Method for estimating the capacity retention rate of a secondary battery, program for estimating the capacity retention rate of a secondary battery, and apparatus for estimating the capacity retention rate of a secondary battery

Abstract

Provided is a secondary battery capacity retention rate (SOH) estimation method which uses the Weibull law to estimate the capacity retention rate of a secondary battery, wherein: the Weibull coefficient mf corresponding to the float capacity retention rate, ηf, and the float deterioration capacity retention rate represented by formula (1) are calculated from a measurement value of a float test for determining the capacity retention rate; the Weibull coefficient mc corresponding to the cycle capacity retention rate, ηc, and the cycle capacity retention rate represented by formula (2) are calculated from a measurement value of a cycle test for determining the capacity retention rate; and the float capacity retention rate and cycle capacity retention rate of the secondary battery are used to estimate the capacity retention rate at a time period t or at a cycle number N.

Description

[Technical Field]

[0001] The present invention relates to a method for estimating the capacity retention rate of a secondary battery, a program for estimating the capacity retention rate of a secondary battery, and a device for estimating the capacity retention rate of a secondary battery in a battery storage system comprising a single rechargeable secondary battery cell or a plurality of rechargeable secondary battery cells connected in series or parallel, and a power adjustment device connected to a commercial power source or power generation device and capable of supplying power to a connected load. [Background technology]

[0002] To reduce the use of commercial power, energy storage systems have been proposed that store electricity generated using natural energy sources such as solar power in batteries, and then supply the stored electricity to loads that require power instead of using commercial power.

[0003] For example, energy storage systems like the one described above are often used with one to two charge-discharge cycles per day, and it is known that the capacity retention rate of secondary batteries gradually decreases due to cycle degradation and aging. The capacity of secondary batteries after prolonged use cannot be determined without actual measurement, but since this would require shutting down the energy storage system in use, it is becoming necessary to predict the capacity retention rate of secondary batteries in order to understand the current state of the secondary batteries while they are in operation. Furthermore, in order to meet the demand for reducing environmental impact, there are requests for extending the lifespan of energy storage systems, and it is becoming necessary to predict the future lifespan of secondary batteries.

[0004] Conventional methods include preparing a large number of complex charge state maps to correct for capacity retention rates, and there are known formulas for predicting capacity retention rates. Known formulas for predicting capacity retention rates include those using the square root law and the power law. It is known that the capacity degradation rate of a secondary battery can be determined by the square root law, which states that it is proportional to the amount of current (integrated current) flowing through the secondary battery raised to the power of 1 / 2, or to the square root of the time the secondary battery has been left unused. The formula is as follows, and by finding the square root of the amount of current flowing through the secondary battery, which is the amount of current flowing through the secondary battery, the degradation state (or capacity degradation rate) of that secondary battery can be estimated.

[0005]

number

[0006] Furthermore, a technique has been disclosed that allows for the estimation of the full charge capacity or remaining capacity (remaining capacity) of a secondary battery by determining the accumulated charge amount based on the time course of the charge and discharge current, without interrupting the power supply from the secondary battery or discharging the secondary battery to its discharge termination state (see, for example, Patent Document 1). The formula is as follows (power law).

[0007]

number

[0008] However, the square root rule has the problem of large discrepancies with actual measurements. On the other hand, the power law shows good fitting but similarly experiences discrepancies with reality. Furthermore, since both the square root rule and the power law can mathematically allow for capacity retention rates below 0%, there is a problem that predicted values ​​in long-term forecasts tend to be lower than the actual values.

[0009] Therefore, the inventors proposed a novel method for estimating capacity retention rate using Weibull's law (see Non-Patent Document 1). This method treats a battery as an assembly of partial batteries and can estimate its lifespan by predicting its failure rate, and is expressed by the following formula.

[0010]

number

[0011] [Patent Document 1] Japanese Patent Publication No. 2009-52974 [Non-patent literature]

[0012] [Non-Patent Document 1] Abstracts of the 59th Battery Symposium, p. 212, published November 26, 2018. [Overview of the project] [Problems that the invention aims to solve]

[0013] The above method for estimating capacity retention using Weibull's law shows good agreement with measured values ​​compared to the square root law and power law, but it has a large error in long-term predictions. Therefore, more accurate life prediction is needed for practical application.

[0014] In view of these circumstances, the present invention aims to provide a method for estimating the state of health (SOH) of a secondary battery, a program for estimating the state of health (SOH) of a secondary battery, and a device for estimating the state of health (SOH) of a secondary battery, which can be estimated more accurately over a longer period of time. [Means for solving the problem]

[0015] A first aspect of the present invention that solves the above problems is: In a secondary battery capacity retention rate (SOH) estimation method that uses Weibull's law to estimate the capacity retention rate of a secondary battery, The Weibull coefficient m corresponding to the float capacity retention rate is obtained from the measured values ​​of the float test to determine the capacity retention rate. f η fand determine the float capacity retention rate of the following formula (1), From the measured values of the cycle test for obtaining the capacity retention rate, the Weibull coefficient m corresponding to the cycle capacity retention rate c , η c and determine the cycle capacity retention rate of the following formula (2), Estimate the capacity retention rate at the period t or the number of cycles N based on the float capacity retention rate and the cycle capacity retention rate of the secondary battery There is a method for estimating the capacity retention rate of a secondary battery, characterized by this.

[0016] [Number]

[0017] The second aspect of the present invention is Regarding the capacity retention rate obtained from the float test as the measured float capacity retention rate, create a Weibull plot of the float capacity retention rate by performing a Weibull plot of the measured float capacity retention rate in relation to ln(period) and ln(ln(1 / capacity retention rate)), Estimate a linear float degradation prediction line from the Weibull plot of the float capacity retention rate, From the slope and intercept of the float degradation prediction line, obtain the Weibull coefficient m f and η f and obtain Regarding the Weibull coefficient m f and η f and obtain the float capacity retention rate from the above formula (1), Regarding the capacity retention rate obtained from the cycle test as the measured cycle capacity retention rate, create a Weibull plot of the cycle capacity retention rate by performing a Weibull plot of the measured cycle capacity retention rate in relation to ln(number of cycles) and ln(ln(1 / capacity retention rate)), Estimate a linear cycle degradation prediction line from the Weibull plot of the cycle capacity retention rate, From the slope and intercept of the cycle degradation prediction line, obtain the Weibull coefficient m c and η c and obtain Regarding the Weibull coefficient m cand η c The cycle capacity retention rate is determined from equation (2) above. The present invention relates to a method for estimating the capacity retention rate of a secondary battery according to a first embodiment, characterized by the following:

[0018] A third aspect of the present invention is: The capacity retention rate is determined by performing arithmetic operations on the float capacity retention rate and the cycle capacity retention rate. The present invention relates to a method for estimating the capacity retention rate of a secondary battery according to a first or second embodiment, characterized by the above.

