CN117851731A

CN117851731A - Method, device, medium and computing equipment for estimating quality based on running vehicle

Info

Publication number: CN117851731A
Application number: CN202410254762.8A
Authority: CN
Inventors: 董鑫涛; 袁凯; 杨庆保; 孟蓉歌; 张耀峰; 连秦剑
Original assignee: Proton Automotive Technology Co Ltd
Current assignee: Proton Automotive Technology Co Ltd
Priority date: 2024-03-06
Filing date: 2024-03-06
Publication date: 2024-04-09
Anticipated expiration: 2044-03-06

Abstract

The embodiment of the invention provides a method, a device, a medium and a computing device for estimating the quality of a running vehicle. The method comprises the following steps: collecting first driving data and second driving data of a driving vehicle and vehicle parameters of the driving vehicle; the time interval between the first acquisition time of the first driving data and the second acquisition time of the second driving data is smaller than or equal to a preset acquisition interval threshold value; the first driving data at least comprises first longitudinal acceleration, first driving force and first wind resistance; the second driving data at least comprises a second longitudinal acceleration, a second driving force and a second wind resistance; calculating to obtain an original estimated value of the vehicle quality based on the first driving data, the second driving data and the vehicle parameters; and screening the original estimated value of the vehicle quality based on the first driving data, the second driving data and the vehicle parameters to obtain the trust estimated value of the vehicle quality. The method and the device improve the accuracy of the estimated vehicle quality.

Description

Method, device, medium and computing equipment for estimating quality based on running vehicle

Technical Field

Embodiments of the present invention relate to the field of vehicle technologies, and more particularly, to a method, an apparatus, a medium, and a computing device for estimating a mass of a traveling vehicle.

Background

With the development of the vehicle industry, various control algorithms are adopted by various large vehicle manufacturers to improve the safety and riding comfort of the vehicle. The premise of accurately controlling the vehicle through a control algorithm is that parameters such as the vehicle quality and the like are required to be obtained. However, in practice, it is found that in the current vehicle mass estimation method, it is generally necessary to consider vehicle running resistance parameters such as rolling resistance and gradient resistance, and the running resistance parameters of the vehicle during running are often difficult to accurately acquire, so that the estimated vehicle mass is not accurate enough when the vehicle is running.

Disclosure of Invention

In this context, embodiments of the present invention desire to provide a method, apparatus, medium, and computing device for estimating mass based on a traveling vehicle.

In a first aspect of the embodiments of the present invention, there is provided a mass estimation method based on a running vehicle, including:

collecting first driving data and second driving data of a driving vehicle and vehicle parameters of the driving vehicle; the time interval between the first acquisition time of the first driving data and the second acquisition time of the second driving data is smaller than or equal to a preset acquisition interval threshold value; the first driving data at least comprise first longitudinal acceleration, first driving force and first wind resistance; the second driving data at least comprise a second longitudinal acceleration, a second driving force and a second wind resistance;

Calculating to obtain an original estimated value of the vehicle quality based on the first driving data, the second driving data and the vehicle parameters;

and screening the vehicle quality original estimated value based on the first driving data, the second driving data and the vehicle parameters to obtain a vehicle quality trust estimated value.

In an example of this embodiment, the calculating, based on the first driving data, the second driving data, and the vehicle parameter, an original estimated value of the vehicle mass includes:

constructing a first vehicle dynamics equation based on the first driving data and the vehicle parameters;

constructing a second vehicle dynamics equation based on the second driving data and the vehicle parameters;

and calculating to obtain an original estimated value of the vehicle mass based on the first vehicle dynamics equation and the second vehicle dynamics equation.

In an example of this embodiment, the screening the vehicle quality raw estimation value based on the first driving data, the second driving data and the vehicle parameter to obtain a vehicle quality trust estimation value includes:

screening the original estimated value of the vehicle quality based on the first driving data, the second driving data and the vehicle parameters to obtain an intermediate estimated value of the vehicle quality;

Acquiring a vehicle mass data set corresponding to the vehicle mass median estimate; the vehicle quality data set comprises the vehicle quality intermediate estimated value and a historical vehicle quality intermediate estimated value corresponding to the vehicle quality intermediate estimated value, wherein the historical vehicle quality intermediate estimated value is obtained before the vehicle quality intermediate estimated value, and the historical vehicle quality intermediate estimated value and the vehicle quality intermediate estimated value are continuously obtained data;

and calculating to obtain a vehicle quality trust estimation value according to the vehicle quality data set.

