Side slip angle estimation method based on intelligent tire and related equipment

Abstract

The invention provides a side slip angle estimation method based on an intelligent tire and related equipment, and relates to the field of vehicle motion state estimation.The method comprises the steps that an on-line circulation estimation process is carried out, and the one-time process comprises the steps that the friction coefficient dynamic adjusting amount in the last estimation process is obtained; acquiring a current first friction coefficient output by the intelligent tire system and a corresponding confidence coefficient; outputting a second friction coefficient at the current time according to a second friction coefficient, the friction coefficient dynamic adjusting quantity, the first friction coefficient and the corresponding confidence coefficient which are output by the self-adaptive Kalman filtering last time through the self-adaptive Kalman filtering; and inputting the chassis signal and the second friction coefficient into a nonlinear observer to obtain an estimated value of the side slip angle of the vehicle. A new friction coefficient information source is provided for side slip angle estimation, the side slip angle is estimated through friction coefficients of multiple sources, and decoupling estimation is achieved.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle motion state estimation, and in particular to a method for estimating the sideslip angle of the center of mass based on an intelligent tire and related equipment. Background Art

[0002] As autonomous driving technology continues to advance toward higher levels of automation and the boundaries of vehicle performance are gradually being pushed, achieving robust vehicle safety control in various driving scenarios, especially in emergency situations, has become a major challenge. Accurate and timely estimation of the vehicle's side slip angle is crucial for vehicle motion control. Advanced chassis control systems require this information to improve vehicle handling and stability control.

[0003] The vehicle's slip angle, defined as the inverse tangent of the ratio of the lateral velocity to the longitudinal velocity at the vehicle's center of mass, is a key indicator of vehicle lateral stability. However, due to the complex, multidimensional, and nonlinear nature of tire dynamics and the strong coupling between vehicle velocity and the tire-road friction coefficient, high-precision estimation of the vehicle's slip angle is difficult.

[0004] In related technologies, methods for estimating the vehicle's center of mass slip angle are mainly divided into kinematic and dynamic methods. The kinematic method calculates the vehicle's center of mass slip angle by integrating the longitudinal, lateral acceleration, and yaw rate signals measured by the vehicle's inertial measurement unit (IMU). However, since integration leads to accumulated sensor errors, long-term integration can cause the estimated result to drift, gradually deviating from the true value. The kinematic method uses the Global Navigation Satellite System (GNSS) to estimate the vehicle's center of mass slip angle. When the GNSS signal is good, this method can achieve high accuracy in estimating the vehicle's center of mass slip angle. However, since GNSS signals are easily affected by signal obstruction and multipath effects, it performs poorly when the vehicle is traveling in areas such as urban high-rise buildings, tunnels, and forests.

[0005] Dynamic methods are based on vehicle and tire dynamic models. These methods typically produce accurate estimates when the vehicle and tire model parameters are accurate. However, in real-world road environments, due to variable road conditions, the tire-road friction coefficient, a key parameter in the tire model, varies significantly, leading to large estimation errors in dynamic methods. To adapt to changing road conditions, some methods have proposed a joint estimation method for the vehicle's slip angle and the tire-road friction coefficient. However, due to the strong coupling between the slip angle and the road friction coefficient, these methods require continuous and sufficient lateral / longitudinal excitation of the tire to converge, resulting in significant errors before the estimation results converge.

[0006] Based on this, there is an urgent need for a method that can support both the joint estimation of the center of mass sideslip angle and the friction coefficient, and the decoupled estimation of the friction coefficient and the center of mass sideslip angle.

[0007] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0008] The present disclosure provides a center of mass sideslip angle estimation method based on an intelligent tire and related equipment, which at least to a certain extent overcomes the problem of strong coupling between the friction coefficient and the center of mass sideslip angle when the center of mass sideslip angle and the friction coefficient are jointly estimated in the related art.

[0009] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0010] In a first aspect, embodiments of the present disclosure provide a method for estimating a sideslip angle of a center of mass based on a smart tire, the method comprising: The cyclic estimation process of the sideslip angle of the center of mass is performed online, wherein one estimation process in the cyclic estimation process includes: Obtain the dynamic adjustment value of the friction coefficient in the last estimation process; Obtaining the current first friction coefficient and the confidence level corresponding to the first friction coefficient output by the intelligent tire system; Outputting the current second friction coefficient through an adaptive Kalman filter based on the second friction coefficient last output by the adaptive Kalman filter, the dynamic adjustment amount of the friction coefficient in the last estimation process, the first friction coefficient, and the confidence level corresponding to the first friction coefficient; The chassis signal and the second friction coefficient are input into the nonlinear observer to obtain the estimated value of the vehicle's center of mass sideslip angle.

[0011] In one possible embodiment, the nonlinear observer includes: a tire model; The chassis signal and the second friction coefficient are input into the nonlinear observer to obtain the estimated value of the vehicle's center of mass sideslip angle, including: Inputting the chassis signal, the vehicle's last longitudinal velocity estimate, the vehicle's last lateral velocity estimate, and the current second friction coefficient into a tire model to determine the vehicle's current longitudinal tire force estimate and lateral tire force estimate; determining a derivative of the vehicle's longitudinal velocity estimate and a derivative of the vehicle's lateral velocity estimate based on the vehicle's longitudinal tire force estimate, the vehicle's lateral tire force estimate, a chassis signal, a previous vehicle longitudinal velocity estimate, a previous vehicle lateral velocity estimate, the vehicle's curb weight, and an observer gain of a nonlinear observer; Integrating the derivative of the vehicle's longitudinal velocity estimate and the derivative of the vehicle's lateral velocity estimate to obtain the vehicle's current longitudinal velocity estimate and lateral velocity estimate; The current estimated value of the center of mass sideslip angle is determined based on the current estimated value of the longitudinal speed and the current estimated value of the lateral speed of the vehicle.

