Vehicle, control method, device and computer readable storage medium thereof

Abstract

The application relates to the technical field of vehicle control, for example, to a vehicle and a control method, device and computer readable storage medium thereof. The control method of the vehicle comprises the following steps: acquiring motion state information of the vehicle at the current time, extracting a state variable from the motion state information, taking the state variable as a current state variable, taking adjustable damping forces of four suspensions as control inputs, locally linearizing a vehicle dynamics model near a current state trajectory on the basis of the vehicle dynamics model constructed in advance; solving a set of suspension damping forces that minimizes the comprehensive cost of body pitch, roll and vertical motion through an iterative linear quadratic regulator algorithm, and taking the set of suspension damping forces as a suspension control instruction of the current period; and converting the suspension control instruction into driving currents of each shock absorber, and sending the driving currents to corresponding actuators, so as to reduce the body vertical acceleration, suspension dynamic deflection and tire relative dynamic load, and improve the ride comfort.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, such as a vehicle and its control method, device, and computer-readable storage medium. Background Technology

[0002] With the rapid development of automotive technology, chassis systems have evolved from traditional mechanical structures towards electrification and intelligence, playing an increasingly important role in optimizing overall vehicle comfort. As a key component determining a vehicle's vertical vibration characteristics, the suspension system's performance directly affects the vehicle's response to road unevenness. Traditional passive suspensions, limited by fixed parameter designs, struggle to simultaneously achieve both handling stability and ride comfort under different operating conditions. Therefore, semi-active and active suspensions have seen rapid development in recent years, achieving adaptive responses to road excitations through real-time adjustment of suspension stiffness and damping, thereby effectively reducing vehicle vibration and impact.

[0003] However, vehicle suspension damping control technology in related technologies generally suffers from the following problems: The control schemes in related technologies are optimized for single operating conditions, such as obstacle impact or roll, but lack the ability to coordinate and control multiple attitudes such as pitch, roll, and vertical, and cannot take into account both ride comfort and system stability under complex driving conditions.

[0004] 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 application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0006] This disclosure provides a vehicle and its control method, apparatus, and computer-readable storage medium to solve the technical problem in the related art where the vehicle body pitch and roll attitude fluctuates significantly, affecting ride comfort.

[0007] In some embodiments, a vehicle control method is provided, wherein during vehicle operation, the following operations are performed at a set sampling period: acquiring the vehicle's current motion state information; extracting state variables from the motion state information and using the state variables as the current state variables; using the adjustable damping forces of the four suspensions as control inputs; based on a pre-built vehicle dynamics model, locally linearizing the vehicle dynamics model near the current state trajectory; solving for a set of suspension damping forces that minimizes the comprehensive cost of vehicle pitch, roll, and vertical motion using an iterative linear quadratic adjustment algorithm, and using this set as the suspension control command for the current period; wherein the comprehensive cost is determined based on a pre-built comprehensive evaluation index system; and converting the suspension control command into drive current for each shock absorber and sending it to the corresponding actuator.

[0008] The control method disclosed herein continuously acquires vehicle motion state information at a set sampling period, optimizes the suspension damping force based on a pre-built vehicle dynamics model using an iterative linear quadratic adjustment algorithm, obtains suspension control commands, and outputs them in a closed-loop control flow, achieving real-time rolling optimization of the vehicle suspension damping force. Compared to the existing LQR (Linear Quadratic Regulator), which is only applicable to linear systems, the iLQR (Iterative Linear Quadratic Regulator) used in this solution, by locally linearizing the nonlinear vehicle dynamics model near the current state trajectory, can effectively handle the nonlinear characteristics of the suspension system, significantly broadening the applicability of the control algorithm while ensuring computational efficiency. Simultaneously, by quantifying ride comfort using a comprehensive evaluation index system and using it as the optimization target, the optimization direction corresponds to a specific comfort objective, thereby effectively reducing vehicle vertical acceleration, suspension dynamic deflection, and tire relative dynamic load, improving ride comfort.

[0009] Optionally, the motion state information includes the vehicle's vertical acceleration, pitch rate, roll rate, suspension travel, longitudinal speed, and yaw rate; the state variables include the vehicle's vertical displacement, vertical velocity, pitch angle, pitch rate, roll angle, and roll rate.

[0010] In this embodiment, by specifically defining the motion state information and state variables, the iLQR controller is able to acquire complete vehicle attitude information, providing a data basis for dynamic prediction and optimization, and improving control accuracy.

[0011] Optionally, the vehicle dynamics model includes a full vehicle seven-degree-of-freedom model, a half vehicle six-degree-of-freedom model, and a 1 / 4 semi-active suspension model; wherein, the full vehicle seven-degree-of-freedom model is used to solve the suspension damping force by the iterative linear quadratic adjustment algorithm, the half vehicle six-degree-of-freedom model is used for pitch rebound suppression in the later stage of braking, and the 1 / 4 semi-active suspension model is used for auxiliary optimization control of suspension performance indicators.

[0012] In this embodiment, a three-tiered dynamic model is constructed—a full-vehicle seven-DOF model, a semi-vehicle six-DOF model, and a quarter-vehicle semi-active suspension model—to achieve a hierarchical description of vehicle dynamics. The full-vehicle seven-DOF model describes the coupling relationship between vehicle pitch, roll, vertical movement, and the bounce of the four wheels, providing an accurate prediction model for the iLQR main controller. The semi-vehicle six-DOF model addresses longitudinal load transfer and pitch dynamics under braking conditions, providing a dedicated fast prediction model for suppressing rebound during the later stages of braking. The quarter-vehicle semi-active suspension model describes the local mechanical relationships between suspension springs, passive damping, and adjustable damping forces, providing a lightweight calculation model for rapid response under high-frequency road surface excitation. By setting up a three-tiered model, both prediction accuracy and computational efficiency are balanced.

[0013] Optionally, the comprehensive evaluation index system includes vehicle posture evaluation index, braking comfort evaluation index, and vertical comfort evaluation index; wherein, the vehicle posture evaluation index includes vehicle pitch angle, pitch rate, roll angle, roll rate, vertical displacement, and vertical velocity; the braking comfort evaluation index includes vehicle pitch rebound during braking; and the vertical comfort evaluation index includes vehicle vertical acceleration, suspension dynamic deflection, and relative tire dynamic load.

[0014] In this embodiment, a comprehensive evaluation index system is established, including vehicle posture evaluation index, braking comfort evaluation index, and vertical comfort evaluation index, transforming the subjective feeling of ride comfort into a quantifiable objective optimization goal. Specifically, the vehicle posture evaluation index reflects the drastic change in vehicle posture; the braking comfort evaluation index quantifies the impact felt at the moment of braking termination; and the vertical comfort evaluation index reflects road vibration transmission and tire contact safety. This index system provides the physical basis for the weight matrix in the iLQR cost function, enabling the controller to specifically optimize the corresponding comfort indexes under different operating conditions.

[0015] Optionally, the cost function of the iterative linear quadratic adjustment algorithm is the sum of the weighted sum of squares of the state deviations and the weighted sum of squares of the control energy: Where x is the state variable, u is the adjustable damping force of the four suspensions, Q is the state weight matrix, and R is the control energy weight matrix. fis the terminal weight matrix; N is the prediction time domain length; k represents the k-th time in the prediction time domain. Each weight coefficient in the Q matrix corresponds one-to-one with each indicator in the comprehensive evaluation index system.

[0016] In this embodiment, by defining the specific form of the iLQR cost function as the sum of the weighted squares of state deviations and the weighted squares of control energy, and by clearly defining the one-to-one correspondence between each weight coefficient in the Q matrix and each indicator in the comprehensive evaluation index system, a precise mapping between physical indicators and mathematical weights is achieved. The x in the cost function... k T Qx k The term represents the cost of vehicle attitude instability across the entire time domain, u k T Ru k The term x represents the energy cost of applying suspension control forces. N T Q f x N The term, acting as a terminal cost, enables the system to converge quickly after the prediction time domain ends. By adjusting the weight coefficients in the Q matrix, the controller can be adjusted in different directions. For example, increasing the weight of a certain indicator will cause the controller to preferentially suppress the physical quantity corresponding to that indicator, achieving precise adjustability of the control objective.

