Road surface actual adhesion coefficient estimation method and device based on tire slippage state and storage medium

Abstract

The invention provides a pavement actual adhesion coefficient estimation method and device based on a tire slippage state and a storage medium, and relates to the technical field of vehicle motion control. According to the method, dynamics basic parameters during normal driving of a vehicle are collected, vertical loads and transverse and longitudinal forces of a front axle and a rear axle are calculated, after the longitudinal force is calibrated by adopting a correction coefficient, an adhesion coefficient is calculated and utilized in combination with the vertical loads, a complex tire model and a camera are not needed, the calculation force requirement is reduced, the transverse and longitudinal force coupling working condition is adapted, and the adaptability of a complex road surface is improved; tracking and utilizing the maximum value of the adhesion coefficient through a stepped memory storage mechanism, storing an effective peak value for a long time, performing gradient forgetting, and providing a stable reference basis; then, vehicle dynamic parameters during tire slippage are collected, and the flag bit activation state is determined; and finally, when the flag bit is activated, outputting a memory adhesion coefficient as an actual pavement adhesion coefficient, so that the output is more fit with a real pavement, and the transverse and longitudinal stability of the vehicle and the torque vector control accuracy are improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle motion control technology, specifically to a method, device, and storage medium for predicting the actual road surface adhesion coefficient based on tire slippage state. Background Technology

[0002] In the field of vehicle motion control, the actual road surface adhesion coefficient is a core input parameter for active safety systems such as ESP (Electronic Stability Program) and TCS (Traction Control System), and its estimation accuracy directly determines the safety and smoothness of vehicle dynamic control. Currently, the mainstream methods for estimating the road surface adhesion coefficient are mainly divided into two categories: one is the tire model-based estimation method, which establishes a complex tire mechanical model and uses parameters such as wheel speed and torque to infer the adhesion coefficient. However, this method requires extremely high accuracy in tire parameter calibration, has high model complexity, and is easily affected by environmental factors such as temperature and tire pressure, making it difficult to balance real-time performance and robustness in mass-produced vehicles. The other category is the road surface feature recognition method, which directly detects road surface texture and water / snow accumulation using visual sensors or LiDAR. This method is significantly affected by severe weather (such as heavy rain and fog), has high sensor costs, is prone to data failure, and is difficult to adapt to all driving scenarios.

[0003] Meanwhile, existing technologies generally suffer from two major pain points: First, in tire slippage scenarios (such as brake lock-up and drive slippage), the real-time collected vehicle dynamic parameters (such as wheel speed and acceleration) are prone to drastic fluctuations, resulting in a large deviation in the adhesion coefficient calculated based on real-time parameters, which cannot accurately reflect the true adhesion limit of the road surface; Second, there is a lack of reasonable use of historical effective adhesion information. Most methods rely solely on data at the current moment for estimation. When the road surface condition changes abruptly (such as driving from a dry road surface to a wet road surface) or when there is instantaneous noise interference from the sensor, the estimation results are prone to jump, which can lead to false triggering of the vehicle control system, affecting the driving experience and safety.

[0004] Furthermore, existing methods often contain redundant logical architectures. Some solutions overly rely on multiple types of high-precision sensors during parameter acquisition or introduce excessive iterative calculations during coefficient calculation. This not only increases hardware costs but also raises the computational load on the vehicle controller, making it difficult to meet the engineering requirements of mass-produced vehicles for "low complexity and high real-time performance." Therefore, there is an urgent need for a method to predict the actual road adhesion coefficient that can balance accuracy, real-time performance, and engineering feasibility, and effectively address tire slippage scenarios, in order to overcome the shortcomings of existing technologies. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method, device and storage medium for predicting the actual road surface adhesion coefficient based on the tire slip state, which addresses the shortcomings of the prior art.

[0006] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A method for predicting the actual road surface adhesion coefficient based on tire slippage state, comprising the following steps: The vehicle dynamics parameters under normal driving conditions are collected. Based on the vehicle dynamics parameters, the front and rear axle vertical loads, front and rear axle lateral forces and front and rear axle longitudinal forces are calculated respectively. The front and rear axle longitudinal forces are corrected based on the correction coefficient to obtain the corrected front and rear axle longitudinal forces. The vehicle's adhesion coefficient is calculated based on the front and rear axle vertical loads, front and rear axle lateral forces and the corrected front and rear axle longitudinal forces. The numerical changes of the adhesion coefficient are tracked and recorded based on the stepped memory storage mechanism to obtain the maximum value of the adhesion coefficient, and the memory adhesion coefficient is calculated based on the maximum value of the adhesion coefficient. Collect vehicle dynamic parameters under tire slippage conditions, and determine the activation status information of the flag bit based on the vehicle dynamic parameters; When the flag is active, the memory adhesion coefficient is output as the actual adhesion coefficient of the road surface.

