A Modeling Method for Vehicle Dynamic Inertia Suspension Based on Trapezoidal Network

Abstract

The present invention discloses a method for modeling a vehicle dynamic inertial suspension based on a ladder network, including: Step (1): Establish a 1 / 4 dynamic inertial suspension model including the outer circuit of the electromechanical inertance container; Step (2): Design a ladder outer circuit of the electromechanical inertance container with series-arm capacitors and shunt-arm inductors based on the ladder network; Step (3): Select the vertical input displacement z of the road surface unevenness r ; Step (4): Considering the constraints of the suspension performance indicators, solve the suspension model parameters through an optimization algorithm to obtain the optimal parameters of the vehicle dynamic inertial suspension based on the ladder network.

Description

Technical Field

[0001] The present invention relates to an optimized design of the outer circuit structure of a vehicle dynamic inertial suspension, and particularly to a method for modeling a vehicle dynamic inertial suspension based on a ladder network. Background Art

[0002] A vehicle suspension refers to the general term for all force-transmitting connecting devices between the vehicle body and the wheels. Its main functions are to transmit the forces and torques between the vehicle body and the wheels, buffer the impacts caused by uneven road surfaces, and ensure the ride comfort, handling stability, and driving safety of the vehicle.

[0003] Traditional suspension systems are limited to the parallel structure form of "spring - damper" two elements and lack mass impedance. In 2002, Professor Smith of the University of Cambridge in the UK proposed the idea of an inertor, achieving a complete correspondence relationship of the second type of electromechanical similarity theory. The inertor has a function similar to that of a capacitor and can block the transmission of low-frequency signals. The introduction of the inertor makes the suspension system form a new suspension structure system of "inertor - spring - damper". During the vertical vibration process, it forms a coupled vibration system of "sprung mass - inertor - unsprung mass", increasing the vertical motion inertia (i.e., "virtual mass") and stability of the suspension system without increasing the self-weight of the suspension system, so it is called a dynamic inertial suspension. Subsequently, Professor Wang Fuzheng proposed a mechatronic inertor device designed by coupling a mechanical inertor device and a rotary motor (Reference: Wang F C, Chan H A. Vehicle suspensions with a mechatronic network strut[J]. Vehicle System Dynamics, 2011, 49(5): 811 - 830.). Its advantage is that the outer circuit impedance of the mechatronic inertor can be used to equivalently simulate the mechanical impedance to achieve the purpose of passive structure design of a complex mechanical network.

[0004] Currently, the research direction of mechatronic inertors mainly focuses on integrated design or inertance ratio range adjustment, while there is less research on the outer circuit of mechatronic inertors and its optimized design, and the performance of dynamic inertial suspensions needs to be further improved. Summary of the Invention

[0005] For the above reasons, the present invention provides a method for modeling a vehicle dynamic inertial suspension based on a ladder network, which can be used to optimize the design of the outer circuit of the mechatronic inertor, improve the mechanical performance of the mechatronic inertor, and enhance the ride comfort, driving safety, and road friendliness of the vehicle.

[0006] To achieve the above improvement effects, the technical solution adopted by the present invention is: A method for modeling a vehicle dynamic inertial suspension based on a ladder network, including:

[0007] Step (1): Establish a 1 / 4 dynamic inertia suspension model including the outer circuit of the electromechanical inertia capacitor:

[0008]

[0009] where, m s is the sprung mass, m u is the unsprung mass, k is the suspension spring stiffness, k r is the equivalent stiffness of the tire, c is the damping coefficient of the shock absorber, F is the output force at both ends of the electromechanical inertia capacitor, g represents the force at both ends of the series branch where the electromechanical inertia capacitor and the shock absorber are located in the suspension system, 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 velocity of the unsprung mass, is the vertical acceleration of the unsprung mass, z r is the vertical input displacement of the road surface unevenness, is the vertical velocity of the electromechanical inertia capacitor;

[0010] Step (2): Design the ladder-shaped outer circuit of the series-arm capacitor and parallel-arm inductor of the electromechanical inertia capacitor based on the ladder network. The complex impedance Z e (s) of the ladder-shaped outer circuit of the electromechanical inertia capacitor is expressed as:

