A method and related equipment for estimating the center of gravity and sideslip angle based on smart tires.

Abstract

This disclosure provides a method and related equipment for estimating the center of gravity sideslip angle based on intelligent tires, relating to the field of vehicle motion state estimation. The method includes: an online cyclic estimation process, where each step includes: acquiring the dynamic adjustment amount of the friction coefficient from the previous estimation process; acquiring the first friction coefficient and corresponding confidence level output by the intelligent tire system for the current estimation; outputting the second friction coefficient for the current estimation using an adaptive Kalman filter, based on the second friction coefficient, the dynamic adjustment amount of the friction coefficient, the first friction coefficient, and the corresponding confidence level output by the adaptive Kalman filter; and inputting the chassis signal and the second friction coefficient into a nonlinear observer to obtain the estimated value of the vehicle's center of gravity sideslip angle. This provides a new source of friction coefficient information for center of gravity sideslip angle estimation, achieving decoupled estimation by estimating the center of gravity sideslip angle through friction coefficients from multiple sources.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle motion state estimation, and in particular to a method and related equipment for estimating the center of gravity sideslip angle based on smart tires. Background Technology

[0002] As autonomous driving technology continues to advance to higher levels of automation and vehicle performance boundaries gradually expand, achieving robust vehicle safety control in various driving scenarios, especially in emergency situations, has become a significant challenge. In the field of vehicle motion control, accurate and timely estimation of the vehicle's center of gravity sideslip angle is crucial; advanced chassis control systems require this information to improve vehicle handling stability control performance.

[0003] The vehicle's sideslip angle, defined as the arctangent of the ratio of the lateral velocity to the longitudinal velocity at the vehicle's center of gravity, is an important indicator of vehicle lateral stability. However, due to the complex, multidimensional, and nonlinear nature of tire dynamics, and the strong coupling relationship between vehicle speed and the tire-road friction coefficient, high-precision estimation of the vehicle's sideslip angle is extremely difficult.

[0004] In related technologies, methods for estimating the vehicle's center of gravity sideslip angle are mainly divided into kinematic methods and dynamic methods. The kinematic method calculates the vehicle's center of gravity sideslip angle by integrating the longitudinal and lateral acceleration and yaw rate signals measured by the onboard inertial measurement unit (IMU). However, integration leads to the accumulation of sensor errors, and prolonged integration causes the estimation results to drift, gradually deviating from the true value. The kinematic method incorporates the Global Navigation Satellite System (GNSS) for vehicle center of gravity sideslip angle estimation. When GNSS signals are good, this method can achieve high accuracy in estimating the vehicle's center of gravity sideslip angle. However, because GNSS signals are easily affected by signal blockage and multipath effects, its performance is poor when the vehicle is traveling in areas with tall buildings in cities, tunnels, and forests.

[0005] Dynamic methods, based on vehicle and tire dynamics models, typically yield accurate estimates when the vehicle and tire model parameters are accurate. However, in real-world road environments, varying road conditions lead to significant changes in the tire-road friction coefficient, a key parameter in the tire model, resulting in substantial estimation errors in dynamic methods. To adapt to changing road conditions, some methods have proposed joint estimation approaches for the vehicle's sideslip angle and the tire-road friction coefficient. However, due to the strong coupling between the sideslip angle and the road friction coefficient, these methods require continuous and sufficient lateral / longitudinal excitation from the tire to converge, resulting in significant errors before the estimation converges.

[0006] Therefore, there is an urgent need for a method that can support both joint estimation of the centroid sideslip angle and the friction coefficient, and decoupled estimation of the friction coefficient and the centroid sideslip angle.

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

[0008] This disclosure provides a smart tire-based center of gravity sideslip angle estimation and related equipment, which at least to some extent overcomes the problem of strong coupling between the friction coefficient and the center of gravity sideslip angle when the center of gravity sideslip angle and the friction coefficient are jointly estimated in related technologies.

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

[0010] In a first aspect, embodiments of this disclosure provide a method for estimating the center of gravity sideslip angle based on a smart tire, the method comprising:

[0011] The process involves an online cyclic estimation of the centroid sideslip angle. One estimation step within this cyclic process includes:

[0012] Obtain the dynamic adjustment amount of the friction coefficient during the previous estimation process;

[0013] Obtain the first friction coefficient and the corresponding confidence level of the first friction coefficient output by the intelligent tire system;

[0014] The second friction coefficient for the current time is output by means of adaptive Kalman filtering, based on the second friction coefficient output by the previous 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;

[0015] 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 gravity sideslip angle.

[0016] In one possible embodiment, the nonlinear observer includes: a tire model;

[0017] 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 gravity sideslip angle, including:

[0018] The chassis signal, the vehicle's previous longitudinal velocity estimate, the vehicle's previous lateral velocity estimate, and the current second friction coefficient are input into the tire model to determine the vehicle's current longitudinal tire force estimate and lateral tire force estimate.

[0019] Based on the vehicle's longitudinal tire force estimates, lateral tire force estimates, chassis signals, the vehicle's previous longitudinal velocity estimates, the vehicle's previous lateral velocity estimates, the vehicle's total mass, and the observer gain of the nonlinear observer, the derivatives of the vehicle's longitudinal velocity estimates and lateral velocity estimates are determined.

[0020] By integrating the derivatives of the vehicle's longitudinal velocity estimate and the vehicle's lateral velocity estimate, we can obtain the vehicle's current longitudinal velocity estimate and lateral velocity estimate.

[0021] Based on the vehicle's current longitudinal and lateral velocity estimates, determine the current center of gravity sideslip angle estimate.

[0022] In one possible embodiment, the nonlinear observer includes: a tire model and a dual-track vehicle model; the method further includes:

[0023] The chassis signal, the vehicle's previous longitudinal velocity estimate, the vehicle's previous lateral velocity estimate, and the current second friction coefficient are input into the tire model to determine the vehicle's current longitudinal tire force estimate and lateral tire force estimate.

