Driving risk assessment method based on coupled vibration model

Abstract

The invention discloses a driving risk assessment method based on a coupled vibration model, and the method comprises the steps: S1, obtaining a vehicle dynamic parameter and a geometric dimension parameter, and building a vehicle dynamic model based on the vehicle dynamic parameter and the geometric dimension parameter by employing finite element analysis software or directly calculating a dynamic differential equation set; s2, acquiring bridge vibration excitation, roughness excitation and crosswind excitation; s3, inputting the bridge vibration excitation, the roughness excitation and the crosswind excitation into the vehicle model for coupled vibration analysis, and obtaining vehicle vibration response data; and S4, based on the vehicle vibration response data, obtaining a driving safety index and a comfort index, and performing risk assessment according to preset threshold values of safety and comfort. According to the method, multi-dimensional factors such as the wind environment, the vehicle dynamic response, the bridge structure vibration characteristic and the driving comfort degree are considered, and a high-precision and high-reliability risk assessment basis can be provided for a bridge operation unit.

Description

Technical Field

[0001] The present invention belongs to the field of bridge traffic safety management and control, and in particular relates to a traffic risk assessment method based on a coupled vibration model. Background Art

[0002] As modern bridges extend their spans, their damping and stiffness decrease. Their dynamic response increases as they face a variety of external loads. In fact, the larger the span, the more critical the design decision becomes regarding wind resistance. Bridges in sea-crossing areas, in particular, experience higher base wind speeds than inland areas due to the lower surface roughness at sea level. They are also frequently affected by unusual winds, such as typhoons, making them susceptible to wind-induced bridge vibrations, such as vortex vibration and buffeting. This results in a less favorable wind environment for sea-crossing bridges.

[0003] The safety issues of vehicles traveling on bridges are more prominent than those on roads. The reasons why the safety issues of driving are more prominent are: (1) Bridges are usually high above the ground, and the wind speed conversion follows a power exponential law, with the wind speed on the bridge deck being greater than the wind speed on the road surface. (2) At the towers of long-span bridges, the wind shielding effect causes the wind environment parameters there to change dramatically, and the wind load acting on the vehicle also changes accordingly. Such rapidly changing loads will have a certain impact on driving. (3) Long-span bridges are flexible structures and will vibrate when driving and when there is wind, causing the vehicle to be in a constant state of vibration.

[0004] Based on calculations of bridge wind resistance requirements during design, wind-induced bridge vibrations, such as buffeting and vortex vibration, rarely damage the bridge structure. However, they can increase the difficulty of controlling vehicles on the bridge, cause severe vibrations, and affect the comfort and subjective psychological experience of occupants. In extreme cases, these conditions can induce lateral deviation, side sliding, or even rollovers in vehicles traveling on the bridge. In existing traffic management systems, speed limits based on real-time wind speeds are commonly used to ensure driving safety. However, current wind speed limit standards are largely determined empirically and lack effective scientific experimental evidence. Summary of the Invention

[0005] This paper proposes a driving risk assessment method based on a coupled vibration model, which is suitable for driving safety warning and control of large-span bridges in canyon areas under strong wind environments. Through multi-excitation coupling analysis, it realizes the quantitative assessment of driving safety in strong / unique wind environments in canyon areas.

[0006] The present invention is achieved through the following technical solutions:

[0007] A driving risk assessment method based on a coupled vibration model includes:

[0008] S1. Obtaining vehicle dynamic parameters and geometric dimension parameters, and establishing a vehicle dynamics model based on the vehicle dynamic parameters and geometric dimension parameters using finite element analysis software or directly calculating a dynamic differential equation system;

[0009] S2. Obtain bridge vibration excitation, roughness excitation and crosswind excitation;

[0010] S3, inputting the bridge vibration excitation, roughness excitation, and crosswind excitation into the vehicle model to perform coupled vibration analysis and obtain vehicle vibration response data;

[0011] S4. Based on the vehicle vibration response data, a driving safety index and a comfort index are obtained, and a risk assessment is performed according to preset thresholds of safety and comfort.

