Vehicle resistance calibration method and device, terminal equipment and storage medium

Abstract

The invention relates to the technical field of automobile control, in particular to a vehicle resistance calibration method and device, terminal equipment and a storage medium. The method comprises the following steps: testing the sliding resistance of a test vehicle, and obtaining multiple groups of test data; preprocessing the multiple groups of test data to obtain multiple groups of smooth test data; fusing the smooth test data to obtain a group of fused test data; according to the fusion test data, fitting to obtain a speed-time curve function, deriving the speed-time curve function, substituting time to obtain an acceleration-time curve function, and fitting the acceleration-time curve function and the speed-time curve function to obtain a speed-acceleration curve function; and obtaining a sliding resistance model according to the speed acceleration curve function and the Newton second law. According to the method, data smoothness is ensured by performing fusion and fitting operation on the test data, and the sliding resistance model is obtained through a calculation mode, so that the precision of the sliding resistance is improved, and the acquisition difficulty of the sliding resistance is simplified.

Description

Technical Field

[0001] The present application relates to the field of automobile control technology, and in particular to a vehicle resistance calibration method, device, terminal equipment and storage medium. Background Art

[0002] In adaptive cruise control, after calculating the required acceleration of the vehicle, it is necessary to accurately control the torque of the engine or drive motor so that the vehicle can travel according to the expected acceleration, thereby achieving stable control of the vehicle's speed and position. The acceleration of the vehicle depends on the difference between the driving force and the driving resistance. Therefore, finding the accurate driving resistance can greatly improve the accuracy and response speed of acceleration control. However, the current calculation method of driving resistance is not accurate and the acquisition method is cumbersome. Summary of the invention

[0003] In view of this, the embodiments of the present application provide a vehicle resistance calibration method, apparatus, terminal device and storage medium, which can effectively solve the problem of inaccurate calculation method of driving resistance.

[0004] In a first aspect, an embodiment of the present application provides a vehicle resistance calibration method, comprising: The test vehicle is subjected to a sliding resistance test, and multiple sets of test data are obtained.

[0005] Preprocessing the multiple groups of test data to obtain multiple groups of smoothed test data; Fusing the smoothed test data to obtain a set of fused test data; According to the fusion test data, a velocity time curve function is fitted, the velocity time curve function is differentiated, and time is substituted into the acceleration time curve function to obtain the acceleration time curve function, and then the acceleration time curve function and the velocity time curve function are fitted to obtain the velocity acceleration curve function; According to the velocity acceleration curve function and Newton's second law, a sliding resistance model is obtained.

[0006] In one embodiment, the test vehicle performs a sliding resistance test and obtains multiple sets of test data, including: Accelerating the test vehicle to a target speed and then shifting the vehicle into neutral, so that the vehicle coasts until it stops; Record the vehicle speed and corresponding time data of the test vehicle respectively by means of a first sensor and a second sensor; Repeat the steps of accelerating the test vehicle to a target speed and then shifting the gear to neutral, so that the vehicle coasts until it stops, and obtain multiple sets of vehicle speeds and corresponding time data as test data.

[0007] In one embodiment, the preprocessing of the test data to obtain smoothed test data includes: Selecting test data in a time period from when the test vehicle accelerates to the target speed until it stops; The speed-time curve function is obtained by fitting; At the current time point, a first speed measured by the first sensor is subtracted from the fitted speed to obtain a first speed difference; If the first speed difference is not greater than a preset error, retaining the first speed; If the first speed difference is greater than the preset error, obtaining a second speed recorded by the second sensor, calculating a difference between the second speed and the fitting speed, and obtaining a second speed difference; If the second speed difference is greater than the preset error, the data set is discarded; if the second speed difference is not greater than the preset error, the first speed is replaced by the second speed, and finally smooth test data is obtained.

[0008] In one embodiment, the preset error is a preset fixed error value; Or a dynamic error value calculated based on a preset error ratio and the fitting speed.

[0009] In one embodiment, the sliding resistance includes: rolling resistance of the wheels, air resistance of the air on the vehicle body, and transmission resistance of the vehicle's conventional system; The expression of the sliding resistance model is determined based on the expressions of the rolling resistance, the air resistance and the powertrain resistance.

