Vehicle eps median self-learning method, system, storage medium and computer

Abstract

The application provides a kind of vehicle EPS midpoint self-learning method, system, storage medium and computer, the method includes: based on the steering wheel information of vehicle determines whether the driver exists steering state;If the driver does not exist steering intention, and the duration exceeds the first threshold, the output torque of the EPS motor of vehicle is compared with the preset output torque threshold;If the output torque is in the preset output torque threshold, and the duration exceeds the first threshold, determine the current driving state of vehicle based on the output torque;If the current driving state of vehicle is in straight-line driving state, the system midpoint data in EPS motor is compared with the initial midpoint data of vehicle;If system midpoint data is not equal to the initial midpoint data of vehicle, the current vehicle speed of vehicle is compared with the preset vehicle speed threshold;If the current vehicle speed is in the preset vehicle speed threshold, and the duration exceeds the first threshold, midpoint self-learning is carried out on EPS motor based on initial midpoint data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building structure evaluation, and in particular to a vehicle EPS mid-position self-learning method, system, storage medium and computer. BACKGROUND

[0002] With the update iteration of technology, in the automobile industry, EPS has gradually replaced the traditional hydraulic steering system to become a standard configuration due to the advantages of providing a comfortable feel and supporting more automatic driving auxiliary functions.

[0003] When a new vehicle equipped with EPS is off the line, the steering wheel angle mid-position needs to be calibrated. One of the functions of the mid-position calibration is to provide a reference for the EPS active return. However, the steering wheel angle mid-position calibration is done in a static condition, and the EPS mid-position calibration may not completely coincide with the actual straight-line driving mid-position. During driving, the vehicle may have a deviation problem. Secondly, as the mileage of the vehicle increases, related parts may wear out, such as tire wear. During driving, the vehicle may have a deviation problem. In addition, when the front suspension key parts of the vehicle are repaired and the four-wheel alignment is performed again, without a steering wheel mid-position calibration tool, the steering wheel angle mid-position calibration may not coincide with the driving mid-position, and during driving, the vehicle may have a deviation problem. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a vehicle EPS mid-position self-learning method, system, storage medium and computer to at least solve the above technical problems.

[0005] The present application provides a vehicle EPS mid-position self-learning method, which comprises:

[0006] acquiring steering wheel information of the vehicle during driving, and determining whether the driver has a steering state based on the steering wheel information;

[0007] if the driver does not have a steering intention and the duration exceeds a first threshold, acquiring an output torque of an EPS motor of the vehicle, and comparing the output torque with a preset output torque threshold;

[0008] if the output torque is equal to the preset output torque threshold and the duration exceeds the first threshold, determining a current driving state of the vehicle based on the output torque;

[0009] if the current driving state of the vehicle is in a straight-line driving state, acquiring system mid-position data in the EPS motor, and comparing the system mid-position data with initial mid-position data of the vehicle;

[0010] if the system mid-position data is not equal to the initial mid-position data of the vehicle, comparing a current vehicle speed of the vehicle with a preset vehicle speed threshold;

[0011] If the current vehicle speed is within the preset vehicle speed threshold and the duration exceeds the first threshold, the EPS motor is subjected to median self-learning based on the initial median data.

[0012] Furthermore, the step of acquiring steering wheel information while the vehicle is in motion, and determining whether the driver is in a steering state based on the steering wheel information, includes:

[0013] The steering wheel angle data of the vehicle while it is in motion is obtained, and the steering wheel angle data is compared with a preset angle threshold.

[0014] If the steering wheel angle data is within the preset angle threshold and the steering wheel angle data is not zero, the steering wheel torque of the vehicle is obtained, and the steering wheel torque is compared with the preset steering wheel torque threshold.

[0015] If the steering wheel torque is within the preset steering wheel torque threshold, it is determined that the driver does not have a steering intention.

