JP2017015594A - Battery, power supply management device, and power supply management method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve the estimation accuracy of the charge rate of a power storage part.SOLUTION: The battery includes: a power storage part; a measurement part for measuring the current value and voltage value of the power storage part; a charge rate calculation part for calculating the charge rate of the power storage part by using the current value and voltage value measured by the measurement part; a history storage part for, when the current value changes, storing the history of a change in the current value; and a rule storage part for storing a rule for determining a parameter value to be used by the charge rate calculation part from the history of the change in the current value, therein the charge rate calculation part calculates the charge rate by using the parameter value determined from the measured current value and the history of the change in the current value stored in the history storage part by referring to the rule storage part.SELECTED DRAWING: Figure 8

Description

  The present invention relates to a battery, a power management device, and a power management method.

  In recent years, lithium ion batteries have been used as storage batteries, and their use in mobile devices such as vehicles and portable information processing apparatuses has been put into practical use. In the mobile device, the state of charge (SoC) of the lithium ion battery is estimated and displayed on the display unit of the mobile device.

  To estimate the charging rate, a battery model equation parameter that changes according to the current battery state is stored, the battery state data is collected during charging, and the charging rate is calculated by correcting the parameter based on the value, A technique for sequentially estimating battery model parameters based on a detected current value of a secondary battery, a technique for estimating parameters by applying an adaptive digital filter such as a least square method, and the like are known.

JP 2011-015520 A JP 2006-105821 A JP 2010-060384 A Japanese Patent Application Laid-Open No. 2003-075518

  When estimating the charging rate (SoC) of a lithium ion battery used as a storage battery, the operation of the lithium ion battery is modeled by an equivalent circuit of R, C, (and L), as represented by the Kalman filter. Since each parameter of the model is identified from the terminal voltage estimated in advance, it is difficult to completely represent the actual operation of the lithium ion battery with an equivalent circuit, and an error occurs in the estimation of the charging rate. .

  Therefore, in one aspect, the present invention aims to improve the estimation accuracy of the charging rate of the power storage unit.

  According to one aspect, a power storage unit, a measurement unit that measures a current value and a voltage value of the power storage unit, and a charging rate of the power storage unit using the current value and the voltage value measured by the measurement unit A charging rate calculation unit that calculates the current value, a history storage unit that stores a history of changes in the current value when the current value changes, and a parameter value used in the charging rate calculation unit from the history of the change in current value. A rule storage unit that stores a rule to be determined, and the charge rate calculation unit refers to the rule storage unit, and the history of changes in the measured current value and the current value stored in the history storage unit A battery for calculating a charging rate using a parameter value determined from the above is provided.

  According to another aspect of the present invention, a charging rate calculation unit that calculates and calculates a charging rate of the power storage unit using a current value and a voltage value of the power storage unit, and when the current value changes, A history storage unit for storing a history of change in value; and a rule storage unit for storing a rule for determining a parameter value used in the charging rate calculation unit from a history of a change in current value. Refers to the rule storage unit, and calculates a charging rate using a parameter value determined from the measured current value and a history of changes in the current value stored in the history storage unit. A power management device is provided.

  According to the other viewpoint of this invention, the charging rate of the said storage battery is calculated using the measurement part which measures the electric current value and voltage value of a storage battery, and the said current value and the said voltage value which were measured by the said measurement part. Rules for determining a parameter value to be used in the charging rate calculation unit from a charging rate calculation unit, a history storage unit that stores a history of changes in the current value when the current value changes, and a history of changes in the current value The charge rate calculation unit is determined from the measured current value and a history of changes in the current value stored in the history storage unit with reference to the rule storage unit. A storage battery management device is provided that calculates a charging rate of a storage battery using a parameter value.

  The estimation accuracy of the charging rate of the power storage unit can be improved.

It is a figure which shows the example of the equivalent circuit model of a lithium ion battery. It is a figure which shows the example of an OCV characteristic curve. It is a figure for demonstrating the SoC estimation method by a Kalman filter. It is a figure which shows the example of a Kalman filter. It is a figure which shows the example of the estimation precision by the SoC estimation method by Kalman filter FK. It is a diagram showing a comparative example of the estimation accuracy due to modification of the parameter C _2. It is a figure which shows the structural example of a battery. It is a figure which shows the function structural example of the charging rate estimation part in 1st Example. It is a figure for demonstrating a parameter correction process. It is a figure which shows the application state of the parameter correction value in a charging rate calculation part. It is a figure which shows the example of a current history pattern. It is a figure for demonstrating the method to determine a correction rule. It is a figure which shows the function structural example of the charging rate estimation part in 2nd Example. It is a flowchart for demonstrating the correction rule preparation process by the correction rule preparation part. It is a figure which shows the example of a reference current value. It is a figure for demonstrating SoC estimation using an extraction pattern. It is a figure for demonstrating the example of fitting. It is a figure which shows the example of a branch threshold value and a branch condition. It is a figure for demonstrating the threshold value determination method. It is a figure which shows the example of a correction rule. It is a figure which shows the example of a correction rule group. It is a figure which shows the example of the selection method of the optimal correction rule. It is a figure which shows the hardware constitutions of the information processing apparatus in 3rd Example. It is a figure which shows the function structural example of the information processing apparatus in 3rd Example. It is a figure which shows the comparison of SoC estimation precision.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. First, a method for estimating a charging rate (State of Charge, hereinafter referred to as SoC) of a lithium ion battery using an equivalent circuit model will be described. Hereinafter, the lithium ion battery is simply referred to as a storage battery.

FIG. 1 is a diagram illustrating an example of an equivalent circuit model of a lithium ion battery. In the equivalent circuit model 2 shown in FIG. 1, the terminal voltage v of the lithium ion battery is expressed as the sum of the voltages of the following three parts.
A potential difference that changes in response to a change in SoC is represented by an open circuit voltage (hereinafter referred to as OCV). In the equivalent circuit model 2, OCV is simply expressed as a voltage source.
-The voltage change which generate | occur | produces with respect to an electric current change is represented by resistance _R0 .
A voltage change that is transient with respect to a current change is represented by an RC partial circuit. In the example of FIG. 1, two RC partial circuits are used, but three or more RC partial circuits may be used.

  Therefore, the terminal voltage v is

より、上記３つの部分の電圧ＯＣＶ、Ｖ_０、Ｖ_１、及びＶ_２の和で表される。

Therefore, it is represented by the sum of the voltages OCV, V ₀ , V ₁ , and V ₂ of the three portions.

When estimating the SoC of the storage battery, the terminal voltage OBS of the actually measured storage battery and the terminal voltage v predicted by the equivalent circuit model 2 are matched.
The resistors R ₀ , R ₁ and R _{2 of the} equivalent circuit model 2 and the capacitors C ₁ and C ₂ ,
An OCV characteristic curve expressing OCV that changes according to SoC;
Is identified in advance.

