CN109733466B - Automobile electro-hydraulic intelligent steering system and multi-objective optimization method thereof - Google Patents
Automobile electro-hydraulic intelligent steering system and multi-objective optimization method thereof Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D3/00—Steering gears
- B62D3/02—Steering gears mechanical
- B62D3/12—Steering gears mechanical of rack-and-pinion type
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D5/00—Power-assisted or power-driven steering
- B62D5/04—Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D5/00—Power-assisted or power-driven steering
- B62D5/06—Power-assisted or power-driven steering fluid, i.e. using a pressurised fluid for most or all the force required for steering a vehicle
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
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Abstract
The invention discloses an automobile electro-hydraulic intelligent steering system and a multi-objective optimization method thereof. The system can intelligently select the proportion of the electric power-assisted module and the electric hydraulic power-assisted module participating in the steering power-assisted; aiming at the complex electromechanical-hydraulic coupling relation existing in the system, a multi-objective optimization method is provided, key design variables which have large influence on the system performance are selected through parameter coupling analysis, optimization is carried out by adopting a multi-objective particle swarm optimization based on a shared niche technology, the optimal design parameters are obtained, the optimal performance of steering road feel, steering energy consumption and steering power is realized, and the overall performance of a steering system is improved.
Description
Technical Field
The invention belongs to the technical field of automobile steering systems, and particularly relates to an automobile electro-hydraulic intelligent steering system and a multi-objective optimization method thereof.
Background
The automobile steering system gradually develops from mechanization to hydraulicization and electronization, thereby not only lightening the operation burden of a driver and obtaining comfortable driving feeling, but also reducing the steering energy consumption and enhancing the driving safety. In the existing automobile steering system, the most applied is an electric hydraulic power steering system and an electric power steering system. The electric power-assisted steering system still has the inherent energy loss of a hydraulic system to cause higher steering energy consumption, has better energy-saving characteristic, but is not good in high-speed road feel as well as hydraulic steering, is limited by motor power and is not suitable for vehicles with larger front axle loads. Therefore, it is difficult to achieve both of a good road feel, a sufficient assist force, and a low energy consumption in both the electro-hydraulic power steering and the electric power steering.
The advantages of electric hydraulic power assistance and electric power assistance are combined, and an electro-hydraulic composite steering system is used as a development direction. For example, the Chinese patent with the application number of CN201721192203.0, named as 'a double-steering power-assisted system' discloses the arrangement of two power-assisted steering systems, so as to realize the high-power steering power-assisted requirement of a pure electric bus; the Chinese patent application No. CN201710587904.2, entitled "an electro-hydraulic hybrid unmanned automobile steering system", discloses a scheme of electric power-assisted active control and hydraulic power-assisted follow-up steering, and solves the problem of steering hysteresis. The Chinese patent application No. CN201610050308.6, entitled "an electric hydraulic steering device for commercial vehicle" discloses that the electric power and the hydraulic power are simultaneously operated, so that the better energy-saving type is obtained and the safety of emergency steering is met. The electro-hydraulic hybrid steering provided by the patent application is only to simply superpose the steering assistance, cannot intelligently coordinate the proportional relation between the electric hydraulic assistance and the electric assistance according to the style of a driver and actual road information, does not consider the coupling between a plurality of steering performances such as steering road feel and steering energy consumption, and does not relate to the complicated electromechanical-hydraulic coupling relation between the electric assistance and the hydraulic assistance and the parameter optimization design scheme thereof.