[0012] Optionally, in S1, the vehicle dynamic parameters include: the mass of the vehicle body, the mass of the wheels, the moment of inertia of the vehicle body around three axes, and the vertical stiffness and damping of the primary and secondary suspensions of the vehicle;

[0013] The geometrical dimension parameters include: the position of the center of mass of the vehicle body relative to the front and rear axles, the driver's position and the ground, the lateral spacing between the wheels and the windward area.

[0014] Optionally, in S2, obtaining bridge vibration excitation includes:

[0015] Given the main beam excitation wind speed time history and bridge aerodynamic parameters;

[0016] Time domain calculation of bridge buffeting force and self-excited force or vortex-excited force load;

[0017] Iteratively calculate the full-bridge amplitude-displacement time history. If a health monitoring system exists for the bridge, the full-bridge amplitude-displacement time history can also be directly obtained using the health monitoring system.

[0018] Obtaining real-time bridge vibration excitation displacement at the vehicle's driving position;

[0019] The bridge vibration excitation is obtained based on the given main beam excitation wind speed time history, bridge aerodynamic parameters, excitation load, full bridge amplitude displacement time history and bridge vibration excitation displacement.

[0020] Optionally, in S2, obtaining the roughness stimulus includes:

[0021] Obtain the power spectral density function of road surface roughness;

[0022] The roughness excitation is obtained based on the trigonometric series method and combined with the power spectral density function.

[0023] Optionally, in S2, obtaining the side wind excitation includes:

[0024] Calculate the average crosswind speed and downwind turbulence based on the measured data;

[0025] Obtaining the integral scale of the downwind turbulence and generating a downwind fluctuating wind speed power spectrum;

[0026] Harmonic synthesis method is used to generate wind speed time history;

[0027] Calculate wind angle and aerodynamic coefficients;

[0028] The crosswind excitation is obtained based on the crosswind average wind speed, the power spectrum of the fluctuating wind speed in the downwind direction, the wind speed time history, the wind deflection angle and the aerodynamic force coefficient.

[0029] Optionally, in S3, obtaining vehicle vibration data includes:

[0030] Establish the state space equation of the vehicle-bridge-wind field coupled vibration field;

[0031] The state space equation is solved by a numerical integration method to obtain vehicle vibration data.

[0032] Optionally, in S4, obtaining a driving comfort index includes:

[0033] A driving comfort index based on the weighted total value of whole-body vibration.

[0034] Driving comfort index based on the probability of driver motion sickness.

[0035] Driving comfort index based on vehicle vibration acceleration.

[0036] Optionally, in step S4, obtaining a driving safety index includes:

[0037] Extract the contact force between the left and right wheels and the ground;

[0038] Based on the contact force, the vehicle rollover stability coefficient, that is, the driving safety index, is obtained.

[0039] Compared with the prior art, the present invention has the following advantages and technical effects:

[0040] The present invention discloses a driving risk assessment method based on a coupled vibration model. This method utilizes finite element simulation software to establish an accurate coupled vibration model and import the dynamic parameters and collective dimensional parameters of the vehicle and bridge, as well as vibration excitation, roughness excitation, and crosswind excitation, ensuring the accuracy of the simulation process. The wind-vehicle-bridge coupled vibration characteristics vary significantly under different wind conditions and driving states. By correcting the aerodynamic coefficients based on measured results, the method accurately reflects the dynamic changes in the crosswind field and simulates the aerodynamic loads on the vehicle. Driving safety is assessed through wheel loads. Since there is no unified evaluation standard for driving comfort during vehicle operation, the method calculates the weighted root mean square value based on the acceleration of each degree of freedom at the driver's position, obtains the total whole-body vibration value and the probability of motion sickness, and evaluates driving comfort from two perspectives. Driving safety is assessed from two dimensions: the driver's subjective perception and the vehicle dynamics.

