A control method and device for road feel simulation of a steer-by-wire system based on Kalman filtering

Abstract

The application discloses a road feeling simulation control method and device for a steer-by-wire system based on Kalman filtering, and solves the problems of current sensor being easily disturbed, imperfect fault diagnosis mechanism and insufficient continuity of road feeling feedback. The method collects actual current signals of an execution motor in real time, dynamically estimates the current by using a Kalman filter, and generates high-precision estimated current signals. When it is detected that the actual current signal fluctuates too much and the error e between the actual current signal and the estimated current continuously exceeds a preset threshold, it is determined that the current sensor is invalid, and the estimated current signal is switched to as a control input for road feeling simulation. When the measured value and the estimated value basically coincide and continuously stabilize, the actual current signal is switched to again. By means of the Kalman filtering and the dynamic switching mechanism, the application realizes high reliability and continuity of road feeling feedback, has fault diagnosis and fault-tolerant control capabilities, adapts to complex vehicle driving conditions, and significantly improves the safety and driving experience of the steer-by-wire system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of patents and belongs to the field of vehicle engineering and automatic control technology, and particularly relates to a road feel simulation control method and device for a steer-by-wire system based on Kalman filtering. BACKGROUND

[0002] With the rapid development of automotive electronics, steer-by-wire systems (SBW) as an advanced steering control technology, have gradually become a research hotspot in the field of intelligent driving and electric vehicles. The steer-by-wire system replaces the traditional mechanical connection through electronic signals, eliminating the direct mechanical linkage between the steering wheel and the steering wheel, so as to realize more flexible steering control, higher energy efficiency and better driving experience. However, the steer-by-wire system also faces many technical challenges, one of which is how to provide real and reliable road feel feedback to the driver in the absence of mechanical connection.

[0003] Road feel feedback is an important way for drivers to perceive the driving state of the vehicle, and directly affects driving safety and handling experience. In the steer-by-wire system, road feel feedback is usually simulated through the current signal of the execution motor. The current signal of the execution motor can reflect the load change of the steering system, and then generate a feedback torque matching the road conditions. However, the measurement accuracy and reliability of the current signal directly determine the quality of the road feel feedback. In actual application, the current sensor may be affected by electromagnetic interference, mechanical vibration or hardware aging, etc., resulting in fluctuations or distortion of the measured signal. Once the current sensor fails, the accuracy of the road feel feedback will be seriously affected, and even may cause safety hazards.

[0004] In order to solve the problem of current sensor failure, the existing technology usually adopts redundant design or fault diagnosis mechanism. For example, some schemes realize redundant measurement by increasing multiple current sensors, but this method will increase the complexity and cost of the system. Some other schemes detect sensor failure through simple threshold judgment or filtering algorithm, but these methods are often difficult to cope with complex dynamic working conditions, and are prone to misjudgment. In addition, the existing technology lacks a mechanism that can seamlessly switch to a backup signal source when the sensor fails, resulting in insufficient continuity and stability of the road feel feedback.

[0005] As a classical state estimation algorithm, Kalman filter is widely used in automatic control, signal processing and fault diagnosis. Through dynamic prediction and correction process, it can effectively suppress noise interference and improve the accuracy of state estimation. In the online control steering system, Kalman filter can be used to estimate the current signal of the execution motor, so as to provide a reliable backup signal source for road feeling feedback. However, the application potential of Kalman filter in road feeling simulation of online control steering system has not been fully tapped in the prior art, especially in seamless switching and dynamic recovery when the sensor fails.

[0006] In summary, the prior art online control steering system road feeling simulation control still has the following shortcomings:

[0007] 1. The measurement signal of the current sensor is susceptible to interference, and lacks efficient fault detection and fault tolerance mechanism;

[0008] 2. The existing fault diagnosis method is difficult to adapt to complex dynamic working conditions, and is prone to misjudgment;

[0009] 3. There is a lack of a control strategy that can seamlessly switch to a backup signal source and dynamically recover when the sensor fails. SUMMARY

[0010] To solve the above problems, the present application provides a Kalman filter-based online control steering system road feeling simulation control method and device to improve the reliability, continuity and dynamic adaptability of road feeling feedback, thereby providing a safer and more comfortable driving experience for the driver.

