CN113103836B - Asymmetric reciprocating damping-based vehicle ISD suspension structure and optimal design method - Google Patents

Abstract

The present invention discloses a vehicle ISD suspension structure based on asymmetric reciprocating damping and an optimization design method. The proposed vehicle ISD suspension structure is a four-element structure including a main spring and a secondary spring, wherein the first parallel element is a main spring, the second parallel element is an integral element consisting of an inertia container and a secondary spring, and the third parallel element is an asymmetric reciprocating damper. The asymmetric characteristics of the asymmetric reciprocating damper are utilized to better suppress the vibration input from the road surface. The parameters of the suspension are optimized using a genetic algorithm. The results show that by using the vehicle ISD suspension structure based on asymmetric reciprocating damping and an optimization design method proposed by the present invention, the dynamic performance index of the suspension is significantly improved compared with the traditional passive suspension under random, pulse and sinusoidal inputs, thereby achieving an improvement in the overall dynamic performance of the vehicle.

Description

A vehicle ISD suspension structure and optimization design method based on asymmetric reciprocating damping

Technical Field

The invention relates to an optimization design method for a vehicle ISD suspension based on asymmetric reciprocating damping, and belongs to the technical field of vehicle suspension vibration reduction.

Background Art

As one of the most important components of the vehicle chassis, the suspension plays a vital role in the smoothness and stability of the vehicle's driving. At present, the ISD (Inter-Spring-Damper) suspension with inertial elements has attracted widespread attention due to its low energy consumption and excellent vibration isolation effect. Chinese patent 201410637469.6 discloses a vehicle inertial suspension structure and a parameter determination method thereof, and optimizes the parameters using an improved genetic algorithm. In the article "Performance investigation of vehicle system with nonlinear ball-screw inerter", X.Q.Sun established a nonlinear mechanical model of the ball screw pair considering the friction and elastic effect of the ball screw pair, and based on experimental data, used the recursive least squares algorithm to identify the parameters of the nonlinear mechanical model. Then, the nonlinear ball screw inertial element was applied to the suspension analysis of the three-passive suspension half-car model, and finally the performance of the nonlinear ball screw inertial suspension system and the linear inertial suspension system were compared. It is a bit regrettable that the above research is based on the working condition of ideal symmetrical reciprocating damping, but in fact the damping will show asymmetrical changes due to the two different conditions of suspension tension and compression. Therefore, this paper proposes a vehicle ISD suspension structure based on asymmetric reciprocating damping, and conducts optimization design and performance impact analysis to improve its practical value.

Summary of the invention

The present invention aims to select a vehicle ISD suspension with application prospects and simple structure, consider the asymmetric characteristics of compression damping and extension damping, effectively establish a 1/4 vehicle suspension dynamics model, and use genetic algorithms to perform single-objective optimization and multi-objective optimization respectively, simulate and analyze the suspension, verify the application prospects of the selected ISD suspension, and provide a theoretical basis for the practical application of vehicle ISD suspension.

To achieve the above invention objectives, a vehicle ISD suspension based on asymmetric reciprocating damping is provided, wherein the suspension structure is a four-element parallel structure including a main spring and a secondary spring, wherein the first parallel element is composed of a main spring (1), the second parallel element is composed of a secondary spring (2) and an inertia container (4), and the third parallel element is composed of an asymmetric reciprocating damping damper (3).

A vehicle ISD suspension optimization design method based on asymmetric reciprocating damping of the present invention comprises the following steps:

1) Population initialization: Initialize the population of the genetic algorithm, that is, set the number of suspension parameter groups, parameter optimization algebra, suspension parameter optimization range, fitness value calculation rules and penalty value selection rules;

2) Measure each individual of the initial population, i.e., the initial suspension parameter group, to obtain a state, obtain the corresponding definite solution, calculate the fitness of each definite solution and record the optimal individual and the corresponding fitness value;

3) Determine whether the evolutionary algebra conditions are met, if so, exit, otherwise continue calculation;

