JP2015038444A - Secondary battery remaining amount estimation method and apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a secondary battery remaining capacity estimation method and a secondary battery remaining capacity estimation apparatus using a Kalman filter, capable of achieving high accuracy SOC estimation using a simple equivalent circuit model and improving profitability and reliability.SOLUTION: An arithmetic processing unit integrates current values for a determination period, the determination period being defined as a period of latest three minutes. If a current integrated value is not less than 2.5% in a SOC change width, the arithmetic processing unit sets a Kalman gain Gk to "0" to invalidate a Kalman filter. At this time, the arithmetic processing unit calculates a current SOC estimation value by adding a previous SOC estimation value to a SOC change width. If the current integrated value is less than 2.5% in SOC change width, the arithmetic processing unit sets the Kalman gain Gk to "1" to perform SOC estimation based on Kalman filter scheme.

Description

  Embodiments described herein relate generally to a secondary battery remaining amount estimation method and apparatus using a Kalman filter.

  In recent years, secondary batteries have been used in a variety of applications, from those mounted on mobile objects such as electric vehicles and hybrid vehicles, portable devices, etc. to those used for suppressing fluctuations in power demand and peak shifts. Yes. Further, in a power generation system using natural energy such as sunlight and wind power, a secondary battery is used for the purpose of suppressing fluctuations in the amount of power generation.

  In such a secondary battery, in order to make the best use of its capacity, it is necessary to accurately estimate the remaining amount of the secondary battery (State of Charge, hereinafter referred to as SOC) at an arbitrary time. Various methods have been proposed for the SOC estimation technique. The basic method is to obtain the absolute value of the SOC. When obtaining the absolute value of the SOC, it is common to use an open circuit voltage (hereinafter referred to as OCV) or a fully charged state or fully discharged state of a secondary battery (hereinafter referred to as full charge / complete discharge). Is.

  However, depending on the application of the secondary battery, there are cases where OCV or full charge / complete discharge cannot be used. For example, a secondary battery used for the purpose of suppressing fluctuations in a wind power generation system or the like cannot use OCV because there is no guarantee that the state of zero current will continue for a certain period (for example, 10 minutes or more). Further, in a wind power generation system or the like, it cannot be expected that the secondary battery will surely reach full charge / complete discharge.

  In such a case, a secondary battery in which charging / discharging is frequently switched in the middle region of the SOC cannot use OCV or full charge / complete discharge, and it is difficult to calculate the absolute value of the SOC. Therefore, a current integration method is known in which the SOC value in a predetermined period is grasped by integrating the current value, and the SOC of the secondary battery is estimated from the SOC change.

  Even if the secondary battery repeats charging and discharging in the middle region of the SOC, the OCV can be used before the secondary battery starts operation. Find the value. In addition, in the current integration method, the SOC value is obtained by integrating the current value per processing cycle, and the SOC is estimated by adding the SOC change amount to the SOC initial value.

  Since the above current integration method is a method of estimating the SOC using the SOC change obtained by current integration, if this method is implemented for a long period of time, the integration error increases. Therefore, in the current integration type SOC estimation method, it is important to periodically reset the SOC estimation error.

  However, when resetting the SOC estimation error, it is necessary to define the SOC absolute value at the time of reset. That is, even if the secondary battery is difficult to use OCV or full charge / complete discharge, the absolute value of the SOC must be obtained by some method. For example, the OCV availability state can be forcibly created by, for example, continuing a zero current state for a certain period of time. Therefore, the operating secondary battery must be temporarily stopped, and a reduction in operating rate becomes a problem.

  As a method for dealing with the above problems, an SOC estimation method for a secondary battery using a Kalman filter has been proposed (for example, Patent Document 1). In this SOC estimation method, a secondary battery cell behavior is simulated by a Kalman filter using an equivalent circuit model, and a predicted value of an observable parameter (such as voltage) at a certain time is obtained. Further, an actual measurement value at the time when the predicted value is predicted is measured. Then, the estimated value of the SOC is continuously updated by correcting the internal parameters (SOC and the like) on which the prediction is based so that the predicted value matches the actually measured value.

