A rollover index optimization method for multi-axle special vehicles

Abstract

The present invention belongs to the field of vehicle engineering technology, and specifically relates to a method for optimizing the rollover index of a multi-axle special vehicle. Step 1: Establish a dynamic model of a multi-axle special vehicle, and test the established multi-axle whole vehicle dynamic model; Step 2: Based on the multi-axle special vehicle dynamic model established in step 1, further establish a rollover dynamic model of the multi-axle special vehicle, and establish a rollover evaluation index LTR for the rollover dynamic model; Step 3: Establish an optimization method for the LTR transient value and warning threshold of step 2. Trucksim is used to establish a dynamic model of a multi-axle special vehicle whose dynamic characteristics are highly consistent with those of the actual vehicle. The influence of the roll moment of the sprung mass and the unsprung mass on the vertical load difference of the wheels on both sides, as well as the influence of vehicle speed and road environment changes on the rollover warning threshold are comprehensively considered. The calculation of LTR and LTR threshold is optimized, thereby improving the rollover warning accuracy of the special vehicle anti-rollover control.

Description

Technical Field

[0001] The invention belongs to the technical field of vehicle engineering, and in particular relates to a rollover index optimization method for a multi-axle special vehicle. Background Art

[0002] Multi-axle special-purpose vehicles, as the primary transport platform and load-bearing medium for large equipment, play a crucial role in the defense and military industries. Modern special-purpose vehicles often feature high curb weights, high centers of mass, long bodies, and narrow wheelbases. These vehicles, facing complex wartime driving environments, are prone to instability, such as rollovers and rollovers. Therefore, anti-rollover control is essential for improving vehicle stability. However, a key consideration in anti-rollover control is the use of appropriate evaluation metrics to accurately assess the risk of rollover.

[0003] Currently, research on vehicle rollover prevention and rollover warning has developed several different evaluation metrics. These primarily include roll angle, lateral acceleration, LTR, and ZMP theory. Among these commonly used metrics, LTR is the most widely used due to its clear definition and critical conditions. However, LTR also has certain limitations. According to its definition, a vehicle rollover is considered to have occurred when one wheel leaves the ground (i.e., an LTR value of 1). In reality, a vehicle does not necessarily roll over when a wheel momentarily leaves the ground due to an impact. Studies have shown that the roll moment of the vehicle's unsprung mass significantly affects the accuracy of LTR calculations, and many researchers fail to consider the influence of the sprung mass roll moment when calculating LTR. Furthermore, current research on vehicle rollover prevention and warning thresholds often sets a fixed value, failing to consider the effects of vehicle speed and road conditions. This makes these rollover metrics applicable only to trip-induced rollovers and less applicable to non-trip-induced rollovers. Multi-axle special vehicles often need to travel in off-road environments. Faced with complex and changeable road driving environments, using a fixed warning threshold to prevent rollover control may result in insufficient control system response time due to late warnings, thus affecting the effectiveness of rollover control.

[0004] Therefore, it is necessary to conduct a deeper study on the calculation accuracy of the vehicle's rollover index and the determination of the warning threshold, to establish a rollover index that can more accurately evaluate the degree of vehicle rollover risk and a warning threshold calculation method with better warning effect, as the basis for vehicle anti-rollover control and warning control. Summary of the Invention

[0005] To address these issues, this paper, based on a dynamic analysis of the commonly used LTR and ZMP, proposes a new rollover evaluation metric that simultaneously considers the roll moment effects of both sprung and unsprung mass, as well as a calculation method that adaptively determines the warning threshold based on vehicle speed and road adhesion. Using a TruckSim vehicle dynamics model and real-world vehicle testing, the accuracy of the optimized LTR and the effectiveness of the new warning threshold were compared, providing insights into rollover control and warning for specialized vehicles.

[0006] In order to achieve the above purpose, the technical solution adopted by the present invention is as follows

[0007] A rollover index optimization method for a multi-axle special vehicle, comprising:

[0008] Step 1: Establish a multi-axle special vehicle dynamics model and test the established multi-axle vehicle dynamics model;

[0009] Step 2: Based on the multi-axle special vehicle dynamics model established in step 1, a rollover dynamics model of the multi-axle special vehicle is further established, and a rollover evaluation index LTR is established for the rollover dynamics model;

[0010] Step 3: Establish an optimization method for the LTR transient value and warning threshold in step 2.

