Electromagnetic thermal double-coupling temperature rise prediction and double-cycle optimization method for active suspension actuators

Abstract

The application discloses an electromagnetic-thermal double-coupling temperature rise prediction and double-cycle optimization method for an active suspension actuator, establishes an electric-magnetic-thermal double-direction coupling relationship transfer model of the active suspension system actuator, iteratively solves steady-state / transient temperature distribution functions of each net point of the actuator based on a mapping relationship between road excitation and linear thrust of the actuator, and obtains electric-magnetic-thermal coupling characteristics of the actuator under each iteration algebra; the steady-state / transient temperature distribution functions are used for temperature rise prediction of the active suspension actuator; the electric-magnetic-thermal coupling performance of the actuator is taken as an optimization target, vehicle suspension performance is taken as a constraint condition, a double-cycle optimization strategy of the active suspension system is constructed, and optimal parameters required when the active suspension system is designed are obtained. The design parameters obtained by the method can improve the design efficiency and effect of the active suspension system, and the active suspension system designed has the advantages of low energy consumption, high control precision and good reliability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of active suspension, in particular to an electromagnetic-thermal double coupling temperature rise prediction and double cycle optimization method of an active suspension actuator. BACKGROUND

[0002] At present, in the related research of the active suspension actuator for vehicles, research is mainly focused on the electromagnetic performance of the actuator, and few scholars study the working thermal characteristics of the actuator, which leads to the fact that accurate guidance cannot be provided in the actuator design optimization link. Moreover, in the existing research, an analytical equation is established based on a two-body two-degree-of-freedom active suspension structure, which is quite different from the actual vehicle suspension structure, resulting in a large error between the suspension dynamics state variables in the calculation results and the actual physical situation, which is not conducive to the temperature rise prediction of the actuator and the development of related technologies. Therefore, it is necessary to consider the electromagnetic-thermal double coupling effect of the actuator, predict the temperature rise, and establish a more accurate active suspension model.

[0003] The working efficiency, reliability and active force control accuracy of the active suspension system for vehicles are particularly important performances. The less the electromagnetic loss generated by the operation of the actuator as the core component, the higher the system efficiency. When the actuator is operating, the temperature rises. If the actuator is in a high-temperature working point for a long time, the risk of structural deformation, insulation failure and the like is easy to occur, which reduces the reliability of the system. Due to the influence of ripple disturbance, slot effect, edge effect, friction disturbance and current time-lag harmonic, electromagnetic thrust fluctuation phenomenon inevitably occurs when the actuator is under load, which reduces the control accuracy of the system to the active force. Therefore, in view of the influence of the negative effects of motor operation on the performance of the system, the electromagnetic-thermal coupling performance of the actuator needs to be further optimized, aiming at breaking through the technical bottleneck of low efficiency and poor effect of the optimization design of the actuator type active suspension system, and solving the problems of high energy consumption, low control accuracy and poor reliability of the electromagnetic actuator type active suspension system. SUMMARY

[0004] Therefore, the present application provides an electromagnetic-thermal double coupling temperature rise prediction and double cycle optimization method of an active suspension actuator.

[0005] The present application achieves the above technical object through the following technical means.

[0006] The electromagnetic-thermal double coupling temperature rise prediction and double cycle optimization method of the active suspension actuator comprises the following steps:

[0007] establishing a mapping relationship between the road excitation Z r and the linear thrust F d of the actuator based on a BP neural network algorithm;

[0008] establishing an electromagnetic-thermal double coupling relationship transmission model of the actuator of the active suspension system, and establishing a mapping relationship between the road excitation Z ra mapping relationship between a road surface excitation Z d and a linear thrust F b of the actuator is iteratively solved to obtain a steady-state / transient temperature distribution function of each node of the actuator at each iteration number, and an electro-magnetic-thermal coupling characteristic of the actuator is obtained at each iteration number; the steady-state / transient temperature distribution function is used for temperature rise prediction of the active suspension actuator;

[0009] A double-loop optimization strategy of the active suspension system is constructed by taking the electro-magnetic-thermal coupling performance of the actuator as an optimization target and taking the vehicle suspension performance as a constraint condition, and optimal parameters required in design of the active suspension system are obtained.

[0010] The electro-magnetic-thermal coupling performance of the actuator includes thrust fluctuation F b and maximum temperature rise T g , and the vehicle suspension performance includes dynamic travel, body acceleration and root mean square value of wheel dynamic load.

[0011] Further, the electro-magnetic-thermal bidirectional coupling relationship transmission model of the active suspension system actuator is composed of six units: a first unit is a force-electricity coupling transmission unit I i (F d , P εi ), which participates in the electro-magnetic-thermal forward coupling and reverse coupling processes; a second unit is an electricity-magnetism coupling transmission unit P i (I abci ), which only participates in the electro-magnetic-thermal forward coupling process; a third unit is a magnetism-heat coupling transmission unit T i (P Qi ), which only participates in the electro-magnetic-thermal forward coupling process; a fourth unit is a heat-magnetism coupling transmission unit P i+1 (T xyi ), which only participates in the electro-magnetic-thermal reverse coupling process; a fifth unit is a heat-electricity coupling transmission unit I i+1 (T xyi ), which only participates in the electro-magnetic-thermal reverse coupling process; and a sixth unit is a heat-heat coupling transmission unit T i+2 (T xyi+1 ), which is at a critical position of the electro-magnetic-thermal forward and reverse coupling processes and is used for re-modification of temperature distribution in a temperature field; wherein: I i is a set of electric field physical parameters at the i th iteration calculation, P i is a set of magnetic field physical parameters at the i th iteration calculation, T i is a set of heat field physical parameters at the i th iteration calculation, and i = 0, 1, 2…N.