  That is, in the Kalman filter-type SOC estimation method, the “prediction” of obtaining a predicted value and the “update” of the estimated value by error correction are repeated, so that the SOC is not used without using OCV or full charge / complete discharge. The estimated value of is obtained stably. Therefore, forcible suspension of charge / discharge is unnecessary, and the operation efficiency of the secondary battery can be maintained.

Special table 2008-546989 gazette

  By the way, in the Kalman filter type SOC estimation method, the accuracy of the equivalent circuit model used in the Kalman filter itself determines the estimation accuracy. It is known that the accuracy of the equivalent circuit model depends on the characteristic parameters of the model itself. Here, the influence of the characteristic parameter on the accuracy of the model will be specifically described using a typical equivalent circuit model of the lithium ion battery cell.

  As shown in FIG. 9, the equivalent circuit model includes an OCV generation unit 11 that is a voltage source, and an Rs generation unit 12 connected in series to the OCV generation unit 11. Furthermore, the equivalent circuit model has a time constant unit 13 connected in series to the OCV generation unit 11 and the Rs generation unit 12. The time constant unit 13 includes a parallel portion of a resistor R1 and a capacitor C1 that form a first-order lag time constant.

The characteristic parameters of OCV, Rs, R1, and C1, which are components of the equivalent circuit model, vary depending on the SOC and temperature of the secondary battery cell. That is, each characteristic parameter is
Open circuit voltage OCV = OCV (SOC, Temp)
Series resistance Rs = Rs (SOC, Temp)
Time constant resistance R1 = R1 (SOC, Temp)
Time constant capacity C1 = C1 (SOC, Temp)
It can be expressed as

  Of these characteristic parameters, OCV is a static characteristic parameter, and it is possible to identify the parameter with high accuracy if a period in which the current is zero is appropriately secured. Therefore, in the state where the simulation of the behavior of the secondary battery cell by the equivalent circuit model mainly depends on the OCV, the parameter identification can be performed with high accuracy. In this case, the error between the predicted value and the measured value is small, and the equivalent circuit model can exhibit high accuracy.

  On the other hand, the other three characteristic parameters Rs, R1, and C1 correspond to transient response characteristics, and are related to dynamic characteristics. Moreover, the equivalent circuit model of the first order lag is only a simple approximation of the secondary battery cell. For this reason, compared with OCV, Rs, R1, and C1 have larger parameter identification errors.

  Therefore, in a state where the simulation of the behavior of the secondary battery cell by the equivalent circuit model mainly depends on Rs, R1, and C1, the error between the predicted value and the measured value increases, and the accuracy of the equivalent circuit model decreases. Resulting in. If the accuracy of the equivalent circuit model is low, an error in parameter identification in the Kalman filter, that is, a Kalman filter error increases, resulting in a decrease in SOC estimation accuracy.

  In order to avoid this problem, it has been conventionally required to construct a complex and advanced battery model in order to reduce the Kalman filter error. As a result, the development of the battery model not only becomes difficult, but the processing load at the time of executing the SOC estimation increases, which causes a cost increase.

  Embodiments of the present invention have been proposed in order to solve the above-described problems, and in a secondary battery remaining amount estimation method and apparatus using a Kalman filter, a simple equivalent circuit model is used. An object of the present invention is to provide a method and apparatus for estimating the remaining amount of a secondary battery that realizes accurate SOC estimation and improves economy and reliability.

In order to achieve the above object, an embodiment of the present invention provides:
(A) a current measurement step for measuring the current of the secondary battery cell;
(B) a voltage measurement step for measuring the voltage of the secondary battery cell;
(C) a temperature measurement step for measuring the temperature of the secondary battery cell;
(D) The measurement values measured in the current measurement step, the temperature measurement step, and the voltage measurement step are input, and the remaining amount of the secondary battery cell is determined by a Kalman filter using an equivalent circuit model of the secondary battery cell. A remaining capacity estimating step for estimating the remaining capacity of the secondary battery,
(E) a current value integration step of integrating current values of the secondary battery cells in a past fixed period to obtain a current integrated value;
(F) including a Kalman gain control step of changing the Kalman gain according to the magnitude of the current integrated value.

