Vehicle RMI anti-rolling regulation and control method fusing load and tire pressure estimation

Abstract

The invention discloses a vehicle RM I anti-rolling regulation and control method fusing load and tire pressure estimation, and belongs to the field of vehicle safety, and the method comprises the following steps: S1, obtaining vehicle chassis pressure data, suspension displacement data and tire pressure data in real time; s2, estimating and optimizing load distribution based on the acquired data; s3, realizing adaptive adjustment of an RM I trigger threshold based on an optimized load distribution estimation result and a driving condition; and S4, judging whether a trigger threshold value is reached or not under the current driving working condition, if so, executing a preset anti-rolling control strategy, and otherwise, returning to the step S1 for continuous monitoring. According to the vehicle RM I anti-rolling regulation and control method fusing the load and the tire pressure estimation, the load distribution is accurately estimated through multi-source data fusion, high-precision and intelligent regulation and control of vehicle rolling prevention are achieved in combination with self-adaptive threshold adjustment and a multi-system cooperative control strategy, and the safety and stability of driving under complex working conditions are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle safety, and particularly to a vehicle RMI anti-roll control method integrating load and tire pressure estimation. Background Art

[0002] Vehicle rollover accidents are extremely harmful safety hazards in road traffic, often resulting in serious casualties and property losses. The anti-roll performance of vehicles is affected by multiple factors, among which the vehicle weight distribution is one of the core influencing factors. In actual driving scenarios, changes in the number of passengers and differences in cargo loading conditions will cause the vehicle load to change dynamically, while an increase in driving mileage and changes in environmental temperature will lead to tire pressure fluctuations. The combined effect of the above factors causes the vehicle weight distribution to change in real time, thereby causing dynamic changes in the vehicle's center of gravity position and roll moment.

[0003] However, traditional vehicle anti-roll systems have significant defects. Most of their control thresholds are set based on fixed parameters and do not fully consider the dynamic change characteristics of vehicle weight distribution. When the vehicle load increases or the load distribution is uneven, the vehicle's center of gravity position and roll moment will change significantly. At this time, because the traditional anti-roll system cannot dynamically adjust the threshold value, it is prone to problems such as inaccurate intervention timing, manifested as premature or late triggering of anti-roll intervention, which not only reduces driving safety but also has an adverse impact on the driving experience.

[0004] In terms of load detection technology, existing solutions mostly rely on dedicated sensors, which have the problems of high cost and susceptibility to environmental interference. Although the tire pressure monitoring system can indirectly reflect the load change, its integration with the RMI (Roll Mitigation Intervention) system is insufficient, and its potential value in load estimation cannot be fully utilized. Summary of the Invention

[0005] The purpose of the present invention is to provide a vehicle RMI anti-roll control method integrating load and tire pressure estimation to solve the above technical problems.

[0006] To achieve the above purpose, the present invention provides a vehicle RMI anti-roll control method integrating load and tire pressure estimation, including the following steps:

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

[0008] S2. Load distribution estimation: Based on the obtained vehicle chassis pressure data, suspension displacement data, and tire pressure data, estimate and optimize the load distribution;

[0009] S3. Threshold Adaptive Adjustment: Based on the optimized load distribution prediction result and driving conditions, by constructing a dynamic threshold decision model, integrating a multi-condition strategy library and a fuzzy decision algorithm, the adaptive adjustment of the RMI trigger threshold is realized;

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

[0011] Therefore, the vehicle RMI anti-roll regulation method adopting the above-mentioned integration of 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, constructing a physical model and combining with a dual unscented Kalman filter, the accurate prediction of the vehicle's total mass, center of gravity position, and axle load distribution is realized, providing a reliable data basis for subsequent anti-roll control, 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 a fuzzy decision algorithm, and dynamically adjust the RMI trigger threshold according to the real-time load distribution and driving conditions (such as load, vehicle speed, road conditions), avoiding the limitations of a fixed threshold, improving the timeliness and accuracy of anti-roll intervention, and reducing false triggers or intervention lags;

