Method and System for Determining Vehicle Driving Style Based on Vehicle Model

Abstract

The present application provides a method and system for determining a vehicle driving style based on a vehicle model. The method includes: obtaining a plurality of historical driving style data sets; obtaining the type of the target vehicle, the target vehicle model, and the initial driving style data set; determining the target driving style data set of the target vehicle at least according to the plurality of historical driving style data sets and the type of the target vehicle; and in the case where the initial driving style data set does not conform to the target driving style data set, adjusting the preset parameters so that the driving style data set of the target vehicle model is the target driving style data set. This method can quickly determine the target driving style data set of the target vehicle through the plurality of historical driving style data sets and the type of the target vehicle, and then use the target vehicle model for testing, which can quickly and accurately adjust the driving style data of the target vehicle model to the target driving style data set, thereby solving the problem that the driving style cannot be efficiently calibrated in the prior art.

Description

Technical Field

[0001] The present application relates to the field of automobiles, and in particular, to a method for determining a driving style of a vehicle based on a vehicle model, a computer-readable storage medium, a processor, and a system. Background Art

[0002] The driving style is the inherent gene of a vehicle model and also a distinct feature that differentiates it from other competing vehicle models. The distinction between driving styles of different control modes refers to the difference in driving sensations shown when driving different control modes using the same driving operation. Driveability is an important performance of an automobile, and the setting of driving styles for each control mode has an important and direct impact on the overall vehicle driveability. With the increasingly fierce competition in the automobile market and the increasingly mature driving experience of automobile drivers, the requirements for automobile driveability are also getting higher and higher. Automobile driveability is related to many components such as the engine, transmission, mounts, suspension, and final drive. Therefore, during the vehicle model development stage, there are many calibration tasks for driveability, so the basis for driveability and driving style setting is particularly important.

[0003] In the existing publicly available technologies, they all focus on identifying the current driver's driving operation actions to determine the appropriate driving control mode and have a preliminary description of the setting principles for each mode. None of them provide a detailed description of the method and basis for setting the overall vehicle driving style for different modes, and the calibration methods are all relatively traditional without applying digital and efficient calibration methods. In addition, there is little research on setting driving styles for virtual test benches.

[0004] Therefore, there is an urgent need for a digital system that can set driving styles and a method for efficient driving style calibration.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background art of the technology described in this article. Therefore, the background art may contain certain information that is not prior art known to those skilled in the art in this country. Summary of the Invention

[0006] The main objective of the present application is to provide a method for determining a driving style of a vehicle based on a vehicle model, a computer-readable storage medium, a processor, and a system to solve the problem of the lack of an efficient driving style calibration method in the prior art.

[0007] To achieve the above object, according to one aspect of the present application, there is provided a method for determining a vehicle driving style based on a vehicle model, including: obtaining a plurality of historical driving style data sets, each of the historical driving style data sets including the vehicle speed and the corresponding acceleration response of the vehicle under different driving modes, where the acceleration response is the acceleration generated per one percent of the throttle pedal opening at different vehicle speeds and different throttle pedal openings, and the types of the vehicles corresponding to any two of the historical driving style data sets are different; obtaining the type of the target vehicle, the target vehicle model, and the initial driving style data set, where the target vehicle model is the model of the target vehicle and is used for virtual testing of the target vehicle, and the initial driving style data set is the driving style data set of the target vehicle model; determining the target driving style data set of the target vehicle at least based on the plurality of historical driving style data sets and the type of the target vehicle; in the case where the initial driving style data set does not conform to the target driving style data set, adjusting the preset parameters so that the driving style data set of the target vehicle model is the target driving style data set, and the preset parameters include at least one of the following: engine speed, output torque, vehicle speed, throttle pedal opening, and gear used.

[0008] Optionally, obtaining a plurality of historical driving style data sets includes: obtaining a plurality of driving style curves, one driving style curve corresponding to one of the historical driving style data sets, and the driving style curve being a relationship curve between the vehicle speed and the acceleration response of the vehicle under different driving modes.

[0009] Optionally, obtaining the target vehicle model of the target vehicle includes: obtaining the first operating parameter signals of the target vehicle under different working conditions, where the first operating parameter signals are used to characterize the parameters related to vehicle power when the target vehicle is running; obtaining the component parameters of the target vehicle and determining a preliminary vehicle model according to the component parameters, where the component parameters are used to characterize the performance of each component of the target vehicle; obtaining the second operating parameter signals of the preliminary vehicle model under different working conditions and calculating the difference between the second operating parameter signals and the first operating parameter signals to obtain a first difference, where the second operating parameter signals are used to characterize the parameters related to vehicle power when the preliminary vehicle model is simulated to run; in the case where the first difference is not within the first predetermined range, adjusting the component parameters so that the first difference is within the first predetermined range, and determining the preliminary vehicle model corresponding to the first difference within the first predetermined range as the target vehicle model.

[0010] Optionally, the working conditions include one of the following: crawling condition, starting and upshifting condition with different throttles, coasting and downshifting condition with different braking intensities, and accelerating and decelerating condition at different vehicle speeds.

[0011] Optionally, obtain an initial driving style data set, including: obtaining an automated test program for testing the acceleration response of the target vehicle model under different driving modes; using the automated test program to test the target vehicle model, and determining the initial driving style data set according to the test results of the automated test program.

[0012] Optionally, determine the target driving style data set of the target vehicle at least according to a plurality of historical driving style data sets and the type of the target vehicle, including: determining the driving style data set of the vehicle with the same target vehicle type as the target driving style data set among the plurality of historical driving style data sets.

[0013] Optionally, in the case that the initial driving style data set does not conform to the target driving style data set, adjust the preset parameters so that the driving style data set of the target vehicle model is the target driving style data set, including: calculating the difference between the acceleration responses of the initial driving style data set and the target driving style data set to obtain a second difference; in the case that the second difference is not within a second predetermined range, adjusting the preset parameters so that the second difference is within the second predetermined range, and determining the driving style data set corresponding to the second difference within the second predetermined range as the target driving style data set.

