A method and device for design optimization of an electric power steering system

Abstract

The application discloses a kind of design optimization method and device of electric power steering system, wherein method, comprising: establishing automobile electric power steering system model and whole vehicle dynamics model;With the steering feeling of automobile electric power steering system, steering sensitivity, steering stability as the performance evaluation index of electric power steering system, the quantization formula of steering feeling, steering sensitivity and steering stability three performance evaluation indexes is established;Select structural parameter as the optimization variable of electric power steering system;Based on collaborative optimization algorithm, establish the two-layer optimization model of electric power steering system, and obtain optimal Pareto solution set according to optimization result, and select optimal compromise solution.

Description

Technical Field

[0001] This invention relates to the field of electric power steering system technology, and in particular to a design optimization method and apparatus for an electric power steering system. Background Technology

[0002] The statements in this section merely refer to the background art related to this invention and do not necessarily constitute prior art.

[0003] Electric power steering (EPS) systems combine mechanical steering systems with a controlled electric motor, using the electric motor as the power source and an electronic control unit (ECU) as the core. The ECU determines the steering assist torque value based on vehicle speed and torque sensor signals, and then drives the motor to provide auxiliary steering force via a motor drive component. Compared to traditional steering systems, EPS systems are simpler in structure, more flexible, and can achieve ideal steering assist characteristics, adapting to changes in driving conditions. They also offer advantages in handling comfort, safety, and energy efficiency. However, current research on EPS systems mainly focuses on their assist characteristics, with structural design relying heavily on designer experience. Existing technologies suffer from high time costs in structural design, and current EPS systems exhibit low sensitivity, weak road feel, and a poor driving experience. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a design optimization method and apparatus for an electric power steering system. By establishing evaluation indicators for the steering system's road feel, sensitivity, and stability, its performance is comprehensively optimized. Furthermore, to further improve the optimization efficiency of the steering system, this invention treats each evaluation indicator of the steering system as an independent discipline and optimizes them separately, achieving distributed and parallel optimization, thereby saving time and costs while ensuring optimization quality.

[0005] On the one hand, a design optimization method for an electric power steering system is provided;

[0006] A design optimization method for an electric power steering system includes:

[0007] Establish a model of the electric power steering system and a vehicle dynamics model; wherein, the electric steering system model includes: a steering wheel model, a rack and pinion model, a power steering motor model, and a tire model;

[0008] The steering feel, steering sensitivity, and steering stability of the electric power steering system are used as performance evaluation indicators for the electric power steering system. Quantitative formulas for these three performance evaluation indicators are established.

[0009] Structural parameters are selected as optimization variables for the electric power steering system; the structural parameters include: the gear radius r of the rack and pinion. p Steering column stiffness K s The equivalent moment of inertia J of the angle motor m1 Motor assist coefficient K a And the front wheel lateral stiffness k1;

[0010] Based on the collaborative optimization algorithm, a two-layer optimization model for the electric power steering system is established. Steering road feel, steering sensitivity, and steering stability are each treated as subsystems, while the comprehensive evaluation index of the steering system is treated as the overall system. In the subsystems, the NLPQL (Non-Linear Programming by Quadratic Lagrangian) algorithm is used for fast retrieval, and in the overall system, the NSGA-II (Non-dominated sorting genetic algorithm-II) algorithm is used for global search. Based on the optimization results, the optimal Pareto solution set is obtained, and the optimal compromise solution is selected.

[0011] On the other hand, a design optimization device for an electric power steering system is provided;

[0012] A design optimization device for an electric power steering system, comprising:

[0013] The model building module is configured to: build a model of the electric power steering system and a vehicle dynamics model; wherein, the electric power steering system model includes: a steering wheel model, a rack and pinion model, a power steering motor model, and a tire model;

[0014] The index establishment module is configured to: use the steering feel, steering sensitivity, and steering stability of the electric steering system as performance evaluation indicators of the electric steering system, and establish quantitative formulas for the three performance evaluation indicators of steering feel, steering sensitivity, and steering stability.