[0014] 3. Multiple systems cooperate to control stability efficiently: Systems such as braking, power, and suspension cooperate to execute anti-roll strategies, such as differential braking to generate a yaw moment to suppress roll, engine torque reduction and transmission gear shifting to assist in deceleration, and active suspension to adjust stiffness and damping to optimize the vehicle attitude, and cooperate to control from multiple dimensions of longitudinal, lateral, and vertical directions, effectively reducing the risk of rollover and improving the stability and safety of vehicle driving under complex conditions;

[0015] 4. Enhance risk prediction and control accuracy: Use the Kalman filter to predict the lateral load transfer rate (LTR), and combine with the LQR algorithm for trajectory tracking to achieve multi-objective coordinated control, predict the rollover risk in advance and optimize the control strategy, with a more timely response than traditional passive control, and improve the system's precise regulation ability of the vehicle's motion state;

[0016] 5. Continuously optimize the system performance: By setting up an early warning mechanism, optimize the parameters of the threshold adjustment algorithm according to the feedback of intervention effects (such as the number of false triggers and risk exposure time), trigger a secondary correction mechanism to cope with the instability risk, enable the system to adapt to different working conditions and vehicle state changes, and maintain the reliability and efficiency of anti-roll performance through iterative optimization.

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

[0018] Figure 1 This is a flow chart of a vehicle RMI anti-rollover control method that integrates load and tire pressure estimation according to the present invention. DETAILED DESCRIPTION

[0019] In order to make the purposes, technical solutions and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar numbers throughout represent the same or similar elements or elements with the same or similar functions.

[0020] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

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

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

[0023] Based on the above analysis, the present invention is designed as follows: Figure 1 As shown, a vehicle RMI anti-rollover control method integrating load and tire pressure estimation 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, the vehicle chassis pressure data at the four corners of the vehicle chassis and the middle position of the axle are collected using a strain gauge pressure sensor, and the single wheel vertical load F is calculated based on the collected vehicle chassis pressure data. z_sensor :

[0026]

[0027] In the formula, U out represents the output voltage of the strain type pressure sensor; K cal represents the calibration coefficient of the strain type pressure sensor; K i represents the force transmission coefficient of the i-th strain type pressure sensor, and F sensor represents the force received by the strain type pressure sensor, F wheel represents the vertical force received by the wheel, l eff represents the effective force arm length, with the unit of m, l total represents the total length of the rod, with the unit of m, θ represents the included angle between the force transmission direction and the axial direction of the reference rod, with the unit of rad;

[0028] Collect the displacement data of 4 groups of suspensions corresponding to 4 wheels by using a displacement sensor to obtain the suspension compression Δx, and then combine the suspension stiffness k spring and the damping parameter c damper to calculate the single-wheel suspension force F z_susp :

[0029]

[0030] In the formula, represents the suspension speed;

[0031] The tire pressure data includes the front tire pressure and the rear tire pressure; among them, the tire pressure P raw of the vehicle's front wheels is collected by using a built-in pressure sensor and temperature compensation is performed to obtain the temperature-compensated tire pressure P corrected :

[0032]

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

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

[0035] Step 1: Perform Fourier transform on the wheel speed signal obtained 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 (τ) and w(τ-t) respectively represent the sampling values of the wheel speed signal at time τ and time τ-t; w(·) represents the Hanning window function; T represents the window length;

[0038] Step 2: Extract the radial vibration feature E of the tire by band-pass filtering with a frequency range of 30 - 60 Hz 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 respectively represent the tire pressure deviations of the two rear wheels of the vehicle; ω1 and ω2 respectively represent the angular velocities measured by the wheel speed sensors on the two rear wheels of the vehicle

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

[0043]

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

[0045] Step 3.3: Based on the dual unscented Kalman filter, iteratively correct the state vector:

[0046]

[0047] In the formula, and respectively represent the corrected state estimate value at time t and the state estimate value before update; K t represents the Kalman gain; z t represents the actual observed wheel speed vector at time t; represents the predicted observed value calculated through the observation model;

[0048] Step 3.4: Extract the tire pressure deviations of each tire from the corrected state vector ;

[0049]

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

[0051]

[0052] where w t represents the weight of the maximum value method at time t; σ max represents the standard deviation of the estimation error of the maximum value method; σ cos represents the standard deviation of the estimation error of the centroid method;

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

[0054]

[0055] where P nom represents the standard tire pressure; E nom represents the energy of the characteristic frequency band under the standard tire pressure; f peak represents the peak frequency of the wheel speed signal; f nom represents the peak frequency under the standard tire pressure.

