A method and system for end dynamic self-learning of steer-by-wire execution

Abstract

The present application relates to the technical field of steering system, and particularly relates to a method and system for end dynamic self-learning of drive-by-wire steering. The method for end dynamic self-learning of drive-by-wire steering is characterized in that: when a vehicle is started and a steering wheel reaches an end to the left or to the right, a current TAS value is recorded as a limit value to the left or to the right; when the steering wheel returns to a center position from the left or right end, the current recorded TAS value is stored in a data storage function module; an end self-learning module reads the value and compensates the current left or right end use value, performs angle calculation of the left or right end according to the current center value, and provides the value to each function module; and if the left or right end stroke of a rack exceeds a default stroke value, the left or right end is safely reset to an initial default value. Compared with the prior art, the method and system for end dynamic self-learning of drive-by-wire steering are provided to ensure that a center position of a corner sensor matches a real steering wheel angle of the vehicle.

Description

Technical Field

[0001] This invention relates to the field of steering system technology, specifically to a method and system for end-effector dynamic self-learning of steer-by-wire. Background Technology

[0002] With the development of the automotive industry and the increasing demand from society for vehicle safety, energy conservation and environmental protection, more and more passenger cars are equipped with electric power steering systems and electronic stability control systems. As with the related driver assistance systems and chassis electronic control systems, they all require accurate steering angle signals. The functions derived from them (soft stop protection, active self-centering, automatic parking, etc.) need to identify the extreme positions of the steering angle and actively limit and precisely control them. In particular, current steer-by-wire systems lack an intermediate shaft compared to traditional steering systems, and the precise control of the upper steering wheel end locking function depends on the angle of the lower steering rack end.

[0003] In addition, it is necessary to implement different functional controls for EPS (Electric Power Steering) based on the maximum left and right limit angle values ​​of the vehicle's steering wheel, such as rack end protection and motor stall protection control.

[0004] However, the mechanical manufacturing of the steering system, the assembly of the entire vehicle, wear and tear, and four-wheel alignment can all cause deviations, asymmetries, and changes in the left and right limit angles of the vehicle's steering wheel compared to the theoretical design values. This negatively impacts the functions and performance of EPS control related to the steering wheel's limit angle position. Therefore, it is necessary to improve the accuracy of the steering wheel's limit angle values.

[0005] The current maximum steering angle calibration for automotive electric power steering systems is determined by the rack and pinion travel. During end-of-line (EOL) testing, the left limit angle is determined by turning the steering wheel to the left to calibrate the maximum left steering angle; the right limit angle is determined by turning the steering wheel to the right to calibrate the maximum right steering angle. This current maximum steering angle calibration method requires recalibration for different rack and pinion systems. Furthermore, when the steering mechanism is maintained or parts are replaced, the calibrated maximum steering angle also needs to be fine-tuned due to the inherent differences between the components.

[0006] Current technology typically uses manual calibration to determine the maximum steering angle of a steering system. This method has several drawbacks: first, it incurs high labor costs; second, it requires a complex calibration process, making production cumbersome; and third, recalibration is necessary when replacing gears, racks, or other related components, increasing maintenance costs. Furthermore, existing methods for calibrating the maximum steering angle of automotive electric power steering systems require high precision in components, which can lead to insufficient scalability of the calibration data. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, this invention provides a method and system for end-effector dynamic self-learning of steer-by-wire, so as to ensure that the center position of the steering angle sensor matches the actual steering wheel angle of the vehicle.

[0008] To achieve the above objectives, a method for end-effector dynamic self-learning in steer-by-wire is designed, characterized by the following specific method:

[0009] (1) When the vehicle is started and in motion: the speed is greater than 3km / h;

[0010] (2) When the steering wheel reaches the end of the left or right turn, if the TAS angle received by the current steering wheel angle acquisition module at the left or right end exceeds the default left or right limit TAS value, the left and right end learning conditions are met, and the current TAS value is recorded as the left or right limit value.

[0011] (3) When the steering wheel returns to the center position from the left and right ends, the currently recorded TAS value is stored in the data storage function module;

[0012] (4) The end self-learning module reads the values ​​and compensates them for the current left and right end values. Then, based on the current median value, it calculates the angles of the left and right ends and provides them to each functional module.

[0013] (5) If the travel of the left and right ends of the rack exceeds the default travel value, the left and right ends will be safely reset to the initial default value.