[0024] The current longitudinal tire force estimate, the current lateral tire force estimate, and the steering angle from the chassis signal are input into the dual-track vehicle model to determine the current longitudinal acceleration estimate and the current lateral acceleration estimate of the vehicle.

[0025] Based on the longitudinal acceleration measurement value, lateral acceleration measurement value in the chassis signal, and the current longitudinal acceleration estimate value and the current lateral acceleration estimate value, determine the current friction coefficient dynamic adjustment amount.

[0026] In one possible embodiment, adaptive Kalman filtering includes: a prediction update process and a measurement update process;

[0027] Using adaptive Kalman filtering, based on the second friction coefficient output from the previous adaptive Kalman filter, the dynamic adjustment of the friction coefficient during the previous estimation process, the first friction coefficient, and the confidence level corresponding to the first friction coefficient, the current second friction coefficient is output, including:

[0028] During the prediction update process, the predicted value of the friction coefficient for the current time is determined based on the second friction coefficient output in the previous time and the dynamic adjustment amount of the friction coefficient in the previous estimation process.

[0029] Obtain the current error covariance prediction value;

[0030] During the measurement update process, the Kalman gain is determined based on the current error covariance prediction value and confidence level.

[0031] The second friction coefficient for the current iteration is determined based on the Kalman gain, the first friction coefficient, and the predicted friction coefficient for the current iteration.

[0032] In one possible embodiment, obtaining the current error covariance prediction value includes:

[0033] Obtain the error covariance update value of the previous adaptive Kalman filter measurement update process;

[0034] Based on the updated error covariance value, determine the current predicted error covariance value.

[0035] In one possible embodiment, obtaining the error covariance update value of the previous adaptive Kalman filter measurement update process includes:

[0036] Based on the previous Kalman gain and the predicted error covariance value in the previous adaptive Kalman filter prediction update process, determine the updated error covariance value in the previous adaptive Kalman filter measurement update process.

[0037] In one possible embodiment, during the measurement update process, determining the Kalman gain based on the current error covariance prediction and confidence level includes:

[0038] The measurement covariance is determined based on the confidence level; where the higher the confidence level, the smaller the measurement covariance.

[0039] The Kalman gain is determined based on the measurement covariance and the predicted error covariance of the current iteration.

[0040] In one possible embodiment, the method further includes:

[0041] During the process of processing the first friction coefficient and the confidence level corresponding to the first friction coefficient through adaptive Kalman filtering, it is determined whether there is a valid first friction coefficient.

[0042] If it exists, then perform the measurement update process;

[0043] 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.

[0044] Secondly, embodiments of this disclosure provide a center-of-gravity sideslip angle estimation device based on a smart tire, comprising:

[0045] The smart tire-based center-of-gravity sideslip angle estimation device is used to execute an online cyclic estimation process for the center-of-gravity sideslip angle. One estimation step in this cyclic estimation process includes:

[0046] The first acquisition unit is used to acquire the dynamic adjustment amount of the friction coefficient in the previous estimation process;

[0047] The second acquisition unit is used to acquire the first friction coefficient and the confidence level corresponding to the first friction coefficient output by the intelligent tire system.

[0048] The filtering unit is used to output the second friction coefficient of the current time through adaptive Kalman filtering, based on the second friction coefficient output by the previous 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;

[0049] The estimation unit is used to input the chassis signal and the second friction coefficient into the nonlinear observer to obtain the estimated value of the vehicle's center of gravity sideslip angle.

[0050] In one possible embodiment, the first acquiring unit is further configured to:

[0051] Input the chassis signal and the current second friction coefficient into the tire model to determine the estimated longitudinal tire force and the estimated lateral tire force in the current tire coordinate system of the vehicle.

[0052] The current longitudinal tire force estimate, the current lateral tire force estimate, and the steering angle from the chassis signal are input into the dual-track vehicle model to determine the current longitudinal acceleration estimate and the current lateral acceleration estimate of the vehicle.

[0053] Based on the longitudinal acceleration measurement value, lateral acceleration measurement value in the chassis signal, and the current longitudinal acceleration estimate value and the current lateral acceleration estimate value, determine the current friction coefficient dynamic adjustment amount.

[0054] Thirdly, embodiments of this disclosure provide an electronic device, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the method of the first aspect described above by executing the executable instructions.

[0055] Fourthly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect above.

[0056] Fifthly, according to another aspect of this disclosure, a computer program product or computer program is also 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 the processor executes the computer instructions, causing the computer device to perform any of the methods described above.

[0057] This disclosure provides a method and related equipment for estimating the center of gravity sideslip angle based on a smart tire, relating to the field of vehicle motion state estimation. The method includes: executing an online cyclic estimation process for the center of gravity sideslip angle, wherein one estimation step in the cyclic estimation process includes: acquiring the dynamic adjustment amount of the friction coefficient from the previous estimation process; acquiring the current first friction coefficient and its corresponding confidence level output by the smart tire system; outputting the current second friction coefficient using an adaptive Kalman filter, based on the previous second friction coefficient output by the adaptive Kalman filter, the dynamic adjustment amount of the friction coefficient from the previous estimation process, the first friction coefficient, and its corresponding confidence level; and inputting the chassis signal and the second friction coefficient into a nonlinear observer to obtain an estimated value of the vehicle's center of gravity sideslip angle. The dynamic adjustment amount of the friction coefficient and the first friction coefficient provide data support for the final center of gravity sideslip angle, providing a new source of friction coefficient information for center of gravity sideslip angle estimation. By estimating the center of gravity sideslip angle using friction coefficients from multiple sources, decoupled estimation is achieved.