[0041] The driving risk assessment method based on the coupled vibration model has the advantages of high-precision simulation and multi-parameter safety assessment in analyzing the driving process of vehicles in crosswind environments in canyon areas. By dynamically updating the coupled response relationship between vehicles, bridges and wind farms, the model can predict the vehicle response status under different wind speeds, vehicle speeds and load conditions, and provide early warnings of possible dangerous situations. During the bridge design phase, it can provide quantitative risk assessment indicators for road traffic management departments to optimize bridge speed limit strategies and the formulation of emergency response mechanisms. In actual bridge operations, a digital twin model of driver and passenger driving safety and comfort can be established by combining the real-time wind speed and bridge vibration status obtained by the health monitoring system, and the driving risk level in the current traffic environment can be assessed in real time.

[0042] In summary, the present invention takes into account multiple factors such as wind environment, vehicle dynamic response, bridge structure vibration characteristics, and driving comfort, and can provide bridge operating units with a high-precision and high-reliability risk assessment basis. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0044] Figure 1 This is a flow chart of a driving risk assessment method based on a coupled vibration model according to an embodiment of the present invention;

[0045] Figure 2 is a side view of a vehicle model according to an embodiment of the present invention;

[0046] Figure 3 is a front view of a vehicle model according to an embodiment of the present invention;

[0047] Figure 4 Schematic diagram of mid-span vertical displacement under simple harmonic load according to an embodiment of the present invention;

[0048] Figure 5 1 is a schematic diagram of vertical displacement at a moving load under first-order positive symmetrical vertical bending vibration according to an embodiment of the present invention;

[0049] Figure 6 1 is a schematic diagram of R1 roughness according to an embodiment of the present invention;

[0050] Figure 7 2 is a schematic diagram of R2 roughness according to an embodiment of the present invention;

[0051] Figure 8 1 is a comparison chart of the theoretical spectrum and the simulated spectrum of R1 roughness of an embodiment of the present invention;

[0052] Figure 9 is a cross-sectional view of average wind speed on a bridge according to an embodiment of the present invention;

[0053] Figure 10 : is a fitted linear relationship diagram between downwind turbulence and wind speed according to an embodiment of the present invention;

[0054] Figure 11 1 is a wind speed time history diagram of an embodiment of the present invention when the average crosswind speed is 30 m / s;

[0055] Figure 12 This is a comparison diagram between the simulated spectrum and the theoretical spectrum of an embodiment of the present invention with an average crosswind speed of 30 m / s;

[0056] Figure 13 is a graph of aerodynamic parameters according to an embodiment of the present invention;

[0057] Figure 14 is a curve diagram of the vertical acceleration of the vehicle body according to an embodiment of the present invention;

[0058] Figure 15 is a schematic diagram of the degree of freedom of the driver's position according to an embodiment of the present invention;

[0059] Figure 16 is a schematic diagram of a comfort frequency weighting function according to an embodiment of the present invention;

[0060] Figure 17 2 is a schematic diagram of a motion sickness frequency weighting function according to an embodiment of the present invention;

[0061] Figure 18 2. This is a schematic diagram showing how the OVTV value, an indicator of ride comfort, varies with roughness according to an embodiment of the present invention;

[0062] Figure 19 is a schematic diagram showing changes in motion sickness indicators along with bridge vibration and roughness according to an embodiment of the present invention;

[0063] Figure 20 Schematic diagram of the rollover coefficient according to the embodiment of the present invention as it changes with wind speed and roughness. DETAILED DESCRIPTION

[0064] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be fully described below in conjunction with the accompanying drawings of the embodiments of the present invention. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be arranged and designed in various different configurations.

[0065] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0066] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort shall fall within the scope of protection of the present invention.

