Variable transmission ratio design method and system for steer-by-wire system

Abstract

The invention discloses a variable transmission ratio design method and system for a steer-by-wire system. The method comprises the following steps: establishing a basic model of a basic transmission ratio changing along with vehicle speed and a three-dimensional transmission ratio model for acquiring an actual transmission ratio; and performing iterative optimization by taking the steady-state yaw velocity gain and the center ratio gradient coefficient in the model as optimization targets, and obtaining a three-dimensional transmission ratio model of an actual transmission ratio according to the steady-state yaw velocity gain and the center ratio gradient coefficient obtained by optimization. The basic model with the fixed value steady-state yaw velocity gain is established, and the three-dimensional transmission ratio model is established by combining the transmission ratio increment with the basic transmission ratio output by the basic model, so that the transmission ratio can be smoothly changed along with the vehicle speed and the steering wheel angle, and the operation comfort of the steer-by-wire system is improved. The improved grey wolf optimization algorithm is utilized to carry out iterative optimization on the steady-state yaw velocity gain and the center ratio gradient coefficient, and the optimization efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of steer-by-wire control for electric vehicles, and particularly relates to a method and system for designing a variable transmission ratio of a steer-by-wire system. Background Art

[0002] As a new generation of automotive steering technology, the steer-by-wire system realizes steering operation through electronic signal transmission and actuators, eliminating the mechanical connection between the steering wheel and the steering wheel in the traditional steering system. This design enables the steering transmission ratio to be flexibly designed and adjusted, thereby improving the handling stability and ride comfort of the vehicle. The design and optimization of the transmission ratio play an important role in the development of the steer-by-wire system, and it has become the core focus of the research and development of electric vehicle steering technology.

[0003] In the prior art, the design of the variable angle transmission ratio of the steer-by-wire system usually first designs the relationship between the transmission ratio and the vehicle speed, and then designs the relationship between the transmission ratio and the steering wheel angle. Therefore, there is currently a lack of a design scheme that comprehensively considers the steering wheel angle, vehicle speed, and transmission ratio, and it is necessary to establish a unified optimization model among the three to improve the design efficiency of the steer-by-wire system. Summary of the Invention

[0004] The object of the present invention is to overcome the deficiencies of the prior art and propose a method and system for designing a variable transmission ratio of a steer-by-wire system based on an improved grey wolf optimization algorithm to solve the problem of low efficiency in designing the transmission ratio of the steer-by-wire system in the prior art.

[0005] In a first aspect, the present invention provides a method for designing a variable transmission ratio of a steer-by-wire system, which includes:

[0006] Establish a basic model in which the basic transmission ratio varies with the vehicle speed; the basic model includes a steady-state yaw rate gain with a fixed value.

[0007] Establish a three-dimensional transmission ratio model for obtaining the actual transmission ratio; the actual transmission ratio in the three-dimensional transmission ratio model is obtained through the basic transmission ratio and the transmission ratio increment. The transmission ratio increment has a coefficient that varies with the steering wheel angle and follows a normal distribution; the center ratio gradient coefficient is used as the standard deviation of the normal distribution.

[0008] Iteratively optimize with the steady-state yaw rate gain and the center ratio gradient coefficient as the optimization objectives, and obtain the three-dimensional transmission ratio model of the actual transmission ratio according to the optimized steady-state yaw rate gain and center ratio gradient coefficient.

[0009] Preferably, the steady-state yaw rate gain and the center ratio gradient coefficient are iteratively optimized by the grey wolf optimization algorithm.

[0010] Preferably, in the grey wolf optimization algorithm, the vehicle handling stability evaluation index is used as the fitness value of the grey wolf individuals.

[0011] Preferably, the grey wolf population is initialized by an optimized chaotic map. The expression of the optimized chaotic map is as follows:

[0012]

[0013] where, X i+1 is the chaotic value of the next state; X i is the chaotic value of the current state; a0 is the control parameter.

[0014] Preferably, the expression of the convergence factor a in the grey wolf optimization algorithm is as follows:

[0015] a = 1 + tanh(G(H - (t / Maxiter) P ))

[0016] where, t is the current iteration number, Maxiter is the maximum iteration number. G, H, and P are the adjustment parameters of the convergence factor a.

