WO2018029849A1 - Estimation device, estimation program, and charging control device - Google Patents

Estimation device, estimation program, and charging control device 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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circuit voltage
rate
change rate
estimation
correction
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PCT/JP2016/073741
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French (fr)
Japanese (ja)
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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/en
Publication of WO2018029849A1 publication Critical patent/WO2018029849A1/en

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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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Abstract

The estimation device according to the present invention is provided with: a calculation unit for estimating the charging rate and a predicted terminal voltage of a chargeable cell through use of a Kalman filter which uses a model function of the open circuit voltage and the charging rate of the cell, and calculating the difference between the predicted terminal voltage and the actual measured value of the terminal voltage of the cell; and a correction unit for correcting the Kalman gain of the Kalman filter in accordance with the rate of change of the open circuit voltage with respect to the charging rate in the model function and correcting the estimated charging rate on the basis of the corrected Kalman gain and the difference.

Description

推定装置、推定プログラムおよび充電制御装置Estimation device, estimation program, and charge control device
　本件は、推定装置、推定プログラムおよび充電制御装置に関する。 This case relates to an estimation device, an estimation program, and a charge control device.
　リチウムイオン電池などの２次電池は、電動モビリティ(電気自動車等)、定置蓄電システム等の蓄電用途として注目されている。電動モビリティ用途では、残走行距離を運転者に表示するために充電率ＳＯＣを得る技術が望まれている。定置蓄電システムにおいても、正確なＳＯＣを得ることは正確なシステム制御のために重要である。 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. In 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.
　正確な制御が望まれる理由として、過充電や過放電を防止することが挙げられる。不正確なＳＯＣしか得られない場合には、大きい充放電マージンを取らざるを得ないので、電池本来の蓄電性能を発揮することが困難となる。これは電池の数を増やすことになり、システムのコストの増加につながる。 The reason why accurate control is desired is to prevent overcharge and overdischarge. When only an inaccurate SOC can be obtained, a large charge / discharge margin must be taken, and it becomes difficult to exhibit the battery's original power storage performance. This increases the number of batteries, leading to an increase in system cost.
　そこで、カルマンフィルタでＳＯＣを推定する技術が開示されている（例えば、特許文献１，２参照）。 Therefore, a technique for estimating the SOC with a Kalman filter is disclosed (for example, see Patent Documents 1 and 2).
特開２０１５－１０５８６３号公報JP2015-105863A 特開２０１２－４７５８０号公報JP 2012-47580 A
　しかしながら、上記技術では、カルマンゲインとして適切な値を使用できていないおそれがある。その結果、高い推定精度が得られないおそれがある。 However, there is a possibility that an appropriate value cannot be used as the Kalman gain in the above technique. As a result, high estimation accuracy may not be obtained.
　１つの側面では、高い推定精度でＳＯＣを推定することができる推定装置、推定プログラムおよび充電制御装置を提供することを目的とする。 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.
　１つの態様では、推定装置は、充電可能な電池の開放電圧と充電率とのモデル関数を用いたカルマンフィルタにより、前記電池の充電率および予測端子電圧を推定し、前記予測端子電圧と前記電池の端子電圧の実測値との差分を算出する算出部と、前記モデル関数における充電率に対する開放電圧の変化率に応じて、前記カルマンフィルタのカルマンゲインを補正し、補正されたカルマンゲインと、前記差分とに基づいて、推定された前記充電率を補正する補正部と、を備える。 In one aspect, 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 And a correction unit that corrects the estimated charging rate.
　高い推定精度でＳＯＣを推定することができる。 SOC can be estimated with high estimation accuracy.
実施例１に係る推定装置のブロック図である。1 is a block diagram of an estimation device according to Embodiment 1. FIG. ２次電池の等価電気回路モデルを例示する図である。It is a figure which illustrates the equivalent electrical circuit model of a secondary battery. ＯＣＶ－ＳＯＣ特性モデル関数を例示する図である。It is a figure which illustrates an OCV-SOC characteristic model function. カルマンフィルタを用いたＳＯＣ推定処理の一例を示す説明図である。It is explanatory drawing which shows an example of the SOC estimation process using a Kalman filter. カルマンフィルタを用いたＳＯＣの推定結果を例示する図である。It is a figure which illustrates the estimation result of SOC using a Kalman filter. 冪関数で近似したＯＣＶ－ＳＯＣモデル関数を例示する。An OCV-SOC model function approximated by a power function is illustrated. 式（１０）を用いて修正値を算出した場合および式（１１）を用いて修正値を算出した場合の、カルマンフィルタを用いたＳＯＣ推定精度について例示する図である。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). 図７の一部拡大図である。FIG. 8 is a partially enlarged view of FIG. 7. 図７の一部拡大図である。FIG. 8 is a partially enlarged view of FIG. 7. 推定装置のハードウェア構成の一例を説明するためのブロック図である。It is a block diagram for demonstrating an example of the hardware constitutions of an estimation apparatus. 変形例にかかる推定システムについて例示する図である。It is a figure which illustrates about the estimation system concerning a modification.
　以下、図面を参照しつつ、実施例について説明する。 Hereinafter, embodiments will be described with reference to the drawings.
　図１は、実施例１に係る推定装置１００のブロック図である。図１で例示するように、推定装置１００は、測定部１０、パラメータ決定部２０、記憶部３０、演算部４０、および出力部５０を備える。演算部４０は、算出部４１および補正部４２を備える。推定装置１００は、例えば、２次電池の充電制御装置に組み込まれる。なお、推定装置１００は、例えば、電気自動車や電動バイク等の制御装置の一機能として実装して、２次電池の充電制御装置を制御するようにしてもよい。 FIG. 1 is a block diagram of the estimation apparatus 100 according to the first embodiment. As illustrated in FIG. 1, 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.
　測定部１０は、２次電池２００の電流、端子電圧等を所定のサンプリング周期で測定する。測定された電流および端子電圧を、測定電流Ｉおよび測定端子電圧ＶＯＢＳと称する。測定部１０は、例えば、電流計、電圧計等で、測定値をパラメータ決定部２０および演算部４０に出力する。 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.