[0010] In one embodiment, the sliding resistance model is expressed as: F=a+b*v+c*v 2 ; Wherein, F is the sliding resistance, v is the speed, and a, b, and c are the coefficients of the sliding resistance model; The sliding resistance model is obtained according to the velocity acceleration curve function and Newton's second law, including: Combining Newton's second law and the velocity acceleration curve function, fitting is performed according to the expression of the sliding resistance model to obtain the values ​​of various coefficients, thereby determining the sliding resistance model.

[0011] In one embodiment, fusing the smoothed test data to obtain a set of fused test data includes: The time average values ​​of various times corresponding to the same speed are calculated, and each of the time average values ​​and the speed are matched one by one to obtain a set of fusion test data consisting of multiple pairs of the time average values ​​and the speed.

[0012] In a second aspect, the present application also provides a vehicle resistance calibration device, comprising: A data acquisition module is used to test the vehicle for sliding resistance test and obtain multiple sets of test data; A data preprocessing module, used for preprocessing the multiple groups of test data to obtain multiple groups of smooth test data; A data fusion module, used for fusing the smoothed test data to obtain a set of fused test data; A data fitting module is used to fit the speed time curve function according to the fusion test data, derive the speed time curve function, and substitute the time into the acceleration time curve function to obtain the acceleration time curve function, and then fit the acceleration time curve function and the speed time curve function to obtain the speed acceleration curve function; The model fitting module is used to obtain a sliding resistance model according to the speed acceleration curve function and Newton's second law.

[0013] In a third aspect, the present application further provides a terminal device, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the vehicle resistance calibration method.

[0014] In a fourth aspect, the present application also provides a computer-readable storage medium storing a computer program, which, when executed on a processor, implements the vehicle resistance calibration method.

[0015] The embodiments of the present application have the following beneficial effects: The present application ensures data smoothness by fusing and fitting the test data, and then establishes a velocity-acceleration relationship function through multiple fittings, so that the second Newton's law can be combined to construct a sliding resistance model. The process of constructing the sliding resistance model only requires one test and calculation. The construction method is simple, and the data is smooth enough to ensure that the constructed sliding resistance model can calculate more accurate results in actual applications, thereby improving the calculation accuracy of the sliding resistance, thereby improving the accuracy of the vehicle in adaptive cruise control. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A schematic diagram of a vehicle resistance calibration method process according to an embodiment of the present application is shown; Figure 2A schematic diagram of a test scenario in an embodiment of the present application is shown; Figure 3 A schematic diagram of a fitting curve of collected data in an embodiment of the present application is shown; Figure 4 A schematic diagram of a data fusion curve collected in an embodiment of the present application is shown; Figure 5 A schematic diagram of a velocity acceleration fitting curve of an embodiment of the present application is shown; Figure 6 A schematic diagram of a sliding resistance speed fitting curve of an embodiment of the present application is shown; Figure 7 A schematic structural diagram of a vehicle resistance calibration device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0019] The components of the embodiments of the present application generally described and shown in the drawings herein may be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0020] Hereinafter, the terms "including", "having" and their cognates that can be used in various embodiments of the present application are intended only to indicate specific features, numbers, steps, operations, elements, components or a combination of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or a combination of the foregoing items or increasing the possibility of one or more features, numbers, steps, operations, elements, components or a combination of the foregoing items. In addition, the terms "first", "second", "third" and the like are only used to distinguish descriptions and cannot be understood as indicating or implying relative importance.

[0021] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art to which the various embodiments of the present application belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present application.

[0022] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0023] In adaptive cruise control, it is necessary to accurately obtain the vehicle's driving resistance. The vehicle's acceleration depends on the difference between the driving force and the driving resistance. Therefore, obtaining the accurate driving resistance can greatly improve the accuracy and response speed of acceleration control. To this end, the present application provides a vehicle resistance calibration method. The method obtains test data from multiple coasting tests of the vehicle, processes the data, and performs multiple fittings to obtain the relationship between speed and acceleration. The coasting resistance model is then obtained in combination with Newton's second law. When the vehicle is moving, the coasting resistance model can be used to calculate the coasting resistance.

