WO2018029849A1

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

Info

Publication number: WO2018029849A1
Application number: PCT/JP2016/073741
Authority: WO
Inventors: 池田和人
Original assignee: 富士通株式会社
Priority date: 2016-08-12
Filing date: 2016-08-12
Publication date: 2018-02-15

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

Estimation device, estimation program, and charge control device

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. 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.

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.

Therefore, a technique for estimating the SOC with a Kalman filter is disclosed (for example, see Patent Documents 1 and 2).

JP2015-105863A JP 2012-47580 A

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.

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.

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.

SOC can be estimated with high estimation accuracy.

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.

Hereinafter, embodiments will be described with reference to the drawings.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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]

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. Σv is a covariance matrix indicating estimated noise. Σw is a covariance matrix indicating measurement noise.

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.

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).

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).

Next, 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 Σv, 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 Σw (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.

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.

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.

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.

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).

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.

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.

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

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

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.

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.

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.

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).

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).

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.

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%.

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.

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.

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.

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.

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).

(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.

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.

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

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. The estimation apparatus according to claim 1, wherein the correction unit performs correction such that the Kalman gain decreases as the change rate increases.
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. 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. 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.
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.
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.
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.
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.
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.
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. 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. 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. 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.
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.
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.
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.
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. 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.
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.