　出力部５０は、演算部４０で推定された充電率（ＳＯＣ）に係る情報が入力されると（あるいは外部装置３００からの要求があった場合に）、ＳＯＣに係る情報を、例えば外部装置３００に出力する。外部装置３００は、推定されたＳＯＣに基づいて、２次電池２００の充放電を制御する。 When 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.
　記憶部３０は、パラメータ決定部２０および演算部４０における処理に用いる情報を記憶する。記憶部３０は、ＯＣＶ（Open　Circuit　Voltage）－ＳＯＣ特性モデル関数、カルマンフィルタに用いる関数や各種パラメータ、等価電気回路モデルの構成素子パラメータおよびこれらを決定するための計算および関数のパラメータ等を記憶する。ＯＣＶ－ＳＯＣ特性モデル関数は、２次電池２００のＯＣＶ－ＳＯＣ関数を示す曲線の関数あるいは近似する関数である。カルマンフィルタの各種パラメータとしては、例えば、予測ノイズを示すΣｖ、測定ノイズを示すΣｗ等が挙げられる。 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 Σv indicating prediction noise, Σw indicating measurement noise, and the like.
　パラメータ決定部２０は、まず、測定部１０から測定端子電圧ＶＯＢＳおよび測定電流Ｉが入力されると、記憶部３０からパラメータを取得して、等価電気回路モデルの構成素子パラメータを、予め決定された計算式を用いて算出する。また、記憶部３０に記憶されている等価電気回路モデルの構成素子パラメータそのものを選定する場合もある。 First, when the measurement terminal voltage V OBS and the measurement current I are input from the measurement unit 10, 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.
　図２は、２次電池２００の等価電気回路モデルを例示する図である。図２で例示するように、等価電気回路モデルは、電流変化に対して過渡的な電圧の変化を表すＲＣ回路であって、電源と、直流抵抗Ｒ０と、２つのＲＣ回路（Ｃ１およびＲ１と、Ｃ２およびＲ２）とが直列に接続された構成を有する。ＲＣ回路Ｒ１Ｃ１は、抵抗Ｒ１とコンデンサＣ１とが並列に接続されて構成されている。ＲＣ回路Ｒ２Ｃ２は、抵抗Ｒ２とコンデンサＣ２とが並列に接続されて構成されている。パラメータ決定部２０は、予め決定された計算式を用いてＲ０，Ｒ１，Ｒ２，Ｃ１およびＣ２の値を算出する。あるいは、予め決定されたＲ０，Ｒ１，Ｒ２，Ｃ１およびＣ２の値を選択する。 FIG. 2 is a diagram illustrating an equivalent electric circuit model of the secondary battery 200. As illustrated in FIG. 2, 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.
　なお、等価電気回路モデルにおいて、電源では、蓄積された電力により電圧が生じる。この電源で生じる電圧が開回路電圧（ＯＣＶ：Open　Circuit　Voltage）である。電源は、ＳＯＣによってＯＣＶが変化する。また、電源は、ＳＯＣが同一でも充電時と放電時とでＯＣＶが変化することを想定して例示している。このため、電源は、ＳＯＣの変化に応じて変化する電位差ＯＣＶを表す電流源ＶＯＣＶ＿ＤＣ（ＳＯＣ）およびＶＯＣＶ＿ＣＣ（ＳＯＣ）を有する。ここで、電流源ＶＯＣＶ＿ＤＣ（ＳＯＣ）は、放電時の電位差ＯＣＶを表す。電流源ＶＯＣＶ＿ＣＣ（ＳＯＣ）は、充電時の電位差ＯＣＶを表す。 In the equivalent electric circuit model, 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). 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. Here, 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.
　直流抵抗Ｒ０の両端の電位差をｖ０とし、ＲＣ回路Ｒ１Ｃ１の両端の電位差をｖ１とし、ＲＣ回路Ｒ２Ｃ２の両端の電位差をｖ２とする。この場合、等価電気回路モデルの端子電圧として表される２次電池２００の予測端子電圧Ｖ－は、電位差ＯＣＶと、電圧ｖ０と、電圧ｖ１と、電圧ｖ２とを用いて、Ｖ－＝ＯＣＶ（ＳＯＣ）－ｖ０－ｖ１－ｖ２で表される。 The potential difference between both ends of the DC resistor R0 is v0, the potential difference between both ends of the RC circuit R1C1 is v1, and the potential difference between both ends of the RC circuit R2C2 is v2. In this case, the predicted terminal voltage V − of the secondary battery 200 expressed as the terminal voltage of the equivalent electric circuit model is obtained by using the potential difference OCV, the voltage v 0, the voltage v 1, and the voltage v 2, V − = OCV ( SOC) -v0-v1-v2.
　算出部４１は、測定部１０から測定電流Ｉおよび測定端子電圧ＶＯＢＳが入力されると、カルマンフィルタ：ＫＦ（あるいは拡張カルマンフィルタ：ＥＫＦ）を用いて、ＳＯＣを推定する。カルマンフィルタにおいては、演算部４０は、記憶部３０からＯＣＶ－ＳＯＣ特性モデル関数、ＫＦ用各種パラメータ等を入手し、パラメータ決定部２０から入力された等価電気回路モデルの各パラメータを用いて、ＳＯＣの推定処理を行う。等価電気回路モデルの各パラメータについては、パラメータ決定部２０によるパラメータの決定を行わずに、記憶部３０に記憶された予め決定しておいたパラメータを入手して用いる場合もある。なお、ＫＦの計算ステップ毎に、測定値の入力、パラメータの入力・決定が行われる。ここで、パラメータの決定には、前ステップのパラメータを使用するという判断を含む。 When the measurement current I and the measurement terminal voltage V OBS are input from the measurement unit 10, the calculation unit 41 estimates the SOC using a Kalman filter: KF (or an extended Kalman filter: EKF). In the 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. As for each parameter of the equivalent electric circuit model, 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. Here, the determination of the parameter includes a determination that the parameter of the previous step is used.