[0019] A fourth aspect of the present invention is: The aforementioned capacity retention rate is estimated as the capacity retention rate over a period t or number of cycles N using the following formula (A). The present invention relates to a method for estimating the capacity retention rate of a secondary battery, characterized by any one of the first to third embodiments.

[0020]

number

[0021] A fifth aspect of the present invention is: The aforementioned capacity retention rate is estimated as the capacity retention rate over a period t or number of cycles N using the following formula (B). The present invention relates to a method for estimating the capacity retention rate of a secondary battery, characterized by any one of the first to third embodiments.

[0022]

number

[0023] A sixth aspect of the present invention is: In a secondary battery capacity retention rate estimation program that estimates the state of health (SOH) of a secondary battery using Weibull's law, The Weibull coefficient m corresponding to the float capacity retention rate is obtained from the measured values ​​of the float test to determine the capacity retention rate. f η f And the procedure for determining the float capacity retention rate in the following formula (1), The Weibull coefficient m corresponding to the cycle capacity retention rate is obtained from the measured values ​​of the cycle test to determine the capacity retention rate. c η c And the procedure for determining the cycle capacity retention rate in the following formula (2), The computer is made to perform a procedure to estimate the capacity retention rate over a period of time t or a number of cycles N, based on the float capacity retention rate and the cycle capacity retention rate of the secondary battery. The present invention relates to a program for estimating the capacity retention rate of secondary batteries.

[0024]

number

[0025] A seventh aspect of the present invention is: The volume retention rate obtained from the float test is defined as the measured float volume retention rate, and this flow To contents The procedure for creating a Weibull plot of float volume retention by plotting the volume retention rate in relation to ln(period) and ln(ln(1 / volume retention rate)), and A procedure for estimating a linear float degradation prediction line from the Weibull plot of the float capacity retention rate, The slope and intercept of the float deterioration prediction line are used to determine the Weibull coefficient m. f and η f The procedure for finding and The Weibull coefficient m f and η f And from the above formula (1) float Procedure for determining the capacity retention rate, The procedure for creating a Weibull plot of the cycle capacity retention rate is as follows: the capacity retention rate obtained from the cycle test is defined as the measured cycle capacity retention rate, and this cycle capacity retention rate is plotted in relation to ln(number of cycles) and ln(ln(1 / capacity retention rate)). A procedure for estimating a linear cycle degradation prediction line from the Weibull plot of the cycle capacity retention rate, From the slope and intercept of the cycle degradation prediction line, the Weibull coefficient m c and η cThe procedure for finding and The Weibull coefficient m c and η c The procedure for determining the cycle capacity retention rate from equation (2) above. The sixth embodiment of the secondary battery capacity retention rate estimation program is characterized by using a computer to perform the operation.

[0026] An eighth aspect of the present invention is: The aforementioned capacity retention rate is calculated by having a computer perform a procedure that involves arithmetic operations on the float capacity retention rate and the cycle capacity retention rate. The present invention relates to a secondary battery capacity retention rate estimation program according to a sixth or seventh embodiment, characterized by the following:

[0027] A ninth aspect of the present invention is: The capacity retention rate of a secondary battery is estimated in any one of the sixth to eighth embodiments of a secondary battery capacity retention rate estimation program, characterized in that the computer is made to perform a procedure to estimate the capacity retention rate as the capacity retention rate over a period t or number of cycles N using the following formula (A).

[0028]

number

[0029] A tenth aspect of the present invention is: The capacity retention rate is found in a secondary battery capacity retention rate estimation program of any 6th to 8th embodiment, characterized by causing a computer to perform a procedure for estimating the capacity retention rate over a period t or number of cycles N using the following formula (B).

[0030]

number

[0031] An eleventh aspect of the present invention is: A secondary battery capacity retention rate estimation device that performs a method for estimating the capacity retention rate of a secondary battery, A storage means for storing data from cycle tests and float tests, The system includes data acquisition means for obtaining operational secondary battery data, period t, and cycle count N from the secondary battery. From the measured values ​​of the float test described above, the Weibull coefficient m corresponding to the float volume retention rate is obtained. f η f And the procedure for determining the float capacity retention rate in the following formula (1), From the measured values ​​of the aforementioned cycle test, the Weibull coefficient m corresponding to the cycle capacity retention rate was obtained. c η c And the procedure for determining the cycle capacity retention rate in the following formula (2), The state of health (SOH) of a secondary battery is estimated by performing a procedure to estimate the capacity retention rate over a period of t or a number of cycles N, based on the float capacity retention rate and the cycle capacity retention rate of the secondary battery. The present invention relates to a device for estimating the capacity retention rate of a secondary battery, characterized by the following features:

number

[0032] A twelfth aspect of the present invention is: The volume retention rate obtained from the float test is defined as the measured float volume retention rate, and this flow To contents The procedure for creating a Weibull plot of float volume retention by plotting the volume retention rate in relation to ln(period) and ln(ln(1 / volume retention rate)), and A procedure for estimating a linear float degradation prediction line from the Weibull plot of the float capacity retention rate, The slope and intercept of the float deterioration prediction line are used to determine the Weibull coefficient m. f and η f The procedure for finding and The Weibull coefficient m f and η f And from the above formula (1) float Procedure for determining the capacity retention rate, The procedure for creating a Weibull plot of the cycle capacity retention rate is as follows: the capacity retention rate obtained from the cycle test is defined as the measured cycle capacity retention rate, and this cycle capacity retention rate is plotted in relation to ln(number of cycles) and ln(ln(1 / capacity retention rate)). A procedure for estimating a linear cycle degradation prediction line from the Weibull plot of the cycle capacity retention rate, From the slope and intercept of the cycle degradation prediction line, the Weibull coefficient m c and η c The procedure for finding and The Weibull coefficient m c and η c The procedure for determining the cycle capacity retention rate from equation (2) above. The eleventh embodiment of a secondary battery capacity retention rate estimation device is characterized by performing the following.

