JP6221884B2 - Estimation program, estimation method, and estimation apparatus - Google Patents

Description

  The present invention relates to an estimation program, an estimation method, and an estimation apparatus.

  2. Description of the Related Art In recent years, in an electric vehicle, an electric motorcycle, or the like, an operation is known in which a use time loss due to a charging time is eliminated by exchanging a secondary battery for each module. Moreover, a lithium ion secondary battery may be used as a secondary battery used for an electric vehicle, an electric motorcycle, or the like. In the operation of replacing the battery module, it is necessary to manage the SoC (States of Charge) that is the charging rate of the lithium ion secondary battery in each battery module. SoC can be measured by measuring the amount of electricity inside the lithium ion secondary battery, but it is difficult to measure during actual operation. For this reason, the SoC in actual operation is estimated from the measurable terminal voltage and current. The SoC is estimated by applying a Kalman filter to an equivalent electric circuit model in which a lithium ion secondary battery is regarded as an electric circuit.

JP 2013-072677 A JP 2008-010420 A

  However, since the internal resistance of the lithium ion secondary battery changes depending on factors such as temperature, SoC, and current, an error from the equivalent electric circuit model to which the Kalman filter is applied occurs. Therefore, an error from the measured terminal voltage occurs in the terminal voltage predicted from the voltage based on the internal resistance. The Kalman filter corrects the prediction based on the error of the terminal voltage. However, since the error of the terminal voltage is highly dependent on the current, the error becomes large at a large current. For this reason, the Kalman filter performs excessive correction on the terminal voltage at the time of a large current, so that the estimation accuracy of SoC is significantly deteriorated.

  In one aspect, the present invention is to provide an estimation program, an estimation method, and an estimation apparatus that can prevent deterioration in estimation accuracy of a charging rate.

  In one aspect, the estimation program calculates a charging rate and a predicted terminal voltage of the battery using a Kalman filter based on the measured current and measured terminal voltage of the battery, and the measured terminal voltage and the predicted terminal voltage. The process which calculates the difference with is executed. The estimation program causes the computer to execute a process of calculating a correction value based on a value obtained by dividing the difference by the absolute value of the measured current and a Kalman gain of the Kalman filter. Further, the estimation program performs a process of correcting the charging rate by multiplying a component corresponding to a transient voltage with respect to a current change of the equivalent electric circuit model of the battery at the corrected value by the absolute value of the measured current. Let it run.

  It is possible to prevent the deterioration of the charging rate estimation accuracy.

FIG. 1 is a block diagram illustrating an example of the configuration of the estimation apparatus according to the first embodiment. FIG. 2 is an explanatory diagram illustrating an example of an equivalent electric circuit model of a lithium ion secondary battery. FIG. 3 is a graph showing an example of the OCV characteristic curve. FIG. 4 is an explanatory diagram illustrating an example of the SoC estimation process using the Kalman filter. FIG. 5 is a flowchart illustrating an example of processing performed by the estimation apparatus according to the first embodiment. FIG. 6 is a block diagram illustrating an example of the configuration of the estimation apparatus according to the second embodiment. FIG. 7 is an explanatory diagram illustrating an example of the SoC estimation process using the Kalman filter according to the second embodiment. FIG. 8 is a flowchart illustrating an example of processing performed by the estimation apparatus according to the second embodiment. FIG. 9 is an explanatory diagram illustrating an example of a computer that executes an estimation program.

  Hereinafter, embodiments of an estimation program, an estimation method, and an estimation apparatus disclosed in the present application will be described in detail based on the drawings. The disclosed technology is not limited by the present embodiment. Further, the following embodiments may be appropriately combined within a consistent range.

  FIG. 1 is a block diagram illustrating an example of the configuration of the estimation apparatus according to the first embodiment. The estimation apparatus 100 illustrated in FIG. 1 includes a measurement unit 101, an output unit 102, a storage unit 120, and a control unit 130. The estimation apparatus 100 is incorporated in, for example, a lithium ion secondary battery module. Moreover, the estimation apparatus 100 may include a control unit that controls charging / discharging of the cell. Note that the estimation device 100 may be mounted as a function of a control device such as an electric vehicle or an electric motorcycle to control the lithium ion secondary battery module.

  Measurement unit 101 measures the current and terminal voltage of the lithium ion secondary battery. The measurement part 101 measures an electric current and a terminal voltage, for example with an ammeter and a voltmeter, respectively. The measurement unit 101 outputs the measured current and terminal voltage to the control unit 130 as a measurement current and a measurement terminal voltage, respectively. In addition, the measurement unit 101 acquires the operation mode of the lithium ion secondary battery and outputs it to the control unit 130.

  When the charging rate information is input from the control unit 130, the output unit 102 outputs the charging rate information to, for example, another device. Here, the other device is, for example, a vehicle control device if it is an electric vehicle, an electric motorcycle, or the like, and is a display unit that displays a screen if it is a portable information terminal such as a smartphone. When the charging rate information is input, the other device displays the charging rate on a meter or a display unit of the vehicle so that the user can visually recognize the charging rate information.

  The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 includes a condition storage unit 121. In addition, the storage unit 120 stores information used for processing in the control unit 130.

The condition storage unit 121 stores an OCV (Open Circuit Voltage) characteristic curve, various parameters of a Kalman filter, each parameter of an equivalent electric circuit model, a threshold value r, a threshold value a, and the like. The OCV characteristic curve is a graph showing the OCV-SoC characteristic of the lithium ion secondary battery, and details will be described later. Examples of the various parameters of the Kalman filter include Σv indicating prediction noise, Σw indicating measurement noise, and the like. Examples of each parameter of the equivalent electric circuit model include R ₀ , R ₁ , R ₂ , C ₁ , and C ₂ described later. Each parameter of the equivalent electric circuit model is set in advance so as to match an actual lithium ion secondary battery.

  The threshold value r is a threshold value that performs case classification when an error, which is a difference between the measured terminal voltage and the predicted terminal voltage predicted by the Kalman filter, is normalized with respect to the current. The threshold value r can be set to r = 5, for example. The threshold value a is a threshold value for not performing correction due to excessive error when a sudden excessive error occurs in the correction value of the charging rate by the Kalman filter.

