KR20170052936A - Prediction Method of State of Function of Car Battery Using Battery Sensor - Google Patents

Prediction Method of State of Function of Car Battery Using Battery Sensor Download PDF

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KR20170052936A
KR20170052936A KR1020150155083A KR20150155083A KR20170052936A KR 20170052936 A KR20170052936 A KR 20170052936A KR 1020150155083 A KR1020150155083 A KR 1020150155083A KR 20150155083 A KR20150155083 A KR 20150155083A KR 20170052936 A KR20170052936 A KR 20170052936A
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South Korea
Prior art keywords
battery
voltage
internal resistance
current
sof
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KR1020150155083A
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Korean (ko)
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KR101776840B1 (en
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송민수
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영화테크(주)
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    • G01R31/3624—
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/03—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for supply of electrical power to vehicle subsystems or for
    • B60R16/033—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for supply of electrical power to vehicle subsystems or for characterised by the use of electrical cells or batteries
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
    • G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
    • G01R27/08—Measuring resistance by measuring both voltage and current
    • G01R31/3658—

Abstract

The present invention relates to a method for predicting a functional state of an automotive battery using a battery sensor. The object of the present invention is to predict an SOF value by applying the RLS technique that can estimate the internal resistance of a battery on the internet and by using the internal resistance of the battery estimated in real time, so that the ISG system can operate smoothly. The present invention comprises the following steps: measuring battery voltage and current in a starting mode to obtain load resistance; measuring the battery voltage and current during driving and applying the value to the RLS technique to estimate the internal resistance of the battery; dividing the open-circuit voltage (OCV) of the battery by the sum of the load resistance and the internal resistance of the battery to obtain a predicted current; and obtaining the minimum voltage of the battery at the time of restarting by using an SOF prediction formula.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a method of predicting a function state of an automobile battery using a battery sensor,

The present invention relates to a method of predicting a functional state of an automotive battery using a battery sensor, and more particularly, to a method of predicting a function state of an automotive battery by using an internal resistance of a battery estimated in real time by applying an RLS The present invention relates to a method for predicting a functional state of an automotive battery using a battery sensor that smoothly operates an ISG system by predicting an SOF value.

Recently, an idle-stop & go system (hereinafter referred to as an 'ISG system') has been developed and applied to automobiles as a countermeasure for reducing energy waste due to idling of the engine.

The ISG system is a system for stopping the engine when the vehicle is stopped and restarting the engine when the user's intention is to be sensed or when the engine must be restarted. The user's willingness to run the vehicle is determined by the user's willingness to brake the accelerator pedal ≪ / RTI >

In order to apply the ISG system, it is necessary to accurately check the state of the vehicle and the state of the battery. In order to monitor the state of the battery at all times, a battery sensor (BS) is used.

The battery sensor is classified into a general analog type battery sensor and an intelligent battery sensor (IBS). IBS is mainly used in automobiles.

The battery sensor is mounted on a battery of an automobile and collects information on the battery voltage, the battery current, and the temperature of the battery negative terminal, and collects information on the state-of-charge (SOC) Estimates the temperature, and predicts the SOF (State-of-Function).

Here, the SOF is a value predicted from the lowest voltage of the battery required when the engine is restarted, and is related to the restart of the engine. The battery sensor provides SOF information required for application of the ISG system.

The ISG system stops the engine when the vehicle is stopped while the vehicle is running, and automatically starts when the engine is restarted and the restartability is secured.

To this end, the battery sensor predicts the lowest voltage of the battery necessary for restarting the engine and provides the SOF information, thereby determining whether or not to stop the engine when the vehicle is stationary while stopping.

A general SOF prediction method uses SOF prediction formula consisting of battery voltage, current and resistance.

Among them, the resistance is divided into the load resistance according to the load of the vehicle and the internal resistance of the battery itself, and the internal resistance of the battery is estimated by collecting the voltage, current and temperature of the battery at startup.

Here, the SOF prediction equation is expressed by the following equation (1).

Figure pat00001

Accordingly, it is important to accurately grasp the internal resistance of the battery in order to improve the accuracy of SOF prediction. It is difficult to directly measure the internal resistance of the battery in the online situation. Therefore, the internal resistance of the battery is calculated through the battery voltage and current at the start-up.

However, in collecting the battery voltage and current at the start, the internal resistance value calculated according to the basic sampling of the battery sensor can be changed, and the chemical reaction of the battery becomes active while the running and stopping are repeated.

Accordingly, there arises a difference between the calculated value of the internal resistance through the voltage and the current at the time of starting the battery in a stabilized state, and the calculated value of the internal resistance of the battery through the voltage and the current at the time of starting in a state where the chemical reaction is active. As a result, the accuracy of SOF prediction is deteriorated.

