WO2019044067A1 - 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

State prediction method for secondary battery, charge control method, and system

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

In recent years, sealed secondary batteries (hereinafter sometimes simply referred to as "secondary batteries") represented by lithium ion secondary batteries are not only mobile devices such as mobile phones and laptop computers but also electric vehicles and hybrids. It is also used as a power source for electric vehicles such as cars. The secondary battery is deteriorated by repeating charge and discharge cycles, and with the progress of the deterioration, it becomes difficult to accurately grasp the state of the remaining capacity, the deteriorated capacity of the battery and the like.

Patent Document 1 describes that the remaining capacity is estimated from the relationship between the pressure between the batteries and the SOC (State of Charge). However, in this method, it is necessary to obtain charge behavior of a cycle from full charge to near complete discharge, and it is difficult to estimate in real use where charge and discharge are randomly performed.

Patent Document 2 describes measuring the thickness of the secondary battery and measuring the remaining capacity from the thickness. However, the change in remaining capacity due to deterioration is not taken into consideration.

JP, 2016-101048, A JP 2004-14462 A

The present invention has been made in view of such problems, and an object thereof is a method of predicting a state of a secondary battery usable in actual use where charging and discharging are performed randomly, a charging method, and a system. It is to provide.

The secondary battery state prediction method according to the present invention is
Acquiring an actual measurement value corresponding to the charge / discharge capacity and deformation amount of the secondary battery;
Extracting at least one inflection point in a characteristic curve indicating the relationship between the charge / discharge capacity of the secondary battery and the deformation amount from the time-series data of the measured value;
Acquiring past characteristic data having at least a full charge time point, a remaining time zero time point, and a stage inflection point in a characteristic curve indicating a relation between charge and discharge capacity and deformation amount of the secondary battery;
The characteristic curve indicated by the characteristic data in the past is fitted to the characteristic curve indicated by the time series data of the actual measurement value on the basis of the extracted inflection point, and a portion not included in the time series data of the actual measurement value is interpolated Generating predicted characteristic data indicating a different characteristic curve;
including.

In the characteristic curve showing the relationship between the charge / discharge capacity of the secondary battery and the deformation amount, there are two inflection points at which the slope of the characteristic curve largely changes with the stage change. According to this method, the inflection point in the characteristic curve is extracted from the time-series data of the measured values, and the past characteristic data and the measured value data are fitted with reference to the extracted inflection point. Even if there is no measurement data for a long time from full discharge to complete discharge, if there is measurement data of a short period, such as from full charge to the inflection point, from one inflection point to the other inflection point, Predicted characteristic data can be generated with a certain degree of accuracy secured.
Therefore, in actual use where charging and discharging are performed randomly instead of a series of charging and discharging such as from full charge to complete discharge, predicted characteristic data securing a certain degree of accuracy can be generated, and the state of the secondary battery is predicted It becomes possible.

FIG. 1 is a block diagram showing an example of a system for executing a secondary battery state prediction method. FIG. 2 is a perspective view schematically showing a sealed secondary battery. AA sectional drawing of FIG. 2A. The graph which shows the characteristic curve which past characteristic data show. The graph which shows the characteristic curve which the time series data of an actual value, and the past characteristic data show. The graph which shows the time-series data of actual value, and the derivative value of actual value. Explanatory drawing regarding the process which produces | generates prediction characteristic data. Explanatory drawing regarding the process which produces | generates prediction characteristic data. Explanatory drawing regarding the process which produces | generates prediction characteristic data. Explanatory drawing regarding the process which produces | generates prediction characteristic data. Explanatory drawing regarding the process which produces | generates prediction characteristic data. Explanatory drawing regarding the process which produces | generates prediction characteristic data. Explanatory drawing regarding the process which produces | generates prediction characteristic data. Explanatory drawing regarding the process which produces | generates prediction characteristic data. Explanatory drawing regarding calculation processing of remaining capacity. Explanatory drawing regarding the fitting process of initial stage characteristic data and prediction characteristic data. Explanatory drawing regarding the fitting process of initial stage characteristic data and prediction characteristic data. Explanatory drawing regarding calculation processing of a degradation state. Explanatory drawing regarding calculation processing of a degradation state. Explanatory drawing regarding calculation processing of a degradation state. Explanatory drawing regarding the process which calculates the thickness change amount to lithium precipitation. Explanatory drawing regarding the process which calculates the thickness change amount to lithium precipitation. Explanatory drawing regarding charge control. The flowchart which shows the state prediction process routine performed by a system.

Hereinafter, embodiments of the present invention will be described.

FIG. 1 shows a system mounted on an electric vehicle such as an electric vehicle or a hybrid vehicle. This system includes a battery module 1 formed by housing a battery assembly formed of a plurality of sealed secondary batteries 2 in a housing. In the present embodiment, four secondary batteries 2 are connected in two parallel two series, but the number of batteries and the connection form are not limited thereto. Although only one battery module 1 is shown in FIG. 1, it is actually equipped as 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 they are housed in a housing together with various devices such as a controller. The housing of the battery pack is formed in a shape suitable for being mounted on a vehicle, for example, in a shape conforming to the shape under the floor of a vehicle.

The secondary battery 2 shown in FIG. 2 is configured as a cell (unit cell) in which the electrode group 22 is accommodated inside the sealed exterior body 21. The electrode group 22 has a structure in which a positive electrode 23 and a negative electrode 24 are stacked or wound between them via a separator 25. The separator 25 holds an electrolytic solution. The secondary battery 2 of the present embodiment is a laminate battery using a laminate film such as an aluminum laminate foil as the exterior body 21, and specifically, is a laminate type lithium ion secondary battery having a capacity of 1.44 Ah. The secondary battery 2 is formed in a thin rectangular parallelepiped shape as a whole, and the X, Y, and Z directions correspond to the length direction, the width direction, and the thickness direction of the secondary battery 2, respectively. The Z direction is also the thickness direction of the positive electrode 23 and the negative electrode 24.

The secondary battery 2 is attached with a detection sensor 5 for detecting deformation of the secondary battery 2. The detection sensor 5 includes a polymer matrix layer 3 attached to the secondary battery 2 and a detection unit 4. The polymer matrix layer 3 contains a filler that disperses the external field in response to the deformation of the polymer matrix layer 3. The polymer matrix layer 3 of the present embodiment is formed in a sheet shape by a flexible deformable elastomer material. The detection 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 accordingly, and the change in the external field caused by the deformation of the polymer matrix layer 3 is detected by the detection unit 4. Thus, 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 outer package 21 of the secondary battery 2, the polymer matrix layer 3 may be deformed according to the deformation (mainly bulging) of the outer package 21. it can. On the other hand, the polymer matrix layer 3 may be attached to the electrode group 22 of the secondary battery 2. According to such a configuration, the polymer matrix layer 3 is deformed according to the deformation (mainly swelling) of the electrode group 22. be able to. The deformation of the secondary battery 2 to be detected may be any deformation of the exterior body 21 and the electrode group 22.

A signal detected by the detection sensor 5 is transmitted to the control device 6, whereby information on deformation of the secondary battery 2 is supplied to the control device 6.

<State prediction system for secondary battery>
As shown in FIG. 1, in the state prediction system of the secondary battery, the past characteristic data acquisition unit 60, the actual value acquisition unit 61, the inflection point extraction unit 62, the prediction characteristic data generation unit 63, and the remaining capacity calculation. A unit 64, an initial characteristic data acquisition unit 65, and a deterioration information generation unit 66 are included. In the present embodiment, the units 60 to 66 are realized by the processor 6 B such as a CPU executing a program in the control device 6, but the present invention is not limited to this. For example, the present invention may be realized by an information processing apparatus located far away via a communication network. The remaining capacity calculation unit 64, the initial characteristic data acquisition unit 65, and the deterioration information generation unit 66 can be omitted as needed.

<Acquisition of past characteristic data>
The past characteristic data acquisition unit 60 acquires past characteristic data of the secondary battery 2. As shown in FIG. 3, the characteristic data in the past are at least the full charge time point Pf, the remaining time zero time point Pe, and the stage change in the characteristic curve L1 showing the relationship between the charge / discharge capacity Q of the secondary battery and the deformation amount T. It has curve points P1 and P2. This is because the characteristic curve L1 can be reproduced to some extent with these four points. The state of the secondary battery 2 can be expressed by a characteristic curve L1.

In the graph of FIG. 3, the horizontal axis is the discharge capacity Q with the origin as the fully charged time point Pf, and the vertical axis is the detected deformation amount T of the secondary battery 2. As the discharge capacity Q from the full charge time point Pf increases, the deformation amount T of the secondary battery 2 changes in the direction in which the thickness of the secondary battery 2 becomes smaller. This is because in the charged secondary battery 2, the electrode group 22 swells (hereinafter sometimes referred to as “electrode swelling”) due to the volume change of the negative electrode active material, and the electrode swelling is accompanied by the discharge. It is because it becomes small. Due to the stage change of the electrode, as shown in FIG. 3, the shape includes two inflection points P1 and P2 (change in slope). For example, in the case of a lithium ion secondary battery using graphite (graphite) for the negative electrode, it is known that the crystalline state of the graphite changes in stages sequentially as it is discharged from the fully charged time point Pf. This is because the distance between the graphene layers increases stepwise in accordance with the insertion amount of lithium ions, so that the negative electrode active material expands. In short, as the stage changes, the volume of the active material changes stepwise, which is reflected in the characteristic curve L1. When the discharge capacity Q further increases, the remaining amount zero point Pe is reached.

In the present embodiment, data is also included between the full charge time point Pf, the remaining amount time zero time point Pe, and the inflection points P1 and P2. The characteristic data may be discrete data in which data of a plurality of points are provided between four points, or continuous data in which four points are connected by a curve. The past characteristic data is not an initial characteristic set at the time of shipment of the secondary battery 2, but is past data from the start of use to the present time. If it is data of the latest charge cycle, it is more preferable because there is little difference between the present and past data. The past characteristic data is stored in the memory 6A of the control device 6, and the past characteristic data acquisition unit 60 acquires data from the memory 6A.

