CN113103836A - Vehicle ISD suspension structure based on asymmetric reciprocating damping and optimal design method - Google Patents
Vehicle ISD suspension structure based on asymmetric reciprocating damping and optimal design method Download PDFInfo
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Abstract
The invention discloses a vehicle ISD suspension structure based on asymmetric reciprocating damping and an optimized design method. By utilizing the asymmetric characteristic of the asymmetric reciprocating damping damper, better inhibiting effect is generated on the vibration input on the road surface. And optimizing the parameters of the suspension by using a genetic algorithm. The results show that: by applying the vehicle ISD suspension structure based on asymmetric reciprocating damping and the optimization design method provided by the invention, the dynamic performance indexes of the suspension are remarkably improved under random, pulse and sine input compared with the traditional passive suspension, and the improvement on the overall dynamic performance of the vehicle is realized.
Description
Technical Field
The invention relates to an optimal design method of a vehicle ISD suspension based on asymmetric reciprocating damping, and belongs to the technical field of vehicle suspension vibration reduction.
Background
As one of the most important parts of a vehicle chassis, a suspension plays a crucial role in the smoothness and stability of the vehicle. At present, ISD (inertial-Spring-Damper) suspension with an inertia element is widely concerned due to low energy consumption and excellent vibration isolation effect. Chinese patent 201410637469.6 discloses a vehicle inerter suspension structure and a method for determining parameters thereof, which optimizes the parameters using an improved genetic algorithm. And in the text of Performance excitation of vehicle system with nonlinear ball-screw inerter, the method comprises the steps of establishing a nonlinear mechanical model of a ball screw pair considering friction of the ball screw pair and elastic effect of a screw, identifying parameters of the nonlinear mechanical model by using a recursive least square algorithm based on experimental data, applying a nonlinear ball screw inerter element to suspension analysis of a three-passive suspension semi-vehicle model, and finally comparing the performances of the nonlinear ball screw inerter suspension system and a linear inerter suspension system. Unfortunately, the above research is based on the working condition of ideal symmetric reciprocating damping, and actually, damping may present asymmetric change due to two different situations of stretching and compressing of the suspension, so a vehicle ISD suspension structure based on asymmetric reciprocating damping is proposed herein, optimized design and performance impact analysis are performed, and practical value is improved.
Disclosure of Invention
The invention aims to select a vehicle ISD suspension with a simple structure and an application prospect, effectively establish 1/4 a vehicle suspension dynamic model by considering the asymmetric characteristics of compression damping and extension damping, respectively carry out single-target optimization and multi-target optimization by utilizing a genetic algorithm, carry out simulation analysis on the suspension, verify the application prospect of the selected ISD suspension and provide a theoretical basis for the practical application of the vehicle ISD suspension.
In order to achieve the aim, the vehicle ISD suspension based on asymmetric reciprocating damping is a four-element parallel structure comprising a main spring and an auxiliary spring, wherein a first parallel element is composed of the main spring (1), a second parallel element is composed of the auxiliary spring (2) and an inertia container (4), and a third parallel element is composed of an asymmetric reciprocating damping damper (3).
The invention discloses an optimal design method of an ISD suspension of a vehicle based on asymmetric reciprocating damping, which comprises the following steps:
1) population initialization: initializing the population of the genetic algorithm, namely setting the number of suspension parameter groups, parameter optimization algebra, suspension parameter optimization range, an adaptability value calculation rule and a penalty value dereferencing rule;
2) measuring each individual of an initial population, namely an initial suspension parameter group, once to obtain a state, acquiring corresponding determination solutions, calculating the fitness of each determination solution and recording an optimal individual and a 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 to obtain a state and a corresponding determination solution, and calculating a fitness value;
5) the selection of individuals in the population is completed in a roulette mode;
6) under the condition that the crossing probability is a default value, randomly crossing the individuals selected in the step 5) to obtain a filial generation group;
7) under the condition that the variation probability is a default value, completing the step 6) to obtain the variation of the filial generation individuals;
8) recording the optimal individuals of the filial generations and the corresponding fitness values;
9) iteration times are +1, judgment of ending conditions is carried out, if the evolution algebra is met, the operation is exited, and if the iteration times are not met, the operation returns to the step 4);
10) after the optimization is completed, the superior performance of the suspension is further verified under other road surface input conditions based on the optimization result.