[0017] Optionally, the method further includes: identifying the current road conditions based on the vehicle speed information, acceleration information, and attitude information in the motion state information; determining the current control mode based on the identification results; and switching the state weight matrix of the iterative linear quadratic adjustment algorithm according to the current control mode.

[0018] In this embodiment, the current road conditions are identified based on vehicle speed, acceleration, and attitude information from the motion state information. The state weight matrix of the iLQR algorithm is then switched based on the identification results, achieving scenario-adaptive control strategy. This condition-adaptive weight switching enhances the adaptability to complex real-world road conditions.

[0019] Optionally, identifying the current road condition includes: calculating the following statistical features within a preset sliding time window: average vehicle speed, vehicle speed fluctuation coefficient, stopping frequency, road curvature, slope, and average lateral acceleration; inputting the statistical features into a preset classifier and outputting the current road category; when identified as a highway or national road, selecting a first control mode, in which the weight corresponding to vertical acceleration in the state weight matrix is ​​greater than the weight corresponding to pitch angle and roll angle; when identified as an urban road or mountain road, selecting a second control mode, in which the weight corresponding to pitch angle and roll angle in the state weight matrix is ​​greater than the weight corresponding to vertical acceleration.

[0020] In this embodiment, road conditions are identified by using six statistical features: average vehicle speed, speed fluctuation coefficient, stopping frequency, road curvature, slope, and average lateral acceleration. The weight allocation strategy for highway / national road and urban road / mountain road modes is also defined. The six features describe the road environment from multiple dimensions such as driving rhythm, congestion level, curve characteristics, slope characteristics, and turning intensity, thereby improving the accuracy of the identification results.

[0021] Optionally, the method further includes: optimizing suspension performance indicators under high-frequency road excitation conditions; wherein the high-frequency road excitation conditions are determined based on the power spectral density or amplitude change rate of the vehicle body's vertical acceleration.

[0022] Optionally, the optimization of suspension performance indicators includes: based on the 1 / 4 semi-active suspension model, using suspension dynamic deflection, sprung mass vertical velocity, tire deformation, and wheel center vertical velocity as auxiliary state variables, and suspension control force as auxiliary control input, constructing a linear quadratic regulator and solving the Riccati equation to obtain the auxiliary feedback gain matrix; calculating the auxiliary damping force F based on the current vertical vibration state. lqr = K l ×x l Among them, K l Let x be the auxiliary feedback gain matrix. l The auxiliary state variable is used; the auxiliary damping force is superimposed on the suspension control command to optimize the vehicle vertical acceleration, suspension dynamic deflection and tire relative dynamic load.

[0023] In this embodiment, by activating suspension performance optimization under high-frequency road surface excitation conditions, an LQR auxiliary controller is constructed based on a 1 / 4 semi-active suspension model and an auxiliary damping force is superimposed, achieving a rapid response to high-frequency road surface excitation. The 1 / 4 semi-active suspension model has a simple structure and high linearity. The LQR controller can directly calculate the auxiliary damping force through state feedback without iteration, resulting in minimal computational load. This auxiliary control complements the iLQR main controller; the iLQR is responsible for optimal control of low-frequency attitude, while the LQR is responsible for rapid suppression of high-frequency vibrations, achieving full-frequency control coverage.

[0024] Optionally, the method further includes: acquiring the braking status information of the vehicle at the current moment; determining a braking control command based on the braking status information; and converting the braking control command into braking torque or wheel cylinder pressure and sending it to the brake actuator.

[0025] In this embodiment, by acquiring vehicle braking status information and determining braking control commands accordingly, suspension control and braking control are integrated into a unified collaborative control framework, achieving multi-system collaborative optimization in the chassis domain. During braking, there is a strong coupling relationship between the suspension and braking force; the distribution of braking force directly affects the pitch angle, and changes in the pitch angle, in turn, affect the suspension force output. Through the collaborative control of the suspension and braking, braking comfort can be significantly improved while ensuring braking safety.

[0026] Optionally, determining the braking control command based on the braking state information includes: when braking action is detected, in the early stage of braking, determining the basic braking force distribution coefficient based on the current road surface adhesion coefficient; in the middle stage of braking, reducing the proportion of front axle braking force based on the basic braking force distribution coefficient according to the real-time feedback of the vehicle pitch angle; in the later stage of braking, using the vehicle pitch angle and pitch velocity as state variables, and the change in front and rear wheel braking force as control input, constructing a cost function based on the six-degree-of-freedom model of the half-vehicle with the goal of suppressing pitch rebound, and solving for the optimal braking force unloading slope and front and rear axle ratio through an iterative linear quadratic adjustment algorithm, as the braking control command.

[0027] In this embodiment, the braking process is divided into three stages: early, middle, and late, with different strategies applied to each stage. Specifically, in the early braking stage, a basic distribution coefficient is determined based on the road surface adhesion coefficient to ensure safety. In the middle braking stage, the proportion of front axle braking force is actively reduced based on pitch angle feedback to lower the "nose-diving" peak. In the late braking stage, the optimal braking force unloading slope is solved based on a half-vehicle six-degree-of-freedom model and the iLQR algorithm to suppress rebound. This achieves refined control of pitch motion throughout the entire braking process. In particular, the iLQR rebound suppression in the late braking stage provides optimal intervention for the rebound impact caused by the sudden release of front suspension compression energy at the end of braking, completely eliminating the nod-nose reciprocating oscillation problem in traditional braking control.

[0028] Optionally, the determination condition for the early stage of braking is that the deceleration is less than a first threshold; the determination condition for the middle stage of braking is that the deceleration is greater than or equal to the first threshold and less than a second threshold; and the determination condition for the late stage of braking is that the vehicle speed drops below a preset threshold.

[0029] In this embodiment, by clearly defining the judgment conditions for the early, middle, and late stages of braking, the braking stages are divided, enabling accurate identification of the current stage and execution of corresponding control strategies under different braking intensities.

[0030] Optionally, the cost function aimed at suppressing pitch rebound is: ; Where, x b,k Let u be the pitch state variable at time k;b,k The control input at time k, Q b This is the pitch state weight matrix, where the weight corresponding to the pitch angle is greater than the weights corresponding to other states. R b This is the weight matrix for the change in braking force; N b The number of time-domain steps is used to predict the later stage of braking.

[0031] In this embodiment, the specific form x of the late-stage braking rebound suppression cost function is defined. b = u b =[ΔF f ,ΔF r ] T and Q b In the matrix, the weight corresponding to the pitch angle is greater than the weight corresponding to other states, ensuring that the iLQR iteration in the later stage of braking optimizes for pitch rebound. Since this problem dimension only sets 2 state variables and 2 control inputs, the iteration can converge quickly within a single control cycle, balancing control effectiveness and real-time performance.

[0032] In some embodiments, a vehicle control device is provided, including a processor and a memory storing program instructions, the processor being configured to execute a vehicle control method as described in any of the above embodiments when the program instructions are executed.

[0033] In some embodiments, a vehicle is provided, including a body, wheels, and a suspension system, wherein the suspension system is connected between the body and the wheels, the suspension system includes springs and continuously damped controlled shock absorbers, one end of the shock absorber is connected to the body and the other end is connected to the wheel, the shock absorber has a built-in solenoid valve, the solenoid valve is used to adjust the oil flow area inside the shock absorber to change the damping force output by the shock absorber; the vehicle further includes: a drive unit mounted on the shock absorber for driving the solenoid valve; a sensor group mounted on the body and / or the suspension system for collecting motion state information of the vehicle; and a control device for the vehicle as described in any of the above embodiments, the control device being electrically connected to the sensor group and the drive unit respectively.

[0034] In some embodiments, a computer-readable storage medium is provided storing program instructions that, when executed, cause a computer to perform a vehicle control method as described in any of the above embodiments.

[0035] The vehicle, control method, apparatus, and computer-readable storage medium provided in this disclosure can achieve the following technical effects: By constructing a chassis-domain collaborative control framework, the semi-active suspension system and braking control are coupled in a single design, achieving comprehensive control over vehicle pitch, roll, and vertical motion. Compared to traditional single control strategies, this method effectively suppresses vehicle attitude changes under multiple operating conditions, significantly improving overall ride comfort. An optimal control algorithm based on iLQR is introduced, achieving an optimal balance between vehicle attitude and control energy while ensuring system stability. This method not only exhibits good robustness but also adaptively adjusts control parameters according to different operating conditions, thereby improving the system's adaptability to complex road conditions.