[0012] In a possible embodiment, the nonlinear observer includes: a tire model and a dual-track vehicle model; the method further includes: Inputting the chassis signal, the vehicle's last longitudinal velocity estimate, the vehicle's last lateral velocity estimate, and the current second friction coefficient into a tire model to determine the vehicle's current longitudinal tire force estimate and lateral tire force estimate; Inputting the current longitudinal tire force estimate, the current lateral tire force estimate, and the steering angle in the chassis signal into a dual-track vehicle model to determine the current longitudinal acceleration estimate and the current lateral acceleration estimate of the vehicle; The current dynamic adjustment amount of the friction coefficient is determined according to the longitudinal acceleration measurement value, the lateral acceleration measurement value, the current longitudinal acceleration estimation value, and the current lateral acceleration estimation value in the chassis signal.

[0013] In a possible embodiment, the adaptive Kalman filter includes: a prediction update process and a measurement update process; Outputting the current second friction coefficient through the adaptive Kalman filter according to the second friction coefficient last output by the adaptive Kalman filter, the dynamic adjustment amount of the friction coefficient in the last estimation process, the first friction coefficient, and the confidence level corresponding to the first friction coefficient, including: During the prediction update process, the current friction coefficient prediction value is determined based on the second friction coefficient output last time and the dynamic adjustment amount of the friction coefficient in the last estimation process; Get the current error covariance prediction value; During the measurement update process, the Kalman gain is determined based on the current error covariance prediction value and confidence level; The second friction coefficient of the current time is determined according to the Kalman gain, the first friction coefficient and the predicted value of the friction coefficient of the current time.

[0014] In a possible embodiment, obtaining the current error covariance prediction value includes: Obtain the error covariance update value of the last adaptive Kalman filter measurement update process; According to the error covariance update value, the current error covariance prediction value is determined.

[0015] In a possible embodiment, obtaining an error covariance update value of a last measurement update process of an adaptive Kalman filter includes: An error covariance update value of a last measurement update process of an adaptive Kalman filter is determined according to a last Kalman gain and an error covariance prediction value of a last prediction update process of an adaptive Kalman filter.

[0016] In a possible embodiment, during the measurement update process, the Kalman gain is determined based on the current error covariance prediction value and the confidence level, including: Determine the measurement covariance according to the confidence level; wherein, the greater the confidence level, the smaller the measurement covariance; The Kalman gain is determined based on the measurement covariance and the current error covariance prediction value.

[0017] In a possible embodiment, the method further includes: In the process of processing the first friction coefficient and the confidence level corresponding to the first friction coefficient by using an adaptive Kalman filter, determining whether there is a valid first friction coefficient; If it exists, the measurement update process is performed; If it does not exist, the measurement update process is skipped, and the current friction coefficient prediction value is used as the current second friction coefficient, and the current error covariance prediction value is used as the current error covariance update value.

[0018] In a second aspect, an embodiment of the present disclosure provides a device for estimating a center of mass side slip angle based on a smart tire, comprising: The center of mass sideslip angle estimation device based on the intelligent tire is used to perform an online cyclic estimation process of the center of mass sideslip angle, wherein one estimation process in the cyclic estimation process includes: A first acquisition unit is used to obtain the dynamic adjustment amount of the friction coefficient in the last estimation process; a second acquiring unit, configured to acquire a current first friction coefficient and a confidence level corresponding to the first friction coefficient output by the intelligent tire system; a filtering unit configured to output a second friction coefficient for a current time through an adaptive Kalman filter based on the second friction coefficient last output by the adaptive Kalman filter, the dynamic adjustment amount of the friction coefficient in the last estimation process, the first friction coefficient, and the confidence level corresponding to the first friction coefficient; The estimation unit is used to input the chassis signal and the second friction coefficient into the nonlinear observer to obtain an estimated value of the vehicle's center of mass sideslip angle.

[0019] In a possible embodiment, the first acquiring unit is further configured to: Inputting the chassis signal and the current second friction coefficient into the tire model to determine an estimated longitudinal tire force value and an estimated lateral tire force value of the vehicle in the current tire coordinate system; Inputting the current longitudinal tire force estimate, the current lateral tire force estimate, and the steering angle in the chassis signal into a dual-track vehicle model to determine the current longitudinal acceleration estimate and the current lateral acceleration estimate of the vehicle; The current dynamic adjustment amount of the friction coefficient is determined according to the longitudinal acceleration measurement value, the lateral acceleration measurement value, the current longitudinal acceleration estimation value, and the current lateral acceleration estimation value in the chassis signal.

[0020] In a third aspect, an embodiment of the present disclosure provides an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the method in the first aspect above by executing the executable instructions.

[0021] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which implements the method in the first aspect when the computer program is executed by a processor.

[0022] In a fifth aspect, according to another aspect of the present disclosure, a computer program product or computer program is further provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the above methods.

[0023] Embodiments of the present disclosure provide a method and related equipment for estimating the center of mass sideslip angle based on an intelligent tire, relating to the field of vehicle motion state estimation. The method comprises: executing an online cyclic estimation process for the center of mass sideslip angle, wherein one estimation process in the cyclic estimation process includes: obtaining a dynamic adjustment value of the friction coefficient from the previous estimation process; obtaining a first friction coefficient and a confidence level corresponding to the first friction coefficient output by the intelligent tire system; outputting a second friction coefficient based on the second friction coefficient output by the adaptive Kalman filter, the dynamic adjustment value of the friction coefficient from the previous estimation process, the first friction coefficient, and the confidence level corresponding to the first friction coefficient; and inputting a chassis signal and the second friction coefficient into a nonlinear observer to obtain an estimated value of the vehicle's center of mass sideslip angle. The dynamic adjustment value of the friction coefficient and the first friction coefficient provide data support for the final center of mass sideslip angle, providing a new source of friction coefficient information for center of mass sideslip angle estimation. Estimating the center of mass sideslip angle using friction coefficients from multiple sources achieves decoupled estimation.

