KR101835761B1 - Apparatus and method for learning slope of road - Google Patents

Abstract

The present invention discloses an apparatus and method for learning a running road gradient of a vehicle. An apparatus and method for learning a running road gradient of a vehicle according to the present invention is characterized in that it comprises at least one of inertial torque derived from the acceleration information of the vehicle and the rolling resistance generated by the engine and the resistance torque to the air resistance, By deriving the calibration data for each resistance torque and the inertial torque from the tilt information and learning about the tilt based on the derived calibration data for each torque, the learning time and load of the tilt of the running road can be minimized, The responsiveness of the transmission according to the present invention can be further improved.

Description

[0001] APPARATUS AND METHOD FOR LEARNING SLOPE OF ROAD [0002]

The present invention relates to an apparatus and method for learning the inclination of a running road of a vehicle, and more particularly, to a method and apparatus for learning the inclination of a running road, And more particularly,

The running road gradient is predicted based on the engine torque received in a general automatic transmission, the target speed change stage is set according to the predicted traveling road inclination, and then the constant speed running and the shifting are executed based on the set target speed change stage.

The inclination degree of the running road is learned from the calibration data reflecting the rolling resistance and the air resistance with respect to the running speed and the inclination degree of the running road based on the calibration data reflecting the inertia resistance according to the acceleration / deceleration of the vehicle speed.

That is, the control unit classifies the speed of the vehicle into a predetermined unit and then travels at a constant speed to calculate the calibration data reflecting the rolling resistance and the air resistance with respect to the running speed of each predetermined unit. Thereafter, the vehicle runs at the same acceleration, And calculates the calibrated calibration data.

That is, the inclination is derived from the sum of the frictional torque of the engine, the engine inertial torque, and the engine output torque.

That is, the engine torque is equal to the frictional torque of the engine + the engine inertia torque + the engine output torque, where the engine input torque is the engine output torque, the torque converter amplification factor, and the torque converter efficiency, Input torque + gear ratio + transmission friction torque + transmission inertia torque. Based on the engine torque, the running road gradient is learned.

That is, since the torque satisfies the output torque-vehicle speed drag torque-vehicle speed inertia torque of the transmission, the running road gradient is learned by the torque, the final gear ratio, and the wheel radius and weight.

However, a series of processes of learning the gradient of the traveling road of the vehicle is very complicated, and the load on the transmission is increased, so that the response to the transmission is degraded.

SUMMARY OF THE INVENTION Accordingly, the present invention has been made keeping in mind the above problems occurring in the prior art, and it is an object of the present invention to provide a vehicle- Calibration data for each resistance torque and inertial torque are derived from the radius and inclination information of the vehicle wheel and learning is performed on the inclination based on the calibration data for each derived torque so that the inclination of the running road is set to the minimum Which can reduce the speed of the vehicle, and further improve the responsiveness of the transmission.

In order to achieve the above object, according to a first aspect of the present invention, there is provided a driving-

An engine torque receiving section,

An engine torque modeling unit for generating and storing a modeling value for the received engine torque;

And an inclination learning unit for learning an inclination of the running road based on a modeling value for the engine torque,

Wherein the gradient learning unit comprises:

Learning is performed on the inclination from the resistance of the engine derived from the modeling value of the engine torque and the resistance torque by the air resistance, the inertia torque according to the vehicle acceleration / deceleration, the weight of the vehicle, the radius information of the vehicle's wheel, Deriving a calibration data value for the calibration,

Learning is performed on the travel road gradient based on the derived calibration data values to generate and store learning values for the gradient.

Preferably, the inclination degree is characterized by satisfying the following expression (1).

..식 1

.. Equation 1

Wherein T is a first resistance torque against rolling resistance, Td is a second resistance torque with respect to air resistance, Ti is an inertia torque with respect to acceleration / deceleration of the vehicle, g is a gravitational acceleration, And R is the radius of the wheel.

Preferably, the first resistance torque is a torque

Is calculated as the product of the engine output torque and the reduction ratio from the engine to the output shaft.

Further, the second resistance torque

And the second resistance torque is calculated based on the calibration data for the air resistance and the air resistance calculated from the vehicle speed.

Preferably, the inertia torque is

Wherein the inertia torque is calculated by multiplying the inertia calibration data defined for the inertia resistance and the acceleration and the radius of the wheel.

Preferably, the inclination learning unit,

Calculating the calibration data for each torque from the following equation (5), deriving learning values for the gradient from the calculated calibration data, and storing the learned values.

.. Equation 5

,

,

,

는 각 토크에 대한 켈리브레이션 데이터값이고, slope는 소정 주기로 입력되는 경사도 정보이고, a는 가속도 정보이며, R은 차량의 바퀴의 반지름이고, v는 차량의 속도이며, 및 rrg 는 엔진에서부터 출력 샤프트까지의 감속비 정보이다.here,

,

,

,

Is the acceleration information, R is the radius of the vehicle's wheel, v is the speed of the vehicle, and rrg is the acceleration information from the engine to the output shaft Speed ratio information.

