WO2020134621A1 - Automobile electro-hydraulic intelligent steering system and multi-objective optimization method therefor - Google Patents

Abstract

An automobile electro-hydraulic intelligent steering system and a multi-objective optimization method therefor, the system comprising a mechanical steering module, an electric power assist module, an electro-hydraulic power assist module, and a control module. The system may intelligently select the proportions by which the electric power assist module and the electro-hydraulic power assist module participate in steering power assist. For the complex coupling relationship of electro-mechanical fluid in the system, a multi-objective optimization method is proposed. By means of parameter coupling analysis, key design variables that have a relatively large impact on system performance are selected, and a shared niche technology-based multi-objective particle swarm algorithm is used for optimization to obtain optimal design parameters, achieve the optimal performance of steering road feel, steering energy consumption and steering assistance, and improve the overall performance of the steering system.

Description

Automobile electro-hydraulic intelligent steering system and its multi-objective optimization method Technical field

The invention belongs to the technical field of automobile steering systems, and specifically refers to an automobile electro-hydraulic intelligent steering system and its multi-objective optimization method.

Background technique

Automobile steering system has gradually developed from mechanized to hydraulic and electronic, which not only reduces the driver's handling burden, obtains a comfortable driving feeling, but also reduces steering energy consumption and enhances driving safety. Among the existing automobile steering systems, electrohydraulic power steering systems and electric power steering systems are the most widely used. The electro-hydraulic power steering system still has the inherent energy loss of the hydraulic system, which leads to higher steering energy consumption. The electric power steering has better energy-saving characteristics, but the high-speed road feel is not as good as that of hydraulic steering, and the limited power of the motor is not suitable for front axle loads. Larger vehicles. Therefore, whether it is electro-hydraulic power steering or electric power steering, it is difficult to take into account good road feel, sufficient power assistance and low energy consumption.

Combining the advantages of electro-hydraulic power assist and electric power assist, the use of electro-hydraulic compound steering system is a development direction. For example, the Chinese patent application number is CN201721192203.0, and the name "a dual-steering power assist system" discloses the arrangement of two power assist steering systems to realize the high-power steering assistance needs of a pure electric bus; the Chinese patent application number is CN201710587904.2, The name "An Electro-hydraulic Hybrid Unmanned Vehicle Steering System" discloses the scheme of electric power active control and hydraulic power steering, which solves the problem of steering hysteresis. The Chinese patent application number is CN201610050308.6, and the name "An Electro-Hydraulic Steering Device for Commercial Vehicles" discloses the use of electric power and hydraulic power to work at the same time to obtain a better energy-saving type and meet the safety of emergency steering. The electro-hydraulic compound steering proposed in the above patent application simply superimposes the steering assistance, and cannot intelligently coordinate the proportional relationship between the electro-hydraulic assistance and the electric assistance according to the driver's style and actual road information. , The steering energy consumption and other couplings between multiple steering performances, nor does it involve the complicated electromechanical-hydraulic coupling relationship between electric power and hydraulic power and its parameter optimization design.

Therefore, an automotive electro-hydraulic intelligent steering system is proposed, and a reasonable multi-objective parameter optimization design is carried out to solve the defect that it is currently unable to intelligently coordinate electric power steering and electro-hydraulic power steering, which is helpful for the development and application of automobile steering systems. Certain market value.

Summary of the invention

In view of the above-mentioned shortcomings of the prior art, the object of the present invention is to provide a multi-objective optimization method for an automobile electro-hydraulic intelligent steering system to overcome the problems in the prior art. The invention solves the problem that it is difficult for the automobile steering system to take into account both low energy consumption and sufficient assistance at the same time by proposing an electro-hydraulic intelligent steering system integrating electric power steering and electro-hydraulic power steering, and considering the electromechanical-hydraulic coupling relationship for multi-objective optimization 3. The problem of proper road sense.

