A bridge network-based modeling method for vehicle electromechanical inertial suspension

Abstract

The present invention discloses a vehicle electromechanical inertial suspension modeling method based on a bridge network. The induced current generated by the bridge network can form an electromagnetic resistance that hinders the motor, thereby achieving the output of the system target impedance. The bridge network can realize a high-order impedance transfer function, thereby improving the overall performance of the vehicle suspension. The component parameters of the bridge network are optimized and calculated using a pattern search method. Simulation analysis shows that the vehicle electromechanical inertial suspension structure and parameter optimization method using the bridge network proposed in the present invention have significantly improved suspension performance and vehicle ride smoothness compared to passive suspension.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle suspension, and in particular relates to a vehicle electromechanical inertial suspension modeling method based on a bridge network. Background Art

[0002] Vehicle suspension refers to the general term for all force-transmitting connection devices between the vehicle body and wheels. Its main function is to transmit force and torque between the vehicle body and wheels, cushion the impact caused by uneven roads, and ensure the vehicle's ride comfort, handling stability and driving safety.

[0003] Traditional suspension systems are limited to a parallel "spring-damper" structure, lacking mass impedance. In 2002, Professor Smith of the University of Cambridge in the UK proposed the concept of an inertia chamber, achieving a complete correspondence with the second-order electromechanical similarity theory. An inertia chamber, similar to a capacitor, can block the transmission of low-frequency signals. The introduction of an inertia chamber creates a novel "inertia chamber-spring-damper" suspension system. During vertical vibration, this system forms a coupled vibration system of "sprung mass-inertia chamber-unsprung mass," increasing the suspension system's vertical inertia (i.e., "virtual mass") and stability without increasing the system's own weight. This is why it's called a dynamic inertia suspension. Subsequently, Professor Wang Fuzheng proposed an electromechanical inertial device designed by coupling a mechanical inertial device with a rotating motor (Reference: Wang FC, Chan HA. Vehicle suspensions with a mechatronic network strut [J]. Vehicle System Dynamics, 2011, 49 (5): 811-830.). Its advantage is that the external circuit impedance of the electromechanical inertial device can be used to equivalently simulate the mechanical impedance, thereby achieving the passive structure design purpose of complex mechanical networks.

[0004] The main research directions for electromechanical inertial devices currently include: the mechanical network containing the inertial device and its corresponding electrical network, and the realization and high-level simplification of the external circuit and mechanical impedance of the electromechanical inertial device. The development of electromechanical inertial devices faces the following challenges: while the vehicle electromechanical inertial system can achieve the target mechanical impedance using the external electrical network, the external electrical network structure remains relatively complex. Furthermore, as the number of electrical components increases, it becomes difficult to obtain a unified impedance transfer function, making it difficult to achieve high-level impedance simplification, such as with existing series-parallel and ladder networks.

[0005] Chinese patent CN113158370A proposes a vehicle electromechanical ISD suspension using a fractional-order electrical network. Compared to traditional passive suspension and vehicle electromechanical ISD suspension systems using integer-order electrical networks, this system achieves excellent vibration isolation performance. However, the complex structure of the fractional-order electrical network is not conducive to practical engineering implementation, and the simplification of high-order impedances makes it difficult to achieve significant improvements. Summary of the Invention

[0006] In response to the technical problems raised above, a bridge-network-based electromechanical inertial suspension system structure is provided. The present invention proposes an optimal method for determining the parameters of the electrical components and inertia coefficient in the bridge network. The present invention differs from series-parallel and ladder networks in terms of the connection and construction of the electrical network, and its effectiveness is more focused on improving the ride comfort of the vehicle by improving the dynamic travel of the suspension. By converting the equivalent structure between the Δ structure and the Y structure, it is first converted into a series-parallel network and then optimized. This makes its impedance expression more complex, achieving a higher-order impedance with fewer components.