[0011] Preferably, the step 1 comprises:

[0012] Step 1.1: Create a multi-axle special vehicle dynamics model in Trucksim, including the vehicle body, tire system, suspension system, steering system, powertrain system, and braking system.

[0013] Step 1.2: Establish a test system to collect displacement, velocity, acceleration, angular velocity, coordinates of the road surface, slope change, and altitude data of the multi-axis special vehicle dynamics model, and analyze and process the collected data;

[0014] Step 1.3: Select a driving trajectory from the actual vehicle experiment and build the same road spectrum information as that of the trajectory in Trucksim in step 1, including the road slope change, turning radius, and friction coefficient. Set the vehicle speed to the same speed as the actual vehicle running speed for simulation testing.

[0015] Preferably, the step 2 is specifically as follows:

[0016] Step 2.1: Based on the multi-axle special vehicle dynamics model established in step 1, further establish a rollover dynamics model of the multi-axle special vehicle;

[0017] Step 2.2: Establish a rollover evaluation index LTR based on the vehicle mass for the rollover dynamics model in step 2.1.

[0018] Preferably, the step 2.1 is specifically as follows:

[0019] Step 2.1.1: Establish a rollover dynamics model for a multi-axle special vehicle;

[0020] Step 2.1.2: Based on step 2.1.1, establish the balance equations for roll moment, yaw moment, and lateral force:

[0021] Rolling moment balance equation:

[0022]

[0023] Yaw moment balance equation:

[0024]

[0025] Lateral force balance equation:

[0026]

[0027] Where m is the vehicle mass, m s is the sprung mass, m u is the unsprung mass, J zz and J xx are the vehicle's moments of inertia on the z-axis and x-axis, φ is the vehicle's roll angle, is the body roll angular velocity, is the body roll acceleration, ω z is the vehicle's yaw rate, is the yaw angular acceleration, K φ is the equivalent body roll stiffness, C φ is the equivalent body roll damping, a y with a ys are the lateral accelerations of the vehicle and sprung mass, h x is the distance from the center of gravity of the sprung mass to the roll center, F yi (i=1,2,3,4,5) is the lateral force of the first to fifth axles, l i (i=1,2,3,4,5) is the distance from the center of the axle of the first to fifth bridges to the center of mass.

[0028] Preferably, the step 2.2 is specifically as follows:

[0029] The rollover evaluation index LTR based on vehicle mass is:

[0030]

[0031] Where B is the wheelbase on both sides, and H is the height from the center of gravity of the sprung mass to the ground.

[0032] Preferably, the optimization of the LTR transient value in step 3 is specifically as follows:

[0033] The lateral acceleration of the vehicle's sprung mass is written as:

[0034]

[0035] Then the roll moment equilibrium equation of the sprung mass at the roll center is:

[0036]

[0037] The roll moment equilibrium equation for the sprung mass at the center of the contact point between the wheels and the ground is:

[0038]

[0039] Combining equations (1), (6) and (7), we get the optimized:

[0040]

[0041] is the derivative of the vehicle's lateral velocity, v x is the longitudinal speed, ω z is the yaw angular velocity, m u is the unsprung mass, F ZR is the sum of the vertical loads on all wheels on the left side of the vehicle, F ZL is the sum of the vertical loads on all wheels on the right side;

[0042] Finally, build the fishhook steering condition for LTR in Trucksim o The calculation accuracy is verified.

[0043] Preferably, the LTR warning threshold LTR in step 3 t The optimization is as follows:

[0044] Using the constructed fishhook steering condition, simulation tests were conducted under different friction coefficients and vehicle speeds. The road friction coefficient increased from 0.1 to 1 in increments of 0.05, and the vehicle speed increased from 50 km / h to the maximum required speed of 100 km / h in increments of 10 km / h. 114 sets of LTR values were obtained for different road friction coefficients and vehicle speeds. For test conditions where the vehicle would roll over, the LTR value 0.1 seconds before |LTR| first reached 1 was defined as the LTR warning threshold. For conditions where the vehicle would not roll over, the LTR warning threshold was defined as 1. The results are shown in Table 1:

[0045] Table 1 LTR warning thresholds under different friction coefficients and vehicle speeds

[0046]

[0047] The data in Table 1 are fitted with polynomials to obtain the warning threshold LTR: t Relationship with vehicle speed and friction coefficient:

[0048]

[0049] f(i,j)=a0+a1i+a2j+a3i 2 +a4j+a5j 2 +a6i 3 +a7i 2 j+a8ij 2 +a9i 4 +a 10 i 3 j+a 11 i 2 j 2 (10)

