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WO2016059869A1 - Dispositif d'estimation de l'état de charge d'une batterie rechargeable et procédé d'estimation de l'état de charge de la batterie rechargeable - Google Patents

Dispositif d'estimation de l'état de charge d'une batterie rechargeable et procédé d'estimation de l'état de charge de la batterie rechargeable Download PDF

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Publication number
WO2016059869A1
WO2016059869A1 PCT/JP2015/073296 JP2015073296W WO2016059869A1 WO 2016059869 A1 WO2016059869 A1 WO 2016059869A1 JP 2015073296 W JP2015073296 W JP 2015073296W WO 2016059869 A1 WO2016059869 A1 WO 2016059869A1
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Prior art keywords
state
secondary battery
battery
internal resistance
soh
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English (en)
Japanese (ja)
Inventor
坂田　康治
麻美 水谷
小林　武則
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Toshiba Corp
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Toshiba Corp
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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
    • 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

  • Embodiments of the present invention relate to a secondary battery charging state estimation device and a charging state estimation method thereof in a secondary battery system for power storage connected to an electric power system or the like.
  • Secondary batteries are used as batteries for electric vehicles and hybrid electric vehicles, and for stationary battery storage systems for power storage.
  • the state of charge of the secondary battery (SOC: State Of Charge) is represented by the charging rate, and the state where the secondary battery is discharged to the end-of-discharge voltage (complete discharge) is defined as SOC 0%.
  • SOC 100% The state in which the battery is charged until (charge) is defined as SOC 100%.
  • the charging rate can be said to be the ratio of the current charging amount to the battery capacity.
  • the charge rate of the secondary battery is important for estimating the chargeable / dischargeable capacity of the secondary battery, and in the case of an in-vehicle secondary battery, it greatly affects the calculation of the cruising range. Further, in the stationary secondary battery system, when an error occurs in the estimated value of the charging rate, problems such as unexpectedly stopping charging / discharging or being unable to use the secondary battery efficiently occur.
  • the open circuit voltage at each charging rate (for example, every 10% of SOC) of the battery is measured in advance, and the battery characteristics are acquired.
  • the charging rate is measured by the current integration method, the open circuit voltage of the battery cell is measured when there is no current during charging / discharging, and the charging rate of the battery is calculated from the previously obtained battery characteristics (OCV-SOC characteristics). It is to correct.
  • a simulation is performed with an equivalent circuit model of a secondary battery, a closed circuit voltage (CCV) of the secondary battery is estimated from the charge / discharge current and the battery temperature, and charging is performed using the Kalman filter from the measured battery voltage. There is a way to correct the rate.
  • CCV closed circuit voltage
  • the method of estimating the charging rate using the battery characteristics takes into account the state of deterioration of the battery (SOH: State Of Health). There is a need.
  • SOH State Of Health
  • the conventional method of estimating the charging rate using the battery characteristics does not take into account the deterioration of the battery, and the charging rate is calculated using only the initial battery characteristics, so that the estimation error increases. Has occurred.
  • the charging state estimation device and the charging state estimation method for a secondary battery according to the present embodiment are made to solve the above-described problems, and can accurately estimate the charging state of the secondary battery.
  • An object of the present invention is to provide a state of charge estimation device and a state of charge estimation method for a secondary battery.
  • the secondary battery charge state estimation device of the present embodiment estimates the deterioration state of the secondary battery based on the measurement data of the state quantities of the battery cells constituting the secondary battery. Estimated by the SOH estimation unit from the SOH estimation unit, the storage unit storing the battery characteristic information in which at least the deterioration state and the value of the internal state parameter of the secondary battery are associated, and the battery characteristic information of the storage unit An SOC estimation unit that obtains an internal state parameter corresponding to the deteriorated state and calculates a charging rate of the secondary battery based on the internal state parameter is provided.
  • the method for estimating the state of charge of the secondary battery quantitatively calculates an indicator of deterioration of the secondary battery based on the measurement data of the state quantity of the battery cell constituting the secondary battery, and the secondary battery An internal state parameter corresponding to the deterioration state estimated in the SOH estimation step based on an SOH estimation step for estimating the deterioration state of the battery and battery characteristic information in which at least the deterioration state and the value of the internal state parameter of the secondary battery are associated And a SOC estimation step of calculating a charging rate of the secondary battery based on the internal state parameter.
