[go: up one dir, main page]

WO2019044067A1 - Procédé de prédiction d'état d'accumulateur, procédé de contrôle de charge, et système - Google Patents

Procédé de prédiction d'état d'accumulateur, procédé de contrôle de charge, et système Download PDF

Info

Publication number
WO2019044067A1
WO2019044067A1 PCT/JP2018/019812 JP2018019812W WO2019044067A1 WO 2019044067 A1 WO2019044067 A1 WO 2019044067A1 JP 2018019812 W JP2018019812 W JP 2018019812W WO 2019044067 A1 WO2019044067 A1 WO 2019044067A1
Authority
WO
WIPO (PCT)
Prior art keywords
characteristic data
point
charge
characteristic curve
inflection
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/JP2018/019812
Other languages
English (en)
Japanese (ja)
Inventor
南方　伸之
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Toyo Tire Corp
Original Assignee
Toyo Tire Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Toyo Tire Corp filed Critical Toyo Tire Corp
Publication of WO2019044067A1 publication Critical patent/WO2019044067A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Definitions

  • the present invention relates to a method of predicting a state of a secondary battery, a charge control method, and a system.
  • 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).
  • SOC State of Charge
  • 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.
  • 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 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.
  • 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.
  • generates prediction characteristic data Explanatory drawing regarding the process which produces
  • 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.
  • four secondary batteries 2 are connected in two parallel two series, but the number of batteries and the connection form are not limited thereto.
  • FIG. 1 shows 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.
  • 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.
  • the deformation of the secondary battery 2 can be detected with high sensitivity.
  • the polymer matrix layer 3 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.
  • 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.
  • the past characteristic data acquisition unit 60 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.
  • 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.
  • 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.
  • the past characteristic data acquisition unit 60 acquires past characteristic data of the secondary battery 2.
  • 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.
  • the horizontal axis is the discharge capacity Q with the origin as the fully charged time point Pf
  • the vertical axis is the detected deformation amount T of the secondary battery 2.
  • 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.
  • the shape includes two inflection points P1 and P2 (change in slope).
  • 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.
  • 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.
  • the voltage or magnetic flux density is expressed as a change amount or the like.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 ( ⁇ T / ⁇ Q) 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.
  • 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
  • ⁇ T / ⁇ Q the actual measurement value is extracted as an inflection point. Examples will be described next.
  • 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
  • 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.
  • the inflection point P1 is extracted when the differential value based on the actual measurement value passes the threshold value Th P1 .
  • 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.
  • 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.
  • 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 [ ⁇ mAh] ⁇ 3. This period can be changed as appropriate.
  • 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.
  • 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.
  • 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.
  • the entire characteristic curve L1 indicated by the past characteristic data is stretched and adjusted to generate a reduced-scale adjusted characteristic curve L1 ′.
  • 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.
  • 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 '.
  • 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.
  • 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.
  • 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.
  • 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.
  • the enlargement ratio of the horizontal axis Xr a '/ a
  • the enlargement ratio of the vertical axis Yr b' / b
  • the inclination can be mentioned.
  • 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 ′.
  • 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 ′.
  • 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.
  • 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 '.
  • 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.
  • 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.
  • 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.
  • commercially available software such as Solver (registered trademark) in Excel (registered trademark) manufactured by Microsoft Corporation is also possible.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the enlargement factor of the horizontal axis the enlargement factor of charge / discharge capacity (a ′ / a).
  • 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
  • the enlargement factor of the charge / discharge capacity is ⁇ b '/ b ⁇ 1 + a' / a ⁇ 8 + c '/ c ⁇ 1 ⁇ / ⁇ 1 + 8 + 1 ⁇ .
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • the present invention is not limited thereto.
  • 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.
  • coefficients on both the horizontal axis and the vertical axis are calculated.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • T1 may be set as the target value.
  • 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.
  • 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.
  • 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).
  • 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 ⁇ mV / ⁇ mAh] satisfies the extraction condition, the actual measurement value is extracted as the inflection point.
  • 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.
  • 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.
  • 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.
  • step S7 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • degradation information may be generated in a state where only one inflection point is extracted.
  • 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.
  • 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. .
  • 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.
  • 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).
  • coefficients expansion ratio of charge and discharge capacity, expansion ratio of deformation 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.
  • 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.
  • 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.
  • step S12 When the process of step S12 is completed, the process proceeds to step S13.
  • 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.
  • 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.
  • 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
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the magnetic filler examples 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 ⁇ m, more preferably 0.1 to 400 ⁇ m, and still more preferably 0.5 to 300 ⁇ m. When the average particle size is smaller than 0.02 ⁇ m, the magnetic properties of the magnetic filler tend to be deteriorated, and when the average particle size exceeds 500 ⁇ m, 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.
  • thermoplastic elastomers examples 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • isocyanate component that can be used for the polyurethane elastomer
  • compounds known in the field of polyurethane can be used.
  • 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.
  • 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.
  • 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.
  • 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.
  • the polymer matrix layer 3 may be a foam containing dispersed fillers and cells.
  • 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.
  • a thermosetting resin foam a polyurethane resin foam, a silicone resin foam, etc. are mentioned, Among these, a polyurethane resin foam is suitable.
  • 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.
  • a thermoplastic resin, a thermosetting resin, or a mixture thereof can be used for the sealing material.
  • 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.
  • 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.
  • 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.
  • the filler maldistribution rate can be adjusted.
  • the filler may be unevenly distributed using physical force such as centrifugal force or magnetic force.
  • 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.
  • SEM-EDS scanning electron microscope-energy dispersive X-ray analyzer
  • the region on the other side with a relatively small amount of filler may be a structure formed of foam containing bubbles.
  • the polymer matrix layer 3 is more easily deformed, and the sensor sensitivity is enhanced.
  • region of one side may be formed with a foam with the area
  • 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.
  • 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.
  • the magnetoresistive element include semiconductor compound magnetoresistive elements, anisotropic magnetoresistive elements (AMR), giant magnetoresistive elements (GMR), and tunnel magnetoresistive elements (TMR).
  • Hall elements are preferable, because they have high sensitivity over a wide range and are useful as the detection unit 4.
  • EQ-430L manufactured by Asahi Kasei Electronics Co., Ltd. can be used as the hall element.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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
  • the inflection point extraction unit 62 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) .
  • 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).
  • 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.
  • 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.
  • step S9 Interpolate to generate prediction characteristic data (step S9), Or
  • 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).
  • 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.
  • step S4 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.
  • 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).
  • the prediction characteristic data includes data of the remaining amount zero point Pe, it becomes possible to calculate the remaining amount accurately.
  • 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).
  • 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).
  • 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).
  • 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.
  • This value is an indicator of charge control and is useful.
  • 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).
  • the fully charged time point Pf in the prediction characteristic data is the thickness maximum point PL
  • 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.
  • This value is an indicator of charge control and is useful.
  • 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.
  • a charge control unit 67 that controls charging.
  • 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.
  • the secondary battery state prediction system 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 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.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGAs field programmable gate arrays
  • 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.
  • the program may be stored in a computer readable recording medium.
  • 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.
  • 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.
  • generation process 1 is the highest, and the generation process 4 is the lowest.
  • 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.