[0036] By dividing the braking process into three stages—early, middle, and late—and applying differentiated strategies to each, especially by introducing a low-dimensional iLQR algorithm in the late braking stage to optimize the braking force unloading slope, active suppression of reciprocating oscillations at the end of braking is achieved. Through optimization of the braking force distribution coefficient and the design of a staged control strategy, the pitch change of the vehicle body during braking and the rebound phenomenon in the late braking stage are effectively reduced, alleviating passenger discomfort. Especially under non-emergency braking conditions, the riding experience can be significantly improved.

[0037] Compared to the limitations of traditional linear quadratic regulators, which are only applicable to linear time-invariant systems, the iLQR algorithm used in this disclosure can achieve local linearization of the nonlinear vehicle dynamics model near the current state trajectory by performing a first-order Taylor expansion. It also effectively handles the strong nonlinear characteristics of the suspension system through iterative solutions, significantly broadening the applicability of the control algorithm. Furthermore, this disclosure uses the optimal control sequence from the previous cycle as the initial value for the current cycle iteration, reducing the number of iterations from the conventional 10+ to 3-5, and controlling the single-cycle calculation time to within 10ms, fully meeting the real-time requirements of a 20ms sampling period. In addition, the cost function includes both attitude deviation and control energy terms, enabling the controller to achieve an optimal balance between vehicle attitude suppression and actuator energy consumption. Compared to control strategies that prioritize attitude suppression while neglecting energy consumption, this reduces control energy consumption by approximately 15% to 20%.

[0038] This disclosure uses a seven-degree-of-freedom model of the entire vehicle as the basis for prediction, achieving joint optimization of pitch, roll, and vertical motion. This overcomes the inherent flaw of traditional four-wheel independent control, which neglects the coupling effect between the various degrees of freedom of the vehicle body, enabling the four suspensions to work collaboratively. Simultaneously, this invention incorporates suspension control and braking control into a unified collaborative framework, allowing pitch angle information during braking to be fed back to the suspension controller in real time, achieving deep coupling optimization of multiple systems in the chassis domain. Furthermore, through a frequency-band collaborative strategy where the iLQR main controller is responsible for low-frequency attitude control and the LQR auxiliary controller is responsible for high-frequency road vibration control, full-frequency control coverage is achieved, filling the performance blind spots of a single controller across the entire frequency range.

[0039] By calculating six statistical features in real time within a sliding time window—average vehicle speed, speed fluctuation coefficient, stopping frequency, road curvature, gradient, and mean lateral acceleration—and combining them with a pre-built decision tree classifier, real-time online identification of road conditions is achieved, and the state weight matrix in the iLQR cost function is dynamically switched accordingly. Specifically, on highways, the focus is on vertical ride comfort optimization, while on mountain curves, the focus is on roll and pitch attitude suppression. Compared to control strategies with fixed weights, this scenario-adaptive weight switching mechanism results in better vehicle dynamic performance under each condition.

[0040] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0041] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein: Figure 1 This is a schematic diagram of the seven-degree-of-freedom dynamics model of the whole vehicle; Figure 2 This is a schematic diagram of a six-and-a-half-degree-of-freedom vehicle body dynamics model; Figure 3 This is a schematic diagram of a 1 / 4 scale semi-active suspension model; Figure 4 This is a schematic flowchart of a vehicle control method according to an embodiment of the present disclosure; Figure 5 The variation law of vehicle pitch angle under slight braking conditions on urban roads; Figure 6 The variation law of vehicle pitch angular velocity under slight braking conditions on urban roads; Figure 7 The variation law of the vertical acceleration of the vehicle body under highway conditions; Figure 8 The variation law of the average relative dynamic load of the four wheels under highway working conditions; Figure 9 The variation law of suspension dynamic deflection under highway working conditions; Figure 10 This is a schematic diagram of a vehicle control device provided in an embodiment of this disclosure. Detailed Implementation

[0042] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0043] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0044] Unless otherwise stated, the term "multiple" means two or more.

[0045] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0046] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0047] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0048] The vehicle control method provided by this invention is applied to real-time control during vehicle operation. The vehicle includes a body, wheels, and a suspension system connecting the body and wheels. The suspension system includes springs and continuously damped control shock absorbers, with one end of the shock absorber connected to the body and the other end connected to the wheel. Each shock absorber has a built-in solenoid valve, which adjusts the fluid flow area inside the shock absorber to change the output damping force. A drive device is mounted on the shock absorber to actuate the solenoid valve. In this embodiment, each of the four suspensions has an independent shock absorber and its drive device, forming a four-channel independent control architecture.

[0049] The vehicle is also equipped with a sensor array, including: a 6-axis inertial measurement unit mounted at the vehicle's center of gravity to measure the vehicle's vertical acceleration, pitch rate, roll rate, and yaw rate; height sensors mounted on each suspension unit to measure the suspension travel; wheel speed sensors mounted on each wheel to measure wheel speed and calculate longitudinal vehicle speed; displacement sensors mounted on the brake pedal or pressure sensors mounted on the brake master cylinder to obtain braking status information; and temperature sensors mounted on the outer wall of the shock absorber cylinder to measure fluid temperature to compensate for the effect of temperature on damping force-current characteristics.

[0050] The vehicle's control unit is electrically connected to both the sensor array and the drive unit, receiving motion state information collected by the sensor array. The vehicle's control unit includes a processor and a memory storing program instructions. The processor is configured to execute the vehicle control method as described in any of the following embodiments when running the program instructions.

[0051] In some embodiments, during the development phase before mass production of the vehicle, the following model is constructed and parameters are calibrated, and all results are stored in the memory of the control device.

[0052] Specifically, a method for constructing multi-level vehicle dynamics models is provided, including: Construct a seven-degree-of-freedom model of the entire vehicle.

[0053] like Figure 1 As shown, the seven-DOF model of the vehicle includes pitch, roll, and vertical degrees of freedom, as well as vertical sway degrees of freedom for the four wheels. This model fully describes the coupled dynamics between the vehicle body and the four suspensions under acceleration, braking, cornering, and uneven road conditions. In this embodiment, it is used as the controlled object for attitude prediction and optimization by the iLQR main controller.

[0054] The equation for vehicle pitch dynamics is: ; In the formula, I y The pitch moment of inertia is the moment of inertia of the vehicle body about the Y-axis, expressed in kg·m². Let be the vehicle's pitch acceleration, which is the second derivative of the pitch angle with respect to time, in rad / s. 2 a is the horizontal distance from the vehicle's center of gravity to the front axle, in meters; b is the horizontal distance from the vehicle's center of gravity to the rear axle, in meters; F s＿L1 Force F is the force applied to the left front suspension, measured in N (N). s＿L2 F represents the force on the left rear suspension, in N (N). s＿R1 F represents the force on the right front suspension, in N (N). s＿R2 θ represents the right rear suspension force, in N; θ represents the vehicle pitch angle, in rad.

[0055] The equation for vehicle roll dynamics is: ; In the formula, I x The moment of inertia is the body roll, expressed in kg·m. 2 That is, the inertia of the vehicle body rotating around the X-axis; The vehicle body roll angle acceleration, in rad / s 2 That is, the second derivative of the roll angle with respect to time; F s＿L1 Force F is the force applied to the left front suspension, measured in N (N). s＿L2 F represents the force on the left rear suspension, in N (N). s＿R1 F represents the force on the right front suspension, in N (N). s＿R2 The force is the force on the right rear suspension, in N; t f t r These are the left and right wheel tracks on the front and rear sides, respectively, in meters (m), which is the lateral distance between the left and right wheels. The vehicle body roll angle is expressed in rad.

[0056] The vertical dynamic equation of the vehicle body is: ; In the formula, m is the sprung mass, in kg, which is the total mass of the vehicle body and all its accessories; The vertical acceleration at the vehicle's center of mass, in m / s². 2 That is, the second derivative of the vertical displacement with respect to time; F s＿L1 Force F is the force applied to the left front suspension, measured in N (N). s＿L2 F represents the force on the left rear suspension, in N (N). s＿R1 F represents the force on the right front suspension, in N (N). s＿R2 The force is the force on the right rear suspension, in N; z is the vertical displacement at the vehicle's center of gravity, in m; V x V is the longitudinal velocity, in m / s. y The value represents the lateral velocity, expressed in m / s.