In addition, according to an embodiment of the present invention, there is provided a secondary battery remaining amount estimation method including the steps (A) to (D).
(G) a digital filter output value calculating step for obtaining a digital filter output value for the current measurement value;
(H) including a Kalman gain control step of changing the Kalman gain according to the magnitude of the output value of the digital filter.
Furthermore, the embodiment of the present invention is also an aspect of a secondary battery remaining amount estimation device.

1 is a block diagram showing a system configuration of a first embodiment of the present invention. The graph for showing the relationship between the determination period and the SOC change width for the control of the Kalman gain in the first embodiment. The graph which shows the example of control of the Kalman gain in 1st Embodiment. It is a graph which shows the log | history of the charging / discharging electric current in 1st Embodiment, (A) is when the absolute value of charging / discharging electric current is changing small, (B) is the absolute value of charging / discharging electric current changing greatly. (C) shows a case where the absolute value of the charge / discharge current is large and is biased in the charging direction. The fixed-cycle process flowchart regarding the measurement process of 1st Embodiment. The fixed-cycle process flowchart regarding the SOC estimation process of 1st Embodiment. It is a graph which shows the example of control of the Kalman gain in the embodiment of the present invention, and (A) is a case where the Kalman gain is switched to three or more stages. When the Kalman gain is continuously variable in stages, (C) shows the case where the Kalman gain is continuously variable as a whole. It is a graph which shows a plurality of examples about control of a Kalman gain in an embodiment of the present invention, and (A) is (B) when a judgment time is set to 3.6 seconds and a reference SOC change width is set to 0.05%. ) Shows a case where the determination time is 30 minutes and the reference SOC change width is set to 25%. The equivalent circuit diagram of a lithium ion battery.

(1) First Embodiment Hereinafter, the configuration of a first embodiment according to the present invention will be described with reference to FIGS. The first embodiment is an SOC estimation method for a secondary battery to which a Kalman filter is applied, and is applied to an apparatus for carrying out this method.

(Constitution)
As shown in FIG. 1, the secondary battery cell 1 includes a voltage measurement unit 2 that measures the voltage of the secondary battery cell 1, a current measurement unit 3 that measures the current of the secondary battery cell 1, and a secondary battery. A temperature measuring unit 4 that measures the temperature of the cell 1 is installed. An arithmetic processing unit 5 is connected to these measuring units 2, 3, 4.

  A storage unit 6 and a communication interface 7 are connected to the arithmetic processing unit 5. The storage unit 6 is a part that stores measurement values actually measured by the measurement units 2, 3, and 4, calculation results of the calculation processing unit 5, and the like. The storage unit 6 stores an equivalent circuit model of the secondary battery cell 1 and its parameter group. The communication interface 7 transmits battery information and the like of the secondary battery cell 1 to an application system (not shown).

  The arithmetic processing unit 5 is a main part of the present embodiment, performs measurement processing by inputting measurement values from the measurement units 2, 3, and 4, and performs SOC estimation processing by a Kalman filter using an equivalent circuit model. It is a remaining amount estimation part. The arithmetic processing unit 5 is also a current value integrating unit that calculates the current integrated value by integrating the current values of the secondary battery cells 1 in a past fixed period.

  In the arithmetic processing unit 5 for obtaining the current integrated value, it is possible to grasp the SOC change due to the current integration for a predetermined period. Therefore, the arithmetic processing unit 5 also performs SOC estimation based on the SOC change due to current integration. That is, the arithmetic processing unit 5 performs SOC estimation using the Kalman filter together with SOC estimation as a basis.

  Furthermore, the arithmetic processing unit 5 functions as a Kalman gain control unit that changes the Kalman gain in accordance with the integrated current value. In the present embodiment, a period in which current values are integrated is defined as a determination period, and an SOC change due to current integration in the determination period is defined as an SOC change width. Here, the relationship between the determination period and the SOC change width in the Kalman gain switching control will be described with reference to FIG.