[0056] S2. Load distribution estimation: Based on the obtained vehicle chassis pressure data, suspension displacement data, and tire pressure data, estimate the load distribution and optimize it;

[0057] Step S2 specifically includes the following steps:

[0058] S21. Estimate the load through the vehicle chassis pressure data, suspension displacement data, and tire pressure data, and implement multi-source data fusion by combining the dual unscented Kalman filter to calculate the total vehicle mass and the three-dimensional centroid position;

[0059] Step S21 specifically includes the following steps:

[0060] S211. Indirectly estimate the vertical load of a single tire based on the tire pressure method:

[0061]

[0062] where F tire,j represents the load of the j-th wheel; P corrected,j represents the tire pressure of the j-th wheel compensated for temperature; A j represents the tire contact projection area of the j-th wheel;

[0063] S212. Directly calculate the vertical load of a single suspension point based on the suspension compression amount and suspension stiffness:

[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 represents the suspension stiffness at the j-th suspension point; Δx j represents the compression amount at the j-th suspension point; R I represents the leverage ratio at the j-th suspension point; F preload,j represents the suspension pre-tightening force at the j-th suspension point;

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

[0067]

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

[0069] S214. Obtain the fused single-wheel load F final,j by weighted fusion:

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

[0071] Among them,

[0072]

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

[0074] S215. Calculate the error ΔF between the single-tire vertical load F tire,j and the single-suspension-point vertical load F susp,j :

[0075]

[0076] S216. Judge whether the error ΔF is greater than the threshold value. 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 the total vehicle mass m total , the single-wheel load F final,j and the coordinates of the center of gravity position (X cg , Y cg , Z cg ).

[0080] S22. Based on the physical relationship between the vertical stiffness of the tire and the load and tire pressure, establish a load-tire pressure mapping model through orthogonal experiments, and introduce the load transfer rate as a correction factor to achieve the 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 represents the vertical stiffness of the tire; a, b, and c all represent the model parameters corrected by the load transfer rate LTR;

[0085] Among them,

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

[0087]

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

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

[0090] S23. Analyze the wear degree of the wheels based on the acceleration, strain, and temperature data of the wheels, and optimize the load distribution prediction by integrating the vertical loads and wear degrees of the four wheels.

[0091] Step S23 specifically includes the following steps:

[0092] S231. Calculate the wear degree W of the wheel based on the current driving condition j :

[0093]

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

[0095] S232. Integrate the vertical loads and wear degrees of the four wheels:

[0096]

[0097] In the formula, F z,fusion represents the optimized load prediction result; ω W and both represent weight coefficients; F tire,nom represents the nominal single-tire vertical load.

[0098] S3. Threshold adaptive adjustment: Based on the optimized load distribution prediction result and the driving condition, by constructing a dynamic threshold decision model and integrating a multi-condition strategy library and a fuzzy decision algorithm, realize the 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. Calculate the roll critical lateral acceleration a without intervention based on vehicle dynamics theory y,crit , as the reference value for threshold adjustment;

[0103]

[0104] In the formula, h total represents the height of the total center of mass from the ground; B represents the front wheel track;

[0105] Among them,

[0106]

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

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

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

[0110] In the formula, a y,threshold represents the corrected critical rollover lateral acceleration and is regarded as the initial threshold acceleration; η represents the safety factor;

[0111] S33. Build a multi-condition strategy library: Set the strategies matching the vehicle speed, total mass, road surface adhesion coefficient, and yaw rate, and calculate the membership degree of each strategy through fuzzy logic for the 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 the real-time vehicle speed, total mass, road surface adhesion coefficient, and yaw rate, screen the matching strategy from the multi-condition strategy library to determine the threshold adjustment parameter Δa y,threshold , and then calculate the current RMI threshold parameter through the threshold adjustment algorithm:

[0113]

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

[0115] S35. Calculate the trigger threshold value θ pred according to the lateral load transfer ratio LTR current :

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

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

[0118] Among them,

[0119]

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

[0121] S36. Determine whether there is a risk of vehicle instability after RMI intervention based on the trigger threshold θ current . If not, continue monitoring; otherwise, trigger secondary correction:

