Battery degradation detection device, battery degradation detection method, and program

Abstract

We provide technology that enables highly accurate degradation detection of storage batteries. [Solution] The battery degradation determination device (100) includes: an approximation range selection unit (114) that causes an approximation formula derivation unit (112) to derive an approximation formula and an approximation index calculation unit (113) to calculate an approximation index in a plurality of approximation ranges that include at least a portion of the measured internal resistance values ​​in a time series, and selects an approximation range in which the approximation index improves; an approximation value calculation unit (115) that calculates an approximation value of the internal resistance of the storage battery (2) using the approximation formula derived based on the measured internal resistance values ​​within the approximation range selected by the approximation range selection unit (114); and a degradation determination unit (116) that determines the degradation of the storage battery (2) based on the approximation value.

Description

[Technical Field]

[0001] This invention relates to a battery degradation detection device, a battery degradation detection method, and a program. [Background technology]

[0002] Determining the degradation of a storage battery based on its internal resistance is being considered. Patent Document 1 discloses a technology for determining degradation based on the time-series data of the measured internal resistance during the operation of a storage battery. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-069223 [Overview of the Initiative] [Problems that the invention aims to solve]

[0004] Depending on the characteristics of the battery, the internal resistance does not necessarily increase monotonically after operation begins. In this case, it has been found that the method described in Patent Document 1 does not always have sufficient accuracy in determining degradation. One aspect of the present invention aims to achieve highly accurate degradation determination of a battery. [Means for solving the problem]

[0005] To solve the above problems, a battery degradation determination device according to one aspect of the present invention includes: an acquisition unit that acquires current and voltage values ​​of a storage battery; an internal resistance calculation unit that calculates actual internal resistance values ​​of the storage battery based on the current and voltage values; an approximation formula derivation unit that derives an approximation formula that approximates the internal resistance of the storage battery in a time series based on the time series actual internal resistance values ​​calculated by the internal resistance calculation unit; an approximation index calculation unit that calculates an approximation index for determining whether the approximation formula derived by the approximation formula derivation unit is good or bad; an approximation range selection unit that selects an approximation range in which the approximation index is good, by causing the approximation formula derivation unit to derive the approximation formula and the approximation index calculation unit to calculate the approximation index within a plurality of approximation ranges including at least a part of the time series actual internal resistance values; an approximation value calculation unit that calculates an approximation value of the internal resistance of the storage battery using the approximation formula derived based on the actual internal resistance values ​​within the approximation range selected by the approximation range selection unit; and a degradation determination unit that determines the degradation of the storage battery based on the approximation value.

[0006] To solve the above problems, a battery degradation determination method according to one aspect of the present invention includes the steps of: acquiring the current value and voltage value of a storage battery; calculating the measured internal resistance value of the storage battery based on the current value and the voltage value; deriving an approximate formula that approximates the internal resistance of the storage battery in a time series based on the calculated time-series measured internal resistance value; calculating an approximate index for determining the quality of the derived approximate formula; deriving the approximate formula and calculating the approximate index in a plurality of approximate ranges that include at least a portion of the time-series measured internal resistance value, and selecting an approximate range in which the approximate index is good; calculating an approximate value of the internal resistance of the storage battery using the approximate formula derived based on the measured internal resistance value within the selected approximate range; and determining the degradation of the storage battery based on the approximate value.

[0007] To solve the above problems, a program according to one aspect of the present invention causes a computer to perform the following processes: acquiring the current value and voltage value of a storage battery; calculating the measured internal resistance value of the storage battery based on the current value and voltage value; deriving an approximate formula that approximates the internal resistance of the storage battery in a time series based on the calculated time-series measured internal resistance value; calculating an approximate index for determining the quality of the derived approximate formula; deriving the approximate formula and calculating the approximate index in a plurality of approximate ranges that include at least a portion of the time-series measured internal resistance value, and selecting an approximate range in which the approximate index is best; calculating an approximate value of the internal resistance of the storage battery using the approximate formula derived based on the measured internal resistance value within the selected approximate range; and determining the degradation of the storage battery based on the approximate value. [Effects of the Invention]

[0008] According to one aspect of the present invention, it is possible to achieve highly accurate degradation detection of storage batteries. [Brief explanation of the drawing]

[0009] [Figure 1] This figure shows an example configuration of a battery degradation determination device according to one embodiment of the present invention. [Figure 2] This figure illustrates the approximation formula derived in Patent Document 1 and Comparative Example 1. [Figure 3] This figure illustrates the approximation formula derived in Comparative Example 2. [Figure 4] This figure illustrates the problems with the approximate straight line in Patent Document 1 and Comparative Example 1. [Figure 5] This figure shows the relationship between the number of charge / discharge cycles and the slope of the regression line. [Figure 6] This is a flowchart illustrating the processing procedure of a battery degradation detection device according to one embodiment of the present invention. [Figure 7] This figure shows the relationship between the number of charge / discharge cycles and the measured internal resistance. [Figure 8]This is a diagram for explaining the approximate formulas calculated in Patent Document 1, Comparative Example 1, Comparative Example 2, and the present embodiment.

