A multi-module fused vehicle state estimation model and a control method thereof

Abstract

The application discloses a multi-module fusion vehicle state estimation model and a control method thereof, relates to the technical field of vehicle engineering and intelligent vehicle control, and comprises a tire force estimation module, a vehicle pose estimation module, an adhesion coefficient calculation module, a slip rate calculation module, a road surface friction coefficient identification module and a yaw angular acceleration calculation module; the tire force estimation module acquires four-wheel speeds, four-wheel driving torques, four-wheel braking torques, a vehicle speed, longitudinal acceleration, lateral acceleration, vertical acceleration, yaw angular velocity, pitch angular velocity and roll angular velocity, performs tire rotation dynamics calculation and axle load transfer calculation, and obtains longitudinal forces and vertical forces of each wheel. The application adopts a layered module architecture and multi-parameter fusion calculation, realizes accurate estimation of vehicle states, optimizes calculation logic and constraint mechanisms in different scenes through a working condition self-adaptive strategy, and improves overall estimation accuracy, system stability and engineering adaptation capability.

Description

Technical Field

[0001] This invention relates to the fields of vehicle engineering and intelligent vehicle control technology, specifically to a multi-module integrated vehicle state estimation model and its control method. Background Technology

[0002] In modern vehicle control systems, Vehicle State Estimation (VSE) is a core technology for improving the accuracy of vehicle dynamic control and driving safety. The implementation of vehicle dynamics control systems such as Anti-lock Braking System (ABS), Traction Control System (TCS), and Active Yaw Control (AYC) all heavily rely on the accurate perception and calculation of vehicle state parameters. However, in actual driving, vehicles experience complex and varied operating conditions, including acceleration and deceleration, steering, body roll and pitch, sudden changes in road adhesion, and intervention of electronic control systems, all of which overlap. Furthermore, issues such as sensor noise and signal coupling interference also exist. Against this backdrop, achieving accurate estimation of multi-dimensional vehicle state parameters has become an important technical direction for improving the active safety performance of vehicles.

[0003] Existing vehicle state estimation technologies mainly include single-parameter independent estimation schemes, single-dimensional shallow fusion estimation schemes, and simple integrated estimation schemes. Among these, single-parameter independent estimation schemes have modules that operate independently and lack data interaction, only capable of estimating basic parameters; single-dimensional shallow fusion schemes only perform simple fusion within a local parameter range, making them difficult to adapt to complex driving conditions; and simple integrated schemes, while integrating some parameters, have relatively simple judgment logic and lack auxiliary corrections based on vehicle attitude and tire force information. These schemes generally suffer from unclear module functional hierarchy, low parameter cross-fusion degree, and poor adaptability to complex driving conditions: on the one hand, the internal calculation process of each module is lengthy and difficult to optimize; on the other hand, various dynamic parameters are not fused together based on their inherent relationships, resulting in limited estimation accuracy; furthermore, under different driving conditions such as free rolling, strong drive, and forced braking, as well as with the intervention of systems such as ABS, TCS, and AYC, the algorithms lack adaptive capabilities and are prone to distortion in the calculation of slip ratio and adhesion coefficient, thus affecting the stability and reliability of the entire vehicle control system.

[0004] The information disclosed in the background section is only intended 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

[0005] The purpose of this invention is to provide a multi-module fusion vehicle state estimation model and its control method to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-module fusion vehicle state estimation model, including a tire force estimation module, a vehicle pose estimation module, an adhesion coefficient calculation module, a slip ratio calculation module, a road surface friction coefficient identification module, and a yaw acceleration calculation module: The tire force estimation module acquires the wheel speed, driving torque, braking torque, vehicle speed, longitudinal acceleration, lateral acceleration, vertical acceleration, yaw rate, pitch rate, and roll rate of the four wheels, and performs tire rotational dynamics calculations and axle load transfer calculations to obtain the longitudinal force and vertical force of each wheel. The vehicle pose estimation module, based on the longitudinal and vertical forces of each wheel, uses yaw rate, pitch rate, roll rate and acceleration information to perform state recursion and measurement update calculations to obtain the vehicle roll angle and pitch angle. The adhesion coefficient calculation module calculates and weights the longitudinal and lateral adhesion coefficients based on the longitudinal and vertical forces of each wheel, as well as the vehicle's roll and pitch angles, and combines them with vehicle speed, acceleration, and yaw rate to obtain the instantaneous friction coefficient and overall adhesion coefficient of each wheel. The slip ratio calculation module calculates the theoretical slip ratio based on the longitudinal force and instantaneous friction coefficient of each wheel, using the wheel speed and vehicle speed of the four wheels. After differentiating the working conditions according to the longitudinal force, it performs filtering and amplitude constraints to obtain the instantaneous slip ratio of each wheel. The road surface friction coefficient identification module determines the road surface type for each wheel and obtains the corresponding maximum friction coefficient based on the instantaneous friction coefficient and instantaneous slip ratio of each wheel through standard friction coefficient curve calculation and error comparison. The yaw acceleration calculation module, based on the vehicle roll angle and pitch angle, performs attitude decoupling and filtering on the yaw rate and then performs numerical differential calculation to obtain the yaw acceleration. This yaw acceleration is then output together with the longitudinal force, the comprehensive adhesion coefficient, the instantaneous slip ratio, and the road friction coefficient for vehicle state control.

[0007] The analysis of tire force changes caused by load distribution variations during vehicle operation, combined with the calculation of vertical force using overall vehicle structural parameters, follows these steps: The longitudinal and lateral acceleration signals of the vehicle are collected, the raw signals are filtered and converted into a vehicle coordinate system expression, and the acceleration measurement units are standardized. Construct a geometric relationship model that includes the distance from the center of gravity to the front axle and the distance from the center of gravity to the rear axle, calculate the distribution ratio of the vehicle's static load between the front and rear axles, and form an initial axle load expression; By incorporating the center of gravity height parameter into the longitudinal acceleration term and the wheelbase parameter into the lateral acceleration term, expressions for front-to-rear load transfer and left-to-right load transfer are established respectively. The load expressions of the front and rear axles and the left and right wheels are superimposed to form a set of vertical force equations for the four wheels. The vertical force values ​​of each wheel are obtained by algebraic solution.

[0008] The longitudinal forces on the tires are calculated based on the relationship between wheel rotation and power input, following these steps: Read the driving torque and braking torque signals, and simultaneously perform time difference calculation on the wheel angular velocity to obtain the angular acceleration sequence; Establish a mapping relationship between tire rotational inertia parameters and rolling radius parameters, and convert angular velocity into corresponding linear velocity expressions; Substituting the driving torque, braking torque, and inertia term into the rotational dynamics equation, a wheel rotational balance expression is constructed. The balance expression is simplified and intermediate variables are eliminated to obtain the longitudinal force calculation results for each wheel and generate the output.