[0122]

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

[0124] In step 34, the threshold parameter is optimized by gradient descent through the objective function to achieve closed-loop iteration of the threshold parameter;

[0125] Among them, the expression of the objective function J is as follows:

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

[0127]

[0128] In the formula, both w1 and w2 represent the weight coefficient dynamic compensation coefficient; OverIntervention represents the false trigger rate; RiskExposure represents the risk exposure integral; LTR actual represents the actual lateral load transfer ratio; θ current represents the current trigger threshold;

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

[0130]

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

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

[0133] S41. Differential braking: Apply braking pressure to the outer front wheels to generate a reverse yaw moment, offset the roll tendency caused by steering, and reduce the lateral acceleration:

[0134]

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

[0136] S42. Classify the threshold trigger intensity described in step S3 and adjust the engine torque and transmission gear based on the classification;

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

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

[0139]

[0140] Among them,

[0141]

[0142] In the formula, K target represents the target stiffness value; K base represents the basic stiffness; α s represents the stiffness adjustment gain; β Rs represents the risk sensitivity coefficient; R roll represents the roll risk-related parameter; C final represents the final damping coefficient; C target represents the target damping coefficient; f vib represents the main body vibration frequency; δ fsc represents the frequency sensitivity coefficient; f ref represents the reference frequency; C base represents the basic damping coefficient; γ vs represents the vibration suppression gain; az Denote the vertical acceleration; T vib Denote the integral time constant of vibration energy;

[0143] In step S42, when LTR pred > 0.7, it is determined as a high-risk state, and at this time, the engine torque is reduced by 30% - 50%;

[0144] When 0.5 < LTR pred ≤ 0.7, it is determined as a medium-risk state, and at this time, the engine torque is reduced by 10% - 30%;

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

[0146] The gear determination method of the transmission is as follows:

[0147]

[0148] Among them,

[0149]

[0150] In the formula, i gear Denote the transmission ratio; N opt Denote the optimal engine speed, N opt = 80%N max ,N max Denote the maximum torque speed; r denotes the dynamic tire radius, and r = r0 + α1·ΔP corr ,r0 denotes the initial radius; K rmi Denote the RMI correction coefficient, and 0.8 ≤ K rmi ≤ 1.2.

[0151] S43. Predict the lateral load transfer ratio through Kalman filtering, and fuse longitudinal braking, lateral trajectory tracking, and vertical suspension control to achieve multi-system collaborative optimization;

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

[0153]

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

[0155] In the formula, represents the state at time k predicted based on the state 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 represents the control input vector at time k-1; P k|k-1 represents the predicted covariance; P k-1 represents the covariance at time k-1; Q1 represents the process noise covariance matrix;

[0156] S432. Predict the lateral load transfer rate through the Kalman gain:

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

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

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

[0160] S433. Longitudinal braking: Calculate the rollover threshold LTR threshold :

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

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

[0163] And when LTR pred >LTR threshold perform the differential braking described in step S41;

[0164] S434. Lateral trajectory tracking;

[0165] S4341. Define the state equation:

[0166]

[0167] Among them,

[0168]

[0169] In the formula, represents the derivative of the state variable; A2 and B2 respectively represent the system matrix and the control input matrix; δ f represents the steering input; d represents the external disturbance; v x represents the longitudinal vehicle speed; C αf represents the front wheel cornering stiffness; C αr represents the rear wheel cornering stiffness;

[0170] S4342. Set the objective function J:

[0171]

[0172] In the formula, Q2 represents the state weight matrix; δ f 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 respectively represent the distances from the front and rear axles to the center of mass;

[0176] S435. Vertical control strategy;

[0177] Roll stiffness adjustment:

[0178]

[0179] In the formula, K φ,target represents the target roll stiffness; K φ0 represents the basic roll stiffness; α a and β a both represent adjustment coefficients; represents the roll angle rate of change;

[0180] Pitch damping adjustment:

[0181]

[0182] In the formula, C θ,target represents the target pitch damping coefficient; C θ0 represents the basic pitch damping coefficient; γ a and δ a are both adjustment coefficients; θ and respectively represent the pitch angle and the pitch angle rate of change.