<Acquisition of measured value data>
The actual measurement value acquisition unit 61 acquires an actual measurement value corresponding to the charge / discharge capacity and deformation amount of the secondary battery 2. The value corresponding to the deformation amount T of the secondary battery is a value detected by the detection sensor 5. In the present embodiment, the voltage or magnetic flux density is expressed as a change amount or the like. However, the present invention is not limited to this, and various changes can be made as long as the physical amount corresponds to the thickness or the deformation amount. The charge / discharge capacity of the secondary battery 2 is a generic term for the discharge capacity and the charge capacity. In the present embodiment, although the discharge capacity, specifically, the discharge capacity from the full charge point is used, the present invention is not limited to this. The value corresponding to the charge and discharge capacity can be represented by current, charge amount or discharge amount, and can be variously changed. The measured values corresponding to the charge / discharge capacity and the deformation amount of the secondary battery 2 are repeatedly acquired and become time series data. FIG. 4 shows the characteristic curve Ln represented by the time series data of the actual measurement values as a solid line. In the example of FIG. 4, the measurement is started from the full charge time point Pf, and the discharge is continued, and the example in which the inflection point P1 is not reached yet is shown. A characteristic curve L1 indicated by a broken line in FIG. 4 is exaggerated and illustrated so that the characteristic curve L1 indicated by the past characteristic data shown in FIG. 3 is different for comparison.

Although the characteristic curve Ln is indicated by a solid line in FIG. 4, actually, the raw data which is an actual measurement value often includes large noise vibrating on the vertical axis, and data per unit discharge capacity is Exist in the form of vibrating up and down at multiple points. In order to remove noise on the vertical axis to facilitate data processing, filter processing such as moving average or median value is performed on the vertical axis so that data per unit discharge capacity becomes one plot. There is. Thereby, it becomes possible to calculate a differential value indicating the slope of the characteristic curve Ln.

<Extraction of inflection point>
The inflection point extraction unit 62 extracts at least one inflection point (P1 or P2) in the characteristic curve Ln indicating the relationship between the charge / discharge capacity of the secondary battery 2 and the deformation amount from the time-series data of the measured values. . Specifically, the inflection point is extracted based on the differential value (ΔT / ΔQ) of the deformation amount T related to the charge / discharge capacity Q determined based on the actual value. This is because the differential value is the slope of the characteristic curve Ln, and the location where the change in the differential value is large is the inflection point. It is possible to extract an inflection point with an extraction condition that the amount of change of the differential value is larger than a certain value.

In the present embodiment, in order to enhance the extraction accuracy, the extraction condition is set according to the charge / discharge capacity Q indicated by the acquired measured value, and the differential value of the deformation amount T related to the charge / discharge capacity Q determined based on the acquired measured value When ΔT / ΔQ) satisfies the extraction condition, the actual measurement value is extracted as an inflection point. Examples will be described next.

As shown in the upper graph of FIG. 5, as shown in the upper graph of FIG. 5, as shown in FIG. 5, 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. Be If this discrete data is differentiated, as shown in the lower graph of the figure, the differential value [ΔmV / ΔmAh] of the deformation amount with respect to the discharge capacity can be obtained. This differential value is one for each unit charge and discharge capacity (unit discharge capacity), and if it is illustrated as in the lower graph of the figure, it is one point.

Setting the extraction conditions, as shown in FIG. 4, the charge-discharge capacity Q _Pn showing actual measurement values acquired in a certain time Pn is the charge-discharge capacity Q _P1 inflection point P1 (P2) in the past of the characteristic data When it falls within the predetermined range centered on (Q _P2 ), as shown in the lower graph of FIG. 5, the threshold value Th _P1 (Th _P2 ) of the differential value is set based on the past characteristic data. For example, as shown in FIG. 4, when the charge / discharge capacity Q _Pn indicated by the measured value data at a certain point Pn is near the inflection point P1, as shown in the lower graph of FIG. A threshold Th _P1 is set. Then, acquisition (measurement) of the actual measurement value further progresses, and as shown in FIG. 5, the inflection point P1 is extracted when the differential value based on the actual measurement value passes the threshold value Th _P1 . In the lower graph of FIG. 5, the extraction condition for extracting the inflection point P1 during discharge is that the threshold Th _P1 is passed downward, and the extraction for extracting the inflection point P1 during charging is performed. The condition is that the threshold Th _P1 is passed upward. Similarly, the extraction condition for extracting the inflection point P2 during discharge is that the threshold Th _P2 is passed upward, and the extraction condition for extracting the inflection point P2 during charging is the threshold It is passing Th _P2 downward.

In the method using the threshold values Th _P1 and Th _P2 of the differential value, there is a possibility that an inflection point may be extracted even if the threshold values Th _P1 and Th _P2 are passed primarily due to the influence of noise or the like. Therefore, in addition to the differential value passing through the threshold values Th _P1 and Th _P2 , it is set as the extraction condition that the differential value continues to continuously rise or falls for a certain period during which the charge / discharge capacity changes. ing. In the present embodiment, since the differential value is calculated for each unit discharge capacity, the extraction condition is that at least three differential values continue to rise or fall continuously. In this case, a certain period during which the charge / discharge capacity changes is for three points, which is unit discharge capacity [ΔmAh] × 3. This period can be changed as appropriate.

<Generation of prediction characteristic data>
As shown in FIG. 6C and FIG. 7C, the prediction characteristic data generation unit 63 sets the characteristic curve L1 indicated by the characteristic data in the past to the characteristic curve Ln indicated by time series data of the actual measurement value with reference to the extracted inflection point. Fitting processing is performed to generate predicted characteristic data indicating a characteristic curve L2 in which a portion not included in the time-series data of the actual measurement value is interpolated. The prediction characteristic data generation unit 63 stores the generated prediction 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 adopted.

<Generation Process 1 of Prediction Characteristic Data>
FIG. 6A shows an example in which two inflection points P1 and P2 are extracted. The inflection point P2 is an inflection point extracted most recently. When the inflection point extraction unit 62 extracts two inflection points P1 and P2, the prediction characteristic data generation unit 63 generates two inflection points P1 and P2 in the past characteristic data as time-series data of measured values. The coefficients for making the corresponding two inflection points P1 and P2 at the same point coincide with each other by the expansion and contraction of the characteristic curve are calculated. As an example of the coefficient, the enlargement ratio of the horizontal axis Xr = a '/ a, the enlargement ratio of the vertical axis Yr = b' / b, and the inclination can be mentioned.
Next, as shown in FIG. 6B, using the coefficients, the entire characteristic curve L1 indicated by the past characteristic data is stretched and adjusted to generate a reduced-scale adjusted characteristic curve L1 ′.
Next, as shown in FIG. 6C, the adjusted characteristic curve L1 ′ is moved, and the inflection point P2 most recently extracted in the characteristic curve Ln indicated by the time-series data of the measured values and the adjusted characteristic curve L1 ′ Are made to coincide with the corresponding inflection point P2 of to generate prediction characteristic data. Then, as shown in the figure, predicted characteristic data is generated which indicates a characteristic curve L2 in which a portion (indicated by a broken line in the figure) not included in the time series data of the actual measurement value is interpolated. A characteristic curve L2 indicated by the prediction characteristic data includes a characteristic curve Ln and a characteristic curve L1 '.

<Generation Process 2 of Predicted Characteristic Data>
FIG. 7A shows an example in which one inflection point P1 is extracted and an actual measurement value corresponding to any one of a full charge time point Pf or a remaining charge zero time point Pe is included in time series data of actual measurement values. When the inflection point extraction unit 62 extracts one inflection point P1, the prediction characteristic data generation unit 63 determines whether the inflection point is any one of the full charge time point Pf or the remaining charge time zero point Pe in the past characteristic data. A coefficient for making two points at the point P1 coincide with corresponding two points in the time-series data of the measured values by the expansion and contraction of the characteristic curve is calculated. In the example of FIG. 7A, the coefficient for making the full charge time point Pf and the inflection point P1 coincide with each other in the vertical and horizontal directions is calculated. As an example of the coefficient, the enlargement ratio of the horizontal axis Xr = a '/ a, the enlargement ratio of the vertical axis Yr = b' / b, and the inclination can be mentioned.
Next, as shown in FIG. 7B, using the coefficients, the entire characteristic curve L1 indicated by the past characteristic data is stretched and adjusted to generate a reduced-scale adjusted characteristic curve L1 ′.
Next, as shown in FIG. 7C, the adjusted characteristic curve L1 ′ is moved, and the inflection point P1 most recently extracted in the characteristic curve Ln indicated by the time-series data of the measured values and the adjusted characteristic curve L1 ′ Are made to coincide with the corresponding inflection point P1 of to generate prediction characteristic data. Then, as shown in the figure, predicted characteristic data is generated which indicates a characteristic curve L2 in which a portion (indicated by a broken line in the figure) not included in the time series data of the actual measurement value is interpolated. A characteristic curve L2 indicated by the prediction characteristic data includes a characteristic curve Ln and a characteristic curve L1 '.

<Generation Process 3 of Predicted Characteristic Data>
FIG. 7D shows an example in which one inflection point P1 is extracted but the measured value corresponding to any one of the fully charged time point Pf or the remaining zero time point Pe is not included in the time series data of the measured value. Show. As shown in FIGS. 7D and 7E, the prediction characteristic data generation unit 63 extracts the inflection point most recently extracted as a coefficient 1 (equal magnification) without enlarging or reducing the characteristic curve L1 indicated by the past characteristic data. The characteristic curve L1 indicated by the past characteristic data is moved and interpolated such that P1 coincides with the inflection point P1 in the past characteristic data, thereby generating predicted characteristic data.

The predicted characteristic data includes a full charge time point Pf, a remaining amount time zero point Pe, and two inflection points P1 and P2. 6A to 6C and FIGS. 7A to 7B show the way of thinking, and the procedure is not limited to this.

Although the reproduction accuracy of the characteristic curve of the secondary battery 2 may be inferior to the generation process 1 shown in FIGS. 6A to 6C and the generation process 2 shown in FIGS. 7A to 7C, the least squares method is used as the fitting process. Other methods such as can be implemented. For example, commercially available software such as Solver (registered trademark) in Excel (registered trademark) manufactured by Microsoft Corporation is also possible.

<Calculation of remaining capacity>
The calculation of the remaining capacity uses predicted characteristic data and an actual measured value at the time of predicting the remaining capacity. Specifically, as shown in FIG. 8, remaining capacity calculation unit 64 indicates charge / discharge capacity Q _Pn indicated by an actual measured value at the time of predicting remaining capacity, and charge / discharge capacity Q of remaining quantity zero point Pe in predicted characteristic data. The difference with _Pe is calculated as the remaining capacity Qr.

<Generation of degradation information>
In order to generate the degradation information, the predicted characteristic data obtained above and the initial characteristic data are used. In some cases, it is possible to generate degradation information using only prediction characteristic data, which will be described later. The deterioration information includes the side reaction balance of the electrode, the change in the amount of active material contributing to charge and discharge, and the amount of thickness change up to lithium deposition.