[0017] Preferably, in the prey hunting stage of the grey wolf optimization algorithm, the position of the grey wolf individuals is dynamically weighted and updated according to the step size of the grey wolf individuals affected by the α, β, and δ wolves. The weights of the α, β, and δ wolves with the fitness values from large to small are w1, w2, and w3 respectively; the weight w1 continuously increases with the iteration process; the weights w2 and w3 continuously decrease with the iteration process; the decreasing amplitude of the weight w2 relative to the initial value is less than the decreasing amplitude of the weight w3 relative to the initial value.

[0018] Preferably, multiple different vehicle speed conditions are set, and the iterative optimization of the steady-state yaw rate gain and the center ratio gradient coefficient is carried out respectively. The final steady-state yaw rate gain and the center ratio gradient coefficient are screened from the optimization results obtained under multiple conditions.

[0019] Preferably, the three-dimensional transmission ratio model is as follows:

[0020]

[0021] where, i is the actual transmission ratio; i w is the basic transmission ratio; Δi is the basic increment of the transmission ratio; δ f is the steering wheel angle, σ is the center ratio gradient coefficient; normpdf(·,·,·) is the normal function.

[0022] Preferably, the basic increment Δi of the transmission ratio is calculated from the normalized value of the vehicle speed and the basic transmission ratio; the preferred expression is as follows:

[0023]

[0024] Among them, S max and S min are the maximum and minimum increment coefficients respectively. V1 and V2 are the lower critical vehicle speed and the upper critical vehicle speed respectively.

[0025] Preferably, in the basic model, the lower critical vehicle speed and the upper critical vehicle speed are set; when the vehicle speed is less than the lower critical vehicle speed, the basic transmission ratio remains unchanged; when the vehicle speed is less than the upper critical vehicle speed, the basic transmission ratio remains unchanged.

[0026] In a second aspect, the present invention provides a variable transmission ratio design system for a steer-by-wire system, which is used to execute the aforementioned variable transmission ratio design method for a steer-by-wire system; the variable transmission ratio design system for a steer-by-wire system includes a vehicle handling test module and a parameter optimization module. The vehicle handling test module is used to test the vehicle handling stability evaluation indexes corresponding to different three-dimensional transmission ratio models; the parameter optimization module is used to iteratively optimize the steady-state yaw rate gain and the center ratio gradient coefficient.

[0027] The present invention has the following beneficial effects.

[0028] 1. The present invention establishes a basic model with a fixed steady-state yaw rate gain, and uses the transmission ratio increment to combine with the basic transmission ratio output by the basic model to establish a three-dimensional transmission ratio model, which can make the transmission ratio change smoothly with the vehicle speed and the steering wheel angle, improving the operation comfort of the steer-by-wire system.

[0029] 2. The present invention uses the improved grey wolf optimization algorithm to iteratively optimize the steady-state yaw rate gain and the center ratio gradient coefficient, and can quickly obtain a three-dimensional transmission ratio model that effectively improves the vehicle handling stability evaluation index.

[0030] 3. The improved grey wolf optimization algorithm in the present invention introduces the ArccosineCosine-Tangent chaotic sequence, the non-linear decreasing convergence factor and the grey wolf individual update formula with dynamic change of the weight in the prey pursuit stage, so that the grey wolf optimization algorithm can effectively balance the global and local search capabilities, thereby improving the optimization efficiency of the transmission ratio of the steer-by-wire system. Description of the Drawings

[0031] Figure 1 It is the flow chart of the improved grey wolf optimization algorithm in step 3 of Embodiment 1 of the present invention.

[0032] Figure 2 It is the comparison diagram of the sequences obtained by the Arccosine-Cosine-Tangent chaotic mapping and the Logistic chaotic mapping in step 3 of Embodiment 1 of the present invention.

[0033] Figure 3 It is a comparison graph of the non-linear decreasing convergence factor and the linear decreasing convergence factor used in step 3 of Embodiment 1 of the present invention.

[0034] Figure 4 It is a comparison graph of the fitness value iteration of the improved grey wolf optimization algorithm and the traditional grey wolf optimization algorithm in step 3 of Embodiment 1 of the present invention.

[0035] Figure 5 It is a comparison graph of the vehicle stability evaluation indexes under different speed conditions with different parameter schemes in step 4 of Embodiment 1 of the present invention.

[0036] Figure 6 It is a schematic diagram of the transmission ratio change of the three-dimensional transmission ratio model optimized in Embodiment 1 of the present invention. Detailed implementation manners

[0037] To better illustrate the technical solutions and advantages of the present invention, the present invention will be further described below in conjunction with specific embodiments and drawings. The described embodiments are part of the embodiments of the present application, rather than all of the embodiments, but do not serve as the basis for limiting the present invention.

