Wheel radius self-learning method, storage medium, and vehicle

Abstract

The application provides a wheel radius self-learning method, a storage medium and a vehicle, and belongs to the technical field of vehicles. In the case that a target wheel meets a self-learning enabling condition, a wheel radius measurement value of the target wheel is determined first, then an initial radius ratio coefficient is determined based on the wheel radius measurement value and a wheel radius reference value, and the initial radius ratio coefficient is filtered, which can effectively reduce the influence of changes in the driving state of the vehicle 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, so as to ensure the reliability of the target radius ratio coefficient, and then based on the target radius ratio coefficient, accurate self-learning of the wheel radius learning value can be realized in the current self-learning cycle, and the accuracy and safety of the vehicle control system are ensured.

Description

Technical Field

[0001] This 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 Technology

[0002] Wheel radius is a crucial parameter in vehicle control systems, playing a key role in functions such as braking systems, traction control, and speed measurement. Accurate calculation of wheel radius during vehicle operation ensures the accuracy and safety of the vehicle control system.

[0003] Currently, the wheel radius is typically calculated by combining wheel speed signals collected by wheel speed sensors with the rotational speed of the power source. However, this method is affected by the detection accuracy and changes in vehicle driving conditions, often resulting in a wheel radius that does not accurately represent the actual wheel radius. This inaccurate wheel radius calculation affects the accuracy and safety of the vehicle control system. Summary of the Invention

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

[0005] To solve the above problems, this application adopts the following technical solution:

[0006] In a first aspect, embodiments of this application provide a wheel radius self-learning method, the method comprising:

[0007] If the target wheel meets the self-learning enable condition, determine the calculated value of the target wheel's radius.

[0008] Based on the calculated wheel radius and the reference wheel radius, the initial radius ratio coefficient is determined;

[0009] The initial radius ratio coefficient is filtered to obtain the target radius ratio coefficient;

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

[0011] In one embodiment of this application, the method further includes:

[0012] When the drive axle containing the target wheel 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.

[0013] In one embodiment of this application, the step of determining the calculated wheel radius of the target wheel includes:

[0014] The target motor's rotational speed, the target transmission's gear ratio, and the target wheel's average wheel speed are obtained; wherein the target motor is connected to the target wheel via the target transmission.

[0015] The calculated wheel radius of the target wheel is determined based on the motor speed, the gear ratio, and the average wheel speed.

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

[0017] 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 that is located 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 one embodiment of this application, the method further includes:

[0020] The stability factor is determined based on the ratio between the target radius ratio coefficient and the initial radius ratio coefficient.

[0021] If the stability factor is within a preset range and the axle speed of the drive shaft where the target wheel is located is greater than the axle speed threshold, a timer is triggered to determine the duration of the first state.

[0022] If the duration of the first state is greater than the first duration threshold, the target radius ratio coefficient is determined to satisfy the stability condition.

[0023] In one embodiment of this application, after 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] If the stability factor is not within the preset range and the shaft speed is greater than the shaft speed threshold, a timer for the duration of the second state is triggered.

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

[0026] In one embodiment of this application, the method further includes:

[0027] If the target wheel does not meet the self-learning enabling condition, or if the target radius ratio coefficient does not meet the stability condition, the target radius ratio coefficient is determined as a preset coefficient.

[0028] In one embodiment of this application, after 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] If the shaft speed is detected to be less than or equal to the shaft speed threshold, the historical target radius ratio coefficient corresponding to the previous self-learning cycle of the current self-learning cycle is obtained;

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

[0031] Secondly, based on the same inventive concept, embodiments of this application provide a wheel radius self-learning device, the device comprising:

[0032] The radius calculation module is used to determine the radius calculation value of the target wheel when the target wheel meets the self-learning enable condition;

[0033] The coefficient determination module is used to determine the initial radius ratio coefficient based on the calculated wheel radius value and the wheel radius reference value;

[0034] The coefficient filtering module is used to filter the initial radius ratio coefficient to obtain the 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, provided that the target radius ratio coefficient meets the stability condition.

[0036] In one embodiment of this 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 the target driving mode.

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

[0039] 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;

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

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

[0042] The historical coefficient acquisition submodule is used to acquire 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 located 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 this application, the wheel radius self-learning device further includes:

[0045] The stability factor determination module is used to determine the stability factor based on the ratio between the target radius ratio coefficient and the initial radius ratio coefficient;

[0046] The first timing module is used to trigger timing for the duration of the first state when the stability factor is within a preset range and the axle speed of the drive shaft where the target wheel is located is greater than the axle 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 the first duration threshold.

