Vehicle RMI rollover prevention control method fusing load and tire pressure estimation

Abstract

The application discloses a vehicle RMI anti-rollover control method fusing load and tire pressure estimation, and belongs to the field of automobile safety, and comprises the following steps: S1, acquiring vehicle chassis pressure data, suspension displacement data and tire pressure data in real time; S2, based on the acquired data, estimating load distribution and optimization; S3, based on the optimized load distribution estimation result and driving conditions, realizing adaptive adjustment of the RMI trigger threshold; S4, judging whether the trigger threshold value is reached under the current driving condition, if yes, executing the preset anti-rollover control strategy, otherwise returning to step S1 for continuous monitoring. The vehicle RMI anti-rollover control method fusing load and tire pressure estimation adopts the above method, accurately estimates the load distribution through multi-source data fusion, combines adaptive threshold adjustment and multi-system collaborative control strategy, realizes high-precision and intelligent control of vehicle anti-rollover, and significantly improves the safety and stability of driving under complex conditions.

Description

Technical Field

[0001] This invention relates to the field of automotive safety technology, and in particular to a vehicle RMI rollover prevention control method that integrates load and tire pressure prediction. Background Technology

[0002] Vehicle rollover accidents are a highly dangerous safety hazard in road traffic, often resulting in serious injuries and property damage. A vehicle's rollover resistance is affected by many factors, with weight distribution being one of the core influencing factors. In real-world driving scenarios, changes in passenger numbers and cargo loading dynamically alter the vehicle's load, while increased mileage and changes in ambient temperature cause tire pressure fluctuations. These factors combined result in real-time changes in the vehicle's weight distribution, which in turn cause dynamic changes in the vehicle's center of gravity and roll moment.

[0003] However, traditional vehicle rollover prevention systems have significant drawbacks. Their control thresholds are mostly set based on fixed parameters, failing to fully consider the dynamic changes in vehicle weight distribution. When vehicle load increases or load distribution becomes uneven, the vehicle's center of gravity and roll moment change significantly. Because traditional rollover prevention systems cannot dynamically adjust threshold values, they are prone to inaccurate intervention timing, triggering rollover prevention intervention too early or too late. This not only reduces driving safety but also negatively impacts the driving experience.

[0004] In terms of load detection technology, existing solutions mostly rely on dedicated sensors, which are costly and susceptible to environmental interference. While tire pressure monitoring systems can indirectly reflect load changes, their integration with RMI (Roll Mitigation Intervention) systems is insufficient, failing to fully realize their potential value in load prediction. Summary of the Invention

[0005] The purpose of this invention is to provide a vehicle RMI rollover prevention control method that integrates load and tire pressure prediction, thereby solving the above-mentioned technical problems.

[0006] To achieve the above objectives, the present invention provides a vehicle RMI rollover prevention control method that integrates load and tire pressure prediction, comprising the following steps:

[0007] S1. Multi-source data acquisition and preprocessing: Real-time acquisition of vehicle chassis pressure data, suspension displacement data, and tire pressure data;

[0008] S2. Load distribution prediction: Based on the acquired vehicle chassis pressure data, suspension displacement data, and tire pressure data, the load distribution is predicted and optimized.

[0009] S3. Adaptive Threshold Adjustment: Based on the optimized load distribution prediction results and driving conditions, a dynamic threshold decision model is constructed, and a multi-condition strategy library and fuzzy decision algorithm are integrated to achieve adaptive adjustment of the RMI trigger threshold.

[0010] S4. Determine whether the trigger threshold value has been reached under the current driving conditions. If it has been reached, execute the preset anti-rollover control strategy; otherwise, return to step S1 for continuous monitoring.

[0011] Therefore, the vehicle RMI rollover prevention control method based on the above-mentioned fusion load and tire pressure prediction has the following beneficial effects:

[0012] 1. Improve the accuracy of load distribution prediction: By integrating vehicle chassis pressure, suspension displacement and tire pressure data, a physical model is constructed and combined with dual unscented Kalman filtering to achieve accurate prediction of vehicle total mass, center of gravity position and axle load distribution, providing a reliable data foundation for subsequent rollover control, and enabling the system to more accurately judge the actual load state of the vehicle.

[0013] 2. Achieve dynamic adaptive threshold: Construct a dynamic threshold decision model, integrate a multi-condition strategy library and fuzzy decision algorithm, and dynamically adjust the RMI trigger threshold according to real-time load distribution and driving conditions (such as load, vehicle speed and road conditions) to avoid the limitations of fixed thresholds, improve the timeliness and accuracy of anti-rollover intervention, and reduce false triggering or intervention lag.

[0014] 3. Multi-system coordinated and efficient stability control: Braking, power, suspension and other systems work together to implement anti-rollover strategies, such as differential braking to generate yaw moment to suppress roll, engine torque reduction and transmission gear shifting to assist deceleration, and active suspension to adjust stiffness and damping to optimize vehicle posture. The coordinated control from longitudinal, lateral and vertical dimensions effectively reduces the risk of rollover and improves the stability and safety of vehicle driving under complex conditions.