Therefore, the electro-hydraulic intelligent steering system for the automobile is provided, reasonable multi-objective parameter optimization design is carried out, the defect that the electric power steering and the electric hydraulic power steering cannot be intelligently coordinated at present is overcome, the development and the application of the automobile steering system are facilitated, and certain market value is achieved.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a multi-objective optimization method of an automotive electro-hydraulic intelligent steering system, so as to overcome the problems in the prior art. The invention provides the electro-hydraulic intelligent steering system integrating the electric power-assisted steering and the electro-hydraulic power-assisted steering, and multi-objective optimization is carried out by considering the electromechanical-hydraulic coupling relation, so that the problem that the automobile steering system is difficult to simultaneously consider lower energy consumption, sufficient power assistance and appropriate road feel is solved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention relates to an automobile electrohydraulic intelligent steering system, which comprises: the device comprises a mechanical steering module, an electric power-assisted module, an electric hydraulic power-assisted module and a control module;
the mechanical steering module comprises a steering wheel, a torsion bar, a lower pipe column, a steering pinion, a steering rack and a wheel unit which are connected in sequence;
the electric power-assisted module comprises a power-assisted motor and a worm and gear reducer; the output end of the power-assisted motor is connected with a worm and gear reducer, the worm and gear reducer acts between the torsion bar and the lower tubular column, and the electric power-assisted torque and the torque of a driver are superposed on the lower tubular column;
the electro-hydraulic power-assisted module comprises an oil tank, an oil pump motor, an oil pump, a reversing valve, a piston and a hydraulic cylinder; the piston is positioned in the hydraulic cylinder and divides the hydraulic cylinder into a left side and a right side, and the two sides of the hydraulic cylinder are respectively communicated with the oil circuit of the reversing valve; the output end of the oil pump motor is connected with an oil pump, and the oil pump transfers the hydraulic oil from the oil tank to the reversing valve and distributes the hydraulic oil to two sides of the hydraulic cylinder;
the control module comprises a main controller, a sensor group, a driver database and a road information database;
the input end of the main controller is electrically connected with the sensor group, and the output end of the main controller is respectively electrically connected with the power-assisted motor, the oil pump motor and the reversing valve;
the sensor group comprises a torque sensor, a corner sensor, a displacement sensor, a vehicle speed sensor, a camera and a GPS receiver; the corner sensor is arranged on the lower pipe column and receives a corner signal of the lower pipe column; the torque sensor is arranged on the torsion bar and receives a torque signal input by a driver; the displacement sensor is arranged at the tail end of the steering rack and receives a displacement signal output by the hydraulic cylinder; the GPS receiver, the camera and the vehicle speed sensor are arranged on the automobile;
the driver database is electrically connected with the main controller and is used for storing the driving data of the current automobile driver and various driver data models downloaded in an off-line mode, selecting the data models according with the driving style of the current driver through data comparison and transmitting the data models to the main controller;
the road information database is electrically connected with the main controller, stores road information downloaded in an off-line mode, is connected with the GPS receiver, and transmits the current road information to the main controller in real time.
Furthermore, the hydraulic cylinder is fixedly connected with the tail end of the lower pipe column, the steering rack is coaxially installed on the inner side of the hydraulic cylinder, and the piston is coaxially and fixedly installed on the part, located in the hydraulic cylinder, of the steering rack; the part of the steering rack axially extending out of the hydraulic cylinder towards the right side is meshed with a steering pinion, the steering pinion transmits the resultant torque of the driver torque and the electric power-assisted torque to the steering rack and converts the resultant torque into rack force, the rack force and hydraulic power generated by pressure difference on two sides of the hydraulic cylinder are superposed and output, and output ends on two sides of the steering rack are connected with wheel units.
Furthermore, the main controller judges the current vehicle state through each input signal of the sensor group, judges the current driving style of the driver through the input signal of the driver database, judges and predicts the current road information and the steering demand through the input signal of the road information database, carries out the steering decision by integrating the information, outputs corresponding electric power-assisted signal, electric hydraulic power-assisted signal and reversing valve control signal, respectively controls the work of the power-assisted motor, the oil pump motor and the reversing valve, and adjusts the proportion of the electric power-assisted module and the electric hydraulic power-assisted module participating in the steering power assistance.
Furthermore, the steering operation characteristics of the current driver are extracted through signals input by a driver database, wherein the steering operation characteristics comprise steering rate, steering time lag and steering amplitude; and comparing the extracted characteristic data with an offline driver data model, selecting one with the highest similarity, and judging the current driving style of the driver.
Further, the road information database receives the GPS signal, obtains the real-time position information of the vehicle, corresponds to the off-line road information, obtains the real-time road information of the vehicle, and predicts the vehicle steering demand according to the information of curve distribution, curve curvature and curve length in the road information.
