Commercial vehicle seat suspension model construction and parameter determination method

Abstract

The invention discloses a commercial vehicle seat suspension model construction and parameter determination method. The method comprises the following working steps that 1, a commercial vehicle seat suspension model is constructed; 2, constructing a seat suspension performance evaluation index; 3, optimizing and solving an objective function and model parameters; step 4, electromechanical network comprehensive implementation based on optimization parameters; and 5, carrying out simulation analysis on the performance of the commercial vehicle seat suspension. The passive seat suspension has the beneficial effects that compared with a traditional passive seat suspension, the riding comfort of a commercial vehicle can be effectively improved, and the vibration isolation performance of the passive seat suspension is more excellent.

Description

Technical Field

[0001] The invention relates to the technical field of commercial vehicle seat suspension vibration isolation, and in particular to a commercial vehicle seat suspension model construction and parameter determination method. Background Art

[0002] Some commercial vehicles operate in harsh road conditions, and long-term severe vibrations can cause physical and mental fatigue of the driver and even lead to various physical diseases. Therefore, the research and design of high-performance vibration isolation seat suspensions becomes particularly important; traditional seat suspensions are mainly passive seat suspensions with poor vibration isolation performance, and are all seat suspensions based on the "spring-damper" two-component system, lacking mass components; and the vibration isolation performance of some active seat suspensions is much better than that of passive seat suspensions, but the cost of active seat suspensions is higher; in recent years, a new type of vibration isolation element (inertia container) has been applied to the field of automotive vibration isolation, and vibration isolation systems based on the "spring-damper-inertia container" three-component system have been widely studied; the emergence of inertia containers has promoted the research and development of automotive vibration isolation systems, and a large number of studies in recent years have shown that introducing inertia containers into automotive suspension systems It can effectively improve the vibration isolation performance of automobile suspension, especially the excellent vibration isolation performance in the low frequency band; for the vibration isolation problem of seat suspension, there are currently scissor seat suspension and seat suspension based on stiffness characteristics, but the vibration isolation performance of these two seat suspensions has been limited; there is also an active seat suspension to suppress seat vibration, but the active seat suspension is expensive, and the time lag problem in the control process will affect the control effect; and the existing model generally regards the mass of the human body as a rigid body mass, ignoring the inherent stiffness and damping of the human body, resulting in the model cannot accurately describe the vibration of the human body, and will also have a certain impact on the parameter solution results; and the current evaluation indicators for the vibration isolation performance of the seat suspension generally include seat acceleration, seat suspension dynamic travel and seat vibration transmission rate, which can only intuitively describe the dynamic characteristics of the suspension, and cannot reflect the vibration state of the human body;

[0003] In view of the above situation, the present invention proposes a commercial vehicle seat suspension model construction and parameter determination method. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the design difficulties of commercial vehicle seat suspension, provide a commercial vehicle seat suspension model construction and parameter determination method, and further improve the vehicle's ride comfort.

[0005] To achieve the above-mentioned purpose, the technical solution of the present invention is a method for constructing a commercial vehicle seat suspension model and determining parameters, comprising the following working steps:

[0006] Step 1: Construction of commercial vehicle seat suspension model;

[0007] Step 2: Construct seat suspension performance evaluation index;

[0008] Step 3: Optimize and solve the objective function and model parameters;

[0009] Step 4: Comprehensive realization of electromechanical network based on optimized parameters;

[0010] Step 5: Simulation analysis of commercial vehicle seat suspension performance.