[0067] The present invention is described in further detail below with reference to the accompanying drawings:

[0068] Example 1:

[0069] like Figure 1 As shown, the present invention discloses a driving risk assessment method based on a coupled vibration model, which specifically includes the following steps:

[0070] S1: Obtain vehicle dynamic parameters and geometric size parameters, and establish a vehicle dynamics model based on the vehicle dynamic parameters and geometric size parameters using finite element analysis software or directly calculating the dynamic differential equations;

[0071] Vehicle dynamic parameters include: the mass of the vehicle body and wheels, the moment of inertia of the vehicle body around the three axes, the vertical stiffness and damping of the primary and secondary suspension of the vehicle;

[0072] Geometric parameters include: the position of the vehicle's center of mass relative to the front and rear axles, the driver's position, and the ground, the lateral spacing between the wheels, and the frontal area;

[0073] S2: Obtain bridge vibration excitation, roughness excitation and crosswind excitation;

[0074] Obtain bridge vibration excitation by: giving the main beam excitation wind speed time history and the bridge's three-force coefficient, flutter derivative and other aerodynamic parameters; calculating the bridge buffeting force and self-excited force or vortex-excited force load; iteratively calculating the full-bridge amplitude displacement time history. If the bridge has a health monitoring system, the full-bridge amplitude displacement time history can also be directly obtained from the health monitoring system; and obtaining the real-time bridge vibration excitation displacement at the vehicle's driving position.

[0075] Obtaining roughness excitation, specifically: obtaining the power spectral density function of road surface roughness; obtaining roughness excitation based on the trigonometric series method;

[0076] Obtain crosswind excitation, specifically: calculate the crosswind average wind speed and downwind turbulence degree based on measured data; obtain the downwind turbulence integral scale; generate the downwind pulsating wind speed power spectrum; use the harmonic synthesis method to generate the wind speed time history; calculate the wind deflection angle and aerodynamic coefficient; calculate the crosswind excitation.

[0077] S3: Input bridge vibration excitation, roughness excitation, and crosswind excitation into the vehicle model for coupled vibration analysis to obtain vehicle vibration response data;

[0078] Obtain vehicle coupled vibration data, specifically by establishing the state space equation of the vehicle-bridge-wind field coupled vibration field; solving the equation group through numerical integration method to obtain the vehicle response.

[0079] S4: Based on the vehicle vibration response data, obtain driving safety and comfort indicators and perform risk assessment according to preset thresholds.

[0080] Driving comfort indices include: a driving comfort index based on the weighted total value of whole-body vibration, a driving comfort index based on the probability of driver motion sickness, and a driving comfort index based on vehicle vibration acceleration.

[0081] The specific driving safety indicators are: extracting the contact force between the left and right wheels and the ground; based on the contact force, obtaining the vehicle rollover stability coefficient.

[0082] Example 2:

[0083] In order to further explain the scheme of the present invention, it is described by the following examples:

[0084] A driving risk assessment method based on a coupled vibration model includes the following steps:

[0085] Step 1: Establish a vehicle dynamics model.

[0086] Specifically, this embodiment establishes a seven-degree-of-freedom vehicle model, with the degree-of-freedom vector Among them, Z v is the vertical degree of freedom of the vehicle body, θ vis the pitch freedom of the vehicle body around the y-axis, is the rolling freedom of the vehicle body around the x-axis, Z s1 is the vertical degree of freedom of the right front wheel, Z s2 is the vertical degree of freedom of the right rear wheel, Z s3 is the vertical degree of freedom of the left front wheel, Z s4 is the vertical degree of freedom of the left rear wheel. Figure 2 、 Figure 3 For detailed parameter meanings, see Table 1-2.

[0087] Table 1

[0088]

[0089] Table 2

[0090]

[0091]

[0092] The vehicle is axisymmetric along the longitudinal axis, so the relevant parameters of the left front wheel and the right front wheel, and the left rear wheel and the right rear wheel are equal.