The invention discloses a multi-objective optimization method of an automotive electro-hydraulic intelligent steering system, which is based on the system and comprises the following steps:
step 1: initializing parameters of the electro-hydraulic steering system, and establishing a simulation model of the electro-hydraulic steering system in multidisciplinary modeling software AMEstim;
step 2: analyzing steering energy consumption, steering road feel and aligning error of the electro-hydraulic steering system;
and step 3: analyzing the coupling relation among mechanical, hydraulic and electronic parameters of the electro-hydraulic steering system;
and 4, step 4: according to the analysis results of the step 2 and the step 3, selecting torsion bar rigidity Ks, pinion radius Rp, reduction ratio G of a worm and gear reducer, piston cross-sectional area Ap and reversing valve port area gain Ka as design variables, and outputting the design variables by adopting an AMEpilot module of AMEsim software;
and 5: setting a target function as steering energy consumption, steering road feel and aligning error, setting a constraint condition as a value range of a design variable, and establishing an optimization model of the electro-hydraulic steering system;
step 6: performing multi-target optimization solution on the electro-hydraulic steering system by adopting a multi-target particle swarm algorithm based on a shared niche technology;
and 7: and obtaining an optimization result, inputting the optimized design variable into Amesim software, and verifying the optimization effect.
Further, the multi-target particle swarm algorithm based on the shared niche technology in the step 6 specifically comprises:
step 61: establishing a particle swarm model according to an optimization model of the electro-hydraulic steering system, defining algorithm parameters, and initializing the particle swarm by adopting initial values of design variables;
step 62: initializing the position and speed information of a particle swarm in a given solution space range;
and step 63: in the solution space range, updating the position and speed information of the particle swarm, generating a new population, and adjusting the historical optimal position of an individual;
step 64: calculating the fitness value of each particle, finding out an initial global optimal position, and adding the solved non-inferior solution into an external storage set Ar;
step 65: calculating the target function values of steering energy consumption, steering road feel and aligning error of each particle, selecting the global optimal position of each particle, selecting a new non-inferior solution under the current state by adopting a arena competition method, and updating an external storage set Ar by using the new non-inferior solution;
and step 66: judging whether an external storage set Ar is full, if not, adjusting the global optimal position, if so, firstly executing a niche maintenance strategy based on a sharing mechanism to ensure the diversity and uniformity of the particle swarm, and then adjusting the global optimal position;
step 67: and (6) circulating the steps 63-66 until the maximum iteration times or convergence is reached, and outputting the optimization result of the electro-hydraulic steering system.
Further, the niche maintenance strategy based on the sharing mechanism in step 66 adjusts the fitness of the niche individuals by using a sharing function, and specifically includes the following steps:
step 661: initializing an algorithm, establishing an initial population and initializing parameters;
step 662: calculating individual fitness, and executing operations such as selection, crossing, mutation and the like of a genetic algorithm;
step 663: calculating individual sharing degree, and updating individual fitness according to the individual sharing degree;
step 664: comparing the fitness of the offspring and the parent, and replacing the parent with the offspring with higher fitness to generate a new population;
step 665: if the termination condition is met, the algorithm is exited and the niche maintenance strategy is completed, otherwise, the process returns to 662.
Further, the calculation formula of the individual sharing degree in the step 663 is as follows:
wherein, share (d)ij) As a function of the degree of sharing, dijIs the Hamming distance, σ0Is the niche boundary parameter and λ is the shared function shape parameter.
Further, the formula for updating the position and the velocity of the particle swarm in step 63 is as follows:
vi(t)＝ωvi(t-1)+c1r1(xpbest-xi)+c2r2(xgbest-xi)
xi(t)＝xi(t-1)vi
in the formula, viDenotes the particle velocity, xiDenotes the position of the particle, xpbestRepresenting individual historical optimal positions, x, of the particlesgbestRepresents the global optimal position of the particle, ω is the inertial weight; r is1And r2Is a random number between 0 and 1, c1And c2Are the global incremental control coefficients and the individual incremental control coefficients.
