TW201913428A - State prediction method for secondary battery, charge control method, and system - Google Patents

Abstract

The present invention provides a state prediction method for a secondary battery, which is available for practical use in which charge and discharge are performed randomly. This method comprises: a step S1 for acquiring an actual measured value corresponding to a charge/discharge capacity Q and a deformation amount T of a secondary battery 2; a step S6 for extracting, from time-series data of the actual measured value, at least one inflection point P1 (P2) of a characteristic curve Ln indicating the relationship between the charge/discharge capacity Q and the deformation amount T of the secondary battery; a step S8 for acquiring past characteristic data having at least a full charge time point Pf, a zero remaining amount time point Pe and stage inflection points P1, P2 out of a characteristic curve L1 indicating the relationship between the charge/discharge capacity and the deformation amount of the secondary battery; and a step S9 for generating predicted characteristic data indicating a characteristic curve L2 in which a portion lacking from the time-series data of the actual measured value is interpolated by performing processing to fit the characteristic curve L1 indicated by the past characteristic data to the characteristic curve Ln indicated by the time-series data of the actual measured value using the extracted inflection point as a reference.

Description

 Secondary battery state prediction method, charging control method, system and program  

The present invention relates to a method of predicting the state of a secondary battery, a charging control method, a system, and a program.

In recent years, a sealed secondary battery (hereinafter sometimes referred to simply as "secondary battery") represented by a lithium ion secondary battery is used not only as a power source for mobile devices such as mobile phones and notebook computers, but also as electric power. A power source for electric vehicles such as automobiles or hybrid vehicles. The secondary battery is deteriorated by repeated charge and discharge cycles, and as the deterioration progresses, it is difficult to accurately grasp the state of the residual capacitance, the deterioration capacitance of the battery, and the like.

Patent Document 1 describes estimating the residual capacitance based on the relationship between the pressure between the batteries and the SOC (State of charge). However, this method must obtain a charging behavior from a full charge to a cycle close to full discharge, which is difficult to infer in practical use in which charging and discharging are randomly performed.

Patent Document 2 describes measuring the thickness of a secondary battery and measuring the residual capacitance based on the thickness. However, the change in the residual capacitance accompanying the deterioration is not considered.

[Previous Technical Literature]

[Patent Literature]

[Patent Document 1] Japanese Patent Laid-Open Publication No. 2016-101048

[Patent Document 2] Japanese Patent Laid-Open Publication No. 2004-14462

The present invention has been made in view of such a problem, and an object of the present invention is to provide a secondary battery state prediction method, a charging method, a system, and a program that can be used in actual use for charging and discharging at random.

The method for predicting the state of the secondary battery of the present invention includes the steps of: obtaining an actual measured value corresponding to the charge and discharge capacitance and the deformation amount of the secondary battery; and extracting the charge and discharge capacitance of the secondary battery from the time series data of the measured value a step of at least one inflection point in a characteristic curve relating to the amount of deformation; obtaining a characteristic curve indicating a relationship between a charge and discharge capacitance of the secondary battery and a deformation amount, at least a time period of full charge, a time point of remaining power, and a time point of zero a step of the previous characteristic data of the inflection point of the stage; and fitting, by using the extracted inflection point, a characteristic curve indicated by the previous characteristic data and a characteristic curve indicated by the time series data of the measured value, And a step of generating a predicted characteristic data, wherein the predicted characteristic data shows a characteristic curve obtained by interpolating a portion of the time-series data of the measured value.

In the characteristic curve indicating the relationship between the charge and discharge capacitance of the secondary battery and the amount of deformation, there is an inflection point in which the slope of the two characteristic curves largely changes with the change of the phase. According to the method, the inflection point in the characteristic curve is extracted from the time series data of the measured value, and the previous characteristic data is matched with the measured value data based on the extracted inflection point, so even if there is no self Measurement data for a long period of time from full charge to full discharge may be ensured as long as there is a certain amount of measured data from a full charge to an inflection point, from one of the inflection points to another, such as a recurve point. Predicted characteristic data with a certain degree of precision.

Therefore, in actual use in which a series of charging and discharging are not performed from full charge to full discharge, and charging and discharging are performed at random, it is possible to generate predictive characteristic data that ensures a certain degree of accuracy, thereby predicting the state of the secondary battery. .

Characteristic curve shown in the initial characteristics of L0‧‧‧

Characteristic curve indicated by Ln‧‧‧ measured value

Characteristic curve shown in the previous characteristic data of L1‧‧

Characteristic curve indicated by L2‧‧‧ predictive characteristic data

P1, P2‧‧‧ recurve points

Pf‧‧‧ Fully charged time

Pe‧‧‧Remaining power zero time point

The biggest point of PL‧‧‧ thickness

Th _P1 , Th _P2 ‧‧‧ threshold

Qr‧‧‧Residual capacitance

6A‧‧‧ memory

6B‧‧‧ processor

Fig. 1 is a block diagram showing an example of a system for performing a secondary battery state prediction method.

Fig. 2A is a perspective view schematically showing a sealed secondary battery.

Figure 2B is a cross-sectional view taken along line A-A of Figure 2A.

Fig. 3 is a graph showing a characteristic curve shown in the previous characteristic data.

Fig. 4 is a graph showing time-series data of measured values and characteristic curves shown by previous characteristic data.

Fig. 5 is a graph showing the time series data of the measured values and the differential values of the measured values.

Fig. 6A is an explanatory diagram related to processing for generating predicted characteristic data.

Fig. 6B is an explanatory diagram related to the process of generating predicted characteristic data.

Fig. 6C is an explanatory diagram related to the process of generating predicted characteristic data.

Fig. 7A is an explanatory diagram related to processing for generating predicted characteristic data.

Fig. 7B is an explanatory diagram related to the process of generating predicted characteristic data.

Fig. 7C is an explanatory diagram related to the process of generating predicted characteristic data.

Fig. 7D is an explanatory diagram related to the process of generating predicted characteristic data.

Fig. 7E is an explanatory diagram related to the process of generating predicted characteristic data.

Fig. 8 is an explanatory diagram related to calculation processing of the remaining capacitance.

Fig. 9A is an explanatory diagram relating to fitting processing of initial characteristic data and predicted characteristic data.

Fig. 9B is an explanatory diagram relating to fitting processing of initial characteristic data and predicted characteristic data.

FIG. 10A is an explanatory diagram related to a process of calculating a deterioration state.

FIG. 10B is an explanatory diagram related to the calculation process of the deterioration state.

FIG. 10C is an explanatory diagram related to the calculation process of the deterioration state.

FIG. 11A is an explanatory diagram related to a process of calculating a thickness change amount until lithium deposition.

FIG. 11B is an explanatory diagram related to a process of calculating a thickness change amount until lithium is deposited.

Fig. 12 is an explanatory diagram related to charging control.

Figure 13 is a flow chart showing a state prediction processing routine executed in the system.

Hereinafter, embodiments of the present invention will be described.

Fig. 1 shows a system mounted in an electric vehicle such as an electric car or a hybrid electric vehicle. This system includes a battery module 1 in which a battery pack composed of a plurality of sealed secondary batteries 2 is housed in a casing. In the present embodiment, four secondary batteries 2 are connected in parallel and two in series, but the number of batteries or the connection form is not limited thereto. Only one battery module 1 is shown in FIG. 1, but it is actually mounted in the form of a battery pack including a plurality of battery modules 1. In the battery pack, a plurality of battery modules 1 are connected in series, and these are housed in a casing together with various devices such as a controller. The housing of the battery pack is shaped to fit the shape of the vehicle, such as a shape that conforms to the shape of the chassis of the vehicle.

The secondary battery 2 shown in FIG. 2 is configured as a battery (single cell) in which the electrode group 22 is housed inside the sealed outer casing 21. The electrode group 22 has a structure in which the positive electrode 23 and the negative electrode 24 are laminated or wound with the separator 25 interposed therebetween, and the separator 25 holds the electrolytic solution. In the secondary battery 2 of the present embodiment, a laminate film such as an aluminum laminate foil is used as the laminate battery of the exterior body 21. Specifically, the secondary battery 2 of the present embodiment is a laminated lithium ion having a capacitance of 1.44 Ah. Secondary battery. The secondary battery 2 is formed into a thin rectangular parallelepiped shape as a whole, and the X, Y, and Z directions correspond to the longitudinal direction, the width direction, and the thickness direction of the secondary battery 2, respectively. Further, the Z direction is also the thickness direction of the positive electrode 23 and the negative electrode 24.

A detection sensor 5 that detects deformation of the secondary battery 2 is mounted on the secondary battery 2. The detection sensor 5 includes a polymer matrix layer 3 and a detection unit 4 that are attached to the secondary battery 2 . The polymer matrix layer 3 is dispersedly contained in a filler corresponding to the deformation of the polymer matrix layer 3 to impart a change to the external field. The polymer matrix layer 3 of the present embodiment is formed into a sheet shape by a soft deformable elastomer material. The detecting unit 4 detects a change in the external field. When the secondary battery 2 is expanded and deformed, the polymer matrix layer 3 is deformed corresponding thereto, and the detecting portion 4 detects a change in the field accompanying the deformation of the polymer matrix layer 3. In this way, the deformation of the secondary battery 2 can be detected with high sensitivity.

In the example of FIG. 2, since the polymer matrix layer 3 is attached to the package 21 of the secondary battery 2, the polymer matrix layer 3 can be deformed in accordance with the deformation (mainly expansion) of the exterior body 21. . On the other hand, the polymer matrix layer 3 may be attached to the electrode group 22 of the secondary battery 2, and with this configuration, the polymer matrix layer 3 may be deformed in accordance with the deformation (mainly expansion) of the electrode group 22. . The deformation of the detected secondary battery 2 can be a deformation of either the outer casing 21 or the electrode group 22.

The signal detected by the detecting sensor 5 is transmitted to the control device 6, whereby information relating to the deformation of the secondary battery 2 is supplied to the control device 6.

<Secondary battery state prediction system>

As shown in FIG. 1, the secondary battery state prediction system includes a previous characteristic data acquisition unit 60, an actual measurement value acquisition unit 61, an inflection point extraction unit 62, a predicted characteristic data generation unit 63, a residual capacitance calculation unit 64, and initial characteristic data. The acquisition unit 65 and the degradation information generation unit 66. In the present embodiment, each of the units 60 to 66 is realized by causing the processor 6 such as a CPU to execute a program in the control device 6, but the present invention is not limited thereto. For example, it can also be implemented by using a remotely located information processing device via a communication network. In addition, the remaining capacitance calculation unit 64, the initial characteristic data acquisition unit 65, and the deterioration information generation unit 66 may be omitted as necessary.

<Getting the previous characteristic data>

The previous characteristic data acquisition unit 60 acquires the previous characteristic data of the secondary battery 2. As shown in FIG. 3, the characteristic data in the characteristic curve L1 indicating the relationship between the charge and discharge capacitance Q of the secondary battery and the deformation amount T has at least a full charge time point Pf, a remaining charge time zero point Pe, and a phase inflection point P1. , P2. The reason for this is that if the four points are present, the characteristic curve L1 can be reproduced to some extent. The state of the secondary battery 2 can be expressed by the characteristic curve L1.