  As shown in FIG. 2, the arithmetic processing unit 5 sets the determination period to the last three minutes in the past, and sets the SOC change width, which is a determination criterion for Kalman gain control, to 2.5%. Note that the constant current value at which the SOC changes by 2.5% in 3 minutes is 50% of the SOC change width per 60 minutes, which corresponds to 0.5 C in terms of the C rate.

  The arithmetic processing unit 5 obtains the SOC change width in the determination period based on the SOC change due to current integration, and the arithmetic processing unit 5 determines whether or not the obtained SOC change width is less than 2.5%. If the arithmetic processing unit 5 determines that the SOC change width in the determination period is less than 2.5%, the Kalman filter is enabled and the Kalman gain is set to “1”. That is, the range in which the SOC change width in the fixed period is less than 2.5% is the effective range of the Kalman filter. When the Kalman gain is set to “1”, the arithmetic processing unit 5 performs the SOC estimation process using a Kalman filter using an equivalent circuit model.

  If the arithmetic processing unit 5 determines that the SOC change width is 2.5% or more, the arithmetic processing unit 5 invalidates the Kalman filter and changes the Kalman gain to “0”. When the Kalman gain is set to “0”, the arithmetic processing unit 5 calculates the SOC change amount only by integrating the current of the secondary battery without using the Kalman filter, and estimates the SOC of the secondary battery based on this.

  As described above, the arithmetic processing unit 5 controls the Kalman gain to “1” or “0” based on the SOC change width of 2.5% for 3 minutes (see the graph of FIG. 3). Here, when the current integrated value for 3 minutes, which is the SOC change width, is expressed as a charge / discharge current history, the arithmetic processing unit 5 sets the following Kalman gain in the following charge / discharge current history [1] or It can be said that it is controlled as in [2].

[1] When the integrated current value is less than 2.5% in the change range of the SOC, the charge amount and the discharge amount of the secondary battery cell 1 are almost balanced (as shown in FIGS. 4A and 4B). The arithmetic processing unit 5 controls the Kalman gain to “1”. It is important whether the amount of charge and the amount of discharge are balanced, and the control does not change depending on the size of each of the amount of charge and the amount of discharge.

  That is, even when the charge amount and the discharge amount are small as shown in FIG. 4A, or when the charge amount and the discharge amount are large as shown in FIG. 4B, the charge amount and the discharge amount are almost balanced. If so, the change width of the SOC is small. When the SOC change width is small as described above, that is, when the secondary battery cell 1 is less than 0.5 C on average and charging and discharging are balanced, the arithmetic processing unit 5 applies the Kalman filter to predict the value. And error correction.

[2] When the integrated current value is 2.5% or more in terms of the change in the SOC, the charge amount and the discharge amount of the secondary battery cell 1 become a history in which the charge direction or the discharge direction is biased (see FIG. 4). (Shown in (C)), the arithmetic processing unit 5 controls the Kalman gain to “0”. Also at this time, the amount of charge or discharge does not matter, and if only one of charging or discharging continues, the SOC change width naturally becomes large. When the SOC change width is large as described above, that is, when the secondary battery cell 1 is continuously charged or discharged, the arithmetic processing unit 5 does not apply the Kalman filter, and the SOC change width by current integration is set. Based on this, SOC estimation is performed.

(Function)
As described above, the first embodiment is based on the SOC estimation based on the SOC change width by the current integration (in the case of [2] above), and the predicted value by the Kalman filter method, that is, the battery model according to the current integration value. SOC estimation (in the case of [1] above) is performed by error correction based on the difference between the measured value and the measured value. The operation of the arithmetic processing unit 5 that performs such SOC estimation will be described with reference to the flowcharts of FIGS.

  5 and 6 are flowcharts of the periodic processing, and are executed, for example, every 0.1 second. FIG. 5 shows the measurement process of the secondary battery cell 1. That is, the voltage measurement unit 2 measures the voltage value of the secondary battery cell 1 (voltage measurement step S1), the current measurement unit 3 measures the current value of the secondary battery cell 1 (current measurement step S2), and measures the temperature. The unit 4 measures the temperature of the secondary battery cell 1 (temperature measurement step S3). The arithmetic processing unit 5 reads the voltage measurement value from the voltage measurement unit 2, the current measurement value from the current measurement unit 3, and the temperature measurement value from the temperature measurement unit 4 (step S 4), and stamps these measurement values with time stamps. And stored in the storage unit 6 (step S5).

