KR102032229B1 - System and method for estimating state of health for battery - Google Patents

System and method for estimating state of health for battery Download PDF

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KR102032229B1
KR102032229B1 KR1020150162031A KR20150162031A KR102032229B1 KR 102032229 B1 KR102032229 B1 KR 102032229B1 KR 1020150162031 A KR1020150162031 A KR 1020150162031A KR 20150162031 A KR20150162031 A KR 20150162031A KR 102032229 B1 KR102032229 B1 KR 102032229B1
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battery
life state
battery life
parameter
soh
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KR20170058165A (en
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황병국
이호일
김정윤
권균
박건희
정재흠
한종훈
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현대일렉트릭앤에너지시스템(주)
서울대학교산학협력단
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery

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Abstract

The present invention relates to a technology for accurately predicting a current battery life state by introducing a parameter representing a battery life state and performing parameter optimization based on input charge / discharge data. A data set setting unit for setting an operation data set of battery charge / discharge, a control unit connected to the data set setting unit, a control unit for controlling a battery life state estimation operation, and a capacity deteriorated based on a mathematical model equation derived from a battery equivalent circuit model (Q) a parameter determination unit for determining a parameter and an estimation result derivation unit for calculating a battery life state (SOH) based on the capacity parameter determined by the parameter determination unit, and updating the calculated battery life state information, characterized in that it comprises a .

Description

System and method for estimating state of health for battery

The present invention relates to a battery life estimation technique. More particularly, the present invention relates to a technique for accurately predicting a current battery life state by introducing a parameter representing a battery life state and performing parameter optimization based on input charge / discharge data. It is about.

In general, the SOH-State Of Health refers to a figure of merit that compares an ideal state of a battery with a current battery state, and is expressed in percentage (%).

If the SOH is 100%, the current battery condition satisfactorily satisfies the specifications and specifications of the initial battery. In theory, the SOH is 100% at the time of battery manufacture. Since various errors may occur in the process, the SOH of all batteries at the time of manufacture does not satisfy 100%.

The SOH of such a battery determines the current SOH by measuring the SOH of the initial battery state, storing the value, and comparing it with the stored initial SOH value based on the values that change as the battery is used. Information such as internal resistance, impedance, conductance, capacity, voltage, self-discharge, charging performance, and number of charge / discharge cycles is considered.

Conventional battery life estimation techniques include a battery life estimation apparatus and a battery life estimation method disclosed in Korean Patent Publication No. 10-1486629 (January 20, 2015), which is based on experiments. Since the SOC estimation focuses on estimating using a table or deterioration curve or estimating from cell voltage or electromotive force measurement, accurate SOH estimation techniques are insufficient.

Republic of Korea Patent Publication 10-1486629B, 2015. 01. 20.

SUMMARY OF THE INVENTION The present invention has been proposed to solve the above conventional problems, and an object of the battery life state estimation system and method according to the present invention is to adopt an equivalent circuit model with low complexity, and to parameterize the initial electromotive force in the parameter estimation process. It is introduced to enable the prediction of the life state of the current battery based on the initial state of charge.

Battery life state estimation system according to the present invention includes a data set setting unit for setting the operation data set of the battery charge and discharge; A control unit connected to the data set setting unit to control a battery life state estimation operation; Calculating a battery life state (SOH) based on a parameter determining unit determining a deteriorated capacity (Q) parameter based on a mathematical model equation derived from a battery equivalent circuit model and the capacity parameter determined by the parameter determining unit, and calculating the calculated battery An estimation result derivation unit for updating life state information, wherein the parameter determination unit calculates a battery residual amount SOC from current data of the data set, and calculates an open circuit voltage OCC of a battery equivalent circuit model. -OCV calculator; The model voltage is calculated based on the capacity (Q), electrolyte resistance parameter (R0), alpha (α), beta (β), and initial open circuit voltage (OCV0) values initially set at the current and time intervals of the data set. A model voltage calculator; An object function setting unit for setting an object function for minimizing the sum of error squares between the calculated model voltage and the actual voltage difference and deterioration for estimating the deteriorated battery capacity Q by applying the object function; And a capacity estimating unit, wherein the objective function is calculated by Equation 2.
[Equation 2]