  The control unit 130 is realized, for example, by executing a program stored in an internal storage device using a RAM as a work area by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. Further, the control unit 130 may be realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). The control unit 130 includes a calculation unit 131, a correction unit 132, and a determination unit 133, and realizes or executes functions and operations of information processing described below. Note that the internal configuration of the control unit 130 is not limited to the configuration illustrated in FIG. 1, and may be another configuration as long as the information processing described below is performed.

When the measurement current and the measurement terminal voltage are input from the measurement unit 101, the calculation unit 131 calculates the SoC (charge rate) and the predicted terminal voltage using a Kalman filter. In addition, the calculation unit 131 calculates the difference y ^* (k) between the measurement terminal voltage and the predicted terminal voltage as an error. Further, in order to correct the SoC, the calculation unit 131 calculates an inverse matrix of a state estimation value calculated by a Kalman filter described later, a difference y ^* (k), and a Kalman gain G (k) for each step. The data is output to the correction unit 132. In addition, the calculation unit 131 receives the state estimation value of the previous step from the correction unit 132 for each step.

Here, the SoC estimation process using the Kalman filter will be described with reference to FIGS. 2 and 3. FIG. 2 is an explanatory diagram illustrating an example of an equivalent electric circuit model of a lithium ion secondary battery. As shown in FIG. 2, the equivalent electric circuit model of the lithium ion secondary battery has a current source V _{OCV_DC} (SoC) and a V _{OCV_CC} (SoC) representing a potential difference OCV that changes in accordance with a change in SoC. Here, the current sources V _{OCV_DC} (SoC) and V _{OCV_CC} (SoC) represent the potential difference OCV during discharging and the potential difference OCV during charging, respectively. Also, the equivalent electric circuit model shown in FIG. 2 includes a resistor R ₀ that represents a change in voltage v ₀ generated with respect to a current change, and an RC circuit that represents a change in transient voltages v ₁ and v ₂ with respect to the current change. (R ₁ C ₁ and R ₂ C ₂ ). That is, the equivalent electric circuit model is used to make the internal resistance of the lithium ion secondary battery a function that depends on the measurement current i. Note that the accuracy of the internal resistance greatly affects the SoC estimation accuracy using the Kalman filter.

The terminal voltage v of the equivalent electric circuit model shown in FIG. 2 is expressed by the sum of the potential difference OCV, the voltage v ₀ , the voltage v _1, and the voltage v ₂ . That is, the terminal voltage v can be expressed by the following formula (1). The calculation unit 131 estimates the SoC using the Kalman filter and using the fact that the OCV changes according to the SoC.

  FIG. 3 is a graph showing an example of the OCV characteristic curve. The OCV characteristic curve shown in FIG. 3 is stored in the condition storage unit 121. The calculation unit 131 refers to the OCV characteristic curve stored in the condition storage unit 121 when performing SoC estimation using a Kalman filter.

The calculation unit 131 uses the equivalent electric circuit model to calculate the current v ₁ , v ₂ , and OCV based on v ₁ , v ₂ , and OCV one step before and the input measurement current i, Predict the predicted terminal voltage. In addition, the calculation unit 131 calculates SoC from the OCV using the OCV characteristic curve. Here, the corresponding equations are shown in the following equations (2) to (4). Here, Formula (2) shows the current state estimated value. Formula (3) shows the terminal voltage based on the OCV characteristic. Formula (4) shows the state estimated value one step before.

Next, the calculation unit 131 calculates the difference between the actually measured measurement terminal voltage and the predicted terminal voltage. The calculation unit 131 considers noise assumed for v ₁ , v ₂ , and SoC and the terminal voltage, and causes v ₁ , v ₂ , and SoC that cause an error between the measurement terminal voltage and the predicted terminal voltage. To estimate the error. The calculating unit 131 corrects the estimated v ₁ , v ₂ , and SoC errors in addition to the predicted v ₁ , v ₂ , and SoC, and thereby outputs the correct estimated SoC value as the charging rate information. Output to.

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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正規化した差分を示し、以下、差分の正規化値という。
［文字５］

は、ステップｋのカルマンフィルタの状態推定値の逆行列を示し、以下、状態推定値の逆行列という。
［文字６］

は、ステップｋのカルマンフィルタの状態推定値の修正値を示し、以下、修正値という。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の誤差の共分散行列、つまり推定値の精度を示す。Σｖは、予測ノイズを示す。Σｗは、測定ノイズを示す。 Next, details of the SoC estimation process using the Kalman filter will be described with reference to FIG. FIG. 4 is an explanatory diagram illustrating an example of the SoC estimation process using the Kalman filter. First, characters used in the description of FIG. 4 will be described. k indicates the number of steps of the Kalman filter.
[Character 1]

Indicates a 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 estimated state value of the Kalman filter in step k, and is hereinafter referred to as the estimated state value.
[Character 3]

Indicates the measurement terminal voltage of step k, and hereinafter referred to as the measurement terminal voltage.
[Character 4]

Indicates a difference obtained by normalizing the difference between the measured terminal voltage and the predicted terminal voltage in step k, and is hereinafter referred to as a normalized value of the difference.
[Character 5]

Indicates an inverse matrix of the state estimation value of the Kalman filter in step k, and is hereinafter referred to as an inverse matrix of the state estimation value.
[Character 6]

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 step k, that is, the accuracy of the estimated value. Σv indicates prediction noise. Σw represents measurement noise.

  Since the Kalman filter is a feedback system process, in the description of the SoC estimation process in FIG. 4, the process will be described from “S” for convenience. In the present embodiment, the calculation unit 131 executes processes corresponding to steps S11 to S16 of the following Kalman filter, and the correction unit 132 executes processes corresponding to steps S17 to S19. Therefore, in the following description, it is assumed that the control unit 130 executes a Kalman filter process.

  When the measurement current i (k) is input, the control unit 130 uses the following equation (5) to calculate the state estimation value based on the state estimation value one step before and the measurement current i (k). An inverse matrix, that is, a predicted value is calculated (step S11). Here, the measurement current i (k) indicates a case where the measurement current i (k) is input every second, and has the relationship of the following formula (6).

When the measurement terminal voltage is input, the control unit 130 uses the following equation (7) to measure the measurement terminal voltage based on the measurement terminal voltage, the inverse matrix of the state estimation value, and the measurement current i (k). And the difference y ^* (k) between the predicted terminal voltage y (k) and the predicted terminal voltage y (k) (step S12). Moreover, Formula (7) can also be expressed as the following Formula (8) using the predicted terminal voltage y (k). Here, the measurement terminal voltage has the relationship of the following formula (9).