[0007] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: A device for predicting the actual road surface adhesion coefficient based on tire slippage state, comprising: The adhesion coefficient calculation module is used to collect the basic vehicle dynamic parameters under normal driving conditions. Based on the basic vehicle dynamic parameters, the vertical load, lateral force and longitudinal force of the front and rear axles are calculated respectively. The longitudinal force of the front and rear axles is corrected based on the correction coefficient to obtain the corrected longitudinal force of the front and rear axles. The adhesion coefficient of the vehicle is calculated based on the vertical load, lateral force and corrected longitudinal force of the front and rear axles. The memory adhesion coefficient calculation module is used to track and record the numerical changes of the adhesion coefficient based on a stepped memory storage mechanism, obtain the maximum value of the adhesion coefficient, and calculate the memory adhesion coefficient based on the maximum value of the adhesion coefficient. The flag determination module is used to collect vehicle dynamic parameters under tire slippage conditions and determine the activation status information of the flag based on the vehicle dynamic parameters. The output module is used to output the memory adhesion coefficient as the actual adhesion coefficient of the road surface when the flag bit is active.

[0008] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: a device for predicting the actual road surface adhesion coefficient based on tire slip state, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for predicting the actual road surface adhesion coefficient based on tire slip state as described above.

[0009] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for predicting the actual road surface adhesion coefficient based on tire slip state as described above.

[0010] The beneficial effects of this invention are: calculating the front and rear axle force parameters based on the basic parameters of vehicle dynamics and correcting the longitudinal force, combining the lateral and longitudinal forces with the vertical load to calculate the adhesion coefficient, avoiding reliance on complex tire models and cameras, reducing chip computing power requirements, adapting to the coupled working conditions of longitudinal and lateral forces, and improving the estimation adaptability of complex road surfaces; A stepped memory storage mechanism is adopted to track and utilize the maximum value of the adhesion coefficient, preserve the effective peak value for a long time and gradually forget it, so as to provide a stable reference benchmark for estimation. Based on the dynamic parameters of tire slip state, the marker position is determined to accurately capture scenarios such as tire slip and abnormal steering, ensuring accurate activation timing. This makes the output actual road adhesion coefficient more consistent with the real road surface, improving the accuracy of vehicle lateral and longitudinal stability control and torque vector control. Attached Figure Description

[0011] Figure 1 A flowchart of the method for predicting the actual road surface adhesion coefficient provided in an embodiment of the present invention; Figure 2 A block diagram of the road surface actual adhesion coefficient prediction device provided in an embodiment of the present invention; Figure 3 The graph showing the relationship between the adhesion coefficient and the memory adhesion coefficient over time is provided for an embodiment of the present invention. Detailed Implementation

[0012] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0013] Example 1: As Figure 1 As shown, this embodiment of the invention provides a method for predicting the actual road surface adhesion coefficient based on tire slippage state, including the following steps: S1. Collect the basic vehicle dynamics parameters under normal driving conditions. Calculate the vertical load, lateral force, and longitudinal force of the front and rear axles based on the basic vehicle dynamics parameters. Correct the longitudinal force of the front and rear axles based on the correction coefficient to obtain the corrected longitudinal force of the front and rear axles. Calculate the vehicle's adhesion coefficient based on the vertical load, lateral force, and corrected longitudinal force of the front and rear axles. S2. Based on the ladder-style memory storage mechanism, the numerical change of the adhesion coefficient is tracked and recorded to obtain the maximum value of the adhesion coefficient, and the memory adhesion coefficient is calculated based on the maximum value of the adhesion coefficient. S3. Collect vehicle dynamic parameters under tire slippage conditions, and determine the activation status information of the flag based on the vehicle dynamic parameters; S4. When the flag is active, the memory adhesion coefficient is output as the actual adhesion coefficient of the road surface.

[0014] In the above embodiments, the front and rear axle force parameters are calculated based on the basic parameters of vehicle dynamics and the longitudinal force is corrected. The adhesion coefficient is used in combination with the lateral and longitudinal forces and the vertical load. This avoids reliance on complex tire models and cameras, reduces the chip computing power requirements, adapts to the coupled working conditions of longitudinal force and lateral force, and improves the applicability of estimation on complex road surfaces. A stepped memory storage mechanism is adopted to track and utilize the maximum value of the adhesion coefficient, preserve the effective peak value for a long time and gradually forget it, so as to provide a stable reference benchmark for estimation. Based on the dynamic parameters of tire slip state, the marker position is determined to accurately capture scenarios such as tire slip and abnormal steering, ensuring accurate activation timing. This makes the output actual road adhesion coefficient more consistent with the real road surface, improving the accuracy of vehicle lateral and longitudinal stability control and torque vector control.