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

[0049] The second timing module is used to trigger timing for the duration of the second state when the stability factor is not within the preset range 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 the second duration threshold.

[0051] In one embodiment of this 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 the target radius ratio coefficient does not meet the stability condition.

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

[0054] The coefficient acquisition module is used to acquire 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.

[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] Thirdly, based on the same inventive concept, embodiments of this application provide a computer-readable storage medium having an executable program stored thereon, wherein the executable program, when executed by a processor, implements the wheel radius self-learning method proposed in the first aspect of this application.

[0057] Fourthly, based on the same inventive concept, embodiments of this application provide a vehicle, including:

[0058] Memory, used to store executable programs;

[0059] processor;

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

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

[0062] This application provides a wheel radius self-learning method. First, under the condition that the target wheel meets the self-learning enabling conditions, the calculated wheel radius value of the target wheel is determined. Then, based on the calculated wheel radius value and the wheel radius reference value, an initial radius ratio coefficient is determined, and the initial radius ratio coefficient is filtered to obtain the target radius ratio coefficient. Finally, under the condition that the target radius ratio coefficient meets the stability conditions, based on the target radius ratio coefficient and the wheel radius reference value, the wheel radius learning value of the target wheel in this self-learning cycle is determined. By filtering the initial radius ratio coefficient, this application can effectively reduce the impact of changes in vehicle driving state on wheel self-learning, improve the accuracy of the target radius ratio coefficient, and ensure the reliability of the target radius ratio coefficient through stability detection. Therefore, based on the target radius ratio coefficient, accurate self-learning of the wheel radius learning value can be achieved within this self-learning cycle, thereby ensuring the accuracy and safety of the vehicle control system. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

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

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

[0066] Figure 3 This is a structural schematic diagram of a vehicle according to one embodiment of this application. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

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

[0069] Anti-lock Braking System (ABS): ABS relies on the rotational speed signal of each wheel to detect whether any wheel is locked. The accuracy of the wheel radius directly affects the accuracy of the ABS in judging vehicle speed, and thus affects the distribution of braking force.

[0070] Traction Control System (TCS): The TCS needs to determine whether any wheels are spinning and slipping based on their rotational speed, and then adjust the torque distribution accordingly. Incorrect wheel radii will lead to misjudgment and affect system performance.

[0071] Adaptive Cruise Control (ACC): ACC estimates the distance to the vehicle ahead using radar ranging and calculates the vehicle's speed based on wheel rotation speed, thereby controlling the distance to the vehicle ahead. Changes in wheel radius directly affect the accuracy of distance and speed calculations.

[0072] Electronic Stability Program (ESP): ESP monitors the deviation between the vehicle's actual direction of travel and the driver's intention, and corrects this deviation by distributing braking and driving forces. Errors in wheel radius can affect ESP's assessment of the vehicle's actual motion.

[0073] For the aforementioned and many other vehicle control and driver assistance systems, accurate wheel radius self-learning is crucial for the normal and efficient operation of the system.

[0074] Currently, the wheel radius is typically calculated by combining wheel speed signals collected by wheel speed sensors with the rotational speed of the power source. However, this method is affected by the detection accuracy and changes in vehicle driving conditions, often resulting in a wheel radius that does not accurately represent the actual wheel radius. This inaccurate wheel radius calculation affects the accuracy and safety of the vehicle control system.

[0075] To address the current problem of accurately learning wheel radii during vehicle use, this application aims to provide a wheel radius self-learning method. This method determines an initial radius ratio coefficient based on the calculated wheel radius of the target wheel and a baseline wheel radius value. Filtering this initial radius ratio coefficient effectively reduces the impact of changes in vehicle driving conditions on wheel self-learning, improving the accuracy of the target radius ratio coefficient. Furthermore, stability testing of the target radius ratio coefficient ensures its reliability. Based on this target radius ratio coefficient, accurate self-learning of the wheel radius 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 This application illustrates a wheel radius self-learning method, which may include the following steps:

[0077] S101: Determine the calculated value of the target wheel radius when the target wheel meets the self-learning enable condition.

[0078] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, or an electronic device with the above functions, such as a vehicle computer, an on-board computer, or an ECU (Electronic Control Unit) or VCU (Vehicle Control Unit). This embodiment will use a VCU as the executing entity for description, and this embodiment does not impose specific limitations on the type of executing entity.

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

[0080] It should be noted that the target wheel refers to the wheel that needs to undergo radius self-learning, such as the front axle wheel or the rear axle wheel. The VCU can perform self-learning on both the front axle wheel and the rear axle wheel simultaneously to obtain the wheel radius learning values ​​for the front axle wheel and the rear axle wheel, respectively.

