Control method and device of electric power steering system, control unit and medium

Abstract

The invention provides a control method and device of an electric power steering system, a control unit and a medium, and relates to the technical field of vehicle electronic control. The method comprises the following steps: acquiring a steering operation input quantity of a driver and a driving state signal of a vehicle, and determining a dynamic compensation parameter of the vehicle based on the driving state signal; determining an estimated value of the stress of the steering rack based on the steering operation input quantity of the driver and the dynamic compensation parameters; and obtaining the actual measurement value of the stress of the steering rack, and dynamically adjusting the output torque of the power-assisted motor according to the deviation between the actual measurement value and the estimated value. According to the method, the adaptive dynamic compensation parameters are generated by fusing the multi-source driving state signals, so that the precision of rack stress estimation is remarkably improved; and closed-loop control is formed based on the deviation of a measured value and an estimated value, and the output torque of the power-assisted motor is dynamically adjusted, so that the problems of power-assisted mismatch and lag are effectively solved, the phenomenon of hand beating in the steering process is effectively inhibited, and the system response time is remarkably shortened.

Description

Technical Field

[0001] This application relates to the field of vehicle electronic control technology, and in particular to a control method, device, control unit and medium for an electric power steering system. Background Technology

[0002] With the continuous improvement of vehicle intelligence and electrification, the electric power steering (EPS) system, as the core underlying actuator for realizing autonomous driving and improving driving quality, is facing unprecedentedly high performance requirements. The EPS system not only needs to provide comfortable assistance but also needs to maintain precise, stable, and predictable handling feedback in various dynamic driving scenarios. How to accurately sense steering load in real time and implement precise control accordingly has become a key industry challenge restricting the leap in the performance of the EPS system.

[0003] In related technologies, mainstream electric power steering system control schemes typically employ an open-loop control architecture based on estimation and lookup tables. Specifically, this scheme first acquires the steering wheel input torque through a torque sensor, then combines it with preset fixed transmission ratios and mechanical efficiency parameters of the steering system to indirectly estimate the force on the steering rack, i.e., rack force, using a static mechanical model. The control unit uses the estimated value and the current vehicle speed as input parameters, queries a pre-calibrated fixed assist characteristic curve, maps it to the corresponding target torque command for the assist motor, and uses this to drive the motor to perform assist. However, this approach suffers from problems such as response lag and frequent "kickback" issues in electric power steering systems. Summary of the Invention

[0004] This application provides a control method, device, control unit, and medium for an electric power steering system, in order to improve the problems of lag response and frequent "kickback" in electric power steering systems.

[0005] In a first aspect, this application provides a control method for an electric power steering system, comprising:

[0006] Acquire driver steering input and vehicle driving status signals;

[0007] Based on the driving status signal, determine the vehicle's dynamic compensation parameters;

[0008] Based on the driver's steering input and dynamic compensation parameters, the estimated value of the steering rack force in the electric power steering system is determined;

[0009] Obtain the measured values ​​of the force on the steering rack;

[0010] The output torque of the power steering motor in the electric power steering system is dynamically adjusted based on the deviation between the measured and estimated values.

[0011] In one possible implementation, the dynamic compensation parameters include a road surface adhesion coefficient compensation factor, and the driving state signals include vehicle speed, lateral acceleration, and the difference in steering angle between the left and right wheels. Based on the driving state signals, the dynamic compensation parameters of the vehicle are determined, including: fusing the vehicle speed, lateral acceleration, and the difference in steering angle between the left and right wheels through a fuzzy controller to obtain the road surface adhesion coefficient compensation factor.

[0012] In one possible implementation, the dynamic compensation parameters further include suspension deformation compensation amount, and the driving state signal further includes wheel angle. Based on the driving state signal, the dynamic compensation parameters of the vehicle are determined, including: determining the suspension deformation amount according to the wheel angle and preset suspension geometric parameters; and generating a suspension deformation compensation amount for compensating the steering trapezoidal gear ratio based on the suspension deformation amount.

[0013] In one possible implementation, the estimated value of the steering rack force in the electric power steering system is determined based on the driver's steering input and dynamic compensation parameters, including: correcting transmission-related parameters based on suspension deformation compensation; determining a first rack force component contributed by the driver's input based on the driver's steering input, road adhesion coefficient compensation factor, and corrected transmission-related parameters; acquiring the real-time torque of the power assist motor, and determining a second rack force component contributed by the power assist motor based on the real-time torque and a preset power assist ratio; and adding the first rack force component and the second rack force component to obtain the estimated value of the steering rack force.

[0014] In one possible implementation, transmission-related parameters include steering trapezoidal efficiency and / or steering gear angle ratio.

[0015] In one possible implementation, the output torque of the power steering motor in the electric power steering system is dynamically adjusted based on the deviation between the measured value and the estimated value, including: determining the base power steering torque of the power steering motor; generating a dynamic adjustment amount based on the deviation between the measured value and the estimated value; and combining the base power steering torque with the dynamic adjustment amount to obtain the output torque of the power steering motor.

[0016] In one possible implementation, a dynamic adjustment amount is generated based on the deviation between the measured value and the estimated value, including: determining whether the absolute value of the deviation is greater than a preset deviation threshold; if the absolute value of the deviation is less than or equal to the deviation threshold, then determining the dynamic adjustment amount as 0; if the absolute value of the deviation is greater than the deviation threshold, then determining the dynamic adjustment amount through a feedback controller.

[0017] In one possible implementation, the feedback controller is a fuzzy proportional-integral-derivative (PID) controller. The dynamic adjustment amount is determined by the feedback controller, including: adjusting the proportional coefficient, integral coefficient, and derivative coefficient of the fuzzy PID controller through fuzzy logic reasoning based on the magnitude and rate of change of the deviation; and performing a composite operation of proportional, integral, and derivative on the deviation based on the adjusted proportional coefficient, adjusted integral coefficient, and adjusted derivative coefficient to generate the dynamic adjustment amount.

[0018] In one possible implementation, determining the basic assist torque of the power steering motor includes: obtaining the corresponding assist gain value from multiple pre-stored assist characteristic curves based on the vehicle speed and steering wheel operation angular velocity in the driving status signal; and determining the basic assist torque based on the assist gain value.

[0019] Secondly, this application provides a control device for an electric power steering system, comprising:

[0020] The acquisition module is used to acquire the driver's steering input and the vehicle's driving status signals;

[0021] The determination module is used to determine the dynamic compensation parameters of the vehicle based on the driving status signal; and to determine the estimated value of the steering rack force in the electric power steering system based on the driver's steering operation input and the dynamic compensation parameters.

[0022] The acquisition module is also used to acquire the measured values ​​of the force on the steering rack;

[0023] The control processing module is used to dynamically adjust the output torque of the power steering motor in the electric power steering system based on the deviation between the measured value and the estimated value.

[0024] Thirdly, this application provides an electric power steering system, comprising:

[0025] Torque sensor is used to sense the amount of steering input from the driver;

[0026] Force sensor, used to sense the measured value of the force on the steering rack;

[0027] Sensor array, used to acquire vehicle driving status signals;

[0028] Assist motor;

[0029] The control unit is connected to the torque sensor, the force sensor signal, the sensor group and the assist motor respectively, and is used to perform the method as described in any of the first aspects.