  FIG. 2 is a diagram illustrating an example of the OCV characteristic curve. In FIG. 2, the vertical axis represents the voltage OCV (mV), and the horizontal axis represents SoC. At the beginning of the charge start when SoC is 0 (depleted state) to approximately 0.1, voltage OCV rises rapidly. Thereafter, the voltage gradually rises within a predetermined range (for example, a range from about 3100 mV to about 3300 mV) until just before SoC becomes 1 (full charge state). Then, the SoC rapidly rises to a predetermined voltage value (for example, approximately 3800 mV) from immediately before 1 (full charge), and the storage battery becomes fully charged.

  Next, as an example of the SoC estimation method using the equivalent circuit model 2 shown in FIG. 1, the SoC estimation method using the Kalman filter KF (FIG. 4) will be described with reference to FIG. FIG. 3 is a diagram for explaining a SoC estimation method using a Kalman filter.

Step S1: Using the equivalent circuit model 2, from the values of V ₁ , V ₂ , and SoC of the previous one (section k−1) and the value of the input current i, the current V ₁ , V ₂ , SoC, and the current terminal voltage v. The predicted current terminal voltage v is referred to as a current terminal voltage estimated value.

  The estimation in the interval k is performed by the following equivalent circuit equations of Equation 2, Equation 3, Equation 4, and Equation 5.

In _Expressions 2 to 5, Δt represents a time width (mesh width) by discretization, S _ca represents a capacity of the storage battery, and V _ocv is a function representing an OCV characteristic curve of the storage battery illustrated in FIG. 2.

  Step S2: The difference between the actually measured terminal voltage measurement value (OBS) and the current terminal voltage estimated value is calculated.

Step S3: Considering the current V ₁ , V ₂ and SoC and the noise assumed for the current terminal voltage v, the cause of the error between the terminal voltage measurement value and the current terminal voltage estimation value Estimate current V ₁ , V ₂ , and SoC errors.

Step S4: By correcting the current V ₁ , V ₂ , and SoC errors in addition to the prediction, a more accurate Soc estimation value is output.

  Here, an example of the Kalman filter KF is shown. The symbol of the Kalman filter KF is defined as follows.

Ｎ_１は、システムノイズを表し、平均０、共分散Σｖの３次元正規分布ノイズに相当する。

N ₁ represents system noise and corresponds to three-dimensional normal distribution noise having an average of 0 and a covariance Σv.

Ｎ_１は、観測ノイズを表し、平均０、分散Σｗの正規分布ノイズに相当する。

N ₁ represents observation noise, and corresponds to normal distribution noise with an average of 0 and variance Σw.

Ａは、対角行列によって表される。

A is represented by a diagonal matrix.

  The Kalman filter KF in the SoC estimation method described above will be described. FIG. 4 is a diagram illustrating an example of the Kalman filter. In FIG.

区間ｋ−１での推定値ｘを示す。

The estimated value x in the section k-1 is shown.

区間ｋ−１での推定誤差共分散（３×３行列）を示す。

The estimated error covariance (3 × 3 matrix) in the interval k−1 is shown.

区間ｋでの端子電圧測定値を示す。これらの数１０〜数１２がカルマンフィルタＫＦに入力される。

The terminal voltage measured value in the section k is shown. These equations 10 to 12 are input to the Kalman filter KF.

  The algorithm of the Kalman filter KF will be described with the following equations 13 to 18. .

は、区間ｋでの端子電圧測定値（数１２）を与える前の、区間ｋ−１での推定誤差共分散（数１１）に基づいて推定した、区間ｋでの推定誤差共分散Ｐを示す。

Indicates the estimated error covariance P in section k, estimated based on the estimated error covariance (section 11) in section k-1 before giving the terminal voltage measurement value (section 12) in section k. .

は、区間ｋでの端子電圧測定値（数１２）を与える前の、区間ｋ−１での推定値ｘ及び電流値ｉとから推定した、区間ｋでの推定値ｘを示す。

Indicates the estimated value x in the section k estimated from the estimated value x and current value i in the section k−1 before giving the terminal voltage measurement value (formula 12) in the section k.

は、数１６のカルマンゲインＧを算出するための調整値に相当し、３次元ベクトルで表される。

Corresponds to an adjustment value for calculating the Kalman gain G of Equation 16, and is represented by a three-dimensional vector.

は、共分散Ｐを最小とする。

Minimizes the covariance P.

は、数１２で与えられた端子電圧測定値と、端子電圧推定値との差（電圧差）を示す。

Indicates the difference (voltage difference) between the terminal voltage measurement value given in Equation 12 and the terminal voltage estimation value.

は、数１６のカルマンゲインＧと、数１７の電圧差とから得た推定値ｘの誤差を示す。

Indicates an error of the estimated value x obtained from the Kalman gain G in Expression 16 and the voltage difference in Expression 17.

  The following estimated value can be obtained by the Kalman filter KF described above.

推定値ｘの誤差（数１８）を、区間ｋ−１での推定値ｘ（数１０）に加算することで、区間ｋでの推定値ｘを得る。

By adding the error (Equation 18) of the estimated value x to the estimated value x (Equation 10) in the section k-1, the estimated value x in the section k is obtained.

区間ｋでの推定値ｘから区間ｋでのＳｏＣ推定値を得る。

The estimated SoC value in the section k is obtained from the estimated value x in the section k.

区間ｋでの推定誤差共分散Ｐを取得する。

The estimated error covariance P in the interval k is acquired.

  The estimation accuracy by the SoC estimation method using the Kalman filter FK will be described. FIG. 5 is a diagram illustrating an example of estimation accuracy by the SoC estimation method using the Kalman filter FK. In FIG. 5A, the change in the current value of the storage battery is shown by a graph in which the vertical axis indicates the input current (mA) and the horizontal axis indicates time (seconds). In FIG. 5A, the positive input current is charging, and the negative input current is discharging. From the graph of FIG. 5 (A), it changes from charge to discharge in time “6000” seconds.

  In FIG. 5B, the actual SoC measurement value 5a and the SoC estimation value 5b estimated by the above-described SoC estimation method using the Kalman filter FK are shown by a graph in which the vertical axis indicates SoC and the horizontal axis indicates time (second). Shows changes.

  From the graph of FIG. 5B, the estimated SoC value 5b substantially matches the measured SoC value 5a up to the time of “6000” seconds when changing from charging to discharging. However, when the current value suddenly becomes negative (discharge) from positive (charge) at time “6000” seconds, the difference between the SoC measurement value 5a and the SoC estimation value 5b widens, and the SoC estimation accuracy deteriorates. I understand.