In the graph of Fig. 3, the horizontal axis represents the discharge capacity Q at which the charging time point Pf is the origin, and the vertical axis represents the deformation amount T of the secondary battery 2 detected. As the discharge capacity Q increases from the fully charged time point Pf, the amount of deformation T of the secondary battery 2 changes in the direction in which the thickness of the secondary battery 2 decreases. The reason for this is that in the secondary battery 2 that is charged, the expansion of the electrode group 22 due to the volume change of the negative electrode active material (hereinafter sometimes referred to as "electrode expansion") occurs, and the electrode expands with discharge. Reduced. As shown in FIG. 3, the shape of the two inflection points P1 and P2 (change in slope) is formed as shown in FIG. For example, in the case where a lithium ion secondary battery using graphite is used for the negative electrode, the crystal state of the graphite changes in order as the discharge starts from the charging time point Pf. The reason for this is that the distance between the graphene layers is gradually increased with the insertion amount of lithium ions, and thus the negative electrode active material is expanded. In summary, the volume of the active material changes stepwise due to phase changes and is reflected in the characteristic curve L1. If the discharge capacitance Q is further increased, the remaining electric quantity zero time point Pe is reached.

In the present embodiment, there is also information between the full charge time point Pf, the remaining charge time zero point Pe, and the four points of the inflection points P1, P2. The characteristic data may be discrete data in which data of a plurality of points is set between 4 points, and continuous data in which 4 points are connected by a curve. The previous characteristic data is not the initial characteristics set at the time of shipment of the secondary battery 2, but the past data from the start of use to the current time point. If it is the data of the most recent charging cycle, the current time point is smaller than the past data, so it is better. The previous characteristic data is memorized in the memory 6A of the control device 6, and the previous characteristic data acquisition unit 60 acquires data from the memory 6A.

<Acquisition of measured data>

The actual value acquisition unit 61 obtains an actual measurement value corresponding to the charge and discharge capacitance and the deformation amount of the secondary battery 2. The value corresponding to the deformation amount T of the secondary battery detects the value detected by the sensor 5. In the present embodiment, the amount of change in voltage or magnetic flux density is expressed, but the present invention is not limited thereto, and various changes can be made as long as it is a physical quantity corresponding to the thickness or the amount of deformation. The charge and discharge capacitor of the secondary battery 2 is a general term for the discharge capacitor and the charge capacitor. In the present embodiment, specifically, the discharge capacity is a discharge capacitance from the time of full charge, but is not limited thereto. The value corresponding to the charge and discharge capacitance can be expressed by current, charge amount, or discharge amount, and various changes can be made. The measured value corresponding to the charge and discharge capacitance and the amount of deformation of the secondary battery 2 is repeatedly obtained and becomes time-series data. Fig. 4 shows the characteristic curve Ln represented by the time series data of the measured values by a solid line. In the example of FIG. 4, an example is shown in which the actual measurement is performed from the fully charged time point Pf, the discharge is continued, and the inflection point P1 has not yet been reached. The characteristic curve L1 shown by the broken line in Fig. 4 is exaggeratedly illustrated in a different manner in comparison with the characteristic curve L1 shown in the previous characteristic data shown in Fig. 3.

Furthermore, the characteristic curve Ln is indicated by a solid line in FIG. 4, but in practice, the raw data as the measured value in many cases contains a large amount of noise that vibrates along the vertical axis, and the data per unit discharge capacitance is along multiple The point of vibration up and down exists. In order to remove the noise on the vertical axis and facilitate data processing, a filtering process such as a moving average or a central value is performed on the vertical axis, and the data per unit discharge capacitance is set to one curve. Thereby, the differential value indicating the slope of the characteristic curve Ln can be calculated.

<Extraction of recurve points>

The inflection point extraction unit 62 extracts at least one inflection point (P1 or P2) of the characteristic curve Ln indicating the relationship between the charge and discharge capacitance of the secondary battery 2 and the amount of deformation from the time series data of the actual measurement values. Specifically, the inflection point is extracted based on the differential value (ΔT/ΔQ) of the deformation amount T associated with the charge and discharge capacitance Q determined by the actual value. The reason is that the differential value is the slope of the characteristic curve Ln, and the portion where the variation of the differential value is large is the inflection point. The amount of change in the differential value may be greater than a certain value as an extraction condition to extract an inflection point.

In the present embodiment, in order to improve the extraction accuracy, the extraction condition is set in accordance with the charge and discharge capacitance Q indicated by the obtained measured value, and the amount of deformation T associated with the charge and discharge capacitance Q determined based on the obtained measured value is used. When the differential value (ΔT/ΔQ) satisfies the extraction condition, the measured value is extracted as an inflection point. The embodiment will be described later.

As shown in FIG. 5, the measured values are continuously obtained, and as shown in the graph above the graph, discrete data indicating the relationship between the discharge capacity Q and the amount of deformation (here, the voltage value of the Hall element) is obtained. If the discrete data is subjected to differential processing, the differential value [ΔmV/ΔmAh] of the deformation amount associated with the discharge capacitance is obtained as shown in the graph below the graph. Each unit charge-discharge capacitor (unit discharge capacitor) has one such differential value, and if it is illustrated as a graph below the graph, it becomes one point.

Regarding the setting of the extraction condition, the charge and discharge capacitance Q _Pn indicated by the measured value obtained at a certain time point Pn as shown in FIG. 4 enters the charge and discharge capacitance Q _P1 (the inversion capacitance P1 (P2) in the previous characteristic data) ( When Q _P2 ) is a situation within a specific range of the center, the threshold value Th _P1 (Th _P2 ) of the differential value is set based on the previous characteristic data as shown in the graph below in FIG. 5 . For example, when the charge/discharge capacitance Q _Pn shown by the measured value data of a certain time point Pn is located near the inflection point P1 as shown in FIG. 4, the threshold value Th _{P1 of the} differential value is set as shown in the graph below FIG. . Then, the measured value is further obtained (measured), and the inflection point P1 is extracted by passing the differential value based on the measured value through the threshold Th _P1 as shown in FIG. 5. In the graph below the graph of FIG. 5, the extraction condition for extracting the inflection point P1 in the discharge is passed downward through the threshold Th _P1 , and the extraction condition for extracting the inflection point P1 during charging is upward through the threshold Th _P1 . Similarly, the extraction condition for extracting the inflection point P2 in the discharge is upward through the threshold Th _P2 , and the extraction condition for extracting the inflection point P2 during charging passes downward through the threshold Th _P2 .

Using a threshold value Th _P1 to the differential value of the above, Th _P2 of the method, it is to be due to the influence of noise and the like by one-time threshold Th _P1, when the case of Th _P2 also extracted inflection point of danger. Therefore, in addition to setting the differential value threshold values Th _P1 and Th _P2 as the extraction conditions, the following items are set as the extraction conditions: the differential value continuously continues to rise or continues to decrease during a certain period of change in the charge/discharge capacitance. In the present embodiment, since the differential value is calculated for each unit discharge capacity, at least three differential values are continuously increased or continuously decreased as extraction conditions. In this case, the period during which the charge and discharge capacitance changes is a period of three points, and becomes a unit discharge capacitance [ΔmAh] × 3. This period can be changed as appropriate.

<Predicting the generation of characteristic data>

As shown in FIG. 6C and FIG. 7C, the predicted characteristic data generating unit 63 estimates the characteristic curve L1 indicated by the previous characteristic data and the characteristic curve Ln indicated by the time series data of the measured value based on the extracted inflection point. The processing is combined to generate a predictive characteristic data showing a characteristic curve L2 obtained by interpolating the portion of the time series data of the measured value. The predicted characteristic data generating unit 63 stores the generated predicted characteristic data in the memory 6A. In the present embodiment, in order to improve the prediction accuracy of the characteristic curve, the method described below is employed.

<Predictive characteristic data generation processing 1>

Fig. 6A shows an example in which two inflection points P1 and P2 are extracted. The inflection point P2 is the recently extracted inflection point. When the inflection point extraction unit 62 acquires the two inflection points P1 and P2, the prediction characteristic data generation unit 63 calculates the two inflection points P1 and P2 in the previous characteristic data by the expansion and contraction of the characteristic curve. The coefficient that matches the two inflection points P1 and P2 in the time series data of the measured values. An example of the coefficient is a magnification ratio Xr=a'/a on the horizontal axis, a magnification ratio Yr=b'/b on the vertical axis, and a slope.

Then, as shown in FIG. 6B, the entire characteristic curve L1 shown in the previous characteristic data is subjected to the expansion and contract adjustment using the coefficient, and the characteristic curve L1' after the reduction ratio adjustment is generated.

Then, as shown in FIG. 6C, the adjusted characteristic curve L1' is moved so that the newly extracted inflection point P2 in the characteristic curve Ln indicated by the time series data of the measured value corresponds to the adjusted characteristic curve L1'. The inflection point P2 is consistent, and the predicted characteristic data is generated. Thus, as shown in the figure, predictive characteristic data can be generated, and the predicted characteristic data shows a characteristic curve L2 obtained by interpolating the portion of the time-series data of the measured value (indicated by a broken line in the figure). The characteristic curve L2 shown in the prediction characteristic data includes the characteristic curve Ln and the characteristic curve L1'.

<Predictive characteristic data generation processing 2>

Fig. 7A shows an example in which one of the inflection points P1 is extracted and the time-series data of the actual measurement value includes the measured value corresponding to any one of the full-charge time point Pf or the remaining power-time zero time point Pe. When the inflection point extraction unit 62 extracts one inflection point P1, the prediction characteristic data generation unit 63 calculates the "full-charge time point Pf or the remaining electric quantity zero time in the previous characteristic data by the expansion and contraction of the characteristic curve. The coefficient of the point Pe and the point 2 of the inflection point P1 are the same as the corresponding point in the time series data of the measured value. In the example of FIG. 7A, a coefficient for matching the full-charge time point Pf with the inflection point P1 by the vertical and horizontal expansion and contraction is calculated. An example of the coefficient is a magnification ratio Xr=a'/a on the horizontal axis, a magnification ratio Yr=b'/b on the vertical axis, and a slope.

Then, as shown in FIG. 7B, the entire characteristic curve L1 shown by the previous characteristic data is subjected to expansion and contract adjustment using the coefficient, and the characteristic curve L1' after the reduction ratio adjustment is generated.

Then, as shown in FIG. 7C, the adjusted characteristic curve L1' is moved so that the newly extracted inflection point P1 in the characteristic curve Ln indicated by the time series data of the measured value corresponds to the adjusted characteristic curve L1'. The inflection point P1 is identical, and the predicted characteristic data is generated. Thus, as shown in the figure, predictive characteristic data can be generated, and the predicted characteristic data shows a characteristic curve L2 obtained by interpolating the portion of the time-series data of the measured value (indicated by a broken line in the figure). The characteristic curve L2 shown in the prediction characteristic data includes the characteristic curve Ln and the characteristic curve L1'.

<Predictive characteristic data generation processing 3>

7D shows an example in which the time-series data of the measured value does not include the actual measured value corresponding to any one of the full-charge time point Pf or the remaining power-time zero time point Pe, although one inflection point P1 is extracted. As shown in FIG. 7D and FIG. 7E, the predicted characteristic data generating unit 63 does not enlarge or reduce the characteristic curve L1 indicated by the previous characteristic data, but causes the newly extracted inverse curved point P1 with the coefficient 1 (equal magnification). The characteristic curve L1 shown in the previous characteristic data is interpolated in such a manner that the inflection point P1 in the previous characteristic data is matched to generate the predicted characteristic data.