Figure 112019073134350-pat00022
Where V k is the k-th actual voltage in the estimated data and V mod, k is the k-th model voltage.

delete

delete

The battery life state estimation method according to the present invention comprises the steps of: (a) setting an operation data set of battery charge / discharge using a data set setting unit; (b) determining a deteriorated capacity (Q) parameter based on a mathematical model equation derived from a battery equivalent circuit model using a parameter determining unit and (c) using a estimation result derivation unit, battery life based on the determined capacity parameter Calculating a state (SOH) and updating the calculated battery life state information, wherein step (b) includes (b-1) remaining of the battery as current data of the data set using the SOC-OCV calculation unit; Calculating a quantity SOC and calculating an open circuit voltage OCV of the battery equivalent circuit model; (b-2) The capacity (Q), electrolyte resistance parameter (R0), alpha (α), beta (β), and initial open circuit voltage initially set at the current and time intervals of the data set using the model voltage calculator. Calculating a model voltage based on an (OCV0) value; (b-3) setting an objective function for minimizing the sum of error squares between the calculated model voltage and the actual voltage difference using the objective function setting unit, and (b-4) using the deterioration capacity estimating unit, in the objective function, Estimating the deteriorated battery capacity Q, and in the step (b-1), the objective function is calculated by Equation 2.
[Equation 2]

Figure 112019073134350-pat00023
Where V k is the k-th actual voltage in the estimated data and V mod, k is the k-th model voltage.

delete

In the battery life state estimation method according to the present invention, the step (c) includes (c-1) calculating and storing a battery life state (SOH) as a ratio of an initial battery capacity parameter and a current battery capacity parameter, ( c-2) the battery life state (SOH) is the step of calculating the average value of the calculated value at least twice and (c-3) checking whether the calculated average SOH value is less than the previously stored SOH stored value And, if it is determined in step (c-3) that the average SOH value is less than the previously stored SOH stored value, further comprising updating the calculated SOH value.

Battery life state estimation system and method according to the present invention adopts a low complexity equivalent circuit model, and by introducing the initial electromotive force as a parameter in the parameter estimation process, it is possible to accurately predict the life state of the current battery based on the initial state of charge It has an effect.

In addition, the present invention can exhibit an excellent technical effect to check the battery life state of the peak grid ESS system or smart grid field without a specific operation pattern by the constant current operation.

In addition, the present invention has an effect that can be used to update the information on the SOC-OCV curve even if the electrode or electrolyte type of the battery cell used is changed.

1 is a block diagram showing the overall configuration of a battery life state estimation system according to the present invention.
2 is a detailed block diagram of the parameter determining unit 50 in the battery life state estimation system according to the present invention.
3 is a diagram showing an equivalent circuit model in the battery life state estimation system according to the present invention.
4 is an overall flowchart of a battery life state estimation method in accordance with the present invention.
5 is a detailed flowchart of the step S40 in the battery life state estimation method according to the present invention.
6 is a detailed flowchart of the step S50 in the battery life state estimation method according to the present invention.
7 to 12 are diagrams showing experimental data and results for confirming the performance of the battery life state estimation system and method according to the present invention.

Hereinafter, a detailed description for implementing the battery life state estimation system and method according to the present invention will be described.

1 is a view showing the overall configuration of the battery life state estimation system according to the present invention, the sensor 10, the data storage unit 20, the control unit 30, the data set setting unit 40, the parameter determination unit 50 and the estimation result derivation unit 60.

The sensing sensor unit 10 detects a battery operating state according to battery charging and discharging according to a set time interval, and specifically detects current and voltage data, and in the embodiment of the present invention, displays the battery operating state at an interval of 10 seconds. Set to detect.

The battery operation data sensed by the sensor 10 is stored in the data storage 20, and performs battery life estimation according to the control signal of the controller 30.

The data set setting unit 40 sets the operation data set for charging and discharging the battery according to a control signal of the controller 30, and the number of the data sets can be adjusted according to the user's convenience. In the example, 1000 is set in consideration of the estimation accuracy and the estimation time, and the data set is ideal when one or more charge / discharge cycles are performed.

The data set setting unit 40 according to the present invention checks and sets whether the initial and last SOC-state of charge of the data set differs by more than a set ratio.