The inventor paid attention to the fact that the estimation accuracy of SoC deteriorates from the time when the state of charge / discharge of the storage battery is switched. In the example of FIG. 5, specifically, the accuracy of the SoC estimated value 5b can be improved by correcting the parameters so as to match the SoC measurement value 5a in the time “6000” seconds when the SoC estimation accuracy deteriorates. Among the parameters of the equivalent circuit model 2 shown in FIG. 1, a description will be given of a case that fix C _2.

Figure 6 is a diagram showing a comparative example of the estimation accuracy due to modification of the parameter C _2. In FIG. 6, the vertical axis indicates SoC and the horizontal axis indicates time (seconds), and the voltage values of V ₁ and V ₂ are shown. For measurement values 6a, C ₂ revised estimate 6b that fixes C ₂ in the charge and discharge state switched time "6000" seconds of storage battery, than without modifying estimate 6c did not modify, measurements 6a The difference is significantly improved.

In addition to the correction of C ₂ , the accuracy of the estimated value can be further improved by correcting other parameters R ₀ , R ₁ , R ₂ , and C ₁ .

In the example of FIGS. 5 and 6, the state where switching from charging to discharging has been described as an example. However, even in the state where switching from discharging to charging is performed, the SoC estimation accuracy deteriorates. Therefore, it is only necessary to correct the parameters R ₀ , R ₁ , R ₂ , C ₁ , and C ₂ when detecting such a state where charging / discharging is switched.

From the above, the first embodiment for correcting the parameters R ₀ , R ₁ , R ₂ , C ₁ , and C ₂ in a state where the SoC estimated value is deteriorated will be described below.

  FIG. 7 is a diagram illustrating a configuration example of a battery. In FIG. 7, a battery 900 mainly includes a storage battery 90, a power management device 100, a positive voltage terminal (+), a negative voltage terminal (−), and an output terminal that outputs SoC.

  The storage battery 90 corresponds to, for example, a storage battery main body such as a lithium ion battery, and is connected to a positive voltage terminal (+) and a negative voltage terminal (−). When the voltage is applied to the storage battery 90 from the positive voltage terminal (+) or the negative voltage terminal (−) of the battery 900, the storage battery 90 is charged and discharged.

The power management device 100 is a device that corrects the parameters R ₀ , R ₁ , R ₂ , C ₁ , and C ₂ in a state where the estimated SoC value is deteriorated, and outputs the estimated SoC. The estimated SoC is output from the output terminal.

  The power management apparatus 100 further includes a current measurement unit 30, a voltage measurement unit 40, and a charge rate estimation unit 50.

  The current measuring unit 30 is an integrating ammeter or the like that measures the current of the storage battery 90 and inputs a current value to the charging rate estimating unit 50. The voltage measuring unit 40 inputs the OBS obtained by measuring the voltage of the storage battery 90 to the charging rate estimating unit 50. OBS is the terminal voltage measurement value described above.

  The charge rate estimation unit 50 is a device that estimates the SoC using the current value input from the current measurement unit 30 and the OBS input from the voltage measurement unit 40. The charging rate estimation unit 50 includes a CPU 51 and a memory 52, and the SoC estimation process in the first embodiment described in detail is realized by software.

The CPU 51 estimates the SoC by executing the program stored in the memory 52. The memory 52 stores a program for estimating the SoC, various data related to the SoC estimation, and the like. In the first embodiment, the memory 52 has parameters R ₀ , R ₁ , R ₂ , C ₁ , which are obtained in advance and improve the accuracy of SoC estimation, determined in advance for each current state in which the SoC estimation value deteriorates. optimal modification rule 70 showing the value of C ₂ stores (Fig. 8).

The CPU 51 changes the values of the parameters R ₀ , R ₁ , R ₂ , C ₁ , and C ₂ indicated by the optimal correction rule 70 when the current change state corresponds to the current state indicated by the optimal correction rule 70. Then, the SoC is estimated.

  The charging rate estimation unit 50 may be a hardware circuit that realizes the SoC estimation process in the first embodiment to be described later.

  FIG. 8 is a diagram illustrating a functional configuration example of the charging rate estimation unit in the first embodiment. In FIG. 8, the charging rate estimation unit 50 includes a parameter correction unit 41 and a charging rate calculation unit 43. The memory 52 stores two previous current values 82 and one previous current value 81 sampled every two Δt in the past, an optimal correction rule 70 prepared in advance, and the like.

  Each time the current current value 80 is input from the current measuring unit 30, the parameter correcting unit 41 stores the two previous current values 82 and the previous current value 81 stored in the memory 52, When the current change obtained from the input current value corresponds to a current history pattern indicated by the optimal correction rule 70 and whose accuracy deteriorates (hereinafter referred to as accuracy deterioration current history pattern 68), the parameter correction value 69 Is input to the charging rate calculation unit 43. The current parameter value of the equivalent circuit model 2 is replaced with the parameter correction value 69.

  The charging rate calculation unit 43 inputs the current value 80 and the terminal voltage measurement value OBS sampled for each Δt, and performs SoC estimation processing by the Kalman filter KF using the current parameter value. The SOC estimated by the charging rate calculation unit 43 is output from the output terminal of the battery 900.

  The optimal correction rule 70 is a program that outputs a parameter correction value 69 for maintaining accuracy for each accuracy deterioration current history pattern 68, data in a table format in which the parameter correction value 69 is associated with each accuracy deterioration current history pattern 68, etc. Indicated by.

  A parameter correction process performed by the parameter correction unit 41 will be described. FIG. 9 is a diagram for explaining the parameter correction processing. In the parameter correction unit 41, the current change indicated by the previous current value 82, the previous current value 81, and the current current value 80 corresponds to one of the accuracy deterioration current history patterns 68 of the optimal correction rule 70. When this is detected, the parameter correction value 69 associated with the accuracy deterioration current history pattern 68 is acquired using the optimal correction rule 70 and input to the charging rate calculation unit 43.

  FIG. 10 is a diagram illustrating an application state of the parameter correction value in the charging rate calculation unit. In FIG. 10, by replacing the parameter of the equivalent circuit model 2 with the parameter correction value 69, the parameter of the equivalent circuit model 2 in the various calculation formulas of steps S1, S2, and S3 performed by the charging rate calculation unit 43 is changed. Adjusted.

  The parameter correction value 69 is applied to Equations 2 to 18 described above, so that the estimated SoC is output with high accuracy.

  Next, a second embodiment for generating the optimum correction rule 70 will be described. First, the outline of the second embodiment will be described.