  Next, the control unit 130 calculates Jacobian A using the following equation (10) based on the state estimated value one step before (Step S13).

The control unit 130 uses the following equation (11) based on the Jacobian A, the covariance matrix P (k−1) one step before, and the prediction noise Σv, and the inverse matrix P ⁻ ( k) is calculated (step S14).

The control unit 130 calculates the Kalman gain G (k) using the following equation (12) based on the inverse matrix P ⁻ (k) of the covariance matrix and the measurement noise Σw (step S15).

The control unit 130 calculates the covariance matrix P (k) using the following equation (13) based on the Kalman gain G (k) and the inverse matrix P ⁻ (k) of the covariance matrix (step) S16). The control unit 130 repeats steps S14 to S16 for each step.

Next, the control unit 130 uses the following equation (14) based on the difference y ^* (k) calculated in step S12 and the Kalman gain G (k) calculated in step S15 to A correction value for correcting the estimated value is calculated (step S17).

  Based on the inverse matrix of the state estimated value calculated in step S11 and the corrected value calculated in step S17, the control unit 130 calculates the state estimated value using the following equation (15) (step S18). ). Here, the state estimated value can also be expressed by the following equation (16).

  Based on the estimated state value, control unit 130 calculates SoC using the following equation (17) (step S19).

  Thus, the control part 130 can calculate SoC every second by repeating the process of step S11-S19 for every step as SoC estimation processing, for example.

Returning to FIG. 1, in the correction unit 132, first, as an initial setting, the control unit 130 refers to the condition storage unit 121 and sets a threshold value r and a threshold value a. The correction unit 132 receives the measurement current from the measurement unit 101, that is, the measurement current i (k) of the step. Further, the correction unit 132 receives the inverse matrix of the state estimation value, the difference y ^* (k), and the Kalman gain G (k) from the calculation unit 131 for each step.

The correcting unit 132 normalizes the difference y ^* (k) using the measured current i (k). First, the correction unit 132 determines whether or not the measured current i (k) is larger than the threshold value r. When the measurement current i (k) is larger than the threshold value r, the correction unit 132 divides the difference y ^* (k) by the absolute value of the measurement current i (k) as shown in the following equation (18). The normalized value of the difference is calculated by multiplying the value by the threshold value r. When the measured current i (k) is equal to or smaller than the threshold value r, the correction unit 132 sets the difference y ^* (k) as the normalized value of the difference as shown in the following equation (19).

  Further, the correction unit 132 receives mode information from the determination unit 133. When the mode information is CC (Constant Current) mode, that is, constant current mode, the correction unit 132, as shown in the following equation (20), the Kalman gain G (k), the normalized value of the difference, It is determined whether or not the matrix component corresponding to the SoC value based on is greater than the threshold value a. This corresponds to a case where the error of the predicted terminal voltage suddenly becomes extremely large. For example, there are a case where a model with a simplified Kalman filter is used for the estimation of SoC, and a case where the accuracy of the measuring instrument in the battery pack is poor.

  When the component of the matrix corresponding to the SoC of the value based on the Kalman gain G (k) and the normalized value of the difference is equal to or less than the threshold value a, the correcting unit 132 calculates the difference between the Kalman gain G (k) and the difference. A value obtained by multiplying the normalized value is calculated as a normalized correction value Gy (k) of the state estimated value. That is, the normalization correction value Gy (k) of the state estimated value can be calculated by the following equation (21).

  When the measurement current i (k) is larger than the threshold value r, that is, when the calculation of Expression (18) is performed, the correction unit 132 returns the current dependency for parameters other than the SoC, so ) To calculate a correction correction value Gyr (k) of the state estimation value.

  Here, the expression (22) can be expressed in more detail as, for example, the following expression (23) and expression (24).

  The correction unit 132 adds the correction correction value Gyr (k) to the inverse matrix of the state estimated value to obtain the state estimated value. The estimated state value in this case can be calculated by the following equation (25).

  When the component of the matrix corresponding to the SoC of the value based on the Kalman gain G (k) and the normalized value of the difference is larger than the threshold value a, the correction unit 132 is represented by the following equation (26). In addition, an inverse matrix of the state estimation value is used as the state estimation value.

  The correction unit 132 outputs the state estimated value calculated by the equation (25) or the equation (26) to the calculation unit 131 for each step. Moreover, the correction | amendment part 132 calculates SoC by Formula (17) from the calculated state estimated value. The correction unit 132 outputs the calculated SoC to the output unit 102.

When the mode information is CV (Constant Voltage) mode, that is, constant voltage mode, correction unit 132 estimates SoC using a coulomb counter. The correction unit 132 calculates a state estimation value based on the estimated SoC and v ₁ and v ₂ . The correction unit 132 outputs the calculated state estimation value to the calculation unit 131 for each step. The correction unit 132 outputs the estimated SoC to the output unit 102.

  When the operation mode is input from the output unit 102, the determination unit 133 determines whether the operation mode is the CV mode (constant voltage mode). When the operation mode is the CV mode, the determination unit 133 outputs the mode information to the correction unit 132 as the CV mode. When the operation mode is not the CV mode, the determination unit 133 outputs the mode information to the correction unit 132 as the CC mode.

  Next, operation | movement of the estimation apparatus 100 of Example 1 is demonstrated.

  FIG. 5 is a flowchart illustrating an example of processing performed by the estimation apparatus according to the first embodiment. The control unit 130 of the estimation apparatus 100 reads various parameters from the condition storage unit 121 and initializes them in the calculation unit 131, the correction unit 132, and the determination unit 133. For example, the control unit 130 refers to the condition storage unit 121 and sets the threshold value r and the threshold value a in the correction unit 132 (step S101).

  The estimation apparatus 100 starts estimating the SoC of the connected lithium ion secondary battery. The measurement unit 101 outputs the measured current and terminal voltage to the control unit 130 as a measurement current and a measurement terminal voltage, respectively. In addition, the measurement unit 101 acquires the operation mode of the lithium ion secondary battery and outputs it to the control unit 130.

  When the measurement current and the measurement terminal voltage are input from the measurement unit 101, the calculation unit 131 uses the Kalman filter to predict the SoC based on the state estimated value one step before and the measurement current i (k). A terminal voltage y (k), an inverse matrix of state estimation values, and a Kalman gain G (k) are calculated (step S102).