Embodiments for Carrying Out the Invention

[0010] 〔Embodiment 1〕 Hereinafter, embodiments according to one aspect of the present invention (hereinafter also referred to as "the present embodiment") will be described based on the drawings. In the drawings, the same or corresponding parts are denoted by the same reference numerals and their descriptions will not be repeated.

[0011] (Configuration of Battery System 1) FIG. 1 is a block diagram showing the configuration of the main part of a battery system 1 according to the present embodiment. The battery system 1 includes a battery 2, an ammeter 3, a voltmeter 4, an RTC (Real Time Clock) 5, and a battery degradation determination device 100.

[0012] The battery 2 is a charging device for storing electricity, such as a lithium-ion battery or the like. The ammeter 3 measures the current value of charging and discharging of the battery 2. The voltmeter 4 measures the voltage value of charging and discharging of the battery 2. The ammeter 3 and the voltmeter 4 output the current value and the voltage value respectively to the battery degradation determination device 100. Also, the RTC 5 outputs the current time to the battery degradation determination device 100. The RTC 5 may output the current time according to the request of the battery degradation determination device 100.

[0013] (Configuration of Battery Degradation Determination Device 100) The battery degradation determination device 100 includes an acquisition unit 101, an operation time measurement unit 102, a stop time measurement unit 103, a charge-discharge cycle number measurement unit 104, an internal resistance calculation unit 111, an approximate formula derivation unit 112, an approximate index calculation unit 113, an approximate range selection unit 114, an approximate value calculation unit 115, a degradation determination unit 116, a diagnosis availability determination unit 117, and a storage unit 120.

[0014] The acquisition unit 101 acquires the current value of the battery 2 during charging and discharging from the ammeter 3, and the voltage value of the battery 2 during charging and discharging from the voltmeter 4. The storage unit 120 may store the acquired current and voltage values. The acquisition unit 101 outputs the acquired current and voltage values ​​to the internal resistance calculation unit 111.

[0015] The operating time measurement unit 102 obtains the current time from the RTC 5. The operating time measurement unit 102 monitors the operating status of the battery system 1, calculates the total operating time, and stores it sequentially in the storage unit 120 along with the measured value of the internal resistance (measured internal resistance). Here, the operating time is the sum of the time the battery 2 is charging and the time the battery 2 is discharging.

[0016] The stop time measurement unit 103 obtains the current time from the RTC 5. The stop time measurement unit 103 monitors the stopped state of the battery system 1, calculates the total stop time, and stores it sequentially in the storage unit 120 along with the measured value of the internal resistance. Here, stop time is the time when the battery 2 is neither charging nor discharging, and this value is also the total time minus the operating time.

[0017] The charge / discharge cycle counting unit 104 counts the number of charge / discharge cycles of the battery 2 and stores the number of charge / discharge cycles together with the measured value of the internal resistance in the storage unit 120. For example, the charge / discharge cycle counting unit 104 starts charging from a state of charge (SOC) of 10% or less of the battery 2, and counts one cycle when the SOC reaches 90% or more. However, the method of counting charge / discharge cycles is not limited to this.

[0018] Here, the State of Charge (SOC) of battery 2 is calculated from the current capacity (in Ah) charged and discharged to battery 2 after it has reached a fully charged state (SOC 100%) or a completely discharged state (SOC 0%). Alternatively, the SOC of battery 2 may be calculated from the current capacity charged and discharged to battery 2 from a stopped state where no charging or discharging is occurring.

[0019] The internal resistance calculation unit 111 calculates the measured value of the internal resistance of the storage battery 2 based on the current value and voltage value acquired by the acquisition unit 101. The internal resistance calculation unit 111 stores the measured value in the storage unit 120. Various conventional methods may be used to calculate the measured value of the internal resistance in the internal resistance calculation unit 111. For example, it may be calculated using the voltage value obtained by charging at a constant current value for a predetermined time from a standby state in which no charging or discharging is taking place.

[0020] The approximation formula derivation unit 112 derives an approximation formula for approximating the internal resistance of the battery 2 over time, based on the measured internal resistance value. The approximation formula derivation unit 112 may also use the operating time, downtime, and number of charge / discharge cycles.

[0021] For example, possible approximation formulas include the following: R1 = R0 + a × t1 α +b × t² β +c×n γ ...(Formula 1) Here, R1 is the internal resistance of battery 2 calculated using the approximation formula (Equation 1), and is a value used when determining degradation. When deriving the approximation formula, the measured value of the internal resistance is substituted for R1.

[0022] R0 is the initial value of the internal resistance of the battery 2 before or immediately after the start of operation. Preferably, this initial value R0 is calculated by the internal resistance calculation unit 111 immediately after operation. Alternatively, it may be calculated before operation.