[0016] Furthermore, the step of determining the current driving state of the vehicle based on the output torque includes:

[0017] The lead screw, belt drive ratio, and transmission efficiency of the EPS motor are obtained, and the assist data of the EPS motor are calculated based on the lead screw, belt drive ratio, transmission efficiency, and output torque.

[0018] The internal friction force of the vehicle's steering system and the active self-centering force of the suspension are obtained, and the power assist data is compared with the internal friction force and the active self-centering force of the suspension.

[0019] If the assist data is less than or equal to the sum of the internal friction force and the suspension active self-centering force, then the current driving state of the vehicle is determined to be a straight-line driving state.

[0020] Furthermore, after the step of performing median self-learning on the EPS motor based on the initial median data, the method further includes:

[0021] The vehicle's steering wheel information, the EPS motor's output torque, and the vehicle's current speed are continuously monitored.

[0022] If any of the following data is abnormal: the steering wheel information of the vehicle, the output torque of the EPS motor, or the current speed of the vehicle, the mid-level self-learning is stopped and an abnormal signal is output.

[0023] This invention also proposes a vehicle EPS median self-learning system, comprising:

[0024] an information acquisition module, configured to acquire steering wheel information of the vehicle when the vehicle is running, and determine whether the driver has a steering state based on the steering wheel information;

[0025] a torque comparison module, configured to acquire output torque of an EPS motor of the vehicle if the driver does not have a steering intention and the duration exceeds a first threshold value, and compare the output torque with a preset output torque threshold value;

[0026] a state determination module, configured to determine a current driving state of the vehicle based on the output torque if the output torque is at the preset output torque threshold value and the duration exceeds the first threshold value;

[0027] a data comparison module, configured to acquire system median data in the EPS motor if the current driving state of the vehicle is in a straight driving state, and compare the system median data with initial median data of the vehicle;

[0028] a vehicle speed comparison module, configured to compare a current vehicle speed of the vehicle with a preset vehicle speed threshold value if the system median data is not equal to the initial median data of the vehicle;

[0029] a self-learning module, configured to perform median self-learning on the EPS motor based on the initial median data if the current vehicle speed is within the preset vehicle speed threshold value and the duration exceeds the first threshold value.

[0030] Further, the information acquisition module comprises:

[0031] a steering angle data acquisition unit, configured to acquire steering wheel steering angle data of the vehicle when the vehicle is running, and compare the steering wheel steering angle data with a preset steering angle threshold value;

[0032] a torque comparison unit, configured to acquire steering wheel torque of the vehicle if the steering wheel steering angle data is within the preset steering angle threshold value and the steering wheel steering angle data is not zero, and compare the steering wheel torque with a preset steering wheel torque threshold value;

[0033] an intention determination unit, configured to determine that the driver does not have a steering intention if the steering wheel torque is within the preset steering wheel torque threshold value.

[0034] Further, the state determination module comprises:

[0035] a boost data calculation unit, configured to acquire lead screw lead of the EPS motor, belt transmission ratio, and transmission efficiency, and calculate boost data of the EPS motor according to the lead screw lead, the belt transmission ratio, the transmission efficiency, and the output torque;

[0036] The data comparison unit is used to obtain the internal friction force of the steering system and the active self-centering force of the suspension of the vehicle, and compare the power assist data with the internal friction force and the active self-centering force of the suspension.

[0037] The state determination unit is used to determine that the current driving state of the vehicle is a straight-line driving state if the assist data is less than or equal to the sum of the internal friction force and the suspension active self-centering force.

[0038] Furthermore, the system also includes:

[0039] The data monitoring module is used to continuously monitor the vehicle's steering wheel information, the output torque of the EPS motor, and the vehicle's current speed.

[0040] The signal output module is used to stop the mid-level self-learning and output an abnormal signal if any of the following data is abnormal: the steering wheel information of the vehicle, the output torque of the EPS motor, and the current speed of the vehicle.

[0041] The present invention also proposes a storage medium storing a computer program that, when executed by a processor, implements the above-described vehicle EPS median self-learning method.

