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WO2017038749A1 - Dispositif de diagnostic de dégradation, procédé de diagnostic de dégradation, et système de diagnostic de dégradation pour batteries - Google Patents

Dispositif de diagnostic de dégradation, procédé de diagnostic de dégradation, et système de diagnostic de dégradation pour batteries Download PDF

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Publication number
WO2017038749A1
WO2017038749A1 PCT/JP2016/075162 JP2016075162W WO2017038749A1 WO 2017038749 A1 WO2017038749 A1 WO 2017038749A1 JP 2016075162 W JP2016075162 W JP 2016075162W WO 2017038749 A1 WO2017038749 A1 WO 2017038749A1
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WIPO (PCT)
Prior art keywords
battery
deterioration
unit
characteristic
operation history
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Ceased
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PCT/JP2016/075162
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English (en)
Japanese (ja)
Inventor
浩臣 音田
洋平 河原
涼次 朝倉
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Resonac Corp
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Hitachi Chemical Co Ltd
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Priority claimed from JP2016068896A external-priority patent/JP2018169161A/ja
Application filed by Hitachi Chemical Co Ltd filed Critical Hitachi Chemical Co Ltd
Publication of WO2017038749A1 publication Critical patent/WO2017038749A1/fr
Anticipated expiration legal-status Critical
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    • 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
    • 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
    • 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 battery deterioration diagnosis.
  • Patent Document 1 calculates current battery characteristics including at least an internal resistance value with respect to a secondary battery, usage condition data including temperature time distribution and current value time distribution in an estimation period, battery voltage, Storing a deterioration characteristic map indicating a distribution of deterioration constants with respect to temperature, and integrating the battery voltage-temperature plane residence time in the deterioration characteristic map based on the current battery characteristic data and use condition data. It is described that the amount of deterioration is estimated.
  • Patent Document 1 describes a technique for estimating the deterioration amount of a secondary battery.
  • it is necessary to prepare in advance a deterioration characteristic map showing the distribution of deterioration constants with respect to battery voltage and temperature.
  • it is necessary to create a battery for experiments and perform a float test that maintains a constant potential, or a storage test that is stored after being adjusted to a constant potential, which takes time and cost. It was.
  • the test can be performed only under limited conditions, there is a case where sufficient estimation accuracy cannot be obtained unlike the condition in which the product is actually used.
  • the present invention provides a technique for diagnosing the degree of deterioration from the actual operation history of the product without obtaining the deterioration constant with respect to the battery voltage and temperature in advance through experiments or the like. Furthermore, a technique for estimating the degree of future deterioration and deriving a use condition for approaching an expected life when the deterioration progresses more than expected.
  • a battery deterioration diagnosis apparatus which is a communication unit that collects battery states, and an operation history storage that stores battery states.
  • a characteristic measuring unit for measuring characteristics that change due to deterioration of the battery from the state of the battery, a characteristic variation model creating unit for creating a model of the relationship between the characteristic and the state of the battery, and a characteristic calculated based on the model
  • a deteriorated battery diagnosis unit that diagnoses battery deterioration based on the estimated value.
  • Degradation can be diagnosed accurately from the actual operation history and characteristics of an actual product without obtaining a degradation constant with respect to battery voltage and temperature in advance through experiments. Furthermore, the use condition for approaching the expected life can be obtained from a plurality of use conditions.
  • FIG. 1 is a block diagram of a battery deterioration diagnosis system in Embodiment 1.
  • FIG. It is a structure figure of the system master table in Example 1. It is a structure figure of the operation history table in Example 1.
  • 6 is a structural diagram of a characteristic variation model table in Embodiment 1.
  • FIG. It is a structure figure of the diagnostic result table in Example 1.
  • 4 is a flowchart of battery deterioration diagnosis in the first embodiment. 6 is a flowchart of battery deterioration diagnosis in Example 2.
  • 10 is a flowchart of battery deterioration diagnosis in Example 3.
  • FIG. It is an example of the abnormally deteriorated battery display screen in Example 4. It is an example of the early deterioration battery display screen in Example 4.
  • FIG. 10 is a block diagram of a battery deterioration diagnosis system according to a fifth embodiment.
  • FIG. 10 is a structural diagram of an operation history table in the fifth embodiment.
  • FIG. 10 is a structural diagram of a characteristic variation model table in Embodiment 5.
  • 10 is a flowchart of battery deterioration diagnosis in Example 5.
  • 10 is a flowchart of battery deterioration diagnosis in Example 6.
  • 10 is an example of a characteristic estimation screen in Example 7.
  • FIG. 1 shows a block diagram of a battery deterioration diagnosis system in the present embodiment.
  • the power storage system 101 includes one or more batteries 102 and a controller 103 connected to the batteries 102.
  • the controller 103 controls the power storage system 101 and the battery 102 and steadily collects measured values of the battery state such as voltage, current, and temperature as operation history.
  • the collected operation history is regularly transmitted through the communication network 105 by the communication unit (storage system side) 104 connected to the controller.
