Methods, devices and electronic equipment for identifying vehicle axle lateral stiffness

Abstract

This application relates to a method, apparatus, and electronic device for identifying the axle lateral stiffness of a vehicle. The method includes: acquiring multiple sets of actual response data and multiple sets of axle lateral stiffness parameters generated by a real vehicle under different operating conditions; fitting the multiple sets of actual response data under a target operating condition to obtain multiple sets of fitted response data; acquiring a pre-established vehicle dynamics model, and inputting any set of simulation parameters into the vehicle dynamics model to obtain simulation data output by the vehicle dynamics model corresponding to any set of simulation parameters; and determining the target axle lateral stiffness parameter set of the real vehicle based on the simulation data. The solution provided by this application can accurately identify the axle lateral stiffness of a vehicle based on the actual response data of the real vehicle, which is beneficial for providing precise control in both the linear and nonlinear regions of the vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a method, apparatus and electronic device for identifying the axle lateral stiffness of a vehicle. Background Technology

[0002] In related technologies, existing technologies only use vehicle simulation response data to fit the stiffness model of the axle when identifying the lateral stiffness of the axle.

[0003] However, the lateral slip characteristics of the axle are influenced by numerous factors, and the simulation model may not fully consider these factors. Furthermore, even if these factors are fully considered, the simulation accuracy will still decrease once the vehicle enters the nonlinear region. Summary of the Invention

[0004] To address or partially address the problems existing in related technologies, this application provides a method, apparatus, and electronic device for identifying the axle lateral stiffness of a vehicle. This device can accurately identify the axle lateral stiffness of a vehicle based on the actual response data of a real vehicle, which is beneficial for providing precise control in the linear and nonlinear regions of the vehicle.

[0005] The first aspect of this application provides a method for identifying the axle side stiffness of a vehicle, comprising: acquiring multiple sets of actual response data and multiple sets of axle side stiffness parameters generated by a real vehicle under different operating conditions; wherein, the multiple sets of axle side stiffness parameters include sets of axle side stiffness parameters corresponding to each set of actual response data; fitting the multiple sets of actual response data under the target operating 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 rate, and fitted lateral acceleration; acquiring a pre-established vehicle dynamics model, and inputting any set of simulation parameters into the vehicle dynamics model to obtain simulation data output by the vehicle dynamics model corresponding to any set of simulation parameters; wherein, the simulation data includes: simulated yaw rate values ​​and simulated lateral acceleration values; any set of simulation parameters includes: basic parameters of the real vehicle, vehicle speed, front wheel steering angle, and a set of axle side stiffness parameters; and determining the target axle side stiffness parameter set of the real vehicle based on the simulation data.

[0006] In some implementations, determining the target axle lateral stiffness parameter set of the real vehicle based on simulation data includes: calculating a target error value; if the target error value is less than a threshold, taking one axle lateral stiffness parameter set from any set 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, a second error value, or the sum of the first and second error values; the first error value is obtained based on the simulated yaw rate value and the fitted yaw rate, and the second error value is obtained based on the simulated lateral acceleration value and the fitted lateral acceleration.

[0007] In some implementations, the method further includes: inputting a target axial side stiffness parameter set into a pre-established axial side stiffness model, and verifying the target axial side stiffness parameters based on multiple sets of actual response data.

[0008] In some implementations, multiple sets of actual response data under target operating conditions are fitted 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 implementations, the method further includes: performing fitting processing on the yaw rate and lateral acceleration respectively to obtain fitting relationships, and obtaining multiple sets of fitted response data based on the fitting relationships.

[0010] In some implementations, the axial side stiffness parameter set includes the axial side stiffness parameter sets for both the front and rear axles.

[0011] In some implementations, the method further includes disabling rear-wheel steering control of the real vehicle while collecting multiple sets of actual response data.

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

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

[0014] A second aspect of this application provides an apparatus for identifying the axle lateral stiffness of a vehicle. The apparatus includes: an acquisition module for acquiring multiple sets of actual response data and multiple sets of axle lateral stiffness parameters generated by a real vehicle under different operating conditions; wherein the multiple sets of axle lateral stiffness parameters include sets of axle lateral stiffness parameters corresponding to each set of actual response data; a fitting module for fitting the multiple sets of actual response data under a target operating condition to obtain multiple sets of fitted response data; wherein any set 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 set of simulation parameters into the vehicle dynamics model to obtain simulation data output by the vehicle dynamics model corresponding to any set of simulation parameters; wherein the simulation data includes: simulated yaw rate values ​​and simulated lateral acceleration values; any set of simulation parameters includes: basic parameters of the real vehicle, vehicle speed, front wheel steering angle, and a set of axle lateral stiffness parameters; and a determination module for determining the target axle lateral stiffness parameter set of the real vehicle based on the simulation data.