[0058] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0059] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0060] Figure 1 A flowchart illustrating a method for estimating the center of gravity sideslip angle based on a smart tire according to an embodiment of this disclosure is shown.

[0061] Figure 2 A schematic diagram of a method for estimating the center of gravity sideslip angle based on a smart tire according to an embodiment of this disclosure is shown.

[0062] Figure 3 This diagram illustrates a flowchart of determining an estimated value of the centroid sideslip angle according to an embodiment of the present disclosure;

[0063] Figure 4 This document illustrates a flowchart of a method for determining the dynamic adjustment amount of the friction coefficient according to an embodiment of the present disclosure.

[0064] Figure 5 A schematic diagram of a dual-track vehicle model is shown in an embodiment of this disclosure;

[0065] Figure 6 This diagram illustrates a method for determining the dynamic adjustment amount of the friction coefficient according to an embodiment of the present disclosure.

[0066] Figure 7 This diagram illustrates a flowchart of determining a second coefficient of friction according to an embodiment of the present disclosure;

[0067] Figure 8 A flowchart of another adaptive Kalman filtering process in an embodiment of this disclosure is shown;

[0068] Figure 9 A schematic diagram illustrating an evaluation result in an embodiment of this disclosure is shown;

[0069] Figure 10 A schematic diagram illustrating another evaluation result in an embodiment of this disclosure;

[0070] Figure 11 A schematic diagram illustrating another evaluation result in an embodiment of this disclosure is shown;

[0071] Figure 12 A schematic diagram of the structure of a smart tire-based center of gravity sideslip angle estimation device is shown in an embodiment of this disclosure;

[0072] Figure 13 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0073] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary 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.

[0074] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0075] The following detailed description of this exemplary implementation method is provided in conjunction with the accompanying drawings and embodiments.

[0076] First, this disclosure provides a method for estimating the center of gravity sideslip angle based on a smart tire. This method can be executed by any electronic device with computing capabilities. In the following process, an electronic device is used as an example as the terminal device.

[0077] Figure 1 This document illustrates a flowchart of a method for estimating the center of gravity sideslip angle based on a smart tire, as shown in an embodiment of this disclosure. Figure 1 As shown in the embodiments of this disclosure, the method for estimating the center of gravity sideslip angle based on a smart tire includes the following steps:

[0078] S102: Obtain the dynamic adjustment amount of the friction coefficient in the previous estimation process.

[0079] S104: Obtain the first friction coefficient and the confidence level corresponding to the first friction coefficient output by the intelligent tire system.

[0080] In one 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 convolutional neural networks, etc., which is not limited in the embodiments disclosed herein.

[0081] S106: Using adaptive Kalman filtering, based on the second friction coefficient output from the previous adaptive Kalman filter, the dynamic adjustment of the friction coefficient during the previous estimation process, the first friction coefficient, and the confidence level corresponding to the first friction coefficient, the current second friction coefficient is output.

[0082] S108: Input the chassis signal and the second friction coefficient into the nonlinear observer to obtain the estimated value of the vehicle's center of gravity sideslip angle.

[0083] Among them, chassis signals are sensor signals commonly found in mass-produced vehicles, which may include: longitudinal acceleration, lateral acceleration, yaw rate, steering angle, and wheel speed of each wheel.

[0084] In one possible embodiment, the convergence of the cyclic estimation process is determined offline. When the second friction coefficient of any given time in the adaptive Kalman filter output is compared with the actual friction coefficient, and the adaptive Kalman filter process is determined to have converged, the cyclic estimation process is determined to have reached the convergence state. The second friction coefficient at the time of convergence is determined, and the chassis signal at the corresponding time is input into the nonlinear observer based on the second friction coefficient at the time of convergence to obtain the estimated value A of the vehicle's center of gravity sideslip angle. The estimated value A of the center of gravity sideslip angle is the output obtained in the offline stage at the time of convergence. At this point, the convergence state is reached, and the convergence speed can be determined and the data can be analyzed based on the estimated value A of the center of gravity sideslip angle.

[0085] Figure 2 A schematic diagram of a method for estimating the center of gravity sideslip angle based on a smart tire, as shown in an embodiment of this disclosure, is illustrated. Figure 2 As shown, it includes: intelligent tire system, adaptive Kalman filter, nonlinear observer, dynamic friction adjustment and chassis signals.

[0086] Its iterative estimation process is as follows: Figure 2 As shown, firstly, the adaptive Kalman filter process provides an initial second friction coefficient. This initial second friction coefficient can be a preset value. During the verification phase, it can be set to an extreme value far removed from the actual friction coefficient corresponding to the real road surface type, used to verify the ability of the adaptive Kalman filter process to correct the friction coefficient in this embodiment. During the usage phase, the initial second friction coefficient can be set to be close to the actual friction coefficient for ease of use. This embodiment does not impose specific limitations on this.

[0087] Based on the initial second friction coefficient and chassis signal inputs, the nonlinear observer first determines the estimated value of the center of gravity sideslip angle, and then, through the dynamic friction adjustment part, determines the dynamic adjustment amount of the friction coefficient, which is input into the adaptive Kalman filtering process for data support of the second friction coefficient in the next iteration. The chassis signals include: longitudinal acceleration measurement, lateral acceleration measurement, yaw rate, steering angle, and wheel speed.

[0088] The longitudinal and lateral velocities of the vehicle are determined by the nonlinear observer to determine the estimated value of the vehicle's center of gravity sideslip angle.

[0089] The intelligent tire system provides the first friction coefficient and the corresponding confidence level for each adaptive Kalman filtering process.

[0090] The inputs for dynamic friction adjustment are the estimated lateral acceleration, the estimated longitudinal acceleration, the measured longitudinal acceleration from the chassis signal, and the measured lateral acceleration from the chassis signal.

[0091] Based on the above process, online cyclic estimation essentially means applying the above process to the vehicle's driving process, continuously outputting the vehicle's motion state information.