[0057] Suspension force is expressed as: ; In the above formula, For suspension spring stiffness, This is the passive damping coefficient of the suspension. This is the suspension compression. For adjustable damping forces, ij represents the four suspension components: left front, right front, left rear, and right rear. This is used in calculating suspension compression. To make the model more accurate, it is necessary to consider the roll and pitch motions of the vehicle body to obtain the compression values ​​of the four suspensions. The expression:

[0058] In the formula, D L1 D represents the compression of the left front suspension. L2 D represents the compression of the left rear suspension. R1 This represents the compression of the right front suspension; D R2 This represents the compression of the right rear suspension; z t＿L1 Z represents the vertical displacement of the left front wheel, in meters (m). t＿L2 Z represents the vertical displacement of the left rear wheel, in meters (m). t＿R1 z represents the vertical displacement of the right front wheel, in meters (m). t＿R2 denoted as z, representing the vertical displacement of the right rear wheel in meters; z represents the vertical displacement at the vehicle's center of gravity in meters.

[0059] Constructing a six-degree-of-freedom model of a half-vehicle includes: Figure 2 As shown, the six-DOF (degrees of freedom) model of the semi-vehicle takes a longitudinal section of the vehicle and includes the vertical degrees of freedom of the body, pitch, and the sprung and unsprung masses of the front and rear axles. It is used to describe the longitudinal load transfer and pitch dynamic response during braking. This model is used for pitch rebound suppression control in the later stages of braking. Compared with the seven-DOF model of the whole vehicle, this model has lower dimensions, and the computational cost of a single iteration is about 1 / 3 of that of the whole vehicle model, which can ensure that multiple iterations of optimization can be completed in the later stages of braking.

[0060] The six-degree-of-freedom half-body vehicle dynamics model is as follows: Its pitch dynamics equation is: ; In the formula, J is the moment of inertia of the vehicle body about the y-axis, with the unit being kg·m. 2 θ is the vehicle pitch angle, in rad. The vehicle body pitch acceleration is expressed in rad / s. The acceleration due to the pitch angle of the vehicle body, measured in rad / s. 2 a and b are the distances from the center of mass to the front and rear axes, respectively, in meters. , These are the equivalent stiffnesses of the front and rear suspensions, respectively, in N / m. , These are the equivalent damping coefficients of the front and rear suspensions, respectively, in N·s / m. , These are the vehicle's vertical acceleration and jerk, respectively, in m / s² and m / s². 2 ; , These are the heights of the braking force application points on the front and rear axles, respectively, in meters (m). , These are the horizontal distances from the points of application of the braking forces on the front and rear axles to the center of mass, respectively, in meters. , These are the braking forces of the front and rear wheels, respectively, in N; , These represent the changes in braking force for the front and rear wheels, respectively, in N.

[0061] Its vertical motion equation is: ; In the formula, denoted as sprung mass in kg; g is the acceleration due to gravity.

[0062] Construct a 1 / 4 semi-active suspension model, including: Figure 3 As shown, the 1 / 4 semi-active suspension model takes the sprung mass of a single wheel and its upper surface. The 1 / 4 semi-active suspension model is as follows: The equation of motion for the sprung mass is: ; The equation of motion for the unsprung mass is: ; In the above formula, z s The vertical displacement of the sprung mass is expressed in meters (m), which is the vertical displacement of the body portion above the suspension. The vertical velocity of the sprung mass, in m / s; Vertical acceleration of the sprung mass, unit: m / s² 2 ;z u The vertical displacement of the unsprung mass, in meters, is the vertical displacement of the wheel and steering knuckle below the suspension. Vertical velocity of the unsprung mass, unit: m / s; Vertical acceleration of the unsprung mass, unit: m / s² 2 m u ks represents the unsprung mass, in kg, i.e., the mass of components below the suspension, such as wheels and steering knuckles; ks represents the suspension spring stiffness, in N / m, i.e., the spring constant of the suspension spring; c s This refers to the passive damping coefficient of the suspension, measured in N·s / m, which is the basic damping coefficient of the shock absorber; k t Tire stiffness, measured in N / m, is the vertical elastic modulus of the tire; z r The input displacement is the road surface displacement, in meters, which is the vertical excitation caused by the unevenness of the road surface at the wheel location.

[0063] This model is used for high-frequency auxiliary optimization control of suspension performance indicators. Since the model is a linear time-invariant system, the LQR controller can calculate the feedback gain matrix offline, eliminating the need for iteration during online operation.

[0064] Optionally, a comprehensive evaluation index system can be constructed, establishing the following three types of evaluation indicators: Vehicle attitude evaluation indicators include pitch angle, pitch rate, roll angle, roll rate, vertical displacement, and vertical velocity. These indicators reflect the severity of changes in vehicle attitude. The larger the absolute value of the attitude angle and the faster the rate of change, the stronger the feeling of being thrown around.

[0065] Braking comfort evaluation index: including the amount of body pitch rebound during the later stage of braking. This index is specifically used to quantify the nose-up impact caused by the suspension releasing energy at the moment braking ends.

[0066] Vertical comfort evaluation indicators include vehicle vertical acceleration, suspension dynamic deflection, and tire relative dynamic load.

[0067] The aforementioned metrics are used to determine the weight coefficients of the Q matrix in the iLQR cost function. The adjustment of the weight coefficients follows this principle: the more important a metric is, the larger its corresponding weight coefficient, and the controller will prioritize suppressing the physical quantity corresponding to that metric.

[0068] Optionally, the weight matrix of the iLQR cost function is calibrated. The cost function of the iLQR algorithm is: ; Where x is a state variable; x k Let k be the state variable at time k. It is a 6×1 column vector; u represents the adjustable damping force of the four suspensions; u k This is the control input at time k. That is, the adjustable damping force of the four suspensions is a 4×1 column vector; Q is the state weight matrix. In this embodiment, a 6×6 state weight matrix is ​​used, which is a diagonal matrix. Each diagonal element corresponds to the weight coefficient of vertical displacement, vertical velocity, pitch angle, pitch angular velocity, roll angle, and roll angular velocity, respectively. Each weight coefficient in the Q matrix corresponds one-to-one with each index in the comprehensive evaluation index system. R is the control energy weight matrix. In this embodiment, a 4×4 control energy weight matrix is ​​used, which is a diagonal matrix. Each diagonal element corresponds to the weight coefficient of the four adjustable suspension damping forces. Q f The terminal weight matrix is ​​used; in this embodiment, a 6×6 terminal state weight matrix is ​​used to ensure that the system state converges quickly after the prediction time domain ends. N is the prediction time domain length; x N Let N be the state variable at time N.

[0069] k represents the k-th time point in the prediction time domain, k=0,1,…,N 1, where k=0 corresponds to the current time.

[0070] The selection of the prediction time domain length N needs to balance control performance and computational real-time performance. In this embodiment, the sampling period T... s With a time limit of 20ms, and a prediction time N of 30 steps, the total prediction time is N×Ts=600ms. Simultaneously, it is ensured that the computation time for a single iLQR iteration of 3-5 times on mainstream automotive controllers does not exceed 10ms. For different vehicle models or different controller computing power, the value of N can be adjusted within the range of 20-50 steps based on the actual vehicle calibration results.

[0071] Multiple Q matrices were calibrated for different road patterns: Highway / National Highway Mode: Increase the weighting coefficient corresponding to vertical acceleration; Urban road / mountain mode: Increase the weighting coefficients corresponding to pitch and roll angles.

[0072] Weight calibration was performed offline in a simulation environment using a genetic algorithm. Specifically, a full vehicle model of the target model was built in CarSim, and two typical scenarios—highway and urban driving conditions—were set up. The root mean square value of each evaluation index was used as the optimization objective, and a genetic algorithm was employed to search for the optimal diagonal elements of the QQ matrix. The fitness function was a weighted sum of the evaluation indices, and the weights were determined based on the influence coefficients of vibrations at different frequencies on human comfort.

[0073] Road recognition threshold calibration includes: determining classification thresholds for six indicators used to distinguish between highways, national roads, mountain roads, and urban roads based on real-vehicle road test data. The specific calibration method involves collecting no less than 1000 kilometers of real-vehicle road test data, covering no less than 250 kilometers for each of the four road types. Statistical analysis is performed on the collected six features, and the mean ± 1.5 times the standard deviation of each feature for each road type is used as the classification threshold.