4) Measure each individual in the population, i.e. each set of suspension parameters, obtain a state and a corresponding definite solution, and calculate the fitness value;

5) The selection of individuals in the population is completed through roulette;

6) When the hybridization probability is the default value, random hybridization is performed among the individuals selected in step 5) to obtain a progeny population;

7) When the mutation probability is the default value, complete step 6) to obtain the mutation of the offspring individuals;

8) Record the best individual of the offspring and the corresponding fitness value;

9) The number of iterations + 1, enter the end condition judgment, if the evolutionary generation is satisfied, exit, otherwise return to step 4);

10) After completing the above optimization, the superior performance of the suspension is further verified under other road inputs based on the optimization results.

Furthermore, the calculation rule steps of the fitness value are as follows:

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

2.2) Establish a 1/4 vehicle suspension vibration model that includes a passive suspension "spring-damper" two-element parallel connection, use integrated white noise as input, and through time domain simulation analysis, obtain the body acceleration response root mean square value BA _BD , suspension dynamic travel response root mean square value SWS _BD , tire dynamic load response root mean square value DTL _BD of the suspension under random road input at a vehicle speed of 20m/s;

2.3) Establish a 1/4 vehicle suspension vibration model including a vehicle ISD suspension based on asymmetric reciprocating damping proposed in the present invention, and also use integrated white noise as input. Through time domain simulation analysis, obtain the body acceleration response root mean square value BA, suspension dynamic stroke response root mean square value SWS, and tire dynamic load response root mean square value DTL of the suspension under random road input at a vehicle speed of 20m/s;

2.4) The calculation formula of the multi-objective optimization fitness function of the genetic algorithm is:

2.5) Taking vehicle acceleration as an example, the single-objective optimization fitness function calculation formula of the genetic algorithm is:

Among them, Punishment is the penalty number.

Furthermore, the body acceleration response root mean square value BA _BD , the suspension dynamic travel response root mean square value SWS _BD , and the tire dynamic load response root mean square value DTL _BD under the random road input are 1.1948 m·s ^-2 , 0.0119 m, and 839.2 N respectively.

Furthermore, the size of the suspension parameter groups is 100, and the number of parameter optimization generations is 20;

The value of the penalty number Punishment in the multi-objective optimization fitness function calculation formula is as follows: as long as one of the body acceleration response root mean square value BA, the suspension dynamic stroke response root mean square value SWS, and the tire dynamic load response root mean square value DTL is greater than the body acceleration response root mean square value BA _BD , the suspension dynamic stroke response root mean square value SWS _BD , and the tire dynamic load response root mean square value DTL _BD in the traditional passive suspension, the penalty number Punishment takes a value of 100, otherwise it takes a value of 0;

The value of the penalty number Punishment in the single-objective optimization fitness function calculation formula of the genetic algorithm is as follows: as long as the body acceleration response root mean square value BA is greater than the body acceleration response root mean square value BA _BD in the traditional passive suspension, the penalty number Punishment takes a value of 100, otherwise it takes a value of 0.

Further, the optimization range of suspension parameters is set as follows: the selected parameters to be optimized are the inertia coefficient b, the main spring stiffness k ₁ , the auxiliary spring stiffness k ₂ , the tension damping coefficient c ₁ and the compression damping coefficient c ₂ ; wherein the optimization range of the inertia coefficient is: [0,8000]kg, the optimization range of the main spring stiffness is: [0,30000]N·m ^-1 , the optimization range of the auxiliary spring stiffness is: [0,20000]N·m ^-1 , and the optimization range of the damping coefficient is: [0,4000]N·s·m ^-1 .

Furthermore, in step 10), the process of verifying the superior performance of the suspension is as follows: based on the parameter optimization results obtained in step 3), simulation analysis is performed to obtain the dynamic improvement of the suspension proposed by the present invention under pulse and sinusoidal road surface inputs respectively.