[0038] Embodiment 1

[0039] A transmission ratio design method for a steer-by-wire system based on an improved grey wolf optimization algorithm includes the following steps:

[0040] Step 1, construct a unified optimization model of steering wheel angle, vehicle speed and transmission ratio

[0041] Divide the speed segments based on the speed of the vehicle. To prevent too small a transmission ratio from making the steering too sensitive at low speeds and too large a transmission ratio from making the steering response too sluggish at high speeds. Set the minimum transmission ratio i for the speed segment less than the lower critical vehicle speed V1 min . Set the maximum transmission ratio i for the speed segment greater than the upper critical vehicle speed V2 max . In this embodiment, the lower critical vehicle speed is set to 20 km / h, the minimum transmission ratio i min is 6, the upper critical vehicle speed is 120 km / h, and the maximum transmission ratio i max is 23.

[0042] Specifically, when the vehicle speed is between the lower critical vehicle speed and the upper critical vehicle speed, a transmission ratio design scheme with a constant yaw rate gain is adopted. The transmission ratio i w can be expressed within different speed segments as:

[0043]

[0044] Among them, L is the wheelbase of the vehicle, taking 2.796 m; m is the vehicle mass, taking 1540 kg; a1 and b1 represent the distances from the front axle and rear axle of the vehicle to the center of mass, taking 1.04 m and 1.756 m respectively; k1 and k2 are the cornering stiffnesses of the front and rear tires respectively, taking -112546.54 N / rad and -87054.24 N / rad; K w is the steady-state yaw rate gain. It can be seen from the formula that in the speed range with a constant yaw rate gain, the transmission ratio increases with the increase of vehicle speed.

[0045] Furthermore, in the speed range with a constant yaw rate gain, a unified optimization model among the steering wheel angle, vehicle speed, and transmission ratio is established. Such as the formula:

[0046]

[0047] Among them, δ f is the steering wheel angle. In this embodiment, the angle range is taken as [-240°, 240°], σ is the center ratio gradient coefficient; normpdf(·, ·, ·) is the normal function.

[0048] Δi is the transmission ratio increment corresponding to the vehicle speed, and it is expressed as:

[0049]

[0050] Among them, S max and S min are the maximum and minimum increment coefficients respectively. In this embodiment, they are taken as 0.5 and 0.2 respectively. It can be known from the formula that the proportional coefficient of the transmission ratio increment decreases with the increase of vehicle speed.

[0051] Step 2: Establish the vehicle handling stability evaluation index as the optimization objective function.

[0052] The expression for establishing the vehicle handling stability evaluation index is shown in formula (4):

[0053]

[0054] Among them, J E is the total driving path tracking goodness error index. J B is the total driver operation burden index. J R is the roll hazard index. J S is the sideslip hazard index. W1, W2, W3, and W4 are the weight coefficients of each evaluation index respectively. In this embodiment, all four weight coefficients are taken as 0.25.

[0055] Specifically, the total driving path tracking goodness error index J E is shown in formula (5):

[0056]

[0057] Among them, J e1 is the track error index, and J e2 is the direction error index.

[0058] Specifically, the track error index J e1 is shown in Equation (6):

[0059]

[0060] Among them, f(t) is the expected path; is the trajectory error standard threshold value, taking 0.4 m; y(t) is the actual trajectory of the vehicle during the test; t n is the test time.

[0061] Specifically, the direction error index J e2 is shown in Equation (7):

[0062]

[0063] Among them, is the yaw rate of the vehicle's center of mass; is the yaw rate standard threshold value, taking 0.8 rad / s.

[0064] Specifically, the total driver operation burden index J B is shown in Equation (8):

[0065]

[0066] Among them, J b1 is the busyness degree, and J b2 is the heaviness degree.

[0067] Specifically, the busyness degree J b1 is shown in Equation (9):

[0068]

[0069] Among them, is the angular velocity of the steering wheel; is the angular velocity standard threshold value of the steering wheel, taking 1.0 rad / s.

[0070] Specifically, the heaviness degree J b2 is shown in Equation (10):

[0071]

[0072] Among them, T sw is the steering wheel torque; is the standard threshold value of the steering wheel torque, taking 8.0 N·m.

[0073] Specifically, the roll risk index J R is shown in Equation (11):

[0074]

[0075] where J r1 is the lateral acceleration evaluation index, and J r2 is the roll angle evaluation index.

[0076] Specifically, the lateral acceleration evaluation index J r1 is shown in Equation (12):

[0077]

[0078] where is the lateral acceleration, is the standard threshold value of the lateral acceleration, taking 3 m / s 2 .

