Road feel simulation method and device for steer-by-wire vehicle, storage medium, and electronic device

Abstract

The present invention provides a method and device, storage medium, and electronic device for simulating road feel of a steer-by-wire vehicle, wherein the method comprises: obtaining historical driving style data of the steer-by-wire vehicle, and collecting the actual hand torque of the driver of the steer-by-wire vehicle; calculating the driver's gain coefficient based on the historical driving style data, wherein the gain coefficient is used to characterize the driver's desired clarity of road feel; obtaining the theoretical road feel feedback hand torque of the vehicle, calculating the expected hand torque based on the theoretical road feel feedback hand torque and the gain coefficient; and simulating the road feel of the steer-by-wire vehicle using the expected hand torque and the actual hand torque. This embodiment solves the technical problem in the related art that steer-by-wire vehicles cannot transmit road feel according to the driver's habits, prevents the driver from losing judgment of the vehicle's dynamics, reduces driving safety risks, and improves driving safety, control experience, and system stability.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a method and device for simulating road feel of a steer-by-wire vehicle, a storage medium, and an electronic device. Background Art

[0002] In related technologies, steer-by-wire systems control vehicle steering by transmitting steering commands to the steering gear via electrical signals. This system eliminates the mechanical connection between the steering wheel and the steering wheel, freeing it from the limitations of traditional steering systems. This reduces the complexity of the steering system while significantly improving its responsiveness. Furthermore, because the intermediate shaft is eliminated, there is no direct force feedback to the driver. To simulate the feel of a traditional steering system, a motor is required to emulate road feel.

[0003] In related technologies, there are two main types of road feel feedback: one is to obtain the actual vehicle's road feel through real-time measurement of the rack force feedback based on a dynamic model simulation or by installing corresponding sensors on the steering actuator of the steer-by-wire system; the other is to obtain the road feel feedback torque based on experience and a simple linear dynamic model. Due to the current problems of difficult sensor installation, increased costs, inaccurate rack force estimation, and difficulty in model establishment, road feel feedback is not ideal, affecting the driver's judgment of road conditions and vehicle safety.

[0004] For the above-mentioned problems existing in related technologies, no efficient and accurate solutions have been found yet. Summary of the Invention

[0005] The present invention provides a method and device for simulating road feel of a steer-by-wire vehicle, a storage medium, and an electronic device to solve technical problems in related technologies.

[0006] According to one embodiment of the present invention, a method for simulating road feel of a steer-by-wire vehicle is provided, comprising: obtaining historical driving style data of the steer-by-wire vehicle, and collecting the actual hand torque of the driver of the steer-by-wire vehicle; calculating the driver's gain coefficient based on the historical driving style data, wherein the gain coefficient is used to characterize the driver's desired clarity of road feel; obtaining a theoretical road feel feedback hand torque of the vehicle, and calculating an expected hand torque based on the theoretical road feel feedback hand torque and the gain coefficient; calculating a motor torque through a closed-loop PID controller based on the expected hand torque and the actual hand torque; and calculating a high-frequency road feel compensation torque of the vehicle; and simulating the road feel of the steer-by-wire vehicle after adding the motor torque and the high-frequency road feel compensation torque.

[0007] Optionally, calculating the driver's gain coefficient based on the historical driving style data includes: identifying the driver's driving habit type based on the historical driving style data; and calculating the gain coefficient of the driving habit type.

[0008] Optionally, identifying the driver's driving habit type based on the historical driving style data includes: preprocessing the historical driving style data to obtain characteristic parameters in a preset distribution format; selecting a target posterior probability that best matches the characteristic parameters from a pre-constructed posterior probability set, wherein the pre-constructed posterior probability set includes multiple posterior probabilities, each posterior probability corresponding to a driving habit type; and determining the driving habit type corresponding to the target posterior probability as the driver's driving habit type.

[0009] Optionally, before selecting the target posterior probability that best matches the feature parameters from the pre-constructed posterior probability set, the method further includes: obtaining multiple sample data; randomly initializing the model parameters of a Gaussian mixture model, wherein the number of Gaussian distributions of the Gaussian mixture model is the same as the number of driving habit types; iteratively performing the following steps until the model parameters of the Gaussian mixture model meet preset conditions: calculating the posterior probability that each sample data in the multiple sample data belongs to each cluster in the Gaussian mixture model to obtain a probability set for all sample data; updating the model parameters of the Gaussian mixture model based on the probability set; extracting the updated model parameters of the Gaussian mixture model; and outputting the posterior probability set after the optimization of the Gaussian mixture model is completed.

[0010] Optionally, calculating the gain coefficient of the driving habit type includes: obtaining the target posterior probability of the driving habit type; calculating the gain coefficient of the driving habit type using the following formula: : ;in, The driver's driving habits The corresponding gain reference coefficient, The driver's driving habit type The corresponding target posterior probability.

[0011] Optionally, obtaining the theoretical road feel feedback hand torque of the vehicle includes: obtaining the rack force signal and vehicle speed of the vehicle in real time, wherein the rack force signal is used to characterize the force feedback transmitted from the road load to the steering wheel; and searching for the theoretical road feel feedback hand torque that matches the rack force signal and the vehicle speed in a preset mapping table of the vehicle.

[0012] Optionally, calculating the expected hand torque based on the theoretical road feel feedback hand torque and the gain coefficient includes: obtaining a compensation torque set of the vehicle, and superimposing the compensation torque set and the theoretical road feel feedback hand torque to obtain an intermediate hand torque; multiplying the intermediate hand torque by the gain coefficient to obtain the expected hand torque.