[0042] The present invention also proposes a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described vehicle EPS median self-learning method.

[0043] The vehicle EPS center position self-learning method, system, storage medium, and computer of this invention determine the driver's driving state through steering wheel information and judge the current driving state of the vehicle based on the output torque of the EPS motor. When the vehicle is in a straight driving state, the system center position data of the EPS motor is compared with the initial center position data of the vehicle to determine whether the vehicle has deviated. The current vehicle speed is judged. When the vehicle speed is within a preset speed threshold, the center position self-learning of the EPS motor is performed using the initial center position data to achieve real-time angle correction. Attached Figure Description

[0044] Figure 1 This is a flowchart of the vehicle EPS median self-learning method in the first embodiment of the present invention;

[0045] Figure 2 for Figure 1 Detailed flowchart of step S101;

[0046] Figure 3 for Figure 1 Detailed flowchart of step S103;

[0047] Figure 4 a structural block diagram of a vehicle EPS neutral self-learning system in a second embodiment of the present application;

[0048] Figure 5 a structural block diagram of a computer in a third embodiment of the present application.

[0049] The following detailed description will further describe the present application with reference to the above-mentioned drawings. DETAILED DESCRIPTION

[0050] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The present application is shown in several embodiments in the drawings. However, the present application can be realized in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the present application is more thorough and complete.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0052] Embodiment One

[0053] Referring to Figure 1 , a vehicle EPS neutral self-learning method in a first embodiment of the present application is shown, which specifically includes steps S101 to S106:

[0054] S101, obtaining steering wheel information of a vehicle when the vehicle is running, and determining whether a driver is in a steering state based on the steering wheel information;

[0055] Further, referring to Figure 2 , the step S101 specifically includes steps S1011-S1013:

[0056] S1011, obtaining steering wheel angle data of the vehicle when the vehicle is running, and comparing the steering wheel angle data with a preset angle threshold;

[0057] S1012, if the steering wheel angle data is within the preset angle threshold and the steering wheel angle data is not zero, obtaining steering wheel torque of the vehicle, and comparing the steering wheel torque with a preset steering wheel torque threshold;

[0058] S1013, if the steering wheel torque is within the preset steering wheel torque threshold, it is determined that the driver has no steering intention.

[0059] In a specific implementation, the steering wheel is connected to an electric power steering assembly through a steering column, wherein an angle sensor and a torque sensor are arranged in the electric power steering assembly, the steering wheel angle data of the vehicle during driving is obtained by using the angle sensor, the steering wheel angle data is compared with a preset angle threshold (in this embodiment, the preset angle threshold is -15°-15°, and in other optional embodiments, the preset angle threshold can also be set by the user or automatically generated by the system), if the steering wheel angle data is within the preset angle threshold and is not zero, the steering wheel torque of the vehicle is obtained by using the torque sensor, the steering wheel torque is compared with a preset steering wheel torque threshold (in this embodiment, the preset steering wheel torque threshold is -1Nm-1Nm, and in other optional embodiments, the preset steering wheel torque threshold can also be set by the user or automatically generated by the system), if the steering wheel torque is within the preset steering wheel torque threshold, it is determined that the driver has no steering intention.

[0060] S102, if the driver has no steering intention and the duration exceeds a first threshold, the output torque of the EPS motor of the vehicle is obtained, and the output torque is compared with a preset output torque threshold;

[0061] In a specific implementation, if the driver has no steering intention and the duration exceeds a first threshold (in this embodiment, the first threshold is 1s, and in other optional embodiments, the first threshold can also be set by the user or automatically generated by the system), it means that the driver does not turn the steering wheel, at this time, the output torque of the EPS motor of the vehicle is obtained, and the output torque is compared with a preset output torque threshold (in this embodiment, the preset output torque threshold is -1Nm-1Nm, and in other optional embodiments, the preset output torque threshold can also be set by the user or automatically generated by the system).