[0079] Specifically, the roll angle evaluation index J r2 is shown in Equation (13):

[0080]

[0081] where is the roll angle of the vehicle; is the standard threshold value of the roll angle of the vehicle, taking 3°.

[0082] Specifically, the sideslip risk index J S is shown in Equation (14):

[0083]

[0084] where J s1 and J s2 are the sideslip risk indices of the front and rear axles respectively.

[0085] Specifically, the sideslip risk index J si of the front and rear axles is shown in Equation (15)

[0086]

[0087] In the formula, F y1 , F y2 are the lateral forces of the front and rear wheels respectively; F z1 , F z2 are the vertical loads of the front and rear wheels respectively; is the standard threshold value of F yi / F zi , taking 0.3.

[0088] Step 3: Variable transmission ratio design based on improved grey wolf optimization algorithm

[0089] On the speed segment with a constant steady-state yaw rate gain K w unchanged, equally spaced points are taken as the optimized speed conditions. The speed conditions taken in this embodiment are 20 km / h, 40 km / h, 60 km / h, 80 km / h, 100 km / h, and 120 km / h. Hereinafter, the vehicle speed of 120 km / h will be taken as an example to illustrate the optimization process.

[0090] As Figure 1 shown, it is the flow chart of the improved grey wolf optimization algorithm. The specific optimization process includes:

[0091] Step 3-1: In this embodiment, the yaw angle gain value K w and the center ratio gradient coefficient σ are regarded as grey wolves. First, the grey wolf population is initialized through an optimized chaotic mapping (referred to as Arccosine-Cosine-Tangent in this embodiment). The formula of the chaotic mapping in this embodiment is as follows:

[0092]

[0093] where, X i+1 is the chaotic value of the next state; X i is the chaotic value of the current state; a0 is a control parameter, and its value range is (0, 8), and a0 = 3 is taken in this embodiment.

[0094] The distribution statistical comparison chart of the chaotic values obtained by performing 10,000 chaotic mappings of the Arccosine-Cosine-Tangent chaotic mapping method in this embodiment and the existing Logistic chaotic mapping method is as Figure 2 shown. As can be seen from Figure 2 it, compared with the Logistic chaotic sequence, the random numbers generated by the Arccosine-Cosine-Tangent chaotic sequence in this embodiment are more evenly distributed in the interval (0, 1). The sequence generated by Arccosine-Cosine-Tangent chaos can improve the initial population distribution and make it more evenly distributed in space.

[0095] Step 3-2: Calculate the convergence factor a, the coefficient vectors A and C, where A and C are used to control the movement of grey wolves in the search space.

[0096] Specifically, a non-linear convergence factor is introduced in this embodiment, as shown in formula (15):

[0097] a = 1 + tanh(G(H - (t / Maxiter)P )) (15)

[0098] Among them, t is the current iteration number, and Maxiter is the maximum iteration number. In this embodiment, Maxiter is taken as 40. G, H, and P are adjustment parameters for the convergence factor a. By setting the three adjustment parameters, the changing trend of the non-linear convergence factor a can be controlled. In this embodiment, G = 5, H = 0.5, and P = 1 are taken.

[0099] As Figure 3 shown, it is a comparison between the non-linear convergence factor a introduced in this embodiment and the linearly decreasing convergence factor a in the original algorithm. As can be seen from Figure 3 it, the improved convergence factor a in this embodiment shows non-linear decrease. In the early stage of iteration, its change amplitude and speed are small, which can expand the search range, ensure population diversity, and enhance the global search ability; in the later stage of iteration, the change amplitude and speed are still small, and the grey wolves search in small steps, enhancing the local search ability and improving the solution efficiency. This improvement effectively balances the global and local search abilities.

[0100] Specifically, the update formulas for the coefficient vectors A and C are shown in Formulas (16) and (17):

[0101] A = 2a·r1 - a (16)

[0102] C = 2·r2 (17)

[0103] Among them, r1 and r2 are random numbers in (0, 1).

[0104] Step 3 - 3: Calculate the fitness value of each grey wolf individual. The fitness value is the vehicle stability evaluation index calculated by Formula (4). The vehicle stability evaluation indexes under different vehicle speeds and transmission ratios are obtained through vehicle dynamics simulation. The three individuals with the best fitness values in the current population are respectively denoted as α, β, and δ wolves in descending order of fitness value, and the remaining individuals are uniformly defined as lower-layer wolves.