  Next, FIG. 6 shows the SOC estimation process of the secondary battery cell 1. Steps S11 to S13 in FIG. 6 are the SOC estimation part by the current integration method. In addition, when calculating | requiring the present SOC estimated value SOC (n), the arithmetic processing part 5 presupposes having the SOC estimated value SOC (n-1) calculated | required in the latest past.

  The arithmetic processing unit 5 obtains the average current value I (n) during the latest past one processing cycle based on the current measurement value taken from the current measurement unit 3 (step S11). Subsequently, by multiplying the average current value I (n) by the processing cycle Ts and dividing by the capacity C of the secondary battery cell 1, the SOC change width ΔSOC is obtained from the previous SOC estimated value SOC (n−1) ( Step S12). The current SOC estimated value SOC (n) is obtained by adding this ΔSOC to the previous SOC estimated value SOC (n−1) (step S13).

  Subsequently, the calculation processing unit 5 adds up the current values of the determination period, using the latest three minutes as the determination period (step S14). In step S15, it is determined whether or not the SOC variation by the integrated current value in the determination period, that is, the SOC change width is less than 2.5%. If it is 2.5% or more (Yes in step S15), the calculation is performed. The processing unit 5 sets the Kalman gain Gk to “0” (step S16).

  If the integrated current value is less than 2.5% in SOC change width (No in step S15), the arithmetic processing unit 5 sets the Kalman gain Gk to “1” (step S17). In the flowchart of FIG. 6, step S14 corresponds to the current value integration step, and steps S15 to S17 correspond to the Kalman gain control step.

  Steps S18 and S19 in FIG. 5 are SOC estimation portions by the Kalman filter method, and when the Kalman gain Gk is set to “1” (step S17), correction by the Kalman filter is performed. When the Kalman gain Gk is set to “0” (step S16), the correction by the Kalman filter is not substantially performed. In this case, the SOC estimation is performed by the current integration method. That is, SOC estimation by the Kalman filter method is performed only when the Kalman gain Gk is set to “1”.

  Step S18 corresponds to “prediction” in the Kalman filter. That is, in step S18, the behavior simulation of the secondary battery cell 1 is performed from the previous voltage estimated value V (n-1) and the current measured value and the measured temperature value by the Kalman filter using the equivalent circuit model. The estimated voltage value Ve (n) is predicted.

  Step S19 corresponds to “update” in the Kalman filter. That is, in step S19, the error between the current voltage estimated value Ve (n) predicted in step S18 and the actually measured voltage measured value Vm (n) is multiplied by the Kalman gain Gk, and this value is obtained as the SOC estimated value SOC. By subtracting from (n), the SOC estimated value SOC (n) is corrected.

  As described above, in the present embodiment, the Kalman gain is controlled according to the SOC change width obtained from the current integrated value, and if the SOC change width is less than 2.5%, the Kalman filter is enabled to perform the SOC estimation. When the SOC change width is less than 2.5%, the charge amount and the discharge amount of the secondary battery cell 1 are in a substantially balanced state.

  As described above, Rs, R1, and C1, which are parameters other than the OCV, correspond to the transient response characteristics, and the effect thereof is a canceling direction on the charge side and the discharge side. For this reason, in the state where the charge amount and the discharge amount are almost balanced, the parameter values of Rs, R1, and C1 are almost cancelled. In the behavior simulation of the secondary battery cell 1, the OCV parameter value is dominant. It becomes.

  Here, it is assumed that the OCV characteristic in the secondary battery cell 1 has an error of ± 5 mV with respect to a cell voltage of about 3 V, and the internal resistance characteristic has an error of ± 0.5 mΩ with respect to a resistance value of about 5 mΩ. This assumption is determined with reference to actual measurement results, and is a sufficiently reasonable value. Further, the cell capacity of the secondary battery cell 1 is assumed to be 20 Ah.