[0014] According to another aspect of the present application, there is provided a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, it controls the device where the computer-readable storage medium is located to execute any one of the methods for determining the vehicle driving style based on the vehicle model.

[0015] According to another aspect of the present application, there is also provided a processor for running a program, where when the program runs, it executes any one of the methods for determining the vehicle driving style based on the vehicle model.

[0016] According to another aspect of the present application, there is also provided a system for determining the vehicle driving style based on the vehicle model, including: one or more processors, a memory, a display device, and one or more programs, where the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include those for executing any one of the methods for determining the vehicle driving style based on the vehicle model.

[0017] Applying the technical solution of the present application, in the method for determining the driving style of a vehicle based on a vehicle model, first, a plurality of historical driving style data sets are obtained. Each historical driving style data set includes the vehicle speed and the corresponding acceleration response of the vehicle in different driving modes. The acceleration response is the acceleration generated per one percent of the accelerator pedal opening at different vehicle speeds and different accelerator pedal openings. The types of vehicles corresponding to any two historical driving style data sets are different. Then, the type of the target vehicle, the target vehicle model, and the initial driving style data set are obtained. The target vehicle model is the model of the target vehicle and is used for virtual testing of the target vehicle. The initial driving style data set is the driving style data set of the target vehicle model. Then, at least based on the plurality of historical driving style data sets and the type of the target vehicle, the target driving style data set of the target vehicle is determined. Finally, in the case where the initial driving style data set does not conform to the target driving style data set, the preset parameters are adjusted so that the driving style data set of the target vehicle model is the target driving style data set. The preset parameters include at least one of the following: engine speed, output torque, vehicle speed, accelerator pedal opening, and gear used. This method quickly determines the target driving style data set of the target vehicle through a plurality of historical driving style data sets and the type of the target vehicle, then obtains the target vehicle model of the target vehicle, tests the target vehicle model, and adjusts the preset parameters so that the driving style data set of the target vehicle model is the target driving style data set. Using the target vehicle model for testing can quickly and accurately adjust the driving style data of the target vehicle model to the target driving style data set, so that the driving style data set of the target vehicle can reach the target driving style data set, thereby solving the problem that the driving style cannot be efficiently calibrated in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The specification drawings forming a part of the present application are used to provide a further understanding of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0019] Figure 1 shows a flowchart of a method for determining the driving style of a vehicle based on a vehicle model according to an embodiment of the present application;

[0020] Figure 2 shows the driving style curves of different driving modes of a vehicle according to an embodiment of the present application;

[0021] Figure 3 shows a relationship diagram of the engine pedal torque output of a vehicle according to an embodiment of the present application;

[0022] Figure 4It shows a transmission shift control relationship diagram of a vehicle according to an embodiment of the present application;

[0023] Figure 5 It shows a schematic diagram of a device for determining a vehicle driving style based on a vehicle model according to an embodiment of the present application. Detailed implementation manners

[0024] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0026] 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 do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so as to describe the embodiments of the present application here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these process, method, product or device.

[0027] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element can be directly on the other element, or there may also be an intermediate element. Moreover, in the specification and claims, when an element is described as "connected" to another element, the element can be "directly connected" to the other element, or "connected" to the other element through a third element.

[0028] As mentioned in the background art, there is a problem of lacking an efficient driving style calibration method in the prior art. To solve the above problem, in a typical implementation manner of the present application, a method for determining a vehicle driving style based on a vehicle model, a computer-readable storage medium, a processor, and a system are provided.

[0029] According to an embodiment of the present application, a method for determining a vehicle driving style based on a vehicle model is provided.

[0030] Figure 1 is a flowchart of a method for determining a vehicle driving style based on a vehicle model according to an embodiment of the present application. As Figure 1 shown, the method includes the following steps:

[0031] Step S101, obtain a plurality of historical driving style data sets. Each of the above historical driving style data sets includes the vehicle speed and the corresponding acceleration response of the vehicle in different driving modes. The above acceleration response is the acceleration generated per one percent of the accelerator pedal opening of the vehicle at different vehicle speeds and different accelerator pedal openings. The types of vehicles corresponding to any two of the above historical driving style data sets are different;

[0032] Step S102, obtain the above type of the target vehicle, the target vehicle model, and the initial driving style data set. The above target vehicle model is the model of the target vehicle, and the above target vehicle model is used for virtual testing of the target vehicle. The above initial driving style data set is the driving style data set of the above target vehicle model;

[0033] Step S103, determine the target driving style data set of the above target vehicle at least according to a plurality of historical driving style data sets and the above type of the above target vehicle;

[0034] Step S104, in the case that the initial driving style data set does not conform to the above target driving style data set, adjust the preset parameters so that the above driving style data set of the above target vehicle model is the above target driving style data set. The above preset parameters include at least one of the following: engine speed, output torque, vehicle speed, accelerator pedal opening, and gear used.

[0035] In the above method for determining the driving style of a vehicle based on a vehicle model, first, a plurality of historical driving style data sets are obtained. Each of the above historical driving style data sets includes the vehicle speed and the corresponding acceleration response of the vehicle in different driving modes. The above acceleration response is the acceleration generated per one percent of the throttle pedal opening of the vehicle at different vehicle speeds and different throttle pedal openings. The types of vehicles corresponding to any two of the above historical driving style data sets are different. Then, the type of the target vehicle, the target vehicle model, and the initial driving style data set are obtained. The above target vehicle model is the model of the target vehicle, and the above target vehicle model is used for the virtual test of the target vehicle. The above initial driving style data set is the driving style data set of the above target vehicle model. Then, at least based on the plurality of historical driving style data sets and the type of the above target vehicle, the target driving style data set of the above target vehicle is determined. Finally, in the case where the initial driving style data set does not conform to the above target driving style data set, the preset parameters are adjusted so that the driving style data set of the above target vehicle model is the above target driving style data set. The above preset parameters include at least one of the following: engine speed, output torque, vehicle speed, throttle pedal opening, and gear used. This method quickly determines the target driving style data set of the target vehicle through a plurality of historical driving style data sets and the type of the target vehicle, then obtains the target vehicle model of the above target vehicle, tests the target vehicle model, and adjusts the preset parameters so that the driving style data set of the above target vehicle model is the above target driving style data set. Using the target vehicle model for testing can quickly and accurately adjust the driving style data of the target vehicle model to the target driving style data set, so that the driving style data set of the target vehicle can reach the target driving style data set, thereby solving the problem in the prior art that the driving style cannot be efficiently calibrated.

