Battery inspection method based on big data

Abstract

The present invention relates to a battery inspection method based on big data. Specifically, the present invention is to provide a battery inspection method based on big data, which applies information about the metal parts of the battery measured by an eddy current sensor to a discriminant function to quickly inspect the status of the metal parts.

Description

Technical Field

[0001] This application claims the benefit of priority from Korean Patent Application No. 10-2020-0022634, filed on February 25, 2020, the disclosure of which is incorporated herein by reference in its entirety.

[0002] The present invention relates to a method for inspecting a battery based on big data, and more particularly, to a method for inspecting a battery based on big data for quickly inspecting the condition of a metal portion of a battery by applying information about the metal portion measured by an eddy current sensor to a discriminant function. Background Art

[0003] Generally speaking, a secondary battery is a battery that can be repeatedly used through a discharge process that converts chemical energy into electrical energy and a charging process in the opposite direction, and types of secondary batteries include nickel-cadmium (Ni-Cd) batteries, nickel-metal hydride (Ni-MH) batteries, lithium metal batteries, lithium-ion batteries, lithium-ion polymer batteries, etc. Among these secondary batteries, lithium secondary batteries with high energy density and voltage, long cycle life, and low self-discharge rate have been commercialized and widely used.

[0004] A battery cell can be provided with an electrode assembly composed of a positive electrode, a negative electrode, and a separator built into a battery case. The battery can include an electrode terminal configured by welding an electrode tab protruding from a current collector to an electrode lead.

[0005] At this time, if a process such as welding is not performed normally in the tab-lead connection portion of the secondary battery, current may flow abnormally or the electrical connection may become unstable due to shock or vibration applied to the battery, and in severe cases, the electrode lead and the electrode tab may be separated.

[0006] Therefore, a method is needed to quickly and accurately detect physical and electrical problems in a battery tab-lead connection. Summary of the Invention

[0007] Technical issues

[0008] The present invention relates to a method for inspecting a battery based on big data, and more particularly, the present invention is to provide a method for inspecting a battery based on big data, for quickly inspecting the condition of a metal part of a battery by applying information about the metal part measured by an eddy current sensor to a discriminant function.

[0009] Technical problems to be solved by the present invention are not limited to the above-mentioned technical problems, and other technical problems not mentioned will be clearly understood by those of ordinary skill in the art to which the present invention pertains from the following description.

[0010] Solution to the problem

[0011] A method for inspecting a battery based on big data may include: an eddy current induction step of inputting an input current as an AC current into a transmitting coil and irradiating a primary magnetic field generated in the transmitting coil to a metal part of the battery to induce eddy currents in the metal part of the battery; an output voltage measurement step of inputting a secondary magnetic field generated by the eddy currents generated in the eddy current induction step into a receiving coil and measuring an induced electromotive force generated in the receiving coil by the secondary magnetic field; an impedance calculation step of calculating impedance based on the input current value and the output voltage value; an impedance analysis step of separating a real part and an imaginary part from the impedance value, inputting them into a discriminant function, and outputting a discriminant value from the discriminant function; and a condition judgment step of judging the condition of the metal part of the battery based on the discriminant value output in the impedance analysis step.

[0012] Effects of the present invention

[0013] The method for inspecting batteries based on big data of the present invention is used to inspect the metal parts of the battery, and specifically, according to the present invention, the physical and electrical connection conditions in the battery tab-lead connection part where the electrode tab and the electrode lead are connected can be quickly and accurately analyzed and judged by using the measurement value of the eddy current sensor as a non-destructive testing device.

[0014] The method for inspecting batteries based on big data of the present invention enables the condition of the metal parts of the battery to be inspected quickly and accurately without damaging the object to be inspected, so that it can be applied to batch production lines, thereby allowing the battery to be fully inspected, unlike a sampling inspection system that inspects some objects in a population of objects. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a block diagram illustrating a method for inspecting a battery based on big data.

[0016] Figure 2 is a perspective view showing a metal portion of a battery to which the method for inspecting a battery based on big data of the present invention is applied.