[0011] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0012] The Kalman filter-based online control steering system road feeling simulation control method comprises the following steps:

[0013] (1) Real-time acquisition of the actual current signal of the execution motor in the online control steering system I meas, and dynamic estimation of the current of the execution motor through the Kalman filter to generate the corresponding estimated current signal I est;

[0014] (2) Calculate the real-time error value e between the actual current signal and the estimated current signal;

[0015] (3) When the fluctuation amplitude of the actual current signal exceeds the preset threshold e th, and the error value e continuously exceeds the preset threshold for a first set time T fault, it is determined that the current sensor fails, the fault flag is triggered, and the estimated current signal I est using the Kalman filter is switched to as the control input of road feeling simulation;

[0016] (4) In the current sensor failure state, continuously monitor the error value e of the actual current signal and the estimated current signal; when the error value e is continuously lower than 80% of the threshold value for a second set time length T recover, it is determined that the current sensor is normal, the fault flag is cleared, and the actual current signal is switched to as the control input of the road sense simulation again;

[0017] (5) The road sense feedback torque is generated according to the switched current signal, and is output to the steering wheel through the steering actuator.

[0018] The further improvement of the application is that the state equation and the observation equation in the Kalman filter are constructed based on a dynamic model of the execution motor, the dynamic model includes electrical equations and mechanical equations of the motor, wherein the state variables at least include motor current I, motor speed ω and load torque T, and the observation variable is the actual current signal; the Kalman filter generates the estimated current signal through real-time iteration prediction and correction process.

[0019] The further improvement of the application is that the condition for determining the failure of the current sensor in step (3) further includes:

[0020] The actual current signal is analyzed in the frequency domain, and if the high-frequency noise amplitude is detected to be higher than a preset third threshold value, an adaptive adjustment mechanism of the Kalman filter is triggered to dynamically correct the filter parameters to suppress noise interference;

[0021] The threshold value is a dynamic threshold value, and the value is adaptively adjusted according to the current vehicle speed, steering input torque and vehicle driving state.

[0022] The further improvement of the application is that in steps (3) and (4), the first set time length and the second set time length are dynamically set according to the vehicle driving condition: the set time length is shortened to improve the switching sensitivity in high-speed driving condition, and the set time length is lengthened to reduce the probability of false triggering in low-speed or frequent steering condition.

[0023] The further improvement of the application is that in step (3) when the estimated current signal of the Kalman filter is switched, it further includes:

[0024] The abnormal fluctuation characteristics of the current actual current signal are recorded, and the characteristics are input to the fault diagnosis module to generate a fault code and store it to the vehicle storage;

[0025] The current sensor abnormal prompt information is sent to the driver through the vehicle human-machine interface.

[0026] The further improvement of the application is that before the actual current signal is switched again in step (4), it further includes:

[0027] The actual current signal is subjected to sliding window mean filtering processing, and a sliding average error of the actual current signal and an estimated current signal is calculated;

[0028] When the sliding average error is lower than 80% of the second threshold value for consecutive N sampling periods, a switching operation is performed, wherein N is dynamically set according to a vehicle control period.

[0029] The application further improves that the input of the Kalman filter further comprises a steering wheel angle signal, a vehicle yaw rate signal and a tire lateral force estimated value, for constructing a multi-dimensional fusion observation model, and improving the current estimation accuracy.

[0030] A road feel simulation control device of a steer-by-wire system, which is based on the Kalman filter-based road feel simulation control method of the steer-by-wire system, comprises:

[0031] A current acquisition module is configured to acquire an actual current signal of an executing motor in real time;

[0032] A Kalman filter module is configured to generate an estimated current signal based on a dynamic model, wherein the dynamic model comprises an electrical equation and a mechanical equation of the motor, and wherein state variables at least include a motor current I, a motor speed ω and a load torque T, and an observation variable is the actual current signal;

[0033] A fault diagnosis module is configured to perform error calculation, threshold comparison and switching logic;

[0034] A redundant control module is configured to activate the estimated current signal when the current sensor fails, and to synchronously update a calculation parameter of a road feel feedback torque;

[0035] An output interface module is configured to send the road feel feedback torque to a steering execution mechanism.