[0093] The motion equilibrium equations for each degree of freedom of the vehicle body are as follows:

[0094] The motion equilibrium equation of the vehicle body along the Z direction is:

[0095]

[0096] The equilibrium equation of motion of the vehicle body around the y-axis:

[0097]

[0098] The equilibrium equation of motion of the vehicle body around the X axis:

[0099]

[0100] The motion equation of the right front wheel in the Z direction is:

[0101]

[0102] The motion equation of the right rear wheel in the Z direction is:

[0103]

[0104] The motion equation of the left front wheel in the Z direction is:

[0105]

[0106] The motion equation of the left rear wheel in the Z direction is:

[0107]

[0108] The matrix form of the vehicle's motion equilibrium equation is:

[0109]

[0110] F VZ 、M VY and M VX All are caused by crosswinds. b1 to z b4 The vertical displacements of the right front wheel, right rear wheel, left front wheel, and left rear wheel at the contact points with the bridge are respectively. When bridge vibration and road roughness exist at the same time:

[0111] z bi =z i +r i i=1,2,3,4 (11)

[0112] Where z i is the vertical displacement at the contact point between the wheel and the bridge caused by bridge vibration. i is the road surface roughness at the wheel.

[0113] When calculating F, have:

[0114]

[0115] Since roughness is not directly related to time, an intermediate variable x must be introduced. i =r i (x i ).for have:

[0116]

[0117] Where v is the speed of the car.

[0118] Step 2: Obtain bridge vibration excitation. Here we introduce the vortex vibration excitation method.

[0119] Specifically, the aerodynamic force is simulated as a simple harmonic force, and the simple harmonic aerodynamic force is applied to the entire bridge to excite the bridge vibration and simulate the bridge vibration.

[0120]

[0121] Where, ρ is the air density; U is the incoming wind speed; D is the transverse wind dimension of the main beam; is the aerodynamic lift coefficient; ω n is the nth order natural frequency of the main beam; θ is the phase angle; a is the amplitude of the simple harmonic force:

[0122]

[0123] A1 is the assumed vertical displacement amplitude of the main beam, A0 is the vertical displacement amplitude of the main beam under the known simple harmonic aerodynamic force obtained by numerical calculation, and F0 is the known simple harmonic force. The simple harmonic force amplitude a is determined by the inverse calculation method: Assuming the vertical displacement amplitude A1 of the main beam and the natural frequency ω n ; The amplitude of the full bridge is F0 and the frequency is ω n The simple harmonic force is calculated to obtain the full bridge amplitude A0; the simple harmonic force amplitude a is calculated according to formula (15).

[0124] When the simple harmonic force frequency is 0.1 Hz, the vertical displacement response at mid-span is as follows: Figure 4 shown.

[0125] Preferably, the simple harmonic force amplitude a is obtained through a wind tunnel test.

[0126] Furthermore, after the amplitude response of the bridge under the action of aerodynamic load converges, a moving load is applied to the bridge to simulate the driving state after the bridge vibration occurs.

[0127] Specifically, the vehicle load is equivalent to a moving load. The vertical load at each node is updated to simulate load movement. The vertical displacement of the bridge at the moving load is calculated as the vertical displacement of the vehicle bottom. The vehicle speed is set to 30 km / h. Figure 5 is the vertical displacement of the moving load point under the first-order positive symmetric vertical bending vibration mode.

[0128] Step 4: Get roughness excitation.

[0129] Specifically, roughness is simulated by power spectral density. The road surface roughness PSD function is:

[0130]

[0131] S(n) is the power spectrum density of road roughness, n is the spatial frequency (m -1 ), n0=0.1m -1 The ISO8608 standard classifies road surface roughness into five levels, A to E, based on S(n0). The S(n0) parameters for each level of roughness are shown in Table 3.

[0132] Table 3

[0133]

[0134] Based on the power spectral density function, the roughness time history is obtained using the trigonometric series method:

[0135]

[0136] Where n kis the spatial frequency (m -1 ), A random phase between 0 and 2π.