[0092] For step S108 above, the nonlinear observer may include a tire model. The tire model in the nonlinear observer determines the estimated values ​​of the longitudinal tire force and the lateral tire force of the vehicle. Based on the nonlinear observer, the longitudinal velocity and lateral velocity of the vehicle are further determined, thereby determining the estimated value of the vehicle's center of gravity sideslip angle.

[0093] In this way, the dynamic adjustment of the friction coefficient and the first friction coefficient provide data support for the final centroid sideslip angle. The introduction of an additional first friction coefficient provides data support, so that the estimation process of the centroid sideslip angle does not need to rely on the friction coefficient that the tire needs to be subjected to continuous and sufficient lateral / longitudinal excitation to converge, thus decoupling the estimation process.

[0094] Figure 3 A flowchart illustrating an embodiment of this disclosure for determining an estimated value of the centroid sideslip angle is shown, as follows: Figure 3 As shown, it includes the following steps:

[0095] S302: Input the chassis signal, the vehicle's previous longitudinal velocity estimate, the vehicle's previous 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.

[0096] In one possible embodiment, the tire model can be UniTire-Ctrl, i.e., the unified tire model control board.

[0097] The longitudinal tire force estimates and lateral tire force estimates of a vehicle can be determined using the following formulas.

[0098] (1)

[0099] in, Functions representing the UniTire-Ctrl tire model This represents the estimated longitudinal tire force in the tire coordinate system. This represents the estimated lateral tire force in the tire coordinate system. Indicates vertical load; Indicates the tire slip angle; Indicates the longitudinal slip ratio of the tire; The uniform representation is any second friction coefficient output by the adaptive Kalman filter process; The letters FL, FR, RL, and RR represent four wheels.

[0100] Some parameters in the above formula (1) are determined by the chassis signal and the previous longitudinal speed estimate and the previous lateral speed estimate. The specific solution process will not be repeated here.

[0101] After determining the estimated values ​​of longitudinal tire force and lateral tire force in the tire coordinate system, the estimated values ​​of longitudinal tire force and lateral tire force in the vehicle coordinate system are determined by formula (2), which are the estimated values ​​of longitudinal tire force and lateral tire force of the vehicle in S302.

[0102] (2)

[0103] in, Indicates the wheel's turning angle; This represents the estimated longitudinal tire force in the vehicle coordinate system. This represents the estimated lateral tire force in the vehicle body coordinate system.

[0104] S304: Based on the vehicle's longitudinal tire force estimates, lateral tire force estimates, chassis signals, the vehicle's previous longitudinal velocity estimates, the vehicle's previous lateral velocity estimates, the vehicle's total mass, and the observer gain of the nonlinear observer, determine the derivatives of the vehicle's longitudinal velocity estimates and lateral velocity estimates.

[0105] In one possible embodiment, the longitudinal and lateral velocities of the vehicle are further determined by a nonlinear observer. The derivatives of the estimated longitudinal and lateral velocities of the vehicle can be determined by the following formulas.

[0106] (3)

[0107] (4)

[0108] in, The derivative of the estimated longitudinal velocity of the vehicle; The derivative of the estimated lateral velocity of the vehicle; This represents the estimated lateral speed of the vehicle in the previous test. This represents the estimated longitudinal velocity of the vehicle in the previous test. This indicates the longitudinal acceleration of the vehicle body in the chassis signal; This indicates the lateral acceleration of the vehicle body in the chassis signal; Indicates the overall vehicle weight; This represents the observer gain related to the longitudinal velocity; This represents the observer gain related to lateral velocity.

[0109] S306: Integrate the derivatives of the vehicle's longitudinal velocity estimate and the vehicle's lateral velocity estimate to obtain the vehicle's current longitudinal velocity estimate and lateral velocity estimate.

[0110] in, and The initial inputs used to determine the longitudinal tire force estimates, lateral tire force estimates, and the derivatives of the vehicle's longitudinal velocity estimates and lateral velocity estimates can be preset values.

[0111] Taking the first solution process as an example, it is as follows: and An initial value is preset to determine the estimated lateral and longitudinal velocities of the vehicle during the first solution process. This represents the estimated lateral speed of the vehicle. This represents the estimated longitudinal velocity of the vehicle. In the second solution process, the value obtained from the first solution will be used... and As the second input and In this embodiment of the disclosure, in order to distinguish between the longitudinal velocity estimate and the lateral velocity estimate used as input for solving, and the longitudinal velocity estimate and the lateral velocity estimate of the vehicle output, the parameters are distinguished by different representation methods.

[0112] S308: Determine the estimated value of the center of gravity sideslip angle for the current time based on the estimated values ​​of the vehicle's longitudinal and lateral speeds for the current time.

[0113] In one possible embodiment, the longitudinal and lateral velocities of the vehicle can be determined by using the following formula (5) to determine the estimated value of the centroid sideslip angle.

[0114] (5)

[0115] in, This represents the estimated value of the centroid sideslip angle. This represents the estimated lateral speed of the vehicle. This represents the estimated longitudinal speed of the vehicle.

[0116] In one possible embodiment, regarding the specific method of obtaining the previous dynamic adjustment amount of the friction coefficient in S102, it should be noted that the method of dynamic adjustment amount of the friction coefficient is the same each time, and it is determined based on the current second friction coefficient and chassis signal.

[0117] By inputting the current second friction coefficient and chassis signal into the nonlinear observer, the estimated value of the vehicle's center of gravity sideslip angle for the current time can be obtained, as well as the dynamic adjustment amount of the friction coefficient for the current time.

[0118] Figure 4 A flowchart illustrating the determination of a dynamic adjustment amount of the friction coefficient in an embodiment of this disclosure is shown, such as... Figure 4 As shown, it includes the following steps:

[0119] S402: Input the chassis signal, the vehicle's previous longitudinal velocity estimate, the vehicle's previous 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.

