[go: up one dir, main page]

WO2023181589A1 - Dispositif de détection d'état de batterie et procédé de détection d'état de batterie - Google Patents

Dispositif de détection d'état de batterie et procédé de détection d'état de batterie Download PDF

Info

Publication number
WO2023181589A1
WO2023181589A1 PCT/JP2023/000396 JP2023000396W WO2023181589A1 WO 2023181589 A1 WO2023181589 A1 WO 2023181589A1 JP 2023000396 W JP2023000396 W JP 2023000396W WO 2023181589 A1 WO2023181589 A1 WO 2023181589A1
Authority
WO
WIPO (PCT)
Prior art keywords
battery
charging
resistance
discharging
ocv
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/JP2023/000396
Other languages
English (en)
Japanese (ja)
Inventor
健士 井上
耕平 本蔵
克 上田
雅浩 米元
純 川治
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hitachi Ltd
Original Assignee
Hitachi Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hitachi Ltd filed Critical Hitachi Ltd
Publication of WO2023181589A1 publication Critical patent/WO2023181589A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • 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]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • 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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • 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]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • 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]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • 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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • 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 a battery state detection device and a battery state detection method for a secondary battery.
  • Patent Document 1 discloses a technique for estimating the relationship between OCV and SOC and the relationship between battery resistance and SOC from continuous voltage data in a BMS (battery management system) of a secondary battery.
  • Patent Document 1 uses continuous voltage data to perform discharge curve analysis and identify OCV and resistance, so high calculation performance is required.
  • An object of the present invention is to provide a battery state detection device that detects the state of a secondary battery with high precision using a small amount of data.
  • the battery state detection device of the present invention includes a data acquisition section that acquires battery information including at least voltage and current of the secondary battery, and a positive electrode prepared in advance for each type of the secondary battery. and a storage unit that stores battery parameters including a resistance function of the negative electrode and a resistance function of the positive and negative electrodes; a parameter calculation unit that calculates a fitting coefficient for identifying OCV based on the battery information acquired by the data acquisition unit from the start to the end of discharge; a fitting coefficient calculated by the parameter calculation unit; and the memory.
  • the OCV identification section identifies the SOC-OCV characteristics based on the potential functions of the positive electrode and the negative electrode stored in the section.
  • the battery state is detected from the charging start and charging end states in multiple charges, it is possible to provide a battery state detection device that detects the state of the secondary battery with high accuracy using a small amount of data.
  • FIG. 1 is a diagram showing the configuration of a secondary battery state diagnosis device according to an embodiment.
  • FIG. 3 is a diagram showing an equivalent circuit model of a battery.
  • FIG. 3 is a diagram showing an example of voltage behavior of a battery when a rectangular wave current is applied to the battery.
  • FIG. 2 is a characteristic diagram of the negative electrode potential of a battery using graphite as the negative electrode active material with respect to the discharge amount.
  • FIG. 2 is a characteristic diagram of negative electrode resistance versus discharge amount of a battery using graphite as a negative electrode active material.
  • FIG. 3 is a characteristic diagram of the discharge amount of the positive electrode potential of a battery using a ternary positive electrode material (NMC) as the positive electrode active material.
  • NMC ternary positive electrode material
  • FIG. 2 is a characteristic diagram of negative electrode resistance versus discharge amount of a battery using a ternary positive electrode material (NMC) as a positive electrode active material.
  • FIG. 2 is a characteristic diagram of the discharge amount of the positive electrode potential of a battery using lithium iron phosphate (LFP) as the positive electrode active material.
  • FIG. 2 is a characteristic diagram of negative electrode resistance versus discharge amount of a battery using lithium iron phosphate (LFP) as a positive electrode active material.
  • FIG. 3 is a diagram illustrating an overview of processing by a parameter calculation unit.
  • FIG. 2 is a diagram illustrating an overview of an OCV identification process for battery SOC. It is a figure showing processing of a parameter calculation part.
  • FIG. 3 is a diagram showing the element ⁇ 1(k) of the matrix ⁇ (k) of resistance sensitivity and voltage sensitivity.
  • FIG. 3 is a diagram showing an element ⁇ 2(k) of a matrix ⁇ (k) of resistance sensitivity and voltage sensitivity.
  • FIG. 3 is a diagram showing an element ⁇ 3(k) of a matrix ⁇ (k) of resistance sensitivity and voltage sensitivity.
  • FIG. 4 is a diagram showing element ⁇ 4(k) of matrix ⁇ (k) of resistance sensitivity and voltage sensitivity.
  • FIG. 3 is a diagram of a mathematical formula showing P(k) in the case of recursive least squares.
  • FIG. 3 is a diagram of a mathematical formula showing P(k) in the case of a nonlinear Kalman filter. It is a figure which shows the mathematical formula when calculating
  • FIG. 1 is a diagram showing the configuration of a secondary battery condition diagnostic device according to an embodiment.