[0023] a is a coefficient obtained by approximating the internal resistance of battery 2 in operation, calculated multiple times over time. b is a coefficient obtained by approximating the internal resistance of battery 2 in shutdown, calculated multiple times over time. c is a coefficient obtained by approximating the internal resistance of battery 2 in operation, calculated multiple times according to the number of charge-discharge cycles.

[0024] t1 is the elapsed time during operation of the battery 2. Here, the operation time measured by the operation time measurement unit 102 and stored in the memory unit 120 is used. t2 is the elapsed time during shutdown of the battery 2. Here, the shutdown time measured by the shutdown time measurement unit 103 and stored in the memory unit 120 is used. n is the number of charge-discharge cycles during operation of the battery 2. Here, the number of charge-discharge cycles measured by the charge-discharge cycle count measurement unit 104 and stored in the memory unit 120 is used.

[0025] Ideally, α, β, and γ should be power constants that minimize the error in response to changes in the internal resistance of the battery 2. However, the so-called square root law is known, which states that the decrease in discharge capacity of a lithium-ion battery is proportional to the square root of the operating time. Therefore, these power constants may be set to 1 / 2. Furthermore, since the DC internal resistance of a lithium-ion battery is also said to be proportional to the operating time, these power constants may be set to 1.

[0026] Note that while battery 2 degrades with repeated charging and discharging, it also degrades over time even when not charging or discharging. Therefore, by managing the operating time, downtime, and number of charge / discharge cycles, and calculating the internal resistance of battery 2 using (Equation 1), a highly accurate approximation is possible. If the downtime and number of charge / discharge cycles cannot be measured, the coefficients b and c may be set to 0, and the internal resistance of battery 2 may be approximated using only the operating time.

[0027] In other words, the approximation formula derivation unit 112 derives an approximate formula for internal resistance from the measured value of internal resistance within the approximation range, operating time, downtime, and number of charge / discharge cycles. The approximation formula may be a regression line between the measured value of internal resistance within the approximation range and the operating time. Note that the regression line may be a curve rather than a straight line. It is preferable that the number of measured value data points be at least a predetermined number in order to derive the approximation formula. Therefore, if the number of measured value data points is less than the predetermined number, the approximation formula derivation unit 112 does not need to derive an approximation formula. For example, the predetermined number is 5 points.

[0028] The approximation index calculation unit 113 calculates an approximation index for determining the quality of the approximation formula derived by the approximation formula derivation unit 112. As an approximation index, the coefficient of determination, correlation coefficient, etc., in regression analysis can be used. The coefficient of determination represents the degree of goodness (or goodness) of the derived approximation formula. The coefficient of determination is generally R 2 This is represented by a value between 0 and 1. The closer it is to 1, the better the approximation formula fits the actual data.

[0029] Furthermore, the correlation coefficient can be calculated using a similar method to the coefficient of determination, is generally denoted by R, and takes values ​​from 0 to 1. The closer it is to 1, the better the approximation formula fits the actual data. Note that the coefficient of determination and the correlation coefficient are commonly used methods in regression analysis, so a detailed explanation will not be provided.

[0030] The approximation range selection unit 114 causes the approximation formula derivation unit 112 to derive an approximation formula and the approximation index calculation unit 113 to calculate an approximation index for a plurality of approximation ranges that include at least a portion of the measured internal resistance values ​​in the time series. The approximation range selection unit 114 then selects the approximation range in which the approximation index calculated by the approximation index calculation unit 113 is best. Alternatively, the approximation range selection unit 114 may select the approximation range in which the approximation index calculated by the approximation index calculation unit 113 is optimal.

[0031] For example, the approximation range selection unit 114 may set an approximation range that includes multiple consecutive internal resistance measurements in a time series of internal resistance measurements, and while changing this approximation range, it may cause the approximation formula derivation unit 112 to derive an approximation formula and the approximation index calculation unit 113 to calculate an approximation index.

[0032] Furthermore, the approximation range selection unit 114 changes the approximation range by sequentially excluding older measured data from all measured internal resistance data in a time series. The approximation range selection unit 114 may also cause the approximation formula derivation unit 112 to derive an approximation formula and the approximation index calculation unit 113 to calculate an approximation index. The approximation range change is continued until a predetermined number of measured data points are reached, for example, five measured data points. The number of older measured data points to be excluded may be one or more.

[0033] The approximation formula derivation unit 112 derives an approximation formula using the measured internal resistance values ​​included in the approximation range selected by the approximation range selection unit 114. Then, the approximation value calculation unit 115 substitutes the latest operating time, downtime, and charge / discharge cycle count into the approximation formula derived by the approximation formula derivation unit 112 to calculate an approximate value of the internal resistance of the battery 2.

