Error elimination method and device for three-axle tractor slip rate and electronic equipment

Abstract

The application provides a three-axle tractor slip ratio error elimination method, device and electronic equipment, which comprises the following steps: determining the tractive force and slip ratio of different tractive force sample points of the three-axle tractor; dividing the different tractive force sample points of the three-axle tractor into multiple regions based on the tractive force of the different tractive force sample points of the three-axle tractor; determining the tractive force average value and slip ratio average value of the tractive force sample points in each region based on the tractive force and slip ratio of the different tractive force sample points of the three-axle tractor; fitting the tractive force average value and slip ratio average value based on the weighted least square method, determining the slip ratio error caused by the rolling radius of the three-axle tractor based on the fitting result, and correcting the rolling radius based on the slip ratio error to eliminate the slip ratio error. The application can eliminate the slip ratio calculation error of the three-axle tractor without increasing the hardware cost, and the calculated slip ratio is more accurate.

Description

Technical Field

[0001] This invention relates to the field of signal processing for tractor vehicles, and specifically to a method, apparatus, and electronic device for eliminating slip rate errors in a three-axle tractor vehicle. Background Technology

[0002] With the development of digital and information technology, autonomous vehicle control technology has gradually advanced to a deeper level. The perception module is a crucial component of driver assistance functions, collecting information such as road surface conditions, air quality, and weather conditions along the vehicle's route. Road surface adhesion is a vital piece of road information; different road surfaces have different adhesion coefficients and offer varying maximum adhesion. When road surface adhesion is low, vehicle tires can easily lose traction and slip, causing the vehicle to lose steering and braking capabilities, potentially leading to traffic accidents. Rapidly identifying the road surface adhesion coefficient is of great importance, and this is especially critical for three-axle tractor vehicles.

[0003] Effect-based road surface identification methods are a technology that emerged with the development of vehicle control systems such as traction control systems and anti-lock braking systems. They utilize the vehicle's own dynamic characteristics and established models to quickly identify the road conditions. Existing effect-based road surface identification methods are generally based on tire noise. However, tire noise has complex causes and, like optical sensors, is significantly affected by the environment. For example, tire stiffness varies with temperature, affecting vibration characteristics and thus noise generation. It's difficult to eliminate uncertainties, and simply qualitatively and quantitatively filtering and detecting the road-related components of tire noise makes it hard to obtain accurate road surface information, leading to significant errors in slip ratio calculations. Tire deformation-based identification methods, unable to efficiently and simply obtain tire deformation information indirectly, typically rely on additional sensors mounted on the tire to acquire deformation characteristics, further increasing hardware costs.

[0004] Therefore, there is a need to provide a method that can eliminate the slip ratio calculation error of three-axle tractors and obtain a more accurate slip ratio without increasing hardware costs. Summary of the Invention

[0005] In view of this, it is necessary to provide a method, device and electronic equipment for eliminating slip ratio error of a three-axle tractor, so as to eliminate the slip ratio calculation error of the three-axle tractor without increasing hardware cost and to calculate a more accurate slip ratio.

[0006] To achieve the above objectives, the present invention provides a method for eliminating slip ratio errors in a three-axle tractor, comprising:

[0007] Determine the traction force and slip ratio at different traction force sample points of a three-axle tractor;

[0008] Based on the traction force of the three-axle tractor at different traction force sample points, the three-axle tractor at different traction force sample points are divided into multiple regions;

[0009] Based on the traction force and slip ratio of different traction force sample points of the three-axle tractor, the average traction force and average slip ratio of the traction force sample points in each region are determined.

[0010] The average traction force and the average slip ratio are fitted using the weighted least squares method. Based on the fitting result, the slip ratio error caused by the rolling radius of the three-axle tractor is determined. The rolling radius is then corrected based on the slip ratio error to eliminate the slip ratio error.

[0011] Furthermore, determining the traction force and slip ratio at different traction force sample points of the three-axle tractor includes:

[0012] The CAN signal of the three-axle tractor is acquired, and the signals in the CAN signal that are associated with the adhesion coefficient between the road surfaces are filtered to obtain the original dataset;

[0013] Error data under the target working condition in the original dataset are filtered out, and Gaussian white noise in the original dataset is eliminated to obtain the target dataset;

[0014] Based on the target dataset, the traction force and slip ratio of the three-axle tractor at different traction force sample points are calculated.

[0015] Further, the step of filtering the signals in the CAN signal that are associated with the inter-road adhesion coefficient to obtain the original dataset includes:

[0016] The signals associated with the adhesion coefficient between road surfaces in the CAN signals are filtered, and the asynchronous discrete signals in the filtered signals are time-synchronized and interpolated to construct the original dataset.

[0017] Furthermore, the signals in the CAN signal that are associated with the adhesion coefficient between the road surface include engine torque signal, engine speed signal, drive shaft speed signal, wheel angular velocity signal, and vehicle speed signal.

[0018] Further, the removal of Gaussian white noise in the original dataset includes:

[0019] Gaussian white noise in the original dataset is eliminated based on the sliding window mean.

[0020] Furthermore, the method for eliminating the slip ratio error of a three-axle tractor also includes:

[0021] Before filtering the signals in the CAN signal that are associated with the road surface adhesion coefficient, a tire dynamics model corresponding to the three-axle tractor is constructed to determine the signals in the CAN signal that are associated with the road surface adhesion coefficient based on the tire dynamics model.

[0022] Furthermore, the tire dynamics model includes tire dynamics models for complete vehicles without trailers and tire dynamics models for vehicles with trailers.

[0023] The present invention also provides an error elimination device for the slip ratio of a three-axle tractor, comprising:

[0024] The first determining module is used to determine the traction force and slip ratio at different traction force sample points of the three-axle tractor.