[0012] Further, the mapping relationship between the road surface excitation Z r and the linear thrust F d of the actuator is iteratively solved to obtain a steady-state temperature distribution function of each node of the actuator, and specifically:

[0013] Based on the mapping relationship, in the case of vehicle road surface level and vehicle speed determination, the actuator thrust F is obtained within the road surface excitation time Δt d , which is the first generation I0(F d , P ε0 ) input of the electric coupling transfer unit, and the physical parameters P d and the permanent magnet flux are used to establish the initial characteristic set I0 of the electric field, and the three-phase current I ε0 in I0 is used as the first generation P0(I abc0 ) input of the electric-magnetic coupling transfer unit, and the initial characteristic set P0 of the magnetic field is established, and the electromagnetic loss physical parameter P abc0 in P0 is used as the first generation T0(P Q0 ) input of the magnetic-thermal coupling transfer unit, and the initial characteristic set T0 of the thermal field is established, and the temperature distribution T Q0 in T0 is used as the input of the thermal-magnetic coupling transfer unit P1(T xy0 ) and the thermal-electric coupling transfer unit I1(T xy0 ), and the next generation characteristic set P1 of the magnetic field and the next generation characteristic set I1 of the electric field are established, and the above steps are repeated to calculate the electric, magnetic and thermal field characteristics.

[0014] In the calculation process of algebra i=1,2…N, the physical parameter T xy0 in the thermal field characteristic set is different from the direct input of the thermal-magnetic coupling transfer unit P xyi (T i+1 ) and the thermal-electric coupling transfer unit I xyi (T i+1 ) at algebra i=0, but is first used as the input of the thermal-thermal coupling transfer unit to establish the next generation characteristic set T xyi of the thermal field, and then the temperature distribution T i+1 in T i+1 is used as the input of the thermal-magnetic coupling transfer unit and the thermal-electric coupling transfer unit.

[0015] Further, the determination of the electric field characteristic set I1 is affected by the parameter P xyi in the force-electric coupling transfer unit I1(F d , P ε1 ), and the transfer relationship of I1(F ε1 , P d ) is In order to maintain the required active force of the system, i.e. the actuator thrust F ε1 , it is necessary to increase the three-phase current in I1, which passes through the transfer relationship i d , i d , i qwhere τ is the pole pitch of the actuator, L d and L q are the d, q-axis inductances, i d and i q are the d, q-axis currents.

[0016] Further, the convergence condition of the iteration is that the temperature distribution difference of each point in the last three generations is less than 0.2℃, that is, the steady-state temperature distribution function of the actuator under the road excitation Δt is obtained.

[0017] Further, the electro-magnetic-thermal coupling performance optimization function of the actuator is:

[0018]

[0019] and satisfies the following constraint conditions:

[0020]

[0021] where W is the set of actuator structure parameters, j is the number of large cycle iterations, σ1 is the body acceleration weight, σ2 is the tire dynamic load weight, rms_s is the root mean square value of the suspension dynamic travel, rms_b is the root mean square value of the body acceleration, rms_d is the root mean square value of the tire dynamic load, and a and b are constants.

[0022] Further, the double-loop optimization strategy of the active suspension system is specifically:

[0023] Taking the number of large cycle iterations j=0 and the number of small cycle iterations i=0, the change amount of each structure parameter of the actuator is The quantitative influence degree of the electro-magnetic-thermal coupling performance change amount is denoted as η, and the Taguchi method experiment structure parameter discrete interval z / η is obtained based on η. The sensitivity of each structure parameter of the actuator to the thrust fluctuation and the maximum temperature rise is calculated The structure parameters with a sensitivity greater than 0.5 and less than -0.5 are selected, the response surface function fitting is performed, the function relationship between the high-sensitivity structure parameters and the electromagnetic-thermal coupling performance is established, and based on the function relationship, the genetic algorithm is used to obtain the Pareto frontier curve of the electro-magnetic-thermal coupling performance (T g ) j , (F b ) j in the jth generation, obtain and record the optimal structure parameter combination [w1, w2, w3...] in the jth generation j and the electro-magnetic-thermal coupling performance (T g ) j , (F b ) jthe optimal value of each generation, wherein the minimum corresponding control parameter combination and actuator structure parameter are the global optimal parameter set, which is used as the design parameter of the active suspension system;

[0024] wherein, z is a structure size proportionality coefficient.

[0025] Further, the control parameter combination set U is obtained by:

[0026] Based on the numerical analysis equation of the five-body two-degree-of-freedom active suspension, the mapping relationship between the combination of the skyhook damping constant C sky and the passive damping constant C s and the suspension dynamic travel root mean square value rms_s, the body acceleration root mean square value rms_b, and the tire dynamic load root mean square value rms_d is obtained, and then the constraint condition cloud map is obtained. In the constraint condition region satisfying the electro-magnetic-thermal coupling performance optimization function, the control parameter combination set U that meets the constraint condition is obtained by taking one set element between C sky and C s .