[0050] Where i represents the friction coefficient of the road surface, j represents the speed of the vehicle, a0=7.2098, a1=-2.6168, a2=-0.13157, a3=-6.22118, a4=0.11313, a5=3.4052e-4, a6=-3.28875, a7=0.12287, a8=-4.66202e-4, a9=6.32616, a 10 =-0.11945, a 11 =1.98969e-4.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] (1) Through the model established by the present invention and actual vehicle testing, it can be seen that the unsprung mass roll moment has a significant impact on the calculation accuracy of the rollover evaluation index. The greater the unsprung mass, the greater the impact. Considering the effects of the sprung mass and unsprung mass roll moment on the wheel vertical load difference can effectively improve the calculation accuracy of the LTR. Simulation analysis shows that the calculation accuracy of the LTR after the present invention is improved by approximately 6.3% and 12.4% compared to the commonly used LTR and ZMP methods, respectively.

[0053] (2) The present invention establishes the relationship between the vehicle rollover warning threshold, vehicle speed and road adhesion coefficient by polynomial fitting, LTR tIt can automatically adjust according to the changes in vehicle speed and road environment. Compared with the warning threshold when it is a fixed value, LTR t The warning can reserve more reaction time for the vehicle's anti-rollover control.

[0054] (3) The optimized rollover evaluation index of the present invention is not only applicable to tripping rollovers, but also to non-tripping rollovers. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0056] In the attached figure:

[0057] Figure 1 Integrate models for the entire vehicle;

[0058] Figure 2 This is the experimental test system diagram;

[0059] Figure 3 This is a comparison chart of road spectrum information;

[0060] Figure 4 This is a comparison diagram of the motion state of the real car and the dynamic model;

[0061] Figure 5 Rollover dynamics model for five-axle special vehicles;

[0062] Figure 6 This is the comparison result under the steering wheel angle step input condition;

[0063] Figure 7 This is the comparison result under the steering wheel sinusoidal input condition;

[0064] Figure 8 shows the comparison results of the hook steering working conditions;

[0065] Figure 9 To verify the comparison results in real vehicle experiments;

[0066] Figure 10 Comparison of warning time under different warning thresholds;

[0067] Figure 11 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0068] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0069] Example:

[0070] Refer to the attached Figure 1-10 As shown, a rollover index optimization method for a multi-axle special vehicle includes:

[0071] Step 1: Establish a multi-axle special vehicle dynamics model and test the established multi-axle vehicle dynamics model; including:

[0072] Step 1.1: In this embodiment, a five-axle, horizontal cab trailer special vehicle dynamics model is established in Trucksim, including the body, tire system, suspension system, steering system, power transmission system and braking system. The vehicle type is TS 5ATractor (SS_SSS), as shown in the following example: Figure 1 shown.

[0073] Step 1.2: To verify the accuracy of the established model, the model is compared with the actual vehicle motion state under the same working conditions to determine whether the established model is accurate.

[0074] Establish as Figure 2 The test system shown in the figure consists of a PC, a triaxial accelerometer, a single-antenna sensor, a pedal force sensor, a low-speed data acquisition system, and a dewesoft data acquisition system. During the experiment, the triaxial accelerometers were attached to the double wishbones and the vehicle frame of each axle. The single-antenna sensor was placed at the vehicle's center of mass, and the pedal force sensor was tied to the accelerator pedal with a cable harness. The dewesoft data acquisition system was powered by a 220V mobile power supply, and a 12V DC power supply was used to power the low-speed data acquisition system. After calibrating the sensitivity of each sensor, the system can collect real-time information on the vehicle's displacement, velocity, acceleration, angular velocity, and other motion states, as well as road coordinates, slope changes, and altitude. Dewesoft and Race_Technology software were used to record, analyze, and process the data.

[0075] Step 1.3: Select a driving track of the actual vehicle experiment and build the same road spectrum information as that of the track in Trucksim in step 1, including the road slope change, turning radius, and road surface with the same friction coefficient. The driving tracks of the actual vehicle and the model vehicle, and the road slope change are as follows: Figure 3 As shown in the figure, the vehicle speed is set to the same as the actual vehicle speed during operation for simulation test. The motion state of the vehicle model is compared with that of the actual vehicle test. The comparison results are shown in the figure. Figure 4 shown.

[0076] The comparison results in Figure 8 show that under the same driving conditions, the Trucksim special vehicle dynamics model is highly consistent with the motion state of the actual vehicle, indicating that the constructed special vehicle dynamics model can be used for simulation analysis of vehicle lateral stability.