[0025] The segmentation module is used to divide the three-axle traction force sample points into multiple regions based on the traction force of the three-axle tractor.

[0026] The second determining module is used to determine the average traction force and average slip ratio of the traction force sample points in each region based on the traction force and slip ratio of the three-axle tractor sample points of different traction force sample points.

[0027] The correction module is used to fit the average traction force and the average slip ratio based on the weighted least squares method, determine the slip ratio error caused by the rolling radius of the three-axle tractor based on the fitting result, and correct the rolling radius based on the slip ratio error to eliminate the slip ratio error.

[0028] The present invention also provides an electronic device, including a memory and a processor, wherein,

[0029] The memory is used to store programs;

[0030] The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the error elimination method for the slip ratio of the three-axle tractor as described in any of the preceding claims.

[0031] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the error elimination method for the slip ratio of a three-axle tractor as described in any of the preceding claims.

[0032] The beneficial effects of the above implementation are as follows: The method, device, and electronic equipment for eliminating slip ratio errors of a three-axle tractor provided by this invention determine the average traction force and average slip ratio of traction force sample points in each region by using the traction force and slip ratio of different traction force sample points of the three-axle tractor; the average traction force and average slip ratio are fitted based on the weighted least squares method, and the slip ratio error caused by the rolling radius of the three-axle tractor is determined based on the fitting result, and the rolling radius is corrected based on the slip ratio error. This invention introduces a weighted least squares algorithm to process the data, obtain the deviation caused by the different wheel rolling radii in the slip ratio calculation, and correct the vehicle slip ratio, ultimately eliminating the error in slip ratio calculation. Without increasing hardware costs, it can eliminate the slip ratio calculation error of a three-axle tractor, and the eliminated slip ratio signal no longer has wheel rolling radius error, improving data utilization and providing a high-quality and efficient dataset for subsequent road surface recognition. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0034] Figure 1 A flowchart illustrating an embodiment of the error elimination method for the slip ratio of a three-axle tractor provided by the present invention;

[0035] Figure 2 A schematic diagram of the slip ratio before eliminating the inherent deviation of slip ratio caused by the rolling radius, provided by the present invention;

[0036] Figure 3 A schematic diagram of the slip ratio after eliminating the inherent deviation of slip ratio caused by the rolling radius, provided by the present invention;

[0037] Figure 4 This invention provides a schematic diagram illustrating the relationship between slip ratio and traction force obtained after processing sample points under the target working condition. Figure 1 ;

[0038] Figure 5 This invention provides a schematic diagram illustrating the relationship between slip ratio and traction force obtained after processing sample points under the target working condition. Figure 2 ;

[0039] Figure 6 This is a schematic diagram of the target dataset constructed after time synchronization and data interpolation of the original dataset, as provided by the present invention.

[0040] Figure 7A comparison chart of traction force and slip ratio distributions before sliding window mean filtering provided by the present invention;

[0041] Figure 8 A comparison diagram of the distribution of traction force and slip ratio after sliding window mean filtering provided by the present invention;

[0042] Figure 9 Force analysis diagram of the tire dynamics model provided by this invention;

[0043] Figure 10 This is a schematic diagram of the non-rigid body model of the driven wheel provided by the present invention;

[0044] Figure 11 A schematic flowchart of another embodiment of the error elimination method for the slip ratio of a three-axle tractor provided by the present invention;

[0045] Figure 12 A schematic diagram of an embodiment of the error elimination device for the slip ratio of a three-axle tractor provided by the present invention;

[0046] Figure 13 A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0047] 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 a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0048] In the description of the embodiments of this application, unless otherwise stated, "a plurality of" means two or more.

[0049] In this embodiment of the invention, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, apparatus, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product or device.

[0050] The naming or numbering of steps in the embodiments of the present invention does not mean that the steps in the method flow must be executed in the time / logical order indicated by the naming or numbering. The execution order of the named or numbered process steps can be changed according to the technical purpose to be achieved, as long as the same or similar technical effect can be achieved.

[0051] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0052] This invention provides a method, apparatus, and electronic device for eliminating slip ratio errors in a three-axle tractor, which will be described below.

[0053] like Figure 1 As shown, the present invention provides a method for eliminating slip ratio error of a three-axle tractor, comprising:

[0054] Step 110: Determine the traction force and slip ratio at different traction force sample points of the three-axle tractor.

[0055] Understandably, the main difference between a three-axle tractor and a regular passenger car lies in their powertrain structure. Generally, the transmission path of a single-axle driven gasoline vehicle is engine, clutch, gearbox, driveshaft, differential, and wheels. Vehicle performance parameters can be obtained directly or indirectly from performance parameter tables. The modeling does not consider any power losses due to friction or mechanical losses during transmission, assuming a lossless, idealized transmission system.

[0056] Ordinary passenger cars typically have a differential on the rear axle. The function of the differential is to allow the left and right wheels to rotate at different speeds, preventing mechanical damage to the rear axle, which lacks steering capability, when the vehicle is turning. This also explains why, when one drive wheel gets stuck in mud or other low-traction surfaces, that wheel slips severely, while the other drive wheel does not turn. The solution to this problem is to install a differential lock in the differential. A differential lock is a device that controls the relative speeds of the two sides. This device rigidly connects the left and right axles, ensuring that the wheels on both sides always maintain the same speed, i.e., have the same torque distribution. Thus, when one drive wheel slips, the other drive wheel can still propel the vehicle out of trouble. Therefore, differentials are commonly found in off-road vehicles, military vehicles, and three-axle tractors that require off-road and maneuverability.

