KR20150047280A - Apparatus and method of self-learning adc correction system - Google Patents

Abstract

The present invention relates to an ADC correction apparatus and method using a self-learning algorithm, and more particularly, to a current, voltage, temperature correction apparatus and method of an intelligent battery sensor of a vehicle.
According to another aspect of the present invention, there is provided an information processing apparatus including a data measuring unit for measuring a state variable of a battery and deriving an output value thereof, an error measuring unit for measuring an error with respect to an output value derived from the data measuring unit, And an error correcting unit for calculating the necessary value by itself and correcting the error.

Description

TECHNICAL FIELD [0001] The present invention relates to an apparatus and method for correcting an ADC (Analog Digital Converter)

The present invention relates to an ADC correction apparatus and method using a self-learning algorithm, and more particularly, to a current, voltage, temperature correction apparatus and method of an intelligent battery sensor of a vehicle.

In recent years, various control devices have been installed in a vehicle (for example, power window control, burglar alarm control, etc.) in order to improve the safety and convenience of the driver of the vehicle. Various control devices installed in the vehicle receive various information from various kinds of sensors And controls the system.

Among various vehicle sensors, the Intelligent Battery Sensor (IBS) is mounted on the negative (-) portion of the vehicle battery to periodically monitor the current, voltage and temperature of the battery and to measure the state of charge (SOC) The state of health (SOH), and the state of function (SOF), to the main controller (ECU).

The information provided by the IBS to the main controller thus drives the various devices in the vehicle to operate in accordance with the current state of the battery. For example, not only is it used for optimizing the charging / discharging performance of the battery and improving the fuel efficiency, but also for judging the ISG (Idle Stop & Go) control condition to be developed for the alternator management system or the fuel saving purpose of the vehicle, It is used to cope with exhaust gas (CO2) regulations.

Therefore, it is very important for IBS to transfer the correctly diagnosed value to the main controller based on the current, voltage and temperature of the monitored battery. For this reason, although various methods for correcting the error of the data measured by the IBS have been developed, there is a problem that there is a slight error in the output value with respect to the correction target value.

Of the various IBS correction methods according to the prior art, the ADC-corrected values calculated based on the two-point calibration method also have minute errors relative to the target values. 1 is a graph showing output values calculated by a two-point correction method.

As shown in FIG. 1, GAIN (output ratio for input) and OFFSET (a value other than '0' for an input of '0') obtained by the two-point correction method are calculated as shown in Equation (1) do. This is a formula using arbitrary two points (X1, Y1), (X2, Y2) of the two-point correction method.

Equation (1) is a GAIN, OFFSET formula outputted after the two-point correction method.

Here, the OFFSET value corresponds to the error range. The reason why the two-point correction method can not accurately correct the error is that the result of Equation 1 is not always a real number but an integer.

Therefore, even if the error is corrected through Equation (2), the real part of the output value is not included.

Example) GAIN (before correction) = 1.683 → GAIN (after correction) = 1

    OFFSET (before correction) = 2.356 → OFFSET (after correction) = 2

Equation (2) is a formula indicating the final output value that is finally output after correction.

Even if the data measured by the battery sensor is corrected through the correction such as the two-point correction method, there is a technical limitation that there is a slight error according to the prior art, or additionally re-determination.

SUMMARY OF THE INVENTION It is an object of the present invention to provide an apparatus and method for accurately correcting voltage, current, and temperature using a self-learning algorithm that imitates a human neural network.

Another object of the present invention is to provide an apparatus and method for determining the necessity of error correction by itself and calculating a value requiring correction.

An error correcting apparatus using a self-learning algorithm according to an embodiment of the present invention includes a data measuring unit for measuring a state variable of a battery and deriving an output value, an error measuring unit for measuring an error with respect to an output value measured by the data measuring unit, And an error correcting unit for correcting the measured error, as well as calculating the value required to be corrected on the basis of the error measured by the measuring unit.

The state variable of the present invention means a current, a voltage, a temperature, etc. indicating a state of a battery. In addition, the error measuring unit of the present invention includes a signal generating unit for generating a signal indicating that an error should be corrected when the error is equal to or greater than a predetermined threshold value.

The error correction unit of the present invention receives a signal indicating that correction is required in the error measurement unit, calculates a self-correction value based on the measured error and a preset threshold value, and corrects the error.

An error correction method using a self-learning algorithm according to an embodiment of the present invention includes the steps of measuring a state variable of a battery and deriving an output value, calculating an error of the derived output value, and determining whether the calculated error is equal to or greater than a predetermined threshold Calculating a value required to be corrected on the basis of the measured error when the error is determined to be equal to or larger than the threshold value, and correcting the error using the value requiring correction.