[0036] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And, although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0037] Specifically, there are multiple types of the above driving modes, which can be a comfort mode, an economy mode, and a sport mode. Those skilled in the art can determine the types of driving modes according to actual needs. Measure the acceleration response of different types of vehicles at different vehicle speeds in each driving mode to obtain a plurality of historical driving style data sets, where there can be multiple of any one type of vehicle.

[0038] In an embodiment of the present application, multiple historical driving style data groups are obtained, including: obtaining multiple driving style curves, where one of the above driving style curves corresponds to one driving style data group, and the above driving style curve is a relationship curve between the vehicle speed and the acceleration response under different driving modes. According to the multiple historical driving style data groups, the target driving style data group can be quickly obtained from them.

[0039] In a specific embodiment of the present application, before the test, vehicle speed signals, engine speed signals, engine torque signals, throttle pedal opening signals, gear signals, vehicle acceleration signals, etc. are collected. Among them, the vehicle speed signal can be collected by the vehicle controller or obtained by a vehicle speed sensor; the engine speed signal can be collected by the vehicle controller or obtained by a speed sensor; the engine torque signal can be collected by the vehicle controller or obtained by a torque sensor; the throttle pedal opening signal can be collected by the vehicle controller or obtained by a throttle pedal opening sensor; the gear signal can be collected by the vehicle controller or obtained from the instrument display; the vehicle acceleration signal is collected by the vehicle controller or can be obtained by a vehicle acceleration sensor, and the acceleration sensor is generally installed on the main driver's seat rail of the vehicle. When the driving mode is the comfort mode, when the vehicle speed slides to 20 km / h, 40 km / h, 60 km / h, 80 km / h, 100 km / h, 120 km / h, different throttle pedal openings are stepped on. The maximum values of the throttle pedal openings of different vehicles are different, and the size of the throttle pedal opening can be expressed as a percentage, such as 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%. Under this operation, the acceleration generated by each 1% throttle pedal opening is statistically shown in Table 1. The above operations are repeated for the economic mode and the sport mode to obtain the acceleration response tables corresponding to different vehicle speeds and throttle pedal openings, and they are plotted into Figure 2 the driving style curves of different driving modes as shown, and the unit of the acceleration generated by each 1% throttle pedal opening is m / s 2 / %. The same test is performed on different types of vehicles to form multiple historical driving style data groups.

[0040] Table 1 Acceleration response corresponding to different vehicle speeds and throttle pedal openings

[0041] 20 km / h 40 km / h 60 km / h 80 km / h 100 km / h 120 km / h 10％ 0.012 0.005 -0.009 -0.012 -0.020 -0.034 20％ 0.034 0.018 0.008 -0.002 -0.004 -0.004 30％ 0.053 0.028 0.013 0.006 0.001 -0.002 40％ 0.051 0.029 0.023 0.010 0.005 0.003 50％ 0.054 0.025 0.021 0.014 0.008 0.003 60％ 0.025 0.036 0.020 0.013 0.004 0.006 70％ 0.041 0.025 0.011 0.017 0.011 0.005 80％ 0.018 0.014 0.020 0.018 0.014 0.009 90％ 0.028 0.020 0.020 0.019 0.014 0.011 100％ 0.039 0.026 0.021 0.021 0.014 0.013

[0042] In order to obtain an accurate target vehicle model of the target vehicle, so as to make the subsequent test results more accurate and reduce the test cost, in another embodiment of the present application, obtaining the target vehicle model of the target vehicle includes: obtaining the first operating parameter signals of the target vehicle under different operating conditions, where the first operating parameter signals are used to characterize the parameter signals related to vehicle power during the operation of the target vehicle; obtaining the component parameters of the target vehicle, and determining a preliminary vehicle model according to the component parameters, where the component parameters are used to characterize the performance of each component of the target vehicle; obtaining the second operating parameter signals of the preliminary vehicle model under different operating conditions, and calculating the difference between the second operating parameter signals and the first operating parameter signals to obtain a first difference, where the second operating parameter signals are used to characterize the parameter signals related to vehicle power during the simulated operation of the preliminary vehicle model; in the case where the first difference is not within the first predetermined range, adjusting the component parameters so that the first difference is within the first predetermined range, and determining the preliminary vehicle model corresponding to the first difference within the first predetermined range as the target vehicle model.

[0043] Specifically, the component parameters of the target vehicle mainly include the engine, flywheel, clutch, transmission, drive shaft, main reducer, half shaft, tire, mount, suspension, etc. Among them, the engine parameters include displacement, coolant volume, oil capacity, friction torque characteristics, external characteristics, pedal torque characteristics, etc. The clutch parameters mainly include current pressure characteristics, number of friction surfaces, friction surface area, friction coefficient, etc. The transmission parameters mainly include transmission ratio, transmission efficiency, moment of inertia of each gear, damping characteristics of each gear, stiffness characteristics of each gear, etc. The drive shaft parameters mainly include the mass, moment of inertia, stiffness, damping, etc. of the drive shaft. The main reducer parameters mainly include reduction ratio and stiffness and damping of the main reducer. The half shaft parameters mainly include shaft stiffness and shaft damping. The tire parameters mainly include mass, moment of inertia, tire size, longitudinal slip characteristics, longitudinal stiffness, etc. The parameters of the mount include kinematic hard points, stiffness characteristics, damping characteristics. The parameters of the suspension include unsprung mass, equivalent damping, equivalent stiffness, etc. The vehicle parameters mainly include vehicle mass, center of mass height, front axle load, rear axle load, air resistance, rolling resistance, slope resistance, driving resistance equation, etc.