[0027] Further, in order to obtain the quantitative influence of the change amount of each structure parameter of the actuator on the electro-magnetic-thermal coupling performance change amount , after obtaining the steady-state electro-magnetic-thermal field coupling characteristic set {I j , P j , T j} under the initial structure parameter combination W0 of the actuator, the steady-state electro-magnetic-thermal field coupling characteristic set under other structure parameter combinations in the actuator structure parameter combination set W is further obtained. The force-electricity coupling transfer unit in the electro-magnetic-thermal bidirectional coupling relationship transfer model of the active suspension system actuator needs to be corrected under different actuator structure parameter combinations. The numerical calculation equation in the transfer unit is:

[0028]

[0029] wherein, τ is the pole spacing of the actuator, L d and L q are the d-axis and q-axis inductances, i d and i q are the d-axis and q-axis currents, respectively.

[0030] The change of the actuator structure parameter will change τ, P, L d , L q , and since F d is known, id and i q The values of the numerical changes realize the correction of the force-electric coupling transmission relationship.

[0031] Further, the numerical analysis equation of the five-body two-degree-of-freedom active suspension includes a dynamic synthesis equation of the nonlinear five-body two-degree-of-freedom active suspension:

[0032]

[0033] Wherein, Q j is a generalized external force, T is the total kinetic energy of the five-body two-degree-of-freedom active suspension, q j is a generalized coordinate, V is the total potential energy of the five-body two-degree-of-freedom active suspension, is the force received by the i th mass point, is the position vector of the i th mass point, F ix is the component of the force received by the i th mass point in the x-axis, F iy is the component of the force received by the i th mass point in the y-axis, x i and y i are the x and y coordinates of the i th point, and n is a constant;

[0034] The total kinetic energy of the five-body two-degree-of-freedom active suspension includes the kinetic energy of the tire Wherein, m u is the tire mass, is the velocity of the hinge point A, is the angle change value of the composite structure axis and the vertical direction;

[0035] The moment of inertia of the composite structure AB in the five-body two-degree-of-freedom active suspension Wherein, are the mass of the composite member 2 in the composite structure AB, the mass of the composite member 1 in the composite structure AB, the length of the composite member 1, the length of the composite member 2, L AN is the distance between the hinge point A and the composite structure centroid N.

[0036] The beneficial effects of the present application are:

[0037] (1) The present application establishes a numerical analysis equation of the five-body two-degree-of-freedom active suspension. Compared with the traditional active suspension analysis equation, the present application retains the rebound and lateral movement characteristics of the real wheel, considers the influence of the topological bar connection relationship and the mass of the topological bar on kinematics and dynamics, and greatly improves the calculation accuracy of the performance parameters of the active suspension system and the working parameters of the actuator.

[0038] (2) The electric-magnetic-thermal bidirectional coupling relationship transmission model of the active suspension actuator of the application, considering the influence of temperature on the magnetic flux of the permanent magnet, the resistivity of the winding, the magnetic permeability of the material and the thermal conductivity of the material, can calculate the electric field coupling characteristics, the magnetic field coupling characteristics and the thermal field coupling characteristics of the active suspension actuator under the condition of meeting the convergence condition and each iteration number, in addition, the transient thermal distribution and the steady-state thermal distribution of the active suspension actuator can be calculated.

[0039] (3) The double-cycle optimization strategy of the active suspension system constructed by the application with the electric-magnetic-thermal coupling performance of the actuator as the optimization target and the vehicle suspension performance as the constraint condition, can obtain the optimal parameters required for the design of the active suspension system, improve the efficiency and effect of the optimization design of the actuator type active suspension system, and the active suspension system designed by the parameters obtained from the double-cycle optimization strategy has low energy consumption, high control precision and good reliability. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 The electromagnetic thermal double coupling temperature rise prediction and double cycle optimization method block diagram of the active suspension actuator of the application;

[0041] Figure 2 The structure diagram of the five-body two-degree-of-freedom active suspension of the application;

[0042] Figure 3 The mapping relationship diagram of the road excitation Z r and the actuator thrust F d of the application;

[0043] Figure 4 The iteration process diagram of the electric, magnetic and thermal field physical parameters of the application;

[0044] Fig. 5(a) is a control parameter set contour diagram of the application meeting the constraint condition one;

[0045] Fig. 5(b) is a control parameter set contour diagram of the application meeting the constraint condition two;

[0046] Figure 6 The area diagram meeting the two constraint conditions of the application;

[0047] Figure 7 The double-cycle optimization strategy flowchart of the application. DETAILED DESCRIPTION

[0048] The application will be further described below in combination with the drawings and specific embodiments, but the protection scope of the application is not limited thereto.

[0049] Reference Figure 1The embodiment provides an electromagnetic heat double-coupling temperature rise prediction and double-circulation optimization method for an active suspension actuator, and specifically comprises the following contents.

[0050] S1, a numerical analysis equation of the five-body two-degree-of-freedom active suspension is established, and the accuracy of the analysis equation is verified through a finite element model.

[0051] Based on the topology structure of the double wishbone suspension, a kind of nonlinear five-body two-degree-of-freedom active suspension structure as shown in Figure 2 is built in Viso, and then a numerical analysis equation of the five-body two-degree-of-freedom active suspension is established in MATLAB, including:

[0052] Random road excitation function Wherein, u is the instantaneous speed of the vehicle installed with the topology structure of the double wishbone suspension, G q (n0) is the grade constant of the road on which the vehicle travels, n0 is the reference spatial frequency, and ω(t) represents the Gaussian white noise.

[0053] Skyhook control strategy Wherein, F d is the instantaneous active force of the active suspension actuator, the skyhook damping constant C sky and the passive damping constant C s are active suspension control parameters, z u is the vertical speed of the tire, z s is the vertical speed of the vehicle body.