The beneficial effects of the present invention are as follows: based on the consideration of the asymmetry of reciprocating damping in the vehicle ISD suspension system, a suspension optimization design method is proposed, which effectively improves the accuracy of the suspension dynamics model and improves the vibration isolation performance of the suspension.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be further described below in conjunction with the accompanying drawings.

FIG. 1 is a schematic diagram of a vehicle ISD suspension structure based on asymmetric reciprocating damping.

FIG. 2 is a flow chart of a vehicle ISD suspension structure optimization design method based on asymmetric reciprocating damping.

FIG. 3 is a schematic diagram of a common passive suspension 1/4 vehicle suspension model.

FIG. 4 is a schematic diagram of a quarter vehicle suspension model of an ISD suspension based on asymmetric reciprocating damping.

Figure 5 is a time domain diagram of vehicle body acceleration based on single-objective optimization results under pulse input.

Figure 6 is a time domain diagram of vehicle body acceleration based on single-objective optimization results under sinusoidal input.

Figure 7 is a time domain diagram of the suspension dynamic travel based on the single-objective optimization result under pulse input.

Figure 8 is a time domain diagram of the suspension dynamic travel based on the single-objective optimization result under sinusoidal input.

Figure 9 is a time domain diagram of tire dynamic load based on single-objective optimization results under pulse input.

Figure 10 is a time domain diagram of tire dynamic load based on single-objective optimization results under sinusoidal input.

In the figure, 1. main spring, 2. auxiliary spring, 3. asymmetric reciprocating damper, 4. inertia container, 5. vehicle body mass, 6. vehicle suspension, 7. wheel mass, 8. tire equivalent spring.

DETAILED DESCRIPTION

The present invention is further described below in conjunction with the accompanying drawings and examples. As shown in FIG1 , a schematic diagram of a vehicle ISD suspension structure based on asymmetric reciprocating damping proposed by the present invention is shown, comprising a main spring 1, a secondary spring 2, an asymmetric reciprocating damper 3, and an inertia container 4, wherein the secondary spring 2 and the inertia container 4 are connected in series, and then connected in parallel with the main spring 1 and the asymmetric reciprocating damper 3.

As shown in FIG2 , there is a flow chart of a method for optimizing the design of a vehicle ISD suspension structure based on asymmetric reciprocating damping. In a common passive suspension 1/4 vehicle suspension model, the spring and the damper are connected in parallel. In FIG4 , there is an ISD suspension 1/4 vehicle suspension model based on asymmetric reciprocating damping, in which the auxiliary spring and the inertia container are connected in series, and then the whole is connected in parallel with the main spring and the asymmetric reciprocating damper, thereby constituting a control example of the present invention.

The present invention proposes a vehicle ISD suspension optimization design method based on asymmetric reciprocating damping, and the flow chart is shown in FIG2 . The vehicle ISD suspension based on asymmetric reciprocating damping and the parameter determination method thereof are characterized in that the suspension parameter determination method is as follows:

(1) Population initialization: For the population initialization of the genetic algorithm, the population size (the number of suspension parameter groups) is set to 100 and the evolutionary generation (parameter optimization generation) is set to 20.

The optimization range of each suspension parameter is set as follows:

In the formula, the parameters to be optimized are the inertia coefficient b, the main spring stiffness k ₁ , the auxiliary spring stiffness k ₂ , the tension damping coefficient c ₁ and the compression damping coefficient c ₂ .

The random road surface input z _r uses the following road surface input model:

Where _G0 is the road roughness coefficient; v is the vehicle speed; _f0 is the lower cutoff frequency; w(t) is the Gaussian white noise with a mean equal to zero.

According to Newton's second law, the dynamic equation corresponding to the vehicle ISD suspension based on asymmetric reciprocating damping is:

Where: _ms is the sprung mass; _mu is the unsprung mass; _k1 is the main spring stiffness; _k2 is the auxiliary spring stiffness; b is the inertia coefficient; _kt is the tire stiffness; _zs , _zb , _zu , _zr are the vertical displacements of the vehicle body, inertia container, tire and road surface respectively; F is the force between the inertia container and the auxiliary spring, _c1 represents the tensile damping, and _c2 represents the compression damping. The model parameters selected in the above formula are shown in Table 1.