[0017] Figure 3 Graph showing the correlation between the impedance value measured using a single-frequency input current and the condition of the metal portion of the battery.

[0018] Figure 4 and Figure 5 is a graph showing test results of the method for inspecting a battery based on big data according to the present invention. DETAILED DESCRIPTION

[0019] A method for inspecting a battery based on big data may include: an eddy current induction step of inputting an input current as an AC current into a transmitting coil and irradiating a primary magnetic field generated in the transmitting coil to a metal part of the battery to induce eddy currents in the metal part of the battery; an output voltage measurement step of inputting a secondary magnetic field generated by the eddy currents generated in the eddy current induction step into a receiving coil and measuring an induced electromotive force generated in the receiving coil by the secondary magnetic field; an impedance calculation step of calculating impedance based on the input current value and the output voltage value; an impedance analysis step of separating a real part and an imaginary part from the impedance value, inputting them into a discriminant function, and outputting a discriminant value from the discriminant function; and a condition judgment step of judging the condition of the metal part of the battery based on the discriminant value output in the impedance analysis step.

[0020] In the eddy current induction step of the method for inspecting a battery based on big data of the present invention, the input current may include a plurality of partial AC currents having different set frequencies.

[0021] In the method for inspecting a battery based on big data of the present invention, the input current may be calculated by the following equation 1:

[0022] [Equation 1]

[0023]

[0024] I is the input current, N is the number of partial AC current values, I Pn is a complex number representing the amplitude and phase of the nth part of the AC current, f n is the set frequency of the nth part AC current, and t is the time.

[0025] In the eddy current induction step of the method for inspecting batteries based on big data of the present invention, the transmitting coil can irradiate the primary magnetic field to the surface of the metal part of the battery while the longitudinal direction of the transmitting coil is perpendicular to the surface of the metal part of the battery.

[0026] In the output voltage measuring step of the method for inspecting a battery based on big data of the present invention, the receiving coil may be disposed so that a longitudinal direction of the receiving coil is perpendicular to a surface of a metal portion of the battery.

[0027] In the impedance calculation step of the method for inspecting a battery based on big data of the present invention, the impedance may be calculated by dividing the output voltage value by the input current value.

[0028] In the method for inspecting a battery based on big data of the present invention, the impedance may include a plurality of partial impedances corresponding to each of the set frequencies.

[0029] In the impedance analysis step of the method for inspecting a battery based on big data of the present invention, a plurality of discriminant functions may be set, and each of the discriminant values ​​of the plurality of discriminant functions may be calculated by the following Equation 4:

[0030] [Equation 4]

[0031]

[0032] D m is the discriminant value of the mth discriminant function, N is the number of set frequencies, R n is the real part of the partial impedance corresponding to the nth set frequency, X n is the imaginary part of the partial impedance corresponding to the nth set frequency, and C rm,n and C xm,n is the discrimination coefficient corresponding to the n-th set frequency of the m-th discriminant function.

[0033] In the method for inspecting batteries based on big data of the present invention, the discrimination coefficient may be calculated based on sample impedance values ​​measured in metal portions of a plurality of sample batteries and condition information values ​​of the metal portions of the plurality of sample batteries.

[0034] In the method for inspecting batteries based on big data of the present invention, the condition information value may include at least one of an ohmic resistance value, a weld thickness, and a tensile strength of a metal portion of the sample battery.

[0035] In the impedance analysis step of the method for inspecting batteries based on big data of the present invention, m discrimination values ​​can be calculated for each of the metal parts of the battery, and in the condition judgment step, the condition of the metal part of the battery can be judged by simultaneously considering at least two discrimination values ​​among the m discrimination values.

[0036] In the method for inspecting batteries based on big data of the present invention, the condition judgment step may include a discrimination value selection step of selecting two discrimination values ​​from among m discrimination values, a chart output step of outputting the discrimination values ​​selected in the discrimination value selection step as a two-dimensional discrimination chart for each axis, and a data marking step of displaying the discrimination values ​​of the metal part of the battery as coordinates on the discrimination chart output in the chart output step.

