KR20180058057A - Method, apparatus and computer program for estimating state of battery charge - Google Patents

Abstract

The present invention relates to a method and an apparatus for estimating a battery state, and more specifically, to a method and an apparatus which can improve accuracy of battery state estimation by combining an ampere-hour counting method and a Kalman filter (a hybrid type Kalman filter method). The present invention discloses a battery state estimation method comprising the steps of: forming an equivalent circuit of a battery; calculating a system equation including a state of charge (SOC) of the battery and a polarization capacity of the battery equivalent circuit as state variables; calculating a measurement equation including an SOC observation value of the battery as an output variable; and estimating the SOC of the battery at preset intervals by applying a measured voltage, a measured current, the system equation and the measurement equation to a Kalman filter algorithm.

Description

[0001] METHOD, APPARATUS AND COMPUTER PROGRAM FOR ESTIMATING STATE OF BATTERY CHARGE [0002] BACKGROUND OF THE INVENTION [0003]

[0001] The present invention relates to a method of estimating a battery state, and more particularly, to a method of estimating a state of a battery by combining an amperere-hour counting method and a Kalman filter (hybrid Kalman filter method) A method for performing the method, and a computer program.

In recent years, demand for portable electronic products such as notebook computers, video cameras, and portable telephones has been rapidly increased, and development of batteries, robots, and satellites for energy storage has been accelerated. Thus, a high performance rechargeable battery Researches are being actively conducted.

The secondary rechargeable batteries are nickel-cadmium batteries, nickel-hydrogen batteries, nickel-zinc batteries, and lithium secondary batteries. Among them, lithium secondary batteries have almost no memory effect compared to nickel- It is very popular because of its low self-discharge rate and high energy density.

Since a battery is used in various electronic apparatuses driven by electric power, especially in mobile devices such as a notebook or an automobile, it is important to grasp accurate information on the SOC of the battery because there is a limit in the use time. This SOC is a measure of how long the battery can be used, which is very important information for the user to use the device. Therefore, general battery mounting devices such as a notebook computer, a mobile phone, and a car estimates the SOC of the battery and obtains information on the available time or usable amount of the battery, and provides the information to the user.

The SOC of the battery is generally expressed as a percentage of the remaining capacity of the battery with respect to the full charge capacity (FCC). As a method of estimating the SOC of the battery, various methods can be used. A representative method is a method of estimating the SOC using the current integration method. The SOC is obtained by integrating the input / output current of the battery and adding / subtracting it from the initial capacity.

In such a current integration method, since the SOC is estimated by the current measured through the current sensor installed in the charge / discharge path of the battery, accurate sensing of the current sensor is very important. However, in the case of the current sensor, a current offset which is different from the actual current and the current measurement value occurs due to the degradation or the like, and the error between the actual SOC and the estimated SOC due to accumulation of the current offset becomes time There was a problem that it grew gradually as it passed.

SUMMARY OF THE INVENTION It is an object of the present invention to improve the accuracy of state estimation of a battery by combining an Ampere-hour Counting Method and a Kalman Filter (a hybrid Kalman filter method) A method for performing the method, and a computer program.

According to another aspect of the present invention,

Constituting a battery equivalent circuit; Calculating a system equation including SOC of the battery and polarization capacity of the battery equivalent circuit as state variables; Calculating a measurement equation including an SOC observation value of the battery as an output variable; And estimating the SOC of the battery at a predetermined cycle by applying the measured current to the battery, the system equation, and the measurement equation to a Kalman filter algorithm.

Here, the step of calculating the system equation may calculate the following equation (8).

&Quot; (8) "

Here, S (k + 1) and u _c is the (k + 1) is the polarization capacity of the SOC and the battery of the respective stage, i.e., the predictive value of the polarization capacity of the SOC and the battery, S (k) and u _c (k) is and the SOC and the battery polarization capacity of the respective previous step, I (k) is the battery current, w ₁ (k) and w ₂ (k) is the noise in the system, R _1, R _2, and C is an equivalent circuit wherein each of the battery The internal resistance, the polarization resistance and the polarization capacity.

