CN100545595C

CN100545595C - A kind of automotive quality estimation system and method

Info

Publication number: CN100545595C
Application number: CNB2005101001197A
Authority: CN
Inventors: 宋佑川
Original assignee: BYD Co Ltd
Current assignee: BYD Co Ltd
Priority date: 2005-09-27
Filing date: 2005-09-27
Publication date: 2009-09-30
Anticipated expiration: 2025-09-27
Also published as: CN1940509A

Abstract

A kind of automotive quality estimation system and method that relates to automobile comprises external data equipment, inner parameter equipment, gradient module, Su Du ﹠amp; Acceleration module and mass centre's module, external data equipment receive the automobilism real time data and it are provided to gradient module and Su Du ﹠amp; Acceleration module; Inner parameter equipment receives the intrinsic or inner parameter of automobile and provides the module to mass centre with it; The data that gradient module is provided according to external data equipment are calculated the angle of gradient of automobile and will be had related parameter or data to reach speed; Acceleration module and mass centre's module; Su Du ﹠amp; The data that acceleration module is provided according to external data equipment and gradient module are calculated the speed and the acceleration of automobile, and will be had related parameter or data to reach mass centre's module; Mass centre's module is according to inner parameter equipment, gradient module and Su Du ﹠amp; Parameter that acceleration module provided or data calculate car mass, and to automobile driving system issue quality information, quality accuracy of estimation height of the present invention, real-time is good, and is practical.

Description

Automobile quality estimation system and method

Technical Field

The invention relates to automobiles, in particular to an automobile quality estimation system and method.

Background

In US patent US 6839615, a map of "square of speed-wheel torque" is plotted based on an automobile running equation using wheel speed sensors or wheel angular velocity sensors and accelerometers, the slope of which is the mass of the automobile and the intercept of which is the slope of the road, the speed of the automobile is calculated using the wheel speeds, although corrected, but still not representative of the true automobile speed, and air resistance and rolling resistance are ignored in the automobile running equation, making the estimation of the automobile mass less accurate.

Also disclosed are publications such as "secure left square with formation for Online Estimation of Vehicle Mass and Road Grade: in Theory and Experiments, the mass of the automobile and the gradient of the road are estimated by using data of ports specified in SAE J1939 and a recursive least square method on the basis of an automobile running equation, most of the data required in the method come from the ports specified in SAE J1939, the data provided by the ports are approximate values rather than accurate values, particularly the speed of the automobile is more inaccurate, and therefore, the estimation of the mass of the automobile is not accurate enough.

Disclosure of Invention

The invention aims to provide a high-accuracy automobile mass estimation system and method to overcome the defect that the automobile mass estimation in the prior art is not accurate enough.

The automobile mass estimation system adopted by the invention comprises an external data device, an internal parameter device, a gradient module, a speed and acceleration module and a mass center module, wherein,

the external data device is used for receiving real-time data of automobile operation and providing the real-time data to the gradient module and the speed & acceleration module;

the internal parameter device is used for receiving the inherent or internal parameters of the automobile and providing the inherent or internal parameters to the mass center module;

the slope module calculates the slope angle of the automobile according to data provided by external data equipment and transmits related parameters or data to the speed & acceleration module and the mass center module;

the speed and acceleration module calculates the speed and acceleration of the automobile according to data provided by external data equipment and the gradient module, and transmits related parameters or data to the mass center module;

the mass center module calculates the mass of the automobile according to parameters or data provided by the internal parameter device, the gradient module and the speed and acceleration module, and issues mass information to the automobile driving system.

The external data device includes a GPS antenna, a gyroscope, and an accelerometer, wherein,

the GPS antenna receives GPS original data and measures the horizontal speed V of the automobile_xAnd a vertical velocity V_zAnd transmitting the data to a grade module;

the gyroscope is used for measuring the yaw angular velocity omega of the automobile, judging whether the automobile runs in a straight line or not and transmitting data to the velocity and acceleration module;

the above-mentionedThe accelerometer measures the longitudinal acceleration a of the vehicle_xAnd lateral acceleration a_yAnd transmit the data to the speed&An acceleration module.