[0054] The dynamic comprehensive equation of the nonlinear five-body two-degree-of-freedom active suspension is:

[0055]

[0056] Wherein, Q j is the generalized external force, T is the total kinetic energy of the five-body two-degree-of-freedom active suspension, q j is the generalized coordinate, V is the total potential energy of the five-body two-degree-of-freedom active suspension, is the force received by the i-th particle, is the position vector of the i-th particle, F ix is the component of the force received by the i-th particle in the x-axis, F iy is the component of the force received by the i-th particle in the y-axis, x i and y i are the x and y coordinates of the i-th particle, and n is a constant.

[0057] The above dynamic comprehensive equation is composed of the following sub-formulas:

[0058] The kinetic energy expression of the vehicle body OA is:

[0059]

[0060] Kinetic energy expression of the lower arm OB:

[0061]

[0062] Kinetic energy expression of the composite structure AB:

[0063]

[0064] Kinetic energy expression of the tire:

[0065]

[0066] Overall kinetic energy expression of the five-body two-degree-of-freedom active suspension:

[0067] T = T OA + T OB + T AB + T wheel

[0068] Elastic potential energy expression of the suspension spring:

[0069]

[0070] Elastic potential energy expression of the tire:

[0071]

[0072] Gravitational potential energy expression of the five-body two-degree-of-freedom active suspension:

[0073]

[0074] Generalized external force expression of the five-body two-degree-of-freedom active suspension:

[0075]

[0076] where m OA , m OB , m AB , m u are the body mass, the lower arm mass, the composite structure mass, and the tire mass, respectively, L OB , L AB , L AB0 , L AN , L OM , H s are the lower arm length, the composite component original length, the composite component length change value, the distance between the hinge point A and the composite structure centroid point N, the distance between the hinge point O and the lower arm centroid point M, and the vertical distance between the hinge point A and the body centroid C ms , k s , kt These are the stiffness of the coil spring and the stiffness of the tire, respectively. These represent the initial angle between the axis of the composite structure and the vertical direction, and the angle change value, respectively. θ0 and θ are the initial angle and angle change value between the lower control arm axis and the horizontal direction, respectively. These are suspension parameters. The velocity at point O is vertically upward, with the positive direction being vertically upward. The angular velocity of the lower swing arm is shown, with counterclockwise as the positive direction; Let Z be the velocity at point A, with vertically upward as the positive direction; s Z u Z r These are vehicle displacement, tire bounce displacement, and road surface excitation, respectively; KG AB KG OA KG OB KG wheel These are the gravitational potential energy of the composite structure AB, the gravitational potential energy of the vehicle body OA, the gravitational potential energy of the lower control arm OB, and the gravitational potential energy of the tire, respectively. The driving force from point A1 in the positive direction of A is the force that propels the object. The driving force pointing from point B1 in the positive direction to B is the force that propels the vehicle. Let A1 be the position vector. Let B1 be the position vector.

[0077] L AB J N They respectively satisfy the following relations:

[0078]

[0079] Among them, J N Let m be the moment of inertia of the composite structure AB. A1B1 m AA1 L AA1 L A1B1 Let L represent the mass of composite component 2, the mass of composite component 1, the length of composite component 1, and the length of composite component 2, respectively; α and β represent the angles between the line connecting hinge points OA and the horizontal direction, and the angle between the line connecting hinge points OA and the vertical direction, respectively; L represents the mass of composite component 2, the mass of composite component 1, the length of composite component 1, and the length of composite component 2 ... OA L is the distance between hinge points O and A. AN The distance between hinge point A and the centroid N of the composite structure.

[0080] Based on modern virtual prototype technology, on the basis of ADAMS program secondary development, using ADAMS View build the same as the above five body two degree of freedom active suspension performance parameters, structure parameters and control strategy consistent active suspension finite element dynamics model, in the same road excitation conditions, capture the analytical method model (i.e. numerical analytical equation) of the dynamic characteristics set and the dynamic characteristics set of finite element method model, compare the dynamic characteristics set (vehicle acceleration, suspension dynamic travel, wheel dynamic load, active force), verify the accuracy of numerical analytical equation.

[0081] S2, based on BP neural network algorithm to establish the mapping relationship between road excitation Z r and actuator linear thrust F d .

[0082] According to the related theory knowledge of mechanical degree of freedom, it is known that the five body two degree of freedom active suspension model needs two external inputs, and the unique motion state of the machine can be determined, since the five body two degree of freedom active suspension system establishes a coupling relationship between the two degrees of freedom through the spiral spring, so the motion characteristics and dynamic characteristics of the active suspension system can be determined through the road excitation Z r . The present application establishes the mapping relationship between road excitation Z r and actuator thrust F d based on BP neural network algorithm, and the BP network establishment process is shown in Figure 3 , specifically:

[0083] S21: initialize parameters: initialize the related parameters of neural network, including neural network step, weight, balance coefficient, exponential decay factor, calculation period;

[0084] S22: set discrete set: set the vehicle instantaneous speed u as discrete set I, the value range is [10, 20], every 0.5m / s interval; the road grade constant Gq(n0) is discrete set II, the value range is [4 2 ×10 -6 , 32 2 ×10 -6 ], every 4 2 ×10 -6 m 3 interval;

[0085] S23: determine the number of neural network layers and the number of neurons: since the input layer is relatively simple, the number of hidden layers is determined to be 1, containing 20 neurons, so as to obtain shorter training time;

[0086] S24: select excitation function: respectively select hyperbolic tangent function and purelin function as the excitation function of hidden layer and output layer;

[0087] S25: Selecting training function: adopting batch gradient descent as training function to meet the need of fast calculation of error value of training function, so as to better adjust the weights and thresholds of neural network;

[0088] S25: Calculating error value: taking the sum of squares of the difference E Fd between expected output and actual output as error function value.