  • the secondary battery condition diagnosis device of the embodiment diagnoses the condition of a secondary battery mounted on an EV (Electric Vehicle), but it is not limited to EVs, and is not limited to electric power storage devices for grid connection stabilization. It can also be applied to diagnosis of the condition of secondary batteries, such as power storage devices for railway systems, and industrial battery systems.
  • EV Electric Vehicle
  • the state detection device 1 of the embodiment is provided as a server on a network to which a plurality of EVs are connected, and estimates the state of each battery of each EV from a remote location, and performs remote diagnosis of the battery based on the estimated state. conduct.
  • the condition detection device 1 of the embodiment includes a data acquisition section 2, a parameter calculation section 3, a battery parameter table 4, an OCV identification section 5, a deterioration diagnosis/remaining life determination section 6, and a battery parameter BMS transfer section 8. 7, calculate battery parameters, diagnose deterioration, and determine remaining life.
  • the battery pack 7 is installed in an EV and is a battery cell (hereinafter referred to as a battery) of a plurality of LFP batteries (lithium iron phosphate batteries) or MNC batteries (nickel-cobalt-manganese ternary positive electrode material lithium-ion batteries). ) are connected in series and parallel.
  • LFP batteries lithium iron phosphate batteries
  • MNC batteries nickel-cobalt-manganese ternary positive electrode material lithium-ion batteries
  • positive electrode active materials include LCO (lithium cobalt dioxide), LMO (manganese-based), NCA (nickel-cobalt-aluminum oxide), and negative electrode active materials include graphite, soft carbon, and LTO. (lithium titanate) may also be used.
  • the BMS 71 is a battery management system installed in the battery pack 7.
  • the BMS 71 measures the voltage, charging/discharging current, and temperature of a plurality of parallel-connected batteries, calculates the remaining battery capacity SOC, and determines battery abnormality.
  • the data acquisition unit 2 of the state detection device 1 receives vehicle or battery pack identification information, charging date and time, battery voltage from the BMS 71 of each battery pack 7 via the EV road-to-vehicle communication or mobile communication network such as a mobile phone. , charging/discharging current, and temperature are acquired as battery information and stored in EV data 9 for each EV or battery pack identification information.
  • the data acquisition unit 2 acquires the voltage, charging current, and temperature of the battery during charging for each battery.
  • the data acquisition unit 2 acquires at least any of the following data (1) to (4).
  • Five pieces of data resistance at the start of charging, resistance at the end of charging, amount of charge during charging, and temperature at the start and end of charging.
  • the data acquisition unit 2 acquires the above data, for example, when the vehicle is charged at a charging station or when the vehicle is charged while parked at night. Alternatively, the above data is acquired from when the vehicle starts traveling until just before it ends.
  • the polarization resistance value of the battery is determined using the battery equivalent circuit model shown in FIG.
  • R0 indicates direct current resistance (hereinafter sometimes referred to as DCR)
  • Rpr indicates polarization resistance
  • Cp indicates polarization capacitance.
  • OCV open circuit voltage
  • FIG. 3 is a diagram showing an example of the voltage behavior of a battery when a rectangular wave current I is applied to the battery.
  • the horizontal axis is elapsed time.
  • the voltage V of the battery changes as shown in FIG.
  • This change in voltage V is roughly divided into three components: a DC voltage component I ⁇ R 0 , a polarization voltage component Vpr, and an OCV fluctuation component ⁇ OCV.
  • the DC voltage component I ⁇ R 0 responds instantaneously to changes in the current I. That is, it instantaneously rises as the current I rises, remains at a constant level, and then disappears as the current I falls.
  • the polarization voltage component Vpr varies with a delay with respect to changes in the current I. That is, after the current I rises, it gradually increases, and after the current I falls, it gradually decreases.
  • the OCV fluctuation component ⁇ OCV represents a change in the OCV of the battery, and corresponds to the difference between OCV1, which is the OCV value before the start of charging, and OCV2, which is the OCV value after the start of charging. This OCV fluctuation component ⁇ OCV corresponds to the amount of change in the state of charge of the battery depending on the amount of charge.
  • the voltage V when the current I rises is expressed as current I ⁇ DC resistance R 0 .
  • the above-mentioned X seconds may be, for example, the 30th second or a longer value, but it is a fixed value. Further, a DC resistance may be used as the resistance, or the sum of a polarization resistance and a DC resistance may be used. Regarding the time of discharge, if the initial discharge current from the start of discharge is constant, it can be calculated by subtracting the x-second voltage from the voltage immediately after the start of discharge, and then dividing by the current. The above are the resistances obtained from the measured values compiled in the data acquisition section 2.
  • the EV data 9 includes the battery voltage, charging current, and temperature in a time series at predetermined time intervals until full charge for each battery pack specified by the identification information acquired by the data acquisition unit 2.
  • This is a storage unit that stores information in the memory.
  • the EV data 9 stores in advance a negative electrode potential function, a positive electrode potential function, a negative electrode resistance function, and a positive electrode resistance function prepared for each type of battery.