In an example of this embodiment, the screening the raw estimated value of the vehicle quality based on the first driving data, the second driving data and the vehicle parameter to obtain the intermediate estimated value of the vehicle quality includes:

obtaining the maximum vehicle mass and the minimum vehicle mass corresponding to the running vehicle from the vehicle parameters;

if the original estimated value of the vehicle mass is smaller than or equal to the maximum vehicle mass and the original estimated value of the vehicle mass is larger than or equal to the minimum vehicle mass, identifying the speed information of the running vehicle based on the first running data and the second running data to obtain a speed identification result;

If the vehicle speed identification result indicates that the first vehicle speed of the running vehicle at the first acquisition time and the second vehicle speed at the second acquisition time are both greater than a preset vehicle speed and the absolute value of the difference between the first longitudinal acceleration and the second longitudinal acceleration is greater than a preset acceleration difference, determining a driving force-acceleration ratio based on the first driving force, the second driving force, the first longitudinal acceleration and the second longitudinal acceleration;

and if the driving force-acceleration ratio is larger than or equal to a preset ratio, determining the original estimated value of the vehicle mass as an intermediate estimated value of the vehicle mass.

In one example of the present embodiment, the determining the driving force-acceleration ratio based on the first driving force, the second driving force, the first longitudinal acceleration, and the second longitudinal acceleration includes:

determining a driving force ratio according to the first driving force and the second driving force;

determining an acceleration ratio according to the first longitudinal acceleration and the second longitudinal acceleration;

the ratio between the driving force ratio and the acceleration ratio is determined as a driving force-acceleration ratio.

In one example of this embodiment, the determining the ratio between the driving force ratio and the acceleration ratio as a driving force-acceleration ratio includes:

multiplying the driving force ratio by 0.3 to obtain a target driving force ratio;

determining a ratio between the target driving force ratio and the acceleration ratio as a driving force-acceleration ratio;

and determining the original estimated value of the vehicle mass as an intermediate estimated value of the vehicle mass if the driving force-acceleration ratio is greater than or equal to a preset ratio, including:

and if the driving force-acceleration ratio is greater than or equal to 1, determining the vehicle mass original estimated value as a vehicle mass intermediate estimated value.

In an example of this embodiment, the determining the driving force ratio according to the first driving force and the second driving force includes:

determining an absolute value of a difference between the first driving force and the second driving force as a first driving force absolute value;

determining a sum of an absolute value of the first driving force and an absolute value of the second driving force as a second driving force absolute value;

a ratio between the first driving force absolute value and the second driving force absolute value is determined as a driving force ratio.

In one example of this embodiment, the determining the acceleration ratio according to the first longitudinal acceleration and the second longitudinal acceleration includes:

determining an absolute value of a difference between the first longitudinal acceleration and the second longitudinal acceleration as a first acceleration absolute value;

determining a sum of the absolute value of the first longitudinal acceleration and the absolute value of the second longitudinal acceleration as a second acceleration absolute value;

a ratio between the first absolute acceleration value and the second absolute acceleration value is determined as an acceleration ratio.

In an example of this embodiment, the calculating a vehicle quality trust estimate according to the vehicle quality dataset includes:

deleting a maximum vehicle mass median estimate and a minimum vehicle mass median estimate from the vehicle mass dataset; the maximum vehicle quality intermediate estimated value is the vehicle quality intermediate estimated value with the largest value in the vehicle quality data set, and the minimum vehicle quality intermediate estimated value is the vehicle quality intermediate estimated value with the smallest value in the vehicle quality data set;

determining an average value of all vehicle quality intermediate estimated values contained in the vehicle quality data set as a current vehicle quality trust estimated value;

Acquiring a previous vehicle quality trust estimation value corresponding to the current vehicle quality trust estimation value;

and carrying out weighted summation on the current vehicle quality trust estimation value and the previous vehicle quality trust estimation value to obtain a final vehicle quality trust estimation value.

In one example of this embodiment, after the obtaining the vehicle quality trust estimate, the method further includes:

acquiring estimated times of the running vehicle and a preset middle load lower limit, a preset middle load upper limit and a preset middle load median when the running vehicle is in a middle load state;

if the estimated times are smaller than or equal to a first preset times, comparing the vehicle quality trust estimated value with the medium load lower limit and the medium load upper limit to obtain a first comparison result;

if the first comparison result shows that the vehicle quality trust estimation value is smaller than the medium load lower limit, determining that the running vehicle is in an idle state, determining the maximum vehicle quality corresponding to the running vehicle as the medium load upper limit, and executing the screening of the vehicle quality original estimation value based on the first vehicle data, the second vehicle data and the vehicle parameters to obtain a vehicle quality trust estimation value;

