WO2017179175A1

WO2017179175A1 - Estimation device, estimation program, and charging control device

Info

Publication number: WO2017179175A1
Application number: PCT/JP2016/062022
Authority: WO
Inventors: 池田和人
Original assignee: 富士通株式会社
Priority date: 2016-04-14
Filing date: 2016-04-14
Publication date: 2017-10-19

Abstract

An estimation device is provided with: a measurement unit for measuring a measurement current and measurement terminal voltage of a rechargeable battery at a prescribed sampling period; a determination unit for making DC resistance parameters for an equivalent circuit model of the battery values obtained on the basis of the measurement current and measurement terminal voltage by dividing the measurement terminal voltage differences at the sampling intervals by the measurement current differences; a calculation unit for estimating the state of charge of the battery and a predicted terminal voltage with a Kalman filter using element parameters including the DC resistances of the equivalent circuit model and calculating the difference between the measurement terminal voltage and the predicted terminal voltage; and a correction unit for correcting the state of charge on the basis of the difference and the Kalman gain of the Kalman filter.

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 by estimating an equivalent circuit parameter using a Kalman filter or the like is disclosed (for example, see Patent Document 1).

JP 2012-58089 A

However, the characteristics of the secondary battery change according to conditions such as temperature and aging. In the above technique, since the equivalent circuit parameter is not changed according to the condition, high estimation accuracy cannot 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 apparatus measures the measurement current and the measurement terminal voltage of a rechargeable battery at a predetermined sampling period, and the measurement of the sampling period based on the measurement current and the measurement terminal voltage. A determination unit that determines a value obtained by dividing a difference in terminal voltage by a difference in the measured current as a parameter of DC resistance of the equivalent electric circuit model of the battery; and the DC resistance of the equivalent electric circuit model A calculation unit that estimates a charging rate and a predicted terminal voltage of the battery by a Kalman filter using element parameters, calculates a difference between the measured terminal voltage and the predicted terminal voltage, the difference, and a Kalman gain of the Kalman filter, And a correction unit that corrects the charging rate based on.

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 the relationship between SOC and OCV of a lithium ion battery. It is a figure which illustrates the relationship between SOC of lead acid battery, and OCV. It is explanatory drawing which shows an example of the SOC estimation process using a Kalman filter. It is a figure which illustrates the flowchart performed when a parameter determination part determines each element parameter of an equivalent electrical circuit model. (A) is a figure which illustrates the relationship between (DELTA) V and (DELTA) I, (b) is a figure which illustrates the calculated real R0 value. It is a figure which illustrates the relationship between the SOC estimated by calculating the actual R0 value, the SOC estimated using the R0 value calculated only from the measured current I, and the actual measured value of the SOC. 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, temperature, and the like of the secondary battery 200 at a predetermined sampling period. The measured current, terminal voltage, and temperature are referred to as measurement current I, measurement terminal voltage V _OBS, and measurement temperature. The measurement part 10 is an ammeter, a voltmeter, a thermometer etc., and outputs a measured value to the parameter determination part 20 and the calculating part 40, 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 parameters of a function indicating an OCV (Open Circuit Voltage) characteristic curve, functions used for a Kalman filter, various parameters, calculation parameters for determining constituent element parameters of an equivalent electric circuit model, function parameters, and the like. The OCV characteristic curve is a graph showing the OCV-SOC characteristic of the secondary battery 200. 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.

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.

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. Moreover, even if the SOC is the same, the OCV changes between charging and discharging. 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.

When 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, the terminal voltage v of the equivalent electric circuit model is the potential difference OCV and the voltage v0. And the sum of voltage v1 and voltage v2. That is, the terminal voltage v is represented by v = OCV + 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 calculation unit 40 obtains the OCV characteristic parameter, various parameters for KF, etc. from the storage unit 30 and performs the SOC estimation process using each parameter of the equivalent electric circuit model input from the parameter determination unit 20. Do. Note that measurement values are input and parameters are determined for each KF calculation step. Here, the determination of the parameter includes a determination that the parameter of the previous step is used.

Here, the SOC-OCV characteristics will be described. FIG. 3 is a diagram illustrating the relationship between SOC and OCV of a lithium ion battery. As illustrated in FIG. 3, in the lithium ion battery, the SOC increases from 0% and the OCV increases rapidly, and thereafter, the OCV tends to increase gently as the SOC increases. Using this relationship, the OCV can be predicted from the SOC. The parameter of the function indicating the OCV characteristic curve is stored in the storage unit 30.

In addition, FIG. 4 is a figure which illustrates the relationship between SOC and OCV of a lead storage battery. As illustrated in FIG. 4, in the lead storage battery, the relationship between the SOC and the OCV is substantially proportional. When a lead storage battery is used as the secondary battery 200, the SOC can be estimated by obtaining the OCV using this relationship. In other secondary batteries such as a nickel metal hydride battery, since there is a predetermined relationship between the OCV and the SOC, the SOC can be estimated by obtaining the OCV.