[0034] The degradation determination unit 116 determines the degradation of the battery 2 by comparing the approximate value of the internal resistance of the battery 2 calculated by the approximate value calculation unit 115 with a determination value. The degradation determination unit 116 determines whether the approximate value of the internal resistance exceeds the determination value. The determination value is a threshold value determined by the user. For example, the determination value may be twice the initial value R0, and the degradation determination unit 116 may determine that the battery 2 is degraded when the approximate value of the internal resistance of the battery 2 calculated by the approximate value calculation unit 115 exceeds 2 × R0.

[0035] Furthermore, the degradation determination unit 116 may perform a degradation determination of the storage battery 2 when the slope of the regression line of the approximation formula derived by the approximation formula derivation unit 112 meets a predetermined condition, for example, when the slope of the regression line of the approximation formula becomes positive.

[0036] The diagnostic feasibility determination unit 117 controls whether or not to perform processing in the approximation formula derivation unit 112, the approximation index calculation unit 113, the approximation range selection unit 114, the approximation value calculation unit 115, and the degradation determination unit 116. The diagnostic feasibility determination unit 117 permits the diagnosis of the storage battery (determines whether or not the diagnosis is possible) if the number of measured internal resistance data points is greater than or equal to a predetermined number.

[0037] This is because, when the number of measured data points is small, the accuracy of the approximation formula is considered to be low and sufficient reliability cannot be obtained. Therefore, the calculation of approximation values ​​by the approximation value calculation unit 115 and the diagnosis by the degradation determination unit 116 are not permitted. In other words, the approximation value calculation unit 115 and the degradation determination unit 116 calculate approximation values ​​and make a degradation determination only when the diagnosis feasibility determination unit 117 determines that the battery can be diagnosed.

[0038] The memory unit 120 stores programs, parameters, and data used in each part of the battery degradation determination device 100. For example, the memory unit 120 stores measured values ​​of internal resistance over time, approximation formulas, operating time, downtime, and the number of charge / discharge cycles.

[0039] (Approximate lines of Patent Document 1, Comparative Example 1 and Comparative Example 2) Here, we will briefly explain the approximate lines of Patent Document 1, Comparative Example 1, and Comparative Example 2 using Figures 2 and 3. In Patent Document 1, instead of determining the deterioration state based on the measured value of internal resistance, the approximate formula (Equation 1) above is derived from the measured value data of internal resistance measured periodically, and the deterioration is determined based on the approximate value at the time of diagnosis.

[0040] Figure 2 is a diagram illustrating the approximation formula calculated in Patent Document 1 and Comparative Example 1. In Figure 2, the horizontal axis represents the value t1 when the operating time of the storage battery is t1. α (where α is a constant between 0.1 and 1.5), and the internal resistance is plotted on the vertical axis. Note that in Figure 2, α = 0.5.

[0041] In Figure 2, the dotted line represents the approximation formula derived by the battery degradation determination device disclosed in Patent Document 1. The approximation formula is y = 0.0163x + 1.3076, and the coefficient of determination R 2 However, it is 0.6230.

[0042] Depending on the battery, the internal resistance may not increase in the initial stage of operation, remain unchanged or decrease from the initial internal resistance, and may start to increase after a certain period of time. Therefore, in Comparative Example 1, the time point when the internal resistance starts to increase is taken as the boundary time, and a diagnostic method using the internal resistance approximated from the measured value data after the boundary time is used.

[0043] In FIG. 2, the straight line shown by the solid line represents the approximate formula derived by Comparative Example 1. The approximate formula derived by Comparative Example 1 is y = 0.0414x + 0.8438, and the coefficient of determination R 2 is 0.9558.

[0044] Also, in Comparative Example 1, the boundary time for determining that the internal resistance has started to increase is set to be above a predetermined threshold value. However, in Comparative Example 2, it is set that the slope of the regression line becomes positive for a predetermined number of consecutive times. This enables reliable detection of the entry into the period when the internal resistance increases.

[0045] FIG. 3 shows the approximate formula derived by Comparative Example 2. In FIG. 3, when the operating time of the storage battery on the horizontal axis is t1, the value t1 α (where α is a constant of 0.1 to 1.5) is taken, the slope of the regression line is on the first vertical axis, and the internal resistance is on the second vertical axis. In FIG. !3, α = 0.5 is used.

[0046] As shown in FIG. 3, the time point when the slope of the regression line becomes positive for a predetermined number of consecutive times is taken as the boundary time, and a diagnostic method using the internal resistance approximated from the measured value data after the boundary time is used. The approximate formula derived by Comparative Example 2 is also y = 0.0414x + 0.8438, and the coefficient of determination R 2 is 0.9558.

[0047] FIG. 4 is a diagram for explaining the problems of the approximate straight lines in Patent Document 1 and Comparative Example 1. In FIG. 4, the horizontal axis represents the diagnostic time, and the vertical axis represents the measured value of the internal resistance. The dotted line in FIG. 2 represents the approximate straight line of Patent Document 1. The dashed-dotted line in FIG. 4 represents the approximate straight line of Comparative Example 1.