  • the secondary battery charge state estimation device of this embodiment always estimates the charge rate of the secondary battery with high accuracy. For this purpose, first, the deterioration state (SOH) of the secondary battery is estimated, and then, from a battery characteristic table prepared in advance and showing a relationship between at least the deterioration state and the value of the internal state parameter of the secondary battery. Then, an internal state parameter of the secondary battery corresponding to the estimated deterioration state is obtained, and the charging rate (SOC) is estimated by using the parameter and the equivalent circuit model and the Kalman filter method.
  • SOH deterioration state
  • SOC charging rate
  • Secondary battery deterioration refers to deterioration in battery performance that accompanies the passage of time or charge / discharge, or both.
  • the deterioration state of the secondary battery is expressed based on, for example, the current battery performance with respect to the initial battery performance. Examples of the battery performance include battery capacity, internal resistance, charging time for constant current input, and the like.
  • the deterioration state of the secondary battery is estimated by quantitatively calculating an index indicating the deterioration of the secondary battery.
  • the index indicating the deterioration of the secondary battery may be a battery capacity, internal resistance, charging time for constant current injection, or at least one of these.
  • attention is paid to the internal resistance as an index indicating the deterioration of the secondary battery.
  • FIG. 1 is a model showing a secondary battery in an equivalent circuit, in which an electromotive force (open voltage (OCV)), a reaction resistance Ra, and a diffusion resistance Rb and a capacitor component C connected in parallel to each other are connected in series.
  • OCV electromotive force
  • the reaction resistance Ra is a resistance component that increases instantaneously immediately after application of a current pulse, and is an ion conduction resistance in the separator portion inside the battery and a charge transfer resistance of the positive electrode / negative electrode.
  • the diffusion resistance Rb is a resistance component that gradually increases during current application, and is a resistance component due to the diffusion delay of ions inside the positive and negative electrode active material particles.
  • the electromotive force (open voltage (OCV)), reaction resistance Ra, and diffusion resistance Rb shown in the equivalent circuit model are parameters indicating the internal state of the secondary battery, and change depending on the deterioration state of the battery. Therefore, if the charging rate is estimated with the equivalent circuit model using the parameters in the initial state without considering the deterioration state of the battery, the estimation error increases. Therefore, in the present embodiment, the current deterioration state of the battery is estimated, and an internal state parameter corresponding to the estimated deterioration state is obtained.
  • the voltage between the terminals of the battery cells constituting the secondary battery (hereinafter referred to as battery voltage) is estimated, and the difference between the estimated battery voltage and the measured battery voltage Is multiplied by the Kalman gain to estimate the charging rate by the Kalman filter method for correcting the charging rate.
  • FIG. 2 is a block diagram showing the overall configuration.
  • the state of charge estimating device of the present embodiment includes measuring units 3 to 5 for measuring the state quantity of the battery cell 1 constituting the secondary battery.
  • the voltage measuring unit 3 that measures the voltage between the terminals of each battery cell 1 is connected to the assembled battery 2 that is a target of charge state estimation and is composed of a plurality of battery cells 1 connected in series or in parallel. Has been.
  • the voltage measurement unit 3 includes a voltage measurement sensor, and the voltage measurement sensor measures a terminal voltage (battery voltage) between the positive electrode terminal and the negative electrode terminal of the battery cell 1.
  • a current measuring unit 4 that measures the charge / discharge current to the battery cell 1 is inserted in the current path of the assembled battery 2.
  • the current measuring unit 4 includes a current measurement sensor that measures a charging current to the battery cell 1 or a discharging current of the battery cell 1.
  • a temperature measuring unit 5 that measures the temperature of the battery cell 1 is provided in the vicinity of the assembled battery 2.
  • the temperature measurement unit 5 includes a temperature measurement sensor.
  • the temperature measurement sensor may measure the temperature of the assembled battery 2, and the temperature of the battery cell 1 includes not only the temperature of the battery cell 1 but also the temperature of the assembled battery 2.
  • the temperature of the battery cell 1 or the assembled battery 2 may also be called battery temperature.
  • a monitoring control unit 6 that monitors and controls the assembled battery 2 is connected to these measuring units 3 to 5.
  • the monitoring control unit 6 includes, for example, a computer having a CPU, a memory, and the like.
  • the program is stored in an HDD, an SSD, or the like, appropriately expanded in a RAM, and processed by the CPU, thereby estimating the SOH and SOC. Estimate.