[0024] The vehicle resistance calibration method is described below in conjunction with some specific embodiments.

[0025] Figure 1 A flow chart of a vehicle resistance calibration method according to an embodiment of the present application is shown. Exemplarily, the vehicle resistance calibration method includes the following steps: Step S100, the test vehicle performs a sliding resistance test and obtains multiple sets of test data.

[0026] The sliding resistance test is used to obtain the relationship between the speed and resistance of the vehicle when it is sliding. The test data obtained is the speed of the vehicle during the test and the time corresponding to the speed.

[0027] In one embodiment, in order to ensure that the acquired speed data is sufficiently accurate, two sensors, a first sensor and a second sensor, may be provided on the vehicle, and the vehicle speed may be acquired simultaneously during the test process through the two sensors.

[0028] The first sensor may be a vehicle speed sensor, and the second sensor may be a positioning sensor.

[0029] Positioning sensors are sensors that can perform positioning based on small errors, such as centimeter-level positioning systems that use RTK (Real-time kinematic) real-time dynamic carrier phase differential technology. Vehicle speed sensors can be the vehicle's own wheel speed sensor, which calculates the vehicle's speed by obtaining the vehicle's wheel speed.

[0030] The two sensors can obtain the speed data of the vehicle when it is moving and the corresponding time data through the data obtained by each sensor. It can be understood that the two sensors will be synchronized in time to ensure that the obtained data can correspond to each other on the time axis.

[0031] When testing, you can choose a straight road and then accelerate the vehicle to a preset speed, such as 120 kilometers per hour, and then put the vehicle in neutral and let it start to slide. At this time, the vehicle will slowly slow down under the action of resistance until it stops.

[0032] During the above-mentioned movement process, the vehicle will respectively record the vehicle speed and corresponding time data recorded by the positioning sensor and the vehicle speed sensor.

[0033] In order to ensure the accuracy of the data, the above test will be repeated multiple times to obtain multiple sets of test data.

[0034] like Figure 2 The figure shows a schematic diagram of the scenario for the above test. The vehicle performs the above test operation on a straight road. For example, when completing one test, the vehicle drives from left to right, and the next test is to drive from right to left. Therefore, the multiple sets of data obtained from multiple tests can be divided into two types of data: outbound (left to right) and return (right to left). It can be understood that the two types of data are different because the road parts during gliding are different.

[0035] These test data are stored in pairs of multiple vehicle speeds and corresponding times. Figure 3 As shown, each data point is the embodiment of vehicle speed and time in the coordinate system.

[0036] Step S200, preprocessing the multiple groups of test data to obtain multiple groups of smoothed test data.

[0037] For the convenience of explanation, the speed obtained by the positioning sensor is recorded as v_RTK0, and the speed obtained by the vehicle speed sensor is recorded as v_ESP0.

[0038] Because the test of this embodiment is to establish a sliding resistance model, the data from the time when the vehicle accelerates from 0 to the preset speed is not the required data. This embodiment requires data from the time when the vehicle reaches the preset speed and the neutral vehicle starts to slide to the time when the vehicle stops due to resistance.

[0039] It can be understood that in a test of sliding from a preset speed to a stop, a vehicle speed will be obtained from two sensors at the same time point, so there will be two speeds at one time point. By fitting these speed values, the following can be obtained: Figure 2 The speed time curve and the corresponding function shown in the figure, the speed represented by the curve in the figure is the fitting speed v_Curv. Each test data can get a speed time curve v_Curv-t, here just take a figure as an example to illustrate.

[0040] For each time point, the speed value v_ESP0 measured by the vehicle speed sensor is subtracted from the fitting value v_Curv to obtain the first speed difference det_v1, and then the first speed difference det_v1 is compared with the preset error. If the first speed difference is not greater than the preset error, v_ESP0 is retained, otherwise the difference between v_RTK0 and the fitting speed is calculated to obtain the second speed difference det_v2. If the second speed difference det_v2 is greater than the preset error, the data of this group is discarded. If the second speed difference det_v2 is not greater than the preset error, v_RTK0 is used instead of v_ESP0. In this way, the speed data calculated by the two sensors are fused together to obtain smooth test data.