[0007] The technical solution adopted by the present invention is as follows: a vehicle electromechanical inertia suspension based on a bridge network, comprising:

[0008] Step (1): Establish a quarter-bridge model of the vehicle electromechanical inertial suspension:

[0009]

[0010] m s is the sprung mass, m u is the unsprung mass, k is the suspension spring stiffness, K t is the equivalent stiffness of the tire, F is the output force of the electromechanical inertia container, z s is the vertical displacement of the sprung mass, is the vertical acceleration of the sprung mass, z u is the vertical displacement of the unsprung mass, is the vertical acceleration of the unsprung mass, z r Input displacement for the road surface;

[0011] Step (2): Using capacitors, inductors, and resistors connected in pairs to form a Δ structure and a bridge network consisting of two resistors, the total impedance Z of the external electrical network is calculated in the general form of Z a (s):

[0012]

[0013] in,

[0014]

[0015]

[0016] C1=(R1R2C1+R1R3C1+L1)R1C1+(R1+2R2+2R3)L1C1

[0017] D1=2R1C1(R2+R3)+L1

[0018] E1=R2+R3

[0019]

[0020]

[0021] H1=(R1+R2+R3)R1L1C1+(R1+R2)R3L1C1+(R1R3C1+L1)R1R2C1+R2R3L1C1

[0022] I1=(R1R2C1+R1R3C1+L1)R1+R1R2R3C1+(R1R3C1+L1)R2

[0023] J1=(R2+R3)R1+R2R3

[0024] Among them, A1~J1 are impedance coefficients;

[0025] Step (3): Determine the objective function optimization rule based on the bridge network structure impedance in step 2):

[0026] Select the passive suspension as the reference benchmark and establish the optimization objective function:

[0027]

[0028] Among them, X1, X2 and X3 are the root mean square value of the vehicle body acceleration, the root mean square value of the suspension dynamic travel and the root mean square value of the tire dynamic load of the vehicle electromechanical inertia suspension to be optimized respectively; X 1pas 、X 2pas and X 3pas They are the RMS value of body acceleration, RMS value of suspension dynamic travel and RMS value of tire dynamic load of traditional passive suspension;

[0029] Step (4): Use an optimization algorithm to optimize the parameters of the electrical components in the bridge network and determine the parameter optimization results.

[0030] Furthermore, the second resistors R2 and R3 are connected in series and then connected in parallel with the first inductor L1 . One end of the first inductor L1 is connected to the first capacitor C1 , and the other end of the first inductor L1 is connected to the first resistor R1 .

[0031] Furthermore, in the above step (3), the constraint range of the variable to be optimized is determined based on the significance of the component and the positivity constraint of the function:

[0032]

[0033] Furthermore, the suspension performance is limited to X1≤X 1pas , X2≤X 2pas , X3≤X 3pas During the optimization process, the three performance indicators of the traditional passive suspension are used as comparative constraint indicators. When the optimization objective function value exceeds its constraint conditions, the optimization is unsuccessful. The objective function will be added with 100 and the optimization will be repeated until the optimization objective function value meets the constraint conditions.

[0034] Furthermore, in the above step (4), the optimization algorithm adopts a pattern search method.

[0035] Furthermore, the pattern search method includes:

[0036] Step (4.1): Set the initial iteration point x 1 ∈R n , initial step size δ, error ε>0, acceleration factor α≥1, reduction factor β∈(0,1). Let y 1 =x 1 , k=1, j=1;

[0037] Step (4.2): Axial search: If condition ① is met: function f(y j +δe j )<function f(y j ), then let y j+1 y j +δe j , go to step (4.3); if condition ② is met: function f(y j -δe j )<function f(y j ), then let y j+1 y j -δe j , go to step (4.3); if conditions ① and ② are not satisfied, let y j+1 y j , go to step (4.3);

[0038] Step (4.3): If j < n, set j to j + 1 and go to step (4.2); otherwise, compare the function f(y n+1 )<function f(y k ), if satisfied, go to step (4.4), otherwise, go to step (4.5).

[0039] Step (4.4): Pattern search: Let x k+1 y n+1 ,y 1 is x k+1 +α(x k+1 -x k ), let k be k+1, j=1, and go to step (4.2);

[0040] Step (4.5): If δ≤ε, then get point x k , end; otherwise, let δ be βδ, y 1 is x k , x k+1 is x k , then let k be k+1, j=1, and go to step (4.2);

[0041] The meanings of the variables are as follows: i is the i-th iteration point, R n is an n-dimensional real number set, k is the number of pattern searches, j is the number of axial searches, δ is the initial step size, α is the acceleration factor, ε is the error, β is the reduction factor, and y i is along the i-th coordinate axis e i The starting point for direction search, e i =(0,…,0,1,0,…,0) T , i=1,2,…,n represents n coordinate axis directions.