The predicted characteristic data includes a full-charge time point Pf, a remaining power zero time point Pe, and two inflection points P1 and P2. 6A to 6C and Figs. 7A to 7B show the findings, and the order is not limited thereto.

Further, as compared with the generation processing 1 shown in FIGS. 6A to 6C and the generation processing 2 shown in FIGS. 7A to 7C, the reproduction accuracy of the characteristic curve of the secondary battery 2 may be deteriorated, and the least square method or the like may be used. The method is performed as a fitting process. For example, it can also be executed by a commercially available software such as Solver (registered trademark) in Excel (registered trademark) manufactured by Microsoft Corporation.

<Calculation of residual capacitance>

The calculation of the residual capacitance is performed using the predicted characteristic data and the measured value at the time point at which the residual capacitance is predicted. Specifically, as shown in FIG. 8, the residual capacitance calculation unit 64 calculates the charge and discharge capacitance Q _Pn indicated by the actual measured value at the time point of the predicted residual capacitance and the charge and discharge capacitance Q of the remaining charge amount zero time point Pe in the predicted characteristic data. The difference of _Pe is taken as the residual capacitance Qr.

<Generation of deterioration information>

In order to generate degradation information, the predicted characteristic data obtained above and the initial characteristic data are used. Degradation information may be generated using only the predicted characteristic data as appropriate, as will be described below. Examples of the deterioration information include a side reaction balance of the electrode, a degree of change in the mass of the active material which contributes to charge and discharge, and a thickness change amount until the lithium is precipitated.

As shown in FIG. 9A, the initial characteristic data acquisition unit 65 acquires at least the full charge time point Pf, the remaining charge time zero point Pe, and the characteristic curve L0 indicating the relationship between the charge/discharge capacity Q of the secondary battery 2 and the deformation amount T. Characteristics of the initial characteristics of the inflection points P1 and P2. The initial characteristic data acquisition unit 65 acquires data from the memory 6A. The initial characteristic data is obtained by using the secondary battery 2 in the initial stage which has not deteriorated as a reference state, and is obtained by, for example, the secondary battery 2 at the time of manufacture or before shipment, and the information related to the characteristic curve L0 is previously memorized in control. In the memory 6A provided in the device 6. In the charging and discharging step of determining the characteristic curve L0, the secondary battery 2 before leaving the factory is placed in a thermostatic chamber at 25 ° C, and after standing for 120 minutes, the charging current of 0.144 A is used for constant current charging to 4.32 V. After 4.32V, the constant voltage is charged until the current value is attenuated to 0.07A, and then the circuit state is maintained for 10 minutes, and the current is discharged to 3.0V with a current of 0.144A. The discharge capacity from the fully charged state to the fully discharged state at this time was 1.44 Ah.

<degree of change in the quality of active materials that contribute to charge and discharge>

As shown in FIG. 9A, the degradation information generation unit 66 calculates a correspondence between the "inversion characteristic points (1), the two inflection points P1, P2" and the "predictive characteristic data (L2) by the expansion and contraction of the characteristic curve. The expansion ratio (a'/a) of the charge and discharge capacitors at which the two inflection points P1 and P2 are identical is the degree of change in the mass of the active material which contributes to charge and discharge. In Fig. 9A, the enlargement ratio of the horizontal axis = the expansion ratio of the charge and discharge capacitor (a'/a).

Further, as shown in FIG. 9B, the expansion ratio of the charge and discharge capacitors can be calculated. The degradation information generating unit 66 uses the ratio (a'/a) between the two inflection points P1, P2, the ratio (b'/b) between one of the inflection points P1 and the full charge time point Pf, and the other The ratio (c'/c) between the inflection point P2 and the remaining electric quantity zero time point Pe is set to be larger than the weight of the other sections by the weighting between the two inflection points P1 and P2, and is calculated by the total. For example, a case where the weighting of b:a:c=1:8:1 is totaled will be described. In this case, the expansion ratio of the charge and discharge capacitance becomes {b'/b × 1 + a' / a × 8 + c' / c × 1} / {1 + 8 + 1}. As described above, it is considered that the full-charge time point Pf and the remaining charge time point Pe are close to each other as compared with the ratio between only two inflection points. Therefore, the degree of matching of the entire characteristic curve is improved, and the calculation accuracy is improved.

<degraded state>

As shown in FIGS. 10A to 10C, the deterioration information generating unit 66 uses "the two inflection points P1, P2 in the initial characteristic data (L0)" and the "predicted characteristic data (L2) by the expansion and contraction of the characteristic curve. The coefficient (the expansion ratio of the charge/discharge capacitance and the expansion ratio of the deformation amount) of the two inflection points P1 and P2" is adjusted by the expansion and adjustment of the entire characteristic curve (L0) shown in the initial characteristic data to make the initial characteristic data. The two inflection points P1 and P2 of the adjusted characteristic curve (L0') and the characteristic curve (L2) of the predicted characteristic data are identical to each other, and the left and right dispersion amounts of the charging and discharging capacitances of the charging time points Pf are calculated. The average value [D1+D2)/2 of the left and right dispersion amount D2 of D1 and the remaining charge amount zero time point Pe between each other is taken as the deterioration state of the battery.

In FIGS. 9A and 9B, only the enlargement ratio of the horizontal axis (the expansion ratio of the charge and discharge capacitance) is calculated, and the enlargement ratio of the vertical axis (the expansion ratio of the deformation amount) is also calculated here.

Specific steps are as follows.

First, as shown in FIG. 10A, the deterioration information generating unit 66 calculates two recursions of "two inflection points P1, P2 in the predicted characteristic data (L2)" and "initial characteristic data (L0). The coefficients at which points P1 and P2 are consistent with the expansion and contraction of the characteristic curve. The calculation of the coefficients is the same as the method shown in Figs. 6A to 6C.

Then, as shown in FIG. 10B, the deterioration information generating unit 66 performs expansion and contraction adjustment on the entire characteristic curve (L0) indicated by the initial characteristic data using the coefficient, and generates information indicating the adjusted characteristic curve (L0').

Then, as shown in FIG. 10B and FIG. 10C, the degradation information generation unit 66 reverses the corresponding one of the most recently extracted inflection point P2 and the telescopically adjusted initial characteristic data (L0') in the prediction characteristic data (L2). The characteristic curve (L2, L0') of at least one of the predicted characteristic data or the initial characteristic data is moved in such a manner that the point P2 is identical.

Then, as shown in FIG. 10C, the deterioration information generating unit 66 calculates the left and right dispersion amount D1 and the remaining power amount zero time point Pe of each of the charging and discharging capacitors of the charging time point Pf based on the adjusted initial characteristic curve. The average value [(D1+D2)/2] of the left and right dispersion amount D2 of the discharge capacitance is taken as the deterioration state of the battery.

When the average value is large, when the battery is deteriorated due to the use of the battery, both the positive electrode and the negative electrode are offset with respect to the amount of side reaction, and depending on which direction is shifted to the left and right, it is possible to discriminate whether the positive electrode or the negative electrode has an increased side reaction. .

Further, in FIGS. 10A to 10C, the adjusted initial characteristic curve L0' is moved after the expansion and contraction adjustment of the initial characteristic curve L0, but the present invention is not limited thereto. For example, instead of the adjusted initial characteristic curve L0', the predicted characteristic curve L2 may be moved, or both curves may be moved. Further, after the two curved lines are first moved to match the recently detected inflection point P2, the initial characteristic curve L0 is subjected to the expansion and contraction adjustment, and the movement and the expansion and contraction adjustment can be simultaneously performed.

The coefficient used in the expansion and contraction adjustment of the initial characteristic curve L0 in FIGS. 10A to 10C can be calculated by the same method as the method of calculating the expansion ratio of the charge and discharge capacitor shown in FIG. 9B. That is, the deterioration information generating portion 66 uses the ratio between the two inflection points P1, P2, the ratio between one of the inflection points P1 and the full charge time point Pf, and the other inflection point P2 and the remaining electric quantity zero time point. The ratio between the Pe, the weight between the two inflection points P1 and P2 is set to be larger than the weight of the other sections, and is calculated by the total. Here, the coefficients of both the horizontal axis and the vertical axis are calculated.

<Amount of thickness change until lithium is deposited>

When the amount of thickness change until lithium deposition is known, it becomes possible to control charging to prevent lithium deposition. Here, as shown in FIG. 11A, the maximum thickness point PL is set in the initial characteristic data. The maximum thickness PL is the limit point for lithium deposition if it is further charged.

Further, as long as sufficient safety can be ensured, the full-charge time point Pf=the maximum thickness point PL can also be used. In this case, the amount of change from the fully charged time point Pf to the remaining charge zero time point Pe becomes the following T1, and the full charge time point Pf in the predicted characteristic data can be set to the maximum thickness point even if the initial characteristic data is not used. .

The deterioration information generation unit 66 calculates a characteristic curve L0 indicated by the initial characteristic data and a characteristic curve L2 indicated by the prediction characteristic data by the expansion and contraction of the characteristic curve, using the method shown in FIGS. 9A and 9B. Coefficient (expansion rate of charge and discharge capacitor, expansion ratio of deformation amount). In FIGS. 9A and 9B, only the enlargement ratio of the horizontal axis (the expansion ratio of the charge and discharge capacitance) is calculated, and the enlargement ratio of the vertical axis (the expansion ratio of the deformation amount) is also calculated.

Then, as shown in FIG. 11B, the deterioration information generating unit 66 specifies the thickness maximum point PL' in the predicted characteristic data based on the calculated maximum value PL in the initial characteristic data using the calculated coefficient. Then, as shown in the figure, the deterioration information generating unit 66 calculates the deformation amount T1 of the maximum thickness point PL' to the remaining electric quantity zero time point Pe in the predicted characteristic data (L2) as the base point to the lithium at the time of the remaining electric quantity zero time point. The thickness change amount T1 until precipitation.

Although the state prediction method described above is exemplified in the discharge, it can be realized during charging.

<Charge Control>

The control device 6 may be provided with a charge control unit 67 that controls the charging of the secondary battery 2 by using the thickness change amount T1 until the lithium deposition is predicted by the degradation information generating unit 66. In other words, the charging control unit 67 does not exceed the thickness variation amount T1 until the lithium deposition is based on the detection of the amount of deformation of the secondary battery 2 detected by the sensor 5 during charging. Control charging. For example, as shown in FIG. 12, a threshold value T2 smaller than the thickness variation amount T1 may be set, and the current may be controlled in such a manner as to maintain the threshold value T2. As the current control method, ON/OFF control, P control, I control, D control, PD control, PI control, PID control, pulse control, PWM control, etc., in which T2 is set as a target value can be used. Further, T1 can be set as the target value as long as it can be controlled not to exceed T1.

The operation of the above system will be described using FIG.

First, in step S1, the actually-measured value acquisition unit 61 acquires an actual measurement value corresponding to the charge/discharge capacity Q and the deformation amount T of the secondary battery 2. Since this step is performed repeatedly, time series data of the measured values are obtained.

In the next step S2, it is determined whether or not there is a predicted characteristic data. When it is determined that there is no predicted characteristic data (S2: NO), the process proceeds to step S6. When it is determined that there is a predicted characteristic data (S2: YES), the process proceeds to step S3. The processing of step S3 is as follows.