[0015] The variable selection module is configured to select structural parameters as optimization variables for the electric power steering system; the structural parameters include: the gear radius r of the rack and pinion. p Steering column stiffness K s The equivalent moment of inertia J of the angle motor m1 Motor assist coefficient K a And the front wheel lateral stiffness k1;

[0016] The optimized output module is configured to: establish a two-layer optimization model of the electric power steering system based on a collaborative optimization algorithm, wherein steering road feel, steering sensitivity, and steering stability are respectively treated as subsystems, and the comprehensive evaluation index of the steering system is treated as the overall system; the NLPQL (Non-Linear Programming by Quadratic Lagrangian) algorithm is used to achieve fast retrieval in the subsystems, and the NSGA-II (Non-dominated sorting geneticalgorithm-II) algorithm is used to achieve global search in the overall system; the optimal Pareto solution set is obtained based on the optimization results, and the optimal compromise solution is selected.

[0017] Furthermore, an electronic device is also provided, including:

[0018] Memory, used for non-transitory storage of computer-readable instructions; and

[0019] Processor, for executing the computer-readable instructions,

[0020] When the computer-readable instructions are executed by the processor, they perform the method described in the first aspect above.

[0021] In another aspect, a storage medium is also provided for non-transitory storage of computer-readable instructions, wherein when the non-transitory computer-readable instructions are executed by a computer, the instructions of the method described in the first aspect are executed.

[0022] In another aspect, a computer program product is also provided, including a computer program that, when run on one or more processors, is used to implement the method described in the first aspect above.

[0023] One of the above technical solutions has the following advantages or beneficial effects:

[0024] 1. The evaluation indicators of electric power steering system, such as steering feel and steering sensitivity, are quantified. Multiple evaluation indicators are optimized from the perspective of overall performance optimization. Through this optimization method, the sensitivity of electric power steering system can be comprehensively improved and the steering feel can be improved, thus providing a reference for the structural design of electric power steering system.

[0025] 2. By adopting a multidisciplinary approach, complex problems are decomposed into multiple subsystems or sub-disciplines, with each sub-discipline performing independent calculations. This parallelizes sequential computational tasks, reducing computational time costs while ensuring computational quality. This is a beneficial exploration for the systematic optimization of electric power steering. Attached Figure Description

[0026] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0027] Figure 1 This is a flowchart of the method in Example 1;

[0028] Figure 2 This is a schematic diagram of the system structure of Example 1;

[0029] In this diagram, 1 represents a gear rack, 2 represents a torque sensor, 3 represents a steering wheel, and 4 represents a power steering motor. Detailed Implementation

[0030] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0031] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0032] Example 1

[0033] This embodiment provides a design optimization method for an electric power steering system;

[0034] like Figure 1 As shown, a design optimization method for an electric power steering system includes:

[0035] S101: Establish a model of the electric power steering system and a vehicle dynamics model; wherein, the electric steering system model includes: a steering wheel model, a rack and pinion model, a power steering motor model, and a tire model;

[0036] S102: Use the steering feel, steering sensitivity, and steering stability of the electric power steering system as performance evaluation indicators for the electric power steering system, and establish quantitative formulas for the three performance evaluation indicators of steering feel, steering sensitivity, and steering stability.

[0037] S103: Select structural parameters as optimization variables for the electric power steering system; the structural parameters include: the gear radius r of the rack and pinion. p Steering column stiffness K s The equivalent moment of inertia J of the angle motor m1 Motor assist coefficient K a And the front wheel lateral stiffness k1;

[0038] S104: Based on the collaborative optimization algorithm, a two-layer optimization model of the electric power steering system is established, in which steering road feel, steering sensitivity, and steering stability are respectively treated as subsystems, and the comprehensive evaluation index of the steering system is treated as the overall system. In the subsystems, the NLPQL (Non-Linear Programming by Quadratic Lagrangian) algorithm is used to achieve fast retrieval, and in the overall system, the NSGA-II (Non-dominated sorting genetic algorithm-II) algorithm is used to achieve global search. Based on the optimization results, the optimal Pareto solution set is obtained, and the optimal compromise solution is selected.