As shown in FIG. 9A, the initial characteristic data acquisition unit 65 sets at least the full charge time point Pf and the remaining time point zero time Pe on the characteristic curve L0 indicating the relationship between the charge / discharge capacity Q of the secondary battery 2 and the deformation amount T as shown in FIG. And acquire initial characteristic data having stage inflection points P1 and P2. The initial characteristic data acquisition unit 65 acquires data from the memory 6A. The initial characteristic data is based on the non-degraded initial stage secondary battery 2 as a reference state, and is determined using, for example, the secondary battery 2 at the time of manufacture or before shipment, and information on the characteristic curve L0 is a control device 6 are stored in advance in a memory 6A included in the memory 6. In the charge and discharge process for which the characteristic curve L0 was determined, the secondary battery 2 before shipment was placed in a thermostatic chamber at 25 ° C., allowed to stand for 120 minutes, constant current charge to 4.32 V with a charge current of 0.144 A, 4 After reaching .32 V, constant-voltage charging was performed until the current value decreased to 0.07 A, and then the open circuit state was maintained for 10 minutes, and constant-current discharge was performed to 3.0 V with a current of 0.144 A. The discharge capacity from the fully charged state to the completely discharged state at this time was 1.44 Ah.

<Change degree of active material mass contributing to charge and discharge>
As illustrated in FIG. 9A, the deterioration information generation unit 66 includes two inflection points P1 and P2 in the initial characteristic data (L0) and two corresponding inflection points P1 and P2 in the prediction characteristic data (L2). The expansion ratio (a '/ a) of the charge / discharge capacity for making the characteristics coincide by the expansion and contraction of the characteristic curve is calculated as the degree of change of the active material mass contributing to the charge / discharge. In FIG. 9A, the enlargement factor of the horizontal axis = the enlargement factor of charge / discharge capacity (a ′ / a).

Further, as shown in FIG. 9B, the enlargement factor of the charge and discharge capacity may be calculated. The deterioration information generation unit 66 sets a ratio (a '/ a) between two inflection points P1 and P2, a ratio (b' / b) between one inflection point P1 and a full charge time point Pf, and the other By using the ratio (c '/ c) between the inflection point P2 and the remaining zero time Pe, the weighting between the two inflection points P1 and P2 is set larger than the weighting of the other sections, calculate. For example, the case of tabulating by weighting of b: a: c = 1: 8: 1 will be described. In this case, the enlargement factor of the charge / discharge capacity is {b '/ b × 1 + a' / a × 8 + c '/ c × 1} / {1 + 8 + 1}. In this way, compared to the ratio between the two inflection points alone, the fully charged time point Pf and the remaining zero time point Pe become closer as a fitting result, so the degree of agreement in the entire characteristic curve is improved, and the calculation accuracy is improved. I think it will improve.

<Deterioration state>
As illustrated in FIGS. 10A to 10C, the deterioration information generation unit 66 includes two inflection points P1 and P2 in the initial characteristic data (L0) and two inflection points P1 and P2 in the prediction characteristic data (L2). Stretch and adjust the entire characteristic curve (L0) indicated by the initial characteristic data using coefficients (enlargement ratio of charge and discharge capacity, expansion ratio of deformation amount) for matching by expansion and contraction of characteristic curve, and after adjustment of initial characteristic data The two inflection points P1 and P2 in the characteristic curve (L0 ′) of the characteristic curve (L0 ′) of the prediction characteristic data and the characteristic curve (L2) are made to coincide with each other The average value [(D1 + D2) / 2] of the lateral displacement amount D2 of the charge and discharge capacity between Pe is calculated as the deterioration state of the battery.
In FIGS. 9A and 9B, only the enlargement factor of the horizontal axis (the enlargement factor of the charge / discharge capacity) is calculated, but here, the enlargement factor of the vertical axis (the enlargement factor of the deformation amount) is also calculated.

The specific steps are as follows.
First, as shown in FIG. 10A, the deterioration information generation unit 66 combines two inflection points P1 and P2 in the prediction characteristic data (L2) and two inflection points P1 and P2 in the initial characteristic data (L0). , To calculate the coefficient to match the expansion and contraction of the characteristic curve. The calculation of the coefficients is the same as the method shown in FIGS. 6A-C.

Next, as illustrated in FIG. 10B, the deterioration information generation unit 66 stretches and adjusts the entire characteristic curve (L0) indicated by the initial characteristic data using coefficients, and data indicating the adjusted characteristic curve (L0 ′) is obtained. Generate
Next, as shown in FIGS. 10B and 10C, the deterioration information generation unit 66 generates the inflection point P2 most recently extracted in the prediction characteristic data (L2) and the initial characteristic data (L0 ′) after the expansion / contraction adjustment. The characteristic curve (L2, L0 ′) of at least one of the prediction characteristic data and the initial characteristic data is moved so that the corresponding inflection point P2 coincides.

Next, as shown in FIG. 10C, the deterioration information generation unit 66 charges the lateral displacement amount D1 of the charge / discharge capacity between full charge points Pf and the residual quantity zero time points Pe with reference to the adjusted initial characteristic curve. The average value [(D1 + D2) / 2] of the lateral displacement amount D2 of the discharge capacity is calculated as the deterioration state of the battery.

If this average value is large, when the battery is deteriorated due to use of the battery, the side reaction amount between the positive electrode and the negative electrode is biased, and depending on which of the left and right is shifted, the positive electrode It can be determined which side reaction of the negative electrode is increasing.

Although the initial characteristic curve L0 'after adjustment is moved after the expansion and contraction adjustment of the initial characteristic curve L0 in FIGS. 10A to 10C, 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 of the curves may be moved. Further, after moving both curves first to make the inflection point P2 detected most recently coincide with each other, the initial characteristic curve L0 may be expanded or adjusted, or the movement and expansion may be performed simultaneously.

The coefficients used when adjusting the expansion and contraction of the initial characteristic curve L0 in FIGS. 10A to 10C can be calculated by the same method as the method of calculating the enlargement factor of the charge and discharge capacity shown in FIG. 9B. That is, the deterioration information generation unit 66 determines the ratio between the two inflection points P1 and P2, the ratio between one inflection point P1 and the full charge time Pf, and the other inflection point P2 and the remaining time zero time Pe. The weighting between the two inflection points P1 and P2 is set larger than the weighting of the other sections using the ratio between T. Here, coefficients on both the horizontal axis and the vertical axis are calculated.

<Thickness change until lithium deposition>
If it is possible to know the amount of thickness change until lithium deposition, charge control can be performed so that lithium does not precipitate. Here, as shown in FIG. 11A, the thickness maximum point PL is set in the initial characteristic data. The thickness maximum point PL is a limit point at which lithium is deposited if charging is performed more than this.
Further, if sufficient safety can be ensured, the full charge time point Pf may be the thickness maximum point PL. In this case, the amount of change from the full charge point Pf to the remaining amount zero point Pe is T1 below, and the full charge point Pf in the predicted characteristic data can be made the thickness maximum point without using the initial characteristic data.

The degradation information generation unit 66 uses the method shown in FIGS. 9A and 9B to expand and contract the characteristic curve to fit the characteristic curve L0 indicated by the initial characteristic data to the characteristic curve L2 indicated by the predicted characteristic data. (Expansion rate of charge and discharge capacity, expansion rate of deformation amount) is calculated. In FIGS. 9A and 9B, only the enlargement factor of the horizontal axis (the enlargement factor of the charge / discharge capacity) is calculated, but the enlargement factor of the vertical axis (the enlargement factor of the deformation amount) is also calculated.

Next, as illustrated in FIG. 11B, the deterioration information generation unit 66 specifies the thickness maximum point PL ′ in the prediction characteristic data from the thickness maximum point PL in the initial characteristic data, using the calculated coefficient. Next, as shown in the figure, the deterioration information generation unit 66 uses the deformation amount T1 from the thickness maximum point PL 'to the remaining amount zero point Pe in the prediction characteristic data (L2) as the starting point at the remaining amount zero point. The amount of change in thickness T1 until lithium deposition is calculated.

Although the above-mentioned state prediction method is illustrated and illustrated during discharge, it can be realized even during charge.

<Charge control>
The control device 6 may be provided with a charge control unit 67 that controls charging of the secondary battery 2 using the thickness change amount T1 up to lithium deposition predicted by the deterioration information generation unit 66. That is, charge control portion 67 performs charging so that the deformation amount of secondary battery 2 detected during charging by detection sensor 5 does not exceed thickness change amount T1 up to lithium deposition starting from the remaining amount zero point. Control. For example, as shown in FIG. 12, a threshold T2 smaller than the thickness change amount T1 may be set, and the current may be controlled to maintain the threshold T2. As a current control method, on / off control, P control, I control, D control, PD control, PI control, PID control, pulse control, PWM control or the like with T2 as a target value may be used. Further, if control can be performed so as not to exceed T1, T1 may be set as the target value.

The operation of the above system will be described with reference to FIG.

First, in step S <b> 1, the actual measurement value acquiring 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 repeatedly executed, time-series data of measured values can be obtained.

In the next step S2, it is determined whether or not there is predicted characteristic data. If it is determined that the predicted characteristic data does not exist (S2: NO), the process proceeds to step S6. If it is determined that the prediction characteristic data is present (S2: YES), the process proceeds to step S3. The process of step S3 will be described later.

In step S6, at least one inflection point P1 in the characteristic curve Ln indicating the relationship between the charge / discharge capacity Q of the secondary battery 2 and the deformation amount T from the time-series data of the measured values by the inflection point extraction unit 62 Extract P2). In the present embodiment, the inflection point extraction unit 62 sets the extraction condition according to the charge / discharge capacity Q _Pn indicated by the acquired actual measurement value, and the differential value of the deformation amount related to the charge / discharge capacity determined based on the acquired actual measurement value When ΔmV / ΔmAh] satisfies the extraction condition, the actual measurement value is extracted as the inflection point. Specifically, when the charge / discharge capacity Q _Pn indicated by the acquired actual measurement value falls within a predetermined range centered on the charge / discharge capacity Q _P1 at the inflection point P1 in the past characteristic data, the past characteristic data is based by setting a threshold Th _P1 of the differential value is set as the extraction condition that the differential value passes the threshold Th _P1. Further, in addition to the differential value passing through the threshold values Th _P1 and Th _P2 , it is set as the extraction condition that the differential value continues to continuously rise or falls for a certain period during which the charge / discharge capacity Q changes. doing. In the present embodiment, the differential value is calculated for each unit discharge capacity, and the extraction condition is that at least three differential values continue to rise or fall continuously.

Next, in step S7, it is determined whether an inflection point has been extracted. If the inflection point can not be extracted (S7: NO), it is determined in step S13 whether the termination condition is satisfied, and the process returns to step S1 until the termination condition is satisfied.

If the inflection point can be extracted in step S7 (S7: YES), in the next step S8, the past characteristic data acquisition unit 60 indicates the relationship between the charge / discharge capacity of the secondary battery and the deformation amount. Among the characteristic curves L1, past characteristic data having at least a full charge time Pf, a remaining time zero Pe, and stage inflection points P1 and P2 are acquired.