The calculation unit 131 calculates a difference y ^* (k) between the measurement terminal voltage and the predicted terminal voltage y (k) (step S103).

The correction unit 132 receives the measurement current from the measurement unit 101, that is, the measurement current i (k) of the step. Further, the correction unit 132 receives the inverse matrix of the state estimation value, the difference y ^* (k), and the Kalman gain G (k) from the calculation unit 131 for each step. The correcting unit 132 determines whether or not the measured current i (k) is larger than the threshold value r (step S104). When the measured current i (k) is larger than the threshold value r (step S104: affirmative), the correction unit 132 calculates the difference y ^* (k) as the absolute value of the measured current i (k) as shown in Expression (18). The normalized value of the difference is calculated by multiplying the value divided by the value and the threshold value r (step S105). When the measured current i (k) is equal to or smaller than the threshold value r (No at Step S104), the correcting unit 132 uses the difference y ^* (k) as it is as the normalized value of the difference as shown in Expression (19). (Step S106).

  When the operation mode is input from the output unit 102, the determination unit 133 determines whether or not the operation mode is the CV mode (step S107). When the operation mode is the CV mode (Step S107: Yes), the determination unit 133 outputs the mode information to the correction unit 132 as the CV mode. When the operation mode is not the CV mode (No at Step S107), the determination unit 133 outputs the mode information as the CC mode to the correction unit 132.

  When the mode information indicating the CC mode is input from the determination unit 133, the correction unit 132, as shown in Expression (20), has a SoC value based on the Kalman gain G (k) and the normalized value of the difference. It is determined whether the matrix component corresponding to is greater than the threshold value a (step S108). When the component of the matrix corresponding to the SoC of the value based on the Kalman gain G (k) and the normalized value of the difference is equal to or less than the threshold value a (step S108: negative), the correction unit 132 determines the state estimated value. Is calculated as shown in Expression (21). That is, the correction unit 132 sets a value obtained by multiplying the Kalman gain G (k) and the normalized normalized value as the normalized correction value Gy (k) of the state estimated value (step S109).

  The correcting unit 132 determines whether or not the measured current i (k) is larger than the threshold value r (step S110). When the measured current i (k) is larger than the threshold value r (step S110: affirmative), the correction unit 132 calculates the corrected correction value Gyr (k) of the state estimation value as shown in the equation (22) (step S110). S111). When the measurement current i (k) is equal to or less than the threshold value r (No at Step S110), the correction unit 132 proceeds to Step S112. That is, the correction unit 132 determines that the measured current i (k) is larger than the threshold value r in step S104, and performs the process of step S111 when the process of step S105 is performed.

  The correcting unit 132 calculates the state estimated value as shown in Expression (25). That is, the correction unit 132 adds the correction correction value Gyr (k) to the inverse matrix of the state estimation value to obtain the state estimation value (step S112).

  When the component of the matrix corresponding to the SoC of the value based on the Kalman gain G (k) and the normalized value of the difference is larger than the threshold value a (step S108: affirmative), the correction unit 132 determines that the expression (26 ), An inverse matrix of the state estimation value is set as the state estimation value (step S113).

When the mode information indicating the CV mode is input from the determination unit 133 (step S107: affirmative), the correction unit 132 estimates SoC using a coulomb counter (step S114). The correction unit 132 calculates a state estimation value based on the estimated SoC and v ₁ and v ₂ . The correcting unit 132 outputs the state estimated value calculated in any one of steps S112 to S114 to the calculating unit 131 for each step.

  The correction unit 132 calculates the SoC from the calculated state estimated value according to the equation (17) (step S115). The correcting unit 132 outputs the calculated SoC to the output unit 102 (step S116). The correction unit 132 increments k to advance the Kalman filter to the next step (step S117), and returns to the process of step S102. The estimating apparatus 100 repeats steps S102 to S117. As a result, the current dependence is restored to the transient voltage with respect to the current change of the equivalent electric model of the battery while preventing the excessive correction at the time of the large current, and the excessive correction based on the excessive error that occurs suddenly is prevented. Therefore, it is possible to prevent deterioration of the estimation accuracy of the charging rate.

  As described above, the estimation apparatus 100 calculates the charging rate and the predicted terminal voltage of the battery using the Kalman filter based on the measured current and the measured terminal voltage of the battery, and calculates the difference between the measured terminal voltage and the predicted terminal voltage. . Further, the estimating apparatus 100 calculates a correction value based on the value obtained by dividing the difference by the absolute value of the measured current and the Kalman gain of the Kalman filter. In addition, the estimating apparatus 100 corrects the charging rate by multiplying the component corresponding to the transient voltage with respect to the current change of the equivalent electric circuit model of the battery at the corrected value by the absolute value of the measured current. As a result, it is possible to prevent deterioration in the estimation accuracy of the charging rate. That is, the estimation apparatus 100 can prevent deterioration of the SoC estimation accuracy due to the sudden error due to the difference in equivalent electric circuit model error, measurement error, and operation mode. Moreover, since the estimation apparatus 100 returns the current dependence to the transient voltage with respect to the current change of the equivalent electric circuit model of the battery, the estimation accuracy of the charging rate can be further prevented from deteriorating.

  Further, when the absolute value of the measured current exceeds a predetermined value, the estimating apparatus 100 calculates a correction value based on a value obtained by dividing the difference by the absolute value of the measured current and the Kalman gain of the Kalman filter. In addition, the estimating apparatus 100 corrects the charging rate by multiplying the component corresponding to the transient voltage with respect to the current change of the equivalent electric circuit model of the battery at the corrected value by the absolute value of the measured current. In addition, the estimation device 100 corrects the charging rate based on the difference and the Kalman gain of the Kalman filter when the absolute value of the measured current is equal to or less than a predetermined value. As a result, excessive correction at a large current can be further prevented.

  In addition, when the correction value for correcting the charging rate is equal to or greater than a predetermined value, the estimating apparatus 100 corrects the charging rate with the correction value set to zero. As a result, excessive correction based on excessive errors that occur suddenly can be prevented.

  Further, the estimating apparatus 100 further determines whether or not the battery operation mode is the constant voltage mode. Further, when the battery is in the constant voltage mode, the estimating apparatus 100 corrects the charging rate with a correction value for correcting the charging rate as zero. As a result, it is possible to prevent excessive correction due to different SoC estimation models.