  If the optimal correction rule 70 is created in consideration of many current values in a plurality of time-series sections and each section, it is considered that the SoC estimation accuracy is improved. In this case, in the implementation of the optimal correction rule 70, the number of cases (hereinafter referred to as “branch”) increases, and the calculation cost such as the memory 52 for storing the parameter correction value 69 and the processing time increases.

  Therefore, as described above, when the SoC measurement accuracy is deteriorated, a time-series current history pattern indicating a current change is created based on the fact that the current change occurs due to charging / discharging of the storage battery 90. The time-series current history pattern is a pattern of current values indicating fluctuations in the previous two (section k-2), the previous (section k-1), and the current (section k). There are 3 sections to consider. Furthermore, the current change of the storage battery 90 which deteriorates the SoC measurement accuracy is determined as shown in FIG. By doing in this way, the total number of current history patterns can be reduced, and an increase in resources such as the memory 52 and calculation cost can be suppressed.

  FIG. 11 is a diagram illustrating an example of a current history pattern. In FIG. 11, in order to create a current history pattern indicating current change due to charging / discharging with the vertical axis representing current and the horizontal axis representing time, charging / discharging is performed in the previous, second, and current sections. Determine the condition. The current history pattern corresponds to a combination of charge / discharge states in three time-series sections.

  Two charging / discharging states of charging (high current), charging (low current), discharging (low current), and discharging (high current) are considered in the previous two, and charging (medium current) is in the previous one. There are two charging / discharging states, and charging (high current), charging (low current), discharging (low current), and discharging (high current). The state is considered.

  Therefore, the current history pattern indicating the current change due to charging / discharging of the storage battery 90 is 32 (= 4 × 2 × 4) patterns in the last two sections, the previous section, and the three sections in the current time series. The parameter values are fitted such that the terminal voltage v of the equivalent circuit model 2 approaches the terminal voltage measurement value OBS in each current history pattern.

  A method for determining the optimum correction rule 70 based on the fitted parameter value will be outlined. FIG. 12 is a diagram for explaining a method of determining a correction rule.

  In the determination of the correction rule, those having similar parameter values fitted in each of all 32 current history patterns are clustered with a predetermined approximate width. It is desirable to perform a plurality of clustering based on a plurality of different approximate widths. The approximate width has a correlation with the number of branches described later. That is, the number of branches increases as the approximate width decreases.

In FIG. 12A, the five-dimensional parameter space (R ₀ , R ₁ , R ₂ , C ₁ , C ₂ ) is shown as a two-dimensional parameter space (R, C) for convenience. In the parameter space of FIG. 12A, an example is shown in which the parameter values fitted in each of all 32 current history patterns are clustered with approximate widths 3a and 3b.

  In the case of the approximate width 3a, four clusters surrounded by a solid line are created. That is, the number of branches is four. In the case of the approximate width 3b, seven clusters surrounded by a broken line are created. That is, the number of branches is 7.

  A representative parameter value 69 ′ is obtained for each cluster. The parameter value 69 ′ may be an average value or a center value of the parameter values in the cluster.

  Further, after performing clustering with various different approximate widths and obtaining the parameter value 69 ′, the estimation error of SoC is obtained.

  In FIG. 12B, the vertical axis represents the estimation error and the horizontal axis represents the number of branches. In FIG. 12B, the estimation error 7a indicates the SoC estimation error when clustering with the approximate width 3a, that is, the number of four branches, and the estimation error 7b is when clustering with the approximate width 3b, that is, The SoC estimation error in the case of 7 branches is shown.

  From the graph of FIG. 12B, comparing the estimation error 7a and the estimation error 7b, it can be seen that the estimation error 7a has a smaller number of branches than the estimation error 7b, but has a high SoC estimation error. However, since the estimation error 7b has a larger number of branches than the estimation error 7a, the calculation cost is increased. In the second embodiment, an optimal optimum correction rule 70 is created in consideration of a trade-off between SoC estimation error and calculation cost.

  In the second embodiment, the configuration of the battery 900 is the same as that of the first embodiment, and the description thereof is omitted. In the second embodiment, the functional configuration of the charging rate estimation unit 50 in the first embodiment is different.

  FIG. 13 is a diagram illustrating a functional configuration example of the charging rate estimation unit in the second embodiment. In FIG. 13, the charging rate estimation unit 50 further includes a correction rule creation unit 45 in addition to the functional configuration of the charging rate estimation unit 50 in the first embodiment shown in FIG. 8. The memory 52 further stores experimental data 60, an extraction pattern 61, a fitting result 62, a correction rule group 63, corrected accuracy data 64, and the like. Portions similar to those in FIG. 8, data, and the like are denoted by the same reference numerals, and portions newly added in the second embodiment will be described.

  The correction rule creation unit 45 extracts the 32 current history patterns described above from the experimental data 60, and obtains various measured values related to the extracted current history patterns and various estimated values obtained from the equivalent circuit model 2. The optimal correction rule 70 is created by using this.

  The correction rule creation unit 45 obtains the estimated SoC value by giving the current value and the terminal voltage measurement value of the three sections in a pseudo time-series manner to the charging rate calculation unit 43. The parameter value is calculated using the estimated SoC value obtained from the charging rate calculation unit 43 and the measured SoC value obtained from the experimental data 60. Therefore, the optimal correction rule 70 is created.

  The correction rule creation unit 45 further gives the current values of three sections in a time series in a pseudo time series to the parameter correction unit 41 in order to perform SoC estimation using the optimal correction rule 70, and to the charging rate calculation unit 43. By artificially giving current values and terminal voltage measured values in three sections in time series, the SoC is re-estimated and the parameter values are optimized. For example, a current value may be given to the parameter correction unit 41 and the charging rate calculation unit 43 based on the extracted current history pattern extracted from the experimental data 60 of the extraction pattern 61.

  The experimental data 60 is data including values such as current history patterns, terminal voltage measurement values, and SoC measurement values obtained in advance. The current history pattern indicates current values in three sections, section k-2, section k-1, and section k. The SoC measurement value indicates the charging rate of the storage battery 90 corresponding to the current values of the three sections. The terminal voltage measurement value corresponds to the actually measured value OBS.

  The extracted current history patterns are current history patterns corresponding to the 32 patterns described in FIG. 11 extracted from a plurality of current history patterns of the experimental data 60. The fitting result 62 is data indicating the parameter value of the equivalent circuit model 2 fitted to the extracted current history pattern.

  The correction rule group 63 includes a correction rule 70a for each branch number when the parameter values based on the fitting result 62 are clustered. The post-correction accuracy data 64 is data indicating the post-correction accuracy of the parameter value of the equivalent circuit model 2. The optimal correction rule 70 is a correction rule 70 a that is determined by referring to the corrected accuracy data 64 and has the best accuracy of the parameter value after correction.