[0077] Step 2: Based on the multi-axle special vehicle dynamics model established in step 1, further establish the rollover dynamics model of the multi-axle special vehicle, and establish the rollover evaluation index LTR for the rollover dynamics model. Specifically including:

[0078] Step 2.1: Based on the multi-axle special vehicle dynamics model established in step 1, further establish the rollover dynamics model of the multi-axle special vehicle; specifically:

[0079] Step 2.1.1: Figure 5 Establish a rollover dynamics model for multi-axle special vehicles;

[0080] Step 2.1.2: Based on step 2.1.1, establish the balance equations for roll moment, yaw moment, and lateral force:

[0081] Rolling moment balance equation:

[0082]

[0083] Yaw moment balance equation:

[0084]

[0085] Lateral force balance equation:

[0086]

[0087] Where g is the acceleration due to gravity, m is the mass of the vehicle, and m s is the sprung mass, m u is the unsprung mass, J zz and J xx are the vehicle's moments of inertia on the z-axis and x-axis, φ is the vehicle's roll angle, is the body roll angular velocity, is the roll angular acceleration, is the vehicle's yaw rate, K φ is the equivalent body roll stiffness, C φ is the equivalent body roll damping, a y with a ys are the lateral accelerations of the vehicle and sprung mass, h x is the distance from the center of gravity of the sprung mass to the roll center, F yi (i=1,2,3,4,5) is the lateral force of the first to fifth axles, l i (i=1,2,3,4,5) is the distance from the center of the axle of the first to fifth bridges to the center of mass.

[0088] Step 2.2: Establish a rollover evaluation index LTR based on the vehicle mass for the rollover dynamics model in step 2.1.

[0089] The LTR is defined as follows:

[0090]

[0091] F ZR is the sum of the vertical loads on all wheels on the left side of the vehicle, F ZL is the sum of the vertical loads on all right wheels. As can be seen from the above formula, the range of LTR is [-1 1], and the closer its value is to 1, the greater the risk of vehicle rollover.

[0092] Since the vertical load of a vehicle is difficult to measure, the calculation of LTR is usually based on the vehicle rollover dynamics model, which is converted into some vehicle motion states that are easier to measure, such as the vehicle's lateral acceleration, body roll angle, and roll angular velocity. The rollover evaluation index LTR based on the vehicle mass is then:

[0093]

[0094] Where B is the wheelbase on both sides, and H is the height from the center of gravity of the sprung mass to the ground.

[0095] ZMP can also be used as a rollover evaluation index for vehicles, as shown in the following expression:

[0096]

[0097] Where G represents the center of gravity of the sprung mass.

[0098] Transforming the above equation, we can get

[0099]

[0100] Multiply both sides of the above equation have:

[0101]

[0102] Ignoring the influence of yaw angular acceleration, the roll moment balance of the vehicle can be expressed as:

[0103]

[0104] Combining formula (13) and formula (14), we can get

[0105]

[0106] Where: a yG Indicates the lateral acceleration at the center of gravity of the sprung mass.

[0107] By transforming the above formula, we can get:

[0108]

[0109] It can be seen that the value range of ZMP is also [-1 1]. The closer |ZMP| is to 1, the higher the risk of vehicle rollover.

[0110] As can be seen from Equations (4) and (16), the denominators of LTR and ZMP represent the weight of the vehicle and the sprung mass, respectively. In other words, the two evaluation indicators use the vehicle and sprung mass as research objects to evaluate the vehicle's rollover risk. However, as can be seen from Equations (1) and (14), the numerator of LTR only considers the effect of the sprung mass's inertia on the vertical load difference between the left and right wheels, and the numerator of ZMP only considers the effect of the sprung mass's inertia on the lateral force of the wheels on both sides. Neither considers the effect of the unsprung mass's inertia. Obviously, for some vehicles where the unsprung mass accounts for a large proportion of the vehicle's total mass, the calculation of both evaluation indicators is not accurate enough. For LTR, since the numerator only considers the effect of the sprung mass's inertia, while the denominator represents the vehicle's total weight, its value should be smaller than the actual value. For ZMP, since the denominator only represents the sprung mass, its value should be larger than the actual value.

[0111] Step 3: Establish an optimization method for the LTR transient value and warning threshold in step 2.