[0011] To further supplement the technical solution, the commercial vehicle seat suspension model in step 1 is constructed based on an electromechanical inertia container, including a coupled vibration model of a seat electromechanical ISD suspension model and a human sitting posture vibration model. The dynamic Laplace equation of the constructed five-degree-of-freedom human seat suspension model is:

[0012] m1x1s 2 +(T(s)+b0s)(x1-x s )s+k1(x1-x s )+c2(x1-x2)s+k2(x1-x2)=0

[0013] m2x2s 2 +c3(x2-x3)s+k3(x2-x3)+c2(x2-x1)s+k2(x2-x1)=0

[0014] m3x3s 2 +c5(x3-x5)s+k5(x3-x5)+c3(x3-x2)s+k3(x3-x2)+c4(x3-x4)s+k4(x3-x4)=0

[0015] m4x4s 2 +c4(x4-x3)s+k4(x4-x3)=0

[0016] m5x5s 2 +c5(x5-x3)s+k5(x5-x3)=0

[0017] Where T(s) is the high-order impedance transfer function of the electromechanical inertia device to be solved, b0 is the inertia coefficient of the electromechanical inertia device; m1-m5 are the masses of the upper floor, lower torso, upper torso, internal organs and head of the seat respectively; x1-x5 are the vertical displacements of the upper floor, lower torso, upper torso, internal organs and head of the seat respectively; x s is the vertical displacement input of the seat bottom plate; k1 is the spring stiffness of the seat suspension, k2-k5 are the equivalent stiffness coefficients and equivalent damping coefficients of the lower torso, upper torso, internal organs and head, c2-c5 are the equivalent stiffness coefficients and equivalent damping coefficients of the lower torso, upper torso, internal organs and head, and s is the Laplace variable.

[0018] As a further supplement to the technical solution, T(s) is a biquadratic impedance transfer function, specifically as follows:

[0019]

[0020] Among them, the coefficients A0, A1, A2, B0, B1, B2 are the coefficients of the biquadratic impedance transfer function.

[0021] To further supplement the technical solution, the step 2 constructs the seat suspension performance evaluation index, that is, the performance index of the seat suspension optimization design is selected as the seat acceleration root mean square value, seat suspension dynamic stroke and seat vibration transmissibility under random road input conditions after chassis suspension filtering, and the acceleration root mean square value of each equivalent part of the human body;

[0022] The input expression for random road surface is:

[0023]

[0024] Among them, u is the vehicle speed, z r (t) is the vertical input displacement, w(t) is the white noise with a mean of 0, and Gq(n0) is the road roughness coefficient.

[0025] The suspension filter function is:

[0026]

[0027] Where z0 is the vertical input displacement of the seat suspension, z r is the road surface input displacement, m s is the vehicle body mass, m u is the unsprung mass of the chassis, k t is the tire stiffness coefficient, k is the chassis suspension stiffness coefficient, and c is the chassis suspension damping coefficient.

[0028] The formula for seat vibration transmissibility is as follows:

[0029]

[0030] Where BA is the root mean square value of the acceleration of the seat suspension upper plate, BA s is the RMS value of the acceleration of the bottom plate under the seat suspension.

[0031] As a further supplement to the technical solution, the optimization in step three of optimizing the objective function and model parameters adopts a whale optimization algorithm.

[0032] To further supplement the technical solution, the whale optimization algorithm includes the following steps:

[0033] Step (3.1): Determine the parameters to be optimized as the inertia coefficient b0 of the ball screw type electromechanical inertia container and the biquadratic impedance transfer function coefficients A0, A1, A2, B0, B1, B2. The set optimization variable range is:

[0034]

[0035] Step (3.2): Take the root mean square value of seat acceleration, seat suspension dynamic travel and seat vibration transmissibility as optimization targets, establish the fitness function of the whale optimization algorithm, linearly weight the three indicators, calculate the fitness value of the objective function, and obtain the objective function J in the optimization algorithm as follows:

[0036]

[0037] Its constraints st:

[0038]

[0039] Where BA, SWS, T are the seat acceleration root mean square value, seat suspension dynamic travel and seat vibration transmissibility of the seat suspension to be optimized; BA p 、SWS p 、T p They are the root mean square value of seat acceleration, seat suspension dynamic stroke and seat vibration transmissibility of the transmission passive seat suspension; w1, w2 and w3 are the weighted coefficients of the optimization target.