  FIG. 14 is a flowchart for explaining the correction rule creation processing by the correction rule creation unit. In FIG. 14, the correction rule creation unit 45 extracts a current history pattern corresponding to the 32 current history patterns described in FIG. 11 from the experimental data 60 as an extracted current history pattern (step S21). The extracted current history pattern, the SoC measurement value and the terminal voltage measurement value for the extraction current history pattern are stored in the memory 52 as the extraction pattern 61.

  The correction rule creation unit 45 fits the parameter value of the equivalent circuit model 2 for each extraction current history pattern of the extraction pattern 61 (step S22). Then, the correction rule creation unit 45 clusters the parameter values with different approximate widths, and generates a correction rule group 63 indicating the correction rule 70a for each branch number by clustering (step S23).

  The correction rule creation unit 45 calculates the accuracy of the parameter value for each correction rule 70a based on different approximate widths (step S24). The corrected accuracy data 64 indicating the accuracy of the parameter value for each correction rule 70 a is stored in the memory 52.

  The correction rule creation unit 45 selects an optimal optimal correction rule 70 by multi-objective optimization for the post-correction accuracy data 64 (step S25). The optimal correction rule 70 is stored in the memory 52. Thereafter, the modification rule creation unit 45 ends the modification rule creation process.

  An example of a reference current value for extracting a current history pattern will be described. FIG. 15 is a diagram illustrating an example of the reference current value. In the graph of FIG. 15, as in FIG. 11, the vertical axis represents current and the horizontal axis represents time.

  In FIG. 15, in the previous two sections, −15A, −5A, + 5A, and + 15A are set as reference current values from a state without charge and discharge (for example, current 0A). In the previous section, −10 A and +10 A are set as reference current values from a state where there is no charge or discharge. In the current section, as in the previous section, −15A, −5A, + 5A, and + 15A are set as reference current values.

  The correction rule creation unit 45 extracts from the experimental data 60 a current history pattern corresponding to any combination of reference current values of three time-series sections (step S21 in FIG. 14). A terminal voltage measurement value and a SoC measurement value are extracted together with the current history pattern, and are indicated by an extraction pattern 61.

  In the extraction pattern 61, the current history pattern indicates the previous current value 82, the previous current value 81, and the current current value 80 in time series. The terminal voltage measurement value and the SoC measurement value indicate values in the three sections.

  Next, the correction rule creation unit 45 uses the extraction pattern 61 to control the parameter correction unit 41 and the charging rate calculation unit 42 to estimate the SoC and to fit the parameter value (step S22 in FIG. 14). Will be described with reference to FIGS. 16 and 17.

  FIG. 16 is a diagram for explaining SoC estimation using an extraction pattern. In FIG. 16, the correction rule creation unit 45 uses the extraction pattern 61 to calculate the current values of the section k-2, the section k-1, and the section k, and the terminal voltage measurement value for each extracted current history pattern. The charging rate estimation unit 50 inputs the series.

  The correction rule creation unit 45 compares the estimated SoC value input from the charging rate estimation unit 50 with the measured SoC value obtained from the extraction pattern 61, and obtains the charging rate estimation result 5c. An error 5e between the SoC measurement value 5a and the SoC estimation value 5b can be obtained from the charging rate estimation result 5c.

FIG. 17 is a diagram for explaining an example of fitting. In FIG. 17, the vertical axis indicates SoC, the horizontal axis indicates time (seconds), the voltage values of V ₁ and V ₂ , and the capacitance C ₂ among the plurality of parameters of the equivalent circuit model 2. An example of fitting is shown.

Error 6r represents the difference between the measured value 6a and without modifying the estimate 6c, error 6p shows the difference between the measured value 6a and C ₂ revised estimate 6b. The error 6p is smaller than the error 6r, and the accuracy is improved by fitting.

  A combination of the extracted current history pattern and the corresponding parameter value is defined as a correction rule 70a. The correction rule 70a is determined when the number of branch thresholds related to the current value of each section (currently, one before and two before), each current threshold, and the parameter value corresponding to the branch are given.

  “Branch” refers to a determination process based on comparison with a branch threshold, and the number of branches corresponds to the number of determination processes.

The number of branching threshold, N ₀ or the current interval, the previous _one N in the interval, if the two previous sections N ₂ pieces, the number of branches, the inclusion of the case branched threshold is not in each section , (N ₀ +1) × (N ₁ +1) × (N ₂ +1).

FIG. 18 is a diagram illustrating an example of a branch threshold and a branch condition. In FIG. 18, the branching threshold in each section is
In the current interval, two branch thresholds A and B,
One branch threshold C in the previous section,
If there are two branch thresholds D and E in the previous section,
The branch condition is
In the current section, “I <A or A ≦ I ≦ B or B <I”
In the previous section, “I <C or C ≦ I”
In the previous section, “I <D or D ≦ I ≦ E or E <I”
It becomes.

  A threshold value determination method for the correction rule 70a will be described. FIG. 19 is a diagram for explaining the threshold value determining method. In FIG. 19, the case where the number of branch thresholds is 1 in each of the previous and second previous sections will be described.

In FIG. 19 (A), for simplicity, the vertical axis represents the parameter C _2, shows a graph illustrating the parameters R ₁ on the horizontal axis. In FIG. 19A, since there are a total of 8 combinations of branch conditions, the parameter values after fitting are clustered into 8 clusters having the same branch conditions. At the time of clustering, the branch thresholds I ₀ , I ₁ and I ₂ are optimized so that the maximum value of the diameter of each cluster is minimized. For optimization, a genetic algorithm may be used.

FIG. 19B shows an example of optimized branch thresholds I ₀ , I ₁ , I ₂ . In FIG. 19B, after optimization, the two previous branch thresholds I ₂ are set to the high current side of the charged state, the previous branch threshold I ₁ is set to less than the medium current of the discharged state, The current branching threshold I ₀ shows an example that is set below a low current in the charged state.

Returning in FIG. 19 (A), in this example, indicates the current value before the two _<I 2, 1 preceding the current value _{<I 1,} the current cluster of current> _{I 0} in the cluster 4a, two front The cluster of current value> I ₂ , previous current value <I ₁ , and current current value <I ₀ is indicated by cluster 4b.

The average (P, Q) of (R ₁ , C ₂ ) in the cluster 4a is obtained and used as the values of the parameters R ₁ and C ₂ when the branch condition of the cluster 4a is satisfied. Further, the average (U, V) of (R ₁ , C ₂ ) in the cluster 4b is obtained and used as the values of the parameters R ₁ and C ₂ when the branch condition of the cluster 4b is satisfied. The average is similarly obtained for other clusters.