[0120] In one possible embodiment, the method for determining the longitudinal tire force estimate and the lateral tire force estimate in the current tire coordinate system of the vehicle in S402 is the same as that in S302, and will not be described again.

[0121] S404: Input the current longitudinal tire force estimate, the current lateral tire force estimate, and the steering angle from 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.

[0122] In one possible embodiment, the vehicle is processed using a dual-track vehicle model to determine the current longitudinal acceleration estimate and the current lateral acceleration estimate.

[0123] Figure 5 A schematic diagram of a dual-track vehicle model according to an embodiment of this disclosure is shown, such as... Figure 5 As shown. The meanings of the parameters included are as follows.

[0124] in, These represent the tire slip angles of the four wheels respectively; These represent the longitudinal tire forces of the four wheels in the tire coordinate system. These represent the lateral tire forces of the four wheels in the tire coordinate system; t represents the track width; a and b represent the distances from the vehicle's center of gravity to the front and rear axles, respectively. Indicates the front wheel steering 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 rate.

[0125] Using the dual-track vehicle model described above, the longitudinal acceleration and lateral acceleration can be estimated using the following formula.

[0126] (6)

[0127] (7)

[0128] in, This represents the estimated longitudinal acceleration of the vehicle at the current time. This represents the estimated lateral acceleration value for the current iteration; These represent the estimated longitudinal tire forces of the four wheels in the tire coordinate system. These represent the estimated lateral tire forces of the four wheels in the tire coordinate system. This indicates the overall weight of the vehicle.

[0129] S406: Determine the dynamic adjustment amount of the friction coefficient for the current time based on the longitudinal acceleration measurement value, the lateral acceleration measurement value in the chassis signal, and the estimated longitudinal acceleration value and the estimated lateral acceleration value for the current time.

[0130] In one possible embodiment, the dynamic adjustment amount of the friction coefficient for the current iteration can be determined by the following formula (8).

[0131] (8)

[0132] in, This indicates the dynamic adjustment amount of the friction coefficient; This represents the gain coefficient.

[0133] Figure 6 A schematic diagram illustrating the determination of the dynamic adjustment amount of the friction coefficient in an embodiment of this disclosure is shown, such as... Figure 6 As shown, the estimated longitudinal acceleration and estimated lateral acceleration of the vehicle in the current iteration, as well as the measured longitudinal acceleration and measured lateral acceleration of the vehicle in the current iteration, are calculated and input into the vector sum to determine the dynamic adjustment amount of the friction coefficient.

[0134] In the estimation process of the center of gravity sideslip angle in related technologies, within the linear operating range of the tire, the tire force is mainly determined by the slip stiffness, and the influence of the friction coefficient is relatively small. Therefore, the inaccuracy of the friction coefficient estimation has a limited impact on the resultant force, resulting in weak adaptive updates of the friction coefficient. Conversely, when the tire is in the nonlinear operating region, the friction coefficient becomes the main factor affecting the tire force. In this case, the difference between the measured and estimated resultant acceleration will be more significant, thereby effectively stimulating the adaptive correction process of the friction coefficient. Therefore, traditional model-based friction coefficient estimation methods in related technologies usually require sufficient tire excitation, i.e., the tire must be operating in the nonlinear region to achieve accurate and sensitive friction coefficient estimation. However, the method in this embodiment does not rely entirely on the dynamic adjustment amount of the friction coefficient input each time. It also includes the value of the first friction determined by the intelligent tire system, and performs an adaptive correction process through an adaptive Kalman filter process to complete the accurate estimation of the friction coefficient, obtaining a second friction coefficient for the estimation process of the center of gravity sideslip angle. Even with relatively small tire excitation, accurate and sensitive estimation of the friction coefficient and the center of gravity sideslip angle can be achieved, improving the accuracy of the center of gravity sideslip angle estimation process.

[0135] Figure 7 A flowchart illustrating the determination of a second friction coefficient according to an embodiment of this disclosure is shown. The adaptive Kalman filtering process includes a prediction update process and a measurement update process. Figure 7 As shown, it includes the following steps:

[0136] S702: During the prediction update process, the predicted value of the friction coefficient for the current time is determined based on the second friction coefficient output in the previous time and the dynamic adjustment amount of the friction coefficient in the previous estimation process.

[0137] In one possible embodiment, the predicted value of the friction coefficient can be determined by the following formula (9).

[0138] (9)

[0139] in, This represents the predicted friction coefficient for the kth time, and indicates that it is calculated based on the second friction coefficient and the dynamic adjustment of the friction coefficient for the (k-1)th time. This represents the second friction coefficient in the (k-1)th iteration; denoted by , where represents the dynamic adjustment of the friction coefficient in the (k-1)th iteration; A and B represent the identity matrix, respectively.

[0140] S704: Obtain the current error covariance prediction value.

[0141] In one possible embodiment, obtaining the current error covariance prediction value may include: obtaining the error covariance update value of the previous adaptive Kalman filter measurement update process; and determining the current error covariance prediction value based on the error covariance update value.

[0142] In one possible embodiment, the error covariance prediction value can be determined as shown in the following formula (10).

[0143] (10)

[0144] in, Let represent the predicted value of the error covariance at the k-th iteration, and let represent the value calculated based on the updated value of the error covariance at the (k-1)-th iteration. This represents the error covariance update value for the (k-1)th iteration; Q represents the process noise covariance, which can be set to 0.001.

[0145] S706: During the measurement update process, the Kalman gain is determined based on the current error covariance prediction and confidence level.

[0146] In one possible embodiment, the Kalman gain can be determined by: determining the measurement covariance based on a confidence level, wherein a higher confidence level corresponds to a smaller measurement covariance; and determining the Kalman gain based on the measurement covariance and the predicted error covariance of the current iteration.

