JP6421411B2 - Estimation program for estimating battery charging rate, estimation method for estimating battery charging rate, and estimation device for estimating battery charging rate - 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 loss of usage time due to charging time is eliminated by replacing a secondary battery with a 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, as a process of the estimation program to be executed by the computer, the computer is caused to execute a process of calculating the charging rate and the predicted terminal voltage of the battery using a Kalman filter based on the measured current and the measured terminal voltage of the battery. . Further, the computer is caused to execute a process of calculating a difference between the measurement terminal voltage and the predicted terminal voltage. Further, the computer causes the charging rate to be corrected 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.

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

FIG. 1 is a block diagram illustrating an example of the configuration of the estimation apparatus according to the 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 of the estimation apparatus according to the embodiment. FIG. 6 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 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, Then, it is determined whether or not the value based on the SoC correction value is larger 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 value based on the Kalman gain G (k), the normalized difference value, and the SoC correction value is equal to or smaller than the threshold value a, the correcting unit 132 converts the Kalman gain G to the inverse matrix of the state estimated value. A value obtained by multiplying (k) by the normalized difference value is set as a state estimated value. The estimated state value in this case can be calculated by the following equation (21).

When the value based on the Kalman gain G (k), the normalized value of the difference, and the correction value of the SoC is larger than the threshold value a, the correction unit 132, as shown in the following formula (22), The inverse matrix of the state estimate is used as the state estimate.

  The correcting unit 132 outputs the state estimated value calculated by the equation (21) or the equation (22) 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 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, the operation of the estimation apparatus 100 according to the embodiment will be described.

  FIG. 5 is a flowchart illustrating an example of processing of the estimation apparatus according to the 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 that is the CC mode is input from the determination unit 133, the correction unit 132, as shown in Expression (20), the Kalman gain G (k), the difference normalization value, the SoC correction value, It is determined whether the value based on is greater than the threshold value a (step S108). When the value based on the Kalman gain G (k), the normalized value of the difference, and the correction value of the SoC is equal to or less than the threshold value a (No at Step S108), the correction unit 132 determines the state estimated value as Calculation is performed as shown in Equation (21). That is, the correcting unit 132 sets a value obtained by adding a value obtained by multiplying the inverse matrix of the state estimated value by the Kalman gain G (k) and the normalized value of the difference as the state estimated value (step S109).

  When the value based on the Kalman gain G (k), the normalized value of the difference, and the corrected value of the SoC is larger than the threshold value a (Yes at Step S108), the correcting unit 132 sets the equation (22). As shown, an inverse matrix of state estimation values is used as a state estimation value (step S110).

When the mode information indicating the CV mode is input from the determination unit 133 (step S107: Yes), the correction unit 132 estimates SoC using a coulomb counter (step S111). 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 state estimated value calculated in step S110 or step S111 to the calculation unit 131 for each step.

  The correction unit 132 calculates the SoC from the calculated state estimated value according to the equation (17) (step S112). The correcting unit 132 outputs the calculated SoC to the output unit 102 (step S113). The correction unit 132 increments k to advance the Kalman filter to the next step (step S114), and returns to the process of step S102. The estimating apparatus 100 repeats steps S102 to S114. Thereby, excessive correction at the time of a large current is prevented, and excessive correction based on an excessive error that occurs suddenly is prevented, so that deterioration in charging rate estimation accuracy can be prevented.

  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 corrects the charging rate 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. 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.

  Further, when the absolute value of the measured current exceeds a predetermined value, the estimating apparatus 100 corrects the charging rate 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 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 prevented.

  In addition, when the value obtained by multiplying the correction value for correcting the charging rate by a predetermined correction value is equal to or greater than the predetermined value, the estimating apparatus 100 corrects the charging rate by setting the correction value 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 above embodiment, a lithium ion secondary battery is used as an example of the secondary battery, but the present invention is not limited to this. The estimation device 100 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 the above embodiment, _two RC circuits of R ₁ C ₁ and R ₂ C ₂ are used as an RC circuit representing a transient voltage change with respect to a current change as an equivalent electric circuit model. However, it is not limited to this. The estimation apparatus 100 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 program prepared in advance by a computer. Therefore, in the following, an example of a computer that executes a program having the same function as in the above embodiment will be described. FIG. 6 is an explanatory diagram illustrating an example of a computer that executes an estimation program.

  As illustrated in FIG. 6, the computer 200 includes a CPU 201 that executes various arithmetic processes, an input device 202 that receives data input from a user, and a monitor 203. The computer 200 also includes a medium reading device 204 that reads a program or the like from a storage medium, an interface device 205 for connecting to another device, and a communication device 206 for connecting to another device by wire or wirelessly. . The computer 200 also includes a RAM 207 that temporarily stores various types of information and a hard disk device 208. Each device 201 to 208 is connected to a bus 209. The computer 200 is, for example, a control device for an electric vehicle or an electric motorcycle, a smartphone, a notebook personal computer, or the like.