[0015] A third aspect of this application provides an electronic device, comprising:

[0016] Processor; and

[0017] A memory that stores executable code, which, when executed by the processor, causes the processor to perform the method described above.

[0018] A fourth aspect of this application provides a computer-readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to perform the method described above.

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

[0020] The technical solution of this application can also reduce identification costs.

[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

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

[0023] Figure 1 This is a schematic flowchart illustrating a method for identifying the axle lateral stiffness of a vehicle according to an embodiment of this application.

[0024] Figure 2 This is a schematic diagram of the structure of the device for identifying the axle side stiffness of a vehicle, as shown in an embodiment of this application;

[0025] Figure 3 This is a schematic diagram of the structure of an electronic device shown in an embodiment of this application. Detailed Implementation

[0026] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application more thorough and complete, and to fully convey the scope of this 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. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the 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 information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0029] Accurate axle slip characteristic modeling is fundamental to vehicle lateral control. Currently used methods for identifying vehicle axle slip stiffness have room for improvement in the accuracy of the obtained axle slip stiffness values.

[0030] To address the aforementioned issues, this application provides a method for identifying the axle side stiffness of a vehicle. This method can accurately identify the axle side stiffness of a vehicle based on actual response data of a real vehicle, which is beneficial for providing precise control in both the linear and nonlinear regions of the vehicle.

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

[0032] Figure 1 This is a flowchart illustrating a method for identifying the axle lateral stiffness of a vehicle, as shown in an embodiment of this application.

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

[0034] Step 101: Obtain multiple sets of actual response data and multiple sets of axle side stiffness parameters under different working conditions generated by real vehicles; wherein, the multiple sets of axle side stiffness parameters include sets of axle side stiffness parameters corresponding to each set of actual response data.

[0035] In some embodiments, the real vehicle can be a car, truck, motorcycle, bus, boat, airplane, helicopter, lawnmower, recreational vehicle, amusement park vehicle, construction equipment, tram, golf cart, train, and handcart, etc., and the embodiments of this application do not impose any special limitations.

[0036] In some embodiments, the vehicle may be a three-axle vehicle or a multi-axle vehicle with more 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 can be the lateral acceleration response and yaw rate response data of the vehicle under specific vehicle speed, steering wheel angle ramp input, and sine input.

[0039] In some embodiments, constant speed steering wheel angle ramp input can be used as the target matching condition, or other conditions can be used, such as variable speed steering wheel sine input, variable speed steering wheel ramp input, etc.

[0040] In some embodiments, the lateral stiffness can be the ratio of the lateral force to the lateral angle.

[0041] In some embodiments, the axial side stiffness parameter set includes the axial side stiffness parameter sets for the front axle and the rear axle.

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

[0043] Step 102: Fit multiple sets of actual response data under the target working condition to obtain multiple sets of fitted response data; wherein, any set of fitted response data includes vehicle speed, front wheel steering angle, fitted yaw rate and fitted lateral acceleration.

[0044] In some embodiments, the target operating condition can be a specific vehicle speed and lateral acceleration. For example, the target operating condition is a dataset (actual response data) with a vehicle speed of 60 km / h and a lateral acceleration of 0.8g.

[0045] In some embodiments, a fitting algorithm is used to fit multiple sets of actual response data under the target operating conditions to obtain multiple sets of fitted response data.

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

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

[0048] It is understandable that the least squares principle is used to select the fitting curve for fitting.

[0049] Furthermore, in some embodiments, fitting processes can be performed separately for yaw rate and lateral acceleration to obtain fitting relationships, and multiple sets of fitted response data can be obtained based on the fitting relationships.

[0050] For example, by choosing the least squares method and fitting a fourth-order polynomial as the objective function, the time t-yaw rate γ and the time t-lateral acceleration a are fitted respectively. y Data pairs were used to obtain the fitted relational expressions f(t,γ) and f(t,a). y Then, the fitted relation f(t,γ) and f(t,a) are used. y Regenerate the vehicle response data, i.e., the fitted multiple sets of response data.

[0051] It is understandable that using actual response data as the identification target will introduce interference due to the interference from the actual vehicle response signal acquisition, resulting in poor identification performance based on the actual vehicle response. This step, which involves fitting and preprocessing the actual response data, removes the interference from the actual acquired signals, thus helping to ensure better identification results.

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

[0053] In some embodiments, a vehicle dynamics model can be established based on the magic formula of tires.

