METHOD FOR PREDICTING LIFESPAN CHARACTERISTICS OF LITHIUM SECONDARY BATTERY

Abstract

The present disclosure relates to a method for predicting lifespan characteristics of a lithium secondary battery that can reliably predict the lifespan characteristics of a lithium secondary battery, specifically, the mode of variation in cycle capacity in advance.

Claims

1. A method for predicting lifespan characteristics of a lithium secondary battery, the method comprising: a first step of subjecting a lithium secondary battery in a form of a blocking cell to an impedance spectroscopic analysis under application of multiple frequencies; a second step of deriving a relationship between capacitance for each frequency from a result of the impedance spectroscopic analysis, and calculating a charge amount of the lithium secondary battery therefrom; a third step of repeatedly performing the first and second steps while repeatedly performing an electrochemical reaction for the lithium secondary battery for a number of cycles, to thereby collect charge amount data of the lithium secondary battery for each of the cycles; and a fourth step of measuring a capacity for each of the cycles x for a lithium secondary battery in a form of a non-blocking cell, and allowing the capacity to correspond to the charge amount data for each of the cycles collected in the third step, to thereby derive a prediction expression of the capacity for each of the cycles of the lithium secondary battery.

2. The method for predicting lifespan characteristics of a lithium secondary battery according to claim 1, wherein the lithium secondary battery in the form of the blocking cell includes an electrode, a separator, and an electrolyte facing each other, and the facing electrode includes an electrode of the same polarity or a pristine electrode, or the electrolyte includes a non-intercalation salt.

3. The method for predicting lifespan characteristics of a lithium secondary battery according to claim 1, wherein the impedance spectroscopic analysis of the first step is performed under the application of a frequency of 10.sup.6 to 10-.sup.−4 Hz.

4. The method for predicting lifespan characteristics of a lithium secondary battery according to claim 1, wherein as a given result of the impedance spectroscopic analysis, data of the given result including a Nyquist plot of the lithium secondary battery, real capacitance data, imaginary capacitance data, time constant data, a capacitance relation graph for each frequency, and peak distribution data of the capacitance relation graph for each frequency are derived.

5. The method for predicting lifespan characteristics of a lithium secondary battery according to claim 4, wherein the second step comprises, deriving a relation graph of an imaginary capacitance for each frequency of the lithium secondary battery; and calculating the charge amount of the lithium secondary battery from an integral value of the relation graph.

6. The method for predicting lifespan characteristics of a lithium secondary battery according to claim 5, wherein the step of deriving the relation graph of the imaginary capacitance for each frequency comprises: deriving a Nyquist plot of the lithium secondary battery from the result of the impedance spectroscopic analysis; and substituting a real impedance, an imaginary impedance and a complex impedance for each frequency derived from the Nyquist plot into the following Equation 1, and deriving a real capacitance and an imaginary capacitance for each frequency, respectively: $\begin{array}{cc}{C}^{\prime }=\frac{-Z^{″}\left(\omega \right)}{\omega {.Math.Z\left(\omega \right).Math.}^2}{C}^{″}=\frac{-Z^{\prime }\left(\omega \right)}{\omega {.Math.Z\left(\omega \right).Math.}^2}& \left[\mathrm{Equation}1\right]\end{array}$ in the above Equation 1, C′ represents the real capacitance of the lithium secondary battery, C″ represents the imaginary capacitance, w represents an angular velocity defined as 2*π*frequency, z′(w) represents the real impedance for each angular velocity, z″(w) represents the imaginary impedance for each angular velocity, and z(w) represents the total complex impedance calculated from the real impedance and the imaginary impedance.

7. The method for predicting lifespan characteristics of a lithium secondary battery according to claim 1, further comprising a fifth step of correcting the prediction expression of the fourth step by performing artificial neural network learning based on the result of the impedance spectroscopic analysis.

8. The method for predicting lifespan characteristics of a lithium secondary battery according to claim 1, wherein the prediction expression of the capacity for each of the cycles in the fourth step is derived from a relational expression of the capacity for each of the cycles after 30 or more cycles of electrochemical reactions are performed for the lithium secondary battery.

9. The method for predicting lifespan characteristics of a lithium secondary battery according to claim 7, wherein the allowing step of the fourth step and the artificial neural network learning step of the fifth step are performed simultaneously or within the same system, so that the prediction expression of the capacity for each of the cycles of the lithium secondary battery is derived.

10. The method for predicting lifespan characteristics of a lithium secondary battery according to claim 7, wherein: as a given result of the impedance spectroscopic analysis, data of the given result including a Nyquist plot of the lithium secondary battery, real capacitance data, imaginary capacitance data, time constant data, capacitance relation graph for each frequency, and peak distribution data of the capacitance relation graph for each frequency are derived; and in the fifth step, the artificial neural network learning is performed based on the data including the real capacitance data, the imaginary capacitance data, the time constant data, and the peak distribution data of the capacitance relation graph for each frequency derived from the given result of the impedance spectroscopic analysis.

