the initial radius ratio coefficient is smoothed by performing smoothing filtering, so that the change amplitude of the initial radius ratio coefficient is smoother, thereby effectively avoiding the adverse effect of the abnormal initial radius ratio coefficient on the self-learning effect.

[0113] In a feasible implementation manner, in order to accurately determine the stability condition of the target radius ratio coefficient, the wheel radius self-learning method may further include the following steps:

[0114] S201: Determine a stability factor based on a ratio between a target radius ratio coefficient and an initial radius ratio coefficient.

[0115] In this embodiment, by determining the ratio as the stability factor, the stability factor can effectively reflect the size change relationship between the target radius ratio coefficient after filtering and the initial radius ratio coefficient before filtering. Specifically, when the stability factor is greater than 1, it means that the target radius ratio coefficient after filtering is greater than the initial radius ratio coefficient before filtering; when the stability factor is less than 1, it means that the target radius ratio coefficient after filtering is less than the initial radius ratio coefficient before filtering.

[0116] S202: When the stability factor is within a preset interval and the shaft speed of the drive shaft where the target wheel is located is greater than a shaft speed threshold, triggering timing of the duration of the first state.

[0117] In this embodiment, considering that the wheel radius self-learning is usually inaccurate under low-speed conditions, the accuracy of the target radius ratio coefficient can be further ensured by setting the shaft speed of the drive shaft where the target wheel is located to be greater than the shaft speed threshold.

[0118] It should be noted that the preset interval may be a closed interval consisting of a lower limit value and an upper limit value. For example, the preset interval may be set to [1.05, 0.95], which means that the stability factor is allowed to fluctuate within an error range of 5%.

[0119] In this embodiment, if the VCU detects that the stability factor is within the preset range and the shaft speed of the drive shaft where the target wheel is located is greater than the shaft speed threshold, the timer will be triggered to count the duration of the first state.

[0120] S203: When the duration of the first state is greater than the first duration threshold, determine that the target radius ratio coefficient meets the stability condition.

[0121] In this embodiment, if the VCU detects that the duration of the first state is greater than the first duration threshold, it means that the initial radius ratio coefficient fluctuates less when the vehicle is traveling at a certain speed, so that the ratio between the target radius ratio coefficient after filtering and the initial radius ratio coefficient before filtering fluctuates around 1 with a smaller fluctuation amplitude, and then determines that the target radius ratio coefficient meets the stability condition, that is, the target radius ratio coefficient is considered to be reliable and valid, and can be used to calculate the wheel radius learning value of the target wheel in this round of self-learning cycle, thereby ensuring the accuracy and reliability of the wheel radius learning value.

[0122] It should be noted that if the duration of the first state is less than or equal to the first duration threshold, and the stability factor is not within the preset interval or the shaft speed of the drive shaft where the target wheel is located is less than or equal to the shaft speed threshold, the timing will be stopped and the duration of the first state will be reset to zero.

[0123] In a feasible implementation manner, after S201, the wheel radius self-learning method may further include the following steps:

[0124] S204: When the stability factor is not within the preset interval and the shaft speed is greater than the shaft speed threshold, triggering timing of the duration of the second state.

[0125] In this embodiment, if the VCU detects that the stability factor is not within the preset range when the shaft speed is greater than the shaft speed threshold, the VCU will trigger timing of the duration of the second state through a timer.

[0126] S205: When the duration of the second state is greater than the second duration threshold, it is determined that the target radius ratio coefficient does not meet the stability condition.

[0127] In this embodiment, if the VCU detects that the duration of the second state is greater than the second duration threshold, it is considered that the fluctuation of the stability factor is large within the second duration threshold, and then it is determined that the target radius ratio coefficient does not meet the stability condition, that is, the self-learning result of this round of self-learning cycle is considered to be a failure.

[0128] In a feasible implementation manner, the wheel radius self-learning method may further include the following steps:

[0129] S301: When the target wheel does not meet the self-learning enabling condition, or when it is determined that the target radius ratio coefficient does not meet the stability condition, the target radius ratio coefficient is determined as a preset coefficient.

[0130] In this embodiment, if the target wheel does not meet the self-learning enabling condition, it means that the drive axle where the target wheel is located is not in gear, or the vehicle is not in the target driving mode, and the target radius ratio coefficient is determined as a preset coefficient. The preset coefficient can be set to 1, that is, the control is performed according to the wheel radius reference value.

[0131] In this embodiment, by determining the target radius ratio coefficient as a preset coefficient when the target wheel does not meet the self-learning enabling conditions or the learning result of this round of self-learning cycle is a failure, a default wheel radius reference value can be provided for the target control system to maintain the normal operation of the target control system.

[0132] In a feasible implementation manner, after S104, the wheel radius self-learning method may further include the following steps:

[0133] S401: When it is detected that the shaft speed is less than or equal to the shaft speed threshold, a historical target radius ratio coefficient corresponding to the previous self-learning cycle of the current self-learning cycle is obtained.