Further, in the step of calculating the measurement equation, a cumulative integration method for the measurement current may be applied.

Further, the step of calculating the measurement equation may further include a current observation noise in the measurement current.

Further, in the step of calculating the measurement equation, the following equation (11) can be calculated.

&Quot; (11) "

는 측정 방정식, S(t ₀ )는 SOC 초기값, Q _n 은 배터리 공칭용량, H(k)는 전류 측정 잡음임.here,

S (t ₀ ) is the SOC initial value, Q _n is the battery nominal capacity, and H (k) is the current measurement noise.

The battery state estimating apparatus according to the present invention comprises:

A sensor unit for measuring voltage and current of the battery; And a controller for estimating an SOC of the battery using the measured voltage and current, wherein the controller configures the battery equivalent circuit, and includes the SOC of the battery and the polarization capacity of the battery equivalent circuit as state variables A system equation and a SOC observation value of the battery as output variables to calculate a measurement equation, a measurement current, a system equation, and a measurement equation to a Kalman filter algorithm to calculate a SOC Can be estimated.

According to the embodiment of the present invention, the accuracy of the battery state estimation can be improved by using the Ampere-hour Counting Method and the Kalman Filter.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.
FIG. 1 is a graph showing a battery SOC estimation value and an actual value by the current integration method.
2 is a flowchart illustrating a battery state estimation method according to an embodiment of the present invention.
3 shows a battery equivalent circuit according to the present invention.
Figure 4 shows a Kalman Filter method.
FIG. 5 shows a configuration of a battery state estimation apparatus according to the present invention.
FIG. 6 illustrates a BMS (Battery Management System) for experimentally verifying a battery state estimation method according to the present invention.
FIG. 7 illustrates an S18650 battery pack used for experimentally verifying a battery state estimation method according to the present invention.
FIG. 8 shows a UI (User Interface) for driving a BMS for experimentally verifying a battery state estimation method according to the present invention.
FIG. 9 is a graph showing a result of an experimentally verifying a battery state estimation method according to the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The present invention is capable of various modifications and various embodiments, and specific embodiments will be described in detail below with reference to the accompanying drawings.

The following examples are provided to aid in a comprehensive understanding of the methods, apparatus, and / or systems described herein. However, this is merely an example and the present invention is not limited thereto.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail. The following terms are defined in consideration of the functions of the present invention, and may be changed according to the intention or custom of the user, the operator, and the like. Therefore, the definition should be based on the contents throughout this specification. The terms used in the detailed description are intended only to describe embodiments of the invention and should in no way be limiting. Unless specifically stated otherwise, the singular forms of the expressions include plural forms of meanings. In this description, the expressions "comprising" or "comprising" are intended to indicate certain features, numbers, steps, operations, elements, parts or combinations thereof, Should not be construed to preclude the presence or possibility of other features, numbers, steps, operations, elements, portions or combinations thereof.

It is also to be understood that the terms first, second, etc. may be used to describe various components, but the components are not limited by the terms, and the terms may be used to distinguish one component from another .

Hereinafter, a battery state estimation method according to the present invention capable of improving the accuracy of battery state estimation by combining an Ampere-hour Counting Method and a Kalman Filter (a hybrid Kalman filter method) DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Exemplary embodiments of a device and a computer program for performing the method will be described in detail with reference to the accompanying drawings.

First, an ampere-hour counting method used in the battery state estimation method according to the present invention will be described.

The current integration method is a method of measuring the state of charge (SOC) by converting the actual current value into a standard current value using the Peukert equation as shown in Equation 1 below and integrating the current value.

 [Equation 1]

는 Peukert 방정식에 의해 계산 및 추론하는 전류 효율 인자인데, 일반적인 경우는 1로 설정한다.Here, SOC ₀ denotes a battery initial charging state, Q _n denotes a nominal capacity of the battery, I denotes a current flowing in the battery in real time according to time t,

Is a current efficiency factor that is calculated and deduced by the Peukert equation. In general, it is set to 1.