[0030] Fourthly, this application provides a control unit, including: a processor, and a memory communicatively connected to the processor;

[0031] Memory is used to store instructions executed by the computer;

[0032] A processor for executing computer-executable instructions stored in memory to implement any of the methods of the first aspect.

[0033] Fifthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the method of any one of the first aspects.

[0034] Sixthly, this application provides a computer program product, including a computer program that, when executed, implements the method of any one of the first aspects.

[0035] The control method, device, control unit, and medium for the electric power steering system provided in this application acquire the driver's steering input and the vehicle's driving status signal; determine the vehicle's dynamic compensation parameters based on the driving status signal; determine the estimated value of the steering rack force in the electric power steering system based on the driver's steering input and the dynamic compensation parameters; acquire the measured value of the steering rack force; and dynamically adjust the output torque of the power steering motor in the electric power steering system according to the deviation between the measured value and the estimated value.

[0036] In this process, by acquiring and fusing multi-source driving state signals in real time, adaptive dynamic compensation parameters are generated, enabling the estimated value of the steering rack force to respond accurately and quickly to changes in vehicle motion state, thereby significantly improving the accuracy of rack force estimation under complex working conditions. On this basis, by comparing the high-precision measured value of the steering rack force with the dynamically compensated estimated value in real time, and forming a closed-loop control based on the deviation between the two, the output torque of the power assist motor is dynamically adjusted, thereby achieving precise following and matching between the power assist output and the actual steering resistance. This effectively overcomes the power assist mismatch and lag problems caused by neglecting dynamic factors in traditional static mechanical models. It not only effectively suppresses the "kickback" phenomenon during steering, but also significantly shortens the system response time due to the accurate and rapid estimation of rack force. At the same time, the introduction of the dynamic compensation mechanism ensures the accuracy, smoothness, and overall reliability of the vehicle's steering assist in different driving scenarios. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0038] Figure 1A flowchart illustrating a control method for an electric power steering system provided as an exemplary embodiment of this application;

[0039] Figure 2 Another schematic flowchart of a control method for an electric power steering system provided as an exemplary embodiment of this application;

[0040] Figure 3 A schematic diagram of the structure of a control device for an electric power steering system provided as an exemplary embodiment of this application;

[0041] Figure 4 A schematic diagram of an electric power steering system provided as an exemplary embodiment of this application;

[0042] Figure 5 A schematic diagram of the architecture of an electric power steering system provided for an exemplary embodiment of this application;

[0043] Figure 6 A schematic diagram of the structure of the control unit provided for an exemplary embodiment of this application.

[0044] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0045] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0046] The terms “first,” “second,” etc., used in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, products, or apparatus.

[0047] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0048] In related technologies, when using an open-loop control architecture based on estimation and lookup tables to control an electric power steering system, the core rack force estimation model is a static mechanical model, which ignores key dynamic variables faced by the vehicle in actual driving, such as the real-time changes in the road adhesion coefficient and the geometric deformation caused by the suspension kinematics during steering. Because no dynamic compensation mechanism is introduced, there will be a significant deviation between the estimated rack force and the actual rack force under complex conditions such as slippery roads, bumpy roads, or high-speed steering. In addition, its control architecture is an open-loop feedforward system, which cannot perform real-time closed-loop correction based on the actual rack resistance. This directly leads to lag in the power steering response and distortion of road feel feedback under dynamic conditions. When the tires encounter a unilateral impact, the power steering torque cannot match the actual sudden change in resistance in time, which frequently induces steering wheel reversal (i.e., "kickback"), seriously impairing driving comfort, linearity, and safety.

[0049] To address the aforementioned issues, this application provides a control scheme for an electric power steering system. Through multi-sensor fusion technology, it collects driver steering input and vehicle driving status signals in real time, analyzes them, and generates dynamic compensation parameters reflecting the current operating conditions. Subsequently, combining the driver steering input and the aforementioned dynamic compensation parameters, a more accurate estimate of the steering rack force is determined. Then, a force sensor is introduced to directly acquire the measured value of the steering rack force, and the measured value is compared with the estimated value in real time. Based on the deviation between the two, the output torque of the power steering motor is dynamically adjusted. Through this closed-loop control path of "perception-compensation-estimation-feedback-adjustment," the system can achieve adaptive response to complex dynamic conditions, effectively overcoming the power steering mismatch and lag problems caused by neglecting dynamic factors in traditional static mechanical models. This not only effectively suppresses the "kickback" phenomenon during steering but also significantly shortens the system response time due to the accurate and rapid estimation of the rack force. Furthermore, the introduction of the dynamic compensation mechanism ensures the accuracy, smoothness, and overall reliability of the power steering in different driving scenarios.

[0050] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0051] Figure 1 A schematic flowchart illustrating a control method for an electric power steering system provided as an exemplary embodiment of this application. Figure 1 As shown, the control method of this electric power steering system includes the following steps:

[0052] S101. Acquire the driver's steering input and the vehicle's driving status signal.

[0053] The driver's steering input is a physical quantity characterizing the driver's steering force or intention, such as steering wheel torque, which can be acquired by a torque sensor mounted on the steering column. The vehicle's driving status signal consists of parameters reflecting the vehicle's motion and dynamic characteristics, which can be collected and transmitted via onboard sensor networks and onboard communication buses such as Controller Area Network (CAN) buses. Accordingly, both the driver's steering input and the vehicle's driving status signal are transmitted in real-time as electrical signals to the control unit in the electric power steering system, serving as the basis for subsequent calculations and control.

[0054] S102. Determine the vehicle's dynamic compensation parameters based on the driving status signal.

[0055] For example, after receiving the aforementioned multi-source driving status signals, the control unit processes them using a specific algorithm running internally. This processing, based on the vehicle's real-time kinematics and dynamics, utilizes pre-set algorithmic models such as fuzzy inference models, geometric calculation models, or combinations thereof, to comprehensively calculate correction amounts—i.e., dynamic compensation parameters—to compensate for the impact of the current driving environment and vehicle posture on the steering system. These parameters are used for subsequent real-time, adaptive corrections to the steering system's mechanical calculations, enabling it to adapt to dynamic conditions such as changes in road conditions and vehicle load transfers.

[0056] S103. Based on the driver's steering input and dynamic compensation parameters, determine the estimated value of the steering rack force in the electric power steering system.

[0057] For example, the driver's steering input and dynamic compensation parameters are input into a pre-calibrated steering system rack force calculation model. This calculation model is based on the dynamic relationship of the steering system, and can be implemented, for example, through a mathematical expression that includes system transmission parameters and dynamic compensation parameters. Using this model, combined with the inherent mechanical parameters of the steering system, and by using the dynamic compensation parameters to correct the calculation results in real time, the expected force on the steering rack under the current operating condition can be calculated, i.e., the estimated value of the steering rack force mentioned above.

[0058] S104. Obtain the measured value of the force on the steering rack.

