NAGA-II optimization method based on semi-active suspension mixing coefficient

Abstract

The invention relates to an NAGA-II optimization method based on a semi-active suspension mixing coefficient, and the method comprises the steps: firstly determining the range of the mixing coefficient, optimizing two smoothness indexes of vehicle body acceleration and suspension dynamic deflection at the same time through a multi-objective optimization method NSGA-II, and comparing the optimization effects of the two indexes under different alpha to obtain an optimal set of alpha; a random pavement input model is established by adopting a harmonic superposition method, so that a simulation result has higher randomness and is close to an actual pavement; three automobile ride comfort evaluation indexes are used as output vectors to establish a state-space equation, so that the distortion possibility of a simulation result is relatively low; the selection of a mixed coefficient is combined with a multi-objective genetic algorithm, and two contradictory objective functions are taken as optimization objectives, so that the optimization objectives are closer to actual application; the hybrid coefficient selected by using the NSGA-II multi-objective optimization method can compromise each evaluation index, so that the comprehensive performance of the semi-active suspension about the riding comfort is optimal.

Description

Technical Field

[0001] The invention belongs to the technical field of automobile suspension, and in particular relates to an NSGA-II optimization method based on a semi-active suspension mixing coefficient. Background Art

[0002] With the development of suspension technology, people have put forward higher requirements for the performance of automobile suspension. They not only pay attention to the functionality of automobiles in daily work and life, but also begin to pay attention to the safety of automobiles on various road surfaces and their riding comfort. The suspension system is a mechanism that realizes the elastic connection between the wheel and the vehicle body. It is mainly used to alleviate the vibration impact of the road on the vehicle body and transmit various forces and moments between the wheel and the vehicle body. Generally, the suspension system is divided into active suspension, semi-active suspension and passive suspension. Among them, active suspension can significantly improve the ride comfort of the car at the cost of energy consumption, but its structure is complex and the cost is high. Although the passive suspension has a simple structure, its performance is poor. Therefore, the semi-active suspension with both low cost and good performance advantages has a high theoretical research value.

[0003] There are many control strategies for semi-active suspension. The classic control strategies can be roughly divided into three types: skyhook control strategy, floorhook control strategy and skyhook-floorhook hybrid control strategy. Among them, skyhook-floorhook hybrid control combines the advantages of skyhook control and floorhook control. By controlling the mixing coefficient α, the proportion of the damping force of the skyhook control and the floorhook control in the hybrid control is adjusted, which plays a greater role in the vibration attenuation of the sprung mass and unsprung mass of the suspension. However, due to the limitation of experimental costs, there is no clear method for the selection of the mixing coefficient α at this stage. Most of them use empirical methods or single-objective optimization methods for a certain smoothness index. The disadvantage is that one index is optimized, but other indexes may be degraded in the opposite direction. Therefore, it is very necessary to design a multi-objective optimization algorithm that can compromise various optimization indexes. Summary of the invention

[0004] The purpose of the present invention is to provide a NSGA-II optimization method based on the hybrid coefficient of the semi-active suspension. Taking the two contradictory indicators that affect ride comfort as the starting point, the NSGA-II optimization method in the multi-objective optimization method is used to complete the selection of the hybrid coefficient, so as to solve the problem of making the optimization purpose more practical and optimizing the comprehensive performance of the semi-active suspension regarding ride comfort.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] An NSGA-II optimization method based on the mixing coefficient of semi-active suspension is proposed. The range of the mixing coefficient α is first determined, and then the two smoothness indicators of vehicle body acceleration and suspension dynamic deflection are simultaneously optimized through the multi-objective optimization method NSGA-II. The optimization effects of the two indicators under different α are compared to obtain the optimal set of α.

[0007] Furthermore, the method specifically includes the following steps:

[0008] A. Select Class B road surface as reference and use harmonic superposition method to establish random road surface model as input of subsequent suspension model;

[0009] B. Model the semi-active suspension model based on the ground-shelf hybrid control, establish the state space equation, and determine the selection range of the initial value of the mixing coefficient α;

[0010] C. Taking the vehicle body acceleration and suspension dynamic deflection as the optimization targets and the mixing coefficient α as the optimization variable, the semi-active suspension model under different α is optimized by NSGA-II multi-objective optimization, and the α with the best optimization effect is selected to form the optimal set.

