CN106184351A - A kind of Multipurpose Optimal Method of electric-hydraulic combined power steering system - Google Patents

Abstract

The invention discloses the Multipurpose Optimal Method of a kind of novel electro-hydraulic composite power steering, wherein, the power-assisted part of electric-hydraulic combined steering is made up of electric power steering module and hydraulic booster module, realized the control of two module power-assisteds by bonder, solve the problem that during hydraulic booster system, electric-controlled hydraulic force aid system assist characteristic adjustability and the high speed of tradition motorbus employing, steering response is poor.Simultaneously, by to electric-hydraulic combined power steering system multiple-objection optimization, with steering response, turn to energy consumption as target, steering sensitivity is constraints, improvement archipelago genetic algorithm based on simulated annealing correction, is optimized design to the selected parameter of electric-hydraulic combined power steering system, compared with the optimum results of archipelago genetic algorithm, electric-hydraulic combined power steering system after optimization can obtain preferable global convergence, convergence rate, and preferably steering response and turn to economy.

Description

A kind of Multipurpose Optimal Method of electric-liquid composite power steering

Technical field

The present invention relates to automobile assisted power steering system technical field, refer specifically to generation a kind of electric-liquid composite power steering Multipurpose Optimal Method.

Background technology

At present, the servo steering system that existing automobile is commonly used has: hydraulic power-assist steering system, electric-controlled hydraulic power-assisted Steering and electric boosting steering system.Wherein, hydraulic power-assist steering system, Electro-Hydraulic Power Steering System can be at automobile Bigger power-assisted is provided under speed operation, alleviates burden when driver turns to；But steering response is poor under high-speed working condition, handle steady Qualitative existing problems.Electric boosting steering system is by controller, assist motor, reducing gear steering-wheel torque sensor and car The speed composition such as sensor, controller accepts steering-wheel torque signal that sensor records and GES and processes, controlling Motor exports power torque according to pre-determined assist characteristic.But affected by electrical characteristics such as the own battery tensions of automobile, The maximum power-assisted square of its output is less, is unsatisfactory for the demand of the vehicles such as motorbus.

Domestic bus and coach enterprise, at the beginning of the design of various vehicle passenger vehicles, is also formed without the Optimization Theory of set of system Its steering system structural parameter is designed by method, and the experience tending to rely on designer is designed.At present, for Electric-liquid composite turning system mechanics systematic parameter and hydraulic system parameters carry out multi-objective optimization design of power, make steering keep Obtain good steering response in the case of stability and turn to the method for economy to there is not yet disclosure.

Summary of the invention

It is directed to above-mentioned the deficiencies in the prior art, it is an object of the invention to provide a kind of electric-liquid composite power steering system The Multipurpose Optimal Method of system, to solve hydraulic power-assist steering system in prior art, Electro-Hydraulic Power Steering System at height Under speed operating mode, steering response is poor, and control stability has problems, and the problem that motorbus turns to economy.

For reaching above-mentioned purpose, the Multipurpose Optimal Method of a kind of electric-liquid composite power steering of the present invention, including Step is as follows:

(1) electric-liquid composite power steering is carried out Dynamic Modeling；

(2) steering energy consumption, steering steering response, steering sensitivity are chosen as electric-liquid composite turning system Performance Evaluating Indexes, its quantitative formula is respectively as follows:

Steering response:

$\frac{T_h\left(s\right)}{T_r\left(s\right)}=\frac{K_s{r}_w}{l}\·\frac{1}{X_1{s}^2+Y_{1}s+Z_1}---\left(1\right)$

In formula:

$X_1=J_1+J_{m2}{n}_2{i}_g+J_{m1}{n}_1\frac{{\mathrm{lA}}_p}{q}$

$Y_1=B_1+B_{m2}{n}_2{i}_g+B_{m1}{n}_1\frac{{\mathrm{lA}}_p}{q}+\frac{\mathrm{\ρ}l}{4{C_q}^2{A_1}^2}$

$Z_1=K_s+K_a{K}_2{K}_s{i}_g+K_a{K}_1{K}_s\frac{{\mathrm{lA}}_p}{q}$

$J_1=J_{\lg }+m_{lm}\frac{lP}{2\mathrm{\π}}+J_{cs}\frac{lP}{2\mathrm{\π}}$

$B_1=B_{\lg }+B_{lm}\frac{lP}{2\mathrm{\π}}+B_{cs}\frac{lP}{2\mathrm{\π}}$

T_dMoment steering wheel inputted for driver；T_pFor steering resisting moment equivalent moment on rocker arm shaft；K_sFor turning To axle rigidity；r_wPitch radius is fanned for tooth；J_lgFor electric boosted module reducing gear and the equivalent moment of inertia of steering screw；m_lm Quality for steering nut；J_csFor the rotary inertia turning to tooth to fan；L is the centre-to-centre spacing of screw rod power；P is steering screw pitch；J_m2 Rotary inertia for assist motor B；n₂Speed reducing ratio for assist motor A corner to steering screw corner；i_gSubtracting for reducing gear Speed ratio；J_m1Rotary inertia for assist motor A；n₁Speed reducing ratio for assist motor A corner to steering screw corner；A_pFor hydraulic pressure The effective area of cylinder piston；Q is double-acting vane pump discharge capacity；B_lgEquivalent viscous damping system for steering screw Yu reducing gear Number；B_lmViscous damping coefficient for steering nut；B_csViscous damping coefficient for tooth fan；B_m2Viscous damping for assist motor B Coefficient；B_m1Viscous damping coefficient for assist motor A；ρ is power-assisted fluid density；A_iIt it is the orifice size of i-th valve port；C_qFor Discharge coefficient；A_iOrifice size for i-th valve port；K_sFor torque sensor rigidity；K_aFor motor torque coefficient；K₁、K₂Point Wei the power-assisted gain of assist motor A, B；

Steering sensitivity expression formula is: $\frac{{\mathrm{\ω}}_r\left(s\right)}{{\mathrm{\θ}}_h\left(s\right)}=\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\δ}\left(s\right)}\frac{\mathrm{\δ}\left(s\right)}{{\mathrm{\θ}}_s\left(s\right)}---\left(2\right)$

The transmission function of the ratio of front wheel angle and steering wheel angle is:

In formula:

$X_2=J_1{n}_3+J_{m2}{n}_2{n}_3{i}_g+J_{m1}{n}_1{n}_3\frac{{\mathrm{lA}}_p}{q}$

$Y_2=B_1{n}_3+B_{m2}{n}_2{n}_3{i}_g+B_{m1}{n}_1{n}_3\frac{{\mathrm{lA}}_p}{q}+\frac{{\mathrm{\ρQ}}_s{A}_p{\mathrm{Pn}}_3}{4{{\mathrm{\πC}}_q}^2{A_1}^2}$