Landscapes

  • Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Electrochemistry (AREA)
  • General Chemical & Material Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Power Engineering (AREA)
  • Secondary Cells (AREA)
  • Tests Of Electric Status Of Batteries (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

La présente invention concerne un procédé de prédiction d'état d'un accumulateur, lequel est disponible pour un usage pratique dans lequel la charge et la décharge sont mises en œuvre aléatoirement. Ce procédé consiste : en une étape S1 destinée à acquérir une valeur mesurée réelle correspondant à une capacité de charge/décharge (Q) et à une quantité de déformation (T) d'un accumulateur (2); en une étape S6 destinée à extraire, à partir de données de série de temps de la valeur mesurée réelle, au moins un point d'inflexion (P1) (P2) d'une courbe caractéristique (Ln) indiquant la relation entre la capacité de charge/décharge (Q) et la quantité de déformation (T) de l'accumulateur; en une étape S8 destinée à acquérir des données caractéristiques ayant au moins un instant de charge complète (Pf), un instant de quantité résiduelle nulle (Pe) et des points d'inflexion d'étage (P1, P2) hors d'une courbe caractéristique (L1) indiquant la relation entre la capacité de charge/décharge et la quantité de déformation de l'accumulateur; et en une étape S9 destinée à générer des données caractéristiques prédites indiquant une courbe caractéristique (L2) dans laquelle une partie manquante des données de série de temps de la valeur mesurée réelle est interpolée en effectuant un traitement pour ajuster la courbe caractéristique (L1) indiquée par le données caractéristiques passées à la courbe caractéristique (Ln) indiquée par les données de série de temps de la valeur mesurée réelle en utilisant le point d'inflexion extrait en tant que référence.
PCT/JP2018/019812 2017-08-29 2018-05-23 Procédé de prédiction d'état d'accumulateur, procédé de contrôle de charge, et système Ceased WO2019044067A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2017-164388 2017-08-29
JP2017164388A JP2019040845A (ja) 2017-08-29 2017-08-29 二次電池の状態予測方法、充電制御方法、システム、及びプログラム

Publications (1)

Publication Number Publication Date
WO2019044067A1 true WO2019044067A1 (fr) 2019-03-07

Family

ID=65525218

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2018/019812 Ceased WO2019044067A1 (fr) 2017-08-29 2018-05-23 Procédé de prédiction d'état d'accumulateur, procédé de contrôle de charge, et système

Country Status (3)

Country Link
JP (1) JP2019040845A (fr)
TW (1) TW201913428A (fr)
WO (1) WO2019044067A1 (fr)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113761799A (zh) * 2021-08-31 2021-12-07 东风商用车有限公司 车辆性能曲线趋势拟合方法、装置、设备及存储介质
CN115034146A (zh) * 2022-08-12 2022-09-09 欣旺达电子股份有限公司 电池鼓胀率的模型建立方法、监控方法、装置及存储介质
CN115152077A (zh) * 2020-03-11 2022-10-04 株式会社Lg新能源 二次电池和用于该二次电池的锂析出检测方法
US12000904B2 (en) 2019-09-30 2024-06-04 Gs Yuasa International Ltd. Estimation device, estimation method, and computer program
CN119199561A (zh) * 2024-11-11 2024-12-27 杭州科工电子科技股份有限公司 一种电池健康状态预测方法、装置、设备及介质