[0042] The beneficial effects of this invention include: It proposes using a bridge-type network as the external electrical network for a vehicle's electromechanical inertial suspension system to achieve the target mechanical high-order impedance and improve vehicle ride comfort. Furthermore, while simplifying the external electrical network structure and increasing the number of electrical components, it further simplifies the high-order impedance, significantly improving it and enhancing suspension performance. Furthermore, the optimization algorithm employed in this invention utilizes a pattern search method, offering significant advantages over the often complex derivatives required to determine optimal parameters for high-order impedance, paving the way for further research into the simplification of high-order impedance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will be further described below with reference to the accompanying drawings and examples.

[0044] Figure 1 It is a flow chart of the vehicle dynamic inertia suspension modeling method based on bridge network.

[0045] Figure 2 It is a schematic diagram of a quarter model of the vehicle's electromechanical inertia suspension.

[0046] Figure 3 This is a schematic diagram of the bridge network structure.

[0047] Figure 4 This is the overall flow chart of the pattern search method.

[0048] Figure 5 These are the frequency domain gain diagrams of the electromechanical inertial suspension, where (a) is the vehicle body acceleration frequency domain gain comparison diagram, (b) is the suspension travel frequency domain gain comparison diagram, and (c) is the tire dynamic load frequency domain gain comparison diagram.

[0049] Figure 6 The following are the time domain performance indicators of the electromechanical inertial suspension, where (a) is the vehicle body acceleration response diagram, (b) is the suspension dynamic travel response diagram, and (c) is the tire dynamic load response diagram.

[0050] Explanation of reference numerals: wherein m s is the sprung mass, m u is the unsprung mass, k is the suspension spring stiffness, K t is the equivalent stiffness of the tire, b is the inertia coefficient of the electromechanical inertia container, c is the damping coefficient of the shock absorber, F is the output force of the electromechanical inertia container, z r is the road surface input displacement, z s is the vertical displacement of the sprung mass, is the vertical velocity of the sprung mass, is the vertical acceleration of the sprung mass, z u is the vertical displacement of the unsprung mass, is the vertical velocity of the unsprung mass, is the vertical acceleration of the unsprung mass, T is the torque of the screw, P is the lead of the screw, J is the moment of inertia of the electromechanical inertia vessel, ω is the angular velocity of the screw, ν is the relative linear velocity of the two ends of the electromechanical inertia vessel, V g is the induced voltage, k e is the electromotive force coefficient, T e is the electromagnetic torque, k t is the torque coefficient, I a is the induced current, Z is the total impedance of the external electrical network, R1, R2 and R3 are the first resistor, second resistor and third resistor respectively, L1 is the first inductor, C1 is the first capacitor, and s is a pull-type variable. DETAILED DESCRIPTION

[0051] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereto.

[0052] A bridge network is a special electrical network structure, distinguished from a series-parallel network by its specialized connection method. This leads to a different impedance expression, requiring equivalent conversion between a △ structure and a Y structure, first converting it to a series-parallel network before performing calculations. The external circuit of an electromechanical inertial capacitor based on a bridge network offers the following advantages: First, the unique structure of the bridge network makes its impedance expression more complex, enabling higher-order impedances with fewer components. Furthermore, with the same number of components, the impedance order of a bridge network is higher than that of a series-parallel network. Second, when dealing with unbalanced bridge problems, the bridge network can be converted into a series-parallel equivalent through equivalent conversion between a △ structure and a Y structure, thereby solving the equivalent impedance of the unbalanced bridge.

[0053] like Figure 1 As shown, the present invention proposes a vehicle electromechanical inertial suspension modeling method based on a bridge network, comprising: step (1): establishing a quarter model of the vehicle electromechanical inertial suspension of the bridge network; step (2): calculating the impedance of the bridge network structure; step (3): optimizing the objective function rule; step (4): using an optimization algorithm to optimize the parameters of the electrical components in the bridge network and determining the parameter optimization results. After the model is established, it is verified by the following steps: using Matlab to simulate and verify the optimized parameters.