In the next step S9, with reference to the extracted inflection point, the prediction characteristic data generation unit 63 performs fitting processing of the characteristic curve L1 indicated by the characteristic data in the past to the characteristic curve Ln indicated by time series data of the actual measurement value. The prediction characteristic data indicating a characteristic curve L2 in which a portion not included in the time-series data of the actual measurement value is interpolated is generated.

When the actual value acquisition unit 61 extracts one inflection point in step S6, the prediction characteristic data generation unit 63 changes the point from any one of the full charge point Pf or the remaining amount zero point Pe in the past characteristic data. A coefficient is calculated to make the two points of the inflection point P1 coincide with the corresponding two points in the time-series data of the actual measurement values by the expansion and contraction of the characteristic curve, and the entire characteristic curve is expanded and adjusted using the coefficients, and the inflection point P1 Match to generate predicted characteristic data.

When the actual measurement value acquisition unit 61 extracts two inflection points in step S6, the prediction characteristic data generation unit 63 determines two inflection points P1 and P2 in the past characteristic data in time series data of the actual measurement value. A coefficient for making the corresponding two inflection points coincide with the characteristic curve by expansion and contraction is calculated, the entire characteristic curve is expanded and adjusted using the coefficient, the inflection point P2 extracted most recently is made to coincide, and the prediction characteristic data is Generate

In the next step S10, it is determined whether or not two different inflection points have been extracted from the time-series data of the measured values. The determination as to the different inflection points can be made based on the SOC and the charge and discharge capacity. In step S12, in generating the deterioration information based on the prediction characteristic data, the prediction characteristic data generated based on two different inflection points is more accurate than the data generated at only one inflection point It is because it is considered to be high. Of course, degradation information may be generated in a state where only one inflection point is extracted.

If it is determined in step S10 that two different inflection points are extracted (S10: YES), in step S11, the initial characteristic data acquisition unit 65 determines the charge / discharge capacity and the deformation amount of the secondary battery. Initial characteristic data having at least a thickness maximum point PL, a full charge time point Pf, a remaining charge zero time point Pe, and stage inflection points P1 and P2 among the characteristic curves L0 indicating the relationship of The thickness maximum point PL can be omitted.

In the next step S12, the deterioration information generation unit 66 determines at least one of the deterioration capacity, the change degree of the active material mass contributing to charge and discharge, or the thickness change amount up to lithium deposition starting from the remaining time zero point. .

In step S12, when the change degree of the active material mass contributing to charge and discharge is determined, the deterioration information generation unit 66 determines two inflection points P1 and P2 in the initial characteristic data and two corresponding inflections in the prediction characteristic data. The enlargement factor of the charge and discharge capacity for making points P1 and P2 coincide with each other by the expansion and contraction of the characteristic curve is calculated.

In step S12, when obtaining the deterioration state, the deterioration information generation unit 66 determines two inflection points P1 and P2 in the initial characteristic data (L0) and two inflection points P1 and P2 in the predicted characteristic data (L2). Adjustment of the initial characteristic data by adjusting the entire characteristic curve (L0) indicated by the initial characteristic data using coefficients (enlargement ratio of charge and discharge capacity, expansion ratio of deformation amount) for making the characteristic curves coincide with expansion and contraction of the characteristic curve Match the two inflection points P1 and P2 in the later characteristic curve (L0 ') and the characteristic curve (L2) of the prediction characteristic data, and make the left / right deviation amount D1 of the charge / discharge capacity between full charge points Pf and zero remaining amount The average value [(D1 + D2) / 2] of the lateral displacement amount D2 of the charge and discharge capacity between the points Pe is calculated as the battery degradation state.

In step S12, in the case of obtaining the thickness change amount up to lithium deposition starting from the remaining amount zero time point, the deterioration information generation unit 66 determines two inflection points P1 and P2 in the initial characteristic data and the predicted characteristic data. A coefficient for making the two inflection points P1 and P2 coincide with each other by expansion and contraction of the characteristic curve is calculated, and the thickness maximum point PL 'in the prediction characteristic data is specified from the thickness maximum point PL in the initial characteristic data using the coefficient The deformation amount T1 from the thickness maximum point PL ′ in the prediction characteristic data to the remaining amount zero point Pe is calculated.
When Pf = PL, the deterioration information generation unit 66 calculates a deformation amount T1 from the thickness maximum point PL (Pf) in the prediction characteristic data to the remaining amount zero point Pe.

When the process of step S12 is completed, the process proceeds to step S13.

In step S3, the remaining capacity calculating unit 64 determines the difference between the charge / discharge capacity Q _Pn indicated by the measured value at the time of predicting the remaining capacity and the charge / discharge capacity Q _Pe at the remaining capacity zero point Pe in the predicted characteristic data. Calculated as Qr. That is, once the prediction characteristic data is generated, the remaining capacity is calculated each time the actual measurement value data is acquired.

Steps S4 to S5 are processes for reviewing the prediction characteristic data. Specifically, in step S4, in the case where two inflection points P1 and P2 are included in the time-series data of the actual measurement value and the prediction characteristic data has already been generated, is a predetermined regeneration condition satisfied? It is determined whether or not. In the present embodiment, as a predetermined regeneration condition, it is determined whether or not the difference between predicted characteristic data that has been generated once and an actual measurement value exceeds a threshold. If the difference exceeds the threshold, in step S5, the prediction characteristic data generation unit 63 regenerates prediction characteristic data. This is because although the prediction characteristic data was once generated based on the actual measurement value, it did not match the actual measurement value. Here, as the regeneration condition, attention is paid to the error. However, for example, various conditions such as reaching a predetermined charge / discharge capacity, or an elapse of a predetermined time from predicted characteristic data can be appropriately changed.
Similar to the method shown in FIG. 9B, the method of generating the prediction characteristic data calculates a coefficient for matching the characteristic curve of the prediction characteristic data with the characteristic curve of the time series data of the actual measurement value by stretching the characteristic curve. Is used to adjust the entire characteristic curve, the inflection points extracted most recently are made to coincide, and the prediction characteristic data is regenerated. The coefficient uses at least the ratio between the two inflection points, the ratio between the latest measured value and the inflection point closer to the latest measured value, and the weighting between the two inflection points in the other interval It is preferable to calculate by aggregation by setting larger than the weighting of. In this way, not only one section between two inflection points but also the ratio of two sections is used to fit to the latest measured value so that the predicted characteristic data is immediately It becomes a form.
Furthermore, when the time-series data of the measured value includes one of the fully charged time point Pf and the remaining zero time point Pe that is farther from the latest measured value, among the fully charged time point Pf and the remaining zero time point Pe Using the ratio between the farthest measured value and one inflection point, the ratio between two inflection points, and the ratio between the latest measured value and the other inflection point close to the latest measured value Preferably, the weighting between the two inflection points is set larger than the weighting of the other sections, and calculation is performed by aggregation. In this way, using the ratio of 3 compartments, fitting is performed so that any of the fully charged time point Pf and the remaining zero time point Pe is also matched with the latest measured value, so the predicted characteristic data conforms to the measured value become.

The detection unit 4 is disposed at a place where a change in the external field can be detected, and is preferably attached to a relatively rigid place which is not easily affected by the swelling of the secondary battery 2. In the present embodiment, as illustrated in FIG. 2B, the detection unit 4 is attached to the inner surface of the housing 11 of the battery module facing the wall 28a. The housing 11 of the battery module is formed of, for example, metal or plastic, and a laminate film may be used. Although the detection unit 4 is disposed close to the polymer matrix layer 3 in the drawing, it may be disposed apart from the polymer matrix layer 3.

In the present embodiment, an example is shown in which the polymer matrix layer 3 contains a magnetic filler as the filler, and the detection unit 4 detects 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 made of an elastomer component.

Examples of the magnetic filler include rare earths, irons, cobalts, nickels, oxides and the like, but rare earths capable of obtaining higher magnetic force are preferable. The shape of the magnetic filler is not particularly limited, and may be spherical, flat, needle-like, columnar or indeterminate. The average particle size of the magnetic filler is preferably 0.02 to 500 μm, more preferably 0.1 to 400 μm, and still more preferably 0.5 to 300 μm. When the average particle size is smaller than 0.02 μm, the magnetic properties of the magnetic filler tend to be deteriorated, and when the average particle size exceeds 500 μm, the mechanical properties of the magnetic elastomer layer tend to be reduced and become brittle.

The magnetic filler may be introduced into the elastomer after magnetization, but is preferably magnetized after introduction into the elastomer. By magnetizing after being introduced into the elastomer, control of the polarity of the magnet becomes easy, and detection of the magnetic field becomes easy.

For the elastomeric component, thermoplastic elastomers, thermosetting elastomers or mixtures thereof can be used. Examples of thermoplastic elastomers include styrene thermoplastic elastomers, polyolefin thermoplastic elastomers, polyurethane thermoplastic elastomers, polyester thermoplastic elastomers, polyamide thermoplastic elastomers, polybutadiene thermoplastic elastomers, polyisoprene thermoplastic elastomers, A fluororubber type thermoplastic elastomer etc. can be mentioned. Moreover, as a thermosetting elastomer, for example, polyisoprene rubber, polybutadiene rubber, styrene-butadiene rubber, polychloroprene rubber, nitrile rubber, diene-based synthetic rubber such as ethylene-propylene rubber, ethylene-propylene rubber, butyl rubber, acrylic rubber, Non-diene based synthetic rubbers such as polyurethane rubber, fluororubber, silicone rubber, epichlorohydrin rubber, and natural rubber can be mentioned. Among these, a thermosetting elastomer is preferable, because it can suppress the stagnation of the magnetic elastomer due to heat generation or overload of the battery. More preferably, they are polyurethane rubber (also referred to as polyurethane elastomer) or silicone rubber (also referred to as silicone elastomer).

The polyurethane elastomer is obtained by reacting a polyol and a polyisocyanate. When using a polyurethane elastomer as an elastomer component, an active hydrogen-containing compound and a magnetic filler are mixed, and an isocyanate component is mixed here to obtain a mixed liquid. A mixed solution can also be obtained by mixing a magnetic filler with an isocyanate component and mixing an active hydrogen-containing compound. The magnetic elastomer can be produced by casting the mixed solution in a mold subjected to a release treatment and then heating to a curing temperature to cure. When silicone elastomer is used as an elastomer component, the magnetic elastomer can be produced by adding a magnetic filler to a silicone elastomer precursor, mixing it, placing it in a mold and then heating and curing it. In addition, you may add a solvent as needed.