To achieve the above objectives, the technical solutions adopted by the present invention are as follows:

An automobile electro-hydraulic intelligent steering system of the present invention includes: a mechanical steering module, an electric power assist module, an electric hydraulic power assist module and a control module;

The mechanical steering module includes a steering wheel, a torsion bar, a lower column, a steering pinion, a steering rack, and a wheel unit connected in sequence;

The electric power assist module includes a power assist motor and a worm gear reducer; the output end of the power assist motor is connected to a worm gear reducer, the worm gear reducer acts between the torsion bar and the lower pipe column, and the electric power assist torque and the driver torque are placed in the lower pipe Columns to superimpose;

The electro-hydraulic booster module includes an oil tank, an oil pump motor, an oil pump, a directional valve, a piston, and a hydraulic cylinder; the piston is located in the hydraulic cylinder and is divided into left and right sides, and the two sides of the hydraulic cylinder are respectively communicated with the directional valve oil circuit The output end of the oil pump motor is connected to an oil pump, and the oil pump transfers hydraulic oil from the oil tank to the directional valve and distributes it to both sides of the hydraulic cylinder;

The control module includes a main controller, a sensor group, a driver database, and a road information database;

The input end of the main controller is electrically connected to the sensor group, and the output end is electrically connected to the booster motor, the oil pump motor and the directional valve, respectively;

The sensor group includes a torque sensor, a rotation angle sensor, a displacement sensor, a vehicle speed sensor, a camera, and a GPS receiver; the rotation angle sensor is installed on the lower column to receive the rotation angle signal of the lower column; the torque sensor is installed on the torsion bar to receive the driver The input torque signal; the displacement sensor is installed at the end of the steering rack and receives the displacement signal output by the hydraulic cylinder; the GPS receiver, camera, and vehicle speed sensor are installed on the car;

The driver database is electrically connected to the main controller, which is used to store the current driving data of the car driver and various driver data models downloaded offline, and through data comparison, select a data model that matches the current driver's driving style, and Transmission to the main controller;

The road information database is electrically connected to the main controller, stores road information downloaded offline, and connects to a GPS receiver to transmit current road information to the main controller in real time.

Further, the hydraulic cylinder is fixedly connected to the end of the lower column, the steering rack is coaxially installed inside the hydraulic cylinder, and the piston is coaxially fixedly mounted on the portion of the hydraulic rack; the steering rack is axially oriented The right part of the hydraulic cylinder is engaged with the steering pinion. The steering pinion transmits the combined torque of the driver's torque and the electric assist torque to the steering rack and converts it into a rack force. The rack force and the pressure on both sides of the hydraulic cylinder The hydraulic power generated by the difference is superimposed and output, and the output ends on both sides of the steering rack are connected to the wheel unit.

Further, the main controller judges the current vehicle state through each input signal of the sensor group, judges the driving style of the current driver through the input signal of the driver database, judges the current road information and predicts the steering demand through the input signal of the road information database , Synthesize the above information to make steering decision, output corresponding electric power assist signal, electro-hydraulic power assist signal, and directional valve control signal, respectively control the work of power assist motor, oil pump motor and directional valve, and adjust the participation of electric power assist module and electric hydraulic power assist module. The ratio of steering assistance.

Further, through the signal input from the driver database, the current steering operation characteristics of the driver are extracted, including the size of the steering rate, the size of the steering lag, and the magnitude of the steering amplitude; the extracted feature data is compared with the offline driver data model to select The one with the highest similarity is determined as the current driver's driving style.

Further, the road information database receives GPS signals, obtains real-time position information of the vehicle, corresponds to offline road information, obtains real-time road information of the vehicle, and according to the curve distribution, curve curvature, and curve length in the road information Information predicts vehicle steering needs.