[0147] In one possible embodiment, the Kalman gain can be determined as shown in the following formulas (11) and (12).

[0148] (11)

[0149] (12)

[0150] in, Let H represent the Kalman gain at the k-th iteration, and let H represent the identity matrix. Represents the measurement covariance. This represents the confidence level corresponding to the first friction coefficient in the kth iteration.

[0151] S708: Determine the second friction coefficient for the current iteration based on the Kalman gain, the first friction coefficient, and the predicted friction coefficient for the current iteration.

[0152] In one possible embodiment, the second friction coefficient can be determined by the following formula (13).

[0153] (13)

[0154] in, Let represent the second friction coefficient of the kth iteration, and let represent the friction coefficient prediction of the kth iteration, the Kalman gain of the kth iteration, and the first friction coefficient of the kth iteration. This represents the first friction coefficient in the k-th iteration.

[0155] In one possible embodiment, the error covariance update value can be determined by: determining the error covariance update value based on the previous Kalman gain and the predicted error covariance value in the previous adaptive Kalman filter prediction update process.

[0156] In one possible embodiment, the error covariance update value can be determined by the following formula (14).

[0157] (14)

[0158] in, Let represent the error covariance update value at the k-th iteration, and let represent the value calculated based on the error covariance prediction at the k-th iteration and the Kalman gain at the k-th iteration. I Represents the identity matrix.

[0159] Through the above process, the first friction coefficient and the dynamic adjustment amount of the friction coefficient are adaptively corrected based on the adaptive Kalman filtering process, outputting an accurate second friction coefficient for the estimation of the centroid sideslip angle. Furthermore, the first friction coefficient, confidence level, and dynamic adjustment amount of the friction coefficient are used in the filtering process to re-estimate the friction coefficient, jointly performing adaptive correction on friction coefficients from multiple sources, improving the accuracy of the estimated friction coefficient, reducing dependence on a single source of friction coefficient, and achieving decoupled estimation of the centroid sideslip angle and friction coefficient. This process is not a simple combination, but rather uses the first friction coefficient, confidence level, and dynamic adjustment amount of the friction coefficient in the measurement and prediction updates during the filtering process to adaptively adjust the friction coefficient. The confidence level is used to determine the gain, different predicted friction coefficient values ​​are determined based on different dynamic adjustment amounts, and different gains are dynamically determined based on different confidence levels, thereby adjusting the second friction coefficient output by the filtering process in real time.

[0160] In one possible embodiment, the measurement update process of the adaptive Kalman filter (AKF) mainly involves processing the first friction coefficient and confidence level. The results sampled by the intelligent tire system are not generated at a fixed frequency, but rather based on a preset angle, depending on the tire's rotational speed. The faster the speed, the more samples are obtained in the same time period. However, typically, the sampled results are lower than the sampling rate of the adaptive Kalman filter. Since the AKF operates at a fixed sampling interval, the sampling results of the intelligent tire system are not available at every moment. Therefore, an update flag is introduced to indicate whether a valid first friction coefficient exists at the current moment.

[0161] Based on this, it can be determined whether a valid first friction coefficient exists between the prediction update process and the measurement update process in the adaptive Kalman filtering process. This can include the following steps: during the process of processing the first friction coefficient and its corresponding confidence level using adaptive Kalman filtering, determine whether a valid first friction coefficient exists in the current period; if it exists, execute the measurement update process; if it does not exist, skip the measurement update process, use the current friction coefficient prediction value as the current second friction coefficient, and use the current error covariance prediction value as the current error covariance update value.

[0162] Figure 8 A flowchart of an adaptive Kalman filtering process according to an embodiment of this disclosure is shown, such as... Figure 8 As shown, it includes: prediction update process, judgment process and measurement update process.

[0163] S802: Execute the prediction update process.

[0164] S804: Determine if upflg is 1. If yes, execute S806; otherwise, execute S808.

[0165] Here, upflg is an abbreviation for the update flag, used to determine whether a valid first friction coefficient exists at the current moment.

[0166] S806: Execute the measurement update process.

[0167] S808: Use the current friction coefficient prediction value as the current second friction coefficient, and use the current error covariance prediction value as the current error covariance update value, and return to execute S802.

[0168] Through the methods described in the embodiments of this disclosure, such as Figure 9-11 As shown, the estimated results of the vehicle's center of gravity sideslip angle and friction coefficient under double lane change conditions are presented.

[0169] Figure 9 A schematic diagram of an evaluation result from an embodiment of this disclosure is shown, such as... Figure 9 As shown, taking a vehicle traveling on an asphalt road as an example, there are six coordinate systems. The vertical coordinate of three of these systems represents the friction coefficient (μ), with "-" indicating no unit. These systems are: the true friction coefficient, the first friction coefficient, and the second friction coefficient. The vertical coordinate of one coordinate system represents the lateral acceleration (μ). (Unit: m / s) 2 The vertical axis of the two coordinate systems represents the centroid sideslip angle (β), in degrees (°), which are the estimated and measured centroid sideslip angles, respectively. The horizontal axis of the six coordinate systems represents time (s).

[0170] The coordinate system shows friction coefficients ranging from 0 to 1 (0, 0.2, 0.4, 0.6, 0.8, and 1), centroid side slip angles ranging from -2 to -3 (-2, -1, 0, 1, 2, and 3), lateral accelerations ranging from -10 to -10 (-10, 0, and 10), and time values ​​ranging from 0 to 10 seconds (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10). The time dimension of the horizontal axis is aligned in the six coordinate systems.

[0171] The actual coefficient of friction of the road surface is 1.0, and the initial second coefficient of friction can be set to 0.1.

[0172] 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 if there are a few misjudgments 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-gravity sideslip angle is in high agreement with the measured center-of-gravity sideslip angle, resulting in accurate results.