Further, the calculation rule of the fitness value comprises the following steps:
2.1) establishing a quarter suspension vibration model containing two-degree-of-freedom motion of vehicle body mass and wheel mass according to Newton's second law;
2.2) establishing an 1/4 vehicle suspension vibration model with two elements of a passive suspension spring-damper connected in parallel, inputting by adopting integral white noise, and acquiring a body acceleration response root mean square value BA of the suspension when the suspension is input on a random road surface with the vehicle speed of 20m/s through time domain simulation analysisBDRoot mean square value SWS of suspension dynamic stroke responseBDRoot mean square value DTL of dynamic load response of tireBD；
2.3) establishing a vibration model of the vehicle suspension comprising the vehicle ISD suspension 1/4 based on asymmetric reciprocating damping, inputting by adopting integral white noise, and obtaining a body acceleration response root mean square value BA, a suspension dynamic stroke response root mean square value SWS and a tire dynamic load response root mean square value DTL of the suspension when the suspension is input on a random road surface with the vehicle speed of 20m/s through time domain simulation analysis;
2.4) the calculation formula of the multi-objective optimization fitness function of the genetic algorithm is as follows:
2.5) taking the acceleration of the vehicle body as an example, the calculation formula of the single-target optimization fitness function of the genetic algorithm is as follows:
wherein Punishment is a penalty number.
Further, the acceleration response root mean square value BA of the lower body under the random road surface inputBDRoot mean square value SWS of suspension dynamic stroke responseBDRoot mean square value DTL of dynamic load response of tireBDAnd the three values are 1.1948 m.s in sequence-2，0.0119m，839.2N。
Further, the number of the suspension parameter sets is 100, and the parameter optimization algebra is 20;
the Punishment number Punishment value rule in the multi-objective optimization fitness function calculation formula is as follows: only when the vehicle body acceleration responds to the root mean square value BA, the suspension dynamic stroke responds to the root mean square value SWS, and one of the tire dynamic load response root mean square values DTL is larger than the vehicle body acceleration response root mean square value BA in the traditional passive suspensionBDRoot mean square value SWS of suspension dynamic stroke responseBDRoot mean square value DTL of dynamic load response of tireBDIf so, the Punishment number Punishment is 100, otherwise, the Punishment number Punishment is 0;
the penalty number Punishment value rule in the single-target optimization fitness function calculation formula of the genetic algorithm is as follows: as long as the vehicle body acceleration response root mean square value BA is larger than that in the traditional passive suspensionBDIf so, the penalty number Punishment is 100, otherwise, the penalty number Punishment is 0.
Further, the suspension parameter optimization range is set as: selecting parameters to be optimized as inertia mass coefficient b and main spring stiffness k1Stiffness k of secondary spring2Tensile damping coefficient c1And compression damping coefficient c2(ii) a Wherein the optimization range of the inertia mass coefficient is as follows: [0,8000]kg, the optimized range of the stiffness of the main spring is as follows: [0,30000]N·m-1The optimal range of the stiffness of the auxiliary spring is as follows: [0,20000]N·m-1The damping coefficient optimization range is as follows: [0,4000]N·s·m-1。
Further, in the step 10), the process for verifying the excellent performance of the suspension is as follows: and (3) under the input of pulse and sinusoidal road surfaces respectively, based on the parameter optimization result obtained in the step 3), performing simulation analysis to obtain the dynamics improvement condition of the suspension provided by the invention under two road surfaces.
The beneficial effects of the invention are as follows: on the basis of considering the asymmetry of reciprocating damping in an ISD suspension system of a vehicle, the suspension optimization design method is provided, the accuracy of a suspension dynamic model is effectively improved, and the vibration isolation performance of the suspension is improved.
Drawings
The invention will be further explained with reference to the 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 vehicle suspension model of a generic passive suspension 1/4.
Fig. 4 is a schematic diagram of a vehicle suspension model of an ISD suspension 1/4 based on asymmetric reciprocating damping.