[0015] 4. Enhance risk prediction and control accuracy: Utilize Kalman filtering to predict the lateral load transfer rate (LTR) and combine it with the LQR algorithm for trajectory tracking to achieve multi-objective coordinated control. This allows for early prediction of rollover risks and optimization of control strategies, resulting in a more timely response than traditional passive control and improving the system's ability to accurately regulate vehicle motion.

[0016] 5. Continuously optimize system performance: By setting up an early warning mechanism, the threshold adjustment algorithm parameters are optimized based on the feedback of intervention effects (such as the number of false triggers and the risk exposure time), triggering a secondary correction mechanism to deal with instability risks, so that the system can adapt to different working conditions and vehicle status changes, and maintain the reliability and efficiency of anti-rollover performance through iterative optimization.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] Figure 1 This is a flowchart of a vehicle RMI rollover prevention control method that integrates load and tire pressure prediction according to the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of the present invention and are not intended to limit the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout.

[0020] It should be noted that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, such as a process, method, system, product, or server that includes a series of steps or units, not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or device.

[0021] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0022] With the rapid development of the automotive industry, vehicle speeds are constantly increasing, and load-bearing scenarios are becoming increasingly diverse. Effectively preventing vehicle rollover has become a critical technical problem that the industry urgently needs to solve. Against this backdrop, developing a rollover prevention system that can integrate load and tire pressure data to predict vehicle weight distribution and intelligently adjust the RMI system threshold accordingly is of paramount practical significance for improving vehicle stability and safety under complex operating conditions.

[0023] Based on the above analysis, the present invention is designed as follows: Figure 1 As shown, a vehicle RMI rollover prevention control method integrating load and tire pressure prediction includes the following steps:

[0024] S1. Multi-source data acquisition and preprocessing: Real-time acquisition of vehicle chassis pressure data, suspension displacement data, and tire pressure data;

[0025] In step S1, strain gauge pressure sensors are used to collect vehicle chassis pressure data at the four corners of the vehicle chassis and the middle position of the axle, and the vertical load F of a single wheel is calculated based on the collected vehicle chassis pressure data. z_sensor :

[0026]

[0027] In the formula, U out K represents the output voltage of the strain gauge pressure sensor. cal K represents the correction coefficient of the strain gauge pressure sensor. i Let represent the force transmission coefficient of the i-th strain gauge pressure sensor, and F sensor F represents the force acting on a strain gauge pressure sensor. wheel The vertical force acting on the wheel, l eff The effective lever arm length is expressed in meters (m) and the length is l. total The total length of the rod is expressed in meters (m), and θ represents the angle between the direction of force transmission and the axis of the rod, expressed in rad.

[0028] Displacement sensors are used to collect four sets of suspension displacement data corresponding to the four wheels to obtain the suspension compression Δx, which is then combined with the suspension stiffness k. spring and damping parameter c damper Calculate the single-wheel suspension force F z_susp :

[0029]

[0030] In the formula, Indicates suspension speed;

[0031] Tire pressure data includes front tire pressure and rear tire pressure; the front tire pressure P is collected using built-in pressure sensors. raw Temperature compensation is then performed to obtain the temperature-compensated tire pressure P. corrected :

[0032]

[0033] In the formula, k cal This indicates the calibration coefficient of the pressure sensor; This indicates the conversion of reference temperature to absolute temperature; T measured This indicates the conversion of Celsius temperature to absolute temperature.

[0034] Estimating tire pressure using the indirect wheel speed method:

[0035] Step 1: Perform Fourier transform on the wheel speed signal acquired by the wheel speed sensor:

[0036]

[0037] In the formula, S k (f,t) represents the time-frequency distribution of the wheel speed signal; ω k w(τ) and w(τ-t) represent the sampled values ​​of the wheel speed signal at time τ and time τ-t, respectively; w(·) represents the Hanning window function; T represents the window length;

[0038] Step 2: Extract the radial vibration feature E of the tire using a 30-60Hz bandpass filter. band :

[0039]

[0040] Step 3: Calculate the tire pressure deviation correction amount ΔP corr ;

[0041] Step 3.1: Define the state vector x2 = [ΔP1, ΔP2] T And the observation vector z2=[ω1,ω2] T Where ΔP1 and ΔP2 represent the tire pressure deviations of the two rear wheels of the vehicle, respectively; ω1 and ω2 represent the angular velocities measured by the wheel speed sensors on the two rear wheels of the vehicle, respectively.