[0074] The calibration of the upper and lower limits of the adjustable damping force for each suspension includes: the upper and lower limits of the adjustable damping force for each suspension are determined by bench tests of the shock absorber. The calibration method is as follows: on the shock absorber bench test rig, the shock absorber is driven to reciprocate at a piston speed of 1.0 m / s. The damping force is measured when the solenoid valve drive current is 0 mA (fully open) and when it is at its rated maximum current (fully closed). This damping force is the upper and lower limit of the shock absorber's damping force. The final limit value written into the controller is the measured value multiplied by a safety factor of 0.95.

[0075] For vehicles with significant differences in load between the front and rear axles, the upper limit of the front suspension shock absorber can be set at 3500N, and the upper limit of the rear suspension shock absorber can be set at 2500N, with a lower limit of 100N for both. The minimum value is not 0 to ensure that there is always basic damping inside the shock absorber and to prevent the suspension from oscillating out of control.

[0076] The construction of the actuator inverse characteristic mapping table includes: The inverse characteristic mapping table is used to convert the target damping force into the drive current of the vibration damper. The construction method is as follows: On a vibration damper test bench, the vibration damper is driven at multiple fixed piston speeds, such as 0.1 m / s, 0.3 m / s, 0.5 m / s, 1.0 m / s, and 1.5 m / s. At each speed, the damping force corresponding to different drive currents is measured, for example, from 0 mA to the rated maximum current, with a step size of 50 mA. The measurement data is organized into a two-dimensional lookup table. The horizontal axis represents the target damping force, ranging from 0 to 3000 N with a step size of 100 N. The vertical axis represents the current relative velocity of the vibration damper, ranging from -2.0 to +2.0 m / s with a step size of 0.1 m / s. The table stores the corresponding drive current values.

[0077] During online operation, the target drive current is obtained by bilinear interpolation based on the target damping force of the current cycle and the measured relative velocity of the shock absorber. To compensate for the effect of temperature on oil viscosity, a temperature sensor is installed on the outer wall of the shock absorber cylinder. When the oil temperature exceeds 80℃, a temperature compensation coefficient is added based on the table lookup result. The compensation coefficient is pre-calibrated through bench testing: the drive current corresponding to the same damping force is measured at oil temperatures of 20℃, 40℃, 60℃, 80℃, and 100℃, and a temperature compensation curve is fitted.

[0078] In some embodiments, combined with Figure 4 As shown, a vehicle control method is provided, including: during vehicle operation, the control device continuously performs the following operations at a set sampling period.

[0079] S401, obtain the vehicle's current motion status information.

[0080] The sensor array is used to acquire the vehicle's vertical acceleration, pitch rate, roll rate, suspension travel, longitudinal speed, and yaw rate.

[0081] S402 extracts state variables from motion state information and uses these state variables as the current state variables. It uses the adjustable damping forces of the four suspensions as control inputs and performs local linearization of the vehicle dynamics model near the current state trajectory based on the pre-built vehicle dynamics model.

[0082] S403 uses an iterative linear quadratic adjustment algorithm to find the set of suspension damping forces that minimizes the overall cost of vehicle pitch, roll, and vertical motion, and uses this as the suspension control command for the current cycle.

[0083] The overall cost is determined based on a pre-constructed comprehensive evaluation index system.

[0084] S404 converts suspension control commands into drive currents for each shock absorber and sends them to the corresponding actuators.

[0085] The control method disclosed herein first performs an initialization warm start, using the optimal control sequence from the previous cycle as the initial value for the current iteration. In the first cycle, where there are no historical values, the initial values ​​are set as the passive damping forces of each suspension, i.e., the damping forces corresponding to 50% solenoid valve opening. Then, forward prediction is performed, starting from the current state x0, using the vehicle's seven-degree-of-freedom model, under a given control sequence u. (0) In this case, the fourth-order Runge-Kutta method is used to extrapolate the vehicle's trajectory x1, x2, ..., x30 at N future moments, corresponding to 600ms. For example, in this embodiment, N=30, corresponding to 600ms. N At each prediction time, the spring force and passive damping force of each suspension are calculated based on the current state. The adjustable damping force is added and then substituted into the dynamic equation to solve for the acceleration. Finally, the velocity and displacement are updated through numerical integration.

[0086] The predicted x1...x N and u0...u N 1. Substitute into the cost function: ; Next, a reverse correction is performed. Because the seven-degree-of-freedom model contains nonlinear terms, iLQR in the current predicted trajectory (x k u k A first-order Taylor expansion of the system is performed near the x-th node: k+1 =A k x k +B k u k ;in, , which is a 6×6 system matrix, representing the coupling relationship between state variables; , is a 6×4 input matrix, representing the influence of control inputs on state variables.

[0087] Solve the Ricardi difference equation recursively from the terminal time N: ; Among them, the terminal condition is S N =Qf,S k It is a 6×6 matrix.

[0088] Calculate the optimal control increment: .

[0089] Perform iterative convergence and update the control sequence: , use u new Replace u (0) Then, jump back to step (2) and re-predict. The convergence condition is: Where ε=1.0N, convergence is considered achieved when the change in damping force of each wheel is less than 1N. When this condition is met or the number of iterations reaches a preset maximum value (5 iterations in this embodiment), the iteration stops and the current control sequence is output.

[0090] Take the first term of the converged control sequence The target damping force for the four wheels in the current cycle.

[0091] Optionally, the suspension control command is converted into drive current, including: using a preset target damping force-drive current inverse characteristic mapping table, bilinearly interpolating the target damping force of the four wheels and the current relative motion speed of the shock absorbers, and converting it into the target drive current I of the four shock absorbers. i .

[0092] Safety limiting is applied to each drive current: I i =clip(I i I min， I max) This ensures that it does not exceed the physical drive range of the shock absorber solenoid valve. In this embodiment, I min =0mA, I max =2000mA.

[0093] The target drive current I1~I4 is sent to the solenoid valve driver of each shock absorber via the CAN / FlexRay bus, which drives the solenoid valve to change the oil flow area inside the shock absorber and output the target damping force.

[0094] The optimal control sequence for this cycle is cached in memory as the initial value for the next iLQR iteration. Vehicle speed, acceleration, attitude angle, and other data for this cycle are pushed into the sliding window to update the historical database. The system then waits for the next sampling cycle to trigger.

[0095] The control method disclosed herein continuously acquires vehicle motion state information at a set sampling period, optimizes the suspension damping force based on a pre-built vehicle dynamics model using an iterative linear quadratic adjustment algorithm, obtains suspension control commands, and outputs them in a closed-loop control flow, achieving real-time rolling optimization of the vehicle suspension damping force. Compared to the existing LQR (Linear Quadratic Regulator), which is only applicable to linear systems, the iLQR (Iterative Linear Quadratic Regulator) used in this solution, by locally linearizing the nonlinear vehicle dynamics model near the current state trajectory, can effectively handle the nonlinear characteristics of the suspension system, significantly broadening the applicability of the control algorithm while ensuring computational efficiency. Simultaneously, by quantifying ride comfort using a comprehensive evaluation index system and using it as the optimization target, the optimization direction corresponds to a specific comfort objective, thereby effectively reducing vehicle vertical acceleration, suspension dynamic deflection, and tire relative dynamic load, improving ride comfort.

[0096] Optionally, the method further includes: identifying the current road conditions based on the vehicle speed information, acceleration information, and attitude information in the motion state information; determining the current control mode based on the identification results; and switching the state weight matrix of the iterative linear quadratic adjustment algorithm according to the current control mode.

[0097] Optionally, identifying the current road condition includes: calculating the following statistical features within a preset sliding time window: average vehicle speed, vehicle speed fluctuation coefficient, stopping frequency, road curvature, slope, and average lateral acceleration; inputting the statistical features into a preset classifier and outputting the current road category; when identified as a highway or national road, selecting a first control mode, in which the weight corresponding to vertical acceleration in the state weight matrix is ​​greater than the weight corresponding to pitch angle and roll angle; when identified as an urban road or mountain road, selecting a second control mode, in which the weight corresponding to pitch angle and roll angle in the state weight matrix is ​​greater than the weight corresponding to vertical acceleration.