The invention has the beneficial effects that:
compared with the existing automobile steering system, the electric power-assisted and electric hydraulic power-assisted integrated steering system has the advantages of integrating the advantages of electric power-assisted and electric hydraulic power-assisted, being capable of obtaining better economy, good driving feeling and sufficient steering power-assisted at the same time, carrying out redundancy backup on safety and reliability through the electric power-assisted and electric hydraulic power-assisted, and being suitable for not only automobiles driven by drivers, but also unmanned automobiles.
The method considers the coupling relation of multiple subjects such as machinery, electronics, hydraulic pressure and the like in the electro-hydraulic steering system, determines key design variables through parameter coupling analysis, adopts multi-target particle swarm optimization based on shared niche technology for optimization, has good convergence, and is easy to obtain global optimization so as to obtain good overall steering performance.
Drawings
FIG. 1 is a block diagram of the principle structure of an electro-hydraulic intelligent steering system of an automobile;
FIG. 2 is a flow chart of the multi-objective optimization of the method of the present invention;
FIG. 3 is a flow chart of a multi-target particle swarm algorithm based on the shared niche technology;
FIG. 4 is a graph of the steering road feel optimization routine of the present invention;
FIG. 5 is a diagram of the alignment error optimization routine of the present invention;
FIG. 6 is a graph illustrating a transition energy consumption optimization process of the present invention;
in the figure, 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-reversing 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-power motor, 21-worm gear reducer, E-electric hydraulic power signal, F-electric power signal, G-reversing valve signal.
Detailed Description
In order to facilitate understanding of those skilled in the art, the present invention will be further described with reference to the following examples and drawings, which are not intended to limit the present invention.
Referring to fig. 1, the invention relates to an electro-hydraulic intelligent steering system for an automobile, which comprises: the device comprises a mechanical steering module, an electric power-assisted module, an electric hydraulic power-assisted module and a control module;
the mechanical steering module comprises a steering wheel 1, a torsion bar 2, a lower pipe column 5, a steering pinion 6, a steering rack 7 and a wheel unit 8 which are connected in sequence;
the electric power-assisted module comprises a power-assisted motor 20 and a worm and gear reducer 21; the output end of the power-assisted motor 20 is connected with a worm and gear reducer 21, the worm and gear reducer acts between the torsion bar 2 and the lower tubular column 5, and the electric power-assisted torque and the torque of a driver are superposed on the lower tubular column 5;
the electro-hydraulic power-assisted module comprises an oil tank 13, an oil pump motor 15, an oil pump 14, a reversing valve 12, a piston 11 and a hydraulic cylinder 10; the piston is positioned in the hydraulic cylinder and divides the hydraulic cylinder into a left side and a right side, and the two sides of the hydraulic cylinder are respectively communicated with the oil way of the reversing valve 12; the output end of the oil pump motor 15 is connected with an oil pump 14, and the oil pump 14 transmits hydraulic oil from the oil tank 13 to the reversing valve 12 and distributes the hydraulic oil to two sides of the hydraulic cylinder 10;
the control module comprises 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 with the sensor group 17, and the output end of the main controller is respectively electrically connected with the power-assisted motor 20, the oil pump motor 15 and the reversing valve 12;
the sensor group comprises a torque sensor 3, a corner sensor 4, a displacement sensor 9, a vehicle speed sensor, a camera and a GPS receiver; the corner sensor 4 is arranged on the lower pipe column and receives a corner signal of the lower pipe column; the torque sensor 3 is arranged on the torsion bar 2 and receives a torque signal input by a driver; the displacement sensor 9 is arranged at the tail end of the steering rack 7 and receives a displacement signal output by the hydraulic cylinder 10; the GPS receiver, the camera and the vehicle speed sensor are arranged on the automobile;
the driver database is electrically connected with the main controller and is used for storing the driving data of the current automobile driver and various driver data models downloaded in an off-line mode, selecting the data model closest to the driving style of the current driver through data comparison and transmitting the data model to the main controller;
the road information database is electrically connected with the main controller, stores road information downloaded in an off-line mode, is connected with the GPS receiver, and transmits the current road information to the main controller in real time.