The multi-objective optimization method of the automobile electro-hydraulic intelligent steering system of the present invention is based on the above system and includes the following steps:

Step 1: Initialize the parameters of the electro-hydraulic steering system, and establish a simulation model of the electro-hydraulic steering system in the multidisciplinary modeling software AMEsim;

Step 2: Analyze the steering energy consumption, steering feel and return error of the electro-hydraulic steering system;

Step 3: Analyze the coupling relationship between the mechanical, hydraulic and electronic parameters of the electro-hydraulic steering system;

Step 4: According to the analysis results of steps 2 and 3 above, select torsion bar stiffness Ks, pinion radius Rp, worm gear reducer reduction ratio G, piston cross-sectional area Ap, reversing valve port area gain Ka as design variables , AMEpilot module using AMEsim software to output design variables;

Step 5: Set the objective function as steering energy consumption, steering feel, and return error, and the constraint condition is the design variable value range, and establish an electro-hydraulic steering system optimization model;

Step 6: A multi-objective particle swarm optimization algorithm based on shared niche technology is used to optimize the multi-objective solution of the electro-hydraulic steering system;

Step 7: Obtain the optimization result and input the optimized design variables into Amesim software to verify the optimization effect.

Further, the multi-objective particle swarm algorithm based on the shared niche technology in step 6 specifically includes:

Step 61: According to the electro-hydraulic steering system optimization model, a particle swarm model is established and the algorithm parameters are defined, and the initial value of the design variable is used to initialize the particle swarm;

Step 62: Within the given solution space, initialize the particle swarm position and velocity information;

Step 63: Within the scope of the solution space, update the position and velocity information of the particle swarm, generate a new population, and adjust the historical optimal position of the individual;

Step 64: Calculate the fitness value of each particle, find the initial global optimal position, and add the obtained non-inferior solution to the external storage set Ar;

Step 65: Calculate the objective function values of each particle's steering energy consumption, steering feel, and return error, and select the global optimal position of each particle, use the ring game method to select the new non-inferior solution in the current state, and use the new Non-inferior solutions update the external storage set Ar;

Step 66: Determine whether the external storage set Ar is full. If it is not full, adjust the global optimal position. If it is full, first execute the niche maintenance strategy based on the sharing mechanism to ensure the diversity and uniformity of the particle swarm, and then adjust the global Optimal location

Step 67: Loop steps 63-66 until it reaches the maximum number of iterations or stops when it converges, and outputs the optimization result of the electro-hydraulic steering system.

Further, in step 66, the niche maintenance strategy based on the sharing mechanism adopts a sharing function to adjust the fitness of niche individuals. The specific steps are as follows:

Step 661: Initialize the algorithm, establish the initial population, and initialize the parameters;

Step 662: Calculate the fitness of individuals and perform operations such as selection, crossover and mutation of genetic algorithms;

Step 663: Calculate the individual sharing degree, and update the individual's fitness according to the individual sharing degree;

Step 664: Compare the fitness of the offspring and the parent, and replace the parent individual with an individual with a higher fitness to generate a new population;

Step 665: If the termination condition is satisfied, then exit the algorithm and complete the niche maintenance strategy, otherwise return to 662.

Further, the calculation formula of the individual sharing degree in the step 663 is:

In the formula, share(d _ij ) is the sharing degree function, d _ij is the Hamming distance, σ ₀ is the niche boundary parameter, and λ is the sharing function shape parameter.

Further, the formula for updating the position and velocity of the particle swarm in step 63 is:

v _i (t)=ωv _i (t-1)+c ₁ r ₁ (x _pbest -x _i )+c ₂ r ₂ (x _gbest -x _i )

x _i (t) = x _i (t-1)v _i

Where v _i is the particle velocity, x _i is the particle position, x _pbest is the individual historical optimal position of the particle, x _gbest is the global optimal position of the particle, ω is the inertial weight; r ₁ and r ₂ are 0 to 1 Between random numbers, c ₁ and c ₂ are global incremental control coefficients and individual incremental control coefficients.