In step S6, the inflection point extraction unit 62 extracts at least one inflection point P1 of the characteristic curve Ln indicating the relationship between the charge and discharge capacitance Q of the secondary battery 2 and the deformation amount T from the time series data of the actual measurement values ( P2). In the present embodiment, the inflection point extraction unit 62 sets the extraction condition in accordance with the charge and discharge capacitance Q _{Pn indicated} by the obtained actual measurement value, and the amount of deformation associated with the charge and discharge capacitance determined based on the obtained actual measurement value. When the differential value [ΔmV/ΔmAh] satisfies the extraction condition, the measured value is extracted as an inflection point. Specifically, when the charge-discharge capacitance Q _Pn shown by the actually measured value enters a specific range centered on the charge-discharge capacitor Q _P1 of the inflection point P1 in the previous characteristic data, it is set based on the previous characteristic data. The threshold value of the differential value is Th _P1 , and the differential value is set as the extraction condition by the threshold Th _P1 . Further, in addition to the setting of the differential value threshold values Th _P1 and Th _P2 as the extraction conditions, the following items are also set as the extraction conditions: the differential value continuously continues to rise or continues to decrease during a certain period in which the charge/discharge capacitance Q changes. In the present embodiment, the differential value is calculated for each unit discharge capacity, and at least three differential values are continuously increased or continuously decreased as extraction conditions.

In the next step S7, it is determined whether or not the inflection point is extracted. When the inflection point is not extracted (S7: NO), it is determined in step S13 whether or not the end condition is satisfied, and the process returns to step S1 before the end condition is satisfied.

When the inflection point is extracted in step S7 (S7: YES), in the next step S8, the previous characteristic data acquisition unit 60 acquires the characteristic curve L1 indicating the relationship between the charge and discharge capacitance of the secondary battery and the amount of deformation. There is at least a previous characteristic data of the full-charge time point Pf, the remaining charge zero time point Pe, and the phase inflection points P1, P2.

In the next step S9, the predicted characteristic data generating unit 63 fits the characteristic curve L1 indicated by the previous characteristic data and the characteristic curve Ln indicated by the time series data of the measured value with reference to the extracted inflection point. A predictive characteristic data is generated, and the predicted characteristic data shows a characteristic curve L2 obtained by interpolating the portion of the time-series data of the measured measured value.

When the actual measurement value acquisition unit 61 extracts one inflection point in the step S6, the prediction characteristic data generation unit 63 calculates the "full charge time point Pf or the remaining power amount in the previous characteristic data by the expansion and contraction of the characteristic curve. The coefficient of 1 point of the zero time point Pe and the 2 point of the inflection point P1 is the same as the "2 points corresponding to the time series data of the measured value", and the entire characteristic curve is adjusted and adjusted by the coefficient to make the inflection point. P1 is consistent, and the predicted characteristic data is generated.

When the actual value acquisition unit 61 extracts two inflection points in the step S6, the prediction characteristic data generation unit 63 calculates the "two inflection points P1, P2 in the previous characteristic data" by the expansion and contraction of the characteristic curve. The coefficient corresponding to the "two inflection points corresponding to the time series data of the measured values" is used to adjust the entire characteristic curve by using the coefficient, so that the recently extracted inflection point P2 is coincident, and the predicted characteristic data is generated.

In the next step S10, it is determined whether two different inflection points are extracted from the time series data of the measured values. Whether or not the different inflection points are judged can be judged by the SOC or the charge and discharge capacitor. The reason for this is that it is considered that when the degradation information is generated based on the predicted characteristic data in step S12, the accuracy of the predicted characteristic data generated based on the two different inflection points is higher than the data generated using only one inflection point. Of course, it is also possible to generate deterioration information in a state in which only one inflection point is extracted.

When it is determined in the step S10 that the two different inflection points are extracted (S10: YES), the initial characteristic data acquisition unit 65 acquires the relationship between the charge and discharge capacitance of the secondary battery and the amount of deformation in the step S11. The characteristic curve L0 has at least the initial characteristic data of the maximum thickness point PL, the full-charge time point Pf, the remaining electric quantity zero time point Pe, and the stage inflection points P1 and P2. The maximum thickness point PL can be omitted.

In the next step S12, the degradation information generation unit 66 obtains at least one of the deterioration capacitance, the degree of change in the mass of the active material that contributes to charge and discharge, or the thickness change amount from the zero point of the remaining amount of electricity to the deposition of lithium. Either.

In step S12, when the degree of change in the mass of the active material that contributes to the charge and discharge is determined, the degradation information generating unit 66 calculates the two inflection points in the initial characteristic data by the expansion and contraction of the characteristic curve. The expansion ratio of the charge and discharge capacitors in which P1 and P2 are identical to the "two inflection points P1 and P2 corresponding to the predicted characteristic data".

In the case where the deterioration state is obtained in step S12, the degradation information generation unit 66 uses the "two inflection points P1, P2 in the initial characteristic data (L0)" and the "prediction" by the expansion and contraction of the characteristic curve. The coefficient (the expansion ratio of the charge/discharge capacitance and the expansion ratio of the deformation amount) at which the two inflection points P1 and P2" in the characteristic data (L2) match, and the entire characteristic curve (L0) shown in the initial characteristic data is adjusted and adjusted. The two inflection points P1 and P2 of the characteristic curve (L0') of the initial characteristic data and the characteristic curve (L2) of the predicted characteristic data are coincident with each other, and the charge and discharge capacitances of the charged time points Pf are calculated. The average value [(D1+D2)/2] of the left and right dispersion amount D2 between the left and right dispersion amount D1 and the remaining charge amount zero time point Pe is used as the deterioration state of the battery.

In the case where the thickness change amount until the lithium deposition is based on the remaining time zero point is obtained in step S12, the deterioration information generating unit 66 calculates the "initial characteristic data" by the expansion and contraction of the characteristic curve. The coefficient of the two inflection points P1, P2" and the "two inflection points P1, P2 in the prediction characteristic data" are used, and the thickness is used in the prediction characteristic data according to the maximum thickness PL in the initial characteristic data. The maximum point PL' is calculated, and the deformation amount T1 of the maximum thickness point PL' in the predicted characteristic data to the remaining electric quantity zero time point Pe is calculated.

When it is set to Pf=PL, the degradation information generation unit 66 calculates the deformation amount T1 of the maximum thickness point PL(Pf) in the predicted characteristic data to the remaining electric quantity zero time point Pe.

When the process of step S12 ends, the process proceeds to step S13.

In step S3, the residual capacitance calculation unit 64 calculates the difference between the charge and discharge capacitance Q _Pn indicated by the actual measured value of the predicted residual capacitance time and the charge and discharge capacitance Q _Pe of the remaining charge amount zero time point Pe in the predicted characteristic data as the remaining Capacitor Qr. That is, if the predicted characteristic data is generated once, the remaining capacitance is calculated every time the measured value data is obtained.

Steps S4 to S5 are used to re-examine the processing of the predicted characteristic data. Specifically, when the two pieces of inflection points P1 and P2 are included in the time-series data of the actually measured value in step S4 and the predicted characteristic data has been generated, it is determined whether or not the specific re-generation condition is satisfied. In the present embodiment, as a specific re-generation condition, it is determined whether or not the difference between the temporarily generated predicted characteristic data and the actual measured value exceeds the threshold. When the difference exceeds the threshold value, in step S5, the predicted characteristic data generating unit 63 regenerates the predicted characteristic data. The reason is that although the predicted characteristic data is temporarily generated based on the measured values, it does not match the measured values. Here, as the re-generation condition, attention may be paid to the error, and various conditions such as reaching a specific charge/discharge capacitance or self-predicted characteristic data for a specific time may be appropriately changed.

In the same manner as the method shown in FIG. 9B, the method for generating the predicted characteristic data is used to calculate the "characteristic curve of the predicted characteristic data" and the "characteristic curve of the time series data of the measured value" by the expansion and contraction of the characteristic curve. Coefficient, the coefficient is used to adjust and adjust the entire characteristic curve so that the recently extracted inflection points are consistent, and then the predicted characteristic data is generated. The coefficient is preferably calculated in such a way that at least the ratio between the two inflection points, the ratio between the latest measured value and the inflection point near the side of the latest measured value, the weighting between the two inflection points The weighting is set to be larger than the other sections, and is calculated by the total. In this way, not only is one interval between the two inflection points, but also the ratio of the two intervals is used, and the latest measured values are also fitted in the same manner, so that the predicted characteristic data conforms to the measured value.

Further, when the time series data of the measured values includes the case where the full charge time point Pf and the remaining power time point Pe are different from the latest measured values, it is preferable to use the full charge time point Pf and the remaining charge time zero time. The ratio between the point Pe and the latest measured value is larger than the ratio between one of the inflection points, the ratio between the two inflection points, the latest measured value and another inflection point close to the latest measured value. The ratio is set to the weighting between the two inflection points to be greater than the weight of the other sections, and is calculated by the total. In this way, by using the ratio of the three sections, the fitting of the full-time time point Pf and the remaining electric quantity zero time point Pe to the latest measured value is performed, and thus the predicted characteristic data conforms to the measured value.

The detecting unit 4 is disposed at a portion capable of detecting a change in the external field, and is preferably attached to a relatively strong portion that is less likely to be affected by the expansion of the secondary battery 2. In the present embodiment, as shown in FIG. 2B, the detecting portion 4 is attached to the inner surface of the casing 11 of the battery module facing the wall portion 28a. The casing 11 of the battery module is formed, for example, of metal or plastic, and there is also a case where a laminated film is used. In the drawing, the detecting portion 4 is disposed close to the polymer matrix layer 3, and may be disposed away from the polymer matrix layer 3.

In the present embodiment, the polymer matrix layer 3 contains a magnetic filler as the filler, and the detecting unit 4 detects an example of a change in the magnetic field as the external field. In this case, the polymer matrix layer 3 is preferably a magnetic elastomer layer in which a magnetic filler is dispersed in a matrix composed of an elastomer component.

Examples of the magnetic filler include a rare earth type, an iron type, a cobalt type, a nickel type, an oxide type, and the like, and a rare earth type which obtains a higher magnetic force is preferable. The shape of the magnetic filler is not particularly limited, and may be any of a spherical shape, a flat shape, a needle shape, a column shape, and a non-fixed shape. The average particle diameter of the magnetic filler is preferably from 0.02 to 500 μm, more preferably from 0.1 to 400 μm, still more preferably from 0.5 to 300 μm. When the average particle diameter is less than 0.02 μm, the magnetic properties of the magnetic filler tend to be lowered. When the average particle diameter exceeds 500 μm, the mechanical properties of the magnetic elastomer layer tend to be lowered and become brittle.

The magnetic filler may also be introduced into the elastomer after magnetization, but is preferably magnetized after being introduced into the elastomer. By magnetizing after being introduced into the elastomer, it is easy to control the polarity of the magnet, and it is easy to detect the magnetic field.

