CN115503737A

CN115503737A - Vehicle mass estimation method, device, medium, equipment and vehicle

Info

Publication number: CN115503737A
Application number: CN202211355318.2A
Authority: CN
Inventors: 白羽鹤; 匡齐; 张利红; 孟美荣; 冯旺旺
Original assignee: Uisee Shanghai Automotive Technologies Ltd
Current assignee: Uisee Shanghai Automotive Technologies Ltd
Priority date: 2022-11-01
Filing date: 2022-11-01
Publication date: 2022-12-23

Abstract

The present disclosure relates to a mass estimation method, apparatus, medium, device, and vehicle of a vehicle, the method comprising: acquiring driving state data of a vehicle; the driving state data comprises a current gear command, a current road gradient, a current acceleration, a current vehicle speed and a current driving torque of the motor; judging whether a quality estimation triggering condition is met or not based on the driving state data; when the quality estimation triggering condition is met, continuously acquiring the road rolling resistance; an estimated mass of the vehicle is determined based on the current vehicle speed, the current acceleration, the current road grade, the road rolling resistance, and the current drive torque. Therefore, additional sensors are not required to be added, the space of the vehicle is not required to be occupied, the investment cost of the vehicle is not increased, and the popularization and the use are convenient; meanwhile, the mass estimation can be carried out by combining the actual conditions of the vehicle and the road, the accuracy of the mass estimation is higher, and the vehicle speed can be conveniently followed in time.

Description

Vehicle mass estimation method, device, medium, equipment and vehicle

Technical Field

The present disclosure relates to the field of vehicle technologies, and in particular, to a method, an apparatus, a medium, a device, and a vehicle for estimating a mass of a vehicle.

Background

Vehicles, such as electric driverless vehicles, and particularly electric driverless tractors, have mass variations with large range fluctuations, and for example, there are various load cases such as only empty heads (only tractors), empty buckets (no cargo), loaded buckets (cargo), and the like.

Generally, the longitudinal speed control of automatic driving is greatly influenced by the mass of a vehicle, and when the mass of the vehicle cannot be known or cannot be accurately predicted, the longitudinal speed control of the vehicle is difficult to achieve an ideal control effect, namely the real-time speed of the vehicle cannot well follow the expected vehicle speed, so that the vehicle speed response of the vehicle is slow, and particularly the response is not timely during acceleration and braking, the following performance is poor, and the energy utilization rate is reduced. Therefore, a suitable quality estimation method is needed to solve the problems faced by the prior art.

Currently, mass estimation identification methods for vehicles are mainly classified into two categories: one method is that a sensor is additionally arranged on a vehicle, and the mass of the vehicle is estimated by using the sensor, but in the method, the sensor occupies the space of the vehicle, the input cost of the vehicle is increased, and the method is difficult to popularize and use; another method is to estimate the vehicle mass by a vehicle dynamics model, and although this method has certain application convenience, it also has a problem of low recognition accuracy.

Disclosure of Invention

In order to solve the technical problem or at least partially solve the technical problem, the present disclosure provides a mass estimation method, apparatus, medium, device and vehicle of a vehicle.

The present disclosure provides a mass estimation method of a vehicle, the method including:

acquiring driving state data of a vehicle; the driving state data comprises a current gear command, a current road gradient, a current acceleration, a current vehicle speed and a current driving torque of the motor;

judging whether a quality estimation triggering condition is met or not based on the driving state data;

when the quality estimation triggering condition is judged to be met, continuously acquiring the road rolling resistance;

determining an estimated mass of the vehicle based on the current vehicle speed, the current acceleration, the current road grade, the road rolling resistance, and the current drive torque.

Optionally, the quality estimation trigger condition comprises:

the driving state data satisfies the following conditions and the holding time is equal to or greater than a preset time threshold:

the current gear command is in a forward gear or a reverse gear;

the current road gradient is equal to or greater than zero and less than or equal to a preset gradient threshold;

the current acceleration is equal to or greater than a first acceleration threshold and less than or equal to a second acceleration threshold; the first acceleration threshold is less than the second acceleration threshold;

the current vehicle speed is equal to or greater than a first speed threshold value and less than or equal to a second speed threshold value; the first speed threshold is smaller than the second speed threshold and both are smaller than the maximum available vehicle speed;

and, the current drive torque is equal to or greater than a first torque threshold and less than or equal to a second torque threshold; the first torque threshold is less than the second torque threshold and each is less than a maximum available torque.

Optionally, the determining an estimated mass of the vehicle comprises:

determining a calculated mass using a vehicle longitudinal dynamics model based on the current vehicle speed, the current acceleration, the current road grade, the road rolling resistance, and the current drive torque;

performing limiting processing based on the calculated quality, and determining a limiting quality; the lower limit value of the mass interval corresponding to the limiting processing is the vehicle no-load mass, and the upper limit value is the vehicle full-load mass;

updating and averaging the stored vehicle masses by using the limit masses to obtain updated average masses;

and performing Kalman filtering quality estimation processing based on the updated average quality to determine the estimation quality.

Optionally, said determining a calculated mass using a vehicle longitudinal dynamics model comprises:

determining the calculated mass using the formula:

wherein m is _F Represents the calculated mass; t is _mot Represents the current drive torque, K represents the transmission ratio, η represents the driveline mechanical efficiency, and r represents the tire effective radius; ρ represents the air density and is a constant; a represents the frontal area; c _D Is the air resistance coefficient; v is the moving speed of the air relative to the vehicle, and the value is equal to the current vehicle speed; f is a resistance coefficient corresponding to the rolling resistance of the road, and is determined based on real-time pavement characteristics; g is gravity acceleration, i represents the current road gradient; delta represents the conversion coefficient of the rotating mass of the automobile after the inertia moment of the rotating mass is counted, and a represents the current acceleration output by the closed-loop control of the speed of the automobile.

Optionally, the limit mass is used to update and average the stored vehicle mass, and the updated average mass is obtained, including an initial stage, an alternately performed average stage and an update stage;

in the initial stage, a mass storage unit group is set, and the masses corresponding to the mass storage units in the mass storage unit group are set to be the memory storage masses of the vehicle after the vehicle operates last time;

in the average stage, calculating the average mass of the mass storage unit group, and updating, triggering and timing; the average mass of the mass storage unit group is the average value of the masses corresponding to the mass storage units in the mass storage unit group; the mean phase starts when the holding duration of the initial phase is longer than the duration of a single updating period;

in the updating stage, the quality corresponding to the first or last quality storage unit in the quality storage unit group is updated by using the limited quality, and updating exit timing is carried out; wherein, the updating stage starts when the quality estimation triggering condition is satisfied and the time length of the updating triggering timing is equal to or greater than the preset updating time length;

when the time length of the update exit timing is equal to or greater than the preset exit time length or does not meet the quality estimation triggering condition, exiting the update stage and entering the average stage, and recalculating the average quality of the quality storage unit group;

wherein the average mass of the mass storage unit group is the updated average mass.