  • the monitoring control unit 6 includes a battery state acquisition unit 7 that controls the acquisition of measurement data from the measurement units 3 to 5, and an SOH estimation unit 8 that estimates the deterioration state of the battery based on each measurement data.
  • the monitoring control unit 6 is connected to a communication interface unit 12 for performing communication with the outside.
  • the battery state acquisition unit 7 includes an interface and a driver, for example, and is connected to the measurement units 3 to 5 through the interface, and acquires measurement data of the measurement units 3 to 5 at a predetermined time interval or timing according to the driver. To do. Further, the battery state acquisition unit 7 outputs the acquired measurement data to the SOH estimation unit 8 and the battery state storage unit 9.
  • the SOH estimation unit 8 includes a CPU and a program, and quantitatively calculates a secondary battery deterioration index based on measurement data obtained at predetermined time intervals or timings. Estimate the degradation state.
  • the internal resistance value R of the assembled battery 2 is calculated using the current I and the battery voltage V of the battery state acquisition unit 7 obtained at a predetermined time interval or timing as inputs. That is, since the internal resistance value of the secondary battery increases with the aging of the battery and the charge / discharge cycle, the internal resistance value obtained by dividing the calculated current internal resistance value R by the internal resistance value R BOL in the initial state.
  • the ratio (R / R BOL ) is taken as an indicator of battery deterioration, and the battery deterioration state (SOH) is estimated.
  • the AC impedance method is a method of extracting an AC component of a current output by connecting a battery to a potentiostat and applying a sine wave voltage.
  • the resistance component for each battery material can be analyzed by changing the frequency of the sine wave.
  • the constant current pulse method does not require a special device. As shown in FIG. 3, this method applies a pulsed constant current to a battery and measures the battery voltage at that time to calculate the internal resistance value from Ohm's law. For example, when the storage battery is used for suppressing fluctuations in photovoltaic power generation, the storage battery is in a standby state at night. In other words, there is no current during standby. A charge / discharge command of a constant current pulse is given to the standby storage battery system, and an internal resistance value is obtained from the applied constant current and the measured battery voltage. Alternatively, a constant current pulse may be applied to the battery, and the internal resistance value may be obtained from a rising voltage change at the time of applying the constant current pulse or a voltage change that gradually increases during the application.
  • the constant current pulse method assumes that the internal resistance of the battery does not depend on the applied current. Although the application time of the constant current pulse depends on the evaluation method, for example, in a battery for an electric vehicle, since the battery output is one of the important characteristics, the voltage 10 seconds after the application of the current pulse is used.
  • the internal resistance value of the battery varies depending on the battery temperature and the charging rate (SOC). Therefore, the battery state (battery temperature and SOC) when the constant current pulse is applied is calculated according to the same conditions. However, since the battery temperature is affected by the ambient environment temperature, calculation under the same temperature condition may be difficult. Therefore, the internal resistance value at the reference temperature may be converted using temperature correction.
  • the temperature correction of the internal resistance value is corrected by the temperature correction coefficient Kt. That is, the ratio of the internal resistance value at each battery temperature to the internal resistance value at a reference temperature (for example, battery temperature 25 ° C.) is obtained in advance. This ratio is the temperature correction coefficient Kt and depends on the battery temperature. Then, with respect to the internal resistance calculated by the battery voltage when the battery temperature T A is measured, by multiplying the temperature correction coefficient Kt (T A), is converted to the internal resistance value at a reference temperature. In order to make the charging rate (SOC) the same condition when calculating the internal resistance value, the estimated value fed back from the SOC estimation unit 11 is converted into the internal resistance value in the reference SOC.
  • SOC charging rate
  • a plurality of internal resistance values corrected for temperature as described above may be averaged.
  • an averaging processing unit (not shown) that performs processing for this purpose may be separately provided in the monitoring control unit 6 or may be separately provided in the SOH estimating unit 8.
  • the program of the SOH estimation unit 8 can be configured by including a program for averaging processing and processing by the CPU.
  • the averaging processing unit is provided in the SOH estimation unit 8 as an example.
  • the averaging process an average of a plurality of internal resistance values obtained in a predetermined period of 1 to 3 weeks or the like may be obtained, or may be obtained by a moving average or the like, and other known averaging methods are used. May be.
  • the SOH estimation unit 8 calculates a ratio (R / R BOL ) of the internal resistance value R at each measurement time point with respect to the initial (BOL: Begin OF Life) internal resistance value R BOL as a reference. From the correlation between the ratio and the battery capacity retention rate, the battery deterioration state (SOH) is estimated.