As an isocyanate component that can be used for the polyurethane elastomer, compounds known in the field of polyurethane can be used. For example, 2,4-toluene diisocyanate, 2,6-toluene diisocyanate, 2,2'-diphenylmethane diisocyanate, 2,4'-diphenylmethane diisocyanate, 4,4'-diphenylmethane diisocyanate, 1,5-naphthalene diisocyanate, p-phenylene Aromatic diisocyanates such as diisocyanate, m-phenylene diisocyanate, p-xylylene diisocyanate, m-xylylene diisocyanate, etc. Aliphatic diisocyanates such as ethylene diisocyanate, 2,2,4-trimethylhexamethylene diisocyanate, 1,6-hexamethylene diisocyanate 1,4-cyclohexane diisocyanate, 4,4'-dicyclohexyl methane diisocyanate, isophorone diisocyanate, nor It can be mentioned alicyclic diisocyanates such as Renan diisocyanate. These may be used alone or in combination of two or more. In addition, the isocyanate component may be one modified by urethane modification, allophanate modification, biuret modification, isocyanurate modification, or the like. Preferred isocyanate components are 2,4-toluene diisocyanate, 2,6-toluene diisocyanate, 4,4'-diphenylmethane diisocyanate, more preferably 2,4-toluene diisocyanate, BR> G, 2,6-toluene diisocyanate is there.

As the active hydrogen-containing compound, those commonly used in the technical field of polyurethane can be used. For example, polyetherpolyol represented by polytetramethylene glycol, polypropylene glycol, polyethylene glycol, copolymer of propylene oxide and ethylene oxide, polybutylene adipate, polyethylene adipate, 3-methyl-1,5-pentane adipate Polyester polycarbonate polyol exemplified by the reaction product of polyester glycol such as polycaprolactone polyol, polyester glycol such as polycaprolactone glycol and alkylene carbonate, ethylene carbonate is reacted with polyhydric alcohol, and then the reaction mixture obtained is Polyester polycarbonate polyol reacted with organic dicarboxylic acid, polyhydroxyl compound and aryl carbonate It can be mentioned a high molecular weight polyol and polycarbonate polyols obtained by ester exchange reaction. These may be used alone or in combination of two or more.

Ethylene glycol, 1,2-propylene glycol, 1,3-propylene glycol, 1,4-butanediol, 1,6-hexanediol, neopentyl glycol, in addition to the high molecular weight polyol component described above as the active hydrogen-containing compound 1,4-cyclohexanedimethanol, 3-methyl-1,5-pentanediol, diethylene glycol, triethylene glycol, 1,4-bis (2-hydroxyethoxy) benzene, trimethylolpropane, glycerin, 1,2,6- Hexanetriol, pentaerythritol, tetramethylol cyclohexane, methyl glucoside, sorbitol, mannitol, dulcitol, sucrose, 2,2,6,6-tetrakis (hydroxymethyl) cyclohexanol, and triethanol amine Low molecular weight polyol component of such emissions, ethylenediamine, tolylenediamine, diphenylmethane diamine, may be used low molecular weight polyamine component of diethylenetriamine. These may be used singly or in combination of two or more. Furthermore, 4,4′-methylenebis (o-chloroaniline) (MOCA), 2,6-dichloro-p-phenylenediamine, 4,4′-methylenebis (2,3-dichloroaniline), 3,5-bis ( Methylthio) -2,4-toluenediamine, 3,5-bis (methylthio) -2,6-toluenediamine, 3,5-diethyltoluene-2,4-diamine, 3,5-diethyltoluene-2,6- Diamine, trimethylene glycol-di-p-aminobenzoate, polytetramethylene oxide-di-p-aminobenzoate, 1,2-bis (2-aminophenylthio) ethane, 4,4'-diamino-3,3 ' -Diethyl-5,5'-dimethyldiphenylmethane, N, N'-di-sec-butyl-4,4'-diaminodiphenylmethane, 4,4'-diamide -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, Polyamines exemplified by N, N'-di-sec-butyl-p-phenylenediamine, m-phenylenediamine, and p-xylylenediamine can also be mixed. Preferred active hydrogen-containing compounds are polytetramethylene glycol, polypropylene glycol, copolymers of propylene oxide and ethylene oxide, 3-methyl-1,5-pentane adipate, more preferably polypropylene glycol, and copolymer of propylene oxide and ethylene oxide It is union.

Preferred combinations of the isocyanate component and the active hydrogen-containing compound include one or two or more of 2,4-toluene diisocyanate, 2,6-toluene diisocyanate, and 4,4′-diphenylmethane diisocyanate as the isocyanate component, and active hydrogen. The contained compounds are polytetramethylene glycol, polypropylene glycol, a copolymer of propylene oxide and ethylene oxide, and a combination of one or more of 3-methyl-1,5-pentane adipate. More preferably, it is a combination of 2,4-toluene diisocyanate and / or 2,6-toluene diisocyanate as an isocyanate component, and polypropylene glycol and / or a copolymer of propylene oxide and ethylene oxide as an active hydrogen-containing compound. is there.

The polymer matrix layer 3 may be a foam containing dispersed fillers and cells. Although a general resin foam can be used as the foam, it is preferable to use a thermosetting resin foam in consideration of characteristics such as compression set. As a thermosetting resin foam, a polyurethane resin foam, a silicone resin foam, etc. are mentioned, Among these, a polyurethane resin foam is suitable. For the polyurethane resin foam, the above-mentioned isocyanate component and active hydrogen-containing compound can be used.

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

A 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 for the purpose of preventing corrosion of the magnetic filler and the like. For the sealing material, a thermoplastic resin, a thermosetting resin, or a mixture thereof can be used. Examples of thermoplastic resins include styrene thermoplastic elastomers, polyolefin thermoplastic elastomers, polyurethane thermoplastic elastomers, polyester thermoplastic elastomers, polyamide thermoplastic elastomers, polybutadiene thermoplastic elastomers, polyisoprene thermoplastic elastomers, Fluorine-based thermoplastic elastomer, ethylene / ethyl acrylate copolymer, ethylene / vinyl acetate copolymer, polyvinyl chloride, polyvinylidene chloride, chlorinated polyethylene, fluorine resin, polyamide, polyethylene, polypropylene, polyethylene terephthalate, polybutylene terephthalate, polystyrene, polybutadiene Etc. can be mentioned. The thermosetting resin may be, for example, polyisoprene rubber, polybutadiene rubber, styrene butadiene rubber, polychloroprene rubber, diene based synthetic rubber such as acrylonitrile butadiene rubber, ethylene propylene rubber, ethylene propylene diene rubber, butyl rubber, Non-diene rubbers such as acrylic rubber, polyurethane rubber, fluororubber, silicone rubber and epichlorohydrin rubber, natural rubber, polyurethane resin, silicone resin, epoxy resin and the like can be mentioned. These films may be laminated, or may be a metal foil such as an aluminum foil or a film including a metal vapor-deposited film in which a metal is vapor-deposited on the film.

The polymer matrix layer 3 may have a filler unevenly distributed in the thickness direction. For example, the polymer matrix layer 3 may have a two-layer structure of one region relatively rich in filler and the other region relatively less filler. In the region on one side containing a large amount of filler, the change of the external field to a small deformation of the polymer matrix layer 3 becomes large, so the sensor sensitivity to a low internal pressure can be enhanced. Further, the region on the other side with a relatively small amount of filler is relatively soft and easy to move, and by attaching this region, the polymer matrix layer 3 (particularly, the region on one side) is easily deformed.

The filler uneven distribution rate in the region on one side is preferably more than 50, more preferably 60 or more, and still more preferably 70 or more. In this case, the filler uneven distribution rate in the region on the other side is less than 50. The filler uneven distribution rate in one area is at most 100, and the filler uneven distribution rate in the other area is at least 0. Therefore, a laminate structure of an elastomer layer containing a filler and an elastomer layer not containing a filler may be used. For uneven distribution of the filler, after introducing the filler into the elastomer component, it is possible to use a method of leaving at room temperature or at a predetermined temperature and letting the natural sedimentation by the weight of the filler. The filler maldistribution rate can be adjusted. The filler may be unevenly distributed using physical force such as centrifugal force or magnetic force. Alternatively, the polymer matrix layer may be formed of a laminate of a plurality of layers having different filler contents.

The filler uneven distribution rate is measured by the following method. That is, the cross section of the polymer matrix layer is observed at 100 times using a scanning electron microscope-energy dispersive X-ray analyzer (SEM-EDS). Elemental analysis of the entire area in the thickness direction of the cross section and the two areas obtained by dividing the cross section in the thickness direction into metal elements (for example, Fe element in the case of the magnetic filler of the present embodiment) Find the abundance. With respect to this amount, the ratio of the area on one side to the area in the entire thickness direction is calculated, and this is taken as the filler uneven distribution rate in the area on one side. The filler uneven distribution rate in the other side area is also similar to this.

The region on the other side with a relatively small amount of filler may be a structure formed of foam containing bubbles. As a result, the polymer matrix layer 3 is more easily deformed, and the sensor sensitivity is enhanced. Moreover, the area | region of one side may be formed with a foam with the area | region of the other side, and the polymer matrix layer 3 in that case becomes a foam as a whole. The polymer matrix layer in which at least a part of the thickness direction is a foam is constituted by a laminate of a plurality of layers (for example, a non-foamed layer containing a filler and a foamed layer not containing a filler) It does not matter.

For example, a reed switch, a magnetoresistive element, a Hall element, a coil, an inductor, an MI element, a flux gate sensor, or the like can be used as the detection unit 4 that detects a change in the magnetic field. Examples of the magnetoresistive element include semiconductor compound magnetoresistive elements, anisotropic magnetoresistive elements (AMR), giant magnetoresistive elements (GMR), and tunnel magnetoresistive elements (TMR). Among these, Hall elements are preferable, because they have high sensitivity over a wide range and are useful as the detection unit 4. For example, EQ-430L manufactured by Asahi Kasei Electronics Co., Ltd. can be used as the hall element.

Since the secondary battery 2 in which gas expansion has progressed may lead to problems such as ignition and rupture, in the present embodiment, when the amount of expansion when the secondary battery 2 is deformed is a predetermined amount or more, charge and discharge It is configured to be shut off. Specifically, when the signal detected by the detection sensor 5 is transmitted to the control device 6 and the change of the external field above the set value is detected by the detection sensor 5, the control device 6 sends a signal to the switching circuit 7 Then, the current from the power generation device (or the charging device) 8 is shut off, and charging / discharging of the battery module 1 is shut off. This makes it possible to prevent in advance the trouble caused by the gas expansion.

Although the above-mentioned embodiment showed the example which is a lithium ion rechargeable battery, the rechargeable battery is not restricted to this. The secondary battery to be used is not limited to a non-aqueous electrolytic solution secondary battery such as a lithium ion battery, and may be an aqueous electrolytic solution secondary battery such as a nickel hydrogen battery.