　次に、記憶部３０に記憶されているＯＣＶ－ＳＯＣ特性モデル関数について説明する。本実施例においては、２次電池２００の動作による特性の変化を加味することにより、実特性との誤差を小さくできるＯＣＶ－ＳＯＣ特性モデルを予め決定しておく。例えば、図３で例示するように、特性の曲線を複数のＳＯＣ領域に分け、それぞれの領域で直線関数によって特性曲線を近似することができる。その他、曲線を表現できる多次関数、三角関数、指数関数、対数関数等を用いることが可能で、さらにこれらを複数組み合わせることも可能である。本実施例においては、一例として、複数の直線関数を用いて特性曲線を近似する。 Next, the OCV-SOC characteristic model function stored in the storage unit 30 will be described. In this embodiment, 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. For example, as illustrated in FIG. 3, 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. In addition, 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. In this embodiment, as an example, the characteristic curve is approximated using a plurality of linear functions.
　続いて、図４を参照しつつ、カルマンフィルタの詳細について説明する。図４は、カルマンフィルタを用いたＳＯＣ推定処理の一例を示す説明図である。まず、下記式（１）は、ステップｋのカルマンフィルタの状態推定値の一例であり、ｖ１，ｖ２およびＳＯＣの状態推定値の一例である。ｋは、カルマンフィルタのステップ数を示す。Δｔは、カルマンフィルタが行われる時間間隔であり、通常、測定部１０が測定電流Ｉおよび測定端子電圧ＶＯＢＳを測定するサンプリング周期に相当する。ただし、測定周期とカルマンフィルタ周期は必ずしも一致する必要はない。
Figure JPOXMLDOC01-appb-M000001
 ここで、Ｓｃ，ａは、ＳＯＣ推定の対象である２次電池の蓄電可能容量である。２次電池によりＳｃ，ａは異なる場合がある。また、Ｓｃ，ａは、２次電池の使用仕様と充放電測定により求めることができる。また、Ｓｃ，ａは、温度と劣化により変化するので、測定あるいは推定された２次電池の温度、および測定あるいは推定された劣化度を基に、ＳＯＣ推定周期毎にあるいは定期／不定期に、求めた蓄電可能容量値を適用することができる。 Next, details of the Kalman filter will be described with reference to FIG. FIG. 4 is an explanatory diagram illustrating an example of an SOC estimation process using a Kalman filter. First, the following 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. Δt 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.
Figure JPOXMLDOC01-appb-M000001
 Here, Sc , a is the chargeable capacity of the secondary battery that is the target of SOC estimation. Sc, a may differ depending on the secondary battery. In addition, Sc, a can be obtained by use specifications and charge / discharge measurement of the secondary battery. In addition, since 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.
　次に、図４の説明に用いる文字を説明する。ＶＯＢＳは、ステップｋの測定端子電圧を示し、以下、測定端子電圧という。
［文字１］
Figure JPOXMLDOC01-appb-I000002
は、ステップｋ－１のカルマンフィルタの補正された状態推定値を示し、以下、１ステップ前の状態推定値という。
［文字２］
Figure JPOXMLDOC01-appb-I000003
は、ステップｋの測定端子電圧と予測端子電圧との差分を示し、以下、差分という。
［文字３］
Figure JPOXMLDOC01-appb-I000004
は、ステップｋのカルマンフィルタの補正前の状態推定値を示し、以下、補正前の状態推定値という。
［文字４］
Figure JPOXMLDOC01-appb-I000005
は、ステップｋのカルマンフィルタの状態推定値の補正値を示し、以下、補正値
という。 Next, characters used in the description of FIG. 4 will be described. V OBS represents the measurement terminal voltage at step k, and is hereinafter referred to as measurement terminal voltage.
[Character 1]
Figure JPOXMLDOC01-appb-I000002
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]
Figure JPOXMLDOC01-appb-I000003
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]
Figure JPOXMLDOC01-appb-I000004
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]
Figure JPOXMLDOC01-appb-I000005
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）は、ステップｋのカルマンゲインを示す。Ａは、ヤコビアンを示す。Ｐ（ｋ）は、ステップｋの推定値の誤差の共分散行列、つまり推定値の精度を示す。Σｖは、推定ノイズを示す共分散行列である。Σｗは、測定ノイズを示す共分散行列である。 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. Σv is a covariance matrix indicating estimated noise. Σw is a covariance matrix indicating measurement noise.
　算出部４１は、測定電流ｉ（ｋ）が入力されると、１ステップ前の状態推定値と、測定電流ｉ（ｋ－１）とに基づいて、下記の式（２）を用いて補正前の状態推定値を算出する（ステップＳ１）。ここで、測定電流ｉ（ｋ）は、１秒ごとに入力される場合を示しており、Δｔ＝１であることから、例えば、簡易的に下記に示す式（３）の関係を用いることができる。
Figure JPOXMLDOC01-appb-M000006
Figure JPOXMLDOC01-appb-M000007
 When the measurement current i (k) is input, 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). Here, the measurement current i (k) indicates a case where the measurement current i (k) is input every second, and Δt = 1. Therefore, for example, the relationship of the following formula (3) can be used simply. it can.
Figure JPOXMLDOC01-appb-M000006
Figure JPOXMLDOC01-appb-M000007
　次に、算出部４１は、上記式（２）の結果から、下記式（４）に従って、予測端子電圧Ｖ－を予測する（ステップＳ２）。
Figure JPOXMLDOC01-appb-M000008
 Next, 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).
Figure JPOXMLDOC01-appb-M000008
　算出部４１は、測定端子電圧ＶＯＢＳが入力されると、測定端子電圧ＶＯＢＳと、予測端子電圧Ｖ－とから、下記式（５）に従って、測定端子電圧ＶＯＢＳと予測端子電圧Ｖ－との差分を算出する（ステップＳ３）。
Figure JPOXMLDOC01-appb-M000009
 When the measurement terminal voltage V OBS is input, 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).
Figure JPOXMLDOC01-appb-M000009
　次に、補正部４２は、１ステップ前の状態推定値に基づいて、下記の式（６）を用いてヤコビアンＡを算出する（ステップＳ４）。
Figure JPOXMLDOC01-appb-M000010
 Next, the correction unit 42 calculates Jacobian A using the following equation (6) based on the state estimation value one step before (Step S4).