[0014] In step (4), the end-to-right self-learning scenario of the end-to-end self-learning module is as follows:

[0015] 1) Right learning scenario 1: median learning angle, learned end right limit angle, current end right limit self-learning angle, and both on a sawtooth edge;

[0016] 2) Right-side learning scenario two: median learning angle, already learned end right limit angle, current end right limit self-learning angle; only the current end right limit self-learning angle is simultaneously on another serrated edge; other angles are on one serrated edge;

[0017] 3) Right-learning scenario three: median learning angle, learned end right limit angle, current end right limit self-learning angle; current end right limit self-learning angle and learned end right limit angle are both on another serrated edge; other angles are on one serrated edge;

[0018] 4) Right Learning Scenario 4: Median learning angle, learned end right limit angle, current end right limit self-learning angle; median learning angle, one end angle of the current end right limit self-learning angle and one end angle of the learned end right limit angle are on the same zigzag edge; current end right limit self-learning angle and other angles and one end angle of the learned end right limit angle are on another zigzag edge.

[0019] When learning scenario one to the right, the judgment logic is as follows:

[0020] fESSStrAngleRightIncreaseflag=[(wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR<(MAX_TASANGLE / 2));

[0021] And (wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR>0);

[0022] And (wConvTASAngle-wSAStrAngleOffsetDyNVR<(-MAX_TASANGLE / 2));

[0023] Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleRightNVR is the right end value of the rack stored in EEPROM; wEsStrAngleRightLearnDy is the right end value of the rack being learned; wConvTASAngle is the current angle value of TAS; fESStrAngleRightIncreaseflag is the condition for enabling rightward learning; and MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

[0024] When learning scenario two to the right, the judgment logic is as follows:

[0025] fESSStrAngleRightIncreaseflag=[(wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR<(MAX_TASANGLE / 2));

[0026] And (wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR<(MAX_TASANGLE / 2));

[0027] And (wConvTASAngle-wSAStrAngleOffsetDyNVR>0);

[0028] And (wConvTASAngle-wSAStrAngleOffsetDyNVR<(MAX_TASANGLE / 2));

[0029] And (wConvTASAngle>wEsStrAngleRightNVR)];

[0030] Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleRightNVR is the right end value of the rack stored in EEPROM; and wEsStrAngleRightLearnDy is the right end value of the rack learned.

[0031] wConvTASAngle is the current TAS angle value; fESStrAngleRightIncreaseflag is the condition for enabling right-side learning; MAX_TASANGLE is the maximum value of the TAS feature, i.e., 2016.

[0032] When learning scenario three to the right, the judgment logic is as follows:

[0033] fESSStrAngleRightIncreaseflag=[(wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR<(-MAX_TASANGLE / 2));

[0034] And (wConvTASAngle-wSAStrAngleOffsetDyNVR<(-MAX_TASANGLE / 2));

[0035] And (wConvTASAngle>wEsStrAngleRightLearnDy)];

[0036] Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleRightNVR is the right end value of the rack stored in EEPROM; and wEsStrAngleRightLearnDy is the right end value of the rack learned.

[0037] wConvTASAngle is the current TAS angle value; fESStrAngleRightIncreaseflag is the condition for enabling right-side learning; MAX_TASANGLE is the maximum value of the TAS feature, i.e., 2016.

[0038] In step (4), the end-to-left self-learning scenario of the end-to-end self-learning module is as follows:

[0039] 1) Learning to the left scenario 1: median learning angle, learned end left limit angle, current end left limit self-learning angle, and both on a sawtooth edge;

[0040] 2) Learning to the left scenario 2: median learning angle, learned end left limit angle, current end left limit self-learning angle; only the current end left limit self-learning angle is on another serrated edge at the same time; other angles are on one serrated edge;

[0041] 3) Learning to the left, scenario three: median learning angle, learned end left limit angle, current end left limit self-learning angle; current end left limit self-learning angle and learned end left limit angle are both on another serrated edge; other angles are on one serrated edge;

[0042] 4) Learning to the left scenario 4: median learning angle, learned end left limit angle, current end left limit self-learning angle; median learning angle, one end angle of the current end left limit self-learning angle and one end angle of the learned end left limit angle are on the same serrated edge; current end left limit self-learning angle and other angles and one end angle of the learned end left limit angle are on another serrated edge.