[0062] S103, if the output torque is within the preset output torque threshold and the duration exceeds the first threshold, the current driving state of the vehicle is determined based on the output torque;

[0063] Further, please refer to Figure 3 , the step S103 specifically includes steps S1031-S1033:

[0064] S1031, the lead screw pitch, belt transmission ratio and transmission efficiency of the EPS motor are obtained, and the assist data of the EPS motor is calculated according to the lead screw pitch, the belt transmission ratio, the transmission efficiency and the output torque;

[0065] S1032, obtaining the internal friction of the steering system of the vehicle and the suspension active return force, and comparing the assist data with the internal friction and the suspension active return force;

[0066] S1033, if the assist data is less than or equal to the sum of the internal friction and the suspension active return force, it is determined that the current driving state of the vehicle is a straight driving state.

[0067] In a specific implementation, if the output torque is at a preset output torque threshold and the duration exceeds a first threshold, the lead screw pitch, the belt transmission ratio, and the transmission efficiency of the EPS motor are obtained, and the assist data of the EPS motor (in this embodiment, R-EPS is taken as an example) is calculated according to the lead screw pitch, the belt transmission ratio, the transmission efficiency, and the output torque according to the following formula:

[0068] F 助力 = Tmotor / lead screw pitch * (2 * Pi * belt transmission ratio * transmission efficiency);

[0069] Wherein, Tmotor is the output torque of the assist motor, that is, the output torque of the EPS motor of the vehicle.

[0070] Further, the internal friction of the vehicle steering system and the suspension active return force are the keys to maintain the straight driving of the vehicle, and the motor output torque only needs to overcome the internal friction of the vehicle steering system and the suspension active return force. The suspension active return force is provided by the wheel side assembly, and the main friction of the steering system is provided by the steering column and the electric power steering gear assembly, that is, 助力 ≤ F 摩擦力 + F 回正力 , it can be confirmed that the vehicle is in a straight driving state.

[0071] S104, if the current driving state of the vehicle is in a straight driving state, obtaining the system median data in the EPS motor, and comparing the system median data with the initial median data of the vehicle;

[0072] In a specific implementation, when the current driving state of the vehicle is in a straight driving state, and the steering wheel angle is not zero, it means that the steering system median does not coincide with the vehicle straight driving median. Due to the EPS belt active return function, the active return is to make the vehicle return to the steering system median, so the vehicle may exist deviation.

[0073] In this embodiment, 0° output by the angle sensor refers to the steering system median (i.e. system median data), and the initial 0° before self-learning is a calibration value; the straight driving direction of the wheel side assembly after four-wheel alignment refers to the vehicle straight driving median (i.e. initial median data).

[0074] S105, if the system median data is not equal to the initial median data of the vehicle, comparing the current vehicle speed with a preset speed threshold value;

[0075] In a specific implementation, if the system median data is not equal to the initial median data of the vehicle, the current vehicle speed is compared with a preset speed threshold value (in this embodiment, the preset speed threshold value is > 10 Kph, and in other optional embodiments, the preset speed threshold value can also be set by the user or automatically generated by the system).

[0076] S106, if the current vehicle speed is within the preset speed threshold value and the duration exceeds the first threshold value, the EPS motor is self-learned based on the initial median data.

[0077] In a specific implementation, if the current vehicle speed is within the preset speed threshold value and the duration exceeds the first threshold value, the EPS starts self-learning. At this time, the absolute value of the steering wheel angle (i.e. the initial median data) is the target value for self-learning strategy correction, and the self-learning is corrected by 0.1° each time.

[0078] In other optional embodiments, after the step of self-learning the EPS motor based on the initial median data, the method further comprises:

[0079] continuously monitoring the steering wheel information of the vehicle, the output torque of the EPS motor, and the current vehicle speed;

[0080] If any of the steering wheel information of the vehicle, the output torque of the EPS motor, and the current vehicle speed of the vehicle is abnormal, the self-learning is stopped and an abnormal signal is output.