[0040] Step (3.3): Calculate the individual extreme value and group extreme value of the whale, where the individual extreme value is the value of the objective function J obtained by the individual whale at the optimal position, and the global extreme value is the optimal value among the individual extreme values ​​of all whales;

[0041] Step (3.4): The position update rule of individual whales is:

[0042]

[0043] Among them, X p (t) is the optimal whale position of the tth generation, X rand is the position of the random individual whale of the tth generation, X(t+1) is the position of the individual whale of the t+1th generation, A and D are coefficient vectors, D′ represents the distance from the ith whale to the prey, b is the spiral constant, l is a random number in [-1, 1], and e is a random number in [0, 1].

[0044] Step (3.5): If the set performance boundary condition is exceeded, the objective function is penalized. The penalty rule is: add a larger number to the objective function. The set penalty value is 1000.

[0045] To further supplement the technical solution, the fourth step is to determine the structure and parameters of the external electrical network of the electromechanical inertial container by the electromechanical network synthesis method; wherein the transfer function expression corresponding to the mechanical network structure is as follows:

[0046]

[0047] Among them, A0, A1, A2, B0, B1, and B2 are the transfer function coefficients after optimization and solution, and the corresponding mechanical network component parameters and electrical network component parameters can be solved accordingly.

[0048] To further supplement the present technical solution, the specific implementation method of the commercial vehicle seat suspension performance simulation analysis in step five is as follows: determine the commercial vehicle seat suspension model and model parameters, drive over a Class C road at a speed of 36 km / h, the simulation time is 10 s, the sampling interval is 0.02 s, and a random road model determined by Gaussian white noise with a mean of zero is selected. The seat acceleration of the commercial vehicle seat suspension based on an electromechanical inertia vessel and the root mean square value of the acceleration of each equivalent part of the human body under random road input conditions are calculated, and compared with the corresponding indicators of the passive suspension.

[0049] As a further supplement to the technical solution, the commercial vehicle seat suspension model and model parameters are determined in step five by respectively building a traditional passive seat suspension system and a seat suspension system model based on an electromechanical inertia container through Matlab / Simulink.

[0050] The beneficial effects are as follows: (1) the model constructed in the present invention is a five-degree-of-freedom human-seat suspension model, which can more directly reflect the effect of the parameter design results of the seat suspension on human body vibration; (2) the seat dynamic inertia suspension designed by the present invention adopts the impedance method. Compared with the structural method, the impedance method can effectively cover a wider range of structures, is general, and has a relatively small workload; (3) the present invention realizes complex mechanical impedance through an electromechanical inertia vessel, which improves the convenience of engineering application; (4) compared with the traditional passive seat suspension, the seat electromechanical ISD suspension can effectively improve the riding comfort of commercial vehicle seats. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of a commercial vehicle seat suspension model construction and parameter determination method;

[0052] Figure 2 It is a five-degree-of-freedom human seat suspension model diagram;

[0053] Figure 3 It is the mechanical network structure of the present invention;

[0054] Figure 4 It is the electrical network structure corresponding to the mechanical network structure of the present invention;

[0055] Figure 5 It is a time domain response comparison diagram of seat suspension performance, where (a) is the time domain response comparison diagram of seat acceleration, (b) is the time domain response comparison diagram of lower torso seat acceleration, (c) is the time domain response comparison diagram of visceral acceleration, (d) is the time domain response comparison diagram of upper torso acceleration, and (e) is the time domain response comparison diagram of head acceleration. DETAILED DESCRIPTION

[0056] In order to make the technical solution more clear to those skilled in the art, Figure 1-5 The technical solution of the present invention is described in detail:

[0057] like Figure 1 As shown, a commercial vehicle seat suspension model construction and parameter determination method includes the following working steps:

[0058] Step 1: Construction of commercial vehicle seat suspension model;

[0059] Step 2: Construct seat suspension performance evaluation index;

[0060] Step 3: Optimize and solve the objective function and model parameters;

[0061] Step 4: Comprehensive realization of electromechanical network based on optimized parameters;

[0062] Step 5: Simulation analysis of commercial vehicle seat suspension performance.