[0057] Similarly, the inter-axle differential functions similarly, but it's applied between drive shafts instead of between the left and right wheels. Here, FL, FR, RLf, RLr, RRf, and RRR are used to represent the left front wheel, right front wheel, left wheel on the center axle, left wheel on the rear axle, right wheel on the center axle, and right wheel on the rear axle, respectively. Note that both the center and rear axles have two tires, but since these tires are rigidly connected via a driveshaft, they rotate at the same speed and do not need to be represented separately. When both the inter-wheel differential and the inter-axle differential are activated, let the torque and speed of the center axle driveshaft be T. shaftM and ω shaftM The torque and speed of the rear axle drive shaft are T respectively. shaftR and ω shaftR The following relationship exists:

[0058]

[0059] To investigate the impact of road excitation on the suspension and body of a three-axle tractor, a multi-degree-of-freedom model of the body and suspension was established. Stiffness and damping coefficient models were developed for the suspension, and the relationship between the vehicle weight's normal force on the tires, the road's support force on the tires, and the road excitation and suspension damping was analyzed, thereby analyzing the stability of the heavy-duty truck. xFL and F xFR F represents the lateral force exerted by the road surface on the tire along the x-axis. rFL and F rFR These are the rolling resistance F experienced by the tire along the y-axis. zFL and F zFR This is the supporting force exerted by the road surface on the tire along the z-axis. In the formula, the pronouns p in each quantity are represented by i and o, respectively, indicating the inner and outer tires, as in F. xRLFi and F xRLFo These represent the lateral forces exerted by the ground on the inner and outer tires on the left side of the central axle, respectively, with the same force F. xRRFp This represents the lateral force exerted by the ground on the tire on the right side of the center axle. F yRLFp and F yRRFp F represents the adhesion force experienced on the left and right sides. rRLFp and F rRRFp F is the rolling resistance of the tire. zRLFp and F zRRFp This represents the supporting force on the tires. In addition to the forces acting on each tire, the entire vehicle also experiences air resistance F along the y-axis. Taero and the vehicle's gravitational component F Tgrade The acceleration of the tractor in the x-axis and y-axis directions is expressed as a. Tx and a Ty According to Newton's laws, the following relationship holds:

[0060]

[0061]

[0062]

[0063]

[0064] F Tgrade =M T gsinθ

[0065]

[0066] M in the above formula T Let ρ be the mass of the tractor, g be the acceleration due to gravity, θ be the angle between the road surface and the horizontal plane, and ρ be the acceleration due to gravity. a It is the density of air, S T It is the frontal area of ​​the tractor unit, C T V is the drag coefficient of the vehicle, and V is the speed of the tractor. The above equation ignores some other forces acting on the vehicle, such as the additional resistance generated during braking, and only considers the conditions under which the vehicle is traveling at a constant speed or accelerating.

[0067] The tractor unit is a three-axle semi-trailer, a non-powered cargo box connected to the tractor unit via a towing pin. The tractor unit provides support and all traction for the semi-trailer. When the tractor unit and semi-trailer are connected, the tractor unit experiences downward pressure along its rear axle. This means that the downward force along the z-axis changes from the tractor unit's weight alone to the combined force of the tractor unit's weight and part of the semi-trailer's weight. Consequently, the pressure exerted on the ground by the tractor unit's center and rear axle tires increases, resulting in changes in adhesion and rolling resistance.

[0068] When the wheel is considered a rigid body, the tire deformation can be ignored. In this case, all excitations from the road surface onto the wheel can be considered as acting on a cross section of the tire tread. Under this premise, we can perform a dynamic analysis on the driven and driven wheels. The difference between the driven and driven wheels is that the force pulling the wheel forward is no longer a forward traction force, but rather a forward torque T from the drive shaft. d Furthermore, it will be subject to resistance F transmitted from the driven wheel along the axle. shaftIn a non-rigid model, the deformation of any object is not negligible. When the tire interacts with the ground, both the wheel and the ground deform. If the deformation of the ground is negligible relative to the deformation of the tire, this type of road surface is called a hard road surface. Conversely, if the deformation of the road surface is not negligible relative to the deformation of the tire, this type of road surface is called a soft road surface. Three-axle tractors typically operate on provincial highways, national highways, urban roads, and expressways, so we will only analyze the situation where the vehicle travels on hard road surfaces. To simplify the model, for the non-rigid model of the wheel, the angle between the road surface and the horizontal plane is not considered, air resistance is ignored, and braking is also disregarded.

[0069] Due to the elasticity of the tire upon contact with the ground, the rubber tends to return to its original shape. Therefore, the actual deformation of the tire is not symmetrical, but rather divided into two parts. With the vertical centerline of the wheel as the dividing line, the side in the direction of the wheel's rolling motion is subjected to the compressive force of the load, and gradually returns to its original shape after passing the centerline. Because the compression and recovery of the tire are asymmetrical, the normal support force of the road surface on the tire is not along the vertical centerline, but deviates from the vertical centerline by a distance d. Therefore, when the wheel rotates, the support force F of the road surface on the tire... s A rolling torque is generated around the center of the wheel, that is:

[0070] T f =F s d

[0071] a ω Let ω represent the angular acceleration of the wheel, r represent the rolling radius of the wheel, and ω represent the angular velocity of the wheel. According to the principle of force balance, for the driven wheel:

[0072]

[0073] When air resistance and road surface gradient are ignored, the resistance experienced by the driven wheel mainly consists of two parts: rolling resistance and frictional resistance. The rolling resistance coefficient is defined as follows:

[0074]

[0075] Numerically, the rolling resistance coefficient is the ratio of the normal support force of the road surface on the wheel to the offset distance d of the tire centerline to the wheel radius r. It can characterize the deformation characteristics between the tire and the road surface.