[0036] The application further improves that the redundant control module further comprises:

[0037] A historical data storage unit is configured to buffer the actual current signal and the estimated current signal in the last T seconds;

[0038] A data compensation unit is configured to perform weighted correction on a current estimated value based on historical data when switching to the estimated current signal, wherein a weight coefficient is negatively correlated with a duration of sensor failure.

[0039] A vehicle electronic control unit comprises a processor and a memory, and the memory stores a computer program, when the program is executed by the processor, the steps of the Kalman filter-based road feel simulation control method of the steer-by-wire system are implemented.

[0040] Compared with the prior art, the application has at least the following beneficial technical effects:

[0041] The present application realizes at least the following advantages by introducing Kalman filtering algorithm and dynamic switching mechanism:

[0042] Real-time estimation of motor current signal by Kalman filtering can provide a reliable backup signal source when the current sensor fails, ensuring the continuity and stability of road feel feedback.

[0043] The fault detection mechanism based on error value e and dynamic threshold can accurately identify the abnormal state of the current sensor and effectively reduce the probability of misjudgment.

[0044] When the sensor fails and recovers, the system can seamlessly switch the signal source, and through the sliding window mean filtering and dynamic threshold judgment, it can ensure the smoothness and reliability of the switching process.

[0045] By dynamically adjusting the threshold and filtering parameters, the present application can adapt to different vehicle driving conditions, including high-speed driving, low-speed steering, and frequent lane changing scenarios.

[0046] Compared with traditional redundant design, the present application realizes fault tolerance through software algorithm, without increasing additional hardware cost, and has high economic efficiency and practicality. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0048] Figure 1 It is a structure schematic diagram of the steer-by-wire system of the present application.

[0049] Figure 2 It is a schematic diagram of the road feel simulation control method of the steer-by-wire system in the present application.

[0050] Figure 3 It is a flow chart of the fault-tolerant control method provided in the present application.

[0051] Figure 4 It is an angle input diagram in embodiment 2 of the present application.

[0052] Figure 5 It is a schematic diagram of the sensor measured current value Imeas and the Kalman filtering estimated current value Iest in embodiment 2 of the present application.

[0053] Figure 6 It is a diagram of the current correction value changing with time in embodiment 2 of the present application.

[0054] Figure 7 A graph of road feel feedback torque calculated according to the current correction value in Embodiment 2 of the present application.

[0055] Figure 8 A comparison graph of road feel feedback values using the road feel simulation control method of the present application and not using the road feel simulation control method of the present application. DETAILED DESCRIPTION

[0056] Hereinafter, only certain exemplary embodiments are simply described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present application. Therefore, the drawings and description are considered to be exemplary in nature rather than limiting.

[0057] In the description of the present application, it should be understood that the terms "comprise" and "include" as used in the specification and the appended claims indicate the presence of the described features, integers, steps, operations, elements, and / or components but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0058] It should also be understood that the terms used in the present application specification are only for the purpose of describing particular embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, unless otherwise clearly indicated by the context, the singular forms "a," "an," and "the" are intended to include the plural forms as well.

[0059] It should be further understood that the term "and / or" as used in the present application specification and the appended claims means any one or more of the associated listed items as well as all possible combinations of the items.

[0060] Various structural diagrams according to the disclosed embodiments of the present application are shown in the drawings. These diagrams are not drawn to scale, in which certain details are exaggerated for the purpose of clarity and certain details can be omitted. The shapes of various regions, layers, and their relative sizes and positional relationships shown in the diagrams are only exemplary, and in actuality can be deviated due to manufacturing tolerances or technical limitations, and regions / layers with different shapes, sizes, and relative positions can be additionally designed by those skilled in the art according to actual needs.

[0061] Embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0062] Embodiment 1

[0063] The road feel simulation control method for a steer-by-wire system based on Kalman filtering provided by the present application specifically includes the following contents:

[0064] Real-time acquisition of actual current signal of motor in steer-by-wire system I meas, and a corresponding estimated current signal I est is generated by a Kalman filter dynamically estimating the current of the motor. The state equation and observation equation of the Kalman filter are constructed based on the dynamic model of the motor, and the state variables include motor current I, motor speed ω and load torque T, and the observation variable is the actual current signal. The Kalman filter generates a high-precision estimated current signal through real-time iterative prediction and correction process.