As the elastomer component, a thermoplastic elastomer, a thermosetting elastomer or a mixture of these may be used. Examples of the thermoplastic elastomer include a styrene-based thermoplastic elastomer, a polyolefin-based thermoplastic elastomer, a polyurethane-based thermoplastic elastomer, a polyester-based thermoplastic elastomer, and a polyamide-based thermoplastic elastomer. A polybutadiene-based thermoplastic elastomer, a polyisoprene-based thermoplastic elastomer, a fluororubber-based thermoplastic elastomer, or the like. Further, examples of the thermosetting elastomer include polyisoprene rubber, polybutadiene rubber, styrene-butadiene rubber, polychloroprene rubber, nitrile rubber, and ethylene-propylene rubber. Non-diene synthetic rubbers such as olefinic synthetic rubber, ethylene-propylene rubber, butyl rubber, acrylic rubber, polyurethane rubber, fluororubber, polyoxyxene rubber, and epichlorohydrin rubber, and natural rubber. Among them, a thermosetting elastomer is preferred because it suppresses deterioration of the magnetic elastomer accompanying heat generation or overload of the battery. Further, a polyurethane rubber (also referred to as a polyurethane elastomer) or a polyoxyxylene rubber (also referred to as a polyoxyxene elastomer) is preferred.

The polyurethane elastomer can be obtained by reacting a polyol with a polyisocyanate. In the case where a polyurethane elastomer is used as the elastomer component, the active hydrogen-containing compound is mixed with a magnetic filler, and the isocyanate component is mixed thereto to obtain a mixed solution. Further, a mixed liquid can also be obtained by mixing a magnetic filler with an isocyanate component and mixing the active hydrogen-containing compound. The mixed solution is cast in a mold which has been subjected to a mold release treatment, and then heated to a hardening temperature to be hardened, whereby a magnetic elastic body can be produced. Further, in the case where a polysiloxane elastomer is used as the elastomer component, the magnetic filler is added to the precursor of the polysiloxane elastomer, mixed, placed in a mold, and then heated to harden it. A magnetic elastomer is produced. Further, a solvent may be added as needed.

As the isocyanate component which can be used for the polyurethane elastomer, a compound known in the art of polyurethanes can be used. For example, 2,4-toluene diisocyanate, 2,6-toluene diisocyanate, 2,2'- diphenylmethane diisocyanate, 2,4'- diphenylmethane diisocyanate, 4, 4'- Phenylmethane diisocyanate, 1,5-naphthalene diisocyanate, p-phenylene diisocyanate, m-phenylene diisocyanate, p-xylylene diisocyanate, m-xylylene diisocyanate, etc. An aromatic diisocyanate; an ethylene diisocyanate; an aliphatic diisocyanate such as 2,2,4-trimethylhexamethylene diisocyanate or 1,6-hexamethylene diisocyanate; 4-cyclohexyl diisocyanate, 4,4'-dicyclohexylmethane diisocyanate, isophorone diisocyanate, lower An alicyclic diisocyanate such as a norbornane diisocyanate. These may be used alone or in combination of two or more. Further, the isocyanate component may be modified by urethane modification, allophanate modification, biuret modification, and isocyanurate modification. Preferred isocyanate components are 2,4-toluene diisocyanate, 2,6-toluene diisocyanate, 4,4'-diphenylmethane diisocyanate, more preferably 2,4-toluene diisocyanate, 2,6-toluene Isocyanate.

As the active hydrogen-containing compound, a general user of the art of polyurethane can be used. For example, a polyether polyol represented by polytetramethylene glycol, polypropylene glycol, polyethylene glycol, a copolymer of propylene oxide and ethylene oxide, and polybutylene adipate (polybutylene adipate) may be mentioned. Polybutylene adipate), polyethylene adipate, polyester methyl alcohol represented by 3-methyl-1,5-pentane adipate a polyester polycarbonate polyol exemplified by a reaction of a polycaprolactone polyol, a polyester glycol such as polycaprolactone diol, and an alkylene carbonate; a polyester polycarbonate polyol obtained by reacting an ethyl ester with a polyol, and then reacting the obtained reaction mixture with an organic dicarboxylic acid; obtained by transesterification of a polyhydroxy compound with an aryl carbonate A high molecular weight polyol such as a polycarbonate polyol. These may be used alone or in combination of two or more.

As the active hydrogen-containing compound, in addition to the above high molecular weight polyol component, ethylene glycol, 1,2-propanediol, 1,3-propanediol, 1,4-butanediol, 1,6-hexane may be used. Alcohol, neopentyl glycol, 1,4-cyclohexanedimethanol, 3-methyl-1,5-pentanediol, diethylene glycol, triethylene glycol, 1,4-bis(2-hydroxyl) Ethoxy)benzene, trimethylolpropane, glycerin, 1,2,6-hexanetriol, pentaerythritol, tetramethylolcyclohexane, glucoside, sorbitol, mannitol, half Low molecular weight polyol component such as lactitol, sucrose, 2,2,6,6-tetrakis (hydroxymethyl)cyclohexanol, and triethanolamine, ethylenediamine, toluenediamine, diphenylmethanediamine, diammine A low molecular weight polyamine component such as ethylene triamine. These may be used alone or in combination of two or more. Further, 4,4'-methylenebis(o-chloroaniline) (MOCA), 2,6-dichloro-p-phenylenediamine, 4,4'-methylenebis(2,3-dichloro) may be mixed. Aniline), 3,5-bis(methylthio)-2,4-toluenediamine, 3,5-bis(methylthio)-2,6-toluenediamine, 3,5-diethyl Toluene-2,4-diamine, 3,5-diethyltoluene-2,6-diamine, triethylene glycol di(p-aminobenzoic acid ester), polytetrahydrofuran bis(p-aminobenzoic acid) Ester), 1,2-bis(2-aminophenylthio)ethane, 4,4'-diamino-3,3'-diethyl-5,5'-dimethyldiphenyl Methane, N,N'-di-second butyl-4,4'-diaminodiphenylmethane, 4,4'-diamino-3,3'-diethyldiphenylmethane, 4, 4'-Diamino-3,3'-diethyl-5,5'-dimethyldiphenylmethane, 4,4'-diamino-3,3'-diisopropyl-5, 5'-Dimethyldiphenylmethane, 4,4'-diamino-3,3',5,5'-tetraethyldiphenylmethane, 4,4'-diamino-3,3 ',5,5'-tetraisopropyldiphenylmethane, m-xylylenediamine, N,N'-di-t-butyl-p-phenylenediamine, m-phenylenediamine and p-phenylene A polyamine exemplified by p-xylylenediamine or the like. Preferred active hydrogen-containing compounds are polybutanediol, polypropylene glycol, a copolymer of propylene oxide and ethylene oxide, 3-methyl-1,5-pentanedicarboxylate, more preferably polypropylene glycol. a copolymer of propylene oxide and ethylene oxide.

A preferred combination of an isocyanate component and an active hydrogen-containing compound is 2,4-toluene diisocyanate, 2,6-toluene diisocyanate, and 4,4'-diphenylmethane diisocyanate as an isocyanate component. Or two or more kinds of polybutanediol, polypropylene glycol, a copolymer of propylene oxide and ethylene oxide as a compound containing active hydrogen, and 3-methyl-1,5-pentanedicarboxylate of adipic acid One type or a combination of two or more types. More preferably, it is a combination of 2,4-toluene diisocyanate and/or 2,6-toluene diisocyanate as an isocyanate component and a polypropylene glycol as an active hydrogen-containing compound and/or a copolymer of propylene oxide and ethylene oxide. .

The polymer matrix layer 3 may also be a foam containing dispersed fillers and bubbles. As the foam, a general resin foam can be used, and in consideration of characteristics such as compression set, it is preferred to use a thermosetting resin foam. Examples of the thermosetting resin foam include a polyurethane resin foam, a polyoxymethylene resin foam, and the like, and among them, a polyurethane foam is preferred. As the polyurethane foam, the above isocyanate component or active hydrogen-containing compound can be used.

The amount of the magnetic filler in the magnetic elastomer is preferably from 1 to 450 parts by weight, more preferably from 2 to 400 parts by weight, per 100 parts by weight of the elastomer component. If it is less than 1 part by weight, it is difficult to detect a change in the magnetic field, and if it exceeds 450 parts by weight, the magnetic elastic body itself may become brittle.

In order to achieve rust prevention of the magnetic filler or the like, the sealing material for sealing the polymer matrix layer 3 may be provided to the extent that the flexibility of the polymer matrix layer 3 is not impaired. A thermoplastic resin, a thermosetting resin, or a mixture of these may be used as the sealing material. Examples of the thermoplastic resin include a styrene-based thermoplastic elastomer, a polyolefin-based thermoplastic elastomer, a polyurethane-based thermoplastic elastomer, a polyester-based thermoplastic elastomer, a polyamide-based thermoplastic elastomer, and a polybutylene. Diene thermoplastic elastomer, polyisoprene thermoplastic elastomer, fluorine-based thermoplastic elastomer, ethylene-ethyl acrylate copolymer, ethylene-vinyl acetate copolymer, polyvinyl chloride, polyvinylidene chloride, chlorine Polyethylene, fluororesin, polyamine, polyethylene, polypropylene, polyethylene terephthalate, polybutylene terephthalate, polystyrene, polybutadiene, and the like. Further, examples of the thermosetting resin include diene such as polyisoprene rubber, polybutadiene rubber, styrene-butadiene rubber, polychloroprene rubber, and acrylonitrile-butadiene rubber. Synthetic rubber; non-diene such as ethylene-propylene rubber, ethylene-propylene-butadiene rubber, butyl rubber, nitrile rubber, polyurethane rubber, fluororubber, polyoxyxene rubber, epichlorohydrin rubber Rubber; natural rubber, polyurethane resin, polyoxyl resin, epoxy resin, etc. These films may be laminated, and may be a film containing a metal foil such as an aluminum foil or a metal deposited film on which the metal is vapor-deposited.

The polymer matrix layer 3 may also be one in which the filler is present on one side in the thickness direction thereof. For example, the polymer matrix layer 3 may have a structure in which two regions of the region on the side opposite to the filler and the region on the other side of the filler are relatively small. In a region where a large amount of the side of the filler is contained, the deformation of the external field with respect to the small deformation of the polymer matrix layer 3 is large, so that the sensitivity of the sensor to the low internal pressure can be improved. Further, the region on the other side where the filler is relatively small is relatively soft and easily moved, and by attaching the region, the polymer matrix layer 3 (particularly, the region on one side) is easily deformed.

The existence ratio of the filler in the region on one side is preferably more than 50, more preferably 60 or more, still more preferably 70 or more. In this case, the existence ratio of the packing of the filler on the other side becomes less than 50. The area of the one side of the packing has a maximum concentration of 100, and the area of the other side has a packing partiality of at least zero. Therefore, it may be a laminate structure of an elastomer layer containing a filler and an elastomer layer containing no filler. For the presence of the partial concentration of the filler, it is possible to use a method of allowing the elastomer component to be allowed to stand at room temperature or at a specific temperature, and allowing the natural precipitation by the weight of the filler. Temperature or time changes, while adjusting the existence of packing bias. It is also possible to use a physical force such as centrifugal force or magnetic force to bias the filler. Alternatively, a polymer matrix layer may be formed by using a laminate composed of a plurality of layers having different filler contents.

The existence ratio of the packing partial concentration was measured by the following method. That is, the cross section of the polymer matrix layer was observed at 100 times using a scanning electron microscope-energy dispersive X-ray analyzer (SEM-EDS). The metal element inherent in the filler is obtained by elemental analysis for the region in the thickness direction of the cross section and the two regions in which the cross section is halved in the thickness direction (if the magnetic filler of the embodiment is used, for example It is the amount of the Fe element). Regarding the amount of existence, the ratio of the region on one side to the region in the thickness direction as a whole is calculated, and the ratio of the partial concentration of the filler in the region on one side is calculated. The rate of packing partial packing on the other side is also the same.