11. The method for predicting lifespan characteristics of a lithium secondary battery according to claim 7, further comprising a sixth step of predicting capacity characteristics for each of the cycles of the lithium secondary battery to be measured, based on the prediction expression of the fourth step or the corrected prediction expression of the fifth step.

12. A system for predicting lifespan characteristics of a lithium secondary battery, the system comprising: a first measurement unit including an impedance spectroscopic analysis device; a second measurement unit that is configured to measure a capacity for each cycle while subjecting a lithium secondary battery in a form of a non-block cell to an electrochemical reaction; a data processing unit that is configured to calculate a charge amount and a charge amount for each cycle from an impedance spectroscopic analysis result data of the lithium secondary battery in a form of a blocking cell derived from the first measurement unit; and a calculation unit that is configured to derive a prediction expression of the capacity for each cycle of a lithium secondary battery, from the capacity for each cycle of the lithium secondary battery in the form of the non-blocking cell that is measured by the second measurement unit and the charge amount for each cycle of the lithium secondary battery in the form of the blocking cell that is calculated by the data processing unit.

13. The system for predicting lifespan characteristics of a lithium secondary battery according to claim 12, wherein the data processing unit is configured to calculate the charge amount for each cycle, from a relation data of an imaginary capacitance for each frequency included in the impedance spectroscopic analysis result data.

14. The system for predicting lifespan characteristics of a lithium secondary battery according to claim 12, further comprising an artificial neural network learning unit configured to perform artificial neural network learning based on the impedance spectroscopic analysis result data that is inputted from the first measurement unit, and to correct the prediction expression of the capacity for each cycle derived from the calculation unit.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0091] FIG. 1 is a diagram schematically showing diffusion and adsorption reactions of lithium ions that occur during impedance spectroscopic analysis inside a lithium secondary battery in a form of a blocking cell used in a prediction method according to an embodiment of the present disclosure.

[0092] FIG. 2 is a diagram showing an example of a Nyquist plot derived as a result of impedance spectroscopy in a prediction method according to an embodiment of the present disclosure.

[0093] FIG. 3a is a diagram showing an example of a relation graph of real capacitance (F/cm.sup.2) for each frequency (Hz) derived from a Nyquist plot in a prediction method according to an embodiment of the present disclosure, and FIG. 3b is a diagram showing an example of a relation graph of an imaginary capacitance (F/cm.sup.2) for each frequency (Hz) derived from the Nyquist plot.

[0094] FIG. 4 is a diagram showing a comparison of the graph of the calculation result of the amount of charge for each cycle (red) and the prediction expression graph of the capacity per cycle (blue) for a lithium secondary battery in a form of a blocking cell derived from the prediction method according to an embodiment of the disclosure, and the measured graph of the capacities for each cycle (black) for an actual lithium secondary battery in a form of a non-blocking cell.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0095] Hereinafter, preferred embodiments are described to help the understanding of the present disclosure. However, the following examples are for illustrative purposes only and the present disclosure is not intended thereby.

Preparation Example: Preparation of Lithium Secondary Battery in a Form of a Non-Blocking Cell and a Blocking Cell

[0096] First, a lithium secondary battery in the form of a non-blocking cell was prepared by the following method.

[0097] LiNi.sub.0.6Co.sub.0.1Mn.sub.0.1O.sub.2 was used as a cathode active material. 90 wt % of this LiNi.sub.0.6Co.sub.0.1Mn.sub.0.1O.sub.2 cathode active material, 5 wt % of Super C65 conductive material and 5 wt % of PVdF as a binder was added to NMP as a solvent, and mixed to prepare a cathode slurry. The cathode slurry was applied to an aluminum foil having a thickness of 20 μm, and then rolled and dried to produce an active material layer and a cathode.

[0098] Meanwhile, 1 mm lithium metal was used as the anode active material.

[0099] The anode and cathode respectively produced above were cut, a porous polyethylene separator was interposed between the cathode and the anode, and then an electrolyte solution in which 1M of LiPF.sub.6 was dissolved in a mixed solvent of EC:DEC=1:1 (volume ratio) was injected to produce a lithium secondary battery in a form of a non-blocking cell. For such a lithium secondary battery in a form of a non-blocking cell, a total of 75 cycles of electrochemical reaction (charge/discharge) proceeded at voltage range of 3.0 to 4.2V under the conditions of charge CC (0.5 C), CV (0.05 C) and discharge CC (0.5 C), CV (0.05 C).