[0043] When learning scenario one to the left, the judgment logic is as follows:

[0044] fESSStrAngleLeftIncreaseflag=(wEsStrAngleLeftLearnDy-wSAStrAngleOffsetDyNVR>(MAX_TASANGLE / 2);

[0045] And (wEsStrAngleLeftLearnDy-wSAStrAngleOffsetDyNVR<0);

[0046] And (wConvTASAngle-wSAStrAngleOffsetDyNVR>(MAX_TASANGLE / 2);

[0047] Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleLeftNVR is the left end value of the EEPROM rack; wEsStrAngleLeftLearnDy is the left end value of the rack being learned; wConvTASAngle is the current TAS angle value; fESStrAngleLeftIncreaseflag is the condition for enabling leftward learning; MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

[0048] When learning scenario two to the left, the judgment logic is as follows:

[0049] fESSStrAngleLeftIncreaseflag=[wEsStrAngleLeftLearnDy-wSteeringAngleOffsetDyNVR>(-MAX_TASANGLE / 2);

[0050] And wEsStrAngleLeftLearnDy-wSteeringAngleOffsetDyNVR<0;

[0051] And wConvTASAngle-wSteeringAngleOffsetDyNVR>(-MAX_TASANGLE / 2);

[0052] And wConvTASAngle-wSteeringAngleOffsetDyNVR<0;

[0053] And (wConvTASAngle) <wEsStrAngleLeftLearnDy);

[0054] Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleLeftNVR is the left end value of the EEPROM rack; wEsStrAngleLeftLearnDy is the left end value of the rack being learned; wConvTASAngle is the current TAS angle value; fESStrAngleLeftIncreaseflag is the condition for enabling leftward learning; MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

[0055] When learning scenario three to the left, the judgment logic is as follows:

[0056] fESSStrAngleLeftIncreaseflag=(wEsStrAngleLeftLearnDy-wSAStrAngleOffsetDyNVR>(MAX_TASANGLE / 2));

[0057] And (wConvTASAngle-wSAStrAngleOffsetDyNVR>(MAX_TASANGLE / 2));

[0058] And (wConvTASAngle) <wEsStrAngleLeftLearnDy);

[0059] Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleLeftNVR is the left end value of the EEPROM rack; wEsStrAngleLeftLearnDy is the left end value of the rack being learned; wConvTASAngle is the current TAS angle value; fESStrAngleLeftIncreaseflag is the condition for enabling leftward learning; MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

[0060] A system employing any one of the above-mentioned end-effector dynamic self-learning methods, characterized in that: the system comprises:

[0061] Upper steering road feel module: Includes steering wheel angle acquisition module, mainly used to calculate the angle of upper steering road feel module, and transmit it to lower steering execution module via private CAN. In the end dynamic self-learning function, it is used to store the activation conditions of the angle for end learning.

[0062] The vehicle signal processing system includes a vehicle speed acquisition module, which is mainly used to calculate the vehicle speed signal and transmit it to the lower steering execution module via CAN. It is used as one of the activation conditions for end angle self-learning in the end dynamic self-learning function.

[0063] The lower steering execution module includes a signal processing module, an end-effector self-learning module, a TAS angle acquisition module, a fault handling module, a data storage module, and other drive-by-wire angle-related modules. The signal processing module is used to store the end-effector self-learning activation conditions and learning values, and the signals involved include the vehicle speed signal from the vehicle's CAN bus and the steering wheel angle signal from the private CAN bus.

[0064] Compared with the prior art, the present invention provides a method and system for end-effector dynamic self-learning for steer-by-wire, so as to ensure that the center position of the steering angle sensor matches the actual steering wheel angle of the vehicle. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of a dual-redundant power steering system.

[0066] Figure 2 This is a schematic diagram of the end-effector dynamic self-learning function system of the present invention.

[0067] Figure 3 This is a flowchart of the dynamic end-point self-learning process of the steer-by-wire system of the present invention.

[0068] Figure 4 This is a schematic diagram of the dynamic end-effector self-learning "right" learning scenario of the power steering system of the present invention.

[0069] Figure 5 for Figure 4 Enlarged illustration of scene 1.

[0070] Figure 6 for Figure 4 Enlarged illustration of scene 2.

[0071] Figure 7 for Figure 4 Enlarged diagram of scene 3 in the middle.

[0072] Figure 8 This is a schematic diagram of the learning process of "learning to the right" in this invention.

[0073] Figure 9 This is a schematic diagram of the dynamic end-effector self-learning "leftward" learning scenario of the steer-by-wire system of the present invention.

[0074] Figure 10 for Figure 9 Enlarged illustration of scene 1.

[0075] Figure 11 for Figure 9 Enlarged illustration of scene 2.

[0076] Figure 12 for Figure 9 Enlarged diagram of scene 3 in the middle.

[0077] Figure 13 This is a schematic diagram of the learning process of "learning to the left" according to the present invention.

[0078] Figure 14 This is a schematic diagram of end-angle learning value compensation.

[0079] Figure 15 This is a schematic diagram of a scenario for resetting end-point security. Detailed Implementation

[0080] The present invention will now be further described with reference to the accompanying drawings.