  • the negative electrode potential function represents the relationship between the charging state, the discharge amount or the charging amount, and the negative electrode potential
  • the positive electrode potential function represents the relationship between the charging state, the discharge amount or the charging amount, and the positive electrode potential
  • the negative electrode resistance function represents the relationship between the state of charge, discharge amount or amount of charge, and negative electrode resistance
  • the positive electrode resistance function represents the relationship between the state of charge, amount of discharge or amount of charge, and positive electrode resistance. It represents a relationship.
  • the parameter calculation unit 3 calculates any of the data (1) to (8) acquired by the data acquisition unit 2 and the negative electrode potential function and positive electrode potential function, or the negative electrode resistance function and the positive electrode resistance stored in the EV data 9.
  • a parameter also referred to as a fitting coefficient for identifying the OCV is calculated from the function and .
  • the OCV identification section 5 calculates an OCV table, an SOC table, and a resistance table based on the fitting coefficients obtained by the parameter calculation section 3 using a calculation method whose details will be described later, and calculates the relationship of OCV to the SOC of the battery (SOC- OCV characteristics) are specified and stored in the battery parameter table 4.
  • the battery parameter table 4 is a storage unit that stores parameters for the negative electrode potential function, positive electrode potential function, negative electrode resistance function, and positive electrode resistance function, as well as the OCV table, SOC table, and resistance table for each battery.
  • FIGS. 4A to 6B examples of the characteristics of the battery's positive electrode resistance, negative electrode resistance, negative electrode potential of the battery, and positive electrode potential with respect to the discharge amount are shown in FIGS. 4A to 6B.
  • FIG. 4A is a diagram showing the characteristics of the negative electrode potential with respect to the discharge amount of a battery using graphite as the negative electrode active material
  • FIG. 4B is a diagram showing the characteristics of the negative electrode resistance with respect to the discharge amount
  • FIG. 5A is a diagram showing the characteristics of the positive electrode potential with respect to the discharge amount of a battery using a ternary positive electrode material (NMC) as the positive electrode active material
  • NMC ternary positive electrode material
  • FIG. 5B is a diagram showing the characteristics of the negative electrode resistance with respect to the discharge amount. be.
  • NMC ternary positive electrode material
  • FIG. 6A is a diagram showing the characteristics of the positive electrode potential with respect to the discharge amount of a battery using lithium iron phosphate (LFP) as the positive electrode active material
  • FIG. 6B is a diagram showing the characteristics of the negative electrode resistance with respect to the discharge amount.
  • 4A to 6B show the characteristics of potential or resistance with respect to the amount of discharge, and the curves showing the characteristics of potential or resistance with respect to the amount of charge are inverted from left to right in the figures.
  • the resistance curves of negative electrode resistance and positive electrode resistance shown in FIGS. 4B, 6B, and 6B are stored in the battery parameter table 4 as negative electrode resistance functions and positive electrode resistance functions.
  • the potential curves of the negative electrode potential and positive electrode potential shown in FIGS. 4A, 5A, and 6A are stored in the battery parameter table 4 as a negative electrode potential function and a positive electrode potential function.
  • the deterioration diagnosis/remaining life determination unit 6 performs battery deterioration diagnosis and remaining life determination based on changes in battery parameters from initial values, although details will be described later. Thereby, the deterioration diagnosis/remaining life determination unit 6 prompts the EV to replace the battery, or determines the end of life period from the battery deterioration history, predicts the battery replacement time, and notifies the EV.
  • the battery parameter BMS transfer unit 8 transfers the OCV table and resistance table to the BMS 71 of each EV when the OCV table and resistance table of the battery parameter table 4 are updated.
  • the state detection device 1 of the embodiment is configured by a computer including a CPU that performs arithmetic processing, a memory, a communication section, an operation section, a display section, and a nonvolatile storage medium.
  • the CPU realizes the functions of the parameter calculation section 3 and the OCV identification section 5 by executing a program stored in a nonvolatile storage medium.
  • the data acquisition unit 2 and the battery parameter BMS transfer unit 8 are configured by a communication unit, and the battery parameter table 4 and the EV data 9 are configured by a nonvolatile storage medium.
  • the input is one of the following. The following describes the case where all information is available.
  • an is how much the position of the negative pole function shifts when SOC changes by 1%
  • ap Qmax indicates how much the position of the positive pole function shifts when the SOC changes by 1%
  • Qmax indicates the Ah capacity of the battery.
  • Vp The function of Vp is expressed as Vp(bp-SOC ⁇ ap), and the function of Vn is expressed as Vn(bn-SOC ⁇ an).
  • Vs(k) Vp(bp-SOCi(k) ⁇ ap)-Vn(bn-SOCi(k) ⁇ bp)
  • Ve(k) Vp(bp- ⁇ SOCi(k)+100Q(k)/ Qmax ⁇ ap)-Vn(bn- ⁇ SOCi(k)+100Q(k)/Qmax ⁇ bp).
  • R the theoretical formula for the resistance estimation function R.
  • R(SOC,Temp; an,bn,ap,bp,Qmax,A,B,C) A ⁇ Rp(SOC,Temp;ap,bp,Qmax)+B Expressed as ⁇ Rn(SOC,Temp;an,bn,Qmax)+C.
  • Rp is the positive electrode resistance stored in the battery parameter table 4 described above