[0013] Optionally, obtaining the compensation torque set of the vehicle includes: collecting the first compensation torque output by the vehicle's return torque module, collecting the second compensation torque output by the vehicle's damping compensation module, and collecting the third compensation torque output by the vehicle's friction compensation module; and determining the first compensation torque, the second compensation torque, and the third compensation torque as the compensation torque set.

[0014] Optionally, calculating the motor torque through the PID controller in a closed loop according to the desired hand torque and the actual hand torque includes: calculating the motor torque using the following formula: : ; where t is the closed loop period, , represents the expected hand torque, is the actual hand torque, , , are the proportional, integral and differential gains of the PID controller respectively, is the feedforward control term, , is the gain coefficient, is the steering wheel angular velocity, is the steering wheel angular acceleration, a, b, c are constants.

[0015] Optionally, calculating the high-frequency road feel compensation torque of the vehicle includes: acquiring the rack force signal of the vehicle in real time, wherein the rack force signal is used to characterize the force feedback transmitted from the road load to the steering wheel; performing high-pass filtering on the rack force signal to extract a high-frequency road excitation component; and determining the high-frequency road excitation component as the high-frequency road feel compensation torque of the vehicle.

[0016] According to another embodiment of the present invention, a road feel simulation device for a steer-by-wire vehicle is provided, comprising: an acquisition module for acquiring historical driving style data of the steer-by-wire vehicle and actual hand torque of the driver of the steer-by-wire vehicle; a first calculation module for calculating a gain coefficient of the driver based on the historical driving style data, wherein the gain coefficient is used to characterize the driver's desired clarity of road feel; a second calculation module for acquiring a theoretical road feel feedback hand torque of the vehicle and calculating an expected hand torque based on the theoretical road feel feedback hand torque and the gain coefficient; a simulation module comprising: a calculation unit for calculating a motor torque through a closed-loop PID controller based on the expected hand torque and the actual hand torque; and calculating a high-frequency road feel compensation torque of the vehicle; and a simulation unit for simulating the road feel of the steer-by-wire vehicle after adding the motor torque and the high-frequency road feel compensation torque.

[0017] Optionally, the first calculation module includes: an identification unit for identifying the driver's driving habit type based on the historical driving style data; and a calculation unit for calculating a gain coefficient of the driving habit type.

[0018] Optionally, the identification unit includes: a processing subunit, used to preprocess the historical driving style data to obtain characteristic parameters in a preset distribution format; a selection subunit, used to select a target posterior probability that best matches the characteristic parameters from a pre-constructed posterior probability set, wherein the pre-constructed posterior probability set includes multiple posterior probabilities, each posterior probability corresponding to a driving habit type; and a determination subunit, used to determine the driving habit type corresponding to the target posterior probability as the driving habit type of the driver.

[0019] Optionally, the device also includes: an acquisition module for acquiring multiple sample data before the first calculation module selects the target posterior probability that best matches the feature parameter in the pre-constructed posterior probability set; an initialization module for randomly initializing the model parameters of the Gaussian mixture model, wherein the number of Gaussian distributions in the Gaussian mixture model is the same as the number of driving habit types; an iteration module for iteratively executing the following steps until the model parameters of the Gaussian mixture model meet preset conditions: calculating the posterior probability that each sample data in the multiple sample data belongs to each cluster in the Gaussian mixture model to obtain a probability set of all sample data; updating the model parameters of the Gaussian mixture model based on the probability set; extracting the updated model parameters of the Gaussian mixture model; and an output module for outputting the posterior probability set after the optimization of the Gaussian mixture model is completed.

[0020] Optionally, the calculation unit includes: an acquisition subunit for acquiring the target posterior probability of the driving habit type; a calculation subunit for calculating the gain coefficient of the driving habit type using the following formula: : ;in, The driver's driving habits The corresponding gain reference coefficient, The driver's driving habit type The corresponding target posterior probability.

[0021] Optionally, the second calculation module includes: a first acquisition unit, used to obtain the rack force signal and vehicle speed of the vehicle in real time, wherein the rack force signal is used to characterize the force feedback transmitted from the road load to the steering wheel; and a search unit, used to search for a theoretical road feel feedback hand torque matching the rack force signal and the vehicle speed in a preset mapping table of the vehicle.

[0022] Optionally, the second calculation module includes: a second acquisition unit, used to obtain the compensation torque set of the vehicle, and superimpose the compensation torque set and the theoretical road feel feedback hand torque to obtain an intermediate hand torque; an operation unit, used to multiply the intermediate hand torque with the gain coefficient to obtain a desired hand torque.

[0023] Optionally, the second acquisition unit includes: a collection subunit, used to collect the first compensation torque output by the vehicle's return torque module, the second compensation torque output by the vehicle's damping compensation module, and the third compensation torque output by the vehicle's friction compensation module; a determination subunit, used to determine the first compensation torque, the second compensation torque, and the third compensation torque as a compensation torque set.

[0024] Optionally, the calculation unit includes: a calculation subunit, configured to calculate the motor torque using the following formula: : ; where t is the closed loop period, , represents the expected hand torque, is the actual hand torque, , , are the proportional, integral and differential gains of the PID controller respectively, is the feedforward control term, , is the gain coefficient, is the steering wheel angular velocity, is the steering wheel angular acceleration, a, b, c are constants.

[0025] Optionally, the calculation unit includes: an acquisition subunit, used to acquire the rack force signal of the vehicle in real time, wherein the rack force signal is used to characterize the force feedback transmitted from the road load to the steering wheel; a filtering subunit, used to perform high-pass filtering on the rack force signal to extract a high-frequency road excitation component; and a determination subunit, used to determine the high-frequency road excitation component as the high-frequency road feel compensation torque of the vehicle.