[0134] In this embodiment, since the wheel radius learning value of this round of self-learning cycle is learned when the shaft speed of the drive shaft where the target wheel is located is greater than the shaft speed threshold, when the shaft speed is less than or equal to the shaft speed threshold, that is, when the vehicle speed is low, the wheel radius learning value of this round of self-learning cycle has less reference significance. Therefore, the VCU will adopt the historical target radius ratio coefficient corresponding to the previous self-learning cycle of this round of self-learning cycle to realize the calculation of the wheel radius.

[0135] S402: Determine the historical target radius ratio coefficient as the target radius ratio coefficient of the current self-learning cycle.

[0136] In this embodiment, when the VCU detects that the target vehicle control system needs to use the wheel radius parameter, if the axle speed is less than or equal to the axle speed threshold, it will determine the historical target radius ratio coefficient as the target radius ratio coefficient of this round of self-learning cycle. In this way, based on the historical target radius ratio coefficient and the wheel radius reference value, the wheel radius learning value of the target wheel can be determined, so that the target control system can control the vehicle according to the wheel radius learning value.

[0137] In the second aspect, based on the same inventive concept, refer to Figure 2 The embodiment of the present application provides a wheel radius self-learning device, and the wheel radius self-learning device 200 includes:

[0138] The radius calculation module 201 is used to determine the wheel radius calculation value of the target wheel when the target wheel meets the self-learning enabling condition;

[0139] A coefficient determination module 202, for determining an initial radius ratio coefficient based on a wheel radius measurement value and a wheel radius reference value;

[0140] A coefficient filtering module 203 is used to filter the initial radius ratio coefficient to obtain a target radius ratio coefficient;

[0141] The radius determination module 204 is used to determine the wheel radius learning value of the target wheel in the current self-learning cycle based on the target radius ratio coefficient and the wheel radius reference value when it is determined that the target radius ratio coefficient meets the stability condition.

[0142] In one embodiment of the present application, the wheel radius self-learning device 200 further includes:

[0143] The enabling condition determination module is used to determine that the target wheel meets the self-learning enabling condition when the drive axle where the target wheel is located is in gear and the vehicle is in a target driving mode.

[0144] In one embodiment of the present application, the radius calculation module 201 includes:

[0145] The information acquisition submodule is used to acquire the motor speed of the target motor, the gear ratio of the target transmission, and the average wheel speed of the target wheel; wherein the target motor is connected to the target wheel through the target transmission;

[0146] The radius calculation submodule is used to determine the wheel radius measurement value of the target wheel based on the motor speed, the gear ratio and the average wheel speed.

[0147] In one embodiment of the present application, the coefficient filtering module 203 includes:

[0148] The historical coefficient acquisition submodule is used to obtain the historical initial radius ratio coefficient corresponding to the historical self-learning cycle; the historical self-learning cycle is at least one self-learning cycle before the current self-learning cycle;

[0149] The coefficient filtering submodule is used to filter the initial radius ratio coefficient based on the historical initial radius ratio coefficient to obtain the target radius ratio coefficient.

[0150] In one embodiment of the present application, the wheel radius self-learning device 200 further includes:

[0151] A stability factor determination module, for determining a stability factor based on a ratio between a target radius ratio coefficient and an initial radius ratio coefficient;

[0152] A first timing module, configured to trigger timing for a duration of a first state when the stability factor is within a preset interval and the shaft speed of the drive shaft where the target wheel is located is greater than a shaft speed threshold;

[0153] The first condition determination module is used to determine that the target radius ratio coefficient meets the stability condition when the duration of the first state is greater than a first duration threshold.

[0154] In one embodiment of the present application, the wheel radius self-learning device 200 further includes:

[0155] A second timing module is used to trigger timing for the duration of the second state when the stability factor is not within the preset interval and the shaft speed is greater than the shaft speed threshold;

[0156] The second condition determination module is used to determine that the target radius ratio coefficient does not meet the stability condition when the duration of the second state is greater than a second duration threshold.

[0157] In one embodiment of the present application, the wheel radius self-learning device 200 further includes:

[0158] The first coefficient determination module is used to determine the target radius ratio coefficient as a preset coefficient when the target wheel does not meet the self-learning enabling condition or when it is determined that the target radius ratio coefficient does not meet the stability condition.

[0159] In one embodiment of the present application, the wheel radius self-learning device 200 further includes:

[0160] A coefficient acquisition module is used to obtain the historical target radius ratio coefficient corresponding to the previous self-learning cycle of the current self-learning cycle when the shaft speed is detected to be less than or equal to the shaft speed threshold;

[0161] The second coefficient determination module is used to determine the historical target radius ratio coefficient as the target radius ratio coefficient of the current self-learning cycle.