  • Rn is the negative electrode resistance stored in the battery parameter table 4 described above
  • a and B are the respective resistance multipliers
  • C is the bias component.
  • FIG. 7 is a diagram showing the relationship between the polarization resistance of a battery (hereinafter referred to as battery resistance) and the charging capacity during charging. Since battery resistance changes as the battery deteriorates, the measured value of polarization resistance at a predetermined charging capacity of a battery in use deviates from the initial battery resistance curve of the battery, as shown in FIG.
  • the fitting of the battery resistance curve to the measured value of polarization resistance by the parameter calculation unit 3 is nothing but calculation of ⁇ , A, B, and C as parameters for OCV identification.
  • the OCV identification unit 5 determines the OCV.
  • the graph in FIG. 8 shows SOC-OCV characteristics, with the horizontal axis representing SOC (%) and the vertical axis representing OCV (V). Note that the vertical axis in FIG. 8 also represents battery resistance (m ⁇ ).
  • the deterioration diagnosis/remaining life determination unit 6 compares Qmax, which indicates the Ah capacity of the battery when it is fully charged, between the beginning of use and the current state, and determines that the battery is in a deteriorated state when the capacity decrease reaches a predetermined value.
  • the deterioration diagnosis/remaining life determination section 6 may determine the deterioration state according to the rate of increase in the polarization resistance calculated by the battery parameter calculation section 3 from the time of the start of use.
  • the deterioration diagnosis/remaining life determination unit 6 obtains a trend curve for battery operating time with respect to Qmax, which indicates the Ah capacity when the battery is fully charged, and predicts the number of days until Qmax, which is a guideline for battery replacement, is reached. , the remaining life of the battery.
  • the deterioration diagnosis/remaining life determination unit 6 obtains the trend curve of the polarization resistance calculated by the battery parameter calculation unit 3 instead of Qmax, predicts the number of days until the resistance value reaches the standard value for battery replacement, and determines the battery life. It may also be used as the remaining life.
  • charging may be read as discharging.
  • start of charging should be read as the start of discharging
  • end of charging should be read as the end of discharging
  • full charge should be read as the end of discharging.
  • the parameter calculation unit 3 uses equation (1) from the periodic battery voltage, current value, and temperature during one charge from the state where the battery is not fully charged to the state where it is fully charged.
  • the polarization resistance Rpr shown in FIG. explain the method.
  • ⁇ is a forgetting coefficient, and may be set to a predetermined value as a constant satisfying 0 ⁇ 1, for example, a value of 0.99.
  • Equation (2) there is an unknown variable SOCi(k) for each k-th charge, and if this variable is optimized, the amount of calculation will increase. Therefore, SOCi(k) is numerically calculated each time so that the squared error E(k) at the k-th charge/discharge is minimized. This calculates SOCi(k) numerically by inserting the current ⁇ , A, B, and C into the equation that partially differentiates E(k) with respect to SOCi(k). Newton's method or bisection method may be used for this.
  • ⁇ , A, B, and C may be calculated at once using the quasi-Newton method using all charging data, or sequentially by expanding the nonlinear Kalman filter. ⁇ , A, B, and C may also be determined. When calculating sequentially, initial values are first set for ⁇ , A, B, and C.
  • step S91 initial values of ⁇ (vector of an, bn, ap, bp, Qmax), A, B, and C are determined.
  • the initial value sets possible values.
  • bn and bp may be the maximum value of the domain of the function or a smaller value.
  • an,ap may be a value that is the range of the function's domain divided by 100 or a smaller value.
  • Qmax may also include the battery catalog Ah.
  • the forgetting coefficient ⁇ may be set to 0.99, for example, or may be set to a value close to 1.
  • S(0) 0, P(0) is set to a constant ⁇ (a large value), and ⁇ I is set.
  • I is an identity matrix.
  • step S92 the kth charging/discharging data from the data acquisition unit 2 is Ts(k), Te(k), Rs(k), Re(k), Q(k), Vs(k), Ve(k). ) to get.
  • step S93 the k-th charging/discharging start SOCSOCi(k) is numerically determined. This determination method uses the method described above.
  • step S94 the resistance sensitivity and voltage sensitivity (numerical differentiation) matrix ⁇ (k) for the current parameters ⁇ (k-1), A(k-1), B(k-1), C(k-1) are calculated.
  • ⁇ (k) is ( ⁇ 1(k), ⁇ 2(k), ⁇ 3(k), ⁇ 4(k))T, and ⁇ 1, ⁇ 2, ⁇ 3, and ⁇ 4 are shown in FIGS. 10A, 10B, and 10C, respectively.
  • FIGS. 10A, 10B, and 10C is an eight-dimensional vertical vector shown in FIG. 10D.
  • the equations in FIGS. 10A to 10D are partial differentials, but here, the partial differentials are determined numerically.
  • step S95 the variance-covariance matrix S is updated.
  • P is obtained as a general inverse matrix of S.
  • the general inverse matrix will be explained.
  • the Moore-Penrose type general inverse matrix is uniquely determined. There are an infinite number of x for which
  • 2 is the minimum if D is not a full rank, but the x for which the distance from the origin is the minimum is uniquely determined, and x D + b. If D has full rank, the general inverse matrix is the same as the inverse matrix. That is, the general inverse matrix is a method of calculating as if it were an inverse matrix even if D is degenerate.
  • P(k) may be obtained from S(k) using this QR decomposition operation.
  • step S96 the observation error and the theoretical error ⁇ are determined, and ⁇ , A, B, and C are updated with the obtained ⁇ .
