KR102016715B1 - Method, apparatus and computer program for estimating state of battery charge - Google Patents

Abstract

The present invention relates to a method and apparatus for estimating a battery state, and more specifically, to the accuracy of battery state estimation by combining an ampere-hour counting method and a Kalman filter (hybrid type Kalman filter method). It relates to a method and apparatus that can improve the.
The present invention comprises the steps of constructing a battery equivalent circuit; Calculating a system equation including the SOC of the battery and the 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 voltage, the measured current, the system equation, and the measured equation to a Kalman Filter algorithm.

Description

Battery status estimation method, apparatus and computer program performing the method {METHOD, APPARATUS AND COMPUTER PROGRAM FOR ESTIMATING STATE OF BATTERY CHARGE}

The present invention relates to a method for estimating a battery state, and more particularly, by combining an ampere-hour counting method and a Kalman filter (hybrid type Kalman filter method) to improve the accuracy of battery state estimation. The present invention relates to a method that can be used, an apparatus for performing the method, and a computer program.

In recent years, as the demand for portable electronic products such as laptops, video cameras, portable telephones, etc. is rapidly increased, and development of energy storage batteries, robots, satellites, etc. is in earnest, high-performance secondary batteries capable of repeating charging and discharging are possible. Research is actively being conducted.

Commercially available secondary batteries include nickel cadmium batteries, nickel hydride batteries, nickel zinc batteries, and lithium secondary batteries. Among them, lithium secondary batteries have almost no memory effect compared to nickel-based secondary batteries, and thus are free of charge and discharge. The self-discharge rate is very low and the energy density is high.

Batteries are used in various electronic devices powered by electric power, especially mobile devices such as laptops and automobiles. Because of the limited use time, it is important to obtain accurate information about the SOC of the battery. This SOC is a measure of how much time a battery can be used, which is very important information for the user to use the device. As a result, general battery-mounted devices such as laptops, mobile phones, and automobiles estimate the SOC of the battery and provide the user with information such as the available time or the amount of available battery.

The SOC of a battery is a form of displaying a percentage of the remaining charge of the battery (FCC, Full Charge Capacity). Various methods may be used for estimating the SOC of the battery. A representative method is a method of estimating the SOC by using a current integration method, in which the SOC is obtained by integrating the input / output current of the battery and adding or subtracting the initial capacity.

In this current integration method, since the SOC is estimated by the current measured by 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 occurs that is different from the actual current and the current measured value due to degradation, and an error between the actual SOC and the estimated SOC may be delayed due to the accumulation of the current offset. There was a problem that gradually increases.

The present invention was devised to solve the above problems, by combining an ampere-hour counting method and a Kalman filter (hybrid type Kalman filter method) to improve the accuracy of battery state estimation. It is an object of the present invention to provide a method, an apparatus and a computer program for performing the method.

Battery state estimation method according to the invention,

Configuring a battery equivalent circuit; Calculating a system equation including the SOC of the battery and the 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 applying the measured current, the system equation, and the measured equation for the battery to a Kalman Filter algorithm to estimate the SOC of the battery at a predetermined period.

Here, in the calculating of the system equation, Equation 8 may be calculated.

[Equation 8]

Where S (k + 1) and u _c (k + 1) are the estimated values of the polarization capacities of the SOC and the battery, that is, the SOC and the battery, respectively, and S (k) and u _c (k) are Respectively, the SOC and battery polarization capacity of the previous stage, I (k) is the battery current, w ₁ (k) and w ₂ (k) are the noise of the system, and R ₁ , R ₂ and C are the battery equivalent circuits respectively. Internal resistance, polarization resistance and polarization capacity.

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

In addition, the calculating of the measurement equation may further include the current observation noise to the measurement current.

In the calculating of the measurement equation, Equation 11 below may be calculated.

[Equation 11]

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

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

Battery state estimation apparatus according to the present invention,

Sensor unit for measuring the voltage and current of the battery; And a controller for estimating the SOC of the battery using the measured voltage and current, wherein the controller comprises the battery equivalent circuit, and includes the polarization capacity of the battery SOC and the battery equivalent circuit as state variables. A measurement equation including a system equation and an SOC observation value of the battery as an output variable is calculated, and the measured voltage, measurement current, the system equation, and the measurement equation are applied to a Kalman Filter algorithm to set the SOC of the battery at predetermined intervals. Can be estimated.

According to an embodiment of the present invention, accuracy of battery state estimation may be improved by using an ampere-hour counting method and a Kalman filter.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, included as part of the detailed description in order to provide a thorough understanding of the present invention, provide examples of the present invention and together with the description, describe the technical idea of the present invention.
1 shows a graph of battery SOC estimated value and actual value by current integration method.
2 is a flowchart illustrating a battery state estimation method according to the present invention.
3 shows a battery equivalent circuit according to the invention.
4 shows a Kalman Filter method.
5 illustrates a configuration of a battery state estimating apparatus according to the present invention.
6 illustrates a battery management system (BMS) for experimentally verifying a battery state estimation method according to the present invention.
7 illustrates an S18650 battery pack used to experimentally verify a battery state estimation method according to the present invention.
8 illustrates a user interface (UI) for driving a BMS for experimentally verifying a battery state estimation method according to the present invention.
9 is a graph illustrating experimentally verified results of a battery state estimation method according to the present invention.