[0011] Furthermore, in step A, based on the harmonic superposition method, a discrete spectrum is used to approximate the target random process, and the road surface roughness is represented by the sum of a large number of sines or cosines with random phases. The road surface model expression of the harmonic superposition method is as follows:

[0012]

[0013] Where n1 and n2 are the lower and upper limits of the spatial frequency, respectively, Δn i The frequency interval is divided into m intervals, and then the center frequency n in the i-th interval is used m,i The power spectral density G q (n m,i ) to approximate the value of the power spectral density of the entire interval; θ i is a random number in the interval [0, 2π], and each θ i Independent of each other; l is the longitudinal length of the road the car is traveling on;

[0014] The road roughness q is represented by a large number of sine values ​​with random phases, which can be used as the input of the subsequent semi-active suspension state space equation.

[0015] Furthermore, in step B, a hybrid damping two-degree-of-freedom quarter semi-active suspension model is established. The hybrid control suspension model has two dampers c connected to the outside. sky and c gud In the ceiling control, a virtual ceiling damper c is introduced sky, connected to the sprung mass m2 to simulate skyhook control; while groundhook control introduces a virtual groundhook damper c between the wheel and the road surface gud , to simulate ground shed control.

[0016] Furthermore, in step B, the initial value of the mixed damping coefficient α is selected in the range of 0-1, and when α is 1, the suspension control strategy is pure ceiling control, and when α is 0, the ceiling control is ineffective and the suspension becomes pure floor-hook control.

[0017] Furthermore, in step B, when establishing the differential equation of motion of the suspension model, the linear description of the damping force of the ceiling damper and the floor damper in this case should be determined in combination with the mixed damping coefficient α. According to Newton's second law, the damping force of the two dampers is:

[0018]

[0019] In the formula, is the vertical vibration velocity of the unsprung mass (m / s); is the vertical vibration velocity of the sprung mass (m / s).

[0020] Combined with the Lagrange equation, the entire mechanical model is described in the form of energy conversion. sky and F gud As a non-force other than gravity and elastic force, the differential equation of motion of the suspension model is:

[0021]

[0022] According to the differential equation of motion, construct the state space equation of the system; let the state vector , the input vector is u=q, and the output vector is Then the following single-input state-space equation can be constructed:

[0023]

[0024] y=Cx+Du (8)

[0025] Where A is the system matrix; B is the input (or control) matrix; C is the output matrix; D is the direct transfer matrix;

[0026]

[0027] By changing the value of the mixing coefficient α, different output vectors y can be obtained. By comparing the three output quantities in y, the performance of the semi-active suspension in this case can be judged.

[0028] Furthermore, step C specifically includes the following steps:

[0029] C1. To optimize the ride comfort of the vehicle, the vertical acceleration of the vehicle body And the suspension dynamic deflection z2-z1 is used as the optimization target of multi-objective optimization, that is:

[0030]

[0031] C2. Taking the mixing coefficient α as the optimization variable, the NSGA-II fast non-dominated sorting optimization method is used for multi-objective optimization, and the α with the best optimization effect is selected to form the optimal set.

[0032] Furthermore, in step C2, in the NSGA-II fast non-dominated sorting method, the specific steps of NSGA-II are: suppose a set Pop contains all populations, loop once for each point p in Pop, and then set the set S p and integer n p , respectively including the individuals dominated by individual p and the number of individuals that can dominate the current individual p, and then search for other individuals q. If pπq, then put individual q into the set S p If it is a non-dominant relationship, then n p +1; if n p =0, then put the individual p into the first sequence solution set, that is, rank = 1; then, set a set ζ, and put p into the set ζ; after traversing all individuals in Pop, proceed to the next step, that is, for each first sequence solution p in the set ζ, take these points as the first batch of output results to form the first layer of Pareto optimal frontier (Pareto front), after removing these points, for the set S p For each individual q, its corresponding n p -1, then determine if n p = 0, then the individual q is placed in the second-layer Pareto optimal frontier as the second batch of output points, and then the next batch of individual q’s n p -1, and then judge if n p =0, then the individual q is placed in the third-layer Pareto optimal frontier as the third batch of output points, and so on, until all individuals are output, and the first layer or other layers of Pareto optimal frontiers are displayed as required.

[0033] Furthermore, by using the optimization method of NSGA-II to optimize different mixing coefficients α within the value range, multiple Pareto optimal fronts can be obtained; the crowding distance d of individual i on the Pareto optimal front is defined as i :

[0034] d i =|x i+1 -x i-1 |+|yi+1 -y i-1 | (12)

[0035] Where x i+1 and x i-1 are the horizontal coordinates of the i+1th point and the i-1th point, y i+1 and i-1 They are the ordinates of the i+1th point and the i-1th point respectively.