[0024] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0026] Figure 1 A flow chart of a method for estimating the sideslip angle of the center of mass based on a smart tire in an embodiment of the present disclosure is shown; Figure 2 A schematic diagram showing a method for estimating the sideslip angle of the center of mass based on a smart tire in an embodiment of the present disclosure is shown; Figure 3 A flow chart for determining an estimated value of a center of mass sideslip angle according to an embodiment of the present disclosure is shown; Figure 4 A flow chart for determining a dynamic adjustment amount of a friction coefficient according to an embodiment of the present disclosure is shown; Figure 5 A schematic diagram showing a double-track vehicle model according to an embodiment of the present disclosure is shown; Figure 6 A schematic diagram showing a method for determining a dynamic adjustment amount of a friction coefficient according to an embodiment of the present disclosure is shown; Figure 7 A flow chart for determining a second friction coefficient according to an embodiment of the present disclosure is shown; Figure 8 A flowchart showing another adaptive Kalman filtering process in an embodiment of the present disclosure is shown; Figure 9 A schematic diagram showing an evaluation result in an embodiment of the present disclosure; Figure 10 A schematic diagram showing another evaluation result in an embodiment of the present disclosure; Figure 11 A schematic diagram showing another evaluation result in an embodiment of the present disclosure; Figure 12 A schematic structural diagram of a center of mass side slip angle estimation device based on an intelligent tire in an embodiment of the present disclosure is shown; Figure 13 A schematic structural diagram of an electronic device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0027] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0028] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0029] This exemplary implementation is described in detail below with reference to the accompanying drawings and examples.

[0030] First, an embodiment of the present disclosure provides a method for estimating the center of mass sideslip angle based on an intelligent tire. The method can be executed by any electronic device with computing and processing capabilities. In the following process, the electronic device is taken as an example of a terminal device.

[0031] Figure 1 A flow chart of a method for estimating the center of mass side slip angle based on an intelligent tire in an embodiment of the present disclosure is shown. Figure 1 As shown, the method for estimating the sideslip angle of the center of mass based on the intelligent tire provided in the embodiment of the present disclosure includes the following steps: S102: Obtaining the dynamic adjustment value of the friction coefficient in the last estimation process.

[0032] S104: Obtaining the current first friction coefficient and the confidence level corresponding to the first friction coefficient output by the intelligent tire system.

[0033] In a possible embodiment, the intelligent tire system may include a system composed of multiple models such as machine learning models, for example, an intelligent tire system composed of a convolutional neural network, etc., which is not limited in the embodiments of the present disclosure.

[0034] S106: Outputting the second friction coefficient of the current time through adaptive Kalman filtering according to the second friction coefficient output by the adaptive Kalman filtering last time, the dynamic adjustment amount of the friction coefficient in the last estimation process, the first friction coefficient and the confidence level corresponding to the first friction coefficient.

[0035] S108: Input the chassis signal and the second friction coefficient into a nonlinear observer to obtain an estimated value of the vehicle's center of mass sideslip angle.

[0036] The chassis signal is a common sensor signal in mass-produced vehicles, and may include measurement values ​​such as longitudinal acceleration, lateral acceleration, yaw angular velocity, steering angle, and wheel speed of each wheel.

[0037] In one possible embodiment, the convergence of the cyclic estimation process is judged in an offline manner. When the comparison between the second friction coefficient output by the adaptive Kalman filter at any time and the true friction coefficient determines that the adaptive Kalman filter process has converged, it is determined that the cyclic estimation process has reached a convergence state, and the second friction coefficient at the time of convergence is determined. The second friction coefficient at the time of convergence and the chassis signal at the corresponding moment are input into the nonlinear observer to obtain the vehicle's center of mass sideslip angle estimation value A. The center of mass sideslip angle estimation value A is the output at the time of convergence obtained in the offline stage. At this time, the convergence state is reached, and the convergence speed can be determined based on the center of mass sideslip angle estimation value A and the data can be analyzed.

[0038] Figure 2 A schematic diagram of a method for estimating the center of mass side slip angle based on a smart tire in an embodiment of the present disclosure is shown. Figure 2 As shown, it includes: intelligent tire system, adaptive Kalman filter, nonlinear observer, dynamic friction regulation and chassis signal.

[0039] The process of cyclic estimation is as follows Figure 2 As shown, first, the adaptive Kalman filter process provides an initial second friction coefficient. The initial second friction coefficient can be a preset value. During the verification phase, it can be set to an extreme value far from the actual friction coefficient corresponding to the actual road type to verify the adaptive Kalman filter process's ability to correct the friction coefficient in the disclosed embodiment. During the use phase, the initial second friction coefficient can be set close to the actual friction coefficient for ease of use. This is not specifically limited in the disclosed embodiment.

[0040] The initial second friction coefficient and chassis signals are input into a nonlinear observer. The nonlinear observer first determines an estimated slip angle, then, through the dynamic friction adjustment component, determines a dynamic friction coefficient adjustment. This is then input into the adaptive Kalman filter process to provide data support for the next second friction coefficient. Chassis signals include longitudinal acceleration measurements, lateral acceleration measurements, yaw rate, steering angle, and wheel speed.

[0041] The nonlinear observer part determines the longitudinal velocity and lateral velocity of the vehicle to determine the estimated value of the vehicle's center of mass sideslip angle.

[0042] The intelligent tire system provides the first friction coefficient and the corresponding confidence level for each adaptive Kalman filter process.

[0043] The inputs to the dynamic friction regulation are the lateral acceleration estimate, the longitudinal acceleration estimate, the longitudinal acceleration measurement from the chassis signal, and the lateral acceleration measurement from the chassis signal.

[0044] Based on the above process, online cyclic estimation essentially means applying the above process to the vehicle during driving and continuously outputting the vehicle's motion state information.

[0045] With respect to step S108 above, the nonlinear observer may include a tire model, and the tire model in the nonlinear observer determines the estimated longitudinal tire force and the estimated lateral tire force of the vehicle, and further determines the longitudinal speed and the lateral speed of the vehicle based on the nonlinear observer, thereby determining the estimated side slip angle of the center of mass of the vehicle.