The hydraulic cylinder 10 is fixedly connected with the tail end of the lower pipe column 5, the steering rack 7 is coaxially installed on the inner side of the hydraulic cylinder 10, and the piston is coaxially and fixedly installed on the part, located in the hydraulic cylinder 10, of the steering rack 7. The part of the steering rack axially extending out of the hydraulic cylinder towards the right side is meshed with a steering pinion, the steering pinion transmits the resultant torque of the driver torque and the electric power-assisted torque to the steering rack and converts the resultant torque into rack force, the rack force and hydraulic power generated by pressure difference on two sides of the hydraulic cylinder are superposed and output, and output ends on two sides of the steering rack are connected with wheel units.
The main controller judges the current vehicle state through input signals of the sensor groups respectively, judges the current driving style of a driver through input signals of a driver database, judges and predicts the current road information and the steering demand through input signals of a road information database, carries out steering decision by integrating the information, outputs corresponding electric power-assisted signals F, electric hydraulic power-assisted signals E and reversing valve control signals G, controls the work of a power-assisted motor, an oil pump motor and a reversing valve respectively, and adjusts the proportion of the electric power-assisted module and the electric hydraulic power-assisted module participating in steering power assistance.
Extracting the steering operation characteristics of the current driver through signals input by a driver database, wherein the steering operation characteristics comprise steering rate, steering time lag and steering amplitude; and comparing the extracted characteristic data with an offline driver data model, selecting one with the highest similarity, and judging the current driving style of the driver.
The road information database receives GPS signals, obtains real-time position information of vehicles, corresponds to off-line road information, obtains real-time road information of the vehicles, and predicts vehicle steering requirements according to information such as curve distribution, curve curvature, curve length and the like in the road information.
Referring to fig. 2, the multi-objective optimization method for the electro-hydraulic intelligent steering system of the automobile, based on the system, comprises the following steps:
step 1: initializing parameters of the electro-hydraulic intelligent steering system, and establishing a simulation model of the electro-hydraulic intelligent steering system in multidisciplinary modeling software AMEstim;
step 2: analyzing steering energy consumption, steering road feel and aligning error of the electro-hydraulic intelligent steering system;
and step 3: analyzing the coupling relation among mechanical, hydraulic and electronic parameters of the electro-hydraulic intelligent steering system;
and 4, step 4: according to the analysis results of the step 2 and the step 3, selecting torsion bar rigidity Ks, pinion radius Rp, reduction ratio G of a worm and gear reducer, piston cross-sectional area Ap and reversing valve port area gain Ka as design variables, and outputting the design variables by adopting an AMEpilot module of AMEsim software;
and 5: setting a target function as steering energy consumption, steering road feel and aligning error, setting a constraint condition as a value range of a design variable, and establishing an optimization model of the electro-hydraulic intelligent steering system;
step 6: performing multi-target optimization solution on the electro-hydraulic intelligent steering system by adopting a multi-target particle swarm algorithm based on a shared niche technology;
and 7: and obtaining an optimization result, inputting the optimized design variable into Amesim software, and verifying the optimization effect.
In the example, the simulation time is set to be 20 seconds, 3 times of continuous steering operations with the same steering wheel angle of +/-120 degrees are carried out within 3-18 seconds, and the steering wheel has no input in the rest time; and analyzing the steering energy consumption, steering road feel and aligning error of the electro-hydraulic intelligent steering system. The steering energy consumption comprises energy consumption of a mechanical steering module, an electric power-assisted module and an electric hydraulic power-assisted module within 3-18 seconds, steering road feel is measured by the peak value and fluctuation condition of the acting force of a torsion bar within 3-18 seconds, and the return error is analyzed by the position of a steering wheel within 19 seconds.
The values of mechanical, hydraulic and electronic parameters in the system are respectively changed, the influence of the parameters on three performances of steering energy consumption, steering road feel and aligning error is judged, and the coupling relation among the mechanical, hydraulic and electronic parameters of the electro-hydraulic intelligent steering system is analyzed.