That is, since the dynamic behavior of the battery can be grasped in the section in which there is sufficient SOC change in the driving section, the difference between the initial and the last SOC of the data set is set at a predetermined ratio, and in the embodiment of the present invention, 30% is set.

The parameter determiner 50 determines the deteriorated capacity (Q) parameter based on a mathematical model derived from a battery equivalent circuit model. The parameter determiner 50 according to the present invention is illustrated in FIG. 2. The SOC-OCV calculator 51, the model voltage calculator 52, the objective function setting unit 53, and the deterioration capacity estimating unit 54 are included.

The SOC-OCV calculator 51 calculates a battery residual amount SOC based on current data of the data set, calculates an open circuit voltage OCV of a battery equivalent circuit model, and calculates the model voltage calculator 52. Calculates the model voltage based on the values of capacity (Q), electrolyte resistance parameter (R0), alpha (α), beta (β), and initial open circuit voltage (OCV0) initially set at the current and time intervals of the data set. do.

The objective function setter 53 is connected to the model voltage calculator 52 to set an objective function that minimizes the sum of error squares of the difference between the calculated model voltage and the actual voltage, and the degradation capacity estimator 54 The deteriorated battery capacity Q is estimated by applying the objective function.

In the present invention, as shown in Figure 3, the battery equivalent circuit model, the capacitor (C) and the resistor (R) is connected in parallel to the resistor (R 0 ) to determine the diffusion resistance and capacitance parameters of the electrolyte in the battery The mathematical model derived from the equivalent circuit model is derived as shown in Equation 1 below.

Figure 112015112654919-pat00001

By using the mathematical model according to the present invention it is to determine the parameters that enable to estimate the correct model.

Equations 2 to 7 including Equation 1 list the objective functions used in the parameter optimization process and the model equations calculated in the process, and the following Table 1 summarizes the definitions of the variables used in the equations. It is.

Figure 112015112654919-pat00002

Figure 112015112654919-pat00003

Figure 112015112654919-pat00004

Figure 112015112654919-pat00005

Figure 112015112654919-pat00006

Figure 112015112654919-pat00007

Equation 2 is an objective function of the optimization according to the present invention as the sum of the least square error between the model voltage and the actual measured voltage. Optimization parameters R0, alpha, beta, and OCV0 of Equation 3 for minimizing Equation 2 above. , Q) is the purpose of the estimation algorithm according to the present invention, where the capacity (Q) parameter represents the current degraded capacity of the battery, and SOH can be estimated based on this capacity (Q) parameter.

In order to do this, the SOC needs to be determined. In order to determine the initial electromotive force, the SOC can be determined.

That is, when the SOC is determined based on the current integration method for each time, the electromotive force corresponding to each SOC can be obtained through Equation 6. In addition, based on the initially set Q, R0, α, β, and OCV0 values Calculation model voltage (V mod, k ) can be calculated (Equation 1). Equation 5 actually summarizes the parameters in the form of redefining the equation to reduce the complexity of the parameter optimization process.

The estimation result derivation unit 60 calculates a battery life state SOH based on the capacity parameter determined by the parameter determination unit 50, and updates the calculated battery life state information.

The estimation result deriving unit 60 according to the present invention calculates the SOH by the ratio of the initial battery capacity parameter and the current battery capacity parameter, wherein the battery life state (SOH) is an average value of values calculated at least twice. It is defined as in Equation 7 below.

Figure 112015112654919-pat00008

In this case, Q aged is a deterioration capacity and Q initial is an initial capacity.

In the embodiment of the present invention, the estimation result derivation unit 60 calculates the SOH cumulatively two or more times, and when the average value of SOH is lower than the previously stored SOH value, the calculated SOH average value is calculated. Updated.

That is, in the case of the present invention, since the estimated data is obtained from the capacity obtained through the optimization process, the estimated SOH value may be larger than the previous SOH value according to the characteristics of the input estimated data. The average of more than one estimate is used as an SOH value, which includes comparing the previous SOH estimate.

The battery life state estimation method using the battery life state estimation system according to the present invention will be described below.

4 is a view showing the overall flow of the battery life state estimation method according to the present invention, first, by using the sensor 10 to detect the battery operating state according to the battery charge and discharge at a set time interval (S10, The detected battery operation data is stored in the data storage unit 20 (S20), and the data stored in the step S10 includes current and voltage data.