The region on the other side where the filler is relatively small may also be a structure formed of a foam containing bubbles. Thereby, the polymer matrix layer 3 is more easily deformed, and the sensitivity of the sensor is improved. Further, the region on one side and the region on the other side may be formed of a foam, and in this case, the entire polymer matrix layer 3 is a foam. The polymer matrix layer in which at least a part of the thickness direction is a foam may be formed of a laminate comprising a plurality of layers (for example, a foam-free layer containing a filler and a foam layer containing no filler).

As the detecting portion 4 that detects a change in the magnetic field, for example, a reed switch, a magnetoresistive element, a Hall element, a coil, an inductor, an MI element, a fluxgate sensor, or the like can be used. Examples of the magnetoresistive element include a semiconductor compound magnetoresistive element, an anisotropic magnetoresistive element (AMR), a giant magnetoresistive element (GMR), and a tunneling magnetoresistive element (TMR). Among them, a Hall element is preferable because it has a high sensitivity in a wide range and is useful as the detecting portion 4. For the Hall element, for example, EQ-430L manufactured by Asahi Kasei Electronics Co., Ltd. can be used.

In the secondary battery 2 in which the gas is expanded, there is a failure such as a fire or a rupture. Therefore, in the present embodiment, when the amount of expansion when the secondary battery 2 is deformed is a specific value or more, the charging and discharging are blocked. . Specifically, when the signal detected by the detecting sensor 5 is transmitted to the control device 6 and the detecting sensor 5 detects a change in the field other than the set value, the control device 6 turns to the switching circuit 7 The signal is transmitted to block the current from the power generating device (or the charging device) 8, thereby forming a state in which the charging and discharging of the battery module 1 is blocked. Thereby, malfunction due to gas expansion can be prevented.

In the above embodiment, the secondary battery is an example of a lithium ion secondary battery, but the invention is not limited thereto. The secondary battery to be used is not limited to a nonaqueous electrolyte secondary battery such as a lithium ion battery, and may be a water-based electrolyte secondary battery such as a nickel hydride battery.

In the above embodiment, an example in which the magnetic field change accompanying the deformation of the polymer matrix layer is detected by the detecting portion is disclosed, and a configuration for detecting changes in other external fields may be employed. For example, the polymer matrix layer contains a conductive filler such as metal particles, carbon black, or a carbon nanotube as a filler, and the detecting portion detects a change in electric field (change in electric resistance and dielectric constant) as an external field.

As described above, the state prediction method of the secondary battery of the present embodiment includes: step S1: obtaining an actual measurement value corresponding to the charge and discharge capacitance Q and the deformation amount T of the secondary battery 2; and step S6: time-series data from the actual measurement value Extracting at least one inflection point P1 (P2) of the characteristic curve Ln indicating the relationship between the charge and discharge capacitance Q of the secondary battery and the deformation amount T; Step 8: Obtaining a charge and discharge capacitance and a deformation amount indicating the secondary battery The characteristic curve L1 of the relationship has at least the previous characteristic data of the full charge time point Pf, the remaining charge zero time point Pe, and the stage inflection point P1, P2; and step 9: the previous characteristic is based on the extracted inflection point The characteristic curve L1 shown in the data is fitted to the characteristic curve Ln shown in the time series data of the measured value to generate prediction characteristic data, which shows the part of the time series data of the interpolated measured value. The characteristic curve L2.

The state prediction system for a secondary battery of the present embodiment includes an actual measurement value acquisition unit 61 that acquires an actual measurement value corresponding to the charge/discharge capacitance Q and the deformation amount T of the secondary battery 2, and an inflection point extraction unit 62: from the actual measurement value. At least one inflection point P1 (P2) of the characteristic curve Ln indicating the relationship between the charge and discharge capacitance Q of the secondary battery and the deformation amount T is extracted from the time series data; the previous characteristic data acquisition unit 60: obtains a secondary battery The characteristic curve L1 of the relationship between the charge and discharge capacitance and the amount of deformation has at least a full-time time point Pf, a remaining charge time point Pe, and a previous characteristic data of the stage inflection points P1, P2; and a predicted characteristic data generating unit 63: The extracted inflection point is used as a reference, and the characteristic curve L1 indicated by the previous characteristic data is fitted to the characteristic curve Ln indicated by the time series data of the measured value to generate a predicted characteristic data, and the predicted characteristic data is displayed. Insert the characteristic curve L2 which is not part of the measured time series data.

In the characteristic curve indicating the relationship between the charge and discharge capacitance of the secondary battery and the amount of deformation, there are the inflection points P1 and P2 in which the slopes of the two characteristic curves largely change with the change of the phase. According to the method, the inflection point in the characteristic curve is extracted from the time series data of the measured value, and the previous characteristic data is matched with the measured value data based on the extracted inflection point, so even if there is no self-charging The measurement data for a long period of time until the full discharge can be ensured as long as it has measured data for a short period of time from a full charge to an inflection point, from one of the inflection points to another inflection point. Predictive characteristics of a certain degree of precision.

Therefore, in actual use in which charging and discharging are not performed in a random manner from a series of charging and discharging, such as self-charging to full discharge, the predictive characteristic data can be detected, and the state of the secondary battery can be predicted.

In the present embodiment, the inflection point extraction unit 62 sets the extraction condition in accordance with the charge and discharge capacitance indicated by the obtained actual measurement value, and differentiates the deformation amount associated with the charge and discharge capacitance determined based on the obtained actual measurement value. When the value satisfies the extraction condition, the measured value is extracted as an inflection point (step S7).

The two inflection points P1 and P2 included in the characteristic curve can be identified by the charge and discharge capacitor Q. In the present embodiment, since the inflection point extraction unit 62 sets the extraction condition in accordance with the charge and discharge capacitance Q indicated by the actual measurement value, compared with the case where the inflection point is extracted only by the change of the differential value, Can improve the extraction accuracy.

In the present embodiment, the inflection point extraction unit 62 enters the charge and discharge capacitance Q _Pn indicated by the obtained measured value as the center of the charge and discharge capacitances Q _P1 and Q _P2 at the inflection points P1 and P2 in the previous characteristic data. In the case of the specific range, the threshold values Th _P1 and Th _{P2 of the} differential values are set based on the previous characteristic data, and the differential values are set as the extraction conditions by the thresholds Th _P1 and Th _P2 (step S7).

In this way, the extraction processing of the inflection points P1, P2 can be realized by a simple calculation.

In the present embodiment, the inflection point extraction unit 62 sets the following items as the extraction conditions in addition to the threshold values Th _P1 and Th _P2 as the extraction conditions: during a certain period of change in the charge and discharge capacitance, the differential value is continuously Continue to rise or continue to fall (step S7).

According to such an extraction condition, even if the differential value temporarily passes through the thresholds Th _P1 and Th _P2 , the extraction condition is not satisfied, so that the extraction accuracy can be further improved.

In the present embodiment, the predicted characteristic data generating unit 63 extracts one of the inflection points P1 and the time-series data of the measured value includes the measured value corresponding to any one of the full-charge time point Pf or the remaining charge time zero point Pe. In the case of the measured value, the "full-time time point Pf in the previous characteristic data or the two points of the remaining power zero time point Pe and the two points of the inflection point P1" and the "measured value" are calculated by the expansion and contraction of the characteristic curve. The corresponding two points in the time series data are consistent with each other. The entire characteristic curve is adjusted and adjusted using the coefficient to make the inflection point P1 coincide, and the predicted characteristic data is generated (step S9), or when one inflection point is extracted. When the measured time series data does not include the measured value corresponding to any one of the full-charge time point Pf or the remaining power zero time point Pe, the characteristic curve L1 indicated by the previous characteristic data is not enlarged or reduced. Rather, the characteristic curve L1 indicated by the previous characteristic data is moved in such a manner that the recently extracted inflection point P1 coincides with the inflection point P1 in the previous characteristic data, and the predicted characteristic data is generated (step S9), or extracted. To 2 In the case of the curved points P1 and P2, the two recursions corresponding to the "two inflection points P1 and P2 in the previous characteristic data" and the "time-series data of the measured values" are calculated by the expansion and contraction of the characteristic curve. The point "consistent coefficient" is used to adjust and adjust the entire characteristic curve by using the coefficient so that the recently extracted inflection point P2 is coincident, and the predicted characteristic data is generated (step S9).

According to this method, the inflection points are preferentially aligned with each other or the inflection point and the end of the characteristic curve. Therefore, the shape of the characteristic curve accompanying the phase change is less likely to be deformed than the general fitting, and the prediction accuracy can be improved.

In the present embodiment, when the predicted characteristic data generating unit 63 includes two inflection points in the time-series data of the actual measurement value and the predicted characteristic data has been generated, and the specific re-generation condition is satisfied (step S4: YES), Calculate a coefficient for matching the "predictive characteristic data characteristic curve L2" with the "measured value time series data characteristic curve Ln" by the expansion and contraction of the characteristic curve, and use the coefficient to adjust the entire characteristic curve L2 to be recently extracted. The recursive points are consistent, and the predicted characteristic data is generated. The coefficient is calculated in such a way that the weighting between the two inflection points is set using at least the ratio between the two inflection points, the ratio between the latest measured value and the inflection point of the side close to the latest measured value. It is weighted larger than other intervals and is calculated by total.

In this way, not only is one interval between the two inflection points, but also the ratio of the two intervals is used, and the latest measured values are also matched, so that the predicted characteristic data conforms to the measured value.

In the present embodiment, the residual capacitance calculation unit 64 calculates the difference between the charge and discharge capacitance Q _Pn indicated by the actual measured value at the time when the residual capacitance is predicted and the charge and discharge capacitance Q _Pe of the remaining charge amount zero point Pe in the predicted characteristic data. The remaining capacitance Qr (step S3).

In this way, since the predicted characteristic data includes the data of the remaining electric power zero time point Pe, the remaining amount can be calculated with high precision.

In the present embodiment, the initial characteristic data acquisition unit 65 and the deterioration information generation unit 66 are provided. The initial characteristic data acquisition unit 65 acquires at least the initial characteristics of the characteristic curve L0 indicating the relationship between the charge and discharge capacitance of the secondary battery and the amount of deformation, at least the full charge time point Pf, the remaining charge time point Pe, and the stage inflection points P1 and P2. Information (step S11). The deterioration information generating unit 66 calculates the "two inflection points P1, P2 in the initial characteristic data" and the "two inflection points P1, P2 corresponding to the predicted characteristic data" by the expansion and contraction of the characteristic curve. The expansion ratio of the charge and discharge capacitance is a degree of change in the mass of the active material that contributes to charge and discharge (step S12).

Thus, the degree of change in the quality of the active material which contributes to charge and discharge can be known.

In the present embodiment, the degradation information generation unit 66 uses the ratio between the two inflection points, the ratio between one of the inflection points and the fully charged time point Pf, and the other recursion with respect to the expansion ratio of the charge and discharge capacitance. The ratio between the point and the remaining charge zero time point Pe is set to a weight greater than the other intervals, and is calculated by the total (step S12).

As described above, it is considered that the full charge time point Pf and the remaining charge time point Pe are close to each other as compared with the ratio between only two inflection points. Therefore, the degree of matching of the entire characteristic curve is improved, and the calculation accuracy is improved.