[0063] Among them, in step 1, the commercial vehicle seat suspension model is constructed based on the electromechanical inertia device, which has the advantages that the electromechanical inertia device can realize the combination of mechanical network and electrical network, and realize the complex impedance of the suspension system through the external electrical network of the motor inertia device, which greatly reduces the complexity of the suspension mechanical structure. The mechanical network structure involved in the present invention is relatively complex, so the external electrical network of the electromechanical inertia device is used to equivalently realize the complex mechanical impedance of the suspension system. Specifically, it includes a coupled vibration model of the seat electromechanical ISD suspension model and the human sitting posture vibration model. The dynamic Laplace square of the constructed five-degree-of-freedom human seat suspension model

[0064] Cheng Wei:

[0065] m1x1s 2 +(T(s)+b0s)(x1-x s )s+k1(x1-x s )+c2(x1-x2)s+k2(x1-x2)=0

[0066] m2x2s 2 +c3(x2-x3)s+k3(x2-x3)+c2(x2-x1)s+k2(x2-x1)=0

[0067] m3x3s 2 +c5(x3-x5)s+k5(x3-x5)+c3(x3-x2)s+k3(x3-x2)+c4(x3-x4)s+k4(x3-x4)=0

[0068] m4x4s 2 +c4(x4-x3)s+k4(x4-x3)=0

[0069] m5x5s 2 +c5(x5-x3)s+k5(x5-x3)=0

[0070] Where T(s) is the high-order impedance transfer function of the electromechanical inertia device to be solved, b0 is the inertia coefficient of the electromechanical inertia device; m1-m5 are the masses of the upper floor, lower torso, upper torso, internal organs and head of the seat respectively; x1-x5 are the vertical displacements of the upper floor, lower torso, upper torso, internal organs and head of the seat respectively; x s is the vertical displacement input of the seat bottom plate; k1 is the spring stiffness of the seat suspension, k2-k5 are the equivalent stiffness coefficients and equivalent damping coefficients of the lower torso, upper torso, internal organs and head, c2-c5 are the equivalent stiffness coefficients and equivalent damping coefficients of the lower torso, upper torso, internal organs and head, and s is the Laplace variable.

[0071] Where T(s) is the biquadratic impedance transfer function, as shown below:

[0072]

[0073] Among them, the coefficients A0, A1, A2, B0, B1, B2 are the coefficients of the biquadratic impedance transfer function.

[0074] The known parameters of the kinetic model are shown in the following table:

[0075]

[0076] Step 2: constructing seat suspension performance evaluation index;

[0077] In this embodiment, the performance indicators of the seat suspension optimization design are the root mean square value of seat acceleration under random road input conditions after chassis suspension filtering, seat suspension dynamic travel and seat vibration transmissibility, and the root mean square value of acceleration of each equivalent part of the human body;

[0078] Furthermore, the input expression of random road surface is:

[0079]

[0080] Among them, u is the vehicle speed, z r(t) is the vertical input displacement, w(t) is the white noise with a mean of 0, and Gq(n0) is the road roughness coefficient.

[0081] The suspension filter function is:

[0082]

[0083] Where z0 is the vertical input displacement of the seat suspension, z r is the road surface input displacement, m s is the vehicle body mass, m u is the unsprung mass of the chassis, k t is the tire stiffness coefficient, k is the chassis suspension stiffness coefficient, and c is the chassis suspension damping coefficient.

[0084] The formula for seat vibration transmissibility is as follows:

[0085]

[0086] BA is the root mean square value of the acceleration of the seat suspension upper plate, BA s is the RMS value of the acceleration of the bottom plate under the seat suspension.