On the other hand, as a result of investigation of the prior art relating to the present invention, the following patent literature was searched.

Patent Document 1 discloses a method of estimating an internal resistance of a vehicle by calculating a previous internal resistance of a vehicle, comparing a magnitude of a calculated previous internal resistance with a predicted previous internal resistance, updating a first aging coefficient of the vehicle to a second aging coefficient Estimating the current internal resistance of the vehicle on the basis of the second senescence coefficient, and estimating the lowest voltage of the battery according to the step of predicting the lowest voltage using the current internal resistance. And an apparatus.

KR 10-2015-0040599 A

SUMMARY OF THE INVENTION The present invention has been made in order to solve the above problems of the prior art, and it is an object of the present invention to provide a method and apparatus for predicting an SOF value by using an internal resistance of a battery, , So that the ISG system works smoothly.

According to an aspect of the present invention, there is provided a method of controlling a battery, including: obtaining a load resistance by measuring a battery voltage and a current at a start; Measuring the battery voltage and current at the time of traveling and applying the measured voltage and current to the RLS technique to estimate the internal resistance of the battery; Dividing the open-circuit voltage (OCV) of the battery by the sum of the load resistance and the battery internal resistance; And obtaining a battery minimum voltage at the time of restarting by using the following SOF prediction formula.

Figure pat00002

According to the method for predicting the functional state of an automobile battery using the battery sensor of the present invention, the battery equivalent model is used to estimate the internal resistance of the battery, and the RLS formula for estimating the parameters of the battery equivalent model is expressed by the following equation Is used;

Figure pat00003

Here, when λ <1, numerical stability is guaranteed,

Figure pat00004
Is an output vector,
Figure pat00005
Is an error vector,
Figure pat00006
Is a parameter vector, P (n)
Figure pat00007
Lambda] is a covariance matrix of Forgetting Factor,
Figure pat00008
Means gain.

According to the method for predicting the function state of an automotive battery using the battery sensor according to the present invention, the internal resistance of the battery estimated on-line on-line and the load resistance of the battery calculated through the voltage and current at the start are inputted, Since the SOF is predicted from the prediction formula, the ISG system is effective according to the accurate SOF value.

In addition, there is an effect that the ISG system can be smoothly operated by reducing the internal resistance of the battery through a method of estimating the internal resistance of the battery while driving while eliminating the internal resistance due to the error that may occur at the time of starting .

1 is a block diagram illustrating a method of predicting a functional state of an automotive battery using the battery sensor of the present invention.
2 is a flowchart illustrating a method of predicting a functional state of an automotive battery using the battery sensor of the present invention.
3 is a reference diagram showing a battery equivalent model applied to the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, preferred embodiments of a method for predicting a function state of an automotive battery using the battery sensor of the present invention will be described with reference to the accompanying drawings.

A method for predicting a function state of an automotive battery using a battery sensor according to the present invention comprises the steps of: measuring a battery voltage and a current at start-up to obtain a load resistance, as shown in FIGS. 1 and 2; Measuring the battery voltage and current at the time of traveling and applying the measured voltage and current to the RLS technique to estimate the internal resistance of the battery; Dividing the open-circuit voltage (OCV) of the battery by the sum of the load resistance and the battery internal resistance; And obtaining a battery minimum voltage at the time of restarting by using the following SOF prediction formula.

Figure pat00009

Here, the load resistance obtained by the battery voltage and the current at the start is obtained by dividing the voltage value at the start by the current value at the start, and expressing it by the expression "load resistance = voltage at start / current at start" .

At this time, in order to estimate the internal resistance of the battery, the battery equivalent model shown in FIG. 3 is used and the following Equation 2 is used as the RLS formula for estimating the parameters of the battery equivalent model.

Figure pat00010

Here, when λ <1, numerical stability is guaranteed,

Figure pat00011
Is an output vector,
Figure pat00012
Is an error vector,
Figure pat00013
Is a parameter vector, P (n)
Figure pat00014
Lambda] is a covariance matrix of Forgetting Factor,
Figure pat00015
Means gain.

On the other hand, the RLS formula according to the equivalent model of the battery is expressed by the following equation (3).

Figure pat00016

Then, by bilinear transforming Equation (3), the following Equation (4) can be obtained.

Figure pat00017

At this time, the respective coefficients of the equation (4) can be obtained from the parameters of the battery equivalent model as shown in the following equation (5).

Figure pat00018

Then, by converting the above equation (5) into the ARX model, it can be expressed by the following equation (6).

Figure pat00019

The load resistance and the internal resistance obtained through the above process are applied to predict the SOF.