Figure JPOXMLDOC01-appb-M000010
　補正部４２は、ヤコビアンＡと、１ステップ前の共分散行列Ｐ（ｋ－１）と、予測ノイズΣｖとに基づいて、下記の式（７）を用いて事前共分散行列Ｐ－（ｋ）を算出する（ステップＳ５）。
Figure JPOXMLDOC01-appb-M000011
 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 Σv, and uses the prior covariance matrix P − (k). Is calculated (step S5).
Figure JPOXMLDOC01-appb-M000011
　補正部４２は、事前共分散行列Ｐ－（ｋ）と、測定ノイズΣｗとに基づいて、下記の式（８）を用いてカルマンゲインＧ（ｋ）を算出する（ステップＳ６）。
Figure JPOXMLDOC01-appb-M000012
 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 Σw (step S6).
Figure JPOXMLDOC01-appb-M000012
　補正部４２は、カルマンゲインＧ（ｋ）と、事前共分散行列Ｐ－（ｋ）とに基づいて、下記の式（９）を用いて共分散行列Ｐ（ｋ）を算出する（ステップＳ７）。補正部４２は、ステップＳ５～ステップＳ７を１ステップごとに繰り返す。
Figure JPOXMLDOC01-appb-M000013
 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.
Figure JPOXMLDOC01-appb-M000013
　次に、補正部４２は、算出された差分と、ステップＳ６で算出されたカルマンゲインＧ（ｋ）とに基づいて、下記の式（１０）を用いて、状態推定値を修正するための修正値を算出することが考えられる。
Figure JPOXMLDOC01-appb-M000014
 Next, 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.
Figure JPOXMLDOC01-appb-M000014
　しかしながら、この場合、必ずしも適切な修正値が得られない場合がある。以下、その理由について説明する。ＯＣＶ－ＳＯＣ特性モデル関数は非線形であるので、ＳＯＣに応じてＯＣＶの変化率が変化する。カルマンフィルタを用いたＳＯＣの推定では、カルマンゲインの計算において、実測値を予測するための行列式の行列が用いられる。そのため、例えば、等価回路モデルを用いた推定値から端子電圧を予測する場合には、カルマンゲインの値はＯＣＶ－ＳＯＣ関数モデル関数、特にＯＣＶの変化率に影響される。例えば、ＯＣＶの変化率が大きくなるとカルマンゲインが大きくなる傾向にある。このため、推定すべきＳＯＣによっては、ＯＣＶの変化率が大きくなり、大きなカルマンゲインが算出され、過剰な補正が行われるおそれがある。 However, in this case, an appropriate correction value may not always be obtained. The reason will be described below. Since the OCV-SOC characteristic model function is non-linear, the OCV change rate changes according to the SOC. In the estimation of the SOC using the Kalman filter, 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.
　図５は、カルマンフィルタを用いたＳＯＣの推定結果を例示する図である。ここでは、ＯＣＶ－ＳＯＣ特性モデル関数は、複数の直線関数で近似したものである。図５の例におけるＳＯＣ範囲では、ＳＯＣ＝３０％に直線関数の切り替わりがあり、３０％より下の領域で直線の傾きが大きくなっている。直線の傾き増加＝ＯＣＶ変化率増加であるため、図５の例では３０％以下でカルマンゲインが大きくなる傾向となり、過剰に推定補正がなされたため推定誤差が大きくなってしまっている。そこで、本実施例においては、ＯＣＶの変化率に応じてカルマンゲインを補正することで、高精度でＳＯＣの推定を行う。具体的には、ＯＣＶの変化率がカルマンゲインに及ぼす影響を減ずる方向に補正項を修正する。例えば、ＯＣＶの変化率が大きい場合には補正項が減少するような修正を行い、ＯＣＶの変化率が小さい場合には補正項が大きくなるような修正を行う。 FIG. 5 is a diagram illustrating an estimation result of SOC using a Kalman filter. Here, the OCV-SOC characteristic model function is approximated by a plurality of linear functions. In the SOC range in the example of FIG. 5, the linear function is switched at SOC = 30%, and the slope of the straight line is large in the region below 30%. Since the increase in the slope of the straight line = the increase in the OCV change rate, in the example of FIG. 5, the Kalman gain tends to increase at 30% or less, and the estimation error has increased due to excessive estimation correction. Therefore, in this embodiment, the SOC is estimated with high accuracy by correcting the Kalman gain according to the change rate of the OCV. Specifically, 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.
　修正係数をｍとした場合、補正部４２は、上記式（１０）の代わりに、下記式（１１）によって修正値を算出する（ステップＳ８）。
Figure JPOXMLDOC01-appb-M000015
 When the correction coefficient is m, the correction unit 42 calculates a correction value by the following formula (11) instead of the above formula (10) (step S8).
Figure JPOXMLDOC01-appb-M000015
　続いて、修正係数ｍの具体的な算出手法について説明する。一例として、ＯＣＶ－ＳＯＣ特性モデル関数を図３のように複数の直線関数で近似した場合を例に説明する。ここで、それぞれのＳＯＣ範囲での直線の式は、例えば
０％～ＳＯＣ１ 　　　ＯＣＶ＝ａ１＊ＳＯＣ＋ｂ１
ＳＯＣ１～ＳＯＣ２　ＯＣＶ＝ａ２＊ＳＯＣ＋ｂ２
ＳＯＣ２～ＳＯＣ３　ＯＣＶ＝ａ３＊ＳＯＣ＋ｂ３
ＳＯＣ３～１００％　ＯＣＶ＝ａ４＊ＳＯＣ＋ｂ４
と設定できる。まず、補正部４２は、ＯＣＶの変化率を算出する（ステップＳ９）。この例の場合、推定されたＳＯＣが位置する範囲により、直線関数が決定される。決定された直線関数の一次の項の係数が変化率となる。当該係数は、当該直線の関数を微分したもの（ｄＯＣＶ／ｄＳＯＣ）である。微分によって得られる関数に、事前測定したＳＯＣを代入することで、ＯＣＶの変化率を算出することができる。例えば、図３の例においてカルマンフィルタで推定されたＳＯＣが５０％だった場合、ＳＯＣ２～ＳＯＣ３の範囲であるため、ＯＣＶの変化率はａ３となる。 Next, a specific method for calculating the correction coefficient m will be described. As an example, a case where the OCV-SOC characteristic model function is approximated by a plurality of linear functions as shown in FIG. 3 will be described as an example. Here, the equation of the straight line in each SOC range is, for example, 0% to SOC1 OCV = a1 * SOC + b1
SOC1 to SOC2 OCV = a2 * SOC + b2
SOC2 to SOC3 OCV = a3 * SOC + b3
SOC3 ~ 100% OCV = a4 * SOC + b4
Can be set. First, the correction unit 42 calculates the OCV change rate (step S9). In this example, 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.