[0098] In this embodiment, during vehicle operation, the following data is acquired through onboard sensors and a controller: vehicle longitudinal speed. Vehicle yaw rate Steering wheel angle Longitudinal acceleration lateral acceleration Road surface slope Location and altitude information. Acquire information such as the number of lane lines identified by the cameras, the number of traffic lights, and traffic signs.

[0099] Within a preset sliding time window T, the above data is statistically processed, and the following six statistical characteristics are calculated: Average vehicle speed: .

[0100] Vehicle speed fluctuation coefficient .

[0101] in, .

[0102] Parking frequency: Set within a time window when the vehicle speed is less than a threshold. And the duration exceeds If an event is recorded as one parking session, then the parking frequency is: .

[0103] Road curvature characteristics: Road curvature can be estimated based on yaw rate and vehicle speed. .

[0104] Slope characteristics: Calculating slope using elevation changes: .

[0105] Mean lateral acceleration: .

[0106] In the above formula, T is the length of the sliding time window, in seconds; v(t) is the longitudinal vehicle speed at time t, in meters per second; S is the number of sampling points within the time window; v i N represents the longitudinal vehicle speed at the i-th sampling point; stop Δh(t) represents the number of stops within the time window; r(t) represents the yaw rate at time t, in rad / s; μ is a positive number less than 1 and greater than 0, for example, 0.01 to prevent division by zero; Δh(t) represents the elevation change at time t, in meters; Δs(t) represents the corresponding travel distance, in meters; a y,i The lateral acceleration at the i-th sampling point, in m / s². 2 .

[0107] Six statistical features are input into a pre-set classifier, which outputs the current road category. In this embodiment, the classifier is implemented using a decision tree. The decision tree's judgment logic is as follows: the root node determines the average vehicle speed. ,like ≥80km / h to enter the candidate branch of the expressway, if 40km / h≤ If you enter a candidate branch of the national highway at a speed of <80km / h, Vehicles traveling at speeds below 40 km / h are considered for entry into urban / mountainous candidate branches. The second layer further refines the judgment based on factors such as speed fluctuation coefficient, road curvature, gradient, and parking frequency, which will not be detailed here.

[0108] The current control mode is determined based on the recognition results: when the road is identified as a highway or national road, the first control mode is selected, where the weight corresponding to vertical acceleration in the Q matrix is ​​greater than the weight corresponding to pitch and roll angles; when the road is identified as an urban road or mountain road, the second control mode is selected, where the weight corresponding to pitch and roll angles in the Q matrix is ​​greater than the weight corresponding to vertical acceleration. The classifier's output is processed by a first-order low-pass filter, for example, with a filter time constant of 1.0 second, to prevent frequent mode switching caused by instantaneous fluctuations in features.

[0109] In this way, highways, national roads, mountain roads, and urban roads can be distinguished based on six indicators: average vehicle speed, speed fluctuation coefficient, stopping frequency, road curvature, gradient, and lateral acceleration. Highways typically exhibit high average speed, low speed fluctuation, extremely low stopping frequency, low road curvature, gentle gradient changes, and low lateral acceleration, generally exhibiting a continuous and stable high-speed traffic condition. National roads generally have lower average speeds than highways, with some speed fluctuations. Stopping frequency is low but not zero, with moderate curvature and gradient, and slightly higher lateral acceleration than highways, reflecting their characteristics of both trunk road traffic and roadside interference. Mountain roads typically have lower average speeds, more significant speed fluctuations, and low stopping frequency, but they have high road curvature, many consecutive curves, and significant longitudinal slope undulations, resulting in relatively high lateral acceleration, reflecting their typical characteristics of numerous curves and steep slopes. Urban roads are characterized by lower average vehicle speeds, greater speed fluctuations, and higher frequency of stops. While road curvature and gradient are typically not prominent, their operational status changes most frequently due to frequent starts, decelerations, turns, and traffic interference. A comprehensive analysis of these six indicators can effectively identify different road types.

[0110] Optionally, the method further includes: optimizing suspension performance indicators under high-frequency road excitation conditions; wherein the high-frequency road excitation conditions are determined based on the power spectral density or amplitude change rate of the vehicle body's vertical acceleration.

[0111] Optionally, the optimization of suspension performance indicators includes: based on the 1 / 4 semi-active suspension model, using suspension dynamic deflection, sprung mass vertical velocity, tire deformation, and wheel center vertical velocity as auxiliary state variables, and suspension control force as auxiliary control input, constructing a linear quadratic regulator and solving the Riccati equation to obtain the auxiliary feedback gain matrix; calculating the auxiliary damping force F based on the current vertical vibration state. lqr = K l ×x l Among them, Kl Let x be the auxiliary feedback gain matrix. l The auxiliary state variable is used; the auxiliary damping force is superimposed on the suspension control command to optimize the vehicle vertical acceleration, suspension dynamic deflection and tire relative dynamic load.

[0112] In this embodiment, the suspension performance is optimized under high-frequency road surface excitation conditions as a supplement to iLQR control.

[0113] In some examples, the determination of high-frequency road excitation conditions includes: real-time power spectral density estimation of the vehicle body vertical acceleration signal, and calculation of the power spectral density integral value P in the 8Hz~20Hz frequency band. high When P high Exceeding the preset threshold P th When this occurs, it is determined to be a high-frequency road surface excitation condition. Optionally, this embodiment presets a threshold P. th Take 0.05 (m / s) 2 ) 2 .

[0114] The LQR auxiliary controller is calculated based on a 1 / 4 semi-active suspension model. The LQR auxiliary controller is designed to optimize the vehicle's vertical acceleration, suspension dynamic deflection, and relative dynamic load on the tires. Specifically, it uses the suspension dynamic deflection z... s z u Vertical velocity of sprung mass Tire deformation amount z u z r and the vertical velocity of the wheel center As an auxiliary state variable x l Using suspension control force as auxiliary control input u l .

[0115] The auxiliary cost function is: ; Among them, Q l =diag(1000, 100, 10000, 10) is the state weight matrix, R l =1×10 6 To control the weight matrix.

[0116] Solve the Riccati equation: ; Among them, A l The system matrix (4×4) for the 1 / 4 suspension model; B l The input matrix for the 1 / 4 suspension model is (4×1); P l This is a solution to the Riccati equation (a 4×4 symmetric positive definite matrix).

[0117] Obtain the auxiliary feedback gain matrix: ; Among them, K l It is a 1×4 feedback gain matrix.

[0118] The auxiliary damping force is directly calculated based on the current vertical vibration state: F lqr = K l ×x l .

[0119] The activation and deactivation logic of the LQR auxiliary controller is as follows: The activation conditions are met simultaneously for the following two conditions: (1) the high-frequency road excitation condition is determined to be true; (2) the current vehicle speed is greater than 30km / h.

[0120] Exit conditions are met if any of the following conditions are met: (1) the high-frequency road excitation condition is false for 0.5 seconds; (2) the vehicle speed drops to below 20 km / h; (3) a braking signal is detected.

[0121] Superposition method: Total control force F total =F iLQR +α×F lqr , of which F iLQR To solve the suspension damping force based on the iLQR algorithm, α is an auxiliary weighting coefficient, with a value ranging from 0 to 1, α = min(P high / P th , 1.0), that is, the more intense the high-frequency vibration, the greater the weight of the auxiliary control.

[0122] Optionally, the method further includes: acquiring the braking status information of the vehicle at the current moment; determining a braking control command based on the braking status information; and converting the braking control command into braking torque or wheel cylinder pressure and sending it to the brake actuator.

[0123] Optionally, determining the braking control command based on the braking state information includes: when braking action is detected, in the early stage of braking, determining the basic braking force distribution coefficient based on the current road surface adhesion coefficient; in the middle stage of braking, reducing the proportion of front axle braking force based on the basic braking force distribution coefficient according to the real-time feedback of the vehicle pitch angle; in the later stage of braking, using the vehicle pitch angle and pitch velocity as state variables, and the change in front and rear wheel braking force as control input, constructing a cost function based on the six-degree-of-freedom model of the half-vehicle with the goal of suppressing pitch rebound, and solving for the optimal braking force unloading slope and front and rear axle ratio through an iterative linear quadratic adjustment algorithm, as the braking control command.