[0026] According to another aspect of an embodiment of the present application, a storage medium is further provided, which includes a stored program, and the above steps are executed when the program is run.

[0027] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; wherein: the memory is used to store computer programs; the processor is used to execute the steps in the above method by running the program stored in the memory.

[0028] An embodiment of the present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the steps in the above method.

[0029] Beneficial effects of the present invention:

[0030] 1. Accurately identify driving habits and provide personalized road feedback for different drivers;

[0031] 2. Utilize the posterior probability of the model algorithm to calculate the gain coefficient corresponding to the driving style in real time, achieving driver-friendly hand torque adjustment;

[0032] 3. The Gaussian mixture model is used for cluster analysis and parameter optimization, which is highly adaptable to noise and abnormal data, making steer-by-wire more stable in complex road conditions and extreme scenarios.

[0033] 4. Support long-term optimization, and conduct long-term tracking and analysis of changes in driver habits through probabilistic modeling to facilitate system learning and adaptive adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0035] Figure 1 This is a hardware structure block diagram of a car according to an embodiment of the present invention;

[0036] Figure 2 is a flow chart of a method for simulating road feel of a steer-by-wire vehicle according to an embodiment of the present invention;

[0037] Figure 3 is a schematic diagram of the principle of identifying driving habit types in an embodiment of the present invention;

[0038] Figure 4 is a schematic diagram of the cluster analysis in an embodiment of the present invention;

[0039] Figure 5 is a mapping curve diagram of the rack force and the theoretical road feel feedback hand torque under a certain vehicle speed condition in an embodiment of the present invention;

[0040] Figure 6 is a schematic diagram of the principle of calculating the desired hand torque in an embodiment of the present invention;

[0041] Figure 7 is a schematic diagram of a road feel simulation based on closed-loop control in an embodiment of the present invention;

[0042] Figure 8 is a schematic diagram of a road feel simulation system according to an embodiment of the present invention;

[0043] Figure 9 4 is a structural block diagram of a road feel simulation device for a steer-by-wire vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a part of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of this application. It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0045] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0046] Example 1

[0047] The method embodiment provided in the first embodiment of the present application can be executed in a car, a server, a processor, a controller or a similar processing device. Taking running on a car as an example, Figure 1 This is a hardware structure diagram of a car according to an embodiment of the present invention. Figure 1 As shown, a car may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 104 for storing data. Optionally, the above-mentioned car may also include a transmission device 106 for communication functions and an input and output device 108. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned automobile. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0048] The memory 104 can be used to store vehicle programs, for example, software programs and modules of application software, such as the vehicle program corresponding to the road feel simulation method of a steer-by-wire vehicle in an embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the vehicle program stored in the memory 104, thereby implementing the above-mentioned method. The memory 104 may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the vehicle via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0049] Transmission device 106 is used to receive or transmit data via a network. A specific example of such a network may include a wireless network provided by the vehicle's communications provider. In one embodiment, transmission device 106 includes a network interface controller (NIC), which can connect to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0050] In this embodiment, a method for simulating road feel of a steer-by-wire vehicle is provided. Figure 2 FIG. 1 is a flow chart of a method for simulating road feel of a steer-by-wire vehicle according to an embodiment of the present invention. Figure 2As shown, the process includes the following steps:

[0051] Step S200, acquiring historical driving style data of a steer-by-wire vehicle and collecting actual hand torque of a driver of the steer-by-wire vehicle;

[0052] The actual hand torque in this embodiment is the real-time torque of the hand force applied to the steering wheel by the driver of the steer-by-wire vehicle, as collected by the reverse wheel sensor, when the driver operates the steering wheel.

[0053] Optionally, historical driving style data is driving data collected from a steer-by-wire vehicle reflecting the driver's driving style before road feel simulation is performed on the actual hand torque collected at the current time. This data includes vehicle speed, actual hand torque, and ideal road feel torque, and the ideal road feel torque difference between the maximum and minimum ideal road feel torques during the historical period is calculated. Historical driving style data includes historical average vehicle speed, historical average actual hand torque, and ideal road feel torque difference.

[0054] Step S201, calculating a driver's gain coefficient based on the historical driving style data, wherein the gain coefficient is used to represent the driver's desired clarity of road feel;

[0055] Different drivers have different requirements for road feel. Some drivers like a clear road feel, while others do not. For example, drivers who prefer economical driving (focusing on driving power consumption or fuel economy) expect a less clear road feel, drivers who prefer comfortable driving (focusing on driving comfort) expect a moderate degree of road feel, and drivers who prefer sporty driving (focusing on driving experience) expect a greater degree of road feel.

[0056] The road feel feedback in this embodiment is an ideal road feel simulated by a motor. By providing different road feel feedback for different drivers, a steer-by-wire road feel feedback control method that is adaptive to the driver's habits is designed.

[0057] Step S202, obtaining a theoretical road feel feedback hand torque of the vehicle, and calculating an expected hand torque according to the theoretical road feel feedback hand torque and the gain coefficient;

[0058] Step S203 , calculating the motor torque through a PID (proportional-integral-derivative) controller closed loop according to the desired hand torque and the actual hand torque; and calculating the high-frequency road feel compensation torque of the vehicle;

[0059] Step S204 , performing road feel simulation on the steer-by-wire vehicle after adding the motor torque and the high-frequency road feel compensation torque;

[0060] The total torque obtained by adding the motor torque and the high-frequency road feel compensation torque is applied in reverse to the motor of the steering wheel of the steer-by-wire vehicle, thereby realizing road feel simulation.