[0044] In another specific embodiment of the present application, the modeling software uses the physical model module in Matlab software for physical modeling to determine a preliminary vehicle model. Using physical modeling can effectively improve the model accuracy. Compare the second operating parameter signals of the preliminary vehicle model under different working conditions with the first operating parameter signals of the target vehicle. If the difference is within the first predetermined range, it indicates that the preliminary vehicle model has a high matching degree and high accuracy with the actual vehicle of the target vehicle, and this preliminary vehicle model is the target vehicle model. If the difference is not within the first predetermined range, it is necessary to optimize the model modeling method and the parameters of each component until the model accuracy meets the standard and is determined as the target vehicle model. The above first predetermined range can be -5% to 5%.

[0045] In order to further improve the accuracy of the target vehicle model, in another embodiment of the present application, the above working conditions include one of the following: crawling condition, starting and upshifting conditions with different throttle positions, coasting and downshifting conditions with different braking intensities, and accelerating and decelerating conditions at different vehicle speeds.

[0046] In practical applications, the above crawling condition is the condition where the vehicle coasts with the throttle released. The above starting and upshifting conditions with different throttle positions can be divided into three throttle size conditions: 0 - 30%, 30 - 60%, and 60 - 100%. The above coasting and downshifting conditions with different braking intensities can be divided into three braking intensity conditions: 0 - 30%, 30 - 60%, and 60 - 100%. The above accelerating and decelerating conditions at different vehicle speeds can be divided into three vehicle speed conditions: below 40 km / h, 40 - 80 km / h, and above 80 km / h.

[0047] In still another embodiment of the present application, obtain an initial driving style data set, including: obtain an automated test program, and the above automated test program is used to test the above acceleration response of the above target vehicle model under different above driving modes; use the above automated test program to test the above target vehicle model, and determine the above initial driving style data set according to the test results of the above automated test program. Through the automated test program, the test results can be quickly obtained without the need for actual vehicle verification, making the test process efficient and cost - reducing.

[0048] In another specific embodiment of the present application, the above automated test program is an automated test program written in advance according to the determined working conditions and written into the virtual test bench. When performing an automated test on the target vehicle model, obtain the automated test program and complete the automated test on the target vehicle model in the virtual test bench.

[0049] In order to obtain a suitable target driving style data set, in another embodiment of the present application, the target driving style data set of the target vehicle is determined at least according to a plurality of historical driving style data sets and the type of the target vehicle, including: determining the driving style data set of the vehicle with the same type as the target vehicle among the plurality of historical driving style data sets as the target driving style data set.

[0050] In practical applications, according to a plurality of historical driving style data sets and the type of the target vehicle, the driving styles of each driving mode of the target vehicle can be determined. The driving style data set of the target vehicle can be determined by the majority of the same styles of the same type of vehicle models in a plurality of historical driving style data sets, or can be determined by a certain benchmark vehicle in a plurality of historical driving style data sets.

[0051] In another embodiment of the present application, in the case where the initial driving style data set does not conform to the target driving style data set, the preset parameters are adjusted so that the driving style data set of the target vehicle model is the target driving style data set, including: calculating the difference in the acceleration response between the initial driving style data set and the target driving style data set to obtain a second difference; in the case where the second difference is not within the second predetermined range, adjusting the preset parameters so that the second difference is within the second predetermined range, and determining the driving style data set corresponding to the second difference within the second predetermined range as the target driving style data set. By adjusting the preset parameters, the driving style data set of the target vehicle model can conform to the target driving style data set, so that the driving style data set of the target vehicle reaches the target driving style data set.

[0052] In practical applications, when the initial driving style data set does not conform to the target driving style data set, the preset parameters are adjusted so that the driving style data set of the target vehicle model is the target driving style data set. Specifically, it can be achieved by optimizing the Figure 3 engine pedal torque output relationship diagram shown as Figure 4 and the transmission shift control relationship diagram shown as Figure 3 which shows the correspondence between the engine speed, different percentage throttle pedal openings and the output torque. Different correspondences can be obtained by adjusting the engine speed, throttle pedal opening or output torque. Figure 4 which shows the correspondence between the vehicle speed, throttle opening and the gear used. Different correspondences can be obtained by adjusting the vehicle speed, throttle opening or the gear used. The new adjusted parameters are used to conduct automated tests again. Repeating this process multiple times until the difference in the acceleration response between the driving style data set of the target vehicle model and the target driving style data set is within the second predetermined range. The second predetermined range can be -0.005 to 0.005 m / s. 2 / %。

[0053] In yet another specific embodiment of the present application, the above method further includes: saving the above target driving style data group. After the optimization of a certain driving mode is completed, the remaining driving modes are automatically tested and calibrated in the same manner. After the debugging of each driving mode is completed, the target driving style data group is obtained and saved to the controller to support the vehicle's overall vehicle development.

[0054] The embodiment of the present application also provides a device for determining a vehicle driving style based on a vehicle model. It should be noted that the device for determining a vehicle driving style based on a vehicle model in the embodiment of the present application can be used to execute the method for determining a vehicle driving style based on a vehicle model provided in the embodiment of the present application. The following introduces the device for determining a vehicle driving style based on a vehicle model provided in the embodiment of the present application.

[0055] Figure 5 is a schematic diagram of the device for determining a vehicle driving style based on a vehicle model according to the embodiment of the present application. As Figure 5 shown, the device includes:

[0056] A first acquisition unit 10, configured to acquire a plurality of historical driving style data groups, each of the above historical driving style data groups including the vehicle speed and the corresponding acceleration response of the vehicle under different driving modes, the above acceleration response being the acceleration generated per one percent of the throttle pedal opening at different vehicle speeds and different throttle pedal openings, and the types of the vehicles corresponding to any two of the above historical driving style data groups being different;

[0057] A second acquisition unit 20, configured to acquire the above type of the target vehicle, the above target vehicle model, and the initial driving style data group, the above target vehicle model being the model of the target vehicle, the above target vehicle model being used for the virtual test of the target vehicle, and the above initial driving style data group being the driving style data group of the above target vehicle model;

[0058] A determination unit 30, configured to determine the target driving style data group of the above target vehicle at least according to the plurality of historical driving style data groups and the above type of the above target vehicle;

[0059] An adjustment unit 40, configured to adjust the preset parameters to make the above driving style data group of the above target vehicle model be the above target driving style data group when the initial driving style data group does not conform to the above target driving style data group, the above preset parameters including at least one of the following: engine speed, output torque, vehicle speed, throttle pedal opening, and gear used.