[0009] The dynamic characteristics of vehicle spatial attitude change over time are analyzed, and attitude estimation is performed through sensor data fusion. The steps are as follows: Collect triaxial angular velocity signals and construct the third row component of the rotation matrix as the state variable, while initializing the state vector and error covariance matrix; A state transition matrix is ​​constructed using angular velocity information, and a recursive operation is performed on the state variables to obtain the predicted attitude vector. The acceleration signal is decomposed into components to remove the dynamic components generated by vehicle acceleration, deceleration and steering, and only the gravity direction component is retained as the observation. The observed and predicted attitudes are fused and updated to calculate the updated state vector, and the roll and pitch angles are solved using inverse trigonometric function relationships.

[0010] The quantitative description of the longitudinal interaction between the tire and the road surface involves obtaining adhesion characteristic parameters through multi-level data processing, as follows: Calculate the ratio of longitudinal force to vertical force for each wheel to form a set of longitudinal adhesion parameters for a single wheel; The adhesion parameters of the left and right wheels of the same axle are subjected to interval filtering, including limiting the extreme value range and constraining the rate of change. The filtered attachment parameters are averaged and smoothed using a filtering algorithm to obtain the shaft-level attachment parameter sequence. The adhesion parameters of the front and rear axles are calculated in combination, and braking control status information is incorporated for correction, and the longitudinal adhesion coefficient is output.

[0011] The correlation between lateral force characteristics and vehicle lateral motion was analyzed, and adhesion capability was assessed by combining multi-source information. The steps are as follows: The first set of adhesion parameters is calculated based on the relationship between tire lateral force and vertical load, and wheel-level lateral adhesion data is generated. The second set of adhesion parameters is calculated by the ratio between lateral acceleration and gravitational acceleration, and a vehicle-level adhesion characterization quantity is formed. The roll and pitch angle information is introduced to identify the current driving state, and corresponding weight coefficients are assigned to different states; A weighted fusion operation is performed on the two sets of adhesion parameters to obtain the lateral adhesion coefficient output.

[0012] The influence of differences in wheel motion states on slip characteristics was analyzed. Stable slip results were obtained through a case-by-case processing approach. The steps are as follows: By constructing an expression for slip ratio calculation based on the relationship between wheel speed and vehicle speed, the initial value of slip ratio for each wheel is obtained. By comparing the relationship between longitudinal force and preset threshold, the wheel state is divided into driving condition, braking condition and free rolling condition. The slip ratio is filtered for both driving and braking conditions, while the original slip ratio calculation result for free rolling conditions is maintained. The slip ratio is divided into intervals based on the operating status of the control system, and amplitude limiting is performed to output a stable slip ratio result.

[0013] The longitudinal force change rate information is further introduced into the wheel slip ratio calculation process to dynamically adjust the slip ratio signal. The wheel state transition interval is identified by the longitudinal force change trend. During the state transition phase, the slip ratio is smoothed and the slip ratio amplitude is synchronously corrected in combination with the wheel speed change rate, thus forming a continuously changing slip ratio output result.

[0014] The identification and processing of differences in tire-road friction behavior involves determining road characteristic parameters through model matching. The steps are as follows: A functional relationship model between the friction coefficient and the slip ratio was constructed, and multiple sets of road surface parameters were configured to form a set of standard curves; The real-time friction coefficient data is matched point by point with each standard curve to obtain the corresponding error value sequence; The error values ​​are compared and sorted, and the curve corresponding to the smallest error is selected as the matching result to determine the road surface type; The corresponding peak parameter is retrieved based on the matching curve, and the maximum road surface friction coefficient is output.

[0015] A multi-module integrated vehicle state control method includes the following steps: The system obtains the wheel speed, driving torque, braking torque, vehicle speed, longitudinal acceleration, lateral acceleration, vertical acceleration, yaw rate, pitch rate, and roll rate of the four wheels, performs tire rotational dynamics calculations and axle load transfer calculations, and obtains the longitudinal force and vertical force of each wheel. Based on the longitudinal and vertical forces of each wheel, the vehicle roll angle and pitch angle are obtained by recursively calculating and updating the state using yaw rate, pitch rate, roll rate and acceleration information. Based on the longitudinal force and vertical force of each wheel, as well as the vehicle roll angle and pitch angle, and combined with the vehicle speed, acceleration and yaw rate, the longitudinal adhesion coefficient and lateral adhesion coefficient are calculated and weighted to obtain the instantaneous friction coefficient and comprehensive adhesion coefficient of each wheel. Based on the longitudinal force and instantaneous friction coefficient of each wheel, the theoretical slip ratio is calculated using the wheel speed and vehicle speed of the four wheels. After differentiating the working conditions according to the longitudinal force, filtering and amplitude constraints are implemented to obtain the instantaneous slip ratio of each wheel. Based on the instantaneous friction coefficient and instantaneous slip ratio of each wheel, the road surface type of each wheel is determined and the maximum friction coefficient of the corresponding road surface is obtained through standard friction coefficient curve calculation and error comparison. Based on the vehicle roll angle and pitch angle, the yaw rate is decoupled and filtered by attitude and then numerically differentiated to obtain the yaw acceleration. This acceleration is then output together with the longitudinal force, the comprehensive adhesion coefficient, the instantaneous slip ratio, and the road friction coefficient for vehicle state control.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention constructs a refined, layered modular architecture, dividing the overall computation process into six main modules. Each main module is further subdivided into multiple functionally independent sub-units, forming a clearly hierarchical and structurally standardized solution system. Each functional unit maintains clear boundary divisions in its design, reducing coupling between them and making data flow and computational logic more intuitive. In practical applications, this structure not only facilitates independent debugging and problem localization of single functional links but also supports rapid optimization and replacement of local algorithms, effectively improving system development and maintenance efficiency. Simultaneously, this architecture possesses excellent scalability and adaptability, allowing for flexible trimming or combination of modules according to different vehicle configurations and functional requirements, thereby enabling engineering applications on multiple vehicle platforms while ensuring the integrity of core functions.

[0017] This invention utilizes a multi-parameter cross-fusion calculation mechanism to unify and integrate dynamic state information from different modules, fully exploring the inherent correlations between various parameters. During the calculation process, multi-dimensional data such as tire forces, vehicle attitude, slip state, adhesion characteristics, road surface information, and yaw dynamics participate in the solution, forming an integrated state estimation system. This cross-fusion approach effectively avoids the error accumulation problem caused by traditional single-parameter independent calculations, allowing each parameter to continuously converge towards the true state through mutual constraints and corrections. This method significantly improves overall estimation accuracy, making the calculation results closer to the actual operating state of the vehicle, providing a more reliable data foundation for subsequent control decisions, and enhancing the robustness and stability of the system under complex operating conditions.

[0018] This invention introduces an adaptive design mechanism for complex operating conditions to dynamically adjust the calculation strategy under different operating states. During vehicle operation, it automatically identifies different wheel conditions such as free rolling, driving, and braking, and comprehensively assesses the current operating environment by combining the operating status of control systems such as ABS, TCS, and AYC. Based on this, corresponding calculation logic, filtering methods, and dynamic amplitude constraint strategies are matched for different operating conditions, ensuring that various parameters maintain a reasonable range of variation in different scenarios. Whether in normal stable driving conditions or in complex conditions such as emergency braking, strong driving, or extreme handling, the continuity and stability of the output results are guaranteed, thereby improving the safety and control reliability of the vehicle throughout all operating scenarios. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0020] Figure 1 This is a schematic diagram of the modules of a multi-module fusion vehicle state estimation model according to the present invention.

