CN104494387B - A vehicle inertial suspension structure and its parameter determination method - Google Patents

Abstract

The invention discloses a kind of vehicle inertial matter suspension frame structure and parameter determination method thereof, belong to automotive suspension vibration isolation technique field.The vehicle inertial matter suspension proposed is made up of two-stage series connection, and the first order is " spring-damper " two element in parallel, and the second level is " used container spring " two element in parallel." used container spring " two element in parallel is utilized to produce the action effect of parallel resonance under given conditions, it is achieved the suppression to body quality vibration, the road roughness that can effectively buffer and decay impacts.Utilize quantum genetic algorithm that suspension parameter is optimized calculating, simulation analysis shows, using vehicle inertial matter suspension frame structure proposed by the invention and parameter determination method, suspension property index has clear improvement compared to the passive suspension of transmission, and vehicle ride performance, safety are the most effectively promoted.

Description

A vehicle inertial suspension structure and its parameter determination method

technical field

The invention belongs to the field of vehicle suspension vibration isolation, and relates to a vehicle inertia suspension structure and a parameter determination method thereof.

Background technique

Since the concept of "inertial mass" was proposed, it has received extensive attention from scholars in the fields of control engineering and mechanical vibration isolation. So far, the vibration isolation potential of mechanical isolation networks using inerters has been demonstrated in the fields of vehicle suspension, building vibration isolation, and high-performance motorcycle steering compensation.

The inerter has the same physical properties as the mass element, and the characteristics of its two ends are more suitable for engineering applications. Michael Z.Q.Chen pointed out in the article "Influence of inerter on natural frequencies of vibration systems" that the inerter has an important effect on the natural frequency of the vibration system. The influence can make the resonance frequency of the vibration system advance, and the multi-degree-of-freedom vibration model is theoretically deduced and proved. Judging from the current practical research, the main method of vibration control for the main vibration system of the vibration system is to use a tuned mass damper. However, due to the heavy weight of the mass and the limitation of installation conditions, this method is still in the theoretical research stage.

Chinese patent 201010281992.1 discloses a dynamic vibration absorber with two free ends, which uses an inerter to replace the original mass element, and can realize the vibration control of the main vibration system. However, through the analysis of its structural characteristics, it can be seen that since the inerter and the damping element can only function under the support and protection of the elastic element, the dynamic shock absorber at both ends cannot be directly used as a vehicle suspension structure, so it needs to be improved Design in order to obtain better practical effect.

Contents of the invention

In order to solve the application problem of the dynamic shock absorber with two free ends in the vehicle suspension system, this invention proposes a vehicle inertial suspension structure, and provides a method for determining its structural parameters, so that the driving performance of the vehicle using this suspension can be improved. Effective promotion.

In order to achieve the above object of the invention, the technical solution adopted by the present invention is: to propose a two-stage tandem vehicle inertial suspension, the first-stage structure is composed of the first-stage spring and the inerter connected in parallel, and the second-stage structure is composed of the second The stage spring and damper are connected in parallel. The upper hinge point of the first-stage spring and the inerter is connected to the vehicle body, and the lower hinge point of the second-stage spring and the damper is connected to the wheel.

Preferably, according to the suspension motion dynamics and the quantum genetic algorithm fitness calculation rule, the method for determining the parameters of the inertial suspension of the vehicle includes the following steps:

(1) Population initialization: initialize the population Q(t)(t=0) of the quantum genetic algorithm, and set the population size, evolution algebra, qubit coding, and suspension parameter optimization range;

(2) Perform a measurement on each individual of the initial population Q(t), calculate the quantum genetic algorithm fitness value of each individual, and record the optimal individual and the corresponding fitness value;

(3) Judging whether the evolution algebra condition is met, if the condition is met, the algorithm ends, otherwise proceed to the next step; (4) Population algebra t=t+1;

(5) Use the quantum revolving door U(t) to perform evolutionary update on each individual to obtain a new population Q(t);

(6) Measure each individual of the population Q(t), and calculate the fitness value;

(7) Record the optimal individual and the corresponding fitness value;

(8) Judging whether the evolutionary algebraic condition is satisfied, if the evolutionary algebraic condition is satisfied, the algorithm ends, otherwise return to step (4).