Hereinafter, SOC estimation processing using a Kalman filter will be described. The calculation unit 41 estimates the current v1, v2 and SOC based on v1, v2 and SOC one step before and the input measurement current I using an equivalent electric circuit model. Moreover, the calculation part 41 calculates OCV from SOC estimated using the OCV characteristic curve. Furthermore, the calculation unit 41 predicts the predicted terminal voltage from the current v1, v2, and OCV.

Next, the calculation unit 41 calculates the difference between the actually measured measurement terminal voltage V _OBS and the predicted terminal voltage.

Next, details of the Kalman filter will be described with reference to FIG. FIG. 5 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 corresponds to a sampling period in which the measurement unit 10 measures the measurement current I and the measurement terminal voltage V _OBS .

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. 5 will be described. V _OBS (k) represents the measurement terminal voltage in 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.

When the measurement terminal voltage V _OBS is input, the calculation unit 41 uses the following formula (4) based on the measurement terminal voltage V _OBS , the state estimation value before correction, and the measurement current i (k). The difference between the measurement terminal voltage V _OBS and the predicted terminal voltage y (k) is calculated (step S2). The difference is expressed as the following formula (4). Note that the predicted terminal voltage y (k) in the above example of the equivalent electric circuit model is the sum of the OCV obtained from the estimated value of SOC and v0, v1, and v2.

Moreover, Formula (4) can also be expressed as the following Formula (5) using the predicted terminal voltage y (k).

Next, the correction | amendment part 42 calculates Jacobian A using the following formula | equation (6) based on the state estimated value one step before (step S3).

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 S4).

The correction 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 S5).

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 S6). . The correcting unit 42 repeats steps S4 to S6 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 S5 to correct the state estimated value. A value is calculated (step S7).

The correcting unit 42 calculates a state estimated value using the following equation (11) based on the state estimated value before correction calculated in step S1 and the corrected value calculated in step S7 (step S8). ). Here, the state estimated value can also be expressed by the following equation (12).

Based on the state estimated value, the correction unit 42 calculates the SOC using the following equation (13) in the case of this example (step S9).

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 S9 as the SOC estimation process for each step. In the above Kalman filter, SOC, v1, and v2 are estimated using the difference between the 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 step every step, the estimated values of SOC, v1, and v2 are gradually brought closer to the true value.

Incidentally, the characteristics of the secondary battery 200 change due to the temperature of the secondary battery 200, aging, and the like. Regarding temperature, it depends on operating conditions (mainly current value) and ambient temperature. It is ideal to change each parameter of the equivalent electric circuit model in accordance with this, but the temperature for determining the characteristic is the temperature inside the secondary battery 200, and the internal temperature of the secondary battery 200 as a product is determined in real time. It is very difficult to measure. Even if a temperature sensor is installed inside the secondary battery 200, the change in the current flowing through the secondary battery 200 may be very fast. In this case, it is considered that the temperature sensor cannot sufficiently follow the temperature change. Although a method for estimating the internal temperature from the current value and the ambient temperature has been proposed, an error is accompanied with the estimated temperature, and as a result, the SOC estimation accuracy is deteriorated. It is also difficult to estimate degradation in real time. There is also a proposal for a degradation estimation method, but it causes an error as with temperature estimation.

As a method for solving this problem, the characteristics of the secondary battery 200 are measured by changing the current value, the ambient temperature, and the degree of deterioration, and the parameters of the equivalent electric circuit model are used as a function of the current value, the ambient temperature, and the degree of deterioration. A method of creating a table of parameters, current values, ambient temperature, and deterioration degree of an equivalent electric circuit model is conceivable. However, since there are so many combinations of the above parameters, it is not practical to measure all characteristics. In addition, there is a possibility (other factors) that the parameters reflecting actual characteristics cannot be determined with the above parameters alone.

As a method for easily changing the parameters of the equivalent electric circuit model in accordance with the internal temperature change of the secondary battery 200, a method has been proposed in which each parameter is described and used as a function of current. Although the accuracy is improved, the ambient temperature and other factors are not taken into account, so the internal temperature cannot be completely reflected, and the accuracy is not sufficient.
Example of the above-described equivalent electric circuit model: R0 = f0 (I), R1 = fa (I), C1 = fb (I), R2 = fc (I), C2 = fd (I)

As described above, since there was no method for correctly changing the parameters of the equivalent electric circuit model in real time due to the temperature and aging of the secondary battery 200, high SOC estimation accuracy could not be obtained under various battery usage conditions.