[0048] As shown in Figure 4, when there are periods in which the slopes of the three approximate lines representing the period of invariance of the measured internal resistance, degradation period 1, and degradation period 2 are different, Comparative Example 1 fails to account for degradation period 2, resulting in a large error with the measured value. The same applies to Comparative Example 2, although it is not shown in the figure.

[0049] In the battery degradation determination device 100 according to this embodiment, the approximation range selection unit 114 changes the approximation range by sequentially excluding older measured data from all measured internal resistance data in a time series. Therefore, the approximation formula derivation unit 112 can derive the approximation formula using only the measured data for degradation period 2, and can handle cases where there are periods in which the slopes of the three approximation lines, as shown in Figure 4, are different.

[0050] Figure 5 shows the relationship between the number of charge / discharge cycles and the slope of the regression line. In Figure 5, the horizontal axis represents the number of charge / discharge cycles, the first vertical axis represents the internal resistance, and the second vertical axis represents the slope of the regression line.

[0051] Figure 5 shows the distribution of measured internal resistance values ​​as the number of charge-discharge cycles increases, and it can be seen that the internal resistance begins to increase around 400 cycles. However, when using the technology of Comparative Example 1, the threshold is set based on the internal resistance of the first cycle, resulting in a lower value being calculated compared to other internal resistances. As a result, it may be mistakenly determined that the resistance increase period has begun in the first few cycles.

[0052] Furthermore, although the slope of the regression line changes from negative to positive around 400 cycles, the fact that it remains positive for the first 50 cycles or so could lead to the misconception that there is no period during which the internal resistance does not change.

[0053] In the battery degradation determination device 100 according to this embodiment, the approximation range selection unit 114 changes the approximation range by sequentially excluding older measured data from all measured internal resistance data in a time series. Therefore, it is possible to prevent the occurrence of the above-mentioned misjudgment.

[0054] (Processing flow of battery degradation detection device 100) Figure 6 is a flowchart illustrating the processing procedure of a battery degradation determination device 100 according to one embodiment of the present invention. First, the internal resistance calculation unit 111 calculates the measured internal resistance of the storage battery 2 based on the current value and voltage value acquired by the acquisition unit 101 (S1).

[0055] Next, the diagnostic feasibility determination unit 117 determines whether or not the battery can be diagnosed by determining whether or not the number of measured data points is equal to or greater than a predetermined number (S2). If the number of measured data points is less than the predetermined number (S2, No), the diagnostic feasibility determination unit 117 determines that the number of measured data points is insufficient (S3) and terminates the process.

[0056] Furthermore, if the number of measured data points is greater than or equal to a predetermined number (S2, Yes), the approximation formula derivation unit 112 derives an approximation formula using all the measured data points (S4). Then, the approximation formula derivation unit 112 determines whether or not the slope of the regression line of the approximation formula is positive (S5).

[0057] If the slope of the regression line of the approximation formula is not positive (S5, No), the degradation determination unit 116 determines that the battery 2 is not degraded (S6) and terminates the process. If the slope of the regression line of the approximation formula is positive (S5, Yes), the approximation index calculation unit 113 calculates the approximation index using the measured internal resistance data within the approximation range (S7).

[0058] Next, the approximation range selection unit 114 determines whether or not approximation with a predetermined number of measured data points has been completed (S8). If approximation with a predetermined number of measured data points has not been completed (S8, No), the approximation range selection unit 114 changes the approximation range by excluding the old measured data points (S9). Then, the approximation formula derivation unit 112 derives an approximation formula using the measured data points within the changed approximation range (S10), and returns to step S7 to repeat the subsequent processing.

[0059] Furthermore, if approximation with a predetermined number of measured data points is completed (S8, Yes), the approximation range selection unit 114 selects the approximation range in which the best approximation index was obtained (S11). Then, the internal resistance calculation unit 111 uses the approximation formula derived in the approximation range in which the best approximation index was obtained to calculate the approximate value R at the time of diagnosis. ana Calculate (S12).

[0060] Next, the deterioration determination unit 116 determines the approximate value R at the time of diagnosis. ana The threshold R x Determine whether the above is true or not (S13). Approximate value R at the time of diagnosis. ana The threshold R x If it is less than (S13, No), the degradation determination unit 116 determines that the storage battery 2 is not degraded (S6) and terminates the process. Also, the approximate value R at the time of diagnosis ana The threshold R x If the above conditions are met (S13, Yes), the degradation determination unit 116 determines that the storage battery 2 is degraded (S14) and terminates the process.

[0061] (Test data for lithium-ion battery cells) The following describes the test data obtained when an accelerated degradation charge-discharge cycle test (DOD 100%) was performed at 50°C using an industrial lithium iron phosphate battery cell.