The beneficial effects of the invention:

Compared with the existing automobile steering system, the invention combines the advantageous functions of electric power assist and electrohydraulic power assist, and can simultaneously obtain better economy, good driving feeling, sufficient steering power assist, and through electric power assist and electric hydraulic power assist Redundant backup of safety and reliability is not only suitable for cars driven by drivers, but also for driverless cars.

The present invention considers the multidisciplinary coupling relationship of electro-hydraulic steering system in mechanics, electronics, hydraulic pressure, etc., determines the key design variables through parameter coupling analysis, uses multi-objective particle swarm optimization algorithm based on shared niche technology for optimization, and has good convergence. It is easy to get the global optimum and to get good overall steering performance.

BRIEF DESCRIPTION

1 is a block diagram of the principle structure of the automobile electro-hydraulic intelligent steering system of the present invention;

2 is a flowchart of multi-objective optimization of the method of the present invention;

3 is a flowchart of a multi-objective particle swarm algorithm based on shared niche technology of the present invention;

4 is a diagram of the optimization process of the steering feel of the present invention;

FIG. 5 is a graph of the optimization history of the correction error of the present invention;

FIG. 6 is a diagram of the optimization history of the energy consumption of the present invention;

In the picture, 1-steering wheel, 2-torsion bar, 3-torque sensor, 4-rotation angle sensor, 5-lower column, 6-steering pinion, 7-steering rack, 8-wheel unit, 9-displacement sensor , 10-hydraulic cylinder, 11-piston, 12-directional valve, 13-oil tank, 14-oil pump, 15-oil pump motor, 16-main controller, 17-sensor group, 18-driver database, 19-road information Database, 20-booster motor, 21-worm gear reducer, E-electrohydraulic booster signal, F-electric booster signal, G-reversing valve signal.

detailed description

In order to facilitate the understanding of those skilled in the art, the present invention will be further described below in conjunction with the embodiments and drawings, and the content mentioned in the embodiments does not limit the present invention.

Referring to FIG. 1, an automobile electro-hydraulic intelligent steering system of the present invention includes: a mechanical steering module, an electric power assist module, an electro-hydraulic power assist module, and a control module;

The mechanical steering module includes a steering wheel 1, a torsion bar 2, a lower column 5, a steering pinion 6, a steering rack 7, and a wheel unit 8 connected in sequence;

The electric booster module includes a booster motor 20 and a worm gear reducer 21; the output end of the booster motor 20 is connected to the worm gear reducer 21, and the worm gear reducer acts between the torsion bar 2 and the lower column 5 to transfer the electric boost torque Superimposed with the driver's torque on the lower column 5;

The electro-hydraulic booster module includes an oil tank 13, an oil pump motor 15, an oil pump 14, a directional valve 12, a piston 11, and a hydraulic cylinder 10; the piston is located in the hydraulic cylinder and is divided into left and right sides. The oil path of the directional valve 12 is connected; the output end of the oil pump motor 15 is connected to the oil pump 14, and the oil pump 14 transfers the hydraulic oil from the oil tank 13 to the directional valve 12, which is distributed to both sides of the hydraulic cylinder 10;

The control module includes a main controller 16, a sensor group 17, a driver database 18, and a road information database 19;

The input end of the main controller 16 is electrically connected to the sensor group 17, and the output end is electrically connected to the booster motor 20, the oil pump motor 15, and the reversing valve 12, respectively;

The sensor group includes a torque sensor 3, a rotation angle sensor 4, a displacement sensor 9, a vehicle speed sensor, a camera, and a GPS receiver; the rotation angle sensor 4 is installed on the lower column and receives the rotation angle signal of the lower column; the torque sensor 3 is installed on the torsion bar 2, receiving the torque signal input by the driver; the displacement sensor 9 is installed at the end of the steering rack 7 and receives the displacement signal output by the hydraulic cylinder 10; the GPS receiver, camera, and vehicle speed sensor are installed on the car;

The driver database is electrically connected to the main controller. It is used to store the current driving data of the car driver and various driver data models downloaded offline, and through data comparison, select the data model that is closest to the current driver's driving style. And transmit to the main controller;

The road information database is electrically connected to the main controller, stores road information downloaded offline, and connects to a GPS receiver to transmit current road information to the main controller in real time.