[0081] like Figure 1 The diagram shown is a structural schematic of a dual-redundant power steering system provided in an embodiment of the present invention. The system mainly consists of an upper steering road feel system and a lower steering execution system. The upper steering road feel module mainly includes a steering wheel 1, an upper steering TAS torque sensor 2, an upper steering column assembly 3, an upper steering road feel dual-redundant ECU 10, and an upper steering turbine assembly 11. The lower steering execution module mainly includes a tie rod assembly 4, a lower steering turbine assembly 5, a rack assembly 6, a lower steering execution dual-redundant ECU 7, a motor rotor position sensor 8, and a lower steering TAS angle sensor 9.

[0082] The upper steering feel module and the lower steering actuation module communicate with each other via a private CAN bus 13, and both modules communicate with the vehicle via the CAN bus 12. The steering wheel 1 is driven by the driver, and the input of the upper steering column assembly 3 is connected to the steering wheel 1. The upper steering TAS torque sensor 2 detects the driver's steering torque and angle. The upper steering feel dual-redundant ECU 10 collects and processes the steering torque, transmitting it via the private CAN bus 13 to the lower steering actuation dual-redundant ECU 7 for use as a judgment condition in the end-of-line self-learning process. The road feel motor is mounted on the upper steering column assembly 3 and provides road feel torque to the driver through a reduction mechanism.

[0083] The lower power steering motor is mounted on the lower steering turbine assembly 5 and provides assistance torque to the driver through the reduction mechanism. The torque of the lower power steering motor is applied to the rack assembly 6 and the tie rod assembly 4 through the lower steering turbine assembly 5 to achieve steering assistance. The lower steering TAS angle sensor 9 is used to detect the angle value of the steering shaft to determine the lower steering angle center value. The motor rotor position sensor 8 is used to detect the rotor position angle of the lower power steering motor and feeds back the detected lower power steering motor angle to the lower steering execution dual redundant ECU 7 for calculating the lower steering angle.

[0084] like Figure 2 The diagram shown is a schematic of the end-effector dynamic self-learning function system of the present invention, which includes three functional systems:

[0085] 1. Upper Steering Road Sensing Module: Includes a steering wheel angle acquisition module, which mainly calculates the angle of the upper steering road sensing module and transmits it to the lower steering execution module via private CAN. It is used to store the activation conditions for the angle learned in the end-of-line dynamic self-learning function.

[0086] 2. Vehicle signal processing system: Includes a vehicle speed acquisition module, which is mainly used to calculate the vehicle speed signal and transmit it to the lower steering execution module via CAN. It is used as one of the activation conditions for end angle self-learning in the end dynamic self-learning function.

[0087] 3. Downward Steering Execution Module: Includes a signal processing module, an end-effector self-learning module, a TAS angle acquisition module, a fault handling module, a data storage module (EEPROM), and other drive-by-wire angle-related modules; the signal processing module is used to store the end-effector angle self-learning activation conditions and learning values, and the signals involved include the vehicle speed signal from the vehicle CAN signal and the steering wheel angle signal from the private CAN signal.

[0088] The TAS angle acquisition module provides the learnable end-point limit angle. The end-point self-learning function uses the self-learned end-point (left / right) angle exceeding the default limit angle. When the steering wheel returns to the target angle, the end-point learned value is stored and provided to relevant functional modules. The data storage module (EEPROM) stores the end-point (left / right) self-learned values, ensuring they remain even after the EPS is powered on or off, thus meeting the non-volatility requirement of the end-point self-learned values. The fault handling module stores the fault and executes relevant safety measures when unexpected faults are encountered during learning.

[0089] like Figure 3 The diagram shown is a flowchart of the dynamic end-effector self-learning process of the power steering system of the present invention, which includes the following steps:

[0090] (6) When the vehicle is started and in motion: the speed is greater than 3km / h;

[0091] (7) When the steering wheel reaches the end of "turning left" or "turning right", if the TAS angle received by the current steering wheel angle acquisition module at the left and right ends exceeds the default left and right limit TAS values, the left and right end learning conditions are met, and the current TAS value is recorded as the left and right limit value.

[0092] (8) When the steering wheel returns to the center position from the left and right ends, the currently recorded TAS value is stored in the data storage function module (EEPROM);

[0093] (9) The end self-learning module reads the values ​​and compensates them for the current left and right end values. Then, based on the current median value, it calculates the angles of the left and right ends and provides them to each functional module.

[0094] (10) If the travel of the left and right ends of the rack exceeds the default travel value, the left and right ends are safely reset to the initial default value.