  • the updated parameters are passed to the OCV identification section 5 to identify the OCV function. Then, the process returns to step S92.
  • Equation (10) holds true based on the conditions when SOC is 0%.
  • Equation (11) holds true under the conditions when SOC is 100%.
  • the maximum eigenvalue ⁇ M and the corresponding eigenvector q in FIG. 14 may be determined by the power law (
  • , ⁇ (k+1)
  • (k 0,1,...), give an initial value x(0) ⁇ 0, and sequentially find x(k) and ⁇ (k). ⁇ ( ⁇ ) becomes ⁇ M, and x( ⁇ ) becomes q. Although it is a repetitive calculation, it can be calculated quickly.
  • the OCV identification unit 5 calculates that if an, bn, ap, bp are determined, the OCV function can be expressed by equation (12). become.
  • the battery current I is expressed by Butler-Volmer equation (13).
  • Q is the discharge amount
  • I0(Q) is a function that changes depending on the discharge amount Q.
  • T is absolute temperature.
  • the positive electrode resistance will be expressed as Rp and the negative electrode resistance will be expressed as Rn below.
  • Rp and the negative electrode resistance Rn have the same Q, their values change depending on the current I and the temperature T. For this reason, some kind of current and temperature correction is required. For example, convert it to the resistance value when the current I is approximately 0 and 25°C. Alternatively, only data in which the current I is around 90 A may be targeted. Note that when the current I is approximately 0, the voltage V hardly increases, so the measurement error is large and it cannot be used as measurement data.
  • positive electrode resistance Rp and negative electrode resistance Rn are expressed as equation (18) using Rn25 and Rp25.
  • the parameter calculation unit 3 sets different constraints on the OCV voltage function shown in equation (18) depending on when LFP is used for the positive electrode (constraint 1) and when NMC is used for the positive electrode (constraint 2). .
  • 0.09V is a voltage at a portion where the negative electrode potential of graphite shown in FIG. 6A becomes flat, and indicates a range rather than a numerically fixed value.
  • bn is fixed
  • bp is determined from ap
  • bp 100ap+Vp -1 (OCV(100)+0.09V).
  • this constraint condition can also be applied to a positive electrode having a monotonically decreasing potential curve like NMC. For example, LCO, LMO, NCA, etc.
  • the battery state can be estimated even if the battery uses an active material with a constant OCV relative to the SOC, such as an LFP. Furthermore, since the state of charge can be estimated with a small amount of data, battery diagnosis is possible even when there is a limit on the amount of communication.
  • the parameter calculation unit 3 of this embodiment sets the SOC of the battery at the start of charging as SOC0(k), sets the battery resistance (polarization resistance Rpr) at the start of charging as Rpr_b(k), and terminates charging before reaching full charge. Let the battery resistance at the time be Rpr_a(k). The parameter calculation unit 3 then fits the battery resistance curve to the battery resistance Rpr_b(k) and the battery resistance Rpr_a(k) to calculate a fitting coefficient.
  • the battery resistance Rpr_b(k) at the start of charging is calculated using equation (1)
  • the battery resistance Rpr_a(k) at the end of charging is calculated as follows.
  • Example 1 and Example 2 above it has been explained that the resistance curves of the polarization resistors are matched, but in this example, the SOC of the battery at the start of charging is set to an unknown value SOC0(k), and the An OCV function curve representing the OCV is fitted to the two battery voltages: the voltage at which the battery is charged, and the voltage at which a sufficient amount of time has passed after the end of charging, to determine the OCV characteristics relative to the SOC.
  • the parameter calculation unit 3 and the OCV identification unit 5 perform integral processing.
  • the SOC of the battery at the start of charging is assumed to be an unknown quantity SOC0(k), and the battery resistance Rpr is determined from the voltage immediately after the current rises, and the voltage and current after X seconds have passed from that time.
  • OCV characteristics with respect to battery parameters and SOC are determined based on the voltage and amount of charge after a sufficient period of time has passed after the end of charging. In this case, ⁇ 1 and ⁇ 4 are used for the aforementioned ⁇ .
  • ⁇ 2 and ⁇ 3 may be used for ⁇ described above.
  • the parameter calculation unit 3 and the OCV identification unit 5 perform integral processing.
  • the parameter calculation unit 3 and the OCV identification unit 5 perform integrated processing, and the SOC of the battery at the start of charging is set as an unknown quantity SOC0(k), and the battery resistance Rpr_b(k) and the voltage OCVb at the start of charging are (k), and the battery resistance Rpr_a(k) and voltage OCVa(k) at the end of charging, the OCV characteristics with respect to the battery parameters and SOC are determined.
  • battery resistance Rpr_b(k) is the battery resistance at the start of charging calculated using equation (1)
  • battery resistance Rpr_a(k) is the battery resistance at the end of charging calculated using equation (22).
  • voltage OCVb(k) is the battery voltage immediately before charging
  • voltage OCVa(k) is the battery voltage when a sufficient amount of time has passed after charging is completed. In this case, all of ⁇ 1, ⁇ 2, ⁇ 3, and ⁇ 4 are used for the aforementioned ⁇ .
  • the state detection device 1 of the embodiment obtains the battery voltage, charging current, and temperature from the BMS 71 and obtains the parameters and OCV.
  • the function may be implemented to obtain the battery parameters and OCV of the batteries in the battery pack 7.
  • the state detection device 1 of the embodiment calculates the fitting coefficient and OCV from the charging current of the battery, but the fitting coefficient and OCV can be similarly calculated from the voltage and discharge current of the battery. .