[0039] Further, in S101: establishing a model of the electric power steering system and a vehicle dynamics model; wherein, the electric power steering system model includes: a steering wheel model, a rack and pinion model, a power steering motor model, and a tire model; wherein,

[0040] The vehicle dynamics model is expressed by the following formula:

[0041]

[0042] Where, a is the distance from the vehicle's center of gravity to the front axle; b is the distance from the vehicle's center of gravity to the rear axle; I x Let I be the moment of inertia of the suspended mass about the x-axis; y Let I be the moment of inertia of the suspension about the y-axis; z Let I be the moment of inertia of the car's mass about the z-axis; xz Let m be the product of inertia of the car's mass about the x and z axes; m is the total vehicle mass; m s ω is the sprung mass; u is the vehicle speed; ω r yaw rate; β is the sideslip angle; g is the acceleration due to gravity; d is half the wheelbase of the vehicle; h is the roll arm; δ is the vehicle roll angle; θ is the front wheel steering angle; F is the vehicle pitch angle; y1 The lateral force of the right front tire; F y2 F is the lateral force of the left front tire; y3 The lateral force of the left rear tire; F y4 The lateral force of the right rear tire; F 11 F 12 F 13 F 14 This represents the total force exerted by the suspension on the vehicle body.

[0043] The steering wheel model, expressed by the formula:

[0044]

[0045] Among them, J sLet θ be the moment of inertia of the steering wheel and the steering input shaft. sw B is the steering wheel angle. s T is the damping coefficient between the steering wheel and the steering input shaft. h For steering hand force, T sen The torque is output by the torque sensor.

[0046] The gear and rack model is expressed by the following formula:

[0047]

[0048] Where, m r B is the equivalent mass of the gear and rack; r For gear and rack equivalent damping; x r r is the rack displacement; p F is the gear radius; a This is equivalent to hydraulic power assist on the steering column; F r This is the steering resistance equivalent to that on the steering column.

[0049] The formula for the power assist motor model is as follows:

[0050]

[0051] Where L e To assist the motor inductance; I e To assist the motor's operating current; R e To assist the motor's internal resistance; E e To assist the motor's back electromotive force; U e To assist the motor terminal voltage; K ee To assist the motor's back electromotive force coefficient; ω e To increase the angular velocity of the motor.

[0052] The tire model, expressed by the formula:

[0053]

[0054] α1 is the front wheel slip angle, and n2 is the transmission ratio from the steering screw to the front wheel.

[0055] Further, in S102: the steering feel, steering sensitivity, and steering stability of the electric power steering system are used as performance evaluation indicators of the electric power steering system, and quantitative formulas are established for these three performance evaluation indicators; wherein,

[0056] The formula for quantifying steering feel is:

[0057]

[0058] in,

[0059] Among them, J e It is the moment of inertia of the output shaft, B e B is the damping coefficient of the output shaft. m It is the viscous damping coefficient of the assist motor, K s For input shaft stiffness, J m b is the equivalent moment of inertia of the angle motor. r G is the damping inertia of the power steering motor, G is the transmission ratio from the power steering motor to the steering rack, and K is the power gain of the power steering motor. a It is the torque coefficient of the motor, r p Let m be the radius of the gear in the gear and rack system. r The equivalent mass of the gear and rack.

[0060] To facilitate the measurement of the steering system's transmission of low-frequency road information, the average value of the steering feel's frequency domain energy within the 0-40Hz frequency range is used to characterize the magnitude of the road feel. The road feel's frequency domain energy can be expressed as:

[0061]

[0062] The formula for quantifying steering sensitivity is:

[0063]

[0064] in:

[0065] A3=hmu(I xz Y δ -N δ hm)+I xz L δ mu+I x N δ mu,

[0066] A2 = L β (I xz Y δ -N δ hm)-I x (N δ Y β -N β Y δ )-L δ (I xz Y β -N β hm)-L p N δ mu,

[0067] A1 = L p (N δ Y β -N β Yδ )-Hmu(N δ Y φ -N φ Y δ )+L δ N φ in L φ N δ in,

[0068] A0=L φ (N δ Y β -N β Y δ )-L β (N δ Y φ -N φ Y δ )+L δ (N β Y φ -N φ Y β ),

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] Q0=−n2(K s +(KK a K s ) / G)((L r +hm) (N β Y φ -N φ Y β )+L φ (N r Y β -N β (Y r -in))-L β (N r Y φ -N φ (Y r -mu)))-d(k1+k2)((L r +hm) (N δ Y φ-N φ Y δ )-E1((L r +hmu)(N δ Y β -N β Y δ )+L β (N r Y δ -N δ (Y r -mu))-L δ (N r Y β -N β (Y r -mu)))+L φ (N r Y δ -N δ (Y r -mu))-L δ (N r Y φ -N φ (Y r -mu))+(a(L φ (N δ Y β -N β Y δ )-L β (N δ Y φ -N φ Y δ )+L δ (N β Y φ -N φ Y β ))) / u)