[0042] Step 3.2: Based on the linear approximate relationship between tire rolling radius and tire pressure, establish an observation model:

[0043]

[0044] In the formula, ω j R represents the angular velocity of the j-th wheel; v represents the vehicle speed; j α1 represents the nominal rolling radius of the j-th wheel; α1 represents the tire pressure-radius sensitivity coefficient; ΔP j Let represent the tire pressure deviation of the j-th wheel, where j∈(1,2,3,4);

[0045] Step 3.3: Iteratively correct the state vector based on dual unscented Kalman filtering:

[0046]

[0047] In the formula, and K represents the corrected state estimate at time t and the state estimate before the update, respectively; t Indicates Kalman gain; z t This represents the actual observed wheel velocity vector at time t; This indicates that the predicted observations are calculated using an observation model;

[0048] Step 3.4: From the corrected state vector Extract the tire pressure deviation of each tire:

[0049]

[0050] Step 4: Dynamically adjust the weights using the centroid method and the maximum value method:

[0051]

[0052] In the formula, w t σ represents the weight of the maximum value method at time t; max σ represents the standard deviation of the estimation error using the maximum value method. cos This represents the standard deviation of the centroid method estimation error;

[0053] Step 5: Based on dynamic weight w t Integrating tire radial vibration characteristics E band and tire pressure deviation correction amount ΔP corr Estimate the rear tire pressure P est,j :

[0054]

[0055] In the formula, P nom Indicates standard tire pressure; E nom This represents the characteristic frequency band energy under standard tire pressure; f peak f represents the peak frequency of the wheel speed signal; nom This indicates the peak frequency at standard tire pressure.

[0056] S2. Load distribution prediction: Based on the acquired vehicle chassis pressure data, suspension displacement data, and tire pressure data, the load distribution is predicted and optimized.

[0057] Step S2 specifically includes the following steps:

[0058] S21. By using vehicle chassis pressure data, suspension displacement data, and tire pressure data, the load is estimated, and multi-source data fusion is achieved by combining dual unscented Kalman filtering to calculate the total mass of the vehicle and the three-dimensional center of gravity position.

[0059] Step S21 specifically includes the following steps:

[0060] S211. Indirect estimation of single tire vertical load based on tire pressure method:

[0061]

[0062] In the formula, F tire,j P represents the load on the j-th wheel; corrected,j A represents the tire pressure of the j-th wheel after temperature compensation; j This represents the tire contact projection area of ​​the j-th wheel;

[0063] S212. Based on suspension compression and suspension stiffness, directly calculate the vertical load at a single suspension point:

[0064] F susp,j =k j ·Δx j ·R j +Fpreload,j (13);

[0065] In the formula, F susp,j Represents the vertical load at the j-th suspension point; k j Δx represents the suspension stiffness at the j-th suspension point. j R represents the compression at the j-th suspension point; I F represents the lever ratio at the j-th suspension point; preload,j This represents the suspension preload at the j-th suspension point;

[0066] S213, Calculate the total mass m of the vehicle. total and the coordinates of the center of gravity (X) cg ,Y cg Z cg ):

[0067]

[0068] In the formula, (x j ,y j ,z j () represents the position coordinates of the j-th vertical load application point;

[0069] S214. Obtaining the fused single-wheel load F based on weighted fusion. final,j :

[0070] F final,j =α·F susp,j +(1-α)·F tire,j (16);

[0071] in,

[0072]

[0073] In the formula, σ tire α represents the standard deviation of the tire pressure load estimation error; α represents the weighting coefficient; σ susp This represents the standard deviation of the suspension load estimation error;

[0074] S215, Calculate the vertical load F of a single tire tire,j Vertical load F at a single suspension point susp,j The error ΔF between them:

[0075]

[0076] S216. Determine if the error ΔF is greater than the threshold. If not, output the fused single-wheel load F. final,j Otherwise, trigger the following compensation and output the compensated single-wheel load F. final,j :

[0077] F final,j=F susp,j +β·(F tire,j -F susp , j (19);

[0078] In the formula, β represents the adaptive compensation coefficient, and 0.2≤β≤0.5;

[0079] S217, Output vehicle total mass m total Single wheel load F final,j and the coordinates of the center of gravity (X) cg ,Y cg Z cg ).

[0080] S22. Based on the physical relationship between tire vertical stiffness and load and tire pressure, a load-tire pressure mapping model is established through orthogonal experiments, and the load transfer rate is introduced as a correction factor to achieve adaptive model parameters under different working conditions.

[0081] Step S22 specifically includes the following steps:

[0082] S221. Establish a load-tire pressure mapping model:

[0083]

[0084] In the formula, K z denoted as tire vertical stiffness; a, b, and c all represent model parameters corrected by load transfer ratio (LTR).

[0085] in,

[0086] a=a0+a1·LTR, b=b0+b1·LTR, c=c0+c1·LTR (21);

[0087]

[0088] In the formula, a0, b0, and c0 represent the initial parameters of the model when LTR = 0; a1, b1, and c1 represent the correction amounts of LTR to the model parameters, which are calibrated through bench tests; F zl and F zr These represent the vertical loads on the left and right wheels, respectively.