　次に、補正部４２は、修正係数ｍを決定する（ステップＳ１０）。変化率の関数として算出する場合には、前述の問題を解決するために、基本的に変化率が大きくなるほど修正係数ｍが小さくなるような関数を選ぶ。例えば、ｍ＝１／変化率、ｍ＝１／√（変化率）、ｍ＝１／（変化率）２のような関数を適用することができる。また、基準となる変化率を設定し、これとの比率から修正係数ｍを決定することもできる。すなわち、変化率の規格値を修正係数ｍとして用いることができる。例えば、直線関数の最も小さな傾きａ４を基準とすることが好ましい。例えば、ｍ＝１／（変化率／基準変化率）、ｍ＝１／√（変化率／基準変化率）、ｍ＝１／（変化率／基準変化率）２のような関数を適用することができる。最も小さな傾きを基準とした規格値を用いることにより、最も小さな変化率の場合に安易にｍ＝１つまり補正なしに設定することが可能になる。 Next, the correction unit 42 determines the correction coefficient m (step S10). When calculating as a function of the rate of change, in order to solve the above-described problem, basically, a function is selected such that the correction coefficient m decreases as the rate of change increases. For example, functions such as m = 1 / change rate, m = 1 / √ (change rate), m = 1 / (change rate) 2 can be applied. It is also possible to set a reference change rate and determine the correction coefficient m from the ratio. That is, the standard value of the change rate can be used as the correction coefficient m. For example, it is preferable to use the smallest inclination a4 of the linear function as a reference. For example, a function such as m = 1 / (change rate / reference change rate), m = 1 / √ (change rate / reference change rate), m = 1 / (change rate / reference change rate) 2 is applied. Can do. By using the standard value based on the smallest inclination, it is possible to easily set m = 1, that is, without correction, at the smallest change rate.
　また、変化率（あるいは、基準変化率との比率）のマップデータから修正係数ｍを決定することもできる。例えば、複数の直線関数による近似で基準変化率を最も小さな直線の傾きとした場合には、例えば、以下のように修正係数ｍを求めることができる。この手法では、計算量が少なくなるという効果が得られる。
変化率／基準変化率＝１　→　ｍ＝１
１　＜　変化率／基準変化率　≦　４　→　ｍ＝０．５
４　＜　変化率／基準変化率　≦　２０　→　ｍ＝０．２５
２０　＜　変化率／基準変化率　→　ｍ＝０．２ Further, the correction coefficient m can be determined from map data of the change rate (or the ratio with the reference change rate). For example, when the reference change rate is set to the smallest straight line slope by approximation with a plurality of linear functions, for example, the correction coefficient m can be obtained as follows. This method has the effect of reducing the amount of calculation.
Change rate / reference change rate = 1 → m = 1
1 <change rate / reference change rate ≦ 4 → m = 0.5
4 <change rate / reference change rate ≦ 20 → m = 0.25
20 <change rate / reference change rate → m = 0.2
　または、関数とマップデータとの組み合わせを用いることもできる。例えば、以下のように修正係数ｍを求めることもできる。この手法では、計算量を少なくしつつ適切な修正係数を取得することができるという効果が得られる。
１　≦　変化率／基準変化率　≦　４　→　ｍ＝１／（変化率／基準変化率）
４　＜　変化率／基準変化率　≦　２０　→　ｍ＝０．２５
２０　＜　変化率／基準変化率　→　ｍ＝０．２ Alternatively, a combination of a function and map data can be used. For example, the correction coefficient m can be obtained as follows. This method has an effect that an appropriate correction coefficient can be acquired while reducing the amount of calculation.
1 ≦ change rate / reference change rate ≦ 4 → m = 1 / (change rate / reference change rate)
4 <change rate / reference change rate ≦ 20 → m = 0.25
20 <change rate / reference change rate → m = 0.2
　ここで、変化率の関数として修正係数ｍを算出する場合の関数を先にあげた平方根の他、三角関数や指数・対数関数を用いるよりも、直線関数等を用いると、計算量が少なくなる。例えば、多数の電池セルを有する電気自動車等に平方根の他、三角関数や指数・対数関数を適用した場合には高い計算能力が必要になるが、直線関数等を用いれば計算能力が要求されない。そこで、上述のマップを用いる方法や、単純な関数とマップを併用する手法が望ましいと考えられる。 Here, in addition to the square root given above for calculating the correction coefficient m as a function of the rate of change, a linear function or the like is used rather than a trigonometric function or an exponent / logarithmic function. . For example, when 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.
　事前推定値の補正を修正する係数の決定方法について、その他の例としてＯＣＶ－ＳＯＣ特性モデル関数を冪関数で近似した場合を説明する。まず、ＯＣＶの変化率を算出する。この例の場合には、モデル関数（冪関数）を微分した下記式（１２）に、推定されたＳＯＣを代入することにより変化率を算出することができる。修正係数ｍの決定に関しては、前述の直線関数の場合と同様である。図６は、冪関数で近似したＯＣＶ－ＳＯＣモデル関数を例示する。
Figure JPOXMLDOC01-appb-M000016
 As another example of the method for determining the coefficient for correcting the correction of the pre-estimated value, a case where the OCV-SOC characteristic model function is approximated by a power function will be described. First, the OCV change rate is calculated. In the case of this example, 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.