[0033] A thirteenth aspect of the present invention is: The capacity retention rate is determined by performing an arithmetic operation on the float capacity retention rate and the cycle capacity retention rate. The present invention relates to a secondary battery capacity retention rate estimation device according to an eleventh or twelfth embodiment, characterized by the following:

[0034] A fourteenth aspect of the present invention is: The aforementioned capacity retention rate is estimated as the capacity retention rate over a period of t or a number of cycles N using the following formula (A). The present invention relates to a secondary battery capacity retention rate estimation device according to any one of the 11th to 13th embodiments, characterized by the above.

[0035]

number

[0036] A fifteenth aspect of the present invention is: The aforementioned capacity retention rate is estimated as the capacity retention rate over a period t or number of cycles N using the following formula (B). The present invention relates to a secondary battery capacity retention rate estimation device according to any one of the 11th to 13th embodiments, characterized by the above.

[0037]

number

[0038] According to the present invention, a method for estimating the capacity retention rate of a secondary battery, a program for estimating the capacity retention rate of a secondary battery, and a device for estimating the capacity retention rate of a secondary battery can be provided, which accurately estimate the capacity retention rate of a secondary battery over a long period of time by separately calculating the float capacity retention rate due to degradation over time and the cycle capacity retention rate due to degradation over the number of charge-discharge cycles. [Brief explanation of the drawing]

[0039] [Figure 1] This figure shows a schematic configuration of a battery storage system, which is an example of a secondary battery to which the method of the present invention is applied. [Figure 2] Figure 1 is a functional block diagram showing an example of the schematic configuration of the control unit. [Figure 3] This is a schematic flowchart of the method of the present invention. [Figure 4] This figure shows an example of a Weibull plot of float test results. [Figure 5] This figure shows an example of a Weibull plot of cycle test results. [Figure 6] This figure shows the results of Example 1. [Figure 7] This figure shows the results of Example 1. [Figure 8] This figure shows the results of Example 2. [Figure 9] This figure shows the results of Example 2. [Figure 10] This figure shows the results of Example 3. [Figure 11] This figure shows the results of Example 3. [Figure 12] This figure shows the results of Example 4. [Figure 13] This figure shows the results of Example 4. [Figure 14] This figure shows the results of Example 5. [Figure 15] This figure shows the results of Example 5. [Figure 16] This figure shows the results of Example 6. [Figure 17] This figure shows the results of Example 6. [Figure 18] This figure shows the results of Example 7. [Figure 19] This figure shows the results of Example 7. [Figure 20] This figure shows the results of Example 8. [Figure 21] This figure shows the results of Example 8. [Modes for carrying out the invention]

[0040] The present invention will be described in detail below based on embodiments.

[0041] (Embodiment 1) Figure 1 shows an example of a schematic configuration of a battery storage system to which the method of the present invention is to be implemented. As shown in Figure 1, the battery storage system 1 comprises a battery storage system 2, which is a secondary battery for storing electricity, and a power adjustment device 3. The power adjustment device 3 is connected to a commercial power source 4, a load 5, and further to a solar power generation device 6, which is a power generation device that generates electricity using natural energy, for example.

[0042] The storage battery 2 is configured to be able to charge and discharge power, and is composed of one or more rechargeable secondary battery cells 2a. Examples of secondary battery cells 2a that make up such a storage battery 2 include lithium-ion batteries, nickel-metal hydride batteries, nickel-cadmium batteries, lead-acid batteries, etc. In this embodiment, a lithium-ion battery was used as the storage battery 2. Furthermore, the multiple secondary battery cells 2a are connected based on the functions and capabilities required of the storage battery, and may be connected in series or in parallel.

[0043] The power adjustment device 3 is a so-called power conditioner and includes an inverter 30 that rectifies the AC power supplied as commercial power 4 and converts the DC power from the storage battery 2 and the solar power generation device 6 into AC power and outputs it to the load 5, as well as a plurality of switches (first switch 31 to fifth switch 35) and a control unit 40 that controls the power adjustment device 3 overall. In this embodiment, the inverter 30 is a bidirectional inverter that has the function of converting input DC power into AC power and outputting it, and the function of converting input AC power into DC power and outputting it.

[0044] The commercial power supply 4 is supplied with electricity from an electric utility company, and in this embodiment, AC power is supplied.

[0045] The power generation device is a power generation device that generates electricity using natural energy (renewable energy) such as sunlight, solar thermal energy, hydroelectric power, wind power, geothermal energy, wave power, temperature difference, and biomass. In this embodiment, a solar power generation device 6 was used as the power generation device.

[0046] In this battery storage system 1, the power adjustment device 3 is controlled by the control unit 40 to convert the DC power generated by the solar power generation device 6 into AC power via the battery 2 and inverter 30 and supply it to the load 5 at a desired timing and rate. The power adjustment device 3 is also controlled by the control unit 40 to convert the AC power supplied from the commercial power supply 4 into DC power via the load 5 and inverter 30 and supply it to the battery 2 at a desired timing.

[0047] Furthermore, the power adjustment device 3 is equipped with a plurality of switches (first switch 31 to fifth switch 35) which are switches that can be opened and closed by the control unit 40. Specifically, it comprises a first switch 31 located between the solar power generation device 6 and the inverter 30, a second switch 32 located between the inverter 30 and the storage battery 2, a third switch 33 located between the branching point 50 between the commercial power supply 4 and the load 5 and the inverter 30, a fourth switch 34 located between the branching point 50 and the commercial power supply 4, and a fifth switch 35 located between the branching point 50 and the load 5. The control unit 40 controls the opening and closing of the first switch 31 to the fifth switch 35 to perform the charging and discharging of the storage battery 2 and control the supply destination of the commercial power supply 4. In other words, the first switch 31 to the fifth switch 35 are controllable by the control unit 40. Note that the fifth switch 35 may be a circuit breaker and not controlled by the control unit 40.

[0048] Here, the control unit 40 of the power adjustment device 3 will be further explained with reference to Figure 2. Figure 2 is a functional block diagram showing the schematic configuration of the control unit 40.

[0049] As shown in Figure 2, the control unit 40 controls the entire power adjustment device 3 and comprises a power generation device monitoring means 41, a storage battery monitoring means 42, and a charge / discharge control means 43.

[0050] The power generation device monitoring means 41 detects the power generation status of the photovoltaic power generation device 6. That is, the power generation device monitoring means 41 detects whether the photovoltaic power generation device 6 is generating power or not due to sunlight. The power generation device monitoring means 41 may also detect the amount of power generated when the photovoltaic power generation device 6 is generating power.