  • a plurality of power storage systems 101 may exist.
  • the power storage system may be structured so as to be easily managed, for example, by bundling a plurality of batteries 102 into a battery module.
  • the power storage system 101 may be a power storage system that incorporates the communication unit (power storage system side) 104 and is integrated with the communication unit.
  • the transmitted operation history is transmitted through the communication network 105 to the deterioration diagnosis device that functions as a monitoring device.
  • the deterioration diagnosis apparatus includes a communication unit (storage unit side) 106, a storage unit 107, a processing unit 112, and a display unit 119 shown in FIG.
  • the transmitted operation history is regularly stored in the storage unit 107
  • the storage unit 107 is a system master table 108 that stores basic data to be diagnosed, and an operation that stores an operation history.
  • An operation history table 109 that is a history storage unit, a characteristic variation model table 110 that stores a characteristic variation model that indicates the relationship between the battery state and characteristics, and a difference between a measured value of the characteristic and an estimated value obtained from the characteristic variation model is stored.
  • the storage unit 107 is connected to the processing unit 112 to exchange data.
  • the processing unit 112 includes a characteristic measurement unit 113 that measures characteristics related to deterioration that changes due to deterioration based on the operation history, and a characteristic variation model generation unit that calculates a relationship between the state of the battery and the characteristics based on the operation history and creates a model.
  • a characteristic measurement unit 113 that measures characteristics related to deterioration that changes due to deterioration based on the operation history
  • a characteristic variation model generation unit that calculates a relationship between the state of the battery and the characteristics based on the operation history and creates a model.
  • an abnormally deteriorated battery diagnosis unit 115 that diagnoses a battery that deteriorates abnormally from the operation history and the model
  • an early deterioration battery diagnosis unit 116 that diagnoses a battery that deteriorates early from the operation history and the model.
  • the abnormally deteriorated battery diagnosis unit 115 and the early deteriorated battery diagnosis unit 116 are collectively used as a deteriorated battery diagnosis unit, and a deteriorated battery diagnosis unit that diagnoses battery deterioration based on an estimated value related to characteristics calculated based on a model. Good.
  • the processing unit 112 includes an alert issuing unit 117 that issues an abnormal sign as an alert by a desired method such as e-mail, telephone, or fax, and a result display unit 118 that generates a screen for displaying the contents of the alert. Then, the display unit 119 connected to the processing unit 112 displays the screen generated by the result display unit 118.
  • the deterioration diagnosis device functions as remote monitoring, and may be, for example, a stand-alone type including a personal computer and a monitor, or a client server type including a server, a personal computer, and a monitor. Moreover, it is good also as a cloud type which preserve
  • FIG. 2 shows the structure of the system master table 108 in this embodiment.
  • the power storage system ID column stores an ID uniquely assigned to the power storage system to be diagnosed
  • the power storage system type column stores the type of the power storage system
  • the battery type column stores the type of battery constituting the power storage system.
  • the table may have information such as information on customers who own the power storage system, installation location, usage, and the like.
  • FIG. 3 shows the structure of the operation history table 109 in this embodiment.
  • the operation history table 109 is a table that records the state of the battery 102 constituting the power storage system, which is the state of the power storage system 101, in time series.
  • the time column is the time when the state is measured
  • the power storage system ID column is an ID for uniquely identifying the power storage system to be diagnosed
  • the battery ID column is the ID for uniquely identifying the battery to be diagnosed
  • Temperature / Humidity column stores the value of the battery state measured with a sensor.
  • the internal resistance / full charge capacity column stores respective calculated values obtained from the actually measured values of the battery state.
  • the internal resistance represents a phenomenon in which a voltage drop occurs when a load is connected to an open circuit voltage as a resistance.
  • the full charge capacity is an amount of electricity that can be taken out until the battery is fully charged and fully discharged.
  • FIG. 4 shows the structure of the characteristic variation model table 110 in this embodiment.
  • the characteristic variation model table 110 based on the data of the operation history table 109, the relationship between the state of the battery and the characteristic is digitized as a model, and the result is stored. Note that the shape and configuration of the power storage system varies depending on the type, and the material and shape of the battery differ depending on the type. Therefore, the relationship between the state and characteristics may vary depending on the type of power storage system and battery. Therefore, in this embodiment, the characteristic variation model is calculated for each combination of the power storage system type and the battery type.
  • the power storage system type column stores the type of the power storage system 101
  • the battery type column stores the type of the battery 102.
  • the characteristic column stores the characteristic of the object of the model. In this embodiment, the internal resistance and the full charge capacity are treated as characteristics.
  • the internal resistance and full charge capacity vary depending on the battery condition at the time of measurement and the influence of deterioration until measurement.
  • the fluctuations due to each influence are hereinafter referred to as measurement fluctuations and deterioration fluctuations.
  • the initial (product shipping) characteristic, the measurement-time fluctuation, and the deterioration fluctuation are linear with respect to the characteristic, and the characteristic fluctuation model is expressed by the following formulas 1, 2, and 3. To do.