Figure JPOXMLDOC01-appb-M000016
　さらに、２次電池２００の電流の状況において（例えば充放電別に）、複数のＯＣＶ－ＳＯＣ関数を使用する場合も、関数の選択（例えば充放電判断）の後、同様の方法で修正係数ｍを決定することができる。この場合、２次電池２００の電流に状況に応じた適切なＯＣＶ－ＳＯＣ関数を用いることができるため、高精度でＳＯＣを推定することができる。 Further, when a plurality of OCV-SOC functions are used in the current situation of the secondary battery 200 (for example, for each charge / discharge), the correction coefficient m is set in the same manner after selecting the function (for example, charge / discharge determination). Can be determined. In this case, since an appropriate OCV-SOC function corresponding to the situation can be used for the current of the secondary battery 200, the SOC can be estimated with high accuracy.
　上記式（１１）に従って修正値が算出された後、補正部４２は、ステップＳ１で算出された補正前の状態推定値と、ステップＳ８で算出された修正値とに基づいて、下記の式（１３）を用いて状態推定値を算出する（ステップＳ１１）。
Figure JPOXMLDOC01-appb-M000017
 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).
Figure JPOXMLDOC01-appb-M000017
　補正部４２は、状態推定値に基づいて、今回の例の場合、下記の式（１４）を用いてＳＯＣを算出する（ステップＳ１２）。
Figure JPOXMLDOC01-appb-M000018
 Based on the state estimated value, the correcting unit 42 calculates the SOC using the following formula (14) in the case of this example (step S12).
Figure JPOXMLDOC01-appb-M000018
　このように、算出部４１および補正部４２は、ＳＯＣ推定処理としてステップＳ１～Ｓ１２の処理を１ステップごとに繰り返すことによって、例えば、１秒ごとにＳＯＣを推定できる。以上のカルマンフィルタでは、実測した測定端子電圧ＶＯＢＳとＳＯＣ，ｖ１，ｖ２の推定値から予測した端子電圧ｙ（ｋ）との差分と、カルマンゲインＧとを用いて、ＳＯＣ，ｖ１，ｖ２の推定値を補正している。これを毎ステップ繰り返すことで、ＳＯＣ，ｖ１，ｖ２の推定値を真値に近づけている。 As described above, 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. In the Kalman filter described above, 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.
　図７は、上記式（１０）を用いて修正値を算出した場合（ゲインの修正無し）および上記式（１１）を用いて修正値を算出した場合の、カルマンフィルタを用いたＳＯＣ推定精度について例示する図である。図８は、図７の一部拡大図である。ここでは、ＯＣＶ－ＳＯＣ特性モデル関数は複数の直線関数で近似したものである。図７および図８の例におけるＳＯＣ範囲では、ＳＯＣ＝３０％に直線関数の切り替わりがあり、３０％より下の領域で直線の傾きが大きくなっている。 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. Here, the OCV-SOC characteristic model function is approximated by a plurality of linear functions. In the SOC range in the examples of FIGS. 7 and 8, the linear function is switched at SOC = 30%, and the slope of the straight line is large in the region below 30%.
　直線の傾き増加＝ＯＣＶ変化率増加であるため、上記式（１０）を用いた場合には３０％以下でカルマンゲインが大きくなる傾向となり、過剰に推定補正がなされたため推定誤差が大きくなってしまっている。これに対し、上記式（１１）を用いた場合には、ＯＣＶ変化率が大きくなっても補正項が過剰に大きくなることが抑制され、過剰な補正による推定誤差の増大を抑えることができる。このように、図７および図８で例示するように、本実施例によれば、推定誤差を減少することができる。 Since the increase in the slope of the straight line = the increase in the OCV change rate, when the above equation (10) is used, the Kalman gain tends to increase at 30% or less, and the estimation error becomes large due to excessive estimation correction. ing. On the other hand, when the above equation (11) is used, it is possible to suppress the correction term from becoming excessively large even if the OCV change rate increases, and to suppress an increase in estimation error due to excessive correction. Thus, as illustrated in FIGS. 7 and 8, according to the present embodiment, the estimation error can be reduced.
　図９も、図７の一部拡大図である。図９の左図は、ＯＣＶの変化率が小さいＳＯＣ範囲のＳＯＣ推定結果および絶対誤差を例示している。図９の右図は、ＯＣＶの変化率が大きいＳＯＣ範囲のＳＯＣ推定結果および絶対誤差を例示している。ＯＣＶの変化率が小さい範囲では、ゲインの修正の有無にかかわらず差がほとんど見られない。一方で、ＯＣＶの変化率が大きい範囲では、ゲインの修正が無い場合には誤差が大きく、ゲインの修正が有る場合には誤差が小さくなっている。これは、ＯＣＶ変化率が大きくなっても補正項が過剰に大きくなることが抑制されているからである。 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. In 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. On the other hand, in the range in which 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.
　本実施例によれば、２次電池２００の開放電圧と充電率とのモデル関数における充電率に対する開放電圧の変化率に応じて、カルマンフィルタのカルマンゲインが補正されている。この場合、カルマンゲインは開放電圧の変化率に影響を受けやすいため、開放電圧の変化率に応じてカルマンゲインを補正することで、開放電圧の変化率の影響を抑制することができる。すなわち、カルマンゲインが適切に補正される。それにより、高精度にＳＯＣを推定することができる。例えば、開放電圧の変化率が大きいほどカルマンゲインが小さくなるように補正することで、補正項が過剰に大きくなることが抑制される。その結果、高精度にＳＯＣを推定することができる。 According to the present embodiment, 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. In this case, since 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. Thereby, the SOC can be estimated with high accuracy. For example, 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.
　上記例において、算出部４１が、充電可能な電池の開放電圧と充電率とのモデル関数を用いたカルマンフィルタにより、前記電池の充電率および予測端子電圧を推定し、前記予測端子電圧と前記電池の端子電圧の実測値との差分を算出する算出部の一例として機能する。補正部４２が、モデル関数における充電率に対する開放電圧の変化率に応じて、カルマンフィルタのカルマンゲインを補正し、補正されたカルマンゲインと、前記差分とに基づいて、推定された前記充電率を補正する補正部の一例として機能する。 In the above example, 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.