FIG. 5 is a time domain plot of vehicle body acceleration based on a single target optimization result with pulse input.
FIG. 6 is a time domain plot of body acceleration based on a single target optimization result with sinusoidal input.
FIG. 7 is a time domain graph of suspension dynamic travel based on single objective optimization results with pulse input.
FIG. 8 is a time domain plot of suspension dynamic travel based on the single objective optimization results with sinusoidal input.
FIG. 9 is a time domain plot of tire dynamic load based on a single target optimization result with pulse input.
FIG. 10 is a time domain plot of tire dynamic load based on a single target optimization result with sinusoidal input.
In the figure, 1 is a main spring, 2 is an auxiliary spring, 3 is an asymmetric reciprocating damper, 4 is an inerter, 5 is a vehicle body mass, 6 is a vehicle suspension, 7 is a wheel mass, and 8 is a tire equivalent spring.
Detailed Description
The invention is further explained with reference to the accompanying drawings and examples, and fig. 1 is a schematic diagram of a vehicle ISD suspension structure based on asymmetric reciprocating damping, which comprises a main spring 1, an auxiliary spring 2, an asymmetric reciprocating damping damper 3 and an inerter 4, wherein the auxiliary spring 2 and the inerter 4 are connected in series and then connected in parallel with the main spring 1 and the asymmetric reciprocating damping damper 3.
Fig. 2 is a flow chart of a method for optimally designing an ISD suspension structure of a vehicle based on asymmetric reciprocating damping, a spring and a damper in a common passive suspension 1/4 vehicle suspension model are connected in parallel, and an auxiliary spring and an inertia container are connected in series in an ISD suspension 1/4 vehicle suspension model based on asymmetric reciprocating damping in fig. 4, and then the whole is connected in parallel with a main spring and an asymmetric reciprocating damper, so that a comparison example of the invention is formed.
The invention provides an optimal design method of a vehicle ISD suspension based on asymmetric reciprocating damping, a flow chart is shown in figure 2, the vehicle ISD suspension based on asymmetric reciprocating damping and a parameter determination method thereof are characterized in that the determination method of suspension parameters is as follows:
(1) and (5) initializing a population. For population initialization of the genetic algorithm, the population size (the number of sets of suspension parameters) is set to 100, and evolution algebra (parameter optimization algebra) is set to 20.
The optimized range of each suspension parameter is set as follows:
in the formula, the parameters to be optimized are inertia coefficient b and main spring stiffness k1Stiffness k of secondary spring2Tensile damping coefficient c1And compression damping coefficient c2。
Random road surface input zrThe following road surface input model was used:
in the formula, G0The coefficient of road surface unevenness; v is the vehicle speed; f. of0Is the lower cut-off frequency; w (t) is white gaussian noise with a mean equal to zero.
According to Newton's second law, the corresponding dynamic equation of the vehicle ISD suspension based on asymmetric reciprocating damping is as follows:
in the formula, msIs a sprung mass; m isuIs an unsprung mass; k is a radical of1Is the main spring stiffness; k is a radical of2Is the secondary spring rate; b isThe inerter coefficient; k is a radical oftIs the tire stiffness; z is a radical ofs,zb,zu,zrRespectively the vertical displacement of the vehicle body, the inertial container, the tire and the road surface; f is the acting force between the inerter and the secondary spring, c1Representing the tensile damping, c2Representing compression damping. The above formula selects the model parameters as shown in table 1.
TABLE 1
Establishing an 1/4 vehicle suspension vibration model comprising two elements of a traditional passive suspension spring-damper in parallel, wherein the corresponding dynamic model comprises the following steps:
wherein m issIs a sprung mass; m isuIs an unsprung mass; k is the spring rate; k is a radical oftIs the tire stiffness; z is a radical ofs,zu,zrRespectively the vertical displacement of the vehicle body, the tire and the road surface;
vehicle body and tire vertical speeds, respectively;
vehicle body and tire vertical acceleration, respectively; c represents damping.