Wherein, the hydraulic cylinder 10 is fixedly connected to the end of the lower pipe column 5, the steering rack 7 is coaxially installed inside the hydraulic cylinder 10, and the piston of the steering rack 7 is coaxially fixedly mounted on the portion of the hydraulic cylinder 10. The part of the steering rack that extends axially to the right of the hydraulic cylinder meshes with the steering pinion. The steering pinion transmits the combined torque of the driver's torque and the electric assist torque to the steering rack and converts it into a rack force. The hydraulic power generated by the pressure difference between the two sides of the hydraulic cylinder is superimposed and output, and the output ends on both sides of the steering rack are connected to the wheel unit.

Among them, the main controller judges the current vehicle state by each input signal of the sensor group, judges the driving style of the current driver by the input signal of the driver database, judges the current road information and predicts the steering demand by the input signal of the road information database, Synthesize the above information to make steering decisions, output corresponding electric power assist signal F, electro-hydraulic power assist signal E, and directional valve control signal G to control the operation of power assist motor, oil pump motor and directional valve, adjust electric power assist module and electro-hydraulic power assist The proportion of modules involved in steering assistance.

Extract the current driver's steering operation characteristics through the signal input from the driver database, including the steering rate, steering delay and steering amplitude; compare the extracted feature data with the offline driver data model to select the highest similarity Is determined as the current driver’s driving style.

The road information database receives GPS signals, obtains real-time position information of the vehicle, corresponds to offline road information, obtains real-time road information of the vehicle, and predicts the vehicle based on the curve distribution, curve curvature, curve length and other information in the road information Turn to demand.

Referring to FIG. 2, a multi-objective optimization method for an automobile electro-hydraulic intelligent steering system according to the present invention is based on the above system and includes the following steps:

Step 1: Initialize the parameters of the electro-hydraulic intelligent steering system, and establish the simulation model of the electro-hydraulic intelligent steering system in the multidisciplinary modeling software AMEsim;

Step 2: Analyze the steering energy consumption, steering feel and return error of the electro-hydraulic intelligent steering system;

Step 3: Analyze the coupling relationship between the mechanical, hydraulic and electronic parameters of the electro-hydraulic intelligent steering system;

Step 4: According to the analysis results of steps 2 and 3 above, select torsion bar stiffness Ks, pinion radius Rp, worm gear reducer reduction ratio G, piston cross-sectional area Ap, reversing valve port area gain Ka as design variables , AMEpilot module using AMEsim software to output design variables;

Step 5: Set the objective function as the steering energy consumption, steering feel, and return error, and the constraint condition is the design variable value range, and establish the electro-hydraulic intelligent steering system optimization model;

Step 6: A multi-objective particle swarm optimization algorithm based on shared niche technology is used to optimize and solve the multi-objective of the electro-hydraulic intelligent steering system;

Step 7: Obtain the optimization result and input the optimized design variables into Amesim software to verify the optimization effect.

In the example, set the simulation time to 20 seconds, perform 3 consecutive steering operations with the same steering wheel angle of ±120° within 3-18 seconds, and the steering wheel has no input for the rest of the time; analyze the steering energy consumption of the electro-hydraulic intelligent steering system, Turning sense and correcting error. The steering energy consumption includes the energy loss of the mechanical steering module, electric power assist module, and electro-hydraulic power assist module in 3-18 seconds. The steering feel is measured by the peak and fluctuation of the torsion bar force in 3-18 seconds, and the correction error is passed through the steering wheel. Analyze at the 19th second.

Change the values of mechanical, hydraulic and electronic parameters in the system respectively, determine the influence of parameters on the three performances of steering energy consumption, steering feel and return error, and analyze the coupling between the mechanical, hydraulic and electronic parameters of the electro-hydraulic intelligent steering system relationship.