　図１０は、推定装置１００のハードウェア構成の一例を説明するためのブロック図である。図１０で例示するように、推定装置１００は、ＣＰＵ１０１、ＲＡＭ１０２、記憶装置１０３、インタフェース１０４などを備える。これらの各機器は、バスなどによって接続されている。ＣＰＵ（Ｃｅｎｔｒａｌ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）１０１は、中央演算処理装置である。ＣＰＵ１０１は、１以上のコアを含む。ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）１０２は、ＣＰＵ１０１が実行するプログラム、ＣＰＵ１０１が処理するデータなどを一時的に記憶する揮発性メモリである。記憶装置１０３は、不揮発性記憶装置である。記憶装置１０３として、例えば、ＲＯＭ（Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、フラッシュメモリなどのソリッド・ステート・ドライブ（ＳＳＤ）、ハードディスクドライブに駆動されるハードディスクなどを用いることができる。インタフェース１０４は、外部機器との信号の送受信を行う機器である。ＣＰＵ１０１が記憶装置１０３に記憶されているプログラムを実行することによって、推定装置１００の各部が実現される。または、ＣＰＵの代わりにＭＰＵ（Ｍｉｃｒｏ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）等を用いても良い。または、例えば、ＡＳＩＣ（Ａｐｐｌｉｃａｔｉｏｎ　Ｓｐｅｃｉｆｉｃ　Ｉｎｔｅｇｒａｔｅｄ　Ｃｉｒｃｕｉｔ）やＦＰＧＡ（Ｆｉｅｌｄ　Ｐｒｏｇｒａｍｍａｂｌｅ　Ｇａｔｅ　Ａｒｒａｙ）等の集積回路により実現されるようにしてもよい。 FIG. 10 is a block diagram for explaining an example of the hardware configuration of the estimation apparatus 100. As illustrated in FIG. 10, 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. As 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. When the CPU 101 executes a program stored in the storage device 103, each unit of the estimation device 100 is realized. Alternatively, an MPU (Micro Processing Unit) or the like may be used instead of the CPU. Alternatively, for example, it may be realized by an integrated circuit such as ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).
（変形例）
　図１１は、変形例にかかる推定システムについて例示する図である。上記各例においては、パラメータ決定部２０および演算部４０は、測定部１０から電流値、端子電圧などの測定値を取得している。これに対して、パラメータ決定部２０および演算部４０の機能を有するサーバが、電気通信回線を通じて測定部１０から測定データを取得してもよい。例えば、サーバは、図１０のＣＰＵ１０１、ＲＡＭ１０２、記憶装置１０３、インタフェース１０４などを備え、パラメータ決定部２０および演算部４０としての機能を実現する。 (Modification)
FIG. 11 is a diagram illustrating an estimation system according to a modification. In each of the above examples, 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. On the other hand, 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. For example, 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.
　以上、本発明の実施例について詳述したが、本発明は係る特定の実施例に限定されるものではなく、特許請求の範囲に記載された本発明の要旨の範囲内において、種々の変形・変更が可能である。 Although the embodiments of the present invention have been described in detail above, the present invention is not limited to such specific embodiments, and various modifications and changes can be made within the scope of the gist of the present invention described in the claims. It can be changed.
　１０　測定部
　２０　パラメータ決定部
　３０　記憶部
　４０　演算部
　５０　出力部
　１００　推定装置
　２００　２次電池
　３００　外部装置 DESCRIPTION OF SYMBOLS 10 Measurement part 20 Parameter determination part 30 Memory | storage part 40 Calculation part 50 Output part 100 Estimation apparatus 200 Secondary battery 300 External apparatus

Claims (20)

  1. 　充電可能な電池の開放電圧と充電率とのモデル関数を用いたカルマンフィルタにより、前記電池の充電率および予測端子電圧を推定し、前記予測端子電圧と前記電池の端子電圧の実測値との差分を算出する算出部と、
    　前記モデル関数における充電率に対する開放電圧の変化率に応じて、前記カルマンフィルタのカルマンゲインを補正し、補正されたカルマンゲインと、前記差分とに基づいて、推定された前記充電率を補正する補正部と、を備えることを特徴とする推定装置。     Using a Kalman filter that uses a model function of the rechargeable battery open-circuit voltage and the charge rate, the battery charge rate and the predicted terminal voltage are estimated, and the difference between the predicted terminal voltage and the measured value of the battery terminal voltage is calculated. A calculation unit for calculating,
    A correction unit that corrects the Kalman gain of the Kalman filter according to the rate of change 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. An estimation device comprising:
  2. 　前記補正部は、前記変化率が大きいほどカルマンゲインが小さくなるような補正を行うことを特徴とする請求項１記載の推定装置。 2. The estimation apparatus according to claim 1, wherein the correction unit performs correction such that the Kalman gain decreases as the change rate increases.
  3. 　前記補正部は、前記モデル関数を充電率で微分した関数に、事前推定した充電率を代入することで、前記開放電圧の変化率を算出することを特徴とする請求項１または２に記載の推定装置。 The said correction | amendment part calculates the rate of change of the said open circuit voltage by substituting the charge rate estimated beforehand to the function which differentiated the said model function with the charge rate, The said open circuit voltage is calculated. Estimating device.
  4. 　前記補正部は、前記開放電圧の変化率に応じて変化する変数を前記カルマンゲインに乗じることで、前記カルマンゲインを補正することを特徴とする請求項１～３のいずれか一項に記載の推定装置。 4. The correction unit according to claim 1, wherein the correction unit corrects the Kalman gain by multiplying the Kalman gain by a variable that changes in accordance with a change rate of the open circuit voltage. Estimating device.
  5. 　前記補正部は、前記変数を、前記開放電圧の変化率の関数を用いて算出することを特徴とする請求項１～４のいずれか一項に記載の推定装置。 5. The estimation apparatus according to claim 1, wherein the correction unit calculates the variable using a function of a change rate of the open circuit voltage.
  6. 　前記補正部は、前記変数を、前記変数と前記開放電圧の変化率とのマップデータから決定することを特徴とする請求項１～４のいずれか一項に記載の推定装置。 The estimation device according to any one of claims 1 to 4, wherein the correction unit determines the variable from map data of the variable and a change rate of the open circuit voltage.
  7. 　前記補正部は、前記開放電圧の変化率により、関数によって前記変数を算出することと、マップデータから前記変数を決定することと、を切り替えることを特徴とする請求項１～４のいずれか一項に記載の推定装置。 The correction unit switches between calculating the variable by a function and determining the variable from map data according to a change rate of the open circuit voltage. The estimation apparatus according to item.