[0051] The battery monitoring means 42 measures the charge / discharge current, voltage, ambient and battery temperature, operating time, cycle count, etc. of the battery 2 and estimates the State of Charge (SOC), State of Health (SOH), etc. In addition, the battery monitoring means 42 in this embodiment also functions as a monitoring device (CMU: Cell Monitor Unit) that monitors voltage, current, temperature, etc. for each secondary battery cell 2a or for each group of battery cells composed of multiple secondary battery cells 2a. The battery monitoring means 42 detects abnormalities in the voltage, current, temperature, etc. of the secondary battery cell 2a or the group of multiple secondary battery cells 2a and transmits the occurrence of the abnormality to the charge / discharge control means 43.

[0052] The charge / discharge control means 43 controls the inverter 30 and the first to fifth switches 31 to 5 35 based on various conditions to control the charging and discharging of the storage battery 2 and the power supply from the commercial power source 4.

[0053] The power generation device monitoring means 41, the battery monitoring means 42, and the charge / discharge control means 43, although not specifically shown in the figures, can be implemented by a central processing unit (CPU), a read-and-write memory (RAM), and a read-only memory (ROM) for storing various programs, which constitute the power adjustment device 3.

[0054] The method of the present invention is applied to such battery storage systems, but of course, it is not limited to these battery storage systems.

[0055] The present invention provides a method for estimating the capacity retention rate of a secondary battery that is actually in use. Specifically, it obtains data such as temperature, current, voltage, operating time, and cycle count of an operating secondary battery, and estimates the state of health (SOH) based on this data.

[0056] While the battery monitoring means 42 may perform the method of the present invention, the battery monitoring means 42 may also transmit the data it collects to an external computing device, and the computing device that acquired the data may then perform the method of the present invention.

[0057] A secondary battery capacity retention rate estimation device that implements the method of the present invention comprises, for example, a storage means for storing data from at least one of a cycle test and a float test, a data acquisition means for obtaining operational secondary battery data, period t, and number of cycles N from the secondary battery, and a calculation device for performing various calculations.

[0058] The storage means, data acquisition means, and computing device are preferably provided by a server connected via a network such as the Internet, but they may also be provided by a computer connected to the network.

[0059] In this invention, the terms are defined and used as follows. The State of Health (SOH) of a secondary battery is determined by distinguishing between the float capacity retention rate, which is due to degradation from battery use, and the cycle capacity retention rate, which is due to degradation from charging and discharging. The estimated float capacity retention rate is based on the measured value of the float test. The cycle capacity retention rate is based on the measured value of the cycle test.

[0060] A schematic flowchart of an example of the method of the present invention is shown in Figure 3. In this invention, the state of health (SOH) of a secondary battery is estimated by separately estimating the float capacity retention rate obtained from a float test and the cycle capacity retention rate obtained from a cycle test.

[0061] In the method of the present invention, a float test (S10) is performed on a predetermined type of secondary battery, in which the secondary battery is operated and the change in capacity over the operating time is measured for each state of charge (SOC) (S1) and each temperature (S2). A cycle test S20 is performed on a predetermined type of secondary battery, in which the SOC is repeatedly increased and decreased at a predetermined temperature S2, with one cycle consisting of charging the battery from SOC 0% to 100% and discharging it from SOC 100% to 0%, while the cycle period is constant. In estimating the float capacity retention rate, the float degradation coefficient (S11) and time (S12) obtained from the float test measurements are introduced into the float degradation equation (1) (S13) to determine the float capacity retention rate (S14).

[0062] On the other hand, in estimating the cycle capacity retention rate, the cycle degradation coefficient (S21) and the number of cycles (S22) obtained from the measured values ​​of the cycle test are introduced into the cycle degradation equation (2) (S23) to determine the cycle capacity retention rate (S24). In this invention, the capacity retention rate (SOH(t)) of the target battery cell, etc., is estimated using the following capacity retention rate SOH estimation formula (A) (step S30).

[0063] In the energy storage system described above, the battery monitoring means 42 acquires temperature, current, voltage, operating period, etc. for each secondary battery cell 2a or for each group of battery cells composed of multiple secondary battery cells 2a, and uses this to estimate the capacity retention rate of the target battery cell, etc., using the following capacity retention rate SOH estimation formula (A).

[0064] Here, when using the capacity retention rate estimation formula (A), the Weibull coefficient m is determined in advance by float tests and cycle tests using test battery cells of the same type as the target battery cell. f η f , m c and η c It is preferable to obtain this information beforehand.

[0065] Weibull coefficient m f η f , m c and η cThis depends on the capacity, structure, and materials of the battery cell, and therefore differs for each type of battery cell. For this reason, it is preferable to use the same type of test battery cell. If the same type of test battery cell is not used, the estimated capacity retention rate will deviate from that of the energy storage system. Even with the same type of test battery cell, it will differ depending on the average charge rate (average SOC) under its usage conditions, so the Weibull coefficient m should be used for each average SOC. f η f , m c and η c It is necessary to determine the Weibull coefficient m. f η f , m c and η c Since this value also changes depending on the operating temperature, it is preferable to calculate it for each temperature range.

[0066] Then, the battery monitoring means 42 obtains the temperature, current, and voltage of the battery cells, etc., and calculates the average State of Charge (SOC) of the operating battery, and the Weibull coefficient m corresponding to the calculated average SOC and temperature. f η f , m c and η c A suitable battery is selected, and the capacity retention rate (SOH) over a period of t or number of cycles (N) is estimated based on the float capacity retention rate and cycle capacity retention rate of the secondary battery.

[0067]

number

[0068] The following describes in detail the method for estimating the capacity retention rate of a secondary battery using the capacity retention rate estimation formula (A) described above.

[0069] The present invention is based on a method for estimating the capacity retention rate of a secondary battery, in which the secondary battery is treated as an assembly of partial batteries, and the capacity retention rate of the secondary battery is predicted by Weibull's law based on the prediction of the failure rate of the partial batteries. However, the present invention distinguishes between the float capacity retention rate obtained by a float test and the cycle capacity retention rate obtained by a cycle test, and estimates the capacity retention rate over a period of t or number of cycles N based on the float capacity retention rate and the cycle capacity retention rate of the secondary battery. By estimating the float capacity retention rate and the cycle capacity retention rate separately, a more accurate capacity retention rate can be estimated, and as will be described in detail later, in particular, the capacity retention rate after long-term use can be estimated more accurately, making the present invention highly effective in determining the lifespan of a secondary battery.