[0095] Due to the inherent characteristics of the TAS angle sensor, it provides cyclic, sawtooth-shaped angle data. During use, four types of scenarios are typically encountered, such as... Figure 4 As shown, this is the right limit, i.e., the learning scenario "to the right" is as follows:

[0096] like Figure 5As shown, in the first learning scenario of "to the right", the median learning angle, the learned right limit angle at the end, and the current right limit self-learning angle at the end are all on a jagged edge.

[0097] therefore,

[0098] fESSStrAngleRightIncreaseflag=[(wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR<(MAX_TASANGLE / 2));

[0099] And (wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR>0);

[0100] And (wConvTASAngle-wSAStrAngleOffsetDyNVR<(-MAX_TASANGLE / 2)).

[0101] Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleRightNVR is the right end value of the rack stored in EEPROM; wEsStrAngleRightLearnDy is the right end value of the rack being learned; wConvTASAngle is the current angle value of TAS; fESStrAngleRightIncreaseflag is the condition for enabling rightward learning; and MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

[0102] like Figure 6 As shown, in the second learning scenario of "to the right": the median learning angle, the already learned right limit angle at the end, and the current right limit self-learning angle at the end; only the current right limit self-learning angle at the end is simultaneously on another serrated edge; the other angles are on one serrated edge.

[0103] therefore,

[0104] fESSStrAngleRightIncreaseflag=[(wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR<(MAX_TASANGLE / 2));

[0105] And (wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR<(MAX_TASANGLE / 2));

[0106] And (wConvTASAngle-wSAStrAngleOffsetDyNVR>0);

[0107] And (wConvTASAngle-wSAStrAngleOffsetDyNVR<(MAX_TASANGLE / 2));

[0108] and (wConvTASAngle>wEsStrAngleRightNVR)].

[0109] Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleRightNVR is the right end value of the rack stored in EEPROM; wEsStrAngleRightLearnDy is the right end value of the rack being learned; wConvTASAngle is the current angle value of TAS; fESStrAngleRightIncreaseflag is the condition for enabling rightward learning; and MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

[0110] like Figure 7 As shown, in the "right" learning scenario three: the median learning angle, the already learned right limit angle at the end, and the current right limit self-learning angle at the end; the current right limit self-learning angle at the end and the already learned right limit angle at the end are both on another serrated edge; other angles are on one serrated edge.

[0111] therefore,

[0112] fESSStrAngleRightIncreaseflag=[(wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR<(-MAX_TASANGLE / 2));

[0113] And (wConvTASAngle-wSAStrAngleOffsetDyNVR<(-MAX_TASANGLE / 2));

[0114] and (wConvTASAngle>wEsStrAngleRightLearnDy)].

[0115] Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleRightNVR is the right end value of the rack stored in EEPROM; wEsStrAngleRightLearnDy is the right end value of the rack being learned; wConvTASAngle is the current angle value of TAS; fESStrAngleRightIncreaseflag is the condition for enabling rightward learning; and MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

[0116] "Right" learning scenario four: median learning angle, learned end right limit angle, current end right limit self-learning angle; median learning angle, one end angle of the current end right limit self-learning angle and one end angle of the learned end right limit angle are both on the same serrated edge; current end right limit self-learning angle and other angles and one end angle of the learned end right limit angle are both on another serrated edge.

[0117] During driving, when the vehicle speed is >3 km / h, and the steering wheel is turned to the vicinity of the right end and the learning activation condition fESStrAngleRightIncreaseflag==1, the current TAS value wConvTASAngle is the newly learned end angle value wEsStrAngleRightLearnDy=wConvTASAngle, which is then recorded.

[0118] When the steering wheel returns to near the center position, the learned data wEsStrAngleRightLearnDy is stored in the data storage module (EEPROM), and the value read from the data storage module (EEPROM) is wEsStrAngleRightNVR. The entire right-turn learning process is as follows. Figure 8 As shown.

[0119] like Figure 9 As shown, this is the right limit, i.e., the learning scenario "to the left" is as follows:

[0120] like Figure 10 As shown, in the "leftward" learning scenario 1: the median learning angle, the already learned end left limit angle, the current end left limit self-learning angle, and both are on a jagged edge.

[0121] therefore,

[0122] fESSStrAngleLeftIncreaseflag=(wEsStrAngleLeftLearnDy-wSAStrAngleOffsetDyNVR>(MAX_TASANGLE / 2);

[0123] And (wEsStrAngleLeftLearnDy-wSAStrAngleOffsetDyNVR<0);

[0124] And (wConvTASAngle-wSAStrAngleOffsetDyNVR>(MAX_TASANGLE / 2).