In the operation of the secondary battery 200, the voltage changes (ΔV) with the change of current (ΔI) due to charging / discharging. Of the parameters of the equivalent electric circuit model, the DC resistance parameter R0 having no time constant does not have transient characteristics. Therefore, in this embodiment, attention is paid to the fact that R0 can be calculated without including a large error in the above-described calculation of ΔV / ΔI. As a result, R0 can be calculated in real time when the secondary battery 200 is charged and discharged. This calculated R0 value is referred to as “actual R0 value”. ΔV is a difference between the terminal voltage measured last time and the terminal voltage measured this time. ΔI is the difference between the current value measured last time and the current value measured this time.

The measurement unit 10 sequentially measures the current flowing through the secondary battery 200 and the terminal voltage at a predetermined sampling period. Therefore, the parameter determining unit 20 first determines R0 among the parameters R0, R1, R2, C1, and C2 of the equivalent electric circuit model. Specifically, the parameter determination unit 20 calculates the actual R0 value using the measurement current I and the measurement terminal voltage V _OBS . The actual R0 value is calculated from R0 = ΔV / ΔI from the current change ΔI and the voltage change ΔV from the previous measurement for each sampling period.

FIG. 6 is a diagram illustrating a flowchart executed when the parameter determining unit 20 determines each element parameter of the equivalent electric circuit model. As illustrated in FIG. 6, the parameter determination unit 20 calculates an actual R0 value from the current change ΔI and the voltage change ΔV by R0 = ΔV / ΔI (step S11). Next, the parameter determination unit 20 determines whether ΔI is equal to or less than a threshold value (step S12). If “Yes” is determined in step S12, the parameter determination unit 20 does not calculate the actual R0 value because the error of the actual R0 value increases. The threshold value can be appropriately determined. In this case, the previous actual R0 value is used for the Kalman filter (step S13).

If it is determined “No” in step S12, the parameter determination unit 20 determines whether or not the actual R0 value is negative (step S14). If it is determined as “Yes” in step S14, the parameter determination unit 20 excludes the actual R0 value. In this case, the previous actual R0 value is used for the Kalman filter (step S13).

When it is determined “No” in step S14, the parameter determination unit 20 determines whether or not the difference between the actual R0 value determined last time and the actual R0 value determined this time is equal to or greater than a threshold value (step S15). . If it is determined as “Yes” in step S15, the current actual R0 value is excluded. In this case, the previous actual R0 value is used for the Kalman filter (step S13). There may be other exclusions. When the calculated value is not adopted, the calculation unit 40 uses the previous R0 value for the Kalman filter.

The parameter determination unit 20 uses the average (moving average) value of the calculated values as necessary. The number of calculations to be averaged can be set appropriately. When ΔI is smaller than the threshold value and the actual R0 value is negative, the previous average value is used for the Kalman filter.

If “No” is determined in step S15 or after execution of step S13, the parameter determination unit 20 uses the determined actual R0 value to calculate the parameters of the equivalent electric circuit model having a time constant other than R0 in the sampling period. Calculate (step S16). The parameter is at least one function (f) of the actual R0 value, the measured current I, the deterioration amount, the temperature, and the sampling period, and is obtained by experiment for each battery having different characteristics. Example of the above-described equivalent circuit model: R1 = f1 (R0, I,...), C1 = f2 (R0, I,...), R2 = f3 (R0, I,...), C2 = f4 (R0, I , ...). Alternatively, the function f′1 (I) determined from the real R0 is used as R1, f′2 (I) determined from the real R0 is used as C1, and f′3 (I determined from the real R0 is used as R2. ) And f′4 (I) determined from the actual R0 may be used as C2. Each component parameter of the determined equivalent electric circuit model is output to the calculation unit 40. The calculation unit 41 and the correction unit 42 estimate the SOC using a Kalman filter that uses the determined parameter of the equivalent electric circuit model.

FIG. 7A is a diagram illustrating the relationship between ΔV and ΔI. The parameter determination unit 20 calculates R0 from ΔV and ΔI at each measurement timing. FIG. 7B is a diagram illustrating the calculated actual R0 value. The battery environmental temperature here is 50 ° C. In FIG. 7B, the R0 value calculated from only the current value I is also illustrated. As illustrated in FIG. 7B, there is a difference in the value between when the actual R0 value is calculated from ΔV and ΔI and when the R0 value is calculated from only the current value I. Specifically, when the actual R0 value is calculated from ΔV and ΔI, R0 is a lower value than when the R0 value is calculated from only the current value I. Here, the reason why the actual R0 value is low is that the change in characteristics due to the influence of the environmental temperature is reflected.

By calculating an actual R0 value reflecting changes in the operating state, ambient temperature, and deterioration degree, and determining each element parameter of the equivalent electric circuit of the battery using this actual R0 value, the operating state, ambient temperature, deterioration degree Is changed to an appropriate parameter corresponding to the change, and the SOC can be estimated with high accuracy.