According to a first aspect of the present invention, there is provided a traveling road gradient learning method for a vehicle,

An engine torque receiving step,

An engine torque modeling step of generating and storing a modeling value for the received engine torque,

And an inclination learning step of learning the vehicle based on the modeling value of the engine torque at an inclination with respect to the running road,

The inclination learning step comprises:

Based on the rolling resistance derived from the modeling value of the engine torque and the resistance torque by the air resistance, the inertia torque according to the vehicle acceleration / deceleration, the weight of the vehicle, the radius information of the vehicle's wheel, Calculation of the calibration data for torque,

Learning is performed on the inclination based on the calculated calibration data value, and a learning value for the generated inclination is generated and stored.

As described above, according to the apparatus and the method for learning the running road gradient of the vehicle according to the present invention, the inertia torque derived from the acceleration information of the vehicle, the rolling resistance generated by the engine, The calibration information for each resistance torque and the inertial torque is derived from the weight of the vehicle and the radius and inclination information of the vehicle wheel, and the learning of the inclination is performed based on the calibration data for each derived torque, The load can be reduced to a minimum and the responsiveness of the transmission can be further improved.

1 is a diagram showing a configuration of a traveling road gradient learning apparatus according to an embodiment of the present invention.
Fig. 2 is a waveform diagram showing the inclination of the vehicle shown in Fig.

In order to fully understand the present invention, operational advantages of the present invention, and objects achieved by the practice of the present invention, reference should be made to the accompanying drawings and the accompanying drawings which illustrate preferred embodiments of the present invention. The same reference numerals and the same names are used for the same parts as in the conventional configuration.

FIG. 1 is a diagram showing the configuration of a driving road gradient learning device for a vehicle according to an embodiment of the present invention. FIG. 2 is a waveform diagram showing a learning value for the gradient calculated by the driving road gradient learning part shown in FIG. admit. As shown in Figs. 1 and 2, the traveling road gradient learning device of the vehicle according to the present invention includes an inertial torque derived from acceleration information, a rolling resistance generated by the engine, a resistance torque generated by the air resistance, And derives the calibration data for each torque from the inclination information input at a predetermined cycle, and executes learning about the vehicle gradient based on the derived calibration data. The apparatus includes an engine torque receiving unit 10 An engine torque modeling unit 30, and a traveling road gradient learning unit 50. [

The engine torque receiving section 10 of the transmission receives the torque supplied from the engine, and the received engine torque is supplied to the engine torque modeling section 30.

The engine torque modeling unit 30 performs modeling on the received engine torque to generate an engine torque modeling value and stores the generated engine torque modeling value in a predetermined position.

Here, since the series of processes for receiving the engine torque and performing the modeling on the received engine torque are well known in the art, a detailed description of the process for generating and storing the modeling value for the engine torque will be omitted.

The inclination learning unit 50 calculates an inclination from a modeling value for the engine torque. The inclination is calculated by subtracting a first resistance torque To against a rolling resistance and a second resistance against an air resistance The inertial torque Ti with respect to the acceleration / deceleration of the vehicle and the weight g xW of the vehicle and the wheel radius R are calculated.

That is, the slope satisfies the following equation (1).

..식 1

.. Equation 1

Here, the first resistance torque To satisfies the following equation (2).

..식 2

.. Equation 2

Here, Te is the final output torque, and rw is the overall reduction ratio from the engine to the output shaft.

The second resistance torque Td satisfies the following equation (3).

..식 3

.. Equation 3

,

,

는 공기 저항에 대해 기 정의된 켈리브레이션 데이터들이고, v는 차속이다.Here,

,

,

Is the pre-defined calibration data for the air resistance, and v is the vehicle speed.

On the other hand, the inertia torque Ti satisfies the following expression (4).

..식 4

.. Equation 4

는 차량의 가감속에 대해 기 정의된 켈리브레이션 데이터값이고, a는 가속도이며, R은 차량의 반지름이다.here,

Is the predefined calibration data value for the acceleration / deceleration of the vehicle, a is the acceleration, and R is the radius of the vehicle.

From the above equations (1) to (4), the learning value for the inclination satisfies the following equation (5).

.. Equation 5

,

,

,

각 저항 토크 및 관성 토크에 대한 켈리브레이션 데이터값으로서, 소정 주기로 입력되는 경사도 정보((slope), 가속도 정보(a), 차량의 바퀴의 반지름(R), 차량의 속도(v), 및 감속비 정보로부터 도출된다.here,

,

,

,

(A), the radius (R) of the vehicle, the speed (v) of the vehicle, and the reduction gear ratio information, which are inputted as a calibration data value for each of the resistance torques and the inertia torque, Lt; / RTI >

That is, based on the slope information (slope), the acceleration information (a), the radius (R) of the vehicle, the vehicle speed (v), and the gear ratio information input at the predetermined period, the calibration data value The learning value for the gradient is executed from the calculated calibration data, and the error or noise component for the learning value of the gradient is minimized by using the least squares method.