[0070] Furthermore, float capacity retention rate is the capacity retention rate based on measurements in the float test and represents degradation that depends on the usage period of the secondary battery, while cycle capacity retention rate is the capacity retention rate based on measurements in the cycle test and represents degradation that depends on the number of cycles, with one cycle being one charge-discharge cycle.

[0071] Here, the method for estimating the capacity retention rate over a period t or number of cycles N using the float capacity retention rate and cycle capacity retention rate of the secondary battery is not particularly limited, and an estimation method may be defined as appropriate depending on the purpose. However, by using the float capacity retention rate and cycle capacity retention rate for estimation, it is possible to estimate the capacity retention rate after long-term use more accurately, and the present invention has a significant effect in determining the lifespan of a secondary battery.

[0072] A specific example of a method for estimating the capacity retention rate over a period t or number of cycles N of a secondary battery, based on the float capacity retention rate and the cycle capacity retention rate, is to determine it by performing arithmetic operations on the float capacity retention rate and the cycle capacity retention rate.

[0073] The choice of which of the four arithmetic operations to use for the float capacity retention rate and the cycle capacity retention rate can be appropriately selected depending on the estimation system, but it is preferable to use either formula (A) or formula (B) below.

[0074] In other words, it is preferable to estimate the capacity retention rate as the capacity retention rate over a period t or number of cycles N using the following formula (A) or formula (B). Formula (A) is preferably used when there is a strong correlation between cycle degradation and float degradation; otherwise, formula (B) is preferable.

[0075]

number

[0076] The following describes the procedure for determining the Weibull coefficient. (Float test for coefficient determination) For a specified type of secondary battery, the battery is operated at a specified temperature and fixed at a specified state of charge (SOC), and the change in capacity over the operating time is measured. If necessary, the same test is performed under different operating temperatures and SOCs. For example, the capacity is checked by fixing the voltage at 50% SOC and leaving the battery at different temperatures such as 25°C, 45°C, and 60°C for extended periods. Alternatively, the capacity is checked at 25°C, 45°C, and 60°C while fixing the voltage at other SOC percentages.

[0077] (Cycle test for coefficient determination) For a specified type of secondary battery, one cycle is defined as the period during which the battery is charged from 0% to 100% SOC and then discharged from 100% SOC to 0%. The SOC is repeatedly increased or decreased while the cycle period is constant. For example, if charging and discharging are performed three times a day, three cycles can be completed in one day, resulting in 300 cycles in 100 days and 3000 cycles in 1000 days. The capacity corresponding to the period corresponding to the number of cycles is measured, and the total cycle test capacity retention rate is obtained to determine the Weibull coefficient.

[0078] The measured values ​​to be acquired are the capacity for each cycle number when the secondary battery is operated at a predetermined temperature. The measurement should correspond to the number of cycles; it can be performed after each cycle, at predetermined intervals, or irregularly. To more accurately estimate the State of Health (SOH), it is preferable to have as many measured values ​​as possible.

[0079] The capacity retention rate is calculated by dividing the degraded capacity by the capacity at the start of the test, and is the ratio of the degraded capacity to the initial capacity retention rate. In other words, the total cycle test capacity retention rate is the value obtained by dividing the measured value obtained from the cycle test by the initial full charge capacity.

[0080] (Measurement method for determining coefficients from operational secondary batteries) The measured values ​​for determining the coefficient may be directly obtained from the secondary battery, for example, the energy storage system described above, which is the target of the estimation of the capacity retention rate (SOH). The measured values ​​for determining the float coefficient can be obtained by measuring the capacity when a predetermined SOC is reached during a predetermined operating period from the start of operation, and the measured values ​​for determining the cycle coefficient can be obtained by measuring the capacity when a predetermined number of cycles have been completed from the start of operation. The method for counting the number of cycles when obtaining the measured values ​​for determining the cycle coefficient can be determined as appropriate. For example, one cycle may be counted as a set of charge and discharge where the capacity moved by 50% or more of the full charge capacity, or one cycle may be counted every two passes through a specific percentage of SOC, or one cycle may be counted when the accumulated actual charge and discharge capacity matches the capacity of one charge and discharge cycle of that battery. Since the battery temperature does not change significantly depending on the environment in which the energy storage system is placed, the battery temperature can be measured and the average temperature can be used.

[0081] The capacity retention rate is calculated by dividing the capacity after degradation by the capacity at the start of the test, and is the ratio of the capacity after degradation to the initial capacity retention rate. In other words, the capacity retention rate is the value obtained by dividing the measured value from the energy storage system by the initial full charge capacity.

[0082] The capacity retention rate of the energy storage system can be estimated by determining a predetermined Weibull coefficient using the following procedure.

[0083] (From the measured values ​​of the float test, the degradation coefficient m f η f (Method for finding the answer) The volume retention rate obtained from the float test is defined as the measured float volume retention rate. A Weibull plot of this float volume retention rate is created by plotting it in relation to ln(period) and ln(ln(1 / volume retention rate)). A linear float degradation prediction line is estimated from the Weibull plot of the float volume retention rate, and the Weibull coefficient m is calculated from the slope and intercept of the float degradation prediction line. f and η f We seek.

[0084] Figure 4 shows an example of a Weibull plot of the results of this float test, plotting the measured values ​​at 25°C and 45°C with a SOC of 50%, and at 60°C with a SOC of 50%, and the Weibull coefficient m under each condition. f η f This can be determined. Furthermore, the float component volume retention rate can be determined.

[0085] (Degradation coefficient m from measured values ​​in cycle tests) c η c (Method for finding the answer) The capacity retention rate obtained from the cycle test is defined as the measured cycle capacity retention rate. A Weibull plot of this cycle capacity retention rate is created by plotting it in relation to ln(number of cycles) and ln(ln(1 / capacity retention rate)). A linear cycle degradation prediction line is estimated from the Weibull plot of the cycle capacity retention rate, and the Weibull coefficient m is calculated from the slope and intercept of the cycle degradation prediction line. c and η c We seek.

[0086] Figure 5 shows an example of a Weibull plot of the cycle test results. The Weibull coefficient m under the measurement conditions. c η cThis can be calculated. Furthermore, the retention rate of the cycle component volume can be determined.