[0037] In the chart output step of the method for inspecting a battery based on big data of the present invention, a plurality of condition areas indicating the conditions of the metal portion of the battery may be displayed on the discrimination chart.

[0038] Mode for Carrying Out the Invention

[0039] Hereinafter, embodiments according to the present invention will be described in detail with reference to the accompanying drawings. In the process, the size or shape of components illustrated in the drawings may be exaggerated for clarity and convenience of description. Furthermore, considering that the configuration and operation of the present invention may vary depending on the intentions or habits of the user or operator, the definitions of these terms should be based on the content throughout this specification.

[0040] Figure 1 is a block diagram illustrating a method for inspecting a battery based on big data. Figure 2 is a perspective view showing a metal portion of a battery to which the method for inspecting a battery based on big data of the present invention is applied. Figure 3 Graph showing the correlation between the impedance value measured using a single-frequency input current and the condition of the metal portion of the battery. Figure 4 and Figure 5 is a graph showing test results of the method for inspecting a battery based on big data according to the present invention.

[0041] In the following, reference will be made to Figures 1 to 5 A method for inspecting a battery based on big data according to the present invention is described in detail.

[0042] like Figure 1 As shown, the method for inspecting a battery based on big data of the present invention may include: an eddy current induction step (S100) of inputting an input current as an AC current into a transmitting coil and irradiating a primary magnetic field generated in the transmitting coil to a metal part of the battery to induce eddy currents in the metal part of the battery; an output voltage measurement step (S200) of inputting a secondary magnetic field generated by the eddy current generated in the eddy current induction step (S100) into a receiving coil and measuring an induced electromotive force in the receiving coil generated by the secondary magnetic field; an impedance calculation step (S300) of calculating an impedance based on an input current value and an output voltage value; an impedance analysis step (S400) of separating a real part and an imaginary part from the impedance value, inputting them into a discriminant function, and outputting a discriminant value from the discriminant function; and a condition judgment step (S500) of judging a condition of the metal part of the battery based on the discriminant value output in the impedance analysis step (S400).

[0043] In the method for inspecting batteries based on big data of the present invention, the metal parts of the battery included in the battery can be measured by an eddy current sensor as a non-destructive testing device, and the impedance value measured by the eddy current sensor can be input into a discriminant function, thereby quickly and accurately inspecting the condition of the metal parts of the battery.

[0044] In the method for inspecting a battery based on big data of the present invention, the metal portion of the battery to be inspected may be an electrode collector, an electrode tab 11, and an electrode lead 12 included in the battery, or may be a connection area where the electrode collector, the electrode tab 11, and the electrode lead 12 are connected to each other. Specifically, as Figure 2 As shown, the metal portion of the battery may be a tab-lead connection region 13 where an electrode tab 11 protruding from an electrode current collector and an electrode lead 12 engaging an electrical terminal of an external device are connected to each other by welding or the like.

[0045] The eddy current sensor used in the big data-based battery inspection method of the present invention may include a transmitting coil for radiating a primary magnetic field, an alternating magnetic field, toward the metal portion of the battery to be inspected, and a receiving coil for receiving a secondary magnetic field radiated by eddy currents generated in the metal portion of the battery by the primary magnetic field, thereby generating an electromotive force. The transmitting coil and the receiving coil may be arranged in a non-contact state by being spaced apart from the metal portion of the battery by a predetermined distance. The transmitting coil and the receiving coil may be arranged in a cylindrical, square, polygonal, or other shape and may be wires wound so as to generate a magnetic field in the longitudinal direction of the transmitting coil and the receiving coil.

[0046] In the eddy current inducing step ( S100 ), the input current may include a plurality of partial AC currents having different set frequencies. Figure 3 : is a graph showing the correlation between the impedance value measured with a single frequency input current and the condition of the metal part of the battery, where the x-axis is tensile strength and the y-axis is impedance at 8500 Hz. Figure 3 As shown, the impedance measured at a single frequency lacks direct correlation with the condition of the metal portion of the battery, and therefore, it is difficult to distinguish between a defective condition and a normal condition using only a single frequency.