[0087] In step three, the optimization of the objective function and model parameters is to use the whale optimization algorithm to obtain the optimal model of the seat suspension; its advantage is that the whale optimization algorithm is a meta-heuristic intelligent optimization algorithm, which has been widely used in theoretical research and engineering practice since it was proposed; the whale optimization algorithm has the following advantages: the whale optimization algorithm is simple, easy to implement, and has a wide range of applications; it does not require gradient information, has a certain degree of adaptability, and can adaptively adjust the search strategy; it has strong global search capabilities and can avoid local optimal situations. Therefore, the present invention selects the whale optimization algorithm to solve the model solving problem.

[0088] In detail, the whale optimization algorithm includes the following steps:

[0089] Step (3.1): Determine the parameters to be optimized as the inertia coefficient b0 of the ball screw type electromechanical inertia container and the biquadratic impedance transfer function coefficients A0, A1, A2, B0, B1, B2. The set optimization variable range is:

[0090]

[0091] Step (3.2): Take the root mean square value of seat acceleration, seat suspension dynamic travel and seat vibration transmissibility as optimization targets, establish the fitness function of the whale optimization algorithm, linearly weight the three indicators, calculate the fitness value of the objective function, and obtain the objective function J in the optimization algorithm as follows:

[0092]

[0093] Its constraints st:

[0094]

[0095] Where BA, SWS, T are the seat acceleration root mean square value, seat suspension dynamic travel and seat vibration transmissibility of the seat suspension to be optimized; BA p 、SWS p 、T p They are the root mean square value of seat acceleration, seat suspension dynamic stroke and seat vibration transmissibility of the transmission passive seat suspension; w1, w2 and w3 are the weighted coefficients of the optimization target.

[0096] Step (3.3): Calculate the individual extreme value and group extreme value of the whale, where the individual extreme value is the value of the objective function J obtained by the individual whale at the optimal position, and the global extreme value is the optimal value among the individual extreme values ​​of all whales;

[0097] Step (3.4): The position update rule of individual whales is:

[0098]

[0099] Among them, X p (t) is the optimal whale position of the tth generation, X rand is the position of the random individual whale of the tth generation, X(t+1) is the position of the individual whale of the t+1th generation, A and D are coefficient vectors, D′ represents the distance from the ith whale to the prey, b is the spiral constant, l is a random number in [-1, 1], and e is a random number in [0, 1].

[0100] Step (3.5): If the set performance boundary condition is exceeded, the objective function is penalized. The penalty rule is: add a larger number to the objective function. The set penalty value is 1000.

[0101] The optimization process outputs the optimal solution after multiple iterative calculations, as shown in the following table.

[0102]

[0103] Step 4: Comprehensive realization of electromechanical network based on optimized parameters;

[0104] The specific implementation method is: determine the structure and parameters of the external electrical network of the electromechanical inertial container through the electromechanical network synthesis method. Figure 3 The corresponding transfer function expression is as follows:

[0105]

[0106] The electrical network structure corresponding to the mechanical network structure is as follows Figure 4 As shown;

[0107] Among them, A0, A1, A2, B0, B1, B2 are the transfer function coefficients after optimization and solution, and the corresponding mechanical network component parameters are shown in the following table:

[0108]

[0109] The parameters of the electrical network components are shown in the following table:

[0110]

[0111] Step 5: Simulation analysis of commercial vehicle seat suspension performance;

[0112] The specific implementation method is as follows: Matlab / Simulink is used to build models of the traditional passive seat suspension system and the seat suspension system based on an electromechanical inertia device respectively, and the vehicle drives over a Class C road at a speed of 36 km / h. The simulation time is 10 s, the sampling interval is 0.02 s, and a random road model determined by Gaussian white noise with a mean of zero is selected. The seat acceleration of the commercial vehicle seat suspension based on an electromechanical inertia device and the root mean square value of the acceleration of each equivalent part of the human body under random road input conditions are calculated, and compared with the corresponding indicators of the passive suspension.