Table 1

A 1/4 vehicle suspension vibration model consisting of two elements of the traditional passive suspension "spring-damper" in parallel is established, and its corresponding dynamic model is:

Where, _ms is the sprung mass; _mu is the unsprung mass; k is the spring stiffness; _kt is the tire stiffness; _zs , _zu , _zr are the vertical displacements of the vehicle body, tire, and road surface, respectively; are the vertical speeds of the vehicle body and tire respectively; are the vertical accelerations of the vehicle body and tire respectively; c represents damping.

Using integrated white noise as input, through time domain simulation analysis, the root mean square value BA _BD of the body acceleration response, the root mean square value SWS _BD of the suspension dynamic travel response, and the root mean square value DTL _BD of the tire dynamic load response of the suspension under the random road input of the vehicle speed of 20m/s are obtained, and the above three values are 1.1948m·s ^-2 , 0.0119m, and 839.2N respectively.

A 1/4 vehicle suspension vibration model of an ISD suspension based on asymmetric reciprocating damping proposed by the present invention is established, and its corresponding dynamic model is:

Wherein, _ms is the sprung mass; _mu is the unsprung mass; _k1 is the main spring stiffness; _k2 is the auxiliary spring stiffness; b is the inertia coefficient; _kt is the tire stiffness; _zs , _zb , _zu , _zr are the vertical displacements of the vehicle body, inertia container, tire and road surface respectively; are the vertical speeds of the vehicle body and tire respectively; are the vertical accelerations of the vehicle body, inertia container and tire respectively; F is the force between the inertia container and the auxiliary spring, _c1 represents the tensile damping, and _c2 represents the compression damping.

Integral white noise is also used as input. Through time domain simulation analysis, the root mean square value BA of the body acceleration response, the root mean square value SWS of the suspension dynamic stroke response, and the root mean square value DTL of the tire dynamic load response are obtained under the random road input of the vehicle speed of 20m/s.

The calculation formula of the multi-objective optimization fitness function of the genetic algorithm is:

Taking the vehicle body acceleration as an example, the single-objective optimization fitness function calculation formula of the genetic algorithm is:

The penalty number Punishment value rule in the fitness function calculation formula of the above-mentioned genetic algorithm multi-objective optimization is: as long as one of the body acceleration response root mean square value BA, the suspension dynamic stroke response root mean square value SWS, and the tire dynamic load response root mean square value DTL is greater than the body acceleration response root mean square value BA _BD , the suspension dynamic stroke response root mean square value SWS _BD , and the tire dynamic load response root mean square value DTL _BD in the traditional passive suspension, the penalty number Punishment value is 100, otherwise it is 0. The penalty number Punishment value rule in the fitness function calculation formula of the genetic algorithm single-objective optimization is: as long as the body acceleration response root mean square value BA is greater than the body acceleration response root mean square value BA _BD in the traditional passive suspension, the penalty number Punishment value is 100, otherwise it is 0.

(2) Measure each individual in the initial population once, obtain a state, obtain the corresponding deterministic solution, calculate the fitness of each deterministic solution, and record the optimal individual and its corresponding fitness value.

(3) Determine whether the evolutionary algebra conditions are met. If so, exit; otherwise, continue calculating.

(4) Measure each individual in the population, obtain a state and a corresponding definite solution, and calculate the fitness value.

(5) The selection of individuals in the population is completed through roulette.

(6) When the hybridization probability is the default value, random hybridization is performed among the individuals selected in step (5) to obtain a progeny population.

(7) When the mutation probability is the default value, complete step (6) to obtain the mutation of the offspring individuals.

(8) Record the best individual of the offspring and the corresponding fitness value.

(9) The number of iterations + 1, enter the end condition judgment, if the evolutionary algebra is satisfied, exit, otherwise return to step (4).