In the first embodiment, the charging rate is calculated by normalizing the difference y ^* (k) between the measured terminal voltage and the predicted terminal voltage according to the measured current i (k). However, the present invention is not limited to this. For example, the predicted noise Σv of the Kalman filter may be corrected according to the measured current i (k).

Therefore, an embodiment for correcting the predicted noise Σv of the Kalman filter according to the measured current i (k) will be described below as Example 2. Note that the same components as those in the estimation apparatus 100 of the first embodiment are denoted by the same reference numerals, and the description of the overlapping configuration and operation is omitted. The difference between the estimation apparatus 200 according to the second embodiment and the estimation apparatus 100 according to the first embodiment is that the difference y ^* (k) between the measurement terminal voltage and the predicted terminal voltage is not normalized, but according to the measurement current i (k). Thus, the prediction noise Σv of the Kalman filter is corrected.

  FIG. 6 is a block diagram illustrating an example of the configuration of the estimation apparatus according to the second embodiment. The estimation apparatus 200 according to the second embodiment is different from the first embodiment in that a control unit 230 is included instead of the control unit 130 according to the first embodiment. The control unit 230 is different from the control unit 130 of the first embodiment in that it includes a calculation unit 231 and a correction unit 232 instead of the calculation unit 131 and the correction unit 132. The estimation apparatus 200 according to the second embodiment is the same process as the Kalman filter process except that the Σv modified in step S14 is used as compared with the Kalman filter according to the first embodiment. Is omitted.

  The calculation unit 231 receives the predicted noise Σv or the corrected predicted noise Σvm from the correction unit 232. The calculation unit 231 uses the Kalman filter to calculate the SoC, the predicted terminal voltage y (k), and the state estimate based on the state estimated value, the measured current i (k), and the predicted noise Σv or the corrected predicted noise Σvm. An inverse matrix of values and a Kalman gain G (k) are calculated.

In the correction unit 232, first, as an initial setting, the control unit 230 refers to the condition storage unit 121, and sets a threshold value r and a threshold value a. The correction unit 232 receives the measurement current from the measurement unit 101, that is, the measurement current i (k) of the step. In addition, the correction unit 232 receives the inverse matrix of the state estimation value, the difference y ^* (k), and the Kalman gain G (k) from the calculation unit 231 for each step.

  The correcting unit 232 determines whether or not the measured current i (k) is larger than the threshold value r. When the measurement current i (k) is larger than the threshold value r, the correction unit 232 measures the threshold value r for a plurality of components that do not correspond to SoC among the predicted noise Σv as shown in the following equation (27). The corrected prediction noise Σvm is calculated by multiplying the value based on the value divided by the current i (k).

As shown in the equation (27), for example, the correction unit 232 converts the (1, 1), (1, 2), (2, 1), (2, 2) components of the prediction noise Σv to (rb / | i (k) |) ² times. In addition, the correction unit 232, for example, increases (rb / | i (k) |) times the (1, 3), (2, 3), (3, 1), (3, 2) components of the prediction noise Σv. And Note that the (3, 3) component of the prediction noise Σv corresponds to the SoC and is left as it is. B is an arbitrary setting parameter. Note that “x” in Expression (27) indicates that each component of the matrix of Σv is (rb / | i (k) |) ² times, (rb / | i (k) |) times, Or it is used to mean 1 time. That is, each component (rb / | i (k) |) 2 times, (rb / | i (k ) |) times, or when a multiplying matrices and _{Σm 1, Σvpq × Σm 1 pq} (p = 1 ~ 3, q = 1 to 3).

Here, an example of the prediction noise Σv is shown in the following equation (28). In the equation (28), l, m, and n represent parameters corresponding to v ₁ , v ₂ , and SoC, respectively.

  When the measured current i (k) is equal to or less than the threshold value r, the correction unit 232 does not correct the predicted noise Σv and keeps it as it is. The correcting unit 232 outputs the predicted noise Σv or the corrected predicted noise Σvm to the calculating unit 231.

Further, the correction unit 232 receives mode information from the determination unit 133. When the mode information is the CC mode, that is, the constant current mode, the correction unit 232 is based on the Kalman gain G (k) and the difference y ^* (k) as shown in the following equation (29). It is determined whether or not the matrix component corresponding to the value SoC is greater than the threshold value a.

When the component of the matrix corresponding to the SoC of the value based on the Kalman gain G (k) and the difference y ^* (k) is equal to or less than the threshold value a, the correction unit 232 determines the Kalman gain G (k), A value obtained by multiplying the difference y ^* (k) is calculated as a correction value as shown in Expression (14). The correction unit 232 adds the correction value to the inverse matrix of the state estimated value to obtain the state estimated value, as shown in Expression (15).

When the component of the matrix corresponding to the SoC of the value based on the Kalman gain G (k) and the difference y ^* (k) is larger than the threshold value a, the correction unit 232, as shown in Expression (26) Let the inverse matrix of the state estimate be the state estimate.

  The correcting unit 232 outputs the state estimated value calculated by the equation (15) or the equation (26) to the calculating unit 131 for each step. Moreover, the correction | amendment part 132 calculates SoC by Formula (17) from the calculated state estimated value. The correction unit 132 outputs the calculated SoC to the output unit 102.

When the mode information is the CV mode, that is, the constant voltage mode, the correction unit 232 estimates SoC using a coulomb counter. The correction unit 232 calculates a state estimation value based on the estimated SoC and v ₁ and v ₂ . The correcting unit 232 outputs the calculated state estimated value to the calculating unit 231 for each step. The correction unit 232 outputs the estimated SoC to the output unit 102.

  FIG. 7 is an explanatory diagram illustrating an example of the SoC estimation process using the Kalman filter according to the second embodiment. Here, in the second embodiment, the calculation unit 231 executes processing corresponding to steps S11 to S16 of the following Kalman filter, and the correction unit 232 executes processing corresponding to steps S10 and S17 to S19. Since the Kalman filter of the second embodiment is different from the Kalman filter of the first embodiment in steps S10 and S14, only those steps will be described. In the following description, it is assumed that the control unit 230 executes the Kalman filter process as in the first embodiment.