Optionally, acquiring the current gear command includes: acquiring a gear command from a domain controller in an automatic driving mode;

acquiring the current road gradient, including: acquiring the current road gradient determined by a domain controller based on an intelligent driving CAN bus; the domain controller determines the current road gradient of the current position of the vehicle based on the current position of the vehicle and prior road ramp map information; and/or the domain controller determines the current road gradient based on gradient information acquired by a gradient sensor loaded on a vehicle;

obtaining the current vehicle speed, including: obtaining the rotating speed of a motor, the radius of a wheel and a transmission ratio, calculating to obtain an initial speed, and then carrying out filtering processing to obtain a current speed;

acquiring the current acceleration, including: performing mathematical processing based on the current vehicle speed to determine the current acceleration;

obtaining the current drive torque, including: receiving a current drive torque from a motor controller based on a power CAN bus;

acquiring a resistance coefficient corresponding to the road rolling resistance, wherein the resistance coefficient comprises the following steps: acquiring a resistance coefficient determined by a domain controller based on an intelligent driving CAN bus; the domain controller identifies the current road condition based on the image acquisition sensor and matches the corresponding resistance coefficient.

The present disclosure also provides a mass estimation device of a vehicle, including:

the first acquisition module is used for acquiring the driving state data of the vehicle; the driving state data comprises a current gear command, a current road gradient, a current acceleration, a current vehicle speed and a current driving torque of the motor;

a condition judgment module for judging whether a quality estimation trigger condition is satisfied based on the driving state data;

the second acquisition module is used for continuously acquiring at least road rolling resistance when the quality estimation triggering condition is judged to be met;

a mass estimation module to determine an estimated mass of the vehicle based on the current vehicle speed, the current acceleration, the current road grade, the road rolling resistance, and the current drive torque.

The present disclosure also provides a computer-readable storage medium having stored thereon a computer program for performing the steps of any of the above-described methods.

The present disclosure also provides an apparatus for a vehicle, including: a processor; a memory for storing the processor-executable instructions; the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the steps of any one of the above methods.

The present disclosure also provides a vehicle including any of the above-described automotive apparatuses.

Compared with the prior art, the technical scheme provided by the disclosure has the following advantages:

the present disclosure provides a method for estimating a mass of a vehicle, including: acquiring driving state data of a vehicle; the driving state data comprises a current gear command, a current road gradient, a current acceleration, a current vehicle speed and a current driving torque of the motor; judging whether a quality estimation triggering condition is met or not based on the driving state data; when the quality estimation triggering condition is met, continuously acquiring the road rolling resistance; an estimated mass of the vehicle is determined based on the current vehicle speed, the current acceleration, the current road grade, the road rolling resistance, and the current drive torque. Therefore, additional sensors are not required to be added, the space of a vehicle opening is not required to be occupied, the input cost of the vehicle is not increased, and the popularization and the use are convenient; meanwhile, the mass estimation can be carried out by combining the actual conditions of the vehicle and the road, the accuracy of the mass estimation is higher, and the timely following of the vehicle speed is facilitated.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.

In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.

FIG. 1 is a schematic flow chart diagram of a mass estimation method for a vehicle in an embodiment of the present disclosure;

FIG. 2 is a schematic flow chart diagram of another vehicle mass estimation method in an embodiment of the present disclosure;

FIG. 3 is a schematic structural diagram of a mass estimation device for a vehicle according to an embodiment of the present disclosure;

fig. 4 is a schematic structural diagram of a vehicle device in an embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of a vehicle in an embodiment of the present disclosure.

Detailed Description

In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways than those described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.

The vehicle quality estimation method provided by the embodiment of the disclosure can be used for controlling the whole vehicle of a pure electric unmanned vehicle, and realizes accurate quality estimation of the pure electric automatic driving tractor, so that vehicle speed following is facilitated, the vehicle speed response speed is increased, the vehicle speed following is better, and the energy utilization rate is improved.

The following describes an exemplary method, apparatus, medium, device and vehicle for estimating vehicle mass according to an embodiment of the present disclosure, with reference to the accompanying drawings.

For example, fig. 1 is a schematic flow chart of a method for estimating a mass of a Vehicle according to an embodiment of the present disclosure, which may be implemented by a Vehicle Control Unit (VCU). Referring to fig. 1, the method may include the steps of:

and S110, acquiring the driving state data of the vehicle.

The driving state data is data related to the driving state of the vehicle and comprises data representing the current driving state of the vehicle, data used for controlling the driving state of the vehicle and data influencing the driving state of the vehicle. For example, the driving state data may include a current gear command, a current road gradient, a current acceleration, a current vehicle speed, and a current driving torque of the motor.

The current gear command is used for controlling the running state of the vehicle, the current road gradient can influence the running state of the vehicle, and the current acceleration, the current vehicle speed and the current driving torque of the motor can represent the current running state of the vehicle.

In this step, the acquiring of the driving state data of the vehicle may include the vehicle control unit receiving related data transmitted by a sensing component or a control component and the like in communication with the vehicle control unit.

The manner of acquiring the above-described respective traveling state data is exemplarily described below.

In some embodiments, obtaining the current gear command may specifically include: in the automatic driving mode, a gear command from the domain controller is acquired.

In the embodiment of the disclosure, the gear command is recognized and processed based on the domain controller, and is transmitted to the vehicle control unit.

Specifically, in the automatic driving mode, the shift command is from a Domain Control Unit (DCU), which may be an automatic driving Domain controller. Therefore, the gear command can be conveniently and accurately acquired.

In other embodiments, in the auxiliary driving mode, the gear command transmitted to the vehicle control unit may be determined based on the recognition of the physical gear and the recognition of the automatic driving gear command, and further, the comprehensive determination may be performed, which is not limited herein.

In some embodiments, obtaining the current road gradient may specifically include: and acquiring the current road gradient determined by the domain controller based on the intelligent driving CAN bus.

The domain controller determines the current road gradient of the current position of the vehicle based on the current position of the vehicle and prior road ramp map information; and/or the domain controller determines the current road grade based on grade information collected by a grade sensor mounted on the vehicle.

In the embodiment of the disclosure, the acquisition, identification and processing of the current road gradient (i represents the current road gradient in this document) are realized based on the domain controller, and are transmitted to the vehicle controller.