[0021] Figure 2 This is a flowchart of a multi-module integrated vehicle state control method according to the present invention. Detailed Implementation

[0022] 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 the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0023] This invention provides, for example Figure 1 The vehicle state estimation model shown includes a multi-module fusion module, a vehicle pose estimation module, an adhesion coefficient calculation module, a slip ratio calculation module, a road surface friction coefficient identification module, and a yaw acceleration calculation module. The tire force estimation module includes a tire vertical force estimation submodule and a tire longitudinal force estimation submodule. Based on the tire rotational dynamics equations and axle load transfer model, this module estimates the longitudinal forces of each tire in real time under different driving conditions. With vertical force .

[0024] During vehicle operation, the load distribution between the front and rear axles and between the left and right wheels dynamically shifts due to changes in operating conditions such as acceleration, braking, and steering. The tire vertical force estimation submodule models and analyzes parameters such as the vehicle's center of gravity position, center of gravity height, wheelbase, and track width to characterize the load transfer process, thereby obtaining the vertical force of each wheel. The changes.

[0025] The tire longitudinal force estimation submodule, based on tire rotational dynamics, comprehensively analyzes factors such as wheel moment of inertia, tire rolling radius, and driving and braking torques to establish the relationship between wheel rotation state and driving or braking input, thereby obtaining the longitudinal force of each wheel. This allows for the effective characterization of the longitudinal interaction characteristics between the tire and the road surface.

[0026] The vehicle pose estimation module includes a time update submodule, an observation construction submodule, a measurement update submodule, and an attitude calculation submodule. This module estimates the vehicle's roll angle using Kalman filtering based on data from sensors such as accelerometers and gyroscopes. Pitch angle Attitude information, etc.

[0027] In a dynamic driving environment, the vehicle's posture is constantly changing due to a variety of factors: Time update submodule: Recursively predicts attitude using angular velocity information to obtain the prior state at the current moment; The observation construction submodule processes the acceleration signal, extracts the component related to the direction of gravity, and reduces the interference caused by the vehicle's acceleration. Measurement Update Submodule: Fuses observation information with predicted state to correct attitude estimation results; Attitude calculation submodule: Calculates the roll angle based on the relationship between state variables and spatial geometry. and pitch angle This enables continuous estimation of vehicle attitude.

[0028] The adhesion coefficient calculation module includes a longitudinal adhesion coefficient calculation submodule, a lateral adhesion coefficient calculation submodule, and a comprehensive adhesion coefficient calculation submodule. This module is used to calculate the instantaneous friction coefficient between the tire and the road surface in real time. and overall adhesion coefficient This provides a basis for the distribution of vehicle driving force and braking force.

[0029] Longitudinal adhesion coefficient calculation submodule: It is calculated by the ratio between the longitudinal force and the vertical force of the tire, and processed at the level of single wheel and whole vehicle to improve stability; Lateral adhesion coefficient calculation submodule: It obtains the coefficient through two methods: lateral force and lateral acceleration, and adjusts the weights according to the vehicle's operating status to make the calculation results more consistent with actual working conditions; The comprehensive adhesion coefficient calculation submodule provides a unified description based on longitudinal and lateral adhesion characteristics, and considers the impact of vehicle control state on adhesion ability, thereby reflecting the tire's comprehensive grip capability under the current working conditions.

[0030] The slip ratio calculation module includes a theoretical slip ratio calculation submodule, a working condition correction submodule, and an amplitude constraint submodule. This module is used to calculate the slip ratio of each tire in real time. This provides key parameters for vehicle dynamics control.

[0031] Theoretical slip ratio calculation submodule: It calculates the slip ratio based on the relationship between wheel speed and vehicle speed, and is used to reflect the difference between the wheel rolling state and the actual vehicle motion; Working condition correction submodule: It distinguishes between driving, braking and free rolling states based on the magnitude of longitudinal force, and performs filtering on slip ratio to improve stability; Amplitude constraint submodule: Limits the slip ratio range under different control states to avoid distortion of calculation results due to wheel speed fluctuations, thereby ensuring the reliability of slip ratio.

[0032] The road surface friction coefficient identification module includes a standard friction coefficient calculation submodule, a road surface type identification submodule, and a road surface friction coefficient query submodule. This module is used to collect interaction information between the tires and the road surface during vehicle operation, and to identify the friction coefficient of different road surfaces such as dry, wet, and icy surfaces in real time. .

[0033] Standard friction coefficient calculation submodule: Based on typical road surface models, it establishes the relationship curve between friction coefficient and slip ratio, providing reference characteristics for different road surfaces; Road surface type identification submodule: By comparing the instantaneous friction coefficient obtained from actual calculation with the standard curve, the error is calculated and the best matching road surface type is determined; Road surface friction coefficient query submodule: Based on the identification results, obtain the maximum friction capacity of the corresponding road surface to provide environmental information support for vehicle control.

[0034] The yaw acceleration calculation module includes an attitude decoupling submodule, a signal preprocessing submodule, and a differential solution submodule. This module is used to calculate the vehicle's yaw acceleration. This provides basic data for vehicle yaw stability control.

[0035] Attitude Decoupling Submodule: Performs coordinate transformation on the yaw rate using attitude angle information to eliminate the coupling effects caused by changes in vehicle attitude; Signal preprocessing submodule: performs filtering on the converted signal to reduce noise interference; Differential solution submodule: Performs numerical differentiation on the processed yaw rate and constrains the result by combining it with the vehicle speed, thereby obtaining stable and reliable yaw acceleration information to support the vehicle yaw stability control process.

[0036] The multimodal fusion vehicle state estimation model relies on onboard sensors and the vehicle's electronic control system to acquire multi-source raw signals generated during driving. By constructing a multi-level information processing link, it unifies and collaboratively processes various dynamic data to achieve systematic calculation of vehicle operating state parameters. The results are then output to the vehicle's active control system to support control decisions under different operating conditions. In the initial stage of signal processing, data from wheel speed sensors, accelerometers, gyroscopes, and various electronic control units first enter the preprocessing stage. By performing noise reduction and time synchronization correction on the raw signals, the impact of sensor measurement errors and signal transmission delays is effectively reduced, thereby ensuring the consistency and reliability of various data in the time dimension during subsequent calculations and providing a stable input foundation for subsequent multi-source information interaction.

[0037] After preprocessing, basic state parameters are calculated based on the processed input data. Tire force information and vehicle attitude information are acquired independently as two core fundamental quantities. Tire force calculation focuses on the relationship between wheel rotation and power input. By analyzing driving torque, braking torque, and wheel speed information, the longitudinal and vertical forces of each wheel are obtained. Vehicle attitude calculation continuously estimates the roll and pitch angles of the vehicle body by fusing angular velocity and acceleration information and combining recursive and correction mechanisms. These two types of fundamental parameters play a crucial role in subsequent calculations, reflecting the current dynamic state of the vehicle and providing necessary prerequisites for the calculation of other parameters.