The present invention is advantageous in that, in outputting state variables (for example, current, voltage, temperature, etc.) of a battery measured by an intelligent battery sensor mounted on a vehicle, an error is corrected through a self-learning algorithm method to detect a more accurate output value to provide.

FIG. 1 is a diagram for explaining a two-point calibration method according to the related art.
2 is a block diagram illustrating an apparatus for correcting errors using a self-learning algorithm according to an embodiment of the present invention.
3 is a diagram illustrating a data flow of an error correction unit and a signal generation unit according to an embodiment of the present invention.
4 is a flowchart illustrating an error correction method according to an embodiment of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention and the manner of achieving them will become apparent with reference to the embodiments described in detail below with reference to the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Is provided to fully convey the scope of the invention to those skilled in the art, and the invention is defined by the scope of the claims.

It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular forms herein include plural forms unless the context clearly dictates otherwise. &Quot; comprises " and / or "comprising" when used in this specification is taken to specify the presence or absence of one or more other components, steps, operations and / Or add-ons.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

2 is a block diagram illustrating an apparatus for correcting errors using a self-learning algorithm according to an embodiment of the present invention.

The apparatus for correcting error of a battery state variable according to an embodiment of the present invention includes a data measuring unit 210 for measuring a state variable of a battery and deriving an output value, an error measuring unit 210 for measuring an error of an output value derived from the data measuring unit 210 And an error corrector 230 for calculating a value required to be corrected on the basis of the measured error and correcting the error by itself.

The state variable referred to in the present invention indicates the state of the battery mounted on the vehicle, which means the current, voltage, temperature, etc. measured by the sensor of the vehicle battery.

Although a typical example of the present invention is a battery sensor for a vehicle battery, the battery sensor of the present invention may be applied to an analog signal converter that receives a data signal to obtain a more accurate output value as a result, and various other types of ADC (Analog- Are all applicable.

For example, in a clean room of a clean room and a semiconductor manufacturing process line in which the width of environment change can be sensitized according to the sensitivity of the measured value (a room where air temperature, humidity and dust particles are artificially controlled / Space) may be another embodiment of the present invention.

In the apparatus of the present invention, the error measuring unit 220 obtains the error of the output value with respect to the target value. If the measured error is equal to or greater than a predetermined threshold value, the error measuring unit 220 itself determines that error correction is necessary, And a signal generating unit 221.

Here, the target value used in the error measurement is a value finally output by the error correction unit 230, and it is possible to set the target value by the user in advance or flexibly according to the environment given in the system. In other words, the target value is a value that is an operation target of the error correction unit 230 so that the measured input value is suitable for what purpose.

As described above, the error measuring unit 220 not only measures the error itself, but also determines a range of the error. When the determined range is large enough to correct the error, the error measuring unit 220 generates a signal indicating that error correction is necessary.

If the measured error is larger than the minimum correction unit, the error is at least 1 LSB. If the error is less than 1 LSB, Means having a correction unit.

For example, if the measured error is 4 mV and the predetermined LSB on the system is 12 uV, even if the error is corrected by 1 LSB, the error can be further reduced by 12 uV and a more accurate value can be outputted. Therefore, the signal generating unit 221 generates a signal indicating that an error correction is necessary.

The error correcting unit 230 receives a signal generated by the error measuring unit 220 and calculates a self correction value based on the error and the LSB, And corrects the basic input value.

),

A system with an error of 4 mV and an LSB of 12 uV is corrected by 1 LSB, and the error is reduced by about 12 uV. Therefore, if 1 LSB is corrected, the error will decrease from 4 mV to 3.988 mV. (

),

)보정될 경우, 오차는 3.996mV만큼(

)감소하게 되고, 보정된 오차는 0.004mV가 된다.(

)If the error correcting unit 230 calculates the maximum correctable value based on the LSB, the LSB is approximately 333 (

) When corrected, the error is 3.996 mV (

), And the corrected error is 0.004 mV. (

)

In other words, although the first measured error is 4mV, the error range is reduced by 99.9% from the initial measurement to 0.004mV through the self-learning algorithm correction of the present invention.

Hereinafter, the flow of the measured data and the apparatus of the present invention will be described in detail with reference to FIG.

3 is a diagram illustrating a data flow of the error corrector 230 and the signal generator 221 according to an embodiment of the present invention.

As shown in FIG. 3, it is determined whether the signal generator 221 needs correction for the measured error. If the measured error has a value larger than a predetermined threshold value (e.g., LSB), the signal generating unit 221 sends a signal to the error correcting unit 230 that error correction is necessary.