[0125] Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleLeftNVR is the left end value of the EEPROM rack; wEsStrAngleLeftLearnDy is the left end value of the rack being learned; wConvTASAngle is the current TAS angle value; fESStrAngleLeftIncreaseflag is the condition for enabling leftward learning; MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

[0126] like Figure 11 As shown, in the second learning scenario "to the left": the median learning angle, the already learned end left limit angle, and the current end left limit self-learning angle; only the current end left limit self-learning angle is on another serrated edge at the same time; the other angles are on one serrated edge.

[0127] therefore,

[0128] fESSStrAngleLeftIncreaseflag=[wEsStrAngleLeftLearnDy-wSteeringAngleOffsetDyNVR>(-MAX_TASANGLE / 2);

[0129] And wEsStrAngleLeftLearnDy-wSteeringAngleOffsetDyNVR<0;

[0130] And wConvTASAngle-wSteeringAngleOffsetDyNVR>(-MAX_TASANGLE / 2);

[0131] And wConvTASAngle-wSteeringAngleOffsetDyNVR<0;

[0132] And (wConvTASAngle) <wEsStrAngleLeftLearnDy)。

[0133] Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleLeftNVR is the left end value of the EEPROM rack; wEsStrAngleLeftLearnDy is the left end value of the rack being learned; wConvTASAngle is the current TAS angle value; fESStrAngleLeftIncreaseflag is the condition for enabling leftward learning; MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

[0134] like Figure 12 As shown, in the "leftward" learning scenario three: the median learning angle, the already learned end left limit angle, and the current end left limit self-learning angle; the current end left limit self-learning angle and the already learned end left limit angle are simultaneously on another serrated edge; other angles are on one serrated edge.

[0135] therefore,

[0136] fESSStrAngleLeftIncreaseflag=(wEsStrAngleLeftLearnDy-wSAStrAngleOffsetDyNVR>(MAX_TASANGLE / 2));

[0137] And (wConvTASAngle-wSAStrAngleOffsetDyNVR>(MAX_TASANGLE / 2));

[0138] And (wConvTASAngle) <wEsStrAngleLeftLearnDy)。

[0139] Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleLeftNVR is the left end value of the EEPROM rack; wEsStrAngleLeftLearnDy is the left end value of the rack being learned; wConvTASAngle is the current TAS angle value; fESStrAngleLeftIncreaseflag is the condition for enabling leftward learning; MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

[0140] "Leftward" learning scenario four: median learning angle, learned end left limit angle, current end left limit self-learning angle; median learning angle, one end angle of the current end left limit self-learning angle and one end angle of the learned end left limit angle are on the same serrated edge; current end left limit self-learning angle and other angles and one end angle of the learned end left limit angle are on another serrated edge.

[0141] During driving, when the vehicle speed is >3 km / h, and the steering wheel is turned to the vicinity of the left end and the learning activation condition fESStrAngleLeftIncreaseflag==1, the current TAS value wConvTASAngle is the newly learned end angle wEsStrAngleLeftLearnDy=wConvTASAngle data is recorded.

[0142] When the steering wheel returns to near the center position, the learned value wEsStrAngleLeftLearnDy is stored in the data storage module (EEPROM), and the value read is wEsStrAngleLeftNVR. The entire right-turn learning process is as follows. Figure 13 As shown.

[0143] like Figure 14 As shown, the self-learning value compensation strategy of the end-position dynamic self-learning of the power steering system of the present invention stores the dynamic left and right end values ​​of the steering wheel returning to center in the data storage function module (EEPROM). After storage, the currently used left and right end values ​​are updated in a timely manner. In order to avoid instantaneous changes in angle caused by instantaneous updates of end-position values, the original current end-position learning value is gradually compensated to the actual left and right end values ​​of the steering wheel through a low-pass filter, thus completing the entire process of dynamic end-position angle self-learning.

[0144] Therefore, the compensation strategy is determined according to the following formula: D n =p*Δ+(1-p)*D n-1 .

[0145] Among them, D n This is the current end-angle compensation value; D n-1 Δ is the previous end-angle compensation value; Δ is the difference between the target end-angle and the actual end-angle; p is the compensation coefficient.

[0146] The present invention provides a dynamic end-effector self-learning safety reset for the power steering system. To avoid the offset of the end-effector rack support mechanism due to assembly or wear, which would cause a special situation in the end-effector's learning to the "left" and "right" directions, the system continuously learns in one direction and the values ​​are constantly updated; the other direction will never learn and the values ​​will remain unchanged, resulting in an ever-increasing learning stroke and a loss of relative accuracy in the end-effector angle-related functions.