[0173] Figure 10 A schematic diagram illustrating another evaluation result in an embodiment of this disclosure is shown, such as... Figure 10 As shown, the estimated results of the centroid sideslip angle and friction coefficient on snow are presented. The actual friction coefficient of snow is approximately 0.37, and the initial second friction coefficient can be set to 1.0.

[0174] Figure 10 There are 6 coordinate systems in the text, and... Figure 9 The coordinate system in the text is similar and will not be described in detail here. Figure 10 The ordinate of the centroid sideslip angle includes -10, 0, and 10; the coefficient of friction includes 0, 0.2, 0.4, 0.6, 0.8, and 1; the lateral acceleration includes -5, 0, and 5; and the time includes 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, and 20.

[0175] Under this operating condition, the tires fully utilize the available road friction, achieving a lateral acceleration of approximately 4 m / s². 2 This indicates a high degree of tire excitation. The adaptive Kalman filtering method in this 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 estimation result to adaptively adjust to the actual friction state during the high-excitation phase, rather than being limited by the first friction coefficient output by the intelligent tire system. Therefore, the friction coefficient determination process in this embodiment is not affected by the friction coefficient from a single data source. Consequently, its side slip angle estimation result is highly consistent with the measured data.

[0176] Figure 11 A schematic diagram illustrating another evaluation result in an embodiment of this disclosure is shown, such as... Figure 11 The figure shows the estimated friction coefficient and sideslip angle of a vehicle traveling on ice. It includes six coordinate systems, the parameters of which are related to... Figure 9 and Figure 10 Similarly, this will not be repeated here. The specific coordinate values ​​involved include: time values ​​of 0, 2, 4, 6, 8, 10, 12, 14, and 16; friction coefficient values ​​of 0, 0.2, 0.4, 0.6, 0.8, and 1; and centroid sideslip angle values ​​of -10, 0, and 10.

[0177] The actual coefficient of friction of the ice surface is approximately 0.15, and the initial value of the second coefficient of friction can be set to 1.0.

[0178] The adaptive Kalman filtering process achieves accurate estimation of friction coefficients from multiple sources, thereby ensuring accurate estimation of the vehicle's center of gravity sideslip angle. See [link to documentation] for details. Figure 11 As shown, no further details will be provided.

[0179] in, Figure 9 , Figure 10 and Figure 11 The coefficient of friction is represented as follows: a rhombus represents the first coefficient of friction, a dashed line represents the second coefficient of friction, and a solid line represents the actual coefficient of friction. The rhombuses are connected by lines, making the change of the first coefficient of friction over time more clearly illustrated. The sideslip angle is represented as follows: a solid line represents the measured sideslip angle, and a dashed line represents the estimated sideslip angle.

[0180] Based on the same inventive concept, this disclosure also provides a centroid sideslip angle estimation device based on a smart tire, as shown in the following embodiment. Since the principle by which this device embodiment solves the problem is similar to that of the above-described method embodiment, the implementation of this device embodiment can refer to the implementation of the above-described method embodiment, and repeated details will not be elaborated further.

[0181] Figure 12 This diagram illustrates the structure of a smart tire-based center of gravity sideslip angle estimation device according to an embodiment of the present disclosure. Figure 12 As shown, the smart tire-based center of gravity sideslip angle estimation device 120 includes: a center of gravity sideslip angle estimation device 120 for executing an online cyclic estimation process of the center of gravity sideslip angle, wherein one estimation process in the cyclic estimation process includes: a first acquisition unit 1201 for acquiring the dynamic adjustment amount of the friction coefficient in the previous estimation process; a second acquisition unit 1202 for acquiring the current first friction coefficient and the confidence level corresponding to the first friction coefficient output by the smart tire system; a filtering unit 1203 for outputting the current second friction coefficient through adaptive Kalman filtering, based on the second friction coefficient output by the adaptive Kalman filter in the previous estimation process, 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; and an estimation unit 1204 for inputting the chassis signal and the second friction coefficient into a nonlinear observer to obtain the estimated value of the vehicle's center of gravity sideslip angle.

[0182] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0183] The following reference Figure 13 To describe an electronic device 1300 according to such an embodiment of the present disclosure. Figure 13 The electronic device 1300 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0184] like Figure 13As shown, the electronic device 1300 is presented in the form of a general-purpose computing device. The components of the electronic device 1300 may include, but are not limited to: at least one processing unit 1310, at least one storage unit 1320, and a bus 1330 connecting different system components (including storage unit 1320 and processing unit 1310).

[0185] The storage unit stores program code that can be executed by the processing unit 1310, causing the processing unit 1310 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. For example, the processing unit 1310 can perform the steps of any of the above-described method embodiments.

[0186] Storage unit 1320 may include readable media in the form of volatile storage units, such as random access memory (RAM) 13201 and / or cache unit 13202, and may further include read-only memory (ROM) 13203.

[0187] 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 or some combination of these examples may include an implementation of a network environment.

[0188] Bus 1330 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0189] Electronic device 1300 can also communicate with one or more external devices 1340 (e.g., 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., router, modem, etc.). This communication can be performed via input / output (I / O) interface 1350. Furthermore, electronic device 1300 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, 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 figures, 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.

[0190] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this 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, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0191] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as a computer program product or a 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 described above.