Specifically, the DCU may obtain gradient information of the current position of the vehicle based on the current position of the vehicle and road slope map information measured in advance (i.e., a priori), i.e., obtain the current road gradient; the current road gradient is sent to the VCU by the DCU through an intelligent driving CAN bus; correspondingly, the VUC acquires the current road grade.

Or, the vehicle is provided with a gradient sensor, and the gradient sensor acquires the gradient information of the current road and transmits the gradient information to the DCU; the DCU determines the gradient of the current road based on the received gradient information of the current road, and sends the gradient to the VCU through an intelligent driving CAN bus; correspondingly, the VCU obtains the current road grade.

So set up, can acquire current road slope conveniently accurately, do benefit to the accurate estimation to vehicle mass.

In some embodiments, obtaining the current vehicle speed may specifically include: and obtaining the rotating speed of the motor, the radius of the wheel and the transmission ratio (namely the speed ratio of the vehicle), calculating to obtain the initial vehicle speed, and then performing filtering processing to obtain the current vehicle speed.

In the embodiment of the disclosure, the vehicle control unit identifies and processes the current vehicle speed. Where v may represent the current vehicle speed, the physical unit may be meters per second (m/s), kilometers per hour (km/h), or other units for representation, and is not limited herein.

Specifically, the VCU calculates and obtains an initial speed through the motor speed, the wheel radius and the vehicle speed ratio, and obtains a current speed after filtering processing, so that the current speed can be conveniently and accurately obtained, and accurate estimation of the vehicle quality is facilitated.

The filtering process is to filter out the too large or too small initial vehicle speed to avoid the step change of the vehicle speed, so that the vehicle speed smoothly changes within a certain range, and the smoothness of the running of the vehicle is favorably ensured.

In some embodiments, obtaining the current acceleration may specifically include: the current acceleration is determined by performing mathematical processing based on the current vehicle speed.

In the embodiment of the disclosure, the vehicle control unit identifies and processes the current acceleration (i.e., the real-time acceleration). Where, a may represent the current acceleration, which may be in physical units of meters per square second (m/s) ² )。

Specifically, the vehicle control unit differentiates and changes the acquired current vehicle speed, and identifies the current acceleration of the vehicle. Therefore, the current acceleration can be conveniently and accurately acquired, and the accurate estimation of the vehicle quality is facilitated.

In some embodiments, obtaining the current driving torque may specifically include: the current drive torque is received from the motor controller based on the power CAN bus.

In the embodiment of the disclosure, a Motor Control Unit (MCU) recognizes a current driving torque and transmits the current driving torque to a vehicle controller. The current driving torque is a current real torque of the motor, which may also be referred to as a real-time torque of the motor, and may be Tmot representing the current driving torque, which may be in physical units of Nm (Nm).

Specifically, the vehicle control unit receives a current driving torque from the motor controller through the power CAN bus.

The manner of acquiring the driving state data is exemplarily described above, and the current gear command, the current road gradient, the current acceleration, the current vehicle speed, and the current driving torque of the motor may be acquired in other manners in other embodiments, which are not limited herein.

And S120, judging whether a quality estimation triggering condition is met or not based on the driving state data.

The quality estimation triggering condition is a condition for triggering the quality estimation step to be executed, and when the quality estimation triggering condition is met, the quality estimation step is triggered to be executed.

In the embodiment of the disclosure, the quality estimation triggering condition is judged by combining the driving state data, so that frequent triggering of quality estimation can be avoided, and the quality stability and the stability of vehicle speed control can be ensured.

In some embodiments, the quality estimation trigger conditions include:

the current gear command is in a forward gear or a reverse gear;

the current vehicle speed is equal to or greater than a first speed threshold value and less than or equal to a second speed threshold value; the first speed threshold is smaller than the second speed threshold and is smaller than the maximum available vehicle speed;

and, the current drive torque is equal to or greater than the first torque threshold and less than or equal to the second torque threshold; the first torque threshold is less than the second torque threshold and each is less than the maximum available torque.

In the embodiments of the present disclosure, it is,quality estimation trigger, also called quality estimation enable, which may be E _m Is represented by when E _m When =1, represents mass estimation enable; when E is _m =0, the representative mass estimation is not enabled. In combination with the above, the step of mass estimation is triggered only when the driving state data satisfies the mass estimation triggering condition, and the mass estimation triggering condition is as follows:

the following five conditions are simultaneously met and maintained for a time equal to or greater than a preset time threshold:

the first condition is as follows: the current gear command is in a driving gear, such as a forward gear (D gear) or a reverse gear (R gear), i.e., the vehicle is in a driven state;

and a second condition: the current road gradient is less than or equal to a preset gradient threshold value and is within an adjustable and controllable range of automatic driving; for example, the preset grade threshold may be 5% or other grade values, without limitation;

and (3) carrying out a third condition: the current acceleration is within an acceleration threshold range defined by a first acceleration threshold and a second acceleration threshold; illustratively, the first acceleration threshold may be 0.1m/s ² The second acceleration threshold may be 1m/s ² Then the current acceleration a satisfies: 0.1m/s ² ≤a≤1m/s ² (ii) a Other acceleration threshold ranges can also be set based on method requirements, without limitation;

and a fourth condition: the current vehicle speed is within a vehicle speed threshold range defined by a first vehicle speed threshold and a second vehicle speed threshold; illustratively, the first vehicle speed threshold is 0.1km/h, the second vehicle speed threshold is a maximum available vehicle speed, which may also be referred to as a maximum design vehicle speed, and then the current vehicle speed v satisfies: v is more than or equal to 0.1km/h and less than or equal to the maximum available vehicle speed; other vehicle speed threshold ranges can also be set based on method requirements, without limitation;

and a fifth condition: the current drive torque is within a torque threshold range defined by a first torque threshold and a second torque threshold; for example, the first torque threshold may be 2Nm, the second torque threshold may be the maximum driving torque, and the current driving torque Tmot satisfies: 2Nm is less than or equal to Tmot and less than or equal to the maximum driving torque; other torque threshold ranges may also be set based on method requirements, without limitation.

Wherein, the above five conditions need to be satisfied simultaneously, that is, the five conditions are in logical relation of and; the preset time threshold is on the order of seconds, such as 2 seconds, but is not limited thereto.

The vehicle mass estimation method provided by the embodiment of the disclosure does not perform real-time estimation in a whole scene, and therefore the mass estimation trigger condition needs to be limited by using the condition; and when the quality estimation triggering condition is not met, the external disturbance is too large, and the quality estimation and the updating are not continued.