If the first comparison result shows that the vehicle quality trust estimated value is greater than the medium load upper limit, determining that the running vehicle is in a full load state, determining the minimum vehicle quality corresponding to the running vehicle as the medium load lower limit, and executing the screening of the vehicle quality original estimated value based on the first running data, the second running data and the vehicle parameters to obtain a vehicle quality trust estimated value;

if the first comparison result shows that the vehicle quality trust estimated value is greater than or equal to the middle load lower limit and the vehicle quality trust estimated value is less than or equal to the middle load upper limit, determining that the running vehicle is in a middle load state, and acquiring a previous vehicle quality trust estimated value;

if the previous vehicle quality trust estimated value is smaller than the medium load lower limit, determining the maximum vehicle quality corresponding to the running vehicle as the medium load upper limit, and executing the screening of the vehicle quality original estimated value based on the first vehicle data, the second vehicle data and the vehicle parameters to obtain a vehicle quality trust estimated value;

if the previous vehicle quality trust estimated value is greater than the medium load upper limit, determining the minimum vehicle quality corresponding to the running vehicle as the medium load lower limit, and executing the screening of the vehicle quality original estimated value based on the first vehicle data, the second vehicle data and the vehicle parameters to obtain a vehicle quality trust estimated value;

And if the previous vehicle quality trust estimated value is greater than or equal to the middle load lower limit and the vehicle quality trust estimated value is less than or equal to the middle load upper limit, determining the maximum vehicle quality corresponding to the running vehicle as the middle load upper limit, determining the minimum vehicle quality corresponding to the running vehicle as the middle load lower limit, and executing the screening of the vehicle quality original estimated value based on the first driving data, the second driving data and the vehicle parameters to obtain the vehicle quality trust estimated value.

In an example of this embodiment, if the estimated number of times is greater than the first preset number of times and less than or equal to a second preset number of times, the method further includes:

comparing the vehicle quality trust estimation value with the medium load lower limit and the medium load upper limit to obtain a second comparison result;

if the second comparison result shows that the vehicle quality trust estimated value is smaller than the medium load lower limit, determining the maximum vehicle quality corresponding to the running vehicle as the medium load median value, and executing the screening of the vehicle quality original estimated value based on the first driving data, the second driving data and the vehicle parameters to obtain a vehicle quality trust estimated value;

If the second comparison result shows that the vehicle quality trust estimated value is greater than the medium load upper limit, determining the minimum vehicle quality corresponding to the running vehicle as the medium load median value, and executing the screening of the vehicle quality original estimated value based on the first driving data, the second driving data and the vehicle parameters to obtain a vehicle quality trust estimated value;

if the second comparison result shows that the vehicle quality trust estimated value is greater than or equal to the middle load lower limit and the vehicle quality trust estimated value is less than or equal to the middle load upper limit, determining that the running vehicle is in a middle load state, and acquiring a previous vehicle quality trust estimated value;

if the previous vehicle quality trust estimated value is smaller than the medium load lower limit, determining the maximum vehicle quality corresponding to the running vehicle as the medium load median value, and executing the screening of the vehicle quality original estimated value based on the first vehicle data, the second vehicle data and the vehicle parameters to obtain a vehicle quality trust estimated value;

if the previous vehicle quality trust estimated value is greater than the medium load upper limit, determining the minimum vehicle quality corresponding to the running vehicle as the medium load middle value, and executing the screening of the vehicle quality original estimated value based on the first vehicle data, the second vehicle data and the vehicle parameters to obtain a vehicle quality trust estimated value;

In an example of this embodiment, if the estimated number of times is greater than the second preset number of times, the method further includes:

comparing the vehicle quality trust estimation value with the medium load lower limit and the medium load upper limit to obtain a third comparison result;

if the third comparison result shows that the vehicle quality trust estimated value is smaller than the medium load lower limit, determining the maximum vehicle quality corresponding to the running vehicle as the medium load lower limit, and executing the screening of the vehicle quality original estimated value based on the first driving data, the second driving data and the vehicle parameters to obtain a vehicle quality trust estimated value;