[0087] (Estimation of capacity retention rate) For secondary batteries of the same type as the one being estimated, the Weibull coefficient m corresponds to a predetermined temperature and predetermined SOC. f η f , m c and η c Select the appropriate value and introduce it into the prediction formula (A) described above to estimate the volume retention rate SOH for period t.

[0088] By more accurately estimating the future State of Health (SOH), it becomes possible to appropriately set the charge and discharge parameters for degraded secondary batteries that have been used for a long period of time, thereby preventing overcharging and over-discharging and enabling safer battery use. Furthermore, it becomes possible to more accurately determine the timing of remaining charge detection, maintenance, and battery replacement in the energy storage system. [Examples]

[0089] (Example 1) Secondary battery (PD50S03): The positive electrode is lithium iron phosphate, the negative electrode is graphite, and the electrolyte is ethylene carbonate (EC):dimethyl carbonate (DMC) = 3:7 with 1.2M lithium hexafluoride phosphate (LiPF6) as a supporting electrolyte. The positive and negative electrodes are stacked elements facing each other with a polyolefin separator in between. The capacity is 50Ah. The elements are housed in a stainless steel metal case. Float and cycle tests were conducted using this secondary battery. Float tests were conducted at various SOCs and temperatures. The cycle test consisted of 6 cycles per day at various SOCs and temperatures.

[0090] Data was collected for a system (battery capacity 2.5kWh) consisting of 16 identical secondary batteries connected in series, and the average State of Charge (SOC) was calculated to be 92.1%. The average temperature was 28.2°C. Furthermore, the Weibull coefficient m from float tests corresponding to the average SOC and average temperature. f is 0.270603, η f The value was 9.341202. Furthermore, the Weibull coefficient m from the cycle test c is 0.659873, η c The value was 10.65732.

[0091] The results of estimating the capacity retention rate from equations (1), (2), and (A) using these Weibull coefficients are shown in Figure 6 as Weibull prediction A. Figure 6 shows the actual data along with the predicted values, and it was confirmed that the predicted values ​​were in close agreement with the actual data. Figure 7(a) shows the change in the number of cycles of the actual device, Figure 7(b) shows the change in the State of Charge (SOC) of the actual device, and Figure 7(c) shows the change in the temperature of the actual device.

[0092] (Example 2) Float tests and cycle tests were performed using the same type of secondary battery as in Example 1. Float tests were conducted at various SOCs and temperatures. The cycle test consisted of 6 cycles per day at various SOCs and temperatures. Data was collected for a system (battery capacity 2.5kWh) consisting of 16 identical secondary batteries connected in series, and the average State of Charge (SOC) was calculated to be 97.0%. The average temperature was 27.8°C. Furthermore, the Weibull coefficient m from float tests corresponding to the average SOC and average temperature. f is 0.962004, η f The value was 4.269083. Furthermore, the Weibull coefficient m from the cycle test c is 0.04926, η c The value was 57.4858.

[0093] The results of estimating the capacity retention rate from equations (1), (2), and (A) using these Weibull coefficients are shown in Figure 8 as Weibull prediction A. Figure 8 shows the actual data along with the predicted values, and it was confirmed that the predicted values ​​were in close agreement with the actual data. Figure 9(a) shows the change in the number of cycles of the actual device, Figure 9(b) shows the change in the State of Charge (SOC) of the actual device, and Figure 9(c) shows the change in the temperature of the actual device.

[0094] (Example 3) Float tests and cycle tests were performed using the same type of secondary battery as in Example 1. Float tests were conducted at various SOCs and temperatures. The cycle test consisted of 6 cycles per day at various SOCs and temperatures. Data was collected for a system (battery capacity 2.5kWh) consisting of 16 identical secondary batteries connected in series, and the average State of Charge (SOC) was calculated to be 77.9%. The average temperature was 31.5°C. Furthermore, the Weibull coefficient m from the float test results corresponding to the average SOC and average temperature. f is 0.355503, η f The value was 7.313371. Furthermore, the Weibull coefficient m from the cycle test results. c is 0.665076, η c The value was 10.78985.

[0095] The results of estimating the capacity retention rate from equations (1), (2), and (A) using these Weibull coefficients are shown in Figure 10 as Weibull prediction A. Figure 10 shows the actual data along with the predicted values, and it was confirmed that the predicted values ​​were in close agreement with the actual data. Figure 11(a) shows the change in the number of cycles of the actual device, Figure 9(b) shows the change in the State of Charge (SOC) of the actual device, and Figure 9(c) shows the change in the temperature of the actual device.

[0096] (Example 4) Float tests and cycle tests were performed using the same type of secondary battery as in Example 1. Float tests were conducted at various SOCs and temperatures. The cycle test consisted of 6 cycles per day at various SOCs and temperatures. Data was obtained for a system in which 16 secondary batteries of the same type were connected in series (battery capacity: 2.5 kWh), and the average SOC was calculated to be 96.7%. The average temperature was 30.5°C. Also, the Weibull coefficient m from the float test corresponding to the average SOC and average temperature f was 0.507544, and η f was 5.396954. Also, the Weibull coefficient m from the cycle test c was 0.105096, and η c was 44.51101.

[0097] The results of estimating the capacity retention rate from Equations (1), (2), and (A) using these Weibull coefficients are shown as Weibull prediction A in Fig. 12. Although the actual data is shown together with the predicted values in Fig. 12, it was confirmed that the predicted values are almost identical to the actual data. Note that Fig. 13(a) shows the change in the number of cycles of the actual machine, Fig. 13(b) shows the change in the SOC of the actual machine, and Fig. 13(c) shows the change in the temperature of the actual machine.

[0098] (Example 5) Float tests and cycle tests were performed using secondary batteries of the same type as in Example 1. The float tests were conducted at various SOCs and temperatures. The cycle tests were performed at 6 cycles per day at various SOCs and temperatures. Data was obtained for a system in which 16 secondary batteries of the same type were connected in series (battery capacity: 2.5 kWh), and the average SOC was calculated to be 92.1%. The average temperature was 28.2°C. Also, the Weibull coefficient m from the float test corresponding to the average SOC and average temperature f was 0.270603, and η f was 9.341202. Also, the Weibull coefficient m from the cycle test c was 0.659873, and η c was 10.65732.

[0099] Using these Weibull coefficients, the capacity retention rate was estimated from equations (1), (2), and (B), and the result is shown in Figure 14 as Weibull prediction B. Figure 14 shows the actual data along with the predicted values, and it was confirmed that the predicted values ​​were in close agreement with the actual data. Figure 15(a) shows the change in the number of cycles of the actual device, Figure 15(b) shows the change in the State of Charge (SOC) of the actual device, and Figure 15(c) shows the change in the temperature of the actual device.