[0137] In this example, we consider three conditions: no roughness, A, B, and C, which are named R0, R1, R2, and R3 respectively. The roughness generation results are as follows: Figure 6 、 Figure 7 shown.

[0138] Preferably, after the roughness is generated, the power spectrum of the generated result is calculated and compared with the theoretical spectrum. The comparison results of R1 working condition are as follows: Figure 8 As shown in Figure 2, it can be seen that the roughness power spectrum fitting result is good.

[0139] Step 5: Obtain crosswind excitation.

[0140] Specifically, crosswind excitation includes average crosswind and fluctuating crosswind. Consider applying crosswind excitation with average wind speeds of 10m / s, 20m / s, 30m / s, and 40m / s, and fluctuating wind speeds with a random time course. The fluctuating wind speed time course is generated using harmonic synthesis based on the fluctuating wind power spectrum. The generation method is as follows:

[0141] According to the measured data, the average wind speed profile of the bridge is obtained as follows: Figure 9 The turbulence degree is calculated by measuring the wind speed time history. The linear relationship between the turbulence degree and wind speed in the downwind direction is shown in Figure 10 shown.

[0142] The fitting formula of downwind turbulence intensity and downwind turbulence integral scale is:

[0143] log 10 L u =-4.961I u +2.379 (18)

[0144] The turbulence integral scale of high turbulence specific wind under different average wind speed conditions is shown in Table 4:

[0145] Table 4

[0146]

[0147] In this embodiment, in order to simulate good wind conditions, the turbulence degree measured in strong winds is reduced, and the turbulence integral scale is obtained as shown in Table 5:

[0148] Table 5

[0149]

[0150] The theoretical model of the power spectrum of fluctuating wind speed along the wind direction is:

[0151]

[0152] Where ω is the frequency; u * is the friction speed; S u (n) is the power spectrum of fluctuating wind speed along the wind direction; σ u is the mean square error of the fluctuating wind speed along the wind direction; It is the integral scale of the fluctuating wind speed along the wind direction.

[0153] The formula for generating the wind speed time history is as follows:

[0154]

[0155] Where N is a sufficiently large positive integer, Δω is the frequency increment, is a random phase uniformly distributed in the range of (0, 2π); H jm (ω ml ) is an element in the matrix H, j, m represents its position in the matrix. The matrix H is the Cholesky decomposition of the power spectral density, that is

[0156] S u =HHT * (20)

[0157] ω ml The value is obtained according to the following formula:

[0158]

[0159] The average crosswind speed is 30m / s, and the turbulence degree along the wind is I u =0.07, downwind turbulence integral scale L u =107.58 The wind speed time history generated is as follows Figure 11 The comparison between the power spectrum of this wind speed time history and the theoretical spectrum is shown in the figure below. Figure 12 As shown in Figure 2, the two fit well in most frequency bands.

[0160] The force exerted by the fluctuating crosswind on the vehicle is calculated using the aerodynamic coefficients, including:

[0161] The lift force on the car is:

[0162]

[0163] The pitching moment on the car is:

[0164]

[0165] The rolling moment of the car is:

[0166]

[0167] Among them, A f is the frontal area of the vehicle, C l is the lift coefficient, C p is the pitching moment coefficient, C r is the rolling moment coefficient, h v C is the height of the vehicle's center of mass. l 、C p and C r It is related to the wind angle. The wind angle is defined as the angle between the wind direction and the vehicle's direction of travel. m Perpendicular to the longitudinal axis of the road, the vehicle moves at a speed U v Moving forward, the wind angle for

[0168]

[0169] Aerodynamic coefficient C p 、C r The relationship with wind angle is as follows Figure 13 shown.

[0170] To supplement the lift coefficient C l The relationship between the lift coefficient of the truck and the wind angle is determined by the actual measurement method. The actual measurement results show that the pitching moment coefficient C p The correlation with the wind angle is small in the range of 0-90°, so the conservative value is 0.2.