[0081] In a specific implementation, during the compensation process, the EPS continuously monitors the steering wheel information of the vehicle, the output torque of the EPS motor, and the current vehicle speed, and if any of the conditions is abnormal (i.e. exceeds the corresponding threshold value), the self-learning is stopped.

[0082] In summary, the vehicle EPS median self-learning method in the above embodiments determines the driving state of the driver through the steering wheel information, and judges the current driving state of the vehicle based on the output torque of the EPS motor. When the vehicle is in a straight driving state, the system median data of the EPS motor is compared with the initial median data of the vehicle to determine whether the vehicle has deviated. The current vehicle speed is judged, and when the vehicle speed is within the preset speed threshold value, the initial median data is used to self-learn the EPS motor to realize real-time correction of the angle.

[0083] Embodiment two

[0084] The application further provides a vehicle EPS neutral self-learning system, please refer to Figure 4 , which is a vehicle EPS neutral self-learning system in the second embodiment of the application, and the system comprises:

[0085] an information acquisition module 11, which is configured to acquire steering wheel information of the vehicle during driving and determine whether the driver has a steering state based on the steering wheel information;

[0086] Further, the information acquisition module 11 comprises:

[0087] a steering angle data acquisition unit, which is configured to acquire steering angle data of the steering wheel of the vehicle during driving and compare the steering angle data with a preset steering angle threshold value;

[0088] a torque comparison unit, which is configured to acquire a steering wheel torque of the vehicle if the steering angle data is within the preset steering angle threshold value and the steering angle data is not zero, and compare the steering wheel torque with a preset steering wheel torque threshold value;

[0089] an intention determination unit, which is configured to determine that the driver has no steering intention if the steering wheel torque is within the preset steering wheel torque threshold value.

[0090] a torque comparison module 12, which is configured to acquire an output torque of an EPS motor of the vehicle if the driver has no steering intention and the duration exceeds a first threshold value, and compare the output torque with a preset output torque threshold value;

[0091] a state determination module 13, which is configured to determine a current driving state of the vehicle based on the output torque if the output torque is within the preset output torque threshold value and the duration exceeds the first threshold value;

[0092] Further, the state determination module 13 comprises:

[0093] a boost data calculation unit, which is configured to acquire a lead screw pitch of the EPS motor, a belt transmission ratio and a transmission efficiency, and calculate boost data of the EPS motor according to the lead screw pitch, the belt transmission ratio, the transmission efficiency and the output torque;

[0094] a data comparison unit, which is configured to acquire internal friction of a steering system of the vehicle and suspension active return force, and compare the boost data with the internal friction and the suspension active return force;

[0095] a state determination unit, which is configured to determine that the current driving state of the vehicle is a straight driving state if the boost data is less than or equal to the sum of the internal friction and the suspension active return force.

[0096] a data comparison module 14, configured to acquire system median data in the EPS motor if the current driving state of the vehicle is in the straight driving state, and compare the system median data with initial median data of the vehicle;

[0097] a vehicle speed comparison module 15, configured to compare a current vehicle speed of the vehicle with a preset vehicle speed threshold if the system median data is not equal to the initial median data of the vehicle;

[0098] a self-learning module 16, configured to perform median self-learning on the EPS motor based on the initial median data if the current vehicle speed is within the preset vehicle speed threshold and the duration exceeds the first threshold.

[0099] In some optional embodiments, the system further comprises:

[0100] a data monitoring module, configured to continuously monitor steering wheel information of the vehicle, output torque of the EPS motor, and current vehicle speed of the vehicle;

[0101] a signal output module, configured to stop the median self-learning and output an abnormal signal if any of the steering wheel information of the vehicle, the output torque of the EPS motor, and the current vehicle speed of the vehicle is abnormal.

[0102] The functions or operation steps realized when each module or unit is executed are substantially the same as those of the above method embodiments, and thus will not be described here.