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

Claims

1. A vehicle RMI anti-roll control method integrating load and tire pressure estimation, characterized in that: It includes the following steps: S1. Multi-source data acquisition and preprocessing: Real-time obtain 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, estimate and optimize the load distribution; S3. Threshold adaptive adjustment: Based on the optimized load distribution estimation result and driving conditions, by constructing a dynamic threshold decision model, integrating a multi-condition strategy library and a fuzzy decision algorithm, realize the adaptive adjustment of the RMI trigger threshold; S4. Determine whether the trigger threshold value is reached under the current driving conditions. If it is reached, execute the preset anti-roll control strategy; otherwise, return to step S1 for continuous monitoring.

2. The vehicle RMI anti-roll control method integrating load and tire pressure prediction according to claim 1, wherein: In step S1, a strain pressure sensor is used to collect the vehicle chassis pressure data at the four corners of the vehicle chassis and the middle position of the axle, and the single-wheel vertical load F is calculated based on the collected vehicle chassis pressure data z_sensor : Where, U out represents the output voltage of the strain type pressure sensor; K cal represents the calibration coefficient of the strain type pressure sensor; K i represents the force transfer coefficient of the i-th strain type pressure sensor, and F sensor represents the force received by the strain type pressure sensor, F wheel represents the vertical force received by the wheel, l eff represents the effective force arm length, with the unit of m, l total represents the total length of the rod, with the unit of m, θ represents the angle between the force transfer direction and the axial direction of the reference rod, with the unit of rad; Collect the displacement data of 4 groups of suspensions corresponding to 4 wheels by using displacement sensors to obtain the suspension compression Δx, and then combine with the suspension stiffness k spring and the damping parameter c damper Calculate the single-wheel suspension force F z_susp : In the formula, represents the suspension speed; The tire pressure data includes the front tire pressure and the rear tire pressure; among them, the pressure sensor built in is used to collect the tire pressure P of the front wheels of the vehicle raw , and temperature compensation is performed to obtain the tire pressure P after temperature compensation corrected : where k cal represents the calibration coefficient of the pressure sensor; T ref_K represents the conversion of the reference temperature to the absolute temperature; T measured represents the conversion of the Celsius temperature to the absolute temperature; Estimate tire pressure using the indirect wheel speed method: Step 1. Utilize Fourier transform on the wheel speed signal obtained by the wheel speed sensor: where S k (f,t) represents the time-frequency distribution of the wheel speed signal; ω k (τ) and w(τ - t) respectively represent the sampled values of the wheel speed signal at time τ and time τ - t; w(·) represents the Hanning window function; T represents the window length; Step 2: Extract the radial vibration feature E of the tire by 30 - 60 Hz band-pass filtering band : Step 3, calculate the tire pressure deviation correction amount ΔP corr ; Step 3.1: Define the state vector x2 = [ΔP1, ΔP2] T and the observation vector z2 = [ω1, ω2] T ; where ΔP1 and ΔP2 respectively represent the tire pressure deviations of the two rear wheels of the vehicle; ω1 and ω2 respectively represent the angular velocities measured by the 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 tire pressure, establish an observation model: where ω j represents the angular velocity of the j-th wheel; v represents the vehicle speed; R j represents the nominal rolling radius of the j-th wheel; α1 represents the tire pressure-radius sensitivity coefficient; ΔP j represents the tire pressure deviation of the j-th wheel, and j ∈ (1, 2, 3, 4); Step 3.

3. Iteratively correct the state vector based on the dual unscented Kalman filter: In the formula, and respectively represent the corrected state estimate value at time t and the state estimate value before update; K t represents the Kalman gain; z t represents the actual observed wheel speed vector at time t; represents the predicted observed value calculated through the observation model; Step 3.

4. Extract the tire pressure deviation of each tire from the corrected state vector : Step 4. Dynamically adjust the weights through the centroid method and the maximum value method: where, w t represents the weight of the maximum value method at time t; σ max represents the standard deviation of the estimation error of the maximum value method; σ cos represents the standard deviation of the estimation error of the centroid method; Step 5: Based on the dynamic weight w t , fuse the radial vibration feature E of the tire band and the tire pressure deviation correction amount ΔP corr to estimate the rear tire pressure P est,j : Where, P nom represents the standard tire pressure; E nom represents the characteristic frequency band energy under the standard tire pressure; f peak represents the peak frequency of the wheel speed signal; f nom represents the peak frequency under the standard tire pressure.