[0089] S223. Output the model parameters a, b, and c under the current driving conditions, as well as the tire vertical stiffness K. z .

[0090] S23. Based on the acceleration, strain, and temperature data of the wheels, analyze the wear degree of the wheels, and integrate the vertical load and wear degree of the four wheels to optimize the load distribution prediction.

[0091] Step S23 specifically includes the following steps:

[0092] S231. Calculate the wear level W of the wheels based on the current driving conditions. j :

[0093]

[0094] In the formula, λ1, λ2, and λ3 all represent weighting coefficients; K z0 D represents the initial vertical stiffness of the wheel. vib D represents the energy of the tire vibration spectrum. vib,0 Indicates the reference value of tire vibration spectrum energy; F tire,j ,max represents the maximum vertical load per tire;

[0095] S232, Combine the vertical load and wear degree of the four wheels:

[0096]

[0097] In the formula, F z,fusion This represents the optimized load prediction result; ω W and All represent weighting coefficients; F tire,nom This indicates the nominal vertical load per tire.

[0098] S3. Adaptive Threshold Adjustment: Based on the optimized load distribution prediction results and driving conditions, a dynamic threshold decision model is constructed, and a multi-condition strategy library and fuzzy decision algorithm are integrated to achieve adaptive adjustment of the RMI trigger threshold.

[0099] Table 1 Multi-condition Strategy Library

[0100]

[0101] Step S3 specifically includes the following steps:

[0102] S31. Based on vehicle dynamics theory, calculate the critical lateral acceleration a for rollover without intervention. y,crit This serves as the baseline value for threshold adjustment;

[0103]

[0104] In the formula, h total B represents the total center of gravity height above the ground; B represents the front wheel track.

[0105] in,

[0106]

[0107] In the formula, m sprung and m unsprungRepresenting the sprung mass and unsprung mass respectively; h sprung and h sprung These represent the height of the center of gravity of the sprung mass from the ground, respectively;

[0108] S32. Considering the influence of unsprung mass, correct the critical lateral acceleration for rollover:

[0109] a y,threshold =η·a y,crit (27);

[0110] In the formula, a y,threshold This represents the corrected critical lateral acceleration for rollover, which is considered as the initial threshold acceleration; η represents the safety factor.

[0111] S33. Construct a multi-condition strategy library: Set strategies that match vehicle speed, total mass, road surface adhesion coefficient and yaw rate, and use fuzzy logic to calculate the membership degree of each strategy for vehicle speed, total mass, road surface adhesion coefficient and yaw rate, and select the strategy with the highest membership degree to output the correction parameters.

[0112] S34. Based on real-time vehicle speed, total mass, road adhesion coefficient, and yaw rate, select matching strategies from the multi-condition strategy library to determine the threshold adjustment parameter Δa. y,threshold The threshold adjustment algorithm then calculates the current RMI threshold parameters:

[0113]

[0114] In the formula, α RA β RA and γ RA These represent the weights for the rate of change of roll angle, the rate of change of lateral acceleration, and the sensitivity coefficient for load loss on the inner wheel, respectively; φ roll Indicates the roll angle; a y,final Indicates the final lateral acceleration threshold; φ roll,final Indicates the final roll angle threshold; F z,inner This indicates the real-time vertical load on the inner wheel; F z,nom Indicates the nominal vertical load;

[0115] S35, Based on the transverse load transfer rate LTR pred Calculate the trigger threshold θ current :

[0116] θ current =θ base ·(1+K dyn ·|LTR pred |) (30);

[0117] In the formula, θ base Indicates the basic threshold value;

[0118] in,

[0119]

[0120] In the formula, φ represents the vehicle roll angle; K dyn This represents the dynamic compensation coefficient, and 0.2 ≤ K. dyn ≤0.5;

[0121] S36. Determine whether the trigger threshold value θ is met. current After RMI intervention, is there a risk of vehicle instability? If not, continue monitoring; otherwise, trigger a second correction.

[0122]

[0123] In the formula, θ emergency Indicates the trigger threshold value after secondary correction; δ represents the secondary correction gain exponent; a y,final,max This indicates the maximum safe lateral acceleration.

[0124] In step 34, the threshold parameters are optimized by gradient descent using the objective function to achieve closed-loop iteration of the threshold parameters;

[0125] The objective function J is expressed as follows:

[0126] J=w1·OverIntervention+w2·RiskExposure (33);

[0127]

[0128] In the formula, w1 and w2 both represent dynamic compensation coefficients for weighting; OverIntervention represents the false trigger rate; RiskExposure represents the risk exposure score; LTR actual θ represents the actual lateral load transfer rate. current Indicates the current trigger threshold;

[0129] The gradient descent update expression is as follows:

[0130]

[0131] In the formula, and K represents the dynamic compensation coefficients at time t+1 and time t, respectively; dyn α represents the dynamic compensation coefficient; η represents the learning rate, and 0.01≤η≤0.1; t+1 and α t These represent the weighting coefficients at time t+1 and time t, respectively.