[0038] After the basic parameters are acquired, various types of data are transferred between different computing units according to preset data flow relationships, realizing the coordinated processing of multi-source information. Tire force information is input into the adhesion characteristic analysis and slip characteristic calculation process to characterize the interaction between the tire and the road surface; vehicle attitude information participates in adhesion characteristic analysis and yaw motion calculation to describe the influence of the vehicle's spatial state on dynamic parameters. At the same time, the adhesion characteristic calculation results and slip characteristic calculation results continue to be passed on, providing a basis for road surface characteristic identification, forming a data link structure that progresses step by step from basic parameters to higher-order environmental parameters.

[0039] In the functional calculation phase, adhesion characteristic analysis obtains the tire's friction capability under the current working conditions by integrating vehicle posture and tire force information, and forms a comprehensive adhesion index through a unified description of longitudinal and lateral characteristics; slip characteristic analysis corrects slip behavior based on the relationship between wheel speed and vehicle speed, combined with the tire force state, so that the slip ratio can reflect the actual tire rolling situation; road surface characteristic identification uses the friction and slip characteristics obtained in the previous step, and determines the current road surface type and the corresponding maximum friction capability through a matching process with the standard model; yaw motion calculation, after obtaining vehicle posture information, decouples and filters the yaw angular velocity, and obtains the yaw angular acceleration through numerical differentiation, thereby describing the dynamic change process of vehicle yaw.

[0040] After calculating various parameters, all vehicle dynamics information is integrated, including tire forces, vehicle attitude, instantaneous friction characteristics, overall adhesion, slip state, and road surface characteristics. This information together constitutes a complete description of the vehicle's operating state. The integrated results are output to the vehicle's active control system to support functions such as braking control, drive control, and yaw stability control. This allows the vehicle to dynamically adjust based on real-time status information in complex driving environments, thereby improving overall driving stability and safety.

[0041] Tire force estimation module: Input signals include the speeds of all four wheels. Driving torque Braking torque Longitudinal acceleration With lateral acceleration Output tire longitudinal force With vertical force The calculation logic is as follows. During vehicle operation, the loads and mechanical states of the wheels continuously change under different working conditions such as driving, braking, and steering. By performing unified modeling and analysis of the input signals, the dynamic characterization of the tire's force state is achieved, thus providing a basic support for subsequent dynamic parameter calculations.

[0042] Tire vertical force estimation submodule: Calculates the vertical force of all four wheels based on an axle load transfer model. Among them, , , , These represent the vertical forces on the left front, right front, left rear, and right rear wheels, respectively. This represents the horizontal distance from the center of gravity to the front axle. This represents the horizontal distance from the center of mass to the rear axle. Indicates the height of the center of mass. Indicates wheelbase. Indicates the overall vehicle weight. Represents gravitational acceleration. Indicates longitudinal acceleration. This represents lateral acceleration. By considering the load transfer caused by the vehicle's acceleration, braking, and lateral movement, the influence of longitudinal and lateral acceleration on the vertical load distribution is incorporated into the calculation, yielding the vertical force at each wheel: The above formula, by unifying the expression of static load distribution and dynamic load transfer of a vehicle, can reflect the changes in the vertical load of each wheel under different longitudinal and lateral motion states of the vehicle. The front and rear axle load distribution is determined by... and The decision was made that longitudinal acceleration would pass through Introducing the effect of load transfer between the front and rear, lateral acceleration is affected by the wheelbase. By introducing variations in the left and right load distribution, a more refined calculation of the vertical force on all four wheels can be achieved.

[0043] Tire longitudinal force estimation submodule: based on tire rotational dynamics equations Calculate the longitudinal forces on all four wheels. Among them, Indicates the longitudinal force of the tire. Indicates driving torque. Indicates braking torque, This represents the moment of inertia of the tire. This represents the angular acceleration of the wheel. This represents the tire rolling radius. This relationship, by analyzing the balance between drive input, braking action, and rotational inertia during wheel rotation, incorporates the influence of wheel angular acceleration on force in the calculation. This ensures that the longitudinal force reflects not only drive and braking inputs but also the dynamic characteristics of the wheel. Through the above calculations, the longitudinal force between each wheel and the ground under driving or braking conditions can be obtained, providing crucial foundational data for slip ratio calculation and adhesion characteristic analysis.

[0044] Through vertical force With longitudinal force The synchronous calculation can fully describe the stress state of the tire under complex working conditions, effectively characterize the interaction between the tire and the road surface, and provide accurate and reliable input information for the subsequent vehicle dynamic state estimation process.

[0045] The vehicle pose estimation module uses the three-axis angular velocity (tilt velocity) of the gyroscope. Pitch angular velocity yaw rate ) and accelerometer triaxial acceleration (longitudinal acceleration) lateral acceleration Vertical acceleration () is the input, with the vehicle roll angle as the input. and pitch angle For the output, optimal attitude estimation is achieved through Kalman filtering. This process combines sensor measurements with system state predictions by continuously and dynamically modeling the vehicle attitude, thereby obtaining stable and reliable attitude information in complex motion environments. The state variable is selected as the third row component of the rotation matrix representing the vehicle attitude. Its expression is: The three components in this state vector represent the projection values ​​of gravitational acceleration in the three directions of the vehicle body coordinate system, where Indicates pitch angle, Indicates the roll angle. This represents the transpose operation. This vector establishes a unique correspondence between attitude and gravity projection, enabling the calculation of roll and pitch angles.

[0046] During the state recursion process, based on the optimal state estimate at the current moment, and combined with the angular velocity information provided by the gyroscope, the state is predicted. The attitude is dynamically updated over time through the state transition relationship. Its calculation form is as follows: in, express Estimation of the optimal state at time t. express Prior state estimation at time 10:00 This represents the state transition matrix, used to describe how the system state changes over time. This represents the error covariance matrix at the current moment, reflecting the uncertainty of the state estimation; This represents the prediction error covariance; This represents the process noise covariance matrix, used to describe system modeling errors and the effects of external disturbances.

[0047] During the observation construction process, the raw accelerometer signal Motion disturbance compensation is performed by removing the dynamic acceleration components generated by vehicle steering and acceleration / deceleration, and extracting the attitude-related gravity component as the observation value, which is expressed as follows: in, This represents the processed observation vector. This represents the raw output of the accelerometer. Indicates the longitudinal speed of the vehicle. For acceleration compensation coefficient, This represents the vehicle's acceleration-related disturbance term. This processing effectively reduces the impact of motion on the observed signal, making the observed values ​​closer to the true attitude information.

[0048] During the measurement update phase, the predicted state is corrected by introducing Kalman gain, achieving optimal fusion between prediction and observation. The relevant calculations are as follows: in, This represents the Kalman gain, used to balance the confidence level between predicted and observed values; This represents the observation matrix, used to establish the mapping relationship between state variables and observations; This represents the observation noise covariance matrix, reflecting the sensor measurement error; Represents the identity matrix; This represents the updated optimal state estimate; This represents the updated error covariance matrix.