It can be understood that the threshold may be adjusted or modified based on the method requirement, and the specific value is not limited in the embodiment of the disclosure.

And S130, when the quality estimation triggering condition is judged to be met, continuously acquiring the road rolling resistance.

In the embodiment of the present disclosure, when it is determined that the quality estimation trigger condition is satisfied, the quality estimation step is triggered to be executed, and in order to accurately implement quality estimation, a road condition-related parameter, for example, road rolling resistance obtained in the step, is applied to the quality estimation step to ensure accurate estimation of quality.

In some embodiments, obtaining the resistance coefficient corresponding to the road rolling resistance may specifically include: and acquiring the resistance coefficient determined by the domain controller based on the intelligent driving CAN bus.

The domain controller identifies the current road condition based on the image acquisition sensor and matches the corresponding resistance coefficient.

In the embodiment of the disclosure, the domain controller identifies and processes the resistance coefficient corresponding to the road rolling resistance (which may be simply referred to as the road rolling resistance coefficient), and transmits the resistance coefficient to the vehicle control unit.

Specifically, the domain controller may identify road conditions through an image acquisition component, such as a camera, for example, identify the material of the current road and the dryness and wetness degree of the road surface by applying an image identification processing technology, and match the corresponding road rolling resistance coefficient; for example, the material of the current road may include asphalt pavement, concrete pavement, gravel pavement, ice and snow pavement, and the like, and the matching correspondence between the resistance coefficient and the material and the degree of dryness and wetness may be measured and stored based on a test for being called in this step. The road rolling resistance coefficient is sent to the vehicle control unit by the domain controller through an intelligent driving CAN bus.

S140, determining the estimated mass of the vehicle based on the current vehicle speed, the current acceleration, the current road gradient, the road rolling resistance and the current driving torque.

The current driving torque, the current vehicle speed and the current acceleration can represent the current vehicle condition, and the current road gradient and the road rolling resistance can represent the current road condition. In the step, the data used for representing the current vehicle condition and the current road condition obtained in the step are combined into the vehicle quality estimation, so that the estimation of the vehicle quality aiming at the actual vehicle condition and the road condition is realized, and the method can be used in various scenes of different vehicle conditions and road conditions to realize the accurate estimation of the vehicle quality.

The mass estimation method of the vehicle provided by the embodiment of the disclosure comprises the steps of acquiring driving state data of the vehicle; the driving state data includes a current gear command, a current road gradient, a current acceleration, a current vehicle speed, and a current driving torque of the motor; judging whether a quality estimation trigger condition is satisfied based on the driving state data; when the quality estimation triggering condition is judged to be met, continuously acquiring the road rolling resistance; an estimated mass of the vehicle is determined based on the current vehicle speed, the current acceleration, the current road grade, the road rolling resistance, and the current drive torque. Therefore, additional sensors are not required to be added, the space of the vehicle is not required to be occupied, the investment cost of the vehicle is not increased, and the popularization and the use are convenient; meanwhile, the mass estimation can be carried out by combining the actual conditions of the vehicle and the road, the accuracy of the mass estimation is higher, and the vehicle speed can be conveniently followed in time.

In some embodiments, based on fig. 1, the determining the estimated mass of the vehicle in S140 may specifically include the following steps:

determining a calculated mass based on a current vehicle speed, a current acceleration, a current road grade, a road rolling resistance and a current driving torque by using a vehicle longitudinal dynamics model;

performing limiting processing based on the calculated quality, and determining limiting quality; the lower limit value of the mass interval corresponding to the limiting processing is the vehicle no-load mass, and the upper limit value is the vehicle full-load mass;

In the embodiment of the disclosure, firstly, a longitudinal vehicle dynamics model is used, and the calculated quality is determined based on the data corresponding to the vehicle condition and the road condition acquired in the previous steps; then, limiting the calculated mass by using the vehicle no-load mass and the vehicle full-load mass to obtain the limited mass within the range of the vehicle no-load mass and the vehicle full-load mass; then, the stored vehicle mass is updated and subjected to mean value processing by utilizing the limiting mass to obtain an updated average mass; and finally, performing Kalman filtering quality estimation processing on the updated average quality to determine the estimation quality.

Therefore, the vehicle quality estimation method provided by the embodiment of the disclosure combines the vehicle condition and road condition identification, the vehicle longitudinal dynamics model calculation, the calculated quality limitation processing, the updating and mean value processing of the memory quality and the limited quality stored by the controller, the Kalman filtering of the average quality and other processing processes, and the quality estimation method integrated with the above processes can accurately estimate the quality of the vehicle and improve the accuracy of vehicle quality estimation, and is particularly favorable for realizing the accurate estimation of the quality of the electric unmanned automatic tractor with large quality change, providing necessary conditions for parameter adjustment for accurate vehicle speed control and further favorable for realizing the timely following of the vehicle speed.

In some embodiments, determining the calculated mass using a vehicle longitudinal dynamics model comprises:

the calculated mass was determined using the following formula:

wherein m is _F Represents the calculated mass; t is _mot Representing the current drive torque, K representing the gear ratio, η representing the driveline mechanical efficiency, r representing the tire effective radius; ρ represents air density, which is a constant; a represents the windward area; c _D Is the air resistance coefficient; v is the moving speed of the air relative to the vehicle, and the value is equal to the current vehicle speed; f is a resistance coefficient corresponding to the road rolling resistance, and is determined based on real-time pavement characteristics; g is gravity acceleration, i represents the current road gradient; delta represents the conversion coefficient of the rotating mass of the automobile after the inertia moment of the rotating mass is counted, and a represents the current acceleration output by the closed-loop control of the speed of the automobile.

In the embodiment of the disclosure, the mass of the vehicle is calculated based on the longitudinal dynamic model of the vehicle to obtain the calculated mass (in m) _F Representation). Specifically, the vehicle control unit takes the current vehicle speed v, the current acceleration a, the current road gradient i identified by the DCU, the resistance coefficient f corresponding to the road rolling resistance identified by the DCU and the current driving torque Tmot as input, and obtains the calculated mass m through an equation corresponding to a vehicle longitudinal dynamic model _F 。

The corresponding equation of the vehicle longitudinal dynamic model can be derived based on the following process.

The resistance to which the vehicle is subjected being wind resistance F _w Rolling resistance F _f Slope resistance F _i And acceleration resistance F _j The vehicle running balance equation is:

F＝F _w +F _f +F _i +F _j (1)

where F represents the tangential reaction force, i.e. the driving force, of the ground on the driving wheel of the wheel.