  • t is a certain time
  • x11 (t), x12 (t), x21 (t), and x22 (t) each represent a battery state at time t.
  • f11, f12, f21, and f22 represent functions suitable for expressing the relationship between the battery state and characteristics. For example, a power function ( ⁇ ), a logarithmic function (log), an exponential function (exp), and the like, which have good fitness are selected. a11, a12, a21, and a22 are coefficients indicating the degree of influence on the characteristics.
  • the battery state to be assigned to the term of the model formula is selected from the column name of the operation history table 109 and stored in the variable column.
  • a calculated value using a function can be input based on the value of the operation history table. For example, in the data 401 on the fourth row in FIG. 4, “sum (abs (current))” is stored in the variable column. sum is a function that outputs a cumulative sum, and abs is a function that outputs an absolute value. In this case, it represents the amount of current that has been accumulated and charged up to time t in the operation history table 109.
  • the variable string “time-init (time)” of the data 402 in the fifth row represents an elapsed time since the start of recording.
  • an interaction term may be added.
  • the term of interaction is expressed by putting the product of variables in a variable string, such as f3 (x1 ⁇ x2).
  • the constraint condition column stores the constraint condition based on the physical relationship between the characteristics and the battery state in order to guarantee the validity of the model.
  • the constraint condition is described as “ ⁇ 0”, and in the model expression of the characteristic “internal resistance”, the coefficient “only” is a negative value in the variable “temperature” term.
  • the coefficient of temperature at the time of measurement can only take a negative value. It shows that.
  • FIG. 5 shows the structure of the diagnosis result table 111 in this embodiment.
  • the diagnosis result table 111 stores the result of diagnosing the battery using the characteristic variation model.
  • the time column is the battery characteristic measurement time
  • the storage system ID is the diagnosis target ID
  • the internal resistance (residual) column is the difference between the value estimated from the characteristic variation model and the measured value (residual).
  • the estimation in the characteristic variation model is performed by substituting the data in the corresponding columns of the current, voltage, SOC (State of Charge), temperature, and humidity columns in the operation history table 109 into the battery states of Equations 1, 2, and 3.
  • the estimated value is a value obtained by estimating an average battery characteristic
  • the difference between the estimated value and the measured value is an index indicating how far the target battery is separated from the normal battery.
  • the internal resistance (deterioration fluctuation) column stores the value obtained by estimating the fluctuation at the time of measurement using the characteristic fluctuation model according to the following equation 4 and removing the fluctuation at the time of measurement from the measured value.
  • Internal resistance (deterioration fluctuation) Internal resistance measurement -A1 x f1 (x1 (t)-(average value of x1 over all periods)) -A2 ⁇ f2 (x2 (t)-(average value of x2 over all periods)) (Equation 4) Since the internal resistance (deterioration variation) column is a value obtained by extracting the initial characteristics and the degradation variation, the tendency of degradation is confirmed by confirming the trend in time series. Similarly, the full charge capacity (residual) column and the full charge capacity (deterioration variation) column calculate and store the full charge capacity residual and degradation variation.
  • FIG. 6 shows a flowchart of battery deterioration diagnosis in this embodiment.
  • the characteristic measurement unit 113 obtains the measured value of the characteristic affected by the deterioration based on the data read from the operation history table 109, and writes it in the operation history table (601).
  • the internal resistance can be measured by dividing the voltage fluctuation amount by the current fluctuation amount according to the following equation 5 based on Ohm's law.
  • the full charge capacity can be measured by extracting two points when the battery is fully charged and when the battery is fully discharged, and integrating the current value between the two points.
  • the characteristic variation model creation unit 114 creates a characteristic variation model based on the data read from the operation history table 109, and writes it in the characteristic variation model table 110 (602).
  • the model coefficient is obtained by multiple regression analysis.
  • the coefficients of the model formulas of Formula 1, Formula 2, Formula 3 are calculated for each term stored in the characteristic column and variable column of the characteristic variation model table 110 for each combination of power storage system type and battery type.
  • the data of the operation history table 109 is calculated so that the residual sum of squares is minimized.
  • the initial (product shipment) characteristic uses a value calculated as a constant term of an initial value of multiple regression analysis.
  • the variable name “initial value” is stored in the characteristic variation model table 110.
  • model abnormality report / display (604).
  • an abnormality is reported through the alert reporting unit 117, and a model abnormality display screen is generated by the result display unit 118 and displayed on the display unit 119.
  • Model abnormalities may be due to abnormal data in the operation history table 109, incorrect variables or constraint conditions set in the characteristic variation model table 110, etc.
  • the data of each table is corrected (605).
  • the model validity check (603) is necessary for the purpose of preventing erroneous determination, but is not essential and may be omitted.
  • the abnormally deteriorated battery diagnosis unit 115 calculates the residual of each characteristic based on the data of the operation history table 109 and the characteristic variation model table 110.