If the third comparison result shows that the vehicle quality trust estimated value is greater than the medium load upper limit, determining the minimum vehicle quality corresponding to the running vehicle as the medium load upper limit, and executing the screening of the vehicle quality original estimated value based on the first driving data, the second driving data and the vehicle parameters to obtain a vehicle quality trust estimated value;

if the third comparison result shows that the vehicle quality trust estimated value is greater than or equal to the middle load lower limit and the vehicle quality trust estimated value is less than or equal to the middle load upper limit, determining that the running vehicle is in a middle load state, and acquiring a previous vehicle quality trust estimated value;

if the previous vehicle quality trust estimated value is smaller than the medium load lower limit, determining the maximum vehicle quality corresponding to the running vehicle as the medium load median value, determining the minimum vehicle quality corresponding to the running vehicle as the medium load lower limit, and executing the screening of the vehicle quality original estimated value based on the first vehicle data, the second vehicle data and the vehicle parameters to obtain a vehicle quality trust estimated value;

If the previous vehicle quality trust estimated value is greater than the medium load upper limit, determining the maximum vehicle quality corresponding to the running vehicle as the medium load upper limit, determining the minimum vehicle quality corresponding to the running vehicle as the medium load median value, and executing the screening of the vehicle quality original estimated value based on the first vehicle data, the second vehicle data and the vehicle parameters to obtain a vehicle quality trust estimated value;

and if the previous vehicle quality trust estimated value is greater than or equal to the middle load lower limit and the vehicle quality trust estimated value is less than or equal to the middle load upper limit, determining the maximum vehicle quality corresponding to the running vehicle as the middle load middle value, determining the minimum vehicle quality corresponding to the running vehicle as the middle load lower limit, and executing the screening of the vehicle quality original estimated value based on the first driving data, the second driving data and the vehicle parameters to obtain the vehicle quality trust estimated value.

In a second aspect of the embodiments of the present invention, there is provided a mass estimation device based on a running vehicle, including:

the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring first driving data and second driving data of a driving vehicle and vehicle parameters of the driving vehicle; the time interval between the first acquisition time of the first driving data and the second acquisition time of the second driving data is smaller than or equal to a preset acquisition interval threshold value; the first driving data at least comprise first longitudinal acceleration, first driving force and first wind resistance; the second driving data at least comprise a second longitudinal acceleration, a second driving force and a second wind resistance;

The calculation unit is used for calculating and obtaining an original estimated value of the vehicle quality based on the first driving data, the second driving data and the vehicle parameters;

and the screening unit is used for screening the original estimated value of the vehicle quality based on the first driving data, the second driving data and the vehicle parameters to obtain a trust estimated value of the vehicle quality.

In a third aspect of embodiments of the present invention, there is provided a computing device comprising: at least one processor, memory, and input output unit; wherein the memory is for storing a computer program and the processor is for invoking the computer program stored in the memory to perform the method of any of the first aspects.

In a fourth aspect of the embodiments of the present invention, there is provided a computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of any of the first aspects.

According to the mass estimation method, the mass estimation device, the mass estimation medium and the mass estimation computing equipment based on the running vehicle, which are disclosed by the embodiment of the invention, the vehicle mass trust estimated value can be obtained through calculation according to two sets of running data comprising the longitudinal acceleration, the driving force and the wind resistance of the vehicle, which are acquired in a short time; the vehicle running resistance parameters such as rolling resistance, gradient resistance and the like do not need to be considered, and adverse effects on vehicle mass estimation caused by inaccurate vehicle running resistance parameters are avoided, so that the accuracy of the estimated vehicle mass is improved.

Drawings

The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. Several embodiments of the present invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:

fig. 1 is a flow chart of a method for estimating a mass of a traveling vehicle according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of a device for estimating mass of a traveling vehicle according to an embodiment of the present invention;

FIG. 3 schematically illustrates a schematic structural diagram of a medium according to an embodiment of the present invention;

FIG. 4 schematically illustrates a structural diagram of a computing device in accordance with embodiments of the present invention.

In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

Detailed Description

The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are presented merely to enable those skilled in the art to better understand and practice the invention and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

Those skilled in the art will appreciate that embodiments of the invention may be implemented as a system, apparatus, device, method, or computer program product. Accordingly, the present disclosure may be embodied in the following forms, namely: complete hardware, complete software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.

According to an embodiment of the invention, a method, a device, a medium and a computing device for estimating the quality of a running vehicle are provided.

It should be noted that any number of elements in the figures are for illustration and not limitation, and that any naming is used for distinction only and not for limitation.

The principles and spirit of the present invention are explained in detail below with reference to several representative embodiments thereof.

Referring to fig. 1, fig. 1 is a flowchart of a method for estimating a mass of a traveling vehicle according to an embodiment of the present invention. It should be noted that embodiments of the present invention may be applied to any scenario where applicable.