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • 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)
  • Tests Of Electric Status Of Batteries (AREA)
  • Secondary Cells (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

L'invention concerne un dispositif de détection d'état de batterie (1) comprenant : une unité d'acquisition de données (2) qui acquiert des informations de batterie comprenant au moins la tension et le courant d'une batterie secondaire ; une unité de stockage (4) qui stocke des paramètres de batterie comprenant une fonction de résistance, pour une électrode positive et une électrode négative, qui est préparée à l'avance pour chaque type de la batterie secondaire, et une fonction de résistance pour l'électrode positive et l'électrode négative ; une unité de calcul de paramètres (3) qui calcule un coefficient d'ajustement pour identifier une OCV sur la base d'informations de batterie acquises par l'unité d'acquisition de données d'un temps de début de charge à un temps de fin de charge ou d'un temps de début de décharge à un temps de fin de décharge, pour chaque occurrence d'une pluralité d'occurrences de la charge/décharge de batterie secondaire ; et une unité d'identification d'OCV (5) qui identifie des caractéristiques de SOC-OCV sur la base du coefficient d'ajustement calculé par l'unité de calcul de paramètres et d'une fonction de potentiel pour l'électrode positive et l'électrode négative stockée dans l'unité de stockage. Le dispositif de détection d'état de batterie détecte ainsi l'état de la batterie secondaire avec une précision élevée en utilisant une petite quantité de données.
PCT/JP2023/000396 2022-03-23 2023-01-11 Dispositif de détection d'état de batterie et procédé de détection d'état de batterie Ceased WO2023181589A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2022047026A JP7721473B2 (ja) 2022-03-23 2022-03-23 電池状態検出装置および電池状態検出方法
JP2022-047026 2022-03-23