  When measurement current i (k) is input, control unit 230 determines whether measurement current i (k) is greater than threshold value r. When the measurement current i (k) is larger than the threshold r, the correction unit 232 sets the threshold r to the plurality of components that do not correspond to SoC in the predicted noise Σv as shown in the equation (27). A corrected prediction noise Σvm is calculated by multiplying the value based on the value divided by (k). That is, the control unit 230 corrects the predicted noise Σv to the corrected predicted noise Σvm. When the measured current i (k) is equal to or smaller than the threshold value r, the correction unit 232 does not correct the predicted noise Σv and keeps it as it is (step S10).

Based on the Jacobian A, the one-step previous covariance matrix P (k−1), and the prediction noise Σv or the corrected prediction noise Σvm, the control unit 230 uses the equation (11) to inverse the covariance matrix. P ⁻ (k) is calculated (step S14). As in the first embodiment, the control unit 230 repeats the processes of steps S10 to S19 for each step as the SoC estimation process, for example, to generate a predicted noise corresponding to the measured current i (k) every second. Can be used to calculate the SoC.

  Next, operation | movement of the estimation apparatus 200 of Example 2 is demonstrated.

  FIG. 8 is a flowchart illustrating an example of processing performed by the estimation apparatus according to the second embodiment. The control unit 230 of the estimation apparatus 200 reads various parameters from the condition storage unit 121 and initializes them in the calculation unit 231, the correction unit 232, and the determination unit 133. For example, the control unit 230 refers to the condition storage unit 121 and sets the threshold value r and the threshold value a in the correction unit 232 (step S201).

  The estimation apparatus 200 starts estimating the SoC of the connected lithium ion secondary battery. The measurement unit 101 outputs the measured current and terminal voltage to the control unit 230 as a measurement current and a measurement terminal voltage, respectively. Further, the measurement unit 101 acquires the operation mode of the lithium ion secondary battery and outputs it to the control unit 230.

  When the measurement current i (k) is input from the measurement unit 101, the correction unit 232 determines whether the measurement current i (k) is larger than the threshold value r (step S202). When the measurement current i (k) is larger than the threshold r (Step S202: Yes), the correction unit 232 sets the threshold r to the measurement current i (k) for a plurality of components that do not correspond to SoC among the predicted noise Σv. The corrected prediction noise Σvm is calculated by multiplying the value based on the value divided by. The correction unit 232 corrects the predicted noise Σv to the calculated corrected predicted noise Σvm (step S203). The correction unit 232 does not correct the predicted noise Σv when the measured current i (k) is equal to or less than the threshold value r (No at Step S202). The correcting unit 232 outputs the predicted noise Σv or the corrected predicted noise Σvm to the calculating unit 231.

  The calculation unit 231 receives the predicted noise Σv or the corrected predicted noise Σvm from the correction unit 232. The calculation unit 231 uses the Kalman filter to calculate the SoC, the predicted terminal voltage y (k), the state based on the state estimated value one step before, the measured current i (k), and the predicted noise Σv or Σvm. An inverse matrix of estimated values and a Kalman gain G (k) are calculated (step S204).

The calculation unit 231 calculates a difference y ^* (k) between the measurement terminal voltage and the predicted terminal voltage y (k) (step S205).

  When the operation mode is input from the output unit 102, the determination unit 133 determines whether the operation mode is the CV mode (step S206). When the operation mode is the CV mode (Step S206: Yes), the determination unit 133 outputs the mode information to the correction unit 232 as the CV mode. If the operation mode is not the CV mode (No at Step S206), the determination unit 133 outputs the mode information to the correction unit 232 as the CC mode.

When the mode information that is the CC mode is input from the determination unit 133, the correction unit 232 has a value based on the Kalman gain G (k) and the difference y ^* (k), as shown in Expression (29). It is determined whether or not the matrix component corresponding to SoC is greater than the threshold value a (step S207). When the component of the matrix corresponding to the SoC of the value based on the Kalman gain G (k) and the difference y ^* (k) is equal to or less than the threshold a (No at Step S207), the correction unit 232 estimates the state. Calculate the corrected value. The correction value is calculated as a value obtained by multiplying the Kalman gain G (k) and the difference y ^* (k) as shown in the equation (14) (step S208). The correction unit 232 adds the correction value to the inverse matrix of the state estimated value as shown in the equation (15) to obtain the state estimated value (step S209).

When the component of the matrix corresponding to the SoC of the value based on the Kalman gain G (k) and the difference y ^* (k) is larger than the threshold value a, the correction unit 232, as shown in Expression (26) Then, an inverse matrix of the state estimation value is set as the state estimation value (step S210).

When the mode information that is the CV mode is input from the determination unit 133 (step S206: Yes), the correction unit 232 estimates the SoC using a coulomb counter (step S211). The correction unit 232 calculates a state estimation value based on the estimated SoC and v ₁ and v ₂ . The correction unit 232 outputs the estimated state value calculated in any one of steps S209 to S211 to the calculation unit 231 for each step.

  The correction unit 232 calculates the SoC from the calculated state estimated value according to the equation (17) (step S212). The correction unit 232 outputs the calculated SoC to the output unit 102 (step S213). The correction unit 232 increments k to advance the Kalman filter to the next step (step S214), and returns to the process of step S202. The estimating apparatus 200 repeats steps S202 to S214. This prevents excessive correction at high currents, corrects the current dependence of the transient voltage on the current change in the equivalent electrical model of the battery, and corrects excessive correction based on excessive errors that occur suddenly. Therefore, it is possible to prevent deterioration of the estimation accuracy of the charging rate.

  As described above, the estimation apparatus 200 calculates the charging rate of the battery using the Kalman filter based on the measured current and the measured terminal voltage of the battery. In addition, when the absolute value of the measured current exceeds a predetermined value, the estimating apparatus 200 converts the predetermined value into a value obtained by dividing the predetermined value by the measured current into a plurality of components that do not correspond to the charging rate in the matrix representing the predicted noise of the Kalman filter. The charge rate is corrected by multiplying the value based on it. As a result, it is possible to prevent deterioration in the estimation accuracy of the charging rate. That is, the estimation apparatus 200 can prevent deterioration of the SoC estimation accuracy due to sudden errors due to differences in equivalent electric circuit model errors, measurement errors, and operation modes. Moreover, since the estimation apparatus 200 corrects the current dependency of the transient voltage with respect to the current change of the equivalent electric circuit model of the battery, it is possible to further prevent deterioration in the estimation accuracy of the charging rate.