[0011]

[0012] where, C1 is the series-arm capacitor of the outer circuit of the rotating motor, C2 is the series-arm capacitor of the outer circuit of the rotating motor, L1 is the parallel-arm inductor of the outer circuit of the rotating motor, L2 is the parallel-arm inductor of the outer circuit of the rotating motor, and s is the Laplace variable;

[0013] Step (3): Select the vertical input displacement z r ;

[0014] Step (4): Considering the constraints of the suspension performance index, solve the suspension model parameters through an optimization algorithm to obtain the optimal parameters of the vehicle dynamic inertia suspension based on the ladder network.

[0015] Furthermore, the transfer function model of the output force F at both ends of the electromechanical inertia capacitor in the step (1) is specifically:

[0016]

[0017] where, p represents the lead of the ball screw, J m represents the moment of inertia of the rotating motor, J represents the moment of inertia of the ball screw inertia capacitor, B m represents the damping coefficient of the rotating motor, kt Denote the torque coefficient of the rotating electrical machine as k e Denote the electromotive force coefficient of the rotating electrical machine as R a Denote the armature resistance of the rotating electrical machine as L a Denote the armature inductance of the rotating electrical machine as is the complex domain form of the vertical velocity of the sprung mass is the complex domain form of the vertical velocity of the electromechanical inertial capacitor is the inertance coefficient of the electromechanical inertial capacitor

[0018] Furthermore, the optimization algorithm in step (4) adopts an improved genetic algorithm

[0019] Furthermore, the improved genetic algorithm is an elite genetic algorithm, including

[0020] Step (4.1): Determine the parameters to be optimized M = [b, c, C1, C2, L1, L2], and initialize the population

[0021] Step (4.2): Take the root mean square value of the vehicle body acceleration, the root mean square value of the suspension dynamic stroke, and the root mean square value of the tire dynamic load as the optimization objectives, and calculate the sum of their ratios with the corresponding performance indicators of the traditional passive suspension as the fitness function of the genetic algorithm, calculate the fitness value of each individual, and obtain the optimization objective function J and its constraint conditions s.t.:

[0022]

[0023]

[0024] Among them, BA(M), SWS(M), and DTL(M) respectively represent the root mean square value of the vehicle body acceleration, the root mean square value of the suspension dynamic stroke, and the root mean square value of the tire dynamic load of the vehicle dynamic inertial suspension based on the trapezoidal network; BA pas , SWS pas , DTL pas respectively represent the root mean square values of the three performance indicators of the traditional passive suspension; w1, w2, and w3 respectively represent their weighting coefficients; UB and LB respectively represent the upper and lower limits of the optimization parameters

[0025] Step (4.3): Evaluate the fitness of the population individuals, determine the elite selection operator, that is, replace the individual with the lowest fitness value in the new population with the optimal individual with the highest fitness value, and the optimal individual does not participate in the crossover and mutation operations to prevent it from being lost and damaged

[0026] Step (4.4): Crossover operation: Replace and recombine some genes of two individuals to generate new individuals

[0027] Step (4.5): Mutation operation: Make changes to the gene values at some gene loci of the individuals in the population

[0028] Step (4.6): Calculate the individual fitness value again, and determine whether the fitness value reaches the expected value or the number of iterations reaches the maximum number. If not, continue to repeat the above steps; if either condition is met, end the optimization and obtain the optimized parameter values.

[0029] Step (4.7): The optimization process finally obtains the optimal solutions of the six parameters to be optimized in Step (4.1).

[0030] Further, in the said step (3), the vertical input displacement z of the road surface unevenness is selected r Specifically:

[0031]

[0032] where v represents the driving speed, n0 represents the reference spatial frequency, w(t) represents Gaussian white noise with a mean of 0, and z r (t) is the vertical input displacement of the road surface unevenness, is the vertical input speed of the road surface unevenness, and G q (n0) is the road surface unevenness coefficient.