[0100] (Example 6) Float tests and cycle tests were performed using the same type of secondary battery as in Example 1. Float tests were conducted at various SOCs and temperatures. The cycle test consisted of 6 cycles per day at various SOCs and temperatures. Data was collected for a system (battery capacity 2.5kWh) consisting of 16 identical secondary batteries connected in series, and the average State of Charge (SOC) was calculated to be 97.0%. The average temperature was 27.8°C. Furthermore, the Weibull coefficient m from float tests corresponding to the average SOC and average temperature. f is 0.962004, η f The value was 4.269083. Furthermore, the Weibull coefficient m from the cycle test c is 0.04926, η c The value was 57.4858.

[0101] The results of estimating the capacity retention rate from equations (1), (2), and (B) using these Weibull coefficients are shown in Figure 16 as Weibull prediction B. Figure 16 shows the actual data along with the predicted values, and it was confirmed that the predicted values ​​were in close agreement with the actual data. Figure 17(a) shows the change in the number of cycles of the actual device, Figure 17(b) shows the change in the State of Charge (SOC) of the actual device, and Figure 17(c) shows the change in the temperature of the actual device.

[0102] (Example 7) Float tests and cycle tests were performed using the same type of secondary battery as in Example 1. The float test was conducted at various SOCs and temperatures. The cycle test was set to 6 cycles per day at various SOCs and temperatures. When data was obtained for a system in which 16 secondary batteries of the same type were connected in series (battery capacity: 2.5 kWh) and the average SOC was determined, it was 77.9%. Also, the average temperature was 31.5°C. Also, the Weibull coefficient m from the float test corresponding to the average SOC and average temperature f was 0.962004, and η f was 4.269083. Also, the Weibull coefficient m from the cycle test c was 0.04926, and η c was 57.4858.

[0103] The results of estimating the capacity retention rate from Equations (1), (2), and (B) using these Weibull coefficients are shown as Weibull prediction B in FIG. 18. Although the actual data is shown together with the predicted values in FIG. 18, it was confirmed that the predicted values almost match the actual data. Note that FIG. 19(a) shows the change in the cycle count of the actual machine, FIG. 19(b) shows the change in the SOC of the actual machine, and FIG. 19(c) shows the change in the temperature of the actual machine.

[0104] (Example 8) A float test and a cycle test were conducted using a secondary battery of the same type as in Example 1. The float test was conducted at various SOCs and temperatures. The cycle test was set to 6 cycles per day at various SOCs and temperatures. When data was obtained for a system in which 16 secondary batteries of the same type were connected in series (battery capacity: 2.5 kWh) and the average SOC was determined, it was 96.7%. Also, the average temperature was 30.5°C. Also, the Weibull coefficient m from the float test corresponding to the average SOC and average temperature f was 0.507544, and η f was 5.396954. Also, the Weibull coefficient m from the cycle test cis 0.105096, η c The value was 44.51101.

[0105] The results of estimating the capacity retention rate from equations (1), (2), and (B) using these Weibull coefficients are shown in Figure 20 as Weibull prediction B. Figure 20 shows both the predicted values ​​and the actual data, and it was confirmed that the predicted values ​​were in close agreement with the actual data. Figure 21(a) shows the change in the number of cycles of the actual device, Figure 21(b) shows the change in the State of Charge (SOC) of the actual device, and Figure 21(c) shows the change in the temperature of the actual device. [Industrial applicability]

[0106] This invention can be effectively utilized in industrial fields that construct battery storage systems using batteries as a power source, as well as in industrial fields that perform their maintenance and operation. [Explanation of Symbols]

[0107] 1…Battery storage system 2… Storage battery 2a…Secondary battery cell 3…Power adjustment device 4…Commercial power supply 5…Load 6…Solar power generation equipment (power generation equipment) 30…Inverter 31…First switch 32...Second switch 33... Third switch 34…Fourth switch 35…5th switch 40... Control Unit 41... Power generation equipment monitoring means 42...Battery monitoring means 43...Charge / Discharge Control Means

Claims

1. In a method for estimating the capacity retention rate (SOH) of a secondary battery using Weibull's law, The Weibull coefficient m corresponding to the float capacity retention rate is obtained from the measured values ​​of the float test to determine the capacity retention rate. ｆ η ｆ And calculate the float capacity retention rate using the following formula (1): The Weibull coefficient m corresponding to the cycle capacity retention rate is obtained from the measured values ​​of the cycle test to determine the capacity retention rate. ｃ η ｃ And calculate the cycle capacity retention rate using the following formula (2): The capacity retention rate over a period of t or number of cycles N is estimated based on the float capacity retention rate and the cycle capacity retention rate of the secondary battery. A method for estimating the capacity retention rate of a secondary battery, characterized by the features described herein. [Math 1]

2. The volume retention rate obtained from the float test is defined as the measured float volume retention rate, and a Weibull plot of the measured float volume retention rate is created by plotting the relationship between ln (period) and ln (ln(1 / volume retention rate)). A linear float degradation prediction line is estimated from the Weibull plot of the float capacity retention rate. The slope and intercept of the float deterioration prediction line are used to determine the Weibull coefficient m. ｆ and η ｆ Seeking, The Weibull coefficient m ｆ and η ｆ And the float capacity retention rate is determined from the above formula (1), The capacity retention rate obtained from the cycle test is defined as the measured cycle capacity retention rate, and a Weibull plot of the measured cycle capacity retention rate is created by plotting the relationship between ln(number of cycles) and ln(ln(1 / capacity retention rate)). A linear cycle degradation prediction line is estimated from the Weibull plot of the cycle capacity retention rate. From the slope and intercept of the cycle deterioration prediction line, the Weibull coefficients m ｃ and η ｃ are obtained, The Weibull coefficient m ｃ and η ｃ The cycle capacity retention rate is determined from the above equation (2). The method for estimating the capacity retention rate of a secondary battery according to claim 1.

3. The capacity retention rate is determined by performing arithmetic operations on the float capacity retention rate and the cycle capacity retention rate. A method for estimating the capacity retention rate of a secondary battery according to claim 1 or 2, characterized by the above.