Although the current integration method is easy to implement, there is a problem of initial value problem and accumulation of SOC estimation error due to sensor noise, so that regular calibration such as resetting the SOC is required when the battery is fully charged.

FIG. 1 is a graph of estimated SOC values and actual values of a battery by a current integration method, in which 1 C is charged and discharged for a S18650 battery pack. It can be seen that the error between the estimated value of the battery SOC and the actual value gradually increases with the time of charging (FIG. 1 (a)) or discharging (FIG. 1 (b)).

2 is a flowchart illustrating a battery state estimation method according to an embodiment of the present invention.

Referring to FIG. 2, the battery state estimation method according to the present invention includes a battery equivalent circuit configuration step S110, a system equation calculation step S120, a measurement equation calculation step S130, and a battery SOC estimation step S140 can do.

First, in step S110, the battery condition estimating apparatus 100 according to the present invention may model the equivalent circuit by reflecting the electrochemical characteristics of the battery. E (t) is the open circuit voltage (OCV), I (t) is the current, R ₁ is the internal resistance of the battery, R ₂ is the polarization resistance, and C is the polarization capacity.

The following equations (2) to (5) can be obtained by applying the Kirchhoff law and the current integration method to the equivalent circuit of the battery.

&Quot; (2) "

&Quot; (3) "

&Quot; (4) "

&Quot; (5) "

Where S (t) is the SOC value, Q ₀ is the nominal capacity, and F [S (t)] is a function of SOC and OCV (Open Circuit Voltage).

Next, in step S120, the system equation can be calculated to apply the Kalman filter method.

First, let's look at the Kalman filter.

The Kalman filter method was developed by Rudolf E. Kalman in the early 1960s and is an optimal estimation technique that finds recursive noise-containing linear state variables using probabilistic models and measurements of the target system. Kalman filters are widely used in many fields such as computer vision, robotics, and radar.

The Kalman filter's algorithm uses the following system equation (or system model) (Equation 6) and the measurement equation (or measurement model) (Equation 7).

&Quot; (6) "

&Quot; (7) "

Referring to FIG. 4, the algorithm of the Kalman filter can be largely divided into a prediction process and an update process. The prediction process implies a prediction of the current state, and the update means making a more precise prediction through the values including the observed measurement from the current state.

는 추정값,

는 예측값,

는 측정값,

는 입력 변수,

는 오차 공분산,

는 이전 추정치 기반 상태 전이 행렬,

는 입력 변수기반 상태 전이행렬,

는 관측모델,

는 칼만이득,

는 시스템 모델의 오차 공분산,

은 관측값의 오차 공분산을 의미한다.More specifically, the Kalman filter algorithm is repeatedly performed according to a predetermined period through initial value determination, estimation and error covariance prediction, estimation value calculation, and error covariance calculation. here,

Is an estimate,

Is a predicted value,

Lt; / RTI >

Is an input variable,

Is an error covariance,

Is a previous estimate-based state transition matrix,

Is an input variable-based state transition matrix,

Is an observation model,

However,

Is the error covariance of the system model,

Is the error covariance of the observed values.

와

가 가질 오차 공분산을 예측하여 입력해준다. 이 과정은 최초에 한번만 수행된다.The initial value determination process is performed in such a manner that,

Wow

The error covariance to be obtained is predicted and input. This process is performed only once at the beginning.

)은 이전에 계산된 오차 공분산(

), 이전 추정치 기반 상태 전이 행렬(

) 그리고 오차 공분산(

)를 이용하여 예측하며, 이렇게 획득된 오차 공분산(

)를 이용하여 칼만 이득(

)값을 계산할 수 있다.The process of estimating the estimates and the error covariance is such that the estimates are closely related to the overall system equation and the predicted error covariance

) Is the previously calculated error covariance (

), A previous estimate-based state transition matrix (

) And error covariance (

), And the obtained error covariance (

) Was used to calculate the Kalman gain

) Can be calculated.