And, wind resistance F _w Calculated using the formula:

wherein: rho generationThe air density can be 1.2258Ns ² m ^-4 (ii) a A is the frontal area, and the physical unit can be square meter (m) ² )；C _D Represents an air resistance coefficient; v is the speed of movement of the air relative to the vehicle, and is numerically equal to the current vehicle speed.

Rolling resistance F _f Calculated using the formula:

F _f ＝f·m _F g·cos(θ) (3)

wherein: f represents a resistance coefficient corresponding to the road rolling resistance, and corresponds to a result of identification and analysis of the DCU by using the image acquisition assembly for the road surface; m is _F Typically, mass is calculated, in physical units of kilograms (kg), and ultimately calculated by the VCU; g represents the acceleration of gravity, which may be in physical units of meters per square meter (m/s) ² ) (ii) a θ represents a road angle in radians (rad) and a relationship with the current road grade i is θ = arctan (i); based on this, (3) formula can be changed to:

F _f ＝f·m _F g·cos(arctan(i)) (4)

illustratively, when the current vehicle speed is 0, the rolling resistance F _f Is 0.

Slope resistance F _i Calculated using the formula:

F _i ＝m _F g·sin(θ) (5)

in combination with the above conversion of the road angle and the current road gradient, equation (5) can be converted into:

F _i ＝m _F g·sin(arctan(i)) (6)

acceleration resistance F _j Calculated using the formula:

F _j ＝δm _F a (7)

wherein: a represents the current acceleration output by the vehicle speed closed-loop control; and delta represents the conversion coefficient of the rotating mass of the automobile after the inertia moment of the rotating mass is taken into account.

Substituting and organizing the above equations (1), (2), (4), (6) and (7), the obtained vehicle running balance equation is:

the current drive torque is expressed as:

wherein, T _mot Representing the current driving torque, r represents the effective radius of the tire, which may be in meters (m) in physical units; k represents a transmission ratio; eta represents the mechanical efficiency of the drive train.

By combining the formulae (8) and (9), it is possible to obtain:

further, by converting the expression (10), the calculated mass m of the vehicle determined by the equation corresponding to the vehicle longitudinal dynamics model can be obtained _F The following formula:

therefore, the equation corresponding to the vehicle longitudinal dynamics model provided by the embodiment of the disclosure comprises data corresponding to the vehicle condition and the road condition, the calculation of the quality of the vehicle is realized by combining the vehicle condition and the road condition, and the calculation accuracy can be improved for scenes with different vehicle conditions and road conditions.

In some embodiments, after obtaining the calculated mass, the calculated mass is limited using the vehicle empty mass and the vehicle full mass as upper and lower limits to obtain a limited mass in the range between the vehicle empty mass and the vehicle full mass, where the limited mass may be m _L And (4) showing. Therefore, the mass limitation and the finally obtained estimated mass are ensured to be in a reasonable mass range, and the accuracy of the estimated mass is improved.

In some embodiments, after obtaining the limiting mass, performing an update and an averaging process in combination with the stored vehicle masses to obtain an updated average mass, the process specifically includes: an initial stage, an alternately performed mean stage and an updating stage;

in the initial stage, setting a mass storage unit group, and setting the mass corresponding to the mass storage units in the mass storage unit group to be the memory storage mass of the vehicle after the vehicle operates last time;

in the average value stage, calculating the average mass of the mass storage unit group, and updating triggering timing; the average quality of the quality storage unit group is the average value of the qualities corresponding to all the quality storage units in the quality storage unit group; the mean phase starts when the holding duration of the initial phase is longer than that of a single updating period;

in the updating stage, the quality corresponding to the first or the last quality storage unit in the quality storage unit group is updated by using the limited quality, and the updating exit timing is carried out; wherein, the updating stage starts when the quality estimation triggering condition is met and the time length of the updating triggering timing is equal to or greater than the preset updating time length;

and when the time length of the update exit timing is equal to or greater than the preset exit time length or does not meet the quality estimation triggering condition, exiting the update stage and entering a mean value stage, and recalculating the average quality of the quality storage unit group;

After the initial stage, the mean stage and the updating stage are executed for the first time, the mean stage and the updating stage are continuously and alternately carried out until the conditions are met, and the operation is quitted; in the updating stage, the quality corresponding to the first or last quality storage unit in the quality storage unit group is updated by using the limited quality, and the quality corresponding to the original storage of each quality storage unit is continuously migrated and replaced backwards or forwards, so that the quality stored by each quality storage unit in the quality storage unit group is updated; in the average stage, an average mass is calculated based on the masses stored in the mass storage units in the mass storage unit group, and the average mass is used as the updated average mass. Thus, the quality updating and the average value processing are realized.

In the embodiment of the disclosure, firstly, the memory storage quality of the vehicle after the last operation is identified and processed. Specifically, the vehicle controller records and stores the estimated mass of the vehicle at the end of the power-on period through the control storage unit (namely, the vehicle mass is stored and updated once when the vehicle is powered off), and after the vehicle controller is powered on again, the vehicle controller can read the stored mass. Then, the updated average quality is obtained by carrying out the updated average value processing of the quality containing the memory storage, and the average quality can be m _A And (4) showing.

Specifically, taking an example that the mass storage unit group includes 6 mass storage units, the process may include three stages:

an initial stage: m is _A ＝m _M When the vehicle is just powered on, the vehicle is in the stage, the driving state data does not meet the quality estimation triggering condition, and the average quality is the memory storage quality of the vehicle after the vehicle runs last time. Illustratively, 6 mass storage units may be m, respectively ₁ 、m ₂ 、m ₃ 、m ₄ 、m ₅ And m ₆ Is represented by, and m ₁ ＝m _M 、m ₂ ＝m _M 、m ₃ ＝m _M 、m ₄ ＝m _M 、m ₅ ＝m _M 、m ₆ ＝m _M . Exemplarily, m _M 1kg, then m ₁ ＝1kg、m ₂ ＝1kg、m ₃ ＝1kg、m ₄ ＝1kg、m ₅ ＝1kg、m ₆ ＝1kg。

And (3) a mean value stage: after the vehicle is powered on, when the holding time length of the initial stage is greater than the time length of a single updating period, immediately entering a mean value stage; in the mean phase, the mean mass m is calculated _A So that m is _A ＝(m ₁ +m ₂ +m ₃ +m ₄ +m ₅ +m ₆ ) And/6, timing at the same time, wherein the timing duration is t _A 。

Illustratively, in combination with the above: m is a unit of _A ＝(1kg+1kg+1kg+1kg+1kg+1kg)/6＝1kg。

