JP6330605B2 - Estimation program, estimation method, and estimation apparatus - Google Patents

Description

  Embodiments described herein relate generally 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 replacing 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 2008-010420 A JP 2012-149947 A

  However, in the estimation of the SoC by the Kalman filter, since the SoC is estimated while balancing the coulomb counter and the estimation of the SoC by back calculation using the equivalent electric circuit model, the convergence of the subsequent estimated value when the estimated value deviates. There is a problem that the estimation accuracy is deteriorated.

  For example, in the estimation of the SoC using the Kalman filter, since the error of the equivalent electric circuit model is large, the accuracy is improved by setting the contribution degree of the equivalent electric circuit model to a small value and making the balance of the coulomb counter important. For this reason, when the estimated value is shifted due to a deviation at the start of estimation or abnormal data, it takes time until the SoC converges thereafter. Although it may be possible to increase the contribution degree of the equivalent electric circuit model in advance, in this case, the convergence is improved, but the estimation accuracy of the SoC is deteriorated due to the influence of the error of the equivalent electric circuit model.

  An object of one aspect is to provide an estimation program, an estimation method, and an estimation apparatus that can improve convergence when estimating a charging rate.

  In the first plan, the estimation program calculates the charging rate and the predicted terminal voltage of the battery using a Kalman filter based on the measured current and measured terminal voltage of the battery, and calculates the measured terminal voltage and the predicted terminal voltage. A process for calculating the difference is executed. Further, the estimation program causes the computer to execute a process of increasing the contribution degree of the predicted noise of the Kalman filter to a predetermined value when the calculation of the charging rate using the Kalman filter is started or when the abnormality of the difference ends. Further, the estimation program causes the computer to execute a process of controlling the contribution degree of the predicted noise of the Kalman filter to be a predetermined value when a predetermined time has elapsed since the contribution degree is increased.

  According to one embodiment of the present invention, the convergence when estimating the charging rate can be improved.

FIG. 1 is a block diagram illustrating a configuration example 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 the Σv setting process. FIG. 6 is an explanatory diagram illustrating an example of the transition of current and charging rate. FIG. 7 is an explanatory diagram illustrating an example of a computer that executes an estimation program.

  Hereinafter, an estimation program, an estimation method, and an estimation apparatus according to embodiments will be described with reference to the drawings. In the embodiment, configurations having the same functions are denoted by the same reference numerals, and redundant description is omitted. Note that the estimation program, the estimation method, and the estimation device described in the following embodiments are merely examples, and do not limit the embodiments. In addition, the following embodiments may be appropriately combined within a consistent range.

  FIG. 1 is a block diagram illustrating a configuration example of an estimation apparatus 100 according to the embodiment. As illustrated in FIG. 1, the estimation apparatus 100 includes a measurement unit 101, an output unit 102, an operation unit 103, 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 operation unit 103 receives an operation input from the user through an operation of inputting an operation key. The operation unit 103 outputs the received operation input to the control unit 130 as input information. The input information may be, for example, various parameters of the Kalman filter, each parameter of the equivalent electric circuit model, a threshold value, a counter value or a coefficient at the time of setting processing in the setting unit 132, and the like. The control unit 130 updates parameters and numerical values stored in the condition storage unit 121 based on the input information from the operation unit 103.

  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, 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 various parameters of the Kalman filter include Σv indicating prediction noise, counter value / coefficient at the time of setting processing of Σv, 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 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 and a setting unit 132, and realizes or executes information processing functions and operations 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. Furthermore, the calculation unit 131 calculates a correction value (details will be described later) for correcting the state estimated value calculated based on the calculated difference y ^* (k) and the Kalman gain G (k) for each step. To the setting unit 132. In addition, the calculation unit 131 receives and sets the predicted noise Σv, which has been processed by the setting unit 132, from the setting 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.