3. The vehicle RMI anti-roll control method for integrating load and tire pressure estimation according to claim 2, characterized in that: Step S2 specifically includes the following steps: S21. Estimate the load through the vehicle chassis pressure data, suspension displacement data, and tire pressure data, and combine the dual unscented Kalman filter to achieve multi-source data fusion, and calculate the vehicle total mass and three-dimensional center of gravity position; S22. Based on the physical relationship between the tire vertical stiffness, load, and tire pressure, establish a load-tire pressure mapping model through orthogonal experiments, and introduce the load transfer rate as a correction factor to achieve the adaptive model parameters under different conditions; S23. Based on the acceleration, strain, and temperature data of the wheels, analyze the wear degree of the wheels, and integrate the vertical loads and wear degrees of the four wheels to optimize the load distribution estimation.

4. The vehicle RMI anti-roll regulation method for integrated load and tire pressure prediction according to claim 3, wherein: Step S21 specifically includes the following steps: S211. Indirectly estimate the single tire vertical load based on the tire pressure method: where F tire,j represents the load of the j-th wheel; P corrected,j represents the tire pressure of the j-th wheel after temperature compensation; A j represents the tire ground contact projected area of the j-th wheel; S212. Directly calculate the single suspension point vertical load based on the suspension compression amount and suspension stiffness: F susp,j = k j ·Δx j ·R j + F preload,j (13); where F susp,j represents the vertical load of the j-th suspension point; k j represents the suspension stiffness of the j-th suspension point; Δx j represents the compression of the j-th suspension point; R I represents the leverage ratio of the j-th suspension point; F preload,j represents the suspension pre-tightening force of the j-th suspension point; S213. Calculate the total vehicle mass m total and the coordinates of the center of gravity position (X cg , Y cg , Z cg ): where (x j , y j , z j ) represents the position coordinates of the application point of the j-th vertical load; S214. Obtain the single-round load F after fusion based on weighted fusion final,j : F final,j = α·F susp,j + (1 - α)·F tire,j (16); Wherein, Where, σ tire represents the standard deviation of the estimated error of the tire pressure load; α represents the weighting coefficient; σ susp represents the standard deviation of the estimated error of the suspension load; S215. Calculate the vertical load F of a single tire tire,j and the vertical load F susp,j of a single suspension point, and the error ΔF therebetween: S216. Determine whether the error ΔF is greater than the threshold. If not, output the single-wheel load F after fusion. final,j Otherwise, trigger the following compensation and output the single-wheel load F after compensation. final,j : F final,j = F susp,j + β · (F tire,j - F susp,j ) (19); In the formula, β represents the adaptive compensation coefficient, and 0.2 ≤ β ≤ 0.5; S217. Output the total vehicle mass m total , the single-wheel load F final,j and the coordinates of the center of gravity position (X cg , Y cg , Z cg ).

5. The vehicle RMI anti-roll control method for fusion load and tire pressure prediction according to claim 4, characterized in that: Step S22 specifically includes the following steps: S221. Establish a load-tire pressure mapping model: where K z represents the vertical stiffness of the tire; a, b, and c all represent the model parameters corrected by the load transfer rate LTR; Wherein, a = a0 + a1·LTR, b = b0 + b1·LTR, c = c0 + c1·LTR (21); Wherein, a0, b0, and c0 all represent the initial parameters of the model when LTR = 0; a1, b1, and c1 all represent the correction amounts of the model parameters by LTR, which are calibrated through bench tests; F zl and F zr respectively represent the vertical loads of the left and right wheels; S223. Output the model parameters a, b, and c and the tire vertical stiffness K under the current driving condition z .

6. The vehicle RMI anti-roll regulation method integrating load and tire pressure estimation according to claim 5, characterized in that: Step S23 specifically includes the following steps: S231. Calculate the wear degree W of the wheel based on the current driving condition. j : wherein, λ1, λ2, and λ3 all represent weight coefficients; K z0 represents the initial vertical stiffness of the wheel; D vib represents the tire vibration spectrum energy; D vib,0 represents the reference value of the tire vibration spectrum energy; F tire,j ,max represents the maximum single-tire vertical load; S232. Integrate the vertical loads and wear degrees of the four wheels: Where, F z,fusion represents the optimized load prediction result; ω W and both represent weight coefficients; F tire,nom represents the nominal single-tire vertical load.