The constraints are the range of design variables, as shown in Table 1:

Table 1

Serial number	design variable	Initial value	Ranges
1	Torsion bar stiffness Ks	15	3-50
2	Pinion radius Rp	7.5	6.5-9.5
3	Worm gear reducer reduction ratio G	18	15-28
4	Piston cross-sectional area Ap	625	200-900
5	Area gain of directional valve valve Ka	0.95	0.9-1.1

Referring to FIG. 3, the multi-objective particle swarm algorithm based on the shared niche technology in step 6 specifically includes:

Step 61: According to the optimization model of the electro-hydraulic intelligent steering system, a particle swarm model is established and the algorithm parameters are defined, and the initial values of the design variables are used to initialize the particle swarm;

Step 62: Within the given solution space, initialize the particle swarm position and velocity information;

Step 63: Within the scope of the solution space, update the position and velocity information of the particle swarm, generate a new population, and adjust the historical optimal position of the individual;

Step 64: Calculate the fitness value of each particle, find the initial global optimal position, and add the obtained non-inferior solution to the external storage set Ar;

Step 65: Calculate the objective function values of each particle's steering energy consumption, steering feel, and return error, and select the global optimal position of each particle, use the ring game method to select the new non-inferior solution in the current state, and use the new Non-inferior solutions update the external storage set Ar;

Step 66: Determine whether the external storage set Ar is full. If it is not full, adjust the global optimal position. If it is full, first execute the niche maintenance strategy based on the sharing mechanism to ensure the diversity and uniformity of the particle swarm, and then adjust the global Optimal location

Step 67: Loop steps 63-66 until it reaches the maximum number of iterations or stops when it converges, and output the optimization result of the electro-hydraulic intelligent steering system.

In the example, the particle swarm model is established and the algorithm parameters are defined, as shown in Table 2:

Table 2

Figure 4 shows the optimization process of steering feel, the abscissa is evolution algebra, and the ordinate is the target value of steering feel. It can be seen from the figure that the target value of the steering road feel is about 0.0111 and the highest is about 0.0197. After the evolutionary generation is greater than 100 generations, the steering road sense takes the most dense value near 0.00193;

Figure 5 shows the optimization process of the return error. The abscissa is the evolutionary algebra, and the ordinate is the target value of the return error. It can be seen from the figure that during the evolution from the first generation to the 400th generation, the value of the recovery error is basically stable around 0.00153, the lowest value is about 0.0017, and the highest value is about 0.00158, the change trend is relatively smooth;

Figure 6 reflects the change in target energy consumption as the evolutionary algebra increases. It can be seen from the figure that after the evolutionary generation is greater than 50 generations, the value of the steering energy consumption gradually decreases, and the value points that are stable near 110 are the most dense.

It can be seen from Figures 4-6 that during the optimization process, the target values of steering feel, return error and steering energy consumption are coupled with each other, and the trend of optimization changes basically converges, and each target value is stable within a certain range , To meet the design needs.

There are many specific application ways of the present invention, and the above are only preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, several improvements can be made without departing from the principles of the present invention. Improvements should also be regarded as the scope of protection of the present invention.

Claims (10)

1. An automobile electro-hydraulic intelligent steering system is characterized by comprising: a mechanical steering module, an electric power assist module, an electric hydraulic power assist module and a control module;

   The mechanical steering module includes a steering wheel, a torsion bar, a lower column, a steering pinion, a steering rack, and a wheel unit connected in sequence;

   The electric power assist module includes a power assist motor and a worm gear reducer; the output end of the power assist motor is connected to a worm gear reducer, the worm gear reducer acts between the torsion bar and the lower pipe column, and the electric power assist torque and the driver torque are placed in the lower pipe Columns to superimpose;