[0076] Combining the above three equations and the principle of force balance, we have:

[0077]

[0078] J is the moment of inertia of the wheel. Rolling resistance is equal to the rolling resistance coefficient λ and the vertical load F on the tire. pThe product of and , in physical terms, is the force required to propel the wheel.

[0079] Unlike the driven wheel, the axle of the drive wheel experiences resistance that hinders its rolling motion, so F... shaft As for resistance, according to the force balance, we have:

[0080]

[0081] Furthermore, during the tire's compression and subsequent recovery process, some energy is converted into internal energy loss, which is why the tire temperature rises rapidly when rolling. This energy loss is not listed in the above formula. Moreover, different tire pressures also affect the degree of tire deformation. Generally, higher tire pressure makes the tire less prone to deformation, resulting in lower rolling resistance with the ground; lower tire pressure makes the tire more prone to deformation, thus increasing the wheel's rolling resistance.

[0082] During normal vehicle operation, the following should be observed:

[0083]

[0084] Define the slip ratio S between the drive wheels and the vehicle. V for:

[0085]

[0086] For the drive wheels, in addition to providing the force to maintain the wheel's own rolling, they also need to provide the traction force required by the rest of the vehicle, so S V It is always greater than 0.

[0087] Step 120: Based on the traction force of the three-axle tractor at different traction force sample points, divide the three-axle tractor at different traction force sample points into multiple regions.

[0088] It is understandable that dividing the traction force from minimum to maximum value into m equally spaced regions allows for more regions, i.e., a larger m, to have more sample points with uniform weights. However, in actual data, not every traction force value within the range of 0 to maximum will have sample points. Considering the computational resource consumption, the algorithm used in this invention does not treat sample points with the same traction force as a single region. Instead, it treats sample points within a range of traction forces as a single region. If a region has no sample points, it is ignored instead of being calculated as 0. Furthermore, considering the reliability of the samples, if the number of sample points in a region is too small, the region is considered unreliable and is discarded. k and b are the slope and intercept of the least squares fitted line.

[0089] Step 130: Based on the traction force and slip ratio of different traction force sample points of the three-axle tractor, determine the average traction force and average slip ratio of the traction force sample points in each region.

[0090] It is understandable that by dividing the traction force into m equally spaced regions from minimum to maximum, the average traction force and average slip ratio of the sample points falling within these m regions can be calculated separately. and These are the average traction force and average slip ratio of all samples in the j-th region, respectively.

[0091] Step 140: Fit the average traction force and the average slip ratio using the weighted least squares method, determine the slip ratio error caused by the rolling radius of the three-axle tractor based on the fitting result, and correct the rolling radius based on the slip ratio error to eliminate the slip ratio error.

[0092] Understandably, in actual working conditions, the rolling radius of each tire on a three-axle tractor will differ. This is due to factors such as tire manufacturing errors, tire wear, and tire pressure. Therefore, when the vehicle travels in a straight line, the angular velocity of the tires on the left and right sides will also differ due to the different rolling radii. Since the rolling radius of the wheels mounted on a vehicle is difficult to measure precisely, and each vehicle and each wheel is different, accurate measurement of the rolling radius is time-consuming and laborious. Therefore, using mathematical methods to eliminate this limitation is a more economical approach.

[0093] Since vehicles typically operate in the linear zone on roads with high and medium adhesion coefficients, the simplest approach is to use the least squares method to linearly fit the calculated traction force and slip ratio, thereby obtaining the intersection point of the fitted line and the axis containing the slip ratio. This intersection point represents the inherent deviation caused by the different rolling radii.

[0094] This embodiment proposes a weighted least squares method. Since this algorithm aims to unify the weights of sample points with different traction forces, the sample points are divided into multiple parts according to the traction force. The average values ​​of the traction force and slip ratio in each part are calculated separately. Then, the points calculated in each part are used as sample points for least squares fitting, thus achieving the unification of weights.

[0095] Dividing the traction force into m equally spaced regions from minimum to maximum, we can then calculate the average traction force and average slip ratio of the sample points falling within these m regions:

[0096]

[0097]

[0098] In the above formula, n j y is the number of sample points in the j-th region. ij and x ij Let be the traction force and slip ratio of the i-th sample within the j-th region, respectively. and These are the average traction force and average slip ratio of all samples in the j-th region, respectively.

[0099] The average values ​​of traction force and slip ratio in each region are used as sample points for least squares fitting:

[0100]

[0101]

[0102]

[0103]

[0104] The more regions are divided, i.e., the larger m is, the more sample points will have their weights unified. In actual data, not every traction force value within the range of 0 to the maximum traction force will have a sample point. Considering the consumption of computational resources, the algorithm used in this invention does not treat sample points with the same traction force as a single region, but rather treats sample points within a traction force range as a single region. If a region has no sample points, it is ignored instead of being calculated as 0. Furthermore, considering the reliability of the samples, if the number of sample points in a region is too small, the region is considered unreliable and is discarded. The least squares method can be used to eliminate errors in the original signal of the three-axle tractor.

[0105] A comparison chart showing the difference before and after eliminating the inherent deviation in slip ratio caused by the rolling radius is shown below. Figure 2 and Figure 3As shown, after obtaining the intercept b, subtracting b from the slip ratio of all sample points eliminates the error caused by different rolling radii. Here, we take data collected from two different vehicles as an example. Data 1 is collected from an unloaded vehicle traveling at approximately 50 km / h on a high-traction road surface, and data 2 is collected from another fully loaded vehicle traveling at approximately 50 km / h on the same high-traction road surface. In the left figure, besides the difference in slip ratio caused by the different road adhesion coefficients due to different vehicle weights, the sample points of the two data points also show an offset due to the different rolling radii, resulting in a non-zero slip ratio during free-gliding. The offset in data 1 is negative, and the offset in data 2 is positive. In the right figure, after eliminating the rolling radius, the data before data 1 and data 2 only show differences in slope due to different vehicle weights, without differences in slip ratio offset caused by different rolling radii. This eliminates the inherent deviation in slip ratio caused by the rolling radius.