[0054] Among them, step 1) is specifically: according to Figure 2 The vehicle electromechanical inertial suspension quarter model of the bridge network is shown, and the dynamic equations are established:

[0055]

[0056] The electromechanical inertia device used is a ball screw type electromechanical inertia device. The structure of this inertia device is conventional technology and will not be described in detail here. This inertia device converts linear motion into rotational motion through a ball screw. The dynamic formula is:

[0057]

[0058]

[0059]

[0060] Since the lead screw is connected to the motor shaft, the angular velocity of the motor shaft and the lead screw can be considered to be the same, both ω. The motor shaft rotates at an angular velocity ω, driving the armature coil of the motor to cut the magnetic flux lines, generating an induced voltage at the two ends of the rotating motor, which is:

[0061] V g =k e ω (4)

[0062] When the external terminal network of the rotating motor forms a closed loop, the induced voltage will cause an induced current I in the loop. a , when the induced current flows through the motor armature, the rotating motor will generate electromagnetic torque, which is:

[0063] T e =k t I a (5)

[0064]

[0065] Ignoring the rotational inertia of the motor shaft itself, when the electrical network at the motor's outer end is closed, the force analysis of the motor shaft is:

[0066]

[0067] Substituting equations (1) to (6) into equation (7), we can obtain:

[0068]

[0069] Where b = J(2π / P) 2 is the inertia coefficient of the electromechanical inertia vessel.

[0070] Among them, step 2) is specifically: according to Figure 3 The electrical components shown are a Δ structure of capacitors, inductors, and resistors connected in pairs, and a bridge network consisting of two resistors. Specifically, second resistors R2 and R3 are connected in series and then connected in parallel with the first inductor L1. One end of the first inductor L1 is connected to the first capacitor C1, and the other end of the first inductor L1 is connected to the first resistor R1.

[0071] Calculate the impedance of the bridge network structure (the total impedance Z of the external electrical network is generally in the form of Z a (s)):

[0072]

[0073] in,

[0074]

[0075]

[0076] C1=(R1R2C1+R1R3C1+L1)R1C1+(R1+2R2+2R3)L1C1

[0077] D1=2R1C1(R2+R3)+L1

[0078] E1=R2+R3

[0079]

[0080]

[0081] H1=(R1+R2+R3)R1L1C1+(R1+R2)R3L1C1+(R1R3C1+L1)R1R2C1+R2R3L1C1

[0082] I1=(R1R2C1+R1R3C1+L1)R1+R1R2R3C1+(R1R3C1+L1)R2

[0083] J1=(R2+R3)R1+R2R3

[0084] Among them, A1~J1 are impedance coefficients.

[0085] Wherein, step 3) specifically comprises: determining the objective function optimization rule according to the impedance relationship of the bridge network structure in step 2).

[0086] Furthermore, the passive suspension is selected as a reference benchmark to establish the optimization objective function:

[0087]

[0088] Among them, X1, X2 and X3 are the root mean square value of the vehicle body acceleration, the root mean square value of the suspension dynamic travel and the root mean square value of the tire dynamic load of the vehicle electromechanical inertia suspension to be optimized. 1pas 、X 2pas and X 3pas They are the root mean square value of the body acceleration, the root mean square value of the suspension dynamic travel, and the root mean square value of the tire dynamic load of the traditional passive suspension. Furthermore, based on the significance of the existence of the components and the positivity constraint of the function, the constraint range of the variables to be optimized is determined:

[0089]

[0090] Furthermore, the suspension performance is limited to X1≤X 1pas , X2≤X 2pas , X3≤X 3pas During the optimization process, the three performance indicators of the traditional passive suspension are used as comparative constraints. When the optimization objective function value exceeds its constraint conditions, the optimization is unsuccessful. The objective function is increased by 100 and the optimization is repeated until the optimization objective function value satisfies the constraint conditions.

[0091] Specifically, step 4) includes: using an optimization algorithm to optimize the parameters of the electrical components in the bridge network and determining the parameter optimization results.