[0076] Among them, I z It is the moment of inertia of the car about the Z-axis, I x It is the moment of inertia of the car about the X-axis, I y It is the moment of inertia of the car about the Y-axis, I xz It is the product of the car's moments of inertia about the X and Z axes, where m is the car's mass and u is the car's velocity. s The spring mass h is the tilt arm, ω r φ is the yaw rate, θ is the roll angle, δ is the pitch angle, and N is the front wheel steering angle. r N β N φ N δ It is the coefficient related to the vehicle's yaw motion, Y r ,Y β ,Y φ ,Yδ It is the coefficient related to vehicle roll motion, L r ,L β ,L φ ,L θ ,L δ ,L p ,L e C1 is the coefficient related to vehicle lateral motion, H φ H θ H p H e C2 is the coefficient related to the vehicle's pitch motion;

[0077] Similar to road feel, steering sensitivity is also characterized in this invention as the average value of frequency domain energy within the 0-40Hz frequency range:

[0078]

[0079] The formula for quantifying steering stability is:

[0080]

[0081] To ensure the stability of the steering system, the following requirements must be met:

[0082] Q6>0; Q5>0; a1>0; b1>0; c1>0; Q0>0.

[0083] Further, in S104: Based on a collaborative optimization algorithm, a two-layer optimization model of the electric power steering system is established, wherein steering feel, steering sensitivity, and steering stability are respectively treated as subsystems, and the comprehensive evaluation index of the steering system is treated as the overall system; NLPQL algorithm is used for fast retrieval in the subsystems, and NSGA-II algorithm is used for global search in the overall system; the optimal Pareto solution set is obtained based on the optimization results, and the optimal compromise solution is selected, specifically including:

[0084] The steering feel subsystem is represented as:

[0085]

[0086] The steering sensitivity subsystem is represented as:

[0087]

[0088] The steering stability subsystem is represented as:

[0089]

[0090] The comprehensive evaluation index of the steering system is expressed as:

[0091]

[0092] In the overall system, the NSGA-II multi-objective algorithm is selected as the optimization algorithm, and in each subsystem, the NLPQL algorithm is selected as the optimization algorithm. Optimization is carried out according to the set step size to obtain the final optimization result.

[0093] By establishing evaluation indicators for the road feel, sensitivity, and stability of the steering system, its performance is comprehensively optimized. At the same time, in order to further improve the optimization efficiency of the steering system, this invention treats each evaluation indicator of the steering system as an independent discipline and optimizes them separately, realizing distributed and parallel optimization, thereby saving time and costs while taking into account the optimization quality.

[0094] like Figure 2 As shown, an electric power steering system includes a steering mechanical unit, an electric power steering unit, an electric control unit (ECU), and on-board sensors. The steering mechanical unit includes a steering wheel 3, a steering column, a rack and pinion 1, a steering tie rod, etc., connected in sequence. The electric power steering unit includes a power steering motor 4, a reduction mechanism, etc., and the on-board sensors include a vehicle speed sensor, a torque sensor 2, etc.

[0095] When a car is turning, the torque sensor and vehicle speed sensor transmit the detected steering wheel torque and vehicle speed signals to the electronic control unit (ECU). The ECU determines the magnitude of the assist torque of the power steering motor based on the torque sensor signal and vehicle speed signal and outputs the corresponding control signal to the drive circuit. The drive circuit provides the corresponding voltage to the power steering motor. The power steering motor amplifies the assist torque through the reduction mechanism and transmits it to the mechanical steering mechanism. It then acts on the tires through the steering column, rack and pinion, and tie rod, ultimately providing steering assistance to the driver.

[0096] Example 2

[0097] This embodiment provides a design optimization device for an electric power steering system;

[0098] A design optimization device for an electric power steering system, comprising:

[0099] The model building module is configured to: build a model of the electric power steering system and a vehicle dynamics model; wherein, the electric power steering system model includes: a steering wheel model, a rack and pinion model, a power steering motor model, and a tire model;

[0100] The index establishment module is configured to: use the steering feel, steering sensitivity, and steering stability of the electric steering system as performance evaluation indicators of the electric steering system, and establish quantitative formulas for the three performance evaluation indicators of steering feel, steering sensitivity, and steering stability.