The flow of the method for estimating the mass of the running vehicle according to the embodiment of the invention shown in fig. 1 includes:

step S101, collecting first driving data, second driving data of a driving vehicle and vehicle parameters of the driving vehicle.

In this embodiment of the present application, a time interval between a first acquisition time of the first driving data and a second acquisition time of the second driving data is less than or equal to a preset acquisition interval threshold; the first driving data at least comprise first longitudinal acceleration, first driving force and first wind resistance; the second driving data at least comprises a second longitudinal acceleration, a second driving force and a second wind resistance.

In this embodiment, the first driving data and the second driving data may be driving data, where the driving data may further include data such as a controller clock, a motor rotation speed, a motor torque, a gearbox output shaft rotation speed, a gearbox actual gear, a road air resistance coefficient, and a windward area.

The vehicle parameters may include transmission gear ratios, final drive ratio, tire radius, driveline efficiency, etc. The embodiment of the present application is not limited in this regard.

In the present embodiment, the resistance experienced by the running vehicle may include rolling resistance and gradient resistance, but the running resistance does not include wind resistance. Therefore, the vehicle wind resistance can be extracted because the ratio of the vehicle wind resistance exceeds the ratio of the rolling resistance and the gradient resistance only when the vehicle is traveling at a high speed; when the vehicle is running at a medium or low speed, the wind resistance of the vehicle is small in the running resistance. And the triggering time of the vehicle mass estimation in the application is mainly concentrated in the middle-low speed section.

In addition, most of vehicles aimed at by the method are commercial vehicles, the operation route of the commercial vehicles is relatively stable, and a relatively accurate vehicle wind resistance calculation formula can be given through statistics of historical data of vehicle operation.

In the embodiment of the present application, it is assumed that the running resistance change rate of the vehicle approaches 0 in a short time. Based on this, two longitudinal dynamics equations at adjacent extremely short time points are combined, and the variable of the running resistance is eliminated by a Gaussian elimination method, so that the vehicle mass raw estimated value is calculated. Therefore, the first driving data and the second driving data can be acquired at the first acquisition time and the second acquisition time respectively, and a kinetic equation set is constructed based on the acquired first driving data and second driving data, so that the quality estimated value of the driving vehicle is calculated.

Step S102, calculating a vehicle quality original estimated value based on the first driving data, the second driving data and the vehicle parameters.

Specifically, the method for calculating the vehicle quality raw estimation value in step S102 based on the first driving data, the second driving data and the vehicle parameters may include the following steps:

According to the implementation mode, the original estimated value of the vehicle mass can be obtained through calculation of the dynamic equation, the running resistance of the vehicle can be eliminated through Gaussian elimination, the influence of the running resistance of the vehicle on the original estimated value of the vehicle mass is avoided, and the accuracy of estimating the original estimated value of the vehicle mass is improved.

In this embodiment of the present application, the first vehicle dynamics equation is:

；

wherein,for the raw estimate of the vehicle mass, +.>For the first longitudinal acceleration +>As the first driving force, a first driving force,for the first wind resistance->Is the first running resistance of the running vehicle.

And, the second vehicle dynamics equation is:

；

wherein,for the raw estimate of the vehicle mass, +.>For the second longitudinal acceleration +>As a result of the second wind resistance force,for the second driving force, ">For a second driving resistance of the driving vehicle +. >. At this time, the first running resistance and the second running resistance of the running vehicle may be eliminated by gaussian elimination.

Taking the first vehicle dynamics equation as an example, a calculation mode of the parameters in the first vehicle dynamics equation is specifically described, and a calculation mode of the parameters in the second vehicle dynamics equation is the same as that of the parameters in the first vehicle dynamics equation, and is not described in detail later.

Specifically, a first longitudinal accelerationThe calculation formula of (2) can be:

；

wherein,is the difference of the vehicle speed; />In order for the time difference to be a function of the time difference,

vehicle speed differenceThe calculation formula of (2) can be:

；

wherein,is->The speed of the vehicle at the moment; />Is->Speed of the vehicle at time>Is greater than->, />Less than 2 seconds.

First wind resistanceThe calculation formula of (2) can be:

；

wherein,for air density->Is the aerodynamic drag coefficient; />Is the windward area of the vehicle; />Is the longitudinal speed of the vehicle.

A first driving forceThe calculation formula of (2) can be:

；

wherein,the torque is output for the motor currently; />The current speed ratio of the gearbox; />Is the rear axle speed ratio; />Is the rotational inertia of the motor rotor; />Adding the rotational inertia of an intermediate shaft and an output shaft to the input shaft of the gearbox; />The rotational speed and the acceleration of the motor; / >Is the rotational inertia of the rear axle; />The rotational speed acceleration of the output shaft of the gearbox; />Is the rotational inertia of the wheel; />For the rotational speed acceleration of the wheel->Is the radius of the wheel.