  • the battery capacity ratio (Cap / Cap BOL ) of the battery capacity Cap (Ah) at each measurement time with respect to the initial battery capacity Cap BOL (Ah) is used as an indicator of battery deterioration.
  • the secondary battery deteriorates due to the formation of a film on the surface of the positive electrode and the negative electrode, the internal resistance value increases, and the battery capacity decreases as the internal resistance value increases. I will do it.
  • the deterioration state (SOH) is estimated from the calculated internal resistance ratio (R / R BOL ) based on the correlation between the internal resistance ratio and the battery capacity retention rate as shown in FIG.
  • the initial internal resistance value R BOL and the internal resistance value R during measurement are unified under the same conditions, for example, a reference temperature of 25 ° C. and SOC of 50%.
  • the battery capacity maintenance ratio is the ratio (%) of the current battery capacity Cap (Ah) to the initial battery capacity Cap BOL (Ah), and is synonymous with the above formula (1).
  • a plurality of data points of the internal resistance ratio and the battery capacity maintenance ratio are prepared in advance, etc., and the battery capacity maintenance ratio (That is, SOH) is estimated.
  • the deterioration state (SOH) is estimated from the calculated internal resistance ratio from the relational expression between the internal resistance ratio and the ionization capacity maintenance ratio obtained by statistical processing or the like for the data points of the internal resistance ratio and the battery capacity maintenance ratio.
  • SOH can be obtained from the following equation (2).
  • ⁇ and ⁇ are coefficients obtained by, for example, the least square method.
  • the correlation between the internal resistance ratio and the battery capacity maintenance rate may be stored in advance by the SOH estimation unit 8 or stored in another component such as the battery state storage unit 9 so that it can be acquired. May be. Further, the relational expression between the internal resistance ratio and the battery capacity maintenance ratio may be updated at a predetermined interval by adding data points.
  • the SOH estimation unit 8 calculates the current battery capacity Cap from the estimated current SOH value and the equation (1) for estimating the charging rate (SOC) as will be described later. If the calculation of the internal resistance includes an error, the battery capacity also includes an error. Therefore, the current battery capacity may be obtained by observing the transition of SOH over a long period. That is, a moving average in units of one to several weeks or months may be obtained.
  • the SOH estimation unit 8 is connected to the battery state storage unit 9 and the SOC estimation unit 11, and outputs the SOH obtained as described above to the battery state storage unit 9 and the SOC estimation unit 11.
  • the battery state storage unit 9 includes a recording medium such as an HDD or an SSD, for example.
  • the battery state storage unit 9 includes measurement data input from the battery state acquisition unit 7, and SOH and SOC estimated by the SOH estimation unit 8 and the SOC estimation unit 11. Stores values sequentially.
  • the battery state storage unit 9 stores in advance the initial battery capacity Cap BOL used in the SOH estimation unit 8 and the initial internal resistance value R BOL , and the initial charge rate used in the SOH estimation unit 11.
  • the battery state storage unit 9 outputs data indicating the stored battery state to the SOH estimation unit 8 and the SOC estimation unit 11.
  • the battery characteristic information storage unit 10 includes a recording medium such as an HDD or an SSD.
  • the battery characteristic information storage unit 10 stores battery characteristic information in which at least the deterioration state and the value of the internal state parameter of the secondary battery are associated with each other.
  • the battery characteristic information is, for example, a battery characteristic table having values of internal state parameters associated with the deterioration state, battery temperature, and SOC or values of internal state parameters for three variables of deterioration state, battery temperature, and SOC. Is stored for each type of internal state parameter of the secondary battery.
  • an electrical characteristic table is stored, which will be specifically described by way of example.
  • the battery characteristic table has internal state parameter values corresponding to each battery temperature and SOC, for example, as shown in FIGS.
  • Examples of the battery characteristic table include a characteristic table of electromotive force (open voltage OCV), reaction resistance Ra, and diffusion resistance Rb.
  • each type of battery characteristic table exists for each different deterioration state.
  • the present invention is not limited to this, and a plurality of middle-term (for example, 70% ⁇ SOH ⁇ 100%) tables in which the SOH fills the space between the initial table and the final table may be provided.
  • the SOC estimation unit 11 includes a CPU and a program, calculates a current internal state parameter based on the current deterioration state, and estimates a battery voltage and a charge rate (SOC) by an equivalent circuit model.