[0062] Figure 7 shows the distribution of measured internal resistance values ​​for each charge-discharge cycle count. In Figure 7, the horizontal axis represents the number of charge-discharge cycles, and the vertical axis represents the measured internal resistance value. As shown in Figure 7, the measured internal resistance value tends to decrease up to 50 cycles, does not increase until around 500 cycles, and then gradually increases from around 500 cycles onward.

[0063] Furthermore, the interruption of the charge-discharge cycle test to conduct capacity verification tests at 25°C every 50 cycles has resulted in variations in the measured internal resistance. Because measured data that deviates significantly from the actual internal resistance can occur in this way, it is preferable to use approximate values ​​rather than the actual measured values ​​for accurate diagnosis.

[0064] Figure 8 shows the data obtained by adding data assuming that the degradation slope changed after 601 cycles using the measured internal resistance values ​​shown in Figure 7, and is a diagram showing the approximate formulas calculated by Patent Document 1, Comparative Example 1, Comparative Example 2, and this embodiment. As an approximate formula, the following equation (Equation 2) is used, in which the time-dependent term of (Equation 1) above is removed and the power of the cycle-dependent term γ=1.

[0065] R1=R0+c×n (Formula 2) As shown in Figure 8, which illustrates the progression of measured internal resistance data, the true value of the internal resistance at 701 cycles, the latest diagnostic result, can be estimated to be around 0.585 to 0.59 mΩ. Therefore, we will compare whether the approximate values ​​calculated using various approximation methods are close to the true value.

[0066] First, let's explain the approximation results in Patent Document 1. In Patent Document 1, an approximation formula was derived using all measured data, and the approximation value calculated using this formula for 701 cycles was 0.563 mΩ. Since the error with the true value is large, it can be said that it is not suitable for diagnosis during periods when the internal resistance does not increase or when the slope of resistance increase changes.

[0067] Next, the approximation results for Comparative Example 1 will be explained. In Comparative Example 1, considering the variation in internal resistance up to around the first 20 cycles, the threshold for internal resistance to determine whether the resistance increase period had begun was set to 0.555 mΩ, and approximation was performed using measured data from 466 cycles onward, when the resistance was 0.555 mΩ or higher. As a result, the approximate value at 701 cycles calculated by Comparative Example 1 was 0.578 mΩ, which was an improvement in accuracy compared to Patent Document 1, but was still slightly lower than the true value.

[0068] Next, we will explain the approximation results for Comparative Example 2. In Comparative Example 2, the approximation was performed using measured data from cycle 444 onwards, where the slope of the regression line changed from negative to positive and positive values ​​occurred five times in a row. As a result, the approximate value at cycle 701 calculated using Comparative Example 2 was 0.577 mΩ, which was almost the same result as in Comparative Example 1.

[0069] In the verification of Comparative Examples 1 and 2, the threshold for internal resistance and the number of cycles at which the slope of the regression line becomes positive were set after knowing the results up to 701 cycles. However, as mentioned above, in practical use, these settings must be made before operation, so if unexpected behavior occurs, it may not be possible to properly capture the timing at which the resistance begins to increase.

[0070] Finally, the approximation results of this embodiment will be explained. In this embodiment, all measured data was used as the starting point, and the approximation range was changed while reducing the amount of data until the number of data points became 5, and the derivation of the approximation formula and the calculation of the coefficient of determination (approximation index) were repeated.

[0071] As a result, the coefficient of determination was closest to 1 when the measured data from cycles 603 to 701 was set as the approximation range. Since the slope of the increase in internal resistance changed from cycle 600, it can be said that this method was able to appropriately capture the timing of the change in internal resistance. Furthermore, the approximate value at cycle 701 calculated by this embodiment was 0.586 mΩ, which can be said to be a highly accurate result close to the true value.

[0072] (Effects of the battery degradation detection device 100) As described above, according to the battery degradation determination device 100 of this embodiment, the approximation range selection unit 114 causes the approximation formula derivation unit 112 to derive an approximation formula and the approximation index calculation unit 113 to calculate an approximation index within a plurality of approximation ranges that include at least a portion of the measured internal resistance values ​​in time series, and selects the approximation range in which the approximation index is best. Therefore, even when different trends are observed in the changes in the measured internal resistance values, highly accurate battery degradation determination can be achieved.

[0073] Furthermore, the approximation range selection unit 114 selects the approximation range in which the approximation index is optimal. Therefore, the approximation formula derivation unit 112 can derive an approximation formula in which the approximation index is optimal.

[0074] Furthermore, the approximation range selection unit 114 sets an approximation range that includes multiple consecutive internal resistance measurement values ​​in the time-series internal resistance measurement values, and while changing this approximation range, it causes the approximation formula derivation unit 112 to derive an approximation formula and the approximation index calculation unit 113 to calculate an approximation index. Therefore, the approximation range selection unit 114 can set a more appropriate approximation range.