[0015] Preferably, basic vehicle dynamics parameters under normal driving conditions are collected, and based on these parameters, the vertical loads, lateral forces, and longitudinal forces of the front and rear axles are calculated, including... Collect basic vehicle dynamics parameters under normal driving conditions. These parameters include the distance from the center of gravity to the front and rear axles, the lateral and longitudinal acceleration at the center of gravity, the yaw acceleration, the curb weight, the front and rear axle drive torque, the front and rear axle braking torque, and the front axle steering angle. Calculate the static vertical loads on the front and rear axles based on the distances from the center of mass to the front and rear axles and the curb weight. The front axle static vertical load of the front and rear axle static vertical loads is expressed as: , The rear axle static vertical load of the aforementioned front and rear axle static vertical loads is expressed as: ; The static vertical loads of the front and rear axles are corrected based on the vertical load correction term and the longitudinal acceleration at the center of mass, resulting in the corrected front and rear axle vertical loads. The front axle vertical load of the aforementioned front and rear axle vertical loads is expressed as: , The rear axle vertical load of the aforementioned front and rear axle vertical loads is expressed as: , in, This is a correction term for the front axle vertical load; the negative sign indicates that the front axle load decreases and the rear axle load increases during acceleration. This is a correction term for the rear axle vertical load; the positive sign indicates that the rear axle load increases and the front axle load decreases during acceleration. This is the distance from the center of gravity to the front axle. This is the distance from the center of mass to the rear axle. The longitudinal acceleration at the center of mass is For curb weight, It is the acceleration due to gravity. The distance from the center of mass to the ground.

[0016] The lateral force on the front axle is calculated based on the lateral acceleration and yaw rate at the center of mass. The front axle lateral force of the front and rear axle lateral forces is expressed as: , The rear axle lateral force of the front and rear axle lateral forces is expressed as: , in, The lateral acceleration at the center of mass is The yaw acceleration is... For rotational inertia, The longitudinal forces on the front and rear axles are calculated based on the front and rear axle drive torque, front and rear axle braking torque, and front axle steering angle. The longitudinal force of the front axle of the longitudinal forces of the front and rear axles is expressed as: , The rear longitudinal force of the front and rear longitudinal forces is expressed as: , in, For front axle drive torque, This is the braking torque of the front axle. This is the rear axle drive torque. This is the braking torque for the rear axle. Front axle steering angle, The radius of the front tire. This is the radius of the rear tire.

[0017] In the above embodiments, by accurately collecting basic vehicle dynamic parameters and combining static vertical load calculation with dynamic correction, the influence of multiple factors such as center of gravity position, acceleration, and torque on the front and rear axle forces is fully considered, ensuring the accuracy of the calculation of front and rear axle vertical loads and lateral and longitudinal forces, and providing reliable basic data support for subsequent adhesion coefficient estimation.

[0018] Preferably, the longitudinal forces of the front and rear axles are corrected based on a correction factor to obtain the corrected longitudinal forces of the front and rear axles, and the vehicle's utilization coefficient of adhesion is calculated based on the vertical loads of the front and rear axles, the lateral forces of the front and rear axles, and the corrected longitudinal forces of the front and rear axles, including: The front axle correction factor was obtained by looking up the tire slip ratio table. and rear axle correction factor , Based on the front axle correction coefficient and rear axle correction factor longitudinal force on the front axle and rear axle longitudinal force Make corrections. The corrected longitudinal force on the front axle is expressed as: , The corrected longitudinal force on the rear axle is expressed as: ; The ratio of the lateral force to the vertical load on the front axle is calculated to obtain the coefficient of adhesion for lateral force utilization on the front axle. , is represented as: , The ratio of the corrected front axle longitudinal force to the front axle vertical load is calculated to obtain the front axle longitudinal force utilizing the adhesion coefficient. , is represented as: , The front axle is laterally utilized using the adhesion coefficient and the longitudinal adhesion coefficient of the front axle Perform coupling calculations to obtain the total adhesion coefficient of the front axle. , is represented as: ; The ratio of the lateral force to the vertical load on the rear axle is calculated to obtain the lateral adhesion coefficient of the rear axle. , is represented as: , The ratio of the corrected longitudinal force to the vertical load on the rear axle is calculated to obtain the longitudinal force of the rear axle utilizing the adhesion coefficient. , is represented as: , The rear axle is laterally utilized using the adhesion coefficient and the longitudinal adhesion coefficient of the rear axle Perform coupling calculations to obtain the total adhesion coefficient of the rear axle. , is represented as: ; Based on a weighted average algorithm, the rear axle is laterally adjusted using the adhesion coefficient. and the longitudinal adhesion coefficient of the rear axle Converting from the shaft end to the center of mass end, the center of mass end is obtained using the adhesion coefficient. , is represented as: , in, For the front axis weight, , For the front axis weight, ,and .

[0019] In the above embodiments, in response to the force deviation caused by longitudinal tire slippage, a correction coefficient related to the slip ratio is introduced to calibrate the longitudinal force. At the same time, by using the coupling calculation of the adhesion coefficient in the lateral and longitudinal directions and the weighted conversion from the shaft end to the center of mass end, the estimation problem under the coupled lateral and longitudinal forces is effectively solved, and the calculation accuracy using the adhesion coefficient is improved.

