WO2018029849A1 - Dispositif d'estimation, programme d'estimation et dispositif de commande de charge - Google Patents

Dispositif d'estimation, programme d'estimation et dispositif de commande de charge Download PDF

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Publication number
WO2018029849A1
WO2018029849A1 PCT/JP2016/073741 JP2016073741W WO2018029849A1 WO 2018029849 A1 WO2018029849 A1 WO 2018029849A1 JP 2016073741 W JP2016073741 W JP 2016073741W WO 2018029849 A1 WO2018029849 A1 WO 2018029849A1
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WO
WIPO (PCT)
Prior art keywords
circuit voltage
rate
change rate
estimation
correction
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PCT/JP2016/073741
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English (en)
Japanese (ja)
Inventor
池田和人
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富士通株式会社
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Priority to PCT/JP2016/073741 priority Critical patent/WO2018029849A1/fr
Publication of WO2018029849A1 publication Critical patent/WO2018029849A1/fr

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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

  • This case relates to an estimation device, an estimation program, and a charge control device.
  • Secondary batteries such as lithium ion batteries are attracting attention as power storage applications such as electric mobility (electric vehicles, etc.) and stationary power storage systems.
  • electric mobility applications a technique for obtaining a charge rate SOC in order to display the remaining travel distance to the driver is desired.
  • Even in a stationary power storage system, obtaining an accurate SOC is important for accurate system control.
  • An object of one aspect is to provide an estimation device, an estimation program, and a charge control device that can estimate SOC with high estimation accuracy.
  • the estimation device estimates a charging rate and a predicted terminal voltage of the battery by a Kalman filter using a model function of an open voltage and a charging rate of the rechargeable battery, and the predicted terminal voltage and the battery
  • a calculating unit that calculates a difference between the measured value of the terminal voltage, a Kalman gain of the Kalman filter is corrected according to a change rate of the open-circuit voltage with respect to a charging rate in the model function, the corrected Kalman gain, and the difference
  • a correction unit that corrects the estimated charging rate.
  • SOC can be estimated with high estimation accuracy.
  • FIG. 1 is a block diagram of an estimation device according to Embodiment 1.
  • FIG. It is a figure which illustrates the equivalent electrical circuit model of a secondary battery. It is a figure which illustrates an OCV-SOC characteristic model function. It is explanatory drawing which shows an example of the SOC estimation process using a Kalman filter. It is a figure which illustrates the estimation result of SOC using a Kalman filter. An OCV-SOC model function approximated by a power function is illustrated. It is a figure which illustrates about the SOC estimation precision using a Kalman filter when a correction value is calculated using Formula (10) and when a correction value is calculated using Formula (11).
  • FIG. 8 is a partially enlarged view of FIG. 7.
  • FIG. 8 is a partially enlarged view of FIG. 7. It is a block diagram for demonstrating an example of the hardware constitutions of an estimation apparatus. It is a figure which illustrates about the estimation system concerning a modification.
  • FIG. 1 is a block diagram of the estimation apparatus 100 according to the first embodiment.
  • the estimation apparatus 100 includes a measurement unit 10, a parameter determination unit 20, a storage unit 30, a calculation unit 40, and an output unit 50.
  • the calculation unit 40 includes a calculation unit 41 and a correction unit 42.
  • the estimation device 100 is incorporated in, for example, a charge control device for a secondary battery. Note that the estimation device 100 may be implemented as a function of a control device such as an electric vehicle or an electric motorcycle, and may control a charging control device for a secondary battery.
  • the measurement unit 10 measures the current, terminal voltage, and the like of the secondary battery 200 at a predetermined sampling period.
  • the measured current and terminal voltage are referred to as measurement current I and measurement terminal voltage V OBS .
  • the measurement unit 10 outputs a measurement value to the parameter determination unit 20 and the calculation unit 40 with an ammeter, a voltmeter, or the like, for example.
  • the information related to the charging rate (SOC) estimated by the calculation unit 40 is input to the output unit 50 (or when there is a request from the external device 300), the information related to the SOC is output to the external device 300, for example. Output to. External device 300 controls charging / discharging of secondary battery 200 based on the estimated SOC.
  • the storage unit 30 stores information used for processing in the parameter determination unit 20 and the calculation unit 40.
  • the storage unit 30 stores an OCV (Open Circuit Voltage) -SOC characteristic model function, functions and various parameters used for the Kalman filter, constituent element parameters of the equivalent electric circuit model, calculation parameters for determining them, function parameters, and the like.