The internal parameter device comprises an external memory and a CAN bus module, wherein,

the external memory provides the vehicle intrinsic and relative parameters to the center of mass module, including the main reducer transmission ratio i₀Mechanical efficiency eta of the drive train_TRadius r of wheel, gravity acceleration g, air resistance coefficient C_DThe device comprises a windward area A, an air density rho and an automobile rotating mass conversion coefficient delta;

the CAN bus module receives data of other function modules on the automobile and provides internal parameters for the mass center module, and the provided parameters comprise engine output torque T_tqTransmission ratio i of the transmission_gAnd a rolling resistance coefficient f.

The automobile quality estimation method is characterized in that: it comprises the following steps:

A. obtaining or calculating relevant parameters through an external data device, an internal parameter device, a gradient module and a speed and acceleration module, and transmitting the relevant parameters to a mass center module;

B. the mass center module calculates the automobile classification power F according to the relevant parameters;

C. the mass center module calculates the automobile normalized acceleration a according to the relevant parameters;

D. and the mass center module calculates the mass m of the automobile and issues mass information to an automobile driving system.

The step A comprises the following steps:

a1, calculating a road slope angle alpha by the slope module according to data provided by an external data device and providing the road slope angle alpha to the speed & acceleration module and the mass center module;

a2, speed&The acceleration module calculates the longitudinal speed u of the automobile according to the data provided by the external data equipment_xAnd longitudinal acceleration a_xAnd provide it to the center of mass module;

a3, intrinsic parameters device provides the car intrinsic or intrinsic parameters to the center of mass module.

In step A1, the slope module provides the horizontal velocity V according to the GPS antenna_xAnd a vertical velocity V_zThe road slope angle α is calculated as follows:

<math> <mrow> <mi>α</mi> <mo>=</mo> <mi>arctan</mi> <mrow> <mo>(</mo> <mfrac> <msub> <mi>V</mi> <mi>z</mi> </msub> <msub> <mi>V</mi> <mi>x</mi> </msub> </mfrac> <mo>)</mo> </mrow> </mrow> </math>

the step A2 comprises the following steps:

a21, a speed and acceleration module receives real-time running data of a GPS antenna, a gyroscope and an accelerometer and a road slope angle alpha;

a22, judging whether the automobile runs straight or not, and performing the following operations:

and A221, if the vehicle is in straight line running, judging whether the road has a gradient, and performing the following operations:

a2211, if the road has no slope, calculating the longitudinal speed u of the automobile by using the data provided by the GPS antenna_xAnd longitudinal acceleration a_x；

A2212, if the road has a slope, calculating the longitudinal speed u of the automobile by using a Kalman filter_xAnd longitudinal acceleration a_x；

A222, if the vehicle does not run in a straight line, using a Kalman filter meterCalculating the longitudinal speed u of the vehicle_xAnd longitudinal acceleration a_x；

A23, sending the longitudinal speed u of the automobile to the mass center module_xAnd longitudinal acceleration a_x。

In step a22, a positive yaw threshold ω is preset_eComparing the threshold value with the yaw angular speed omega of the automobile, if | omega | is less than or equal to ω_eJudging that the automobile runs in a straight line; otherwise, judging that the automobile does not run in a straight line.

In step a221, a positive gradient threshold α is preset_eComparing the threshold value with the road slope angle alpha, if the alpha is less than or equal to alpha_eJudging that the road has no gradient; otherwise, judging that the road has the gradient.