Adopting integral white noise for input, and obtaining the vehicle body acceleration response root mean square value BA of the suspension frame under the condition of random road surface input with the vehicle speed of 20m/s through time domain simulation analysisBDRoot mean square value SWS of suspension dynamic stroke responseBDRoot mean square value DTL of dynamic load response of tireBDAnd the three values are 1.1948 m.s in sequence-2，0.0119m，839.2N。
The vibration model of the ISD suspension 1/4 vehicle suspension based on asymmetric reciprocating damping is established, and the corresponding dynamic model is as follows:
wherein m issIs a sprung mass; m isuIs an unsprung mass; k is a radical of1Is the main spring stiffness; k is a radical of2Is the secondary spring rate; b is the inertia mass coefficient; k is a radical oftIs the tire stiffness; z is a radical ofs,zb,zu,zrRespectively the vertical displacement of the vehicle body, the inertial container, the tire and the road surface;
vehicle body and tire vertical speeds, respectively;
respectively the vertical acceleration of the vehicle body, the inerter and the tire; f is the acting force between the inerter and the secondary spring, c1Representing the tensile damping, c2Representing compression damping.
And similarly, integral white noise is input, and a body acceleration response root mean square value BA, a suspension dynamic stroke response root mean square value SWS and a tire dynamic load response root mean square value DTL of the suspension are obtained through time domain simulation analysis when the suspension is input on a random road surface with the vehicle speed of 20 m/s.
The multi-objective optimization fitness function calculation formula of the genetic algorithm is as follows:
taking the acceleration of the vehicle body as an example, the calculation formula of the single-target optimization fitness function of the genetic algorithm is as follows:
the Punishment number Punishment value rule in the genetic algorithm multi-objective optimization fitness function calculation formula is as follows: only when the vehicle body acceleration responds to the root mean square value BA, the suspension dynamic stroke responds to the root mean square value SWS, and one of the tire dynamic load response root mean square values DTL is larger than the vehicle body acceleration response root mean square value BA in the traditional passive suspensionBDRoot mean square value SWS of suspension dynamic stroke responseBDRoot mean square value DTL of dynamic load response of tireBDIf so, the penalty number Punishment is 100, otherwise, the penalty number Punishment is 0. The Punishment number Punishment value rule in the genetic algorithm single-target optimization fitness function calculation formula is as follows: as long as the vehicle body acceleration response root mean square value BA is larger than that in the traditional passive suspensionBDIf so, the penalty number Punishment is 100, otherwise, the penalty number Punishment is 0.
(2) And measuring each individual of the initial population once to obtain a state, acquiring corresponding determination solutions, calculating the fitness of each determination solution and recording the optimal individual and the corresponding fitness value.
(3) And judging whether the evolution algebra condition is met, if so, exiting, and otherwise, continuing to calculate.
(4) Each individual of the population is measured to obtain a state and a corresponding determination solution, and a fitness value is calculated.
(5) The selection of individuals in the population is completed by means of roulette.
(6) And (5) in the case that the crossing probability is a default value, randomly crossing the individuals selected in the step (5) to obtain a filial generation population.
(7) And (5) under the condition that the variation probability is a default value, completing the step (6) to obtain the variation of the filial generation individuals.
(8) And recording the optimal individuals of the filial generation and the corresponding fitness values.
(9) And (4) judging the iteration times by +1, exiting if the iteration times meet the evolution algebra, and returning to the step (4) if the iteration times do not meet the evolution algebra.
(10) In the same way, the superior dynamic performance of the suspension provided by the invention under two road surfaces is verified based on the parameter optimization results obtained by the steps under the input of the pulse road surface and the sine road surface respectively.