Although the above-mentioned embodiment showed an example which detects change of a magnetic field accompanying modification of a polymer matrix layer by a detection part, it may be the composition of detecting change of other external fields. For example, it is considered that the polymer matrix layer contains a conductive filler such as metal particles, carbon black, carbon nanotubes, etc. as a filler, and the detection unit detects a change in electric field (change in resistance and dielectric constant) as an external field. Be

As described above, the method of predicting the state of the secondary battery of the present embodiment
Step S1 of acquiring an actual measurement value corresponding to the charge / discharge capacity Q and the deformation amount T of the secondary battery 2;
Step S6 of extracting at least one inflection point P1 (P2) in the characteristic curve Ln indicating the relationship between the charge / discharge capacity Q of the secondary battery and the deformation amount T from the time series data of the measured values;
A step of acquiring past characteristic data having at least a full charge time point Pf, a remaining time zero time point Pe, and stage inflection points P1 and P2 out of a characteristic curve L1 indicating the relationship between the charge / discharge capacity of the secondary battery and the deformation amount S8,
The characteristic curve L1 indicated by the past characteristic data is fitted to the characteristic curve Ln indicated by the time series data of the actual measurement value on the basis of the extracted inflection point, and a portion not included in the time series data of the actual value is interpolated. Generating predicted characteristic data indicating a characteristic curve L2;
including.

The secondary battery state prediction system of the present embodiment is
A measured value acquisition unit 61 that acquires measured values corresponding to the charge / discharge capacity Q and the deformation amount T of the secondary battery 2;
An inflection point extraction unit 62 for extracting at least one inflection point P1 (P2) in a characteristic curve Ln indicating the relationship between the charge / discharge capacity Q of the secondary battery and the deformation amount T from time series data of measured values;
Of the characteristic curve L1 indicating the relationship between the charge and discharge capacity of the secondary battery and the deformation amount, the past acquires characteristic data of at least the full charge time point Pf, the remaining time zero time point Pe and the stage inflection points P1 and P2. Characteristic data acquisition unit 60,
The characteristic curve L1 indicated by the past characteristic data is fitted to the characteristic curve Ln indicated by the time series data of the actual measurement value on the basis of the extracted inflection point, and a portion not included in the time series data of the actual value is interpolated. A prediction characteristic data generation unit 63 that generates prediction characteristic data indicating a characteristic curve L2;
Equipped with

There are two inflection points P1 and P2 at which the slope of the characteristic curve largely changes with the stage change in the characteristic curve showing the relationship between the charge and discharge capacity and the deformation amount of the secondary battery. According to this method, the inflection point in the characteristic curve is extracted from the time-series data of the measured values, and the past characteristic data and the measured value data are fitted with reference to the extracted inflection point. Even if there is no measurement data for a long time from full discharge to complete discharge, if there is measurement data of a short period, such as from full charge to the inflection point, from one inflection point to the other inflection point, Predicted characteristic data can be generated with a certain degree of accuracy secured.
Therefore, the predicted characteristic data can be detected and the state of the secondary battery can be predicted in actual use where charging and discharging are performed randomly, not a series of charging and discharging such as from full charge to complete discharge.

In the present embodiment, the inflection point extraction unit 62 sets extraction conditions according to the charge / discharge capacity indicated by the acquired actual measurement value, and the differential value of the deformation amount related to the charge / discharge capacity determined based on the acquired actual measurement value is the extraction condition If the above condition is satisfied, the actual measurement 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 capacity Q. In this embodiment, since the inflection point extraction unit 62 sets the extraction condition according to the charge / discharge capacity Q indicated by the actual measurement value, the extraction accuracy is higher than in the case where the inflection point is extracted only by the change of the differential value. It is possible to improve the

In the present embodiment, the inflection point extraction unit 62, the charge-discharge capacity Q _Pn showing actual measurement value acquired is centered on the charge and discharge capacity Q _P1, Q _P2 past the inflection point in the characteristic data P1, P2 If falling within the predetermined range, sets the threshold value _Th P1, _{Th P2} of the differential value based on historical characteristic data set as an extraction condition that the differential value has passed the threshold value _Th P1, _{Th P2} (step S7) .

This makes it possible to realize the extraction process of the inflection points P1 and P2 by a simple calculation.

In the present embodiment, the inflection point extraction unit 62 continuously increases the derivative value during a certain period in which the charge / discharge capacity changes in addition to the derivative value passing the threshold values Th _P1 and Th _P2. It sets that continuing or falling as an extraction condition (step S7).

With such an extraction condition, even if the differential value temporarily passes through the threshold values 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 prediction characteristic data generation unit 63
When one inflection point P1 is extracted and the time-series data of actual values includes an actual measurement value corresponding to any one of the full charge time point Pf or the remaining zero time point Pe, in the past characteristic data Calculate a coefficient to make one of the fully charged time point Pf or the remaining zero time point Pe and two points at the inflection point P1 coincide with the corresponding two points in the time series data of the measured values by the expansion and contraction of the characteristic curve. , Adjust the entire characteristic curve using coefficients, make the inflection point P1 coincide, and generate prediction characteristic data (step S9),
Or
If one inflection point is extracted and the time-series data of the measured values does not include the measured value corresponding to any one of the fully charged time point Pf or the remaining zero time point Pe, the past characteristic data indicates Move the characteristic curve L1 indicated by the past characteristic data so that the inflection point P1 extracted most recently matches the inflection point P1 in the past characteristic data without enlarging or reducing the characteristic curve L1. Interpolate to generate prediction characteristic data (step S9),
Or
When two inflection points P1 and P2 are extracted, the two inflection points P1 and P2 in the past characteristic data are expanded and contracted to the corresponding two inflection points in the time-series data of the measured value. A coefficient for matching is calculated, and the entire characteristic curve is stretched and adjusted using the coefficient, and the inflection point P2 extracted most recently is matched to generate prediction characteristic data (step S9).

According to this method, since the inflection points or the inflection points and the end of the characteristic curve coincide with each other with priority, the shape of the characteristic curve with stage change is less likely to collapse compared to general fitting, and thus prediction It is possible to improve the accuracy.

In the present embodiment, when the predicted characteristic data generation unit 63 includes two inflection points in the time series data of the actual measurement value and the predicted characteristic data has already been generated, and a predetermined regeneration condition is satisfied (step S4: YES), calculate the coefficient for matching the characteristic curve L2 of the prediction characteristic data to the characteristic curve Ln of the time series data of the actual measurement value by the expansion and contraction of the characteristic curve, and adjust the expansion and contraction of the entire characteristic curve L2 using the coefficient Then, the inflection points extracted most recently are made to coincide, and prediction characteristic data is regenerated. The coefficient uses at least the ratio between the two inflection points, the ratio between the latest measured value and the inflection point closer to the latest measured value, and the weighting between the two inflection points in the other interval Set larger than the weighting of, and calculate by aggregation.

In this way, not only one section between two inflection points but also the ratio of two sections is used to fit to the latest measured value so that the predicted characteristic data is immediately It becomes a form.

In the present embodiment, the remaining capacity calculation unit 64 determines the difference between the charge / discharge capacity Q _Pn indicated by the measured value at the time of predicting the remaining capacity and the charge / discharge capacity Q _Pe at the remaining capacity zero point Pe in the predicted characteristic data. Calculated as the capacity Qr (step S3).

As described above, since the prediction characteristic data includes data of the remaining amount zero point Pe, it becomes possible to calculate the remaining amount accurately.

In the present embodiment, an initial characteristic data acquisition unit 65 and a deterioration information generation unit 66 are provided. Initial characteristic data acquisition portion 65 has at least a full charge time point Pf, a remaining amount time zero point Pe, and stage inflection points P1 and P2 in a characteristic curve L0 indicating the relationship between the charge and discharge capacity of the secondary battery and the deformation amount. Initial characteristic data is acquired (step S11). The deterioration information generation unit 66 has a charge / discharge capacity for causing the two inflection points P1 and P2 in the initial characteristic data and the corresponding two inflection points P1 and P2 in the predicted characteristic data to coincide with each other by the expansion and contraction of the characteristic curve. The enlargement factor of is calculated as the degree of change of the amount of active material contributing to charge and discharge (step S12).

In this way, it is possible to know the degree of change of the amount of active material contributing to charge and discharge.

In the present embodiment, the degradation information generation unit 66 determines the ratio between two inflection points, the ratio between one inflection point and the full charge time point Pf, and the other inflection point with respect to the enlargement factor of the charge and discharge capacity. The weighting between the two inflection points is set larger than the weightings of the other sections using the ratio between the remaining amount zero point Pe and the remaining points, and the calculation is made by aggregation (step S12).

In this way, compared to the ratio between the two inflection points alone, the fully charged time point Pf and the remaining zero time point Pe become closer as a fitting result, so the degree of agreement in the entire characteristic curve is improved, and the calculation accuracy is improved. I think it will improve.

In the present embodiment, an initial characteristic data acquisition unit 65 and a deterioration information generation unit 66 are provided. Initial characteristic data acquisition portion 65 has at least a full charge time point Pf, a remaining amount time zero point Pe, and stage inflection points P1 and P2 in a characteristic curve L0 indicating the relationship between the charge and discharge capacity of the secondary battery and the deformation amount. Initial characteristic data is acquired (step S11). The deterioration information generation unit 66 calculates coefficients for making the two inflection points P1 and P2 in the initial characteristic data coincide with the two inflection points P1 and P2 in the prediction characteristic data by the expansion and contraction of the characteristic curve, While adjusting the entire characteristic curve L0 indicated by the initial characteristic data using coefficients, the two inflection points P1 and P2 in the characteristic curve L0 'after adjustment of the initial characteristic data and the characteristic curve L2 of the predicted characteristic data coincide with each other And calculate the average value [(D1 + D2) / 2] of the lateral displacement amount D1 of the charge and discharge capacity between the full charge time points Pf and the lateral displacement amount D2 of the charge and discharge capacity between the remaining time zero point Pe as the battery degradation state. (Step S12).

In this way, it is possible to calculate the deterioration state of the battery as to how much the side reaction of one of the positive electrode and the negative electrode is greater than the other side reaction.

In the present embodiment, an initial characteristic data acquisition unit 65 and a deterioration information generation unit 66 are provided. Initial characteristic data acquisition unit 65 sets at least a thickness maximum point PL, a full charge time point Pf, a remaining time zero time point Pe, and a stage inflection point in a characteristic curve L0 indicating the relationship between the charge and discharge capacity of the secondary battery and the deformation amount. Initial characteristic data having P1 and P2 is acquired (step S11). The deterioration information generation unit 66 calculates coefficients for making the two inflection points P1 and P2 in the initial characteristic data coincide with the two inflection points P1 and P2 in the prediction characteristic data by the expansion and contraction of the characteristic curve, The thickness maximum point PL 'in the prediction characteristic data is specified from the thickness maximum point PL in the initial characteristic data using the coefficient, and the deformation amount T1 from the thickness maximum point PL' in the prediction characteristic data to the remaining amount zero point Pe is It is calculated as the amount of thickness change up to lithium deposition starting from the zero point.