For an example of modification rules 70a when the parameters _{R 1} and _{C 2} will be described. FIG. 20 is a diagram illustrating an example of a correction rule. The correction rule 70a illustrated in FIG. 20 is based on FIG. For simplicity, only parameters R ₁ and C ₂ are shown, but the same applies to the case where all parameters R ₀ , R ₁ , R ₂ , C ₁ , and C ₂ of the equivalent circuit model ₂ are included.

Fixed rule 70a illustrated in FIG. 20 shows the values of the parameters R ₁ and C ₂ which is set when the branch condition and the branch condition is satisfied. The branch condition indicates the current value of the three sections and represents the accuracy deterioration current history pattern 68a. The values of the parameters R ₁ and C ₂ when the branch condition is satisfied represent the parameter correction value 69a.

  Such a correction rule 70a is created for each branch number (combination of branch thresholds in three sections), and a correction rule group 63 is created (step S23 in FIG. 14).

  And the correction rule preparation part 45 calculates | requires the error (accuracy) of each correction rule 70a of the correction rule group 63 (step S24 of FIG. 14). The correction rule creation unit 45 applies the correction rule 70a, causes the parameter correction unit 41 and the charging rate calculation unit 43 to re-estimate the SoC using each of the previously prepared current patterns and measurement terminal voltages. Calculate the estimation error.

  FIG. 21 is a diagram illustrating an example of a correction rule group. In FIG. 21, a correction rule group 63 including a plurality of correction rules 70a shown on the left is simply shown on the right. The plurality of correction rules 70a are identified by the correction rules 1 and 2, respectively.

  The correction rule group 63 represents the correction rule 70a with an identifier, the number of branches, the number of branch thresholds, an error, and the like. The identifier indicates information for identifying the correction rule 70a. The number of branches corresponds to the number of accuracy deterioration current history patterns 68 (number of clusters). The number of branch thresholds indicates the total number of three sections of the branch threshold. The error indicates the value of the average estimation error of the correction rule 70a.

  For the correction rule group 63, an optimal correction rule 70 is selected in consideration of the trade-off between accuracy and calculation cost by multi-objective optimization that reduces errors and reduces calculation costs (step S25 in FIG. 14). The accuracy corresponds to an error, and the calculation cost corresponds to the number of branches and the number of branch thresholds.

  The optimal correction rule 70 may be selected using a genetic algorithm. For example, Pareto optimal, weighted sum optimal, or the like may be used, or may be used in combination.

  FIG. 22 is a diagram illustrating an example of a method for selecting an optimal correction rule. In Pareto optimization in FIG. 22, the correction rule 71 is the optimal rule in relation to the error and the number of branch thresholds. On the other hand, a point 79 indicates an error when the parameter value is not corrected.

  The weighted sum optimum is calculated by the following equation (1).

Weighted sum = a x error + b x number of branches + c x number of thresholds (1)
The coefficients a, b, and c can be determined as appropriate. In the weighted sum optimum, the correction rule 72 is the optimum rule.

  Any one of the correction rule 71 and the correction rule 72 may be selected as the optimum correction rule 70.

  In the second embodiment described above, when the SoC of the storage battery 90 is estimated, the parameter correction is performed when the parameter correction unit 41 indicates the degraded accuracy current history pattern based on the optimal correction rule 70 generated by the charging rate estimation unit 50. By applying the value 69, the charging rate calculation unit 43 can keep estimating the SoC while maintaining the accuracy.

  In the second embodiment, the optimum correction rule 70 is created in the power management apparatus 100, but it may be created in advance by the information processing apparatus 200 (FIG. 23) and stored in the memory 52. The case where the optimal correction rule 70 is created by the information processing apparatus 200 is a third embodiment.

  The information processing apparatus 200 according to the third embodiment has a hardware configuration as shown in FIG. FIG. 23 is a diagram illustrating a hardware configuration of the information processing apparatus according to the third embodiment. In FIG. 23, an information processing device 200 is a device controlled by a computer, and includes a CPU (Central Processing Unit) 11, a main storage device 12, an auxiliary storage device 13, an input device 14, and a display device 15. , A communication I / F (interface) 17 and a drive device 18 are connected to the bus B.

  The CPU 11 controls the information processing apparatus 200 according to a program stored in the main storage device 12. The main storage device 12 uses a RAM (Random Access Memory), a ROM (Read Only Memory) or the like, and is obtained by a program executed by the CPU 11, data necessary for processing by the CPU 11, and processing by the CPU 11. Store or temporarily store the data.

  The auxiliary storage device 13 uses an HDD (Hard Disk Drive) or the like, and stores data such as programs for executing various processes. A part of the program stored in the auxiliary storage device 13 is loaded into the main storage device 12 and executed by the CPU 11, whereby various processes are realized. The storage unit 130 corresponds to the main storage device 12 and / or the auxiliary storage device 13.

  The input device 14 includes a mouse, a keyboard, and the like, and is used for a user to input various information necessary for processing by the information processing device 200. The display device 15 displays various information required under the control of the CPU 11. The input device 14 and the display device 15 may be a user interface such as an integrated touch panel. The communication I / F 17 performs communication through a wired or wireless network. Communication by the communication I / F 17 is not limited to wireless or wired.

  A program that realizes processing performed by the information processing apparatus 200 is provided to the information processing apparatus 200 by a storage medium 19 such as a CD-ROM (Compact Disc Read-Only Memory).

  The drive device 18 provides an interface between the information processing device 200 and a storage medium 19 (for example, a CD-ROM) set in the drive device 18.

  Further, the storage medium 19 stores a program that realizes various processes according to the present embodiment, which will be described later, and the program stored in the storage medium 19 is installed in the information processing apparatus 200 via the drive device 18. Is done. The installed program can be executed by the information processing apparatus 200.

The storage medium 19 for storing the program is not limited to a CD-ROM, but one or more non-transitory tangible media having a structure that can be read by a computer. If it is. As a computer-readable storage medium, in addition to a CD-ROM, a portable recording medium such as a DVD disk or a USB memory, or a semiconductor memory such as a flash memory may be used.
FIG. 24 is a diagram illustrating a functional configuration example of the information processing apparatus according to the third embodiment. The information processing apparatus 200 includes a parameter correction unit 41, a charge rate calculation unit 43, and a correction rule creation unit 45, similar to the charge rate estimation unit 50 of the power management device 100 in the second embodiment.

  Further, the storage unit 130 stores the previous current value 82 and the previous current value 81, the experimental data 60, the extraction pattern 61, the fitting result 62, the correction rule group 63, the corrected accuracy data 64, the optimum correction rule 70, and the like. Is memorized.