  • the OCV-SOC characteristic model function is a function of a curve indicating the OCV-SOC function of the secondary battery 200 or an approximate function. Examples of various parameters of the Kalman filter include ⁇ v indicating prediction noise, ⁇ w indicating measurement noise, and the like.
  • the parameter determination unit 20 acquires the parameters from the storage unit 30, and the constituent element parameters of the equivalent electric circuit model are determined in advance. Calculate using the following formula. In some cases, the component parameters of the equivalent electric circuit model stored in the storage unit 30 are selected.
  • FIG. 2 is a diagram illustrating an equivalent electric circuit model of the secondary battery 200.
  • the equivalent electric circuit model is an RC circuit that represents a transient voltage change with respect to a current change, and includes a power supply, a DC resistance R0, and two RC circuits (C1 and R1). , C2 and R2) are connected in series.
  • the RC circuit R1C1 is configured by connecting a resistor R1 and a capacitor C1 in parallel.
  • the RC circuit R2C2 is configured by connecting a resistor R2 and a capacitor C2 in parallel.
  • the parameter determination unit 20 calculates the values of R0, R1, R2, C1, and C2 using a predetermined calculation formula. Alternatively, predetermined values of R0, R1, R2, C1, and C2 are selected.
  • a voltage is generated in the power supply by the accumulated power.
  • the voltage generated by this power supply is an open circuit voltage (OCV).
  • OCV open circuit voltage
  • the OCV of the power supply varies depending on the SOC. Further, the power source is illustrated assuming that the OCV changes between charging and discharging even if the SOC is the same. For this reason, the power supply has current sources V OCV_DC (SOC) and V OCV_CC (SOC) that represent potential differences OCV that change according to changes in the SOC.
  • the current source V OCV_DC (SOC) represents the potential difference OCV during discharge.
  • a current source V OCV_CC (SOC) represents a potential difference OCV during charging.
  • the calculation unit 41 estimates the SOC using a Kalman filter: KF (or an extended Kalman filter: EKF).
  • KF Kalman filter
  • the arithmetic unit 40 obtains an OCV-SOC characteristic model function, various parameters for KF, etc. from the storage unit 30 and uses each parameter of the equivalent electric circuit model input from the parameter determination unit 20 to calculate the SOC. Perform estimation processing.
  • a parameter determined in advance stored in the storage unit 30 may be obtained and used without determining the parameter by the parameter determination unit 20. Note that for each KF calculation step, measurement values are input and parameters are input and determined.
  • the determination of the parameter includes a determination that the parameter of the previous step is used.
  • an OCV-SOC characteristic model that can reduce an error from an actual characteristic by taking into account a change in characteristics due to the operation of the secondary battery 200 is determined in advance.
  • the characteristic curve can be divided into a plurality of SOC regions, and the characteristic curve can be approximated by a linear function in each region.
  • a multi-order function, a trigonometric function, an exponential function, a logarithmic function or the like that can express a curve can be used, and a plurality of these can be combined.
  • the characteristic curve is approximated using a plurality of linear functions.
  • FIG. 4 is an explanatory diagram illustrating an example of an SOC estimation process using a Kalman filter.
  • equation (1) is an example of the state estimated value of the Kalman filter in step k, and is an example of the state estimated values of v1, v2, and SOC.
  • k indicates the number of steps of the Kalman filter.
  • ⁇ t is a time interval in which the Kalman filter is performed, and usually corresponds to a sampling period in which the measurement unit 10 measures the measurement current I and the measurement terminal voltage V OBS . However, the measurement period and the Kalman filter period do not necessarily coincide.
  • Sc a is the chargeable capacity of the secondary battery that is the target of SOC estimation.
  • Sc may differ depending on the secondary battery.
  • Sc a can be obtained by use specifications and charge / discharge measurement of the secondary battery.
  • Sc and a change with temperature and deterioration based on the measured or estimated secondary battery temperature and the measured or estimated deterioration degree, every SOC estimation period or periodically / irregularly The obtained chargeable capacity value can be applied.
  • V OBS represents the measurement terminal voltage at step k, and is hereinafter referred to as measurement terminal voltage.
  • [Character 1] Indicates a corrected state estimated value of the Kalman filter in step k ⁇ 1, and is hereinafter referred to as a state estimated value one step before.
  • [Character 2] Indicates the difference between the measured terminal voltage and the predicted terminal voltage in step k, and hereinafter referred to as the difference.
  • [Character 3] Indicates a state estimation value before correction of the Kalman filter in step k, and is hereinafter referred to as a state estimation value before correction.