[0036] A major requirement for selecting the optimal range of the Pareto optimal frontier is uniformity. i Filter out points that are too small and select the crowding distance d i Each individual is large enough to obtain the vertical acceleration of the vehicle body and the optimal range of suspension dynamic deflection z2-z1. Finally, by comparing the RMS values ​​of the two optimization objectives within the range, multiple mixing coefficients α that meet the optimization purpose can be selected to form the optimal set of α values.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. The present invention adopts the harmonic superposition method to establish a random road surface input model, making the simulation results more random and closer to the actual road surface;

[0039] 2. The state space equation is established using the three major vehicle ride comfort evaluation indicators as output vectors, making the simulation results less likely to be distorted;

[0040] 3. Combine the selection of the mixing coefficient α with the multi-objective genetic algorithm NSGA-II, and use two contradictory objective functions as optimization targets, so that the optimization purpose is closer to practical application;

[0041] 4. The mixed coefficient α selected using the NSGA-II multi-objective optimization method can compromise various evaluation indicators and optimize the comprehensive performance of the semi-active suspension regarding ride comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 Hybrid damping control two-degree-of-freedom quarter suspension model;

[0044] Figure 2The Pareto optimal frontier when the mixing coefficient is 0.5;

[0045] Figure 3 Flowchart of the steps of the NAGA-II optimization method based on the mixing coefficient of the semi-active suspension. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with embodiments:

[0047] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0048] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0049] The present invention proposes an NSGA-II optimization method based on a semi-active suspension mixing coefficient. Firstly, the range of the mixing coefficient α is determined. Then, two smoothness indicators, vehicle body acceleration and suspension dynamic deflection, are simultaneously optimized through a multi-objective optimization method NSGA-II. Furthermore, the optimization effects of the two indicators under different α are compared. Finally, the optimal set of α is obtained.

[0050] Specifically, the steps include:

[0051] Step 1: Select Class B road surface as reference and use harmonic superposition method to establish a random road surface model as input for subsequent suspension model;

[0052] Step 2: Model the semi-active suspension model based on the ground-shelf hybrid control, establish the state space equation, and determine the selection range of the initial value of the mixing coefficient α;

[0053] Step 3: Taking the vehicle body acceleration and suspension dynamic deflection as the optimization targets and the mixing coefficient α as the optimization variable, the semi-active suspension model under different α is optimized by NSGA-II multi-objective optimization, and the α with the best optimization effect is selected to form the optimal set.

[0054] The international standard ISO8608:2016 defines the power spectral density G of the road surface as q (n0) is used as the classification standard for road roughness level, G q The fitting expression of (n0) is:

[0055]

[0056] Where W is the frequency index; n is the spatial frequency (m -1 ); n0 is the reference spatial frequency (m -1 ), n0=0.1m -1 ; G q (n0) is the road roughness coefficient (m 3 );G q (n) is the power spectral density function of road roughness (m 3 ). Generally, W is 2, and G q (n0) is obtained by looking up the table.

[0057] International standard ISO8608:2016 passed G q The division of (n0) values ​​thus specifies different levels of road surfaces, and also clearly specifies the upper and lower limits of the road surface roughness coefficient under each level of road surface. The present invention is based on the harmonic superposition method, uses a discrete spectrum to approximate the random process of the target, and represents the road surface roughness by the sum of a large number of sines or cosines with random phases. The road surface model expression of the harmonic superposition method is as follows:

[0058]

[0059] Where n1 and n2 are the lower and upper limits of the spatial frequency, respectively, Δn i The frequency interval is divided into m intervals, and then the center frequency n in the i-th interval is used m,i The power spectral density G q (n m,i ) to approximate the value of the power spectral density of the entire interval; θ i is a random number in the interval [0, 2π], and each θ i Independent of each other; l is the longitudinal length of the road on which the car is traveling.

[0060] The road roughness q is represented by a large number of sine values ​​with random phases, which can be used as the input of the subsequent semi-active suspension state space equation.