  The information processing apparatus 200 is different from the power management apparatus 100 in that it does not include a positive voltage terminal (+), a negative voltage terminal (−), and an output terminal that outputs SoC. Since the processing content by each component is as described above, the same reference numerals as those in FIGS. 8 and 13 are given and detailed description thereof is omitted.

  The optimal correction rule 70 created by the information processing apparatus 200 is stored in advance in the memory 52 of the power management apparatus 100, so that the SoC can be accurately estimated even when the voltage due to charging / discharging of the storage battery 90 changes. be able to.

  Next, the accuracy of SoC estimation when the storage battery 90 is repeatedly charged and discharged will be described. FIG. 25 is a diagram showing a comparison of SoC estimation accuracy. In FIG. 25, it is a graph which shows transition of SoC from the time "180000" (second) which repeated charging / discharging of the storage battery 90 to the time "210000" (second).

  In FIG. 25, the SoC measurement value 91 indicates the actual measurement value of the SoC, and the related technique 92 indicates the SoC estimated by the technique to which any of the first to third examples in the present embodiment is not applied. Form 93 shows SoC estimated by correcting the parameter value in the state of the specific voltage change as described above.

  As described above, since the parameter value of the equivalent circuit model 2 is corrected in a voltage change state in which the estimation accuracy deteriorates, this Embodiment 93 has a higher SoC estimation accuracy than the related technology 92. It has been improved.

  The present invention is not limited to the specifically disclosed embodiments, and can be principally modified and changed without departing from the scope of the claims.

The following appendices are further disclosed with respect to the embodiments including the first to third examples.
(Appendix 1)
A power storage unit;
A measuring unit for measuring a current value and a voltage value of the power storage unit;
A charge rate calculation unit that calculates a charge rate of the power storage unit using the current value and the voltage value measured by the measurement unit;
A history storage unit for storing a history of changes in the current value when the current value has changed;
A rule storage unit for storing a rule for determining a parameter value used in the charging rate calculation unit from a history of changes in the current value;
The charging rate calculation unit refers to the rule storage unit and calculates a charging rate using a parameter value determined from the measured current value and a history of changes in the current value stored in the history storage unit. A battery characterized by that.
(Appendix 2)
The rules stored in the rule storage unit include
A current history pattern in which the estimation accuracy of the charging rate deteriorates;
A correction rule indicating a parameter value used in the charging rate calculation unit for the current history pattern is stored,
The charging rate calculation unit
The supplementary note 1, wherein when the measured current change of the power storage unit corresponds to the current history pattern in which the estimation accuracy deteriorates, the charging rate is calculated by correcting the parameter value according to the correction rule. Battery.
(Appendix 3)
The battery according to claim 2, wherein the current history pattern includes a current value at the time of switching between charge and discharge of the power storage unit.
(Appendix 4)
The charging rate is calculated by giving the charging rate calculation unit a plurality of current values representing the current history pattern in which the estimation accuracy obtained from the experimental data is deteriorated, and the calculated charging rate is obtained from the experimental data. The battery according to appendix 2 or 3, further comprising a correction rule creating unit that obtains the parameter value by fitting to a measured charge rate value and creates the correction rule.
(Appendix 5)
The correction rule creation unit
Appendix 4 characterized by calculating the accuracy of each parameter value of a plurality of correction rules created by clustering the parameter values under different branching conditions and selecting an optimal correction rule based on the calculated accuracy The battery described.
(Appendix 6)
The correction rule creation unit
The battery according to claim 5, wherein the parameter value is optimized using at least one of the number of branches according to the branch condition, at least one of the branch threshold numbers used in the branch condition, and the accuracy.
(Appendix 7)
The battery according to appendix 6, wherein the current history pattern is represented by three or more current values obtained in time series.
(Appendix 8)
A measuring unit for measuring the current value and the voltage value of the power storage unit;
A charge rate calculation unit that calculates a charge rate of the power storage unit using the current value and the voltage value measured by the measurement unit;
A history storage unit for storing a history of changes in the current value when the current value has changed;
A rule storage unit for storing a rule for determining a parameter value used in the charging rate calculation unit from a history of changes in the current value;
The charging rate calculation unit refers to the rule storage unit and calculates a charging rate using a parameter value determined from the measured current value and a history of changes in the current value stored in the history storage unit. A power management device.
(Appendix 9)
The rules stored in the rule storage unit are:
A current history pattern in which the estimation accuracy of the charging rate deteriorates;
A correction rule indicating a parameter value used in the charging rate calculation unit for the current history pattern is stored,
The charging rate calculation unit
Item 8. The charge rate is calculated by correcting the parameter value according to the correction rule when the measured current change of the power storage unit corresponds to the current history pattern in which the estimation accuracy deteriorates. Power management device.
(Appendix 10)
The charging rate is calculated by giving the charging rate calculation unit a plurality of current values representing the current history pattern in which the estimation accuracy obtained from the experimental data is deteriorated, and the calculated charging rate is obtained from the experimental data. The power management apparatus according to appendix 9, further comprising a correction rule creation unit that obtains the parameter value by fitting to a charge rate measurement value and creates the correction rule.
(Appendix 11)
A process of measuring the current value and voltage value of the power storage unit;
A process of calculating a charging rate of the power storage unit using the measured current value and the voltage value;
A process of storing a history of changes in the current value when the current value has changed;
From the history of changes in the current value, the computer performs processing for storing a rule for determining the parameter value for calculating the charging rate in the storage unit,
The process for calculating the charging rate refers to the rule, and calculates a charging rate using a parameter value determined from the measured current value and a history of changes in the current value. Method.
(Appendix 12)
The rules stored in the rules include
A current history pattern in which the estimation accuracy of the charging rate deteriorates;
A correction rule indicating the charging rate calculation parameter value for the current history pattern is stored,
The process of calculating the charging rate is as follows:
Supplementary note 11 wherein when the measured current change of the power storage unit corresponds to the current history pattern in which the estimation accuracy deteriorates, the charging rate is calculated by correcting the parameter value according to the correction rule. Power management method.
(Appendix 13)
The charging rate is calculated by giving a plurality of current values representing the current history pattern, which is obtained from experimental data and the estimation accuracy deteriorates, to the process of calculating the charging rate, and the calculated charging rate is calculated from the experimental data. 13. The power management method according to appendix 12, wherein the computer further performs a process of acquiring the parameter value by fitting to the obtained charging rate measurement value and creating the correction rule.
(Appendix 14)
A measurement unit for measuring the current value and voltage value of the storage battery;
A charge rate calculation unit that calculates a charge rate of the storage battery using the current value and the voltage value measured by the measurement unit;
A history storage unit for storing a history of changes in the current value when the current value has changed;
A rule storage unit for storing a rule for determining a parameter value used in the charging rate calculation unit from a history of changes in the current value;
The charging rate calculation unit refers to the rule storage unit and calculates a charging rate of the storage battery using a parameter value determined from the measured current value and a history of changes in the current value stored in the history storage unit. A storage battery management device characterized by calculating.
(Appendix 15)
A measurement unit for measuring the current value and voltage value of the storage battery;
A charge rate calculation unit that calculates a charge rate of the storage battery using the current value and the voltage value measured by the measurement unit;
A history storage unit for storing a history of changes in the current value when the current value has changed;
A rule storage unit for storing a rule for determining a parameter value used in the charging rate calculation unit from a history of changes in the current value;
A program executed by a storage battery management device having a calculation unit,
The charge rate of the storage battery is calculated using the current value and the voltage value measured by the measurement unit,
The charging rate is calculated by referring to the rule storage unit and calculating the charging rate of the storage battery using a parameter value determined from the measured current value and a history of changes in the current value stored in the history storage unit. A program that causes the calculation unit to execute a calculation process.