In step (2), the steps of the quantum genetic algorithm fitness calculation rule are as follows:

(1) According to Newton's second law, a two-degree-of-freedom quarter suspension vibration model including body mass and wheel mass is established;

(2) Establish a 1/4 vehicle suspension vibration model that includes the parallel connection of the traditional passive suspension "spring-damper" two components, use integral white noise as input, and obtain the suspension at a vehicle speed of 20m/s through time domain simulation The root mean square value of body acceleration response BA _pas , the root mean square value of suspension dynamic stroke response SWS _pas , and the root mean square value of tire dynamic load response DTL _pas under random road surface input;

(3) Establish a 1/4 vehicle suspension vibration model comprising the vehicle inertial suspension proposed by the present invention, also adopt integral white noise to input, and obtain the suspension at a speed of 20m/s on a random road surface by time domain simulation Input the root mean square value BA of the acceleration response of the lower body, the root mean square value SWS of the dynamic stroke response of the suspension, and the root mean square value DTL of the tire dynamic load response.

In step (2), the fitness calculation formula of the quantum genetic algorithm is:

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; BA</span>}}{{\mathrm{<span\; class="notranslate">\; BA</span>}}_{\mathrm{<span\; class="notranslate">\; pas</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; SWS</span>}}{{\mathrm{<span\; class="notranslate">\; SWS</span>}}_{\mathrm{<span\; class="notranslate">\; pas</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; DTL</span>}}{{\mathrm{<span\; class="notranslate">\; DTL</span>}}_{\mathrm{<span\; class="notranslate">\; pas</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span>$

In step (5), the update evolution rule of the quantum revolving door is:

$\left[\begin{array}{c}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{<span\; class="notranslate">\; \&prime;</span>}\\ {\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{<span\; class="notranslate">\; \&prime;</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>\left[\begin{array}{c}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\\ {\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\end{array}\right]$

In step (5), the adjustment operation of the quantum revolving door is:

$u\left({\mathrm{\&theta;}}_i\right)=\left[\begin{array}{cc}\cos \left({\mathrm{\&theta;}}_i\right)& -\sin \left({\mathrm{\&theta;}}_i\right)\\ \sin \left({\mathrm{\&theta;}}_i\right)& \cos \left({\mathrm{\&theta;}}_i\right)\end{array}\right],$ _θi is determined by the rotation angle selection strategy set in Table 1.

Table 1 Rotation angle selection strategy map

In Table 1, xi is the _i -th position of the current chromosome, best _i is the i-th position of the current optimal chromosome, f(x) is the fitness function, and s(α _i , β _i ) is the rotation angle direction.

In step (1), the population initialization is as follows: set the population size to 40, the evolution algebra to 200, and the stiffness optimization range of the first-stage spring (1) and the second-stage spring (2) to be: [10000, 30000]N m ^-1 , the optimization range of the inertial coefficient of the inerter (3): [100,1000]kg, the optimization range of the damper (4) damping coefficient: [1000,3000]N·s·m ^-1 ; The qubit codes are all initialized as: What the chromosome expresses is the equal probability superposition of all possible states.

The beneficial effect of adopting the present invention is: compared with the parallel connection of the "capacitor-inductance" two components in the circuit system, when the parallel connection of the "inverter-spring" two components resonates in parallel, the vibration transmission can be effectively blocked. Based on this, a two-stage tandem vehicle inertial suspension structure is proposed. The application of this suspension structure can effectively suppress and attenuate the impact of road unevenness and improve the suspension performance. The determination method of the suspension parameters is based on the genetic optimization algorithm of quantum computing, and the quantum state vector expression is introduced into the genetic algorithm, which effectively solves the phenomenon that the genetic algorithm is easy to fall into a local extremum, and can be obtained under a small population size. Obtain satisfactory optimization parameters.