[0061] The hybrid damping two-degree-of-freedom quarter semi-active suspension model is established as follows Figure 1 shown. Figure 1 In, c sky is the damping coefficient of the ceiling damper (Ns / m), which will also be referred to as c in the following text. sky represents the ceiling damper; c gud is the damping coefficient of the floor shed damper (Ns / m), which will also be referred to as c in the following text. gudrepresents the floor shed damper; α is the mixed damping coefficient; m1 is the unsprung mass (kg); m2 is the sprung mass (kg); c is the shock absorber damping coefficient (Ns / m); k is the passive spring stiffness (N / m); k t is the tire stiffness (N / m).

[0062] The main difference between the hybrid control suspension model and the traditional two-degree-of-freedom quarter suspension model is that the hybrid control suspension model has two external dampers c sky and c gud In the ceiling control, a virtual ceiling damper c is introduced sky , connected to the sprung mass m2 to simulate skyhook control; while groundhook control introduces a virtual groundhook damper c between the wheel and the road surface gud , to simulate the ground-shelf control. The design of the hybrid damping control suspension model combines these two control strategies. In short, the model not only needs to set up a sky-shelf damper between the vehicle body and the surrounding environment, such as the sky, but also needs to set up a ground-shelf damper between the wheels and the road surface to achieve hybrid damping control.

[0063] The initial value of the mixed damping coefficient α can be selected in the range of 0-1, and when α is 1, the suspension control strategy is pure ceiling control, and when α is 0, the ceiling control is not effective and the suspension becomes pure floor-hook control.

[0064] Pre-built Figure 1 The differential equation of motion of the suspension model must first be combined with the mixed damping coefficient α to determine the linear description of the damping force of the ceiling damper and the floor damper in this case. According to Newton's second law, the damping force of the two dampers is:

[0065]

[0066] In the formula, is the vertical vibration velocity of the unsprung mass (m / s); is the vertical vibration velocity of the sprung mass (m / s).

[0067] Combined with the Lagrange equation, the entire mechanical model is described in the form of energy conversion. sky and F gud As a non-force other than gravity and elastic force, the differential equation of motion of the suspension model is:

[0068]

[0069] Based on the differential equation of motion, the state space equation of the system can be constructed. Let the state vector The input vector is u=q, and the output vector is Then the following single-input state-space equation can be constructed:

[0070]

[0071] y=Cx+Du (8)

[0072] Where A is the system matrix; B is the input (or control) matrix; C is the output matrix; and D is the direct transfer matrix.

[0073]

[0074]

[0075] By changing the value of the mixing coefficient α, different output vectors y can be obtained. By comparing the three output quantities in y, the performance of the semi-active suspension in this case can be judged.

[0076] Multi-objective optimization is an improved version of single-objective optimization. It is used to optimize multiple objective functions at the same time. The increase and decrease relationships between these objective functions conflict with each other. If one objective function is optimized, other objective functions will be degraded. For the present invention, a decrease in the root mean square value of the vertical acceleration of the vehicle body will lead to an increase in the maximum value of the dynamic deflection of the suspension. Conversely, an increase in the root mean square value of the acceleration will lead to a decrease in the maximum value of the dynamic deflection of the suspension. Therefore, the multi-objective optimization method can be used to optimize these two objectives with the purpose of compromise. For the purpose of optimizing the ride comfort of the vehicle, the vertical acceleration of the vehicle body is And the suspension dynamic deflection z2-z1 is used as the optimization target of multi-objective optimization, that is:

[0077]

[0078] Taking the mixing coefficient α as the optimization variable, the NSGA-II fast non-dominated sorting optimization method is used for multi-objective optimization.

[0079] First, for the concept of dominance, two parameters x and y can be set. If the solution that satisfies all the optimization objectives of x is not worse than y, and x contains at least one objective function that is better than y, then it can be said that x dominates y, denoted as xπy, otherwise it is non-dominated.

[0080] The NSGA-II fast non-dominated sorting method is an improved version of the traditional NSGA non-dominated sorting method. The specific steps of NSGA-II are: suppose a set Pop contains all populations, then loop once for each point p in Pop, and then set the set S p and integer n p , respectively including the individuals dominated by individual p and the number of individuals that can dominate the current individual p, and then search for other individuals q. If pπq, then put individual q into the set S p If it is a non-dominant relationship, then np +1. If n p =0, then put the individual p into the first sequence solution set, that is, rank = 1. Then, set another set ζ, and put p into the set ζ. After traversing all individuals in Pop, proceed to the next step, that is, for each first sequence solution p in the set ζ, take these points as the first batch of output results to form the first layer of Pareto optimal frontier (Pareto front), and after removing these points, for the set S p For each individual q, its corresponding n p -1, then determine if n p = 0, then the individual q is placed in the second-layer Pareto optimal frontier as the second batch of output points, and then the next batch of individual q’s n p -1, and then judge if n p = 0, then the individual q is placed in the third layer of Pareto optimal frontier as the third batch of output points, and so on, until all individuals are output, and the first layer or other layers of Pareto optimal frontier are displayed as required. The worst complexity of this method is O(MN 2 ), M is the number of optimization targets, N is the number of individuals in the population, which is lower than the worst complexity of the traditional non-dominated sorting method of NSGA O(MN 3 ) should be better.