Referring to Fig. 2, as shown in a), a waveform diagram showing the slope learning value calculated based on the output torque, the vehicle speed drag torque, and the vehicle speed inertia torque derived from the existing engine torque, and b) This figure shows actual slope.

As shown in the figure, the error between the calculated calculated slope and the actual slope is derived from the calibration data value for each torque.

However, the calibration data for each torque derived from the slope information (slope), the acceleration information (a), the radius R of the vehicle's wheel, the vehicle speed v, and the gear ratio information according to the embodiment of the present invention The learning value for the gradient using the value is as shown in c). That is, as shown in FIG. 2, it can be seen that the error between the slope learning value derived from the calibration data value for each torque calculated according to the embodiment of the present invention and the actual slope is within the predefined tolerance.

The calibration data value for each torque is calculated from the slope information (slope), the acceleration information (a), the radius (R) of the vehicle's wheel, the vehicle speed (v) It takes about 10 seconds or less to derive the calibration data because it takes a total of less than 100 seconds to generate the learning value for the ramp as the derived calibration data value. Therefore, the learning time for the inclination is minimum .

Here, from the slope information (slope), the acceleration information (a), the radius (R) of the vehicle, the vehicle speed (v) A series of processes for calculating the calibration data value for the calibration data and minimizing the error or noise component with respect to the learning value of the gradient based on the calculated calibration data using the least squares method is a well known technique. And a detailed description of the least squares method for minimizing errors or noise components of the learning value for the calculated gradient is omitted.

It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. It is, therefore, to be understood that the above-described embodiments are illustrative in all respects and not as restrictive. The scope of the present invention is defined by the appended claims rather than the foregoing detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be interpreted as being included in the scope of the present invention .

A first resistance torque for the rolling resistance generated by the engine and a second resistance torque for the air resistance, and a second resistance torque for estimating the running road gradient from the weight of the vehicle and the radius and tilt information of the vehicle wheel, based on the inertia torque derived from the acceleration information, Accuracy and reliability of the operation of the learning device and method of running road inclination of the vehicle which can reduce the time and load to the minimum by estimating the inclination of the running road by deriving the calibration data and thereby improve the responsiveness of the transmission accordingly The present invention is industrially applicable because it can bring about a great improvement in terms of performance, efficiency, performance and efficiency, and is not only a possibility of marketing or operating a vehicle to be applied, but also can be practically and practically carried out.

Claims (7)

An engine torque receiving section,
An engine torque modeling unit for generating and storing a modeling value for the engine torque received by the engine torque receiving unit,
And an inclination learning unit for learning an inclination of the running road based on a modeling value for the engine torque,
Wherein the gradient learning unit comprises:
From the modeling value of the engine torque, the resistance of the engine by the rolling resistance and the resistance of the air, the inertial torque of the vehicle acceleration / deceleration, the weight of the vehicle, the radius information of the vehicle wheel, Derive the calibration data value,
Learning is performed on the travel road gradient based on the derived calibration data values to generate and store learning values for the gradient,
Wherein the calibration data values for the respective torques are derived from the following equations.

Where R is the radius of the wheel of the vehicle, a is the acceleration information, Slope is the slope information input at a predetermined cycle, g is the gravitational acceleration, W is the mass of the vehicle , v is the vehicle speed,

,

,

,

Is the calibration data value for each torque. The traveling road gradient learning device according to claim 1, wherein the gradient information satisfies the following equation.

Wherein T is a first resistance torque against rolling resistance, Td is a second resistance torque with respect to air resistance, Ti is an inertia torque with respect to acceleration / deceleration of the vehicle, g is a gravitational acceleration, And R is the radius of the wheel. 3. The apparatus of claim 2, wherein the first resistance torque
And the output shaft is calculated as the product of the engine output torque and the reduction ratio from the engine to the output shaft. 4. The method of claim 3,
Wherein the calculation is made from the pre-defined calibration data on the air resistance and the vehicle speed. 5. The control device according to claim 4,
Is calculated by multiplying inertial calibration data previously defined for the inertia resistance, acceleration, and radius of the wheel. delete Receiving engine torque;
An engine torque modeling step of generating and storing a modeling value for the received engine torque;
And an inclination learning step of learning by an inclination with respect to the running road based on a modeling value for the engine torque,
The inclination learning step comprises:
A calibration value for each torque based on the rolling resistance derived from the modeling value for the engine torque and the resistance torque by the air resistance, the inertia torque according to the vehicle acceleration / deceleration, the weight of the vehicle, the radius information of the vehicle's wheel, ≪ / RTI >
Learning is performed on the travel road gradient based on the derived calibration data values to generate and store learning values for the gradient,
Wherein the calibration data values for the respective torques are derived from the following equations.

Where R is the radius of the wheel of the vehicle, a is the acceleration information, Slope is the slope information input at a predetermined cycle, g is the gravitational acceleration, W is the mass of the vehicle , v is the vehicle speed,

,

,

,

Is the calibration data value for each torque.

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