Method and device for identifying vehicle axle cornering stiffness and electronic equipment

Abstract

The invention relates to a method and device for identifying vehicle axle cornering stiffness and electronic equipment. The method comprises the following steps: acquiring multiple groups of actual response data and multiple axle cornering stiffness parameter sets generated by a real vehicle under different working conditions; performing fitting processing on the multiple groups of actual response data under the target working condition to obtain multiple groups of fitting response data after fitting; obtaining a pre-established vehicle dynamics model, and inputting any group of simulation parameters into the vehicle dynamics model to obtain simulation data which is output by the vehicle dynamics model and corresponds to any group of simulation parameters; and determining a target axle cornering stiffness parameter set of the real vehicle based on the simulation data. According to the scheme provided by the invention, the axle cornering stiffness of the vehicle can be accurately identified according to the actual response data of the real vehicle, and accurate control can be provided for a linear region and a non-linear region of the vehicle.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a method, device and electronic equipment for identifying vehicle axle lateral stiffness. Background Art

[0002] In the related art, when identifying the cornering stiffness of an axle, the prior art only uses vehicle simulation response data to fit the stiffness model of the axle.

[0003] However, there are many factors that affect the axle's side slip characteristics, and the simulation model may not fully consider these factors. Even if these factors are fully considered, the simulation accuracy will still decrease after the vehicle enters the nonlinear region. Summary of the invention

[0004] In order to solve or partially solve the problems existing in the related art, the present application provides a method, device and electronic equipment for identifying the axle lateral stiffness of a vehicle, which can accurately identify the axle lateral stiffness of the vehicle based on the actual response data of the real vehicle, and is conducive to providing precise control for the linear and nonlinear regions of the vehicle.

[0005] The first aspect of the present application provides a method for identifying the axle roll stiffness of a vehicle, comprising: obtaining multiple groups of actual response data and multiple axle roll stiffness parameter sets under different working conditions generated by a real vehicle; wherein the multiple axle roll stiffness parameter sets include an axle roll stiffness parameter set corresponding to each group of actual response data; performing fitting processing on the multiple groups of actual response data under target working conditions to obtain multiple groups of fitted response data; wherein any group of fitted response data includes vehicle speed, front wheel steering angle, fitted yaw rate and fitted lateral acceleration; obtaining a pre-established vehicle dynamics model, and inputting any group of simulation parameters into the vehicle dynamics model respectively to obtain simulation data corresponding to any group of simulation parameters output by the vehicle dynamics model; wherein the simulation data includes: yaw rate simulation value and lateral acceleration simulation value; any group of simulation parameters includes: basic parameters of the real vehicle, vehicle speed, front wheel steering angle and an axle roll stiffness parameter set; determining the target axle roll stiffness parameter set of the real vehicle based on the simulation data.

[0006] In some embodiments, a target axle roll stiffness parameter set of a real vehicle is determined based on simulation data, including: calculating a target error value, and when the target error value is less than a threshold value, using an axle roll stiffness parameter set in any group of simulation parameters as the target axle roll stiffness parameter set of the real vehicle; wherein the target error value is a first error value or a second error value or a sum of the first error value and the second error value; the first error value is obtained based on a yaw velocity simulation value and a fitted yaw velocity, and the second error value is obtained based on a lateral acceleration simulation value and a fitted lateral acceleration.

[0007] In some embodiments, the method further comprises: inputting the target axle lateral stiffness parameter set into a pre-established axle lateral stiffness model, and verifying the target axle lateral stiffness parameters according to a plurality of sets of actual response data.

[0008] In some embodiments, fitting is performed on multiple sets of actual response data under target operating conditions to obtain multiple sets of fitted response data, including: using the least squares method to fit multiple sets of actual response data under target operating conditions to obtain multiple sets of fitted response data.

[0009] In some embodiments, the method further includes: performing fitting processing on the yaw angular velocity and the lateral acceleration respectively to obtain a fitting relationship, and obtaining a plurality of groups of fitting response data after fitting according to the fitting relationship.

[0010] In some embodiments, the axle cornering stiffness parameter set includes axle cornering stiffness parameter sets for a front axle and a rear axle.

[0011] In some implementations, the method further includes: when collecting multiple sets of actual response data, turning off rear wheel steering control of the real vehicle.

[0012] In some embodiments, the method further includes: establishing a vehicle dynamics model based on a magic formula for the tire.