[0060] In the above-mentioned determining device for the driving style of a vehicle based on a vehicle model, a plurality of historical driving style data groups are obtained through the above-mentioned first obtaining unit. Each of the above-mentioned historical driving style data groups includes the vehicle speed and the corresponding acceleration response under different driving modes. The above-mentioned acceleration response is the acceleration generated per one percent of the accelerator pedal opening at different vehicle speeds and different accelerator pedal openings. The types of vehicles corresponding to any two of the above-mentioned historical driving style data groups are different; the above-mentioned type of the target vehicle, the target vehicle model, and the initial driving style data group are obtained through the above-mentioned second obtaining unit. The above-mentioned target vehicle model is the model of the target vehicle, and the above-mentioned target vehicle model is used for the virtual test of the target vehicle. The above-mentioned initial driving style data group is the driving style data group of the above-mentioned target vehicle model; the above-mentioned determining unit determines the target driving style data group of the above-mentioned target vehicle at least according to a plurality of historical driving style data groups and the above-mentioned type of the above-mentioned target vehicle; when the initial driving style data group does not conform to the above-mentioned target driving style data group, the above-mentioned adjusting unit adjusts the preset parameters so that the driving style data group of the above-mentioned target vehicle model is the above-mentioned target driving style data group. The above-mentioned preset parameters include at least one of the following: engine speed, output torque, vehicle speed, accelerator pedal opening, and used gear. This device quickly determines the target driving style data group of the target vehicle through a plurality of historical driving style data groups and the type of the target vehicle, then obtains the above-mentioned target vehicle model of the target vehicle, tests the target vehicle model, and adjusts the preset parameters so that the driving style data group of the above-mentioned target vehicle model is the above-mentioned target driving style data group. Using the target vehicle model for testing can quickly and accurately adjust the driving style data of the target vehicle model to the target driving style data group, so that the driving style data group of the target vehicle can reach the target driving style data group, thereby solving the problem in the prior art that the driving style cannot be efficiently calibrated.

[0061] Specifically, there are multiple types of the above-mentioned driving modes, which can be a comfort mode, an economy mode, and a sport mode. Those skilled in the art can determine the types of driving modes according to actual needs. Measure the acceleration responses of different types of vehicles at different vehicle speeds under each driving mode to obtain a plurality of historical driving style data groups, where there can be multiple vehicles of any one type.

[0062] In an embodiment of the present application, the above-mentioned first obtaining unit includes a first obtaining module. The above-mentioned first obtaining module is used to obtain a plurality of driving style curves. One of the above-mentioned driving style curves corresponds to one driving style data group. The above-mentioned driving style curve is a relationship curve between the vehicle speed and the acceleration response of the vehicle under different driving modes. According to a plurality of historical driving style data groups, the target driving style data group can be quickly obtained therefrom.

[0063] In a specific embodiment of the present application, before the test, vehicle speed signal, engine speed signal, engine torque signal, accelerator pedal opening signal, gear signal, vehicle acceleration signal, etc. are collected. Among them, the vehicle speed signal can be collected by the vehicle controller or obtained by the vehicle speed sensor; the engine speed signal can be collected by the vehicle controller or obtained by the engine speed sensor; the engine torque signal can be collected by the vehicle controller or obtained by the torque sensor; the accelerator pedal opening signal can be collected by the vehicle controller or obtained by the accelerator pedal opening sensor; the gear signal can be collected by the vehicle controller or obtained from the instrument display; the vehicle acceleration signal is collected by the vehicle controller or can be obtained by the vehicle acceleration sensor, and the acceleration sensor is generally installed on the main driver's seat rail of the vehicle. When the driving mode is the comfort mode, when the vehicle speed slides to 20 km / h, 40 km / h, 60 km / h, 80 km / h, 100 km / h, 120 km / h, step on different accelerator pedal openings. The maximum values of the accelerator pedal openings of different vehicles are different, and the size of the accelerator pedal opening can be expressed as a percentage, such as 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%. Under this operation, the acceleration generated by each 1% of the accelerator pedal opening is statistically shown in Table 2. Repeat the above operation for both the economic mode and the sport mode to obtain the acceleration response table corresponding to different vehicle speeds and accelerator pedal openings, and draw the driving style curves of different driving modes as shown in Figure 2 The acceleration unit generated by each 1% of the accelerator pedal opening is m / s 2 / %. Conduct the same test on different types of vehicles to form multiple historical driving style data groups.

[0064] Table 2 Acceleration response corresponding to different vehicle speeds and accelerator pedal openings

[0065] 20 km / h 40 km / h 60 km / h 80 km / h 100 km / h 120 km / h 10％ 0.012 0.005 -0.009 -0.012 -0.020 -0.034 20％ 0.034 0.018 0.008 -0.002 -0.004 -0.004 30％ 0.053 0.028 0.013 0.006 0.001 -0.002 40％ 0.051 0.029 0.023 0.010 0.005 0.003 50％ 0.054 0.025 0.021 0.014 0.008 0.003 60％ 0.025 0.036 0.020 0.013 0.004 0.006 70％ 0.041 0.025 0.011 0.017 0.011 0.005 80％ 0.018 0.014 0.020 0.018 0.014 0.009 90％ 0.028 0.020 0.020 0.019 0.014 0.011 100％ 0.039 0.026 0.021 0.021 0.014 0.013

[0066] In order to obtain an accurate target vehicle model of the target vehicle, so as to make the subsequent test results more accurate and reduce the test cost. In another embodiment of the present application, the second acquisition unit includes a second acquisition module, a third acquisition module, a first calculation module, and a first adjustment module. Among them, the second acquisition module is used to acquire the first operating parameter signals of the target vehicle under different working conditions, and the first operating parameter signals are used to characterize the parameter signals related to vehicle power during the operation of the target vehicle; the third acquisition module is used to acquire the component parameters of the target vehicle and determine a preliminary vehicle model according to the component parameters, and the component parameters are used to characterize the performance of each component of the target vehicle; the first calculation module is used to acquire the second operating parameter signals of the preliminary vehicle model under different working conditions and calculate the difference between the second operating parameter signals and the first operating parameter signals to obtain a first difference, and the second operating parameter signals are used to characterize the parameter signals related to vehicle power during the simulated operation of the preliminary vehicle model; the first adjustment module is used to adjust the component parameters when the first difference is not within the first predetermined range, so that the first difference is within the first predetermined range, and determine the preliminary vehicle model corresponding to the first difference within the first predetermined range as the target vehicle model.