[0081] Using the NSGA-II optimization method, we can optimize different mixing coefficients α within the value range to obtain multiple Pareto optimal frontiers. (m / s 2 ) is the first optimization goal 1 st Objective, taking the suspension dynamic deflection z2-z1(m) as the second optimization objective 2 nd Objective, its Pareto optimal frontier is as follows Figure 2 shown.

[0082] Then, set the crowding distance d of individual i on the Pareto optimal frontier i :

[0083] d i =|x i+1 -x i-1 |+|y i+1 -y i-1 | (12)

[0084] In the formula, x i+1 and x i-1 are the horizontal coordinates of the i+1th point and the i-1th point, y i+1 and i-1 They are the ordinates of the i+1th point and the i-1th point respectively.

[0085] A major requirement for selecting the optimal range of the Pareto optimal front is uniformity. Therefore, for the crowding distance d i Filter out points that are too small and select the crowding distance d i Each individual is large enough. Then, the vertical acceleration of the vehicle body can be obtained and the optimal range of suspension dynamic deflection z2-z1. For different mixing coefficients α, there will be different Pareto optimal frontiers, and the optimal ranges of the corresponding optimization objectives will also be different. Finally, by comparing the RMS values ​​of the two optimization objectives within this range, multiple mixing coefficients α that meet the optimization purpose can be selected to form the optimal set of α values.

[0086] The present invention uses the multi-objective optimization method of NSGA-II to complete the selection of the mixing coefficient, and only uses the vertical acceleration of the vehicle body and suspension dynamic deflection z2-z1 as the optimization targets. This idea can also be used for multi-objective optimization with all smoothness indicators as optimization targets.

[0087] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A NSGA-II optimization method based on a semi-active suspension hybrid coefficient, characterized in that: First, the range of the mixing coefficient α is determined, and then the two smoothness indicators of body acceleration and suspension dynamic deflection are simultaneously optimized through the multi-objective optimization method NSGA-II. The optimization effects of the two indicators under different α are compared to obtain the optimal set of α.

2. The NSGA-II optimization method based on the semi-active suspension mixing coefficient according to claim 1 is characterized in that: The specific steps include: A. Select Class B road surface as reference and use harmonic superposition method to establish random road surface model as input of subsequent suspension model; B. Model the semi-active suspension model based on the ground-shelf hybrid control, establish the state space equation, and determine the selection range of the initial value of the mixing coefficient α; C. Taking the vehicle body acceleration and suspension dynamic deflection as the optimization targets and the mixing coefficient α as the optimization variable, the semi-active suspension model under different α is optimized by NSGA-II multi-objective optimization, and the α with the best optimization effect is selected to form the optimal set.

3. The NSGA-II optimization method based on the semi-active suspension mixing coefficient according to claim 2 is characterized in that: Step A, based on the harmonic superposition method, uses discrete spectrum to approximate the target random process, and represents the road roughness by the sum of a large number of sines or cosines with random phases. The road model expression of the harmonic superposition method is as follows: Where n1 and n2 are the lower and upper limits of the spatial frequency, respectively, Δn i The frequency interval is divided into m intervals, and then the center frequency n in the i-th interval is used m,i The power spectral density G q (n m,i ) to approximate the value of the power spectral density of the entire interval; θ i is a random number in the interval [0, 2π], and each θ i Independent of each other; l is the longitudinal length of the road the car is traveling on; The road roughness q is represented by a large number of sine values ​​with random phases, which can be used as the input of the subsequent semi-active suspension state space equation.

4. The NSGA-II optimization method based on the semi-active suspension mixing coefficient according to claim 2 is characterized in that: Step B, establish a hybrid damping two-degree-of-freedom quarter semi-active suspension model. The hybrid control suspension model has two dampers c connected to the outside sky and c gud In the ceiling control, a virtual ceiling damper c is introduced sky , connected to the sprung mass m2 to simulate skyhook control; while groundhook control introduces a virtual groundhook damper c between the wheel and the road surface gud , to simulate ground shed control.