[0081] In a practical implementation, the status information may include the gear position information of the drive axle where the target wheel is located and the vehicle's driving mode information. 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 determines that the target wheel meets the self-learning enable 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 driving the vehicle. In this case, it is meaningful to perform radius self-learning on the target wheel. The target driving mode refers to the driving mode that requires vehicle control based on the wheel radius. After the wheel radius self-learning is completed, 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 value.

[0083] In this embodiment, by triggering the calculation of the wheel radius measurement value when the target wheel meets the self-learning enable condition, the accuracy and reliability of the wheel radius measurement value can be effectively ensured.

[0084] S102: Determine the initial radius ratio coefficient based on the calculated wheel radius 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 target wheel models correspond to different wheel radius reference values.

[0086] In practical implementation, the ratio between the calculated wheel radius and the reference wheel radius can be determined as the initial radius ratio coefficient. This initial radius ratio coefficient can characterize the deviation between the current calculated radius of the target wheel and the reference radius.

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

[0088] In this embodiment, considering the different driving states of the vehicle, the initial radius ratio coefficient may change significantly. For example, when road conditions change, or when the vehicle is driving on a bumpy road, the vibration and impact of the entire vehicle will cause significant changes in vehicle speed and wheel condition. Therefore, to reduce the adverse effects caused by changes in vehicle driving state, the VCU will filter the initial radius ratio coefficient after calculation 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. Low-pass filtering is used to filter out obviously abnormal initial radius ratio coefficients; smoothing filtering is used to smooth the initial radius ratio coefficients to reduce the magnitude of their variation.

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

[0091] In practical 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 the maximum and minimum coefficient values. Specifically, if the initial radius ratio coefficient is within the coefficient filtering interval, it is considered valid; if it is greater than the maximum coefficient, the maximum coefficient is determined as the initial radius ratio coefficient; if it is less than the minimum coefficient, the minimum coefficient is determined as the initial radius ratio coefficient. This effectively filters out initially radius ratio coefficients that are significantly too large or too small.

[0092] S104: Given that the target radius ratio coefficient satisfies the stability condition, determine the wheel radius learning value of the target wheel in this self-learning cycle based on the target radius ratio coefficient and the wheel radius reference value.

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

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

[0095] In this embodiment, to ensure the wheel radius used by the target vehicle control system remains effective during vehicle operation, the VCU will perform radius self-learning on the target wheels according to a preset self-learning cycle during vehicle travel. Once the VCU determines that the self-learning result of the current self-learning cycle is successful, it will send the wheel radius learning value of that cycle to the target vehicle control system, so that the target vehicle control system can control the vehicle based on the wheel radius learning value. The target vehicle control system may include, but is not limited to, systems such as ABS, ACC, ESP, and TCS.

[0096] In this embodiment, by determining the initial radius ratio coefficient based on the calculated wheel radius of the target wheel and combining it with the wheel radius reference value, and then filtering the initial radius ratio coefficient, the impact of changes in vehicle driving state on wheel self-learning can be effectively reduced, improving the accuracy of the target radius ratio coefficient. At the same time, stability testing of the target radius ratio coefficient can ensure its reliability. Therefore, 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 relevant vehicle control system.

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

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

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

[0100] It should be noted that the average wheel speed of the target wheel refers to the average wheel speed of the left and right wheels within the target wheel. For example, when the target wheel is a front axle wheel, the average wheel speed of the left and right front wheels can be determined as the average wheel speed of the target wheel.

[0101] S101-2: Determine the calculated wheel radius 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 rotated, the wheel radius can be accurately calculated by taking into account the gear ratio of the target transmission based on the motor speed and average wheel speed.

[0103] It should be noted that the above calculation method applies when the vehicle's power source is an electric motor. When the vehicle's power source is an engine, the wheel radius can also be calculated based on the engine speed, the gear ratio of the target transmission, and the average wheel speed of the target wheels. In other words, this method is applicable to both gasoline-powered vehicles and new energy vehicles. Since electric motor speeds are usually more stable than engine speeds, the wheel radius calculation for new energy vehicles is generally more accurate.

[0104] In this embodiment, to effectively filter the calculated wheel radius value, a radius filtering interval can be set for the calculated wheel radius value. This radius filtering interval is a closed interval consisting of the maximum radius value and the minimum radius value. That is, if the calculated wheel radius value is within the radius filtering interval, the calculated wheel radius value is considered valid; if the calculated wheel radius value is greater than the maximum radius value, the maximum radius value is determined as the calculated wheel radius value; if the calculated wheel radius value is less than the minimum radius value, the minimum radius value is determined as the calculated wheel radius value. In this way, significantly larger or smaller calculated wheel radius values ​​can be effectively filtered.