[0020] Preferably, the numerical changes of the adhesion coefficient are tracked and recorded based on a stepped memory storage mechanism to obtain the maximum value of the adhesion coefficient, and the memory adhesion coefficient is calculated based on the maximum value of the adhesion coefficient, including: Phase 1: Define a ladder storage variable, wherein the ladder storage variable includes a first ladder memory value. Second-tier memory value And the initial value is the current centroid end using the adhesion coefficient And set time parameters, including descent confirmation time t and first step memory value. The corresponding first step hold time t 21 And the second-tier memory value The corresponding second-step hold time t 22 ; Initialize the first and second step timers to 0, and initialize the first step memory value. Locking marker and second-level memory value The lock is marked as unlocked; Phase 2: By comparing the current centroid end using the adhesion coefficient Using the adhesion coefficient at the center of mass end of the previous moment Determine the magnitude of the value and the trend of its change: like Then judge It is on an upward trend. For fluctuation threshold, like Then judge To stabilize the trend, If the first-level memory value is in an upward or stable trend. Locking marker and second-level memory value If all the lock markers are unlocked, then , To ensure the first-tier memory value Second-tier memory value All during the unlocked period Maximum value; if the first-level memory value If the locking flag is locked, then the first-level memory value... Keep the current value and only update the unlocked second-tier memory value. ; Phase 3: like Then judge To confirm the downward trend, a downward confirmation timer is started to accumulate time. If within time t: like Always less than Then judge To achieve an effective descent, a tiered locking process is initiated. Then judge For momentary fluctuations, reset the descent confirmation timer and return to stage 2 to continue tracking; The ladder locking process is as follows: If the first-tier memory value If the locking flag is set to unlocked, then the current centroid end will be adjusted using the adhesion coefficient. peak Assigned to the first-order memory value Set the first-tier memory value The lock is marked as locked, and the first step timer is started to accumulate the holding time; If the first-tier memory value The lock flag is locked, and the second-level memory value is... If the lock is marked as unlocked, then the peak value will be... Assigned to the second-order memory value Set the second-tier memory value The lock is marked as locked, and the second-stage timer is started to accumulate the holding time; If the cumulative time of the first-step timer is greater than or equal to the first-step hold time t 21 Then set the first-level memory value. If the lock flag is set to unlocked, the first-stage timer is reset to 0, and the process enters the forgetting phase. If the cumulative time of the second-step timer is greater than or equal to the second-step hold time t 22 Then set the second-tier memory value. If the lock flag is set to unlocked, the second-stage timer is reset to 0, and the forgetting process begins. The forgetting handling process is as follows: Based on the gradient descent formula, the first-stage memory value will enter the forgetting process. Or second-tier memory value Descending to the current center of mass using the adhesion coefficient The gradient descent formula is: , ,in, The decrease coefficient, For time step, when / At that point, the descent stops, and the player returns to the second phase to await the next update. Phase 4: Real-time retrieval of the first-tier memory value Second-tier memory value The maximum value in the range is used as the memory adhesion coefficient. .

[0021] like Figure 3 As shown, the above process can be understood as: 1. Using attachment for memory: Memory adhesion coefficient m The core function of 2 is to continuously track and store the most recently observed friction coefficients over a long period of time. m The maximum value is 1. This mechanism ensures that the system can record the maximum friction conditions that occur during actual operation, thus providing a stable and reliable reference benchmark for the friction estimation system. Once set, m 2 will serve as the basis for triggering all mechanisms that rely on the friction coefficient reset mechanism. That is, when it is necessary to reset or re-evaluate the friction state, the system will prioritize calling this stored value to ensure the continuity and security of the control strategy.

[0022] The tiered storage method can be understood as: During the initial phase of the adhesion-based growth phase, the step value and the adhesion-based growth value are taken as the larger value and continuously updated. No hold-up processing is performed during this phase until the adhesion-based growth value begins to decline. At this point, the step value is held up and the hold-up time begins to accumulate.

[0023] To maximize the use of adhesion within a certain period of time m 1. Stored in Ladder 1 m 21 And keep t 21 s, where storage time t 21 With current vehicle speed and a y Related to the current vehicle speed or a y The larger the value, the greater the probability of good road conditions, and therefore the longer the storage time. If the current vehicle speed is low and ay is small, it means the road conditions are unknown, and the storage time t will be shorter.21 This will reduce the time, facilitating continuous updates of the latest road surface adhesion. If the storage time is t... 21 Inside, utilizing attachment m 1 increased to a level greater than m 21 For larger values, update ladder 2. m 22 The value, and lasting t 22 s. At storage time t 22 Inside, the adhesion μ1 was increased to a level greater than that of the internal components. m 22 For larger values, update ladder 2. m 21 The value, and lasting t 21 s. If the adhesion continues to decrease, then from m 21 They gradually forget things and then gradually return to using the attached values.