[0013] In some embodiments, the method further comprises: determining a set of axle cornering stiffness parameters to be input into the vehicle dynamics model based on an optimization algorithm.

[0014] According to a second aspect of the present application, there is provided a device for identifying the axle roll stiffness of a vehicle, the device comprising: an acquisition module for acquiring multiple groups of actual response data and multiple axle roll stiffness parameter sets under different working conditions generated by a real vehicle; wherein the multiple axle roll stiffness parameter sets include axle roll stiffness parameter sets corresponding to each group of actual response data; a fitting module for fitting the multiple groups of actual response data under target working conditions to obtain multiple groups of fitted response data; wherein any group of fitted response data includes vehicle speed, front wheel steering angle, fitted yaw rate and fitted lateral acceleration; an acquisition module for acquiring a pre-established vehicle dynamics model, and inputting any group of simulation parameters into the vehicle dynamics model respectively to obtain simulation data corresponding to any group of simulation parameters output by the vehicle dynamics model; wherein the simulation data includes: yaw rate simulation value and lateral acceleration simulation value; any group of simulation parameters includes: basic parameters of the real vehicle, vehicle speed, front wheel steering angle and an axle roll stiffness parameter set; a determination module for determining the target axle roll stiffness parameter set of the real vehicle based on the simulation data.

[0015] A third aspect of the present application provides an electronic device, including:

[0016] Processor; and

[0017] The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is caused to execute the method as described above.

[0018] A fourth aspect of the present application provides a computer-readable storage medium having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as described above.

[0019] The technical solution provided by the present application may include the following beneficial effects: it can accurately identify the axle lateral stiffness of the vehicle based on the actual response data of the real vehicle, which is conducive to providing precise control for the linear and nonlinear regions of the vehicle.

[0020] The technical solution of the present application can also: reduce identification costs.

[0021] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and other objects, features and advantages of the present application will become more apparent by describing in more detail the exemplary embodiments of the present application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the present application.

[0023] Figure 1 is a flow chart of a method for identifying the lateral stiffness of a vehicle axle shown in an embodiment of the present application;

[0024] Figure 2 is a schematic structural diagram of a device for identifying the lateral stiffness of a vehicle axle shown in an embodiment of the present application;

[0025] Figure 3 It is a schematic diagram of the structure of an electronic device shown in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0027] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0028] It should be understood that although the terms "first", "second", "third", etc. may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0029] Accurate axle roll characteristics modeling is the basis of vehicle lateral control. The accuracy of the axle roll stiffness obtained by the currently commonly used methods for identifying vehicle axle roll stiffness needs to be improved.

[0030] In response to the above problems, an embodiment of the present application provides a method for identifying the axle lateral stiffness of a vehicle, which can accurately identify the axle lateral stiffness of the vehicle based on the actual response data of a real vehicle, and is conducive to providing precise control for the linear and nonlinear regions of the vehicle.

[0031] The technical solution of the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0032] Figure 1 It is a flow chart of a method for identifying the lateral stiffness of a vehicle axle shown in an embodiment of the present application.

[0033] See also Figure 1 , a method for identifying axle lateral stiffness of a vehicle, the method comprising:

[0034] Step 101, obtaining multiple groups of actual response data and multiple axle lateral stiffness parameter sets under different working conditions generated by a real vehicle; wherein the multiple axle lateral stiffness parameter sets include an axle lateral stiffness parameter set corresponding to each group of actual response data.

[0035] In some embodiments, the real vehicle may be a car, truck, motorcycle, bus, boat, airplane, helicopter, lawn mower, recreational vehicle, amusement park vehicle, construction equipment, tram, golf cart, train, and cart, etc., and the embodiments of the present application are not particularly limited.

[0036] In some embodiments, the vehicle may be a three-axle vehicle, or a multi-axle vehicle greater than three axles.

[0037] In some embodiments, different operating conditions refer to driving conditions such as different vehicle speeds, steering wheel angles, and lateral accelerations.

[0038] In some embodiments, the actual response data may be the lateral acceleration response and yaw rate response data of the vehicle under a specific vehicle speed, a steering wheel angle ramp input, and a sinusoidal input.

[0039] In some embodiments, a constant-speed steering wheel angle ramp input may be used as a target matching condition, and other conditions may also be used, such as a variable-speed steering wheel sinusoidal input, a variable-speed steering wheel ramp input, etc.