[0132] S4. Determine whether the trigger threshold has been reached under the current driving conditions. If it has, execute the preset anti-rollover control strategy; otherwise, return to step S1 for continuous monitoring. Step S4 specifically includes the following steps:

[0133] S41. Differential Braking: By applying braking pressure to the outer front wheel, a reverse yaw moment is generated to counteract the roll tendency caused by steering and reduce lateral acceleration.

[0134]

[0135] In the formula, P brake Indicates the applied braking pressure value; F brake Indicates the braking pressure applied to the front wheels; μ represents the brake pad friction coefficient; A piston Indicates the piston area of ​​a single brake caliper; n pads This represents the number of effective friction surfaces;

[0136] S42. Divide the threshold trigger intensity described in step S3 into levels, and adjust the engine torque and transmission gear based on the divided levels.

[0137] Meanwhile, based on the dynamic compensation coefficient K dyn Adjusting the stiffness and damping coefficient of the suspension with roll angle adjustment:

[0138] K target =K base ·(1+α s ·tanh(β Rs ·R roll )) (39);

[0139]

[0140] in,

[0141]

[0142] In the formula, K target K represents the target stiffness value. base Indicates the foundation stiffness; α s Indicates stiffness adjustment gain; β Rs R represents the risk sensitivity coefficient. roll Indicates parameters related to roll risk; C final Indicates the final damping coefficient; C target f represents the target damping coefficient; vib Indicates the dominant frequency of vehicle body vibration; δ fsc f represents the frequency sensitivity coefficient; ref Indicates the reference frequency; C base Indicates the foundation damping coefficient; γ vs Indicates vibration suppression gain; az T represents vertical acceleration. vib Represents the time constant of the vibration energy integral;

[0143] In step S42, when LTR pred When the torque is greater than 0.7, it is considered a high-risk condition, and the engine torque should be reduced by 30% to 50%.

[0144] When 0.5 <LTR pred When the torque is ≤0.7, it is considered a medium-risk condition, and the engine torque should be reduced by 10%-30%.

[0145] When LTR pred When the value is ≤0.5, it is considered a low-risk state, and the engine torque remains at the original level.

[0146] The method for determining the gears of a transmission is as follows:

[0147]

[0148] in,

[0149]

[0150] In the formula, i gear N represents the gear ratio of the transmission; opt Indicates the engine's optimal speed, N opt =80%N max N max The maximum torque speed is indicated by r; r represents the dynamic radius of the tire, and r = r0 + α1·ΔP corr r0 represents the initial radius; K rmi This represents the RMI correction factor, and 0.8 ≤ K. rmi ≤1.2.

[0151] S43. By predicting the lateral load transfer rate through Kalman filtering, and integrating longitudinal braking, lateral trajectory tracking, and vertical suspension control, multi-system collaborative optimization is achieved.

[0152] S431, Based on the input state variable x = [LTR pred ,φ roll ,γ yr ,a y,final ] T γ yr Indicates the yaw rate, predicts the state at the next moment, and the covariance:

[0153]

[0154] P k|k-1 =A1P k-1 A1 T+Q1 (43);

[0155] In the formula, This represents the state at time k based on the state predicted at time k-1; A1 and B1 represent the state transition matrix and the control input matrix, respectively. Represents the predicted state at time k-1; u k-1 P represents the control input vector at time k-1; k|k-1 P represents the predicted covariance. k-1 Let Q1 represent the covariance at time k-1; Q1 represents the process noise covariance matrix.

[0156] S432. Predicting lateral load transfer rate using Kalman gain:

[0157] K k =P k|k-1 H T HP k|k-1 H T +R) -1 (44);

[0158] P k =(IK k H)P k|k-1 (45);

[0159] In the formula, K k H represents the Kalman gain; H represents the observation matrix; R represents the observation noise covariance; P represents the observation noise covariance. k I represents the observation noise covariance; I represents the identity matrix.

[0160] S433, Longitudinal Braking: Calculate Rollover Threshold (LTR) threshold :

[0161] LTR threshold =η sf ·LTR crit (46);

[0162] In the formula, η sf Indicates the safety factor; LTR crit Indicates the critical lateral load transfer rate;

[0163] And when LTR pred >LTR threshold At that time, the differential braking described in step S41 is executed;

[0164] S434, Lateral trajectory tracking;

[0165] S4341. Define the state equation:

[0166]

[0167] in,

[0168]

[0169] In the formula, The derivatives of the state variables are represented by A2 and B2, respectively, which represent the system matrix and the control input matrix. δ f Indicates steering input; d represents external disturbance; v x Indicates longitudinal vehicle speed; C αf Indicates the front wheel lateral stiffness; C αr Indicates the rear wheel lateral stiffness;

[0170] S4342. Define the objective function J:

[0171]

[0172] In the formula, Q2 represents the state weight matrix; δ f R represents the steering input; R represents the control input weight.