[0024] The memory logic is as follows: 1) When using attachment Increasing, real-time updates of unlocked attachments m 21 , m 22 The value of the memory attachment value must not be lower than the utilization attachment value. If m 21 , m 22 If a certain peak value exists, then that value will not increase with the increase of the attachment. When the attachment reaches a certain peak and begins to decrease, a certain descent confirmation time t is given. If the attachment continues to decrease within time t, then the peak value is assigned to... m 21 To keep the peak value at t 21 If, within time t, the attachment increases to a value greater than the previous peak, then the timing of t is reset and... m 21 It continues to grow by utilizing attachment, without storing it.

[0025] 2) When μ 21 Once the timer has expired, reset the timer to zero. m 21 The value of gradually decreases until it follows the change in adhesion. If at this point adhesion shows a peak again, and t 22 If the timing has already started, then the peak value is determined by... m 21 Responsible for maintaining.

[0026] 3) If the timer t 21 Within a certain timeframe, if the adhesion continues to increase, then... m21 Maintaining the previous peak value, μ 22 It increases with the increase of the utilization adhesion. When the utilization adhesion reaches a certain peak and begins to decrease, a certain descent confirmation time t is given. If the utilization adhesion continues to decrease within time t, then the peak value is assigned to... m 22 To keep the peak value at t 22 If, within time t, the attachment increases to a value greater than the previous peak, then the timing of t is reset and... m 22 Follow by using attachment Continue to increase the size, without storing it.

[0027] 4) When t 22 Once the timer has expired, reset the timer to zero. m 22 The value of gradually decreases until it follows the change in adhesion. If at this point adhesion shows a peak again, and t 21 If the timing has already started, then the peak value is determined by... m 22 Responsible for maintaining.

[0028] 5) Using a tiered storage method, we obtain... m 21 , m 22 Taking the larger of these two values ​​yields the final memory adhesion coefficient. m 2.

[0029] In the above embodiments, an innovative stepped memory storage mechanism is adopted. By dynamically tracking and utilizing the maximum value of the attachment coefficient through dual-step values, combined with descent confirmation, timed retention and gradient forgetting logic, it not only avoids peak misjudgment caused by instantaneous fluctuations, but also retains effective peak information for a long time, providing a stable and reliable memory attachment coefficient for subsequent output.

[0030] Preferably, the process involves collecting vehicle dynamic parameters during tire slippage and determining the activation status information of the flag based on these parameters, including: The vehicle dynamic parameters under tire slip conditions are collected, including wheel speed and center of gravity speed. ; Convert the wheel speed from angular velocity to linear velocity to obtain the wheel linear velocity. , The vehicle operating condition is determined, including braking, driving, and pure rolling conditions, based on the wheel linear velocity. and center of mass speed Calculate the actual slip ratio under various vehicle operating conditions s , When the vehicle is in braking condition (commonly when the brake is applied). The slip ratio under braking conditions is expressed as: , When the vehicle is in driving mode (common during rapid acceleration). The slip ratio under driving conditions is expressed as: , In pure rolling conditions, ; Lateral acceleration at the center of mass With center of gravity speed The ideal yaw rate is obtained by performing ratio calculations. Obtain the actual yaw rate ,based on The difference in yaw rate was calculated. ; Set the road surface adhesion reset flag position as The initial state is 0. When any preset condition is met, it is set to the active state of 1. The preset conditions are: Condition 1: Actual slip ratio s ≥ slip ratio threshold, Condition 2: Difference in yaw rate ≥Yaw rate difference threshold Condition 3: ABS flag activated ( ) or TCS flag activated ( ).

[0031] Specifically, the slip ratio threshold is usually set at 10%-15%, and anything exceeding this value is considered significant slip. The threshold for the difference in yaw rate is usually set at 0.2-0.5 rad / s. Exceeding this value indicates abnormal steering stability, which may be caused by slippage.

[0032] The threshold can be dynamically adjusted and adaptively corrected according to vehicle speed to suit different driving scenarios.

[0033] It should be understood that, in the process of converting the wheel speed from angular velocity to linear velocity, specifically: real-time wheel speed (unit: rad / s, denoted as ω) is obtained through four-wheel wheel speed sensors. , , , The formula for converting wheel speed from "angular velocity (rad / s)" to "linear velocity (m / s)" is as follows: , r This is the effective rolling radius of the wheel.

[0034] In the above embodiments, the focus is on the accurate identification of tire slip state. By calculating the actual slip rate under different working conditions and deriving the yaw rate difference, and with the assistance of ABS / TCS marker positions, a multi-dimensional and comprehensive marker position activation condition is constructed. At the same time, it supports dynamic threshold adaptation to vehicle speed, ensuring the accuracy and robustness of slip state judgment in different driving scenarios.

[0035] Preferably, the method further includes the step of: When the vehicle travels within the set storage distance, the current value of the actual road surface adhesion coefficient remains unchanged. When the vehicle travels a distance exceeding the set storage distance, the actual road surface adhesion coefficient will be reset to 1.0.