[0089] Through the process of S21-S25, the mapping relationship between road excitation Z r and actuator thrust F d is established.

[0090] S3, the electric-magnetic-thermal bidirectional coupling relationship transfer model of the actuator of the active suspension system is established, the steady-state / transient temperature distribution function of each node of the actuator is iteratively solved, and the electric-magnetic-thermal coupling characteristics of the actuator under each iteration algebra are obtained.

[0091] Considering the influence of temperature on the flux of permanent magnet, the resistivity of winding, the magnetic permeability of material and the thermal conductivity of material, six units are arranged and connected to constitute the electric-magnetic-thermal bidirectional coupling relationship transfer model of the active suspension actuator; the first unit is a force-electric coupling transfer unit I i (F d , P εi ), which participates in the forward and reverse coupling processes of electric-magnetic-thermal; the second unit is an electric-magnetic coupling transfer unit P i (I abci ), which only participates in the forward coupling process of electric-magnetic-thermal; the third unit is a magnetic-thermal coupling transfer unit T i (P Qi ), which only participates in the forward coupling process of electric-magnetic-thermal; the fourth unit is a thermal-magnetic coupling transfer unit P i+1 (T xyi ), which only participates in the reverse coupling process of electric-magnetic-thermal; the fifth unit is a thermal-electric coupling transfer unit I i+1 (T xyi ), which only participates in the reverse coupling process of electric-magnetic-thermal; the sixth unit is a thermal-thermal coupling transfer unit T i+2 (T xyi+1 ), which is at the critical point of the forward and reverse coupling processes of electric-magnetic-thermal, and modifies the temperature distribution in the temperature field again. Wherein: I i is the electric field physical parameter set (electric field characteristics) at iteration i (i=0, 1, 2…N), P i is the magnetic field physical parameter set (magnetic field characteristics) at iteration i (i=0, 1, 2…N), and T i is the thermal field physical parameter set (thermal field characteristics) at iteration i (i=0, 1, 2…N).

[0092] See Figure 4 The iterative process of the physical parameters of the electric, magnetic, and thermal fields is as follows: Based on the mapping relationship established in step S2, and given the vehicle road surface grade and vehicle speed, the actuator thrust F within the road surface excitation time Δt is obtained. d This serves as the first generation I0(F) of the first unit force-electric coupling transmission unit. d P ε0 Input items, via F d Physical parameter P of permanent magnet flux linkage in magnetic field characteristics ε0 (Initial magnetic field), establish a set of initial characteristics I0 representing the electric field, and then consider the three-phase currents I in the initial characteristics set I0. abc0 As the first generation of P0(I) as the second unit-electromagnetic coupling transfer unit abc0 Input items are used to establish a set of initial characteristics P0 representing the magnetic field, and the electromagnetic loss physical parameter P in the initial characteristics set P0 is used to... Q0 The first generation T0(P) is the third unit of heat source input - magnetic-thermal coupling transfer unit. Q0 Establish a set of initial thermal field characteristics T0, and define the temperature distribution T in the initial thermal field characteristic set T0. xy0 P1(T) serves as the fourth unit – thermal-magnetic coupling transfer unit. xy0 ), Fifth Unit - Thermal-Electrical Coupling Transfer Unit I1 (T xy0 The input terms are used to establish the next-generation characteristic set P1 of the magnetic field and the next-generation characteristic set I1 of the electric field. The determination of the electric field characteristic set I1 is subject to the force-electric coupling transmission unit I1(F). d P ε1 ) parameter P ε1 The impact (I1(F) d P ε1 The transitive relation is As the temperature of the permanent magnets rises, the magnetic flux linkages of each permanent magnet decrease. In order to maintain the required driving force of the system, i.e., the actuator thrust F, d It is necessary to increase the three-phase current in I1, and this three-phase current is transmitted through the transfer relationship i. d i q (After transformation), repeat the above steps to calculate the electric, magnetic, and thermal field characteristics (i = 1, 2, 3…N); it is worth noting that the physical parameter T in the subsequent thermal field characteristic set... xyi Unlike when the algebraic i=0, the thermo-magnetic coupling transfer unit P is directly input. i+1 (T xyi and thermo-electric coupling transfer unit I i+1 (T xyi Instead, it is first used as the input term of the sixth unit - thermal-thermal coupling transfer unit to establish the next-generation characteristic set T of the thermal field. i+1Then the next generation of thermal field characteristics set T i+1 The temperature distribution T xyi (i≠0) as the input of the fourth unit-thermal-magnetic coupling transfer unit and the fifth unit-thermal-electric coupling transfer unit. Notably, the convergence condition of iteration is that the temperature distribution difference of each point under three consecutive generations (T xyi+2 , T xyi+1 , T xyi ) is less than 0.2℃, that is, the steady-state temperature distribution function T xy of the actuator under the road excitation (for a time length Δt) is obtained; when Δt is extremely small, the transient temperature distribution function of the actuator in the working active suspension system can be calculated.

[0093] In the iteration process, {I0, P0, T0} is the electric-magnetic-thermal coupling characteristic set of the first generation of the actuator, and so on, to obtain the electric-magnetic-thermal coupling characteristics of the actuator under each iteration number.