[0173] S4343, Calculate the feedforward steering angle δ ff :

[0174]

[0175] In the formula, l r and l f These represent the distances from the front and rear axles to the center of mass, respectively.

[0176] S435, Vertical control strategy;

[0177] Roll stiffness adjustment:

[0178]

[0179] In the formula, K φ,target Indicates the target roll stiffness; K φ0 Indicates the lateral tilt stiffness of the foundation; α a and β a All represent adjustment coefficients; Indicates the rate of change of roll angle;

[0180] Pitch damping adjustment:

[0181]

[0182] In the formula, C θ,target C represents the target pitch damping coefficient; θ0 Indicates the pitch damping coefficient of the foundation; γ a and δ a Adjustment coefficients; θ and These represent the pitch angle and the rate of change of the pitch angle, respectively.

[0183] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A vehicle RMI rollover prevention control method integrating load and tire pressure prediction, characterized in that: The method comprises the following steps: S1, multi-source data acquisition and preprocessing: real-time acquisition of vehicle chassis pressure data, suspension displacement data and tire pressure data; S2, load distribution estimation: based on the obtained vehicle chassis pressure data, suspension displacement data and tire pressure data, the load distribution is estimated and optimized; S3, threshold adaptive adjustment: based on the optimized load distribution estimation result and the driving condition, a dynamic threshold decision model is constructed, a multi-condition strategy library and a fuzzy decision algorithm are fused, and the adaptive adjustment of the RMI triggering threshold is realized; S4, judge whether the triggering threshold value is reached under the current driving condition, if yes, execute the preset rollover prevention control strategy, otherwise return to step S1 for continuous monitoring; In step S1, the vehicle chassis pressure data of four corners of the vehicle chassis and the middle position of the axle are collected by using a strain pressure sensor, and the single-wheel vertical load is calculated based on the collected vehicle chassis pressure data : （1）； wherein, represents an output voltage of the strain gauge pressure sensor; represents a correction coefficient of the strain gauge pressure sensor; represents a force transmission coefficient of the first strain gauge pressure sensor, and , represents a force received by the strain gauge pressure sensor, represents a vertical force received by the wheel, represents an effective arm length, in m, represents a total length of the rod, in m, represents an included angle between a force transmission direction and an axial direction of the rod, in rad; The displacement data of the four suspensions corresponding to the four wheels are collected by using displacement sensors to obtain suspension compression amounts , in combination with suspension stiffness and damping parameters , to calculate single-wheel suspension forces : （2）； In the formula, denotes the suspension velocity; The tire pressure data includes front tire pressure and rear tire pressure; wherein the tire pressure of the front wheel of the vehicle is collected by using the built-in pressure sensor , and temperature compensation is performed to obtain the temperature-compensated tire pressure : （3）； In the formula, represents a pressure sensor calibration coefficient; represents a reference temperature conversion to absolute temperature; represents a Celsius temperature conversion to absolute temperature; The tire pressure is estimated by using the indirect wheel speed method: Step 1, the wheel speed signal obtained by the wheel speed sensor is processed by using Fourier transform: （4）； wherein denotes the time-frequency distribution of the wheel speed signal; and denote the sample values of the wheel speed signal at time and time respectively; denotes the Hanning window function; denotes the window length; Step 2, extract the tire radial vibration features by 30-60 Hz band-pass filtering : （5）； Step 3, calculate the tire pressure deviation correction amount ; Step 3.1, define state vector and observation vector ; wherein, respectively represent tire pressure deviations of the two rear wheels of the vehicle; respectively represent angular velocities measured by wheel speed sensors on the two rear wheels of the vehicle; Step 3.2, based on the linear approximation relationship between the tire rolling radius and the tire pressure, an observation model is established: （6）； wherein denotes the angular velocity of the wheel; denotes the vehicle speed; denotes the nominal rolling radius of the wheel; denotes the tire pressure-radius sensitivity factor; denotes the tire pressure deviation of the wheel, and ; Step 3.3, based on double unscented Kalman filter iterative correction state vector: （7）； In the formula, and respectively represent the state estimation value after the time correction and the state estimation value before the update; represents the Kalman gain; represents the actual observed wheel speed vector at the time; represents the predicted observation value calculated through the observation model; Step 3.

4. Extracting tire pressure deviations from the corrected state vector from the modified state vector: （8）； Step 4, the weight is dynamically adjusted by using the barycenter method and the maximum value method: （9）； （10）； wherein denotes denotes the maximum value method weight; denotes the maximum value method estimated error standard deviation; denotes the barycenter method estimated error standard deviation; Step 5, based on dynamic weights , fusing tire radial vibration features and tire pressure deviation correction amount , estimating rear tire pressure : （11）； In the formula, represents the standard tire pressure; represents the characteristic band energy at the standard tire pressure; represents the peak frequency of the wheel speed signal; represents the peak frequency at the standard tire pressure.