$Z_2=Z_1{n}_3+\frac{2{\mathrm{dk}}_{1}l}{r_w{n}_3}$

n₃Speed reducing ratio for wheel steering angle to steering screw corner；

$\left\{\begin{array}{c}\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\δ}\left(s\right)}=\frac{A_3{s}^3+A_2{s}^2+A_{1}s+A_0}{B_4{s}^4+B_3{s}^3+B_2{s}^2+B_{1}s+B_0}\\ \frac{\mathrm{\β}\left(s\right)}{\mathrm{\δ}\left(s\right)}=\frac{F_3{s}^3+F_2{s}^2+F_{1}s+F_0}{B_4{s}^4+B_3{s}^3+B_2{s}^2+B_{1}s+B_0}\\ \frac{\mathrm{\φ}\left(s\right)}{\mathrm{\δ}\left(s\right)}=\frac{H_2{s}^2+H_{1}s+H_0}{B_4{s}^4+B_3{s}^3+B_2{s}^2+B_{1}s+B_0}\end{array}\right.$

$A_3=-mu{I}_x{N}_{\mathrm{\δ}}+h^2{\mathrm{um}}_s^2{N}_{\mathrm{\δ}}-hu{I}_{xz}{m}_s{Y}_{\mathrm{\δ}}$

A₂=muL_pN_δ+I_xN_δY_β-I_xN_βY_δ

A₁=muL_φN_δ-L_pN_δY_β+L_pN_βY_δ-hum_sN_φY_δ+hum_sN_δY_φ

A₀=-L_φN_δY_β+L_φN_βY_δ

$B_4=mu{I}_{xz}^2-mu{I}_x{I}_z+h^{2}u{I}_z{m}_s^2$ $B_3=mu{I}_z{L}_p+mu{I}_x{N}_r-h^2{\mathrm{um}}_s^2{N}_r+h{I}_{xz}{m}_s{N}_{\mathrm{\β}}+hu{I}_{xz}{m}_s{Y}_r-I_{xz}^2{Y}_{\mathrm{\β}}+I_x{I}_z{Y}_{\mathrm{\β}}$

$\begin{array}{c}{B}_2=mu{I}_z{L}_{\mathrm{\φ}}-{\mathrm{muL}}_p{N}_r-mu{I}_x{N}_{\mathrm{\β}}+h^2{\mathrm{um}}_s^2{N}_{\mathrm{\β}}+mu{I}_{xz}{N}_{\mathrm{\φ}}+I_x{N}_{\mathrm{\β}}{Y}_r-I_z{L}_p{Y}_{\mathrm{\β}}\\ -hu{I}_{xz}{m}_s{Y}_{\mathrm{\β}}-I_x{N}_r{Y}_{\mathrm{\β}}+hu{I}_z{m}_s{Y}_{\mathrm{\φ}}\end{array}$

B₁=-muL_φN_r+muL_pN_β-L_pN_βY_r+hum_sN_φY_r-I_zL_φY_β+L_pN_rY_β-I_xzN_φY_β

-hum_sN_rY_φ+I_xzN_βY_φ

B₀=muL_φN_β-L_φN_βY_r+L_φN_rY_β-hum_sN_φY_β+hum_sN_βY_φ

$F_3=-h{I}_{xz}{m}_s{N}_{\mathrm{\δ}}+I_{xz}^2{Y}_{\mathrm{\δ}}-I_x{I}_z{Y}_{\mathrm{\δ}}$

$F_2=mu{I}_x{N}_{\mathrm{\δ}}-h^2{\mathrm{um}}_s^2{N}_{\mathrm{\δ}}-I_x{N}_{\mathrm{\δ}}{Y}_r+I_z{L}_p{Y}_{\mathrm{\δ}}+hu{I}_{xz}{m}_s{Y}_{\mathrm{\δ}}+I_x{N}_r{Y}_{\mathrm{\δ}}$

F₁=-muL_pN_δ+L_pN_δY_r+I_zL_φY_δ-L_pN_rY_δ+I_xzN_φY_δ-I_xzN_δY_φ

F₀=-muL_φN_δ+L_φN_δY_r-L_φN_rY_δ+hum_sN_φY_δ-hum_sN_δY_φ

H₂=-muI_xzN_δ-huI_zm_sY_δ

H₁=-hum_sN_δY_r+I_xzN_δY_β+hum_sN_rY_δ-I_xzN_βY_δ

H₀=hum_sN_δY_β-hum_sN_βY_δ

H0=humsN δ Y β-humsN β Y δ

Turn to economy:

E=P₁+P₂+P₃+P₄ (3)

$\left\{\begin{array}{c}{P}_1=R_A{I_A}^2+\frac{{U_s}^2}{R_{elec}}\\ P_2={\mathrm{\ΣM}}_{ci}+C_{Fr}{\mathrm{\Σ\ω}}_i+C_{Fr2}{{\mathrm{\Σ\ω}}_i}^2+{\mathrm{\ΣC}}_i\\ P_3=\frac{P_{s}q}{2\mathrm{\π}}\mathrm{\ω}-P_s{Q}_s\\ P_4=\frac{\mathrm{\ρ}}{8{\left(C_q{A}_1\right)}^2}{(Q_s+A_p\frac{{\mathrm{dx}}_r}{dt})}^3+\frac{\mathrm{\ρ}}{8{\left(C_q{A}_2\right)}^2}{(Q_s-A_p\frac{{\mathrm{dx}}_r}{dt})}^3\end{array}\right.$

In formula:

P₁For electronic control unit ECU power loss；P₂For assist motor A, assist motor B power loss；P₃Help for hydraulic pressure Power module power steering power loss of pump；P₄For hydraulic booster module rotary valve structure power loss；R_AFor armature resistance；I_AFor electricity Pivot electric current；U_sFor controller both end voltage；R_elecFor controller resistance；M_ciFor the torque loss caused that rubs in motor；C_FrFor Speed ratio coefficient of friction；ω_iFor motor speed；C_Fr2For speed ratio square coefficient of friction；C_iFor motor unknown losses；

(3) using steering response and turn to economy as optimization aim, steering sensitivity as constraints, set up electricity- Liquid composite power steering Model for Multi-Objective Optimization, object function f (x) that electric-liquid composite power steering optimizes is:

Road feel energy function:

$f\left(x_1\right)=\frac{1}{2{\mathrm{\π\ω}}_0}{\∫}_0^{{\mathrm{\ω}}_0}{\left|\frac{T_h\left(s\right)}{T_r\left(s\right)}\right|}_{s=j\mathrm{\ω}}^{2}d\mathrm{\ω}$

In formula:

$\frac{T_h\left(s\right)}{T_r\left(s\right)}=\frac{l\·K_s}{r_w(X_1{s}^2+Y_{1}s+Z_1)}$

$X_1=J_1+J_{m2}{n}_2{i}_g+J_{m1}{n}_1\frac{{\mathrm{lA}}_p}{q}$

$Y_1=B_1+B_{m2}{n}_2{i}_g+B_{m1}{n}_1\frac{{\mathrm{lA}}_p}{q}+\frac{\mathrm{\ρ}l}{4{C_q}^2{A_1}^2}$

$Z_1=K_s+K_a{K}_2{K}_s{i}_g+K_a{K}_1{K}_s\frac{{\mathrm{lA}}_p}{q}$

$J_1=J_{\lg }+m_{lm}\frac{lP}{2\mathrm{\π}}+J_{cs}\frac{lP}{2\mathrm{\π}}$

$B_1=B_{\lg }+B_{lm}\frac{lP}{2\mathrm{\π}}+B_{cs}\frac{lP}{2\mathrm{\π}}$

Turn to economy:

$f_2=\underset{1}{\overset{i}{\mathrm{\Σ}}}{P}_i(i=1,2,3,4)$

$\left\{\begin{array}{c}{P}_1=R_A{I_A}^2+\frac{{U_s}^2}{R_{elec}}\\ P_2={\mathrm{\ΣM}}_{ci}+C_{Fr}{\mathrm{\Σ\ω}}_i+C_{Fr2}{{\mathrm{\Σ\ω}}_i}^2+{\mathrm{\ΣC}}_i\\ P_3=\frac{P_{s}q}{2\mathrm{\π}}\mathrm{\ω}-P_s{Q}_s\\ P_4=\frac{\mathrm{\ρ}}{8{\left(C_q{A}_1\right)}^2}{(Q_s+A_p\frac{{\mathrm{dx}}_r}{dt})}^3+\frac{\mathrm{\ρ}}{8{\left(C_q{A}_2\right)}^2}{(Q_s-A_p\frac{{\mathrm{dx}}_r}{dt})}^3\end{array}\right.$

Optimizing constraints is:

$f\left(x\right)=\frac{1}{2{\mathrm{\π\ω}}_0}{\∫}_0^{{\mathrm{\ω}}_0}{\left|\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\θ}\left(s\right)}\right|}^{2}d\mathrm{\ω}$

$\frac{{\mathrm{\ω}}_r\left(s\right)}{{\mathrm{\θ}}_h\left(s\right)}=\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\δ}\left(s\right)}\frac{\mathrm{\δ}\left(s\right)}{{\mathrm{\θ}}_s\left(s\right)}$

$\frac{\mathrm{\δ}\left(s\right)}{{\mathrm{\θ}}_s\left(s\right)}=\frac{K_s}{(X_2{s}^2+Y_{2}s-Z_2)+\frac{2{\mathrm{dK}}_{1}l}{n_2\·r_w}\[\frac{a}{\mathrm{\μ}}\frac{w_r\left(s\right)}{\mathrm{\δ}\left(s\right)}+\frac{\mathrm{\β}\left(s\right)}{\mathrm{\δ}\left(s\right)}+E_1\frac{\mathrm{\φ}\left(s\right)}{\mathrm{\δ}\left(s\right)}-1\]}$

$X_2=J_1{n}_3+J_{m2}{n}_2{n}_3{i}_g+J_{m1}{n}_1{n}_3\frac{{\mathrm{lA}}_p}{q}$

$Y_2=B_1{n}_3+B_{m2}{n}_2{n}_3{i}_g+B_{m1}{n}_1{n}_3\frac{{\mathrm{lA}}_p}{q}+\frac{{\mathrm{\ρQ}}_s{A}_p{\mathrm{Pn}}_3}{4{{\mathrm{\πC}}_q}^2{A_1}^2}$

$Z_2=Z_1{n}_3+\frac{2{\mathrm{dk}}_{1}l}{r_w{n}_3}$

$\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\δ}\left(s\right)}=\frac{A_3{s}^3+A_2{s}^2+A_{1}s+A_0}{B_4{s}^4+B_3{s}^3+B_2{s}^2+B_{1}s+B_0}$

(4) by rotary inertia, reducing gear speed reducing ratio i of electric boosted module motor_g, torque sensor rigidity, front-wheel Fixed with steering mechanism equivalence to the damped coefficient on steering screw, double-acting vane pump parameter stator thickness B, double-acting vane pump Sub-major axis radius R₂As design variable；

(5) archipelago genetic algorithm based on simulated annealing correction is used to carry out excellent to the selected parameter in step (4) Change, and design variable value when obtaining making steering response according to optimum results in ISIGHT, turn to economy to reach optimal solution；

(6) steering response, the optimal solution turning to economy and the archipelago that meet steering sensitivity constraint optimization obtained Genetic algorithm optimization result compares, and proposes steering response that method obtains according to the present invention, turns to economy optimization to tie Fruit is superior to the optimum results of archipelago genetic algorithm, then it is assumed that this optimization method is effective.

Preferably, the Dynamic Modeling in above-mentioned steps (1) includes: assist motor A model, steering pump model, rotary valve Model, fluid power cylinder model, assist motor B model, reducing gear model, steering wheel model, ball-and-nut steering gear model, wheel Loose tool type, whole vehicle model and energy consumption mathematical model.

Beneficial effects of the present invention:

The present invention is directed to electric-liquid composite power steering be derived by Performance Evaluating Indexes steering steering response, Steering sensitivity and turn to economy, and set up the quantitative formula of three evaluation indexes, with steering response with turn to economy As optimization aim, steering sensitivity, as constraints, sets up electric-liquid composite power steering Model for Multi-Objective Optimization, Use improvement archipelago genetic algorithm optimization algorithm based on simulated annealing correction；The method carries out SA to fitness function and repaiies Just, thus promote the convergence of optimized algorithm, improve electric-liquid composite power steering multiple-objection optimization global convergence, Convergence rate and effect of optimization.

In automobile assisted power steering system, realize multi-steering mode capabilities, can carry out turning to pattern to cut according to different operating modes Change, it is achieved being perfectly combined of motor turning portability and steering response, and can also be by the economy of automobile assisted power steering and spirit Activity combines, and therefore has wide market application foreground.

Accompanying drawing explanation

Fig. 1 illustrates the present invention and is combined servo steering system structure chart；

Fig. 2 illustrates electric-liquid and is combined force aid system optimization method flow chart；

Fig. 3 illustrates improvement archipelago genetic algorithm flow chart based on simulated annealing correction.

Detailed description of the invention

For the ease of the understanding of those skilled in the art, the present invention is made further with accompanying drawing below in conjunction with embodiment Bright, that embodiment is mentioned content not limitation of the invention.

Shown in reference Fig. 1, the Multipurpose Optimal Method of a kind of electric-liquid composite power steering of the present invention, it is applied to Electric-liquid composite power steering, this system includes: mechanical steering module, electric boosted module, hydraulic booster module and ECU4, respective sensor；

Steering wheel 1 that described mechanical steering module includes being sequentially connected with, steering spindle 2, ball-and-nut steering gear 8, pitman arm 9, wheel 10 and torque sensor 3；

Described electric boosted module includes assist motor A6, reducing gear 5；

Described hydraulic booster module includes assist motor B13, power assistance pump for steering 12, rotary valve 11, servohydraulic cylinder 7, storage Oil tank 14；Wherein, hydrostatic sensor original paper is installed in servohydraulic cylinder 7 both sides；

ECU4 sends control signal to assist motor A6, and power-assisted square passes to turn to after reducing gear 5 slows down and increases square and shakes Arm 9；ECU4 sends control signal to assist motor B13, drives power assistance pump for steering 12 to work, makes fluid flow to via high-pressure oil pipe Control valve, rotary valve, servohydraulic cylinder 7, the working solution of end loop is back to fuel tank via oil return oil pipe.ECU is believed by speed Number, angular signal, lateral acceleration signal and steering-wheel torque signal 15 send signal 16, by control to the corresponding motor that controls Current of electric A, B processed switch assistant mode.