[0059] In this step, for example, a force sensor directly mounted on the steering rack or related transmission components can be used to measure the actual force acting on the steering rack in real time, obtaining the measured value of the force on the steering rack. This sensor typically employs strain gauge or piezoelectric principles, and its measurement signal, after conditioning, is sent to the control unit as a true reference for feedback control.

[0060] S105. Based on the deviation between the measured value and the estimated value, dynamically adjust the output torque of the power steering motor in the electric power steering system.

[0061] For example, the control unit continuously calculates the deviation between the measured value and the estimated value; based on the deviation, it calculates in real time the amount of adjustment to the output torque of the power steering motor through a closed-loop control algorithm such as PID control or its variant; the control unit then sends an updated torque command to the power steering motor driver so that the power steering torque output by the motor can follow the change in actual steering resistance, thereby reducing or eliminating the deviation and realizing real-time matching and stable control of the power steering assist.

[0062] The electric power steering system control method provided in this application acquires and fuses multi-source driving state signals in real time to generate adaptive dynamic compensation parameters. This enables the estimated value of the steering rack force to respond accurately and quickly to changes in vehicle motion state, thereby significantly improving the accuracy of rack force estimation under complex working conditions. Furthermore, by comparing the high-precision measured value of the steering rack force with the dynamically compensated estimated value in real time, and forming a closed-loop control based on the deviation between the two, the output torque of the power steering motor is dynamically adjusted. This achieves precise following and matching between the power steering output and the actual steering resistance, effectively overcoming the power steering mismatch and lag problems caused by neglecting dynamic factors in traditional static mechanical models. This not only effectively suppresses the "kickback" phenomenon during steering but also significantly shortens the system response time due to the accurate and rapid estimation of rack force. Simultaneously, the introduction of the dynamic compensation mechanism ensures the accuracy, smoothness, and overall reliability of the vehicle's power steering in different driving scenarios.

[0063] In some embodiments, the dynamic compensation parameters include a road surface adhesion coefficient compensation factor, and the driving state signals include vehicle speed, lateral acceleration, and left and right wheel angle difference. Based on the driving state signals, the dynamic compensation parameters of the vehicle are determined, including: fusing the vehicle speed, lateral acceleration, and left and right wheel angle difference through a fuzzy controller to obtain the road surface adhesion coefficient compensation factor.

[0064] For example, the aforementioned dynamic compensation parameters include a road adhesion coefficient compensation factor to reflect changes in road surface adhesion state; the driving state signals specifically include, but are not limited to, vehicle speed, lateral acceleration, and the difference in steering angle between the left and right wheels. Accordingly, the vehicle speed, lateral acceleration, and the difference in steering angle between the left and right wheels are input to a fuzzy controller, which has a pre-set fuzzy rule base reflecting the correlation between the aforementioned signals and the road surface adhesion state; the fuzzy controller performs intelligent fusion and decision-making on multi-source signals through fuzzification, fuzzy inference, and defuzzification steps, and finally outputs a real-time changing compensation value, namely the aforementioned road surface adhesion coefficient compensation factor.

[0065] In one specific implementation, the road surface adhesion coefficient compensation factor can be determined by a functional relationship of the following form:

[0066]

[0067] in, This is the road surface adhesion coefficient compensation factor; and These are coefficients calibrated based on experience. The typical value range is 0.1-0.5. The typical value range is 0.01-0.05 s / m; The vehicle's speed; This represents the difference in steering angle between the left and right wheels. This formula reflects the dynamic characteristic that the road adhesion coefficient compensation factor decreases exponentially with increasing vehicle speed and is proportional to the difference in steering angle between the left and right wheels.

[0068] In this embodiment, a fuzzy controller is introduced to fuse multi-source information such as vehicle speed, lateral acceleration, and the difference in steering angle between the left and right wheels, enabling intelligent and real-time estimation of the road surface adhesion state. This allows the system to accurately perceive changes in the vehicle's lateral dynamics caused by slippery, icy, or abrupt changes in the road surface adhesion coefficient, and to generate an adaptive road surface adhesion coefficient compensation factor. By dynamically incorporating this compensation factor into the mechanical calculation model of the steering system, the accuracy of steering rack force estimation under complex conditions such as low-adhesion road surfaces is significantly improved. This provides a more accurate and reliable input benchmark for subsequent closed-loop power steering control, effectively enhancing the adaptability and robustness of the electric power steering system to varying driving environments.

[0069] In some embodiments, the dynamic compensation parameters also include suspension deformation compensation amount, and the driving state signal also includes wheel angle. Based on the driving state signal, the dynamic compensation parameters of the vehicle are determined, including: determining the suspension deformation amount according to the wheel angle and preset suspension geometric parameters; and generating a suspension deformation compensation amount for compensating the steering trapezoidal gear ratio based on the suspension deformation amount.

[0070] For example, based on the wheel angle Based on the preset suspension geometry parameters, the geometric deformation of the suspension system is determined. Specifically, if the lower control arm length is... Then, the suspension deformation can be calculated using the following formula: suspension deformation amount. .

[0071] Accordingly, based on suspension deformation Generate suspension deformation compensation amount for compensating for steering trapezoidal gear ratio, for example. ,in, A preset coefficient to characterize the influence of suspension deformation on the transmission ratio.

[0072] Furthermore, the modified steering trapezoidal gear ratio satisfies the following formula:

[0073]

[0074] in, The corrected steering trapezoidal gear ratio; The nominal gear ratio is for the steering trapezoid.

[0075] In this embodiment, by real-time monitoring of wheel angles and combining them with the geometric parameters of the suspension system, the amount of suspension structural deformation caused by steering operations is quantified. Based on this, compensation parameters are generated to correct the steering trapezoidal gear ratio. This allows the system to dynamically sense and compensate for changes in the steering transmission relationship caused by suspension geometric changes when driving on bumpy roads or making large-angle turns. By incorporating this suspension deformation into rack force estimation, the calculation bias of traditional static models under such conditions is effectively eliminated. This significantly improves the accuracy of steering load prediction under uneven road surfaces and extreme steering postures, thereby enhancing the adaptability of the electric power steering system to complex vehicle postures and road surface excitations, and effectively ensuring the accuracy and stability of power steering output in various dynamic driving scenarios.

[0076] In some embodiments, the estimated value of the steering rack force in the electric power steering system is determined based on the driver's steering input and dynamic compensation parameters, including: correcting the transmission-related parameters based on the suspension deformation compensation; determining the first rack force component contributed by the driver input based on the driver's steering input, the road adhesion coefficient compensation factor, and the corrected transmission-related parameters; obtaining the real-time torque of the power assist motor, and determining the second rack force component contributed by the power assist motor based on the real-time torque and a preset power assist ratio; and adding the first rack force component and the second rack force component to obtain the estimated value of the steering rack force.

[0077] In some embodiments, transmission-related parameters include steering trapezoidal efficiency and / or steering gear angle ratio.