The automobile mass estimation method according to claim 4, characterized in that: in the step B, the mass center module calculates the automobile normalized power F according to the following method:

wherein,

each parameter being engine output torque T_tqTransmission ratio i of the transmission_gMain reducer transmission ratio i₀Drive train machineEfficiency eta_TRadius r of wheel, air resistance coefficient C_DFrontal area A, air density rho and longitudinal speed u of automobile_x。

In the step C, the center of mass module calculates the vehicle normalized acceleration a according to the following method:

a＝gf cosα+g sinα+δa_xwherein

the parameters are gravity acceleration g, rolling resistance coefficient f, road slope angle alpha, automobile rotating mass conversion coefficient delta and longitudinal acceleration a_x。

The invention has the beneficial effects that: in the invention, the automobile running equation is used for estimating the mass of the automobile, but air resistance and rolling resistance are not ignored, a large amount of real-time automobile running data are transmitted through external data equipment, intrinsic or internal parameters of the automobile are transmitted through internal parameter equipment, and factors of all aspects are comprehensively considered.

Drawings

FIG. 1 is a schematic diagram of the system of the present invention;

FIG. 2 is a schematic diagram of an exemplary application of the present invention;

FIG. 3 is a schematic control flow chart of the present invention;

FIG. 4 is a Kalman filter update state model.

Detailed Description

The invention is explained in more detail below with reference to the figures and examples:

according to fig. 1 and 2, the invention comprises an external data device 1, an internal parameter device 2, a grade module 3, a speed & acceleration module 4 and a center of mass module 5.

As shown in fig. 1, an external data device 1 is used to receive real-time vehicle operation data and provide it to a grade module 3 and a speed & acceleration module 4.

As shown in FIG. 1, the external data device 1 includes a GPS antenna 11, a gyroscope 12 and an accelerometer 13, wherein the GPS antenna 11 receives GPS raw data and measures the horizontal velocity V of the automobile_xAnd a vertical velocity V_zAnd transmits the data to the gradient module 3, and the gyroscope 12 measures the yaw rate omega of the automobile for judging whether the automobile is in straight line driving and transmits the data to the speed&The acceleration module 4, the accelerometer 13 measures the longitudinal acceleration a of the vehicle_xAnd lateral acceleration a_yAnd transmit the data to the speed&An acceleration module 4.

As shown in fig. 1, the internal parameter device 2 is used to receive vehicle-specific or internal parameters and to provide them to the center of mass module 5.

As shown in FIG. 1, the internal parameter device 2 comprises an external memory 21 and a CAN bus module 22, wherein the external memory 21 provides the center of mass module 5 with vehicle-specific and relevant parameters, including the final drive gear ratio i₀Mechanical efficiency eta of the drive train_TRadius r of wheel, gravity acceleration g, air resistance coefficient C_DThe frontal area A, the air density rho and the conversion coefficient delta of the rotating mass of the automobile, the CAN bus module 22 receives data of other functional modules on the automobile and provides internal parameters for the mass center module 5, and the provided parameters comprise the output torque T of the engine_tqTransmission ratio i of the transmission_gAnd a rolling resistance coefficient f.

As shown in fig. 1, the grade module 3 calculates the grade angle of the vehicle based on data provided by the external data device 1 and passes the relevant parameters or data to the speed & acceleration module 4 and the center of mass module 5.

As shown in fig. 1, the speed & acceleration module 4 calculates the speed and acceleration of the vehicle based on data provided by the external data device 1 and the grade module 3 and transmits the relevant parameters or data to the center of mass module 5.

As shown in fig. 1, the center of mass module 5 calculates the mass of the vehicle based on the parameters or data provided by the internal parameter device 2, the grade module 3, and the speed & acceleration module 4, and distributes the mass information to the vehicle drive system.

The driving equation of the automobile is as follows:

F_i＝F_f+F_w+f_i+F_j

in the formula,

is the driving force of the automobile;

F_fmgf cos α, rolling resistance;

is the air resistance;

F_img sin α, grade drag;

F_j＝δma_xand is acceleration resistance.

Thus, the following transformations may be made:

in the above formula, T_tqFor engine output torque, i_gTo the transmission ratio of the variator, i₀Is the main reducer transmission ratio eta_TFor the mechanical efficiency of the drive train, r is the wheel radius, m is the vehicle mass, g is the gravitational acceleration, f is the rolling resistance coefficient, α is the road bank angle, C_DIs the air resistance coefficient, A is the windward area, rho is the air density, u_xIs the longitudinal speed of the vehicle, delta is the conversion coefficient of the rotating mass of the vehicle, a_xIs the longitudinal acceleration of the vehicle.