As the inventive concept allows for various changes and numerous embodiments, particular embodiments will be described in detail with reference to the accompanying drawings.

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

In describing the embodiments of the present invention, when it is determined that the detailed description of the known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted. In addition, terms to be described below are terms defined in consideration of functions in the present invention, which may vary according to the intention or custom of a user or an operator. Therefore, the definition should be made based on the contents throughout the specification. The terminology used in the description is for the purpose of describing particular embodiments only and should not be limiting. Unless expressly used otherwise, the singular forms “a,” “an,” and “the” include plural forms of meaning. In this description, expressions such as "comprises" or "equipment" are intended to indicate certain features, numbers, steps, actions, elements, portions or combinations thereof, and one or more than those described. It should not be construed to exclude the presence or possibility of other features, numbers, steps, actions, elements, portions or combinations thereof.

In addition, terms such as first and second may be used to describe various components, but the components are not limited by the terms, and the terms are used to distinguish one component from another component. Only used as

Hereinafter, the battery state estimation method according to the present invention, which can improve the accuracy of battery state estimation by combining an ampere-hour counting method and a Kalman filter (hybrid-type Kalman filter method), DETAILED DESCRIPTION An apparatus and a computer program for carrying out a method will now 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 a state of charge (SOC) by converting an actual current value into a standard current value and integrating the value using a Peukert equation as shown in Equation 1 below.

 [Equation 1]

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

Is the current efficiency factor calculated and inferred by the Peukert equation, which is set to 1 in the general case.

The current integration method is easy to implement, but there is a problem of accumulating SOC estimation errors due to initial value and sensor noise. Therefore, regular calibration such as resetting the SOC is required when the battery is fully charged.

FIG. 1 is a graph of battery SOC estimated values and actual values by the current integration method, and illustrates a case of charging and discharging 1C with respect to an S18650 battery pack. It can be seen that as the charge (FIG. 1 (a)) or discharge (FIG. 1 (b)) time passes, the error between the battery SOC estimated value and the actual value by the cumulative integration method gradually increases.

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

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), the SOC estimation step of the battery (S140). can do.

First, in step S110, the battery state estimating apparatus 100 according to the present invention may be modeled as an equivalent circuit by reflecting the electrochemical characteristics of the battery. E (t) is the open circuit voltage (OCV: Open Circuit Voltage), I (t) is the current, R ₁ is the internal resistance of the battery, R ₂ is the polarization resistance, C refers to the polarization capacity.

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

[Equation 2]

[Equation 3]

[Equation 4]

[Equation 5]

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

Next, in step S120 it is possible to calculate a system equation to apply the Kalman Filter (Kalman Filter) method.

First, the Kalman Filter will be described.

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

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

[Equation 6]

[Equation 7]

Referring to FIG. 4, an algorithm of the Kalman filter may be classified into a prediction process and an update process. The prediction process means the prediction of the current state, and the update means the more accurate prediction through the value including the observed measurement in 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 at predetermined intervals through the process of initial value determination, estimated value and error covariance prediction, estimated value calculation, and error covariance calculation. here,

Is an estimate,

Is the predicted value,

Is the measure,

Is an input variable,

Is the error covariance,

Is the state estimate matrix based on the previous estimate,

Is an input variable-based state transition matrix,

Is an observation model,

Is full of swords,

Is the error covariance of the system model,

Is the error covariance of the observations.

와

가 가질 오차 공분산을 예측하여 입력해준다. 이 과정은 최초에 한번만 수행된다.The initial value determination process, initially

Wow

It estimates and inputs the error covariance that will have. 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.In the process of estimating estimates and error covariances, the estimates are closely related to the overall system equation, and the predicted error covariance (

) Is the previously calculated error covariance (

), Previous estimate based state transition matrix (

) And error covariance (

) And predicted error covariance (

) To gain Kalman gain (

) Value can be calculated.

)과 예측값(

)의 차이를 칼만 이득(

)으로 곱해주고 거기에 예측값(

)을 더해주어 최종 추정값(

)을 계산한다.The process of calculating the estimates is based on the current measurements (

) And the predicted value (

Kalman gain () difference

) And multiply it by the predicted value (

) To add the final estimate (

Calculate

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

)을 사용할 것인지 버릴 것인지를 판단할 수 있다.In calculating the error covariance, the error covariance is estimated (

Is a measure of how accurate) is, and by setting a reference value for this error covariance,

) Can be used to decide whether or not to use

In conclusion, the Kalman filter predicts what the next state and error covariance will be based on the system equation, and then computes a new estimate by compensating for the difference between the measured and predicted values. The end result 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 Equation 2 to Equation 5 to obtain a system equation for the battery equivalent circuit configured in the previous step (S110),

,

And

And finally, the system equation and the measurement equation can be calculated as shown in Equations 8 and 9, respectively.