[0103] The vehicle EPS median self-learning system provided in the embodiments of the present application has the same implementation principles and technical effects as the above method embodiments, and for brevity of description, the parts not mentioned in the system embodiments can be referred to the corresponding contents in the above method embodiments.

[0104] Embodiment Three

[0105] The present application also provides a computer, please refer to Figure 5 , which is a computer in the third embodiment of the present application, comprising a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20, wherein the processor 20 implements the above vehicle EPS median self-learning method when executing the computer program 30.

[0106] The memory 10 includes at least one type of storage medium including flash, hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 10 can be an internal storage unit of a computer, such as a hard disk of the computer, in some embodiments. The memory 10 can also be an external storage device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., in other embodiments. Further, the memory 10 can include both an internal storage unit of a computer and an external storage device. The memory 10 can be used to store application software installed in the computer and various data, and to temporarily store data that has been output or is to be output.

[0107] The processor 20 can be an electronic control unit (ECU), a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, for executing program codes or processing data stored in the memory 10, such as executing an access restriction program, in some embodiments.

[0108] It is noted that, Figure 5 The illustrated structure does not constitute a limitation on the computer, which can include fewer or more components than illustrated, or combine certain components, or arrange the components differently, in other embodiments.

[0109] The embodiments of the present application also propose a storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned mid-position self-learning method for a vehicle EPS.

[0110] Those skilled in the art can understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a list of executable instructions for implementing the logic function, which can be embodied in any computer readable medium for use by or in connection with an instruction execution system, apparatus or device, such as a computer-based system, a system including a processor or other system that can fetch and execute instructions from an instruction execution system, apparatus or device, or in conjunction with these instruction execution systems, apparatus or devices. For the present specification, the "computer readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by or in connection with an instruction execution system, apparatus or device, or in conjunction with these instruction execution systems, apparatus or devices.

[0111] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that is then reproducible into a computer readable medium.

[0112] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or combinations thereof, can be used: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), and / or the like.

[0113] The technical features of the above-described embodiments can be combined in any manner. In order to make the description concise, not all possible combinations of the technical features in the above-described embodiments are described, however, as long as the combinations of the technical features do not contradict each other, they should be considered within the scope of the present disclosure.

[0114] The above-described embodiments are merely representative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be construed as limiting the scope of the patent. It should be noted that for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these are within the scope of the present application. Therefore, the scope of the patent of the present application should be based on the appended claims.

Claims

1. A method for self-learning of a neutral position in a vehicle EPS, characterized by, The method comprises the following steps: acquiring steering wheel information of the vehicle during driving, and determining whether the driver has a steering intention based on the steering wheel information; if the driver does not have a steering intention and the duration exceeds a first threshold, acquiring output torque of an EPS motor of the vehicle, and comparing the output torque with a preset output torque threshold; if the output torque is within the preset output torque threshold and the duration exceeds the first threshold, determining a current driving state of the vehicle based on the output torque; if the current driving state of the vehicle is in a straight driving state, acquiring system median data in the EPS motor, and comparing the system median data with initial median data of the vehicle; if the system median data is not equal to the initial median data of the vehicle, comparing a current vehicle speed of the vehicle with a preset vehicle speed threshold; if the current vehicle speed is within the preset vehicle speed threshold and the duration exceeds the first threshold, performing median self-learning on the EPS motor based on the initial median data; wherein the step of determining the current driving state of the vehicle based on the output torque comprises: acquiring lead screw pitch, belt transmission ratio and transmission efficiency of the EPS motor, and calculating assistance data of the EPS motor according to the lead screw pitch, the belt transmission ratio, the transmission efficiency and the output torque; acquiring internal friction of a steering system of the vehicle and suspension active return force, and comparing the assistance data with the internal friction and the suspension active return force; if the assistance data is less than or equal to the sum of the internal friction and the suspension active return force, determining that the current driving state of the vehicle is in a straight driving state.