[0092] The meanings of the variables in this algorithm are as follows: i is the i-th iteration point, Rn is an n-dimensional real number set, k is the number of pattern searches, j is the number of axial searches, δ is the initial step size, α is the acceleration factor, ε is the error, β is the reduction factor, and y i is along the i-th coordinate axis e i The starting point for direction search, e i =(0,…,0,1,0,…,0) T , i=1,2,…,n represents n coordinate axis directions.

[0093] Furthermore, the optimization algorithm in step (4) adopts a pattern search method.

[0094] Further, see Figure 4 , the pattern search method in step (4) includes:

[0095] Step (4.1): Set the initial iteration point x 1 ∈R n , initial step size δ, error ε>0, acceleration factor α≥1, reduction factor β∈(0,1). Let y 1 =x 1 , k=1, j=1.

[0096] Step (4.2): Axial search: If condition ① is met: function f(y j +δe j )<function f(y j ), then let y j+1 y j +δe j , go to step (4.3); if condition ② is met: function f(y j -δe j )<function f(y j ), then let y j+1 y j -δe j , go to step (4.3); if conditions ① and ② are not satisfied, let y j+1 y j , go to step (4.3).

[0097] Step (4.3): If j < n, set j to j + 1 and go to step (4.2); otherwise, compare the function f(y n+1 )<function f(y k ), if satisfied, go to step (4.4), otherwise, go to step (4.5).

[0098] Step (4.4): Pattern search: Let x k+1 y n+1 ,y 1 is x k+1+α(x k+1 -x k ), let k be k+1, j=1, and go to step (4.2).

[0099] Step (4.5): If δ≤ε, then get point x k , end; otherwise, let δ be βδ, y 1 is x k , x k+1 is x k , then let k be k+1, j=1, and go to step (4.2).

[0100] Furthermore, according to the above optimization algorithm, the optimization results of various parameters are obtained, as shown in Table 1.

[0101] Table 1 Bridge network parameter optimization results

[0102]

[0103] After the model is established, it is verified through the following steps: Matlab is used to simulate and verify the optimized parameters.

[0104] Using Matlab / Simulink, we built quarter-models of both a traditional passive suspension and a bridge-type electromechanical inertial suspension. Simulations were performed on both suspension types, using vehicle acceleration, suspension travel, and tire dynamic load as performance indicators. The vehicle was assumed to travel at 20 m / s under random road conditions.

[0105] The simulation can obtain the frequency domain gain comparison diagram of the vehicle electromechanical inertial suspension based on the bridge network, such as Figure 5 As shown, (a) is the vehicle acceleration frequency domain gain comparison diagram, (b) is the suspension dynamic stroke frequency domain gain comparison diagram, and (c) is the tire dynamic load frequency domain gain comparison diagram; the performance time domain index diagram of the suspension can also be obtained, such as Figure 6 As shown in Figure 2, (a) is the vehicle body acceleration response diagram, (b) is the suspension dynamic travel response diagram, and (c) is the tire dynamic load response diagram. The RMS value improvement effect of the suspension is obtained based on the simulation results, as shown in Table 2.

[0106] Table 2 RMS value improvement effect

[0107]

[0108] The above simulation results show that the vehicle electromechanical inertia suspension based on the bridge network of the present invention can effectively reduce the root mean square value of the suspension dynamic travel, and also significantly improve the root mean square value of the tire dynamic load, thereby enhancing its suspension performance and significantly improving the vehicle's driving smoothness.

[0109] The embodiments described are preferred implementations of the present invention, but the present invention is not limited to the above-mentioned implementations. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essence of the present invention are within the scope of protection of the present invention.