[0112] Based on the comparative analysis in step 2, it can be concluded that both LTR and ZMP are inaccurate in assessing the risk of vehicle rollover. In particular, the deviation increases as the proportion of unsprung mass in the vehicle increases. Therefore, further optimization of the accuracy of the rollover evaluation index is necessary. This embodiment comprehensively considers the impact of the sprung mass and unsprung mass moment of inertia on the vertical wheel load difference and optimizes the calculation of LTR. The optimization of the LTR transient value is specifically as follows:

[0113] Depend on Figure 5 , the lateral acceleration of the vehicle's sprung mass is written as:

[0114]

[0115] Then the roll moment equilibrium equation of the sprung mass at the roll center is:

[0116]

[0117] The roll moment equilibrium equation for the sprung mass at the center of the contact point between the wheels and the ground is:

[0118]

[0119] Combining equations (1), (6) and (7), we get the optimized:

[0120]

[0121] is the derivative of the vehicle's lateral velocity, v x is the longitudinal speed, ω z is the yaw angular velocity, m u is the unsprung mass, F ZR is the sum of the vertical loads on all wheels on the left side of the vehicle, F ZL is the sum of the vertical loads on all wheels on the right side.

[0122] To compare LTR o Compared with the commonly used LTR and ZMP evaluation indicators, Trucksim was used to build two working conditions: angle step input and sine angle input. The road adhesion coefficient was set to 0.85 and the simulation speed was set to 80km / h. The three evaluation indicators were co-simulated with the Simulink calculation model. The comparison results of the three evaluation indicators under the two working conditions are shown in the figure. Figure 6 and Figure 7 shown.

[0123] Figure 6 The comparison results show that under the same steering wheel angle step input, the calculated values of the three evaluation indicators have certain deviations. The steady-state value of ZMP is about 0.69, and LTR o The steady-state value of is about 0.65, and the steady-state value of LTR is about 0.57. Figure 7 The comparison results show that under the same amplitude of the steering wheel sinusoidal angle input, the deviations between the three evaluation indicators are the same as the test results under the angle step input condition, and the amplitude of ZMP is about 2.3 times that of LTR. o The amplitude of LTR is about 6.3% larger than that of LTR. o The amplitude is 12.4% smaller. This simulation conclusion is consistent with the analysis conclusion of step 2.2.

[0124] Finally, to verify LTR o The calculation accuracy of LTR is verified based on the built Trucksim dynamic model and real vehicle experimental data. oThe idea for verifying the accuracy is to build a typical stability test condition in Trucksim - the fishhook turn condition. Under this condition, the vehicle's motion state and the vertical loads on both wheels are obtained. The actual LTR of the model is calculated from the vertical loads on both wheels. Three evaluation indicators are calculated based on the vehicle's motion state. The three evaluation indicators are compared with the actual LTR. The comparison results of some vehicle motion states and evaluation indicators are shown in Figure 8. LTR is verified by real vehicle experiments o The idea of precision is to install the data acquisition equipment used in the model verification at various positions of the vehicle in sequence, drive the vehicle along a predetermined S-shaped curve at a certain speed, obtain the vehicle's motion state, calculate three evaluation indicators, and use the dual integral sensor installed on the suspension to obtain the deformation of the suspension on both sides of the vehicle. The vertical load of the wheel is calculated based on the suspension deformation and suspension stiffness, and thus the actual lateral load transfer rate during the actual vehicle movement is calculated. The comparison results of the vehicle's partial motion state with the three evaluation indicators and the actual lateral load transfer rate are shown as follows: Figure 9 shown.

[0125] The comparison results in Figure 8 show that under the same hook turning conditions, the curves of the three evaluation indicators are basically the same as the actual LTR curve. The steady-state value of the commonly used LTR is about -0.57, the steady-state value of ZMP is about -0.74, and the LTR o The steady-state value of the LTR is very close to that of the actual LTR, both around -0.65. In the comparison of the evaluation indicators, the actual LTR oscillates significantly from 1.6s to 2.8s and from 3.3s to 6.5s. This difference from the other three evaluation indicators is due to the fact that in the TruckSim vehicle, only the right and left wheels of axles 4 and 5 are out of contact with the ground from 1.6s to 2.8s and from 3.3s to 6.5s, respectively, and no rollover occurs. At these times, the vehicle exhibits strong nonlinearity. The three evaluation indicators are calculated based on the vehicle's lateral acceleration, roll angle, and roll angular velocity, which weakens the representation of this nonlinearity. However, on the other hand, this better reflects the actual rollover risk of the vehicle. In reality, the wheels on all axles on the same side of the vehicle do not roll over simultaneously. In other words, the wheels on one or more axles on one side of the vehicle breaking contact with the ground does not necessarily mean that the vehicle will roll over.