  • the data of the internal resistance (residual) column and the full charge capacity (residual) column of the diagnosis result table 111 are calculated and stored.
  • the latest residual for each battery in the diagnosis result table 111 is confirmed for each combination of the power storage system type and the battery type, or for each power storage system ID, and a check is made to see if there is a residual deviating from variation. For example, if there is a battery that deviates from the upper limit value or the lower limit value with + 3 ⁇ (a value that is three times the standard deviation) as the upper limit value and ⁇ 3 ⁇ as the lower limit value, it is determined that there is a battery that deviates from the model.
  • an alert is issued and a screen is displayed with the deviated battery as an abnormally deteriorated battery (608).
  • An abnormality is reported through the alert reporting unit 117, and an abnormally deteriorated battery display screen is generated by the result display unit 118 and displayed on the display unit 119.
  • An abnormally deteriorated battery may be replaced with a normal battery as a countermeasure.
  • the data in the operation history table 109 is changed, the data in the operation history table 109 is corrected as necessary (609).
  • the diagnosis ends. Since the diagnosis needs to be performed regularly, the flowchart of this diagnosis is repeatedly executed from the beginning (601) at an appropriate interval (for example, every day).
  • the present embodiment is a battery deterioration diagnosis device, and includes a communication unit that collects battery states, an operation history storage unit that stores battery states, and changes depending on battery deterioration from battery states.
  • a characteristic measurement unit that measures characteristics to be measured, a characteristic variation model creation unit that creates a model of the relationship between the characteristics and the state of the battery, and a deterioration that diagnoses the deterioration of the battery based on an estimated value for the characteristic calculated based on the model It consists of a battery diagnostic unit.
  • the deteriorated battery diagnostic unit compares the measured value of the characteristic output from the characteristic measuring unit with the estimated value of the characteristic based on the model, and determines whether the difference between the measured value and the estimated value exceeds a predetermined reference value.
  • An abnormally deteriorated battery diagnosis unit for diagnosing battery abnormality is provided.
  • a method for diagnosing battery degradation collecting battery status, measuring characteristics that change from battery status due to battery degradation, creating a model of the relationship between characteristics and battery status, and based on the model
  • the battery is configured to be diagnosed for deterioration based on the estimated value related to the calculated characteristic.
  • the estimated value for the characteristic is an estimated value of the characteristic by the model, and the measured value of the characteristic is compared with the estimated value of the characteristic, and the battery is determined by whether or not the difference between the measured value and the estimated value exceeds a predetermined reference value. It is configured so as to diagnose abnormalities.
  • the battery deterioration diagnosis system includes a power storage system, a communication network, and a deterioration diagnosis device.
  • the power storage system is connected to one or a plurality of batteries and the measured value of the battery state as an operation history.
  • a controller that collects, a communication unit that transmits the collected operation history to the communication network, the deterioration diagnosis device includes a communication unit that collects the operation history via the communication network, and an operation history storage unit that stores the operation history
  • a characteristic measurement unit that measures characteristics that change due to battery deterioration from the operation history, a characteristic variation model creation unit that creates a model of the relationship between the characteristics and the operation history, and an estimated value related to the characteristics calculated based on the model
  • a deteriorated battery diagnosis unit that diagnoses battery deterioration.
  • FIG. 7 shows a flowchart of battery deterioration diagnosis in this embodiment.
  • processing steps 601 to 605 are the same processing steps as in FIG. 6, and the same reference numerals as those in FIG.
  • the processing step 610 when the constraint condition is satisfied in the model validity confirmation (603), in the processing step 610, the fluctuation at the time of measurement is removed for each characteristic, the initial characteristic + deterioration fluctuation is extracted, and the diagnosis result table 111. Is stored in a column for the deterioration variation of the characteristic of. Next, for each combination of the power storage system type and the battery type, or for each power storage system ID, the data for deterioration change based on the latest data for each battery in the diagnosis result table 111 is confirmed to determine whether there is an early deterioration battery ( 611).
  • the early deterioration battery diagnosis unit 116 calculates the deterioration fluctuation amount of each characteristic based on the data of the operation history table 109 and the characteristic fluctuation model table 110.
  • an alert is issued and a screen is displayed (612).
  • An abnormality is reported through the alert reporting unit 117, and an early deterioration battery display screen is generated by the result display unit 118 and displayed on the display unit 119.
  • the early deteriorated battery may be replaced with a normal battery as a countermeasure. In this case, since the data such as the battery ID in the operation history table 109 is changed, the data in the operation history table 109 is corrected as necessary (609).
  • the diagnosis ends. Since the diagnosis needs to be performed regularly, the flowchart of this diagnosis is repeatedly executed from the beginning (601) at an appropriate interval (for example, every day).
  • the present embodiment is a battery deterioration diagnosis device, and includes a communication unit that collects battery states, an operation history storage unit that stores battery states, and changes depending on battery deterioration from battery states.