[0040] In some embodiments, the cornering stiffness may be a ratio of the cornering force to the cornering angle.

[0041] In some embodiments, the set of axle cornering stiffness parameters includes a set of axle cornering stiffness parameters for a front axle and a rear axle.

[0042] In some embodiments, the rear wheel steering control of the real vehicle is turned off while collecting multiple sets of actual response data.

[0043] Step 102, performing fitting processing on multiple groups of actual response data under target operating conditions to obtain multiple groups of fitted response data; wherein any group of fitted response data includes vehicle speed, front wheel steering angle, fitted yaw angular velocity and fitted lateral acceleration.

[0044] In some embodiments, the target operating condition may be a specific vehicle speed and lateral acceleration, such as a data set (actual response data) where the target operating condition is a vehicle speed of 60 km / h and a lateral acceleration of 0.8 g.

[0045] In some embodiments, a fitting algorithm is used to perform fitting processing on multiple groups of actual response data under target operating conditions to obtain multiple groups of fitted response data.

[0046] In some embodiments, performing fitting processing on multiple sets of actual response data under target operating conditions to obtain multiple sets of fitted response data may include:

[0047] The least square method is used to fit multiple groups of actual response data under target working conditions to obtain multiple groups of fitted response data.

[0048] It can be understood that the fitting curve is selected for fitting according to the least squares principle.

[0049] Furthermore, in some embodiments, fitting processing may be performed on the yaw angular velocity and the lateral acceleration, respectively, to obtain a fitting relationship, and multiple groups of fitting response data after fitting are obtained according to the fitting relationship.

[0050] For example, the least square method is selected, and a quartic polynomial is used as the objective function for fitting, respectively fitting time t-yaw angular velocity γ, time t-lateral acceleration a y Data pairs, get the fitted relationship f(t,γ) and f(t,a y ). Then, the fitted relationship f(t,γ) and f(t,a y ) regenerates the vehicle response data, that is, multiple groups of fitting response data after fitting.

[0051] It can be understood that using the actual response data as the identification target will introduce interference due to the interference of the vehicle's actual response signal collection, resulting in poor identification effect with the vehicle's actual response as the target. By fitting and preprocessing the actual response data in this step, the interference of the actual collection signal is removed, which is conducive to ensuring the identification effect.

[0052] Step 103, obtaining a pre-established vehicle dynamics model, and inputting any set of simulation parameters into the vehicle dynamics model respectively, to obtain simulation data corresponding to any set of simulation parameters output by the vehicle dynamics model; wherein the simulation data includes: yaw angular velocity simulation value and lateral acceleration simulation value; any set of simulation parameters includes: basic parameters of a real vehicle, vehicle speed, front wheel steering angle and an axle lateral stiffness parameter set.

[0053] In some embodiments, the vehicle dynamics model may be built based on the magic formula for tires.

[0054] It can be understood that by adding simulation parameters to the vehicle dynamics model, simulation data output by the vehicle dynamics model can be obtained.

[0055] In some embodiments, the vehicle dynamics model simulation may obtain a yaw rate simulation value and a lateral acceleration simulation value.

[0056] In some embodiments, the simulation parameters of the vehicle dynamics model may include: first, the basic parameters of the real vehicle input, such as the mass and geometric dimensions of the vehicle; secondly, the vehicle speed and front wheel steering angle input; and for each set of axle lateral stiffness parameters that matches the target operating condition vehicle speed and front wheel steering angle combination, the yaw rate simulation value and lateral acceleration simulation value corresponding to the above contents are obtained.

[0057] In some embodiments, a set of axle cornering stiffness parameters to be input into a vehicle dynamics model may be determined based on an optimization algorithm.

[0058] In some embodiments, the optimization algorithm may include a genetic algorithm, a simulated annealing algorithm, or the like.

[0059] It can be understood that each time a set of simulation parameters is inputted in each iteration, corresponding simulation data is obtained.

[0060] Step 104 : determining a target axle cornering stiffness parameter set of the real vehicle based on the simulation data.

[0061] It can be understood that since the simulation data includes yaw rate simulation values ​​and lateral acceleration simulation values, the target axle lateral stiffness parameter set of the real vehicle can be determined based on the yaw rate simulation values; the target axle lateral stiffness parameter set of the real vehicle can also be determined based on the lateral acceleration simulation values; and the target axle lateral stiffness parameter set of the real vehicle can be determined based on the yaw rate simulation values ​​and the lateral acceleration simulation values.