[0105] Step 3 - 4: Update the position of each grey wolf individual.

[0106] Specifically, in the stage of surrounding the prey, the update formula for the position of the grey wolf individual is as follows:

[0107] D = |C·X P (t) - X(t)| (18)

[0108] X(t + 1) = X p (t) - A·D (19)

[0109] Among them, D is the distance between the grey wolf individual and the prey; X P(t) is the current position of the prey; X(t) is the position of the gray wolf individual; X(t + 1) is the position of the gray wolf individual after update.

[0110] Specifically, in the stage of hunting the prey, the update formula of the gray wolf individual in this embodiment is:

[0111]

[0112] Among them, X(t + 1) is the position of the gray wolf individual after update. Z1, Z2, Z3 are basic weight values, Z is the total weight value, and Z = Z1 + Z2 + Z3. In this embodiment, Z1 = 4, Z2 = 3, Z3 = 2, and Z = 9 are taken. X1, X2, X3 are the step lengths of the gray wolf individual affected by wolves α, β, δ moving respectively.

[0113] In formula (20), (Z1 + t / Maxiter + (t / Maxiter) 2 ) is the actual weight value of wolf α, and its value gradually increases with the iterative process; (Z2 - (t / Maxiter) 2 ) is the actual weight value of wolf β, and its value gradually increases with the iterative process; (Z3 - t / Maxiter) is the actual weight value of wolf δ, and its value gradually increases with the iterative process; the decreasing amplitude of the actual weight value of X2 is less than that of the actual weight value of X3; the above actual weight values are specifically designed based on the status of wolves α, β, δ in the wolf pack.

[0114] Specifically, the expressions of X1, X2, and X3 are:

[0115] X1 = X α -A1·D α (21)

[0116] X2 = X β -A2·D β (22)

[0117] X3 = X δ -A3·D δ (23)

[0118] Among them, X α , X β , X δ are the positions of wolves α, β, δ respectively; A1, A2, A3 are coefficient vectors; D α , D β , D δ are the distances between other wolves and wolves α, β, δ

[0119] Specifically, the expressions of D α , D β , D δ are as follows:

[0120] D α = |C1·X α - X| (24)

[0121] D β = |C2·X β - X| (25)

[0122] D δ = |C3·X δ - X| (26)

[0123] where X is the current position of the wolf; C1, C2, and C3 are coefficient vectors.

[0124] Step 3 - 5: After each iteration, determine whether the convergence condition is satisfied. If the current iteration number t reaches the maximum iteration number Maxiter, use the optimal solution α wolf in the current wolf pack as the optimization result; otherwise, continue to iterate until the algorithm satisfies the convergence condition.

[0125] Specifically, the optimal solution α wolf output corresponds to the optimal yaw rate gain value and the center ratio gradient coefficient at the corresponding speed. In this embodiment, the optimized yaw rate gain value is 0.15 and the center ratio gradient coefficient is 20 under the working condition of 120 km / h. Figure 4 is the fitness value iteration diagram of the conventional Grey Wolf Optimization algorithm (GWO) and the improved Grey Wolf Optimization algorithm (IGWO) in this embodiment. It can be seen from the figure that the improved Grey Wolf Optimization algorithm has a faster convergence speed.

[0126] Step 4: Obtain the comprehensive best yaw rate gain value and the center ratio gradient coefficient for the speed range where the yaw rate gain remains unchanged.

[0127] Specifically, after obtaining the corresponding best yaw rate gain values and center ratio gradient coefficients under six vehicle speed working conditions from 20 km / h to 120 km / h. Based on different yaw rate gain values and center ratio gradient coefficients, design the transmission ratio schemes for six working conditions. Test each transmission ratio scheme under each vehicle speed working condition, calculate the handling stability evaluation index values, and perform weighted analysis on them. Figure 5 is the schematic diagram of the vehicle stability evaluation index values under different speed working conditions for six transmission ratio schemes.

[0128] Furthermore, by comprehensively analyzing the weighted comprehensive handling stability evaluation index values, the transmission ratio scheme with the smallest weighted index value of the test results is the best transmission ratio scheme within the speed range where the yaw rate gain remains unchanged, and it has good handling characteristics within this speed range. In this embodiment, when the yaw rate gain value is 0.2883 and the center ratio gradient coefficient is 20, the weighted index value of the test results of the corresponding transmission ratio scheme is the smallest, and it has good handling characteristics throughout the entire vehicle speed range.Figure 6 Schematic diagram of the final drive ratio for the speed range with a constant yaw rate gain value.