  • [Character 4] Indicates a correction value of the estimated state value of the Kalman filter in step k, and is hereinafter referred to as a correction value.
  • G (k) represents the Kalman gain of step k.
  • A indicates Jacobian.
  • P (k) represents the error covariance matrix of the estimated value in step k, that is, the accuracy of the estimated value.
  • ⁇ v is a covariance matrix indicating estimated noise.
  • ⁇ w is a covariance matrix indicating measurement noise.
  • the calculation unit 41 uses the following equation (2) based on the state estimated value one step before and the measurement current i (k ⁇ 1) before correction.
  • the estimated state value is calculated (step S1).
  • the calculation unit 41 predicts the predicted terminal voltage V ⁇ according to the following formula (4) from the result of the above formula (2) (step S2).
  • the calculation unit 41 calculates the measurement terminal voltage V OBS and the prediction terminal voltage V ⁇ from the measurement terminal voltage V OBS and the prediction terminal voltage V ⁇ according to the following equation (5). Is calculated (step S3).
  • the correction unit 42 calculates Jacobian A using the following equation (6) based on the state estimation value one step before (Step S4).
  • the correction unit 42 uses the following equation (7) based on the Jacobian A, the one-step previous covariance matrix P (k ⁇ 1), and the prediction noise ⁇ v, and uses the prior covariance matrix P ⁇ (k). Is calculated (step S5).
  • the correcting unit 42 calculates the Kalman gain G (k) using the following equation (8) based on the prior covariance matrix P ⁇ (k) and the measurement noise ⁇ w (step S6).
  • the correcting unit 42 calculates the covariance matrix P (k) using the following equation (9) based on the Kalman gain G (k) and the prior covariance matrix P ⁇ (k) (step S7). .
  • the correcting unit 42 repeats steps S5 to S7 for each step.
  • the correction unit 42 uses the following equation (10) based on the calculated difference and the Kalman gain G (k) calculated in step S6 to correct the state estimated value. It is conceivable to calculate the value.
  • the OCV-SOC characteristic model function is non-linear, the OCV change rate changes according to the SOC.
  • a matrix of a determinant for predicting the actual measurement value is used in the calculation of the Kalman gain. Therefore, for example, when the terminal voltage is predicted from the estimated value using the equivalent circuit model, the value of the Kalman gain is influenced by the OCV-SOC function model function, particularly the rate of change of the OCV. For example, as the OCV change rate increases, the Kalman gain tends to increase. For this reason, depending on the SOC to be estimated, the change rate of the OCV becomes large, a large Kalman gain may be calculated, and excessive correction may be performed.
  • FIG. 5 is a diagram illustrating an estimation result of SOC using a Kalman filter.
  • the OCV-SOC characteristic model function is approximated by a plurality of linear functions.
  • the correction term is corrected so as to reduce the influence of the OCV change rate on the Kalman gain. For example, correction is performed such that the correction term decreases when the OCV change rate is large, and correction is performed such that the correction term increases when the OCV change rate is small.
  • the correction unit 42 calculates a correction value by the following formula (11) instead of the above formula (10) (step S8).
  • the correction unit 42 calculates the OCV change rate (step S9).
  • the linear function is determined by the range in which the estimated SOC is located.
  • the coefficient of the first-order term of the determined linear function is the rate of change.
  • the coefficient is obtained by differentiating the function of the straight line (dOCV / dSOC).
  • the change rate of OCV can be calculated by substituting previously measured SOC into the function obtained by differentiation. For example, when the SOC estimated by the Kalman filter in the example of FIG. 3 is 50%, the change rate of the OCV is a3 because it is in the range of SOC2 to SOC3.
  • the correction unit 42 determines the correction coefficient m (step S10).
  • a function is selected such that the correction coefficient m decreases as the rate of change increases.
  • m 1, that is, without correction, at the smallest change rate.
  • a linear function or the like is used rather than a trigonometric function or an exponent / logarithmic function.
  • a trigonometric function or exponential / logarithmic function is applied in addition to the square root to an electric vehicle having a large number of battery cells, a high calculation capability is required. Therefore, a method using the above-described map and a method using a simple function and the map together are considered desirable.
  • the OCV change rate is calculated.
  • the rate of change can be calculated by substituting the estimated SOC into the following equation (12) obtained by differentiating the model function (a power function).
  • the determination of the correction coefficient m is the same as in the case of the linear function described above.
  • FIG. 6 illustrates an OCV-SOC model function approximated by a power function.
  • the correction coefficient m is set in the same manner after selecting the function (for example, charge / discharge determination). Can be determined.
  • the SOC can be estimated with high accuracy.