The constraint is a range of values of the design variable, as shown in table 1:
TABLE 1
| Serial number | Design variables | Initial value | Value range |
| 1 | Torsion |
15 | 3-50 |
| 2 | Pinion radius Rp | 7.5 | 6.5-9.5 |
| 3 | Reduction ratio G of |
18 | 15-28 |
| 4 | Piston cross-sectional area Ap | 625 | 200-900 |
| 5 | Valve port area gain Ka of reversing valve | 0.95 | 0.9-1.1 |
Referring to fig. 3, the step 6 of the multi-target particle swarm algorithm based on the shared niche technology specifically includes:
step 61: establishing a particle swarm model according to an optimization model of the electro-hydraulic intelligent steering system, defining algorithm parameters, and initializing the particle swarm by adopting initial values of design variables;
step 62: initializing the position and speed information of a particle swarm in a given solution space range;
and step 63: in the solution space range, updating the position and speed information of the particle swarm, generating a new population, and adjusting the historical optimal position of an individual;
step 64: calculating the fitness value of each particle, finding out an initial global optimal position, and adding the solved non-inferior solution into an external storage set Ar;
step 65: calculating the target function values of steering energy consumption, steering road feel and aligning error of each particle, selecting the global optimal position of each particle, selecting a new non-inferior solution under the current state by adopting a arena competition method, and updating an external storage set Ar by using the new non-inferior solution;
and step 66: judging whether an external storage set Ar is full, if not, adjusting the global optimal position, if so, firstly executing a niche maintenance strategy based on a sharing mechanism to ensure the diversity and uniformity of the particle swarm, and then adjusting the global optimal position;
step 67: and (4) circulating the steps 63-66 until the maximum iteration times or convergence is reached, and outputting the optimization result of the electro-hydraulic intelligent steering system.
In an example, a particle swarm model is established and algorithm parameter definition is performed, and the specific definition is shown in table 2:
TABLE 2
Fig. 4 shows the optimization course of the steering road feel, the abscissa is the evolution algebra, and the ordinate is the target value of the steering road feel. As seen from the figure, the target value of the steering road feel is about 0.0111 at the lowest and about 0.0197 at the highest, and after the evolution algebra is more than 100, the steering road feel is most densely valued near 0.00193;
fig. 5 shows the optimization course of the aligning error, the abscissa is the evolution algebra, and the ordinate is the target value of the aligning error. It can be seen from the figure that, in the process of evolution from generation 1 to generation 400, the return error value is kept stable basically near 0.00153, the lowest value is about 0.0017, the highest value is about 0.00158, and the change trend is relatively gentle;
fig. 6 reflects the change of the target value of energy consumption as the evolution algebra increases. It can be seen from the figure that after the evolution generation number is greater than 50, the value of the steering energy consumption is gradually reduced, and the value taking points which are stable near 110 are the most dense.
It can be seen from fig. 4-6 that, in the optimization process, the target values of the steering road feel, the aligning error and the steering energy consumption are coupled with each other, the variation trend of the optimization is basically convergent, and each target value is stabilized within a certain range, so that the design requirements can be met.
While the invention has been described in terms of its preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.