[0067] Specifically, the component parameters of the target vehicle mainly include the engine, flywheel, clutch, transmission, drive shaft, main reducer, half shaft, tire, mount, suspension, etc. Among them, the engine parameters include displacement, coolant volume, oil capacity, friction torque characteristics, external characteristics, pedal torque characteristics, etc. The clutch parameters mainly include current pressure characteristics, number of friction surfaces, friction surface area, friction coefficient, etc. The transmission parameters mainly include transmission ratio, transmission efficiency, moment of inertia of each gear, damping characteristics of each gear, stiffness characteristics of each gear, etc. The drive shaft parameters mainly include the mass, moment of inertia, stiffness, damping, etc. of the drive shaft. The main reducer parameters mainly include the reduction ratio and the stiffness and damping of the main reducer. The half shaft parameters mainly include shaft stiffness and shaft damping. The tire parameters mainly include mass, moment of inertia, tire size, longitudinal slip characteristics, longitudinal stiffness, etc. The parameters of the mount include kinematic hard points, stiffness characteristics, damping characteristics. The parameters of the suspension include unsprung mass, equivalent damping, equivalent stiffness, etc. The vehicle parameters mainly include vehicle mass, center of mass height, front axle load, rear axle load, air resistance, rolling resistance, slope resistance, driving resistance equation, etc.

[0068] In another specific embodiment of the present application, the modeling software uses the physical model module in Matlab software for physical modeling to determine a preliminary vehicle model. Using physical modeling can effectively improve the model accuracy. Compare the second operating parameter signals of the preliminary vehicle model under different working conditions with the first operating parameter signals of the target vehicle. If the difference is within the first predetermined range, it indicates that the preliminary vehicle model has a high matching degree and high accuracy with the actual vehicle of the target vehicle, and this preliminary vehicle model is the target vehicle model. If the difference is not within the first predetermined range, it is necessary to optimize the model modeling method and the parameters of each component until the model accuracy meets the standard and is determined as the target vehicle model. The above-mentioned first predetermined range can be -5% to 5%.

[0069] In order to further improve the accuracy of the target vehicle model, in another embodiment of the present application, the above-mentioned working conditions include one of the following: crawling condition, starting and upshifting conditions with different throttle positions, coasting and downshifting conditions with different braking intensities, and accelerating and decelerating conditions at different vehicle speeds.

[0070] In practical applications, the above-mentioned crawling condition is the condition where the vehicle coasts with the throttle released. The above-mentioned starting and upshifting conditions with different throttle positions can be divided into three throttle size conditions: 0 - 30%, 30 - 60%, and 60 - 100%. The above-mentioned coasting and downshifting conditions with different braking intensities can be divided into three braking intensity conditions: 0 - 30%, 30 - 60%, and 60 - 100%. The above-mentioned accelerating and decelerating conditions at different vehicle speeds can be divided into three conditions: vehicle speed below 40 km / h, 40 - 80 km / h, and above 80 km / h.

[0071] In still another embodiment of the present application, the above-mentioned second acquisition unit further includes a fourth acquisition module and a first determination module. Among them, the above-mentioned fourth acquisition module is used to acquire an automated test program, and the automated test program is used to test the acceleration response of the above-mentioned target vehicle model under different above-mentioned driving modes; the above-mentioned first determination module is used to test the above-mentioned target vehicle model using the above-mentioned automated test program and determine the above-mentioned initial driving style data set according to the test results of the above-mentioned automated test program. Through the automated test program, the test results can be quickly obtained without the need for real vehicle verification, making the test process efficient and cost - reducing.

[0072] In another specific embodiment of the present application, the above-mentioned automated test program is an automated test program written in advance according to the determined working conditions and written into the virtual test bench. When performing an automated test on the target vehicle model, the automated test program for the target vehicle model is acquired.

[0073] In order to obtain a suitable target driving style data set, in another embodiment of the present application, the above-mentioned determination unit includes a second determination module, and the second determination module is configured to determine, from multiple historical driving style data sets, the driving style data set of a vehicle of the same type as the target vehicle as the target driving style data set.

[0074] In practical applications, according to multiple historical driving style data sets and the type of the target vehicle, the driving styles of each driving mode of the target vehicle can be determined. The driving style data set of the target vehicle can be determined by the majority of the same styles of the same type of vehicle models in multiple historical driving style data sets, or can be determined by a certain benchmark vehicle in multiple historical driving style data sets.

[0075] In another embodiment of the present application, the above-mentioned adjustment unit includes a second calculation module and a second adjustment module. Among them, the second calculation module is configured to calculate the difference in the acceleration response between the initial driving style data set and the target driving style data set to obtain a second difference; the second adjustment module is configured to, when the second difference is not within the second predetermined range, adjust the above-mentioned preset parameters so that the second difference is within the second predetermined range, and determine the driving style data set corresponding to the second difference within the second predetermined range as the target driving style data set. By adjusting the preset parameters, the driving style data set of the target vehicle model can be made to conform to the target driving style data set, so that the driving style data set of the target vehicle reaches the target driving style data set.