[0124] The cost function aimed at suppressing pitch rebound is: ; Where, x b,k Let u be the pitch state variable at time k; b,k The control input at time k, Q b This is the pitch state weight matrix, where the weight corresponding to the pitch angle is greater than the weights corresponding to other states. R b This is the weight matrix for the change in braking force; N b The number of time-domain steps is used to predict the later stage of braking.

[0125] Optionally, the determination condition for the early stage of braking is that the deceleration is less than a first threshold; the determination condition for the middle stage of braking is that the deceleration is greater than or equal to the first threshold and less than a second threshold; and the determination condition for the late stage of braking is that the vehicle speed drops below a preset threshold.

[0126] In this embodiment, the braking control module is activated when a braking signal is detected.

[0127] The condition for determining the braking stage is that the deceleration is less than the first threshold. In this embodiment, the first threshold is 0.3g.

[0128] Determine the basic braking force distribution coefficient β0 based on the current road surface adhesion coefficient μ: , of which F f Fr is the front wheel braking force, in N; Fr is the rear wheel braking force, in N; β0 is the basic braking force distribution coefficient, a dimensionless number between 0 and 1, representing the proportion of front wheel braking force to total braking force.

[0129] The constraint condition that neither the front nor rear wheels lock up is satisfied: F f ≤μ×F zf F r ≤μ×Fzr; where: μ is the road adhesion coefficient, estimated in real time using the wheel slip ratio estimation method; F zf为 Front axle static axle load, unit: N; Fzr is the axle static axle load, unit: N.

[0130] The criteria for determining the braking mid-term are that the deceleration is greater than or equal to the first threshold and less than the second threshold. In this embodiment, the second threshold ranges from 0.3g to 0.6g.

[0131] Based on real-time feedback of the vehicle pitch angle θ, the distribution coefficient is actively fine-tuned: β=β0 Δβ(θ); where: β is the braking force distribution coefficient at the current moment; Δβ(θ)=Kp×θ, is the adjustment amount of the distribution coefficient; Kp is the proportional coefficient, which is 0.5 in this embodiment.

[0132] During the mid-braking phase, the peak pitch angle can be reduced by appropriately decreasing the proportion of front axle braking force.

[0133] The condition for determining the later stage of braking is that the vehicle speed drops below a preset threshold, which is 5 km / h in this embodiment.

[0134] The braking post-stage control method is as follows: using the vehicle pitch angle and pitch velocity as state variables, and the changes in braking force of the front and rear wheels as control inputs, a state-space model is constructed, and the control quantity is solved based on the optimal control algorithm.

[0135] Among them, the vehicle pitch angle and pitch angular velocity are the state variables: , where x b It is a 2×1 column vector.

[0136] The change in braking force between the front and rear wheels is used as the control input: Where: ΔF f ΔF represents the change in front wheel braking force, expressed in N, as an adjustment relative to the current moment's front wheel braking force. r The unit for the change in rear wheel braking force is N, which is the adjustment value of the rear wheel braking force relative to the current moment.

[0137] A cost function aimed at suppressing pitch rebound is constructed based on a half-vehicle six-degree-of-freedom model: ; Where, x b,k Let u be the pitch state variable at time k; b,k The control input at time k; Q b This is the pitch state weight matrix. In the pitch state weight matrix, the weight corresponding to the pitch angle is greater than the weight corresponding to other states. This embodiment uses a 2×2 pitch state weight matrix, Q. b =diag(10000,100); R b The braking force change weight matrix is ​​used in this embodiment. R is a 2×2 braking force change weight matrix. b =diag(1,1); N b The number of time-domain steps for the later stage of braking prediction is set to 10 in this embodiment, corresponding to a 200ms prediction time domain.

[0138] The iLQR algorithm is used to find the optimal braking force unloading slope and front-rear axle ratio, i.e., to find the sequence of front and rear wheel braking force changes that minimizes pitch rebound. The first term is taken as the braking control command for the current cycle. The braking control command is then converted into braking torque or wheel cylinder pressure and sent to the brake actuator.

[0139] In the later stages of braking, an optimal control algorithm based on iLQR is introduced to iteratively optimize the changes in braking force between the front and rear wheels. By locally approximating the nonlinear longitudinal dynamics, active suppression of vehicle pitch rebound is achieved, thereby improving comfort in the later stages of braking.

[0140] The specific implementation of the present invention will be experimentally verified in conjunction with specific embodiments below.

[0141] We will conduct a real-vehicle verification using a certain SUV model as an example. The vehicle parameters are as follows: curb weight 1800kg, wheelbase 2.8m, front axle load 55%, rear axle load 45%, suspension stiffness 20000N / m, and passive damping coefficient 1500N·s / m.

[0142] Test Condition 1: High-speed straight driving. Vehicle speed 120km / h, Class B road surface. (Combined with...) Figure 7 , Figure 8 , Figure 9 The experimental results shown are as follows: the root mean square value of the vertical acceleration of the vehicle body decreased from 0.004153g to 0.003281g, a decrease of 21.0%; the root mean square value of the average dynamic deflection of the suspension decreased from 272mm to 253mm, a decrease of 7.0%; and the root mean square value of the relative dynamic load of the tires decreased from 0.00514 to 0.00430, a decrease of 16.3%.

[0143] Test Condition 2: Urban Road Braking. Initial speed 60 km / h, moderate braking to a stop, average deceleration 0.4g. Figure 5 and Figure 6 The experimental results shown are as follows: the peak braking pitch angle decreased from 1.8° to 1.2°, a decrease of 33.3%; the pitch rebound amplitude in the later stage of braking decreased from 0.8° to 0.25°, a decrease of 68.8%; and the rebound decay time was shortened from 0.6s to 0.2s, a decrease of 66.7%.

[0144] Test Condition 3: Mountain Curve Condition. Vehicle speed 40~60km / h, continuous curves, road surface adhesion coefficient 0.8. Experimental Results: Peak roll angle decreased from 4.5° to 3.2°, a decrease of 28.9%; root mean square roll rate decreased from 6.8° / s to 4.5° / s, a decrease of 33.8%.

[0145] Experimental data show that the present invention can effectively reduce the amplitude of vehicle body posture fluctuation, suppress late-stage braking rebound, and weaken the vibration transmission of road disturbances to the vehicle body, thus significantly improving vehicle ride comfort.

[0146] This invention constructs a vehicle attitude controller based on the Iterative Linear Quadratic Regulation (iLQR) theory, using four adjustable suspension damping forces as control inputs to jointly regulate the vehicle's pitch, roll, and vertical movements. By locally linearizing the vehicle's nonlinear dynamics system near the current trajectory and approximating the performance index function quadratically, an iterative optimization method is used to solve for the optimal control sequence, thereby effectively suppressing multi-degree-of-freedom coupled vibrations of the vehicle body and improving the vehicle's attitude stability under acceleration, deceleration, and complex road conditions. Simultaneously, considering the phased dynamic characteristics of the vehicle's braking process, the front and rear axle braking force distribution coefficients are adaptively adjusted. In the later stages of braking, an optimal control algorithm based on iLQR is introduced to iteratively optimize the vehicle body rebound caused by longitudinal load transfer and suspension release. By rolling optimization of the control input changes, active suppression of pitch rebound is achieved, thereby reducing discomfort during the braking termination phase. In addition, this invention designs a multi-mode switching control mechanism based on road conditions and vehicle operating status, enabling suspension control and braking control to achieve coordinated switching and parameter reconstruction under different typical operating conditions; at the same time, combined with the adaptive characteristics of the iLQR control strategy to model and operating condition changes, it further improves the environmental adaptability and robustness of the control system.

[0147] This invention optimizes the coupling of continuous damping adjustment of semi-active suspension with dynamic distribution of braking force, thereby achieving coordinated control of the vehicle's longitudinal, lateral, and vertical multi-degree-of-freedom dynamic response. It can effectively reduce the amplitude of vehicle body posture fluctuations, suppress late-stage braking rebound, and weaken the transmission of road disturbances to the vehicle body. This significantly improves vehicle ride comfort, ride smoothness, and overall dynamic performance under complex operating conditions, and has good engineering application value and promotion prospects.

[0148] A vehicle control device is provided, including a processor and a memory storing program instructions, the processor being configured to execute a vehicle control method as described in any of the above embodiments when the program instructions are executed.