Description of drawings

Fig. 1 is a schematic diagram of the structure of a vehicle inertial suspension;

Fig. 2 is a flow chart of a method for determining parameters of a vehicle inertial suspension based on a quantum genetic algorithm;

Fig. 3 is a schematic diagram of a 1/4 suspension vibration model established in the suspension parameter determination method;

Fig. 4 is a comparison diagram of body acceleration frequency domain gain;

Fig. 5 is a comparison diagram of the frequency domain gain of the suspension stroke;

Figure 6 is a comparison diagram of tire dynamic load frequency domain gain.

Reference signs are explained as follows: 1. First-stage spring, 2. Second-stage spring, 3. Inerter, 4. Damper, 5. Body mass, 6. Vehicle suspension, 7. Wheel mass, 8. Tires, etc. effective spring.

detailed description

The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereto.

In Figure 1, a vehicle inertial suspension consists of two stages connected in series. The first stage structure is composed of the first stage spring 1 and the inerter 3 in parallel; the second stage is composed of the second stage spring 2 and the damper 4 in parallel. The upper hinge point of the first-stage spring 1 and the inerter container 3 is connected to the vehicle body, and the lower hinge point of the second-stage spring 2 and the damper 4 is connected to the wheel, thereby completing the installation of a vehicle inertial suspension structure.

In order to determine the specific parameters of each component of the inertial suspension of the vehicle, the present invention provides a method for determining the parameters of the first stage spring 1, the second stage spring 2, the inerter container 3 and the damper 4.

The specific flow chart of the method for determining the parameters of the vehicle inertial suspension based on the quantum genetic algorithm is shown in Figure 3, which mainly includes the following steps:

(1) Population initialization. For the population initialization of the quantum genetic algorithm, the population size is set to 40, the evolution algebra is 200, and the qubit codes of each individual are initialized as: What the chromosome expresses is the equal probability superposition of all possible states.

Here, the optimization range of each parameter of the suspension is set as follows:

$\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; 10000</span>}\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 30000</span>}\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}\\ \mathrm{<span\; class="notranslate">\; 100</span>}\mathrm{<span\; class="notranslate">\; kg</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 1000</span>}\mathrm{<span\; class="notranslate">\; kg</span>}\\ \mathrm{<span\; class="notranslate">\; 1000</span>}\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 3000</span>}\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}\end{array}\right.$

The calculation rules for the fitness value of the quantum genetic algorithm are set as follows:

According to Newton's second law, the dual-mass suspension vibration model is established as shown in Figure 3. In the figure, the mass of the vehicle body 5 is 320kg, the mass of the wheel 7 is 40kg, and the equivalent stiffness of the tire 8 is 190kN·m ^-1 . The road surface input Z _g (t) adopts the following road surface input model:

${\stackrel{<span\; class="notranslate">\; \&CenterDot;</span>}{\mathrm{<span\; class="notranslate">\; z</span>}}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}\sqrt{{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\mathrm{<span\; class="notranslate">\; u</span>}}\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>$

In the formula, the lower cut-off frequency f ₀ is 0.01Hz, the road surface roughness coefficient G ₀ is 5×10 ^-6 m ³ cycle ^-1 , w(t) is integral white noise, and the vehicle speed is 20m/s.

Firstly, a 1/4 vehicle suspension vibration model including the parallel connection of two components of the traditional passive suspension "spring-damper" is established. The spring stiffness of the passive suspension is 22kN·m ^-1 and the damping coefficient is 1000N·s·m ^-1 . Through time-domain simulation, the root mean square value BA _pas of body acceleration response, the root mean square value SWS _pas of suspension dynamic travel response and the root mean square value DTL _pas of tire dynamic load response are obtained under random road input of traditional passive suspension.

Establish a 1/4 vehicle suspension vibration model including the vehicle inertial suspension proposed by the present invention, obtain the vehicle body acceleration response root mean square value BA, suspension dynamic stroke response root mean square value SWS, and tire dynamic load under the same road surface input Response RMS DTL.

The fitness calculation formula of quantum genetic algorithm is:

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; BA</span>}}{{\mathrm{<span\; class="notranslate">\; BA</span>}}_{\mathrm{<span\; class="notranslate">\; pas</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; SWS</span>}}{{\mathrm{<span\; class="notranslate">\; SWS</span>}}_{\mathrm{<span\; class="notranslate">\; pas</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; DTL</span>}}{{\mathrm{<span\; class="notranslate">\; DTL</span>}}_{\mathrm{<span\; class="notranslate">\; pas</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span>$

(2) Measure each individual of the initial population Q(t), calculate the fitness value of the quantum genetic algorithm of each individual, and record the optimal individual and the corresponding fitness value.