[0126] Figure 9 The comparison results are the same as those in Figure 8. o Compared to the actual LTR from real-vehicle testing, the calculated values are more accurate, with the calculated ZMP values slightly larger and the LTR values slightly smaller. The data in the figure shows that the ZMP amplitude is approximately 22% larger than the actual LTR amplitude, while the LTR amplitude is approximately 15% smaller.

[0127] Based on the above analysis, it can be concluded that considering the influence of the rolling moment of the sprung mass and the unsprung mass on the LTR can effectively improve the calculation accuracy of the LTR and make the evaluation of vehicle rollover more accurate.

[0128] LTR warning threshold LTR t The optimization is as follows:

[0129] At present, the threshold values of indicators used for vehicle rollover warning control are mostly set to a certain value, without considering the impact of changes in the road environment. As a result, the rollover evaluation indicators are often only applicable to non-stumbling rollovers and have poor applicability to stumbling rollovers. Whether the driver performs anti-rollover control himself or the active anti-rollover control system performs anti-rollover control, a certain reaction time is required when receiving the warning signal of vehicle rollover. In the case of high vehicle speed and good road adhesion coefficient, if the vehicle is still subject to anti-rollover control according to a fixed warning value, the anti-rollover control may fail due to insufficient reaction time. Based on this, the present invention considers the impact of vehicle speed and road input on the LTR threshold, and further optimizes the LTR warning threshold through data fitting.

[0130] Using the constructed fishhook steering condition, simulation tests were conducted under different friction coefficients and vehicle speeds. The road friction coefficient increased from 0.1 to 1 in increments of 0.05, and the vehicle speed increased from 50 km / h to the maximum required speed of 100 km / h in increments of 10 km / h. 114 sets of LTR values were obtained for different road friction coefficients and vehicle speeds. For test conditions where the vehicle would roll over, the LTR value 0.1 seconds before |LTR| first reached 1 was defined as the LTR warning threshold. For conditions where the vehicle would not roll over, the LTR warning threshold was defined as 1. The results are shown in Table 1:

[0131] Table 1 LTR warning thresholds under different friction coefficients and vehicle speeds

[0132]

[0133] The data in Table 1 are fitted with polynomials to obtain the warning threshold LTR: t Relationship with vehicle speed and friction coefficient:

[0134]

[0135] f(i,j)=a0+a1i+a2j+a3i 2 +a4j+a5j 2 +a6i 3 +a7i 2 j+a8ij 2+a9i 4 +a 10 i 3 j+a 11 i 2 j 2 (10)

[0136] Where i represents the friction coefficient of the road surface, j represents the speed of the vehicle, a0=7.2098, a1=-2.6168, a2=-0.13157, a3=-6.22118, a4=0.11313, a5=3.4052e-4, a6=-3.28875, a7=0.12287, a8=-4.66202e-4, a9=6.32616, a 10 =-0.11945, a 11 =1.98969e-4.

[0137] Simulation results show that the main impact on the warning threshold is the high adhesion coefficient working condition. Therefore, three groups of working conditions with different vehicle speeds and high adhesion coefficients were designed to verify the optimization effect of the warning threshold. The road adhesion coefficient and vehicle speed of the three working conditions are 0.9 and 75km / h, 0.9 and 85km / h, and 0.95 and 75km / h respectively. The warning thresholds under the three working conditions were calculated based on the optimized warning threshold calculation method, and the warning thresholds and the LTR under the corresponding working conditions were defined. o The time when the curves intersect is the warning time. The warning time when the warning threshold is set to a fixed value is T 01 、T 02 、T 03 , the warning threshold is LTR o The warning time is T1, T2, and T3. The warning threshold is set to 0.85, which is commonly used. The new evaluation index curve (LTR) under three working conditions o ) and the warning threshold curve (LTR t )like Figure 10 The comparison results of warning threshold and warning time before and after optimization are shown in Table 2 and Table 3 respectively.