[0171] The values of aerodynamic coefficients are shown in Tables 6 and 7.

[0172] Table 6

[0173]

[0174] Table 7

[0175]

[0176] Step 6: Calculate the vehicle vibration response.

[0177] Specifically, a state-space representation is used to calculate the vehicle's vibration response under excitation. This representation consists of a set of inputs, outputs, and state variables related by a first-order differential equation. State variables are variables whose values change over time, and the values of output variables depend on the values of these state variables.

[0178] The basic equation of state space expression is:

[0179]

[0180] Among them, u is the input variable, x is the state variable, and y is the output variable.

[0181] Reduce the order of the differential equation of motion and set p = Z. have:

[0182]

[0183] By comparing with formula (15), we can see that the state space in this case is expressed as:

[0184]

[0185] C=[II] (31)

[0186] D=[0] (32)

[0187] Solve the state space equations to obtain the response of each degree of freedom of the vehicle body.

[0188] The vertical acceleration curve of the center of mass of the vehicle when the bicycle passes through the R0 and R1 working conditions is as follows: Figure 14 The RMS values of the vehicle body vertical acceleration are shown in Table 8.

[0189] Table 8

[0190]

[0191] Step 7: Calculate driving comfort and driving safety indicators.

[0192] Specifically, the driving comfort index includes the comfort index total whole body vibration value (OVTV) and the probability of motion sickness (MSI), which respectively evaluate the driver's comfort and the proportion of motion sickness. The driving comfort index is related to the acceleration of each degree of freedom at the driver's position. The relevant five degrees of freedom are marked as a zs 、a zb 、a zf 、a ry 、a rx ,like Figure 15 As shown in the figure, s represents the seat support surface, t represents the backrest support surface, and f represents the foot support surface.

[0193]

[0194] Specifically, the steps for calculating the OVTV value are as follows:

[0195] Perform frequency weighting on each axial vibration acceleration. The weighting function is as follows: Figure 16 As shown:

[0196] (1)W k Indicates the z of the seat support surface s Direction, foot support surface z f Frequency weighting function for the direction.

[0197] (2)W d Indicates the backrest support surface z b Frequency weighting function for the direction.

[0198] (3)W e r represents the seat support surface x and r y Frequency weighting function for the direction.

[0199] The acceleration of each degree of freedom in each direction is weighted, as shown in Table 9.

[0200] Table 9

[0201]

[0202]

[0203] For the acceleration of the degree of freedom at the driver's position a(t), the frequency-weighted acceleration time history a is obtained by fast Fourier transform weighting. w (t); Calculate the frequency-weighted acceleration time history a for each degree of freedom w The root mean square value RMS of (t):

[0204]

[0205] Weight the root mean square value of each degree of freedom by multiplying the axis weighting coefficient

[0206] OVTV={∑(M ij RMS ij ) 2} 1 / 2 (37)

[0207] Table 10 shows the comfort level classification of OVTV values.

[0208] Table 10

[0209]

[0210] The steps for calculating the MSI value are as follows:

[0211] Using frequency weighting function W f Perform weighted processing. The weighting function is as follows Figure 17 As shown. Obtain the frequency-weighted acceleration time history a w (t), calculate the motion sickness dose value MSDV:

[0212]

[0213] Calculate the total MSDV of different axial acceleration contributions based on MSDV T:

[0214]

[0215] Calculate the motion sickness index MSI:

[0216] MSI (%) = 1 / 3MSDV T (40)

[0217] Specifically, the driving safety index includes the vehicle rollover coefficient. The rollover coefficient calculation formula is:

[0218]

[0219] When the rollover coefficient is greater than 5 / 6, the vehicle is in danger of rolling over.