[0033] The beneficial effects of the present invention are as follows: The present invention designs the outer circuit of the electromechanical inertance container based on the ladder network, uses the ladder network to realize the high-order suspension impedance to equivalently simulate the mechanical impedance, and improves the mechanical output of the electromechanical inertance container. The simulation results show that: compared with the traditional passive suspension, a vehicle dynamic inertia suspension based on the ladder network has significant performance improvements at different vehicle speeds, effectively improving the ride comfort, driving safety and road friendliness of the vehicle. In addition, the present invention uses the improved genetic algorithm to avoid the optimal individual being damaged and lost by crossover and mutation, and can converge to the global optimal solution, obtaining the best parameters of the vehicle dynamic inertia suspension model based on the ladder network, laying a foundation for the structural optimization design of the electrical network of the electromechanical inertance container. Description of the Drawings

[0034] The present invention will be further described below with reference to the drawings and embodiments.

[0035] Figure 1 It is a flowchart of a method for modeling a vehicle dynamic inertia suspension based on a ladder network.

[0036] Figure 2 It is a schematic diagram of a 1 / 4 vehicle dynamic inertia suspension model based on a ladder network.

[0037] Figure 3 It is a schematic diagram of the ladder-shaped outer circuit structure of the electromechanical inertance container.

[0038] Figure 4 It is a general flowchart of the improved genetic algorithm.

[0039] Figure 5 Time-domain diagrams of the performance indicators of a vehicle's dynamic inertial suspension based on a trapezoidal network at a vehicle speed of 10 m / s, where (a) is the body acceleration response diagram, (b) is the suspension dynamic stroke response diagram, and (c) is the tire dynamic load response diagram.

[0040] Figure 6 Time-domain diagrams of the performance indicators of a vehicle's dynamic inertial suspension based on a trapezoidal network at a vehicle speed of 20 m / s, where (a) is the body acceleration response diagram, (b) is the suspension dynamic stroke response diagram, and (c) is the tire dynamic load response diagram.

[0041] Figure 7 Time-domain diagrams of the performance indicators of a vehicle's dynamic inertial suspension based on a trapezoidal network at a vehicle speed of 30 m / s, where (a) is the body acceleration response diagram, (b) is the suspension dynamic stroke response diagram, and (c) is the tire dynamic load response diagram.

[0042] Description of reference numerals: Among them, m s is the sprung mass, m u is the unsprung mass, k is the suspension spring stiffness, k r is the equivalent stiffness of the tire, b is the inertia mass coefficient of the electromechanical inertor, c is the damping coefficient of the shock absorber, F is the output force at both ends of the electromechanical inertor, g represents Figure 2 the acting force at both ends of the series branch where the electromechanical inertor and the shock absorber are located in the suspension system shown, 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, [[ID=2〕8]] is the vertical velocity of the unsprung mass, is the vertical acceleration of the unsprung mass, z r is the vertical input displacement of the road surface unevenness, z b is the vertical displacement of the electromechanical inertor, is the vertical velocity of the electromechanical inertor, is the vertical acceleration of the electromechanical inertor, U is the induced electromotive force of the rotating motor, R a is the armature resistance of the rotating motor, L a is the armature inductance of the rotating motor, C1 is the series-arm capacitor 1 of the external circuit of the rotating motor, C2 is the series-arm capacitor 2 of the external circuit of the rotating motor, L1 is the parallel-arm inductor 1 of the external circuit of the rotating motor, L2 is the parallel-arm inductor 2 of the external circuit of the rotating motor, s is the Laplace variable. Detailed implementation manners

[0043] 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.

[0044] A ladder network is a circuit network composed of several cascaded L-shaped networks. Its terminal impedance function can be expanded in the form of a continued fraction. The corresponding network implementation structure is a ladder network, and the continued fraction expression is directly related to the parameters of each component of the network. The order of the continued fraction expression is related to the number of components. Applying the ladder network to the outer circuit of the electromechanical inertia capacitor has the following beneficial effects: First, different from the irregular connection of electrical components, the ladder network is designed based on the L-shaped network structure and is more regular, enriching the topological structure form of the outer circuit of the electromechanical inertia capacitor. At the same time, compared with the low-order impedance below the third order, the high-order suspension impedance is beneficial to improving the suspension vibration isolation performance. The impedance transfer function of the ladder network is in the form of a continued fraction, which can realize the high-order suspension impedance. By optimizing the parameters of the outer circuit components, the mechanical output performance of the electromechanical inertia capacitor can be significantly improved.