[0041] It should be noted that when the calculated first speed difference and the second speed difference are both greater than the preset error, it means that the two acquired speeds are too deviated, and such deviated data samples need to be excluded.

[0042] The preset error can be a preset fixed error value, such as 0.1 m / s, etc. It can also be set based on a certain ratio, for example, an error ratio of 5% can be preset, then the corresponding error value can be dynamically set to v_Curv *0.05 relative to the speed obtained by each sample, which means that the deviation between the speed obtained by each sensor and the fitting speed must be within 5%, otherwise it exceeds the preset error.

[0043] The above operation is performed for each set of test data, so multiple sets of smoothed test data can be obtained, which are recorded as v_Merge_i-t. i represents the sequence number of the set of test data.

[0044] Among them Figure 4 As shown, this step is Figure 3 After processing the data in, we get the v_Merge_i-t curve graph.

[0045] Step S300, fusing the smoothed test data to obtain a set of fused test data.

[0046] In the test of this embodiment, the vehicle speed starts from the target speed and slides until it stops. Therefore, for multiple groups of smooth test data, the time average values ​​corresponding to each time at the same speed are calculated. For example, there are 6 groups of fused data in total, then one speed v_Merge corresponds to 6 times t1, t2, t3, t4, t5, and t6. The average time of these 6 times is calculated to obtain an average time corresponding to the speed v_Merge. This calculation is performed for each speed, and all smooth test data can be fused together based on the speed to obtain a group of fused test data vt.

[0047] The fused test data can be used to fit the sliding resistance model.

[0048] Step S400, according to the fusion test data, a velocity time curve function is fitted, the velocity time curve function is differentiated, and time is substituted into the acceleration time curve function to obtain the acceleration time curve function, and then the acceleration time curve function and the velocity time curve function are fitted to obtain the velocity acceleration curve function.

[0049] The fusion test data includes vehicle speed and time, so the speed-time curve function v=f(t) can be obtained by fitting the vehicle speed and time.

[0050] Acceleration is the derivative of velocity, so by taking the derivative of v and substituting it into the corresponding event data t, we can get a set of v' values ​​that change with time t, so that there is a functional relationship between acceleration and time v'=f1(t), where v' is acceleration.

[0051] At this time, combining the velocity time curve function v=f(t) and the acceleration time function v'=f1(t), we can get the corresponding relationship between velocity v and acceleration v'. Therefore, we can also fit v' and v to get the curve v'=f2(v), that is, the curve function relationship between acceleration v' and velocity v.

[0052] The above fitting method can be calculated by combining multiple data obtained in the above steps through polynomial regression or the like. The specific calculation method is not the focus of this embodiment and will not be elaborated here.

[0053] Among them, the curve of velocity v and acceleration v' after fitting is as follows Figure 5 As shown, it can be seen that there is a positive correlation between vehicle speed and acceleration. The greater the vehicle speed, the greater the acceleration.

[0054] Step S500: obtaining a sliding resistance model according to the speed-acceleration curve function and Newton's second law.

[0055] It can be understood that the sliding resistance model ultimately obtained in this embodiment reflects the relationship between the vehicle speed and the vehicle resistance. As for the aforementioned test method of this application, it can be known that the resistance and acceleration of the vehicle when it is in a sliding state are related. Therefore, after obtaining the curve function relationship between the speed and acceleration, the sliding resistance model can be obtained in combination with Newton's second law F=mv'.

[0056] The calculation model of the sliding resistance model is: rolling resistance + air resistance + transmission resistance = sliding resistance, where rolling resistance is the resistance generated when the wheels roll against the ground, air resistance is the resistance of the air to the vehicle body, and transmission resistance is the resistance generated by the transmission system itself when sliding. These three resistances combine to form the sliding resistance.

[0057] Through traditional calculation methods, these three forces can be calculated separately. Therefore, by combining the calculation expressions of these three resistances, the calculation expression of the sliding resistance model can be obtained, in which the air resistance mentioned above is a quadratic term, and the rolling resistance and transmission resistance are linear terms. According to this model, the resistance is finally reflected as a quadratic function of the vehicle speed, that is, F=a+b*v+c*v 2 When the calculation expression of the sliding resistance model is determined, fitting can be performed according to the obtained test data to obtain a sliding resistance model that better fits the test data.