[0061] Through the above steps, historical driving style data of a steer-by-wire vehicle and the actual hand torque of the driver of the steer-by-wire vehicle are obtained; a gain coefficient of the driver is calculated based on the historical driving style data, wherein the gain coefficient is used to characterize the driver's desired clarity of road feel; a theoretical road feel feedback hand torque of the vehicle is obtained, and an expected hand torque is calculated based on the theoretical road feel feedback hand torque and the gain coefficient; a motor torque is calculated through a closed-loop PID controller based on the expected hand torque and the actual hand torque; and a high-frequency road feel compensation torque of the vehicle is calculated; and the motor torque and the high-frequency road feel compensation torque are added together to perform road feel simulation on the steer-by-wire vehicle, thereby providing different road feel feedback to different drivers, thereby adapting to the driver's habits when the steer-by-wire vehicle performs road feel feedback control. This solves the technical problem in the related art that steer-by-wire vehicles cannot transmit road feel according to the driver's habits, prevents the driver from losing judgment of the vehicle dynamics, reduces driving safety risks, and improves driving safety, control experience, and system stability.

[0062] In one implementation of this embodiment, calculating the driver's gain coefficient based on the historical driving style data includes: identifying the driver's driving habit type based on the historical driving style data, wherein the driving habit type includes one of the following: economy, comfort, and sport; and calculating the gain coefficient of the driving habit type.

[0063] In one example, identifying a driver's driving habit type based on the historical driving style data includes: preprocessing the historical driving style data to obtain characteristic parameters in a preset distribution format; selecting a target posterior probability that best matches the characteristic parameters from a pre-constructed posterior probability set, wherein the pre-constructed posterior probability set includes multiple posterior probabilities, each posterior probability corresponding to a driving habit type; and determining the driving habit type corresponding to the target posterior probability as the driver's driving habit type.

[0064] In one example, calculating the gain coefficient of the driving habit type includes: obtaining the target posterior probability of the driving habit type; calculating the gain coefficient of the driving habit type using the following formula: : ;in, The driver's driving habits The corresponding gain reference coefficient, The driver's driving habit type The corresponding target posterior probability.

[0065] When the driver habit recognition module identifies the driving habit type, it analyzes and identifies the current driver's driving habits and selects the category with the highest probability as the current driver's driving habits. Figure 3 This is a schematic diagram of the principle of identifying driving habit types in an embodiment of the present invention. The process includes:

[0066] Preprocess the collected driving data of the current driver to make it consistent with the data distribution of the training model;

[0067] Calculate the current driver's characteristics after selecting the characteristic parameters The posterior probability of belonging to each class: ; is the mixing coefficient of the kth class, is the probability density of the K-th Gaussian distribution;

[0068] According to the posterior probability, select the category with the largest posterior probability As a classification of current driver habits: ;

[0069] The gain coefficient is calculated by weighted averaging the current driver's driving habit type through the posterior probability:

[0070] ;

[0071] Among them, G is the calculated comprehensive gain coefficient of the current driver, is the gain benchmark coefficient corresponding to the k-th driving habit, and K (capital letter) is the total number of driving habits.

[0072] In this embodiment, before selecting the target posterior probability that best matches the feature parameter in the pre-constructed posterior probability set, it also includes: obtaining multiple sample data; randomly initializing the model parameters of the Gaussian mixture model, wherein the number of Gaussian distributions of the Gaussian mixture model is the same as the number of driving habit types; iteratively performing the following steps until the model parameters of the Gaussian mixture model meet the preset conditions: calculating the posterior probability that each sample data in the multiple sample data belongs to each cluster in the Gaussian mixture model to obtain a probability set of all sample data; updating the model parameters of the Gaussian mixture model based on the probability set; extracting the updated model parameters of the Gaussian mixture model; and outputting the posterior probability set after the optimization of the Gaussian mixture model is completed.

[0073] Optionally, updating the model parameters of the Gaussian mixture model based on the probability set includes: updating the mean of the Gaussian mixture model using the following formula: : ; Update the covariance matrix of the Gaussian mixture model using the following formula : ; Use the following formula to update the mixing coefficient of the Gaussian mixture model ; Wherein, N is the total sample size of the sample data, It is a sample Belong to cluster The probability of yes With sample The product of the vector value of the sample data, the model parameters including the mean , covariance matrix , mixing coefficient ,satisfy .

[0074] Figure 4 This is a schematic diagram of the cluster analysis in an embodiment of the present invention. The cluster analysis module uses the Gaussian mixture model algorithm to cluster analyze the collected road feel feedback torque, and divides the driver's habits into three types: economy, comfort, and sports, which are recorded as z1, z2, and z3. Compared with the K-Means algorithm, the Gaussian mixture model has more flexible cluster shape processing and provides soft classification. The K-Means algorithm can only hard-classify the driver into a certain driving style, ignoring the ambiguity of the style, while the Gaussian mixture model algorithm realizes soft classification through posterior probability, which can quantify the possibility of the driver belonging to each style, facilitating personalized adjustment. At the same time, the Gaussian mixture model algorithm is more robust and suitable for processing complex data. The process of cluster analysis includes:

[0075] First, the collected driving data of different drivers are preprocessed to remove missing values and outliers, and standardized to ensure that different features are at the same level;

[0076] The difference between the maximum ideal road feeling torque and the minimum ideal road feeling torque is selected as the characteristic parameter used in the cluster analysis formula;

[0077] Initialize the Gaussian mixture model, determine the number of Gaussian distributions K=3, and randomly initialize the parameters of each Gaussian distribution, the mean , covariance matrix , mixing coefficient ,satisfy ;

[0078] In the expectation step E (Expectation), calculate each sample Which cluster does it belong to? The posterior probability of :

[0079] ;

[0080] in, It is a sample Belong to cluster The probability of is the probability density function of the kth Gaussian distribution, which represents the probability density function of the kth Gaussian distribution given the mean vector and covariance matrix Under the condition that a d-dimensional vector The probability density of occurrence, is the input sample to be evaluated, Represents the dimension of the input sample.