[0045] A vehicle dynamic inertia suspension modeling method based on a ladder network according to the present invention includes: (1) establishing a 1 / 4 dynamic inertia suspension model including the outer circuit of the electromechanical inertia capacitor; (2) designing a ladder-shaped outer circuit of the electromechanical inertia capacitor with series-arm capacitors and shunt-arm inductors based on the ladder network; (3) selecting the vertical input displacement z of the road surface unevenness r ; (4) considering the constraints of the suspension performance index, solving the suspension model parameters through an optimization algorithm to obtain the optimal parameters of the vehicle dynamic inertia suspension based on the ladder network. After the model is established, it is verified through the following steps: (5) simulating the dynamic inertia suspension based on the ladder network; (6) evaluating the influence of the ladder network on the dynamic inertia suspension.

[0046] Among them, step (1) is specifically: According to Figure 2 the 1 / 4 vehicle dynamic inertia suspension model shown, establish its dynamic equation:

[0047]

[0048] Furthermore, F represents the output force at both ends of the electromechanical inertia capacitor.

[0049] Furthermore, the electromechanical inertia capacitor is connected in series with the shock absorber. The output force F at both ends of the electromechanical inertia capacitor and the damping force generated by the shock absorber are a pair of interaction forces that cancel each other out.

[0050] Furthermore, g represents Figure 2 the force acting on both ends of the series branch where the electromechanical inertia capacitor and the shock absorber are located in the suspension system shown (which acts on the sprung mass m s and the unsprung mass m u ), and its value is equal to the output force at both ends of the electromechanical inertia capacitor or the damping force generated by the shock absorber. Using it in the above dynamic equation can make it more concise and intuitive.

[0051] Furthermore, z s -z u 、k r (z u -z r ), respectively, represent the vertical acceleration of the sprung mass, the dynamic suspension travel, and the dynamic tire load, which are the dynamic performance indicators of the suspension system and are used to evaluate the ride comfort, driving safety, and road friendliness.

[0052] Furthermore, the transfer function model of the output force F at both ends of the electromechanical inertance container is specifically as follows:

[0053]

[0054] Among them, p represents the lead of the ball screw, J m represents the moment of inertia of the rotating motor, J represents the moment of inertia of the ball screw inertance container, B m represents the damping coefficient of the rotating motor, k t represents the torque coefficient of the rotating motor, k e represents the back electromotive force coefficient of the rotating motor, R a represents the armature resistance of the rotating motor, L a represents the armature inductance of the rotating motor, Z e (s) represents the complex impedance of the trapezoidal outer circuit of the electromechanical inertance container, is the complex domain form of the vertical velocity of the sprung mass, is the complex domain form of the vertical velocity of the electromechanical inertance container, and s is the Laplace variable.

[0055] Furthermore, is the inertance-mass coefficient of the electromechanical inertance container.

[0056] Furthermore, a 75SZD01 type DC motor of the Moment Company is selected for the rotating motor. The armature resistance R of the rotating motor a is 10.6 Ω, the armature inductance L of the rotating motor a is 0.0186 H, the torque coefficient k of the rotating motor t is 0.98 N·m / A, the back electromotive force coefficient k of the rotating motor e is 0.98 V·s / rad. In addition, the lead p of the ball screw is 0.016 m, that is, 16 mm.

[0057] Furthermore, the rotor shaft of the rotating motor is fixedly connected to the screw of the ball screw inertance container. When relative movement occurs at both ends of the electromechanical inertance container, the screw drives the rotor shaft of the rotating motor to rotate. Due to the principle of electromagnetic induction, the rotating motor generates an induced electromotive force U, thereby supplying power to the outer circuit, as Figure 2-3 shown.

[0058] Among them, step (2) is specifically as follows: As Figure 2-3As shown in the figure, based on the ladder network method, the series arm of the electromechanical inertance container is designed as a capacitor, and the shunt arm is designed as an inductor, forming a "two-capacitor - two-inductor" type ladder external circuit.