[0113] It can be seen from the simulation results that the commercial vehicle seat suspension based on the electromechanical inertia vessel proposed in the present invention can effectively improve the riding comfort of commercial vehicles compared with the traditional passive seat suspension, and its vibration isolation performance is more superior.

[0114] The above technical solutions only reflect the preferred technical solutions of the technical solutions of the present invention. Some changes that may be made to certain parts thereof by technicians in this technical field all reflect the principles of the present invention and fall within the protection scope of the present invention.

Claims

1. A commercial vehicle seat suspension model construction and parameter determination method, characterized in that: The following steps are included: Step 1: Construction of commercial vehicle seat suspension model; Step 2: Construct seat suspension performance evaluation index; Step 3: Optimize and solve the objective function and model parameters; Step 4: Comprehensive realization of electromechanical network based on optimized parameters; Step 5: Simulation analysis of commercial vehicle seat suspension performance.

2. A commercial vehicle seat suspension model construction and parameter determination method according to claim 1, characterized in that: In the step 1, the commercial vehicle seat suspension model is constructed based on an electromechanical inertia container, including a coupled vibration model of a seat electromechanical ISD suspension model and a human sitting posture vibration model. The dynamic Laplace equation of the constructed five-degree-of-freedom human seat suspension model is: m1x1s 2 +(T(s)+b0s)(x1-x s )s+k1(x1-x s )+c2(x1-x2)s+k2(x1-x2)=0 m2x2s 2 +c3(x2-x3)s+k3(x2-x3)+c2(x2-x1)s+k2(x2-x1)=0 m3x3s 2 +c5(x3-x5)s+k5(x3-x5)+c3(x3-x2)s+k3(x3-x2)+c4(x3-x4)s+k4(x3-x4)=0 m4x4s 2 +c4(x4-x3)s+k4(x4-x3)=0 <h2 style=";text-align:left;direction:ltr">m5x5s<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +c5(x5-x3)s+k5(x5-x3)=0 Where T(s) is the high-order impedance transfer function of the electromechanical inertia device to be solved, b0 is the inertia coefficient of the electromechanical inertia device; m1-m5 are the masses of the upper floor, lower torso, upper torso, internal organs and head of the seat respectively; x1-x5 are the vertical displacements of the upper floor, lower torso, upper torso, internal organs and head of the seat respectively; x s is the vertical displacement input of the seat bottom plate; k1 is the spring stiffness of the seat suspension, k2-k5 are the equivalent stiffness coefficients and equivalent damping coefficients of the lower torso, upper torso, internal organs and head, c2-c5 are the equivalent stiffness coefficients and equivalent damping coefficients of the lower torso, upper torso, internal organs and head, and s is the Laplace variable.

3. A commercial vehicle seat suspension model construction and parameter determination method according to claim 2, characterized in that: The T(s) is a biquadratic impedance transfer function, as shown below: Among them, the coefficients A0, A1, A2, B0, B1, B2 are the coefficients of the biquadratic impedance transfer function.

4. A commercial vehicle seat suspension model construction and parameter determination method according to claim 1, characterized in that: The step 2 constructs the seat suspension performance evaluation index, i.e., the performance index of the seat suspension optimization design is selected as the seat acceleration root mean square value, seat suspension dynamic stroke and seat vibration transmissibility under the random road input condition after chassis suspension filtering, and the acceleration root mean square value of each equivalent part of the human body; The input expression for random road surface is: Among them, u is the vehicle speed, z r (t) is the vertical input displacement, w(t) is the white noise with a mean of 0, and Gq(n0) is the road roughness coefficient; The suspension filter function is: Where z0 is the vertical input displacement of the seat suspension, z r is the road surface input displacement, m s is the vehicle body mass, m u is the unsprung mass of the chassis, k t is the tire stiffness coefficient, k is the chassis suspension stiffness coefficient, and c is the chassis suspension damping coefficient; The formula for seat vibration transmissibility is as follows: Where BA is the root mean square value of the acceleration of the seat suspension upper plate, BA s is the RMS value of the acceleration of the bottom plate under the seat suspension.