[0049] During attitude calculation, the optimal state estimation vector is geometrically transformed into an intuitive expression of attitude angles. The calculation relationship is as follows: in, , , These represent the three components of the state vector, corresponding to the projection values ​​of gravity in the vehicle body coordinate system; Indicates the roll angle. This represents the pitch angle. Through the above relationship, the conversion from state vector to attitude angle can be achieved, allowing the vehicle attitude to be expressed in an intuitive form.

[0050] Through the above process, sensor measurement information is combined with dynamic models to achieve continuous estimation and real-time updates of vehicle attitude. Even in the presence of noise interference and complex operating conditions, stable and accurate roll and pitch angle information can still be obtained, providing a reliable foundation for subsequent vehicle dynamics analysis and control.

[0051] Adhesion coefficient calculation module: Input signals include the tire longitudinal force output by the tire force estimation module. and vertical force Roll angle output by the vehicle pose estimation module and pitch angle Speed Longitudinal acceleration lateral acceleration yaw rate Including the operating status of AYC, ABS, and TCS, outputting the instantaneous friction coefficient of the four wheels. and overall adhesion coefficient By comprehensively analyzing the vehicle's dynamic state and the tire's stress state, the mechanical relationship between the tire and the road surface is established, enabling dynamic evaluation of adhesion.

[0052] The longitudinal adhesion coefficient is calculated based on the ratio between the longitudinal force and the vertical force of the tire. The longitudinal adhesion coefficient for a single wheel is calculated, and this value is the instantaneous friction coefficient. Subsequently, processing is performed at the axle level, imposing range constraints on the longitudinal adhesion coefficients of the left and right wheels on the same axle, including limits on minimum, maximum, and gradient values, thereby eliminating the influence of outlier data on the overall results. After filtering, the results for the left and right wheels are averaged and filtered to obtain the longitudinal adhesion coefficients of the front and rear axles, respectively. Finally, ABS operating status information is incorporated to fuse the longitudinal adhesion coefficients of the front and rear axles, resulting in a more accurate longitudinal adhesion coefficient. It can reflect the vehicle's true adhesion when braking control intervenes.

[0053] The calculation of the lateral adhesion coefficient uses two different methods and then merges them: Based on tire force: Based on the relationship between tire lateral force and vertical load, it is used to directly reflect the tire's lateral grip capability; Based on vehicle motion: Based on the ratio between the lateral acceleration of the vehicle's center of gravity and the acceleration due to gravity, the lateral adhesion of the tires is indirectly reflected through the overall motion state of the vehicle.

[0054] During the calculation process, a working condition confidence level is introduced to weight and fuse the calculation results from the two methods, using vehicle attitude angles. and Identify the current driving state. When the vehicle is in a steady-state steering condition, increase the weight of the lateral force calculation results to make the results closer to the actual tire stress. When the vehicle is in extreme conditions or has a large sideslip angle, increase the weight of the lateral acceleration calculation results to enhance the algorithm's stability under extreme conditions. Through dynamic weight allocation, a stable and reliable lateral adhesion coefficient is obtained. .

[0055] The overall adhesion coefficient calculation process is based on the tire adhesion ellipse theory, focusing on the longitudinal adhesion coefficient. With lateral adhesion coefficient A unified description is provided. First, the basic comprehensive adhesion coefficient is calculated using the following formula: in, Indicates the overall adhesion coefficient. Indicates the longitudinal adhesion coefficient. This represents the lateral adhesion coefficient. This formula reflects the adhesion limit relationship of a tire under simultaneous longitudinal and lateral forces.

[0056] After the basic comprehensive adhesion coefficient is calculated, information such as the working conditions of ABS, AYC, and TCS, as well as the yaw rate, is incorporated to correct the results: When ABS or TCS is activated, the dynamic range of the longitudinal adhesion coefficient is adjusted in conjunction with the wheel slip ratio to reflect changes in adhesion during driving or braking. When AYC is active, combined with yaw rate The weight of the lateral adhesion coefficient is adjusted so that the overall adhesion coefficient can adapt to the dynamic changes in vehicle yaw.

[0057] Through the above correction process, the final output comprehensive adhesion coefficient satisfies the tire adhesion limit constraint condition, providing a reliable basis for the distribution of vehicle braking force and driving force, while ensuring the stability and accuracy of adhesion estimation results under complex working conditions.

[0058] Slip ratio calculation module: Input signals include the tire longitudinal force output by the tire force estimation module. Four-wheel speed Speed Including the operating status of ABS and TCS systems, outputting the instantaneous slip ratio of the four wheels. This process analyzes the difference between the wheel rotation state and the overall vehicle motion state to characterize the relative slippage between the wheel and the ground, thus providing key parameter support for vehicle dynamics control.

[0059] The theoretical slip ratio is calculated based on the relationship between wheel speed and vehicle speed, using the following expression: in: It represents the slip ratio, which reflects the degree of slippage between the wheel and the ground; It indicates the longitudinal speed of the vehicle, reflecting the overall forward speed of the vehicle; It represents the angular velocity of the wheel and describes the state of wheel rotation; This indicates the tire's rolling radius, used to convert angular velocity into linear velocity.

[0060] This formula determines the working condition of the wheels by comparing the difference between the vehicle's actual speed and the wheel's rolling speed. When the wheel speed is greater than the vehicle speed, the linear velocity of the wheel exceeds the forward speed of the vehicle, which is considered a driving condition. When the wheel speed is less than the vehicle speed, the wheel rotation is insufficient to match the vehicle speed, which is a braking condition.

[0061] After the theoretical slip ratio is calculated, a working condition correction process is introduced, classifying wheel conditions based on tire longitudinal force as the core criterion. This is achieved by setting a longitudinal force threshold: Strong drive or forced movement state: When the longitudinal force on the wheel exceeds this threshold, it indicates that the wheel is in a strong drive or forced movement state. In this state, the slip ratio is easily affected by noise and transient changes, requiring the use of a Kalman filter algorithm to smooth the theoretical slip ratio, suppressing signal fluctuations and improving computational stability.

[0062] Free-rolling state: When the longitudinal force on the wheel is less than or equal to this threshold, it indicates that the wheel is in a free-rolling state. At this time, the degree of slippage is small and the change is gradual, so no filtering processing is required, thereby reducing computational complexity and improving response speed.

[0063] This process enables adaptive adjustment of the slip ratio calculation strategy under different operating conditions, making the results more consistent with the actual operating conditions.

[0064] In the further processing of the slip ratio, an amplitude constraint mechanism is introduced to dynamically limit the slip ratio. This is combined with the operating states of the ABS and TCS systems: When the control system is not involved: the slip ratio remains within its natural range of variation.