[0106] In some embodiments, determining the traction force and slip ratio at different traction force sample points of the three-axle tractor includes:

[0107] The CAN (Controller Area Network) signal of the three-axle tractor is acquired, and the signals in the CAN signal that are related to the adhesion coefficient between the road surfaces are filtered to obtain the original dataset;

[0108] Error data under the target working condition in the original dataset are filtered out, and Gaussian white noise in the original dataset is eliminated to obtain the target dataset;

[0109] Based on the target dataset, the traction force and slip ratio of the three-axle tractor at different traction force sample points are calculated.

[0110] Understandably, after filtering the signals related to the adhesion coefficient between the CAN signal and the road surface to obtain the original dataset, many situations are not taken into account, such as traction loss during braking and lateral slippage caused by the vehicle when turning at a large angle. These special conditions usually occur randomly and are unpredictable. The impact of these conditions can only be analyzed theoretically, and it is impossible to calculate and correct the impact based on the actual information available. Therefore, for these special conditions (i.e. target conditions), additional analysis is required to filter out the error data under these conditions.

[0111] When the turning angle is too large, it will affect the recognition of the adhesion coefficient. Therefore, in this embodiment, the dataset is filtered according to the vehicle yaw rate, and samples with excessive yaw rates (i.e., yaw rates greater than a threshold) are removed. The threshold for judgment can be calculated based on the main driving conditions of the three-axle tractor. During the vehicle's operation, if the vehicle is driven by a human, the driver's application of the brakes indicates that a road problem has been detected ahead, and at this time, it is not necessary to recognize the road adhesion coefficient. If the vehicle is unmanned, the application of the brakes indicates that the system has detected the change in road conditions and taken measures, and at this time, it is also not necessary to recognize the adhesion coefficient. Recognition can be resumed only after the road ahead is deemed safe or a certain condition is met. Therefore, this invention removes sample points containing braking data from the dataset. When the vehicle is under engine braking, the torque calculated by the vehicle sensors is negative. At this time, the engine does not actually provide power to the drive wheels, but rather acts as resistance. Therefore, this invention removes sample points where the vehicle torque is negative.

[0112] The slip ratio is calculated directly from the wheel rolling angular velocity collected by the vehicle's wheel speed sensor without noise reduction processing. Therefore, white noise is present in the signal, which obscures the signal characteristics and interferes with subsequent processing and recognition in the algorithm. A sliding window mean filtering algorithm is used to eliminate Gaussian white noise. The algorithm is as follows:

[0113]

[0114] In the above formula, S t It is the slip ratio at time t, calculated in practice. Let be the slip ratio at time t after filtering. This is the slip ratio at time ti after filtering. The algorithm uses a window of 10 points, taking the average of all values ​​within the window as the filtered result for the last time step. Filtering is performed on all samples as the window slides forward over time. The advantage of sliding window mean filtering compared to commonly used filtering methods is that it requires less processing power and storage space, making it more suitable for embedded systems such as automotive computers.

[0115] An example graph showing the relationship between slip ratio and traction force after processing sample points under the target working condition is shown below. Figure 4 and Figure 5 As shown, the relationship between the treated slip ratio and traction force is clearly linear. Since the vehicle is traveling on a high-traction surface, it is extremely difficult to exceed the maximum adhesion that the surface can provide. Therefore, when driving on a high-traction surface, the relationship between slip ratio and traction force is usually linear.

[0116] In some embodiments, filtering the signals in the CAN signal associated with the inter-road adhesion coefficient to obtain the original dataset includes:

[0117] The signals associated with the adhesion coefficient between road surfaces in the CAN signals are filtered, and the asynchronous discrete signals in the filtered signals are time-synchronized and interpolated to construct the original dataset.

[0118] It is understandable that, such as Figure 6 As shown, each acquired message (i.e., CAN signal) has a timestamp indicating the specific time it was received. Therefore, all message information is acquired sequentially. However, this does not mean that the information contained in each message frame is synchronized with the time it was received. Typically, automotive sensors receive information in parallel, and the vehicle ECU performs parallel calculations. Due to the characteristics of the CAN network, different messages need to be sent sequentially according to their priority. When the CAN network is overloaded, some messages may even be lost. During processing, the information contained in each message should be restored to its actual occurrence time, i.e., time synchronization.

[0119] The time synchronization method for messages of different sequences in the same period is to take the message with the highest priority and shortest period as the reference message, and group other messages with the timestamps of two adjacent reference messages as the interval. When the reception time of other messages is between these two adjacent reference messages, the occurrence time of the message is synchronized with the previous reference message. This achieves time synchronization between messages of the same period.

[0120] For time synchronization methods involving messages with different periods, it's necessary to find the closest reference frame preceding the longer-period message and synchronize the longer-period message with this reference frame. This completes the time synchronization. However, longer-period messages mean that sampling at the reference frame's period might result in no samples. Therefore, numerical interpolation is needed for the longer-period messages to meet the sampling frequency requirements. The interpolation method involves linearly interpolating the data from the two synchronized longer-period messages using the sampling frequency of the reference message.