[0101] The variable selection module is configured to select structural parameters as optimization variables for the electric power steering system; the structural parameters include: the gear radius r of the rack and pinion. p Steering column stiffness K s The equivalent moment of inertia J of the angle motor m1 Motor assist coefficient K a And the front wheel lateral stiffness k1;

[0102] The optimized output module is configured to: establish a two-layer optimization model of the electric power steering system based on a collaborative optimization algorithm, wherein steering road feel, steering sensitivity, and steering stability are respectively treated as subsystems, and the comprehensive evaluation index of the steering system is treated as the overall system; the NLPQL (Non-Linear Programming by Quadratic Lagrangian) algorithm is used to achieve fast retrieval in the subsystems, and the NSGA-II (Non-dominated sorting geneticalgorithm-II) algorithm is used to achieve global search in the overall system; the optimal Pareto solution set is obtained based on the optimization results, and the optimal compromise solution is selected.

[0103] It should be noted that the model building module, indicator building module, variable selection module, and optimization output module mentioned above correspond to steps S101 to S104 in Embodiment 1. The examples and application scenarios implemented by these modules and their corresponding steps are the same, but they are not limited to the content disclosed in Embodiment 1. It should be noted that these modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0104] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0105] The proposed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and the division of modules described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed.

[0106] Example 3

[0107] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform the method described in Embodiment 1.

[0108] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0109] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0110] In the implementation process, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by software instructions.

[0111] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0112] Those skilled in the art will recognize that the units and algorithm steps described in connection with the various examples of this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0113] Example 4

[0114] This embodiment also provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the method described in Embodiment 1.

[0115] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A design optimization method for an electric power steering system, characterized in that, include: Establish an electric power steering system model and a vehicle dynamics model; wherein, the electric power steering system model includes: a steering wheel model, a rack and pinion model, a power steering motor model, and a tire model; The steering feel, steering sensitivity, and steering stability of the electric power steering system are used as performance evaluation indicators. Quantitative formulas are established for these three performance evaluation indicators; the quantitative formula for steering feel is as follows: in, , , ; in, It is the moment of inertia of the output shaft. It is the damping coefficient of the output shaft. It is the viscous damping coefficient of the assist motor. For steering column stiffness, The equivalent rotational inertia of the angle motor, It is the damping inertia of the assist motor. To improve the transmission ratio from the motor to the steering rack, It is the power gain of the boost motor. It is the motor assist coefficient. Let be the radius of the gear rack. The equivalent mass of the gear and rack; The formula for quantifying steering sensitivity is: Structural parameters are selected as optimization variables for the electric power steering system; these structural parameters include: the gear radius of the rack and pinion. Steering column stiffness Equivalent moment of inertia of the angle motor Motor assist coefficient and front wheel lateral stiffness ; Based on the collaborative optimization algorithm, a two-layer optimization model of the electric power steering system is established. Steering road feel, steering sensitivity, and steering stability are each treated as subsystems, while the comprehensive evaluation index of the electric power steering system is treated as the overall system. The NLPQL algorithm is used to achieve fast retrieval in the subsystems, and the NSGA-II algorithm is used to achieve global search in the overall system. The optimal Pareto solution set is obtained based on the optimization results, and the optimal compromise solution is selected. The comprehensive evaluation index for electric power steering systems is expressed as follows: In the overall system, the NSGA-II multi-objective algorithm is selected as the optimization algorithm, and in each subsystem, the NLPQL algorithm is selected as the optimization algorithm. Optimization is carried out according to the set step size to obtain the final optimization result.