  • a characteristic measurement unit that measures the characteristics to be measured, a characteristic variation model creation unit that creates a model of the relationship between the characteristics and the state of the battery, and a measured value that is output from the characteristic measurement unit varies depending on the battery state at the time of measurement.
  • It is divided into deterioration fluctuations due to the influence of deterioration until measurement, and whether or not the deterioration fluctuation amount excluding the estimated value of fluctuation at the time of measurement from the measurement value of the characteristic output from the characteristic measurement unit exceeds a predetermined reference value It is composed of an early deterioration battery diagnosis unit that diagnoses a sign of battery abnormality.
  • it is a battery deterioration diagnosis method that collects the battery state and measures the characteristics that change from the battery state due to the battery deterioration.
  • the measured value of the characteristic depends on the battery state at the time of measurement and the measurement
  • a model of the relationship between the characteristics and the state of the battery was created due to the effects of the deterioration of the battery, and whether the deterioration fluctuation amount excluding the estimated value of the fluctuation at the time of measurement from the measured value of the characteristics exceeded the predetermined reference value It is configured to diagnose a sign of battery abnormality depending on whether or not.
  • the present embodiment it is possible to diagnose whether there is an early deteriorated battery without being affected by fluctuations during measurement.
  • the battery can be detected at an early stage before it leads to an abnormality in the power storage system, and measures can be taken in advance to prevent an abnormality in the power storage system.
  • FIG. 8 shows a flowchart of battery deterioration diagnosis in this embodiment.
  • This embodiment is a processing flow in which FIG. 6 and FIG. 7 are combined, and the same processing steps as those in FIG. 6 and FIG. Therefore, although a detailed description is omitted, an outline thereof will be described below.
  • a characteristic variation model is created and the validity of the model is confirmed (601-605), an abnormal battery is detected by comparing the measured value and the estimated value by the model (606-609), and the variation at the time of measurement is removed. Then, the early deterioration battery is detected based on the initial characteristics + deterioration fluctuation (610-612).
  • Figure 9 shows the model abnormality display screen.
  • the model type display unit 701 displays the power storage system type, battery type, and characteristics of the model to be displayed.
  • the coefficient stored in the characteristic variation model table 110 is displayed on the graph display unit 702.
  • the data of the variable column and measurement variation / degradation variation column are displayed as labels, and the coefficient size is displayed as a bar graph.
  • the standard partial regression coefficient when the multiple regression analysis is performed may be displayed.
  • the coefficients that deviate from the constraint condition are highlighted, and the labels and the bar graph plot are surrounded by a bold line.
  • the voltage since the voltage does not satisfy the constraint condition, it is surrounded by a thick line.
  • Fig. 10 shows an example of the abnormally deteriorated battery display screen.
  • the type display unit 801 displays the storage system type, battery type, storage system ID, and characteristics to be displayed. This display screen can be displayed for each power storage system type, each battery type, and each power storage system ID. In this example, an abnormally deteriorated battery related to internal resistance is displayed for each power storage system ID.
  • Characteristic and residual graph display section 802 displays characteristics and residuals in time series. As for the characteristics, the data in the characteristic column of the operation history table 109 is displayed for the battery of the type display unit 801. In the residual graph, the residual data of the corresponding characteristic in the diagnosis result table 111 for the battery of the type display unit 801 is displayed.
  • Fig. 11 shows another example of the early deterioration battery display screen.
  • the model type display unit 901 displays the storage system type, battery type, storage system ID, and characteristics to be displayed. This display screen can be displayed for each power storage system type, each battery type, and each power storage system ID. In this example, an example of displaying an early deteriorated battery related to internal resistance for each power storage system ID is shown.
  • Characteristics and deterioration fluctuation graph display section 902 displays characteristics and deterioration fluctuations in time series.
  • the characteristics the data in the characteristic column of the operation history table 109 is displayed for the battery of the type display unit 901.
  • the deterioration variation of the corresponding characteristic in the diagnosis result table 111 for the battery of the type display unit 901 that is, the data of the initial characteristic + degradation variation is displayed.
  • the distribution display section 903 for the latest deterioration variation data at the time point indicated as “latest” in the characteristic and deterioration variation graph display section 902 is displayed as a histogram.
  • the battery ID is displayed for the early deteriorated battery exceeding the upper limit value / lower limit value. In this example, it can be seen that the battery with a battery ID of 5 is an early deteriorated battery.
  • FIG. 12 shows an example of the early deterioration battery display screen different from FIG. 12, unlike the characteristic and deterioration variation graph display unit 902 in FIG. 11, the warning line is shown in the characteristic and deterioration variation graph display unit 1002 in FIG. As a result, it is possible to know whether the deterioration fluctuation amount increases and exceeds the warning line. Unlike the latest degradation change distribution display section 903 in FIG. 11, the latest degradation change distribution display section 1003 also shows a warning line, and there is a battery that exceeds the warning line, and its battery ID is the same. It turns out that it is 6,7.