[0138] Table 2 Comparison of warning thresholds

[0139] Warning threshold i=0.9,j=75km / h i=0.9,j=85km / h i=0.95,j=75km / h Before optimization 0.85 0.85 0.85 After optimization 0.732 0.691 0.705

[0140] Table 3 Comparison results of warning time

[0141] Warning time i=0.9,j=75km / h i=0.9,j=85km / h i=0.95,j=75km / h Before optimization <h2 style=";text-align:left;direction:ltr"><![CDATA[T <h2 style=";text-align:left;direction:ltr"> 01 <h2 style=";text-align:left;direction:ltr"> <1.23s]]><h2 style=";text-align:left;direction:ltr"> <![CDATA[T 02 =1.15s]]> <![CDATA[T 03 =1.22s]]> After optimization <![CDATA[T1=1.08s]]> <![CDATA[T2=0.85s]]> <![CDATA[T3=1.05s]]>

[0142] Figure 10The results from Tables 2 and 3 indicate that the road adhesion coefficient has a greater impact on the warning threshold, while vehicle speed has a smaller impact. The optimized warning threshold allows for more reaction time for the vehicle's rollover prevention control. For the three predefined operating conditions, under the same vehicle speed and road adhesion coefficient, the optimized warning thresholds were reduced by 0.118, 0.159, and 0.145, respectively, compared to the fixed warning thresholds. Compared to the warning times under the fixed thresholds, the optimized warning thresholds were shortened by 0.15s, 0.3s, and 0.17s, respectively. The optimized warning thresholds can adaptively adjust to changes in vehicle speed and road conditions, making the rollover indicator applicable not only to trip-induced rollovers but also to non-trip-induced rollovers.

[0143] in conclusion:

[0144] This paper establishes a dynamic model of a heavy-duty special vehicle using the parametric modeling software Trucksim. Actual vehicle experiments verify the model's accuracy and reliability. A dynamic analysis of the vehicle's rollover evaluation indicators based on the rollover dynamics model is also conducted. The optimization of the LTR evaluation indicator and LTR warning threshold is explored, resulting in the following conclusions:

[0145] (1) The unsprung mass roll moment has a significant impact on the calculation accuracy of the rollover evaluation index. The larger the unsprung mass, the greater the impact. Considering the influence of the sprung mass and unsprung mass roll moment on the wheel vertical load difference can effectively improve the calculation accuracy of LTR. Simulation analysis shows that the calculation accuracy of the optimized LTR is improved by about 6.3% and 12.4% compared with the commonly used LTR and ZMP methods, respectively.

[0146] (2) The relationship between the vehicle rollover warning threshold, vehicle speed, and road adhesion coefficient was established by polynomial fitting. LTRt can automatically adjust according to changes in vehicle speed and road environment. Compared with the warning when the warning threshold is a fixed value, the warning under LTRt can reserve more reaction time for the vehicle's anti-rollover control.

[0147] (3) The optimized rollover evaluation index is not only applicable to tripping rollovers, but also to non-tripping rollovers.

[0148] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the rollover index of a multi-axle special vehicle, characterized by: include: Step 1: Establish a multi-axle special vehicle dynamics model and test the established multi-axle vehicle dynamics model; Step 2: Based on the multi-axle special vehicle dynamics model established in step 1, a rollover dynamics model of the multi-axle special vehicle is further established, and a rollover evaluation index LTR is established for the rollover dynamics model; The rollover evaluation index LTR based on vehicle mass is: Where g is the acceleration due to gravity, B is the wheelbase of the wheels on both sides, H is the height from the center of gravity of the sprung mass to the ground, m is the mass of the vehicle, and m s is the sprung mass, φ is the body roll angle, is the vehicle body roll angular velocity, K φ is the equivalent body roll stiffness, C φ is the equivalent body roll damping, a ys Lateral acceleration of sprung mass, h x is the distance from the center of gravity of the sprung mass to the roll center; Step 3: Establish an optimization method for the LTR transient value and warning threshold in step 2; LTR warning threshold LTR t The optimization is as follows: Using the constructed fishhook steering condition, simulation tests were conducted under different friction coefficients and vehicle speeds. The road friction coefficient increased from 0.1 to 1 in increments of 0.05, and the vehicle speed increased from 50 km / h to the maximum required speed of 100 km / h in increments of 10 km / h. 114 sets of LTR values under different road friction coefficients and vehicle speeds were obtained. For the test condition where the vehicle would roll over, the LTR value 0.1 seconds before |LTR| first reached 1 was defined as the LTR warning threshold. For the condition where the vehicle would not roll over, the LTR warning threshold was defined as 1. The results are shown in Table 1: Table 1 LTR warning thresholds under different friction coefficients and vehicle speeds The data in Table 1 are fitted with polynomials to obtain the warning threshold LTR: t Relationship with vehicle speed and friction coefficient: <h2 style=";text-align:left;direction:ltr">f(i,j) = a0 + a1i + a2j + a3i<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +a4j+a5j<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr">+a6i<h2 style=";text-align:left;direction:ltr"> 3 <h2 style=";text-align:left;direction:ltr"> +a7i<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> j+a8ij<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +a9i<h2 style=";text-align:left;direction:ltr"> 4 <h2 style=";text-align:left;direction:ltr"> +a<h2 style=";text-align:left;direction:ltr"> 10 <h2 style=";text-align:left;direction:ltr"> i<h2 style=";text-align:left;direction:ltr"> 3 <h2 style=";text-align:left;direction:ltr"> j+a<h2 style=";text-align:left;direction:ltr"> 11 <h2 style=";text-align:left;direction:ltr"> i<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> j<h2 style=";text-align:left;direction:ltr"> 2 Where i represents the friction coefficient of the road surface, j represents the speed of the vehicle, a0=7.2098, a1=-2.6168, a2=-0.13157, a3=-6.22118, a4=0.11313, a5=3.4052e-4, <h2 style=";text-align:left;direction:ltr">a6 = 3.28875, a7 = 0.12287, a8 = 4.66202e-4, a9 = 6.32616, a<h2 style=";text-align:left;direction:ltr"> 10 <h2 style=";text-align:left;direction:ltr"> =-0.11945, <h2 style=";text-align:left;direction:ltr">a<h2 style=";text-align:left;direction:ltr"> 11 <h2 style=";text-align:left;direction:ltr"> <1.98969e-4.