The present embodiment uses modeling software to be MATLAB-simulink, and optimization software is isight；Carry out multiple-objection optimization Calculating, Fig. 2 is Multipurpose Optimal Method schematic flow sheet, specifically comprises the following steps that

Step 1: electric-liquid composite power steering is carried out Dynamic Modeling；It is embodied according to " electric hydaulic helps The energy-saving analysis of power steering and modeling and simulating " (Zhao Wanli, Jiangsu University), " design of Electro-Hydraulic Power Steering System Research " (woods is escaped, and Jiangsu University is learned for (monarch Mr. Zhang, Jiangsu University), " exploitation of pure electric coach electric booster steering system controller " Report) structure such as the rotary valve of electric hydaulic force aid system, hydraulic pump disclosed in document, and the modeling side of electric boosting steering system Method, sets up electric-liquid and is combined the model of servo steering system, and the steering for subsequent step emulates and optimizes and lays the foundation；

Step 2: choose steering energy consumption, steering steering response, steering sensitivity as electric-liquid composite turning system The Performance Evaluating Indexes of system, sets up three Performance Evaluating Indexes quantitative formulas:

Steering response:

$\frac{T_h\left(s\right)}{T_r\left(s\right)}=\frac{K_s{r}_w}{l}\·\frac{1}{X_1{s}^2+Y_{1}s+Z_1}---\left(1\right)$

In formula:

$X_1=J_1+J_{m2}{n}_2{i}_g+J_{m1}{n}_1\frac{{\mathrm{lA}}_p}{q}$

$Y_1=B_1+B_{m2}{n}_2{i}_g+B_{m1}{n}_1\frac{{\mathrm{lA}}_p}{q}+\frac{\mathrm{\ρ}l}{4{C_q}^2{A_1}^2}$

$Z_1=K_s+K_a{K}_2{K}_s{i}_g+K_a{K}_1{K}_s\frac{{\mathrm{lA}}_p}{q}$

$J_1=J_{\lg }+m_{lm}\frac{lP}{2\mathrm{\π}}+J_{cs}\frac{lP}{2\mathrm{\π}}$

$B_1=B_{\lg }+B_{lm}\frac{lP}{2\mathrm{\π}}+B_{cs}\frac{lP}{2\mathrm{\π}}$

T_dMoment steering wheel inputted for driver；T_pFor steering resisting moment equivalent moment on rocker arm shaft；K_sFor turning To axle rigidity；r_wPitch radius is fanned for tooth；J_lgFor electric boosted module reducing gear and the equivalent moment of inertia of steering screw；m_lm Quality for steering nut；J_csFor the rotary inertia turning to tooth to fan；L is the centre-to-centre spacing of screw rod power；P is steering screw pitch；J_m2 Rotary inertia for assist motor B；n₂Speed reducing ratio for assist motor A corner to steering screw corner；i_gSubtracting for reducing gear Speed ratio；J_m1Rotary inertia for assist motor A；n₁Speed reducing ratio for assist motor A corner to steering screw corner；A_pFor hydraulic pressure The effective area of cylinder piston；Q is double-acting vane pump discharge capacity；B_lgEquivalent viscous damping system for steering screw Yu reducing gear Number；B_lmViscous damping coefficient for steering nut；B_csViscous damping coefficient for tooth fan；B_m2Viscous damping for assist motor B Coefficient；B_m1Viscous damping coefficient for assist motor A；ρ is power-assisted fluid density；A_iIt it is the orifice size of i-th valve port；C_qFor Discharge coefficient；A_iOrifice size for i-th valve port；K_sFor torque sensor rigidity；K_aFor motor torque coefficient；K₁、K₂Point Wei the power-assisted gain of assist motor A, B；

Steering sensitivity expression formula is:

$\frac{{\mathrm{\ω}}_r\left(s\right)}{{\mathrm{\θ}}_h\left(s\right)}=\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\δ}\left(s\right)}\frac{\mathrm{\δ}\left(s\right)}{{\mathrm{\θ}}_s\left(s\right)}---\left(2\right)$

The transmission function of the ratio of front wheel angle and steering wheel angle is:

In formula:

$X_2=J_1{n}_3+J_{m2}{n}_2{n}_3{i}_g+J_{m1}{n}_1{n}_3\frac{{\mathrm{lA}}_p}{q}$

$Y_2=B_1{n}_3+B_{m2}{n}_2{n}_3{i}_g+B_{m1}{n}_1{n}_3\frac{{\mathrm{lA}}_p}{q}+\frac{{\mathrm{\ρQ}}_s{A}_p{\mathrm{Pn}}_3}{4{{\mathrm{\πC}}_q}^2{A_1}^2}$

$Z_2=Z_1{n}_3+\frac{2{\mathrm{dk}}_{1}l}{r_w{n}_3}$

n₃Speed reducing ratio for wheel steering angle to steering screw corner；

$\left\{\begin{array}{c}\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\δ}\left(s\right)}=\frac{A_3{s}^3+A_2{s}^2+A_{1}s+A_0}{B_4{s}^4+B_3{s}^3+B_2{s}^2+B_{1}s+B_0}\\ \frac{\mathrm{\β}\left(s\right)}{\mathrm{\δ}\left(s\right)}=\frac{F_3{s}^3+F_2{s}^2+F_{1}s+F_0}{B_4{s}^4+B_3{s}^3+B_2{s}^2+B_{1}s+B_0}\\ \frac{\mathrm{\φ}\left(s\right)}{\mathrm{\δ}\left(s\right)}=\frac{H_2{s}^2+H_{1}s+H_0}{B_4{s}^4+B_3{s}^3+B_2{s}^2+B_{1}s+B_0}\end{array}\right.$

$A_3=-mu{I}_x{N}_{\mathrm{\δ}}+h^2{\mathrm{um}}_s^2{N}_{\mathrm{\δ}}-hu{I}_{xz}{m}_s{Y}_{\mathrm{\δ}}$

A₂=muL_pN_δ+I_xN_δY_β-I_xN_βY_δ

A₁=muL_φN_δ-L_pN_δY_β+L_pN_βY_δ-hum_sN_φY_δ+hum_sN_δY_φ

A₀=-L_φN_δY_β+L_φN_βY_δ

$B_4=mu{I}_{xz}^2-mu{I}_x{I}_z+h^{2}u{I}_z{m}_s^2$

$B_3=mu{I}_z{L}_p+mu{I}_x{N}_r-h^2{\mathrm{um}}_s^2{N}_r+h{I}_{xz}{m}_s{N}_{\mathrm{\β}}+hu{I}_{xz}{m}_s{Y}_r-I_{xz}^2{Y}_{\mathrm{\β}}+I_x{I}_z{Y}_{\mathrm{\β}}$

$\begin{array}{c}{B}_2=mu{I}_z{L}_{\mathrm{\φ}}-{\mathrm{muL}}_p{N}_r-mu{I}_x{N}_{\mathrm{\β}}+h^2{\mathrm{um}}_s^2{N}_{\mathrm{\β}}+mu{I}_{xz}{N}_{\mathrm{\φ}}+I_x{N}_{\mathrm{\β}}{Y}_r-I_z{L}_p{Y}_{\mathrm{\β}}\\ -hu{I}_{xz}{m}_s{Y}_{\mathrm{\β}}-I_x{N}_r{Y}_{\mathrm{\β}}+hu{I}_z{m}_s{Y}_{\mathrm{\φ}}\end{array}$