[0054] It is understandable 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, vehicle dynamics model simulation can obtain simulated values ​​of yaw rate and lateral acceleration.

[0056] In some embodiments, the simulation parameters of the vehicle dynamics model may include: first, the basic parameters of the real vehicle, such as the vehicle's mass and geometric dimensions; second, the vehicle speed and front wheel steering angle; and third, the axle side stiffness parameter set for each matching target working condition combination of vehicle speed and front wheel steering angle, to obtain the simulated values ​​of yaw rate and lateral acceleration corresponding to the above contents.

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

[0058] In some embodiments, the optimization algorithm may include optimization algorithms such as genetic algorithms and simulated annealing algorithms.

[0059] It is understandable that each iteration inputs a set of simulation parameters, and then obtains the corresponding simulation data.

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

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

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

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

[0064] Calculate the target error value. If the target error value is less than the threshold, take one of the axle lateral stiffness parameter sets from any set of simulation parameters as the target axle lateral stiffness parameter set of the real vehicle. The target error value is either the first error value, the second error value, or the sum of the first and second error values. The first error value is obtained based on the simulated yaw rate and the fitted yaw rate, and the second error value is obtained based on the simulated lateral acceleration and the fitted lateral acceleration.

[0065] It is understandable that by setting a target to obtain a target error value that is less than a threshold, a preferred set of target axial offset stiffness parameters can be obtained.

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

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

[0068] In some embodiments, the target error value can be a sum, i.e., an error value that is related to both yaw rate and lateral acceleration.

[0069] The method for identifying the axle lateral stiffness of a vehicle according to the embodiments of this application can calculate the target error value in a variety of ways, so as to obtain the target axle lateral stiffness parameter set of the real vehicle based on the target error value.

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

[0071] It is understood that the preferred set of target axial offset stiffness parameters, or the optimal set of target axial offset stiffness parameters, is input into the pre-established axial offset stiffness model, and the target axial offset stiffness parameters are verified based on multiple sets of actual response data.

[0072] For example, the axial side stiffness model can be represented by the tire magic formula.

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

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

[0075] To better understand this application, the following embodiments further illustrate the content of this application, but this application is not limited to the following embodiments.

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

[0077] First, a simplified dynamic model of the vehicle is established using modeling and simulation software, taking a three-axle vehicle as an example.

[0078] The vehicle's mass is m, and its velocity in the x-direction of the vehicle's coordinate system is v. x Let L1 be the distance between the first and second axes, and L2 be the distance between the first and third axes. Let δ be the rotation angles of the first, second, and third axes. f δ m δ r The tire slip angles for the first, second, and third axles are α, ... f α m α r The distances from the first, second, and third axles to the vehicle's center of gravity are l, respectively. a l b l c The lateral stiffnesses of the first, second, and third axes are k, respectively. f k m k r The motion of the three-axle vehicle in steady state then 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 a 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 gravity, and γ is the yaw rate.

[0086] For vehicles that only steer on the first and third axles, i.e., the second axle does not steer, we have:

[0087] δ m =0 Formula 5

[0088] Among the factors affecting the axle side stiffness of a vehicle, tire stiffness is the primary factor. Therefore, a simplified version of the tire magic formula is used to describe the axle side stiffness characteristics as follows:

[0089] F = Dsin(Carctan(Bα)) Formula Six

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

[0091]

[0092] The yaw rate and lateral acceleration of a vehicle at different speeds and front wheel angles can be obtained using formulas one through seven.

[0093] Steering can be achieved using the first and third axes, or any combination of axes other than the first axis.

[0094] Next, the actual response data of the vehicles was collected.

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

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

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

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

[0099] The least squares method can be used to fit the data using a fourth-order polynomial as the objective function, specifically fitting the time t-yaw rate γ and the time t-lateral acceleration a. y Data pairs were used to obtain the fitted relational expressions f(t,γ) and f(t,a). y Then, the fitted relation f(t,γ) and f(t,a) are used. y Regenerate vehicle response data. Vehicle response data refers to the vehicle's lateral acceleration and yaw rate.

[0100] Finally, an optimized algorithm was used to identify the axial side stiffness parameters.

[0101] Axial side 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 sets of axle lateral stiffness parameters are used for the front and rear axles according to the actual situation. Here, the front axle lateral stiffness parameter set is: (B f C f D f The central and rear axle structures have the same rigidity, and both use the parameter set (B). r C r D r ).

[0103] Optimization was performed using simulation software, and the optimization problem was established as follows:

[0104] Optimization variables: Front and rear axle lateral stiffness parameter set (B) f C f D f ) and (Br C r D r There are six parameters in total;

[0105] Optimization condition: Vehicle speed v under the fitted working condition x Front wheel steering angle δ f Input data such as a vehicle speed of around 60 km / h and a lateral acceleration of 0.8g.