And step S103, screening the original estimated value of the vehicle quality based on the first driving data, the second driving data and the vehicle parameters to obtain a trust estimated value of the vehicle quality.

In the embodiment of the application, the vehicle mass estimation is not always continuously iterated to estimate the value, but intermittently iterated, and the vehicle mass original estimated value obtained through screening is determined as the vehicle mass intermediate estimated value only if the condition is met according to a plurality of preset screening conditions.

As an optional implementation manner, the step S103 of screening the vehicle quality raw estimation value based on the first driving data, the second driving data and the vehicle parameter to obtain a vehicle quality trust estimation value may include the following steps:

By implementing the implementation mode, the intermediate estimated value of the vehicle quality obtained by screening can be further calculated so as to exclude data with larger dispersion and obtain the estimated value of the vehicle quality trust, thereby improving the accuracy of the estimated value of the vehicle quality trust.

In this embodiment of the present application, the method for screening the raw estimated value of the vehicle quality based on the first driving data, the second driving data and the vehicle parameter to obtain the intermediate estimated value of the vehicle quality may include the following steps:

By implementing the embodiment, the acquisition requirements of the first driving data and the second driving data can be judged, and the calculated original estimated value of the vehicle quality can be determined as the intermediate estimated value of the vehicle quality only when the judgment conditions all meet the acquisition requirements. And the accuracy of the obtained vehicle quality intermediate estimated value is improved by calculating the data obtained under the condition of meeting the data acquisition requirement.

Optionally, determining a driving force-acceleration ratio based on the first driving force, the second driving force, the first longitudinal acceleration, and the second longitudinal acceleration includes:

Optionally, determining the ratio between the driving force ratio and the acceleration ratio as a driving force-acceleration ratio includes:

As an alternative embodiment, determining a driving force ratio from the first driving force and the second driving force includes:

And determining an acceleration ratio from the first longitudinal acceleration and the second longitudinal acceleration, comprising:

Wherein, implementing this embodiment, the reliability of determining the vehicle mass intermediate estimated value can be improved by calculating the ratio between the driving force and the acceleration.

For example, the following may be the screening conditions for determining the vehicle mass median estimate:

1. during the process of collecting the first driving data, the second driving data and the vehicle parameters, the brake pedal of the vehicle is not depressed, namely the mechanical braking of the vehicle is not triggered.

2. In the process of collecting the first driving data, the second driving data and the vehicle parameters, the first rotation speed difference between the rotation speed of the output shaft of the speed changing box and the current rotation speed of the speed changing box is smaller than 100 than the second rotation speed difference between the rotation speed of the input shaft of the speed changing box and the rotation speed of the motor.

3. The absolute value of the difference between the first longitudinal acceleration and the second longitudinal acceleration is greater than the preset acceleration difference (the preset acceleration difference may be 0.02 ）。

4. The interval between the original estimated values of the vehicle mass is more than 0.5s after each trigger calculation.

5. The actual maximum and minimum vehicle masses of the vehicle mass are determined according to the type of the traveling vehicle, and the calculated raw estimated value of the vehicle mass must be between the maximum and minimum vehicle masses.

6. The driving force-acceleration ratio needs to be larger than a preset ratio (the preset ratio may be preset, for example, 0.3).

7. During the process of collecting the first driving data, the second driving data and the vehicle parameters, the gear of the gearbox is in gear.

8. In the process of collecting the first driving data, the second driving data and the vehicle parameters, the driving speed of the driving vehicle needs to be larger than 0.5m/s.

For example, the vehicle mass dataset may contain 5 data, i.e., 1 vehicle mass median estimate and 4 historical vehicle mass median estimates, with the 5 data being consecutively derived data.

As an alternative embodiment, the manner of calculating the vehicle quality trust estimate according to the vehicle quality dataset may comprise the steps of:

Wherein, the implementation mode can lead the obtained current vehicle quality trust estimated value to be more approximate to the real quality of the vehicle by removing the upper limit and the lower limit; and the accuracy of the estimated final vehicle quality trust estimation value can be improved by carrying out weighted summation on the current vehicle quality trust estimation value and the previous vehicle quality trust estimation value.

In the embodiment of the application, the current vehicle quality trust estimated value and the previous vehicle quality trust estimated value can be calculated in a Kalman low-pass filtering mode, and the final vehicle quality trust estimated value is obtained.