[0121] The reason for using linear interpolation is that the longest message period in this invention is only 20ms, and it is data collected from actual vehicles. Since vehicles cannot undergo large dynamic changes within 20ms, there will be almost no large jumps in values ​​in the data obtained from the messages. Simple linear interpolation can meet the requirements and consume very little computing resources.

[0122] set up Figure 6 The values ​​of the two 10ms periodic messages at time 0 and time 10ms are respectively Value t+1 and Value t-1 The value of the interpolated frame at time 5ms is Value.t According to the linear interpolation method, we have:

[0123]

[0124] In some embodiments, the signals associated with the road surface adhesion coefficient in the CAN signal include engine torque signal, engine speed signal, drive shaft speed signal, wheel angular velocity signal, and vehicle speed signal.

[0125] Understandably, the signals required to identify the adhesion coefficient between a three-axle tractor and the road surface include engine torque, engine speed, driveshaft speed, wheel angular velocity, vehicle speed, vehicle lateral acceleration, vehicle longitudinal acceleration, vehicle yaw rate, clutch status, brake cylinder pressure, and vehicle weight. Some of these signals can be provided by the vehicle's CAN network.

[0126] In a three-axle tractor, the transmission period of each message (i.e., CAN signal) on the CAN bus is different. Common transmission periods are 5ms, 10ms, 20ms, 50ms, and 100ms. Here, the period refers to the interval between two frames of messages with the same sequence number. However, due to physical layer limitations, even messages with the same 5ms period can have different transmission times.

[0127] In some embodiments, the removal of Gaussian white noise in the original dataset includes:

[0128] Gaussian white noise in the original dataset is eliminated based on the sliding window mean.

[0129] It is understandable that, such as Figure 7 and Figure 8 As shown, the Gaussian white noise in the signal is eliminated after sliding window mean filtering. A comparison of the traction force and slip ratio distributions before and after filtering reveals that the distribution of sample points is "thinner" after filtering. This is because removing Gaussian white noise results in a smaller slip ratio fluctuation range, lower variance, and a more compact sample point distribution. Without suppressing or eliminating white noise, the sample distributions of different road surface data would largely overlap in subsequent identification. A "thinner" sample point distribution means more distinct features and less overlap between different data points, which is more beneficial for subsequent processing.

[0130] In some embodiments, the method for eliminating the slip ratio error of a three-axle tractor further includes:

[0131] Before filtering the signals in the CAN signal that are associated with the road surface adhesion coefficient, a tire dynamics model corresponding to the three-axle tractor is constructed to determine the signals in the CAN signal that are associated with the road surface adhesion coefficient based on the tire dynamics model.

[0132] Understandably, the force analysis diagram of the tire dynamics model corresponding to a three-axle tractor is as follows: Figure 9 As shown, the tractor load is a three-axle semi-trailer, consisting of a non-powered cargo box connected to the tractor unit via a towing pin. The tractor unit provides support and all traction for the semi-trailer, enabling its movement. Although the semi-trailer itself has no traction force, and all power comes from the tractor unit, it is equipped with a braking system. Through the connection device with the tractor unit, the driver's braking request can be transmitted to the semi-trailer, achieving synchronized braking between the tractor unit and the semi-trailer. The modeling ignores other forces such as braking during actual braking, considering only the conditions of the semi-trailer traveling at a constant speed or accelerating.

[0133] like Figure 1 As shown, when the tractor and the semi-trailer are connected together, the tractor experiences a downward force along the z-axis on its rear axle. This means that the downward force along the z-axis changes from the weight of the tractor alone to the combined force of the tractor's weight and part of the semi-trailer's weight. As a result, the pressure of the tractor's middle and rear axle tires on the ground increases, and the corresponding adhesion and rolling resistance change. However, when the tractor and the semi-trailer are considered as a whole, their interaction can be disregarded.

[0134] The tire dynamics model for a three-axle tractor can also include a non-rigid body model of the driven wheels of the three-axle tractor, as shown in the schematic diagram below. Figure 10 As shown, considering that the working conditions of three-axle tractors are mostly provincial highways, national highways, urban roads, and expressways, the analysis only considers the situation where the vehicle travels on hard surfaces. To simplify the model, the angle between the road surface and the horizontal plane is not considered for the non-rigid body model of the wheels, and air resistance and braking are ignored. Due to the elasticity of the tire after contact with the ground, the rubber tends to return to its original shape, so the actual deformation of the tire is not... Figure 10 The tire doesn't deform symmetrically from side to side; instead, it's divided into two parts, with the vertical centerline of the wheel as the dividing line. The side in the direction of the wheel's rolling movement is subjected to the compressive force of the load, and gradually returns to its original shape after passing the centerline. Because the compression and recovery of the tire are not symmetrical, the normal support force of the road surface on the tire is not along the vertical centerline, but deviates from it by a distance. Therefore, when the wheel rotates, the support force of the road surface on the tire generates a rolling torque around the center of the wheel.

[0135] In some embodiments, the tire dynamics model includes a tire dynamics model for a complete vehicle without a trailer and a tire dynamics model for a vehicle with a trailer.

[0136] Understandably, the driving conditions of a three-axle tractor are usually divided into two types: without a trailer and with a trailer. The main impact of the presence of a trailer is that the vertical load on the drive wheels will change significantly, causing changes in the adhesion and friction between the vehicle and the road surface. Furthermore, the impact will vary to different degrees depending on the weight of the cargo in the trailer.