[0106] Optimization boundary: such as the vehicle's basic parameters like mass m mentioned above.

[0107] Optimization goal: Where w1 and w2 are the weights for matching the yaw rate target and the lateral acceleration target, respectively, here we take w1,w2=1; γ sim a y_sim These are the yaw rate and lateral acceleration obtained from the model simulation, γ. fit a y_fit These are the fitted yaw rate and lateral acceleration, respectively.

[0108] Furthermore, it should be noted that the optimization objective can be set as the set of lateral stiffness parameters for 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 for each axle, the suspension hard point parameters, the number of suspension bushing and elastic component parameter sets, and axles with the same above factors can share a set of parameters.

[0109] Furthermore, it should be noted that the weights w1, w2 = 1 for the two parts of the optimization objective can also be other values.

[0110] By employing an optimization algorithm to minimize the optimization objective, the optimal set of front and rear axle lateral stiffness parameters (B) that best matches the model response with the actual vehicle response can be obtained. 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 the number of iterations for the optimization algorithm. When the optimization objective is less than the minimum value, or when the number of iterations is reached, the optimal result is obtained.

[0112] The optimization process consists of the following sub-steps:

[0113] Step 1: Input the vehicle's basic parameters such as mass m and geometric dimensions.

[0114] Step 2: For each matching target operating condition, the vehicle speed v xi Front wheel steering angle δ fi Combining, the parameter set (B) of the current iteration input is calculated through the dynamic model. f C f D f ) and (B r C r D r The corresponding simulated values ​​of yaw rate and lateral acceleration γ sim,i and a y_sim,i .

[0115] Step 3: Based on the evaluation function Calculate the current front and rear axle lateral stiffness parameter set (B) f C f D f ) and (B r C r D r Evaluate the function value.

[0116] Step 4: The set of front and rear axle lateral stiffness parameters (B) for the next calculation step is determined by 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 the values, and repeat steps 2 and 3 until optimization terminates, obtaining the set of front and rear axle lateral stiffness parameters (B) that best matches the actual vehicle response. f C f D f ) and (B r C r D r ).

[0117] Step 5: Optimal front and rear axle lateral stiffness parameter set (B) f C f D f ) and (B r C r D r The axle side stiffness model was updated, and the optimization results were verified using the real vehicle response data collected above.

[0118] The method for identifying vehicle axle lateral stiffness in this application directly identifies axle lateral stiffness with the vehicle response as the target, and the modeling process only requires a simple vehicle dynamics model and a simplified axle lateral stiffness model, omitting the refined modeling process of matching the model as the target, thus saving a lot of manpower and material resources.

[0119] Corresponding to the aforementioned application function implementation method embodiments, this application also provides a device, electronic device, and corresponding embodiments for identifying vehicle axle lateral stiffness.

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

[0121] See 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 acquisition module 230, and a determination module 240.

[0122] The acquisition module 210 is used to acquire multiple sets of actual response data and multiple sets of axle side stiffness parameters under different working conditions generated by real vehicles; wherein, the multiple sets of axle side stiffness parameters include sets of axle side stiffness parameters corresponding to each set of actual response data.

[0123] The fitting module 220 is used to fit multiple sets of actual response data under the 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 rate and fitted lateral acceleration.

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

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

[0126] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated further here.

[0127] According to embodiments of this application, any plurality of modules among the acquisition module 210, fitting module 220, obtaining module 230, and determining module 240 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the acquisition module 210, fitting module 220, obtaining module 230, and determining module 240 can be at least partially implemented as hardware circuitry, 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-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the acquisition module 210, fitting module 220, obtaining module 230, and determining module 240 can be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

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

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

[0130] The processor 320 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0131] Memory 310 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM may store static data or instructions required by processor 320 or other modules of the computer. Permanent storage devices may be read-write storage devices. Permanent storage devices may be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. In some embodiments, permanent storage devices use mass storage devices (e.g., magnetic or optical disks, flash memory) as permanent storage devices. In other embodiments, permanent storage devices may be removable storage devices (e.g., floppy disks, optical drives). System memory may be a read-write storage device or a volatile read-write storage device, such as dynamic random access memory. System memory may store some or all of the instructions and data required by the processor during operation. Furthermore, memory 310 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (e.g., DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, memory 310 may include a removable storage device that is readable and / or writable, such as a laser disc (CD), a read-only digital multifunction optical disc (e.g., DVD-ROM, dual-layer DVD-ROM), a read-only Blu-ray disc, an ultra-high-density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not contain carrier waves or transient electronic signals transmitted wirelessly or via wired connections.