[0062] The method for identifying the axle lateral stiffness of a vehicle in the embodiment of the present application can accurately identify the axle lateral stiffness of the vehicle based on the actual response data of a real vehicle, which is beneficial for providing precise control for the linear and nonlinear regions of the vehicle.

[0063] In some embodiments, determining a target axle lateral stiffness parameter set of a real vehicle based on simulation data includes:

[0064] A target error value is calculated. When the target error value is less than a threshold value, an axle roll stiffness parameter set in any group of simulation parameters is used as a target axle roll stiffness parameter set of a real vehicle; wherein the target error value is a first error value or a second error value or a sum of the first error value and the second error value; the first error value is obtained based on a yaw velocity simulation value and a fitted yaw velocity, and the second error value is obtained based on a lateral acceleration simulation value and a fitted lateral acceleration.

[0065] It can be understood that a target error value that satisfies a threshold value can be set as a target to further obtain a preferred target axle lateral stiffness parameter set.

[0066] In some embodiments, the target error value may be a first error value, ie, an error value related to the yaw rate.

[0067] In some embodiments, the target error value may be a second error value, ie, an error value related to the lateral acceleration.

[0068] In some embodiments, the target error value may be a sum value, ie, an error value related to both the yaw rate and the lateral acceleration.

[0069] The method for identifying the axle lateral stiffness of a vehicle in an embodiment of the present application can calculate a target error value in a variety of ways to obtain a target axle lateral stiffness parameter set of a real vehicle based on the target error value.

[0070] In some embodiments, the method for identifying vehicle axle roll stiffness further includes: inputting a target axle roll stiffness parameter set into a pre-established axle roll stiffness model, and verifying the target axle roll stiffness parameters based on multiple sets of actual response data.

[0071] It can be understood that the preferred target axle roll stiffness parameter set, or the optimal target axle roll stiffness parameter set is input into the pre-established axle roll stiffness model, and the target axle roll stiffness parameters are verified according to multiple sets of actual response data.

[0072] For example, the axle cornering stiffness model could be the tire magic formula.

[0073] For example, the target axle roll stiffness parameter set is updated to the pre-established axle roll stiffness model to obtain the corresponding simulation data, which is then compared with the actual response data, thereby verifying the optimization effect by comparing the actual vehicle response with the simulation response.

[0074] The method for identifying the vehicle axle roll stiffness of an embodiment of the present application can ensure the accuracy of the axle roll stiffness by inputting a target axle roll stiffness parameter set into a pre-established axle roll stiffness model and verifying the target axle roll stiffness parameters based on multiple sets of actual response data.

[0075] In order to better understand the present application, the content of the present application is further explained below in conjunction with the embodiments, but the present application is not limited to the following embodiments.

[0076] The process of identifying the vehicle axle lateral stiffness is as follows.

[0077] First, use modeling and simulation software to establish a simplified vehicle dynamics model, taking a three-axle vehicle as an example.

[0078] The mass of the vehicle is m, and the velocity in the x direction of the vehicle coordinate system is v x , let the distance between the first axis and the second axis be L1, and the distance between the first axis and the third axis be L2. The rotation angles of the first, second, and third axes are δ f , δ m , δ r ; The tire slip angles of the first, second and third axles are α f , α m , α r ; The distances from the first, second, and third axes to the center of mass of the vehicle are l a , l b , l c ; The lateral stiffness of the first, second and third axes are k f , k m , k r . Then the motion of the three-axle vehicle in steady state satisfies the following equation:

[0079]

[0080] The turning angle of a three-axle vehicle satisfies the following relationship:

[0081]

[0082]

[0083] The lateral acceleration of the vehicle can be expressed as:

[0084] α y =v x (β+γ) Formula 4

[0085] Where β is the sideslip angle of the vehicle's center of mass, and γ is the yaw rate.