上記の［文字１］は、ステップｋ−１のカルマンフィルタの状態推定値を示し、以下、１ステップ前の状態推定値という。
［文字２］

上記の［文字２］は、ステップｋのカルマンフィルタの状態推定値を示し、以下、状態推定値という。
［文字３］

上記の［文字３］は、ステップｋの測定端子電圧を示し、以下、測定端子電圧という。
［文字４］

上記の［文字４］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５］

上記の［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]

[Character 1] indicates the 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]

[Character 2] indicates the state estimated value of the Kalman filter in step k, and is hereinafter referred to as a state estimated value.
[Character 3]

[Character 3] indicates the measurement terminal voltage of step k, and is hereinafter referred to as the measurement terminal voltage.
[Character 4]

[Character 4] 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 5]

[Character 5] indicates a correction value of the estimated 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. Here, in the present embodiment, the calculation unit 131 executes processing corresponding to S11 to S19 of the following Kalman filter, and the setting unit 132 executes processing corresponding to S20. 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 (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) (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 the Jacobian A using the following equation (10) based on the state estimated value one step before (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 (S14). Here, when executing S14, the control unit 130 calculates the inverse matrix P ⁻ (k) of the covariance matrix using the prediction noise Σv based on the result set in S20 described later.

Based on the inverse matrix P ⁻ (k) of the covariance matrix and the measurement noise Σw, the control unit 130 calculates the Kalman gain G (k) using the following equation (12) (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 (S16). ). The control unit 130 repeats S14 to S16 for each step.

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

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

  Based on the state estimated value, the control unit 130 calculates SoC using the following equation (17) (S19). Further, the control unit 130 refers to the correction value calculated in S17 and performs a setting process for setting the predicted noise Σv in S14 (S20). Details of this setting process will be described later.

  As described above, the control unit 130 can calculate the SoC every second, for example, by repeating the processes of S11 to S19 for each step as the SoC estimation process.

  Returning to FIG. 1, the setting unit 132 performs a setting process for setting the predicted noise Σv in S <b> 14. Specifically, the setting unit 132 sets the contribution of the predicted noise Σv of the Kalman filter to a predetermined value at the start of SoC calculation using the Kalman filter or at the abnormal end when the difference between the measurement terminal voltage and the predicted terminal voltage ends. Set larger. Then, the setting unit 132 sets the contribution degree of the predicted noise Σv to be greater than a predetermined value and then returns the contribution degree to the predetermined value when a predetermined time has elapsed.

Here, an example of the prediction noise Σv is shown in the following formula (18). In the equation (18), l, m, and n represent parameters corresponding to v ₁ , v ₂ , and SoC, respectively. The (3, 3) component, which is a parameter corresponding to SoC, shown in Expression (18) is a value corresponding to an equivalent electric circuit model in the Kalman filter. Therefore, by multiplying the (3, 3) component in the predicted noise Σv by a predetermined coefficient (1 or more), the contribution degree of the equivalent electric circuit model can be made larger than the initial setting.

  In the example of Expression (18), the setting unit 132 sets the contribution of the predicted noise Σv by multiplying the (3, 3) component, which is a component corresponding to SoC, by the coefficient c. For example, the setting unit 132 sets a value of 1 or more to the coefficient c when the contribution is larger than the predicted noise Σv stored in the condition storage unit 121. The setting unit 132 sets the coefficient c = 1 when using the value of the predicted noise Σv stored in the condition storage unit 121 as it is.

  Here, details of the setting process of the prediction noise Σv performed by the setting unit 132 will be described. FIG. 5 is a flowchart illustrating an example of the Σv setting process.

  As illustrated in FIG. 5, when the processing is started, the setting unit 132 refers to the condition storage unit 121 and performs setting processing such as a setting value necessary for calculating the component of the predicted noise Σv, the threshold value, and the coefficient c. Such various parameters are acquired (S201). The threshold values in these various parameters include a threshold value TH for determining an abnormality in the difference between the measured terminal voltage and the predicted terminal voltage. In addition, the setting value necessary for calculating the coefficient c is set to a setting value a that is a value of one or more coefficients when the contribution degree of the prediction noise Σv is set to be large, and after the contribution degree of the prediction noise Σv is set to be large. There is a counter value b indicating a period until the original value is restored.