2 Equivalent circuit model 11 CPU
12 Main storage device 13 Auxiliary storage device 14 Input device 15 Display device 17 Communication I / F
DESCRIPTION OF SYMBOLS 18 Drive apparatus 19 Storage medium 30 Current measurement part 40 Voltage measurement part 41 Parameter correction part 43 Charge rate calculation part 45 Correction rule preparation part 50 Charge rate estimation part 52 Memory 60 Experimental data 61 Extraction pattern 62 Fitting result 63 Correction rule group 64 Correction Post-accuracy data 68 Accuracy deterioration current history pattern 69 Parameter correction value 70 Optimal correction rule 90 Storage battery 100 Power management device 200 Information processing device 900 Battery

Claims (12)

A power storage unit;
A measuring unit for measuring a current value and a voltage value of the power storage unit;
A charge rate calculation unit that calculates a charge rate of the power storage unit using the current value and the voltage value measured by the measurement unit;
A history storage unit for storing a history of changes in the current value when the current value has changed;
A rule storage unit for storing a rule for determining a parameter value used in the charging rate calculation unit from a history of changes in the current value;
The charging rate calculation unit refers to the rule storage unit and calculates a charging rate using a parameter value determined from the measured current value and a history of changes in the current value stored in the history storage unit. A battery characterized by that. The rules stored in the rule storage unit are:
A current history pattern in which the estimation accuracy of the charging rate deteriorates;
A correction rule indicating a parameter value used in the charging rate calculation unit for the current history pattern is stored,
The charging rate calculation unit
The charging rate is calculated by correcting the parameter value according to the correction rule when the measured current change of the power storage unit corresponds to the current history pattern in which the estimation accuracy deteriorates. The battery described.   The battery according to claim 2, wherein the current history pattern includes a current value at the time of switching between charge and discharge of the power storage unit. The charging rate is calculated by giving the charging rate calculation unit a plurality of current values representing the current history pattern in which the estimation accuracy obtained from the experimental data is deteriorated, and the calculated charging rate is obtained from the experimental data. The battery according to claim 2, further comprising a correction rule creation unit that obtains the parameter value by fitting to a measured charge rate value and creates the correction rule. The correction rule creation unit
2. The accuracy of the parameter value of each of a plurality of correction rules created by clustering the parameter values under different branch conditions is calculated, and an optimal correction rule is selected based on the calculated accuracy. 4. The battery according to 4. A charge rate calculation unit that calculates and calculates a charge rate of the power storage unit using a current value and a voltage value of the power storage unit;
A history storage unit for storing a history of changes in the current value when the current value has changed;
A rule storage unit for storing a rule for determining a parameter value used in the charging rate calculation unit from a history of changes in the current value;
The charging rate calculation unit refers to the rule storage unit and calculates a charging rate using a parameter value determined from the measured current value and a history of changes in the current value stored in the history storage unit. A power management device. The rules stored in the rule storage unit are:
A current history pattern in which the estimation accuracy of the charging rate deteriorates;
A correction rule indicating a parameter value used in the charging rate calculation unit for the current history pattern is stored,
The charging rate calculation unit
7. The charging rate is calculated by correcting the parameter value according to the correction rule when the measured current change of the power storage unit corresponds to the current history pattern in which the estimation accuracy deteriorates. The power management device described. The charging rate is calculated by giving the charging rate calculation unit a plurality of current values representing the current history pattern in which the estimation accuracy obtained from the experimental data is deteriorated, and the calculated charging rate is obtained from the experimental data. The power management apparatus according to claim 7, further comprising a correction rule creation unit that obtains the parameter value by fitting to a charge rate measurement value and creates the correction rule. A process of measuring the current value and voltage value of the power storage unit;
A process of calculating a charging rate of the power storage unit using the measured current value and the voltage value;
A process of storing a history of changes in the current value when the current value has changed;
From the history of changes in the current value, the computer performs processing for storing a rule for determining the parameter value for calculating the charging rate in the storage unit,
The process for calculating the charging rate refers to the rule, and calculates a charging rate using a parameter value determined from the measured current value and a history of changes in the current value. Method. The rules stored in the rules include
A current history pattern in which the estimation accuracy of the charging rate deteriorates;
A correction rule indicating the charging rate calculation parameter value for the current history pattern is stored,
The process of calculating the charging rate is as follows:
The charging rate is calculated by correcting the parameter value according to the correction rule when the measured current change of the power storage unit corresponds to the current history pattern in which the estimation accuracy deteriorates. The power management method described. A measurement unit for measuring the current value and voltage value of the storage battery;
A charge rate calculation unit that calculates a charge rate of the storage battery using the current value and the voltage value measured by the measurement unit;
A history storage unit for storing a history of changes in the current value when the current value has changed;
A rule storage unit for storing a rule for determining a parameter value used in the charging rate calculation unit from a history of changes in the current value;
The charging rate calculation unit refers to the rule storage unit and calculates a charging rate of the storage battery using a parameter value determined from the measured current value and a history of changes in the current value stored in the history storage unit. A storage battery management device characterized by calculating. A measurement unit for measuring the current value and voltage value of the storage battery;
A charge rate calculation unit that calculates a charge rate of the storage battery using the current value and the voltage value measured by the measurement unit;
A history storage unit for storing a history of changes in the current value when the current value has changed;
A rule storage unit for storing a rule for determining a parameter value used in the charging rate calculation unit from a history of changes in the current value;
A program executed by a storage battery management device having a calculation unit,
The charge rate of the storage battery is calculated using the current value and the voltage value measured by the measurement unit,
The charging rate is calculated by referring to the rule storage unit and calculating the charging rate of the storage battery using a parameter value determined from the measured current value and a history of changes in the current value stored in the history storage unit. A program that causes the calculation unit to execute a calculation process.

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