[0137] In other embodiments, the method for eliminating the slip ratio error of the three-axle tractor provided by the present invention is as follows: Figure 12 As shown, a dynamic model is constructed to analyze the internal forces and the excitation between the vehicle and the road surface. A tire dynamic model is constructed to analyze the signals related to road adhesion. The raw signals collected from the CAN bus of the three-axle tractor are filtered using time synchronization and interpolation algorithms to obtain the raw dataset of the three-axle tractor. The error sources of the raw dataset under special driving conditions are analyzed and filtered out. Gaussian white noise in the slip ratio is eliminated using sliding window mean filtering. The wheel rolling radius is corrected according to the weighted least squares algorithm to compensate for the system error caused by it and eliminate its impact on the road adhesion recognition of the three-axle tractor.

[0138] The mass M of the tractor and semi-trailer T and M S The vehicle's acceleration a in the x-axis and y-axis directions Tx and a Sx With a Ty and a Sy The vehicle's speed V and the angle θ between the road and the horizontal plane are determined by the vehicle itself or its motion. However, different road surfaces cause different excitations to the vehicle, thus changing the relationships between these quantities. These interrelationships are the key research coefficients in this invention. Rigid and non-rigid body models were established for the drive and driven wheels of a three-axle tractor, respectively. The corresponding changes in the wheels when the road adhesion coefficient changes were studied, thereby obtaining characteristics related to the road adhesion coefficient.

[0139] In the above algorithm, asynchronous discrete signals arranged in chronological order are synchronized and interpolated to reconstruct the original message information collected from the CAN bus of the three-axle tractor, providing data support for the identification of the adhesion coefficient of the three-axle tractor. Some special driving conditions can interfere with the vehicle's motion state. These special conditions are analyzed, and their effects are eliminated by filtering them out.

[0140] To address the significant noise present in the slip ratio signal, this algorithm employs a sliding window mean filtering algorithm to eliminate the noise. Furthermore, since the rolling radii of vehicle tires vary, this introduces systematic errors when calculating the slip ratio. This algorithm uses a multiplication algorithm to correct for the wheel rolling radii, compensating for these systematic errors and eliminating their impact on the road adhesion recognition of the three-axle tractor.

[0141] In summary, the method for eliminating slip ratio error of a three-axle tractor provided by the present invention includes: determining the traction force and slip ratio of different traction force sample points of the three-axle tractor; dividing the different traction force sample points of the three-axle tractor into multiple regions based on the traction force of the different traction force sample points of the three-axle tractor; determining the average traction force and average slip ratio of the traction force sample points in each region based on the traction force and slip ratio of the different traction force sample points of the three-axle tractor; fitting the average traction force and the average slip ratio based on the weighted least squares method; determining the slip ratio error caused by the rolling radius of the three-axle tractor based on the fitting result; and correcting the rolling radius based on the slip ratio error to eliminate the slip ratio error.

[0142] In the method for eliminating slip ratio errors of a three-axle tractor provided by this invention, the average traction force and average slip ratio of traction force sample points in each region are determined by using the traction force and slip ratio of different traction force sample points of the three-axle tractor. The average traction force and average slip ratio are fitted using a weighted least squares method. Based on the fitting result, the slip ratio error caused by the rolling radius of the three-axle tractor is determined, and the rolling radius is corrected based on the slip ratio error. This invention introduces a weighted least squares algorithm to process the data, obtain the deviation in slip ratio calculation caused by different wheel rolling radii, and correct the vehicle slip ratio, ultimately eliminating the error in slip ratio calculation. Without increasing hardware costs, the slip ratio calculation error of a three-axle tractor can be eliminated. The slip ratio signal after elimination no longer has wheel rolling radius errors, improving data utilization and providing a high-quality and efficient dataset for subsequent road surface recognition.

[0143] like Figure 12 As shown, the present invention also provides an error elimination device 1200 for the slip ratio of a three-axle tractor, comprising:

[0144] The first determining module 1210 is used to determine the traction force and slip ratio of different traction force sample points of the three-axle tractor.

[0145] The segmentation module 1220 is used to divide the three-axle traction force sample points into multiple regions based on the traction force of the three-axle tractor sample points with different traction forces.

[0146] The second determining module 1230 is used to determine the average traction force and average slip ratio of the traction force sample points in each region based on the traction force and slip ratio of the traction force sample points of the three-axle tractor.

[0147] The correction module 1240 is used to fit the average traction force and the average slip ratio based on the weighted least squares method, determine the slip ratio error caused by the rolling radius of the three-axle tractor based on the fitting result, and correct the rolling radius based on the slip ratio error to eliminate the slip ratio error.

[0148] The error elimination device for the slip ratio of the three-axle tractor provided in the above embodiments can realize the technical solution described in the above embodiments of the error elimination method for the slip ratio of the three-axle tractor. The specific implementation principle of each module or unit can be found in the corresponding content in the above embodiments of the error elimination method for the slip ratio of the three-axle tractor, which will not be repeated here.

[0149] like Figure 13 As shown, the present invention also provides an electronic device 1300. The electronic device 1300 includes a processor 1301, a memory 1302, and a display 1303. Figure 13 Only some components of the electronic device 1300 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0150] In some embodiments, memory 1302 may be an internal storage unit of electronic device 1300, such as a hard disk or memory of electronic device 1300. In other embodiments, memory 1302 may also be an external storage device of electronic device 1300, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 1300.

[0151] Furthermore, the memory 1302 may include both internal storage units of the electronic device 1300 and external storage devices. The memory 1302 is used to store application software and various types of data installed on the electronic device 1300.

[0152] In some embodiments, processor 1301 may be a central processing unit (CPU), microprocessor or other data processing chip, used to run program code stored in memory 1302 or process data, such as the error elimination method for the slip ratio of a three-axle tractor in this invention.

[0153] In some embodiments, display 1303 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 1303 is used to display information from electronic device 1300 and to display a visual user interface. Components 1301-1303 of electronic device 1300 communicate with each other via a system bus.