(3) Judging whether the condition of evolution algebra is satisfied, if the condition is satisfied, the algorithm ends, otherwise, proceed to the next step.

(4) Population algebra t=t+1.

(5) Use the quantum revolving door U(t) to perform evolutionary update on each individual to obtain a new population Q(t).

The adjustment operation of the quantum revolving door is: $u\left({\mathrm{\&theta;}}_i\right)=\left[\begin{array}{cc}\cos \left({\mathrm{\&theta;}}_i\right)& -\sin \left({\mathrm{\&theta;}}_i\right)\\ \sin \left({\mathrm{\&theta;}}_i\right)& \cos \left({\mathrm{\&theta;}}_i\right)\end{array}\right]$

Its update process is as follows: $\left[\begin{array}{c}{\mathrm{\&alpha;}}_i^{\&prime;}\\ {\mathrm{\&beta;}}_i^{\&prime;}\end{array}\right]=u\left({\mathrm{\&theta;}}_i\right)\left[\begin{array}{c}{\mathrm{\&alpha;}}_i\\ {\mathrm{\&beta;}}_i\end{array}\right]$

Among them, (α _i , β _i ) ^T and (α _i ′, β _i ′) ^T represent the probability amplitudes before and after the revolving door update of the ith qubit of the chromosome. _θi is the rotation angle, which is determined by the rotation angle strategy set in Table 1.

In Table 1, xi is the _i -th position of the current chromosome, best _i is the i-th position of the current optimal chromosome, f(x) is the fitness function, and s(α _i , β _i ) is the rotation angle direction. ,

(6) Measure each individual of the population Q(t), and calculate the fitness value.

(7) Record the optimal individual and the corresponding fitness value.

(8) Judging whether the evolutionary algebraic condition is satisfied, if the evolutionary algebraic condition is satisfied, the algorithm ends, otherwise return to step 4.

The component parameters of the inertial suspension of the vehicle obtained by simulation optimization under the Matlab environment are:

The stiffness of the first stage spring 1 is: 30000kN·m ^-1

The stiffness of the second stage spring 2 is: 10000kN·m ^-1

The inertial coefficient of the inertial vessel 3 is: 312kg

The damping coefficient of the damper 4 is: 2080N·s·m ^-1

Substituting the component parameters obtained by the vehicle inertial suspension parameter determination method proposed by the present invention into the 1/4 suspension vibration model, the frequency domain response analysis diagram of its vehicle body acceleration, suspension dynamic stroke, and tire dynamic load is obtained as shown in Figure 4- As shown in Figure 6, it can be seen from the figure that compared with the traditional passive suspension, the inertial suspension of the vehicle can effectively buffer and attenuate the impact of the unevenness of the road surface, especially at the resonance bias frequency of the vehicle body, the vibration from the unevenness of the road surface The impact is obviously attenuated, and the frequency domain analysis shows that the vehicle inertial suspension proposed by the invention shows better performance advantages.

The described embodiment is a preferred implementation of the present invention, but the present invention is not limited to the above-mentioned implementation, without departing from the essence of the present invention, any obvious improvement, replacement or modification that those skilled in the art can make Modifications all belong to the protection scope of the present invention.

Claims (5)