[0162] It should be noted that the specific implementation of the wheel radius self-learning device 200 of the embodiment of the present application refers to the specific implementation of the wheel radius self-learning method proposed in the first aspect of the embodiment of the present application, and will not be repeated here.

[0163] On the third aspect, based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium on which an executable program is stored. When the executable program is executed by a processor, the wheel radius self-learning method proposed in the first aspect of the present application is implemented.

[0164] It should be noted that the specific implementation of the computer-readable storage medium of the embodiment of the present application refers to the specific implementation of the wheel radius self-learning method proposed in the first aspect of the embodiment of the present application, and will not be repeated here.

[0165] Fourthly, refer to Figure 3 Based on the same inventive concept, the embodiment of the present application provides a vehicle 300, including:

[0166] Memory 301, used for storing executable programs;

[0167] Processor 302;

[0168] When the executable program is executed by the processor 302, the wheel radius self-learning method proposed in the first aspect of the present application is implemented.

[0169] It should be noted that the specific implementation of the vehicle 300 in the embodiment of the present application refers to the specific implementation of the wheel radius self-learning method proposed in the first aspect of the embodiment of the present application, and will not be repeated here.

[0170] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, apparatuses, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0171] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0172] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0173] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0174] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0175] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0176] The above is a detailed introduction to a wheel radius self-learning method, storage medium and vehicle provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A wheel radius self-learning method, characterized in that: The method comprises: When the target wheel satisfies the self-learning enabling condition, determining a wheel radius measurement value of the target wheel; Determining an initial radius ratio coefficient based on the wheel radius measured value and the wheel radius reference value; Filtering the initial radius ratio coefficient to obtain a target radius ratio coefficient; When it is determined that the target radius ratio coefficient satisfies the stability condition, the wheel radius learning value of the target wheel in the current self-learning cycle is determined based on the target radius ratio coefficient and the wheel radius reference value.

2. The wheel radius self-learning method according to claim 1, characterized in that: The method further comprises: When the drive axle where the target wheel is located is in gear and the vehicle is in a target driving mode, it is determined that the target wheel meets the self-learning enabling condition.

3. The wheel radius self-learning method according to claim 1, characterized in that: The step of determining the wheel radius measurement value of the target wheel comprises: Acquire the motor speed of the target motor, the gear ratio of the target transmission, and the average wheel speed of the target wheel; wherein the target motor is connected to the target wheel through the target transmission; A wheel radius measurement value of a target wheel is determined based on the motor speed, the speed ratio and the average wheel speed.

4. The wheel radius self-learning method according to claim 1, characterized in that: The step of filtering the initial radius ratio coefficient to obtain a target radius ratio coefficient comprises: Obtaining a historical initial radius ratio coefficient corresponding to a historical self-learning cycle; the historical self-learning cycle is at least one self-learning cycle before the current self-learning cycle; Based on the historical initial radius ratio coefficient, the initial radius ratio coefficient is filtered to obtain the target radius ratio coefficient.

5. The wheel radius self-learning method according to claim 1, characterized in that: The method further comprises: determining a stability factor based on a ratio between the target radius ratio coefficient and the initial radius ratio coefficient; When the stability factor is within a preset interval and the shaft speed of the drive shaft where the target wheel is located is greater than a shaft speed threshold, triggering timing for the duration of the first state; When the duration of the first state is greater than a first duration threshold, it is determined that the target radius ratio coefficient satisfies the stability condition.

6. The wheel radius self-learning method according to claim 5, characterized in that: After the step of determining a stability factor based on a ratio between the target radius ratio coefficient and the initial radius ratio coefficient, the method further comprises: When the stability factor is not within the preset interval and the shaft speed is greater than the shaft speed threshold, triggering timing of the duration of the second state; When the duration of the second state is greater than a second duration threshold, it is determined that the target radius ratio coefficient does not satisfy the stability condition.

7. The wheel radius self-learning method according to claim 6, characterized in that: The method further comprises: When the target wheel does not satisfy the self-learning enabling condition, or when it is determined that the target radius ratio coefficient does not satisfy the stability condition, the target radius ratio coefficient is determined as a preset coefficient.

8. The wheel radius self-learning method according to claim 5, characterized in that: After the step of determining the wheel radius learning value of the target wheel in the current self-learning cycle based on the target radius ratio coefficient and the wheel radius reference value, the method further includes: When it is detected that the shaft speed is less than or equal to the shaft speed threshold, obtaining a historical target radius ratio coefficient corresponding to a previous self-learning cycle of the current self-learning cycle; The historical target radius ratio coefficient is determined as the target radius ratio coefficient of the current self-learning cycle.

9. A computer-readable storage medium having an executable program stored thereon, characterized in that: When the executable program is executed by a processor, the wheel radius self-learning method according to any one of claims 1 to 8 is implemented.

10. A vehicle, characterized in that: include: A memory for storing an executable program; processor; When the executable program is executed by the processor, the wheel radius self-learning method according to any one of claims 1 to 8 is implemented.

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