  Next, the setting unit 132 determines whether it is time to start the system, that is, whether it is time to start calculating SoC (S202). Specifically, the setting unit 132 determines whether or not it is time to start the SoC estimation process by the calculation unit 131 for the lithium ion secondary battery. When it is time to start the SoC estimation process by the calculation unit 131 (S202: YES), the setting unit 132 advances the process to S203. If it is not the time when the calculation unit 131 starts the SoC estimation process (S202: NO), the setting unit 132 advances the process to S207.

  In S203, the setting unit 132 activates a counter (t) for counting up to the counter value b (S203). Next, the setting unit 132 calculates a coefficient c related to the contribution degree of the prediction noise Σv (S204). Specifically, the setting unit 132 sets the initial value of the coefficient c immediately after starting the counter as the set value a, and the counter (t) so that the value of the coefficient c is gradually decreased from the set value a as the counter (t) advances. The value of the coefficient c corresponding to is calculated. Further, the setting unit 132 calculates the coefficient c corresponding to the counter (t) so that the value of the decreased coefficient c becomes c = 1 when the counter (t) is counted up to the counter value b.

  Specifically, the coefficient c = a when the count of the counter (t) is started (t = 0) as in the following formula (19), (20) or (21). As the counter (t) advances, the coefficient c gradually decreases from a to 1, and when the counter (t) is counted up to the counter value b, the coefficient c = 1.

  Expression (19) is an example of an expression in which the coefficient c is gradually decreased from a to 1 as the counter (t) advances. Expression (20) is an example of an expression in which the coefficient c gradually decreases from a to 1 with a square root change as the counter (t) advances. Expression (21) is an example of an expression in which the coefficient c is gradually decreased from a to 1 with a square change as the counter (t) advances. These formulas (19) to (21) are examples of gradual changes, and formulas corresponding to the characteristics of the lithium ion secondary battery are set in advance.

Next, the setting unit 132 sets the prediction noise Σv obtained by multiplying the prediction noise Σv stored in the condition storage unit 121 by the coefficient c calculated in S204 (S205). Thus, in S14, using the predicted noise Σv that is set, the inverse matrix P of a covariance matrix ^- calculating a (k).

  Next, the setting unit 132 determines whether or not the counter (t) has counted up to the counter value b (S206). When the counter value b has not been counted (S206: NO), the setting unit 132 increments the counter (t) and returns the process to S204. When the counter value b is counted (S206: YES), the setting unit 132 returns the process.

In S207, the setting unit 132 sets in advance a correction value for correcting the state estimated value calculated using Expression (14) based on the difference y ^* (k) and the Kalman gain G (k). It is determined whether the threshold value TH is exceeded (S207). When the threshold value TH is exceeded (S207: YES), this corresponds to a case where a sudden abnormality occurs in the difference y ^* (k) between the measurement terminal voltage and the predicted terminal voltage and the error of the predicted terminal voltage becomes extremely large. For example, a case where a model with a simplified Kalman filter is used for estimation of SoC, or a case where the accuracy of a measuring instrument in a battery pack is bad may be mentioned.

  When the threshold value TH is exceeded (S207: YES), the setting unit 132 counts that there is an abnormality (S208), and advances the process to S210. In S208, while the state where the threshold value TH is exceeded after the process returns (until the state shifts to a state where the threshold value TH is below (S207: NO)), an abnormal time is counted by incrementing a counter or the like.

When the value is below the threshold TH (S207: NO), the setting unit 132 determines whether or not the correction value one step before exceeds a preset threshold TH (S209). When the correction value one step before exceeds a preset threshold value TH (S209: YES), when the current step ends the abnormality that occurred in the difference y ^* (k) between the measured terminal voltage and the predicted terminal voltage That's what it means.

When the correction value one step before exceeds the preset threshold TH, and when the abnormality that occurred in the difference y ^* (k) between the measured terminal voltage and the predicted terminal voltage has ended (S209: YES), the setting unit 132 advances the process to S203. If the correction value one step before exceeds the preset threshold value TH (S209: NO), the setting unit 132 advances the process to S210.