B₁=-muL_φN_r+muL_pN_β-L_pN_βY_r+hum_sN_φY_r-I_zL_φY_β+L_pN_rY_β-I_xzN_φY_β

-hum_sN_rY_φ+I_xzN_βY_φ

B₀=muL_φN_β-L_φN_βY_r+L_φN_rY_β-hum_sN_φY_β+hum_sN_βY_φ

$F_3=-h{I}_{xz}{m}_s{N}_{\mathrm{\δ}}+I_{xz}^2{Y}_{\mathrm{\δ}}-I_x{I}_z{Y}_{\mathrm{\δ}}$

$F_2=mu{I}_x{N}_{\mathrm{\δ}}-h^2{\mathrm{um}}_s^2{N}_{\mathrm{\δ}}-I_x{N}_{\mathrm{\δ}}{Y}_r+I_z{L}_p{Y}_{\mathrm{\δ}}+hu{I}_{xz}{m}_s{Y}_{\mathrm{\δ}}+I_x{N}_r{Y}_{\mathrm{\δ}}$

F₁=-muL_pN_δ+L_pN_δY_r+I_zL_φY_δ-L_pN_rY_δ+I_xzN_φY_δ-I_xzN_δY_φ

F₀=-muL_φN_δ+L_φN_δY_r-L_φN_rY_δ+hum_sN_φY_δ-hum_sN_δY_φ

H₂=-muI_xzN_δ-huI_zm_sY_δ

H₁=-hum_sN_δY_r+I_xzN_δY_β+hum_sN_rY_δ-I_xzN_βY_δ

H₀=hum_sN_δY_β-hum_sN_βY_δ

Turn to economy:

E=P₁+P₂+P₃+P₄ (3)

$\left\{\begin{array}{c}{P}_1=R_A{I_A}^2+\frac{{U_s}^2}{R_{elec}}\\ P_2={\mathrm{\ΣM}}_{ci}+C_{Fr}{\mathrm{\Σ\ω}}_i+C_{Fr2}{{\mathrm{\Σ\ω}}_i}^2+{\mathrm{\ΣC}}_i\\ P_3=\frac{P_{s}q}{2\mathrm{\π}}\mathrm{\ω}-P_s{Q}_s\\ P_4=\frac{\mathrm{\ρ}}{8{\left(C_q{A}_1\right)}^2}{(Q_s+A_p\frac{{\mathrm{dx}}_r}{dt})}^3+\frac{\mathrm{\ρ}}{8{\left(C_q{A}_2\right)}^2}{(Q_s-A_p\frac{{\mathrm{dx}}_r}{dt})}^3\end{array}\right.$

In formula:

P₁For electronic control unit ECU power loss；P₂For assist motor A, assist motor B power loss；P₃Help for hydraulic pressure Power module power steering power loss of pump；P₄For hydraulic booster module rotary valve structure power loss；R_AFor armature resistance；I_AFor electricity Pivot electric current；Us is controller both end voltage；R_elecFor controller resistance；M_ciFor the torque loss caused that rubs in motor；C_FrFor Speed ratio coefficient of friction；ω_iFor motor speed；C_Fr2For speed ratio square coefficient of friction；C_iFor motor unknown losses；

Step 3: using steering response with turn to economy as optimization aim, steering sensitivity, as constraints, is set up Electric-liquid composite power steering Model for Multi-Objective Optimization, object function f (x) that electric-liquid composite power steering optimizes For:

Road feel energy function:

$f\left(x_1\right)=\frac{1}{2{\mathrm{\π\ω}}_0}{\∫}_0^{{\mathrm{\ω}}_0}{{\left|\frac{T_h\left(s\right)}{T_r\left(s\right)}\right|}_{s=j\mathrm{\ω}}}^{2}d\mathrm{\ω}$

In formula:

$\frac{T_h\left(s\right)}{T_r\left(s\right)}=\frac{l\·K_s}{r_w(X_1{s}^2+Y_{1}s+Z_1)}$

$X_1=J_1+J_{m2}{n}_2{i}_g+J_{m1}{n}_1\frac{{\mathrm{lA}}_p}{q}$

$Y_1=B_1+B_{m2}{n}_2{i}_g+B_{m1}{n}_1\frac{{\mathrm{lA}}_p}{q}+\frac{\mathrm{\ρ}l}{4{C_q}^2{A_1}^2}$

$Z_1=K_s+K_a{K}_2{K}_s{i}_g+K_a{K}_1{K}_s\frac{{\mathrm{lA}}_p}{q}$

$J_1=J_{\lg }+m_{lm}\frac{lP}{2\mathrm{\π}}+J_{cs}\frac{lP}{2\mathrm{\π}}$

$B_1=B_{\lg }+B_{lm}\frac{lP}{2\mathrm{\π}}+B_{cs}\frac{lP}{2\mathrm{\π}}$

Turn to economy:

$f_2=\underset{1}{\overset{i}{\mathrm{\Σ}}}{P}_i(i=1,2,3,4)$

$\left\{\begin{array}{c}{P}_1=R_A{I_A}^2+\frac{{U_s}^2}{R_{elec}}\\ P_2={\mathrm{\ΣM}}_{ci}+C_{Fr}{\mathrm{\Σ\ω}}_i+C_{Fr2}{{\mathrm{\Σ\ω}}_i}^2+{\mathrm{\ΣC}}_i\\ P_3=\frac{P_{s}q}{2\mathrm{\π}}\mathrm{\ω}-P_s{Q}_s\\ P_4=\frac{\mathrm{\ρ}}{8{\left(C_q{A}_1\right)}^2}{(Q_s+A_p\frac{{\mathrm{dx}}_r}{dt})}^3+\frac{\mathrm{\ρ}}{8{\left(C_q{A}_2\right)}^2}{(Q_s-A_p\frac{{\mathrm{dx}}_r}{dt})}^3\end{array}\right.$

Optimizing constraints is:

$f\left(x\right)=\frac{1}{2{\mathrm{\π\ω}}_0}{\∫}_0^{{\mathrm{\ω}}_0}{\left|\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\θ}\left(s\right)}\right|}^{2}d\mathrm{\ω}$

$\frac{{\mathrm{\ω}}_r\left(s\right)}{{\mathrm{\θ}}_h\left(s\right)}=\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\δ}\left(s\right)}\frac{\mathrm{\δ}\left(s\right)}{{\mathrm{\θ}}_s\left(s\right)}$

$X_2=J_1{n}_3+J_{m2}{n}_2{n}_3{i}_g+J_{m1}{n}_1{n}_3\frac{{\mathrm{lA}}_p}{q}$

$Y_2=B_1{n}_3+B_{m2}{n}_2{n}_3{i}_g+B_{m1}{n}_1{n}_3\frac{{\mathrm{lA}}_p}{q}+\frac{{\mathrm{\ρQ}}_s{A}_p{\mathrm{Pn}}_3}{4{{\mathrm{\πC}}_q}^2{A_1}^2}$

$Z_2=Z_1{n}_3+\frac{2{\mathrm{dk}}_{1}l}{r_w{n}_3}$

$\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\δ}\left(s\right)}=\frac{A_3{s}^3+A_2{s}^2+A_{1}s+A_0}{B_4{s}^4+B_3{s}^3+B_2{s}^2+B_{1}s+B_0}$