As an alternative embodiment, following step S103, the following steps may also be performed:

By implementing the embodiment, when the running vehicle is in the medium load state, the medium load upper limit and the medium load lower limit of the running vehicle can be obtained, and the relation between the vehicle quality trust estimated value and the medium load upper limit and/or the medium load lower limit can be judged, so that the current load state of the running vehicle is determined, and the accuracy of determining the current load state of the running vehicle is improved.

In the embodiment of the invention, if the estimated frequency is greater than the first preset frequency and less than or equal to the second preset frequency, comparing the vehicle quality trust estimated value with the medium load lower limit and the medium load upper limit to obtain a second comparison result;

If the estimated times are larger than the second preset times, comparing the vehicle quality trust estimated value with the medium load lower limit and the medium load upper limit to obtain a third comparison result;

In the embodiment of the present invention, the first preset number of times may be 5, the second preset number of times may be 10, etc., and the embodiment of the present invention is not limited thereto, and the preset number of times may be a convergence step length, and the convergence step length may be any number between 3 and 7.

According to the invention, the vehicle quality trust estimated value can be calculated according to two sets of driving data comprising the longitudinal acceleration, the driving force and the wind resistance of the vehicle, which are acquired in a short time; the vehicle running resistance parameters such as rolling resistance, gradient resistance and the like do not need to be considered, and adverse effects on vehicle mass estimation caused by inaccurate vehicle running resistance parameters are avoided, so that the accuracy of the estimated vehicle mass is improved. In addition, the method and the device can improve the accuracy of the original estimated value of the vehicle mass obtained through estimation. In addition, the accuracy of the obtained vehicle quality trust estimation value can be improved. In addition, the accuracy of the obtained vehicle quality intermediate estimated value can be improved. In addition, the invention can also improve the reliability of determining the vehicle quality intermediate estimated value.

Having described the method of an exemplary embodiment of the present invention, next, a mass estimation device based on a traveling vehicle of an exemplary embodiment of the present invention will be described with reference to fig. 2, the device including:

An acquisition unit 201, configured to acquire first driving data, second driving data, and vehicle parameters of a driving vehicle; the time interval between the first acquisition time of the first driving data and the second acquisition time of the second driving data is smaller than or equal to a preset acquisition interval threshold value; the first driving data at least comprise first longitudinal acceleration, first driving force and first wind resistance; the second driving data at least comprise a second longitudinal acceleration, a second driving force and a second wind resistance;

a calculating unit 202, configured to calculate, based on the first driving data, the second driving data, and the vehicle parameter, a vehicle quality raw estimation value;

and a screening unit 203, configured to screen the vehicle quality raw estimation value based on the first driving data, the second driving data and the vehicle parameter, so as to obtain a vehicle quality trust estimation value.

Having described the method and apparatus of the exemplary embodiments of the present invention, reference is now made to fig. 3, which illustrates a computer-readable storage medium of the exemplary embodiments of the present invention, and reference is now made to fig. 3, which shows a computer-readable storage medium, an optical disc 30, having a computer program (i.e., a program product) stored thereon, which, when executed by a processor, implements the steps described in the above-described method embodiments, such as collecting first driving data, second driving data, and vehicle parameters of a driving vehicle; the time interval between the first acquisition time of the first driving data and the second acquisition time of the second driving data is smaller than or equal to a preset acquisition interval threshold value; the first driving data at least comprises first longitudinal acceleration, first driving force and first wind resistance; the second driving data at least comprises a second longitudinal acceleration, a second driving force and a second wind resistance; calculating to obtain an original estimated value of the vehicle quality based on the first driving data, the second driving data and the vehicle parameters; screening the original estimated value of the vehicle quality based on the first driving data, the second driving data and the vehicle parameters to obtain a trust estimated value of the vehicle quality; the specific implementation of each step is not repeated here.

It should be noted that examples of the computer readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical or magnetic storage medium, which will not be described in detail herein.

Having described the methods, apparatus and media of exemplary embodiments of the present invention, next, a computing device for mass estimation based on a traveling vehicle of exemplary embodiments of the present invention is described with reference to FIG. 4.

FIG. 4 illustrates a block diagram of an exemplary computing device 40 suitable for use in implementing embodiments of the invention, the computing device 40 may be a computer system or a server. The computing device 40 shown in fig. 4 is merely an example and should not be taken as limiting the functionality and scope of use of embodiments of the present invention.

As shown in fig. 4, components of computing device 40 may include, but are not limited to: one or more processors or processing units 401, a system memory 402, a bus 403 that connects the various system components (including the system memory 402 and the processing units 401).