[0132] The memory 310 stores executable code, which, when processed by the processor 320, can cause the processor 320 to execute part or all of the methods described above.

[0133] Furthermore, the method according to this application can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the method described above.

[0134] Alternatively, this application may be implemented as a computer-readable storage medium (or a non-transitory machine-readable storage medium or a machine-readable storage medium) storing executable code (or computer program or computer instruction code) thereon, which, when executed by a processor of an electronic device (or server, etc.), causes the processor to perform part or all of the steps of the methods described above according to this application.

[0135] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled 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 sets of actual response data and multiple sets of axle side stiffness parameters under different operating conditions generated by real vehicles; wherein, the multiple sets of axle side stiffness parameters include sets of axle side stiffness parameters corresponding to each set of actual response data; Multiple sets of actual response data under target working conditions are fitted to obtain multiple sets of fitted response data; wherein, any set of fitted response data includes vehicle speed, front wheel steering angle, fitted yaw rate and fitted lateral acceleration; it includes: performing fitting processing on yaw rate and lateral acceleration respectively to obtain fitting relationship, and obtaining multiple sets of fitted response data according to the fitting relationship. A pre-established vehicle dynamics model is obtained, and any set of simulation parameters is input into the vehicle dynamics model to obtain simulation data output by the vehicle dynamics model corresponding to the set of simulation parameters; wherein, the simulation data includes: simulated values ​​of yaw rate and simulated values ​​of lateral acceleration; the set of simulation parameters includes: basic parameters of the real vehicle, the vehicle speed, the front wheel steering angle, and a set of axle side stiffness parameters; Determining the target axle lateral stiffness parameter set of the real vehicle based on the simulation data includes: calculating a target error value; if the target error value is less than a threshold, taking one axle lateral stiffness parameter set from any set 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, a second error value, or the sum of the first error value and the second error value; the first error value is obtained based on the simulated yaw rate value and the fitted yaw rate, and the second error value is obtained based on the simulated lateral acceleration value and the fitted lateral acceleration.

2. The method according to claim 1, characterized in that, The method further includes: The target axial side stiffness parameter set is input into a pre-established axial side stiffness model, and the target axial side stiffness parameters are verified based on the multiple sets of actual response data.

3. The method according to claim 1, characterized in that, The process of fitting multiple sets of actual response data under the target operating condition to obtain multiple sets of fitted response data includes: The least squares method is used to fit multiple sets of actual response data under the target working conditions to obtain multiple sets of fitted response data.

4. The method according to claim 1, characterized in that, The axial side stiffness parameter set includes the axial side stiffness parameter sets for the front axle and the rear axle.

5. The method according to claim 1, characterized in that, The method also includes: establishing the vehicle dynamics model based on the magic formula of tires.

6. The method according to claim 1, characterized in that, The method further includes: The set of axle side stiffness parameters to be input into the vehicle dynamics model is determined based on the optimization algorithm.

7. A device for identifying the axle lateral stiffness of a vehicle, characterized in that, include: The acquisition module is used to acquire multiple sets of actual response data and multiple sets of axle side stiffness parameters under different working conditions generated by real vehicles; wherein, the multiple sets of axle side stiffness parameters include sets of axle side stiffness parameters corresponding to each set of actual response data. The fitting module is used to fit 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 rate, and fitted lateral acceleration; it includes: performing fitting processing on yaw rate and lateral acceleration respectively to obtain fitting relationship, and obtaining multiple sets of fitted response data according to the fitting relationship. The acquisition module is used to acquire a pre-established vehicle dynamics model and input any set of simulation parameters into the vehicle dynamics model to obtain simulation data output by the vehicle dynamics model corresponding to the set of simulation parameters; wherein, the simulation data includes: simulated values ​​of yaw rate and simulated values ​​of lateral acceleration; the set of simulation parameters includes: basic parameters of the real vehicle, the vehicle speed, the front wheel steering angle, and a set of axle side stiffness parameters; A determination module is used to determine the target axle lateral stiffness parameter set of the real vehicle based on the simulation data; it includes: calculating a target error value, and if the target error value is less than a threshold, taking one axle lateral stiffness parameter set from any set 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 the sum of the first error value and the second error value; the first error value is obtained based on the simulated yaw rate value and the fitted yaw rate, and the second error value is obtained based on the simulated lateral acceleration value and the fitted lateral acceleration.

8. An electronic device, characterized in that, include: processor; as well as A memory having executable code stored thereon, which, when executed by the processor, causes the processor to perform the method as described in any one of claims 1-6.

Images