[0078] The transmission-related parameters may include steering trapezoidal efficiency, steering gear angle ratio, or a combination thereof. For example, this can be achieved through suspension deformation compensation (or a corrected steering trapezoidal ratio generated accordingly). This is used to update the equivalent values ​​of steering trapezoidal efficiency, steering gear ratio, or combinations thereof. Accordingly, updates to transmission-related parameters can be achieved by establishing an equivalent mapping between suspension deformation compensation and target parameters. For example, if the transmission-related parameter is steering trapezoidal efficiency, the system can pre-calibrate a set of data or functions to correlate suspension deformation. Or the corrected steering trapezoidal gear ratio The numerical relationship between this and the equivalent steering trapezoidal efficiency. In real-time control, based on the currently calculated... Values ​​(e.g.) By looking up a table or calculating the mapping relationship, a dynamic steering trapezoidal efficiency that matches the current suspension deformation state can be obtained, replacing the original fixed nominal value. Similarly, if the parameter is the steering gear angle transmission ratio, it can also be determined based on... The equivalent steering angle transmission ratio is obtained in real time through a similar mapping method. This mechanism essentially quantifies the impact of suspension geometry deformation on the system's transmission characteristics as a dynamic correction to relevant calculation parameters, thereby enabling subsequent rack force estimation to more accurately reflect the actual mechanical state.

[0079] Correspondingly, based on the driver's steering input, such as the steering wheel input torque Road surface adhesion coefficient compensation factor And the corrected transmission-related parameters such as steering trapezoidal efficiency. and steering gear angular ratio The first rack force component contributed by the driver input is calculated using the steering system mechanical model. For example, in a specific implementation, the first rack force component... Satisfy the following formula:

[0080]

[0081] in, Let be the pitch circle radius of the steering gear.

[0082] Simultaneously, the real-time torque of the assist motor is acquired, and based on this real-time torque... With the preset power assist ratio Determine the second rack force component contributed by the assist motor. Specifically, the second rack force component Satisfy the formula .

[0083] Furthermore, the calculated first rack force component is... Force component with the second rack The sum of these values ​​is the estimated value of the force on the steering rack. That is, the estimated value of the force on the steering rack. Satisfy the formula This estimate fully represents the expected total force acting on the steering rack under current driver operation, motor assistance, and dynamic compensation conditions, which is also the estimated force on the steering rack.

[0084] In this embodiment, by dynamically correcting the transmission-related parameters of the steering system using suspension deformation compensation, and combining the road adhesion compensation factor and the driver's steering input, a refined and adaptive calculation of the rack force contributed by the driver's operation is achieved. Simultaneously, by independently calculating the contribution of the motor assist, the two are finally superimposed to obtain a complete rack force estimate. This architecture, combining discrete calculation with dynamic correction, clearly distinguishes and accurately quantifies the impact of human and power assist on the total steering load, significantly improving the overall rack force estimation accuracy under complex dynamic conditions such as suspension deformation and road adhesion changes. This provides a highly reliable and responsive reference signal for subsequent closed-loop feedback control based on measured values, effectively enhancing the matching accuracy and adaptability of the power assist system.

[0085] Based on the above embodiments, in some embodiments, the output torque of the power steering motor in the electric power steering system is dynamically adjusted according to the deviation between the measured value and the estimated value, including: determining the basic power steering torque of the power steering motor; generating a dynamic adjustment amount based on the deviation between the measured value and the estimated value; and combining the basic power steering torque with the dynamic adjustment amount to obtain the output torque of the power steering motor.

[0086] For example, based on the vehicle's current driving status signal, the control unit determines a base torque value that reflects the basic power assist requirement, i.e., the aforementioned base assist torque, through a preset control strategy; simultaneously, the control unit continuously calculates the measured value. Compared with the estimated value Deviation between , and the deviation The feedback signal is input to a closed-loop control unit, which generates a torque correction amount, i.e., a dynamic adjustment amount, for real-time correction based on the deviation information. Further, the control unit superimposes the basic assist torque with the dynamic adjustment amount, and the result is the final output torque command sent to the assist motor. In this way, the organic combination of feedforward basic control and feedback real-time correction is realized, so that the assist output can not only match the macroscopic needs of different driving conditions, but also track and compensate for the microscopic instantaneous fluctuations of steering load in real time.

[0087] In some embodiments, a dynamic adjustment amount is generated based on the deviation between the measured value and the estimated value, including: determining whether the absolute value of the deviation is greater than a preset deviation threshold; if the absolute value of the deviation is less than or equal to the deviation threshold, then determining the dynamic adjustment amount as 0; if the absolute value of the deviation is greater than the deviation threshold, then determining the dynamic adjustment amount through a feedback controller.

[0088] For example, the control unit calculates the absolute value of the deviation between the measured value and the estimated value. And compare it with a preset trigger threshold, such as 50N; if If the value is less than or equal to 50N, the current estimated value is deemed to meet the requirements of the measured value, and no additional correction is needed. In this case, the dynamic adjustment amount is set to 0. If the error exceeds 50N, a significant load estimation error is determined, and the internal closed-loop feedback control mechanism is activated to address the deviation. The process involves processing and analysis, and then calculating and outputting a non-zero correction amount to offset the error, i.e., the state adjustment amount.

[0089] In this embodiment, the generation of dynamic adjustment quantities is intelligently managed by setting a deviation threshold. This allows the system to maintain a zero dynamic adjustment quantity when the estimated and measured values ​​match well, reducing over-response to minor fluctuations or noise. This ensures the continuity and smoothness of the power assist output and effectively reduces unnecessary motor movements and energy consumption. When the deviation significantly exceeds the deviation threshold, the feedback controller is immediately activated to correct the issue, ensuring rapid and accurate compensation for sudden changes in actual steering resistance or model mismatch. This on-demand triggering mechanism effectively optimizes the allocation of control resources while ensuring system control accuracy, contributing to improved overall system robustness and energy efficiency.

[0090] In some embodiments, the feedback controller is a fuzzy PID controller. The dynamic adjustment amount is determined by the feedback controller, including: adjusting the proportional coefficient, integral coefficient, and derivative coefficient of the fuzzy PID controller by fuzzy logic reasoning according to the magnitude and rate of change of the deviation; and performing a composite operation of proportional, integral, and derivative on the deviation based on the adjusted proportional coefficient, adjusted integral coefficient, and adjusted derivative coefficient to generate the dynamic adjustment amount.

[0091] Among them, the feedback controller is a fuzzy PID controller, which is a controller that combines fuzzy logic reasoning (an intelligent algorithm that simulates human experience-based decision-making) with classical PID control (a control method based on proportional, integral, and derivative operations of error).

[0092] For example, the complete process of determining the dynamic adjustment amount by this fuzzy PID controller is as follows: the fuzzy PID controller receives two input signals in real time, namely the absolute value of the rack force deviation. and the rate of change of deviation (i.e., the rate of change of the deviation over time); this fuzzy PID controller converts these two precise numerical signals into fuzzy quantities that conform to natural language descriptions through a fuzzification interface. For example, ... =80N is converted to a membership degree of 0.8 for "large deviation" and 0.2 for "medium deviation," and the rate of change is converted to fuzzy linguistic values ​​such as "rapidly increasing" and "slowly changing." Correspondingly, this fuzzy PID controller has a pre-built fuzzy rule base containing a series of "IF-THEN" rules summarized from expert experience to describe the relationship between the deviation state and the optimal PID parameter adjustment. Each rule has a form similar to: "IF..." For "big" AND To indicate "rapidly increasing", THEN significantly increases the scaling factor. Appropriately increase the differential coefficient Maintain integral coefficient ".