[0094] S4, based on the Taguchi method-response surface method-genetic algorithm, taking the thrust fluctuation and the maximum temperature rise in the electric-magnetic-thermal coupling characteristics of the actuator as the optimization objectives, and taking the vehicle suspension performance (dynamic travel, body acceleration, and wheel dynamic load root mean square value) as the constraint condition, a double-loop optimization strategy of the active suspension system is constructed; wherein the maximum temperature rise is determined by the steady-state temperature distribution function.

[0095] The double-loop optimization strategy of the active suspension system is shown in Figure 7 According to the electromagnetic-thermal coupling relationship of the actuator and the numerical analysis equation of the active suspension, it is known that the maximum temperature rise T g is not only related to the structure of the actuator itself, but also related to the active force of the active suspension system, that is, related to the values of the control parameters C sky , C s ; similarly, the thrust fluctuation F b is not only related to the structure parameters of the actuator, but also related to the maximum temperature rise of the actuator. The dynamic travel, body acceleration, and tire dynamic load of the active suspension are related to the values of C sky , C s , and in order to ensure the performance of the active suspension, the values of C sky , C s need to be constrained.

[0096] Therefore, the electromagnetic-thermal coupling performance (thrust fluctuation and maximum temperature rise) optimization function of the active suspension actuator can be written as:

[0097]

[0098] and satisfies the following constraint conditions:

[0099]

[0100] wherein, W is the actuator structure parameter combination set, j is the large loop iteration number, σ1 is the body acceleration weight, σ2 is the tire dynamic load weight, the values of σ1 and σ2 are determined according to the actual driving conditions of the vehicle, generally, more attention is paid to the driving smoothness on the low-speed bumpy road, that is, σ1 can take a larger value, and more attention is paid to safety on the high-speed flat road, that is, σ2 can take a larger value; rms_s is the suspension dynamic travel root mean square value, rms_b is the body acceleration root mean square value, rms_d is the tire dynamic load root mean square value, and the values of constants a and b are related to the vehicle type and are empirical values.

[0101] Based on the numerical analysis equation of the five-body two-degree-of-freedom active suspension, the C sky (taken in the range of 0Ns / m-2000Ns / m), C s (taken in the range of 0.5Ns / m-2500Ns / m) combination is respectively mapped with the suspension dynamic travel root mean square value rms_s, the body acceleration root mean square value rms_b, and the tire dynamic load root mean square value rms_d, and then the constraint condition cloud map is obtained, which meets the constraint condition rms_s[(c sky ) j , (c s ) j ]≤a, the control parameter set located in the area above the contour curve is shown in FIG. 5(a), which meets the constraint condition 1500·σ1·rms_b[(c sky ) j , (c s ) j ]+σ2·rms_d[(c sky ) j , (c s ) j ]≤b, the control parameter set located in the area below the contour curve is shown in FIG. 5(b); the area meeting both constraint conditions is shown in FIG. 5(c). Figure 6 In this area, C sky , C s take one set element every 150Ns / m to obtain the control parameter combination set U meeting both constraint conditions.

[0102] Taking the large loop iteration number j=0 and the small loop iteration number i=0, according to the mixed-hangar control strategy of the active suspension system, (F d ) j is obtained, based on the electro-magnetic-thermal bidirectional coupling relationship transmission model of the S3 active suspension system actuator, the steady-state electro-magnetic-thermal field coupling characteristic set {I j , P j , T j} under the initial structure parameter combination W0 of the actuator is obtained by iteration, in order to obtain the change amount of each structure parameter of the actuator Quantitative influence on the change of electro-magnetic-thermal coupling performance To continue to obtain the set of steady-state electro-magnetic-thermal field coupling characteristics under other structural parameter combinations in the set W of actuator structural parameter combinations, the force-electricity coupling transfer unit in the transfer model of the bidirectional coupling relationship of the electro-magnetic-thermal coupling of the active suspension system actuator needs to be corrected when calculating different actuator structural parameter combinations. The numerical calculation equation in the transfer unit is:

[0103]

[0104] Where τ is the pole spacing of the actuator, L d and L q are the d-axis and q-axis inductances, i d and i q are the d-axis and q-axis currents, respectively.

[0105] The change of the actuator structural parameters will change τ, P, L d , L q , and since F d is known, the values of i d and i q change, thereby realizing the correction of the force-electricity coupling transfer relationship.

[0106] The above quantitative influence degree is denoted as η, and satisfies the expression:

[0107]

[0108] Based on η, the Taguchi method experiment structural parameter discrete interval z / η is obtained, and is written into the Minitab data processing software. The sensitivity of each structural parameter of the actuator to the thrust fluctuation and the maximum temperature rise is calculated (value range: ), where z is the structural size proportionality coefficient.

[0109] The structural parameters with a sensitivity greater than 0.5 and less than -0.5 are selected, and the insensitive parameters are eliminated. The response surface function fitting is performed, and the functional relationship between the high-sensitivity structural parameters and the electro-magnetic-thermal coupling performance is established: F b (w1, w2, w3 ···), T g (w1, w2, w3 ···), where w1, w2, w3... are high-sensitivity structural parameters.

[0110] Based on the established functional relationship, the genetic algorithm is used to obtain the electro-magnetic-thermal coupling performance (T g ) j , (F b ) jThe optimal structure parameter combination [w1, w2, w3...] of the jth generation is obtained and recorded according to the Pareto frontier curve. j and the jth generation magnetic-thermal coupling performance (T g ) j , (F b ) j The minimum value (the optimal value of the current generation) is obtained, and it is judged whether the elements in the control parameter combination set U are taken or not. If the control parameter combination set U is not taken, the above process is continued, that is, j+1, until the control parameter combination set is taken completely. The optimal values of each generation are compared, wherein the corresponding control parameter combination and actuator structure parameter corresponding to the minimum value are the global optimal parameter set, which is used as the design parameter of the active suspension system.