2. The design optimization method for an electric power steering system as described in claim 1, characterized in that, An electric power steering system model and a vehicle dynamics model are established; wherein, the electric power steering system model includes: a steering wheel model, a rack and pinion model, a power steering motor model, and a tire model; wherein, The vehicle dynamics model is expressed by the following formula: in, This is the distance from the car's center of gravity to the front axle. This is the distance from the car's center of gravity to the rear axle. Let x be the moment of inertia of the suspended mass about the x-axis; Let be the moment of inertia of the suspension about the y-axis; Let be the moment of inertia of the car's mass about the z-axis; Let be the product of inertia of the car's mass about the x and z axes; For the overall vehicle weight; For the sprung mass; u For vehicle speed; ω r This refers to the yaw rate; β It is the centroid sideslip angle; It is the acceleration due to gravity; This is half the vehicle's track width. It is the tilt arm; This refers to the vehicle body roll angle; This refers to the front wheel steering angle; The vehicle body pitch angle; The lateral force on the right front tire; The lateral force of the left front tire; The lateral force is from the left rear tire. The lateral force on the right rear tire; , , , This represents the total force exerted by the suspension on the vehicle body.

3. The design optimization method for an electric power steering system as described in claim 1 or 2, characterized in that, The steering wheel model, expressed by the formula: in, This represents the moment of inertia of the steering wheel and steering input shaft. For steering wheel angle, This is the damping coefficient between the steering wheel and the steering input shaft. For steering hand force, The torque sensor outputs torque; The gear and rack model is expressed by the following formula: in, The equivalent mass of the gear and rack; This is the equivalent damping for the gear and rack; This represents the rack displacement; Where is the gear radius; This is equivalent to hydraulic power assist on the steering column; This is the equivalent steering resistance on the steering column; The formula for the power assist motor model is as follows: in L e To assist the motor inductance; I e To assist the motor's operating current; R e To assist the motor's internal resistance; E e To assist the back electromotive force of the motor; U e To boost the motor terminal voltage; K ee To increase the back electromotive force coefficient of the motor; ω e To increase the angular velocity of the motor; The tire model, expressed by the formula: The front wheel slip angle, This is the transmission ratio from the steering screw to the front wheel.

4. The design optimization method for an electric power steering system as described in claim 1, characterized in that, The formula for quantifying steering stability is: To ensure the stability of the electric power steering system, the following requirements must be met: 。 5. The design optimization method for an electric power steering system as described in claim 1, characterized in that, Based on a collaborative optimization algorithm, a two-layer optimization model for the electric power steering system is established. Steering road feel, steering sensitivity, and steering stability are each considered a subsystem, while the comprehensive evaluation index of the electric power steering system forms the overall system. The NLPQL algorithm is used for fast retrieval within the subsystems, and the NSGA-II algorithm is used for global search in the overall system. The optimal Pareto solution set is obtained based on the optimization results, and the optimal compromise solution is selected, specifically including: The steering feel subsystem is represented as: The steering sensitivity subsystem is represented as: The steering stability subsystem is represented as: 。 6. A design optimization device for an electric power steering system, characterized in that, The method for designing and optimizing an electric power steering system as described in any one of claims 1-5 includes: The model building module is configured to: build an electric power steering system model and a vehicle dynamics model; wherein, the electric power steering system model includes: a steering wheel model, a rack and pinion model, a power steering motor model, and a tire model; The index establishment module is configured to: use the steering feel, steering sensitivity, and steering stability of the electric power steering system as performance evaluation indicators of the electric power steering system, and establish quantitative formulas for the three performance evaluation indicators of steering feel, steering sensitivity, and steering stability. The variable selection module is configured to select structural parameters as optimization variables for the electric power steering system; the structural parameters include: the gear radius of the rack and pinion. Steering column stiffness Equivalent moment of inertia of the angle motor Motor assist coefficient and front wheel lateral stiffness ; The optimized output module is configured to: establish a two-layer optimization model of the electric power steering system based on a collaborative optimization algorithm, wherein steering road feel, steering sensitivity, and steering stability are respectively treated as subsystems, and the comprehensive evaluation index of the electric power steering system is treated as the overall system; the NLPQL algorithm is used to achieve fast retrieval in the subsystems, and the NSGA-II algorithm is used to achieve global search in the overall system; the optimal Pareto solution set is obtained based on the optimization results, and the optimal compromise solution is selected.

7. An electronic device, characterized in that it comprises: Memory is used to store computer-readable instructions in a non-transitory manner. as well as Processor, for executing the computer-readable instructions, When the computer-readable instructions are executed by the processor, they perform the method described in any one of claims 1-5.

8. A storage medium, characterized in that, The computer-readable instructions are stored non-transitory, wherein when the non-transitory computer-readable instructions are executed by a computer, the instructions of the method according to any one of claims 1-5 are executed.

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