2. The vehicle RMI rollover prevention method of claim 1, wherein: Step S2 specifically comprises the following steps: S21, estimate the load by using the vehicle chassis pressure data, suspension displacement data and tire pressure data, realize multi-source data fusion by using double unscented Kalman filter, calculate the total mass of the vehicle and the three-dimensional barycenter position; S22, based on the physical relationship between the tire vertical stiffness, load and tire pressure, a load-tire pressure mapping model is established by using orthogonal test, and the load transfer rate is introduced as a correction factor to realize the adaptive of the model parameters under different conditions; S23, based on the acceleration, strain and temperature data of the wheel, the wear degree of the wheel is analyzed, and the vertical load and wear degree of the four wheels are fused to optimize the load distribution estimation.

3. The vehicle RMI rollover prevention method of claim 2, wherein: Step S21 specifically comprises the following steps: S211, indirectly estimate the single tire vertical load based on the tire pressure method: （12）； In the formula, represents the load of the first wheel; represents the tire pressure of the first wheel after temperature compensation; represents the tire ground projection area of the first wheel; S212, directly calculate the single suspension point vertical load based on the suspension compression amount and the suspension stiffness: （13）； wherein represents the vertical load of the th suspension point; represents the suspension stiffness of the th suspension point; represents the compression amount of the th suspension point; represents the lever ratio of the th suspension point; represents the suspension pre-tightening force of the th suspension point; S213、calculating the total mass of the vehicle and the coordinates of the center of gravity : （14）； （15）； In the formula, denotes the position coordinate of the point of action of the vertical load. S214, obtaining the fused single-wheel load based on the weighted fusion : （16）； wherein, （17）； In the formula, represents the standard deviation of the tire pressure load estimation error; represents the weighting coefficient; represents the standard deviation of the suspension load estimation error; S215, calculating single-tire vertical load error between the single-suspension-point vertical load and the single-tire vertical load : （18）； S216, judging the error whether greater than a threshold, if not, output, the single wheel load after fusion ; otherwise trigger the following compensation, and output the single wheel load after compensation : （19）； wherein denotes the adaptive compensation coefficient, and ; S217, output the total mass of the vehicle , single wheel load and the coordinates of the center of gravity .

4. The vehicle RMI rollover prevention method of claim 3, wherein: Step S22 specifically comprises the following steps: S221, establish a load-tire pressure mapping model: （20）； wherein represents the tire vertical stiffness; , and all represent the load transfer ratio corrected model parameters; wherein, （21）； （22）； wherein, , and each represent an initial parameter of the model; , and each represent a correction amount for the parameter of the model, which is calibrated by a bench test; and represent vertical loads of the left and right wheels, respectively. S223、outputting the model parameters in the current driving condition , and and tire vertical stiffness .

5. The vehicle RMI rollover prevention method of claim 4, wherein: Step S23 specifically comprises the following steps: S231, based on the current driving condition, calculate the wear degree of the wheel : （23）； wherein , and each represent a weight coefficient; represents an initial vertical stiffness of the wheel; represents a tire vibration spectrum energy; represents a tire vibration spectrum energy reference value; represents a maximum single tire vertical load; S232, fuse the vertical load and wear degree of the four wheels: （24）； In the formula, represents the optimized load estimation result; and both represent the weight coefficient; represents the nominal single-tire vertical load.

6. The vehicle RMI rollover prevention method of claim 5, wherein: Step S3 specifically comprises the following steps: S31, calculate the rollover critical lateral acceleration without intervention based on vehicle dynamics theory as the reference value for threshold adjustment; （25）； wherein represents the total height of the center of mass from the ground; represents the front wheel track; wherein, （26）； wherein and represent the sprung mass and the unsprung mass, respectively; and represent the sprung mass center height and the unsprung mass center height, respectively; S32, consider the influence of the unsprung mass, and correct the critical lateral acceleration of rollover: （27）； In the formula, represents the corrected roll-over threshold lateral acceleration and is considered as the initial threshold acceleration; represents the safety factor; S33, construct a multi-condition strategy library: set the strategies matched with the vehicle speed, total mass, road adhesion coefficient and yaw rate, and calculate the membership degrees of each strategy by fuzzy logic, select the strategy with the highest membership degree to output the correction parameters; S34, determining the threshold adjustment parameter from the multi-working condition strategy library based on the real-time vehicle speed, total mass, road adhesion coefficient and yaw rate The current RMI threshold parameter is calculated by the re-threshold adjustment algorithm: （28）； （29） wherein, , and respectively represent a roll angle change rate weight, a lateral acceleration change rate weight, and an inboard wheel load loss sensitivity coefficient; represents a roll angle; represents a final lateral acceleration threshold; represents a final roll angle threshold; represents a real-time vertical load of an inboard wheel; represents a nominal vertical load; S35、According to the transverse load transfer rate Computing the triggering threshold value : （30）； In the formula, denotes a base threshold value; wherein, （31）； wherein represents a vehicle roll angle; represents a dynamic compensation coefficient, and ; S36, judge whether the vehicle has instability risk after the RMI intervention based on the trigger threshold value , if not, continue to monitor, otherwise trigger secondary correction: （32）； In the formula, represents the trigger threshold value after the second correction; represents the second correction gain index; represents the maximum safe lateral acceleration.