  • FIG. 13 shows a block diagram of a battery deterioration diagnosis system in the present embodiment.
  • components 101 to 119 are the same as those in FIG. 1, and are given the same reference numerals as those in FIG.
  • the operation history estimation unit 122 estimates a future operation history based on the past operation history stored in the operation history table 109, and adds the estimation result to the operation history table 109.
  • the characteristic estimation unit 123 estimates future battery characteristics based on the data of the operation history table 109 and the characteristic variation model table 110, and stores the estimation result in the diagnosis result table 111.
  • FIG. 14 shows the structure of the operation history table 109 in this embodiment.
  • case No. is added to the operation history table 109 of the first embodiment shown in FIG. Since the structure is such that columns are added, only the added columns will be described.
  • Case No. The column stores information for distinguishing whether the stored data is a measured value or a value estimated by the operation history estimation unit 122. In this example, the actually measured value stores 0, and the estimated value stores other than 0.
  • FIG. 15 shows the structure of the diagnosis result table 111 in this embodiment.
  • case No. is stored in the diagnosis result table 111 of the first embodiment shown in FIG. Since the structure is such that columns are added, only the added columns will be described.
  • Case No. The column stores information for distinguishing whether the stored data is a value diagnosed based on an actually measured operation history or a value diagnosed based on an estimated operation history. In this example, 0 is stored when diagnosis is performed using the actually measured operation history, and non-zero is stored when diagnosis is performed using the estimated operation history.
  • FIG. 16 shows a flowchart for performing a deterioration diagnosis by estimating a future deterioration of characteristics in this embodiment.
  • processing steps 601 to 605 are the same processing steps as those in FIG. 6, and the same reference numerals as those in FIG.
  • a future operation history is estimated and added to the operation history table 109 (613).
  • the operation history table 109 the actually measured operation history is stored in the case number. Since the column is registered as 0, the power storage system ID column matches the power storage system ID to be diagnosed. Get data whose column is 0.
  • a future value is estimated according to the rule for each battery ID, and case No. Register the column as 1.
  • the operation history of the last one year is repeated until the time when a predetermined expected life (for example, 10 years) elapses.
  • a predetermined expected life for example, 10 years
  • Future battery characteristics are estimated (614).
  • Future battery characteristics can be estimated by Equation 1, Equation 3, and Equation 6 below.
  • the case No. acquired from the operation history table 109 is shown in the column. Stores the same value as the column. Next, it is determined whether the battery characteristics exceed the use limit by the expected life (615). Here, it is determined whether or not the battery characteristics (deterioration fluctuation) exceed the use limit that is a predetermined reference value by the expected life (for example, 10 years) that is a predetermined use period. If it exceeds, an alert is issued and a screen is displayed (616). An abnormality is reported through the alert reporting unit 117, and a characteristic estimation screen is generated by the result display unit 118 and displayed on the display unit 119. A battery that has not reached its expected life may be replaced with a normal battery as a countermeasure. In this case, since the data such as the battery ID in the operation history table 109 is changed, the data in the operation history table 109 is corrected as necessary (609).
  • the present embodiment it is possible to accurately estimate future characteristics from the actual operation history and characteristics of the product without diagnosing the deterioration constant with respect to the battery voltage and temperature in advance through experiments or the like, and diagnose deterioration. It is possible to detect batteries that are rapidly deteriorated and become unusable at an earlier stage than expected, before they lead to abnormalities in the power storage system, and it is possible to prevent power storage system abnormalities in advance by taking countermeasures. Become.
  • FIG. 17 shows a flowchart of battery deterioration diagnosis in this embodiment.
  • processing steps 601 to 615 are the same processing steps as those in FIG. 16, and the same reference numerals as those in FIG.
  • a revision plan of the future operation history is created in order to achieve the expected life (617).
  • a column that affects deterioration is specified. However, columns related to time are excluded because they cannot be controlled.
  • the column corresponding to the variable stored in the variable column is specified for the row in which the measurement variation / degradation variation column of the characteristic variation model table 110 is stored as “degradation”.
  • degradation In the example of FIG.
  • the first method is a method for accepting a correction proposal via a screen.
  • the result display unit 118 displays a variable that affects the deterioration and accepts a change amount of the variable.
  • the operation history estimation unit 122 performs estimation by repeating the operation history of the most recent year until the time when a predetermined expected life elapses.
  • a correction plan is created by multiplying or subtracting the received variable change amount by the value obtained from the operation history table 109.
  • the created correction plan is stored in the operation history table 109.
  • case no The stored case number is stored in the column. Stores the value obtained by incrementing the maximum value of the column.
  • the second method is a method of creating a correction plan with reference to the operation history of another power storage system stored in the operation history table 109.
  • all the power storage system IDs having the same value as the power storage system to be diagnosed are acquired from the system master table 108.
  • Data for which the column matches 0 is acquired for the most recent year from the time of diagnosis.
  • an average value is calculated for a plurality of different batteries of all the battery IDs of the storage system ID to be diagnosed from the acquired data, and the representative value is set to 1 as a representative value.