[0154] In some embodiments of the present invention, when the processor 1301 executes the error elimination program for the slip ratio of the three-axle tractor in the memory 1302, the following steps can be implemented:

[0155] Determine the traction force and slip ratio at different traction force sample points of a three-axle tractor;

[0156] Based on the traction force of the three-axle tractor at different traction force sample points, the three-axle tractor at different traction force sample points are divided into multiple regions;

[0157] Based on the traction force and slip ratio of different traction force sample points of the three-axle tractor, the average traction force and average slip ratio of the traction force sample points in each region are determined.

[0158] The average traction force and the average slip ratio are fitted using the weighted least squares method. Based on the fitting result, the slip ratio error caused by the rolling radius of the three-axle tractor is determined. The rolling radius is then corrected based on the slip ratio error to eliminate the slip ratio error.

[0159] It should be understood that when the processor 1301 executes the error elimination program for the slip ratio of the three-axle tractor in the memory 1302, in addition to the functions mentioned above, it can also perform other functions, as can be found in the description of the corresponding method embodiments above.

[0160] Furthermore, the embodiments of the present invention do not specifically limit the type of the electronic device 1300 mentioned. The electronic device 1300 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 1300 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0161] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an error elimination method for the slip ratio of a three-axle tractor provided by the methods described above, the method comprising:

[0162] Determine the traction force and slip ratio at different traction force sample points of a three-axle tractor;

[0163] Based on the traction force of the three-axle tractor at different traction force sample points, the three-axle tractor at different traction force sample points are divided into multiple regions;

[0164] Based on the traction force and slip ratio of different traction force sample points of the three-axle tractor, the average traction force and average slip ratio of the traction force sample points in each region are determined.

[0165] The average traction force and the average slip ratio are fitted using the weighted least squares method. Based on the fitting result, the slip ratio error caused by the rolling radius of the three-axle tractor is determined. The rolling radius is then corrected based on the slip ratio error to eliminate the slip ratio error.

[0166] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0167] The above provides a detailed description of the error elimination method, device, and electronic equipment for the slip ratio of a three-axle tractor provided by the present invention. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for eliminating slip ratio error in a three-axle tractor, characterized in that, include: Determine the traction force and slip ratio at different traction force sample points of a three-axle tractor; Based on the traction force of the three-axle tractor at different traction force sample points, the three-axle tractor ... Based on the traction force and slip ratio of different traction force sample points of the three-axle tractor, the average traction force and average slip ratio of the traction force sample points in each region are determined. The average traction force and the average slip ratio are fitted using the weighted least squares method. Based on the fitting results, the slip ratio error caused by the rolling radius of the three-axle tractor is determined. The rolling radius is then corrected based on the slip ratio error to eliminate the slip ratio error.

2. The method for eliminating slip ratio error of a three-axle tractor according to claim 1, characterized in that, The determination of traction force and slip ratio at different traction force sample points of the three-axle tractor includes: The CAN signal of the three-axle tractor is acquired, and the signals in the CAN signal that are associated with the adhesion coefficient between the road surfaces are filtered to obtain the original dataset; Error data under the target working condition in the original dataset are filtered out, and Gaussian white noise in the original dataset is eliminated to obtain the target dataset; Based on the target dataset, the traction force and slip ratio of the three-axle tractor at different traction force sample points are calculated.

3. The method for eliminating slip ratio error of a three-axle tractor according to claim 2, characterized in that, The process of filtering the CAN signals associated with the inter-road adhesion coefficient to obtain the original dataset includes: The signals associated with the adhesion coefficient between road surfaces in the CAN signals are filtered, and the asynchronous discrete signals in the filtered signals are time-synchronized and interpolated to construct the original dataset.

4. The method for eliminating slip ratio error of a three-axle tractor according to claim 2, characterized in that, The CAN signals associated with the road surface adhesion coefficient include engine torque signal, engine speed signal, drive shaft speed signal, wheel angular velocity signal, and vehicle speed signal.

5. The method for eliminating slip ratio error of a three-axle tractor according to claim 2, characterized in that, The removal of Gaussian white noise in the original dataset includes: Gaussian white noise in the original dataset is eliminated based on the sliding window mean.

6. The method for eliminating slip ratio error of a three-axle tractor according to claim 2, characterized in that, Also includes: Before filtering the signals in the CAN signal that are associated with the road surface adhesion coefficient, a tire dynamics model corresponding to the three-axle tractor is constructed to determine the signals in the CAN signal that are associated with the road surface adhesion coefficient based on the tire dynamics model.

7. The method for eliminating slip ratio error of a three-axle tractor according to claim 6, characterized in that, The tire dynamics model includes tire dynamics models for complete vehicles without trailers and tire dynamics models for vehicles with trailers.

8. A device for eliminating slip ratio error in a three-axle tractor, characterized in that, include: The first determining module is used to determine the traction force and slip ratio at different traction force sample points of the three-axle tractor. The segmentation module is used to divide the three-axle tractor into multiple regions based on the traction force of different traction force sample points, in order from the minimum to the maximum traction force; sample points within a traction force range are considered as one region. The second determining module is used to determine the average traction force and average slip ratio of the traction force sample points in each region based on the traction force and slip ratio of the three-axle tractor sample points of different traction force sample points. The correction module is used to fit the average traction force and the average slip ratio based on the weighted least squares method, determine the slip ratio error caused by the rolling radius of the three-axle tractor based on the fitting result, and correct the rolling radius based on the slip ratio error to eliminate the slip ratio error.

9. An electronic device, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the error elimination method for the slip ratio of a three-axle tractor as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the error elimination method for the slip ratio of the three-axle tractor as described in any one of claims 1 to 7.

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