  • the SOC estimation unit 11 includes an internal state parameter specifying unit 11a, a battery voltage simulator 11b, and a Kalman filter 11c, as shown in FIG.
  • the internal state parameter specifying unit 11a obtains a current internal state parameter corresponding to the current deterioration state estimated by the SOH estimating unit 8 used by the battery voltage simulator 11b from the battery characteristic information storage unit 10.
  • the internal state parameter is acquired from the battery characteristic table of the corresponding deterioration state.
  • the internal state parameter of the corresponding deterioration state may be calculated by interpolation (interpolation) or extrapolation (extrapolation) from two or more characteristic tables having different deterioration states of the internal state parameters. That is, an internal state parameter value corresponding to a current deterioration state that is expected within or outside the range may be obtained from two or more characteristic tables having different known deterioration states. For example, the value of the reaction resistance Ra at the time of EOL increases several times as compared with that at the time of BOL of the secondary battery.
  • an internal state parameter corresponding to the current deterioration state is calculated by linear interpolation.
  • interpolation or extrapolation is performed based on the correlation between the internal state parameter and the deterioration state, which is previously determined by measurement or the like, or the relational expression derived from the correlation, the current internal state parameter is accurately estimated. Can be done.
  • the internal state parameter specifying unit 11a When the internal state parameter specifying unit 11a obtains the current internal state parameter, it outputs the parameter to the battery voltage simulator 11b. Thus, the internal state parameter specifying unit 11a can update the internal state parameter from the one corresponding to the past deterioration state to the one corresponding to the current deterioration state.
  • the battery voltage simulator 11b estimates the battery voltage by using the obtained internal state parameters and the current value, battery temperature, and battery voltage measurement data as inputs.
  • the internal state of the battery at time t is x t
  • the system noise is w t
  • x t + 1 from the internal state x t of the observation time t to the observation time t + 1 is expressed as x t + 1 .
  • the function to be obtained is f
  • the internal state x t + 1 of the battery at the observation time t + 1 is expressed by the following equation (3).
  • the internal state of the battery at the observation time t + 1 is obtained.
  • OCV, Ra, and Rb are functions that change depending on the charging rate, the deterioration state, and the battery temperature (SOC t , SOH t , T t ) at time t . Since the capacitor component C does not depend on the deterioration state in this embodiment, a constant value is used. However, when the capacitor component C changes with deterioration, the battery characteristic information storage unit 10 stores data having a value corresponding to the deterioration state, The SOC may be calculated by obtaining a value corresponding to the state.
  • the system noises w1 t and w2 t are measurement noises of the measuring devices 3 to 5, for example.
  • the battery voltage simulator 11b receives each measurement data and the current internal state parameter, estimates the battery voltage according to the following expression (4), and outputs it to the Kalman filter 11c.
  • the Kalman filter 11c updates the estimated value at the time t using the latest measurement data at the time t + 1 by the state estimation method using the Kalman filter. That is, assuming that the Kalman gain is K and the prediction error w t , the estimated value at time t + 1 is expressed by the following equation (5).
  • the difference w is fed back, and at time t + 1
  • the estimated SOC value is obtained by the following equation (6).
  • the Kalman gain K may be stored in the SOC estimation unit 11 or may be stored in advance in the battery state storage unit 9.
  • the Kalman gain K may be a fixed value or may be calculated one by one.
  • the Kalman filter 11c acquires the initial charging rate (SOC) from the battery state storage unit 9.
  • the SOC estimation unit 11 outputs the estimated SOC value obtained as described above to the battery state storage unit 9 and the communication interface unit 12.
  • the communication interface unit 12 is a medium for inputting the estimated SOC value to a host device (such as an EMS energy management system or a control system that controls the entire storage battery).
  • FIG. 8 is an operation flowchart of the secondary battery charged state estimation device of the present embodiment.
  • the charging / discharging current, the battery voltage, and the battery temperature are measured for the battery cell 1 or the assembled battery 2 by the measurement units 3 to 5, and the battery state acquisition unit 7 receives the measurement data at a predetermined timing from the measurement units 3 to 5. Is acquired (step S01).
  • the battery state acquisition unit 7 outputs the acquired measurement data to the SOH estimation unit 8 and the battery state storage unit 9 (step S02).
  • the SOH estimation unit 8 calculates an internal resistance value based on each measurement data (step S03) and performs temperature correction (step S04).
  • step S05 when a plurality of (for example, five) internal resistance values are not obtained within a predetermined period (NO in step S05), the process returns to step S01.