[0065] The real-time error value e between the actual current signal and the estimated current signal is calculated. When the fluctuation amplitude of the error value is detected to exceed the preset threshold, and continues to exceed the preset threshold for a first set time length, it is determined that the current sensor fails. The threshold is dynamically adjusted according to the vehicle driving state to adapt to different working condition requirements.

[0066] When it is determined that the current sensor fails, the estimated current signal of the Kalman filter is switched to be used as the control input of the road feel simulation. During the switching process, the system records the abnormal fluctuation characteristics of the current actual current signal, and inputs the characteristics to a fault diagnosis module to generate a fault code and store it to the vehicle storage. At the same time, the vehicle human-machine interface sends an abnormal current sensor prompt information to the driver.

[0067] In the current sensor failure state, the error value e between the actual current signal and the estimated current signal is continuously monitored. When the error value e is continuously lower than the first threshold for a second set time length, it is determined that the current sensor is normal. Before switching back to the actual current signal, the actual current signal is subjected to a sliding window mean filtering process, and the sliding average error between the actual current signal and the estimated current signal is calculated. When the sliding average error is continuously lower than 80% of the second threshold for N sampling periods, the switching operation is performed, and the actual current signal is used again as the control input of the road feel simulation.

[0068] The road feel feedback torque generation generates a road feel feedback torque according to the current signal, and outputs the road feel feedback torque to the steering wheel through the steering actuator. The calculation of the road feel feedback torque comprehensively considers the vehicle speed, steering wheel angle, vehicle yaw rate and tire lateral force, etc., to ensure the authenticity and dynamic adaptability of the feedback torque.

[0069] As shown in Figure 1 The present application is applied to a steer-by-wire system, although the steer-by-wire system has many advantages, but also faces many technical challenges. How to provide real and reliable road feel feedback for the driver is one of them

[0070] As shown in Figure 2As shown, the application uses the execution motor current as the main source of road feel feedback torque to provide appropriate steering resistance for the driver. However, the execution motor current sensor may be affected by unpredictable factors such as electromagnetic interference, mechanical vibration or hardware aging, resulting in sensor failure. Once the current sensor fails, the accuracy of the road feel feedback will be seriously affected, and even safety hazards may be caused. Therefore, this paper provides a steer-by-wire system road feel simulation control method based on Kalman filtering. As shown in Figure 3 .

[0071] When the steer-by-wire system is working normally, first, the required data is collected in real time, including the execution motor current I meas obtained through the current sensor, the motor speed ω obtained through the encoder or speed sensor, etc. Then, the execution motor current is estimated through the designed Kalman filter to obtain the estimated value I est. After that, the execution motor current is detected for error and judged for failure, and the real-time error value e between the actual current signal and the estimated current signal is calculated;

[0072] When the fluctuation amplitude of the actual current signal is detected to exceed the preset threshold e th, and the error value e continuously exceeds the preset threshold for a first set duration T fault, it is determined that the current sensor fails, the fault flag is triggered, and the estimated current signal I est using the Kalman filter is switched to as the control input for road feel simulation; in the current sensor failure state, the error value e of the actual current signal and the estimated current signal is continuously monitored; when the error value e continuously falls below 80% of the threshold for a second set duration T recover, it is determined that the current sensor has recovered to normal, the fault flag is cleared, and the actual current signal is switched back to as the control input for road feel simulation; finally, the road feel feedback torque is generated according to the switched current signal, and output to the steering wheel through the steering execution mechanism to provide appropriate road feel feedback to the driver.

[0073] Example 2

[0074] The sine working condition is selected to illustrate the embodiment of the application,

[0075] Test working condition setting: set the steering wheel angle input with a period of 5s and an amplitude of 300° at a vehicle speed of 20km / h, and the angle input graph is as shown in Figure 4 .