  • the average value for each combination of ID and battery ID is the amount of change. Then, among the values acquired from the operation history table 109, a plurality of modification proposal candidates are created by multiplying or gradually subtracting each change amount for the value of the column that affects the deterioration. The created revision proposal is stored in the operation history table 109. At that time, case no. The column stores a unique number for each change amount.
  • the future characteristics are estimated for all the prepared revision proposals (618).
  • the data in which the power storage system ID string matches the power storage system ID to be diagnosed is obtained from the operation history table 109, and the obtained data is estimated by substituting the data into Equations 1, 3, and 6.
  • the estimated battery characteristics (deterioration variation) are stored in the diagnosis result table 111.
  • the estimation is case no. This is performed for each column value, and the diagnosis result table 111 shows the case number. Stores the estimated result together with the value of.
  • the storage system ID column from the diagnosis result table 111 acquires all data that matches the storage system ID to be diagnosed, and does not exceed a predetermined battery characteristic usage limit by a predetermined expected life (for example, 10 years). Determine if a case exists. If there is a case where the usage limit is not exceeded, the result is displayed (620). In that case, all the data in which the power storage system ID column stored in the diagnosis result table 111 matches the diagnosis target and the amount of change when the operation history correction plan is created are displayed. If there is no case where the usage limit is not exceeded, an alert is issued / displayed that the expected battery has not been reached (621).
  • An abnormality is reported through the alert reporting unit 117, and a characteristic estimation screen is generated by the result display unit 118.
  • the storage unit ID string stored in the diagnosis result table 111 is displayed on the display unit 119 as in step 621. Together with all the data and the amount of change when the revision plan of the operation history is created.
  • the present embodiment it is possible to accurately estimate future characteristics from the actual operation history and characteristics of the product without diagnosing the deterioration constant with respect to the battery voltage and temperature in advance through experiments or the like, and diagnose deterioration. Can be detected at the stage of signing before it leads to an abnormality in the power storage system, and the degree of deterioration is more advanced than expected. From a plurality of use conditions, use conditions that approach the expected life can be obtained.
  • FIG. 18 shows an example of the characteristic estimation screen.
  • the type display unit 1101 displays the power storage system type, battery type, characteristics, and power storage system ID to be diagnosed.
  • This display screen can be displayed for each power storage system type, each battery type, and each power storage system ID.
  • an example is shown in which a battery that has not reached its expected life regarding internal resistance is displayed for each power storage system ID.
  • a characteristic and deterioration fluctuation graph 1102 displays characteristics and deterioration fluctuations in time series.
  • case No. which is a measured value among the data in the characteristic column of the operation history table 109 for the battery of the type display unit 1101. Display data with column 0.
  • the deterioration variation of the corresponding characteristic in the diagnosis result table 111 for the battery of the type display unit 1101 is displayed.
  • the case number of the diagnosis result table 111 is changed.
  • Data with a column of 0 is plotted with a solid line to indicate that it has been calculated based on the measured operating history.
  • Case No. Data other than 0 in the column is plotted with a broken line to indicate that it is calculated based on the estimated operation history.
  • the use limit is indicated by a broken horizontal line in the deterioration change graph, and when the deterioration change of characteristics exceeds the use limit by the expected life, the battery ID is displayed as a battery that has not reached the expected life. In this example, it can be seen that the battery ID is 3.
  • FIG. 19 shows an example of accepting the change amount used for estimating the operation history on the screen from the characteristic estimation screen.
  • the type display unit 1103 displays the power storage system type, battery type, characteristics, power storage system ID, and battery ID to be diagnosed. This display screen can be displayed for each power storage system type, each battery type, each power storage system ID, and each battery ID. In this example, an estimation example of a battery ID of a specific power storage system ID is shown.
  • the operating history change amount receiving unit 1104 identifies and displays variables that affect deterioration. Further, the change amount can be input and accepted by a slide bar or a text box.
  • the operation history is estimated based on the change amount, and the characteristic (deterioration variation) based on the estimated operation history is estimated and displayed on the characteristic and deterioration variation graph 1105.
  • the characteristics case No. indicating the measured value of the data in the characteristic column of the operation history table 109 for the battery of the type display unit 1103. Display data with column 0.
  • the deterioration variation amount of the corresponding characteristic in the diagnosis result table 111 for the battery of the type display unit 1103 is displayed.
  • the case number of the diagnosis result table 111 is changed.
  • Data with a column of 0 is plotted with a solid line to indicate that it has been calculated based on the measured operating history.
  • Data other than 0 in the column is plotted with a broken line to indicate that it is calculated based on the estimated operation history.
  • the variables changed during estimation and the amount of change are also shown.
  • the operation history is estimated with the current change amount set to 1.0, the usage limit is reached by the expected life, but when the operation history is estimated with the change amount set to 0.8, the expected value is expected. It can be seen that the service life can be continued without reaching the service life limit.