  • a plurality of (for example, five) internal resistance values are obtained within a predetermined period (YES in step S05)
  • the average is processed by the averaging processing unit in the SOH estimating unit 8 (step S06), and the SOH estimating unit 8 Performs estimation of the SOH value, and outputs the estimated value to the SOC estimation unit 11 (step S07).
  • the internal state parameter specifying unit 11a of the SOC estimation unit 11 receives each measurement data from the battery state storage unit 9 and the SOH input from the SOH estimation unit 8, and stores each measurement data, current SOH, and battery characteristic information. Based on the battery characteristic table of the unit 10, the current internal state parameters (reaction resistance Ra, diffusion resistance Rb, open circuit voltage OCV) are determined (step S08). The determined current internal state parameter is output to the battery voltage simulator 11b.
  • the battery voltage simulator 11b estimates the battery voltage Vest based on the current internal state parameter and the equivalent circuit model (step S09). Further, this estimated value Vest is output to the Kalman filter 11c. The Kalman filter 11c multiplies the estimation error between the input estimated value Vest and the measured value Vmes by the Kalman gain K, and estimates the SOC based on the equation (6) (step S10).
  • the charging state estimation device for a secondary battery quantitatively calculates an index of deterioration of the secondary battery based on the measurement data of the state quantity of the battery cell 1 constituting the secondary battery.
  • a battery characteristic information storage unit 10 in which a battery characteristic table in which at least the deterioration state and the value of the internal state parameter of the secondary battery are associated is stored;
  • An SOC estimation unit 11 that obtains an internal state parameter corresponding to the deterioration state estimated by the SOH estimation unit 8 from the battery characteristic table of the characteristic information storage unit 10 and calculates a charging rate of the secondary battery based on the internal state parameter; , Was prepared.
  • the SOC estimation unit 8 calculates an internal state parameter corresponding to the deterioration state estimated by the SOH estimation unit 8 by interpolation or extrapolation based on the internal state parameter for different deterioration states in the battery characteristic information storage unit 10. I did it. Thereby, since the internal state parameter according to the present deterioration state can be calculated
  • the SOH estimating unit 8 calculates the internal resistance of the secondary battery based on the measured values from the voltage measuring unit 3 and the current measuring unit 4, and estimates the deterioration state of the secondary battery based on the internal resistance. I made it. Thereby, compared with the case where the battery capacity is obtained by integrating the current value and the deterioration state is estimated, the measurement noise of the current measuring unit 4 is not accumulated, so that the deterioration state can be estimated with high accuracy. .
  • the SOH estimation unit 8 calculates the internal resistance by the constant current pulse method. Thereby, the deterioration state of the secondary battery can be estimated by calculating the internal resistance by a simple method.
  • the temperature measurement unit 5 that measures the temperature of the battery cell 1 is provided, and the SOH estimation unit 8 converts the internal resistance at the temperature measured by the temperature measurement unit 5 into the internal resistance of the reference temperature. Thereby, an accurate degradation state can be estimated.
  • the SOH estimation unit 8 estimates the deterioration state of the secondary battery from the correlation between the ratio of the internal resistance to the initial internal resistance and the maintenance rate of the battery capacity to the initial battery capacity. I tried to do it. As a result, since both measurement and statistical correlation are used, an accurate deterioration state can be estimated. Even when the battery capacity cannot be obtained directly, the deterioration state can be estimated from the internal resistance.
  • the modification of the first embodiment relates to the calculation of the internal resistance value of the SOH estimation unit 8. Since the basic configuration is the same as that of the first embodiment, description of the configuration and operation of the same portion will be omitted as appropriate. This modification is effective when it is difficult to calculate the internal resistance by the constant current pulse method shown in the first embodiment, for example, when the storage battery is used for suppressing fluctuations in wind power generation.
  • the SOH estimation unit 8 of this modification example is the same as the first embodiment in that the current I and the battery voltage V measured within a certain period are input and the internal resistance value R of the secondary battery is calculated.
  • the charge / discharge current waveform and the battery voltage waveform measured by the measuring units 3 and 4 are converted into signal strengths for each frequency by wavelet transformation, and at a frequency where the correlation of each wavelet coefficient is high, The internal resistance R shown in the following formula (7) is estimated.
  • (W ⁇ i ) (a, b) is obtained by wavelet transforming a current measurement waveform, and represents a wavelet coefficient of each analysis level a and shift b.