[0058] In the formula, F is the sliding resistance, a, b, c are coefficients, and v is the vehicle speed. In order to obtain the values ​​of these three coefficients, it is necessary to combine F=mv' and v'=f2(v) obtained in the previous steps to perform fitting. The specific fitting method can be implemented by calling the fitting program in matlab, such as the fitting method of differential equation + least squares method, or other processing methods. The specific calculation process will not be repeated here.

[0059] Finally, the resistance-velocity fitting curve and the corresponding function expression can be obtained. The curve is as follows: Figure 6 As shown, the drag increases with increasing speed, and Figure 5 The velocity-acceleration relationship curve can correspond to the above.

[0060] The sliding resistance model finally obtained can be used for the vehicle's driving resistance in various scenarios. For example, it can be used to estimate the obstacle resistance on a low-speed road. The vehicle is very sensitive to the unevenness of the road when driving at a low speed, especially the bumps on the ground will cause the low-speed vehicle to further reduce its speed or even stop until it slides backward. Therefore, when the vehicle is driving at a low speed, it is necessary to design an algorithm to estimate the driving resistance caused by the bumps on the ground. At this time, the sliding resistance model of this embodiment can be used to calculate the resistance currently required to overcome obstacles according to the vehicle speed, that is, all the elements of the driving resistance on a flat road can be obtained.

[0061] It can be understood that this embodiment obtains the test data of the vehicle in the sliding state through a simple test method, obtains smooth test data through data fitting and fusion, and then obtains the relationship between the vehicle speed and the vehicle acceleration through data fitting, thereby obtaining the sliding resistance model. The sliding resistance model is obtained in a simple manner, and the calculation process can be calculated using various existing data fitting methods to establish a sufficiently reliable sliding resistance model, and finally the vehicle can obtain the sliding resistance according to the sliding resistance model.

[0062] Figure 7 A schematic diagram of the structure of a vehicle resistance calibration device according to an embodiment of the present application is shown. The exemplary device includes: The data acquisition module 10 is used to test the vehicle to perform a sliding resistance test and obtain multiple sets of test data; A data preprocessing module 20, used for preprocessing the multiple groups of test data to obtain multiple groups of smooth test data; A data fusion module 30, used for fusing the smoothed test data to obtain a set of fused test data; The data fitting module 40 is used to fit the velocity time curve function according to the fusion test data, derive the velocity time curve function, and substitute the time into the acceleration time curve function to obtain the acceleration time curve function, and then fit the acceleration time curve function and the velocity time curve function to obtain the velocity acceleration curve function; The model fitting module 50 is used to obtain a sliding resistance model according to the speed acceleration curve function and Newton's second law.

[0063] The present application also provides a terminal device, which includes a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the vehicle resistance calibration method.

[0064] The terminal device can be a terminal device with computing capabilities such as a computer or a vehicle-mounted central control, wherein the terminal device can be mounted on the vehicle or can be an independent device, and can be used to load the calibrated sliding resistance model into the vehicle.

[0065] The present application also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed on a processor, the vehicle resistance calibration method is implemented.

[0066] It can be understood that the device of this embodiment corresponds to the method of the above embodiment, and the options in the above embodiment are also applicable to this embodiment, so they will not be described repeatedly here.

[0067] Exemplarily, the terminal device includes a processor and a memory, wherein the memory stores a computer program, and the processor runs the computer program, thereby causing the terminal device to execute the functions of each module in the above method or the above apparatus.

[0068] Among them, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or at least one of other programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application.

[0069] The memory may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electric erasable programmable read-only memory (EEPROM), etc. The memory is used to store a computer program, and the processor may execute the computer program accordingly after receiving an execution instruction.

[0070] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or the flow diagram, and the combination of boxes in the structure diagram and / or the flow diagram, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0071] In addition, the functional modules or units in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0072] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.