Thereafter, the error corrector 230 calculates the self-correction value on the basis of the data (e.g., error, LSB) output in the intermediate process by a predetermined calculation method, and then outputs the final output value. The final output value at this stage is an approximation to the above-mentioned target value.

In summary, in a system with a target value of 14 V, a primary output value of 13.996 V, and a 1 LSB of 12 uV, the error with respect to the target value becomes 4 mV (14-13.996 V = 4 mV), the error of 4 mV measured by the error measuring unit 220 The signal generator 221 transmits a signal to the error corrector 230 indicating that the correction is necessary. The error correction unit 230 receives the transmitted signal and calculates a self correction value. In this case, since it is determined that the maximum correction is possible when 333 LSB is corrected, the error correction unit 230 corrects 4 mV by 333 LSB 3.996mV is corrected to output the final output value having an error of 0.004mV.

In the meantime, the specific values mentioned in the present invention are introduced to explain a series of processes that the present apparatus takes to correct an error, and it is needless to say that the present invention is not limited to the apparatus for correcting only specific values.

4 is a flowchart illustrating an error correction method according to an embodiment of the present invention.

As shown in FIG. 4, the state of the battery is periodically monitored and measured in a sensor that measures the state of the vehicle battery (S410). A primary output value and a target value of the measured current, voltage, temperature, etc. of the measured battery are derived (S420), and an error range is calculated based on the derived output value and the target value (S430).

Here, the simple method of measuring the error range can not only calculate the difference between the target value and the output value, but also calculate the error range by various other methods.

For example, it is possible to divide the output value by the target value to make it a unit of%. That is, all of the error expressions that measure the data and represent the difference that occurs between the measured value and the true value are all possible.

If it is determined that the measured error is equal to or greater than the minimum unit correction value (S440), the process proceeds to the correction step through the self-learning algorithm of the present invention (S450).

In the correction step using the self-learning algorithm, a value required for correction is calculated by itself and the error is corrected to derive the final output value.

The present invention is a system for calculating an error of a measured value with respect to a target value by itself in a system having an error between a measured value and a target value, applying a self correction value as a value necessary for error correction to the correction step, and deriving a final output value, Or to the step after the two-point calibration mentioned above, or in addition to various other correction methods, it is possible to reduce the error range.

The foregoing description is merely illustrative of the technical idea of the present invention and various changes and modifications may be made without departing from the essential characteristics of the present invention.

Therefore, the embodiments of the present invention are not intended to limit the scope of the present invention, and the scope of the present invention is not limited by these embodiments. It is intended that the present invention cover the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents, which fall within the scope of the present invention as claimed.

210: data measuring unit 220: error measuring unit
221: Signal generator 230: Error corrector

Claims (7)

An apparatus for correcting error of a battery state variable using a self-learning algorithm,
A data measuring unit for measuring a state variable of the battery and deriving an output value;
An error measuring unit for measuring an error with respect to the output value derived from the data measuring unit; And
An error correcting unit for correcting the error by calculating a value required to be corrected on the basis of the error measured by the error measuring unit,
And an ADC correction device using the self-learning algorithm. 2. The method of claim 1,
Means a current, voltage, temperature, etc. indicating the state of the battery
ADC Compensation Device Using Self - Learning Algorithm. The apparatus of claim 1, wherein the error measuring unit
And a signal generating section for generating a signal indicating that the correction of the error is necessary when the error is equal to or greater than a predetermined threshold value
ADC Compensation Device Using Self - Learning Algorithm. 2. The apparatus of claim 1, wherein the error correction unit
Calculating a self-correction value based on the measured error and a predetermined threshold value, and correcting the error by receiving a signal indicating that correction is necessary in the error measuring unit
ADC Compensation Device Using Self - Learning Algorithm. A method for correcting error of a battery state variable using a self-learning algorithm,
Measuring a state variable of the battery and deriving an output value;
Calculating an error of the derived output value, and determining whether the calculated error is greater than or equal to a predetermined threshold;
Calculating a value required to be corrected based on the measured error when the error is determined to be equal to or greater than a threshold value; And
Correcting an error using a value required for the correction
A method for calibrating an ADC using a self-learning algorithm. 5. The method of claim 4, wherein calculating the value requiring correction comprises:
Calculating the self-correction value based on the error and the predetermined threshold value
A method of ADC calibration using self - learning algorithm. 5. The method of claim 4, wherein determining whether the error is greater than or equal to a threshold value comprises:
If the error is greater than or equal to a predetermined threshold value,
A method of ADC calibration using self - learning algorithm.

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