Next, by using the data set setting unit 40, the step (S30) of setting the operation data set of battery charging and discharging is performed, and after the step S20, the initial and last remaining battery SOC of the data set (SOC). ) Is further determined to determine whether the difference is greater than the set ratio (S40), and if the difference is not greater than the ratio set in the step S30, and performs a step (S45) for resetting the data set.

In the exemplary embodiment of the present invention, the SOC difference ratios of the initial and last data sets of the step S30 are set to 30%. When the ratio difference is not more than 30%, the data set is reset.

Next, the step S40 of performing parameter optimization based on a mathematical model derived from the battery equivalent circuit model using the parameter determiner 50 is performed.

In the step S50 according to the present invention, as shown in FIG. 5, the SOC-OCV calculator 51 calculates a battery residual amount SOC using current data of a data set, and calculates a battery equivalent circuit model. After calculating the open circuit voltage (OCV) (S51), and then using the model voltage calculation unit 52, the capacity (Q) initially set by the current and time interval of the data set, the electrolyte resistance A model voltage is calculated based on the parameters R0, alpha α, beta β, and initial open circuit voltage OVC0 (S53).

Next, by using the objective function setting unit 53, the step of setting the objective function for minimizing the sum of error squares of the difference between the calculated model voltage and the actual voltage is performed (S55), and the degradation capacity estimation unit 54 In operation S57, the deteriorated battery capacity Q is estimated in the objective function.

Steps S51 to S57 are performed through Equation 1 to Equation 6, and are not described in detail because they have been described above.

Next, the battery life state SOH is calculated based on the determined capacity parameter using the estimation result derivation unit 60, and the calculated battery life state information is updated (S50).

In step S60 according to the present invention, as shown in FIG. 6, a step (S61) of calculating and storing a battery life state (SOH) is performed according to a ratio of an initial battery capacity parameter and a current battery capacity parameter, and according to the present invention. The step S61 is calculated by the equation (7).

Next, calculating the battery life state (SOH) at least two times (S63), calculating the average of the calculated SOH value (S65) is performed, (c-3) the calculated average SOH value previously A step S67 is performed to determine whether the value is smaller than the stored SOH stored value.

Next, when it is confirmed that the average SOH value calculated in the step S57 is smaller than the previously stored SOH stored value, the step of updating the calculated SOH value is performed (S69).

In order to confirm the performance of the battery life state estimation system and method according to the present invention, experiments and evaluations were performed in terms of accuracy, robustness and convergence, and accuracy was calculated by comparing SOH and estimated SOH for actual capacity. We evaluated the robustness of various currents, SOCs, and temperatures, and their convergence to initial values.

First, as shown in FIGS. 7 to 9, the accuracy of the estimation was evaluated for each cycle, and as a result, it was confirmed that the average error rate was obtained at about 3%.

In addition, in terms of robustness, as shown in FIGS. 10 and 11, the estimation results for various SOCs, current patterns, temperatures, and the like may also be derived from similar estimation values to confirm stable results with respect to the current estimation values. .

Finally, in terms of convergence, as shown in FIG. 12, as a result of evaluating the charging capacity using various initial values, it was confirmed that the convergence was also similar.

As described above, the battery life state estimation system and method according to the present invention adopts an equivalent circuit model with low complexity, and introduces an initial electromotive force as a parameter in the parameter estimation process, and thus, based on the initial state of charge, There is an effect that can accurately predict the life state, the present invention can exhibit an excellent technical effect to check the battery life state of the peak grid ESS system or smart grid field with no specific operation pattern by constant current operation.

In addition, the present invention can be used by updating the information on the SOC-OCV curve even if the electrode or electrolyte type of the battery cell to be used is changed.

Although the embodiments of the present invention have been described above, the technical idea of the present invention is not limited to the above embodiments, and may be implemented by a battery life state estimation system and method according to various embodiments of the present invention in a range that does not depart from the technical idea of the present invention. have.