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

[0106] S103-1: 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 prior to the current self-learning cycle.

[0107] In this embodiment, after each self-learning cycle, the VCU stores the initial radius ratio coefficient calculated in that self-learning cycle, thus obtaining the historical initial radius ratio coefficient. In this way, during the current self-learning cycle, the VCU can retrieve the required data from the stored historical initial radius ratio coefficients as needed.

[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 prior to the current self-learning cycle according to a preset filtering algorithm, and filter the initial radius ratio coefficients of the current self-learning cycle.

[0110] In practical implementation, a moving average filtering algorithm can be used to smooth the initial radius ratio coefficient by using the average value of the historical initial radius ratio coefficients within a preset window size, thereby obtaining the target radius ratio coefficient.

[0111] In practical implementation, the Exponential Moving Average (EMA) algorithm can also be used. By assigning different weights to the historical initial radius ratio coefficient, the closest data point has a larger weight, so as to more accurately reflect the changing trend of the initial radius ratio coefficient, thereby achieving filtering of the initial radius ratio coefficient.

[0112] In this embodiment, by performing smoothing filtering on the initial radius ratio coefficient, the variation range of the initial radius ratio coefficient is made smoother, thereby effectively avoiding the adverse effects of abnormal initial radius ratio coefficient on the self-learning effect.

[0113] In a feasible implementation, 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 the stability factor based on the ratio between the target radius ratio coefficient and the initial radius ratio coefficient.

[0115] In this embodiment, by defining the ratio as a stability factor, the stability factor can effectively reflect the relationship between the magnitude of 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 indicates 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 indicates that the target radius ratio coefficient after filtering is less than the initial radius ratio coefficient before filtering.

[0116] S202: If the stability factor is within the preset range and the axle speed of the drive shaft where the target wheel is located is greater than the axle speed threshold, trigger the timing for the duration of the first state.

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

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

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

[0120] S203: If the duration of the first state is greater than the first duration threshold, determine that the target radius ratio coefficient satisfies 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 indicates that the fluctuation of the initial radius ratio coefficient is small during the vehicle's travel at a certain speed. This causes the ratio between the filtered target radius ratio coefficient and the initial radius ratio coefficient before filtering to fluctuate around 1 with a small fluctuation range. Thus, it is determined that the target radius ratio coefficient meets the stability condition, meaning that the target radius ratio coefficient is considered reliable and effective, and can be used to calculate the wheel radius learning value of the target wheel in this 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 range or the axle speed of the drive shaft where the target wheel is located is less than or equal to the axle speed threshold, the timing will stop and the duration of the first state will be cleared to zero.

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

[0124] S204: If the stability factor is not within the preset range and the shaft speed is greater than the shaft speed threshold, trigger the timing for 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, it will trigger a timer to time the duration of the second state.

[0126] S205: If the duration of the second state is longer than the second duration threshold, the target radius ratio coefficient is determined to 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 considers that the fluctuation of the stability factor is large within the second duration threshold, and thus determines that the target radius ratio coefficient does not meet the stability condition, that is, it considers the self-learning result of this round of self-learning cycle to be a failure.

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

[0129] S301: If the target wheel does not meet the self-learning enable condition, or if the target radius ratio coefficient does not meet the stability condition, the target radius ratio coefficient shall be set as a preset coefficient.

[0130] In this embodiment, if the target wheel does not meet the self-learning enable condition, it indicates that the drive axle containing the target wheel is not in gear, or the vehicle is not in the target driving mode. In this case, the target radius ratio coefficient will be determined as a preset coefficient. This preset coefficient can be set to 1, meaning control is performed according to the wheel radius reference value.

[0131] In this embodiment, when the target wheel does not meet the self-learning enable condition or the learning result of the current self-learning cycle is a failure, the target radius ratio coefficient is determined as a preset coefficient, which can provide a default wheel radius reference value for the target control system to maintain the normal operation of the target control system.

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

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

[0134] In this embodiment, since the wheel radius learning value of the current self-learning cycle is learned when the axle speed of the drive shaft where the target wheel is located is greater than the axle speed threshold, the wheel radius learning value of the current self-learning cycle has little reference significance when the axle speed is less than or equal to the axle speed threshold, i.e. when the vehicle speed is low. Therefore, the VCU will use the historical target radius ratio coefficient corresponding to the previous self-learning cycle to calculate the wheel radius.

[0135] S402: Determine the historical target radius ratio coefficient as the target radius ratio coefficient for this round of 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 for the current 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 based on the wheel radius learning value.