On the other hand, since the voltage of the battery is the sum of the battery open circuit voltage (OCV) voltage and the battery internal voltage, which is a voltage actually flowing from the battery, it can be expressed as "battery voltage = battery OCV voltage + battery internal resistance * battery current".

The battery OCV voltage can be expressed as "battery OCV voltage = battery voltage-battery internal resistance * battery current ".

Accordingly, the predicted current can be expressed by the following equation (7).

Figure pat00020

The SOF can be predicted by applying the predictive current obtained by the above-described expression (7) to the expression (1).

According to the present invention, since the SOF is predicted from the SOF prediction formula using the internal resistance of the battery estimated on-line on-line and the load resistance of the battery calculated through the voltage and current at the start, So that the ISG system can be operated.

In addition, the ISG system can be smoothly operated by reducing the internal resistance of the battery through the method of estimating the internal resistance of the battery while driving while eliminating the internal resistance due to the error that may occur at the time of starting.

While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but on the contrary is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. It will be appreciated by those of ordinary skill in the art that numerous changes and modifications may be made to the invention. And all such modifications and changes as fall within the scope of the present invention are therefore to be regarded as being within the scope of the present invention.

Claims (2)

Obtaining a load resistance by measuring a battery voltage and a current at startup;
Measuring the battery voltage and current at the time of traveling and applying the measured voltage and current to the RLS technique to estimate the internal resistance of the battery;
Dividing the open-circuit voltage (OCV) of the battery by the sum of the load resistance and the battery internal resistance;
And estimating a minimum battery voltage at the time of restarting by using the following SOF prediction formula.
Figure pat00021
 The method according to claim 1,
A method for predicting a functional state of an automobile battery using a battery sensor that uses a battery equivalent model to estimate a battery internal resistance and uses the following formula as an RLS formula for estimating a parameter of a battery equivalent model;
Figure pat00022

Here, when λ <1, numerical stability is guaranteed,
Figure pat00023
Is an output vector,
Figure pat00024
Is an error vector,
Figure pat00025
Is a parameter vector, P (n)
Figure pat00026
Lambda] is a covariance matrix of Forgetting Factor,
Figure pat00027
Means gain.
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Cited By (7)

* Cited by examiner, † Cited by third party
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CN109932662A (en) * 2019-03-06 2019-06-25 重庆雅讯电源技术有限公司 Battery capacity status estimation method, device, equipment and storage medium
KR20190110190A (en) 2018-03-20 2019-09-30 영화테크(주) Minimum Re-start Voltage Presumption Method of Battery for Automotive Vehicles
CN110635735A (en) * 2019-09-27 2019-12-31 华中科技大学 Control method of PMSM servo system current loop
CN111323705A (en) * 2020-03-19 2020-06-23 山东大学 Battery parameter identification method and system based on robust recursive least squares
US11015562B1 (en) 2020-07-16 2021-05-25 Hyundai Motor Company Vehicle and method of controlling the same
CN113466724A (en) * 2020-03-31 2021-10-01 比亚迪股份有限公司 Method and device for determining parameters of battery equivalent circuit model, storage medium and electronic equipment
US11269013B2 (en) 2018-02-01 2022-03-08 Lg Energy Solution, Ltd. Method for estimating parameter of equivalent circuit model for battery, and battery management system

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KR102373449B1 (en) 2018-02-01 2022-03-10 주식회사 엘지에너지솔루션 Method and battery management system for determining power limit of a battery
US11846678B2 (en) 2018-06-07 2023-12-19 Samsung Sdi Co., Ltd. Method and system for validating a temperature sensor in a battery cell

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JP6355919B2 (en) * 2013-12-25 2018-07-11 古河電気工業株式会社 Battery discharge capacity control method and apparatus

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11269013B2 (en) 2018-02-01 2022-03-08 Lg Energy Solution, Ltd. Method for estimating parameter of equivalent circuit model for battery, and battery management system
KR20190110190A (en) 2018-03-20 2019-09-30 영화테크(주) Minimum Re-start Voltage Presumption Method of Battery for Automotive Vehicles
CN109932662A (en) * 2019-03-06 2019-06-25 重庆雅讯电源技术有限公司 Battery capacity status estimation method, device, equipment and storage medium
CN110635735A (en) * 2019-09-27 2019-12-31 华中科技大学 Control method of PMSM servo system current loop
CN111323705A (en) * 2020-03-19 2020-06-23 山东大学 Battery parameter identification method and system based on robust recursive least squares
CN113466724A (en) * 2020-03-31 2021-10-01 比亚迪股份有限公司 Method and device for determining parameters of battery equivalent circuit model, storage medium and electronic equipment
US11015562B1 (en) 2020-07-16 2021-05-25 Hyundai Motor Company Vehicle and method of controlling the same

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