  The hard disk device 208 stores an information processing program having the same functions as the processing units of the calculation unit 131, the correction unit 132, and the determination unit 133 illustrated in FIG. The hard disk device 208 stores a condition storage unit 121. Also, the hard disk device 208 stores various data for realizing the information processing program. The monitor 203 has the same function as the output unit 102 shown in FIG. The interface device 205 has the same function as the measurement unit 101 shown in FIG. For example, when a printing device is connected, the interface device 205 is integrated with the printing device and has the same function as the output unit 102 shown in FIG. Further, the interface device 205 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 communication device 206 has the same functions as the input unit 101 and the output unit 102, for example, when the state of the computer 200 is grasped from another computer.

  The CPU 201 reads out each program stored in the hard disk device 208, develops it in the RAM 207, and executes it to perform various processes. In addition, these programs can cause the computer 200 to function as the calculation unit 131, the correction unit 132, and the determination unit 133 illustrated in FIG.

  Note that the above information processing program is not necessarily stored in the hard disk device 208. For example, the computer 200 may read and execute a program stored in a storage medium readable by the computer 200. The storage medium readable by the computer 200 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 information processing program may be stored in a device connected to a public line, the Internet, a LAN (Local Area Network), etc., and the computer 200 may read out and execute the information processing program therefrom. The computer 200 includes a CPU 201, a RAM 207, a nonvolatile memory (for example, a flash memory) that replaces the hard disk device 208, and an interface device 205 having the same functions as the measurement unit 101 and the output unit 102. Also good. In this configuration, the computer 200 may be, for example, a module incorporated in a lithium ion secondary battery, a so-called embedded microcomputer using an MPU, or the like.

DESCRIPTION OF SYMBOLS 100 Estimation apparatus 101 Measuring part 102 Output part 120 Storage part 121 Condition storage part 130 Control part 131 Calculation part 132 Correction part 133 Determination part

Claims (5)

On the computer,
From the parameters of the equivalent electric circuit model of the battery and the charging rate of the battery obtained from the OCV characteristic curve, the state estimated value of the Kalman filter is defined,
Define a function for calculating the predicted value of the state estimated value from the state estimated value one step before and the measured current of the battery,
By using the Kalman gain before Symbol Kalman filter, a correction value that is generated based on the difference between the measured terminal voltage and the predicted terminal voltage of the battery, it corrects the predicted value,
Extracting a component of the charging rate in the corrected predicted value and estimating the charging rate;
The correction process adopts a value obtained by dividing the difference by the absolute value of the measurement current instead of the difference when the absolute value of the measurement current exceeds a predetermined value, and the absolute value of the measurement current is When the difference is equal to or less than a predetermined value, the difference is adopted.
An estimation program for estimating a charging rate of a battery.   The correction process is performed when the value obtained by multiplying the Kalman gain, the difference or a value adopted instead of the difference, and a correction value for preventing excessive correction is larger than a predetermined value. The estimation program for estimating the charging rate of the battery according to claim 1, wherein the predicted value is corrected with zero being zero. In the computer,
Further, a process for determining whether or not the battery operation mode is a constant voltage mode is executed,
When the battery is in the constant voltage mode, the correcting process estimates the charging rate using a coulomb counter, and calculates a predicted value of the state estimated value calculated based on the estimated charging rate and the parameter. The estimation program for estimating the charging rate of the battery according to claim 1, wherein the predicted value is corrected. Computer
From the parameters of the equivalent electric circuit model of the battery and the charging rate of the battery obtained from the OCV characteristic curve, the state estimated value of the Kalman filter is defined,
Define a function for calculating the predicted value of the state estimated value from the state estimated value one step before and the measured current of the battery,
By using the Kalman gain before Symbol Kalman filter, a correction value that is generated based on the difference between the measured terminal voltage and the predicted terminal voltage of the battery, it corrects the predicted value,
Performing a process of extracting the charge rate component in the corrected predicted value and estimating the charge rate;
The correction process adopts a value obtained by dividing the difference by the absolute value of the measurement current instead of the difference when the absolute value of the measurement current exceeds a predetermined value, and the absolute value of the measurement current is When the difference is equal to or less than a predetermined value, the difference is adopted.
An estimation method for estimating a charging rate of a battery. A state estimated value of the Kalman filter is defined from the parameters of the equivalent electric circuit model of the battery and the charging rate of the battery obtained from the OCV characteristic curve, and the predicted value of the state estimated value is defined as the state estimated value of one step before A definition part for defining a function to be calculated from the measured current of the battery;
And Kalman gain before Symbol Kalman filter, and a correction unit using said correction value that is generated based on the difference between the measured terminal voltage and the predicted terminal voltage of the battery, corrects the predicted value,
An estimation unit that extracts the charge rate component in the corrected predicted value and estimates the charge rate;
When the absolute value of the measurement current exceeds a predetermined value, the correction unit adopts a value obtained by dividing the difference by the absolute value of the measurement current instead of the difference, and the absolute value of the measurement current is predetermined. When the value is less than or equal to the value, the difference is adopted.
An estimation apparatus for estimating a charging rate of a battery.

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