[0081] ;

[0082] After obtaining the probability, update the model parameters of the previous round in the maximization step M (Maximization) and update the mean , update the covariance matrix , update the mixing coefficient , N is the total sample size;

[0083] Repeat the E and M steps until the log-likelihood function converges. , ;

[0084] According to the posterior probability , divide each sample into the cluster with the highest probability, .

[0085] In one implementation of this embodiment, obtaining the theoretical road feel feedback hand torque of the vehicle includes: obtaining the rack force signal and vehicle speed of the vehicle in real time, wherein the rack force signal is used to characterize the force feedback transmitted from the road load to the steering wheel; and searching for the theoretical road feel feedback hand torque that matches the rack force signal and the vehicle speed in a preset mapping table of the vehicle.

[0086] Original rack force Perform low-pass filtering to eliminate high-frequency noise (such as motor vibration and road impact noise). The filter transfer function is: , T is 0.001s, and the smooth rack force is output after filtering , based on the preset rack force-theoretical road feel feedback hand torque mapping curve, Figure 5 This is a mapping curve diagram of the rack force and the theoretical road feel feedback hand torque under a certain vehicle speed condition in an embodiment of the present invention. The theoretical road feel feedback hand torque corresponding to the current rack force is calculated using a two-dimensional linear interpolation method: , where the mapping curve is generated based on experimental calibration data, satisfying the nonlinear characteristics of high gradient at low speed (enhanced road feel) and low gradient at high speed (interference suppression).

[0087] In one example, calculating the expected hand torque based on the theoretical road feel feedback hand torque and the gain coefficient includes: obtaining the compensation torque set of the vehicle, and superimposing the compensation torque set and the theoretical road feel feedback hand torque to obtain an intermediate hand torque; multiplying the intermediate hand torque by the gain coefficient to obtain the expected hand torque.

[0088] Optionally, the compensation torque includes: a set of compensation torques output by other functional modules (such as a return torque module, a damping compensation module, a friction compensation module, etc.) .

[0089] In this embodiment, obtaining the compensation torque set of the vehicle includes: collecting the first compensation torque output by the vehicle's return torque module, collecting the second compensation torque output by the vehicle's damping compensation module, and collecting the third compensation torque output by the vehicle's friction compensation module; determining the first compensation torque, the second compensation torque, and the third compensation torque as the compensation torque set.

[0090] Figure 6 This is a schematic diagram of the principle of calculating the desired hand torque in an embodiment of the present invention. In the road feel feedback control of the wire-controlled steering system, the calculation algorithm of the desired hand torque is used as the core algorithm. Through multi-source torque fusion and driver personalized gain adjustment, the desired hand torque that meets the current driving scene and driver characteristics is generated. The calculation process of the desired hand torque includes: the input signal includes: rack force: the rack axial force calculated in real time by the steering actuator (such as the steering motor); , representing the force feedback transmitted from the road load to the steering wheel; compensation torque: the set of compensation torques output by other functional modules (such as the return torque module, damping compensation module, friction compensation module, etc.) ; Comprehensive gain coefficient: personalized gain coefficient based on the output of the driver style recognition module ; The compensation torque calculated and output by each functional module is used as input and superimposed with the theoretical road feel feedback hand torque as the expected hand torque; the calculated current driver comprehensive gain coefficient is multiplied by the expected hand torque and output.

[0091] In one example, calculating the motor torque through the PID controller in a closed loop based on the desired hand torque and the actual hand torque includes: calculating the motor torque using the following formula: : ; where t is the closed loop period, , represents the expected hand torque, is the actual hand torque, , , are the proportional, integral and differential gains of the PID controller respectively, is the feedforward control term, , is the gain coefficient, is the steering wheel angular velocity, is the steering wheel angular acceleration, a, b, c are constants.

[0092] Figure 7 This is a schematic diagram of the road feel simulation based on closed-loop control in an embodiment of the present invention. The closed-loop control module obtains the closed-loop motor torque by combining the expected hand torque with the real-time collected driver hand torque through a feedforward PID. , The high-frequency road feel compensation torque calculated by the rack force Add together to obtain the final motor torque output to the motor,

[0093] In one example, calculating the high-frequency road feel compensation torque of the vehicle includes: acquiring the rack force signal of the vehicle in real time, wherein the rack force signal is used to represent the force feedback transmitted from the road load to the steering wheel; performing high-pass filtering on the rack force signal to extract the high-frequency road excitation component; and determining the high-frequency road excitation component as the high-frequency road feel compensation torque of the vehicle.

[0094] The high frequency compensation torque calculation method is to calculate the rack force signal Perform high-pass filtering (cut-off frequency 50 Hz) to extract the high-frequency road excitation component:

[0095] ;in, is the cutoff frequency, is the high frequency gain coefficient.

[0096] will be calculated and high frequency compensation torque The final road feel feedback torque is added and output to the motor to achieve road feel simulation.