[0047] In the method for inspecting a battery based on big data of the present invention, in the eddy current induction step (S100), by forming eddy currents in the metal part of the battery through an input current including a plurality of partial AC currents with different set frequencies, the same number of impedance values ​​as the number of partial AC currents can be obtained as measurement values, and the condition of the metal part of the battery can be accurately judged.

[0048] The set frequency for each of the multiple partial AC currents can be selected within the 100 Hz to 200 Hz frequency band. If the set frequency exceeds 200 kHz, excessively strong eddy currents form only on the surface of the metal portion of the battery being inspected, resulting in negligible impedance measurements. If the set frequency is less than 100 Hz, the primary magnetic field penetrates deeply into the metal portion of the battery, but the density of the eddy currents induced at this depth is low, resulting in noise being measured in the receiving coil. Therefore, it is desirable to select the set frequency within the 100 Hz to 200 Hz frequency band.

[0049] The number of partial AC currents, that is, the number of set frequencies, can preferably be 2 to 16. If the number of set frequencies is too small, the accuracy of the inspection is reduced. On the contrary, if the number of set frequencies is too large, not only does it take time to calculate the result, but mining the discriminant function may cost a lot of cost and time, and the measurement results may overlap between frequencies. Therefore, the number of set frequencies can preferably be 2 to 16, and more preferably, the number of set frequencies can be 8. For example, the number of set frequencies can be 8, and each set frequency can be selected from 6000 Hz, 6800 Hz, 7200 Hz, 7500 Hz, 8000 Hz, 8500 Hz, 9200 Hz, and 9700 Hz.

[0050] In the eddy current induction step (S100), although each of the partial AC currents can be sequentially input into the transmitting coil as input current, the value obtained by summing all the partial AC currents can be input into the transmitting coil as the input current. When the input current is input into the transmitting coil, even if the input current is cut off, the excitation current may remain in the transmitting coil, the receiving coil, the metal parts of the battery, etc. For accurate inspection, it is desirable to re-input the input current into the transmitting coil after the excitation current has dissipated. Therefore, when the partial AC currents are sequentially input into the transmitting coil, the greater the number of partial AC currents—that is, the number of set frequencies—the longer the inspection takes. However, in the eddy current induction step (S100) of the method for inspecting batteries based on big data of the present invention, by inputting the value obtained by summing all the partial AC currents into the transmitting coil as the input current, the time required for the eddy current induction step (S100) and the output voltage measurement step (S200) can be reduced. In other words, it is desirable to input the value obtained by summing all the partial AC currents as the input current into the transmitting coil.

[0051] Specifically, when a value obtained by summing partial AC currents is input to the transmitting coil as an input current, the input current can be calculated by the following Equation 1:

[0052] [Equation 1]

[0053]

[0054] I is the input current, N is the number of partial AC current values, I Pn is a complex number representing the amplitude and phase of the nth part of the AC current, f n Is the set frequency of the nth partial AC current, and t is time. Since N is the number of partial AC current values, it is also the number of set frequencies or the number of partial impedance values ​​to be described later.

[0055] In the eddy current induction step (S100), the transmitting coil may irradiate the primary magnetic field onto the surface of the metal portion of the battery while the longitudinal direction of the transmitting coil is perpendicular to the surface of the metal portion of the battery. Specifically, the transmitting coil may irradiate the primary magnetic field in a direction perpendicular to the surface of the metal portion of the battery.

[0056] In the output voltage measurement step (S200), the receiving coil may be arranged so that its longitudinal direction is perpendicular to the surface of the metal portion of the battery. Specifically, the transmitting coil may generate an electromotive force by absorbing the secondary magnetic field radiated perpendicular to the surface of the metal portion of the battery. This generated electromotive force can be measured as an output voltage value.

[0057] In the impedance calculation step (S300), the impedance may be calculated by dividing the output voltage value by the input current value. That is, the impedance may be calculated by the following equation 2.