In this way, it is possible to calculate the amount of thickness change up to lithium deposition starting from the remaining amount zero point. This value is an indicator of charge control and is useful.

In the present embodiment, the deterioration information generation unit 66 determines, for the coefficients, a ratio between two inflection points, a ratio between one inflection point and a full charge time point Pf, and the other inflection point and the remaining time zero time point. Using the ratio between Pe, the weighting between the two inflection points is set to be larger than the weighting of the other sections, and calculation is performed by aggregation (step S12).

In this way, compared to the ratio between the two inflection points alone, the fully charged time point Pf and the remaining zero time point Pe become closer as a fitting result, so the degree of agreement in the entire characteristic curve is improved, and the calculation accuracy is improved. I think it will improve.

In the present embodiment, the fully charged time point Pf in the prediction characteristic data is the thickness maximum point PL, and the deterioration information generation unit 66 determines the deformation T1 from the thickness maximum point PL (Pf) in the prediction characteristic data to the remaining zero time point Pe. Is calculated as the amount of thickness change up to lithium deposition starting from the remaining amount zero point.

In this way, it is possible to calculate the amount of thickness change up to lithium deposition starting from the remaining amount zero point. This value is an indicator of charge control and is useful.

In the charge control system of the present embodiment, the deformation amount T of the secondary battery detected by the deterioration information generation unit 66 and the detection sensor 5 does not exceed the thickness change amount T1 generated by the deterioration information generation unit 66. And a charge control unit 67 that controls charging.

According to this configuration, since charge control is performed so that lithium does not precipitate, in some cases, it may be possible to charge rapidly without causing deterioration due to lithium precipitation.

In the present embodiment, the polymer matrix layer 3 is attached directly or indirectly to the secondary battery 2, and the polymer matrix layer 3 is formed of a filler that changes the external field according to the deformation of the polymer matrix layer 3. The deformation amount T of the secondary battery 2 is detected by detecting the change in the external field according to the deformation of the polymer matrix layer 3.

In this way, an actual measurement value corresponding to the amount of deformation of the secondary battery 2 can be appropriately detected.

The secondary battery state prediction system according to the present embodiment is
A processor 6B and a memory 6A for storing instructions executable by the processor 6B.
Processor 6B is
Measured values corresponding to the charge / discharge capacity Q and deformation amount T of the secondary battery 2 are acquired (step S1),
At least one inflection point P1 (P2) in the characteristic curve Ln indicating the relationship between the charge / discharge capacity Q of the secondary battery and the deformation amount T is extracted from the time-series data of the measured values (step S6),
Of the characteristic curve L1 showing the relationship between the charge and discharge capacity of the secondary battery and the deformation amount, obtain the past characteristic data having at least the full charge time point Pf, the remaining time zero time point Pe and the stage inflection points P1 and P2 Step S8),
The characteristic curve L1 indicated by the past characteristic data is fitted to the characteristic curve Ln indicated by the time series data of the actual measurement value on the basis of the extracted inflection point, and a portion not included in the time series data of the actual value is interpolated. The prediction characteristic data indicating the characteristic curve L2 is generated (step S9).

The processor 6B includes one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, It may be realized by a microcontroller, microprocessor or other electronic component.

The program according to the present embodiment is a program that causes a computer to execute the above method.
By executing these programs, it is also possible to obtain the effects of the above method. In addition, the program may be stored in a computer readable recording medium.

The present invention is not limited to the embodiment described above, and various improvements and modifications can be made without departing from the spirit of the present invention. For example, the order of execution of each process such as operations, procedures, steps, and steps in the apparatuses, systems, programs, and methods shown in the claims, the specification, and the drawings is the output order of the previous process. It can be realized in any order as long as it is not used in processing. Even if the flow in the claims, the description and the drawings is described using “first”, “next” and the like for convenience, it does not mean that execution in this order is essential. .

In the above embodiment, the prediction characteristic data is generated by extracting the inflection point from the time-series data of the actual measurement value and performing the fitting process on the past characteristic data. However, if accuracy is not considered, generation processing 4 of prediction characteristic data can also be mentioned as follows. That is, in generation processing 4 of predicted characteristic data, the characteristic curve L1 indicated by the past characteristic data is fitted as it is without enlargement or reduction based on the charge / discharge capacity Q _Pn indicated by the actual measurement value at the present time. According to this method, it is possible to generate predicted characteristic data although capacity deterioration and balance deviation are not taken into consideration.
At least one of the generation processes 1 to 4 of the prediction characteristic data described above can be implemented. The combination of the generation processes 1 to 4 can be arbitrarily performed. However, in order from the process with high accuracy to the order with low accuracy, it is generation process 1, generation process 2, generation process 3, and generation process 4. The generation process 1 is the highest, and the generation process 4 is the lowest. On the other hand, the time required to generate the prediction characteristic data is the shortest in the generation process 4 and the longest in the generation process 1.

L0 Characteristic curve indicated by initial characteristic data Ln Characteristic curve indicated by actual value L1 Characteristic curve indicated by past characteristic data L2 Characteristic curve indicated by predicted characteristic data P1, P2 Inflection point Pf Full charge point Pe Remaining zero time point PL ... thickness maximum point Th _P1 , Th _P2 ... threshold Qr ... remaining capacity 6A ... memory 6B ... processor

Claims (33)