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019177147A1 (fr) 2018-03-16 2019-09-19 三菱マテリアル株式会社 Élément de conversion thermoélectrique
CN113866649A (zh) * 2020-06-30 2021-12-31 比亚迪股份有限公司 电池状态的计算方法和计算装置以及存储介质
HUE068600T2 (hu) * 2020-12-29 2025-01-28 Lg Energy Solution Ltd Akkumulátor diagnosztikai eszköz, akkumulátor diagnosztikai módszer, akkumulátoregység és elektromos jármû
TWI786770B (zh) * 2021-08-16 2022-12-11 加百裕工業股份有限公司 電池健康管理方法及電池健康管理裝置
TWI786769B (zh) * 2021-08-16 2022-12-11 加百裕工業股份有限公司 電池健康管理方法
CN117872182B (zh) * 2023-12-29 2024-11-12 武汉亿纬储能有限公司 电池的最大膨胀力预测方法、装置、电子设备及存储介质

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016006359A1 (fr) * 2014-07-10 2016-01-14 東洋ゴム工業株式会社 Procédé de diagnostic de la détérioration d'une batterie rechargeable de type étanche et système de diagnostic de détérioration
JP2016508215A (ja) * 2012-12-04 2016-03-17 エルジー・ケム・リミテッド 二次電池の放電深度推定装置及び方法
WO2016135992A1 (fr) * 2015-02-26 2016-09-01 東洋ゴム工業株式会社 Procédé d'évaluation de détérioration et système d'évaluation de détérioration de batterie secondaire de type étanche

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016508215A (ja) * 2012-12-04 2016-03-17 エルジー・ケム・リミテッド 二次電池の放電深度推定装置及び方法
WO2016006359A1 (fr) * 2014-07-10 2016-01-14 東洋ゴム工業株式会社 Procédé de diagnostic de la détérioration d'une batterie rechargeable de type étanche et système de diagnostic de détérioration
WO2016135992A1 (fr) * 2015-02-26 2016-09-01 東洋ゴム工業株式会社 Procédé d'évaluation de détérioration et système d'évaluation de détérioration de batterie secondaire de type étanche

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12000904B2 (en) 2019-09-30 2024-06-04 Gs Yuasa International Ltd. Estimation device, estimation method, and computer program
CN115152077A (zh) * 2020-03-11 2022-10-04 株式会社Lg新能源 二次电池和用于该二次电池的锂析出检测方法
CN115152077B (zh) * 2020-03-11 2025-08-15 株式会社Lg新能源 二次电池和用于该二次电池的锂析出检测方法
CN113761799A (zh) * 2021-08-31 2021-12-07 东风商用车有限公司 车辆性能曲线趋势拟合方法、装置、设备及存储介质
CN113761799B (zh) * 2021-08-31 2024-03-26 东风商用车有限公司 车辆性能曲线趋势拟合方法、装置、设备及存储介质
CN115034146A (zh) * 2022-08-12 2022-09-09 欣旺达电子股份有限公司 电池鼓胀率的模型建立方法、监控方法、装置及存储介质
CN119199561A (zh) * 2024-11-11 2024-12-27 杭州科工电子科技股份有限公司 一种电池健康状态预测方法、装置、设备及介质

Also Published As

Publication number Publication date
TW201913428A (zh) 2019-04-01
JP2019040845A (ja) 2019-03-14

Similar Documents

Publication Publication Date Title
WO2019044067A1 (fr) Procédé de prédiction d'état d'accumulateur, procédé de contrôle de charge, et système
JP6186385B2 (ja) 密閉型二次電池の劣化診断方法及び劣化診断システム
JP6209173B2 (ja) 密閉型二次電池の劣化診断方法及び劣化診断システム
TWI631356B (zh) Residual capacity prediction method for sealed secondary battery, residual capacity prediction system, method for obtaining internal battery information, and battery control method
JP6176632B2 (ja) 組電池の異常判定方法及び組電池の異常判定装置
JP6200880B2 (ja) 密閉型二次電池の変形検出方法、及び、密閉型二次電池
EP3503246A1 (fr) Procédé de fabrication de batterie assemblée utilisant des batteries usagées, et batterie assemblée
WO2016006359A1 (fr) Procédé de diagnostic de la détérioration d'une batterie rechargeable de type étanche et système de diagnostic de détérioration
WO2018173360A1 (fr) Procédé de charge de pile rechargeable non aqueuse
WO2017158923A1 (fr) Procédé de prédiction de capacité restante de batterie secondaire de type scellée, système de prédiction de capacité restante, procédé d'acquisition d'informations internes de batterie, et procédé de commande de batterie
JP2019220348A (ja) 二次電池の変形検出システム、方法及びプログラム

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18851408

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 18851408

Country of ref document: EP

Kind code of ref document: A1