Claims (10)
1. The utility model provides an automobile electricity liquid intelligence a steering system which characterized in that includes: the device comprises a mechanical steering module, an electric power-assisted module, an electric hydraulic power-assisted module and a control module;
the mechanical steering module comprises a steering wheel, a torsion bar, a lower pipe column, a steering pinion, a steering rack and a wheel unit which are connected in sequence;
the electric power-assisted module comprises a power-assisted motor and a worm and gear reducer; the output end of the power-assisted motor is connected with a worm and gear reducer, the worm and gear reducer acts between the torsion bar and the lower tubular column, and the electric power-assisted torque and the torque of a driver are superposed on the lower tubular column;
the electro-hydraulic power-assisted module comprises an oil tank, an oil pump motor, an oil pump, a reversing valve, a piston and a hydraulic cylinder; the piston is positioned in the hydraulic cylinder and divides the hydraulic cylinder into a left side and a right side, and the two sides of the hydraulic cylinder are respectively communicated with the oil circuit of the reversing valve; the output end of the oil pump motor is connected with an oil pump, and the oil pump transfers the hydraulic oil from the oil tank to the reversing valve and distributes the hydraulic oil to two sides of the hydraulic cylinder;
the control module comprises a main controller, a sensor group, a driver database and a road information database;
the input end of the main controller is electrically connected with the sensor group, and the output end of the main controller is respectively electrically connected with the power-assisted motor, the oil pump motor and the reversing valve;
the sensor group comprises a torque sensor, a corner sensor, a displacement sensor, a vehicle speed sensor, a camera and a GPS receiver; the corner sensor is arranged on the lower pipe column and receives a corner signal of the lower pipe column; the torque sensor is arranged on the torsion bar and receives a torque signal input by a driver; the displacement sensor is arranged at the tail end of the steering rack and receives a displacement signal output by the hydraulic cylinder; the GPS receiver, the camera and the vehicle speed sensor are arranged on the automobile;
the driver database is electrically connected with the main controller, stores the driving data of the current automobile driver and various driver data models downloaded in an off-line mode, selects a data model according with the driving style of the current driver through data comparison, and transmits the data model to the main controller;
the road information database is electrically connected with the main controller, stores road information downloaded in an off-line mode, is connected with the GPS receiver, and transmits the current road information to the main controller in real time.
2. The automotive electrohydraulic intelligent steering system according to claim 1, wherein the hydraulic cylinder is fixedly connected with the tail end of the lower pipe column, the steering rack is coaxially installed on the inner side of the hydraulic cylinder, and the piston is coaxially and fixedly installed on the part of the steering rack located in the hydraulic cylinder; the part of the steering rack axially extending out of the hydraulic cylinder towards the right side is meshed with a steering pinion, the steering pinion transmits the resultant torque of the driver torque and the electric power-assisted torque to the steering rack and converts the resultant torque into rack force, the rack force and hydraulic power generated by pressure difference on two sides of the hydraulic cylinder are superposed and output, and output ends on two sides of the steering rack are connected with wheel units.
3. The vehicle electro-hydraulic intelligent steering system according to claim 1, wherein the main controller judges a current vehicle state through each input signal of the sensor group, judges a current driving style of a driver through an input signal of the driver database, judges and predicts current road information and a steering demand through an input signal of the road information database, performs a steering decision by synthesizing the information, outputs a corresponding electric power signal, an electric hydraulic power signal and a reversing valve control signal, controls the work of the power motor, the oil pump motor and the reversing valve respectively, and adjusts the proportion of the electric power module and the electric hydraulic power module participating in the steering power.
4. The automobile electrohydraulic intelligent steering system according to claim 1, characterized in that steering operation characteristics of a current driver, including steering rate, steering time lag and steering amplitude, are extracted through signals input by a driver database; and comparing the extracted characteristic data with an offline driver data model, selecting one with the highest similarity, and judging the current driving style of the driver.
5. The electro-hydraulic intelligent steering system for the automobile according to claim 1, wherein the road information database receives GPS signals, obtains real-time position information of the automobile, corresponds to off-line road information, obtains real-time road information of the automobile, and predicts the steering requirement of the automobile according to information of curve distribution, curve curvature and curve length in the road information.
6. A multi-objective optimization method for an electro-hydraulic intelligent steering system of an automobile, based on the system of any one of the claims 1 to 5, characterized by comprising the following steps:
step 1: initializing parameters of the electro-hydraulic intelligent steering system, and establishing a simulation model of the electro-hydraulic intelligent steering system in multidisciplinary modeling software AMEstim;
step 2: analyzing steering energy consumption, steering road feel and aligning error of the electro-hydraulic intelligent steering system;
and step 3: analyzing the coupling relation among mechanical, hydraulic and electronic parameters of the electro-hydraulic intelligent steering system;
and 4, step 4: according to the analysis results of the step 2 and the step 3, selecting torsion bar rigidity Ks, pinion radius Rp, reduction ratio G of a worm and gear reducer, piston cross-sectional area Ap and reversing valve port area gain Ka as design variables, and outputting the design variables by adopting an AMEpilot module of AMEsim software;
and 5: setting a target function as steering energy consumption, steering road feel and aligning error, setting a constraint condition as a value range of a design variable, and establishing an optimization model of the electro-hydraulic intelligent steering system;
step 6: performing multi-target optimization solution on the electro-hydraulic intelligent steering system by adopting a multi-target particle swarm algorithm based on a shared niche technology;
and 7: and obtaining an optimization result, inputting the optimized design variable into Amesim software, and verifying the optimization effect.