2. The method for optimizing the rollover index of a multi-axle special vehicle according to claim 1, characterized in that: The step 1 comprises: Step 1.1: Create a multi-axle special vehicle dynamics model in Trucksim, including the vehicle body, tire system, suspension system, steering system, powertrain system, and braking system. Step 1.2: Establish a test system to collect displacement, velocity, acceleration, angular velocity, coordinates of the road surface, slope change, and altitude data of the multi-axis special vehicle dynamics model, and analyze and process the collected data; Step 1.3: Select a driving trajectory from the actual vehicle experiment and build the same road spectrum information as that of the trajectory in Trucksim in step 1, including the road slope change, turning radius, and friction coefficient. Set the vehicle speed to the same speed as the actual vehicle running speed for simulation testing.

3. The method for optimizing the rollover index of a multi-axle special vehicle according to claim 2, characterized in that: The step 2 is specifically as follows: Step 2.1: Based on the multi-axle special vehicle dynamics model established in step 1, further establish a rollover dynamics model of the multi-axle special vehicle; Step 2.2: Establish a rollover evaluation index LTR based on the vehicle mass for the rollover dynamics model in step 2.

1.

4. The method for optimizing the rollover index of a multi-axle special vehicle according to claim 3, characterized in that: The step 2.1 is specifically as follows: Step 2.1.1: Establish a rollover dynamics model for a multi-axle special vehicle; Step 2.1.2: Based on step 2.1.1, establish the balance equations for roll moment, yaw moment, and lateral force: Rolling moment balance equation: Yaw moment balance equation: Lateral force balance equation: Where m u is the unsprung mass, J zz and J xx are the vehicle's moment of inertia on the z-axis and x-axis, is the body roll acceleration, ω z is the vehicle's yaw rate, is the yaw angular acceleration, a y is the lateral acceleration of the vehicle, F yi is the lateral force of the first to fifth axles, where i = 1, 2, 3, 4, 5; l i is the distance from the center of the axle of the first to fifth bridges to the center of mass, where i = 1, 2, 3, 4, 5.

5. The method for optimizing the rollover index of a multi-axle special vehicle according to claim 4, characterized in that: The optimization of the LTR transient value in step 3 is specifically as follows: The lateral acceleration of the vehicle's sprung mass is written as: Then the roll moment equilibrium equation of the sprung mass at the roll center is: The roll moment equilibrium equation for the sprung mass at the center of the contact point between the wheels and the ground is: Combining equations (1), (6) and (7), we get the optimized: in, is the derivative of the vehicle's lateral velocity, v x is the longitudinal speed, ω z is the yaw angular velocity, m u is the unsprung mass, F ZR is the sum of the vertical loads on all wheels on the left side of the vehicle, F ZL is the sum of the vertical loads on all wheels on the right side; Finally, build the fishhook steering condition for LTR in Trucksim o The calculation accuracy is verified.

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