[0075] Furthermore, the approximation range selection unit 114 changes the approximation range by sequentially excluding older measured data from all measured internal resistance data in a time series. Therefore, the approximation range selection unit 114 can select an approximation range that gives more importance to the most recent measured data.

[0076] Furthermore, the approximation range selection unit 114 changes the approximation range until the number of measured data points included in the approximation range reaches a predetermined number. Therefore, it is possible to prevent problems such as the inability to derive an appropriate approximation formula due to a small number of measured data points.

[0077] Furthermore, the degradation determination unit 116 performs a degradation determination of the storage battery when the slope of the regression line of the approximation formula derived by the approximation formula derivation unit 112 meets a predetermined condition. Therefore, the degradation determination unit 116 can prevent a degradation determination from being performed when the storage battery 2 is clearly not degraded.

[0078] [Examples of implementation using software] The control block of the battery degradation detection device 100 may be implemented by logic circuits (hardware) formed on an integrated circuit (IC chip) or the like, or by software.

[0079] In the latter case, the battery degradation determination device 100 includes a computer that executes instructions for a program, which is software that realizes each function. This computer includes, for example, one or more processors and a computer-readable recording medium that stores the program. The object of the present invention is achieved when the processor reads the program from the recording medium and executes it in the computer. For example, a CPU (Central Processing Unit) can be used as the processor. As the recording medium, a "tangible medium that is not temporary," such as ROM (Read Only Memory), can be used, as well as tape, disk, card, semiconductor memory, programmable logic circuit, etc. It may also further include RAM (Random Access Memory) for deploying the program. Furthermore, the program may be supplied to the computer via any transmission medium capable of transmitting the program (such as a communication network or broadcast wave). In one aspect of the present invention, the program can also be realized in the form of a data signal embedded in a carrier wave, which is embodied by electronic transmission.

[0080] 〔summary〕 A battery degradation determination device according to embodiment 1 of the present invention includes: an acquisition unit that acquires current and voltage values ​​of a storage battery; an internal resistance calculation unit that calculates actual internal resistance values ​​of the storage battery based on the current and voltage values; an approximation formula derivation unit that derives an approximation formula that approximates the internal resistance of the storage battery in a time series based on the time series of actual internal resistance values ​​calculated by the internal resistance calculation unit; an approximation index calculation unit that calculates an approximation index for determining the quality of the approximation formula derived by the approximation formula derivation unit; an approximation range selection unit that selects an approximation range in which the approximation index is improved by causing the approximation formula derivation unit to derive the approximation formula and the approximation index calculation unit to calculate the approximation index within a plurality of approximation ranges including at least a part of the time series of actual internal resistance values; an approximation value calculation unit that calculates an approximation value of the internal resistance of the storage battery using the approximation formula derived based on the actual internal resistance values ​​within the approximation range selected by the approximation range selection unit; and a degradation determination unit that determines the degradation of the storage battery based on the approximation value.

[0081] In the battery degradation determination device according to aspect 2 of the present invention, in aspect 1, the approximation range selection unit selects the approximation range in which the approximation index is optimal.

[0082] In the battery degradation determination device according to embodiment 3 of the present invention, in embodiment 1 or 2 above, the approximation range selection unit sets an approximation range that includes a plurality of consecutive internal resistance measurement values ​​in the time series of internal resistance measurement values, and while changing the approximation range, causes the approximation formula derivation unit to derive the approximation formula and causes the approximation index calculation unit to calculate the approximation index.

[0083] In the battery degradation determination device according to embodiment 4 of the present invention, in embodiments 1 to 3 above, the approximation range selection unit changes the approximation range by sequentially excluding older measured data from all measured data of the internal resistance measured over time.

[0084] In the battery degradation determination device according to embodiment 5 of the present invention, in embodiment 4, the approximate range selection unit changes the approximate range until the number of measured value data included in the approximate range reaches a predetermined number.

[0085] In the battery degradation determination device according to embodiment 6 of the present invention, in embodiments 1 to 5 above, the degradation determination unit determines the degradation of the storage battery when the slope of the regression line of the approximation formula derived by the approximation formula derivation unit meets a predetermined condition.

[0086] A battery degradation determination method according to embodiment 7 of the present invention includes the steps of: acquiring the current value and voltage value of a storage battery; calculating the measured internal resistance value of the storage battery based on the current value and the voltage value; deriving an approximate formula that approximates the internal resistance of the storage battery in a time series based on the calculated time-series measured internal resistance value; calculating an approximate index for determining the quality of the derived approximate formula; deriving the approximate formula and calculating the approximate index in a plurality of approximate ranges that include at least a portion of the time-series measured internal resistance value, and selecting an approximate range in which the approximate index is good; calculating an approximate value of the internal resistance of the storage battery using the approximate formula derived based on the measured internal resistance value within the selected approximate range; and determining the degradation of the storage battery based on the approximate value.