  • the correction unit 42 After the correction value is calculated according to the above equation (11), the correction unit 42, based on the state estimation value before correction calculated in step S1 and the correction value calculated in step S8, the following equation ( 13) is used to calculate the estimated state value (step S11).
  • the correcting unit 42 calculates the SOC using the following formula (14) in the case of this example (step S12).
  • the calculation unit 41 and the correction unit 42 can estimate the SOC every second, for example, by repeating the processes of steps S1 to S12 as the SOC estimation process for each step.
  • the SOC, v1, and v2 are estimated using the difference between the actually measured terminal voltage VOBS and the terminal voltage y (k) predicted from the estimated values of SOC, v1, and v2, and the Kalman gain G. The value is corrected. By repeating this every step, the estimated values of SOC, v1, and v2 are brought close to the true value.
  • FIG. 7 illustrates the SOC estimation accuracy using the Kalman filter when the correction value is calculated using the above equation (10) (no gain correction) and when the correction value is calculated using the above equation (11). It is a figure to do.
  • FIG. 8 is a partially enlarged view of FIG.
  • the OCV-SOC characteristic model function is approximated by a plurality of linear functions.
  • the estimation error can be reduced.
  • FIG. 9 is also a partially enlarged view of FIG.
  • the left diagram of FIG. 9 illustrates the SOC estimation result and the absolute error in the SOC range where the change rate of the OCV is small.
  • the right diagram of FIG. 9 exemplifies the SOC estimation result and the absolute error in the SOC range where the change rate of the OCV is large.
  • the range where the change rate of the OCV is small there is almost no difference regardless of whether or not the gain is corrected.
  • the OCV change rate is large, the error is large when there is no gain correction, and the error is small when there is gain correction. This is because an excessive increase in the correction term is suppressed even when the OCV change rate increases.
  • the Kalman gain of the Kalman filter is corrected according to the change rate of the open-circuit voltage with respect to the charge rate in the model function of the open-circuit voltage and the charge rate of the secondary battery 200.
  • the Kalman gain is easily affected by the change rate of the open-circuit voltage
  • the influence of the change rate of the open-circuit voltage can be suppressed by correcting the Kalman gain according to the change rate of the open-circuit voltage. That is, the Kalman gain is appropriately corrected.
  • the SOC can be estimated with high accuracy.
  • the correction term is suppressed from becoming excessively large by performing correction so that the Kalman gain decreases as the change rate of the open circuit voltage increases. As a result, the SOC can be estimated with high accuracy.
  • the calculation unit 41 estimates the charging rate and the predicted terminal voltage of the battery by a Kalman filter using a model function of the open voltage and the charging rate of the rechargeable battery, and the predicted terminal voltage and the battery It functions as an example of a calculation unit that calculates a difference from the measured value of the terminal voltage.
  • the correction unit corrects the Kalman gain of the Kalman filter according to the change rate of the open circuit voltage with respect to the charging rate in the model function, and corrects the estimated charging rate based on the corrected Kalman gain and the difference. Functions as an example of a correction unit.
  • FIG. 10 is a block diagram for explaining an example of the hardware configuration of the estimation apparatus 100.
  • the estimation device 100 includes a CPU 101, a RAM 102, a storage device 103, an interface 104, and the like. Each of these devices is connected by a bus or the like.
  • a CPU (Central Processing Unit) 101 is a central processing unit.
  • the CPU 101 includes one or more cores.
  • a RAM (Random Access Memory) 102 is a volatile memory that temporarily stores programs executed by the CPU 101, data processed by the CPU 101, and the like.
  • the storage device 103 is a nonvolatile storage device.
  • the storage device 103 for example, a ROM (Read Only Memory), a solid state drive (SSD) such as a flash memory, a hard disk driven by a hard disk drive, or the like can be used.
  • the interface 104 is a device that transmits and receives signals to and from an external device.
  • the CPU 101 executes a program stored in the storage device 103, each unit of the estimation device 100 is realized.
  • an MPU Micro Processing Unit
  • it may be realized by an integrated circuit such as ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).
  • FIG. 11 is a diagram illustrating an estimation system according to a modification.
  • the parameter determination unit 20 and the calculation unit 40 obtain measurement values such as current values and terminal voltages from the measurement unit 10.
  • a server having the functions of the parameter determination unit 20 and the calculation unit 40 may acquire measurement data from the measurement unit 10 through a telecommunication line.
  • the server includes the CPU 101, the RAM 102, the storage device 103, the interface 104, and the like illustrated in FIG. 10 and realizes the functions as the parameter determination unit 20 and the calculation unit 40.