[0086] For a vehicle with only the first and third axles steering, i.e. the second axle not steering, we have:

[0087] δ m =0 Formula 5

[0088] The tire stiffness is the main factor affecting the axle cornering stiffness of the vehicle, so the simplified tire magic formula is used to describe the axle cornering characteristics as follows:

[0089] F=Dsin(Carctan(Bα)) Formula 6

[0090] Where F is the axial lateral force, and its value is F f 、F m 、F r ; α is the axial deflection angle, the value is α f , α m , α r ; B, C, D are the parameters to be identified. Then:

[0091]

[0092] The vehicle's yaw rate and lateral acceleration at different vehicle speeds and front wheel turning angles can be calculated using Formulas 1 to 7.

[0093] The first and third axes may be turned, or any combination of the turns of axes other than the first axis may be used.

[0094] Next, collect the actual vehicle response data.

[0095] With a sampling period of 10ms, the vehicle's lateral acceleration response data and yaw rate response data are collected under constant speed, steering wheel angle ramp input, and sinusoidal input. The steering wheel ramp input is the target working condition for axle lateral stiffness matching. To ensure the accuracy of axle lateral stiffness matching, the vehicle needs to achieve the largest possible lateral acceleration. To avoid interference of the control system on the vehicle response, the rear-wheel steering control is turned off during the collection process.

[0096] Then, the actual vehicle response data is preprocessed.

[0097] From the vehicle data collected above, select the data segment with constant vehicle speed, steering wheel slope input, and large lateral acceleration, and extract it as the axle lateral stiffness matching target; the data segment with a vehicle speed of around 60km / h and a lateral acceleration of 0.8g can be selected as the matching target.

[0098] The target working condition is refitted using a fitting algorithm to obtain the fitted vehicle response data;

[0099] You can choose the least squares method and fit it with a quartic polynomial as the objective function, fitting time t-yaw angular velocity γ, time t-lateral acceleration a respectively. y Data pairs, get the fitted relationship f(t,γ) and f(t,a y ). Then, the fitted relationship f(t,γ) and f(t,a y ) regenerates vehicle response data. The vehicle response data refers to the lateral acceleration and yaw rate of the vehicle.

[0100] Finally, the optimization algorithm is used to identify the axle lateral stiffness parameters.

[0101] The axle cornering stiffness can be calculated using a simplified version of the tire magic formula, where B, C, and D need to be identified based on the actual response of the vehicle.

[0102] Since the tire stiffness of the front and rear axles of a vehicle is generally different, different axle cornering stiffness parameter sets are used for the front and rear axle cornering stiffness according to actual conditions. Here, the front axle cornering stiffness parameter set is: (B f ,C f ,D f ), the middle axle and rear axle have the same structure and stiffness, and all use parameter set (B r ,C r ,D r ).

[0103] Use simulation software for optimization and establish the optimization problem as follows:

[0104] Optimization variables: front and rear axle cornering stiffness parameter set (B f ,C f ,D f ) and (Br ,C r ,D r ) A total of six parameters;

[0105] Optimization conditions: vehicle speed v of the fitting condition x , front wheel turning angle δ f Input: For example, the vehicle speed is around 60km / h and the lateral acceleration reaches 0.8g.

[0106] Optimization boundaries: basic vehicle parameters such as the mass m mentioned above.

[0107] Optimization goal: Where w1 and w2 are the weights of matching the yaw rate target and the lateral acceleration target, and here w1, w2 = 1; γ sim 、a y_sim are the yaw rate and lateral acceleration obtained by model simulation, γ fit 、a y_fit are the fitted yaw angular velocity and lateral acceleration respectively.

[0108] Furthermore, it should be noted that the optimization target can be set as the parameter set of the lateral stiffness of each axis (B i ,C i ,D i ), i = 1, 2, 3 ... n, where the value of n can be determined by the number of tire specifications, suspension hard point parameters, suspension bushing and elastic component parameter sets of each axle. Axles with the same above factors can share a set of parameter sets.

[0109] Furthermore, it should be noted that the weights w1 and w2 of the two parts in the optimization objective are 1, and other values ​​of w1 and w2 may also be used.

[0110] By using the optimization algorithm to minimize the optimization target, the optimal front and rear axle cornering stiffness parameter set (B f ,C f ,D f ) and (B r ,C r ,D r ).

[0111] It can be understood that the minimum value refers to setting a minimum value and number of iterations for the optimization algorithm. When the optimization target is less than the minimum value or the number of iterations is reached, the optimal result is obtained.

[0112] The optimization process is divided into the following sub-steps:

[0113] Step 1: Input the basic vehicle parameters such as vehicle mass m, geometric dimensions, etc.