[0093] Correspondingly, the fuzzy PID controller will fuzzify the current input ( and The state is matched against all rules in the rule base; each activated rule will produce a conclusion (i.e., a conclusion on the state). , and (Adjustment suggestions); the fuzzy PID controller synthesizes these conclusions through a specific inference algorithm (such as Mamdani inference) to obtain information about... , , The fuzzy set of adjustment values; the fuzzy PID controller, through a defuzzification process (e.g., using the centroid method), transforms these fuzzy output sets back to precise values, which are the required correction values ​​for the three parameters of the PID controller at the current moment; the original values ​​of the controller... , , Adding the respective correction values ​​to the baseline value yields the real-time proportional coefficient used for the current control cycle. Real-time integral coefficient and real-time differential coefficients .

[0094] Furthermore, the controller uses the real-time parameters obtained from the above processing procedure, such as... , and According to the classic PID control law, the original deviation is... Perform composite operations to generate the final dynamic adjustment amount, which satisfies the following formula:

[0095] Dynamic adjustment amount =

[0096] Among them, the proportional term Used to provide the current deviation Proportional instantaneous correction force to quickly reduce errors; integral term The response addresses the cumulative value of the deviation (sum of historical errors) to eliminate persistent steady-state errors; the differential term... It responds to the changing trend of deviation, playing a predictive and damping role, and can effectively suppress system overshoot and oscillation.

[0097] Furthermore, the output torque of the assist motor Satisfy the following formula:

[0098]

[0099] in, This is the basic assist torque for the motor.

[0100] Through the above process, the fuzzy PID controller achieves adaptive parameter tuning. For example, when a vehicle suddenly drives over a pothole, causing a sharp increase in the measured rack force ( large and When the value increases rapidly (indicated by a positive value), the fuzzy PID controller will automatically increase the value. and It generates a fast and powerful corrective torque to counteract the impact and effectively suppress the "hand-slapping" phenomenon; when the deviation is small and the change is gradual, the fuzzy PID controller will use a set of milder parameters to ensure smooth power assist adjustment and reduce unnecessary jitter, so that the system can intelligently adapt to complex dynamic working conditions, ensuring response speed while taking into account the stability of the control process and ride comfort.

[0101] This application embodiment achieves an intelligent upgrade of traditional fixed-parameter PID control by employing a fuzzy PID controller. Specifically, the controller can dynamically and collaboratively adjust the proportional, integral, and derivative coefficients based on the real-time magnitude and trend of the steering rack force deviation through fuzzy logic reasoning. This ensures that the control parameters always maintain an optimal match with the current error characteristics. This adaptive parameter tuning mechanism allows the system to rapidly enhance control when facing severe fluctuations in rack force caused by sudden road impacts, generating strong corrections to quickly suppress the "kickback" phenomenon. Under normal driving conditions with gentler deviations, it automatically adopts milder control parameters to ensure smooth and stable power steering adjustment. Through this mechanism, while significantly improving the system's dynamic response speed and control accuracy, it effectively reduces overshoot and oscillation, significantly optimizing the steering assist's responsiveness, stability, and driving comfort.

[0102] In some embodiments, determining the basic assist torque of the power steering motor includes: obtaining the corresponding assist gain value from multiple pre-stored assist characteristic curves based on the vehicle speed and steering wheel operation angular velocity in the driving status signal; and determining the basic assist torque based on the assist gain value.

[0103] For example, the control unit acquires the vehicle speed from the driving status signal in real time. angular velocity of steering wheel operation (i.e., how fast the steering wheel turns); the system has multiple pre-stored calibrated power assist characteristic curves, which are based on vehicle speed. and steering wheel operation angular velocity As a two-dimensional input, it defines the corresponding assist gain value (i.e., assist strength coefficient) for different driving scenarios. Accordingly, the control unit adjusts the input based on the current (…). , The value is obtained by interpolating from these curves using a lookup table or by direct matching to obtain the most suitable assist gain value for the current operating condition. For example, in low-speed parking conditions ( Low, Under potentially high conditions, a higher assist gain is obtained by looking up the table (e.g., a 30% increase in the nominal value); however, under high-speed cruising conditions ( high, If the value is typically lower, a lower assist gain is obtained by looking up a table (e.g., a 20% reduction from the nominal value). Correspondingly, the control unit compares the obtained assist gain value with the driver's steering input (e.g., steering wheel input torque). By combining these methods, the base assist torque is calculated using a predefined mapping function. This provides the power assist motor with a preliminary torque command reference that matches the driving scenario.

[0104] In this embodiment, by querying pre-stored power assist characteristic curves in real time based on vehicle speed and steering wheel operation angular velocity, intelligent feedforward adjustment of power assist gain is achieved. This allows the system to accurately identify the current driving scenario (such as low-speed parking or high-speed cruising) and actively match a corresponding base power assist level. This provides sufficient power assist gain to reduce driver burden during low-speed, large-angle maneuvers, while moderately reducing gain at high speeds to enhance steering wheel stability and road feedback. This condition-adaptive feedforward strategy provides an accurate and reasonable torque benchmark for subsequent closed-loop feedback control, significantly improving steering feel and ease of operation under different conditions, and effectively reducing the correction burden on the feedback loop. This overall optimizes the system's response characteristics, energy efficiency, and driving experience consistency.

[0105] Figure 2 Another schematic flowchart illustrating the control method of an electric power steering system provided as an exemplary embodiment of this application. (See attached diagram.) Figure 2 As shown, the control method of this electric power steering system includes the following steps:

[0106] S201. Acquire the steering wheel input torque and the vehicle's driving status signal, which includes the vehicle's driving speed, lateral acceleration, steering wheel operation angular velocity, wheel angle, and the difference in steering angle between the left and right wheels.

[0107] S202. Based on the vehicle speed and steering wheel operation angular velocity, obtain the corresponding power assist gain value from multiple pre-stored power assist characteristic curves.

[0108] For example, based on the vehicle's speed angular velocity of steering wheel operation By using a lookup table method, interpolation or direct matching is performed from multiple calibrated power assist characteristic curves to obtain the most suitable power assist gain value for the current operating condition. For example, in low-speed parking conditions ( Low, Under potentially high conditions, a higher assist gain is obtained by looking up the table (e.g., a 30% increase in the nominal value); however, under high-speed cruising conditions ( high, If the value is usually lower, then a lower boost gain can be obtained by looking up the table (e.g., a 20% reduction in the nominal value).

[0109] S203. Determine the basic assist torque based on the assist gain value.

[0110] Correspondingly, the control unit will compare the obtained power assist gain value with the steering wheel input torque. Combined, the base assist torque is calculated using a predefined mapping function. This provides the power assist motor with a preliminary torque command reference that matches the driving scenario.

[0111] S204. The vehicle speed, lateral acceleration and the difference in steering angle between the left and right wheels are fused by a fuzzy controller to obtain the road surface adhesion coefficient compensation factor.