  • (W ⁇ v ) (a, b) is obtained by wavelet transforming the voltage measurement waveform of the battery cell, and represents the wavelet coefficient of each analysis level a and shift b.
  • the internal resistance value calculated here corresponds to the internal resistance value calculated by the constant current pulse method, but the internal resistance value calculated at a low analysis level represents the reaction resistance.
  • the correlation between current and voltage may be low due to the influence of measurement noise or the like, and the internal resistance value is calculated at the analysis level a with high correlation.
  • high correlation means that the determination coefficient is equal to or greater than a predetermined value, and the value is designed in advance.
  • the SOH estimation unit 8 obtains the internal resistance value using the wavelet transform as described above, and calculates the ratio from the estimation result of the internal resistance at each measurement time with the initial (BOL) internal resistance value as a reference. Further, the SOH value is estimated by the above equation (2).
  • the SOH estimation unit 8 calculates the internal resistance from the voltage measurement waveform and the current measurement waveform measured by the voltage measurement unit 3 and the current measurement unit 4 using wavelet transform. Thereby, even when the storage battery system is operating, the internal resistance value can be estimated, so that it is not necessary to stop the system.
  • the deterioration state of the secondary battery is estimated based on the internal resistance, but the present invention is not limited to this, and a known estimation method can be used.
  • the characteristic table of the battery characteristic information storage unit 10 is also in accordance with the definition of the deterioration state of the estimation method.
  • the battery capacity can be obtained only from the measured current value or voltage value such as current integration or power integration. That is, as an indicator of deterioration, the battery capacity may be directly obtained without integrating the internal resistance by integrating the measured current value or voltage value, and the deterioration state may be estimated from the above equation (1).
  • the deterioration state of the secondary battery may be estimated based on the measurement data indicating the state quantity and the relationship.

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  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

L'invention concerne un dispositif d'estimation de l'état de charge d'une batterie rechargeable et un procédé d'estimation de l'état de charge de la batterie rechargeable, ce par quoi l'état de charge d'une batterie rechargeable peut être estimé avec précision. La présente invention comporte : une unité d'estimation de l'état de santé - SOH 8 qui estime l'état de détérioration d'une batterie rechargeable en fonction des données de mesure des quantités en termes d'état des éléments de batterie 1 qui constituent la batterie rechargeable ; une unité de stockage d'informations 10 se rapportant aux caractéristiques de la batterie ayant, stockées dans celle-ci, des informations se rapportant à des caractéristiques de la batterie dans lesquelles au moins l'état de détérioration et les valeurs des paramètres se rapportant à l'état interne d'une batterie rechargeable sont reliées ensemble ; et une unité d'estimation de l'état de charge - SOC 11, qui obtient, à partir d'une table des caractéristiques se rapportant à la batterie, de caractéristiques de batterie dans l'unité de stockage d'informations 10 se rapportant aux caractéristiques de la batterie, les paramètres se rapportant à l'état interne correspondant à l'état de détérioration ayant été estimé au moyen de l'unité d'estimation de l'état de santé - SOH 8, et qui calcule le taux de charge de la batterie rechargeable en fonction des paramètres se rapportant à l'état interne.