[0149] Combination Figure 10 As shown, this embodiment of the present disclosure provides a vehicle control device 10, including a processor 100 and a memory 101. Optionally, the device 10 may further include a communication interface 102 and a bus 103. The processor 100, communication interface 102, and memory 101 can communicate with each other via the bus 103. The communication interface 102 can be used for information transmission. The processor 100 can call logical instructions in the memory 101 to execute the vehicle control method of the above embodiment.

[0150] Furthermore, the logic instructions in the aforementioned memory 101 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0151] The memory 101, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 100 executes functional applications and data processing by running the program instructions / modules stored in the memory 101, thereby implementing the vehicle control method in the above embodiments.

[0152] The memory 101 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 101 may include high-speed random access memory and may also include non-volatile memory.

[0153] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured to execute the aforementioned vehicle control method.

[0154] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc., and other media capable of storing program code.

[0155] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0156] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0157] The methods and products disclosed in the embodiments herein (including but not limited to devices and equipment) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0158] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for controlling a vehicle, characterized in that, During vehicle operation, perform the following operations at a set sampling period: Obtain the vehicle's current motion status information. State variables are extracted from the motion state information and used as the current state variables. The adjustable damping forces of the four suspensions are used as control inputs. Based on the pre-built vehicle dynamics model, the vehicle dynamics model is locally linearized near the current state trajectory. The suspension damping forces that minimize the overall cost of vehicle pitch, roll, and vertical motion are determined by an iterative linear quadratic adjustment algorithm and used as the suspension control command for the current cycle; wherein the overall cost is determined based on a pre-constructed comprehensive evaluation index system. The suspension control commands are converted into drive currents for each shock absorber and sent to the corresponding actuators.

2. The method according to claim 1, characterized in that, The motion status information includes the vehicle's vertical acceleration, pitch rate, roll rate, suspension travel, longitudinal speed, and yaw rate. The state variables include the vehicle's vertical displacement, vertical velocity, pitch angle, pitch rate, roll angle, and roll rate.

3. The method according to claim 1, characterized in that, The vehicle dynamics model includes a full vehicle seven-degree-of-freedom model, a half-vehicle six-degree-of-freedom model, and a 1 / 4 semi-active suspension model; The full vehicle seven-degree-of-freedom model is used to solve the suspension damping force using the iterative linear quadratic adjustment algorithm, the half vehicle six-degree-of-freedom model is used for pitch rebound suppression in the later stages of braking, and the 1 / 4 semi-active suspension model is used for auxiliary optimization control of suspension performance indicators.

4. The method according to claim 1, characterized in that, The comprehensive evaluation index system includes vehicle posture evaluation index, braking comfort evaluation index and vertical comfort evaluation index; The vehicle posture evaluation indicators include vehicle pitch angle, pitch rate, roll angle, roll rate, vertical displacement, and vertical velocity; the braking comfort evaluation indicators include vehicle pitch rebound during the braking process; and the vertical comfort evaluation indicators include vehicle vertical acceleration, suspension dynamic deflection, and relative tire dynamic load.

5. The method according to claim 1, characterized in that, The cost function of the iterative linear quadratic control algorithm is the sum of the weighted square sum of the state deviations and the weighted square sum of the control energy: ； Where x is the state variable, u is the adjustable damping force of the four suspensions, Q is the state weight matrix, and R is the control energy weight matrix. f is the terminal weight matrix; N is the prediction time domain length; k represents the k-th time in the prediction time domain. Each weight coefficient in the Q matrix corresponds one-to-one with each indicator in the comprehensive evaluation index system.

6. The method according to any one of claims 1 to 5, characterized in that, Also includes: The current road conditions are identified based on the vehicle speed, acceleration, and attitude information in the motion state information. The current control mode is determined based on the identification results; Based on the current control mode, switch the state weight matrix of the iterative linear quadratic adjustment algorithm.

7. The method according to claim 6, characterized in that, The identification of current road conditions includes: Within a preset sliding time window, calculate the following statistical characteristics: average vehicle speed, vehicle speed fluctuation coefficient, stopping frequency, road curvature, slope, and mean lateral acceleration. The statistical features are input into a preset classifier, which outputs the current road category. When the highway or national road is identified, the first control mode is selected. In the first control mode, the weight of vertical acceleration in the state weight matrix is ​​greater than the weight of pitch angle and roll angle. When the road is identified as an urban road or a mountain road, the second control mode is selected. In the second control mode, the weights corresponding to the pitch angle and roll angle in the state weight matrix are greater than the weights corresponding to the vertical acceleration.

8. The method according to any one of claims 2 to 5, characterized in that, Also includes: The suspension performance indicators were optimized under high-frequency road excitation conditions. The high-frequency road excitation condition is determined based on the power spectral density or amplitude change rate of the vehicle's vertical acceleration.

9. The method according to claim 7, characterized in that, The optimization of suspension performance indicators includes: Based on the 1 / 4 semi-active suspension model, the suspension dynamic deflection, sprung mass vertical velocity, tire deformation and wheel center vertical velocity are used as auxiliary state variables, and the suspension control force is used as the auxiliary control input. A linear quadratic regulator is constructed and the Riccati equation is solved to obtain the auxiliary feedback gain matrix. Calculate the auxiliary damping force based on the current vertical vibration state: F lqr = K l ×x l ; Among them, K l Let x be the auxiliary feedback gain matrix. l The auxiliary state variable; The auxiliary damping force is superimposed on the suspension control command to optimize the vehicle's vertical acceleration, suspension dynamic deflection, and tire relative dynamic load.

10. The method according to any one of claims 2 to 5, characterized in that, Also includes: Obtain the vehicle's current braking status information; Based on the braking status information, a braking control command is determined, and the braking control command is converted into braking torque or wheel cylinder pressure and sent to the braking actuator.

11. The method according to claim 10, characterized in that, The step of determining the braking control command based on the braking state information includes: When braking action is detected, the basic braking force distribution coefficient is determined based on the current road surface adhesion coefficient in the early stage of braking. During the braking phase, based on real-time feedback of the vehicle pitch angle, the proportion of front axle braking force is reduced on the basis of the aforementioned basic braking force distribution coefficient. In the later stage of braking, the vehicle pitch angle and pitch velocity are used as state variables, and the changes in braking force of the front and rear wheels are used as control inputs. Based on the six-degree-of-freedom model of the half-vehicle, a cost function with the goal of suppressing pitch rebound is constructed. The optimal braking force unloading slope and front and rear axle ratio are solved by an iterative linear quadratic adjustment algorithm, which serves as the braking control command.

12. The method according to claim 11, characterized in that, The condition for determining the pre-braking stage is that the deceleration is less than a first threshold. The condition for determining the braking mid-term is that the deceleration is greater than or equal to the first threshold and less than the second threshold; The condition for determining the later stage of braking is that the vehicle speed drops below a preset threshold.

13. The method according to claim 11, characterized in that, The cost function aimed at suppressing pitch rebound is: ； Where, x b,k Let u be the pitch state variable at time k; b,k The control input at time k, Q b This is the pitch state weight matrix, where the weight corresponding to the pitch angle is greater than the weights corresponding to other states. R b N is the weight matrix for the change in braking force; b The number of time-domain steps for predicting the later stage of braking.

14. A vehicle control device, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to execute the vehicle control method as described in any one of claims 1 to 13 when running the program instructions.

15. A vehicle, characterized in that, The vehicle includes a body, wheels, and a suspension system. The suspension system connects the body and the wheels. The suspension system includes springs and continuously damped controlled shock absorbers. One end of the shock absorber is connected to the body, and the other end is connected to the wheel. The shock absorber has a built-in solenoid valve for adjusting the fluid flow area inside the shock absorber to change the damping force output by the shock absorber. The vehicle also includes: A drive unit, mounted on the vibration damper, is used to drive the solenoid valve to operate; A sensor array, mounted on the vehicle body and / or the suspension system, for collecting vehicle motion state information; and a vehicle control device as claimed in claim 14, wherein the control device is electrically connected to the sensor array and the drive unit, respectively.

16. A computer-readable storage medium storing program instructions, characterized in that, When the program instructions are executed, they cause the computer to perform the vehicle control method as described in any one of claims 1 to 13.

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