In S203 to S206 at the abnormal end of the difference y ^* (k) between the measured terminal voltage and the predicted terminal voltage (S209: YES), the contribution of the equivalent electric circuit model is increased with the coefficient c = a as in the system startup. The predicted noise Σv to be set is set. Then, the coefficient c is decreased, and when a predetermined time has elapsed (when the counter value b is counted), the coefficient c is set so as to return to the initial predicted noise Σv stored in the condition storage unit 121.

In addition, the setting unit 132 sets the set value a and the counter value when the system is started (S202: YES) and when the difference y ^* (k) between the measurement terminal voltage and the predicted terminal voltage ends abnormally (S209: YES). The value of b may be different. For example, the set value a and the counter value b at the time of system startup (S202: YES) may be larger than the values at the time of abnormal termination (S209: YES). This is because the estimated deviation of SoC at the time of system startup is generally larger than the estimated deviation of SoC due to a sudden abnormality during system startup, and it takes time to converge. As described above, the coefficient may be calculated based on the set value a and the counter value b corresponding to the estimated SoC deviation at the time of system startup or the estimated deviation of the SoC due to a sudden abnormality during the system startup. .

In addition, when the difference y ^* (k) between the measurement terminal voltage and the predicted terminal voltage ends abnormally (S209: YES), the setting unit 132 sets the set value a based on the abnormality time measured in S208. The counter value b may be set. For example, the set value “a” and the counter value “b” may be set to be large in accordance with the length of time of abnormality that has been counted. This is because when the sudden abnormality during the system startup lasts longer, the estimated SoC deviation is larger than when it is shorter, and it takes time to converge. As described above, the coefficient may be calculated based on the set value a and the counter value b corresponding to the sudden abnormality time during system startup.

In S210, the setting unit 132 sets the component of the prediction noise Σv stored in the condition storage unit 121 as it is (coefficient c = 1). Accordingly, in S14, the inverse matrix P ⁻ (k) of the covariance matrix is calculated using the prediction noise Σv stored in the condition storage unit 121 as it is.

With the above setting process, when the calculation of SoC starts or when the difference y ^* (k) ends abnormally, the prediction noise Σv that increases the contribution of the equivalent electric circuit model is set as the coefficient c = a, and the inverse of the covariance matrix is set. A matrix P ⁻ (k) is calculated. Therefore, when the calculation of SoC is started or when the difference y ^* (k) ends abnormally, the contribution degree of the equivalent electric circuit model is set to be larger than a predetermined value, so that the convergence of the SoC deviation can be improved. . Next, when the predetermined time has elapsed (when the counter value b is counted), the initial predicted noise Σv stored in the condition storage unit 121 is restored, so that after the SoC deviation converges, the equivalent electric circuit model It can be prevented from being affected by errors. For this reason, in the estimation apparatus 100, deterioration of the estimation accuracy of SoC can be prevented.

  FIG. 6 is an explanatory diagram illustrating an example of the transition of current (Current) and charging rate (SoC). Specifically, FIG. 6 illustrates charge / discharge of a rechargeable battery in an electric vehicle. For example, in a place where the change in current is large, it indicates that the motor is driven or regenerated in the electric vehicle. In addition, the main charging of the rechargeable battery is performed by connecting to a commercial power source or the like when the electric vehicle is stopped. In addition, “Real” in the SoC graph indicates an actual SoC transition. In addition, “off” in the SoC graph indicates the transition of the SoC when the function of increasing the contribution of the equivalent electric circuit model is not used. In addition, “on” in the SoC graph indicates the transition of the SoC when the function of increasing the contribution of the equivalent electric circuit model is used.

  As shown in FIG. 6, in the state C1 at the time of starting the system, by increasing the contribution of the equivalent electric circuit model, the convergence to the “Real” graph converges faster than when the contribution of the equivalent electric circuit model is not increased. ing. Similarly, in the state C2 in which a sudden abnormality occurs in the difference between the measured terminal voltage and the predicted terminal voltage and the deviation of the graph is large, the contribution of the equivalent electric circuit model is increased to increase the “Real” It converges quickly on the graph.

  In the above embodiment, 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 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 calculation unit 131 and the setting unit 132 may be integrated.