[0114] Step 2: For each matching target vehicle speed v xi , front wheel turning angle δ fi Combination, through the dynamic model to calculate the current iteration input parameter set (B f ,C f ,D f ) and (B r ,C r ,D r ) corresponds to the simulated value of yaw angular velocity and lateral acceleration γ sim,i and a y_sim,i .

[0115] Step 3: According to the evaluation function Calculate the current front and rear axle cornering stiffness parameter set (B f ,C f ,D f ) and (B r ,C r ,D r )Evaluation function value.

[0116] Step 4: Determine the front and rear axle cornering stiffness parameter set (B) for the next calculation step by using optimization algorithms including but not limited to genetic algorithms and simulated annealing algorithms. f ,C f ,D f ) and (B r ,C r ,D r ) input, and repeat steps 2 and 3 until the optimization is terminated, and the front and rear axle cornering stiffness parameter set (B f ,C f ,D f ) and (B r ,C r ,D r ).

[0117] Step 5: Set the optimal front and rear axle cornering stiffness parameter set (B f ,C f ,D f ) and (B r ,C r ,D r ) is updated to the axle lateral stiffness model, and the actual vehicle response data collected above are used to verify the optimization results.

[0118] The method for identifying the axle roll stiffness of a vehicle in an embodiment of the present application directly identifies the axle roll stiffness based on the vehicle response, and the modeling process only requires a simple vehicle dynamics model and a simplified axle roll stiffness model, omitting the refined modeling process based on model-target matching, thus saving a lot of manpower and material resources.

[0119] Corresponding to the aforementioned application function realization method embodiment, the present application also provides a device for identifying the lateral stiffness of a vehicle axle, an electronic device and corresponding embodiments.

[0120] Figure 2 It is a schematic diagram of the structure of a device for identifying the lateral stiffness of a vehicle axle shown in an embodiment of the present application.

[0121] See also Figure 2 The device 200 for identifying the lateral stiffness of a vehicle axle in this embodiment includes an acquisition module 210 , a fitting module 220 , an obtaining module 230 and a determination module 240 .

[0122] An acquisition module 210 is used to acquire multiple groups of actual response data and multiple axle lateral stiffness parameter sets under different working conditions generated by a real vehicle; wherein the multiple axle lateral stiffness parameter sets include axle lateral stiffness parameter sets corresponding to each group of actual response data;

[0123] A fitting module 220 is used to perform fitting processing on multiple sets of actual response data under target working conditions to obtain multiple sets of fitted response data; wherein any set of fitted response data includes vehicle speed, front wheel steering angle, fitted yaw angular velocity and fitted lateral acceleration;

[0124] The acquisition module 230 is used to obtain a pre-established vehicle dynamics model, and input any set of simulation parameters into the vehicle dynamics model to obtain simulation data corresponding to any set of simulation parameters output by the vehicle dynamics model; wherein the simulation data includes: a yaw rate simulation value and a lateral acceleration simulation value; and any set of simulation parameters includes: basic parameters of a real vehicle, a vehicle speed, a front wheel steering angle, and a set of axle lateral stiffness parameters;

[0125] The determination module 240 is used to determine a target axle lateral stiffness parameter set of the real vehicle based on the simulation data.

[0126] Regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0127] According to an embodiment of the present application, any multiple modules in the acquisition module 210, the fitting module 220, the acquisition module 230 and the determination module 240 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the acquisition module 210, the fitting module 220, the acquisition module 230 and the determination module 240 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the acquisition module 210, the fitting module 220, the acquisition module 230 and the determination module 240 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be performed.

[0128] Figure 3 It is a schematic diagram of the structure of an electronic device shown in an embodiment of the present application.

[0129] See also Figure 3 , the electronic device 300 includes a memory 310 and a processor 320.

[0130] The processor 320 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0131] The memory 310 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. Among them, ROM can store static data or instructions required by the processor 320 or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory 310 may include any combination of computer-readable storage media, including various types of semiconductor storage chips (such as DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 310 may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (such as a DVD-ROM, a double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a mini SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0132] The memory 310 stores executable codes, and when the executable codes are processed by the processor 320 , the processor 320 can execute part or all of the methods described above.