And (3) an updating stage: when the mass is estimatedThe spring member is satisfied (i.e. Em = 1) and the timing duration of the mean phase is equal to or greater than the preset update duration (i.e. t) _A ≥t _u1 ，t _u1 Representing a preset update duration, i.e., a set update duration for entering an update phase), the quality stored by the quality storage unit group is updated once. For example, the updating manner may be: m is ₁ ＝m ₂ 、m ₂ ＝m ₃ 、m ₃ ＝m ₄ 、m ₄ ＝m ₅ 、m ₅ ＝m ₆ 、m ₆ ＝m _L Or is as follows: m is ₁ ＝m _L 、m ₂ ＝m ₁ 、m ₃ ＝m ₂ 、m ₄ ＝m ₃ 、m ₅ ＝m ₄ 、m ₆ ＝m ₅ (ii) a Timing while updating, wherein the timing duration is the time duration of updating and exiting timing, and can be represented as t _U 。

Exemplarily, m _L If =1.6kg, the updated mass storage unit group is: m is ₁ ＝1.6kg、m ₂ ＝1kg、m ₃ ＝1kg、m ₄ ＝1kg、m ₅ ＝1kg、m ₆ =1kg; or m ₁ ＝1kg、m ₂ ＝1kg、m ₃ ＝1kg、m ₄ ＝1kg、m ₅ ＝1kg、m ₆ ＝1.6kg。

Further, when the time length of the update exit timer is equal to or greater than the preset exit time length (i.e. t) _U ≥t _u2 ，t _u2 Representing a preset exit duration, i.e. a set update duration to exit the update phase) or not satisfying the quality estimation trigger condition (i.e. E) _m = 0), the update phase is exited and the mean phase is entered, and the mean mass is recalculated.

After the re-averaging stage, average mass:

m _A ＝(1.6kg+1kg+1kg+1kg+1kg+1kg)/6＝1.1kg

or m _A ＝(1kg+1kg+1kg+1kg+1kg+1.6kg)/6＝1.1kg。

Then, performing an update stage, m _L If =2.2kg, the updated mass storage unit group is: m is a unit of ₁ ＝2.2kg、m ₂ ＝1.6kg、m ₃ ＝1kg、m ₄ ＝1kg、m ₅ ＝1kg、m ₆ =1kg; or m ₁ ＝1kg、m ₂ ＝1kg、m ₃ ＝1kg、m ₄ ＝1kg、m ₅ ＝1.6kg、m ₆ ＝2.2kg。

After the re-averaging stage, average mass:

m _A ＝(2.2kg+1.6kg+1kg+1kg+1kg+1kg)/6＝1.2kg

or m _A ＝(1kg+1kg+1kg+1kg+1.6kg+2.2kg)/6＝1.2kg。

Thus, updating and mean processing are realized to obtain updated average quality.

In some embodiments, the kalman filter quality estimation process may include the following:

inputting by a Kalman filter module;

processing a state variable, a state equation, a measurement equation and a system state space expression of the Kalman filter module;

the kalman filter module updates time and measurements.

Specifically, the kalman filter module inputs may include: respectively inputting corresponding data based on the data interface, specifically comprising:

input interface 1 (denoted u (1)) inputs the current drive torque, i.e., T _mot ＝u(1)；

Input interface 2 (denoted u (2)) inputs the current vehicle speed, i.e. v = u (2);

input interface 3 (denoted u (3)) inputs the average quality, i.e. m _A ＝u(3)；

The input interface 4 (denoted by u (4)) inputs the current road gradient, i.e., i = u (4);

input interface 5 (denoted u (5)) inputs a speed measurement noise covariance value R _v I.e. R _v ＝u(5)；

Input interface 6 (denoted u (6)) inputs the quality measure noise covariance value R _m I.e. R _m ＝u(6)；

Input interface 7 (denoted u (7)) inputs gradient measurement noise covariance value R _i I.e. R _i ＝u(7)；

Input interface 8 (denoted u (8)) inputsMemory storage mass m _M I.e. m _M ＝u(8)。

Specifically, the processing of the state variables, the state equations, the measurement equations and the system state space expression of the kalman filter module may include:

if the selected state variables are the current vehicle speed v, the vehicle mass m and the current road gradient i, the system state vector is as follows:

x(t)＝(v(t)，m(t)，i(t))

the system state equation is:

wherein the content of the first and second substances,

W _k is a process noise vector, and V _k To measure the noise vector, the process excitation noise covariance matrix is:

Q _k ＝E[W _k W _k ^T ]

the measurement noise covariance matrix is:

the system measurement equation is:

the system state equation and the system measurement equation can be combined into a system state space expression:

in the formula, H is a measurement matrix.

Specifically, the kalman filter module time update and measurement update may include:

the time update equation:

and updating time (priori), and calculating a next state predicted value according to a system state equation.

Covariance prediction:

in the formula:

the optimal estimated value of the state variable at the last moment is obtained; p _k-1 The error covariance of the last moment;

is a prior estimate of the state variable;

is the prior error covariance; j. the design is a square _f The process equation vector function f () is used for solving the state variable to obtain a Jacobian matrix, and the matrix expression is as follows:

measurement update equation:

measurement update (a posteriori), computing kalman gain from the estimated (a priori) error covariance and the measurement noise covariance:

and updating the state and calculating an optimal (posterior) estimation value.

In the above formula, z _k ＝[u(2)；u(3)；u(4)；]。

And updating the covariance, and calculating an error covariance matrix between the optimal estimated value and the true value to prepare for next recursion.

In the formula: k _k Is the Kalman gain;

is a state variable posterior estimate; p _k Is the posterior error covariance; and I is an identity matrix.

Based on the above, the kalman filter module finally outputs the estimation result of the current time, including the estimated vehicle speed, the estimated mass, and the estimated gradient.

The estimated vehicle speed is used for correcting the current vehicle speed calculated by the feedback rotating speed of the motor; the estimated gradient is used for correcting the current road gradient reported by the DCU; the estimated mass is used for longitudinal vehicle speed control of the VCU in the autonomous driving mode.

In some embodiments, fig. 2 is a schematic flow chart of another vehicle mass estimation method in the disclosed embodiments. Referring to fig. 2, the method may include:

and S210, collecting, identifying and processing data.

In conjunction with the above, data related to the vehicle condition and road condition, such as road rolling resistance or corresponding resistance coefficient, current gear command, current road grade, current acceleration, current vehicle speed, and current driving torque of the motor, may be obtained.

And S220, enabling quality estimation.

In connection with the above, the driving state data of the vehicle triggers the opening (updating) of the step corresponding to the mass estimation only if the mass estimation triggering condition is met. Therefore, the influence of disturbance on the quality estimation is reduced, and the accuracy and the credibility of the estimated quality are improved.