[0059] Furthermore, the ladder external circuit of the rotating electrical machine, i.e., the ladder external circuit of the electromechanical inertance container.

[0060] Furthermore, the complex impedance Z e (s) of the ladder external circuit of the electromechanical inertance container is written in the continued fraction form, specifically as:

[0061]

[0062] Furthermore, the complex impedance Z e (s) of the ladder external circuit of the electromechanical inertance container is a high-order impedance transfer function, and the highest order is the fourth order.

[0063] Furthermore, when the complex impedance Z e (s) of the external circuit of the electromechanical inertance container is substituted into the transfer function model of the output force F of the electromechanical inertance container, a reciprocal transformation is required. Therefore, the numerator order of the complex impedance of the external circuit of the electromechanical inertance container is the third order, and the denominator order is the fourth order.

[0064] Among them, step (3) is specifically: select the vertical input displacement z of the road surface unevenness r , specifically as:

[0065]

[0066] Among them, v represents the driving speed, taken as 20 m / s; n0 represents the reference spatial frequency, taken as 0.1 m -1 ; w(t) represents Gaussian white noise with a mean of 0; z r (t) is the vertical input displacement of the road surface unevenness, is the vertical input speed of the road surface unevenness.

[0067] Furthermore, select Class C road surface as the random road surface input, and the road surface unevenness coefficient G q (n0) takes the geometric mean as 2.56×10 -4 m 3 .

[0068] Among them, step (4) is specifically: as Figure 4 shown, considering the constraint conditions of the suspension performance index, use the improved genetic algorithm to solve the model parameters, and obtain the optimal mechanical network and circuit element parameters of the vehicle dynamic inertia suspension based on the ladder network.

[0069] Furthermore, the numerical values of the optimized parameters determine the performance indicators of the suspension, namely the root mean square value of body acceleration BA(M), the root mean square value of suspension working stroke SWS(M), and the root mean square value of tire dynamic load DTL(M).

[0070] Furthermore, determine the parameters to be optimized M = [b, c, C1, C2, L1, L2], and initialize the population.

[0071] Furthermore, take the root mean square value of body acceleration, the root mean square value of suspension working stroke, and the root mean square value of tire dynamic load as the optimization objectives, and calculate the sum of their ratios with the corresponding indicators of the traditional passive suspension respectively as the fitness function of the improved genetic algorithm, calculate the fitness value of each individual, and obtain the objective function J of the dynamic inertial suspension and its constraint conditions s.t.:

[0072]

[0073]

[0074] where BA(M), SWS(M), and DTL(M) respectively represent the root mean square value of body acceleration, the root mean square value of suspension working stroke, and the root mean square value of tire dynamic load of the vehicle's dynamic inertial suspension based on the trapezoidal network; BA pas , SWS pas , DTL pas respectively represent the root mean square values of the three performance indicators of the traditional passive suspension; w1, w2, and w3 respectively represent their weighting coefficients; UB and LB respectively represent the upper and lower limits of the optimized parameters.

[0075] Furthermore, evaluate the fitness of the population individuals, determine the elite selection operator, replace the worst individual with the lowest fitness value in the new population with the best individual with the highest fitness value, and the best individual does not participate in the crossover and mutation operations to prevent loss and damage.

[0076] Furthermore, perform the crossover operation: replace and recombine some genes of the two individuals to generate new individuals.

[0077] Furthermore, perform the mutation operation: change the gene values at some gene loci of the individuals in the population.

[0078] Furthermore, calculate the individual fitness value again, and judge whether the fitness value reaches the expected value or whether the number of iterations reaches the maximum number of times. If not, continue to repeat the above steps; if either condition is satisfied, end the optimization and obtain the optimized parameter values.

[0079] Furthermore, the optimization process finally obtains the optimal solutions of the six parameters to be optimized.

[0080] Furthermore, the improved genetic algorithm adopted here is the elitist genetic algorithm, which is characterized by replacing the worst individual with the best individual. The best individual is surely retained. Compared with the simple genetic algorithm using the roulette wheel rule, it can achieve global convergence and obtain the global optimal solution.

[0081] Furthermore, Tables 1, 2, and 3 show the parameter optimization results at vehicle speeds of 10 m / s, 20 m / s, and 30 m / s.