[0133] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0134] Alternatively, the present application can also be implemented as a computer-readable storage medium (or non-transitory machine-readable storage medium or machine-readable storage medium) on which executable code (or computer program or computer instruction code) is stored. When the executable code (or computer program or computer instruction code) is executed by a processor of an electronic device (or server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0135] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for identifying the axle lateral stiffness of a vehicle, characterized in that: include: Acquire multiple groups of actual response data and multiple axle lateral stiffness parameter sets under different working conditions generated by a real vehicle; wherein the multiple axle lateral stiffness parameter sets include an axle lateral stiffness parameter set corresponding to each group of actual response data; Performing fitting processing on multiple groups of actual response data under target working conditions to obtain multiple groups of fitted response data; wherein any group of fitted response data includes vehicle speed, front wheel steering angle, fitted yaw angular velocity and fitted lateral acceleration; Acquire a pre-established vehicle dynamics model, and input any set of simulation parameters into the vehicle dynamics model respectively, to obtain simulation data corresponding to the any set of simulation parameters output by the vehicle dynamics model; wherein the simulation data includes: a yaw rate simulation value and a lateral acceleration simulation value; and the any set of simulation parameters includes: basic parameters of the real vehicle, the vehicle speed, the front wheel steering angle, and a set of axle lateral stiffness parameters; A target axle cornering stiffness parameter set of the real vehicle is determined based on the simulation data.

2. The method according to claim 1, characterized in that The step of determining a target axle lateral stiffness parameter set of the real vehicle based on the simulation data comprises: Calculate a target error value, and when the target error value is less than a threshold value, use the one axle lateral stiffness parameter set in any one group of simulation parameters as the target axle lateral stiffness parameter set of the real vehicle; wherein the target error value is a first error value or a second error value or a sum of the first error value and the second error value; the first error value is obtained based on the yaw velocity simulation value and the fitted yaw velocity, and the second error value is obtained based on the lateral acceleration simulation value and the fitted lateral acceleration.

3. The method according to claim 1, characterized in that The method further comprises: The target axle lateral stiffness parameter set is input into a pre-established axle lateral stiffness model, and the target axle lateral stiffness parameters are verified according to the multiple groups of actual response data.

4. The method according to claim 1, characterized in that: The fitting process is performed on multiple groups of actual response data under target working conditions to obtain multiple groups of fitted response data, including: The least square method is used to fit multiple groups of actual response data under target working conditions to obtain multiple groups of fitted response data.

5. The method according to claim 4, characterized in that The method further comprises: The fitting process is performed on the yaw angular velocity and the lateral acceleration respectively to obtain a fitting relationship, and a plurality of groups of fitting response data after fitting are obtained according to the fitting relationship.

6. The method according to claim 1, characterized in that The axle cornering stiffness parameter set includes axle cornering stiffness parameter sets of a front axle and a rear axle.

7. The method according to claim 1, characterized in that The method further includes establishing the vehicle dynamics model based on a magic formula for a tire.

8. The method according to claim 1, characterized in that The method further comprises: A set of axle cornering stiffness parameters to be input into the vehicle dynamics model is determined based on an optimization algorithm.

9. A device for identifying the lateral stiffness of a vehicle axle, characterized in that: include: An acquisition module, used to acquire multiple groups of actual response data and multiple axle lateral stiffness parameter sets under different working conditions generated by a real vehicle; wherein the multiple axle lateral stiffness parameter sets include axle lateral stiffness parameter sets corresponding to each group of actual response data; A fitting module is used to perform fitting processing on multiple groups of actual response data under target working conditions to obtain multiple groups of fitted response data; wherein any group of fitted response data includes vehicle speed, front wheel steering angle, fitted yaw angular velocity and fitted lateral acceleration; An acquisition module is used to acquire a pre-established vehicle dynamics model, and input any set of simulation parameters into the vehicle dynamics model respectively, to obtain simulation data corresponding to the any set of simulation parameters output by the vehicle dynamics model; wherein the simulation data includes: a yaw rate simulation value and a lateral acceleration simulation value; and the any set of simulation parameters includes: basic parameters of the real vehicle, the vehicle speed, the front wheel steering angle, and a set of axle lateral stiffness parameters; A determination module is used to determine a target axle lateral stiffness parameter set of the real vehicle based on the simulation data.

10. An electronic device, characterized in that: include: processor; as well as A memory having executable codes stored thereon, which, when executed by the processor, causes the processor to execute the method according to any one of claims 1 to 8.

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