7. The vehicle RMI anti-roll control method for integrating load and tire pressure prediction according to claim 6, characterized in that: Step S3 specifically includes the following steps: S31. Calculate the rollover critical lateral acceleration \(a\) without intervention based on vehicle dynamics theory y,crit , which serves as the reference value for threshold adjustment; where h total represents the height of the total centroid from the ground; B represents the front wheel track; Wherein, where m sprung and m unsprung represent the sprung mass and the unsprung mass respectively; h sprung and h unsprung represent the height of the center of mass of the sprung mass from the ground and the height of the center of mass of the unsprung mass from the ground respectively; S32. Consider the influence of the unsprung mass and correct the roll critical lateral acceleration: a y,threshold = η·a y,crit (27); where a y,threshold represents the corrected rollover critical lateral acceleration and is regarded as the initial threshold acceleration; η represents the safety factor; S33. Construct a multi-condition strategy library: Set strategies matching the vehicle speed, total mass, road surface adhesion coefficient, and yaw rate, and calculate the membership degrees of each strategy through fuzzy logic for the 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; S34. Screen and match a strategy from the multi-condition strategy library based on the real-time vehicle speed, total mass, road surface adhesion coefficient, and yaw rate, and determine the threshold adjustment parameter Δa y,threshold , and then calculate the current RMI threshold parameter through the threshold adjustment algorithm: Where α RA , β RA and γ RA respectively represent the roll angle change rate weight, the lateral acceleration change rate weight, and the inner wheel load loss sensitivity coefficient; φ roll represents the roll angle; a y,final represents the final lateral acceleration threshold; φ roll,final represents the final roll angle threshold; F z,inner represents the real-time vertical load of the inner wheel; F z,nom represents the nominal vertical load; S35. Calculate the trigger threshold θ according to the lateral load transfer ratio LTR pred current :​ θ current = θ base · (1 + K dyn · |LTR pred |) (30); where θ base represents the basic threshold value; Wherein, where φ represents the vehicle roll angle; K dyn represents the dynamic compensation coefficient, and 0.2 ≤ K dyn ≤ 0.5; S36. Determine whether there is a risk of vehicle instability after RMI intervention based on the trigger threshold θ current If not, continue monitoring; otherwise, trigger secondary correction: where θ emergency represents the trigger threshold value after secondary correction; δ represents the secondary correction gain index; a y,final,max represents the maximum safe lateral acceleration.

8. The vehicle RMI anti-roll regulation method for integrating load and tire pressure estimation according to claim 7, wherein: In step 34, optimize the threshold parameters through gradient descent of the objective function to achieve the closed-loop iteration of the threshold parameters; Wherein, the expression of the objective function J is as follows: J = w1·OverIntervention + w2·RiskExposure (33); Wherein, both w1 and w2 represent the dynamic compensation coefficient of the weight coefficient; OverIntervention represents the false trigger rate; RiskExposure represents the risk exposure integral; LTR actual represents the actual lateral load transfer ratio; θ current represents the current trigger threshold; The gradient descent update expression is as follows: In the formula, and respectively represent the dynamic compensation coefficients at the (t + 1)-th moment and the t-th moment; K dyn represents the dynamic compensation coefficient; η represents the learning rate, and 0.01 ≤ η ≤ 0.1; α t+1 and α t respectively represent the weight coefficients at the (t + 1)-th moment and the t-th moment.