After the deformation finishing of the formula, the product is obtained

The automobile classification power F is obtained as follows:

the automobile normalized acceleration a is taken as follows:

a＝m(gf cosα+g sinα+δa_x)

the vehicle mass m is calculated according to the following formula:

m＝F/a

as can be seen from the above equation, as long as the vehicle-normalized power F and the vehicle-normalized acceleration a can be obtained, the vehicle mass m can be calculated by a simple least square method.

In the invention, relevant parameters are obtained or calculated through an external data device 1, an internal parameter device 2, a gradient module 3 and a speed & acceleration module 4, the relevant parameters are transmitted to a mass center module 5, the mass center module 5 calculates the automobile classification power F and the automobile classification acceleration a according to the relevant parameters, calculates the automobile mass m, and issues mass information to an automobile driving system.

As shown in fig. 3, the specific control flow of the present invention is as follows:

1. the slope module 3 provides the horizontal velocity V of the vehicle according to the GPS antenna 11_xAnd a vertical velocity V_zCalculating road slope angle alpha and providing it to speed&The acceleration module 4 and the center of mass module 5, as shown in fig. 2, may calculate the road grade angle α as follows:

2. the speed & acceleration module 4 receives real-time data of the operation of the GPS antenna 11, the gyroscope 12 and the accelerometer 13 and the road slope angle α.

3. Presetting a positive yaw threshold value omega_eAnd comparing the threshold value with the yaw velocity omega of the automobile to judge whether the automobile runs straight or not, and performing the following operations:

31. if | ω | is less than or equal to ω |_eIf the vehicle is judged to be running in a straight line, a positive gradient threshold value alpha e is preset, whether the gradient exists in the road is judged by comparing the positive gradient threshold value alpha with the road gradient angle alpha, and the following operations are carried out:

311. if alpha is less than or equal to alpha_eIf the road has no slope, the longitudinal speed u of the automobile is calculated by adopting the data provided by the GPS antenna 11_xAnd longitudinal acceleration a_xWherein the longitudinal speed u of the vehicle_xHorizontal velocity V measured directly by GPS_xThe result is, that is,

u_x＝V_x

longitudinal acceleration a of automobile_xHorizontal velocity V measured by GPS_xA differential over time is obtained as

a_{x} = \frac{d V_{x}}{dt}

312. If | α | > α_eJudging that the road has a slope, and calculating the longitudinal speed u of the automobile by using a Kalman filter_xAnd longitudinal acceleration a_x。

Kalman filtering is a well-known mathematical algorithm whose mathematical principle is as follows:

x[n+1]＝Φx[n]+ψu[n]+ε[n]

y[n]＝Hx[n]+η[n]

in the form of the state model of the above formula, kalman filtering satisfies the following recursion equation:

A. measurement update (Observation update)

\hat{x} [n | n] = \hat{x} [n | n - 1] + K [n] (y [n] - H \hat{x} [n | n - 1])

K[n]＝P[n|n-1]H^T(HP[n|n-1]H^T+R[n])^-1

P[n|n]＝(I-K[n]H)P[n|n-1]

B. Time updating

P[n+1|n]＝ΦP[n|n]Φ^T+Q[n]

In the above 5 formulas:

indicates the measurement value y [ n ] using the time n]The updated value of x is updated, i.e. the best estimate of x is made at time n, as shown in fig. 4.

Is represented by y [0 ]]Up to y [ n ]]The value of x at time n +1 of the measured value estimate of (a);

i is an identity matrix;

k [ n ] is a Kalman gain matrix;

Q[n]is the model noise epsilon n]Is Q [ n ]]＝E(ε[n]ε[n]^T)；

R[n]Is to measure the noise eta n]Is R [ n ]]＝E(η[n]η[n]^T)；

P [ n | n ] and P [ n | n-1] are error covariance matrices, which are defined as

P [n | n] = E ({x [n] - \hat{x} [n | n]} {x [n] - \hat{x} [n | n]^{T}})

P [n | n - 1] = E ({x [n] - \hat{x} [n | n - 1]} {x [n] - \hat{x} [n | n - 1]^{T}})

Where x [ n ] represents the actual value of x at time n, and the meaning of the symbol E () is the expectation of the expression in parentheses.