7. The multi-objective optimization method of the automotive electro-hydraulic intelligent steering system according to claim 6, wherein the multi-objective particle swarm algorithm based on the shared niche technology in the step 6 specifically comprises the following steps:
step 61: establishing a particle swarm model according to an optimization model of the electro-hydraulic intelligent steering system, defining algorithm parameters, and initializing the particle swarm by adopting initial values of design variables;
step 62: initializing the position and speed information of a particle swarm in a given solution space range;
and step 63: in the solution space range, updating the position and speed information of the particle swarm, generating a new population, and adjusting the historical optimal position of an individual;
step 64: calculating the fitness value of each particle, finding out an initial global optimal position, and adding the solved non-inferior solution into an external storage set Ar;
step 65: calculating the target function values of steering energy consumption, steering road feel and aligning error of each particle, selecting the global optimal position of each particle, selecting a new non-inferior solution in the current state by adopting a arena competition method, and updating an external storage set Ar by using the new non-inferior solution;
and step 66: judging whether an external storage set Ar is full, if not, adjusting the global optimal position, if so, firstly executing a niche maintenance strategy based on a sharing mechanism to ensure the diversity and uniformity of the particle swarm, and then adjusting the global optimal position;
step 67: and (4) circulating the steps 63-66 until the maximum iteration times or convergence is reached, and outputting the optimization result of the electro-hydraulic intelligent steering system.
8. The multi-objective optimization method of the electro-hydraulic intelligent steering system of the automobile according to claim 7, wherein the niche maintenance strategy based on the sharing mechanism in the step 66 adopts a sharing function to adjust the individual fitness of the niche, and the specific steps are as follows:
step 661: initializing an algorithm, establishing an initial population and initializing parameters;
step 662: calculating individual fitness, and executing selection, crossing and mutation operations of a genetic algorithm;
step 663: calculating individual sharing degree, and updating individual fitness according to the individual sharing degree;
step 664: comparing the fitness of the offspring and the parent, and replacing the parent with the offspring with higher fitness to generate a new population;
step 665: if the termination condition is met, the algorithm is exited and the niche maintenance strategy is completed, otherwise, the process returns to 662.
9. The multi-objective optimization method of the electro-hydraulic intelligent steering system of the automobile according to claim 8, wherein the calculation formula of the individual sharing degree in the step 663 is as follows:
wherein, share (d)ij) As a function of the degree of sharing, dijIs the Hamming distance, σ0Is the niche boundary parameter and λ is the shared function shape parameter.
10. The multi-objective optimization method for the electro-hydraulic intelligent steering system of the automobile according to claim 7, wherein the formula for updating the position and the speed of the particle swarm in the step 63 is as follows:
vi(t)＝ωvi(t-1)+c1r1(xpbest-xi)+c2r2(xgbest-xi)
xi(t)＝xi(t-1)vi
in the formula, viDenotes the particle velocity, xiDenotes the position of the particle, xpbestRepresenting individual historical optimal positions, x, of the particlesgbestRepresents the global optimal position of the particle, ω is the inertial weight; r is1And r2Is a random number between 0 and 1, c1And c2Are the global incremental control coefficients and the individual incremental control coefficients.
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| CN110458276B (en) * | 2019-07-23 | 2022-07-08 | 浙江吉利汽车研究院有限公司 | Vehicle control parameter calibration method, device and equipment based on multi-target particle swarm algorithm |
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