5. The method for constructing a commercial vehicle seat suspension model and determining parameters according to claim 1, characterized in that: The optimization in step three for optimizing the objective function and model parameters is performed using the whale optimization algorithm.

6. A commercial vehicle seat suspension model construction and parameter determination method according to claim 5, characterized in that: The whale optimization algorithm includes the following steps: Step (3.1): Determine the parameters to be optimized as the inertia coefficient b0 of the ball screw type electromechanical inertia container and the biquadratic impedance transfer function coefficients A0, A1, A2, B0, B1, B2. The set optimization variable range is: Step (3.2): Take the root mean square value of seat acceleration, seat suspension dynamic travel and seat vibration transmissibility as optimization targets, establish the fitness function of the whale optimization algorithm, linearly weight the three indicators, calculate the fitness value of the objective function, and obtain the objective function J in the optimization algorithm as follows: Its constraints st: Where BA, SWS, T are the seat acceleration root mean square value, seat suspension dynamic travel and seat vibration transmissibility of the seat suspension to be optimized; BA p 、SWS p , T p They are the root mean square value of seat acceleration, seat suspension dynamic travel and seat vibration transmissibility of the transmission passive seat suspension; w1, w2 and w3 are the weighted coefficients of the optimization target; Step (3.3): Calculate the individual extreme value and group extreme value of the whale, where the individual extreme value is the value of the objective function J obtained by the individual whale at the optimal position, and the global extreme value is the optimal value among the individual extreme values ​​of all whales; Step (3.4): The position update rule of individual whales is: Among them, X p (t) is the optimal whale position of the tth generation, X rand is the position of a random individual whale of the tth generation, X(t+1) is the position of an individual whale of the t+1th generation, A and D are coefficient vectors, D′ represents the distance from the ith whale to its prey, b is the spiral constant, l is a random number in [-1, 1], and e is a random number in [0, 1]; Step (3.5): If the set performance boundary condition is exceeded, the objective function is penalized. The penalty rule is: add a larger number to the objective function. The set penalty value is 1000.

7. A commercial vehicle seat suspension model construction and parameter determination method according to claim 1, characterized in that: The fourth step is to determine the structure and parameters of the external electrical network of the electromechanical inertial container by means of an electromechanical network synthesis method; wherein the transfer function expression corresponding to the mechanical network structure is as follows: Among them, A0, A1, A2, B0, B1, and B2 are the transfer function coefficients after optimization and solution, and the corresponding mechanical network component parameters and electrical network component parameters can be solved accordingly.

8. The method for constructing a commercial vehicle seat suspension model and determining parameters according to claim 1, characterized in that: The specific implementation method of the commercial vehicle seat suspension performance simulation analysis in step five is as follows: determine the commercial vehicle seat suspension model and model parameters, drive over a Class C road at a speed of 36 km / h, the simulation time is 10 s, the sampling interval is 0.02 s, a random road model determined by Gaussian white noise with a mean of zero is selected, and the seat acceleration of the commercial vehicle seat suspension based on an electromechanical inertia vessel and the root mean square value of the acceleration of each equivalent part of the human body under random road input conditions are calculated, and compared with the corresponding indicators of the passive suspension.

9. A commercial vehicle seat suspension model construction and parameter determination method according to claim 8, characterized in that: In the step 5, the commercial vehicle seat suspension model and model parameters are determined by respectively building a traditional passive seat suspension system and a seat suspension system model based on an electromechanical inertia container through Matlab / Simulink.

Images