[0112] S205. Determine the suspension deformation amount based on the wheel angle and preset suspension geometry parameters; based on the suspension deformation amount, generate the suspension deformation compensation amount for compensating the steering trapezoidal transmission ratio.

[0113] S206. Based on the suspension deformation compensation amount, the transmission-related parameters are corrected, including the steering trapezoidal efficiency and / or the steering gear angle transmission ratio.

[0114] S207. Based on the steering wheel input torque, road surface adhesion coefficient compensation factor, and corrected transmission-related parameters, as well as the real-time torque of the power assist motor and the preset power assist transmission ratio, determine the estimated value of the steering rack force.

[0115] For example, based on the steering wheel input torque, the road surface adhesion coefficient compensation factor, and the corrected transmission-related parameters, the first rack force component contributed by the driver input is determined; the real-time torque of the power steering motor is obtained, and based on the real-time torque and the preset power steering ratio, the second rack force component contributed by the power steering motor is determined; the first rack force component and the second rack force component are added together to obtain the estimated value of the steering rack force.

[0116] S208. Obtain the measured value of the force on the steering rack.

[0117] S209. Determine whether the deviation between the measured value and the estimated value is greater than the preset deviation threshold.

[0118] If so, execute S210;

[0119] If not, proceed to S213.

[0120] S210. Based on the magnitude and rate of change of the deviation, adjust the proportional coefficient, integral coefficient, and derivative coefficient of the fuzzy PID controller through fuzzy logic reasoning.

[0121] S211. Based on the adjusted proportional coefficient, the adjusted integral coefficient, and the adjusted differential coefficient, perform a composite operation of proportional, integral, and differential on the deviation to generate a dynamic adjustment amount.

[0122] S212. Set the dynamic adjustment amount to 0.

[0123] S213. The output torque of the assist motor is obtained by combining the basic assist torque with the dynamic adjustment amount.

[0124] Accordingly, the electric power steering system control scheme provided in this application achieves significant technical effects in terms of calculation accuracy, response speed, energy efficiency, and overall driving experience by introducing a multi-sensor fusion and dynamic compensation mechanism. Specifically, these effects are as follows: 1) Significantly improved calculation accuracy: By adopting a multi-source information fusion and adaptive compensation mechanism, the rack force calculation can respond in real time to dynamic conditions such as changes in road surface adhesion coefficient and suspension geometric deformation; relevant bench tests show that on slippery surfaces (e.g., with a slippery adhesion coefficient...). Under combined conditions such as 0.3 mm² and suspension compression of 20 mm, the rack force calculation error is reduced by approximately 20% compared to the traditional static model, achieving an accuracy within ±5%. Simultaneously, a calibration mechanism utilizing real-time force sensor monitoring and fuzzy PID closed-loop feedback can promptly correct model deviations, further ensuring the accuracy and reliability of the calculation results. 2) Optimized power steering response performance: Based on the measured rack force value, the closed-loop control architecture enables the system to perceive changes in steering resistance in real time and quickly adjust the motor assist. The overall response time of the electric power steering system is reduced from approximately 80 ms in the traditional scheme to approximately 65 ms, significantly improving steering follow-through and reducing the "kickback" phenomenon by 40%, thus significantly improving the smoothness and operational safety of the steering process. 3) Improved system energy efficiency: Through an intelligent feedforward assist strategy based on vehicle speed and steering wheel operation angular velocity, the system automatically matches the optimal assist gain under different operating conditions, reducing the power consumption of the assist motor by approximately 15% at high speeds, which helps extend the electric vehicle's range by approximately 2%, achieving a balance between handling performance and energy economy. 4) Enhanced Overall Driving Experience: Provides light steering assistance at low speeds, while at high speeds, moderately reducing gain enhances steering stability and road feedback, achieving a balance between ease of operation and driving stability. The enhanced road feedback also helps the driver perceive the vehicle's status more accurately, increasing driving confidence. 5) Improved Safety and Reliability: High-precision rack force estimation and rapid-response closed-loop control enable the steering system to execute driver commands more precisely, improving vehicle handling stability in emergency avoidance scenarios. The system's sensors and control units have fault diagnosis capabilities, monitoring the system's operating status in real time. When a sensor malfunction or excessive calculation error is detected, timely warnings are issued and corresponding measures are taken to ensure the basic functions of the steering system, further improving overall vehicle safety and operational reliability. In summary, through a closed-loop technical path of "perception-compensation-estimation-feedback-adjustment," the overall performance of the electric power steering system in terms of accuracy, response, energy efficiency, experience, and safety is comprehensively improved.

[0125] In summary, this application has at least the following advantages:

[0126] First, by acquiring and fusing multi-source driving status signals in real time, adaptive dynamic compensation parameters are generated, enabling the estimated value of the steering rack force to respond accurately and quickly to changes in vehicle motion, thus significantly improving the accuracy of rack force estimation under complex working conditions. Based on this, by comparing the high-precision measured value of the steering rack force with the dynamically compensated estimated value in real time, and forming a closed-loop control based on the deviation between the two, the output torque of the power assist motor is dynamically adjusted. This achieves precise following and matching between the power assist output and the actual steering resistance, effectively overcoming the power assist mismatch and lag problems caused by neglecting dynamic factors in traditional static mechanical models. This not only effectively suppresses the "kickback" phenomenon during steering but also significantly shortens the system response time due to the accurate and rapid estimation of rack force. Furthermore, the introduction of the dynamic compensation mechanism ensures the accuracy, smoothness, and overall reliability of the vehicle's steering assist in different driving scenarios.

[0127] Second, by introducing a fuzzy controller to fuse multi-source information such as vehicle speed, lateral acceleration, and the difference in steering angle between the left and right wheels, intelligent and real-time estimation of road surface adhesion is achieved. This enables the system to accurately perceive changes in vehicle lateral dynamics caused by slippery, icy, or abrupt changes in the road surface adhesion coefficient, and to generate an adaptive road surface adhesion coefficient compensation factor. By dynamically incorporating this compensation factor into the mechanical calculation model of the steering system, the accuracy of steering rack force estimation under complex conditions such as low-adhesion road surfaces is significantly improved. This provides a more accurate and reliable input benchmark for subsequent closed-loop power steering control, effectively enhancing the adaptability and robustness of the electric power steering system to varying driving environments.

[0128] Third, by monitoring wheel angles in real time and combining them with the geometric parameters of the suspension system, the amount of suspension structural deformation caused by steering operations is quantified. Based on this, compensation parameters are generated to correct the steering trapezoidal gear ratio. This allows the system to dynamically sense and compensate for changes in steering transmission relationships caused by suspension geometric changes when driving on bumpy roads or making large-angle turns. By incorporating this suspension deformation into rack force estimation, the calculation bias of traditional static models under such conditions is effectively eliminated. This significantly improves the accuracy of steering load prediction under uneven road surfaces and extreme steering postures, thereby enhancing the adaptability of the electric power steering system to complex vehicle postures and road surface excitations, and effectively ensuring the accuracy and stability of power steering output in various dynamic driving scenarios.