[0147] The current method involves a safety reset when the distance between the learned left and right ends exceeds the predetermined distance. This resets the initial positions of the left and right ends before resuming self-learning. For example... Figure 15 The image shown is a schematic diagram of an end-point security reset scenario:

[0148] 1) Before the rack moves: S1 is the current end stroke.

[0149] 2) After the rack moves: S2 is the current end stroke.

[0150] 3) Rack movement S3: When S2>S1+ΔS, with the center as the center, the left and right S4 distances are reset to the end; ΔS is generally set at 5~10mm.

Claims

1. A method for end-effector dynamic self-learning in steer-by-wire control, characterized in that: The specific method is as follows: When the vehicle is started and in motion: the speed is greater than 3 km / h; When the steering wheel reaches its end point to the left or right, if the TAS angle received by the current steering wheel angle acquisition module at the left or right end point exceeds the default left or right limit TAS value, the left or right end point learning condition is met, and the current TAS value is recorded as the left or right limit value. When the steering wheel returns from the left or right end to the center position, the currently recorded TAS value is stored in the data storage function module; The end-effector self-learning module reads the values ​​and compensates them for the current left and right end-effector values. Then, based on the current median value, it calculates the angles of the left and right end-effectors and provides them to various functional modules. If the travel of the left or right ends of the rack exceeds the default travel value, the left and right ends will be safely reset to their initial default values. The end-to-right self-learning scenario of the end-to-end self-learning module is as follows: 1) Rightward learning scenario 1: median learning angle, learned end right limit angle, current end right limit self-learning angle, and both on a sawtooth edge; 2) Right-learning scenario two: median learning angle, learned end right limit angle, current end right limit self-learning angle; only the current end right limit self-learning angle is on another serrated edge at the same time; other angles are on one serrated edge; 3) Right-learning scenario three: median learning angle, learned end right limit angle, current end right limit self-learning angle; current end right limit self-learning angle and learned end right limit angle are both on another serrated edge; other angles are on one serrated edge; 4) Right Learning Scenario 4: Median learning angle, learned end right limit angle, current end right limit self-learning angle; median learning angle, one end angle of the current end right limit self-learning angle and one end angle of the learned end right limit angle are on the same zigzag edge; current end right limit self-learning angle and other angles and one end angle of the learned end right limit angle are on another zigzag edge.

2. The method for end-effector dynamic self-learning for steer-by-wire according to claim 1, characterized in that: When learning scenario one to the right, the judgment logic is as follows: fESSStrAngleRightIncreaseflag=[(wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR<(MAX_TASANGLE / 2)); And (wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR>0); And (wConvTASAngle-wSAStrAngleOffsetDyNVR<(-MAX_TASANGLE / 2))]; Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleRightNVR is the right end value of the rack stored in EEPROM; wEsStrAngleRightLearnDy is the right end value of the rack being learned; wConvTASAngle is the current angle value of TAS; fESStrAngleRightIncreaseflag is the condition for enabling rightward learning; and MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

3. The method for end-effector dynamic self-learning for steer-by-wire execution according to claim 1, characterized in that: When learning scenario two to the right, the judgment logic is as follows: fESSStrAngleRightIncreaseflag=[(wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR<(MAX_TASANGLE / 2)); And (wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR<(MAX_TASANGLE / 2)); And (wConvTASAngle-wSAStrAngleOffsetDyNVR>0); And (wConvTASAngle-wSAStrAngleOffsetDyNVR<(MAX_TASANGLE / 2)); And (wConvTASAngle>wEsStrAngleRightNVR)]; Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleRightNVR is the right end value of the rack stored in EEPROM; wEsStrAngleRightLearnDy is the right end value of the rack being learned; wConvTASAngle is the current angle value of TAS; fESStrAngleRightIncreaseflag is the condition for enabling rightward learning; and MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

4. The method for end-effector dynamic self-learning for steer-by-wire execution according to claim 1, characterized in that: When learning scenario three to the right, the judgment logic is as follows: fESSStrAngleRightIncreaseflag=[(wEsStrAngleRightLearnDy-wSAStrAngleOffsetDyNVR<(-MAX_TASANGLE / 2)); And (wConvTASAngle-wSAStrAngleOffsetDyNVR<(-MAX_TASANGLE / 2)); And (wConvTASAngle>wEsStrAngleRightLearnDy)]; Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleRightNVR is the right end value of the rack stored in EEPROM; wEsStrAngleRightLearnDy is the right end value of the rack being learned; wConvTASAngle is the current angle value of TAS; fESStrAngleRightIncreaseflag is the condition for enabling rightward learning; and MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