   The electro-hydraulic booster module includes an oil tank, an oil pump motor, an oil pump, a directional valve, a piston, and a hydraulic cylinder; the piston is located in the hydraulic cylinder and is divided into left and right sides, and the two sides of the hydraulic cylinder are respectively communicated with the directional valve oil circuit The output end of the oil pump motor is connected to an oil pump, and the oil pump transfers hydraulic oil from the oil tank to the directional valve and distributes it to both sides of the hydraulic cylinder;

   The control module includes a main controller, a sensor group, a driver database, and a road information database;

   The input end of the main controller is electrically connected to the sensor group, and the output end is electrically connected to the booster motor, the oil pump motor and the directional valve, respectively;

   The sensor group includes a torque sensor, a rotation angle sensor, a displacement sensor, a vehicle speed sensor, a camera, and a GPS receiver; the rotation angle sensor is installed on the lower column to receive the rotation angle signal of the lower column; the torque sensor is installed on the torsion bar to receive the driver The input torque signal; the displacement sensor is installed at the end of the steering rack and receives the displacement signal output by the hydraulic cylinder; the GPS receiver, camera, and vehicle speed sensor are installed on the car;

   The driver database is electrically connected to the main controller, which stores the current driving data of the car driver and various driver data models downloaded offline, and through data comparison, selects a data model that matches the current driver's driving style, and Controller transmission;

   The road information database is electrically connected to the main controller, which stores road information downloaded in an offline manner, and is connected to a GPS receiver to transmit the current road information to the main controller in real time.
2. The automobile electro-hydraulic intelligent steering system according to claim 1, wherein the hydraulic cylinder is fixedly connected to the end of the lower column, the steering rack is coaxially installed inside the hydraulic cylinder, and the steering rack is located in the hydraulic cylinder The piston is coaxially fixed on the part of the steering rack; the part of the steering rack that extends axially to the right of the hydraulic cylinder meshes with the steering pinion. The steering pinion transmits the combined torque of the driver's torque and the electric assist torque to the steering rack and converts it. It is the rack force, which is superimposed and output by the hydraulic assist generated by the pressure difference between the two sides of the hydraulic cylinder. The output ends of the steering rack are connected to the wheel unit.
3. The automobile electro-hydraulic intelligent steering system according to claim 1, wherein the main controller judges the current vehicle state by each input signal of the sensor group, and judges the current driver's driving style by the input signal of the driver database , Judge the current road information and predict the steering demand through the input signal of the road information database, synthesize the above information to make the steering decision, output the corresponding electric power assist signal, electro-hydraulic power assist signal, reversing valve control signal, and control the power assist motor and oil pump motor And the work of the directional valve, adjust the proportion of the electric power assist module and the electric hydraulic power assist module to participate in the steering assist.
4. The automobile electro-hydraulic intelligent steering system according to claim 1, characterized in that the current driver's steering operation characteristics are extracted through the signal input from the driver database, including the steering rate magnitude, steering time lag magnitude, and steering amplitude magnitude; The extracted feature data is compared with the offline driver data model, and the one with the highest similarity is selected to determine the driving style of the current driver.
5. The automobile electro-hydraulic intelligent steering system according to claim 1, wherein the road information database receives GPS signals, obtains real-time position information of the vehicle, corresponds to offline road information, obtains real-time road information of the vehicle, and according to the road The information of the curve distribution, curve curvature, and curve length in the information predicts the steering demand of the vehicle.
6. A multi-objective optimization method for an automobile electro-hydraulic intelligent steering system is based on the system according to any one of claims 1 to 5, characterized in that it includes the following steps:

   Step 1: Initialize the parameters of the electro-hydraulic intelligent steering system, and establish the simulation model of the electro-hydraulic intelligent steering system in the multidisciplinary modeling software AMEsim;

   Step 2: Analyze the steering energy consumption, steering feel and return error of the electro-hydraulic intelligent steering system;

   Step 3: Analyze the coupling relationship between the mechanical, hydraulic and electronic parameters of the electro-hydraulic intelligent steering system;