[Equation 8]

[Equation 9]

Where S (k + 1) and u _c (k + 1) are the estimated values of the polarization capacities of the SOC and the battery, that is, the SOC and the battery, respectively, and S (k) and u _c (k) are Each is the SOC and battery polarization capacity of the previous stage. 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 operation S130, a measurement equation may be calculated to apply the Kalman filter method. In the conventional battery SOC estimation method, Equation 9 is used as a measurement equation, but the following Equation 10 is used in the present invention.

[Equation 10]

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

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

[Equation 11]

The measurement sensor may measure the voltage and current of the battery at sampling time intervals in real time, and by applying the measured value to Equation 10, the SOC of the battery may be calculated. In the conventional current integration method, current measurement noise may be reflected in the measured current value to improve the accuracy of the estimation.

Finally, in step S140, the SOC of the battery may be estimated by applying the measured current for the battery, the system equation (Equation 8) and the measurement equation (Equation 11) to the Kalman filter algorithm (see FIG. 4 and related description). have.

5 illustrates a configuration of a battery state estimating apparatus 100 according to the present invention.

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

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

The controller 120 configures 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 a state variable, and a measurement equation including the SOC observation value of the battery as an output variable. In this way, the measured voltage, the measured current, the system equation, and the measured equation may be applied to the Kalman Filter algorithm to estimate the SOC of the battery at a predetermined period.

6 to 9, an experiment for verifying a battery state estimation method according to the present invention and a result thereof will be described.

The experiment was conducted using the battery management system (BMS) of FIG. 6, and the battery pack S18650 of FIG. 7 was used. Referring to FIG. 6, a system (BMS) 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 run the system.

9 is a graph showing the results of the present experiment. Specifically, FIGS. 9 (a) and 9 (b) show the battery state estimation value according to the present invention while charging the S18650 battery pack using the system of FIG. 6. It is a graph showing the error of the actual value.

As can be seen in Figures 9 (a) and 9 (b), the battery state estimation value according to the present invention is corrected without continuously increasing the error value compared to the actual battery charge and discharge amount, while the existing error value continuously increases. Experimentally confirmed that more accurate battery state estimation is possible than the current integration method (see FIG. 1).

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

The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on the media may be those specially designed and constructed for the purposes of the present invention, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical media such as CDROMs, DVDs, and magnetic-optical such as floppy disks. Examples of medium-optical, and program instructions such as ROM, RAM, flash memory, and the like, include high-level executables that can be executed by a computer using an interpreter as well as machine code such as that produced by a compiler. Contains the language code. The hardware device described above may be configured to operate as at least one software module to perform the operations of one embodiment of the present invention, and vice versa.

The above description is merely illustrative of the technical idea of the present invention, and those skilled in the art to which the present invention pertains may make various modifications and changes 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 idea of the present invention but to describe the present invention, but are not limited to these embodiments. The scope of protection of the present invention should be interpreted by the following claims, and all technical ideas falling within the scope of the present invention should be construed as being included in the scope of the present invention.

100: battery state estimation device
110: sensor unit
120: control unit

Claims (7)

Configuring a battery equivalent circuit;
Calculating a system equation including the SOC of the battery and the polarization capacity of the battery equivalent circuit as state variables;
Calculating a measurement equation including the SOC observation value of the battery as an output variable and using the state variable calculated through the system equation; And
Estimating the SOC of the battery at a predetermined period by applying a measurement equation including the measured current for the battery, the system equation, and the output variable to a Kalman Filter algorithm. Way.
The method of claim 1,
Computing the system equation,
A method of estimating a battery state according to claim 8 is calculated.
[Equation 8]

Where S (k + 1) and u _c (k + 1) are the estimated values of the polarization capacities of the SOC and the battery, that is, the SOC and the battery, respectively, and S (k) and u _c (k) are Respectively, the SOC and battery polarization capacity of the previous stage, I (k) is the battery current, w ₁ (k) and w ₂ (k) are the noise of the system, and R ₁ , R ₂ and C are the battery equivalent circuits respectively. Internal resistance, polarization resistance and polarization capacity.
The method of claim 1,
Computing the measurement equation,
A method of estimating battery condition, characterized by applying a cumulative integration method to a measured current.
The method of claim 3,
Computing the measurement equation,
And integrating a current observation noise to the measured current.
The method of claim 1,
Computing the measurement equation,
A method of estimating a battery state according to claim 11 is calculated.
[Equation 11]

here,

Is the measurement equation, S (t ₀ ) is the initial SOC value, Q _n is the nominal battery capacity, and H (k) is the current measurement noise.
A computer program stored in a medium in combination with hardware for executing the battery state estimation method of any one of claims 1 to 5.
Sensor unit for measuring the voltage and current of the battery; And
A control unit for estimating the SOC of the battery using the measured voltage and current,
The control unit,
A system equation including a battery equivalent circuit and including a SOC of the battery and a polarization capacity of the battery equivalent circuit as state variables and an SOC observation value of the battery as an output variable and the state variable calculated through the system equation To calculate the measurement equation,
And a measurement equation including a measurement voltage, a measurement current, the system equation, and the output variable is applied to a Kalman Filter algorithm to estimate the SOC of the battery at a predetermined period.

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