  8. 　前記補正部は、前記開放電圧の変化率の規格値に応じて、前記カルマンフィルタのカルマンゲインを補正することを特徴とする請求項１～７のいずれか一項に記載の推定装置。 The estimation apparatus according to any one of claims 1 to 7, wherein the correction unit corrects a Kalman gain of the Kalman filter according to a standard value of a change rate of the open circuit voltage.
  9. 　前記補正部は、前記モデル関数の最も小さな開放電圧変化率に基づいて、前記開放電圧の変化率の規格値を求めることを特徴とする請求項８記載の推定装置。 The estimation device according to claim 8, wherein the correction unit obtains a standard value of the change rate of the open-circuit voltage based on a minimum open-circuit voltage change rate of the model function.
  10. 　前記モデル関数は、前記電池の電流の状況に応じて複数あることを特徴とする請求項１～９のいずれか一項に記載の推定装置。 The estimation apparatus according to any one of claims 1 to 9, wherein there are a plurality of model functions depending on a current state of the battery.
  11. 　コンピュータに、
    　充電可能な電池の開放電圧と充電率とのモデル関数を用いたカルマンフィルタにより、前記電池の充電率および予測端子電圧を推定し、前記予測端子電圧と前記電池の端子電圧の実測値との差分を算出する処理と、
    　前記モデル関数における充電率に対する開放電圧の変化率に応じて、前記カルマンフィルタのカルマンゲインを補正し、補正されたカルマンゲインと、前記差分とに基づいて、推定された前記充電率を補正する処理と、を実行させることを特徴とする推定プログラム。     On the computer,
    Using a Kalman filter that uses a model function of the rechargeable battery open-circuit voltage and the charge rate, the battery charge rate and the predicted terminal voltage are estimated, and the difference between the predicted terminal voltage and the measured value of the battery terminal voltage is calculated. Processing to calculate,
    A process of correcting 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 correcting the estimated charging rate based on the corrected Kalman gain and the difference; The estimation program characterized by performing.
  12. 　前記補正する処理において、前記変化率が大きいほどカルマンゲインが小さくなるような補正を行うことを特徴とする請求項１１記載の推定プログラム。 12. The estimation program according to claim 11, wherein in the correction process, correction is performed such that the Kalman gain decreases as the change rate increases.
  13. 　前記補正する処理において、前記モデル関数を充電率で微分した関数に、事前推定した充電率を代入することで、前記開放電圧の変化率を算出することを特徴とする請求項１１または１２に記載の推定プログラム。 13. The change rate of the open-circuit voltage is calculated by substituting a pre-estimated charging rate into a function obtained by differentiating the model function with a charging rate in the correcting process. Estimation program.
  14. 　前記補正する処理において、前記開放電圧の変化率に応じて変化する変数を前記カルマンゲインに乗じることで、前記カルマンゲインを補正することを特徴とする請求項１１～１３のいずれか一項に記載の推定プログラム。 14. The Kalman gain is corrected by multiplying the Kalman gain by a variable that changes in accordance with a change rate of the open circuit voltage in the correction process. Estimation program.
  15. 　前記補正する処理において、前記変数を、前記開放電圧の変化率の関数を用いて算出することを特徴とする請求項１１～１４のいずれか一項に記載の推定プログラム。 The estimation program according to any one of claims 11 to 14, wherein, in the correction process, the variable is calculated using a function of a change rate of the open circuit voltage.
  16. 　前記補正する処理において、前記変数を、前記変数と前記開放電圧の変化率とのマップデータから決定することを特徴とする請求項１１～１４のいずれか一項に記載の推定プログラム。 The estimation program according to any one of claims 11 to 14, wherein, in the correction process, the variable is determined from map data of the variable and the change rate of the open circuit voltage.
  17. 　前記補正する処理において、前記開放電圧の変化率により、関数によって前記変数を算出することと、マップデータから前記変数を決定することと、を切り替えることを特徴とする請求項１１～１４のいずれか一項に記載の推定プログラム。 15. The correction process switches between calculating the variable by a function and determining the variable from map data according to a change rate of the open circuit voltage. The estimation program according to one item.
  18. 　前記補正する処理において、前記開放電圧の変化率の規格値に応じて、前記カルマンフィルタのカルマンゲインを補正することを特徴とする請求項１１～１７のいずれか一項に記載の推定プログラム。 The estimation program according to any one of claims 11 to 17, wherein, in the correction process, a Kalman gain of the Kalman filter is corrected in accordance with a standard value of a change rate of the open circuit voltage.
  19. 　前記補正する処理において、前記モデル関数の最も小さな開放電圧変化率に基づいて、前記開放電圧の変化率の規格値を求めることを特徴とする請求項１８記載の推定プログラム。 19. The estimation program according to claim 18, wherein, in the correcting process, a standard value of the change rate of the open circuit voltage is obtained based on a minimum open circuit voltage change rate of the model function.
  20. 　充電可能な電池の開放電圧と充電率とのモデル関数を用いたカルマンフィルタにより、前記電池の充電率および予測端子電圧を推定し、前記予測端子電圧と前記電池の端子電圧の実測値との差分を算出する算出部と、前記モデル関数における充電率に対する開放電圧の変化率に応じて、前記カルマンフィルタのカルマンゲインを補正し、補正されたカルマンゲインと、前記差分とに基づいて、推定された前記充電率を補正する補正部と、を備える推定装置と、
    　前記補正部によって補正された前記充電率に基づいて前記電池の充放電制御を行う制御装置と、を備えることを特徴とする充電制御装置。     Using a Kalman filter that uses a model function of the rechargeable battery open-circuit voltage and the charge rate, the battery charge rate and the predicted terminal voltage are estimated, and the difference between the predicted terminal voltage and the measured value of the battery terminal voltage is calculated. The calculation unit for calculating, the Kalman gain of the Kalman filter is corrected according to the change rate of the open circuit voltage with respect to the charging rate in the model function, and the charging estimated based on the corrected Kalman gain and the difference A correction unit that corrects the rate, and an estimation device comprising:
    And a control device that performs charge / discharge control of the battery based on the charge rate corrected by the correction unit.
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