   Step 4: According to the analysis results of steps 2 and 3 above, select torsion bar stiffness Ks, pinion radius Rp, worm gear reducer reduction ratio G, piston cross-sectional area Ap, reversing valve port area gain Ka as design variables , AMEpilot module using AMEsim software to output design variables;

   Step 5: Set the objective function as the steering energy consumption, steering feel, and return error, and the constraint condition is the design variable value range, and establish the electro-hydraulic intelligent steering system optimization model;

   Step 6: A multi-objective particle swarm optimization algorithm based on shared niche technology is used to optimize and solve the multi-objective of the electro-hydraulic intelligent steering system;

   Step 7: Obtain the optimization result and input the optimized design variables into Amesim software to verify the optimization effect.
7. The multi-objective optimization method for an automobile electro-hydraulic intelligent steering system according to claim 6, wherein the multi-objective particle swarm algorithm based on the shared niche technology in step 6 specifically includes:

   Step 61: According to the optimization model of the electro-hydraulic intelligent steering system, a particle swarm model is established and the algorithm parameters are defined, and the initial values of the design variables are used to initialize the particle swarm;

   Step 62: Within the given solution space, initialize the particle swarm position and velocity information;

   Step 63: Within the scope of the solution space, update the position and velocity information of the particle swarm, generate a new population, and adjust the historical optimal position of the individual;

   Step 64: Calculate the fitness value of each particle, find the initial global optimal position, and add the obtained non-inferior solution to the external storage set Ar;

   Step 65: Calculate the objective function values of each particle's steering energy consumption, steering feel, and return error, and select the global optimal position of each particle, use the ring game method to select the new non-inferior solution in the current state, and use the new Non-inferior solutions update the external storage set Ar;

   Step 66: Determine whether the external storage set Ar is full. If it is not full, adjust the global optimal position. If it is full, first execute the niche maintenance strategy based on the sharing mechanism to ensure the diversity and uniformity of the particle swarm, and then adjust the global Optimal location

   Step 67: Loop steps 63-66 until it reaches the maximum number of iterations or stops when it converges, and output the optimization result of the electro-hydraulic intelligent steering system.
8. The multi-objective optimization method for an automobile electro-hydraulic intelligent steering system according to claim 7, wherein the niche maintenance strategy based on the sharing mechanism in step 66 uses a sharing function to adjust the fitness of the niche individual, specifically Proceed as follows:

   Step 661: Initialize the algorithm, establish the initial population, and initialize the parameters;

   Step 662: Calculate the fitness of individuals and perform operations such as selection, crossover and mutation of genetic algorithms;

   Step 663: Calculate the individual sharing degree, and update the individual's fitness according to the individual sharing degree;

   Step 664: Compare the fitness of the offspring and the parent, and replace the parent individual with an individual with a higher fitness to generate a new population;

   Step 665: If the termination condition is satisfied, then exit the algorithm and complete the niche maintenance strategy, otherwise return to 662.
9. The multi-objective optimization method for an automobile electro-hydraulic intelligent steering system according to claim 8, wherein the calculation formula of the individual sharing degree in step 663 is:

   

   In the formula, share(d _ij ) is the sharing degree function, d _ij is the Hamming distance, σ ₀ is the niche boundary parameter, and λ is the sharing function shape parameter.
10. The multi-objective optimization method for an automobile electro-hydraulic intelligent steering system according to claim 7, wherein the formula for updating the position and velocity of the particle swarm in step 63 is:

    v _i (t)=ωv _i (t-1)+c ₁ r ₁ (x _pbest -x _i )+c ₂ r ₂ (x _gbest -x _i )

    x _i (t) = x _i (t-1)v _i

    Where v _i is the particle velocity, x _i is the particle position, x _pbest is the individual historical optimal position of the particle, x _gbest is the global optimal position of the particle, ω is the inertial weight; r ₁ and r ₂ are 0 to 1 Between random numbers, c ₁ and c ₂ are global incremental control coefficients and individual incremental control coefficients.

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