  Under such an assumption, the error of the voltage drop due to the internal resistance with respect to a continuous current of 0.5 C, that is, 10 A is ± 5 mV, and the internal resistance error is a value corresponding to the error of the OCV characteristic.

  Therefore, in a state in which charging and discharging with an SOC change width of less than 2.5% in 3 minutes are almost balanced, the simulation using the equivalent circuit model mainly depends on the OCV. As a result, even with a simple equivalent circuit model, sufficient simulation accuracy can be ensured, and the Kalman filter can be used effectively to obtain excellent SOC estimation accuracy.

  In the present embodiment, if the SOC change width is 2.5% or more in 3 minutes, the Kalman filter is disabled and SOC estimation by current integration is performed. When the SOC change width is 2.5% or more, one of the charge amount and the discharge amount of the secondary battery cell 1 is in an unbalanced state that is significantly biased compared to the other, and is continuously charged or continuously over a long period of time. The discharge state will continue. In this case, the effect of canceling out the parameter values of Rs, R1, and C1 between the charging side and the discharging side cannot be expected so much. In the behavioral simulation of the secondary battery cell 1, Rs, R1, and C1 corresponding to the transient response characteristics are expected. The parameter value becomes dominant.

  That is, when the SOC change width is 2.5% or more = the average current value in one direction of charging or discharging is 0.5C or more, the correction error between the predicted value calculated by the Kalman filter and the measured value is mostly internal resistance. This is due to errors. If the assumption is made that the internal resistance characteristic has an error of ± 0.5 mΩ with respect to the resistance value of about 5 mΩ, the voltage error due to the internal resistance error exceeds ± 5 mV. Moreover, if the current value increases, the internal resistance error further increases in proportion to this.

  Therefore, in the situation of continuous charging or continuous discharging with an SOC change width of 2.5% or more, the parameter identification error becomes large and the accuracy of the equivalent circuit model is lowered. As a result, an appropriate correction value in the Kalman filter cannot be obtained. Such a situation leads to an increase in Kalman filter error. Therefore, in this embodiment, when the SOC change width is 2.5% or more and continuous charging or continuous discharging is performed, the Kalman gain is set to “0”, the application of the Kalman filter is stopped, and the current integration method is adopted.

(effect)
According to the present embodiment described above, in this embodiment, the advantages of both the current integration method and the Kalman filter method are selectively used according to the charge / discharge current history, thereby improving the SOC estimation accuracy. ing. That is, in the case where the error of the equivalent circuit model is small, the “correcting operation” is realized by applying the Kalman filter and reducing the influence of the Kalman filter error.

  On the other hand, when the error of the equivalent circuit model increases, the Kalman filter is not applied, and the Kalman filter error can be reduced to prevent the SOC estimation accuracy from being lowered. Therefore, it is possible to realize highly accurate SOC estimation using a simple equivalent circuit model. For this reason, the difficulty of battery model development can be avoided, and there is no fear that the processing load at the time of SOC estimation execution will increase. As a result, cost can be suppressed and excellent economic efficiency and reliability can be obtained.

  If the unbalanced state of charging or discharging continues for a long time, there is a concern about an error increase due to integration of current integration error. However, if the direction of charging or discharging is biased, the battery capacity is finite, and eventually charging or discharging ends. Therefore, there is always a charge / discharge stop or equilibrium state, a return state from charge to discharge, or a return state from discharge to charge. In any of these states, since an equilibrium state between the charge amount and the discharge amount always appears at a certain timing, the charge or discharge imbalance cannot be continued.

(2) Other Embodiments The above-described embodiments are presented as examples in the present specification, and are not intended to limit the scope of the invention. In other words, the present invention can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the invention described in the claims and equivalents thereof in the same manner as included in the scope and gist of the invention.

  For example, as the variable control of the Kalman gain, the Kalman gain is a binary value of “0” and “1” in the first embodiment (see FIG. 3), but both may be a non-zero value. The Kalman gain may be switched in three steps or more according to the range of the current integrated value (see FIG. 7A).