[0220] In this embodiment, the OVTV value changes with the roughness as shown in the following example: Figure 18 As shown; the OVTV value changes with vehicle speed and roughness as shown Figure 18 As shown in the figure; the change of MSI value with bridge vibration and roughness is shown in the figure Figure 19 As shown; the maximum values of vehicle rollover coefficient under the working conditions of average crosswind speed of 20, 30 and 40m / s are as follows Figure 20 shown.

[0221] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A driving risk assessment method based on a coupled vibration model, characterized in that: include: S1. Obtaining vehicle dynamic parameters and geometric dimension parameters, and establishing a vehicle dynamics model based on the vehicle dynamic parameters and geometric dimension parameters using finite element analysis software or directly calculating a dynamic differential equation system; S2. Obtain bridge vibration excitation, roughness excitation and crosswind excitation; S3, inputting the bridge vibration excitation, roughness excitation, and crosswind excitation into a vehicle model to perform coupled vibration analysis and obtain vehicle vibration response data; S4. Based on the vehicle vibration response data, a driving safety index and a comfort index are obtained, and a risk assessment is performed according to preset thresholds of safety and comfort.

2. The driving risk assessment method based on the coupled vibration model according to claim 1 is characterized in that: In S1, the vehicle dynamic parameters include: the mass of the vehicle body, the mass of the wheels, the moment of inertia of the vehicle body around three axes, and the vertical stiffness and damping of the primary and secondary suspensions of the vehicle; The geometrical dimension parameters include: the position of the center of mass of the vehicle body relative to the front and rear axles, the driver's position and the ground, the lateral spacing between the wheels and the windward area.

3. The driving risk assessment method based on coupled vibration model according to claim 1 is characterized in that: In S2, obtaining bridge vibration excitation includes: Given the main beam excitation wind speed time history and bridge aerodynamic parameters; Time domain calculation of bridge buffeting force and self-excited force or vortex-excited force load; Iteratively calculate the full-bridge amplitude-displacement time history. If a health monitoring system exists for the bridge, the full-bridge amplitude-displacement time history can also be directly obtained using the health monitoring system. Obtain real-time bridge vibration excitation displacement at the vehicle's driving position; The bridge vibration excitation is obtained based on the given main beam excitation wind speed time history, bridge aerodynamic parameters, excitation load, full bridge amplitude displacement time history and bridge vibration excitation displacement.

4. The driving risk assessment method based on coupled vibration model according to claim 1 is characterized in that: In S2, obtaining the roughness stimulus includes: Obtain the power spectral density function of road surface roughness; The roughness excitation is obtained based on the trigonometric series method and combined with the power spectral density function.

5. The driving risk assessment method based on coupled vibration model according to claim 1 is characterized in that: In S2, obtaining the side wind excitation includes: Calculate the average crosswind speed and downwind turbulence based on the measured data; Obtaining the integral scale of the downwind turbulence and generating a downwind fluctuating wind speed power spectrum; Harmonic synthesis method is used to generate wind speed time history; Calculate wind angle and aerodynamic coefficients; The crosswind excitation is obtained based on the crosswind average wind speed, the power spectrum of the fluctuating wind speed in the downwind direction, the wind speed time history, the wind deflection angle and the aerodynamic force coefficient.

6. The driving risk assessment method based on coupled vibration model according to claim 1 is characterized in that: In S3, obtaining vehicle vibration data includes: Establish the state space equation of the vehicle-bridge-wind field coupled vibration field; The state space equation is solved by a numerical integration method to obtain vehicle vibration data.

7. The driving risk assessment method based on coupled vibration model according to claim 1 is characterized in that: In S4, obtaining the driving comfort index includes: Driving comfort index based on the weighted total value of whole body vibration; Driving comfort index based on the probability of driver motion sickness; Driving comfort index based on vehicle vibration acceleration.

8. The driving risk assessment method based on coupled vibration model according to claim 1 is characterized in that: In step S4, obtaining the driving safety index includes: Extract the contact force between the left and right wheels and the ground; Based on the contact force, the vehicle rollover stability coefficient, that is, the driving safety index, is obtained.

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