Computing device 40 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computing device 40 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 402 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 4021 and/or cache memory 4022. Computing device 40 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, ROM4023 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4 and commonly referred to as a "hard disk drive"). Although not shown in fig. 4, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media), may be provided. In such cases, each drive may be coupled to bus 403 through one or more data medium interfaces. The system memory 402 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the embodiments of the invention.

A program/utility 4025 having a set (at least one) of program modules 4024 may be stored, for example, in system memory 402, and such program modules 4024 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 4024 generally perform the functions and/or methodologies of the described embodiments of the present invention.

Computing device 40 may also communicate with one or more external devices 404 (e.g., keyboard, pointing device, display, etc.). Such communication may occur through an input/output (I/O) interface 405. Moreover, computing device 40 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 406. As shown in fig. 4, network adapter 406 communicates with other modules of computing device 40, such as processing unit 401, etc., over bus 403. It should be appreciated that although not shown in fig. 4, other hardware and/or software modules may be used in connection with computing device 40.

The processing unit 401 executes various functional applications and data processing, for example, acquires first traveling data, second traveling data, and vehicle parameters of the traveling vehicle by running a program stored in the system memory 402; the time interval between the first acquisition time of the first driving data and the second acquisition time of the second driving data is smaller than or equal to a preset acquisition interval threshold value; the first driving data at least comprises first longitudinal acceleration, first driving force and first wind resistance; the second driving data at least comprises a second longitudinal acceleration, a second driving force and a second wind resistance; calculating to obtain an original estimated value of the vehicle quality based on the first driving data, the second driving data and the vehicle parameters; and screening the original estimated value of the vehicle quality based on the first driving data, the second driving data and the vehicle parameters to obtain the trust estimated value of the vehicle quality. The specific implementation of each step is not repeated here. It should be noted that although in the above detailed description several units/modules or sub-units/sub-modules of a mass estimation device based on a running vehicle are mentioned, such a division is only exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present invention. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.

In the description of the present invention, it should be noted that the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Furthermore, although the operations of the methods of the present invention are depicted in the drawings in a particular order, this is not required to either imply that the operations must be performed in that particular order or that all of the illustrated operations be performed to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.

Claims

1. A mass estimation method based on a traveling vehicle, comprising:

2. The method for estimating the mass of the traveling vehicle according to claim 1, wherein the calculating the vehicle mass raw estimation value based on the first traveling data, the second traveling data and the vehicle parameter includes:

3. The method for estimating the mass of the traveling vehicle according to claim 1, wherein the screening the vehicle mass raw estimation value based on the first traveling data, the second traveling data and the vehicle parameter to obtain a vehicle mass trust estimation value comprises:

4. The method for estimating a mass of a traveling vehicle according to claim 3, wherein the screening the raw estimated value of the mass of the vehicle based on the first traveling data, the second traveling data and the vehicle parameter to obtain the intermediate estimated value of the mass of the vehicle comprises:

5. The traveling vehicle-based mass estimation method according to claim 4, the determining a driving force-acceleration ratio based on the first driving force, the second driving force, the first longitudinal acceleration, and the second longitudinal acceleration, comprising:

6. The traveling vehicle-based mass estimation method according to claim 5, the determining a ratio between the driving force ratio and the acceleration ratio as a driving force-acceleration ratio, comprising:

7. The traveling vehicle-based mass estimation method according to claim 5, the determining a driving force ratio from the first driving force and the second driving force, comprising:

8. The traveling vehicle-based mass estimation method according to claim 7, the determining an acceleration ratio from the first longitudinal acceleration and the second longitudinal acceleration, comprising:

9. The method for estimating the mass of the traveling vehicle according to claim 8, wherein the calculating a vehicle mass trust estimation value according to the vehicle mass data set includes:

10. The method for estimating a mass of a traveling vehicle according to claim 9, further comprising, after said deriving a vehicle mass trust estimate:

11. The traveling vehicle-based mass estimation method according to claim 10, if the estimated number is greater than the first preset number and equal to or less than a second preset number, the method further comprising:

12. The traveling vehicle-based mass estimation method according to claim 11, further comprising, if the estimated number of times is greater than the second preset number of times:

13. A mass estimation device based on a traveling vehicle, comprising:

14. A computing device, the computing device comprising:

at least one processor, memory, and input output unit;

wherein the memory is configured to store a computer program, and the processor is configured to invoke the computer program stored in the memory to perform the method of any of claims 1-12.

15. A computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of any of claims 1-12.