(10) Similarly, under pulse and sinusoidal road inputs, the parameter optimization results obtained based on the above steps are used to verify the superior dynamic performance of the suspension proposed in the present invention under the two road conditions.

The component parameters of the vehicle ISD suspension based on asymmetric reciprocating damping obtained by simulation optimization in the Matlab/Simulink environment are:

(1) Single-objective optimization based on vehicle body acceleration:

The stiffness of the main spring 1 is: 2860N·m ^-1

The stiffness of the auxiliary spring 2 is: 847N·m ^-1

The tensile damping coefficient of the asymmetric reciprocating damper 3 is: 1149N·s·m ^-1

The compression damping coefficient of the asymmetric reciprocating damper 3 is: 1057N·s·m ^-1

The inertia coefficient of inertia container 4 is: 5351kg

(2) Single-objective optimization based on suspension travel:

The stiffness of the main spring 1 is: 7351N·m ^-1

The stiffness of the auxiliary spring 2 is: 3146N·m ^-1

The tensile damping coefficient of the asymmetric reciprocating damper 3 is: 1797N·s·m ^-1

The compression damping coefficient of the asymmetric reciprocating damper 3 is: 1836N·s·m ^-1

The inertia coefficient of inertia container 4 is: 315kg

(3) Single-objective optimization based on suspension travel:

The stiffness of the main spring 1 is: 11519N·m ^-1

The stiffness of the auxiliary spring 2 is: 4072N·m ^-1

The tensile damping coefficient of the asymmetric reciprocating damper 3 is: 1733N·s·m ^-1

The compression damping coefficient of the asymmetric reciprocating damper 3 is: 1822N·s·m ^-1

The inertia coefficient of inertia container 4 is: 24kg

(4) Multi-objective optimization:

The stiffness of the main spring 1 is: 4446N·m ^-1

The stiffness of the auxiliary spring 2 is: 3948N·m ^-1

The tensile damping coefficient of the asymmetric reciprocating damper 3 is: 17027N·s·m ^-1

The compression damping coefficient of the asymmetric reciprocating damper 3 is: 1686N·s·m ^-1

The inertia coefficient of inertia container 4 is: 1562kg

Through simulation analysis under various working conditions and using the optimization method proposed by the present invention, the selected vehicle ISD suspension based on asymmetric reciprocating damping has excellent low-frequency vibration reduction performance under random road input, and has great room for improvement in dynamic performance. Under pulse road input, the response time and overshoot of various performance indicators of the suspension are significantly improved; under sinusoidal road input, the response peak suppression effect of the asymmetric reciprocating damping ISD suspension is better than that of the symmetric reciprocating damping ISD suspension, showing excellent stability and practicality.

The implementation cases are preferred implementation modes of the present invention, but the present invention is not limited to the above implementation modes. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essential content of the present invention belong to the protection scope of the present invention.

Claims (3)

1. An optimal design method of an ISD suspension of a vehicle based on asymmetric reciprocating damping is characterized in that the suspension structure is a four-element parallel structure containing a main spring and a secondary spring, a first parallel element is composed of the main spring (1), a second parallel element is composed of the secondary spring (2) and an inertial container (4), and a third parallel element is composed of an asymmetric reciprocating damping damper (3);

the method comprises the following steps:

1) Initializing a population: initializing the population of a genetic algorithm, namely setting a suspension parameter group number, a parameter optimization algebra, a suspension parameter optimization range, a fitness value calculation rule and a penalty value rule;

2) Measuring each individual of the initial population, namely the initial suspension parameter set, once to obtain a state, obtaining corresponding determined solutions, calculating the fitness of each determined solution, and recording the optimal individual and the corresponding fitness value;

3) Judging whether the evolution algebra condition is met, if so, exiting, otherwise, continuing to calculate;

4) Measuring each individual of the population, namely each group of suspension parameters, obtaining a state and a corresponding determination solution, and calculating a fitness value;