[0111] The embodiments are preferred embodiments of the present application, but the present application is not limited to the above embodiments. Any obvious improvement, replacement or modification made by those skilled in the art without departing from the essential content of the present application shall fall within the protection scope of the present application.

Claims

1. An electromagnetic-thermal double coupling temperature rise prediction and double cycle optimization method for active suspension actuators, characterized by: establishing a numerical analytical equation of a five-body two-degree-of-freedom active suspension, and verifying the accuracy of the analytical equation through a finite element model; Based on the BP neural network algorithm to establish the road surface excitation Z r The mapping relationship with the linear thrust of the actuator F d ​ Establish a bidirectional coupling model of the electro-magnetic-thermal relationship of the actuators in the active suspension system, based on road excitation. Z r linear thrust of the actuator F d The mapping relationship is iteratively solved to obtain the steady-state / transient temperature distribution function of each point of the actuator, and the electro-magnetic-thermal coupling characteristics of the actuator under each iteration algebra are obtained; the steady-state / transient temperature distribution function is used for temperature rise prediction of the active suspension actuator; taking the electro-magnetic-thermal coupling performance of the actuator as an optimization objective and the vehicle suspension performance as a constraint condition, constructing a double cycle optimization strategy for the active suspension system, and obtaining optimal parameters required for the design of the active suspension system; The actuator electromagnetic thermal coupling performance includes thrust fluctuation and maximum temperature rise The vehicle suspension performance includes dynamic travel, body acceleration, and wheel dynamic load root mean square value The electric-magnetic-thermal bidirectional coupling relationship transmission model of the active suspension system actuator is connected by six units: the first unit is a force-electric coupling transmission unit I i ( F d , ), participating in the electric-magnetic-thermal forward coupling and reverse coupling process; The second unit is an electro-magnetic coupling transfer unit P i ( I abci ), only involved in the electro-magnetic-thermal forward coupling process; the third unit is a magnetic-thermal coupling transfer unit T i ( P Qi ), only involved in the electro-magnetic-thermal forward coupling process; The fourth unit is a thermo-magnetic coupling transfer unit P i+1 T xyi only participates in the electro-magnetic-thermal reverse coupling process;​ The fifth unit is a thermo-electric coupling transfer unit I i+1 T xyi only involved in the electro-magnetic-thermal reverse coupling process;​ The sixth unit is a heat-heat coupling transmission unit T i+2 T xyi+1 , at the critical point of the electric-magnetic-thermal positive and negative coupling process, the temperature distribution in the temperature field is re-modified; wherein: I i is the iteration calculation i of the electric field physical parameter set, P i is the iteration calculation i of the magnetic field physical parameter set, T i is the iteration calculation i of the thermal field physical parameter set, i =0,1,2…N;​ Specifically, the dual-loop optimization strategy of the active suspension system involves taking the largest loop iteration number. j =0, number of small loop iterations i =0, the change in each structural parameter of the actuator Changes in electro-magnetic-thermal coupling performance The quantitative degree of influence is denoted as ,by Based on this, the discrete interval of the experimental structural parameters using the Taguchi method was obtained. z / The sensitivity of each structural parameter of the actuator to thrust fluctuation and maximum temperature rise was calculated. Select sensitivity For structural parameters in the ranges greater than 0.5 and less than -0.5, response surface function fitting is performed to establish a functional relationship between highly sensitive structural parameters and electromagnetic thermal coupling performance. Based on this functional relationship, a genetic algorithm is used to obtain information about the first... j Electromagnetic-thermal coupling performance in the cycle ( T g ) j 、( F b ) j The Pareto front curve was obtained and recorded. j The optimal combination of structural parameters [ w 1. w 2. w 3...] j and the j Thermocouple performance ( T g ) j 、 ( F b ) j To determine the optimal value of the control coefficient combination set. U Whether all elements in the set have been taken, if the control coefficient combination set U If not all items are retrieved, continue the above process, i.e. j +1, until all control coefficient combinations have been taken, compare the optimal values ​​of each generation, and the control parameter combination and actuator structure parameters corresponding to the smallest value are the globally optimal parameter set, which is used as the design parameters for the active suspension system; where... =1,2,3… n , z This is the structural dimension ratio coefficient.

2. The electromagnetic thermal dual-coupling temperature rise prediction and dual- cycle optimization method of claim 1, wherein, The road surface excitation Z r The mapping relationship of the actuator linear thrust F d The steady-state temperature distribution function of each node of the actuator is solved iteratively, specifically: Based on the mapping relationship, in the case of vehicle road surface grade and vehicle speed determination, the road surface excitation action time Δ is obtained t The actuator thrust in the inner part F d , which is the first generation of electric coupling transmission unit I 0 F d , The input item is obtained by F d and the physical parameters of the permanent magnet flux , to establish a set of initial characteristics of the electric field I 0 I The three-phase current in the above-mentioned I abc0 , which is the first generation of electric-magnetic coupling transmission unit P 0 I abc0 The input item is established to establish a set of initial characteristics of the magnetic field P 0 P The electromagnetic loss physical parameters in the above-mentioned P Q0 , which is the first generation of magnetic-thermal coupling transmission unit T 0 P Q0 The input item is established to establish a set of initial characteristics of the thermal field T 0 T The temperature distribution in the above-mentioned T xy0 , which is the first generation of thermal-magnetic coupling transmission unit P 1 T xy0 , the input item of the thermal-electric coupling transmission unit I 1 T xy0 , to establish a set of next-generation characteristics of the magnetic field P 1 and a set of next-generation characteristics of the electric field I 1, repeat the above steps to calculate the characteristics of the electric, magnetic and thermal fields; In algebra i The calculation process for =1,2…N, and the physical parameters in the thermal field characteristic set. T xyi Unlike algebra i When =0, the thermo-magnetic coupling transfer unit is directly input. P i+1 ( T xyi and thermo-electric coupling transfer unit I i+1 ( T xyi Instead, it is first used as an input term for the thermal-thermal coupling transfer unit to establish the next-generation characteristic set of the thermal field. T i+1 Then the T i+1 Temperature distribution in T xyi As input to the thermo-magnetic coupling transfer unit and the thermo-electric coupling transfer unit.