Step 4: by rotary inertia, reducing gear speed reducing ratio i of electric boosted module motor_g, torque sensor rigidity, front Wheel and steering mechanism equivalence are to the damped coefficient on steering screw, double-acting vane pump parameter stator thickness B, double-acting vane pump Stator major axis radius R₂As design variable；

Step 5: use improvement archipelago genetic algorithm based on simulated annealing correction that the selected parameter in step 4 is entered Row optimizes, and chooses optimal solution (with reference to shown in Fig. 3) according to optimum results；

Specifically include:

5.1 initialize colony；

5.2 calculate the fitness value F of each individuality in colony；

5.3 are modified by simulated annealing correcting module and judge, meet condition and then enter 5.4, otherwise return 5.2；

It is as follows that correcting module performs step:

Step1: module initialization

(11) k=1, j ∈ N, Freeze=0, set initial parameter T_k, L_k, s, q, ε；

(12)

(13) initial solution x is produced^k∈ S, makes x^s=x^k；

Step2: selected domain variability carries out field search to each field j ∈ N

(21)

(22) produce field and solve x ∈ N (x^k), calculate δ₁=f (x)-f (x^k),δ₂=f (x)-f (x^s)；

(23) if δ₁＜ 0, thenIf δ₂＜ 0, then x^s=x^k；Otherwise, if exp is (-δ₁/T_k) ＞, random [0,1], then x^k=x,

(24) ifThenTurn (22)；

(25) if all spectra searches for complete (j >=| N |), step3 is turned；Otherwise, j=j+1, turn (22)；

Step 3: algorithm stop technology

(31)Freeze=Freeze+1；

(32) if Freeze >=q, algorithm will terminate, thus exports solution x^s；Otherwise, step 4 is proceeded to；

Step 4: parameter adaptive control and correction fitness value

(41) temperature control coefrficient is calculated

$\mathrm{\σ}=(\underset{j\∈N}{\mathrm{\Σ}}{I}_k^j+1)/(\underset{j\∈N}{\mathrm{\Σ}}{A}_k^j+1)-(\underset{j\∈N}{\mathrm{\Σ}}{I}_{k-1}^j+1)/(\underset{j\∈N}{\mathrm{\Σ}}{A}_{k-1}^j+1)$

(42) temperature is calculated

$T_{k+1}=\[Switch\left(\mathrm{\σ}\right){(1-\frac{1}{k+1})}^s+(1-Switch(\mathrm{\σ}\left)\right)(1+\frac{1}{k+1})\]T_k$

(43) number of times and the field searching intensity of search are calculated；

$L_{k+1}^j=INT\left(\right(I_k^j+1)/({\mathrm{\ΣI}}_k^j+1\left)\right)L_{k-1}$

(44) adaptive response function is revised；

$F^{*}=e^{p^k{T}^{*}F}$

In formula: F^*For fitness value after revising, F is for revising prospective adaptation angle value, and p is decay factor；

(45) k=k+1, turns step 2；

5.4 are selected to enter individuality of future generation by the ideal adaptation angle value revised；

5.5 carry out intersection operation by probability P c；

5.6 carry out mutation operation by probability P c；

5.7 judge modules, if meeting condition, entering 5.8 output optimal solutions, otherwise returning 5.2；

5.8 output optimal solutions.

(6) steering response, the optimal solution turning to economy and the archipelago that meet steering sensitivity constraint optimization obtained Genetic algorithm optimization result compares, and proposes steering response that method obtains according to the present invention, turns to economy optimization to tie Fruit is superior to the optimum results of archipelago genetic algorithm, then it is assumed that this optimization method is effective.

The concrete application approach of the present invention is a lot, and the above is only the preferred embodiment of the present invention, it is noted that for For those skilled in the art, under the premise without departing from the principles of the invention, it is also possible to make some improvement, this A little improvement also should be regarded as protection scope of the present invention.

Claims (3)

1. the Multipurpose Optimal Method of an electric-liquid composite power steering, it is characterised in that comprise the following steps that

(1) electric-liquid composite power steering is carried out Dynamic Modeling；

(2) steering steering response, steering sensitivity, steering economy are chosen as electric-liquid composite turning system Performance Evaluating Indexes, its quantitative formula is respectively as follows:

Steering response:

$\frac{T_h\left(s\right)}{T_r\left(s\right)}=\frac{K_s{r}_w}{l}\·\frac{1}{X_1{s}^2+Y_{1}s+Z_1}---\left(1\right)$

In formula:

$X_1=J_1+J_{m2}{n}_2{i}_g+J_{m1}{n}_1\frac{{\mathrm{lA}}_p}{q}$

$Y_1=B_1+B_{m2}{n}_2{i}_g+B_{m1}{n}_1\frac{{\mathrm{lA}}_p}{q}+\frac{\mathrm{\ρ}l}{4{C_q}^2{A_1}^2}$

$Z_1=K_s+K_a{K}_2{K}_s{i}_g+K_a{K}_1{K}_s\frac{{\mathrm{lA}}_p}{q}$

$J_1=J_{\lg }+m_{lm}\frac{lP}{2\mathrm{\π}}+J_{cs}\frac{lP}{2\mathrm{\π}}$

$B_1=B_{\lg }+B_{lm}\frac{lP}{2\mathrm{\π}}+B_{cs}\frac{lP}{2\mathrm{\π}}$

T_dMoment steering wheel inputted for driver；T_pFor steering resisting moment equivalent moment on rocker arm shaft；K_sFor steering spindle Rigidity；r_wPitch radius is fanned for tooth；J_lgFor electric boosted module reducing gear and the equivalent moment of inertia of steering screw；m_lmFor turning To the quality of nut；J_csFor the rotary inertia turning to tooth to fan；L is the centre-to-centre spacing of screw rod power；P is steering screw pitch；J_m2For helping The rotary inertia of force motor B；n₂Speed reducing ratio for assist motor A corner to steering screw corner；i_gDeceleration for reducing gear Ratio；J_m1Rotary inertia for assist motor A；n₁Speed reducing ratio for assist motor A corner to steering screw corner；A_pFor hydraulic cylinder The effective area of piston；Q is double-acting vane pump discharge capacity；B_lgEquivalent viscous damping ratio for steering screw Yu reducing gear； B_lmViscous damping coefficient for steering nut；B_csViscous damping coefficient for tooth fan；B_m2Viscous damping system for assist motor B Number；B_m1Viscous damping coefficient for assist motor A；ρ is power-assisted fluid density；A_iIt it is the orifice size of i-th valve port；C_qFor stream Coefficient of discharge；A_iOrifice size for i-th valve port；K_sFor torque sensor rigidity；K_aFor motor torque coefficient；K₁、K₂Respectively Power-assisted gain for assist motor A, B；

Steering sensitivity expression formula is:

$\frac{{\mathrm{\ω}}_r\left(s\right)}{{\mathrm{\θ}}_h\left(s\right)}=\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\δ}\left(s\right)}\frac{\mathrm{\δ}\left(s\right)}{{\mathrm{\θ}}_s\left(s\right)}---\left(2\right)$

The transmission function of the ratio of front wheel angle and steering wheel angle is:

In formula:

$X_2=J_1{n}_3+J_{m2}{n}_2{n}_3{i}_g+J_{m1}{n}_1{n}_3\frac{{\mathrm{lA}}_p}{q}$

$Y_2=B_1{n}_3+B_{m2}{n}_2{n}_3{i}_g+B_{m1}{n}_1{n}_3\frac{{\mathrm{lA}}_p}{q}+\frac{{\mathrm{\ρQ}}_s{A}_p{\mathrm{Pn}}_3}{4{{\mathrm{\πC}}_q}^2{A_1}^2}$

$Z_2=Z_1{n}_3+\frac{2{\mathrm{dk}}_{1}l}{r_w{n}_3}$

n₃Speed reducing ratio for wheel steering angle to steering screw corner；

$\left\{\begin{array}{c}\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\δ}\left(s\right)}=\frac{A_3{s}^3+A_2{s}^2+A_{1}s+A_0}{B_4{s}^4+B_3{s}^3+B_2{s}^2+B_{1}s+B_0}\\ \frac{\mathrm{\β}\left(s\right)}{\mathrm{\δ}\left(s\right)}=\frac{F_3{s}^3+F_2{s}^2+F_{1}s+F_0}{B_4{s}^4+B_3{s}^3+B_2{s}^2+B_{1}s+B_0}\\ \frac{\mathrm{\φ}\left(s\right)}{\mathrm{\δ}\left(s\right)}=\frac{H_2{s}^2+H_{1}s+H_0}{B_4{s}^4+B_3{s}^3+B_2{s}^2+B_{1}s+B_0}\end{array}\right.$

$A_3=-mu{I}_x{N}_{\mathrm{\δ}}+h^2{\mathrm{um}}_s^2{N}_{\mathrm{\δ}}-hu{I}_{xz}{m}_s{Y}_{\mathrm{\δ}}$

A₂=muL_pN_δ+I_xN_δY_β-I_xN_βY_δ

A₁=muL_φN_δ-L_pN_δY_β+L_pN_βY_δ-hum_sN_φY_δ+hum_sN_δY_φ

A₀=-L_φN_δY_β+L_φN_βY_δ

$B_4=mu{I}_{xz}^2-{\mathrm{muI}}_x{I}_z+h^2{\mathrm{uI}}_z{m}_s^2$

$B_3=mu{I}_z{L}_p+mu{I}_x{N}_r-h^2{\mathrm{um}}_s^2{N}_r+h{I}_{xz}{m}_s{N}_{\mathrm{\β}}+hu{I}_{xz}{m}_s{Y}_r-I_{xz}^2{Y}_{\mathrm{\β}}+I_x{I}_z{Y}_{\mathrm{\β}}$

$\begin{array}{c}{B}_2=mu{I}_z{L}_{\mathrm{\φ}}-{\mathrm{muL}}_p{N}_r-mu{I}_x{N}_{\mathrm{\β}}+h^2{\mathrm{um}}_s^2{N}_{\mathrm{\β}}+mu{I}_{xz}{N}_{\mathrm{\φ}}+I_x{N}_{\mathrm{\β}}{Y}_r-I_z{L}_p{Y}_{\mathrm{\β}}\\ -hu{I}_{xz}{m}_s{Y}_{\mathrm{\β}}-I_x{N}_r{Y}_{\mathrm{\β}}+hu{I}_z{m}_s{Y}_{\mathrm{\φ}}\end{array}$

B₁=-muL_φN_r+muL_pN_β-L_pN_βY_r+hum_sN_φY_r-I_zL_φY_β+L_pN_rY_β-I_xzN_φY_β

-hum_sN_rY_φ+I_xzN_βY_φ

B₀=muL_φN_β-L_φN_βY_r+L_φN_rY_β-hum_sN_φY_β+hum_sN_βY_φ

$F_3=-{\mathrm{hI}}_{xz}{m}_s{N}_{\mathrm{\δ}}+I_{xz}^2{Y}_{\mathrm{\δ}}-I_x{I}_z{Y}_{\mathrm{\δ}}$

$F_2=mu{I}_x{N}_{\mathrm{\δ}}-h^2{\mathrm{um}}_s^2{N}_{\mathrm{\δ}}-I_x{N}_{\mathrm{\δ}}{Y}_r+I_z{L}_p{Y}_{\mathrm{\δ}}+hu{I}_{xz}{m}_s{Y}_{\mathrm{\δ}}+I_x{N}_r{Y}_{\mathrm{\δ}}$

F₁=-muL_pN_δ+L_pN_δY_r+I_zL_φY_δ-L_pN_rY_δ+I_xzN_φY_δ-I_xzN_δY_φ

F₀=-muL_φN_δ+L_φN_δY_r-L_φN_rY_δ+hum_sN_φY_δ-hum_sN_δY_φ

H₂=-muI_xzN_δ-huI_zm_sY_δ

H₁=-hum_sN_δY_r+I_xzN_δY_β+hum_sN_rY_δ-I_xzN_βY_δ

H₀=hum_sN_δY_β-hum_sN_βY_δ

Turn to economy:

E=P₁+P₂+P₃+P₄ (3)

$\left\{\begin{array}{c}{P}_1=R_A{I_A}^2+\frac{{U_s}^2}{R_{elec}}\\ P_2={\mathrm{\ΣM}}_{ci}+C_{Fr}{\mathrm{\Σ\ω}}_i+C_{Fr2}{{\mathrm{\Σ\ω}}_i}^2+{\mathrm{\ΣC}}_i\\ P_3=\frac{P_{s}q}{2\mathrm{\π}}\mathrm{\ω}-P_s{Q}_s\\ P_4=\frac{\mathrm{\ρ}}{8{\left(C_q{A}_1\right)}^2}{(Q_s+A_p\frac{{\mathrm{dx}}_r}{dt})}^3+\frac{\mathrm{\ρ}}{8{\left(C_q{A}_2\right)}^2}{(Q_s-A_p\frac{{\mathrm{dx}}_r}{dt})}^3\end{array}\right.$

In formula:

P₁For electronic control unit ECU power loss；P₂For assist motor A, assist motor B power loss；P₃For hydraulic booster mould Block power steering power loss of pump；P₄For hydraulic booster module rotary valve structure power loss；R_AFor armature resistance；I_AFor armature electricity Stream；U_sFor controller both end voltage；R_elecFor controller resistance；M_ciFor the torque loss caused that rubs in motor；C_FrFor speed ratio Coefficient of friction；ω_iFor motor speed；C_Fr2For speed ratio square coefficient of friction；C_iFor motor unknown losses；

(3) using steering response with turn to economy as optimization aim, steering sensitivity, as constraints, sets up electric-liquid multiple Closing power steering system Model for Multi-Objective Optimization, object function f (x) that electric-liquid composite power steering optimizes is:

Road feel energy function:

$f\left(x_1\right)=\frac{1}{2{\mathrm{\π\ω}}_0}{\∫}_0^{{\mathrm{\ω}}_0}|\frac{T_h\left(s\right)}{T_r\left(s\right)}{{|}_{s=j\mathrm{\ω}}}^{2}d\mathrm{\ω}$

In formula:

$\frac{T_h\left(s\right)}{T_r\left(s\right)}=\frac{l\·K_s}{r_w(X_1{s}^2+Y_{1}s+Z_1)}$

$X_1=J_1+J_{m2}{n}_2{i}_g+J_{m1}{n}_1\frac{{\mathrm{lA}}_p}{q}$

$Y_1=B_1+B_{m2}{n}_2{i}_g+B_{m1}{n}_1\frac{{\mathrm{lA}}_p}{q}+\frac{\mathrm{\ρ}l}{4{C_q}^2{A_1}^2}$