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  • Manufacturing & Machinery (AREA)
  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Electrochemistry (AREA)
  • General Chemical & Material Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Power Engineering (AREA)
  • Secondary Cells (AREA)
  • Tests Of Electric Status Of Batteries (AREA)

Abstract

Selon la présente invention, le dispositif d'estimation est pourvu : d'une unité de calcul permettant d'estimer le taux de charge et la tension de borne prédite d'une cellule chargeable au moyen d'un filtre de Kalman qui utilise une fonction de modèle de la tension de circuit ouvert et du taux de charge de la cellule, et de calculer la différence entre la tension de borne prédite et la valeur mesurée réelle de la tension de borne de la cellule ; d'une unité de correction permettant de corriger le gain de Kalman du filtre de Kalman en fonction du taux de variation de la tension de circuit ouvert par rapport au taux de charge dans la fonction de modèle, et de corriger le taux de charge estimé en fonction du gain de Kalman corrigé et de la différence.
PCT/JP2016/073741 2016-08-12 2016-08-12 Dispositif d'estimation, programme d'estimation et dispositif de commande de charge WO2018029849A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112470017A (zh) * 2019-02-07 2021-03-09 株式会社Lg化学 电池管理装置、电池管理方法和电池组
WO2023027049A1 (fr) * 2021-08-26 2023-03-02 株式会社Ｇｓユアサ Procédé de correction, programme informatique, appareil de correction et dispositif de stockage d'électricité
CN117741450A (zh) * 2024-02-21 2024-03-22 新风光电子科技股份有限公司 一种电参数分析的储能电池检测方法

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006284431A (ja) * 2005-04-01 2006-10-19 Nissan Motor Co Ltd 二次電池の充電率推定装置
WO2016031191A1 (fr) * 2014-08-28 2016-03-03 日本電気株式会社 Dispositif de traitement d'informations, procédé de traitement d'informations et support de stockage

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006284431A (ja) * 2005-04-01 2006-10-19 Nissan Motor Co Ltd 二次電池の充電率推定装置
WO2016031191A1 (fr) * 2014-08-28 2016-03-03 日本電気株式会社 Dispositif de traitement d'informations, procédé de traitement d'informations et support de stockage

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112470017A (zh) * 2019-02-07 2021-03-09 株式会社Lg化学 电池管理装置、电池管理方法和电池组
CN112470017B (zh) * 2019-02-07 2023-12-01 株式会社Lg新能源 电池管理装置、电池管理方法和电池组
US11923710B2 (en) 2019-02-07 2024-03-05 Lg Energy Solution, Ltd. Battery management apparatus, battery management method and battery pack
WO2023027049A1 (fr) * 2021-08-26 2023-03-02 株式会社Ｇｓユアサ Procédé de correction, programme informatique, appareil de correction et dispositif de stockage d'électricité
CN117741450A (zh) * 2024-02-21 2024-03-22 新风光电子科技股份有限公司 一种电参数分析的储能电池检测方法
CN117741450B (zh) * 2024-02-21 2024-05-14 新风光电子科技股份有限公司 一种电参数分析的储能电池检测方法

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