)과 예측값(

)의 차이를 칼만 이득(

)으로 곱해주고 거기에 예측값(

)을 더해주어 최종 추정값(

)을 계산한다.The process of calculating the estimated value is performed by comparing the current measured value (

) And the predicted value

) To the Kalman gain (

) And multiply it by the predicted value (

) To add the final estimate (

).

)이 얼마나 정확한지를 보여주는 척도이며, 이 오차 공분산에 대한 기준값을 설정하여 최종 추정값(

)을 사용할 것인지 버릴 것인지를 판단할 수 있다.In the error covariance calculation, the error covariance is calculated as

) Is a measure of how accurate the error covariance is, and the final estimate (

Can be used or discarded.

As a result, the Kalman filter estimates the state of the next time point and the value of the error covariance based on the system equation, calculates a new estimated value by compensating for the difference between the measured value and the predicted value, Which is the final product of the filter.

,

및

를 얻을 수 있고, 최종적으로 시스템 방정식 및 측정 방정식은 각각 아래의 수학식 8 및 수학식 9와 같이 산출할 수 있다.Using equations (2) to (5) to obtain system equations for the battery equivalent circuit configured in the previous step (S110)

,

And

And finally the system equation and the measurement equation can be calculated by the following equations (8) and (9), respectively.

&Quot; (8) "

&Quot; (9) "

Here, S (k + 1) and u _c is the (k + 1) is the polarization capacity of the SOC and the battery of the respective stage, i.e., the predictive value of the polarization capacity of the SOC and the battery, S (k) and u _c (k) is And SOC and battery polarization capacity of the previous stage, respectively. In addition, I (k) represents the previous battery current, w ₁ (k) and w ₂ (k) are the noise of the system, and v (k) is the voltage measurement noise.

In step S130, a measurement equation can be calculated to apply the Kalman filter method. In the conventional battery SOC estimation method, Equation (9) is used as a measurement equation, but in the present invention, Equation (10) below is used.

&Quot; (10) "

Here, S (k) is a SOC observation value, S (t ₀ ) is an SOC initial value, Q _n is a battery nominal capacity, and H (k) is a current measurement noise.

Equation (10) is a measurement equation corresponding to Equation (2), and can be ultimately transformed into the following measurement equation (Equation (11)).

&Quot; (11) "

The voltage and current of the battery can be measured in real time at a sampling time interval using a measurement sensor, and the SOC of the battery can be calculated by applying the measured value to Equation (10). The accuracy of the estimation can be improved by reflecting the current measurement noise to the measured current value in the conventional current integration method.

Finally, in S 140, the SOC of the battery can be estimated by applying the measured current to the battery, the system equation (Equation 8) and the measurement equation (Equation 11) to the Kalman filter algorithm have.

FIG. 5 shows a configuration of a battery state estimation apparatus 100 according to the present invention.

Referring to FIG. 5, the battery state estimation apparatus 100 according to the present invention may include a sensor unit 110 and a control unit 120. Although not shown, the display unit may further include a display unit for displaying an estimated battery SOC value by a number, a graph, or the like.

The sensor unit 110 can measure the voltage and current of the battery.

The controller 120 constitutes the battery equivalent circuit and calculates a system equation including the SOC of the battery and the polarization capacity of the battery equivalent circuit as state variables and a measurement equation including an SOC observation value of the battery as an output variable The SOC of the battery can be estimated at a predetermined cycle by applying the measured voltage, the measured current, the system equation, and the measurement equation to the Kalman filter algorithm.

An experiment for verifying the battery state estimation method according to the present invention and results thereof will be described with reference to FIGS. 6 to 9. FIG.