And S230, estimating the mass based on the vehicle longitudinal dynamic model.

In conjunction with the above, a calculated mass is determined using a vehicle longitudinal dynamics model based on a current vehicle speed, a current acceleration, a current road grade, a road rolling resistance, and a current drive torque.

And S240, quality estimation limiting processing.

The calculated mass obtained based on the vehicle longitudinal dynamics model is limited to obtain the limited mass, and the limited mass is within the range of the vehicle no-load mass and the vehicle full-load mass so as to ensure the credibility and accuracy of the estimated mass.

And S250, updating the mean value of the mass estimation with memory storage.

Namely, the mass stored by the memory is combined, and the average is obtained by updating the average value by using the limited mass obtained after the limiting processing.

S260, step six: and (5) Kalman filtering quality estimation processing.

Namely, based on the updated average mass, kalman filtering mass estimation processing is performed to determine the estimated mass.

The vehicle quality estimation method provided by the embodiment of the disclosure can acquire vehicle condition and road condition related data in real time, and realize flexible adjustment of corresponding parameters in a vehicle longitudinal dynamic model, so that the method can be applied to various scenes such as different vehicle conditions and road conditions, and the quality estimation accuracy is improved. The problem that in the related art, due to the fact that related parameters are set to be fixed values, the related parameters cannot be flexibly suitable for different roads, and the quality estimation accuracy is poor is solved.

Based on the same inventive concept, the embodiment of the present disclosure further provides a vehicle mass estimation device, which is used for executing the steps of any one of the methods provided by the above embodiments, so as to achieve corresponding beneficial effects.

Exemplarily, fig. 3 is a schematic structural diagram of a mass estimation device of a vehicle in an embodiment of the present disclosure. Referring to fig. 3, the apparatus 30 may include: a first obtaining module 310, configured to obtain driving state data of a vehicle; the driving state data includes a current gear command, a current road gradient, a current acceleration, a current vehicle speed, and a current driving torque of the motor; a condition determining module 320 for determining whether a quality estimation trigger condition is satisfied based on the driving state data; a second obtaining module 330, configured to, when it is determined that the quality estimation trigger condition is satisfied, continue to obtain at least road rolling resistance; the mass estimation module 340 determines an estimated mass of the vehicle based on the current vehicle speed, the current acceleration, the current road grade, the road rolling resistance, and the current drive torque.

According to the mass estimation device for the vehicle, mass estimation can be achieved without adding an additional sensor through the synergistic effect of the functional modules, so that the space of a vehicle opening is not occupied, the input cost of the vehicle is not increased, and the mass estimation device is convenient to popularize and use; meanwhile, the mass estimation can be carried out by combining the actual conditions of the vehicle and the road, the accuracy of the mass estimation is higher, and the vehicle speed can be conveniently followed in time.

In some embodiments, the quality estimation trigger conditions include:

the current gear command is in a forward gear or a reverse gear;

the current vehicle speed is equal to or greater than a first speed threshold and less than or equal to a second speed threshold; the first speed threshold is smaller than the second speed threshold and is smaller than the maximum available vehicle speed;

In some embodiments, the mass estimation module 340 is configured to determine an estimated mass of the vehicle, including the mass estimation module 340 being configured to:

In some embodiments, the mass estimation module 340 is configured to determine the calculated mass using a longitudinal vehicle dynamics model, including the mass estimation module 340 being configured to:

the calculated mass was determined using the following formula:

wherein m is _F Represents the calculated mass; t is _mot Representing the current drive torque, K representing the gear ratio, η representing the driveline mechanical efficiency, r representing the tire effective radius; ρ represents air density, which is a constant; a represents the frontal area; c _D Is the air resistance coefficient; v is the moving speed of the air relative to the vehicle, and the value is equal to the current vehicle speed; f is a resistance coefficient corresponding to the rolling resistance of the road, and is determined based on real-time pavement characteristics; g is gravity acceleration, i represents the current road gradient; delta represents the conversion coefficient of the rotating mass of the automobile after the inertia moment of the rotating mass is counted, and a represents the current acceleration output by the closed-loop control of the speed of the automobile.

In some embodiments, the mass estimation module 340 is configured to update and average the stored vehicle masses with the limiting masses and obtain an updated average mass, including: the quality estimation module 340 is specifically configured to perform an initial phase, an alternately performed mean phase, and an update phase; wherein the content of the first and second substances,

in the initial stage, setting a mass storage unit group, and setting the mass corresponding to the mass storage unit in the mass storage unit group to be the memory storage mass after the vehicle operates last time;

in the average value stage, calculating the average quality of the quality storage unit group, and updating, triggering and timing; the average quality of the quality storage unit group is the average value of the qualities corresponding to all the quality storage units in the quality storage unit group; the mean phase starts when the holding time length of the initial phase is longer than the time length of a single updating period;

wherein the average quality of the quality memory cell group is the updated average quality.

In some embodiments, the first obtaining module 310 is configured to obtain the current gear command, and includes: acquiring a gear command from a domain controller in an automatic driving mode;

in some embodiments, the first obtaining module 310 is configured to obtain the current road gradient, including: acquiring the current road gradient determined by a domain controller based on an intelligent driving CAN bus; the domain controller determines the current road gradient of the current position of the vehicle based on the current position of the vehicle and prior road ramp map information; and/or the domain controller determines the current road gradient based on gradient information acquired by a gradient sensor loaded on the vehicle;

in some embodiments, the first obtaining module 310 is configured to obtain the current vehicle speed, and includes: obtaining the rotating speed of a motor, the radius of a wheel and a transmission ratio, calculating to obtain an initial speed, and then carrying out filtering processing to obtain a current speed;

in some embodiments, the first obtaining module 310 is configured to obtain the current acceleration, and includes: performing mathematical processing based on the current vehicle speed to determine the current acceleration;

in some embodiments, the first obtaining module 310 is configured to obtain the current driving torque, and includes: receiving a current drive torque from a motor controller based on a power CAN bus;

in some embodiments, the second obtaining module 330 is configured to obtain a resistance coefficient corresponding to a road rolling resistance, and includes: acquiring a resistance coefficient determined by a domain controller based on an intelligent driving CAN bus; the domain controller identifies the current road condition based on the image acquisition sensor and matches the corresponding resistance coefficient.

It should be noted that the apparatus 30 shown in fig. 3 can perform the steps of any one of the methods provided in the above embodiments to achieve the corresponding advantages.

The embodiment of the present disclosure further provides a computer-readable storage medium, where a computer program is stored, where the computer program is used to execute the steps of any one of the methods provided in the foregoing embodiments, so as to achieve corresponding beneficial effects.