3. The electromagnetic thermal double-coupled temperature rise prediction and double-cycle optimization method according to claim 2, characterized in that, The set of electric field characteristics I The determination of 1 is subject to force-electric coupling transmission unit I 1( F d , ) parameters The impact, I 1( F d , The transitive relationship is as follows: In order to maintain the system's required driving force, i.e., actuator thrust F d It is necessary to increase I The three-phase current in 1, which is transmitted through the aforementioned transfer relationship. i d , i q The transformation yields; where, The distance between the magnetic poles of the actuator. L d and L q They are respectively d , q Shaft inductor, i d and i q They are respectively d , q Axis current.

4. The electromagnetic thermal double-coupling temperature rise prediction and double- cycle optimization method according to claim 2, characterized in that, The convergence condition of iteration is that the difference of temperature distribution of each point is less than 0.2℃ when three successive generations are compared, that is, the road surface excitation effect ΔT is obtained t The steady-state temperature distribution function of the lower actuator.

5. The electromagnetic thermal dual-coupled temperature rise prediction and dual- cycle optimization method of claim 1, wherein, The electro-magnetic-thermal coupling performance optimization function of the actuator is: and satisfies the following constraint conditions: wherein, W is a set of actuator structure parameters, j is a number of large loop iterations, σ1 is a body acceleration weight, and σ2 is a tire dynamic load weight, rms_s is a suspension dynamic travel root mean square value, rms_b is a body acceleration root mean square value, rms_d is a tire dynamic load root mean square value, a and b are constants, C sky is a skyhook damping constant coefficient, C s is a passive damping constant coefficient.

6. The electromagnetic thermal dual-coupled temperature rise prediction and dual- cycle optimization method of claim 1, wherein, The control parameter combination set U The acquisition process is: based on the numerical analysis equation of five-body two-degree-of-freedom active suspension, the sky damping constant C sky , passive damping constant C s The mapping relationship of the combination of the constant and the root mean square value of the suspension dynamic travel rms_s、 The root mean square value of the body acceleration rms_b、 The root mean square value of the tire dynamic load rms_d , and then the constraint condition cloud map is obtained, in the constraint condition area meeting the electro-magnetic-thermal coupling performance optimization function, C sky , C s Interval taking one set element, obtaining the control parameter combination set meeting the constraint condition U .

7. The electromagnetic thermal double-coupled temperature rise prediction and double-cycle optimization method according to claim 6, characterized in that, To obtain the change in each structural parameter of the actuator Changes in electro-magnetic-thermal coupling performance The quantitative influence of obtaining the initial structural parameter combination of the actuator. W Set of steady-state electro-magnetic-thermal field coupling characteristics at 0 I j , P j , T j After that, continue to obtain the actuator structural parameter combination set. W The set of steady-state electro-magnetic-thermal field coupling characteristics under other structural parameter combinations requires correction of the force-electric coupling transmission unit in the electro-magnetic-thermal bidirectional coupling relationship transmission model of the active suspension system actuators to calculate different actuator structural parameter combinations. The numerical calculation equation of this transmission unit is as follows: ,in, The distance between the magnetic poles of the actuator. L d and L q They are respectively d , q Shaft inductor, i d and i q They are respectively d , q Shaft current; changes in actuator structural parameters will alter , , L d , L q ,because F d It is known, therefore i d and i q The value changes, thus correcting the force-electric coupling transmission relationship.

8. The electromagnetic thermal double-coupled temperature rise prediction and double-cycle optimization method according to claim 6, characterized in that, The numerical analytical equations of the five-body two-degree-of-freedom active suspension include the comprehensive dynamic equations of the nonlinear five-body two-degree-of-freedom active suspension: ,in, Q j External forces in a broad sense T The overall kinetic energy of the five-body, two-degree-of-freedom active suspension. q j For generalized coordinates, V The overall potential energy of a five-body, two-degree-of-freedom active suspension. For the first i The force acting on a point mass, For the first i The potential vector of a single particle F ix For the first i The force on a point mass is x Components of the axis, F iy For the first i The force on a point mass is y Components of the axis, x i and y i for i point x , y Axis coordinates n The kinetic energy is a constant; the overall kinetic energy of the five-body two-degree-of-freedom active suspension includes the kinetic energy of the tires. ,in, For tire quality, Hinged point A speed, The angle change between the axis of the composite structure and the vertical direction; the moment of inertia of the composite structure AB in the five-body two-degree-of-freedom active suspension. ,in, , , , These represent the mass of composite component 2, the mass of composite component 1, the length of composite component 1, and the length of composite component 2 in composite structure AB, respectively. L AN Hinged point A Composite structure centroid N The distance between them.

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