[0082] Table 1 Parameter Optimization Results at a Vehicle Speed of 10 m / s

[0083]

[0084] Table 2 Parameter Optimization Results at a Vehicle Speed of 20 m / s

[0085]

[0086] Table 3 Parameter Optimization Results at a Vehicle Speed of 30 m / s

[0087]

[0088] The following is the simulation:

[0089] Among them, step (5) is specifically: Build a traditional passive suspension and a vehicle dynamic inertial suspension model based on a ladder network through Matlab / Simulink. Substitute the optimized suspension parameters into the vehicle dynamic inertial suspension model based on the ladder network. Select a Class C random input road surface. The vehicle travels at three different speeds of 10 m / s, 20 m / s, and 30 m / s. Take the body acceleration, suspension dynamic stroke, and tire dynamic load as the indicators to evaluate the suspension performance.

[0090] Figure 5 is the time-domain diagram of the vehicle dynamic inertial suspension performance indicators at a vehicle speed of 10 m / s. Among them, (a) is the body acceleration response diagram, (b) is the suspension dynamic stroke response diagram, and (c) is the tire dynamic load response diagram.

[0091] Table 4 is the comparison table of root mean square values at a vehicle speed of 10 m / s:

[0092]

[0093]

[0094] Figure 6 is the time-domain diagram of the vehicle dynamic inertial suspension performance indicators at a vehicle speed of 20 m / s. Among them, (a) is the body acceleration response diagram, (b) is the suspension dynamic stroke response diagram, and (c) is the tire dynamic load response diagram.

[0095] Table 5 is the comparison table of root mean square values at a vehicle speed of 20 m / s:

[0096]

[0097] Figure 7 Figure 7 shows the time-domain diagrams of the performance indicators of the vehicle dynamic inertia suspension based on the trapezoidal network at a vehicle speed of 30 m / s, where (a) is the body acceleration response diagram, (b) is the suspension dynamic stroke response diagram, and (c) is the tire dynamic load response diagram.

[0098] Table 6 is the comparison table of root mean square values at a vehicle speed of 30 m / s:

[0099]

[0100] The following is an analysis of the simulation results:

[0101] From Tables 4, 5, 6 and Figure 5 , 6 , and 7, it can be seen that at different vehicle speeds (10 m / s, 20 m / s, and 30 m / s), the improvement effect of the root mean square value of the suspension dynamic stroke of the vehicle dynamic inertia suspension based on the trapezoidal network of the present invention is the most significant, which are 20.79%, 13.89%, and 15.98% respectively, and the root mean square value of the tire dynamic load is improved by 13.05%, 9.51%, and 11.86% respectively; compared with the traditional passive suspension, the vehicle dynamic inertia suspension model based on the trapezoidal network of the present invention has a certain improvement effect on the body acceleration, which are 0.21%, 3.59%, and 1.15% respectively. Therefore, using the trapezoidal network as the outer circuit of the electromechanical inertance can effectively improve the ride comfort, driving safety, and road friendliness of the vehicle.

[0102] The above results show that the vehicle dynamic inertia suspension based on the trapezoidal network of the present invention can significantly improve the suspension dynamic stroke and tire dynamic load of the vehicle at different vehicle speeds, and the body acceleration is also reduced to a certain extent, which can effectively improve the ride comfort, driving safety, and road friendliness of the vehicle.

[0103] The described embodiments are the preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Without departing from the essence of the present invention, any obvious improvements, substitutions, or modifications made by those skilled in the art shall fall within the protection scope of the present invention.