  • FIG. 20 shows an example of creating and presenting a plurality of proposals from the characteristic estimation screen without accepting the change amount at the time of estimating the operation history from the screen.
  • This display screen can be displayed for each power storage system type, each battery type, each power storage system ID, and each battery ID.
  • an estimation example of a battery ID of a specific power storage system ID is shown.
  • a characteristic and deterioration change graph 1106 displays a plurality of operation history estimations and estimation results of characteristics (deterioration fluctuations) based on the estimated operation history.
  • case No. indicating the measured value of the data in the characteristic column of the operation history table 109 for the battery of the type display unit 1103. Display data with column 0.
  • the deterioration variation amount of the corresponding characteristic in the diagnosis result table 111 for the battery of the type display unit 1103 is displayed.
  • the case number of the diagnosis result table 111 is changed.
  • Data with a column of 0 is plotted with a solid line to indicate that it has been calculated based on the measured operating history.
  • Case No. Data other than 0 in the column is plotted with a broken line to indicate that it is calculated based on the estimated operation history.
  • the variables changed during estimation and the amount of change are also shown.
  • the operation history is estimated with the current change amount set to 1.2 or 1.0, the use limit is reached by the expected life, but the change amount is set to 0.8 or 0.6.
  • the estimation it is understood that the use limit can not be reached until the expected life and the use can be continued.
  • the battery abnormality predictive diagnosis system allows the battery to be deteriorated at an abnormally fast rate without obtaining a deterioration constant with respect to the battery voltage and temperature by a prior experiment or the like. It is possible to detect the battery that will be used as a sign before the storage system malfunctions due to deterioration, and when the degree of deterioration is more advanced than expected, it is possible to derive conditions that allow it to be used until the expected life It becomes.
  • the present invention is not limited to the above-described embodiments, and includes various modifications.
  • the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Further, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Further, it is possible to add, delete, and replace other configurations for a part of the configuration of each embodiment.

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

Abstract

L'invention concerne un dispositif de diagnostic de dégradation de batterie pour diagnostiquer le degré de dégradation à partir de l'historique de fonctionnement réel d'un produit sans obtenir à l'avance la constante de dégradation par rapport à la tension de batterie et la température par le biais de l'expérimentation ou analogue. Le dispositif est composé de : une unité de communication pour collecter des informations relatives à l'état d'une batterie (courant, tension, température, état de charge, et analogue) ; une unité de mémoire d'historique de fonctionnement pour mémoriser les informations d'état de batterie ; une unité de mesure de caractéristiques pour mesurer, à partir des informations d'état de batterie, des caractéristiques (résistance interne et capacité à pleine charge) qui changent en raison de la dégradation de la batterie ; une unité de création de modèles de fluctuation de caractéristiques pour créer un modèle de la relation entre les caractéristiques et l'état de la batterie sur la base des données mémorisées dans l'unité de mémoire d'historique de fonctionnement ; et une unité de diagnostic de batterie dégradée pour diagnostiquer la dégradation de la batterie sur la base des valeurs de mesure relatives aux caractéristiques calculées sur la base du modèle.
PCT/JP2016/075162 2015-08-31 2016-08-29 Dispositif de diagnostic de dégradation, procédé de diagnostic de dégradation, et système de diagnostic de dégradation pour batteries Ceased WO2017038749A1 (fr)

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CN112685827A (zh) * 2019-10-17 2021-04-20 丰田自动车株式会社 数据记录装置以及数据记录方法
CN112685827B (zh) * 2019-10-17 2023-10-13 丰田自动车株式会社 数据记录装置以及数据记录方法
JP2021113768A (ja) * 2020-01-21 2021-08-05 パナソニックＩｐマネジメント株式会社 車両シミュレーションシステム、車両シミュレーション方法およびコンピュータプログラム
JP7320755B2 (ja) 2020-01-21 2023-08-04 パナソニックＩｐマネジメント株式会社 車両シミュレーションシステム、車両シミュレーション方法およびコンピュータプログラム
CN114002517B (zh) * 2020-07-28 2024-07-16 比亚迪股份有限公司 器件诊断方法、平台、系统及可读存储介质
CN114002517A (zh) * 2020-07-28 2022-02-01 比亚迪股份有限公司 器件诊断方法、平台、系统及可读存储介质
CN112540309A (zh) * 2020-12-10 2021-03-23 广州能源检测研究院 一种基于电池循环数据相似度分析的电池监控系统及方法
CN112540309B (zh) * 2020-12-10 2024-02-06 广州能源检测研究院 一种基于电池循环数据相似度分析的电池监控系统及方法
CN115079005A (zh) * 2021-03-12 2022-09-20 株式会社东芝 电池的诊断方法、电池的诊断装置、电池的诊断系统、电池搭载设备以及存储介质
WO2023181589A1 (fr) * 2022-03-23 2023-09-28 株式会社日立製作所 Dispositif de détection d'état de batterie et procédé de détection d'état de batterie
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