Claims

1. A vehicle electromechanical inertial suspension based on a bridge network, characterized in that: include: Step (1): Establish a quarter-bridge model of the vehicle electromechanical inertial suspension: m s is the sprung mass, m u is the unsprung mass, k is the suspension spring stiffness, K t is the equivalent stiffness of the tire, F is the output force of the electromechanical inertia container, z s is the vertical displacement of the sprung mass, is the vertical acceleration of the sprung mass, z u is the vertical displacement of the unsprung mass, is the vertical acceleration of the unsprung mass, z r Input displacement for the road surface; Step (2): Using capacitors, inductors, and resistors connected in pairs to form a Δ structure and a bridge network consisting of two resistors, the total impedance Z of the external electrical network is calculated in the general form of Z a (s): in, C1=(R1R2C1+R1R3C1+L1)R1C1+(R1+2R2+2R3)L1C1 D1=2R1C1(R2+R3)+L1 E1=R2+R3 H1=(R1+R2+R3)R1L1C1+(R1+R2)R3L1C1+(R1R3C1+L1)R1R2C1+R2R3L1C1 I1=(R1R2C1+R1R3C1+L1)R1+R1R2R3C1+(R1R3C1+L1)R2 J1=(R2+R3)R1+R2R3 Among them, A1~J1 are impedance coefficients; Step (3): Determine the objective function optimization rule based on the bridge network structure impedance in step 2): Select the passive suspension as the reference benchmark and establish the optimization objective function: Among them, X1, X2 and X3 are the root mean square value of the vehicle body acceleration, the root mean square value of the suspension dynamic travel and the root mean square value of the tire dynamic load of the vehicle electromechanical inertia suspension to be optimized respectively; X 1pas 、X 2pas and X 3pas They are the RMS value of body acceleration, RMS value of suspension dynamic travel and RMS value of tire dynamic load of traditional passive suspension; Step (4): Use an optimization algorithm to optimize the parameters of the electrical components in the bridge network and determine the parameter optimization results.

2. The vehicle electromechanical inertial suspension modeling method based on a bridge network according to claim 1, characterized in that: The second resistors R2 and R3 are connected in series and then in parallel with the first inductor L1. One end of the first inductor L1 is connected to the first capacitor C1, and the other end of the first inductor L1 is connected to the first resistor R1.

3. The vehicle electromechanical inertial suspension modeling method based on a bridge network according to claim 1, characterized in that: In the above step (3), the constraint range of the variable to be optimized is determined according to the significance of the component and the positivity constraint of the function: ； Limiting suspension performance to , , During the optimization process, the three performance indicators of the traditional passive suspension are used as comparative constraint indicators. When the optimization objective function value exceeds its constraint conditions, the optimization is unsuccessful. The objective function will be added with 100 and the optimization will be repeated until the optimization objective function value meets the constraint conditions.

4. The vehicle electromechanical inertial suspension modeling method based on a bridge network according to claim 1, characterized in that: In the above step (4), the optimization algorithm adopts the pattern search method.

5. The vehicle electromechanical inertial suspension modeling method based on a bridge network according to claim 4, characterized in that: The pattern search method includes: Step (4.1): Set the initial iteration point x 1 ∈R n , initial step size δ, error ε>0, acceleration factor α≥1, reduction factor β∈(0,1), let y 1 =x 1 , k=1, j=1; Step (4.2): Axial search: If condition ① is met: function f(y j +δe j )<function f(y j ), then let y j+1 y j +δe j , go to step (4.3); if condition ② is met: function f(y j -δe j )<function f(y j ), then let y j+1 y j -δe j , go to step (4.3); if conditions ① and ② are not satisfied, let y j+1 y j , go to step (4.3); Step (4.3): If j < n, set j to j + 1 and go to step (4.2); otherwise, compare the function f(y n+1 )<function f(y k ), if satisfied, go to step (4.4), otherwise, go to step (4.5); Step (4.4): Pattern search: Let x k+1 y n+1 ,y 1 is x k+1 +α(x k+1 -x k ), let k be k+1, j=1, and go to step (4.2); Step (4.5): If δ≤ε, then get point x k , end; otherwise, let δ be βδ, y 1 is x k , x k+1 is x k , then let k be k+1, j=1, and go to step (4.2); The meanings of the variables are as follows: i is the i-th iteration point, R n is an n-dimensional real number set, k is the number of pattern searches, j is the number of axial searches, δ is the initial step size, α is the acceleration factor, ε is the error, β is the reduction factor, and y i is along the i-th coordinate axis e i The starting point for direction search, e i =(0,…,0,1,0,…,0) T , i=1,2,…,n represents n coordinate axis directions.

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