[0100] In the process of proceeding the electrochemical reaction of the 75 cycles, the electrochemical reactions for each cycle of 10, 20, 30, 40, 50, 60, and 70 cycles were performed, and then the lithium secondary battery in a form of a non-blocking cell was completely discharged to control the SOC to 0%. Then, the lithium secondary battery in a form of a non-blocking cell was disassembled, two identical cathodes were faced each other instead of the anodes, the same separator was interposed therebetween, the same electrolyte solution was injected to produce a lithium secondary battery in a form of a blocking cell corresponding to each cycle.

Example: Derivation of a Prediction Expression of Capacity for Each Cycle of a Lithium Secondary Battery and Evaluation of its Reliability

[0101] In the Preparation Example, after the electrochemical reaction of 10, 20, 30, 40, 50, 60 and 70 cycles, impedance spectroscopic analysis was performed for the lithium secondary batteries in the form of blocking cells respectively manufactured by the following method.

[0102] This analysis was performed using impedance spectroscopy device (manufacturer: Biologic; product name: SP-300), and proceeded under the application of an AC signal having an amplitude of 10 mV and a frequency of 106 to 10-4 Hz.

[0103] From these impedance spectroscopic analysis results, a Nyquist plot for a battery in the form of a blocking cell for each cycle of Preparation Example was derived, which was shown in FIG. 2. Then, the real impedance (Ω*cm.sup.2), imaginary impedance (Ω*cm.sup.2), and complex impedance (Ω*cm.sup.2) for each frequency (Hz) were derived from the Nyquist plot, respectively, which were then substituted into the following Equation 1, and real capacitance (F/cm.sup.2) and imaginary capacitance (F/cm.sup.2) for each frequency were derived, respectively.

[00002]$\begin{array}{cc}{C}^{\prime }=\frac{-Z^{″}\left(\omega \right)}{\omega {.Math.Z\left(\omega \right).Math.}^2}{C}^{″}=\frac{-Z^{\prime }\left(\omega \right)}{\omega {.Math.Z\left(\omega \right).Math.}^2}& \left[\mathrm{Equation}1\right]\end{array}$

in the above Equation 1, C′ represents the real capacitance (F/cm.sup.2) of the lithium secondary battery, C″ represents the imaginary capacitance (F/cm.sup.2), w represents the angular velocity defined as 2*π*frequency, z′(w) represents the real impedance (Ω*cm.sup.2) for each angular velocity, z″(w) represents the imaginary impedance (Ω*cm.sup.2) for each angular velocity, and z(w) represents the total complex impedance (Ω*cm.sup.2) calculated from the real impedance and the imaginary impedance.

[0104] From the above derivation results, a relation graph of real and imaginary capacitances (F/cm.sup.2) for each frequency (Hz) for the battery in a form of a blocking cell for each cycle was derived, and shown in FIGS. 3a and 3b, respectively. In addition, as shown in FIG. 3b, in the relation graph of the number of imaginary capacitances for each frequency, the baseline of the single distribution curve (red line in FIG. 3b) was set and then the area under the peak (A) of this single distribution curve was integrated, and substituted into the relation expression of area (A)=0.682*charge amount Co. Thereby, the charge amount (F/cm.sup.2) for each cycle of the blocking cell type battery was calculated, respectively. The data of the charge amount for each cycle calculated and collected through this process is shown in the red graph of FIG. 4.

[0105] Separately from this, in the relation graph of the imaginary capacitance for each frequency, the frequency value of the x-axis corresponding to the peak point was taken as the reciprocal, the time constant and output data of the battery were separately calculated. In addition, the peak distribution (σ) data of the relation graph of capacitance for each frequency were calculated together.

[0106] Meanwhile, separately from the above process, the lithium secondary battery in a form of a non-blocking cell produced in Preparation Example was subjected to a charge/discharge process of 75 cycles. The capacity (mAh/g) for each cycle was measured, and shown as a black graph in FIG. 4.

[0107] Then, the charge amount data for each cycle for the battery in a form of a blocking cell and the capacity data for each cycle for the battery in a form of a non-blocking cell were made to correspond with each other. Thus, the prediction data for predicting the change pattern of capacity y for each x cycle were collected. Among these data, by linear regression of the predicted data of capacity y for each cycle x collected at 30 or more cycles, the prediction expression for capacity per cycle was derived. On the other hand, by using the peak distribution (σ) data of the relation graph of the time constant of the battery and the capacitance for each frequency described above, artificial neural network learning was performed, which was fed back to correct the prediction expression for capacity for each cycle derived above.

[0108] Through the process described above, a graph (blue in FIG. 4; y=−1.40184x+64.2) of the final corrected capacity for each cycle was derived, respectively, and shown in FIG. 4. Comparing this with the actual graph of the capacity (mAh/g) for each cycle shown in the black graph of FIG. 4, it is confirmed that the prediction expression of the capacity for each cycle derived in Examples reflects the actual measurement results with very high reliability.