9. The vehicle RMI anti-roll regulation method for fusion load and tire pressure prediction according to claim 8, characterized in that: Step S4 specifically includes the following steps: S41. Differential braking: By applying braking pressure to the outer front wheels, a reverse yaw moment is generated to counteract the roll tendency caused by steering and reduce the lateral acceleration: where P brake represents the applied braking pressure value; F brake represents the braking pressure value applied to the front wheels; μ represents the friction coefficient of the brake pads; A piston represents the piston area of the single-side brake caliper; n pads represents the number of effective friction surfaces. S42. Classify the threshold trigger intensity described in step S3 and adjust the torque of the engine and the gear position of the transmission based on the classified levels; Meanwhile, based on the dynamic compensation coefficient K dyn and the stiffness and damping coefficients of the roll angle adjustment suspension: K target = K base ·(1 + α s ·tanh(β Rs ·R roll )) (39); Wherein, Where, K target represents the target stiffness value; K base represents the foundation stiffness; α s represents the stiffness adjustment gain; β Rs represents the risk sensitivity coefficient; R roll represents the roll risk related parameter; C final represents the final damping coefficient; C target represents the target damping coefficient; f vib represents the main frequency of vehicle body vibration; δ fsc represents the frequency sensitivity coefficient; f ref represents the reference frequency; C base represents the foundation damping coefficient; γ vs represents the vibration suppression gain; a z represents the vertical acceleration; T vib represents the vibration energy integration time constant; S43. Predict the lateral load transfer ratio through Kalman filtering, and fuse longitudinal braking, lateral trajectory tracking, and vertical suspension control to achieve multi-system collaborative optimization; S431. Based on the input state variables x = [LTR pred , φ roll , γ yr , a y,final T , γ yr represents the yaw rate, predict the state and covariance at the next moment:​ P k|k-1 = A1P k-1 A1 T + Q1 (43); In the formula, represents the state at time k predicted based on the state 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 represents the control input vector at time k-1; P k|k-1 represents the prediction covariance; P k-1 represents the covariance at time k-1; Q1 represents the process noise covariance matrix; S432. Predict the lateral load transfer ratio through the Kalman gain: K k = P k|k-1 H T (HP k|k-1 H T + R) -1 (44); P k =(I - K k H)P k|k-1 (45); Where K k represents the Kalman gain; H represents the observation matrix; R represents the observation noise covariance; P k represents the observation noise covariance; I represents the identity matrix; S433. Longitudinal braking: Calculate the rollover threshold LTR threshold :[[]]END]] LTR threshold = η sf ·LTR crit (46); where η sf represents the safety factor; LTR crit represents the critical lateral load transfer ratio; and when LTR pred >LTR threshold perform the differential braking described in step S41; S434. Lateral trajectory tracking; S4341. Define the state equation: Wherein, In the formula, represents the derivative of the state variable; A2 and B2 represent the system matrix and the control input matrix respectively; δ f represents the steering input; d represents the external disturbance; v x represents the longitudinal vehicle speed; C αf represents the cornering stiffness of the front wheels; C αr represents the cornering stiffness of the rear wheels; S4342. Set the objective function J: wherein, Q2 represents a state weight matrix; δ f represents a steering input; R represents a control input weight; S4343. Calculate the feedforward steering angle δ ff : where \(l\) r and \(l\) f respectively represent the distances from the front and rear axles to the center of mass; S435. Vertical control strategy; Roll stiffness adjustment: where, K φ,target represents the target roll stiffness; K φ0 represents the basic roll stiffness; α a and β a both represent adjustment coefficients; represents the roll angle change rate; Pitch damping adjustment: Where, C θ,target represents the target pitch damping coefficient; C θ0 represents the basic pitch damping coefficient; γ a and δ a are both adjustment coefficients; θ and represent the pitch angle and the pitch angle change rate respectively.

10. The vehicle RMI rollover prevention control method integrating load and tire pressure estimation according to claim 9, characterized in that: In step S42, when LTR pred > 0.7, it is determined as a high-risk state, and at this time, the engine torque is reduced by 30% to 50%; When 0.5 < LTR pred ≤ 0.7, it is determined as a medium-risk state, and at this time, the engine torque is reduced by 10% - 30%; When LTR pred ≤ 0.5, it is determined as a low-risk state, and at this time, the engine torque remains at the original level; The method for determining the gear position of the transmission is as follows: Wherein, where i gear represents the transmission gear ratio; N opt represents the optimum engine speed, N opt = 80%N max , N max represents the maximum torque speed; r represents the dynamic radius of the tire, and r = r0 + α1·ΔP corr , r0 represents the initial radius; K rmi represents the RMI correction factor, and 0.8 ≤ K rmi ≤ 1.2.

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