  In the second embodiment, a plurality of components not corresponding to the SoC of the predicted noise Σv of the Kalman filter are corrected. However, the present invention is not limited to this. For example, the component corresponding to the SoC of the predicted noise Σv of the Kalman filter may be corrected.

  Therefore, an embodiment for correcting the component corresponding to the SoC of the predicted noise Σv of the Kalman filter will be described below as Example 3. Note that the same components as those of the estimation apparatus 200 of the second embodiment are denoted by the same reference numerals, and the description of the overlapping configuration and operation is omitted. The difference between the estimation apparatus of the third embodiment and the estimation apparatus 300 of the second embodiment is that the prediction noise Σv is corrected as shown in the following expression (30) instead of the expression (27).

  As shown in Expression (30), the estimation apparatus according to the third embodiment calculates a value based on a value obtained by dividing the measurement current i (k) by the threshold value r in the component including the component corresponding to SoC in the prediction noise Σv. Multiplication is performed to calculate a corrected prediction noise Σvm.

The estimation apparatus according to the third embodiment, for example, sets the (3, 3) component of the prediction noise Σv to (| i (k) | · c / r) ² times as shown in Expression (30). In addition, the estimation device, for example, uses (1, i), (2, 3), (3, 1), (3, 2) components of the prediction noise Σv as (| i (k) | · c / r). Double. The remaining components of the prediction noise Σv are left as they are. C is an arbitrary setting parameter. Incidentally, "×" of formula (30), for each component of the matrix of [sigma] v, the components (| i (k) | · c / r) 2 times, (| i (k) | · c / r) Used to mean double or 1 times. That is, each component (| i (k) | · c / r) 2 times, (| i (k) | · c / r) times, or when a multiplying matrices and Σm _2, Σvpq × Σm ₂ pq (P = 1-3, q = 1-3).

  As described above, the estimation apparatus according to the third embodiment calculates the charging rate of the battery using the Kalman filter based on the measured current and the measured terminal voltage of the battery. In addition, when the absolute value of the measured current exceeds a predetermined value, the estimation device sets the value obtained by dividing the measured current by the predetermined value to the component including the component corresponding to the charging rate in the matrix representing the predicted noise of the Kalman filter. The charge rate is corrected by multiplying the value based on it. As a result, it is possible to prevent deterioration in the estimation accuracy of the charging rate. That is, the estimation apparatus according to the third embodiment can prevent deterioration in the SoC estimation accuracy due to the sudden error due to the difference in equivalent electric circuit model error, measurement error, and operation mode. In addition, since the estimation apparatus according to the third embodiment corrects the current dependency of the transient voltage with respect to the current change of the equivalent electric circuit model of the battery, it is possible to further prevent deterioration in the estimation accuracy of the charging rate.

  In each of the above embodiments, a lithium ion secondary battery is used as an example of a secondary battery, but the present invention is not limited to this. The estimation apparatus 100 or 200 can estimate the SoC for other types of batteries as long as the OCV characteristic curve of the target battery and the equivalent electric circuit model are obtained. Other batteries can be applied to, for example, lithium ion polymer secondary batteries, calcium ion secondary batteries, nanowire batteries, nickel metal hydride secondary batteries, lead storage batteries, and the like. Moreover, as a lithium ion secondary battery, it can apply to a lithium cobalt oxide battery, a lithium iron phosphate battery, etc. Thereby, SoC of various batteries can be estimated.

In each of the above-described embodiments, as an equivalent electric circuit model, _two RC circuits of R ₁ C ₁ and R ₂ C ₂ are used as RC circuits that represent a transient voltage change with respect to a current change. Although used, it is not limited to this. The estimation apparatus 100 or 200 may use, for example, three or more equivalent electric circuit models as the equivalent electric circuit model. As a result, the accuracy of the equivalent electric circuit model is improved, so that the SoC estimation accuracy by the Kalman filter is improved.

  In addition, each component of each part illustrated does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each part is not limited to the one shown in the figure, and all or a part thereof may be functionally or physically distributed / integrated in arbitrary units according to various loads and usage conditions. Can be configured. For example, the correction unit 132 and the determination unit 133 may be integrated so that the determination based on the battery operation mode is performed simultaneously with the correction.

  Further, various processing functions performed in each unit may be executed entirely or arbitrarily on a CPU (or a microcomputer such as an MPU or MCU (Micro Controller Unit)). The various processing functions may be executed entirely or arbitrarily on a program that is analyzed and executed by a CPU (or a microcomputer such as an MPU or MCU) or hardware based on wired logic. It goes without saying that it is good.

  By the way, the various processes described in the above embodiments can be realized by executing a prepared program on a computer. Therefore, in the following, an example of a computer that executes a program having the same function as each of the above embodiments will be described. FIG. 9 is an explanatory diagram illustrating an example of a computer that executes an estimation program.

  As shown in FIG. 9, the computer 300 includes a CPU 301 that executes various arithmetic processes, a battery information interface 302 that receives information from the battery, and a current measurement interface 303 that receives information on measured current and voltage. In addition, the computer 300 includes an external interface 304 that is used to exchange various information by connecting to other devices, and to operate the computer 300 by providing a UI (User Interface) to the other devices. The external interface 304 is a general-purpose interface that can also be used for writing a program or the like from another device to a flash memory 306 described later. The computer 300 also includes a RAM 305 that temporarily stores various types of information and a flash memory 306. Each component 301 to 306 is connected to a bus 307. The computer 300 is, for example, a control device for an electric vehicle or an electric motorcycle, a smartphone, a notebook personal computer, or the like.

  In the flash memory 306, an estimation program having functions similar to those of the calculation unit 131, the correction unit 132, and the determination unit 133 illustrated in FIG. 1 or the processing units of the calculation unit 231 and the correction unit 232 illustrated in FIG. Remembered. The flash memory 306 stores a condition storage unit 121. The flash memory 306 stores various data for realizing the estimation program. The battery information interface 302 has the same function as the function of acquiring the operation mode of the lithium ion secondary battery of the measurement unit 101 shown in FIG. The current measurement interface 303 has the same functions as the ammeter and voltmeter of the measurement unit 101 shown in FIG. The external interface 304 has the same function as the output unit 102 shown in FIG. In addition, the external interface 304 may be connected to, for example, a data logger so that the charge / discharge state of the lithium ion secondary battery can be recorded.