[0097] The solution of this embodiment is based on the anti-integral saturation strategy. When the error exceeds the limit continuously, the integral term is frozen to avoid output saturation and improve dynamic response. The parameter adaptive mechanism is adopted: PID gain Dynamic adjustment with vehicle speed, enhanced proportional control at low speed, enhanced differential damping at high speed; adopts feedforward-feedback decoupling design: the feedforward term dominates steady-state tracking, PID feedback suppresses disturbances, and the two complement each other in the frequency domain (feedforward compensates low frequency, feedback suppresses high frequency); adopts high-frequency compensation optimization: adopts phase advance correction adjustment This module significantly improves the tracking accuracy of road feel feedback and the ability to restore high-frequency details through feedforward-feedback coordinated control and high-frequency dynamic compensation, enhancing the driver's driving experience and the real-time and accuracy of dynamic feedback torque calculation.

[0098] The solution of this embodiment provides a road feel simulation control method for a steer-by-wire system that is adaptive to driver habits. Figure 8 The schematic diagram of the road feeling simulation system in the embodiment of the present invention includes a cluster analysis module, a driver habit identification module, an expected hand torque calculation module, and a closed-loop control module; the cluster analysis module first collects driving data of different drivers at different speeds, performs cluster analysis on the collected data, and obtains different driving habits. The driver habit identification module identifies the current driver in combination with the current driver's driving data to obtain the current driver's driving habits, and brings the current driver's driving habits into the classification through cluster analysis to obtain a comprehensive gain coefficient, and the expected hand torque calculation module The module calculates the desired hand torque suitable for the current driver by taking over the compensation torques from other modules (such as the return compensation torque and the damping compensation torque) and combining them with the rack force, vehicle speed, and the comprehensive gain coefficient. The closed-loop control module combines the desired hand torque with the actual driver's hand torque collected in real time, along with parameters such as vehicle speed, upper motor speed, and high-frequency compensation torque, to derive the closed-loop motor torque through a feedforward PID. This is then added to the high-frequency road feel compensation torque obtained through the rack force to obtain the final motor control torque output to the motor, which is then transmitted to the HWA road feel simulation motor to complete the road feel simulation.

[0099] The cluster analysis module collects driving data from different drivers at different speeds offline, converts the collected road feel feedback torque into the ideal road feel feedback torque at the driver's current speed, and performs cluster analysis on the collected driving data to classify driver habits into three categories: economy, comfort, and sport.

[0100] The driver habit identification module identifies the current driver's driving data and obtains the gain coefficient corresponding to the current driver's driving habits;

[0101] The expected hand torque calculation module calculates the target hand torque by looking up the rack force signal obtained by downward rotation and the vehicle speed. The output of the return module, damping module, and other modules are converted into hand torque and added to the target hand torque. The gain coefficient used by the driver is substituted into this step to obtain the final expected hand torque.

[0102] The closed-loop control module uses a feedforward PID controller to calculate the closed-loop motor torque, combining the desired hand torque with the real-time collected driver hand torque. This torque is then added to the high-frequency road feel compensation torque derived from the rack force to obtain the final motor torque output. This torque is then transmitted to the HWA road feel simulation motor to complete the road feel simulation.

[0103] This embodiment can also dynamically update the target posterior probability model based on the driver's long-term behavior data to ensure the accuracy and personalization of habit determination.

[0104] The road feel simulation control method of the wire-controlled steer system that is adaptive to driver habits of this embodiment can solve the problem of lack of road feel transmission in traditional steering but in wire-controlled steer, prevent the driver from losing judgment of vehicle dynamics, reduce driving safety risks, and improve driving safety, control experience and system stability.

[0105] Through the description of the above embodiments, those skilled in the art will clearly understand that the methods according to the above embodiments can be implemented using software plus the necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, or optical disk) and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0106] Example 2

[0107] This embodiment also provides a road feel simulation device for a steer-by-wire vehicle. This device is used to implement the above-mentioned embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0108] Figure 9 FIG. 1 is a structural block diagram of a road feel simulation device for a steer-by-wire vehicle according to an embodiment of the present invention. Figure 9 As shown, the device includes:

[0109] an acquisition module 91 for acquiring historical driving style data of a steer-by-wire vehicle and actual hand torque of a driver of the steer-by-wire vehicle;

[0110] a first calculation module 92 for calculating a driver's gain coefficient based on the historical driving style data, wherein the gain coefficient is used to represent the driver's desired clarity of road feel;

[0111] A second calculation module 93 is configured to obtain a theoretical road feel feedback hand torque of the vehicle, and calculate an expected hand torque according to the theoretical road feel feedback hand torque and the gain coefficient;

[0112] The simulation module 94 includes: a calculation unit, which is used to calculate the motor torque through the closed loop of the PID controller according to the desired hand torque and the actual hand torque; and calculate the high-frequency road feel compensation torque of the vehicle; and a simulation unit, which is used to simulate the road feel of the wire-controlled steering vehicle after adding the motor torque and the high-frequency road feel compensation torque.

[0113] Optionally, the first calculation module includes: an identification unit for identifying the driver's driving habit type based on the historical driving style data; and a calculation unit for calculating a gain coefficient of the driving habit type.

[0114] Optionally, the identification unit includes: a processing subunit, used to preprocess the historical driving style data to obtain characteristic parameters in a preset distribution format; a selection subunit, used to select a target posterior probability that best matches the characteristic parameters from a pre-constructed posterior probability set, wherein the pre-constructed posterior probability set includes multiple posterior probabilities, each posterior probability corresponding to a driving habit type; and a determination subunit, used to determine the driving habit type corresponding to the target posterior probability as the driving habit type of the driver.