[0076] In practical applications, when the above-mentioned initial driving style data set does not conform to the target driving style data set, the preset parameters are adjusted so that the driving style data set of the above-mentioned target vehicle model is the target driving style data set. Specifically, it can be achieved by optimizing the engine pedal torque output relationship diagram as shown in Figure 3 and the transmission shift control relationship diagram as shown in Figure 4 . Figure 3 shows the corresponding relationship between the engine speed, different percentage throttle pedal openings, and output torque. By adjusting the engine speed, throttle pedal opening, or output torque, different corresponding relationships can be obtained. Figure 4 shows the corresponding relationship between the vehicle speed, throttle opening, and used gear. By adjusting the vehicle speed, throttle opening, or used gear, different corresponding relationships can be obtained. Then, the new adjusted parameters are used to conduct automated tests again. Repeating this process multiple times until the difference in the acceleration response between the driving style data set of the target vehicle model and the target driving style data set is within the second predetermined range. The second predetermined range can be -0.005 to 0.005 m / s 2 / %.

[0077] In still another specific embodiment of the present application, the above device further includes a storage unit, and the storage unit is used to store the above target driving style data group. After the optimization of a certain driving mode is completed, the remaining driving modes are automatically tested and calibrated in the same manner. After the debugging of each driving mode is completed, the target driving style data group is obtained and stored in the controller to support the vehicle development of the whole vehicle.

[0078] The above device for determining the driving style of a vehicle based on a whole vehicle model includes a processor and a memory. The above first acquisition unit, second acquisition unit, determination unit, adjustment unit, etc. are all stored in the memory as program units, and the processor executes the above program units stored in the memory to implement corresponding functions.

[0079] The processor includes a kernel, and the kernel retrieves the corresponding program units from the memory. One or more kernels can be set, and the problem of the lack of an efficient driving style calibration method in the prior art is solved by adjusting the kernel parameters.

[0080] The memory may include non-permanent memory in a computer-readable medium, forms such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM), and the memory includes at least one storage chip.

[0081] The embodiment of the present invention provides a computer-readable storage medium, and the above computer-readable storage medium includes a stored program. When the above program runs, it controls the device where the computer-readable storage medium is located to execute the above method for determining the driving style of a vehicle based on a whole vehicle model.

[0082] The embodiment of the present invention provides a processor, and the above processor is used to run a program. When the above program runs, it executes the above method for determining the driving style of a vehicle based on a whole vehicle model.

[0083] The embodiment of the present invention provides a device, and the device includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, it realizes at least the following steps:

[0084] Step S101, obtain a plurality of historical driving style data groups, each of the above historical driving style data groups includes the vehicle speed of the vehicle under different driving modes and the corresponding acceleration response. The above acceleration response is the acceleration generated by the vehicle per one percent of the throttle pedal opening at different vehicle speeds and different throttle pedal openings. The types of vehicles corresponding to any two of the above historical driving style data groups are different;

[0085] Step S102: Obtain the above type of the target vehicle, the target vehicle model, and the initial driving style data set. The above target vehicle model is the model of the target vehicle, and the above target vehicle model is used for virtual testing of the target vehicle. The above initial driving style data set is the driving style data set of the above target vehicle model;

[0086] Step S103: Determine the target driving style data set of the above target vehicle at least based on multiple historical driving style data sets and the above type of the above target vehicle;

[0087] Step S104: In the case where the initial driving style data set does not conform to the above target driving style data set, adjust the preset parameters so that the above driving style data set of the above target vehicle model is the above target driving style data set. The above preset parameters include at least one of the following: engine speed, output torque, vehicle speed, accelerator pedal opening, and gear in use.

[0088] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0089] This application also provides a computer program product, which, when executed on a data processing device, is adapted to execute a program initialized with at least the following method steps:

[0090] Step S101: Obtain multiple historical driving style data sets. Each of the above historical driving style data sets includes the vehicle speed and the corresponding acceleration response of the vehicle in different driving modes. The above acceleration response is the acceleration generated per one percent of the accelerator pedal opening at different vehicle speeds and different accelerator pedal openings. The types of the vehicles corresponding to any two of the above historical driving style data sets are different;

[0091] Step S102: Obtain the above type of the target vehicle, the target vehicle model, and the initial driving style data set. The above target vehicle model is the model of the target vehicle, and the above target vehicle model is used for virtual testing of the target vehicle. The above initial driving style data set is the driving style data set of the above target vehicle model;

[0092] Step S103: Determine the target driving style data set of the above target vehicle at least based on multiple historical driving style data sets and the above type of the above target vehicle;

[0093] Step S104: In the case where the initial driving style data set does not conform to the above target driving style data set, adjust the preset parameters so that the above driving style data set of the above target vehicle model is the above target driving style data set. The above preset parameters include at least one of the following: engine speed, output torque, vehicle speed, accelerator pedal opening, and gear in use.

[0094] According to another aspect of the present application, there is also provided a system for determining a vehicle driving style based on a vehicle model, including: one or more processors, a memory, a display device, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include a method for executing any of the above-mentioned methods for determining a vehicle driving style based on a vehicle model.

[0095] The above-mentioned system for determining a vehicle driving style based on a vehicle model quickly determines a target driving style data set of a target vehicle through multiple historical driving style data sets and the type of the target vehicle, then obtains the target vehicle model of the target vehicle, tests the target vehicle model, and adjusts preset parameters so that the driving style data set of the target vehicle model is the target driving style data set. By using the target vehicle model for testing, the driving style data of the target vehicle model can be quickly and accurately adjusted to the target driving style data set, so that the driving style data set of the target vehicle can reach the target driving style data set, thereby solving the problem in the prior art that the driving style cannot be efficiently calibrated.

[0096] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0098] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions in the processFigure 1 one or more processes and / or blocks Figure 1 the functions specified in one or more blocks.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more processes and / or blocks Figure 1 one or more blocks.

[0100] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0101] The memory may include non-permanent memory in the computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0102] Computer-readable media include permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media do not include transitory media, such as modulated data signals and carrier waves.

[0103] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent in such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of another identical element in the process, method, commodity or device comprising the element.