  Alternatively, the Kalman gain value may be fixed in a predetermined range of the current integrated value, and the Kalman gain may be continuously variable when the current integrated value deviates from the predetermined range (see FIG. 7B). Furthermore, the Kalman gain value may be continuously variable according to the current integrated value instead of fixing the Kalman gain value within a predetermined range (see FIG. 7C). Either method has the effect of increasing the Kalman gain if the amount of charge / discharge in the latest fixed period is small, and decreasing the Kalman gain if it is small.

  Further, in the Kalman gain control step, the Kalman gain may be changed according to the magnitude of the “digital filter output value with respect to the current measurement value” instead of the “current integrated value in the past certain period”. Specifically, a moving average, a first-order lag, a second-order lag, or the like may be used as the digital filter output value.

  In the first embodiment, the SOC change width, which is a reference value for Kalman gain switching control, is set to 2.5%, but this reference value is “allowable value of SOC estimation error by the Kalman filter according to the specification (hereinafter referred to as the specification error”). It is desirable that the range is 0.2 to 1 times the “allowable value”. If the SOC change width is set to be narrower than the width of the specification error allowable value, the effective range of the Kalman filter is naturally narrowed. In this case, the “Effectiveness” of the Kalman filter is worse than the specification level. This is to increase the frequency at which the Kalman filter is disabled and to secure an opportunity to estimate the SOC by current integration.

  However, even if the effective range of the Kalman filter is limited, if the effective range of the Kalman filter becomes too narrow, the chance of performing the SOC estimation by the Kalman filter is extremely reduced, and the ratio of the period during which the SOC estimation is performed only by the current integration method increases. To do. As a result, there is a significant risk that the current integration error increases. Therefore, it is preferable that the SOC change width is 0.2 times or more of the specification error allowable value.

  On the other hand, if the SOC change width is increased to exceed the specification error allowable value, the effective range of the Kalman filter becomes too wide, and only the SOC estimation by the Kalman filter is performed. As a result, even when the accuracy of the equivalent circuit model is low, SOC estimation by the Kalman filter is forced, and the risk of increasing the Kalman filter error becomes significant. Therefore, it is preferable to determine whether to apply or not apply before the influence of the estimation exceeds the specification error allowable value with low accuracy by setting the SOC variation width to be not more than 1 times the specification error allowable value at the maximum.

  Further, not only the SOC change width but also the determination time is appropriately adjusted. That is, if the determination time is too short, the SOC change width in the determination period exceeds the reference value even though there is almost no SOC change width on average, and the Kalman filter is invalidated outside the effective range of the Kalman filter. There is a fear.

  For example, even if it is equivalent to 0.5C, it is assumed that the determination time is set to 3.6 seconds and the reference SOC change width is set to 0.05% (see FIG. 8A). At this time, when the reference SOC change width is 0.05%, the effective range of the Kalman filter is too small, and the SOC change width at the determination time of 3.6 seconds rarely falls within the effective range of the Kalman filter. As a result, the Kalman filter becomes inactive and the current integration error increases.

  On the other hand, if the determination time is too long, the effective range of the Kalman filter becomes large, and a Kalman filter error may have already occurred at the time of determination. For example, it is assumed that the determination time is 30 minutes and the reference SOC change width is set to 25% (see FIG. 8B, also in this case 0.5C). At this time, if the reference SOC change width is 25%, the effective range of the Kalman filter is too large, and the SOC change width in the determination time of 30 minutes rarely deviates from the effective range of the Kalman filter. As a result, the Kalman filter is only valid and the Kalman filter error increases.

  Therefore, the determination time is appropriately selected so that the SOC change width is within the range of the specification error allowable value. Since the specification error tolerance depends on the characteristics of the equivalent circuit model, the determination time itself also depends on the characteristics of the equivalent circuit model. That is, the determination time is selected according to the characteristics of the equivalent circuit model.