Publications (1)

Publication Number Publication Date
WO2023181589A1 true WO2023181589A1 (fr) 2023-09-28

Family

ID=88100949

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2023/000396 Ceased WO2023181589A1 (fr) 2022-03-23 2023-01-11 Dispositif de détection d'état de batterie et procédé de détection d'état de batterie

Country Status (2)

Country Link
JP (1) JP7721473B2 (fr)
WO (1) WO2023181589A1 (fr)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2025010856A (ja) * 2023-07-10 2025-01-23 株式会社日立ハイテク リチウムイオン電池劣化推定方法、および、リチウムイオン電池劣化通知方法
KR20250051858A (ko) * 2023-10-10 2025-04-18 주식회사 엘지에너지솔루션 배터리 관리 시스템, 배터리 팩, 전기 차량 및 배터리 관리 방법
WO2025173451A1 (fr) * 2024-02-13 2025-08-21 株式会社Ｇｓユアサ Procédé d'estimation, appareil d'estimation et programme d'ordinateur
KR20250135470A (ko) * 2024-03-06 2025-09-15 주식회사 엘지에너지솔루션 배터리 진단 장치 및 그것의 동작 방법

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007195312A (ja) * 2006-01-18 2007-08-02 Toyota Motor Corp 二次電池の寿命推定装置
JP2011257411A (ja) * 2000-05-23 2011-12-22 Canon Inc 二次電池の内部状態検知用回路並びに該回路を有する電池パック、機器、機械及びシステム
JP2014010003A (ja) * 2012-06-28 2014-01-20 Hitachi Ltd 電池モジュールおよびその状態推定方法
WO2014155726A1 (fr) * 2013-03-29 2014-10-02 株式会社日立製作所 Procédé d'estimation de caractéristiques de pile, dispositif d'estimation de caractéristiques de pile et programme
JP2016012984A (ja) * 2014-06-30 2016-01-21 日立化成株式会社 電池システム
WO2017038749A1 (fr) * 2015-08-31 2017-03-09 日立化成株式会社 Dispositif de diagnostic de dégradation, procédé de diagnostic de dégradation, et système de diagnostic de dégradation pour batteries
JP2018063186A (ja) * 2016-10-13 2018-04-19 カルソニックカンセイ株式会社 バッテリのパラメータ推定装置及びパラメータ推定方法
JP2021110579A (ja) * 2020-01-08 2021-08-02 株式会社豊田自動織機 満充電容量推定装置及び満充電容量推定方法
JP6918433B1 (ja) * 2020-03-10 2021-08-11 三菱電機株式会社 劣化度診断装置
WO2021176748A1 (fr) * 2020-03-06 2021-09-10 株式会社日立ハイテク Dispositif de détermination de caractéristique de batterie et système de batterie secondaire