[0065] When the ABS or TCS system is activated: the wheel state enters the forced control stage, and the slip ratio may produce abnormal values ​​due to drastic fluctuations in wheel speed. At this time, the slip ratio is divided into intervals based on the wheel speed change rate and the longitudinal force change rate. Different amplitude ranges are set for braking and driving conditions, and dynamic amplitude limiting is performed to avoid sudden changes or distortions in the slip ratio.

[0066] The slip ratio after amplitude constraint can more stably reflect the actual sliding situation between the wheel and the ground.

[0067] Through a series of steps including theoretical calculation, operating condition correction, and amplitude constraint, the slip ratio is gradually optimized from the initial estimate to the final output. The resulting four-wheel instantaneous slip ratio not only accurately reflects the tire slip characteristics of the vehicle under different driving and braking conditions, but also maintains good stability and reliability under complex operating conditions and control system intervention, providing key input data for adhesion coefficient calculation, road surface recognition, and vehicle dynamics control.

[0068] Road surface friction coefficient identification module: The input signal includes the instantaneous friction coefficient output by the adhesion coefficient calculation module. The instantaneous slip ratio output by the slip ratio calculation module Output the road surface type and maximum road friction coefficient for each of the four wheels. This process analyzes the interaction characteristics between the tire and the road surface, and compares standard models with real-time data to identify and quantify the road environment characteristics, thereby providing environmental parameter support for vehicle dynamics control.

[0069] Standard friction coefficient calculation In the calculation of the standard friction coefficient, a functional relationship between the friction coefficient and the slip ratio is established based on the Burckhardt model formula, and its expression is as follows: in: Indicates that at a slip ratio Standard coefficient of friction at that time; It represents the slip ratio, reflecting the degree of relative slippage between the tire and the ground; , , These are the inherent fitting parameters for the road surface, used to describe the differences in frictional characteristics among different road surface types.

[0070] parameter It mainly determines the peak level of the friction coefficient. The rate at which the coefficient of friction increases with the slip ratio is affected. It reflects the friction decay trend in high-slip regions. By configuring different parameter combinations for different types of pavements, multiple sets of standard friction coefficient curves can be constructed. For typical working conditions such as dry, wet, and icy conditions, a total of twelve typical pavement types were established, each corresponding to a specific set of parameters, thereby generating differentiated friction coefficient-slip ratio benchmark curves, providing a reference model for the subsequent identification process.

[0071] During road surface type identification, each of the four wheels is analyzed independently. This is achieved by calculating the instantaneous friction coefficient of each wheel in real time. Standard friction coefficient with various typical road surfaces Each model is compared one by one, and the statistical error is calculated to measure the degree of matching between the actual working conditions and each model. The calculation method is as follows: in: Indicates the first The statistical error of a typical road surface is used to measure the deviation between the road surface model and the actual data. This represents the instantaneous friction coefficient of the wheel at the moment of travel, which is calculated from the adhesion coefficient. Indicates the current slip ratio The standard coefficient of friction under the corresponding conditions; Indexes representing different road surface types.

[0072] By calculating the error of all typical road surfaces, the type with the smallest error is selected as the current wheel identification result, thus determining the road surface type. This process fully utilizes the correspondence between real-time measurement data and the standard model to achieve stable and reliable road surface identification in complex environments.

[0073] During the road surface friction coefficient query process, based on the road surface type identified for each wheel, corresponding data is extracted from a pre-established database of standard friction coefficient curve peaks to obtain the maximum friction capacity under the current road surface conditions. This maximum friction coefficient... Reflecting the tire's achievable adhesion limit under current road conditions, this is a crucial basis for the vehicle's braking and driving force distribution. By mapping the identification results to database information, the conversion from road surface type to friction performance parameters is achieved, thereby providing accurate environmental constraints for the vehicle control system.

[0074] Through the coordinated efforts of three processes—standard model construction, error matching and identification, and peak parameter lookup—the road surface friction coefficient identification can maintain high accuracy and stability under different working conditions. The final output includes the road surface type and the maximum road surface friction coefficient. It can comprehensively reflect the friction characteristics of the vehicle's current driving environment, providing key supporting data for slip control, adhesion utilization, and overall vehicle stability control.

[0075] Yaw angle acceleration calculation module: Input signal vehicle pose estimation module output roll angle and pitch angle Speed and yaw rate Output signal: Yaw acceleration .

[0076] This process revolves around the dynamic characteristics of yaw motion. By performing attitude compensation, filtering, and numerical differentiation on the angular velocity signal, the yaw acceleration can accurately reflect the dynamic changes of the vehicle during steering and stability control.

[0077] In the initial stage of yaw rate signal processing, the original yaw rate is decoupled based on vehicle attitude angle information. During actual vehicle operation, due to attitude changes such as roll and pitch, the yaw rate signal measured by the gyroscope often contains attitude coupling components, which can interfere with the true representation of yaw motion.

[0078] By introducing the roll angle and pitch angle The original yaw rate signal is transformed from the sensor coordinate system to the vehicle coordinate system, eliminating the influence of attitude coupling and enabling the processed yaw rate to more accurately reflect the vehicle's rotational motion characteristics around the vertical axis. This processing essentially uses attitude angle information to perform spatial projection correction on the angular velocity component, thereby eliminating interference components outside the yaw direction.

[0079] After attitude decoupling, the yaw rate signal is preprocessed. Since noise and high-frequency interference are unavoidable during sensor measurements, directly using the raw signal for subsequent calculations would lead to large fluctuations in the results. Therefore, a moving average filter is used to smooth the signal.

[0080] Moving average filtering effectively reduces the impact of random noise and high-frequency disturbances on the signal by averaging it over a certain time window, while preserving the signal's trend as much as possible. This process improves the stability of the yaw rate signal without introducing complex calculations, providing a reliable input for subsequent differential calculations.

[0081] After filtering, the band-limited differential algorithm is used to perform numerical differentiation on the yaw rate signal to obtain the yaw acceleration. Yaw acceleration represents the rate of change of yaw rate over time and is an important indicator of a vehicle's dynamic yaw response capability.

[0082] In actual calculations, since differential operations are sensitive to noise and can easily amplify high-frequency disturbances in the signal, a band-limited processing strategy is introduced when performing differential calculations to limit the range of signal variation, thereby suppressing the noise amplification effect and improving the stability of the calculation results.

[0083] In the calculation of yaw acceleration, the vehicle's longitudinal speed is also introduced. Constraints are imposed on the results. Vehicle speed, as an important parameter reflecting the vehicle's operating state, has a significant impact on yaw motion. At low speeds: the yaw rate changes less, and the differential result is easily affected by noise. By constraining the differential result with vehicle speed information, the accuracy of the yaw acceleration calculation can be maintained under different vehicle speed conditions.

[0084] At high speeds: Yaw dynamics become more pronounced. Vehicle speed constraints ensure that the calculation results accurately reflect the actual dynamic characteristics.

[0085] Through a step-by-step process involving attitude decoupling, signal preprocessing, and differential solution, the yaw acceleration is progressively extracted and optimized from the original measurement signal. The final obtained yaw acceleration... It can output stably under complex working conditions, reflecting the dynamic changes of vehicle yaw motion and effectively suppressing the effects of sensor noise and attitude coupling, providing key basic data support for vehicle yaw stability control.