  Furthermore, 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. Needless to say, 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. In the following, an example of a computer that executes a program having the same function as that of the above embodiment will be described. FIG. 7 is an explanatory diagram illustrating an example of a computer that executes an estimation program.

  As illustrated in FIG. 7, the computer 200 includes a CPU 201 that executes various arithmetic processes, a battery information interface 202 that receives information from the battery, and a current measurement interface 203 that receives information on measured current and voltage. Further, the computer 200 has an external interface 204 used for exchange of various information connected to other devices, operation of the computer 200 performed by providing a UI (User Interface) to other devices, and the like. The external interface 204 is a general-purpose interface that can be used for writing a program or the like from another device to a flash memory 206 described later. The computer 200 also includes a RAM 205 that temporarily stores various types of information and a flash memory 206. Each component 201 to 206 is connected to the bus 207. 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 flash memory 206 stores an estimation program having the same functions as the processing units of the calculation unit 131 and the setting unit 132. The flash memory 206 stores a condition storage unit 121. The flash memory 206 stores various data for realizing the estimation program. The battery information interface 202 has a function similar to the function of acquiring the operation mode of the lithium ion secondary battery of the measurement unit 101. The current measurement interface 203 has the same functions as the ammeter and voltmeter of the measurement unit 101. The external interface 204 has the same function as the output unit 102. Further, the external interface 204 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 201 reads out each program stored in the flash memory 206, develops it in the RAM 205, and executes it to perform various processes. In addition, these programs can cause the computer 200 to function as the calculation unit 131 and the setting unit 132.

  Note that the above estimation program is not necessarily stored in the flash memory 206. 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 estimation 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 and execute the estimation program therefrom. 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 ... Measurement part 102 ... Output part 103 ... Operation part 120 ... Storage part 121 ... Condition storage part 130 ... Control part 131 ... Calculation part 132 ... Setting part 200 ... Computer C1, C2 ... State

Claims (8)

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,
When the calculation of the charging rate using the Kalman filter is started or the abnormality of the difference ends, the contribution degree of the predicted noise of the Kalman filter is made larger than a predetermined value, and a predetermined time has elapsed since the contribution degree is increased. An estimation program for executing a process of controlling so that the contribution degree of the predicted noise of the Kalman filter becomes the predetermined value. 2. The estimation program according to claim 1, wherein the control process is performed such that the degree of contribution of the predicted noise of the Kalman filter is made larger than the predetermined value and then gradually decreased to become the predetermined value. The control process differs in the degree of increasing the contribution of predicted noise of the Kalman filter from the predetermined value and the time until the contribution reaches the predetermined value at the start of calculation or the end of the abnormality. The estimation program according to claim 1, wherein the estimation program is controlled as follows. The control process includes, based on an operation input from a user by an operation unit, a degree to which the contribution degree of the predicted noise of the Kalman filter is larger than the predetermined value, and a time period until the contribution degree becomes the predetermined value. The estimation program according to any one of claims 1 to 3, wherein the estimation program is controlled. 5. The estimation program according to claim 1, wherein the control process determines that the abnormality is ended when the difference is switched from a predetermined threshold value or more to a threshold value or less. The control process is based on the time until the difference is switched from the predetermined threshold value or more to the threshold value or less, and the degree of contribution of the predicted noise of the Kalman filter to be larger than the predetermined value, The estimation program according to claim 5, wherein the time until the predetermined value is reached is controlled. 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,
When the calculation of the charging rate using the Kalman filter is started or the abnormality of the difference ends, the contribution degree of the predicted noise of the Kalman filter is made larger than a predetermined value, and a predetermined time has elapsed since the contribution degree is increased. Then, an estimation method is performed, wherein a process of controlling the Kalman filter so that the degree of contribution of the predicted noise becomes the predetermined value is executed. 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;
When the calculation of the charging rate using the Kalman filter is started or the abnormality of the difference ends, the contribution degree of the predicted noise of the Kalman filter is made larger than a predetermined value, and a predetermined time has elapsed since the contribution degree is increased. And a control unit that controls the degree of contribution of the predicted noise of the Kalman filter to be the predetermined value.

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