1. A method for determining a vehicle inertial suspension structure parameter, the vehicle inertial suspension structure includes a first-level structure and a second-level structure, and the first-level structure is composed of a first-level spring (1) and a second-level structure Inerters (3) are connected in parallel, and the second-level structure is composed of second-level springs (2) and dampers (4) in parallel; the first-level structure and the second-level structure are connected in series; the first The upper hinge point of the first-stage spring (1) and the inertia container (3) is connected to the vehicle body, and the lower hinge point of the second-stage spring (2) and the damper (4) is connected to the wheel. It is characterized in that the vehicle inertia The method for determining the structural parameters of the mass suspension includes the following steps: (1) Population initialization: initialize the population Q(t)(t=0) of the quantum genetic algorithm, and set the population size, evolution algebra, qubit coding, and suspension parameter optimization range; (2) Perform a measurement on each individual of the initial population Q(t), calculate the quantum genetic algorithm fitness value of each individual, and record the optimal individual and the corresponding fitness value; (3) Judging whether the evolutionary algebra condition is met, if the condition is met, the algorithm ends, otherwise proceed to the next step; (4) population algebra t=t+1; (5) Use the quantum revolving door U(t) to perform evolutionary update on each individual to obtain a new population Q(t); (6) Measure each individual of the population Q(t), and calculate the fitness value; (7) Record the optimal individual and the corresponding fitness value; (8) Judging whether the evolutionary algebraic condition is satisfied, if the evolutionary algebraic condition is satisfied, the algorithm ends, otherwise return to step (4). 2. the determining method of a kind of vehicle inertial suspension structure parameter as claimed in claim 1, is characterized in that, in step (2), described quantum genetic algorithm fitness calculation rule step is as follows: (1) According to Newton's second law, a two-degree-of-freedom quarter suspension vibration model including body mass and wheel mass is established; (2) Establish a 1/4 vehicle suspension vibration model that includes the parallel connection of the traditional passive suspension "spring-damper" two components, use integral white noise as input, and obtain the suspension at a vehicle speed of 20m/s through time domain simulation The root mean square value of body acceleration response BA _pas , the root mean square value of suspension dynamic stroke response SWS _pas , and the root mean square value of tire dynamic load response DTL _pas under random road surface input; (3) Establish a 1/4 vehicle suspension vibration model including the vehicle inertial suspension, and also use integral white noise for input, and through time domain simulation, obtain the vehicle body acceleration of the suspension on a random road with a vehicle speed of 20m/s Response root mean square value BA, suspension dynamic stroke response root mean square value SWS, tire dynamic load response root mean square value DTL. 3. the determining method of a kind of vehicle inertial suspension structure parameter as claimed in claim 2, is characterized in that, in step (2), the fitness computing formula of described quantum genetic algorithm is: $\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; B</span>}\mathrm{<span\; class="notranslate">\; A</span>}}{{\mathrm{<span\; class="notranslate">\; BA</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; S</span>}\mathrm{<span\; class="notranslate">\; W</span>}\mathrm{<span\; class="notranslate">\; S</span>}}{{\mathrm{<span\; class="notranslate">\; SWS</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; D.</span>}\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; L</span>}}{{\mathrm{<span\; class="notranslate">\; DTL</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span>$ 4. the determining method of a kind of vehicle inertial suspension structure parameter as claimed in claim 1, is characterized in that, in step (5), The update evolution rule of the quantum revolving door is: $\left[\begin{array}{c}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{<span\; class="notranslate">\; \&prime;</span>}\\ {\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{<span\; class="notranslate">\; \&prime;</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>\left[\begin{array}{c}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\\ {\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\end{array}\right]$ The adjustment operation of the quantum revolving door is: _θi is determined by the rotation angle selection strategy set in Table 1; Table 1 Rotation angle selection strategy map In Table 1, x _i is the i-th position of the current chromosome, best _i is the i-th position of the current optimal chromosome, f(x) is the fitness function, and s(α _i , β _i ) is the rotation angle direction; (α _i , β _i ) ^T and (α _i ′, β _i ′) ^T represent the probability amplitudes before and after the revolving door update of the i-th qubit of the chromosome, respectively. 5. the determining method of a kind of vehicle inertial suspension structure parameter as claimed in claim 1, is characterized in that, in step (1), described population initialization is: setting population size is 40, and evolutionary number is 200, The optimization range of the stiffness of the first stage spring (1) and the second stage spring (2) is: [10000,30000]N m ^-1 , the optimization range of the inertial coefficient of the inerter (3) is: [100,1000]kg, Damper (4) damping coefficient optimization range: [1000,3000]N s m ^-1 ; the qubit codes of each individual are initialized as: What the chromosome expresses is the equal probability superposition of all possible states.