FIG. 8 is a diagram illustrating the relationship between the SOC estimated by calculating the actual R0 value, the SOC estimated using the R0 value calculated only from the measured current I, and the actual measured value of the SOC. The battery environmental temperature here is 50 ° C. As illustrated in FIG. 8, when the R0 value calculated from only the measured current I is used, each parameter of the equivalent electric circuit model may greatly deviate from the actual characteristics of the secondary battery 200. In this case, the difference between the estimated SOC and the actual measured value of the SOC can be large. On the other hand, when calculating the actual R0 value and calculating other parameters, it is possible to suppress the deviation of the other parameters. Thereby, the discrepancy between the estimated SOC and the actual measured value of the SOC is reduced.

The relationship between the actual R0 value and the element parameters of other equivalent electric circuit models may be obtained by experiments. Therefore, the work amount and the work time can be greatly reduced as compared to experimentally determining the relationship between the operating state, the ambient temperature, the degree of deterioration, and the element parameters of the equivalent electric circuit model.

Compared with the method of determining parameters based on the relationship between operating conditions, ambient temperature, degree of deterioration, and parameters of the equivalent electric circuit model, the amount of calculation for determining parameters can be reduced, thereby reducing the cost of the calculation device. . Alternatively, SOC estimation of many batteries (100 or more in EV) can be performed at the same time with the same computing device.

FIG. 9 is a block diagram for explaining an example of the hardware configuration of the estimation apparatus 100. As illustrated in FIG. 9, 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. 10 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. 9 and realizes 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

A measurement unit that measures a measurement current and a measurement terminal voltage of a rechargeable battery at a predetermined sampling period;
Based on the measurement current and the measurement terminal voltage, a value obtained by dividing the difference of the measurement terminal voltage in the sampling period by the difference of the measurement current is a parameter of DC resistance of the equivalent electric circuit model of the battery. A decision unit to decide as
A calculation unit that estimates a charging rate and a predicted terminal voltage of the battery by a Kalman filter using an element parameter including the DC resistance of the equivalent electric circuit model, and calculates a difference between the measured terminal voltage and the predicted terminal voltage; ,
An estimation device comprising: a correction unit that corrects the charging rate based on the difference and a Kalman gain of the Kalman filter.
The said determination part determines the element parameters other than the said DC resistance of the said equivalent electrical circuit model from the parameter of the said DC resistance using the function or table | surface defined beforehand. Estimating device.
The estimation device according to claim 1 or 2, wherein the determining unit uses parameters of the DC element obtained at a plurality of times of the sampling period when determining an element parameter other than the DC resistance.
4. The estimation apparatus according to claim 1, wherein the determination unit uses the measurement current when determining an element parameter other than the DC resistance.
The determining unit determines an element parameter other than the DC resistance by using a previously determined DC resistance parameter when the difference between the measured currents is equal to or less than a threshold value. 5. The estimation device according to any one of 4.
When the difference between the parameter determined by the DC element and the parameter determined last time is equal to or greater than the threshold, the determination unit determines an element parameter other than the DC resistance using the parameter determined last time. The estimation apparatus according to any one of claims 1 to 5, wherein:
The measurement unit measures the temperature of the battery,
The determination unit determines an element parameter other than the DC resistance using at least one of the parameter determined last time, the measurement current, the sampling period, and the temperature measured by the measurement unit. The estimation apparatus according to claim 5 or 6.
On the computer,
Based on the measurement current and the measurement terminal voltage measured at a predetermined sampling period of the rechargeable battery, a value obtained by dividing the difference of the measurement terminal voltage of the sampling period by the difference of the measurement current, Processing to determine as a parameter of DC resistance of the equivalent electric circuit model of the battery;
A process of estimating a charging rate and a predicted terminal voltage of the battery by a Kalman filter using an element parameter including the DC resistance of the equivalent electric circuit model, and calculating a difference between the measured terminal voltage and the predicted terminal voltage;
An estimation program for executing the process of correcting the charging rate based on the difference and a Kalman gain of the Kalman filter.
A measurement unit that measures a measurement current and a measurement terminal voltage of a rechargeable battery at a predetermined sampling period, and based on the measurement current and the measurement terminal voltage, a difference between the measurement terminal voltages of the sampling period The value obtained by dividing by the difference is determined by a determining unit that determines the DC resistance parameter of the equivalent electric circuit model of the battery, and the Kalman filter using the element parameter including the DC resistance of the equivalent electric circuit model, A calculation unit that estimates a battery charging rate and a predicted terminal voltage, calculates a difference between the measured terminal voltage and the predicted terminal voltage, and a correction that corrects the charging rate based on the difference and a Kalman gain of the Kalman filter 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.