$Z_1=K_s+K_a{K}_2{K}_s{i}_g+K_a{K}_1{K}_s\frac{{\mathrm{lA}}_p}{q}$

$J_1=J_{\lg }+m_{lm}\frac{lP}{2\mathrm{\π}}+J_{cs}\frac{lP}{2\mathrm{\π}}$

$B_1=B_{\lg }+B_{lm}\frac{lP}{2\mathrm{\π}}+B_{cs}\frac{lP}{2\mathrm{\π}}$

Turn to economy:

$f_2=\underset{1}{\overset{i}{\Σ}}{P}_i,(i=1,2,3,4)$

$\left\{\begin{array}{c}{P}_1=R_A{I_A}^2+\frac{{U_s}^2}{R_{elec}}\\ P_2={\mathrm{\ΣM}}_{ci}+C_{Fr}{\mathrm{\Σ\ω}}_i+C_{Fr2}{{\mathrm{\Σ\ω}}_i}^2+{\mathrm{\ΣC}}_i\\ P_3=\frac{P_{s}q}{2\mathrm{\π}}\mathrm{\ω}-P_s{Q}_s\\ P_4=\frac{\mathrm{\ρ}}{8{\left(C_q{A}_1\right)}^2}{(Q_s+A_p\frac{{\mathrm{dx}}_r}{dt})}^3+\frac{\mathrm{\ρ}}{8{\left(C_q{A}_2\right)}^2}{(Q_s-A_p\frac{{\mathrm{dx}}_r}{dt})}^3\end{array}\right.$

Optimizing constraints is:

$f\left(x\right)=\frac{1}{2{\mathrm{\π\ω}}_0}{\∫}_0^{{\mathrm{\ω}}_0}|\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\θ}\left(s\right)}{|}^{2}d\mathrm{\ω}$

$\frac{{\mathrm{\ω}}_r\left(s\right)}{{\mathrm{\θ}}_h\left(s\right)}=\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\δ}\left(s\right)}\frac{\mathrm{\δ}\left(s\right)}{{\mathrm{\θ}}_s\left(s\right)}$

$X_2=J_1{n}_3+J_{m2}{n}_2{n}_3{i}_g+J_{m1}{n}_1{n}_3\frac{{\mathrm{lA}}_p}{q}$

$Y_2=B_1{n}_3+B_{m2}{n}_2{n}_3{i}_g+B_{m1}{n}_1{n}_3\frac{{\mathrm{lA}}_p}{q}+\frac{{\mathrm{\ρQ}}_s{A}_p{\mathrm{Pn}}_3}{4{{\mathrm{\πC}}_q}^2{A_1}^2}$

$Z_2=Z_1{n}_3+\frac{2{\mathrm{dk}}_{1}l}{r_w{n}_3}$

$\frac{{\mathrm{\ω}}_r\left(s\right)}{\mathrm{\δ}\left(s\right)}=\frac{A_3{s}^3+A_2{s}^3+A_{1}s+A_0}{B_4{s}^4+B_3{s}^3+B_2{s}^2+B_{1}s+B_0}$

(4) by the rotary inertia J of electric boosted module motor_m2, reducing gear speed reducing ratio i_g, torque sensor stiffness K_s, front-wheel with The equivalent damped coefficient B on steering screw of steering mechanism_lg, double-acting vane pump parameter stator thickness B, double-acting vane pump fixed Sub-major axis radius R₂As design variable；

(5) use archipelago genetic algorithm based on simulated annealing correction that the selected parameter in step (4) is optimized, and Design variable value when obtaining making steering response according to optimum results in ISIGHT, turn to economy to reach optimal solution；

(6) steering response meeting steering sensitivity constraint optimization obtained, the optimal solution and the archipelago heredity that turn to economy Algorithm optimization result compares, and proposes steering response that method obtains according to the present invention, turns to economy optimum results equal It is better than the optimum results of archipelago genetic algorithm, then it is assumed that this optimization method is effective.

The Multipurpose Optimal Method of electric-liquid composite power steering the most according to claim 1, it is characterised in that on The Dynamic Modeling stated in step (1) includes: assist motor A model, steering pump model, rotary valve model, fluid power cylinder mould Type, assist motor B model, reducing gear model, steering wheel model, ball-and-nut steering gear model, tire model, whole vehicle model with And energy consumption mathematical model.

The Multipurpose Optimal Method of electric-liquid composite power steering the most according to claim 1, it is characterised in that on State step (5) to specifically include:

5.1 initialize colony；

5.2 calculate the fitness value F of each individuality in colony；

5.3 are modified by simulated annealing correcting module and judge, meet condition and then enter 5.4, otherwise return 5.2；

It is as follows that correcting module performs step:

Step1: module initialization

(11) k=1, j ∈ N, Freeze=0, set initial parameter T_k, L_k, s, q, ε；

(12)

(13) initial solution x is produced^k∈ S, makes x^s=x^k；

Step2: selected domain variability carries out field search to each field j ∈ N

(21)

(22) produce field and solve x ∈ N (x^k), calculate δ₁=f (x)-f (x^k),δ₂=f (x)-f (x^s)；

(23) if δ₁＜ 0, thenIf δ₂＜ 0, then x^s=x^k；Otherwise, if exp is (-δ₁/T_k) ＞, Random [0,1], then x^k=x,

(24) ifThenTurn (22)；

(25) if all spectra searches for complete (j >=| N |), step 3 is turned；Otherwise, j=j+1, turn (22)；

Step 3: algorithm stop technology

(31)Freeze=Freeze⁺¹；

(32) if Freeze >=q, algorithm will terminate, thus exports solution x^s；Otherwise, step 4 is proceeded to；

Step 4: parameter adaptive control and correction fitness value

(41) temperature control coefrficient is calculated

$\mathrm{\σ}=(\underset{j\∈N}{\Σ}{I}_k^j+1)/(\underset{j\∈N}{\Σ}{A}_k^j+1)-(\underset{j\∈N}{\Σ}{I}_{k-1}^j+1)/(\underset{j\∈N}{\Σ}{A}_{k-1}^j+1)$

(42) temperature is calculated

$T_{k+1}=\[Switch\left(\mathrm{\σ}\right){(1-\frac{1}{k+1})}^s+(1-Switch(\mathrm{\σ}\left)\right)(1+\frac{1}{k+1})\]T_k$

(43) number of times and the field searching intensity of search are calculated；

$L_{k+1}^j=INT\left(\right(I_k^j+1)/({\mathrm{\ΣI}}_k^j+1\left)\right)L_{k-1}$

(44) adaptive response function is revised；

$F^{*}=e^{p^k{T}^{*}F}$

In formula: F^*For fitness value after revising, F is for revising prospective adaptation angle value, and p is decay factor；

(45) k=k+1, turns step 2；

5.4 are selected to enter individuality of future generation by the ideal adaptation angle value revised；

5.5 carry out intersection operation by probability P c；

5.6 carry out mutation operation by probability P c；

5.7 judge modules, if meeting condition, entering 5.8 output optimal solutions, otherwise returning 5.2；

5.8 output optimal solutions.