[0076] Set the current sensor fault: 0~5s, no fault; 5~10s, 0.5 times current sensor gain failure; 10~15s, no fault, 15~20s, current sensor complete failure

[0077] The sensor measured current value Imeas and the Kalman filter estimated current value Iest are as shown in the following formula. Figure 5

[0078] According to the road feeling simulation control method of the present application, the image of the current correction value changing with time is as shown in the following formula. Figure 6

[0079] The image of the road feeling feedback torque calculated according to the current correction value is as shown in the following formula. Figure 7

[0080] The comparison of the road feeling feedback values using the road feeling simulation control method of the present application and not using the road feeling simulation control method of the present application is as shown in the following formula. Figure 8 As can be seen from the formula, when the current sensor fails, the proper road feeling torque cannot be fed back without using the method, which causes a safety hazard in the driving process; using the present application can still output the proper road feeling feedback steering wheel torque when the current sensor fails, which improves the safety and fault tolerance of the steer-by-wire system.

[0081] Embodiment 3

[0082] The present application provides a steer-by-wire system road feeling simulation control device, which is based on the steer-by-wire system road feeling simulation control method based on Kalman filtering, and comprises:

[0083] A current acquisition module is configured to acquire the actual current signal of the executing motor in real time.

[0084] A Kalman filtering module is configured to generate an estimated current signal based on a dynamic model, wherein the dynamic model comprises an electrical equation and a mechanical equation of the motor, and the state variables at least include the motor current I, the motor speed ω and the load torque T, and the observation variable is the actual current signal.

[0085] A fault diagnosis module is configured to perform error calculation, threshold comparison and switching logic.

[0086] A redundancy control module is configured to enable the estimated current signal when the current sensor fails, and synchronously update the calculation parameters of the road feeling feedback torque.

[0087] An output interface module is configured to send the road feeling feedback torque to the steering execution mechanism.

[0088] In the present embodiment, the redundancy control module further comprises:

[0089] A historical data storage unit is configured to buffer the actual current signal and the estimated current signal in the last T seconds.

[0090] ​​​The data compensation unit corrects the current estimation value based on historical data when switching to the estimated current signal, wherein the weight coefficient is negatively related to the duration of the sensor failure.

[0091] Embodiment 4

[0092] The application provides a vehicle electronic control unit, comprising a processor and a memory, wherein the memory stores a computer program, and when the program is executed by the processor, the steps of the line control steering system road feeling simulation control method based on Kalman filtering are realized.

[0093] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, device or computer program product. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0094] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one or more flows and / or blocks. Figure 1 The device that implements the functions specified in one or more flows and / or blocks.

[0095] These computer program instructions can also be stored in a computer readable storage medium that can guide the computer or other programmable data processing devices to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction devices, which implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one or more flows and / or blocks. Figure 1 The device that implements the functions specified in one or more flows and / or blocks.

[0096] These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to produce a computer implemented process, so that the instructions executed on the computer or other programmable device provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one or more flows and / or blocks. Figure 1steps of the functions specified in the one or more blocks.

[0097] The foregoing merely illustrates the principles of the application and application of its novel teachings. This description is not intended to limit the application to the exact construction described herein. The application is, therefore, to be construed as including all modifications and equivalents that fall within the scope of the claims. Specific features of the

[0098] Furthermore, it should be appreciated that a presently- described embodiment can be implemented in either hardware, software, or a combination thereof. The present application also can be embodied as computer-readable data and / or computer- executable instructions (e.g., one or more computer programs) embodied on one or more information carriers (e.g., one or more tangible computer-readable media, such as one or more magnetic tapes, one or more floppy disks, one or more optical disks, one or more CD-ROMs, one or more DVDs, one or more RAM, one or more ROM, one or more flash memories, one or more hard disks, one or more solid state drives, one or more solid state memories, one or more USB drives, one or more memory cards, one or more memory sticks, one or more memory modules, one or more memory chips, one or more memory devices, one or more memory units, one or more memory locations, one or more memory addresses, one or more memory registers, one or more memory buffers, one or more memory caches, one or more memory pools, one or more memory banks, one or more memory arrays, one or more memory blocks, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips, one or more memory chips