[0046] In the above manner, the dynamic adjustment amount of the friction coefficient and the first friction coefficient provide data support for the final center of mass slip angle. The introduction of the additional first friction coefficient to provide data support makes it possible for the center of mass slip angle estimation process to no longer rely on the friction coefficient, which can only converge when the tire is subjected to continuous and sufficient lateral / longitudinal excitation, thereby decoupling the estimation process.

[0047] Figure 3 A flow chart of determining a center of mass side slip angle estimation value in an embodiment of the present disclosure is shown. Figure 3 As shown, the following steps are included: S302: Input the chassis signal, the vehicle's last longitudinal velocity estimate, the vehicle's last lateral velocity estimate, and the current second friction coefficient into a tire model to determine the vehicle's current longitudinal tire force estimate and lateral tire force estimate.

[0048] In a possible embodiment, the tire model may be UniTire-Ctrl, ie, a unified tire model control version.

[0049] The estimated longitudinal and lateral tire forces of the vehicle can be determined using the following formulas.

[0050] (1) in, Function representing the UniTire-Ctrl tire model, represents the estimated longitudinal tire force in the tire coordinate system; represents the estimated value of the lateral tire force in the tire coordinate system; represents the vertical load; Indicates tire slip angle; Indicates the longitudinal slip rate of the tire; Unified representation of any second friction coefficient output by the adaptive Kalman filter process; , which means the specific wheels, FL, FR, RL, and RR represent the four wheels respectively.

[0051] Some parameters in the above formula (1) are determined by the chassis signal and the last estimated longitudinal velocity and the last estimated lateral velocity. The specific solution process will not be repeated here.

[0052] After determining the longitudinal tire force estimate and the lateral tire force estimate in the tire coordinate system, the longitudinal tire force estimate and the lateral tire force estimate in the vehicle body coordinate system are determined using formula (2), which are the longitudinal tire force estimate and the lateral tire force estimate of the vehicle in S302.

[0053] (2) in, Indicates the wheel angle; represents the estimated longitudinal tire force in the vehicle body coordinate system; Represents the estimated lateral tire force in the vehicle body coordinate system.

[0054] S304: Determine a derivative of the vehicle's longitudinal velocity estimate and a derivative of the vehicle's lateral velocity estimate based on the vehicle's longitudinal tire force estimate, lateral tire force estimate, chassis signals, the vehicle's last longitudinal velocity estimate, the vehicle's last lateral velocity estimate, the vehicle's overall mass, and an observer gain of a nonlinear observer.

[0055] In a possible embodiment, the longitudinal velocity and lateral velocity of the vehicle are further determined by a nonlinear observer, and the derivative of the estimated value of the longitudinal velocity of the vehicle and the derivative of the estimated value of the lateral velocity of the vehicle can be determined by the following formula.

[0056] (3) (4) in, represents the derivative of the vehicle's longitudinal velocity estimate; represents the derivative of the vehicle's lateral velocity estimate; Indicates the vehicle's last estimated lateral speed; Indicates the vehicle's last estimated longitudinal velocity; Indicates the longitudinal acceleration of the vehicle body in the chassis signal; Indicates the lateral acceleration of the vehicle body in the chassis signal; Indicates the vehicle mass; represents the observer gain related to the longitudinal velocity; represents the observer gain related to the lateral velocity.

[0057] S306: Integrate the derivative of the vehicle's longitudinal velocity estimate and the derivative of the vehicle's lateral velocity estimate to obtain the vehicle's current longitudinal velocity estimate and lateral velocity estimate.

[0058] in, and When the initial input is used to determine the longitudinal tire force estimate, the lateral tire force estimate, and the derivative of the vehicle longitudinal velocity estimate and the derivative of the vehicle lateral velocity estimate, it may be a preset value.

[0059] Taking the first solution process as an example, it is and Preset an initial value to determine the estimated value of the vehicle's lateral velocity and the estimated value of the vehicle's longitudinal velocity in the first solution process. represents the estimated lateral velocity of the vehicle, Indicates the estimated longitudinal velocity of the vehicle. In the second solution process, the first solution is obtained and As the second input and In order to distinguish the longitudinal velocity estimate and the lateral velocity estimate used as input for solution, and the longitudinal velocity estimate and the lateral velocity estimate of the vehicle obtained as output, parameters are distinguished by different representations.

[0060] S308: Determine a current estimated value of the center of mass sideslip angle based on the current estimated value of the longitudinal speed and the current estimated value of the lateral speed of the vehicle.

[0061] In a possible embodiment, the longitudinal velocity and lateral velocity of the vehicle are determined, and the estimated value of the center of mass sideslip angle can be determined by the following formula (5).

[0062] (5) in, represents the estimated value of the center of mass sideslip angle, represents the estimated lateral velocity of the vehicle, Represents the estimated longitudinal velocity of the vehicle.

[0063] In a possible embodiment, regarding the specific method of obtaining the last dynamic adjustment amount of the friction coefficient in S102, it should be noted that the method of dynamically adjusting the friction coefficient each time is the same, and is determined based on the current second friction coefficient and chassis signal.

[0064] After the current second friction coefficient and chassis signal are input into the nonlinear observer, the current estimated value of the vehicle's center of mass sideslip angle can be obtained, and the current dynamic adjustment value of the friction coefficient can also be obtained.

[0065] Figure 4 A flow chart of determining the dynamic adjustment amount of the friction coefficient in an embodiment of the present disclosure is shown. Figure 4 As shown, the following steps are included: S402: Input the chassis signal, the vehicle's last longitudinal velocity estimate, the vehicle's last lateral velocity estimate, and the current second friction coefficient into the tire model to determine the vehicle's current longitudinal tire force estimate and lateral tire force estimate.

[0066] In a possible embodiment, the method of determining the estimated longitudinal tire force value and the estimated lateral tire force value in the current secondary tire coordinate system of the vehicle in S402 is the same as the determination method in S302 and is not repeated here.

[0067] S404: Input the current longitudinal tire force estimate, the current lateral tire force estimate, and the steering angle in the chassis signal into the dual-track vehicle model to determine the current longitudinal acceleration estimate and the current lateral acceleration estimate of the vehicle.