2. The vehicle EPS neutral self-learning method according to claim 1, characterized by, The step of acquiring steering wheel information of the vehicle during driving and determining whether the driver has a steering intention based on the steering wheel information comprises: acquiring steering wheel angle data of the vehicle during driving, and comparing the steering wheel angle data with a preset steering wheel angle threshold; if the steering wheel angle data is within the preset steering wheel angle threshold and the steering wheel angle data is not zero, acquiring steering wheel torque of the vehicle, and comparing the steering wheel torque with a preset steering wheel torque threshold; if the steering wheel torque is within the preset steering wheel torque threshold, determining that the driver does not have a steering intention.

3. The vehicle EPS neutral self-learning method according to claim 1, characterized by, After the step of performing median self-learning on the EPS motor based on the initial median data, the method further comprises: continuously monitoring the steering wheel information of the vehicle, the output torque of the EPS motor and the current vehicle speed of the vehicle; if any of the steering wheel information of the vehicle, the output torque of the EPS motor and the current vehicle speed of the vehicle is abnormal, stopping the median self-learning and outputting an abnormal signal.

4. A vehicle EPS neutral self-learning system characterized by comprising: The method comprises the following steps: an information acquisition module for acquiring steering wheel information of the vehicle during driving, and determining whether the driver has a steering intention based on the steering wheel information; a torque comparison module configured to obtain an output torque of an EPS motor of the vehicle if the driver has no steering intention and the duration exceeds a first threshold value, and compare the output torque with a preset output torque threshold value; a state determination module configured to determine a current driving state of the vehicle based on the output torque if the output torque is at the preset output torque threshold value and the duration exceeds the first threshold value; a data comparison module configured to obtain system median data in the EPS motor and compare the system median data with initial median data of the vehicle if the current driving state of the vehicle is in a straight driving state; a vehicle speed comparison module configured to compare a current vehicle speed of the vehicle with a preset vehicle speed threshold value if the system median data is not equal to the initial median data of the vehicle; a self-learning module configured to perform median self-learning on the EPS motor based on the initial median data if the current vehicle speed is within the preset vehicle speed threshold value and the duration exceeds the first threshold value; wherein the state determination module comprises: a boost data calculation unit configured to obtain a lead screw pitch of the EPS motor, a belt transmission ratio, and a transmission efficiency, and calculate boost data of the EPS motor according to the lead screw pitch, the belt transmission ratio, the transmission efficiency, and the output torque; a data comparison unit configured to obtain internal friction of a steering system of the vehicle and suspension active return force, and compare the boost data with the internal friction and the suspension active return force; a state determination unit configured to determine that the current driving state of the vehicle is in a straight driving state if the boost data is less than or equal to a sum of the internal friction and the suspension active return force.

5. The vehicle EPS neutral self-learning system according to claim 4, characterized by, the information acquisition module comprises: a steering wheel angle data acquisition unit configured to obtain steering wheel angle data of the vehicle when driving, and compare the steering wheel angle data with a preset steering wheel angle threshold value; a torque comparison unit configured to obtain a steering wheel torque of the vehicle if the steering wheel angle data is within the preset steering wheel angle threshold value and the steering wheel angle data is not zero, and compare the steering wheel torque with a preset steering wheel torque threshold value; an intention determination unit configured to determine that the driver has no steering intention if the steering wheel torque is within the preset steering wheel torque threshold value.

6. The vehicle EPS neutral self-learning system according to claim 4, characterized by, the system further comprises: a data monitoring module configured to continuously monitor steering wheel information of the vehicle, the output torque of the EPS motor, and the current vehicle speed of the vehicle; a signal output module configured to stop the median self-learning and output an abnormal signal if any of the steering wheel information of the vehicle, the output torque of the EPS motor, and the current vehicle speed of the vehicle is abnormal.

7. A storage medium having stored thereon a computer program, characterized in that The program is executed by the processor to implement the vehicle EPS median self-learning method of any one of claims 1 to 3.

8. A computer comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the vehicle EPS median self-learning method of any one of claims 1 to 3.

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