Claims

1. A control method for road feel simulation of a steer-by-wire system based on Kalman filtering, characterized by, The method comprises the following steps: (1) Real-time acquisition of the actual current signal of the execution motor in the steer-by-wire system I meas, and through the Kalman filter, dynamic estimation of the current of the execution motor is performed to generate the corresponding estimated current signal I est; (2) calculating the real-time error value e between the actual current signal and the estimated current signal; (3) when the fluctuation amplitude of the actual current signal is detected to exceed a preset threshold e th, and the error value e continuously exceeds the preset threshold for a first set duration T fault, it is determined that the current sensor is failed, a fault flag is triggered, and the estimated current signal using the Kalman filter is switched to as the control input of road feeling simulation I est (4) in the current sensor failure state, continuously monitor the error value e of the actual current signal and the estimated current signal; when the error value e is continuously lower than 80% of the threshold value for a second set time length T when the current sensor recovers, it is determined that the current sensor recovers to normal, the fault flag is cleared, and the actual current signal is switched to the control input of the road feeling simulation again. (5) generating a road feel feedback torque according to the switched current signal, and outputting the road feel feedback torque to the steering wheel through a steering actuator.

2. The control method of claim 1, wherein, The state equation and the observation equation in the Kalman filter are constructed based on a dynamic model of the executing motor, the dynamic model comprising an electrical equation and a mechanical equation of the motor, wherein the state variables at least include motor current I, motor speed ω and load torque T, and the observation variable is the actual current signal; The Kalman filter generates the estimated current signal through a real-time iterative prediction and correction process.

3. The control method of claim 1, wherein, The condition for determining the failure of the current sensor in the step (3) further comprises: The actual current signal is analyzed in the frequency domain, and if the amplitude of the detected high-frequency noise exceeds a preset third threshold value, an adaptive adjustment mechanism of the Kalman filter is triggered to dynamically correct the filtering parameters to suppress noise interference; The third threshold value is a dynamic threshold value, and the value thereof is adaptively adjusted according to the current vehicle speed, the steering wheel input torque and the vehicle driving state.

4. The control method of claim 1, wherein, In the steps (3) and (4), the first set time length and the second set time length are dynamically set according to the vehicle driving condition: the set time length is shortened to improve the switching sensitivity in high-speed driving conditions, and the set time length is lengthened to reduce the probability of false triggering in low-speed or frequent steering conditions.

5. The control method of claim 1, wherein, When the step (3) switches to the estimated current signal of the Kalman filter, further comprising: Recording the abnormal fluctuation characteristics of the current actual current signal, and inputting the characteristics to a fault diagnosis module to generate a fault code and store the fault code to a vehicle-mounted storage; Sending an abnormal current sensor prompt information to the driver through a vehicle-mounted human-machine interface.

6. The control method of claim 1, wherein, Before switching back to the actual current signal in the step (4), further comprising: Performing a sliding window mean filtering process on the actual current signal, and calculating the sliding average error of the actual current signal and the estimated current signal; When the sliding average error is lower than 80% of the threshold value for N consecutive sampling periods, performing the switching operation, wherein N is dynamically set according to the vehicle control period.

7. The control method of claim 1, wherein, The input of the Kalman filter further comprises a steering wheel angle signal, a vehicle yaw rate signal and a tire lateral force estimate value, which are used to construct a multi-dimensional fusion observation model to improve the current estimation accuracy.

8. A road feel simulation control device for a steer-by-wire system, characterized in that, The device is based on the road feel simulation control method of the steer-by-wire system based on the Kalman filtering of claim 1, comprising: A current acquisition module for real-time acquisition of the actual current signal of the executing motor; A Kalman filtering module for generating an estimated current signal based on a dynamic model, the dynamic model comprising an electrical equation and a mechanical equation of the motor, wherein the state variables at least include motor current I, motor speed ω and load torque T, and the observation variable is the actual current signal; A fault diagnosis module for performing error calculation, threshold comparison and switching logic; A redundant control module for enabling the estimated current signal when the current sensor fails, and synchronously updating the calculation parameters of the road feel feedback torque; An output interface module for sending the road feel feedback torque to the steering actuator.

9. A steer-by-wire system road-feel simulation control device according to claim 8, characterized by The redundant control module further comprises: A historical data storage unit for buffering the actual current signal and the estimated current signal in the last T seconds; The data compensation unit corrects the current estimation value based on historical data when switching to the estimated current signal, wherein the weight coefficient is negatively correlated with the duration of sensor failure.

10. A vehicle electronic control unit, characterized by, The system comprises a processor and a memory, and the memory stores a computer program. When the program is executed by the processor, the steps of the method for simulating road feeling of a steer-by-wire system based on Kalman filtering according to any one of claims 1-7 are implemented.

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