7. The vehicle RMI rollover prevention method of claim 6, wherein: In step 34, the threshold parameters are optimized by gradient descent optimization through the objective function, and the closed-loop iteration of the threshold parameters is realized; where the objective function The expression is as follows: （33）； （34）； （35）； wherein and both represent a weight coefficient dynamic compensation coefficient; represents a false trigger rate; represents a risk exposure score; represents an actual lateral load transfer ratio; represents a current trigger threshold; The gradient descent update expression is as follows: （36）； （37）； In the formula, and respectively represent the dynamic compensation coefficient at the time the time represents the dynamic compensation coefficient; represents the learning rate, and ; and respectively represent the weight coefficient at the time the time 8. The vehicle RMI rollover prevention method of claim 7, wherein: Step S4 specifically comprises the following steps: S41, differential braking: by applying brake pressure to the outer front wheel, a reverse yaw moment is generated to offset the roll tendency caused by turning, and the lateral acceleration is reduced: （38）； In the formula, represents the applied brake pressure value; represents the applied brake pressure value of the front wheel; represents the brake pad friction coefficient; represents the single-sided brake caliper piston area; represents the number of effective friction surfaces; S42, dividing the threshold trigger intensity described in step S3 into grades, and adjusting the torque of the engine and the gear of the transmission based on the divided grades; At the same time, based on dynamic compensation coefficient and side angle adjustment suspension stiffness and damping coefficient: （39）； （40）； Wherein, （41）； In the formula, represents a target stiffness value; represents a base stiffness; represents a stiffness adjustment gain; represents a risk sensitivity coefficient; represents a roll risk related parameter; represents a final damping coefficient; represents a target damping coefficient; represents a body vibration main frequency; represents a frequency sensitivity coefficient; represents a reference frequency; represents a base damping coefficient; represents a vibration suppression gain; represents a vertical acceleration; represents a vibration energy integration time constant; S43, predicting the lateral load transfer rate through Kalman filtering, fusing longitudinal braking, lateral trajectory tracking, and vertical suspension control to realize multi-system collaborative optimization; S431、based on the input state variable , denotes the yaw angular velocity, the predicted next time state and covariance: （42）； （43）； In the formula, Indicates based on Time-state prediction Current state; and These represent the state transition matrix and the control input matrix, respectively. express The predicted state at any given moment; express The control input vector at each time step; Indicates the predicted covariance; express Covariance at time; Represents the process noise covariance matrix; S432, predicting the lateral load transfer rate through Kalman gain: （44）； （45）； wherein denotes the Kalman gain; denotes the observation matrix; denotes the observation noise covariance; denotes the observation noise covariance; denotes the identity matrix; S433, longitudinal braking: calculate rollover threshold : （46）； wherein represents a safety factor; represents a critical lateral load transfer rate; and when the differential braking described in step S41 is performed; S434, lateral trajectory tracking; S4341, defining a state equation: （47）； Wherein, （48）； （49）； wherein denotes a derivative of a state variable; and denote a system matrix and a control input matrix, respectively; denotes a steering input; denotes an external disturbance; denotes a longitudinal vehicle speed; denotes a front wheel cornering stiffness; denotes a rear wheel cornering stiffness; S4342, set objective function : （50）； wherein denotes a state weight matrix; denotes a steering input; denotes a control input weight; S4343, calculate feedforward steering angle : （51）； wherein and respectively represent the distance from the front and rear axles to the center of mass. S435, vertical control strategy; Roll stiffness adjustment: （52）； In the formula, represents a target roll stiffness; represents a base roll stiffness; and each represent an adjustment coefficient; represents a roll angle change rate; Pitch damping adjustment: （53）； wherein represents a target pitch damping coefficient; represents a base pitch damping coefficient; and are adjustment coefficients; and respectively represent a pitch angle and a pitch angle rate of change.

9. The vehicle RMI rollover prevention method of claim 8, wherein: In step S42, when a high risk state is determined, at which time the engine torque is reduced by 30% to 50%. When a medium risk condition is determined, 10% - 30% of engine torque is reduced; When When the value of the engine torque is less than or equal to 0.5, the engine torque is maintained at the original level. The gear determination mode of the transmission is as follows: （54）； Wherein, （55）； wherein denotes the transmission gear ratio; denotes the engine optimum rotation speed, , denotes the maximum torque rotation speed; denotes the tire dynamic radius, and , denotes the initial radius; denotes the RMI correction factor, and .

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