5. The method for end-effector dynamic self-learning for steer-by-wire according to claim 1, characterized in that: The following is a scenario for the end-to-left self-learning module: 1) Learning to the left scenario 1: median learning angle, learned end left limit angle, current end left limit self-learning angle, and both on a sawtooth edge; 2) Learning to the left scenario 2: median learning angle, learned end left limit angle, current end left limit self-learning angle; only the current end left limit self-learning angle is on another serrated edge at the same time; other angles are on one serrated edge; 3) Learning to the left, scenario three: median learning angle, learned end left limit angle, current end left limit self-learning angle; current end left limit self-learning angle and learned end left limit angle are both on another serrated edge; other angles are on one serrated edge; 4) Learning to the left scenario 4: median learning angle, learned end left limit angle, current end left limit self-learning angle; median learning angle, one end angle of the current end left limit self-learning angle and one end angle of the learned end left limit angle are on the same serrated edge; current end left limit self-learning angle and other angles and one end angle of the learned end left limit angle are on another serrated edge.

6. The method for end-effector dynamic self-learning for steer-by-wire execution according to claim 5, characterized in that: When learning scenario one to the left, the judgment logic is as follows: fESSStrAngleLeftIncreaseflag=[(wEsStrAngleLeftLearnDy-wSAStrAngleOffsetDyNVR>(MAX_TASANGLE / 2)); And (wEsStrAngleLeftLearnDy-wSAStrAngleOffsetDyNVR<0); And (wConvTASAngle-wSAStrAngleOffsetDyNVR>(MAX_TASANGLE / 2))]; Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleLeftNVR is the left end value of the EEPROM rack; wEsStrAngleLeftLearnDy is the left end value of the rack being learned; wConvTASAngle is the current TAS angle value; fESStrAngleLeftIncreaseflag is the condition for enabling leftward learning; MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

7. A method for end-effector dynamic self-learning for steer-by-wire according to claim 5, characterized in that: When learning scenario two to the left, the judgment logic is as follows: fESSStrAngleLeftIncreaseflag=[(wEsStrAngleLeftLearnDy-wSteeringAngleOffsetDyNVR>(-MAX_TASANGLE / 2)); And (wEsStrAngleLeftLearnDy-wSteeringAngleOffsetDyNVR<0); And (wConvTASAngle-wSteeringAngleOffsetDyNVR>(-MAX_TASANGLE / 2)); And (wConvTASAngle-wSteeringAngleOffsetDyNVR<0); And (wConvTASAngle) <wEsStrAngleLeftLearnDy)]; Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleLeftNVR is the left end value of the EEPROM rack; wEsStrAngleLeftLearnDy is the left end value of the rack being learned; wConvTASAngle is the current TAS angle value; fESStrAngleLeftIncreaseflag is the condition for enabling leftward learning; MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

8. A method for end-effector dynamic self-learning for steer-by-wire according to claim 5, characterized in that: When learning scenario three to the left, the judgment logic is as follows: fESSStrAngleLeftIncreaseflag=[(wEsStrAngleLeftLearnDy-wSAStrAngleOffsetDyNVR>(MAX_TASANGLE / 2)); And (wConvTASAngle-wSAStrAngleOffsetDyNVR>(MAX_TASANGLE / 2)); And (wConvTASAngle) <wEsStrAngleLeftLearnDy)]; Where wSAStrAngleOffsetDyNVR is the median value of the rack; wEsStrAngleLeftNVR is the left end value of the EEPROM rack; wEsStrAngleLeftLearnDy is the left end value of the rack being learned; wConvTASAngle is the current TAS angle value; fESStrAngleLeftIncreaseflag is the condition for enabling leftward learning; MAX_TASANGLE is the maximum value of the TAS characteristic, i.e., 2016.

9. A system employing any one of the above-mentioned end-effector dynamic self-learning methods, characterized in that: The system includes: Upper steering road feel module: Includes steering wheel angle acquisition module, mainly used to calculate the angle of upper steering road feel module, and transmit it to lower steering execution module via private CAN. In the end dynamic self-learning function, it is used to store the activation conditions of the angle for end learning. The vehicle signal processing system includes a vehicle speed acquisition module, which calculates the vehicle speed signal and transmits it to the lower steering execution module via CAN. It is used as one of the activation conditions for end-angle self-learning in the end-effector dynamic self-learning function. The lower steering execution module includes a signal processing module, an end-effector self-learning module, a TAS angle acquisition module, a fault handling module, a data storage module, and other drive-by-wire angle-related modules. The signal processing module is used to store the end-effector self-learning activation conditions and learning values, and the signals involved include the vehicle speed signal from the vehicle's CAN signal and the steering wheel angle signal from the private CAN signal.

Images