[0036] It should be understood that when the trigger flag is activated, the current memory adhesion coefficient μ2 is output as the final road surface adhesion coefficient μ, which cannot be lower than the current real-time utilized adhesion coefficient. μ2 is a friction coefficient value stored based on time, only stored for a few seconds. However, μ is a value stored based on distance, and generally the storage distance is much longer, which can reflect whether the current road surface is a high-friction road surface or a low-friction road surface.

[0037] The actual road surface adhesion coefficient μ will remain stable for a certain distance after the update, and this distance is related to the tire slippage time. If the tire slippage time is short, the distance maintained will be longer. If this distance is exceeded, the road surface adhesion coefficient μ will gradually return to 1.

[0038] In the above embodiments, an adhesion coefficient retention and regression mechanism based on driving distance is designed. Unlike the short-term storage of the adhesion coefficient memory, the mechanism ensures the stable representation of the overall friction state of the road surface by maintaining it over a long distance. At the same time, the logic of regressing to 1.0 is set to avoid the estimation deviation caused by using old values ​​for a long time, thus taking into account both stability and environmental adaptability.

[0039] Example 2: As Figure 2 As shown, this embodiment of the invention also provides a device for predicting the actual road surface adhesion coefficient based on tire slippage state, comprising: The adhesion coefficient calculation module is used to collect the basic vehicle dynamic parameters under normal driving conditions. Based on the basic vehicle dynamic parameters, the vertical load, lateral force and longitudinal force of the front and rear axles are calculated respectively. The longitudinal force of the front and rear axles is corrected based on the correction coefficient to obtain the corrected longitudinal force of the front and rear axles. The adhesion coefficient of the vehicle is calculated based on the vertical load, lateral force and corrected longitudinal force of the front and rear axles. The memory adhesion coefficient calculation module is used to track and record the numerical changes of the adhesion coefficient based on a stepped memory storage mechanism, obtain the maximum value of the adhesion coefficient, and calculate the memory adhesion coefficient based on the maximum value of the adhesion coefficient. The flag determination module is used to collect vehicle dynamic parameters under tire slippage conditions and determine the activation status information of the flag based on the vehicle dynamic parameters. The output module is used to output the memory adhesion coefficient as the actual adhesion coefficient of the road surface when the flag bit is active.

[0040] Preferably, the output module is further configured to: When the vehicle travels within the set storage distance, the current value of the actual road surface adhesion coefficient remains unchanged. When the vehicle travels a distance exceeding the set storage distance, the actual road surface adhesion coefficient will be reset to 1.0.

[0041] Example 3: This embodiment of the invention also provides a device for predicting the actual road surface adhesion coefficient based on tire slip state, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for predicting the actual road surface adhesion coefficient based on tire slip state as described above.

[0042] Example 4: This embodiment of the invention also provides a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the method for predicting the actual road surface adhesion coefficient based on tire slip state as described above.

[0043] 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 apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0044] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0045] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0046] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0047] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0048] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of estimating an actual road surface adhesion coefficient based on a tire slip state, characterized by, The method comprises the following steps: Collecting vehicle dynamics basic parameters in a normal driving state of the vehicle, calculating front and rear axle vertical loads, front and rear axle lateral forces and front and rear axle longitudinal forces based on the vehicle dynamics basic parameters, correcting the front and rear axle longitudinal forces based on a correction coefficient to obtain corrected front and rear axle longitudinal forces, and calculating a utilization adhesion coefficient of the vehicle based on the front and rear axle vertical loads, the front and rear axle lateral forces and the corrected front and rear axle longitudinal forces; Tracking and recording a numerical change of the utilization adhesion coefficient based on a ladder type memory storage mechanism to obtain a maximum utilization adhesion coefficient, and calculating a memory adhesion coefficient based on the maximum utilization adhesion coefficient; Collecting vehicle dynamic parameters in a tire slip state, and determining activation state information of a flag based on the vehicle dynamic parameters; When the flag is in an activated state, outputting the memory adhesion coefficient as an actual road adhesion coefficient.