[0073] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A vehicle resistance calibration method, characterized in that: include: The test vehicle is tested for sliding resistance and multiple sets of test data are obtained; Preprocessing the multiple groups of test data to obtain multiple groups of smoothed test data; Fusing the smoothed test data to obtain a set of fused test data; According to the fusion test data, a velocity time curve function is fitted, the velocity time curve function is differentiated, and time is substituted into the acceleration time curve function, and then the acceleration time curve function and the velocity time curve function are fitted to obtain a velocity acceleration curve function; According to the velocity acceleration curve function and Newton's second law, a sliding resistance model is obtained.

2. The vehicle resistance calibration method according to claim 1, characterized in that: The test vehicle performs a sliding resistance test and obtains multiple sets of test data, including: Accelerating the test vehicle to a target speed and then shifting the vehicle into neutral, so that the vehicle coasts until it stops; Record the vehicle speed of the test vehicle and the corresponding time data respectively by means of a first sensor and a second sensor; Repeat the steps of accelerating the test vehicle to a target speed and then shifting the gear to neutral, so that the vehicle coasts until it stops, and obtain multiple sets of vehicle speeds and corresponding time data as test data.

3. The vehicle resistance calibration method according to claim 2, characterized in that: The preprocessing of the test data to obtain smoothed test data includes: Selecting test data in a time period from when the test vehicle accelerates to the target speed until it stops; The speed-time curve function is obtained by fitting; At the current time point, a first speed measured by the first sensor is subtracted from the fitted speed to obtain a first speed difference; If the first speed difference is not greater than a preset error, retaining the first speed; If the first speed difference is greater than the preset error, obtaining a second speed recorded by the second sensor, and calculating a difference between the second speed and the fitting speed to obtain a second speed difference; If the second speed difference is greater than the preset error, the data set is discarded; if the second speed difference is not greater than the preset error, the first speed is replaced by the second speed, and finally smooth test data is obtained.

4. The vehicle resistance calibration method according to claim 3, characterized in that: The preset error is a preset fixed error value; Or a dynamic error value calculated based on a preset error ratio and the fitting speed.

5. The vehicle resistance calibration method according to claim 1, characterized in that: The sliding resistance includes: the rolling resistance of the wheels, the air resistance of the air on the vehicle body and the transmission resistance of the vehicle's traditional system; The expression of the sliding resistance model is determined based on the expressions of the rolling resistance, the air resistance and the powertrain resistance.

6. The vehicle resistance calibration method according to claim 5, characterized in that: The expression of the sliding resistance model is: F=a+b*v+c*v 2 ; Wherein, F is the sliding resistance, v is the speed, and a, b, and c are the coefficients of the sliding resistance model; The sliding resistance model is obtained according to the velocity acceleration curve function and Newton's second law, including: Combining Newton's second law and the velocity acceleration curve function, fitting is performed according to the expression of the sliding resistance model to obtain the values ​​of various coefficients, thereby determining the sliding resistance model.

7. The vehicle resistance calibration method according to claim 1, characterized in that: The step of fusing the smoothed test data to obtain a set of fused test data includes: The time average values ​​of various times corresponding to the same speed are calculated, and each of the time average values ​​and the speed are matched one by one to obtain a set of fusion test data consisting of multiple pairs of the time average values ​​and the speed.

8. A vehicle resistance calibration device, characterized in that: include: A data acquisition module is used to test the vehicle for sliding resistance test and obtain multiple sets of test data; A data preprocessing module, used for preprocessing the multiple groups of test data to obtain multiple groups of smooth test data; A data fusion module, used for fusing the smoothed test data to obtain a set of fused test data; A data fitting module is used to fit the speed time curve function according to the fusion test data, derive the speed time curve function, and substitute the time into the acceleration time curve function to obtain the acceleration time curve function, and then fit the acceleration time curve function and the speed time curve function to obtain the speed acceleration curve function; The model fitting module is used to obtain a sliding resistance model according to the speed acceleration curve function and Newton's second law.

9. A terminal device, characterized in that: The terminal device comprises a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the vehicle resistance calibration method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored therein, and when the computer program is executed on a processor, the vehicle resistance calibration method according to any one of claims 1 to 7 is implemented.

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