Claims

1. A vehicle dynamic inertia suspension modeling method based on a trapezoidal network, characterized in that, Including: Step (1): Establish a 1 / 4 dynamic inertia suspension model including the outer circuit of the electromechanical inertance container: Among them, m s is the sprung mass, m u is the unsprung mass, k is the suspension spring stiffness, k r is the equivalent stiffness of the tire, c is the damping coefficient of the shock absorber, F is the output force at both ends of the electromechanical inertance container, g represents the force acting on both ends of the series branch where the electromechanical inertance container and the shock absorber are located in the suspension system, 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 velocity of the unsprung mass, is the vertical acceleration of the unsprung mass, z r is the vertical input displacement of the road surface unevenness, is the vertical velocity of the electromechanical inertance container; Step (2): Design the electromechanical inertial capacitor ladder outer circuit of series-arm capacitors and shunt-arm inductors based on the ladder network. The complex impedance Z e (s) of the electromechanical inertial capacitor ladder outer circuit is expressed as: Where, C1 is the series arm capacitor 1 of the outer circuit of the rotating motor, C2 is the series arm capacitor 2 of the outer circuit of the rotating motor, L1 is the parallel arm inductor 1 of the outer circuit of the rotating motor, L2 is the parallel arm inductor 2 of the outer circuit of the rotating motor, and s is the Laplace variable; Step (3): Select the vertical input displacement z of the road surface unevenness r ; Step (4): Considering the constraints of the suspension performance index, solve the suspension model parameters through an optimization algorithm to obtain the optimal parameters of the vehicle dynamic inertia suspension based on the ladder network; The optimization algorithm in the said step (4) adopts an improved genetic algorithm; The said improved genetic algorithm is an elitist genetic algorithm, including: Step (4.1): Determine the parameters to be optimized M = [b, c, C1, C2, L1, L2], and initialize the population; Step (4.2): Take the root mean square value of the body acceleration, the root mean square value of the dynamic stroke of the suspension, and the root mean square value of the dynamic load of the tire as the optimization objectives, and add their ratios to the corresponding performance indexes of the traditional passive suspension as the fitness function of the genetic algorithm, calculate the fitness value of the individual, and obtain the optimization objective function J and its constraint conditions s.t.: Among them, BA(M), SWS(M), and DTL(M) respectively represent the root mean square value of the body acceleration, the root mean square value of the dynamic suspension travel, and the root mean square value of the dynamic tire load of the vehicle dynamic inertial suspension based on the ladder network; BA pas , SWS pas , DTL pas respectively represent the root mean square values of the three performance indicators of the traditional passive suspension; w1, w2, and w3 respectively represent their weighting coefficients; UB and LB respectively represent the upper and lower limits of the optimization parameters; Step (4.3): Evaluate the fitness of the population individuals, determine the elitist selection operator, that is, replace the individual with the lowest fitness value with the optimal individual with the highest fitness value in the new population, and the optimal individual does not participate in the crossover and mutation operations to prevent it from being lost and damaged; Step (4.4): Crossover operation: Replace and recombine some genes of the two individuals to generate new individuals; Step (4.5): Mutation operation: Change the gene values at some gene loci of the individuals in the population; Step (4.6): Calculate the fitness value of the individual again, and judge whether the fitness value reaches the expected value or the number of iterations reaches the maximum number of times. If not, continue to repeat the above steps; if either condition is satisfied, end the optimization and obtain the optimized parameter values; Step (4.7): The optimization process finally obtains the optimal solutions of the six parameters to be optimized in step (4.1); In step (3), the vertical input displacement z of the road surface unevenness is selected r Specifically: where \(v\) represents the driving speed, \(n_0\) represents the reference spatial frequency, \(w(t)\) represents Gaussian white noise with a mean of 0, and \(z r (t)\) is the vertical input displacement of the road surface unevenness, is the vertical input speed of the road surface unevenness, and \(G q (n_0)\) is the road surface unevenness coefficient.

2. The vehicle dynamic inertia suspension modeling method based on a trapezoidal network according to claim 1, characterized in that The transfer function model of the output force F at both ends of the electromechanical inertance container in the said step (1) is specifically: where p represents the lead of the ball screw, J m represents the moment of inertia of the rotary motor, J represents the moment of inertia of the ball screw inertor, B m represents the damping coefficient of the rotary motor, k t represents the torque coefficient of the rotary motor, k e represents the back electromotive force coefficient of the rotary motor, R a represents the armature resistance of the rotary motor, L a represents the armature inductance of the rotary motor, is the complex domain form of the vertical velocity of the sprung mass, is the complex domain form of the vertical velocity of the electromechanical inertor, is the inertance coefficient of the electromechanical inertor.

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