2. The method of claim 1, wherein Collecting vehicle dynamics basic parameters in a normal driving state of the vehicle, calculating front and rear axle vertical loads, front and rear axle lateral forces and front and rear axle longitudinal forces based on the vehicle dynamics basic parameters, including Collecting vehicle dynamics basic parameters in a normal driving state of the vehicle, the vehicle dynamics basic parameters including a center of mass to front and rear axle distance, lateral and longitudinal accelerations at the center of mass, yaw angular acceleration, total mass, front and rear axle driving torques, front and rear axle braking torques and front axle rotation angle; Calculating front and rear axle static vertical loads based on the center of mass to front and rear axle distance and the total mass, The front axle static vertical load of the front and rear axle static vertical loads is represented as: ， The rear axle static vertical load of the front and rear axle static vertical loads is represented as: ； Correcting the front and rear axle static vertical loads based on a vertical load correction term and longitudinal acceleration at the center of mass to obtain front and rear axle vertical loads, The front axle vertical load of the front and rear axle vertical loads is represented as: ， The rear axle vertical load of the front and rear axle vertical loads is represented as: ， wherein, is a front axle vertical load correction term, the negative sign indicating that the front axle load decreases and the rear axle load increases during acceleration, is a rear axle vertical load correction term, the positive sign indicating that the rear axle load increases and the front axle load decreases during acceleration, is the distance from the center of mass to the front axle, is the distance from the center of mass to the rear axle, is the longitudinal acceleration at the center of mass, is the curb weight, is the acceleration due to gravity, is the distance from the center of mass to the ground; Calculating a front axle lateral force based on lateral acceleration at the center of mass and yaw angular acceleration, The front axle lateral force of the front and rear axle lateral forces is represented as: ， The rear axle lateral force of the front and rear axle lateral forces is represented as: ， wherein is the lateral acceleration at the center of mass, is the yaw angular acceleration, is the moment of inertia, Calculating front and rear axle longitudinal forces based on the front and rear axle driving torques, the front and rear axle braking torques and the front axle rotation angle, The front axle longitudinal force of the front and rear axle longitudinal forces is represented as: ， The rear axle longitudinal force of the front and rear axle longitudinal forces is represented as: ， wherein, is the front axle drive torque, is the front axle brake torque, is the rear axle drive torque, is the rear axle brake torque, is the front axle rotation angle, is the front wheel tire radius, is the rear wheel tire radius.

3. The method of claim 2, wherein Correcting the front and rear axle longitudinal forces based on a correction coefficient to obtain corrected front and rear axle longitudinal forces, and calculating a utilization adhesion coefficient of the vehicle based on the front and rear axle vertical loads, the front and rear axle lateral forces and the corrected front and rear axle longitudinal forces, including: The front axle correction factor and the rear axle correction factor are obtained from a tire slip ratio table and a rear axle correction factor , based on the front axle correction factor and the rear axle correction factor correcting the front axle longitudinal force and the rear axle longitudinal force ​ The corrected front axle longitudinal force is represented as: ， The corrected rear axle longitudinal force is represented as: ； The front axle lateral force and the front axle vertical load are ratioed to obtain a front axle lateral utilization adhesion coefficient is expressed as: ， The modified front axle longitudinal force and the front axle vertical load are calculated by ratio, to obtain the front axle longitudinal utilization adhesion coefficient is expressed as: ， The total utilization coefficient of the front axle is calculated by coupling the lateral utilization coefficient of the front axle and the longitudinal utilization coefficient of the front axle The total utilization coefficient of the front axle is calculated by coupling the lateral utilization coefficient of the front axle , expressed as: ； The rear axle lateral force and the rear axle vertical load are ratioed to obtain a rear axle lateral utilization coefficient is expressed as: ， The modified rear axle longitudinal force and the rear axle vertical load are subjected to ratio calculation to obtain the rear axle longitudinal utilization adhesion coefficient is expressed as: ， The total utilization coefficient of the rear axle is calculated by coupling the lateral utilization coefficient of the rear axle and the longitudinal utilization coefficient of the rear axle The total utilization coefficient of the rear axle is calculated by coupling the lateral utilization coefficient of the rear axle is expressed as ； Based on the weighted average algorithm, the rear axle lateral utilization coefficient of adhesion and the rear axle longitudinal utilization coefficient of adhesion converted from the axle end to the center of mass end, the center of mass end utilization coefficient of adhesion is represented as: , wherein is the front axle weight, , is the front axle weight, , and .