  The CPU 301 performs various processes by reading each program stored in the flash memory 306, developing the program in the RAM 305, and executing the program. Further, these programs allow the computer 300 to function as the calculation unit 131, the correction unit 132, and the determination unit 133 illustrated in FIG. 1, or the calculation unit 231, the correction unit 232, and the determination unit 133 illustrated in FIG. it can.

  Note that the above estimation program is not necessarily stored in the flash memory 306. For example, the computer 300 may read and execute a program stored in a storage medium readable by the computer 300. The storage medium readable by the computer 300 corresponds to, for example, a portable recording medium such as a CD-ROM, a DVD disk, a USB (Universal Serial Bus) memory, a semiconductor memory such as a flash memory, a hard disk drive, and the like. Alternatively, the estimation program may be stored in a device connected to a public line, the Internet, a LAN (Local Area Network), etc., and the computer 300 may read and execute the estimation program. The computer 300 may be, for example, a module incorporated in a lithium ion secondary battery, a so-called embedded microcomputer using an MPU, or the like.

100, 200 Estimating device 101 Measuring unit 102 Output unit 120 Storage unit 121 Condition storage unit 130, 230 Control unit 131, 231 Calculation unit 132, 232 Correction unit 133 Determination unit

Claims (12)

On the computer,
Based on the measured current and measured terminal voltage of the battery, the charging rate and predicted terminal voltage of the battery are calculated using a Kalman filter, and the difference between the measured terminal voltage and the predicted terminal voltage is calculated,
A correction value is calculated based on a value obtained by dividing the difference by the absolute value of the measured current and a Kalman gain of the Kalman filter, and a transient voltage with respect to a current change of the equivalent electric circuit model of the battery at the correction value is calculated. An estimation program characterized by causing a corresponding component to be multiplied by an absolute value of the measured current to execute a process of correcting the charging rate.   The correction process calculates a correction value based on a value obtained by dividing the difference by the absolute value of the measurement current when the absolute value of the measurement current exceeds a predetermined value, and a Kalman gain of the Kalman filter, A component corresponding to a transient voltage with respect to a current change of the equivalent electric circuit model of the battery at the corrected value is multiplied by the absolute value of the measured current to correct the charging rate, and the absolute value of the measured current is predetermined. The estimation program according to claim 1, wherein the charging rate is corrected based on the difference and a Kalman gain of the Kalman filter when the value is equal to or less than a value.   The estimation program according to claim 1, wherein the correction process corrects the charging rate with the correction value set to zero when a correction value for correcting the charging rate is equal to or greater than a predetermined value. In the computer,
Further, a process for determining whether or not the battery operation mode is a constant voltage mode is executed,
4. The correction process according to claim 1, wherein when the battery is in the constant voltage mode, the charging rate is corrected by setting a correction value for correcting the charging rate to zero. 5. The estimation program described in. On the computer,
Based on the measured current and measured terminal voltage of the battery, the charging rate of the battery is calculated using a Kalman filter,
When the absolute value of the measured current exceeds a predetermined value, a component that does not correspond to the charging rate in the matrix representing the predicted noise of the Kalman filter is multiplied by a value based on a value obtained by dividing the predetermined value by the measured current. And the estimation program characterized by performing the process which correct | amends the said charging rate. On the computer,
Based on the measured current and measured terminal voltage of the battery, the charging rate of the battery is calculated using a Kalman filter,
When the absolute value of the measured current exceeds a predetermined value, based on a value obtained by dividing the measured current by the predetermined value to a component including a component corresponding to the charging rate in a matrix representing the predicted noise of the Kalman filter An estimation program for executing a process of correcting the charging rate by multiplying a value. Computer
Based on the measured current and measured terminal voltage of the battery, the charging rate and predicted terminal voltage of the battery are calculated using a Kalman filter, and the difference between the measured terminal voltage and the predicted terminal voltage is calculated,
A correction value is calculated based on a value obtained by dividing the difference by the absolute value of the measured current and a Kalman gain of the Kalman filter, and a transient voltage with respect to a current change of the equivalent electric circuit model of the battery at the correction value is calculated. A process for correcting the charging rate by multiplying a corresponding component by an absolute value of the measured current, and executing an estimation method. Computer
Based on the measured current and measured terminal voltage of the battery, the charging rate of the battery is calculated using a Kalman filter,
When the absolute value of the measured current exceeds a predetermined value, a component that does not correspond to the charging rate in the matrix representing the predicted noise of the Kalman filter is multiplied by a value based on a value obtained by dividing the predetermined value by the measured current. Then, a process for correcting the charging rate is executed. Computer
Based on the measured current and measured terminal voltage of the battery, the charging rate of the battery is calculated using a Kalman filter,
When the absolute value of the measured current exceeds a predetermined value, based on a value obtained by dividing the measured current by the predetermined value to a component including a component corresponding to the charging rate in a matrix representing the predicted noise of the Kalman filter An estimation method comprising: performing a process of correcting the charging rate by multiplying a value. Based on the measured current of the battery and the measured terminal voltage, the Kalman filter is used to calculate the charging rate and the predicted terminal voltage of the battery, and a calculation unit that calculates the difference between the measured terminal voltage and the predicted terminal voltage;
A correction value is calculated based on a value obtained by dividing the difference by the absolute value of the measured current and a Kalman gain of the Kalman filter, and a transient voltage with respect to a current change of the equivalent electric circuit model of the battery at the correction value is calculated. An estimation device comprising: a correction unit that corrects the charging rate by multiplying a corresponding component by an absolute value of the measured current. Based on the measured current and measured terminal voltage of the battery, a calculation unit that calculates the charging rate of the battery using a Kalman filter;
When the absolute value of the measured current exceeds a predetermined value, a component that does not correspond to the charging rate in the matrix representing the predicted noise of the Kalman filter is multiplied by a value based on a value obtained by dividing the predetermined value by the measured current. And a correction unit that corrects the charging rate. Based on the measured current and measured terminal voltage of the battery, a calculation unit that calculates the charging rate of the battery using a Kalman filter;
When the absolute value of the measured current exceeds a predetermined value, based on a value obtained by dividing the measured current by the predetermined value to a component including a component corresponding to the charging rate in a matrix representing the predicted noise of the Kalman filter An estimation device comprising: a correction unit that multiplies the values to correct the charging rate.

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