Acquiring an actual measurement value corresponding to the charge / discharge capacity and deformation amount of the secondary battery;
Extracting at least one inflection point in a characteristic curve indicating the relationship between the charge / discharge capacity of the secondary battery and the deformation amount from the time-series data of the measured value;
Acquiring past characteristic data having at least a full charge time point, a remaining time zero time point, and a stage inflection point in a characteristic curve indicating a relation between charge and discharge capacity and deformation amount of the secondary battery;
The characteristic curve indicated by the characteristic data in the past is fitted to the characteristic curve indicated by the time series data of the actual measurement value on the basis of the extracted inflection point, and a portion not included in the time series data of the actual measurement value is interpolated Generating predicted characteristic data indicating a different characteristic curve;
A method of predicting the state of a secondary battery, including: Set extraction conditions according to the charge / discharge capacity indicated by the acquired actual measurement value,
The method according to claim 1, wherein the measured value is extracted as an inflection point when the differential value of the deformation amount related to the charge / discharge capacity determined based on the acquired measured value satisfies the extraction condition. When the charge / discharge capacity indicated by the obtained measured value falls within a predetermined range centered on the charge / discharge capacity at the inflection point in the past characteristic data, the threshold value of the differential value is set based on the past characteristic data, The method according to claim 2, wherein an extraction condition is set that the differential value has passed the threshold value. In addition to the differential value passing the threshold value, it is set as an extraction condition that the differential value continues to rise or fall continuously during a certain period in which the charge / discharge capacity changes. The method described in 3. In the case where one inflection point is extracted and the time-series data of the actual measurement value includes an actual measurement value corresponding to any one of the full charge time or the remaining time zero, the characteristic data of the past The factor for making the characteristic curve match the two points at any one of the fully charged time point or the remaining zero time point and the inflection point at corresponding two points in the time series data of the actually measured value Calculating and scaling the entire characteristic curve using the coefficients, matching the inflection points, and generating the predicted characteristic data;
Or
In the case where one inflection point is extracted and the time-series data of the actual measurement value does not include the actual measurement value corresponding to any one point of the full charge time or the remaining zero time, the characteristic data of the past The characteristic curve indicated by the past characteristic data is moved so that the inflection point extracted most recently matches the inflection point in the past characteristic data without enlarging or reducing the characteristic curve indicated by Interpolate to generate the prediction characteristic data,
Or
When two inflection points are extracted, the two inflection points in the past characteristic data are made to coincide with the corresponding two inflection points in the time-series data of the actual measurement value by the expansion and contraction of the characteristic curve. The method according to any one of claims 1 to 4, wherein a coefficient is calculated, the entire characteristic curve is scaled by using the coefficient, the inflection points extracted most recently are matched, and the prediction characteristic data is generated. In the case where two inflection points are included in the time series data of the actual measurement value, the prediction characteristic data is already generated, and a predetermined regeneration condition is satisfied, the characteristic curve of the prediction characteristic data is the actual measurement value A coefficient for matching the characteristic curve of the time series data by expansion and contraction of the characteristic curve is calculated, the whole characteristic curve is expanded and adjusted using the coefficient, the inflection points extracted most recently are made to coincide, and the predicted characteristic data Including the step of regenerating
The coefficient uses at least the ratio between the two inflection points, the ratio between the latest actual value and the inflection point closer to the latest actual value, and the weighting between the two inflection points The method according to any one of claims 1 to 5, wherein the value is set larger than the weighting of the section of and calculated by aggregation. The difference between the charge / discharge capacity indicated by the actual value at the time of predicting the remaining capacity and the charge / discharge capacity at the remaining time zero in the predicted characteristic data is calculated as the remaining capacity. Method. Acquiring initial characteristic data having at least a full charge time point, a remaining time zero time point, and a stage inflection point in a characteristic curve indicating a relation between charge and discharge capacity of secondary battery and deformation amount;
An activity that contributes to charge and discharge, an enlargement ratio of charge and discharge capacity for matching the two inflection points in the initial characteristic data and the corresponding two inflection points in the predicted characteristic data by the expansion and contraction of the characteristic curve The method according to any one of claims 1 to 7, further comprising the step of: calculating as the degree of change of the amount of substance. The enlargement ratio of the charge and discharge capacity is determined using the ratio between the two inflection points, the ratio between one inflection point and the full charge time, and the ratio between the other inflection point and the remaining time zero. The method according to claim 8, wherein the weight between the two inflection points is set larger than the weight of the other sections and calculated by aggregation. Acquiring initial characteristic data having at least a full charge time point, a remaining time zero time point, and a stage inflection point in a characteristic curve indicating a relation between charge and discharge capacity of secondary battery and deformation amount;
A coefficient for making two inflection points in the initial characteristic data coincide with two inflection points in the prediction characteristic data by expansion and contraction of the characteristic curve is calculated, and the initial characteristic data is indicated using the coefficients The expansion and contraction adjustment of the entire characteristic curve is performed, and the two inflection points in the characteristic curve after adjustment of the initial characteristic data and the characteristic curve of the predicted characteristic data are made to coincide with each other. And calculating the average value of the amount of deviation of the charge / discharge capacity between the remaining amount zero point in time as the deterioration state of the battery, the method according to any one of claims 1 to 7. The coefficients are calculated using the ratio between the two inflection points, the ratio between one inflection point and the full charge time, and the ratio between the other inflection point and the remaining time zero, The method according to claim 10, wherein the weight between the inflection points is set larger than the weight of the other sections and calculated by aggregation. Acquiring initial characteristic data having at least a thickness maximum point, a full charge time, a remaining time zero, and a stage inflection point among characteristic curves showing a relation between charge and discharge capacity and deformation amount of the secondary battery;
A coefficient for causing the two inflection points in the initial characteristic data and the two inflection points in the prediction characteristic data to coincide with each other by expansion and contraction of the characteristic curve is calculated, and using the coefficients, the in the initial characteristic data is calculated The thickness maximum point in the prediction characteristic data is specified from the thickness maximum point, and the amount of deformation from the thickness maximum point in the prediction characteristic data to the remaining amount zero point is determined until lithium deposition starting from the remaining point zero point The method according to any one of claims 1 to 7, comprising the step of calculating as a thickness change amount. The coefficients are calculated using the ratio between the two inflection points, the ratio between one inflection point and the full charge time, and the ratio between the other inflection point and the remaining time zero, The method according to claim 12, wherein the weight between the inflection points is set larger than the weight of the other sections and calculated by aggregation. The point of full charge in the predicted characteristic data is the maximum thickness point,
The method according to any one of claims 1 to 7, further comprising the step of calculating the amount of deformation from the thickness maximum point to the point of remaining amount zero in the predicted characteristic data as the amount of thickness change until lithium deposition starting from the point of remaining amount zero. How to describe. A thickness change amount until lithium deposition starting from the remaining amount zero point is calculated in advance by executing the method according to any one of claims 12 to 14,
A charge control method, comprising: controlling charging such that a deformation amount of a secondary battery detected by a detection sensor does not exceed the thickness change amount. A polymer matrix layer is directly or indirectly attached to the secondary battery, and the polymer matrix layer contains a filler that changes the external field according to the deformation of the polymer matrix layer, and the polymer The method according to any one of claims 1 to 15, wherein the amount of deformation of the secondary battery is detected by detecting a change in the external field in response to a deformation of a matrix layer. A measured value acquisition unit that acquires measured values corresponding to the charge / discharge capacity and deformation amount of the secondary battery;
An inflection point extraction unit for extracting at least one inflection point in a characteristic curve indicating a relationship between a charge / discharge capacity of the secondary battery and a deformation amount from time series data of the measured value;
A past characteristic data acquisition unit for acquiring past characteristic data having at least a full charge point, a residual amount zero point, and a stage inflection point in a characteristic curve indicating a relationship between charge and discharge capacity and deformation amount of a secondary battery;
The characteristic curve indicated by the characteristic data in the past is fitted to the characteristic curve indicated by the time series data of the actual measurement value on the basis of the extracted inflection point, and a portion not included in the time series data of the actual measurement value is interpolated A predicted characteristic data generation unit that generates predicted characteristic data indicating a different characteristic curve;
A secondary battery state prediction system comprising: The inflection point extraction unit
Set extraction conditions according to the charge / discharge capacity indicated by the acquired actual measurement value,
The system according to claim 17, wherein the measured value is extracted as an inflection point when the differential value of the deformation amount related to the charge and discharge capacity determined based on the acquired measured value satisfies the extraction condition. The inflection point extraction unit is configured based on the past characteristic data when the charge / discharge capacity indicated by the acquired actual measurement value falls within a predetermined range centered on the charge / discharge capacity of the inflection point in the past characteristic data. The system according to claim 18, wherein a threshold value of a derivative value is set, and that the derivative value passes the threshold value is set as an extraction condition. In addition to the differential value passing through the threshold value, the inflection point extraction unit may keep the differential value continuously rising or falling for a certain period during which the charge / discharge capacity changes. The system according to claim 19, wherein the system is set as an extraction condition. The predicted characteristic data generation unit
In the case where one inflection point is extracted and the time-series data of the actual measurement value includes an actual measurement value corresponding to any one of the full charge time or the remaining time zero, the characteristic data of the past The factor for making the characteristic curve match the two points at any one of the fully charged time point or the remaining zero time point and the inflection point at corresponding two points in the time series data of the actually measured value Calculating and scaling the entire characteristic curve using the coefficients, matching the inflection points, and generating the predicted characteristic data;
Or
In the case where one inflection point is extracted and the time-series data of the actual measurement value does not include the actual measurement value corresponding to any one point of the full charge time or the remaining zero time, the characteristic data of the past The characteristic curve indicated by the past characteristic data is moved so that the inflection point extracted most recently matches the inflection point in the past characteristic data without enlarging or reducing the characteristic curve indicated by Interpolate to generate the prediction characteristic data,
Or
When two inflection points are extracted, the two inflection points in the past characteristic data are made to coincide with the corresponding two inflection points in the time-series data of the actual measurement value by the expansion and contraction of the characteristic curve. The system according to any one of claims 17 to 20, wherein a coefficient is calculated, and the coefficient is used to stretch and adjust the entire characteristic curve, and the inflection point extracted most recently is matched to generate the predicted characteristic data. The predicted characteristic data generation unit
In the case where two inflection points are included in the time series data of the actual measurement value, the prediction characteristic data is already generated, and a predetermined regeneration condition is satisfied, the characteristic curve of the prediction characteristic data is the actual measurement value A coefficient for matching the characteristic curve of the time series data by expansion and contraction of the characteristic curve is calculated, the whole characteristic curve is expanded and adjusted using the coefficient, the inflection points extracted most recently are made to coincide, and the predicted characteristic data Is configured to regenerate the
The coefficient uses at least the ratio between the two inflection points, the ratio between the latest actual value and the inflection point closer to the latest actual value, and the weighting between the two inflection points The system according to any one of claims 17 to 21, wherein the system is set larger than the weighting of the section of and calculated by aggregation. The remaining capacity calculator configured to calculate, as the remaining capacity, the difference between the charge / discharge capacity indicated by the actual value at the time of predicting the remaining capacity and the charge / discharge capacity at the remaining time zero in the predicted characteristic data. The system described in any of the. An initial characteristic data acquisition unit for acquiring initial characteristic data having at least a full charge point, a remaining amount zero point, and a stage inflection point in a characteristic curve indicating a relation between charge and discharge capacity of secondary battery and deformation amount;
An activity that contributes to charge and discharge, an enlargement ratio of charge and discharge capacity for matching the two inflection points in the initial characteristic data and the corresponding two inflection points in the predicted characteristic data by the expansion and contraction of the characteristic curve A deterioration information generation unit that calculates the degree of change of the object mass;
A system according to any of claims 17-23, comprising The enlargement ratio of the charge and discharge capacity is determined using the ratio between the two inflection points, the ratio between one inflection point and the full charge time, and the ratio between the other inflection point and the remaining time zero. The system according to claim 24, wherein the weight between the two inflection points is set larger than the weight of the other sections and calculated by aggregation. An initial characteristic data acquisition unit for acquiring initial characteristic data having at least a full charge point, a remaining amount zero point, and a stage inflection point in a characteristic curve indicating a relation between charge and discharge capacity of secondary battery and deformation amount;
A coefficient for making two inflection points in the initial characteristic data coincide with two inflection points in the prediction characteristic data by expansion and contraction of the characteristic curve is calculated, and the initial characteristic data is indicated using the coefficients The expansion and contraction adjustment of the entire characteristic curve is performed, and the two inflection points in the characteristic curve after adjustment of the initial characteristic data and the characteristic curve of the predicted characteristic data are made to coincide with each other. And a deterioration information generation unit that calculates an average value of the amount of deviation of charge and discharge capacity between the remaining amount zero time points as a deterioration state of the battery;
A system according to any of claims 17-23, comprising The coefficients are calculated using the ratio between the two inflection points, the ratio between one inflection point and the full charge time, and the ratio between the other inflection point and the remaining time zero, 27. The system according to claim 26, wherein the weight between the inflection points is set larger than the weight of the other sections and calculated by aggregation. Initial characteristic data acquisition for acquiring initial characteristic data having at least a thickness maximum point, a full charge time, a remaining time zero, and a stage inflection point among characteristic curves showing a relation between charge and discharge capacity and deformation amount of a secondary battery Department,
A coefficient for causing the two inflection points in the initial characteristic data and the two inflection points in the prediction characteristic data to coincide with each other by expansion and contraction of the characteristic curve is calculated, and using the coefficients, the in the initial characteristic data is calculated The thickness maximum point in the prediction characteristic data is specified from the thickness maximum point, and the amount of deformation from the thickness maximum point in the prediction characteristic data to the remaining amount zero point is determined until lithium deposition starting from the remaining point zero point A deterioration information generation unit that calculates a thickness change amount,
A system according to any of claims 17-23, comprising The coefficients are calculated using the ratio between the two inflection points, the ratio between one inflection point and the full charge time, and the ratio between the other inflection point and the remaining time zero, The system according to claim 28, wherein the weight between the inflection points is set larger than the weight of the other sections and calculated by aggregation. The point of full charge in the predicted characteristic data is the maximum thickness point,
18. The apparatus according to claim 17, further comprising: a deterioration information generation unit that calculates a deformation amount from the thickness maximum point to the zero residual point in the predicted characteristic data as a thickness change amount until lithium deposition starting from the residual zero point. The system according to any one of 23. A degradation information generation unit according to any of claims 28 to 30,
A charge control system, comprising: a charge control unit that controls charging so that a deformation amount of a secondary battery detected by a detection sensor does not exceed a thickness change amount generated by the deterioration information generation unit. A polymer matrix layer is directly or indirectly attached to the secondary battery, and the polymer matrix layer contains a filler that changes the external field according to the deformation of the polymer matrix layer, and the polymer The system according to any one of claims 17 to 31, wherein the amount of deformation of the secondary battery is detected by detecting a change in the external field in response to a deformation of a matrix layer. A processor,
A memory for storing instructions executable by the processor;
The processor is
Obtain measured values corresponding to the charge and discharge capacity and deformation of the secondary battery,
Extracting at least one inflection point in a characteristic curve indicating a relationship between a charge / discharge capacity of the secondary battery and a deformation amount from time series data of the measured value;
In the characteristic curve showing the relationship between the charge and discharge capacity of the secondary battery and the deformation amount, obtain the past characteristic data having at least the full charge time, the remaining time zero, and the stage inflection point,
The characteristic curve indicated by the characteristic data in the past is fitted to the characteristic curve indicated by the time series data of the actual measurement value on the basis of the extracted inflection point, and a portion not included in the time series data of the actual measurement value is interpolated A secondary battery state prediction system configured to generate predicted characteristic data indicating a characteristic curve.

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