[0087] A program according to aspect 8 of the present invention causes a computer to perform the following processes: acquiring the current value and voltage value of a storage battery; calculating the measured internal resistance of the storage battery based on the current value and voltage value; deriving an approximate formula that approximates the internal resistance of the storage battery in a time series based on the calculated time-series measured internal resistance; calculating an approximate index for determining the quality of the derived approximate formula; deriving the approximate formula and calculating the approximate index in a plurality of approximate ranges that include at least a portion of the time-series measured internal resistance, and selecting an approximate range in which the approximate index is best; calculating an approximate value of the internal resistance of the storage battery using the approximate formula derived based on the measured internal resistance within the selected approximate range; and determining the degradation of the storage battery based on the approximate value.

[0088] [Additional Notes] The present invention is not limited to the embodiments described above, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. [Explanation of Symbols]

[0089] 1. Battery storage system 2. Storage battery 3 Ammeter 4. Voltmeter 5 RTC 100 Battery deterioration determination device 101 Acquisition Department 102 Operating Time Measurement Unit 103 Stop time measurement unit 104 Charge / Discharge Cycle Count Measurement Unit 111 Internal resistance calculation unit 112 Approximate formula derivation part 113 Approximate index calculation section 114 Approximate Range Selection Section 115 Approximate Value Calculation Unit 116 Deterioration judgment section 117 Diagnostic feasibility determination unit 120 Storage section

Claims

1. An acquisition unit that acquires the current value and voltage value of the storage battery, An internal resistance calculation unit that calculates the measured internal resistance of the storage battery based on the current value and the voltage value, Based on the time-series measured internal resistance values ​​calculated by the internal resistance calculation unit, an approximation formula derivation unit derives an approximation formula that approximates the internal resistance of the storage battery in a time series, The approximation index calculation unit calculates an approximation index for determining the quality of the approximation formula derived by the approximation formula derivation unit, An approximation range selection unit selects an approximation range in which the approximation index improves, by having the approximation formula derivation unit derive the approximation formula and the approximation index calculation unit calculate the approximation index within a plurality of approximation ranges that include at least a portion of the measured internal resistance values ​​in a time series, An approximation value calculation unit calculates an approximate value of the internal resistance of the storage battery using an approximation formula derived based on the measured internal resistance within the approximation range selected by the approximation range selection unit, A battery degradation determination device comprising: a degradation determination unit that determines the degradation of the storage battery based on the aforementioned approximate value.

2. The battery degradation determination device according to claim 1, wherein the approximation range selection unit selects an approximation range in which the approximation index is optimal.

3. The battery degradation determination device according to claim 1, wherein the approximation range selection unit sets an approximation range that includes a plurality of consecutive internal resistance measurement values ​​in the time series of internal resistance measurement values, and while changing the approximation range, causes the approximation formula derivation unit to derive the approximation formula and causes the approximation index calculation unit to calculate the approximation index.

4. The battery degradation determination device according to claim 3, wherein the approximate range selection unit changes the approximate range by sequentially excluding older measured data from all measured internal resistance data in a time series.

5. The battery degradation determination device according to claim 4, wherein the approximate range selection unit changes the approximate range until the number of measured value data included in the approximate range reaches a predetermined number.

6. The battery degradation determination device according to any one of claims 1 to 5, wherein the degradation determination unit determines the degradation of the storage battery when the slope of the regression line of the approximation formula derived by the approximation formula derivation unit meets a predetermined condition.

7. Steps include obtaining the current and voltage values ​​of the battery, A step of calculating the measured internal resistance of the storage battery based on the current value and the voltage value, The steps include: deriving an approximate formula that approximates the internal resistance of the storage battery in a time series based on the calculated measured internal resistance values ​​in a time series; The steps include: calculating an approximation index for determining the quality of the derived approximation formula; The steps include: deriving the approximation formula, calculating the approximation index, and selecting an approximation range in which the approximation index improves, within a plurality of approximation ranges that include at least a portion of the measured internal resistance values ​​in a time series; A step of calculating an approximate value of the internal resistance of the storage battery using an approximate formula derived based on the measured internal resistance value within the selected approximate range, A battery degradation determination method comprising the step of determining the degradation of the storage battery based on the aforementioned approximate value.

8. On the computer, The process of obtaining the current and voltage values ​​of the storage battery, A process for calculating the measured internal resistance of the battery based on the current value and the voltage value, Based on the calculated time-series measured internal resistance values, a process is performed to derive an approximate formula that approximates the internal resistance of the storage battery in the time series, A process for calculating an approximation index to determine the quality of the derived approximation formula, A process of deriving the approximation formula, calculating the approximation index, and selecting the approximation range in which the approximation index improves, within a plurality of approximation ranges that include at least a portion of the measured internal resistance values ​​in time series, A process to calculate the approximate value of the internal resistance of the storage battery using an approximation formula derived based on the measured internal resistance value within the selected approximation range, A program that performs a process to determine the degradation of the storage battery based on the aforementioned approximate value.

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