An initial value x [1|0 ] when given 0]And P [1|0 ]]Then, 5 formulas of the Kalman filtering algorithm can be iterated repeatedly, so that the optimal x value can be estimated at any time n, and the longitudinal speed u of the automobile can be calculated by utilizing a Kalman filter_xAnd longitudinal acceleration a_x。

32. If | ω | is greater than ω |)_eJudging that the automobile does not run in a straight line, and calculating the longitudinal speed u of the automobile by using a Kalman filter_xAnd longitudinal acceleration a_x。

4. The speed & acceleration module 4 sends the vehicle longitudinal speed ux and the longitudinal acceleration ax to the center of mass module 5.

5. The internal parameter device 2 supplies the vehicle-specific or internal parameters to the center of mass module 5, wherein the data supplied by the CAN bus module 22 to the center of mass module 5 essentially comprise:

engine output torque T_tqTransmission ratio i of the transmission_gAnd a rolling resistance coefficient f.

The data provided by the external memory 21 to the mass centre module 5 mainly comprises:

main reducer transmission ratio i₀Mechanical efficiency eta of the drive train_TRadius r of wheel, gravity acceleration g, air resistance coefficient C_DThe device comprises a wind facing area A, an air density rho and an automobile rotating mass conversion coefficient delta.

6. The mass center module 5 calculates the vehicle normalized power F according to the following method:

wherein,

each parameter being engine output torque T_tqTransmission ratio i of the transmission_gMain reducer transmission ratio i₀Mechanical efficiency eta of the drive train_TRadius r of wheel, air resistance coefficient C_DFrontal area A, air density rho and longitudinal speed u of automobile_x。

7. The center of mass module 5 calculates the vehicle normalized acceleration a as follows:

a＝gf cosα+g sinα+δa_xwherein

the parameters are gravity acceleration g, rolling resistance coefficient f, road slope angle alpha, automobile rotating mass conversion coefficient delta and longitudinal acceleration ax.

8. And the mass center module 5 calculates the mass m of the automobile according to the m ═ F/a, and distributes mass information to the automobile driving system.

Claims

1. An automobile mass estimation system, characterized in that: it comprises an external data device (1), an internal parameter device (2), a gradient module (3), a speed & acceleration module (4) and a center of mass module (5), wherein,

the external data device (1) is used for receiving real-time data of automobile operation and providing the real-time data to the gradient module (3) and the speed & acceleration module (4);

the internal parameter device (2) is used for receiving the inherent or internal parameters of the automobile including the air resistance coefficient CD and the rolling resistance coefficient f and providing the parameters to the mass center module (5);

the gradient module (3) calculates the gradient angle of the automobile according to data provided by the external data equipment (1), and transmits related parameters or data to the speed & acceleration module (4) and the mass center module (5);

the speed and acceleration module (4) calculates the speed and acceleration of the automobile according to the data provided by the external data device (1) and the gradient module (3), and transmits related parameters or data to the mass center module (5);

and the mass center module (5) calculates the mass of the automobile according to the parameters or data provided by the internal parameter equipment (2), the gradient module (3) and the speed and acceleration module (4), and distributes mass information to the automobile driving system.

2. The automobile mass estimation system of claim 1, wherein: said external data device (1) comprising a GPS antenna (11), a gyroscope (12) and an accelerometer (13), wherein,

the GPS antenna (11) receives GPS original data and measures the horizontal speed V of the automobile_xAnd a vertical velocity V_zAnd transmits the data to the gradient module (3);

the gyroscope (12) is used for measuring the yaw angular velocity omega of the automobile, judging whether the automobile runs in a straight line or not and transmitting data to the velocity and acceleration module (4);

the accelerometer (13) measures the longitudinal acceleration a of the automobile_xAnd lateral acceleration a_yAnd transmit the data to the speed&An acceleration module (4).