[0058] [Equation 2]

[0059]

[0060] Z is impedance and V is output voltage. Here, the output voltage V can be output as an alternating voltage.

[0061] In the impedance calculation step (S300), the impedance may include a plurality of partial impedances corresponding to each of the set frequencies. That is, as many partial impedances as the number of set frequencies may be set. Specifically, the impedance may be the sum of all partial impedances.

[0062] That is, the correlation between the impedance and the partial impedance can be expressed by Equation 3.

[0063] [Equation 3]

[0064]

[0065] R n is the real part of the partial impedance corresponding to the nth set frequency, and X n It is the imaginary part of the partial impedance corresponding to the nth set frequency. nand X n is a real number.

[0066] In the impedance analysis step (S400), a plurality of discriminant functions may be set, and each of the discriminant values ​​for the plurality of discriminant functions may be calculated by the following Equation 4:

[0067] [Equation 4]

[0068]

[0069] D m is the discriminant value of the mth discriminant function, N is the number of set frequencies, R n is the real part of the partial impedance corresponding to the nth set frequency, X n is the imaginary part of the partial impedance corresponding to the nth set frequency, and C rm,n and C xm,n is the discriminant coefficient corresponding to the nth set frequency of the mth discriminant function. The discriminant coefficient is a real number. In the method for inspecting batteries based on big data of the present invention, at least two discriminant functions are provided, and the condition of the metal portion of the battery can be determined based on the correlation between the discriminant values, which are the output values ​​of the discriminant functions. The discriminant function can be set as a linear function, as in Equation 4.

[0070] The discrimination coefficient may be calculated based on the sample impedance values ​​measured in the metal parts of the plurality of sample batteries and the condition information values ​​of the metal parts of the plurality of sample batteries. The metal parts of the sample batteries may be metal parts of sample batteries set separately from the battery to be inspected.

[0071] The sample impedance value may be an impedance value obtained by performing the eddy current induction step ( S100 ), the output voltage measurement step ( S200 ), and the impedance calculation step ( S300 ) on the metal portion of the sample battery.

[0072] The condition information value of the metal portion of the sample battery may include at least one of an ohmic resistance value, a weld thickness, and a tensile strength of the metal portion of the sample battery. The condition information value of the metal portion of the sample battery may be a physical quantity directly measured relative to the metal portion of the sample battery by a measuring device such as a resistance sensor or a tensile strength meter.

[0073] The discriminant function may be data mined based on the sample impedance value and the condition information value, and the value of the discriminant coefficient may be tuned according to the result of the data mining.

[0074] The discriminant function can be data mined using sample impedance values ​​and condition information values ​​extracted from a number N of sample batteries at least 6 times the set frequency. Specifically, the discriminant function can be data mined by applying coefficients derived from statistical programs (R, Mini tap, etc.). In actual mass production of batteries, the number of batches is typically 10,000 or more, and therefore, extracting impedance information from 10,000 or more sample batteries may be ideal. However, when the discriminant function is data mined using sample impedance values ​​and condition information values ​​extracted from a number N of sample batteries at least 6 times the set frequency, a discriminant function with a certain level of reliability can be achieved.

[0075] In the impedance analysis step (S400), m discriminant values ​​are calculated for each metal portion, and in the condition determination step (S500), the condition of the metal portion of the battery is determined by simultaneously considering at least two of the m discriminant values. Specifically, the condition of the metal portion of the battery can be determined by selecting some discriminant values ​​from a plurality of discriminant values ​​based on the condition to be determined, such as the welding condition, the conduction state, etc., and analyzing the correlation between the discriminant values.

[0076] Specifically, the condition judgment step (S500) may include a discrimination value selection step of selecting two discrimination values ​​from among m discrimination values, a chart output step of outputting the discrimination values ​​selected in the discrimination value selection step as a two-dimensional discrimination chart for each axis, and a data marking step of displaying the discrimination values ​​of the metal part of the battery as coordinates on the discrimination chart output in the chart output step.