[0137] Secondly, based on the same inventive concept, and referring to... Figure 2 This application provides a wheel radius self-learning device, the wheel radius self-learning device 200 includes:

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

[0139] The coefficient determination module 202 is used to determine the initial radius ratio coefficient based on the calculated wheel radius value and the wheel radius reference value;

[0140] The coefficient filtering module 203 is used to filter the initial radius ratio coefficient to obtain the 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, provided that the target radius ratio coefficient meets the stability condition.

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

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

[0144] In one embodiment of this 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 radius of the target wheel based on the motor speed, gear ratio, and average wheel speed.

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

[0148] The historical coefficient acquisition submodule is used to acquire 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 that is prior to 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 this application, the wheel radius self-learning device 200 further includes:

[0151] The stability factor determination module is used to determine the stability factor based on the ratio between the target radius ratio coefficient and the initial radius ratio coefficient.

[0152] The first timing module is used to trigger timing for the duration of the first state when the stability factor is within a preset range and the axle speed of the drive shaft where the target wheel is located is greater than the axle speed threshold.

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

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

[0155] The second timing module is used to trigger timing for the duration of the second state when the stability factor is not in the preset range 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 the second duration threshold.

[0157] In one embodiment of this 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 enable condition, or when the target radius ratio coefficient does not meet the stability condition.

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

[0160] The coefficient acquisition module is used to acquire the historical target radius ratio coefficient corresponding to the previous 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 for the current self-learning cycle.

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

[0163] Thirdly, based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing an executable program thereon, which, when executed by a processor, implements the wheel radius self-learning method proposed in the first aspect of this application.

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

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

[0166] Memory 301 is used to store 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 this application is implemented.

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

[0170] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products 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] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0172] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The 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 equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0174] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0175] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes the element.

[0176] The present invention has provided a detailed description of a wheel radius self-learning method, storage medium, and vehicle. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A self-learning method for wheel radius, characterized in that, The method includes: If the target wheel meets the self-learning enable condition, determine the calculated value of the target wheel's radius. Based on the calculated wheel radius and the reference wheel radius, the initial radius ratio coefficient is determined; The initial radius ratio coefficient is filtered to obtain the target radius ratio coefficient; If the target radius ratio coefficient satisfies the stability condition, the wheel radius learning value of the target wheel in this self-learning cycle is determined based on the target radius ratio coefficient and the wheel radius reference value. The method further includes: The stability factor is determined based on the ratio between the target radius ratio coefficient and the initial radius ratio coefficient. If the stability factor is within a preset range and the axle speed of the drive shaft where the target wheel is located is greater than the axle speed threshold, a timer is triggered to determine the duration of the first state. If the duration of the first state is greater than the first duration threshold, the target radius ratio coefficient is determined to satisfy the stability condition.

2. The wheel radius self-learning method according to claim 1, characterized in that, The method further includes: When the drive axle containing the target wheel 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.

3. The wheel radius self-learning method according to claim 1, characterized in that, The steps for determining the calculated wheel radius of the target wheel include: The target motor's rotational speed, the target transmission's gear ratio, and the target wheel's average wheel speed are obtained; wherein the target motor is connected to the target wheel via the target transmission. The calculated wheel radius of the target wheel is determined based on the motor speed, the gear 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 the target radius ratio coefficient includes: 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 that is located 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, After determining the stability factor based on the ratio between the target radius ratio coefficient and the initial radius ratio coefficient, the method further includes: If the stability factor is not within the preset range and the shaft speed is greater than the shaft speed threshold, a timer for the duration of the second state is triggered. If the duration of the second state is greater than the second duration threshold, it is determined that the target radius ratio coefficient does not satisfy the stability condition.

6. The wheel radius self-learning method according to claim 5, characterized in that, The method further includes: If the target wheel does not meet the self-learning enabling condition, or if the target radius ratio coefficient does not meet the stability condition, the target radius ratio coefficient is determined as a preset coefficient.

7. The wheel radius self-learning method according to claim 1, characterized in that, After 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: If the shaft speed is detected to be less than or equal to the shaft speed threshold, the historical target radius ratio coefficient corresponding to the previous self-learning cycle of the current self-learning cycle is obtained; The historical target radius ratio coefficient is determined as the target radius ratio coefficient for the current self-learning cycle.

8. A computer-readable storage medium having an executable program stored thereon, characterized in that, When the executable program is executed by the processor, it implements the wheel radius self-learning method as described in any one of claims 1-7.

9. A vehicle, characterized in that, include: Memory, used to store executable programs; processor; When the executable program is executed by the processor, the wheel radius self-learning method as described in any one of claims 1-7 is implemented.

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