10: detection sensor unit 20: data storage unit
30: control unit 40: data set setting unit
50: parameter determination unit 51: SOC-OCV calculation unit
52: model voltage calculation unit 53: the objective function setting unit
54: deterioration capacity estimation unit 60: estimation result derivation unit
100: battery life state estimation system

Claims (12)

A data set setting unit for setting an operation data set of battery charge / discharge;
A control unit connected to the data set setting unit to control a battery life state estimation operation;
A parameter determining unit that determines a deteriorated capacity (Q) parameter based on a mathematical model derived from a battery equivalent circuit model;
A calculation result derivation unit configured to calculate a battery life state (SOH) based on the capacity parameter determined by the parameter determination unit, and to update the calculated battery life state information;
The parameter determiner,
A SOC-OCV calculator configured to calculate a battery residual amount (SOC) based on current data of the data set and to calculate an open circuit voltage (OCV) of a battery equivalent circuit model;
The model voltage is calculated based on the capacity (Q), electrolyte resistance parameter (R0), alpha (α), beta (β), and initial open circuit voltage (OCV0) values initially set at the current and time intervals of the data set. A model voltage calculator;
An objective function setter connected to the model voltage calculator to set an objective function for minimizing the sum of squared errors between the calculated model voltage and the actual voltage difference;
A deterioration capacity estimator for estimating deteriorated battery capacity (Q) by applying the objective function;
The objective function is a battery life state estimation system, characterized in that calculated by Equation 2.
[Equation 2]
Figure 112019073134350-pat00024

Where V k is the k-th actual voltage in the estimated data and V mod, k is the k-th model voltage. The method of claim 1,
The data set is
Battery life state estimation system, characterized in that the current, voltage, time measurement data in the battery operation data stored at regular time intervals.
The method of claim 2,
The data set setting unit,
A battery life state estimation system for determining whether the initial and last SOC-state of charge of a data set differs by more than a set percentage.
delete delete The method of claim 1,
The estimation result deriving unit
The battery life state (SOH) is calculated from the ratio of the initial battery capacity parameter to the current battery capacity parameter.
The battery life state (SOH) is a battery life state estimation system, characterized in that the average value of at least two times calculated value.
The method of claim 1,
The battery equivalent circuit model
A battery life state estimation system comprising a capacitor (C) and a resistor (R) connected in parallel to a resistor (R 0 ) to include diffusion resistance and capacitance parameters of an electrolyte in the battery.
(a) setting an operation data set of battery charging / discharging using the data set setting unit;
(b) determining a deteriorated capacity (Q) parameter based on a mathematical model derived from a battery equivalent circuit model using a parameter determining unit; and
(c) calculating a battery life state (SOH) based on the determined capacity parameter using the estimation result derivation unit, and updating the calculated battery life state information,
In step (b),
(b-1) calculating a battery residual amount SOC from current data of a data set by using an SOC-OCV calculator and calculating an open circuit voltage OCV of a battery equivalent circuit model;
(b-2) The capacity (Q), electrolyte resistance parameter (R0), alpha (α), beta (β), and initial open circuit voltage initially set at the current and time intervals of the data set using the model voltage calculator. Calculating a model voltage based on an (OCV0) value;
(b-3) setting an objective function that minimizes the sum of squared errors of the difference between the calculated model voltage and the actual voltage using the objective function setting unit;
(b-4) estimating the deteriorated battery capacity Q in the objective function using the deterioration capacity estimating unit,
In the step (b-1),
The objective function is a battery life state estimation method characterized in that it is calculated by the equation (2).
[Equation 2]
Figure 112019073134350-pat00025

Where V k is the k-th actual voltage in the estimated data and V mod, k is the k-th model voltage.
The method of claim 8,
After step (a),
(d) checking whether the initial and last SOC levels of the data set differ by more than a set percentage,
If there is no difference more than the ratio set in step (d),
and (d-1) resetting the data set.
delete delete The method of claim 8,
In step (c),
(c-1) calculating and storing a battery life state (SOH) as a ratio of an initial battery capacity parameter and a current battery capacity parameter;
(c-2) accumulating the battery life state (SOH) at least twice and calculating an average value of the calculated SOH values; and
(c-3) checking whether the calculated average SOH value is smaller than a previously stored SOH stored value,
If it is determined in step (c-3) that the average SOH value is less than the previously stored SOH stored value, further comprising the step of updating the calculated SOH value, characterized in that the battery life state estimation method.
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