3. The automobile mass estimation system of claim 1, wherein: the internal parameter device (2) comprises an external memory (21) and a CAN bus module (22), wherein,

the external memory (21) provides the vehicle intrinsic and relative parameters including the final drive transmission ratio i to the center of mass module (5)₀Mechanical efficiency eta of the drive train_TRadius r of wheel, acceleration by gravityDegree g, coefficient of air resistance C_DThe device comprises a windward area A, an air density rho and an automobile rotating mass conversion coefficient delta;

the CAN bus module (22) receives data of other functional modules on the automobile and provides internal parameters for the mass center module (5), and the provided parameters comprise engine output torque T_tqTransmission ratio i of the transmission_gAnd a rolling resistance coefficient f.

4. A method for estimating the mass of an automobile, characterized in that: it comprises the following steps:

A. obtaining or calculating related parameters through an external data device (1), an internal parameter device (2), a gradient module (3) and a speed & acceleration module (4), and transmitting the related parameters to a mass center module (5);

a1, the gradient module (3) calculates the road gradient angle alpha according to the data provided by the external data device (1) and provides the road gradient angle alpha to the speed & acceleration module (4) and the mass center module (5);

a2, speed&The acceleration module (4) calculates the longitudinal speed u of the vehicle according to the data provided by the external data device (1)_xAnd longitudinal acceleration a_xAnd provides it to the mass centre module (5);

a21, a speed and acceleration module (4) receives real-time running data of a GPS antenna (11), a gyroscope (12) and an accelerometer (13) and a road slope angle alpha;

A222, if the automobile does not run in a straight line, calculating the longitudinal speed u of the automobile by using a Kalman filter_xAnd longitudinal acceleration a_x；

A23, sending the longitudinal speed u of the automobile to the mass center module (5)_xAnd longitudinal acceleration a_x；

A3, the internal parameter device (2) provides the center of mass module (5) with a coefficient of air resistance C_DAnd a rolling resistance coefficient f;

B. the mass center module (5) calculates the automobile classification power F according to the relevant parameters;

C. the mass center module (5) calculates the automobile normalized acceleration a according to the related parameters;

D. and the mass center module (5) calculates the mass m of the automobile and issues mass information to an automobile driving system.

5. The automobile mass estimation method according to claim 4, characterized in that: in the step A1, the gradient module (3) provides the horizontal speed V according to the GPS antenna (11)_xAnd a vertical velocity V_zThe road slope angle α is calculated as follows:

<math> <mrow> <mi>α</mi> <mo>=</mo> <mi>arctan</mi> <mrow> <mo>(</mo> <mfrac> <msub> <mi>V</mi> <mi>z</mi> </msub> <msub> <mi>V</mi> <mi>x</mi> </msub> </mfrac> <mo>)</mo> </mrow> <mo>.</mo> </mrow> </math>

6. the automobile mass estimation method according to claim 4, characterized in that: in step a22, a positive yaw threshold ω is preset_eComparing the threshold value with the yaw angular speed omega of the automobile, if | omega | is less than or equal to ω_eJudging that the automobile runs in a straight line; otherwise, judging that the automobile does not run in a straight line.

7. The automobile quality estimation method according to claim 4,the method is characterized in that: in step a221, a positive gradient threshold α is preset_eComparing the threshold value with the road slope angle alpha, if the alpha is less than or equal to alpha_eJudging that the road has no gradient; otherwise, judging that the road has the gradient.

8. The automobile mass estimation method according to claim 4, characterized in that: in the step B, the mass center module (5) calculates the automobile normalized power F according to the following method:

wherein,

9. The automobile mass estimation method according to claim 4, characterized in that: in the step C, the mass center module (5) calculates the automobile normalized acceleration a according to the following method:

a＝gfcosα+gsinα+δα_xwherein

each parameter is weightAcceleration g, rolling resistance coefficient f, road slope angle alpha, automobile rotating mass conversion coefficient delta and longitudinal acceleration a_x。