[0068] In a possible embodiment, a dual-track vehicle model is used to process and determine a current estimated longitudinal acceleration value and a current estimated lateral acceleration value of the vehicle.

[0069] Figure 5 A schematic diagram of a double-track vehicle model in an embodiment of the present disclosure is shown. Figure 5 The parameters include the following meanings.

[0070] in, Respectively represent the tire slip angles of the four wheels; They represent the longitudinal tire forces of the four wheels in the tire coordinate system; They represent the lateral tire forces of the four wheels in the tire coordinate system; t represents the wheelbase; a and b represent the distances from the vehicle's center of mass to the front and rear axles, respectively; Indicates the front wheel turning angle; Indicates the lateral speed of the vehicle; Indicates the longitudinal speed of the vehicle; represents the sideslip angle of the center of mass; r represents the yaw angular velocity.

[0071] Through the above dual-track vehicle model, the longitudinal acceleration and lateral acceleration can be estimated using the following formulas.

[0072] (6) (7) in, Indicates the estimated value of the vehicle's current longitudinal acceleration; Indicates the current lateral acceleration estimate; Respectively represent the estimated longitudinal tire forces of the four wheels in the tire coordinate system, Respectively represent the estimated values ​​of the lateral tire forces of the four wheels in the tire coordinate system; Indicates the vehicle's overall mass.

[0073] S406: Determine a current dynamic adjustment amount of the friction coefficient based on the longitudinal acceleration measurement value, the lateral acceleration measurement value, the current longitudinal acceleration estimation value, and the current lateral acceleration estimation value in the chassis signal.

[0074] In a possible embodiment, the current dynamic adjustment amount of the friction coefficient can be determined by the following formula (8).

[0075] (8) in, Indicates the dynamic adjustment amount of friction coefficient; Represents the gain coefficient.

[0076] Figure 6 A schematic diagram of determining the dynamic adjustment amount of the friction coefficient in an embodiment of the present disclosure is shown. Figure 6 As shown, the vehicle's current longitudinal acceleration estimate and current lateral acceleration estimate, as well as the vehicle's current longitudinal acceleration measurement and the vehicle's current lateral acceleration measurement are respectively calculated and inputted as a vector to determine the dynamic adjustment amount of the friction coefficient.

[0077] In the related art, when estimating the center of mass slip angle, within the tire's linear operating range, tire forces are primarily determined by slip stiffness, with the friction coefficient having a minor influence. Therefore, inaccurate friction coefficient estimation has a limited impact on the net force, resulting in weaker adaptive updates of the friction coefficient. In contrast, when the tire operates in a nonlinear operating range, the friction coefficient becomes the primary factor influencing the tire forces. In this case, the difference between the measured and estimated net accelerations becomes more pronounced, effectively triggering the adaptive correction process for the friction coefficient. Therefore, traditional model-based friction coefficient estimation methods in the related art typically require sufficient tire excitation—that is, the tire must operate in a nonlinear region—to achieve accurate and sensitive friction coefficient estimation. However, the method in the disclosed embodiments does not rely solely on the dynamic friction coefficient adjustment value input each time. It also includes a first friction value determined by the intelligent tire system and adaptively corrected through an adaptive Kalman filter process to accurately estimate the friction coefficient, obtaining a second friction coefficient for use in the center of mass slip angle estimation process. This allows accurate and sensitive friction coefficient and center of mass slip angle estimation to be achieved even with low tire excitation, improving the accuracy of the center of mass slip angle estimation process.

[0078] Figure 7 FIG1 shows a flow chart of determining the second friction coefficient in an embodiment of the present disclosure. The adaptive Kalman filter process includes: a prediction update process and a measurement update process. Figure 7 As shown, the following steps are included: S702: During the prediction update process, a current friction coefficient prediction value is determined based on the second friction coefficient output last time and the dynamic adjustment amount of the friction coefficient in the last estimation process.

[0079] In a possible embodiment, the friction coefficient prediction value can be determined by the following formula (9).

[0080] (9) in, represents the k-th predicted value of the friction coefficient, and indicates that it is calculated based on the k-1-th second friction coefficient and the dynamic adjustment amount of the friction coefficient. represents the k-1th second friction coefficient; represents the k-1th dynamic adjustment value of the friction coefficient; A and B represent the unit matrix respectively.

[0081] S704: Obtain the current error covariance prediction value.

[0082] In a possible embodiment, a method for obtaining the current error covariance prediction value may include: obtaining the error covariance update value of the measurement update process of the last adaptive Kalman filter; and determining the current error covariance prediction value based on the error covariance update value.

[0083] In a possible embodiment, the error covariance prediction value may be determined as shown in the following formula (10).

[0084] (10) in, Denotes the k-th error covariance prediction value, and indicates that it is calculated based on the k-1-th error covariance update value. represents the k-1th error covariance update value; Q represents the process noise covariance, which can be set to 0.001.

[0085] S706: During the measurement update process, determine the Kalman gain based on the current error covariance prediction value and confidence level.

[0086] In a possible embodiment, the Kalman gain may be determined in the following manner, including: determining a measurement covariance based on a confidence level; wherein the greater the confidence level, the smaller the measurement covariance; and determining the Kalman gain based on the measurement covariance and a current error covariance prediction value.

[0087] In one possible embodiment, the Kalman gain may be determined as shown in the following formulas (11) and (12).

[0088] (11) (12) in, represents the k-th Kalman gain, H represents the unit matrix, represents the measurement covariance, Indicates the confidence level corresponding to the first friction coefficient of the kth time.

[0089] S708: Determine the second friction coefficient for the current time according to the Kalman gain, the first friction coefficient, and the predicted value of the friction coefficient for the current time.

[0090] In a possible embodiment, the second friction coefficient can be determined by the following formula (13).

[0091] (13) in, represents the k-th second friction coefficient, and indicates that it is calculated based on the k-th friction coefficient prediction value, the k-th Kalman gain, and the k-th first friction coefficient; represents the kth first friction coefficient.