5. The NSGA-II optimization method based on the semi-active suspension mixing coefficient according to claim 2 is characterized in that: In step B, the initial value of the mixed damping coefficient α is selected in the range of 0-1, and when α is 1, the suspension control strategy is pure ceiling control, and when α is 0, the ceiling control is not effective and the suspension becomes pure floor-hook control.

6. The NSGA-II optimization method based on the semi-active suspension mixing coefficient according to claim 2 is characterized in that: Step B, when establishing the differential equation of motion of the suspension model, the linear description of the damping force of the ceiling damper and the floor damper in this case should be determined by combining the mixed damping coefficient α. According to Newton's second law, the damping force of the two dampers is: In the formula, is the vertical vibration velocity of the unsprung mass (m / s); is the vertical vibration velocity of the sprung mass (m / s). Combined with the Lagrange equation, the entire mechanical model is described in the form of energy conversion. sky and F gud As a non-force other than gravity and elastic force, the differential equation of motion of the suspension model is: According to the differential equation of motion, construct the state space equation of the system; let the state vector , the input vector is u=q, and the output vector is Then the following single-input state-space equation can be constructed: y=Cx+Du (8) Where A is the system matrix; B is the input (or control) matrix; C is the output matrix; D is the direct transfer matrix; By changing the value of the mixing coefficient α, different output vectors y can be obtained. By comparing the three output quantities in y, the performance of the semi-active suspension in this case can be judged.

7. The NSGA-II optimization method based on the semi-active suspension mixing coefficient according to claim 2 is characterized in that: Step C specifically comprises the following steps: C1. To optimize the ride comfort of the vehicle, the vertical acceleration of the vehicle body And the suspension dynamic deflection z2-z1 is used as the optimization target of multi-objective optimization, that is: C2. Taking the mixing coefficient α as the optimization variable, the NSGA-II fast non-dominated sorting optimization method is used for multi-objective optimization, and the α with the best optimization effect is selected to form the optimal set.

8. The NSGA-II optimization method based on the semi-active suspension mixing coefficient according to claim 7 is characterized in that: Step C2, in the NSGA-II fast non-dominated sorting method, the specific steps of NSGA-II are: suppose a set Pop contains all populations, loop once for each point p in Pop, and then set the set S p and integer n p , respectively including the individuals dominated by individual p and the number of individuals that can dominate the current individual p, and then search for other individuals q. If pπq, then put individual q into the set S p If it is a non-dominant relationship, then n p +1; if n p =0, then the individual p is placed in the first sequence solution set, that is, rank = 1; Then, set up another set ζ, and put p into the set ζ; after traversing all individuals in Pop, proceed to the next step, that is, for each first sequence solution p in the set ζ, take these points as the first batch of output results to form the first layer of Pareto optimal frontier (Pareto front), and after removing these points, for the set S p For each individual q, its corresponding n p -1, then determine if n p = 0, then the individual q is placed in the second-layer Pareto optimal frontier as the second batch of output points, and then the next batch of individual q’s n p -1, and then judge if n p =0, then the individual q is placed in the third-layer Pareto optimal frontier as the third batch of output points, and so on, until all individuals are output, and the first layer or other layers of Pareto optimal frontiers are displayed as required.

9. The NSGA-II optimization method based on the semi-active suspension mixing coefficient according to claim 8 is characterized in that: Using the optimization method of NSGA-II, we can optimize different mixing coefficients α within the value range to obtain multiple Pareto optimal frontiers. We define the crowding distance d of individual i on the Pareto optimal frontier. i : d i =|x i+1 -x i-1 |+|y i+1 -y i-1 | (12) Where x i+1 and x i-1 are the horizontal coordinates of the i+1th point and the i-1th point, y i+1 and i-1 The ordinates of the i+1th point and the i-1th point respectively; A major requirement for selecting the optimal range of the Pareto optimal frontier is uniformity. i Filter out points that are too small and select the crowding distance d i Each individual is large enough to obtain the vertical acceleration of the vehicle body and the optimal range of suspension dynamic deflection z2-z1; finally, by comparing the root mean square values ​​of the two optimization objectives within the range, multiple mixing coefficients α that meet the optimization purpose can be selected to form the optimal set of α values.

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