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013063136A (ja) * 2011-09-16 2013-04-11 Panasonic Corp 生体刺激用パッドとそれを備えた生体刺激装置

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011257411A (ja) * 2000-05-23 2011-12-22 Canon Inc 二次電池の内部状態検知用回路並びに該回路を有する電池パック、機器、機械及びシステム
JP2007195312A (ja) * 2006-01-18 2007-08-02 Toyota Motor Corp 二次電池の寿命推定装置
JP2014010003A (ja) * 2012-06-28 2014-01-20 Hitachi Ltd 電池モジュールおよびその状態推定方法
WO2014155726A1 (fr) * 2013-03-29 2014-10-02 株式会社日立製作所 Procédé d'estimation de caractéristiques de pile, dispositif d'estimation de caractéristiques de pile et programme
JP2016012984A (ja) * 2014-06-30 2016-01-21 日立化成株式会社 電池システム
WO2017038749A1 (fr) * 2015-08-31 2017-03-09 日立化成株式会社 Dispositif de diagnostic de dégradation, procédé de diagnostic de dégradation, et système de diagnostic de dégradation pour batteries
JP2018063186A (ja) * 2016-10-13 2018-04-19 カルソニックカンセイ株式会社 バッテリのパラメータ推定装置及びパラメータ推定方法
JP2021110579A (ja) * 2020-01-08 2021-08-02 株式会社豊田自動織機 満充電容量推定装置及び満充電容量推定方法
WO2021176748A1 (fr) * 2020-03-06 2021-09-10 株式会社日立ハイテク Dispositif de détermination de caractéristique de batterie et système de batterie secondaire
JP6918433B1 (ja) * 2020-03-10 2021-08-11 三菱電機株式会社 劣化度診断装置

Also Published As

Publication number Publication date
JP7721473B2 (ja) 2025-08-12
JP2023140942A (ja) 2023-10-05

Similar Documents

Publication Publication Date Title
WO2023181589A1 (fr) Dispositif de détection d'état de batterie et procédé de détection d'état de batterie
Shahriari et al. Online state-of-health estimation of VRLA batteries using state of charge
CN110914696B (zh) 用于在电池的操作期间估计电池开路池格电压、充电状态以及健康状态的方法和系统
RU2361333C2 (ru) Оценка состояния и параметров гальванического элемента
JP5058814B2 (ja) バッテリーの状態及びパラメーターの推定システム及び方法
Charkhgard et al. State-of-charge estimation for lithium-ion batteries using neural networks and EKF
US8738310B2 (en) Automatic determination of baselines for battery testing
US12252021B2 (en) Method for estimating an operating parameter of a battery unit
KR102871414B1 (ko) 실시간 및 사용 위치에서 배터리의 열역학적 데이터(엔탈피 및 엔트로피)를 측정하는 방법 및 장치
Gong et al. Parameter and state of charge estimation simultaneously for lithium‐ion battery based on improved open circuit voltage estimation method
CN113728242B (zh) 对可充电电池中的析锂进行表征
JP2023541417A (ja) バッテリの充電状態を推定する方法
KR20050013972A (ko) 이차 전지의 잔류 용량 산출 방법 및 배터리 팩
GB2532726A (en) Cell internal impedance diagnostic system
Li et al. A new parameter estimation algorithm for an electrical analogue battery model
US11143705B2 (en) Method and device for detecting battery cell states and battery cell parameters
Gould et al. EV/HEV Li-ion battery modelling and State-of-Function determination
WO2021117300A1 (fr) Procédé de réglage de données de batterie et procédé de fabrication d'unité de gestion de batterie, et unité de gestion de batterie et serveur
US11255916B2 (en) Method and device for monitoring a stable convergence behavior of a Kalman filter
Havangi Adaptive robust unscented Kalman filter with recursive least square for state of charge estimation of batteries
Manthopoulos et al. A review and comparison of lithium-ion battery SOC estimation methods for electric vehicles
CA2588334C (fr) Procede et systeme d'estimation de l'etat et des parametres d'une batterie
EP4024066A1 (fr) Dispositif de diagnostic de batterie, et procédé associé
Ramachandran et al. Recursive estimation of battery pack parameters in electric vehicles
Andalibi et al. A model-based approach for voltage and state-of-charge estimation of lithium-ion batteries

Legal Events

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

Ref document number: 23774190

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 23774190

Country of ref document: EP

Kind code of ref document: A1