4. The aforementioned capacity retention rate is estimated as the capacity retention rate over a period t or number of cycles N using the following formula (A). A method for estimating the capacity retention rate of a secondary battery according to claim 1 or 2, characterized by the above. [Math 2]

5. The aforementioned capacity retention rate is estimated as the capacity retention rate over a period t or number of cycles N using the following formula (B). A method for estimating the capacity retention rate of a secondary battery according to claim 1 or 2, characterized by the above. [Math 3]

6. In a secondary battery capacity retention rate estimation program that estimates the state of health (SOH) of a secondary battery using Weibull's law, The Weibull coefficient m corresponding to the float capacity retention rate is obtained from the measured values ​​of the float test to determine the capacity retention rate. ｆ η ｆ And the procedure for determining the float capacity retention rate in the following formula (1), The Weibull coefficient m corresponding to the cycle capacity retention rate is obtained from the measured values ​​of the cycle test to determine the capacity retention rate. ｃ η ｃ And the procedure for determining the cycle capacity retention rate in the following formula (2), The computer is made to perform a procedure to estimate the capacity retention rate over a period of t or a number of cycles N, based on the float capacity retention rate and the cycle capacity retention rate of the secondary battery. A program for estimating the capacity retention rate of a secondary battery, characterized by the above features. [Math 4]

7. The procedure for creating a Weibull plot of the float capacity retention rate is as follows: the capacity retention rate obtained from the float test is defined as the measured float capacity retention rate, and this float capacity retention rate is plotted in relation to ln (period) and ln (ln(1 / capacity retention rate)). A procedure for estimating a linear float degradation prediction line from the Weibull plot of the float capacity retention rate, The slope and intercept of the float deterioration prediction line are used to determine the Weibull coefficient m. ｆ and η ｆ The procedure for finding and The Weibull coefficient m ｆ and η ｆ The procedure for determining the float capacity retention rate from the above formula (1), The procedure for creating a Weibull plot of the cycle capacity retention rate is as follows: the capacity retention rate obtained from the cycle test is defined as the measured cycle capacity retention rate, and this cycle capacity retention rate is plotted in relation to ln(number of cycles) and ln(ln(1 / capacity retention rate)). A procedure for estimating a linear cycle degradation prediction line from the Weibull plot of the cycle capacity retention rate, From the slope and intercept of the cycle degradation prediction line, the Weibull coefficient m ｃ and η ｃ The procedure for finding and The Weibull coefficient m ｃ and η ｃ The procedure for determining the cycle capacity retention rate from the above formula (2) and Make the computer function and run A program for estimating the capacity retention rate of a secondary battery according to feature 6.

8. The capacity retention rate is calculated by having a computer perform a procedure that involves arithmetic operations on the float capacity retention rate and the cycle capacity retention rate. A program for estimating the capacity retention rate of a secondary battery according to claim 6 or 7, characterized in that it is the same as described in claim 6 or 7.

9. The aforementioned capacity retention rate is estimated as the capacity retention rate over a period t or number of cycles N using the following formula (A), and this procedure is performed by having a computer function to execute this procedure. A program for estimating the capacity retention rate of a secondary battery according to claim 6 or 7, characterized in that it is the same as described in claim 6 or 7. [Math 5]

10. The aforementioned capacity retention rate is estimated as the capacity retention rate over a period t or number of cycles N using the following formula (B), and the computer is made to perform this procedure. A program for estimating the capacity retention rate of a secondary battery according to claim 6 or 7, characterized in that it is the same as described in claim 6 or 7. [Math 6]

11. A secondary battery capacity retention rate estimation device that performs a method for estimating the capacity retention rate of a secondary battery, A storage means for storing data from cycle tests and float tests, The system includes data acquisition means for obtaining operational secondary battery data, period t, and cycle count N from the secondary battery, From the measured values ​​of the float test described above, the Weibull coefficient m corresponding to the float volume retention rate is obtained. ｆ η ｆ And the procedure for determining the float capacity retention rate in the following formula (1), From the measured values ​​of the aforementioned cycle test, the Weibull coefficient m corresponding to the cycle capacity retention rate was obtained. ｃ η ｃ And the procedure for determining the cycle capacity retention rate in the following formula (2), The state of health (SOH) of the secondary battery is estimated by performing a procedure to estimate the capacity retention rate over a period t or number of cycles N, based on the float capacity retention rate and the cycle capacity retention rate of the secondary battery. A device for estimating the capacity retention rate of a secondary battery, characterized by the above. [Number 7]

12. The procedure for creating a Weibull plot of the float capacity retention rate is as follows: the capacity retention rate obtained from the float test is defined as the measured float capacity retention rate, and this float capacity retention rate is plotted in relation to ln (period) and ln (ln(1 / capacity retention rate)). A procedure for estimating a linear float degradation prediction line from the Weibull plot of the float capacity retention rate, The slope and intercept of the float deterioration prediction line are used to determine the Weibull coefficient m. ｆ and η ｆ The procedure for finding and The Weibull coefficient m ｆ and η ｆ The procedure for determining the float capacity retention rate from the above formula (1), The procedure for creating a Weibull plot of the cycle capacity retention rate is as follows: the capacity retention rate obtained from the cycle test is defined as the measured cycle capacity retention rate, and this cycle capacity retention rate is plotted in relation to ln(number of cycles) and ln(ln(1 / capacity retention rate)). A procedure for estimating a linear cycle degradation prediction line from the Weibull plot of the cycle capacity retention rate, From the slope and intercept of the cycle degradation prediction line, the Weibull coefficient m ｃ and η ｃ The procedure for finding and The Weibull coefficient m ｃ and η ｃ The procedure for determining the cycle capacity retention rate from equation (2) is carried out. The capacity retention rate estimation device for a secondary battery according to claim 11.

13. The capacity retention rate is determined by performing an arithmetic operation on the float capacity retention rate and the cycle capacity retention rate. The capacity retention rate estimation device for a secondary battery according to claim 11 or 12, characterized in that it is a secondary battery capacity retention rate estimation device.

14. The aforementioned capacity retention rate is estimated as the capacity retention rate over a period t or number of cycles N using the following formula (A). The capacity retention rate estimation device for a secondary battery according to claim 11 or 12, characterized in that it is a secondary battery capacity retention rate estimation device. [Number 8]

15. The aforementioned capacity retention rate is estimated as the capacity retention rate over a period t or number of cycles N using the following formula (B). The capacity retention rate estimation device for a secondary battery according to claim 11 or 12, characterized in that it is a secondary battery capacity retention rate estimation device. [Number 9]

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