The experiment was conducted using the BMS (Battery Management System) of FIG. 6, and the battery pack was S18650 of FIG. Referring to FIG. 6, a BMS (Battery Management System) provided for verifying a battery state estimation method according to the present invention may include a BMS platform main body, a monitoring system, a charge / discharge system, and a battery pack, 8 shows an actual UI (User Interface) screen for the user to operate the system.

FIG. 9 is a graph showing the results of the present experiment. Specifically, FIGS. 9 (a) and 9 (b) are graphs showing battery state estimation values according to the present invention while charging the S18650 battery pack using the system of FIG. Which is a graph showing an error of an actual value.

9 (a) and 9 (b), the battery state estimation value according to the present invention is corrected without continuously increasing the error value with respect to the actual battery charge current amount, and the existing error value is continuously increased (See FIG. 1). In addition, it is possible to estimate the battery state more accurately than the current integration method (see FIG. 1).

Embodiments of the present invention may be implemented in the form of program instructions that can be executed on various computer means and recorded on a computer readable medium.

The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions recorded on the medium may be those specially designed and constructed for the present invention or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape, optical media such as CDROMs, DVDs, magneto-optical media such as floptical disks, Examples of program instructions, such as magneto-optical and ROM, RAM, flash memory, and the like, include machine code such as those generated by a compiler, as well as machine-readable instructions that may be executed by a computer using an interpreter, Includes language code. The hardware devices described above may be configured to operate as at least one software module to perform operations of one embodiment of the present invention, and vice versa.

The foregoing description is merely illustrative of the technical idea of the present invention, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present invention. Therefore, the embodiments described in the present invention are not intended to limit the technical spirit of the present invention, but are intended to be illustrative and not restrictive. The scope of protection of the present invention should be construed by the following claims, and all technical ideas within the scope of equivalents thereof should be construed as being included in the scope of the present invention.

100: Battery condition estimating device
110:
120:

Claims (7)

Constituting a battery equivalent circuit;
Calculating a system equation including SOC of the battery and polarization capacity of the battery equivalent circuit as state variables;
Calculating a measurement equation including an SOC observation value of the battery as an output variable; And
Estimating the SOC of the battery at a predetermined period by applying the measured current to the battery, the system equation, and the measurement equation to a Kalman filter algorithm.
The method according to claim 1,
Wherein the step of calculating the system equation comprises:
And calculating the following equation (8): " (8) "
&Quot; (8) "

Here, S (k + 1) and u _c is the (k + 1) is the polarization capacity of the SOC and the battery of the respective stage, i.e., the predictive value of the polarization capacity of the SOC and the battery, S (k) and u _c (k) is and the SOC and the battery polarization capacity of the respective previous step, I (k) is the battery current, w ₁ (k) and w ₂ (k) is the noise in the system, R _1, R _2, and C is an equivalent circuit wherein each of the battery The internal resistance, the polarization resistance and the polarization capacity.
The method according to claim 1,
Wherein the step of calculating the measurement equation comprises:
And a charge accumulation method for the measured current is applied.
The method of claim 3,
Wherein the step of calculating the measurement equation comprises:
And a current observation noise is additionally included in the measurement current.
The method according to claim 1,
Wherein the step of calculating the measurement equation comprises:
And calculating the following equation (11): " (11) "
&Quot; (11) "

here,

S (t ₀ ) is the SOC initial value, Q _n is the battery nominal capacity, and H (k) is the current measurement noise.
A computer program stored in a medium for executing the battery state estimation method of any one of claims 1 to 5 in combination with hardware.
A sensor unit for measuring voltage and current of the battery; And
And a controller for estimating an SOC of the battery using the measured voltage and current,
Wherein,
Wherein the battery equipotential circuit comprises a system equation including a SOC of the battery and a polarization capacity of the battery equivalent circuit as state variables and a measurement equation including an SOC observation value of the battery as an output variable,
Wherein the SOC of the battery is estimated at a predetermined cycle by applying the measured voltage, the measured current, the system equation, and the measurement equation to a Kalman filter algorithm.

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