[0104] As can be seen from the above description, the above embodiments of the present application achieve the following technical effects:

[0105] 1), The method for determining the driving style of a vehicle based on a vehicle model of the present application. First, obtain a plurality of historical driving style data groups. Each of the above historical driving style data groups includes the vehicle speed and the corresponding acceleration response under different driving modes. The above acceleration response is the acceleration generated per one percent of the accelerator pedal opening at different vehicle speeds and different accelerator pedal openings. The types of vehicles corresponding to any two of the above historical driving style data groups are different. Then, obtain the type of the target vehicle, the target vehicle model, and the initial driving style data group. The above target vehicle model is the model of the target vehicle, and the above target vehicle model is used for virtual testing of the target vehicle. The above initial driving style data group is the driving style data group of the above target vehicle model. Then, determine the target driving style data group of the above target vehicle at least according to the plurality of historical driving style data groups and the type of the above target vehicle. Finally, in the case where the initial driving style data group does not conform to the above target driving style data group, adjust the preset parameters so that the driving style data group of the above target vehicle model is the above target driving style data group. The above preset parameters include at least one of the following: engine speed, output torque, vehicle speed, accelerator pedal opening, and gear used. This method quickly determines the target driving style data group of the target vehicle through a plurality of historical driving style data groups and the type of the target vehicle, then obtains the target vehicle model of the above target vehicle, tests the target vehicle model, and adjusts the preset parameters so that the driving style data group of the above target vehicle model is the above target driving style data group. Using the target vehicle model for testing can quickly and accurately adjust the driving style data of the target vehicle model to the target driving style data group, so that the driving style data group of the target vehicle can reach the target driving style data group, thereby solving the problem in the prior art that the driving style cannot be efficiently calibrated.

[0106] 2) The system for determining the driving style of a vehicle based on a vehicle model of the present application quickly determines the target driving style data group of the target vehicle through a plurality of historical driving style data groups and the type of the target vehicle, then obtains the target vehicle model of the above target vehicle, tests the target vehicle model, and adjusts the preset parameters so that the driving style data group of the above target vehicle model is the above target driving style data group. Using the target vehicle model for testing can quickly and accurately adjust the driving style data of the target vehicle model to the target driving style data group, so that the driving style data group of the target vehicle can reach the target driving style data group, thereby solving the problem in the prior art that the driving style cannot be efficiently calibrated.

[0107] The above are only the preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various modifications and changes can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for determining a vehicle driving style based on a vehicle model, characterized in that, it includes: Obtain a plurality of historical driving style data sets, each of the historical driving style data sets including the vehicle speed and the corresponding acceleration response of the vehicle in different driving modes, where the acceleration response is the acceleration generated per one percent of the accelerator pedal opening at different vehicle speeds and different accelerator pedal openings, and the types of vehicles corresponding to any two of the historical driving style data sets are different; Obtain the type of the target vehicle, the target vehicle model, and the initial driving style data set. The target vehicle model is the model of the target vehicle and is used for virtual testing of the target vehicle. The initial driving style data set is the driving style data set of the target vehicle model; Determine the target driving style data set of the target vehicle at least according to a plurality of historical driving style data sets and the type of the target vehicle; In the case where the initial driving style data set does not conform to the target driving style data set, adjust the preset parameters so that the driving style data set of the target vehicle model is the target driving style data set. The preset parameters include at least one of the following: engine speed, output torque, vehicle speed, accelerator pedal opening, and gear used; Determine the target driving style data set of the target vehicle at least according to a plurality of historical driving style data sets and the type of the target vehicle, including: Among the plurality of historical driving style data sets, determine the driving style data set of the vehicle with the same type as the target vehicle as the target driving style data set.

2. The method according to claim 1, characterized in that, Obtaining a plurality of historical driving style data sets includes: Obtain a plurality of driving style curves, one driving style curve corresponding to one of the historical driving style data sets, and the driving style curve being a relationship curve between the vehicle speed and the acceleration response of the vehicle in different driving modes.

3. The method according to claim 1, characterized in that, Obtaining the target vehicle model of the target vehicle includes: Obtain the first operating parameter signals of the target vehicle under different operating conditions, where the first operating parameter signals are used to characterize the parameter signals related to vehicle power when the target vehicle is operating; Obtain the component parameters of the target vehicle and determine a preliminary vehicle model according to the component parameters, where the component parameters are used to characterize the performance of each component of the target vehicle; Obtain the second operating parameter signals of the preliminary vehicle model under different operating conditions and calculate the difference between the second operating parameter signals and the first operating parameter signals to obtain a first difference, where the second operating parameter signals are used to characterize the parameter signals related to vehicle power when the preliminary vehicle model is simulated to operate; In the case where the first difference is not within the first predetermined range, adjust the component parameters so that the first difference is within the first predetermined range, and determine the preliminary vehicle model corresponding to the first difference within the first predetermined range as the target vehicle model.

4. The method according to claim 3, characterized in that, The working conditions include any one of the following: creeping condition, starting and upshifting conditions at different throttle positions, coasting and downshifting conditions at different braking intensities, and accelerating and decelerating conditions at different vehicle speeds.

5. The method according to claim 1, wherein, obtain an initial driving style data set, including: obtain an automated test program, which is used to test the acceleration response of the target vehicle model under different driving modes; use the automated test program to test the target vehicle model, and determine the initial driving style data set according to the test results of the automated test program.

6. The method according to claim 1, wherein, in the case that the initial driving style data set does not conform to the target driving style data set, adjust the preset parameters so that the driving style data set of the target vehicle model is the target driving style data set, including: calculate the difference between the acceleration responses of the initial driving style data set and the target driving style data set to obtain a second difference; in the case that the second difference is not within the second predetermined range, adjust the preset parameters so that the second difference is within the second predetermined range, and determine the driving style data set corresponding to the second difference within the second predetermined range as the target driving style data set.

7. A computer-readable storage medium, wherein, the computer-readable storage medium includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the method for determining the vehicle driving style based on the vehicle model according to any one of claims 1 to 6.

8. A processor, wherein, the processor is used to run a program, wherein when the program runs, it executes the method for determining the vehicle driving style based on the vehicle model according to any one of claims 1 to 6.

9. A system for determining the vehicle driving style based on the vehicle model, wherein, comprises: one or more processors, a memory, a display device, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include the method for determining the vehicle driving style based on the vehicle model according to any one of claims 1 to 6.

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