In the present embodiment, the initial characteristic data acquisition unit 65 and the deterioration information generation unit 66 are provided. The initial characteristic data acquisition unit 65 acquires at least the initial characteristics of the characteristic curve L0 indicating the relationship between the charge and discharge capacitance of the secondary battery and the amount of deformation, at least the full charge time point Pf, the remaining charge time point Pe, and the stage inflection points P1 and P2. Information (step S11). The deterioration information generation unit 66 calculates a coefficient for matching the "two inflection points P1, P2 in the initial characteristic data" with the "two inflection points P1, P2 in the prediction characteristic data" by the expansion and contraction of the characteristic curve. The entire characteristic curve L0 shown in the initial characteristic data is adjusted and adjusted using the coefficient, and the two inflection points P1 and P2 of the characteristic curve L0' of the initial characteristic data and the characteristic curve L2 of the predicted characteristic data are identical to each other. The average value [(D1+D2)/2] of the left and right dispersion amount D1 of the charge and discharge capacitances of the charge and discharge capacitors of the charge time and discharge point Pf between the full charge time points Pf is calculated as the battery The deterioration state (step S12).

In this way, it is possible to calculate the extent to which the side reaction of one of the positive electrode or the negative electrode is more than the other party's side reaction.

In the present embodiment, the initial characteristic data acquisition unit 65 and the deterioration information generation unit 66 are provided. The initial characteristic data acquisition unit 65 acquires at least the maximum thickness point PL, the full charge time point Pf, the remaining charge time zero point Pe, and the stage inflection point P1 in the characteristic curve L0 indicating the relationship between the charge and discharge capacitance of the secondary battery and the amount of deformation. The initial characteristic data of P2 (step S11). The deterioration information generation unit 66 calculates a coefficient for matching the "two inflection points P1, P2 in the initial characteristic data" with the "two inflection points P1, P2 in the prediction characteristic data" by the expansion and contraction of the characteristic curve. Using the coefficient and specifying the maximum thickness point PL' in the predicted characteristic data based on the maximum thickness point PL in the initial characteristic data, and calculating the deformation amount T1 of the maximum thickness point PL' in the predicted characteristic data to the zero time point Pe of the remaining electricity amount as The amount of thickness change from the zero point of the remaining amount of electricity to the deposition of lithium.

In this way, the amount of change in thickness until the deposition of lithium based on the remaining time of the remaining amount of electricity is calculated. This value is useful as an indicator of charge control.

In the present embodiment, regarding the coefficient, the degradation information generation unit 66 uses the ratio between the two inflection points, the ratio between one of the inflection points and the fully charged time point Pf, and the other inflection point and the remaining electric quantity zero. The ratio between the time points Pe is set to be greater than the weight of the other sections by the weighting between the two inflection points, and is calculated by the total (step S12).

As described above, it is considered that the full charge time point Pf and the remaining charge time point Pe are close to each other as compared with the ratio between only two inflection points. Therefore, the degree of matching of the entire characteristic curve is improved, and the calculation accuracy is improved.

In the present embodiment, the full-charge time point Pf in the predicted characteristic data is the maximum thickness point PL, and the deterioration information generating unit 66 calculates the deformation amount of the maximum thickness point PL(Pf) in the predicted characteristic data to the remaining electric quantity zero time point Pe. T1 is the thickness change amount until lithium is precipitated based on the remaining time zero point.

In this way, the amount of change in thickness until the deposition of lithium based on the remaining time of the remaining amount of electricity is calculated. This value is useful as an indicator of charge control.

The charging control system of the present embodiment includes the deterioration information generating unit 66 and the charging control unit 67. The charging control unit 67 does not exceed the deterioration information generating unit by the deformation amount T of the secondary battery detected by the detecting sensor 5. The amount of change in thickness T1 produced by 66 controls charging.

According to this configuration, since charging control is performed so that lithium does not precipitate, there is a case where charging can be rapidly performed without causing deterioration due to lithium deposition.

In the present embodiment, the polymer matrix layer 3 is directly or indirectly attached to the secondary battery 2, and the polymer matrix layer 3 contains a filler which is changed in the external field in accordance with the deformation of the polymer matrix layer 3, and is detected by the detection. The deformation amount T of the secondary battery 2 is detected in response to a change in the field corresponding to the deformation of the polymer matrix layer 3.

Thus, the measured value corresponding to the amount of deformation of the secondary battery 2 can be appropriately detected.

The state prediction system for a secondary battery of the present embodiment includes a processor 6B and a memory 6A for storing instructions executable by the processor 6B.

The processor 6B is configured to obtain an actual measurement value corresponding to the charge/discharge capacitance Q and the deformation amount T of the secondary battery 2 (step S1), and extract the charge and discharge capacitance indicating the secondary battery from the time series data of the actual measurement value. At least one inflection point P1 (P2) of the characteristic curve Ln of the relationship between the Q and the deformation amount T (step S6), the characteristic curve L1 indicating the relationship between the charge and discharge capacitance of the secondary battery and the amount of deformation is at least filled. The previous characteristic data of the electric time point Pf, the remaining electric quantity zero time point Pe and the stage inflection points P1, P2 (step S8), based on the extracted inflection point, the characteristic curve L1 shown by the previous characteristic data and the measured The characteristic curve Ln shown by the time series data of the value is subjected to a fitting process to generate predicted characteristic data (step S9), and the predicted characteristic data shows a characteristic curve L2 obtained by interpolating the portion of the time series data of the actually measured value. .

The processor 6B can also be used by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), and field devices. Implemented as a gate array (FPGA), controller, microcontroller, microprocessor or other electronic component.

The program of this embodiment is a program for causing a computer to execute the above method.

By performing these programs, the effects of the above methods can also be obtained. Moreover, it can also be a recording medium readable by a computer having a program.

The present invention is not limited to the above-described embodiments, and various modifications and changes can be made without departing from the spirit and scope of the invention. For example, the order of execution of the processes, sequences, steps, and stages in the devices, systems, programs, and methods shown in the claims, the description, and the drawings is not limited to the use of the pre-processed output in post-processing. Can be implemented in any order. The flow of the patent application, the specification, and the drawings has been described using "first", "continued", etc. for convenience of explanation, but it does not mean that it must be executed in this order.

In the above embodiment, the inflection point is extracted from the time series data of the measured values, and the previous characteristic data is subjected to fitting processing, thereby generating the predicted characteristic data. However, if the accuracy is not considered, the generation processing 4 of the predicted characteristic data may be listed as follows. In other words, the prediction characteristic data generation processing 4 is based on the charge and discharge capacitance Q _Pn indicated by the actual measurement value at the current time point, and is directly fitted without narrowing and reducing the characteristic curve L1 indicated by the previous characteristic data. According to this method, although the capacitance degradation and the balance offset are not considered, the prediction characteristic data can be generated.

At least one of the generation processes 1 to 4 of the above-described predicted characteristic data may be installed. The combination of the generation processes 1 to 4 can be arbitrarily performed. However, if the processing is performed in order of high precision and high precision, the processing 1, the generation processing 2, the generation processing 3, and the generation processing 4 are performed. The generation process 1 is the highest, and the process 4 is generated with the lowest precision. Conversely, with respect to the time required before the prediction property data is generated, the generation process 4 is the shortest, and the generation process 1 becomes the longest.

Claims (33)