[0115] Optionally, the apparatus further includes: an acquisition module configured to acquire a plurality of sample data before the first calculation module selects a target posterior probability that best matches the feature parameter from a pre-constructed posterior probability set; an initialization module configured to randomly initialize model parameters of a Gaussian mixture model, wherein the number of Gaussian distributions in the Gaussian mixture model is the same as the number of driving habit types;

[0116] An iteration module is used to iteratively execute the following steps until the model parameters of the Gaussian mixture model meet preset conditions: calculating the posterior probability that each sample data among the multiple sample data belongs to each cluster in the Gaussian mixture model to obtain a probability set of all sample data; updating the model parameters of the Gaussian mixture model based on the probability set; extracting the model parameters of the Gaussian mixture model after the update; an output module is used to output the posterior probability set after the optimization of the Gaussian mixture model is completed.

[0117] Optionally, the calculation unit includes: an acquisition subunit for acquiring the target posterior probability of the driving habit type; a calculation subunit for calculating the gain coefficient of the driving habit type using the following formula: : ;in, The driver's driving habits The corresponding gain reference coefficient, The driver's driving habit type The corresponding target posterior probability.

[0118] Optionally, the second calculation module includes: a first acquisition unit, used to obtain the rack force signal and vehicle speed of the vehicle in real time, wherein the rack force signal is used to characterize the force feedback transmitted from the road load to the steering wheel; and a search unit, used to search for a theoretical road feel feedback hand torque matching the rack force signal and the vehicle speed in a preset mapping table of the vehicle.

[0119] Optionally, the second calculation module includes: a second acquisition unit, used to obtain the compensation torque set of the vehicle, and superimpose the compensation torque set and the theoretical road feel feedback hand torque to obtain an intermediate hand torque; an operation unit, used to multiply the intermediate hand torque with the gain coefficient to obtain a desired hand torque.

[0120] Optionally, the second acquisition unit includes: a collection subunit, used to collect the first compensation torque output by the vehicle's return torque module, the second compensation torque output by the vehicle's damping compensation module, and the third compensation torque output by the vehicle's friction compensation module; a determination subunit, used to determine the first compensation torque, the second compensation torque, and the third compensation torque as a compensation torque set.

[0121] Optionally, the calculation unit includes: a calculation subunit, configured to calculate the motor torque using the following formula: : ; where t is the closed loop period, , represents the expected hand torque, is the actual hand torque, , , are the proportional, integral and differential gains of the PID controller respectively, is the feedforward control term, , is the gain coefficient, is the steering wheel angular velocity, is the steering wheel angular acceleration, a, b, c are constants.

[0122] Optionally, the calculation unit includes: an acquisition subunit, used to acquire the rack force signal of the vehicle in real time, wherein the rack force signal is used to characterize the force feedback transmitted from the road load to the steering wheel; a filtering subunit, used to perform high-pass filtering on the rack force signal to extract a high-frequency road excitation component; and a determination subunit, used to determine the high-frequency road excitation component as the high-frequency road feel compensation torque of the vehicle.

[0123] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0124] Example 3

[0125] An embodiment of the present invention further provides a storage medium storing a computer program, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0126] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0127] S1, acquiring historical driving style data of a steer-by-wire vehicle and collecting actual hand torque of a driver of the steer-by-wire vehicle;

[0128] S2, calculating a driver's gain coefficient based on the historical driving style data, wherein the gain coefficient is used to represent the driver's desired clarity of road feel;

[0129] S3, obtaining a theoretical road feel feedback hand torque of the vehicle, and calculating an expected hand torque according to the theoretical road feel feedback hand torque and the gain coefficient;

[0130] S4, calculating the motor torque through a closed-loop PID controller according to the desired hand torque and the actual hand torque; and calculating the high-frequency road feel compensation torque of the vehicle;

[0131] S5 , performing road feel simulation on the steer-by-wire vehicle after adding the motor torque and the high-frequency road feel compensation torque.

[0132] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0133] An embodiment of the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0134] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0135] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program:

[0136] S1, acquiring historical driving style data of a steer-by-wire vehicle and collecting actual hand torque of a driver of the steer-by-wire vehicle;

[0137] S2, calculating a driver's gain coefficient based on the historical driving style data, wherein the gain coefficient is used to represent the driver's desired clarity of road feel;

[0138] S3, obtaining a theoretical road feel feedback hand torque of the vehicle, and calculating an expected hand torque according to the theoretical road feel feedback hand torque and the gain coefficient;

[0139] S4, calculating the motor torque through a closed-loop PID controller according to the desired hand torque and the actual hand torque; and calculating the high-frequency road feel compensation torque of the vehicle;

[0140] S5 , performing road feel simulation on the steer-by-wire vehicle after adding the motor torque and the high-frequency road feel compensation torque.