The parameters of the elements of the vehicle ISD suspension based on the asymmetric reciprocating damping obtained by simulation optimization in the Matlab/Simulink environment are as follows:
(1) single-target optimization based on vehicle body acceleration:
the stiffness of the main spring 1 is: 2860 N.m-1
The stiffness of the secondary spring 2 is: 847 N.m-1
The tensile damping coefficient of the asymmetric reciprocating damping damper 3 is: 1149N s m-1
The compression damping coefficient of the asymmetric reciprocating damping damper 3 is: 1057 N.s.m-1
The inerter 4 has an inerter coefficient of: 5351kg
(2) Single-target optimization based on suspension dynamic stroke:
the stiffness of the main spring 1 is: 7351 N.m-1
The stiffness of the secondary spring 2 is: 3146N · m-1
The tensile damping coefficient of the asymmetric reciprocating damping damper 3 is: 1797N s m-1
The compression damping coefficient of the asymmetric reciprocating damping damper 3 is: 1836 N.s.m-1
The inerter 4 has an inerter coefficient of: 315kg
(3) Single-target optimization based on suspension dynamic stroke:
the stiffness of the main spring 1 is: 11519 N.m-1
The stiffness of the secondary spring 2 is: 4072 N.m-1
The tensile damping coefficient of the asymmetric reciprocating damping damper 3 is: 1733 N.s.m-1
The compression damping coefficient of the asymmetric reciprocating damping damper 3 is: 1822N s m-1
The inerter 4 has an inerter coefficient of: 24kg of
(4) Multi-objective optimization:
the stiffness of the main spring 1 is: 4446N · m-1
The stiffness of the secondary spring 2 is: 3948N · m-1
The tensile damping coefficient of the asymmetric reciprocating damping damper 3 is: 17027N s m-1
The compression damping coefficient of the asymmetric reciprocating damping damper 3 is: 1686 Ns.m-1
The inerter 4 has an inerter coefficient of: 1562kg
By simulation analysis under various working conditions and the application of the optimal selection method provided by the invention, the low-frequency vibration damping performance of the selected vehicle ISD suspension based on asymmetric reciprocating damping under random road surface input is very excellent, and the dynamic performance improving space is large. Under the input of a pulse road surface, the response time and the overshoot of each performance index of the suspension are obviously improved; under the input of a sinusoidal road surface, the response peak suppression effect of the asymmetric reciprocating damping ISD suspension is better than that of the symmetric reciprocating damping ISD suspension, and excellent stability and practicability are shown.
The present invention is not limited to the above-described embodiments, and any obvious improvements, substitutions or modifications can be made by those skilled in the art without departing from the spirit of the present invention.
Claims (7)
1. The vehicle ISD suspension is characterized in that the suspension structure is a four-element parallel structure comprising a main spring and an auxiliary spring, a first parallel element is composed of the main spring (1), a second parallel element is composed of the auxiliary spring (2) and an inerter (4), and a third parallel element is composed of an asymmetric reciprocating damper (3).
2. The method for optimally designing the ISD suspension of the vehicle based on the asymmetric reciprocating damping as claimed in claim 1, is characterized by comprising the following steps:
1) population initialization: initializing the population of the genetic algorithm, namely setting the number of suspension parameter groups, parameter optimization algebra, suspension parameter optimization range, an adaptability value calculation rule and a penalty value dereferencing rule;
2) measuring each individual of an initial population, namely an initial suspension parameter group, once to obtain a state, acquiring corresponding determination solutions, calculating the fitness of each determination solution and recording an optimal individual and a 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 to obtain a state and a corresponding determination solution, and calculating a fitness value;
5) the selection of individuals in the population is completed in a roulette mode;
6) under the condition that the crossing probability is a default value, randomly crossing the individuals selected in the step 5) to obtain a filial generation group;
7) under the condition that the variation probability is a default value, completing the step 6) to obtain the variation of the filial generation individuals;
8) recording the optimal individuals of the filial generations and the corresponding fitness values;
9) iteration times are +1, judgment of ending conditions is carried out, if the evolution algebra is met, the operation is exited, and if the iteration times are not met, the operation returns to the step 4);
10) after the optimization is completed, the superior performance of the suspension is further verified under other road surface input conditions based on the optimization result.