[0077] In the chart output step, a plurality of condition areas indicating the condition of the metal portion of the battery may be displayed on the discriminant chart. Therefore, the condition of the metal portion of the battery can be determined based on which area of ​​the discriminant chart the coordinates for the condition of the metal portion of the battery are displayed. The condition area can be determined based on the correlation between the discriminant values.

[0078] Example 1

[0079] Inspection was performed on metal portions of 25 normal batteries and metal portions of 25 defective batteries by applying the method for inspecting batteries based on big data of the present invention.

[0080] The eddy current induction step (S100) and the output voltage measurement step (S200) are performed on the tab-lead connection region 13 where the electrode tab 11 made of aluminum film and the electrode lead 12 made of aluminum alloy are ultrasonically welded. At this time, the sum of eight partial AC currents with set frequencies of 6000 Hz, 6800 Hz, 7200 Hz, 7500 Hz, 8000 Hz, 8500 Hz, 9200 Hz, and 9700 Hz, respectively, is used as the input current value in the eddy current testing instrument.

[0081] The discriminant function is data mined using sample impedance values ​​and condition information values ​​extracted from the metal parts of 50 sample batteries, and the impedance analysis step (S400) is performed using the discriminant function derived from the statistical program and the condition judgment step (S500) is performed using 16 discriminant values.

[0082] Figure 4 The graph is a two-dimensional discriminant graph in which two discriminant values ​​are used as each axis, wherein the coordinates indicated by circles are the metal parts of normal batteries and the coordinates indicated by squares are the metal parts of defective batteries. Figure 4 As shown, it can be seen that the coordinates of the metal portion of the normal battery and the coordinates of the metal portion of the defective battery are concentrated and separated from each other.

[0083] Example 2

[0084] By applying the method for inspecting batteries based on big data of the present invention, inspection was performed on metal portions of a total of 30 batteries, ie, 10 weakly welded batteries, 10 over-welded batteries, and 10 normally welded batteries.

[0085] The eddy current induction step (S100) and the output voltage measurement step (S200) are performed on the tab-lead connection region 13 where the electrode tab 11 made of aluminum film and the electrode lead 12 made of aluminum alloy are ultrasonically welded. At this time, the sum of eight partial AC currents with set frequencies of 30 kHz, 50 kHz, 70 kHz, 90 kHz, 110 kHz, 130 kHz, 150 kHz, and 170 kHz, respectively, is used as the input current value in the eddy current testing instrument.

[0086] The discriminant function is data mined using sample impedance values ​​and condition information values ​​extracted from the metal parts of 30 sample batteries. The impedance analysis step (S400) is performed using the discriminant function derived from the statistical program R, and the condition determination step (S500) is performed using 32 discriminant values. Specifically, LD1 and LD2 are derived for the real part and imaginary part of the impedance corresponding to each of the eight partial input current values, respectively.

[0087] Figure 5 The graph is a two-dimensional discriminant graph in which two discriminant values ​​are used as each axis. A plurality of condition areas representing welding conditions are displayed on the two-dimensional discriminant graph, and the coordinates for the conditions of the metal parts of the battery are overlapped and marked in the condition areas. Figure 5 As shown, for the metal parts of a total of 30 batteries, 10 batteries were judged to be weakly welded, 10 batteries were over-welded, and 10 batteries were normally welded. That is, the method for inspecting batteries based on big data of the present invention produced accurate inspection results for all 30 batteries.

[0088] Although the embodiments of the present invention have been described above, these are merely exemplary, and those skilled in the art will appreciate that various modifications and equivalent scopes of the embodiments thereof are possible. Therefore, the true technical protection scope of the present invention should be determined by the following claims.

[0089] Industrial availability

[0090] The method for inspecting batteries based on big data of the present invention is used to inspect the metal parts of the battery, and specifically, according to the present invention, the physical and electrical connection conditions in the battery tab-lead connection part where the electrode tab and the electrode lead are connected can be quickly and accurately analyzed and judged by using the measurement value of the eddy current sensor as a non-destructive testing device.