4. The actual road adhesion coefficient estimation method according to claim 1, wherein Tracking and recording a numerical change of the utilization adhesion coefficient based on a ladder type memory storage mechanism to obtain a maximum utilization adhesion coefficient, and calculating a memory adhesion coefficient based on the maximum utilization adhesion coefficient, including: Stage 1: A step storage variable is defined, which includes a first step memory value , a second step memory value , and an initial value being a current centroid end with an adhesion coefficient , and a time parameter is set, which includes a falling confirmation time t, a first step memory value corresponding first step holding time t 21 , and a second step memory value corresponding second step holding time t 22 ; initializing the first step counter and the second step counter to 0 and initializing the first step memory value the lock flag of the second step memory value the lock flag to be unlocked; Stage 2: By comparing the value of the centroid end of the current moment with the value of the centroid end of the previous moment using the adhesion coefficient to determine the trend of the value If then determine an upward trend, a volatility threshold, If then determine as a stationary trend, If the first-level memory value is in an upward or stable trend. Locking marker and second-level memory value If all the lock markers are unlocked, then , To ensure the first-tier memory value Second-tier memory value All during the unlocked period Maximum value; if the first-level memory value If the locking flag is locked, then the first-level memory value... Keep the current value and only update the unlocked second-tier memory value. ; Stage 3: If , it is determined that the trend is downward, and a downward confirmation timer is started to accumulate time. If the time t elapses, If is always less than , then determine as valid falling, enter the ladder lock process, if , determine as transient fluctuation, reset the falling confirmation timer, and return to the second stage to continue tracking. The ladder locking process is: If the first-tier memory value If the locking flag is set to unlocked, then the current centroid end will be adjusted using the adhesion coefficient. peak Assigned to the first-order memory value Set the first-tier memory value The lock is marked as locked, and the first step timer is started to accumulate the holding time; If the lock flag of the first step memory value is locked, and the lock flag of the second step memory value is not locked, the peak value is assigned to the second step memory value , the lock flag of the second step memory value is set as locked, and the second step timer is started to accumulate the holding time; If the accumulated time of the first step timer ≥ the first step retention time t 21 , set the first step memory value , set the lock flag to unlocked, reset the first step timer to 0, and enter the forgetting phase processing flow, If the accumulated time of the second step timer ≥ the second step holding time t 22 , set the second step memory value , set the lock flag to unlocked, reset the second step timer to 0, and enter the forgetting process flow. The forgetting processing process is: based on a gradient descent formula or second ladder memory value to the current centroid end using the coefficient of adhesion , the gradient descent formula is: , wherein, is a fall-off coefficient, is a time step, When / the descent is stopped and the second phase waits for the next update. Stage 4: Real-time retrieval of the first-tier memory value Second-tier memory value The maximum value in the range is used as the memory adhesion coefficient. .

5. The method of claim 1, wherein Collecting vehicle dynamic parameters in a tire slip state, and determining activation state information of a flag based on the vehicle dynamic parameters, including: Collecting vehicle dynamic parameters in a tire slip state, the vehicle dynamic parameters including wheel speed and mass center speed ; Converting the wheel wheel speed from angular velocity to linear velocity, resulting in a wheel linear speed , determining a vehicle operating condition, the vehicle operating condition including a braking operating condition, a driving operating condition, and a pure rolling operating condition, and calculating an actual slip ratio in each vehicle operating condition based on a wheel linear velocity and a center of mass vehicle speed calculating an actual slip ratio in each vehicle operating condition s , When the vehicle working condition is a braking working condition, Braking condition slip ratio is expressed as: , When the vehicle working condition is a driving working condition, The drive operating slip is expressed as: , Pure rolling condition, ; Lateral acceleration at the center of mass With center of gravity speed The ideal yaw rate is obtained by performing ratio calculations. Obtain the actual yaw rate ,based on The difference in yaw rate was calculated. ; The road surface adhesion reset flag is set to 0 in an initial state, and is set to 1 in an activated state when any one of preset conditions is satisfied. The preset conditions are that Condition 1 : Actual slip ratio s ≥ Slip ratio threshold value, Condition 2: yaw rate difference ≥ yaw rate difference threshold value, Condition 3: ABS flag bit is activated or TCS flag bit is activated.

6. The method of claim 1, wherein Further comprising steps of: When the vehicle driving distance is within a set storage distance, keeping the current value of the actual road adhesion coefficient unchanged, When the vehicle driving distance exceeds the set storage distance, returning the actual road adhesion coefficient to 1.

0.

7. A device for predicting the actual road surface adhesion coefficient based on tire slippage state, characterized in that, Comprising: an adhesion coefficient calculation module, configured to collect vehicle dynamics basic parameters in a normal driving state of the vehicle, calculate front and rear axle vertical loads, front and rear axle lateral forces and front and rear axle longitudinal forces based on the vehicle dynamics basic parameters, correct the front and rear axle longitudinal forces based on a correction coefficient to obtain corrected front and rear axle longitudinal forces, and calculate an adhesion coefficient of the vehicle based on the front and rear axle vertical loads, the front and rear axle lateral forces and the corrected front and rear axle longitudinal forces; a memory adhesion coefficient calculation module, configured to track and record a value change of the adhesion coefficient based on a step memory storage mechanism to obtain a maximum adhesion coefficient, and calculate a memory adhesion coefficient based on the maximum adhesion coefficient; a flag bit determination module, configured to collect vehicle dynamic parameters in a tire slip state, and determine activation state information of a flag bit based on the vehicle dynamic parameters; an output module, configured to output the memory adhesion coefficient as an actual road adhesion coefficient when the flag bit is in an activated state.

8. The actual road surface coefficient of friction estimation device according to claim 7, characterized by, The output module is further configured to: When the vehicle driving distance is within a set storage distance, keep the current value of the actual road adhesion coefficient unchanged, When the vehicle driving distance exceeds the set storage distance, return the actual road adhesion coefficient to 1.

0.

9. A device for predicting the actual road surface adhesion coefficient based on tire slippage state, characterized in that, A computer program product comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for estimating an actual road adhesion coefficient based on a tire slip state according to any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program product, when executed by the processor, implements the method for estimating an actual road adhesion coefficient based on a tire slip state according to any one of claims 1 to 6.

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