[0141] Optionally, specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0142] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0143] Through the description of the above embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a general hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the relevant technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0144] It should be understood that the terms used herein are for the purpose of describing specific example embodiments only and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "comprise", "include", "contain" and "have" are inclusive and therefore specify the presence of stated features, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring them to be performed in the specific order described or illustrated, unless the order of execution is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0145] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is intended to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for simulating road feel of a steer-by-wire vehicle, characterized in that: include: Acquiring historical driving style data of a steer-by-wire vehicle and collecting actual hand torque of a driver of the steer-by-wire vehicle; Calculating a driver's gain coefficient based on the historical driving style data, wherein the gain coefficient is used to represent the driver's desired clarity of road feel; Acquiring a theoretical road feel feedback hand torque of the vehicle, and calculating an expected hand torque according to the theoretical road feel feedback hand torque and the gain coefficient; Calculating the motor torque through a closed-loop PID controller according to the desired hand torque and the actual hand torque; and calculating the high-frequency road feel compensation torque of the vehicle; performing a road feel simulation on the steer-by-wire vehicle after adding the motor torque and the high-frequency road feel compensation torque; Calculating the driver's gain coefficient based on the historical driving style data includes: identifying the driver's driving habit type based on the historical driving style data; calculating the gain coefficient of the driving habit type; calculating the gain coefficient of the driving habit type includes: obtaining the target posterior probability of the driving habit type; and calculating the gain coefficient of the driving habit type using the following formula: : ;in, The driver's driving habits The corresponding gain reference coefficient, The driver's driving habit type The corresponding target posterior probability.

2. The method according to claim 1, characterized in that Obtaining the theoretical road feel feedback hand torque of the vehicle includes: acquiring a rack force signal and a vehicle speed of the vehicle in real time, wherein the rack force signal is used to represent force feedback transmitted from a road load to a steering wheel; A theoretical road feel feedback hand torque that matches the rack force signal and the vehicle speed is searched in a preset mapping table of the vehicle.

3. The method according to claim 1, characterized in that Calculating the expected hand torque according to the theoretical road feel feedback hand torque and the gain coefficient includes: Obtaining a compensation torque set of the vehicle, and superimposing the compensation torque set and the theoretical road feel feedback hand torque to obtain an intermediate hand torque; The intermediate hand torque is multiplied by the gain coefficient to obtain the desired hand torque.

4. The method according to claim 3, characterized in that Obtaining the compensation torque set of the vehicle includes: collecting a first compensation torque output by a aligning torque module of the vehicle, collecting a second compensation torque output by a damping compensation module of the vehicle, and collecting a third compensation torque output by a friction compensation module of the vehicle; The first compensation torque, the second compensation torque, and the third compensation torque are determined as a compensation torque set.

5. The method according to claim 1, wherein Calculating the motor torque through a closed loop of a PID controller according to the desired hand torque and the actual hand torque includes: Use the following formula to calculate the motor torque : ； Where t is the closed loop period, , represents the expected hand torque, is the actual hand torque, , , are the proportional, integral and differential gains of the PID controller respectively, is the feedforward control term, , is the gain coefficient, is the steering wheel angular velocity, is the steering wheel angular acceleration, a, b, c are constants.

6. The method according to claim 1, characterized in that Calculating the high-frequency road feel compensation torque of the vehicle includes: acquiring a rack force signal of the vehicle in real time, wherein the rack force signal is used to represent force feedback transmitted from a road load to a steering wheel; performing high-pass filtering on the rack force signal to extract a high-frequency road excitation component; The high-frequency road surface excitation component is determined as a high-frequency road feel compensation torque of the vehicle.

7. The method according to claim 1, characterized in that Identifying the driver's driving habit type based on the historical driving style data includes: preprocessing the historical driving style data to obtain characteristic parameters in a preset distribution format; Selecting a target posterior probability that best matches the characteristic parameter from a pre-constructed posterior probability set, wherein the pre-constructed posterior probability set includes a plurality of posterior probabilities, each posterior probability corresponding to a driving habit type; The driving habit type corresponding to the target posterior probability is determined as the driving habit type of the driver.

8. The method according to claim 7, characterized in that Before selecting the target posterior probability that best matches the feature parameter from the pre-constructed posterior probability set, the method further includes: Get multiple sample data; Randomly initializing model parameters of a Gaussian mixture model, wherein the number of Gaussian distributions in the Gaussian mixture model is the same as the number of driving habit types; The following steps are iteratively performed until the model parameters of the Gaussian mixture model meet the preset conditions: calculating the posterior probability that each sample data in the plurality of sample data belongs to each cluster in the Gaussian mixture model to obtain a probability set for all sample data; updating the model parameters of the Gaussian mixture model based on the probability set; and extracting the updated model parameters of the Gaussian mixture model; After the Gaussian mixture model optimization is completed, the posterior probability set is output.

9. A road feel simulation device for a steer-by-wire vehicle, characterized in that: include: an acquisition module, configured to acquire historical driving style data of a steer-by-wire vehicle and an actual hand torque of a driver of the steer-by-wire vehicle; a first calculation module, configured to calculate a driver's gain coefficient based on the historical driving style data, wherein the gain coefficient is used to represent the driver's desired clarity of road feel; a second calculation module, configured to obtain a theoretical road feel feedback hand torque of the vehicle, and calculate an expected hand torque according to the theoretical road feel feedback hand torque and the gain coefficient; The simulation module includes: a calculation unit for calculating the motor torque according to the desired hand torque and the actual hand torque through a PID controller closed loop; and calculating the high-frequency road feel compensation torque of the vehicle; and a simulation unit for simulating the road feel of the steer-by-wire vehicle after adding the motor torque and the high-frequency road feel compensation torque. The first calculation module includes: an identification unit for identifying the driver's driving habit type based on the historical driving style data; a calculation unit for calculating the gain coefficient of the driving habit type; the calculation unit includes: an acquisition subunit for acquiring the target posterior probability of the driving habit type; a calculation subunit for calculating the gain coefficient of the driving habit type using the following formula: : ;in, The driver's driving habits The corresponding gain reference coefficient, The driver's driving habit type The corresponding target posterior probability.

10. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 8 when executed.

11. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 8.

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