[0086] In the process of vehicle dynamics control, this parameter can be used to help determine the vehicle's steering response characteristics and stable state, thus providing an important basis for adjusting the control strategy. This invention constructs a refined, layered modular architecture, dividing the overall computation process into six main modules. Each main module is further subdivided into multiple functionally independent sub-units, forming a clearly hierarchical and structurally standardized solution system. Each functional unit maintains clear boundary divisions in its design, reducing coupling between them and making data flow and computational logic more intuitive. In practical applications, this structure not only facilitates independent debugging and problem localization of single functional links but also supports rapid optimization and replacement of local algorithms, effectively improving system development and maintenance efficiency. Simultaneously, this architecture possesses excellent scalability and adaptability, allowing for flexible trimming or combination of modules according to different vehicle configurations and functional requirements, thereby enabling engineering applications on multiple vehicle platforms while ensuring the integrity of core functions.

[0087] This invention utilizes a multi-parameter cross-fusion calculation mechanism to unify and integrate dynamic state information from different modules, fully exploring the inherent correlations between various parameters. During the calculation process, multi-dimensional data such as tire forces, vehicle attitude, slip state, adhesion characteristics, road surface information, and yaw dynamics participate in the solution, forming an integrated state estimation system. This cross-fusion approach effectively avoids the error accumulation problem caused by traditional single-parameter independent calculations, allowing each parameter to continuously converge towards the true state through mutual constraints and corrections. This method significantly improves overall estimation accuracy, making the calculation results closer to the actual operating state of the vehicle, providing a more reliable data foundation for subsequent control decisions, and enhancing the robustness and stability of the system under complex operating conditions.

[0088] This invention introduces an adaptive design mechanism for complex operating conditions to dynamically adjust the calculation strategy under different operating states. During vehicle operation, it automatically identifies different wheel conditions such as free rolling, driving, and braking, and comprehensively assesses the current operating environment by combining the operating status of control systems such as ABS, TCS, and AYC. Based on this, corresponding calculation logic, filtering methods, and dynamic amplitude constraint strategies are matched for different operating conditions, ensuring that various parameters maintain a reasonable range of variation in different scenarios. Whether in normal stable driving conditions or in complex conditions such as emergency braking, strong driving, or extreme handling, the continuity and stability of the output results are guaranteed, thereby improving the safety and control reliability of the vehicle throughout all operating scenarios.

[0089] This invention provides, for example Figure 2 The multi-module integrated vehicle state control method shown includes the following steps: The system obtains the wheel speed, driving torque, braking torque, vehicle speed, longitudinal acceleration, lateral acceleration, vertical acceleration, yaw rate, pitch rate, and roll rate of the four wheels, performs tire rotational dynamics calculations and axle load transfer calculations, and obtains the longitudinal force and vertical force of each wheel. Based on the longitudinal and vertical forces of each wheel, the vehicle roll angle and pitch angle are obtained by recursively calculating and updating the state using yaw rate, pitch rate, roll rate and acceleration information. Based on the longitudinal force and vertical force of each wheel, as well as the vehicle roll angle and pitch angle, and combined with the vehicle speed, acceleration and yaw rate, the longitudinal adhesion coefficient and lateral adhesion coefficient are calculated and weighted to obtain the instantaneous friction coefficient and comprehensive adhesion coefficient of each wheel. Based on the longitudinal force and instantaneous friction coefficient of each wheel, the theoretical slip ratio is calculated using the wheel speed and vehicle speed of the four wheels. After differentiating the working conditions according to the longitudinal force, filtering and amplitude constraints are implemented to obtain the instantaneous slip ratio of each wheel. Based on the instantaneous friction coefficient and instantaneous slip ratio of each wheel, the road surface type of each wheel is determined and the maximum friction coefficient of the corresponding road surface is obtained through standard friction coefficient curve calculation and error comparison. Based on the vehicle roll angle and pitch angle, the yaw rate is decoupled and filtered by attitude and then numerically differentiated to obtain the yaw acceleration. This acceleration is then output together with the longitudinal force, the comprehensive adhesion coefficient, the instantaneous slip ratio, and the road friction coefficient for vehicle state control.

[0090] This invention provides a multi-module fusion vehicle state estimation model for implementing the aforementioned multi-module fusion vehicle state control method. The specific method and process of the multi-module fusion vehicle state control method are detailed in the embodiment of the aforementioned multi-module fusion vehicle state estimation model, and will not be repeated here.

[0091] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A multi-module fusion vehicle state estimation model, characterized in that, It includes a tire force estimation module, a vehicle pose estimation module, an adhesion coefficient calculation module, a slip ratio calculation module, a road surface friction coefficient identification module, and a yaw acceleration calculation module. The tire force estimation module acquires the wheel speed, driving torque, braking torque, vehicle speed, longitudinal acceleration, lateral acceleration, vertical acceleration, yaw rate, pitch rate, and roll rate of the four wheels, and performs tire rotational dynamics calculations and axle load transfer calculations to obtain the longitudinal force and vertical force of each wheel. The vehicle pose estimation module, based on the longitudinal and vertical forces of each wheel, uses yaw rate, pitch rate, roll rate and acceleration information to perform state recursion and measurement update calculations to obtain the vehicle roll angle and pitch angle. The adhesion coefficient calculation module calculates and weights the longitudinal and lateral adhesion coefficients based on the longitudinal and vertical forces of each wheel, as well as the vehicle's roll and pitch angles, and combines them with vehicle speed, acceleration, and yaw rate to obtain the instantaneous friction coefficient and overall adhesion coefficient of each wheel. The slip ratio calculation module calculates the theoretical slip ratio based on the longitudinal force and instantaneous friction coefficient of each wheel, using the wheel speed and vehicle speed of the four wheels. After differentiating the working conditions according to the longitudinal force, it performs filtering and amplitude constraints to obtain the instantaneous slip ratio of each wheel. The road surface friction coefficient identification module determines the road surface type for each wheel and obtains the corresponding maximum friction coefficient based on the instantaneous friction coefficient and instantaneous slip ratio of each wheel through standard friction coefficient curve calculation and error comparison. The yaw acceleration calculation module, based on the vehicle roll angle and pitch angle, performs attitude decoupling and filtering on the yaw rate and then performs numerical differential calculation to obtain the yaw acceleration. This yaw acceleration is then output together with the longitudinal force, the comprehensive adhesion coefficient, the instantaneous slip ratio, and the road friction coefficient for vehicle state control.