[0092] In a possible embodiment, the error covariance update value may be determined by: determining the error covariance update value according to the last Kalman gain and the error covariance prediction value in the last prediction update process of the adaptive Kalman filter.

[0093] In a possible embodiment, the error covariance update value may be determined by the following formula (14).

[0094] (14) in, represents the k-th error covariance update value, and indicates that it is calculated based on the k-th error covariance prediction value and the k-th Kalman gain; I Represents the identity matrix.

[0095] Through the above process, the first friction coefficient and the dynamic adjustment value of the friction coefficient are adaptively corrected based on the adaptive Kalman filtering process, outputting an accurate second friction coefficient for use in the center of mass slip angle estimation process. Furthermore, the first friction coefficient, the confidence level, and the dynamic adjustment value of the friction coefficient are used in the filtering process to re-estimate the friction coefficient. This adaptively corrects the friction coefficients from multiple sources, improving the accuracy of the estimated friction coefficient and reducing reliance on a single source of friction coefficient, thereby achieving decoupled estimation of the center of mass slip angle and the friction coefficient. The above process is not a simple combination of the two. Instead, the first friction coefficient, the confidence level, and the dynamic adjustment value of the friction coefficient are used in the measurement and prediction updates of the filtering process to adaptively adjust the friction coefficient. The confidence level is used to determine a gain, and different friction coefficient prediction values ​​are determined based on different dynamic adjustment values ​​of the friction coefficient. Different gains are dynamically determined based on different confidence levels, thereby adjusting the second friction coefficient output from the filtering process in real time.

[0096] In one possible embodiment, the measurement update process of the adaptive Kalman filter (AKF) primarily processes the first friction coefficient and confidence level. The sampling results of the intelligent tire system are not generated at a fixed frequency. Instead, they are sampled based on a preset angle and depend on the tire's rotational speed. The faster the rotational speed, the more sampling results are obtained in the same timeframe. However, the sampling results are typically lower than the sampling rate of the adaptive Kalman filter (AKF). Since the AKF operates at a fixed sampling interval, the sampling results of the intelligent tire system are not always available. Therefore, an update flag is introduced to indicate whether a valid first friction coefficient exists at the current moment.

[0097] Based on this, whether a valid first friction coefficient exists can be determined between the prediction update process and the measurement update process of the adaptive Kalman filter process. This can include the following steps: during the process of processing the first friction coefficient and the confidence level corresponding to the first friction coefficient through the adaptive Kalman filter, determining whether a valid first friction coefficient exists in the current cycle; if so, executing the measurement update process; if not, skipping the measurement update process, using the current friction coefficient prediction value as the current second friction coefficient, and using the current error covariance prediction value as the current error covariance update value.

[0098] Figure 8 FIG. 4 shows a flow chart of an adaptive Kalman filtering process in an embodiment of the present disclosure. Figure 8 As shown, it includes: prediction update process, judgment process and measurement update process.

[0099] S802: Execute the prediction update process.

[0100] S804: Determine whether upflg is 1. If so, execute S806; if not, execute S808.

[0101] Here, upflg is the abbreviation of the update flag, which is used to determine whether there is a valid first friction coefficient at the current moment.

[0102] S806: Execute the measurement update process.

[0103] S808: Using the current friction coefficient prediction value as the current second friction coefficient, and using the current error covariance prediction value as the current error covariance update value, and returning to execute S802.

[0104] Through the above method in the embodiment of the present disclosure, as Figure 9-11 As shown, the estimation results of the center of mass sideslip angle and friction coefficient of the vehicle under double lane change conditions are shown.

[0105] Figure 9 A schematic diagram showing an evaluation result in an embodiment of the present disclosure is shown in FIG. Figure 9 As shown, taking a vehicle driving on an asphalt road as an example, there are six coordinate systems. The vertical coordinates of three coordinate systems are the friction coefficient (μ), - represents no unit, respectively: the true friction coefficient, the first friction coefficient, the second friction coefficient, the vertical coordinate of one coordinate system is the lateral acceleration ( ), unit is m / s 2 The vertical coordinates of the two coordinate systems are the center of mass sideslip angle (β), the unit is degree °, which are the estimated value of the center of mass sideslip angle and the measured center of mass sideslip angle, respectively. The horizontal coordinates of the six coordinate systems are time (s).

[0106] The coordinate system shows that the friction coefficient ranges from 0, 0.2, 0.4, 0.6, 0.8 and 1 in 0-1, the sideslip angle of the center of mass includes -2, -1, 0, 1, 2 and 3 in -2-3, the lateral acceleration includes -10, 0 and 10 in -10-10, and the time includes: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 in 0-10s. The time dimensions of the horizontal axes in the six coordinate systems are aligned.

[0107] The actual friction coefficient of the road surface is 1.0, and the initial second friction coefficient can be set to 0.1.

[0108] During the initial straight-line driving phase, the second friction coefficient estimated by the adaptive Kalman filter quickly converges to the true friction coefficient. Even with minor miscalculations in the first friction coefficient output by the intelligent tire system, it remains stable throughout the entire operating condition. Thanks to the accurate friction coefficient estimation, the estimated center of mass slip angle closely matches the measured center of mass slip angle, ensuring accurate results.

[0109] Figure 10 A schematic diagram showing another evaluation result in an embodiment of the present disclosure is shown. Figure 10 As shown in FIG, the estimated results of the sideslip angle and friction coefficient of the center of mass on the snow are shown. The actual friction coefficient of the snow is about 0.37, and the initial second friction coefficient can be set to 1.0.

[0110] Figure 10 There are 6 coordinate systems in Figure 9 The coordinate system in is similar and will not be described again. Figure 10 The ordinates of the sideslip angle at the center of mass include -10, 0, and 10, the friction coefficients include 0, 0.2, 0.4, 0.6, 0.8, and 1, the lateral accelerations include -5, 0, and 5, and the times include 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, and 20.