[0192] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. A program product capable of implementing the methods described above is stored thereon. In some possible implementations, various aspects of this disclosure may also be implemented as a program product including program code, which, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0193] More specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0194] In this disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, carrying 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, capable of transmitting, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0195] Optionally, the program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0196] In practical implementation, program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute 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 cases involving remote computing devices, the remote computing device can 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 it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0197] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0198] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0199] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this 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, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0200] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A method for estimating the center of gravity sideslip angle based on a smart tire, characterized in that, The method includes: The process involves an online cyclic estimation of the centroid sideslip angle. One estimation step within this cyclic process includes: Obtain the dynamic adjustment amount of the friction coefficient during the previous estimation process; Obtain the first friction coefficient and the confidence level corresponding to the first friction coefficient output by the intelligent tire system; The second friction coefficient for the current time is output by means of adaptive Kalman filtering, based on the second friction coefficient output by the previous adaptive Kalman filtering, 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; 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 gravity sideslip angle; The nonlinear observer includes: a tire model; The step of inputting the chassis signal and the second friction coefficient into the nonlinear observer to obtain the estimated value of the vehicle's center of gravity sideslip angle includes: The chassis signal, the vehicle's previous longitudinal velocity estimate, the vehicle's previous lateral velocity estimate, and the current second friction coefficient are input into the tire model to determine the vehicle's current longitudinal tire force estimate and lateral tire force estimate. Based on the vehicle's longitudinal tire force estimate, lateral tire force estimate, chassis signal, previous longitudinal velocity estimate, previous lateral velocity estimate, vehicle mass, and observer gain of the nonlinear observer, determine the derivatives of the vehicle's longitudinal velocity estimate and lateral velocity estimate. By integrating the derivatives of the vehicle's longitudinal velocity estimate and the vehicle's lateral velocity estimate, the vehicle's current longitudinal velocity estimate and lateral velocity estimate are obtained. Based on the estimated longitudinal and lateral speeds of the vehicle in the current iteration, the estimated centroid sideslip angle for the current iteration is determined.

2. 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 includes: The chassis signal, the vehicle's previous longitudinal velocity estimate, the vehicle's previous lateral velocity estimate, and the current second friction coefficient are input into the tire model to determine the vehicle's current longitudinal tire force estimate and lateral tire force estimate. The current longitudinal tire force estimate, the current lateral tire force estimate, and the steering angle from the chassis signal are input into the dual-track vehicle model to determine the current longitudinal acceleration estimate and the current lateral acceleration estimate of the vehicle. Based on the longitudinal acceleration measurement value, the lateral acceleration measurement value in the chassis signal, and the estimated longitudinal acceleration value and the estimated lateral acceleration value of the current time, the dynamic adjustment amount of the friction coefficient for the current time is determined.

3. The method according to claim 1, characterized in that, The adaptive Kalman filter includes: a prediction update process and a measurement update process; The process of using adaptive Kalman filtering to output the current second friction coefficient based on the second friction coefficient output by the previous 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 includes: During the prediction update process, the predicted value of the friction coefficient for the current time is determined based on the second friction coefficient output in the previous time and the dynamic adjustment amount of the friction coefficient in the previous estimation process. Obtain 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 the confidence level. The second friction coefficient for the current iteration is determined based on the Kalman gain, the first friction coefficient, and the predicted friction coefficient for the current iteration.

4. The method according to claim 3, characterized in that, The step of obtaining the current error covariance prediction value includes: Obtain the error covariance update value of the previous adaptive Kalman filter measurement update process; The current error covariance prediction value is determined based on the updated error covariance value.

5. The method according to claim 4, characterized in that, The step of obtaining the error covariance update value of the previous adaptive Kalman filter measurement update process includes: Based on the previous Kalman gain and the predicted error covariance value in the previous adaptive Kalman filter prediction update process, determine the updated error covariance value in the previous adaptive Kalman filter measurement update process.

6. The method according to claim 3, characterized in that, In the measurement update process, determining the Kalman gain based on the current error covariance prediction value and the confidence level includes: The measurement covariance is determined based on the confidence level; wherein, the higher the confidence level, the smaller the measurement covariance. The Kalman gain is determined based on the measurement covariance and the predicted error covariance of the current measurement.

7. The method according to claim 3, characterized in that, The method further includes: During the process of processing the first friction coefficient and the confidence level corresponding to the first friction coefficient through adaptive Kalman filtering, it is determined whether there is a valid first friction coefficient. If it exists, then execute the measurement update process; 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.

8. A centroid sideslip angle estimation device based on a smart tire, applied to the method described in any one of claims 1 to 7, characterized in that, include: The smart tire-based center-of-gravity sideslip angle estimation device is used to execute an online cyclic estimation process for the center-of-gravity sideslip angle, wherein one estimation process in the cyclic estimation process includes: The first acquisition unit is used to acquire the dynamic adjustment amount of the friction coefficient in the previous estimation process; The second acquisition unit is used to acquire the first friction coefficient and the confidence level corresponding to the first friction coefficient output by the intelligent tire system. The filtering unit is used to output the second friction coefficient of the current time through adaptive Kalman filtering, based on the second friction coefficient output by the previous 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; The estimation unit is used to input the chassis signal and the second friction coefficient into the nonlinear observer to obtain the estimated value of the vehicle's center of gravity sideslip angle; The nonlinear observer includes: a tire model; The estimation unit is also used to input the chassis signal, the vehicle's previous longitudinal speed estimate, the vehicle's previous lateral speed 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. Based on the vehicle's longitudinal tire force estimate, lateral tire force estimate, chassis signal, previous longitudinal velocity estimate, previous lateral velocity estimate, vehicle mass, and observer gain of the nonlinear observer, determine the derivatives of the vehicle's longitudinal velocity estimate and lateral velocity estimate. By integrating the derivatives of the vehicle's longitudinal velocity estimate and the vehicle's lateral velocity estimate, the vehicle's current longitudinal velocity estimate and lateral velocity estimate are obtained. Based on the estimated longitudinal and lateral speeds of the vehicle in the current iteration, the estimated centroid sideslip angle for the current iteration is determined.

9. An electronic device, characterized in that, include: processor; and memory for storing the executable instructions of the processor; The processor is configured to execute the method of any one of claims 1 to 7 by executing the executable instructions.

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