DESCRIPTION OF SYMBOLS 1 ... Secondary battery cell 2 ... Voltage measurement part 3 ... Current measurement part 4 ... Temperature measurement part 5 ... Arithmetic processing part 6 ... Memory | storage part 7 ... Communication interface 11 ... OCV production | generation part 12 ... Rs production | generation part 13 ... Time constant part

Claims (9)

A current measurement step for measuring the current of the secondary battery cell;
A voltage measuring step for measuring a voltage of the secondary battery cell;
A temperature measuring step for measuring the temperature of the secondary battery cell;
A residual value for estimating the remaining amount of the secondary battery cell by the Kalman filter using the measured values measured in the current measurement step, the temperature measurement step, and the voltage measurement step and using an equivalent circuit model of the secondary battery cell. A method for estimating the remaining amount of the secondary battery including the amount estimating step,
A current value integrating step for calculating a current integrated value by integrating the current values of the secondary battery cells in a past fixed period;
And a Kalman gain control step of changing a Kalman gain according to the magnitude of the integrated current value.   2. The remaining capacity of the secondary battery according to claim 1, wherein the Kalman gain is reduced when an absolute value of an integrated value of current values in the secondary battery cell is equal to or greater than a predetermined value. Estimation method.   When the absolute value of the integrated value of the current value in the secondary battery cell is equal to or greater than a predetermined value, the Kalman gain is set to zero, and the SOC of the secondary battery is estimated from the SOC change due to the current value integration. The method for estimating the remaining amount of the secondary battery according to claim 1. A current measurement step for measuring the current of the secondary battery cell;
A voltage measuring step for measuring a voltage of the secondary battery cell;
A temperature measuring step for measuring the temperature of the secondary battery cell;
A residual value for estimating the remaining amount of the secondary battery cell by the Kalman filter using the measured values measured in the current measurement step, the temperature measurement step, and the voltage measurement step and using an equivalent circuit model of the secondary battery cell. A method for estimating the remaining amount of the secondary battery including the amount estimating step,
A digital filter output value calculating step for obtaining a digital filter output value for the current measurement value;
And a Kalman gain control step of changing a Kalman gain according to the magnitude of the digital filter output value.   5. The secondary battery according to claim 4, wherein the Kalman gain is reduced when an absolute value of a digital filter output value with respect to a current value in the secondary battery cell is equal to or greater than a predetermined value. Remaining amount estimation method.   When the absolute value of the digital filter output value with respect to the current value in the secondary battery cell is equal to or greater than a predetermined value, the Kalman gain is set to zero, and the SOC of the secondary battery is estimated from the SOC change due to the current value integration. The method for estimating the remaining amount of the secondary battery according to claim 4.   Based on the history of charge current or discharge current during a given judgment period, determine whether the charge current and discharge current are in an equilibrium state, or whether the charge current or discharge current is in an imbalance state biased in only one direction The method for estimating the remaining capacity of a secondary battery according to claim 1, further comprising: A current measuring unit for measuring the current of the secondary battery cell;
A voltage measuring unit for measuring the voltage of the secondary battery cell;
A temperature measurement unit for measuring the temperature of the secondary battery cell;
Remaining power of the secondary battery cell is estimated by a Kalman filter using the measured values measured by the current measuring unit, the temperature measuring unit, and the voltage measuring unit and using an equivalent circuit model of the secondary battery cell. An amount estimation unit, and a secondary battery remaining amount estimation device comprising:
A current value integrating unit that obtains a current integrated value by integrating the current values of the secondary battery cells in a fixed period in the past;
A secondary battery remaining amount estimation device comprising: a Kalman gain control unit that changes a Kalman gain according to the magnitude of the current integrated value. A current measuring unit for measuring the current of the secondary battery cell;
A voltage measuring unit for measuring the voltage of the secondary battery cell;
A temperature measurement unit for measuring the temperature of the secondary battery cell;
Remaining power of the secondary battery cell is estimated by a Kalman filter using the measured values measured by the current measuring unit, the temperature measuring unit, and the voltage measuring unit and using an equivalent circuit model of the secondary battery cell. An amount estimation unit, and a secondary battery remaining amount estimation device comprising:
A digital filter output value calculation unit for obtaining a digital filter output value for the current measurement value;
A secondary battery remaining amount estimation device, comprising: a Kalman gain controller configured to change a Kalman gain in accordance with a magnitude of the digital filter output value.

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