[0129] Embodiment 2

[0130] A variable transmission ratio design system for a steer-by-wire system, which is used to execute the aforementioned variable transmission ratio design method for a steer-by-wire system; the variable transmission ratio design system for the steer-by-wire system includes a vehicle handling test module and a parameter optimization module; the vehicle handling test module is used to test the vehicle handling stability evaluation indexes corresponding to different three-dimensional transmission ratio models; the parameter optimization module is used to iteratively optimize the steady-state yaw rate gain and the center ratio gradient coefficient.

Claims

1. A variable transmission ratio design method for a steer-by-wire system, characterized in that: A basic model of the relationship between the basic transmission ratio and vehicle speed is established; the basic model includes a steady-state yaw rate gain with a fixed value. A three-dimensional transmission ratio model for obtaining the actual transmission ratio is established; the actual transmission ratio in the three-dimensional transmission ratio model is obtained from the basic transmission ratio and the transmission ratio increment; the transmission ratio increment has a coefficient that varies with the steering wheel angle and follows a normal distribution; the center ratio gradient coefficient is used as the standard deviation of the normal distribution. Using the steady-state yaw rate gain and the center ratio gradient coefficient as optimization objectives for iterative optimization, and obtaining the three-dimensional transmission ratio model of the actual transmission ratio according to the optimized steady-state yaw rate gain and center ratio gradient coefficient.

2. The variable gear ratio design method of a steer-by-wire system according to claim 1, wherein: The steady-state yaw rate gain and the center ratio gradient coefficient are iteratively optimized by the grey wolf optimization algorithm.

3. A variable transmission ratio design method for a steer-by-wire system according to claim 2, characterized in that: In the grey wolf optimization algorithm, the vehicle handling stability evaluation index is used as the fitness value of the grey wolf individuals.

4. A variable transmission ratio design method for a steer-by-wire system according to claim 2, characterized in that: The grey wolf population is initialized by an optimized chaotic map; the expression of the optimized chaotic map is as follows: Among them, X i+1 is the chaotic value of the next state; X i is the chaotic value of the current state; a0 is the control parameter.

5. A variable transmission ratio design method for a steer-by-wire system according to claim 2, characterized in that: The expression of the convergence factor a in the grey wolf optimization algorithm is as follows: a = 1 + tanh(G(H - (t / Maxiter) P )) Where t is the current iteration number, Maxiter is the maximum iteration number; G, H, P are the adjustment parameters of the convergence factor a.

6. A variable transmission ratio design method for a steer-by-wire system according to claim 2, characterized in that: In the prey pursuit stage of the grey wolf optimization algorithm, the position of the grey wolf individual is dynamically weighted and updated according to the step size of the grey wolf individual affected by the α, β, δ wolves. The weights of the α, β, δ wolves with the largest to smallest fitness values are w1, w2, w3 respectively; the weight w1 continuously increases during the iteration process; the weights w2, w3 continuously decrease during the iteration process; the reduction amplitude of the weight w2 relative to the initial value is less than the reduction amplitude of the weight w3 relative to the initial value.

7. A design method for variable transmission ratio of a steer-by-wire system according to claim 1, characterized in that: Set multiple different vehicle speed conditions, and perform iterative optimization of the steady-state yaw rate gain and the center ratio gradient coefficient respectively; select the final steady-state yaw rate gain and center ratio gradient coefficient from the optimization results obtained under multiple conditions.

8. A variable transmission ratio design method for a steer-by-wire system according to claim 1, characterized in that: The three-dimensional transmission ratio model is as follows: Among them, i is the actual transmission ratio; i w is the basic transmission ratio; Δi is the basic increment of the transmission ratio; δ f is the steering wheel angle, σ is the center ratio gradient coefficient; normpdf(·,·,·) is the normal function.

9. A variable transmission ratio design method for a steer-by-wire system according to claim 1, characterized in that: The basic transmission ratio increment Δi is calculated from the normalized value of the vehicle speed and the basic transmission ratio.

10. A variable transmission ratio design system for a steer-by-wire system, characterized in that: A system for implementing the variable transmission ratio design method for a steer-by-wire system as claimed in claim 3; the variable transmission ratio design system for the steer-by-wire system includes a vehicle handling test module and a parameter optimization module; The vehicle handling test module is used to test the vehicle handling stability evaluation index corresponding to different three-dimensional transmission ratio models; the parameter optimization module is used to iteratively optimize the steady-state yaw rate gain and the center ratio gradient coefficient.

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