PCT/JP2015/073296 2014-10-17 2015-08-20 Dispositif d'estimation de l'état de charge d'une batterie rechargeable et procédé d'estimation de l'état de charge de la batterie rechargeable Ceased WO2016059869A1 (fr)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2018009963A (ja) * 2016-07-15 2018-01-18 日立化成株式会社 シミュレーション方法及びシミュレーション装置
JP2018009939A (ja) * 2016-07-15 2018-01-18 日立化成株式会社 シミュレーション方法及びシミュレーション装置
JP2018009941A (ja) * 2016-07-15 2018-01-18 日立化成株式会社 蓄電デバイス容量選択方法
JP2018009940A (ja) * 2016-07-15 2018-01-18 日立化成株式会社 シミュレーション方法及びシミュレーション装置
JP2019105589A (ja) * 2017-12-14 2019-06-27 本田技研工業株式会社 電池状態推定装置
JP2019528032A (ja) * 2016-07-22 2019-10-03 エオス エナジー ストレージ， エルエルシー バッテリ管理システム
JP2019175544A (ja) * 2018-03-26 2019-10-10 住友電気工業株式会社 統計処理システム及びパラメータ推定装置
WO2020080176A1 (fr) * 2018-10-19 2020-04-23 三菱日立パワーシステムズ株式会社 Système de gestion de batterie secondaire, procédé de gestion de batterie secondaire et programme de gestion de batterie secondaire dudit système de gestion de batterie secondaire, et système de batterie secondaire
JP2020515859A (ja) * 2017-04-12 2020-05-28 エルジー・ケム・リミテッド ノイズを反映したバッテリーの残存容量の算出装置及び方法
JP2020134279A (ja) * 2019-02-18 2020-08-31 日立オートモティブシステムズ株式会社 電池制御装置
JP2020156228A (ja) * 2019-03-20 2020-09-24 トヨタ自動車株式会社 車両用電池制御装置
JPWO2019138286A1 (ja) * 2018-01-11 2021-02-12 株式会社半導体エネルギー研究所 二次電池の異常検知装置、異常検知方法、及びプログラム
JP2021097503A (ja) * 2019-12-17 2021-06-24 東洋システム株式会社 状態出力システム
EP3889626A1 (fr) * 2020-02-27 2021-10-06 O2Micro, Inc. Contrôleurs de gestion de batterie capables d'estimer l'état de charge
CN113853706A (zh) * 2019-12-11 2021-12-28 株式会社Lg新能源 使用电极的相对劣化程度控制二次电池操作的设备和方法
JP2022069223A (ja) * 2020-10-23 2022-05-11 日新電機株式会社 蓄電池システム、劣化判定装置および劣化判定方法
WO2022107349A1 (fr) * 2020-11-19 2022-05-27 オムロン株式会社 Système de gestion de batterie de stockage et procédé de gestion de batterie de stockage
JP2022085385A (ja) * 2020-11-27 2022-06-08 株式会社東芝 電池の劣化判定装置、電池の管理システム、電池搭載機器、電池の劣化判定方法、及び、電池の劣化判定プログラム
CN115398257A (zh) * 2020-10-22 2022-11-25 株式会社Lg新能源 电池设备及电阻状态估计方法
WO2023085515A1 (fr) * 2021-11-09 2023-05-19 Lg Electronics Inc. Système de stockage d'énergie
US12055591B2 (en) 2020-02-27 2024-08-06 O2Micro Inc. Battery management controllers capable of determining estimate of state of charge

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
HRP20230478T1 (hr) * 2018-03-28 2023-07-21 Toyo System Co., Ltd. Uređaj za određivanje stanja degradacije i postupak određivanja stanja degradacije

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006098135A (ja) * 2004-09-28 2006-04-13 Fuji Heavy Ind Ltd バッテリの劣化度推定装置
JP2006226788A (ja) * 2005-02-16 2006-08-31 Fuji Heavy Ind Ltd バッテリ管理システム
JP2014102188A (ja) * 2012-11-21 2014-06-05 Mitsubishi Heavy Ind Ltd 満充電容量推定装置、満充電容量推定方法及びプログラム
JP2014119397A (ja) * 2012-12-18 2014-06-30 Toshiba Corp 二次電池の電池状態推定装置

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006098135A (ja) * 2004-09-28 2006-04-13 Fuji Heavy Ind Ltd バッテリの劣化度推定装置
JP2006226788A (ja) * 2005-02-16 2006-08-31 Fuji Heavy Ind Ltd バッテリ管理システム
JP2014102188A (ja) * 2012-11-21 2014-06-05 Mitsubishi Heavy Ind Ltd 満充電容量推定装置、満充電容量推定方法及びプログラム
JP2014119397A (ja) * 2012-12-18 2014-06-30 Toshiba Corp 二次電池の電池状態推定装置

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Publication number Priority date Publication date Assignee Title
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US11368030B2 (en) 2016-07-22 2022-06-21 Eos Energy Storage Llc Battery management system
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US11340298B2 (en) 2017-04-12 2022-05-24 Lg Chem, Ltd. Apparatus and method for calculating state of charge of battery by reflecting noise
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US12442862B2 (en) 2018-01-11 2025-10-14 Semiconductor Energy Laboratory Co., Ltd. Device detecting abnormality of secondary battery, abnormality detection method, and program
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GB2592774A (en) * 2018-10-19 2021-09-08 Mitsubishi Power Ltd Secondary battery management system, secondary battery management method and secondary battery management program of said secondary battery management system
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US12283837B2 (en) 2018-10-19 2025-04-22 Mitsubishi Power, Ltd. Secondary battery management system, secondary battery management method and secondary battery management program for said secondary battery management system, and secondary battery system
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