 A method for predicting a state of a secondary battery, comprising the steps of: obtaining an actual measured value corresponding to a charge and discharge capacitance and a deformation amount of the secondary battery; extracting a charge indicating the secondary battery from the time series data of the measured value a step of at least one inflection point in a characteristic curve of a relationship between a discharge capacitance and a deformation amount; and a characteristic curve indicating a relationship between a charge and discharge capacitance of the secondary battery and a deformation amount has at least a full charge time point and a remaining charge time zero time a step of the previous characteristic data of the point and the inversion point; and fitting the characteristic curve indicated by the previous characteristic data to the characteristic curve indicated by the time series data of the measured value based on the extracted inflection point Processing, generating a step of predicting characteristic data, the predicted characteristic data showing a characteristic curve obtained by interpolating a portion of the time-series data of the measured value.    The method according to claim 1, wherein the extraction condition is set according to the charge and discharge capacitance indicated by the obtained measured value, and the differential value of the deformation amount associated with the charge and discharge capacitance determined based on the obtained measured value satisfies the When the condition is extracted, the measured value is extracted as an inflection point.    The method of claim 2, wherein the charge and discharge capacitance indicated by the obtained measured value enters a specific range centered on the charge and discharge capacitance of the inflection point in the previous characteristic data, based on the previous characteristic The data sets a threshold value of the differential value, and sets the differential value as the extraction condition by the threshold.    According to the method of claim 3, in addition to setting the differential value as the extraction condition, the following is also set as the extraction condition: the differential value continuously continues to rise or continue for a certain period of the change of the charge and discharge capacitance. decline.    The method of claim 1, wherein when the one inflection point is extracted and the time series data of the measured value includes the measured value corresponding to the one of the full charge time point or the remaining power zero time point In the case of calculating, the expansion time point or the 1 point of the remaining time zero point and the time of the inversion point in the previous characteristic data and the time of the measured value are calculated by the expansion and contraction of the characteristic curve. A coefficient corresponding to two points in the sequence data, using the coefficient to adjust and adjust the entire characteristic curve so that the inflection point is consistent, and the predicted characteristic data is generated, or when an inflection point is extracted and the measured value is obtained When the time series data does not contain the actual measured value corresponding to any one of the full charge time point or the remaining power zero time point, the characteristic curve indicated by the previous characteristic data is not expanded and reduced, but The recently extracted inflection point moves in the same manner as the inflection point in the previous characteristic data to move the characteristic curve shown in the previous characteristic data, interpolates, and generates the predicted characteristic data, or extracts two recursions. In the case of a point, a coefficient for matching the two inflection points in the previous characteristic data with the corresponding two inflection points in the time series data of the measured value by using the expansion and contraction of the characteristic curve is used, and the coefficient is used. The entire characteristic curve is scaled and adjusted so that the recently extracted inflection point is consistent, and the predicted characteristic data is generated.    The method of claim 1, comprising the steps of: when the time series data of the measured value includes two inflection points and the predicted characteristic data has been generated and the specific regenerating condition is satisfied, By using the expansion and contraction of the characteristic curve to make the characteristic curve of the predicted characteristic data and the characteristic curve of the time-series data of the measured value, the coefficient is used to adjust and adjust the entire characteristic curve so that the recently extracted inflection point is consistent, and a step of generating the predicted characteristic data; the coefficient is calculated by using at least a ratio between the two inflection points, a ratio between the latest measured value and an inflection point on the side close to the latest measured value, The weighting between the two inflection points is set to be larger than the weight of the other sections, and is calculated by the total.    The method according to claim 1, wherein the difference between the charge and discharge capacitance indicated by the measured value at the time when the residual capacitance is predicted and the charge and discharge capacitance at the zero time point of the remaining charge in the predicted characteristic data is calculated as the residual capacitance.    The method of claim 1, comprising the steps of: obtaining a characteristic curve indicating a relationship between a charge and discharge capacitance of the secondary battery and a deformation amount, at least a full charge time point, a remaining charge time zero point, and a phase inflection point. a step of initial characteristic data; and calculating a expansion ratio of a charge and discharge capacitor for making the two inflection points in the initial characteristic data coincide with the two inflection points corresponding to the predicted characteristic data by the expansion and contraction of the characteristic curve A step as a degree of change in the quality of the active material which contributes to charge and discharge.    The method of claim 8, wherein the expansion ratio of the charge and discharge capacitor is calculated by using a ratio between the two inflection points, a ratio between one of the inflection points and a time of full charge. And the ratio between the other inflection point and the remaining electric quantity zero time point, the weighting between the two inflection points is set to be larger than the weight of the other sections, and is calculated by the total.    The method of claim 1, comprising the steps of: obtaining a characteristic curve indicating a relationship between a charge and discharge capacitance of the secondary battery and a deformation amount, at least a full charge time point, a remaining charge time zero point, and a phase inflection point. a step of initial characteristic data; and calculating a coefficient for matching two inflection points in the initial characteristic data with two inflection points in the prediction characteristic data by expansion and contraction of the characteristic curve, and using the coefficient for the initial stage The entire characteristic curve shown in the characteristic data is telescopically adjusted, and the two inflection points of the adjusted characteristic curve of the initial characteristic data and the characteristic curve of the predicted characteristic data are identical to each other, and the charged time points are calculated from each other. The average value of the amount of dispersion of the charge and discharge capacitors and the amount of dispersion of the charge and discharge capacitors of the remaining charge at zero time points is a step of deteriorating the state of the battery.    The method of claim 10, wherein the coefficient is calculated by using a ratio between the two inflection points, a ratio between one of the inflection points and the fully charged time point, and another inflection point The ratio between the remaining electric power zero time points, the weighting between the two inflection points is set to be larger than the weight of the other sections, and is calculated by the total.    The method of claim 1, comprising the steps of: obtaining a characteristic curve indicating a relationship between a charge and discharge capacitance of the secondary battery and a deformation amount, at least a maximum thickness point, a full charge time point, a remaining charge time point, and a phase a step of initial characteristic data of the inflection point; and calculating a coefficient for matching the two inflection points in the initial characteristic data with the two inflection points in the prediction characteristic data by the expansion and contraction of the characteristic curve, And determining a maximum point of the thickness in the predicted characteristic data according to the maximum point of the thickness in the initial characteristic data, and calculating a deformation amount of the maximum point in the predicted characteristic data to the zero time point of the remaining electric quantity as the remaining The power zero time point is a step from the base point to the thickness change amount until lithium is precipitated.    The method of claim 12, wherein the coefficient is calculated by using a ratio between the two inflection points, a ratio between one of the inflection points and the fully charged time point, and another recursion The ratio between the point and the remaining power zero time point, the weight between the two inflection points is set to be larger than the weight of the other sections, and is calculated by the total.    The method of claim 1, wherein the full charge time point in the predicted characteristic data is a maximum thickness point, the method comprising the steps of: calculating the maximum thickness point in the predicted characteristic data to the remaining power zero time The amount of deformation of the dot is the amount of thickness change until the lithium is precipitated based on the zero point of the remaining amount of electricity.    The method according to claim 1, wherein the polymer matrix layer is directly or indirectly attached to the secondary battery, and the polymer matrix layer contains a filler corresponding to the deformation of the polymer matrix layer and imparts a change to the external field. The amount of deformation of the secondary battery is detected by detecting a change in the external field corresponding to the deformation of the polymer matrix layer.    A charging control method for performing the method of claim 12, pre-calculating a thickness variation amount from the zero point of the remaining amount of electricity to the deposition of lithium, and detecting the second time by the detecting sensor The charging is controlled in such a manner that the amount of deformation of the battery does not exceed the amount of change in thickness.    A state prediction system for a secondary battery, comprising: an actual measurement value acquisition unit: obtaining an actual measurement value corresponding to a charge and discharge capacitance and a deformation amount of the secondary battery; and an inflection point extraction unit: extracting from the time series data of the actual measurement value At least one inflection point in a characteristic curve indicating a relationship between a charge and discharge capacitance of the secondary battery and a deformation amount; and a previous characteristic data acquisition unit: obtaining a characteristic curve indicating a relationship between a charge and discharge capacitance of the secondary battery and a deformation amount a prior characteristic data having at least a full charge time point, a remaining charge zero time point, and a phase inflection point; and a predicted characteristic data generation unit: the characteristic curve indicated by the previous characteristic data is based on the extracted inflection point The characteristic curve shown by the time series data of the measured value is subjected to fitting processing to generate predictive characteristic data, and the predicted characteristic data shows a characteristic curve obtained by interpolating the portion of the time series data of the measured value.    The system of claim 17, wherein the inflection point extraction unit sets the extraction condition corresponding to the charge and discharge capacitance indicated by the obtained measured value, and relates to the charge and discharge capacitance determined based on the obtained measured value. When the differential value of the deformation amount satisfies the extraction condition, the measured value is extracted as an inflection point.    The system of claim 18, wherein the inflection point extraction unit enters a specific range of the charge and discharge capacitance indicated by the measured value of the previous characteristic data centered on the charge and discharge capacitance of the inflection point in the previous characteristic data. In the case, the threshold value of the differential value is set based on the previous characteristic data, and the differential value is set as the extraction condition by the threshold value.    The system according to claim 19, wherein the inflection point extraction unit sets the following value as the extraction condition, and sets the following as the extraction condition: within a certain period of the change of the charge and discharge capacitance The differential value continuously continues to rise or continues to decrease.    The system of claim 17, wherein the predicted characteristic data generating unit extracts one inflection point and the time series data of the measured value includes any one of the full charging time point or the remaining power zero time point. Corresponding to the measured value, the expansion point of the characteristic data is used to make the full-time point in the previous characteristic data or any one of the remaining power zero time points and two points of the inflection point The corresponding two-point coefficient in the time-series data of the measured value, using the coefficient to adjust and adjust the entire characteristic curve, so that the inflection point is consistent, and the predicted characteristic data is generated, or when an inflection point is extracted If the measured time series data does not contain the measured value corresponding to any one of the full charge time point or the remaining power zero time point, the characteristic curve indicated by the previous characteristic data is not expanded and reduced. And, by interpolating the characteristic curve indicated by the previous characteristic data in such a manner that the recently extracted inflection point coincides with the inflection point in the previous characteristic data, the predicted characteristic data is generated, or When two inflection points are extracted, it is calculated that the two inflection points in the previous characteristic data are consistent with the corresponding two inflection points in the time series data of the measured values by the expansion and contraction of the characteristic curve. The coefficient is used to adjust and adjust the entire characteristic curve so that the recently extracted inflection point is consistent, and the predicted characteristic data is generated.    The system of claim 17, wherein the predicted characteristic data generating unit is configured to: when the time series data of the measured value includes two inflection points and the predicted characteristic data has been generated and the specific regenerated When the condition is satisfied, a coefficient for matching the characteristic curve of the predicted characteristic data with the characteristic curve of the time series data of the measured value by the expansion and contraction of the characteristic curve is calculated, and the entire characteristic curve is adjusted and adjusted by using the coefficient. The recently extracted inflection point is consistent, and the predicted characteristic data is generated again; the coefficient is calculated in the following manner: using the ratio between the two inflection points, the latest measured value and the recursion of the side close to the latest measured value The ratio between the points, the weight between the two inflection points is set to be larger than the weight of the other sections, and is calculated by the total.    The system according to claim 17, further comprising: a residual capacitance calculation unit that calculates a charge/discharge capacitance indicated by an actual measurement value at a time point at which the residual capacitance is predicted and a remaining time value in the predicted characteristic data The difference between the charge and discharge capacitors is used as the residual capacitance.    The system according to claim 17, further comprising: an initial characteristic data acquisition unit that acquires at least a full charge time point, a remaining charge time zero point, and a phase in a characteristic curve indicating a relationship between a charge and discharge capacitance of the secondary battery and a deformation amount The initial characteristic data of the inflection point; and the degradation information generating unit: calculating the two inflection points in the initial characteristic data by the expansion and contraction of the characteristic curve and the two inflection points corresponding to the prediction characteristic data The expansion ratio of the charge and discharge capacitor serves as a degree of change in the quality of the active material that contributes to charge and discharge.    The system of claim 24, wherein the expansion ratio of the charge and discharge capacitor uses a ratio between the two inflection points, a ratio between one of the inflection points and the fully charged time point, and another inflection point The ratio between the two inflection points is set to be larger than the weight of the other sections, and is calculated by the total.    The system according to claim 17, further comprising: an initial characteristic data acquisition unit that acquires at least a full charge time point, a remaining charge time zero point, and a phase in a characteristic curve indicating a relationship between a charge and discharge capacitance of the secondary battery and a deformation amount The initial characteristic data of the inflection point; and the degradation information generating unit: calculating a coefficient for matching the two inflection points in the initial characteristic data with the two inflection points in the prediction characteristic data by the expansion and contraction of the characteristic curve And using the coefficient to adjust and adjust the entire characteristic curve indicated by the initial characteristic data, and the two inflection points of the adjusted characteristic curve of the initial characteristic data and the characteristic curve of the predicted characteristic data are consistent with each other to calculate The average value of the amount of dispersion of the charge and discharge capacitances of the respective charging time points and the amount of dispersion of the charge and discharge capacitances of the remaining time points is used as the deterioration state of the battery.    The system of claim 26, wherein the coefficient is calculated by using a ratio between the two inflection points, a ratio between one of the inflection points and the fully charged time point, and another recursion The ratio between the point and the remaining power zero time point, the weight between the two inflection points is set to be larger than the weight of the other sections, and is calculated by the total.    The system according to claim 17, further comprising: an initial characteristic data acquisition unit that acquires at least a maximum thickness point, a full-charge time point, and a remaining electric quantity in a characteristic curve indicating a relationship between a charge/discharge capacitance of the secondary battery and a deformation amount. The initial characteristic data of the time point and the inversion point of the phase; and the degradation information generating unit: calculating the two inversions of the initial characteristic data and the two of the predicted characteristic data by the expansion and contraction of the characteristic curve a coefficient of uniformity, using the coefficient and specifying the maximum point of the thickness in the predicted characteristic data according to the maximum point of the thickness in the initial characteristic data, and calculating the maximum point of the thickness in the predicted characteristic data to the remaining electric quantity zero The amount of deformation at the time point is the thickness change amount until the lithium is precipitated based on the zero point of the remaining amount of electricity.    The system of claim 28, wherein the coefficient is calculated by using a ratio between the two inflection points, a ratio between one of the inflection points and the fully charged time point, and another recursion The ratio between the point and the remaining power zero time point, the weight between the two inflection points is set to be larger than the weight of the other sections, and is calculated by the total.    The system of claim 17, wherein the full-charge time point in the predicted characteristic data is a maximum thickness point, the system has a degradation information generating unit, and the degradation information generating unit calculates the maximum thickness point in the predicted characteristic data The amount of deformation up to the time point of the remaining electric quantity is taken as the thickness change amount until the lithium is precipitated based on the zero time point of the remaining electric quantity.    The system according to claim 17, wherein the polymer matrix layer is directly or indirectly attached to the secondary battery, and the polymer matrix layer contains a filler corresponding to the deformation of the polymer matrix layer and imparts a change to the external field. The amount of deformation of the secondary battery is detected by detecting a change in the external field corresponding to the deformation of the polymer matrix layer.    A charging control system comprising: the degradation information generating unit according to claim 28; and a charging control unit: the deformation amount of the secondary battery detected by the detecting sensor does not exceed that generated by the degradation information generating unit The amount of thickness variation controls the charging.    A program that causes a computer to perform the method of any one of claims 1 to 16.