5) The selection of individuals in the population is completed by means of roulette;

6) Under the condition that the hybridization probability is a default value, randomly hybridizing the individuals selected in the step 5) to obtain a offspring population;

7) Under the condition that the mutation probability is a default value, completing the step 6) to obtain mutation of the offspring individuals;

8) Recording optimal individuals of the offspring and corresponding fitness values;

9) Iteration times +1, entering end condition judgment, exiting if the evolution algebra is met, otherwise returning to the step 4);

10 After the optimization is completed, further verifying the superior performance of the suspension under other road surface input based on the optimization result;

the calculation rule of the fitness value comprises the following steps:

2.1 According to Newton's second law, building a quarter suspension vibration model comprising two degrees of freedom motion of the mass of the vehicle body and the mass of the wheel;

2.2 1/4 vehicle suspension vibration model comprising parallel two elements of a passive suspension 'spring-damper', adopting integral white noise for input, and obtaining a vehicle body acceleration response root mean square value BA _BD of the suspension under random road surface input with the vehicle speed of 20m/s through time domain simulation analysis, wherein the dynamic travel response root mean square value SWS _BD of the suspension and the dynamic load response root mean square value DTL _BD of the tire;

2.3 The invention provides an asymmetric reciprocation damping-based vehicle ISD suspension 1/4 vehicle suspension vibration model, which is also input by adopting integral white noise, and obtains a vehicle body acceleration response root mean square value BA and a tire dynamic load response root mean square value DTL of the suspension under the random road surface input of the vehicle speed of 20m/s through time domain simulation analysis;

2.4 Multi-objective optimization fitness function calculation formula of genetic algorithm is:

2.5 Taking the acceleration of the vehicle body as an example, a single-target optimization fitness function calculation formula of the genetic algorithm is as follows:

wherein Punishment is penalty number;

The vehicle body acceleration response root mean square value BA _BD, the suspension dynamic travel response root mean square value SWS _BD and the tire dynamic load response root mean square value DTL _BD under the random road surface input are 1.1948 m.s ^-2, 0.0119m and 839.2N in sequence;

The number of the suspension parameter groups is 100, and the parameter optimization algebra is 20;

The penalty number Punishment in the multi-objective optimization fitness function calculation formula is as follows: as long as one of the suspension dynamic travel response root mean square value SWS and the tire dynamic load response root mean square value DTL is larger than the vehicle acceleration response root mean square value BA _BD in the traditional passive suspension when the vehicle acceleration responds to the root mean square value BA, the suspension dynamic travel response root mean square value SWS _BD and the tire dynamic load response root mean square value DTL _BD, the penalty value Punishment takes on the value of 100, otherwise takes on the value of 0;

The penalty number Punishment in the single-objective optimization fitness function calculation formula of the genetic algorithm is as follows: the penalty Punishment is rated as 100 whenever the body acceleration response rms BA is greater than the body acceleration response rms BA _BD in the conventional passive suspension, or else is rated as 0.

2. The optimal design method for the vehicle ISD suspension based on asymmetric reciprocation damping according to claim 1, wherein the suspension parameter optimization range is set as follows: the parameters to be optimized are selected to be an inertial coefficient b, a primary spring stiffness k ₁, a secondary spring stiffness k ₂, a tensile damping coefficient c ₁ and a compression damping coefficient c ₂; the optimization range of the inertial coefficient is as follows: [0,8000] kg, the main spring stiffness optimization range is: [0,30000] N.m ^-1, the optimization range of the secondary spring stiffness is: [0,20000] n.m ^-1, the damping coefficient optimization range is: [0,4000] N.s.m ^-1.

3. The optimal design method for the vehicle ISD suspension based on asymmetric reciprocation damping according to claim 1, wherein in the step 10), the superior performance verification process of the suspension is as follows: based on the parameter optimization result obtained in the step 3), the dynamics improvement condition of the suspension provided by the invention under two road surfaces is obtained through simulation analysis under the pulse and sine road surface input respectively.