2. The multi-module fusion vehicle state estimation model according to claim 1, characterized in that, The analysis of tire force changes caused by load distribution variations during vehicle operation, combined with the calculation of vertical force using overall vehicle structural parameters, follows these steps: The longitudinal and lateral acceleration signals of the vehicle are collected, the raw signals are filtered and converted into a vehicle coordinate system expression, and the acceleration measurement units are standardized. Construct a geometric relationship model that includes the distance from the center of gravity to the front axle and the distance from the center of gravity to the rear axle, calculate the distribution ratio of the vehicle's static load between the front and rear axles, and form an initial axle load expression; By incorporating the center of gravity height parameter into the longitudinal acceleration term and the wheelbase parameter into the lateral acceleration term, expressions for front-to-rear load transfer and left-to-right load transfer are established respectively. The load expressions of the front and rear axles and the left and right wheels are superimposed to form a set of vertical force equations for the four wheels. The vertical force values ​​of each wheel are obtained by algebraic solution.

3. The multi-module fusion vehicle state estimation model according to claim 1, characterized in that, The longitudinal forces on the tires are calculated based on the relationship between wheel rotation and power input, following these steps: Read the driving torque and braking torque signals, and simultaneously perform time difference calculation on the wheel angular velocity to obtain the angular acceleration sequence; Establish a mapping relationship between tire rotational inertia parameters and rolling radius parameters, and convert angular velocity into corresponding linear velocity expressions; Substituting the driving torque, braking torque, and inertia term into the rotational dynamics equation, a wheel rotational balance expression is constructed. The balance expression is simplified and intermediate variables are eliminated to obtain the longitudinal force calculation results for each wheel and generate the output.

4. The multi-module fusion vehicle state estimation model according to claim 1, characterized in that, The dynamic characteristics of vehicle spatial attitude change over time are analyzed, and attitude estimation is performed through sensor data fusion. The steps are as follows: Collect triaxial angular velocity signals and construct the third row component of the rotation matrix as the state variable, while initializing the state vector and error covariance matrix; A state transition matrix is ​​constructed using angular velocity information, and a recursive operation is performed on the state variables to obtain the predicted attitude vector. The acceleration signal is decomposed into components to remove the dynamic components generated by vehicle acceleration, deceleration and steering, and only the gravity direction component is retained as the observation. The observed and predicted attitudes are fused and updated to calculate the updated state vector, and the roll and pitch angles are solved using inverse trigonometric function relationships.

5. The vehicle state estimation model based on multi-module fusion according to claim 1, characterized in that, The quantitative description of the longitudinal interaction between the tire and the road surface involves obtaining adhesion characteristic parameters through multi-level data processing, as follows: Calculate the ratio of longitudinal force to vertical force for each wheel to form a set of longitudinal adhesion parameters for a single wheel; The adhesion parameters of the left and right wheels of the same axle are subjected to interval filtering, including limiting the extreme value range and constraining the rate of change. The filtered attachment parameters are averaged and smoothed using a filtering algorithm to obtain the shaft-level attachment parameter sequence. The adhesion parameters of the front and rear axles are calculated in combination, and braking control status information is incorporated for correction, and the longitudinal adhesion coefficient is output.

6. The vehicle state estimation model based on multi-module fusion according to claim 1, characterized in that, The correlation between lateral force characteristics and vehicle lateral motion was analyzed, and adhesion capability was assessed by combining multi-source information. The steps are as follows: The first set of adhesion parameters is calculated based on the relationship between tire lateral force and vertical load, and wheel-level lateral adhesion data is generated. The second set of adhesion parameters is calculated by the ratio between lateral acceleration and gravitational acceleration, and a vehicle-level adhesion characterization quantity is formed. The roll and pitch angle information is introduced to identify the current driving state, and corresponding weight coefficients are assigned to different states; A weighted fusion operation is performed on the two sets of adhesion parameters to obtain the lateral adhesion coefficient output.

7. The multi-module fusion vehicle state estimation model according to claim 1, characterized in that, The influence of differences in wheel motion states on slip characteristics was analyzed. Stable slip results were obtained through a case-by-case processing approach. The steps are as follows: By constructing an expression for slip ratio calculation based on the relationship between wheel speed and vehicle speed, the initial value of slip ratio for each wheel is obtained. By comparing the relationship between longitudinal force and preset threshold, the wheel state is divided into driving condition, braking condition and free rolling condition. The slip ratio is filtered for both driving and braking conditions, while the original slip ratio calculation result for free rolling conditions is maintained. The slip ratio is divided into intervals based on the operating status of the control system, and amplitude limiting is performed to output a stable slip ratio result.

8. The multi-module fusion vehicle state estimation model according to claim 7, characterized in that, The longitudinal force change rate information is further introduced into the wheel slip ratio calculation process to dynamically adjust the slip ratio signal. The wheel state transition interval is identified by the longitudinal force change trend. During the state transition phase, the slip ratio is smoothed and the slip ratio amplitude is synchronously corrected in combination with the wheel speed change rate, thus forming a continuously changing slip ratio output result.

9. The vehicle state estimation model based on multi-module fusion according to claim 1, characterized in that, The identification and processing of differences in tire-road friction behavior involves determining road characteristic parameters through model matching. The steps are as follows: A functional relationship model between the friction coefficient and the slip ratio was constructed, and multiple sets of road surface parameters were configured to form a standard curve set; The real-time friction coefficient data is matched point by point with each standard curve to obtain the corresponding error value sequence; The error values ​​are compared and sorted, and the curve corresponding to the smallest error is selected as the matching result to determine the road surface type. Based on the matching curve, query the corresponding peak parameter and output the maximum road surface friction coefficient.

10. A multi-module fusion vehicle state control method, implemented using a multi-module fusion vehicle state estimation model as described in any one of claims 1-9, characterized in that, Includes the following steps: The system obtains the wheel speed, driving torque, braking torque, vehicle speed, longitudinal acceleration, lateral acceleration, vertical acceleration, yaw rate, pitch rate, and roll rate of the four wheels, performs tire rotational dynamics calculations and axle load transfer calculations, and obtains the longitudinal force and vertical force of each wheel. Based on the longitudinal and vertical forces of each wheel, the vehicle roll angle and pitch angle are obtained by recursively calculating and updating the state using yaw rate, pitch rate, roll rate and acceleration information. Based on the longitudinal force and vertical force of each wheel, as well as the vehicle roll angle and pitch angle, and combined with the vehicle speed, acceleration and yaw rate, the longitudinal adhesion coefficient and lateral adhesion coefficient are calculated and weighted to obtain the instantaneous friction coefficient and comprehensive adhesion coefficient of each wheel. Based on the longitudinal force and instantaneous friction coefficient of each wheel, the theoretical slip ratio is calculated using the wheel speed and vehicle speed of the four wheels. After differentiating the working conditions according to the longitudinal force, filtering and amplitude constraints are implemented to obtain the instantaneous slip ratio of each wheel. Based on the instantaneous friction coefficient and instantaneous slip ratio of each wheel, the road surface type of each wheel is determined and the maximum friction coefficient of the corresponding road surface is obtained through standard friction coefficient curve calculation and error comparison. Based on the vehicle roll angle and pitch angle, the yaw rate is decoupled and filtered by attitude and then numerically differentiated to obtain the yaw acceleration. This acceleration is then output together with the longitudinal force, the comprehensive adhesion coefficient, the instantaneous slip ratio, and the road friction coefficient for vehicle state control.

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