[0091] The method for inspecting batteries based on big data of the present invention enables the condition of the metal parts of the battery to be inspected quickly and accurately without damaging the object to be inspected, so that it can be applied to batch production lines, thereby allowing the battery to be fully inspected, unlike a sampling inspection system that inspects some objects in a population of objects.

Claims

1. A method for inspecting a battery based on big data, comprising: an eddy current induction step of inputting an input current as an AC current into a transmitting coil and irradiating a primary magnetic field generated in the transmitting coil to a metal portion of the battery to induce eddy currents in the metal portion of the battery; an output voltage measuring step of inputting a secondary magnetic field generated by the eddy current generated in the eddy current inducing step into a receiving coil and measuring an induced electromotive force generated by the secondary magnetic field in the receiving coil; an impedance calculating step of calculating impedance based on the input current value and the output voltage value; an impedance analysis step of separating a real part and an imaginary part from the impedance value, inputting the real part and the imaginary part into a discriminant function, and outputting a discriminant value from the discriminant function; as well as a condition determination step of determining the condition of the metal portion of the battery based on the determination value outputted in the impedance analysis step, Wherein, in the eddy current induction step, the input current includes a plurality of partial AC currents with different set frequencies, wherein, in the impedance calculation step, the impedance is calculated by dividing the output voltage value by the input current value, and wherein the impedance includes a plurality of partial impedances corresponding to each of the set frequencies, In the impedance analysis step, a plurality of discriminant functions are set, and each of the discriminant values ​​of the plurality of discriminant functions is calculated by the following equation 4: [Equation 4] D m is the discriminant value of the mth discriminant function, N is the number of set frequencies, R n is the real part of the partial impedance corresponding to the nth set frequency, X n is the imaginary part of the partial impedance corresponding to the nth set frequency, and Cr m,n and Cx m,n is the discrimination coefficient corresponding to the n-th set frequency of the m-th discriminant function.

2. The method for inspecting a battery based on big data according to claim 1, wherein: The input current is calculated by the following equation 1: [Equation 1] I is the input current, N is the number of partial AC current values, I Pn is a complex number representing the amplitude and phase of the nth part of the AC current, f n is the set frequency of the nth part AC current, and t is the time.

3. The method for inspecting a battery based on big data according to claim 1, wherein: In the eddy current induction step, The transmitting coil irradiates the primary magnetic field to the surface of the metal part of the battery while a longitudinal direction of the transmitting coil is perpendicular to the surface of the metal part of the battery.

4. The method for inspecting a battery based on big data according to claim 1, wherein: In the output voltage measuring step, The receiving coil is disposed such that a longitudinal direction of the receiving coil is perpendicular to a surface of the metal portion of the battery.

5. The method for inspecting a battery based on big data according to claim 1, wherein: The discrimination coefficient is calculated based on sample impedance values ​​measured in metal portions of a plurality of sample batteries and condition information values ​​of the metal portions of the plurality of sample batteries.

6. The method for inspecting a battery based on big data according to claim 5, wherein: The condition information value includes at least one of an ohmic resistance value, a weld thickness, and a tensile strength of the metal portion of the sample battery.

7. The method for inspecting a battery based on big data according to claim 1, wherein: In the impedance analysis step, m discriminant values ​​are calculated for each of the metal parts of the battery, and In the condition judging step, the condition of the metal portion of the battery is judged by simultaneously considering at least two discrimination values ​​among the m discrimination values.

8. The method for inspecting a battery based on big data according to claim 7, wherein: The condition determination step includes: a discriminant value selection step of selecting two discriminant values ​​from among the m discriminant values, a chart outputting step of outputting a two-dimensional discriminant chart in which the discriminant value selected in the discriminant value selecting step is used as each axis, and a data marking step of displaying the discrimination value of the metal portion of the battery as a coordinate on the discrimination graph outputted in the graph outputting step; 9. The method for inspecting a battery based on big data according to claim 8, wherein: In the graph output step, A plurality of condition areas indicating conditions of the metal portion of the battery are displayed on the discrimination chart.

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