[0111] In this condition, the tire fully utilizes the available road friction and the lateral acceleration reaches about 4m / s 2 , indicating a high degree of tire excitation. The adaptive Kalman filter method in this disclosed embodiment integrates the estimated value (0.3) from the intelligent tire system with dynamic response information based on the vehicle dynamics model. This allows the estimated result to adaptively adjust to the actual friction state during high excitation, rather than being limited to the first friction coefficient output by the intelligent tire system. The friction coefficient determination process in this disclosed embodiment is unaffected by the friction coefficient from a single data source. Therefore, the sideslip angle estimation results are highly consistent with the measured data.

[0112] Figure 11 A schematic diagram showing another evaluation result in an embodiment of the present disclosure is shown. Figure 11As shown in the figure, the friction coefficient and the side slip angle estimation results of the vehicle driving on the ice are shown. It includes six coordinate systems, and the parameters involved in the coordinate systems are the same as Figure 9 and Figure 10 The specific coordinate values ​​involved are similar and will not be repeated here. The time includes: 0, 2, 4, 6, 8, 10, 12, 14 and 16, the friction coefficient includes: 0, 0.2, 0.4, 0.6, 0.8 and 1, and the center of mass slip angle includes: -10, 0 and 10.

[0113] The actual friction coefficient of the ice surface is about 0.15, and the initial value of the second friction coefficient can be set to 1.0.

[0114] The adaptive Kalman filter process accurately estimates the friction coefficient from multiple sources, which in turn makes the vehicle's center of mass and sideslip angle estimation more accurate. Figure 11 As shown, no further details are given.

[0115] in, Figure 9 、 Figure 10 and Figure 11 The friction coefficients are represented as follows: the first friction coefficient is represented by a rhombus, the second friction coefficient is represented by a dashed line, and the actual friction coefficient is represented by a solid line. The rhombuses are connected by lines to clarify the diagram's depiction of the first friction coefficient over time. The sideslip angle is represented as follows: the measured sideslip angle is represented by a solid line, and the estimated sideslip angle is represented by a dashed line.

[0116] Based on the same inventive concept, the present disclosure also provides a device for estimating the center of mass side slip angle based on an intelligent tire, as shown in the following embodiment. Because the principles of this device embodiment are similar to those of the aforementioned method embodiment, the implementation of this device embodiment can be referenced to the implementation of the aforementioned method embodiment, and any repetitions will not be repeated.

[0117] Figure 12 A schematic diagram of the structure of a device for estimating the center of mass side slip angle based on an intelligent tire in an embodiment of the present disclosure is shown. Figure 12As shown, the center of mass sideslip angle estimation device 120 based on the intelligent tire includes: the center of mass sideslip angle estimation device 120 is used to perform an online cyclic estimation process of the center of mass sideslip angle, wherein an estimation process in the cyclic estimation process includes: a first acquisition unit 1201, used to obtain the dynamic adjustment amount of the friction coefficient in the previous estimation process; a second acquisition unit 1202, used to obtain the current first friction coefficient output by the intelligent tire system and the confidence level corresponding to the first friction coefficient; a filtering unit 1203, used to output the current second friction coefficient according to the second friction coefficient last output by the adaptive Kalman filter, the dynamic adjustment amount of the friction coefficient in the previous estimation process, the first friction coefficient and the confidence level corresponding to the first friction coefficient through an adaptive Kalman filter; an estimation unit 1204, used to input the chassis signal and the second friction coefficient into a nonlinear observer to obtain an estimated value of the center of mass sideslip angle of the vehicle.

[0118] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods, or program products. Therefore, various aspects of the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, microcode, etc.), or in a combination of hardware and software, collectively referred to herein as "circuits," "modules," or "systems."

[0119] Refer to the following Figure 13 1300 according to this embodiment of the present disclosure will be described. Figure 13 The electronic device 1300 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0120] like Figure 13 As shown, electronic device 1300 is implemented as a general-purpose computing device. Components of electronic device 1300 may include, but are not limited to, the aforementioned at least one processing unit 1310, the aforementioned at least one storage unit 1320, and a bus 1330 connecting various system components (including storage unit 1320 and processing unit 1310).

[0121] The storage unit stores program code, which can be executed by the processing unit 1310, so that the processing unit 1310 performs the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of the present disclosure. For example, the processing unit 1310 can perform the steps of any of the above method embodiments.

[0122] The storage unit 1320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 13201 and / or a cache unit 13202 , and may further include a read-only memory unit (ROM) 13203 .

[0123] The storage unit 1320 may also include a program / utility 13204 having a set (at least one) of program modules 13205, such program modules 13205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0124] Bus 1330 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0125] Electronic device 1300 can also communicate with one or more external devices 1340 (e.g., a keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with electronic device 1300, and / or any device that enables electronic device 1300 to communicate with one or more other computing devices (e.g., a router, modem, etc.). This communication can occur via input / output (I / O) interface 1350. Furthermore, electronic device 1300 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via network adapter 1360. As shown, network adapter 1360 communicates with other modules of electronic device 1300 via bus 1330. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 1300, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0126] Through the description of the above embodiments, it will be readily understood by those skilled in the art that the example embodiments described herein can be implemented via software or via a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes several instructions for enabling a computing device (such as a personal computer, server, terminal device, or network device) to execute the methods according to the embodiments of the present disclosure.

[0127] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods of the above embodiments.

[0128] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided. The computer-readable storage medium may be a readable signal medium or a readable storage medium. A program product capable of implementing the above-mentioned method of the present disclosure is stored thereon. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Methods" section above of this specification.

[0129] More specific examples of computer-readable storage media in the present disclosure may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0130] In the present disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0131] Alternatively, the program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0132] In a specific implementation, the program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0133] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0134] Furthermore, although the steps of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0135] Through the description of the above embodiments, it will be readily understood by those skilled in the art that the example embodiments described herein can be implemented via software or via a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes several instructions for enabling a computing device (such as a personal computer, server, mobile terminal, or network device) to execute the methods according to the embodiments of the present disclosure.

[0136] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the appended claims.