3. The method for optimally designing the ISD suspension of the vehicle based on the asymmetric reciprocating damping as claimed in claim 2, wherein the calculation rule of the fitness value comprises the following steps:
2.1) establishing a quarter suspension vibration model containing two-degree-of-freedom motion of vehicle body mass and wheel mass according to Newton's second law;
2.2) establishing an 1/4 vehicle suspension vibration model with two elements of a passive suspension spring-damper connected in parallel, inputting by adopting integral white noise, and acquiring a body acceleration response root mean square value BA of the suspension when the suspension is input on a random road surface with the vehicle speed of 20m/s through time domain simulation analysisBDRoot mean square of suspension dynamic travel responseValue SWSBDRoot mean square value DTL of dynamic load response of tireBD；
2.3) establishing a vibration model of the vehicle suspension comprising the vehicle ISD suspension 1/4 based on asymmetric reciprocating damping, inputting by adopting integral white noise, and obtaining a body acceleration response root mean square value BA, a suspension dynamic stroke response root mean square value SWS and a tire dynamic load response root mean square value DTL of the suspension when the suspension is input on a random road surface with the vehicle speed of 20m/s through time domain simulation analysis;
2.4) the calculation formula of the multi-objective optimization fitness function of the genetic algorithm is as follows:
2.5) taking the acceleration of the vehicle body as an example, the calculation formula of the single-target optimization fitness function of the genetic algorithm is as follows:
wherein Punishment is a penalty number.
4. The method as claimed in claim 3, wherein the ISD suspension optimization design method for the vehicle is characterized in that the body acceleration response RMS value BA under the random road surface inputBDRoot mean square value SWS of suspension dynamic stroke responseBDRoot mean square value DTL of dynamic load response of tireBDAnd the three values are 1.1948 m.s in sequence-2，0.0119m，839.2N。
5. The method for optimally designing the ISD suspension of the vehicle based on the asymmetric reciprocating damping as claimed in claim 3, wherein the number of the suspension parameter sets is 100, and the parameter optimization algebra is 20;
the Punishment number Punishment value rule in the multi-objective optimization fitness function calculation formula is as follows: as long as the vehicle body is acceleratedThe root mean square value BA, the suspension dynamic stroke response root mean square value SWS and one of the tire dynamic load response root mean square values DTL are larger than the vehicle body acceleration response root mean square value BA in the traditional passive suspensionBDRoot mean square value SWS of suspension dynamic stroke responseBDRoot mean square value DTL of dynamic load response of tireBDIf so, the Punishment number Punishment is 100, otherwise, the Punishment number Punishment is 0;
the penalty number Punishment value rule in the single-target optimization fitness function calculation formula of the genetic algorithm is as follows: as long as the vehicle body acceleration response root mean square value BA is larger than that in the traditional passive suspensionBDIf so, the penalty number Punishment is 100, otherwise, the penalty number Punishment is 0.
6. The method for optimally designing the ISD suspension of the vehicle based on the asymmetric reciprocating damping as claimed in claim 2, wherein the optimal range of the suspension parameters is set as follows: selecting parameters to be optimized as inertia mass coefficient b and main spring stiffness k1Stiffness k of secondary spring2Tensile damping coefficient c1And compression damping coefficient c2(ii) a Wherein the optimization range of the inertia mass coefficient is as follows: [0,8000]kg, the optimized range of the stiffness of the main spring is as follows: [0,30000]N·m-1The optimal range of the stiffness of the auxiliary spring is as follows: [0,20000]N·m-1The damping coefficient optimization range is as follows: [0,4000]N·s·m-1。
7. The method for optimally designing the ISD suspension of the vehicle based on the asymmetric reciprocating damping as claimed in claim 2, wherein in the step 10), the excellent performance verification process of the suspension is as follows: and (3) under the input of pulse and sinusoidal road surfaces respectively, based on the parameter optimization result obtained in the step 3), performing simulation analysis to obtain the dynamics improvement condition of the suspension provided by the invention under two road surfaces.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114312513A (en) * | 2021-12-06 | 2022-04-12 | 江苏大学 | Mechanical memory element and application thereof |
| CN115848548A (en) * | 2022-11-17 | 2023-03-28 | 昆明理工大学 | ISD multilayer suspension support device of half a whole car |
| CN117251952A (en) * | 2023-09-08 | 2023-12-19 | 海南大学 | Optimal design method of damping structure based on multi-level graded yield damper |
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| CN115848548B (en) * | 2022-11-17 | 2024-04-05 | 昆明理工大学 | ISD multi-layer suspension supporting device for half vehicle |
| CN117251952A (en) * | 2023-09-08 | 2023-12-19 | 海南大学 | Optimal design method of damping structure based on multi-level graded yield damper |
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