The embodiment of the present disclosure also provides an automotive apparatus, including: a processor; a memory for storing processor-executable instructions; and the processor is used for reading the executable instructions from the memory and executing the executable instructions to realize the steps of any one of the methods provided by the above embodiments, so as to realize the corresponding beneficial effects.

Exemplarily, fig. 4 is a schematic structural diagram of a vehicle device in an embodiment of the present disclosure. Referring to fig. 4, the vehicular apparatus 40 includes: a processor 420; a memory 410 for storing instructions executable by processor 420; the processor 420 is configured to read the executable instructions from the memory 410 and execute the executable instructions to implement the steps of any one of the methods provided in the foregoing embodiments, which has corresponding beneficial effects, and is not described herein again to avoid repeated descriptions.

Processor 420 may be, among other things, a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the computer to perform desired functions.

Memory 410 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, random Access Memory (RAM), cache memory (or the like). The non-volatile memory may include, for example, read Only Memory (ROM), a hard disk, flash memory, and the like. One or more computer program instructions may be stored on a computer-readable storage medium and executed by processor 420 to implement the method steps of the various embodiments of the present application described above and/or other desired functions.

In addition to the above-described methods and electronic devices, embodiments of the present application may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the method steps of the various embodiments of the present application.

The computer program product may write program code for carrying out operations for embodiments of the present disclosure in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.

Furthermore, the disclosed embodiments may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by the processor 420, cause the processor 420 to perform the method steps of the various embodiments of the present application.

The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

On the basis of the above embodiments, embodiments of the present disclosure also provide a vehicle including any one of the vehicle apparatuses provided by the above embodiments, with corresponding beneficial effects.

Exemplarily, fig. 5 is a schematic structural diagram of a vehicle in an embodiment of the present disclosure. Referring to fig. 5, the vehicle 50 may be an electric unmanned automatic tractor system device, which may specifically include: the system comprises a driving motor, a speed reducer, a differential mechanism, driving wheels, a power battery, a high-voltage distribution box, a DCDC (direct current), a lead-acid battery, a Vehicle Control Unit (VCU), a Battery Management System (BMS), a Motor Controller (MCU), an automatic driving area controller (DCU) system, an electric power steering system (EPS), an electronic parking brake system (EPB), an electronic hydraulic brake system (EHB), a vehicle body control system (BCM), a Tire Pressure Monitoring System (TPMS), a combination instrument system (ICM), a traction bolt controller (ATB), a trailer and the like; the structures are mechanically connected (shown by a bold solid line in the figure), electrically connected (shown by a broken line in the figure) or connected by a bus (shown by a thin solid line in the figure); for example, the vehicle device may be built into the vehicle control unit.

In other embodiments, the vehicle may also be another type of vehicle, and correspondingly includes other structures, which are not described or limited herein.

It is noted that, in this document, relational terms such as "first" and "second," and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.

The previous description is only for the purpose of describing particular embodiments of the present disclosure, so as to enable those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A mass estimation method of a vehicle, characterized by comprising:

2. The method of claim 1, wherein the quality estimation trigger condition comprises:

the current gear command is in a forward gear or a reverse gear;

and, the current drive torque is equal to or greater than a first torque threshold and less than or equal to a second torque threshold; the first torque threshold is less than the second torque threshold and both are less than a maximum available torque.

3. The method of claim 1 or 2, wherein the determining an estimated mass of the vehicle comprises:

4. The method of claim 3, wherein determining a calculated mass using a vehicle longitudinal dynamics model comprises:

determining the calculated mass using the formula:

wherein m is _F Represents the calculated mass; t is a unit of _mot Representing the current drive torque, K representing the gear ratio, η representing the driveline mechanical efficiency, r representing the tire effective radius; ρ represents the air density and is a constant; a represents the windward area; c _D Is the air resistance coefficient; v is the moving speed of the air relative to the vehicle, and the value is equal to the current vehicle speed; f is a resistance coefficient corresponding to the road rolling resistance, and is determined based on real-time pavement characteristics; g is gravity acceleration, i represents the current road gradient; delta represents the conversion coefficient of the rotating mass of the automobile after the inertia moment of the rotating mass is counted, and a represents the current acceleration output by the closed-loop control of the speed of the automobile.

5. The method of claim 3, wherein the stored vehicle masses are updated and averaged with the limiting masses to obtain an updated average mass, including an initial phase, an alternating averaging phase, and an updating phase;

in the average stage, calculating the average mass of the mass storage unit group, and updating, triggering and timing; the average mass of the mass storage unit group is the average value of the masses corresponding to the mass storage units in the mass storage unit group; the mean phase starts when the holding time length of the initial phase is longer than the time length of a single updating period;

in the updating stage, the quality corresponding to the first or last quality storage unit in the quality storage unit group is updated by using the limited quality, and updating exit timing is carried out; wherein, the updating stage starts when the quality estimation triggering condition is satisfied and the time length of updating triggering timing is equal to or greater than the preset updating time length;

wherein the average quality of the set of quality storage units is the updated average quality.

6. The method according to claim 1 or 4,

acquiring the current gear command, including: acquiring a gear command from a domain controller in an automatic driving mode;

obtaining the current road grade, including: acquiring the current road gradient determined by a domain controller based on an intelligent driving CAN bus; the domain controller determines the current road gradient of the current position of the vehicle based on the current position of the vehicle and prior road ramp map information; and/or the domain controller determines the current road gradient based on gradient information acquired by a gradient sensor loaded on a vehicle;

obtaining the current vehicle speed, including: acquiring the rotating speed of a motor, the radius of a wheel and a transmission ratio, calculating to obtain an initial vehicle speed, and then performing filtering processing to obtain a current vehicle speed;

obtaining the current driving torque, including: receiving a current drive torque from a motor controller based on a power CAN bus;

acquiring a resistance coefficient corresponding to the road rolling resistance, wherein the resistance coefficient comprises the following steps: acquiring a resistance coefficient determined by a domain controller based on an intelligent driving CAN bus; and the domain controller identifies the current road condition based on the image acquisition sensor and matches the corresponding resistance coefficient.

7. A mass estimation device of a vehicle, characterized by comprising:

8. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for performing the steps of the method according to any one of claims 1-6.

9. An apparatus for a vehicle, comprising: a processor; a memory for storing the processor-executable instructions; the processor configured to read the executable instructions from the memory and execute the executable instructions to implement the steps of the method according to any one of claims 1-6.

10. A vehicle characterized by comprising the vehicular apparatus according to claim 9.