Claims

1. A method for estimating the center of mass side slip angle based on an intelligent tire, characterized in that: The method comprises: The cyclic estimation process of the sideslip angle of the center of mass is performed online, wherein one estimation process in the cyclic estimation process includes: Obtain the dynamic adjustment value of the friction coefficient in the last estimation process; Obtaining a first friction coefficient of a current time output by the intelligent tire system and a confidence level corresponding to the first friction coefficient; Outputting a current second friction coefficient through an adaptive Kalman filter based on the second friction coefficient last output by the adaptive Kalman filter, the dynamic adjustment amount of the friction coefficient during the last estimation process, the first friction coefficient, and a confidence level corresponding to the first friction coefficient; The chassis signal and the second friction coefficient are input into a nonlinear observer to obtain an estimated value of the vehicle's center of mass sideslip angle.

2. The method according to claim 1, characterized in that The nonlinear observer includes: a tire model; Inputting the chassis signal and the second friction coefficient into a nonlinear observer to obtain an estimated value of the vehicle's center of mass sideslip angle includes: Inputting the chassis signal, the vehicle's last estimated longitudinal velocity, the vehicle's last estimated lateral velocity, and the current second friction coefficient into the tire model to determine the vehicle's current longitudinal tire force estimate and lateral tire force estimate; determining a derivative of the vehicle's longitudinal velocity estimate and a derivative of the vehicle's lateral velocity estimate based on the vehicle's longitudinal tire force estimate, the vehicle's lateral tire force estimate, the chassis signal, a previous vehicle longitudinal velocity estimate, a previous vehicle lateral velocity estimate, the vehicle's curb weight, and an observer gain of the nonlinear observer; Integrating the derivative of the vehicle's longitudinal velocity estimate and the derivative of the vehicle's lateral velocity estimate to obtain a current longitudinal velocity estimate and a current lateral velocity estimate of the vehicle; The current estimated value of the center of mass sideslip angle is determined based on the current estimated value of the longitudinal speed and the current estimated value of the lateral speed of the vehicle.

3. The method according to claim 1, characterized in that The nonlinear observer includes: a tire model and a dual-track vehicle model; The method further comprises: Inputting the chassis signal, the vehicle's last estimated longitudinal velocity, the vehicle's last estimated lateral velocity, and the current second friction coefficient into the tire model to determine the vehicle's current longitudinal tire force estimate and lateral tire force estimate; Inputting the current longitudinal tire force estimate, the current lateral tire force estimate, and the steering angle in the chassis signal into the dual-track vehicle model to determine the current longitudinal acceleration estimate and the current lateral acceleration estimate of the vehicle; A current dynamic adjustment amount of the friction coefficient is determined according to the longitudinal acceleration measurement value, the lateral acceleration measurement value in the chassis signal, the current longitudinal acceleration estimation value, and the current lateral acceleration estimation value.

4. The method according to claim 1, wherein The adaptive Kalman filter includes: a prediction update process and a measurement update process; Outputting the current second friction coefficient by the adaptive Kalman filter according to the second friction coefficient last output by the adaptive Kalman filter, the dynamic adjustment amount of the friction coefficient in the last estimation process, the first friction coefficient, and the confidence level corresponding to the first friction coefficient includes: During the prediction update process, the current friction coefficient prediction value is determined based on the second friction coefficient output last time and the dynamic adjustment amount of the friction coefficient in the last estimation process; Get the current error covariance prediction value; During the measurement update process, determining a Kalman gain according to the current error covariance prediction value and the confidence level; The second friction coefficient for the current time is determined according to the Kalman gain, the first friction coefficient and the predicted value of the friction coefficient for the current time.

5. The method according to claim 4, characterized in that The obtaining of the current error covariance prediction value includes: Obtain the error covariance update value of the last adaptive Kalman filter measurement update process; The current error covariance prediction value is determined according to the error covariance update value.

6. The method according to claim 5, characterized in that The obtaining of the error covariance update value of the last adaptive Kalman filter measurement update process includes: An error covariance update value of a last measurement update process of an adaptive Kalman filter is determined according to a last Kalman gain and an error covariance prediction value of a last prediction update process of an adaptive Kalman filter.

7. The method according to claim 4, characterized in that The step of determining the Kalman gain according to the current error covariance prediction value and the confidence level during the measurement update process includes: Determining a measurement covariance according to the confidence level; wherein the greater the confidence level, the smaller the measurement covariance; The Kalman gain is determined according to the measurement covariance and the current error covariance prediction value.

8. The method according to claim 4, characterized in that The method further comprises: In a process of processing the first friction coefficient and the confidence level corresponding to the first friction coefficient by using an adaptive Kalman filter, determining whether the first friction coefficient is valid; If so, executing the measurement update process; If it does not exist, the measurement update process is skipped, and the friction coefficient prediction value of the current time is used as the second friction coefficient of the current time, and the error covariance prediction value of the current time is used as the error covariance update value of the current time.

9. A device for estimating the center of mass side slip angle based on an intelligent tire, characterized in that: include: The center of mass sideslip angle estimation device based on the intelligent tire is used to perform an online cyclic estimation process of the center of mass sideslip angle, wherein one estimation process in the cyclic estimation process includes: A first acquisition unit is used to obtain the dynamic adjustment amount of the friction coefficient in the last estimation process; a second acquiring unit, configured to acquire a first friction coefficient outputted by the intelligent tire system and a confidence level corresponding to the first friction coefficient; a filtering unit, configured to output a current second friction coefficient through an adaptive Kalman filter based on the second friction coefficient last output by the adaptive Kalman filter, the dynamic adjustment amount of the friction coefficient during the last estimation process, the first friction coefficient, and a confidence level corresponding to the first friction coefficient; The estimation unit is configured to input the chassis signal and the second friction coefficient into a nonlinear observer to obtain an estimated value of the vehicle's center of mass sideslip angle.

10. An electronic device, characterized in that: include: processor; and a memory for storing executable instructions for the processor; The processor is configured to perform the method according to any one of claims 1 to 8 by executing the executable instructions.

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