[0129] Fourth, by employing a fuzzy PID controller, an intelligent upgrade of traditional fixed-parameter PID control is achieved. Specifically, this controller can dynamically and collaboratively adjust the proportional, integral, and derivative coefficients based on the real-time magnitude and trend of the steering rack force deviation through fuzzy logic reasoning. This ensures that the control parameters always maintain an optimal match with the current error characteristics. This adaptive parameter tuning mechanism allows the system to rapidly enhance control when facing severe fluctuations in rack force caused by sudden road impacts, generating strong corrections to quickly suppress the "kickback" phenomenon. Under normal driving conditions with gentler deviations, it automatically adopts milder control parameters to ensure smooth and stable power steering adjustments. Through this mechanism, while significantly improving the system's dynamic response speed and control accuracy, it effectively reduces overshoot and oscillation, significantly optimizing the steering assist's responsiveness, stability, and driving comfort.

[0130] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0131] Figure 3 A schematic diagram of the control device for an electric power steering system provided as an exemplary embodiment of this application. Figure 3 As shown, the control device 30 of the electric power steering system includes an acquisition module 31, a determination module 32, and a control processing module 33, wherein:

[0132] The acquisition module 31 is used to acquire the driver's steering operation input and the vehicle's driving status signal;

[0133] The determination module 32 is used to determine the dynamic compensation parameters of the vehicle based on the driving status signal; and to determine the estimated value of the steering rack force in the electric power steering system based on the driver's steering operation input and the dynamic compensation parameters.

[0134] The acquisition module 31 is also used to acquire the measured value of the force on the steering rack;

[0135] The control processing module 33 is used to dynamically adjust the output torque of the power steering motor in the electric power steering system based on the deviation between the measured value and the estimated value.

[0136] In one possible implementation, the dynamic compensation parameters include the road surface adhesion coefficient compensation factor, and the driving state signals include vehicle speed, lateral acceleration, and the difference in steering angle between the left and right wheels. The determination module 32 can be specifically used to: perform fusion processing on the vehicle speed, lateral acceleration, and the difference in steering angle between the left and right wheels through a fuzzy controller to obtain the road surface adhesion coefficient compensation factor.

[0137] In one possible implementation, the dynamic compensation parameters also include suspension deformation compensation amount, and the driving state signal also includes wheel angle. The determination module 32 can also be used to: determine the suspension deformation amount based on the wheel angle and preset suspension geometric parameters; and generate a suspension deformation compensation amount for compensating the steering trapezoidal transmission ratio based on the suspension deformation amount.

[0138] In one possible implementation, the determining module 32 can also be used to: correct transmission-related parameters based on suspension deformation compensation; determine the first rack force component contributed by the driver input based on the driver's steering input, road surface adhesion coefficient compensation factor, and the corrected transmission-related parameters; obtain the real-time torque of the power assist motor, and determine the second rack force component contributed by the power assist motor based on the real-time torque and the preset power assist transmission ratio; and add the first rack force component and the second rack force component to obtain an estimated value of the steering rack force.

[0139] In one possible implementation, transmission-related parameters include steering trapezoidal efficiency and / or steering gear angle ratio.

[0140] In one possible implementation, the control processing module 33 may be specifically used to: determine the basic assist torque of the assist motor; generate a dynamic adjustment amount based on the deviation between the measured value and the estimated value; and combine the basic assist torque with the dynamic adjustment amount to obtain the output torque of the assist motor.

[0141] In one possible implementation, the control processing module 33 can also be used to: determine whether the absolute value of the deviation is greater than a preset deviation threshold; when the absolute value of the deviation is less than or equal to the deviation threshold, determine the dynamic adjustment amount as 0; when the absolute value of the deviation is greater than the deviation threshold, determine the dynamic adjustment amount through the feedback controller.

[0142] In one possible implementation, the feedback controller is a fuzzy PID controller, and the control processing module 33 can also be used to: adjust the proportional coefficient, integral coefficient, and derivative coefficient of the fuzzy PID controller through fuzzy logic reasoning according to the magnitude and rate of change of the deviation; and perform a composite operation of proportional, integral, and derivative on the deviation based on the adjusted proportional coefficient, adjusted integral coefficient, and adjusted derivative coefficient to generate a dynamic adjustment amount.

[0143] In one possible implementation, the control processing module 33 can also be used to: obtain the corresponding assist gain value from multiple pre-stored assist characteristic curves based on the vehicle driving speed and steering wheel operation angular velocity in the driving state signal; and determine the basic assist torque based on the assist gain value.

[0144] The control device for the electric power steering system provided in this application embodiment can execute the technical solution shown in the above-described control method embodiment for the electric power steering system. Its implementation principle and beneficial effects are similar, and will not be repeated here.

[0145] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0146] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0147] It should be noted that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways; and it should be understood that the division of the various modules of the above device is only a logical functional division, and in actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can all be implemented in software through processing element calls; they can all be implemented in hardware; or some modules can be implemented in software through processing element calls, and some modules can be implemented in hardware. For example, the control processing module can be a separately established processing element, or it can be integrated into a chip of the above device. Alternatively, it can be stored as program code in the memory of the above device, and its functions can be called and executed by a processing element of the above device. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through hardware integrated logic circuits in the processor element or software instructions.

[0148] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a System-On-a-Chip (SOC).

[0149] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Video Discs, DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0150] Figure 4 A schematic diagram of an electric power steering system provided as an exemplary embodiment of this application. Figure 4 As shown, the electric power steering system 40 includes:

[0151] Torque sensor 41 is used to sense the amount of driver steering input;

[0152] Force sensor 42 is used to sense the measured value of the force on the steering rack;

[0153] Sensor group 43 is used to acquire vehicle driving status signals;

[0154] Assist motor 44;

[0155] The control unit 45 is connected to the torque sensor 41, the force sensor signal 42, the sensor group 43 and the assist motor 44 respectively, and is used to execute the method as described in any of the above embodiments.

[0156] Among them, sensor group 43 includes, but is not limited to, vehicle speed sensor, acceleration sensor and steering angle sensor, etc. The vehicle speed sensor can also be replaced by wheel speed sensor, etc.

[0157] For example, Figure 5 A schematic diagram of the architecture of an electric power steering system provided for an exemplary embodiment of this application. Figure 5 As shown, the control unit determines dynamic compensation parameters, such as road adhesion coefficient compensation factor and suspension deformation compensation amount, based on driving status signals from the sensor group (such as vehicle speed, lateral acceleration, and left and right wheel steering angle difference). Based on the driver's steering input from the torque sensor and the aforementioned dynamic compensation parameters, it determines the estimated value of the steering rack force. According to the deviation between the measured and estimated values ​​of the steering rack force from the force sensor, it dynamically adjusts the output torque of the power steering motor in the electric power steering system and generates corresponding control signals to drive the power steering motor and reduction mechanism. Furthermore, the system has complete fault diagnosis and fault tolerance management functions: the control unit monitors the validity of each sensor signal and the consistency of system calculations in real time. If a sensor fault, signal abnormality, or calculation error continuously exceeds the limit, it sends diagnostic information and alarm commands via the vehicle's CAN bus (e.g., displaying the corresponding fault code on the instrument panel) and automatically switches to a preset degraded control mode to maintain the basic power steering function, thereby improving system performance while effectively ensuring vehicle driving safety and operational reliability.

[0158] Figure 6 A schematic diagram of the structure of a control unit provided for an exemplary embodiment of this application. For example... Figure 6 As shown, the control unit 45 in this embodiment includes:

[0159] At least one processor 451; and a memory 452 communicatively connected to the at least one processor;

[0160] The memory 452 stores instructions that can be executed by at least one processor 451, which, when executed by at least one processor 451, cause the control unit to perform the method as described in any of the above embodiments.

[0161] Alternatively, the memory 452 can be either standalone or integrated with the processor 451.

[0162] The memory 452 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0163] The processor 451 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. Specifically, when implementing the control method of the electric power steering system described in the foregoing method embodiments, the control unit may be, for example, an electronic device with processing capabilities such as a server.

[0164] Optionally, the control unit may also include a communication interface 453. In specific implementations, if the communication interface 453, memory 452, and processor 451 are implemented independently, they can be interconnected via a bus to complete communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not imply that there is only one bus or one type of bus.

[0165] Optionally, in a specific implementation, if the communication interface 453, memory 452 and processor 451 are integrated on a single chip, then the communication interface 453, memory 452 and processor 451 can communicate through an internal interface.

[0166] The implementation principle and technical effects of the control unit provided in this embodiment can be found in the foregoing embodiments, and will not be repeated here.

[0167] This application also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed, they are used to implement the method steps as described in the above method embodiments. The specific implementation methods and technical effects are similar and will not be repeated here.

[0168] The aforementioned computer-readable storage media can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0169] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components within the control unit of the electric power steering system.

[0170] This application also provides a computer program product, including a computer program, which, when executed, implements the method steps as described in the above method embodiments. The specific implementation and technical effects are similar and will not be repeated here.

[0171] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0172] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0173] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A control method for an electric power steering system, characterized in that, include: Acquire driver steering input and vehicle driving status signals; Based on the driving status signal, the dynamic compensation parameters of the vehicle are determined; Based on the driver's steering input and the dynamic compensation parameters, the estimated value of the steering rack force in the electric power steering system is determined; Obtain the measured value of the force on the steering rack; The output torque of the power steering motor in the electric power steering system is dynamically adjusted based on the deviation between the measured value and the estimated value.

2. The control method for the electric power steering system according to claim 1, characterized in that, The dynamic compensation parameters include a road surface adhesion coefficient compensation factor, and the driving state signals include vehicle speed, lateral acceleration, and the difference in steering angle between the left and right wheels. Determining the dynamic compensation parameters of the vehicle based on the driving state signals includes: The road surface adhesion coefficient compensation factor is obtained by fusing the vehicle speed, lateral acceleration, and left and right wheel angle difference using a fuzzy controller.

3. The control method for the electric power steering system according to claim 2, characterized in that, The dynamic compensation parameters also include suspension deformation compensation amount, and the driving state signal also includes wheel angle. Determining the dynamic compensation parameters of the vehicle based on the driving state signal includes: The suspension deformation is determined based on the wheel angle and the preset suspension geometry parameters. Based on the suspension deformation, a suspension deformation compensation amount is generated to compensate for the steering trapezoidal gear ratio.

4. The control method for the electric power steering system according to claim 3, characterized in that, The step of determining the estimated value of the steering rack force in the electric power steering system based on the driver's steering input and the dynamic compensation parameters includes: Based on the aforementioned suspension deformation compensation amount, the transmission-related parameters are corrected; Based on the driver's steering input, the road surface adhesion coefficient compensation factor, and the corrected transmission-related parameters, the first rack force component contributed by the driver's input is determined. The real-time torque of the power assist motor is obtained, and based on the real-time torque and the preset power assist transmission ratio, the second rack force component contributed by the power assist motor is determined. Add the first rack force component to the second rack force component to obtain the estimated value of the force on the steering rack.

5. The control method for the electric power steering system according to claim 4, characterized in that, The transmission-related parameters include steering trapezoidal efficiency and / or steering gear angle transmission ratio.

6. The control method for the electric power steering system according to any one of claims 1 to 5, characterized in that, The step of dynamically adjusting the output torque of the power steering motor in the electric power steering system based on the deviation between the measured value and the estimated value includes: Determine the basic assist torque of the assist motor; Based on the deviation between the measured value and the estimated value, a dynamic adjustment amount is generated; The output torque of the assist motor is obtained by combining the basic assist torque with the dynamic adjustment amount.

7. The control method for the electric power steering system according to claim 6, characterized in that, The step of generating a dynamic adjustment amount based on the deviation between the measured value and the estimated value includes: Determine whether the absolute value of the deviation is greater than a preset deviation threshold; If the absolute value of the deviation is less than or equal to the deviation threshold, then the dynamic adjustment amount is determined to be 0; If the absolute value of the deviation is greater than the deviation threshold, the dynamic adjustment amount is determined by the feedback controller.

8. The control method for the electric power steering system according to claim 7, characterized in that, The feedback controller is a fuzzy PID controller, and determining the dynamic adjustment amount through the feedback controller includes: Based on the magnitude and rate of change of the deviation, the proportional coefficient, integral coefficient, and derivative coefficient of the fuzzy PID controller are adjusted through fuzzy logic reasoning. Based on the adjusted proportional coefficient, the adjusted integral coefficient, and the adjusted differential coefficient, a composite operation of proportional, integral, and differential is performed on the deviation to generate the dynamic adjustment amount.

9. The control method for the electric power steering system according to claim 6, characterized in that, Determining the basic assist torque of the assist motor includes: Based on the vehicle speed and steering wheel operation angular velocity in the driving status signal, the corresponding power assist gain value is obtained by querying multiple pre-stored power assist characteristic curves; Based on the aforementioned assist gain value, the basic assist torque is determined.

10. A control device for an electric power steering system, characterized in that, include: The acquisition module is used to acquire the driver's steering input and the vehicle's driving status signals; The determination module is used to determine the dynamic compensation parameters of the vehicle based on the driving state signal; and to determine the estimated value of the steering rack force in the electric power steering system based on the driver's steering operation input and the dynamic compensation parameters. The acquisition module is also used to acquire the measured value of the force on the steering rack; The control processing module is used to dynamically adjust the output torque of the power steering motor in the electric power steering system based on the deviation between the measured value and the estimated value.

11. A control unit, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory is used to store computer-executed instructions; The processor is configured to execute the computer execution instructions to implement the method as described in any one of claims 1 to 9.

12. An electric power steering system, characterized in that, include: Torque sensor is used to sense the amount of steering input from the driver; Force sensor, used to sense the measured value of the force on the steering rack; Sensor array, used to acquire vehicle driving status signals; Assist motor; The control unit is connected to the torque sensor, the force sensor signal, the sensor group and the assist motor respectively, and is used to perform the method as described in any one of claims 1 to 9.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the method as described in any one of claims 1 to 9.

14. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it implements the method as described in any one of claims 1 to 9.

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