Battery analysis system, battery analysis method, and battery analysis program

Abstract

An SOH calculating unit calculates a plurality of states of health (SOH) of a secondary battery on the basis of an SOC difference between two points obtained by referring to battery data, and an electric current integrated value. A deterioration prediction formula generating unit performs curve regression on the plurality of calculated SOHs to generate a deterioration prediction formula (SOH=m+K×Xf) for the secondary battery. On the basis of actual or predicted battery data of the secondary battery after the start of an (n+1)th use, and deterioration characteristics of the secondary battery, a deterioration rate statistical value calculating unit calculates a statistical value of the rate of deterioration during the (n +1)th use of the secondary battery. The deterioration prediction formula generating unit corrects the rate of deterioration in the deterioration prediction formula during the n-th use of the secondary battery on the basis of the ratio between the statistical value of the rate of deterioration during the n-th use, calculated from the deterioration characteristics of the secondary battery, and the statistical value of the rate of deterioration during the (n+1)th use, calculated from the deterioration characteristics, to generate the deterioration prediction formula during the (n+1)th use.

Description

Battery analysis system, battery analysis method, and battery analysis program

[0001] The present disclosure relates to a battery analysis system, a battery analysis method, and a battery analysis program for analyzing the internal state of a secondary battery.

[0002] Some secondary batteries are operated so that their primary use is terminated and they are put into secondary use according to a predetermined standard. For example, the predetermined standard is often when the SOH (State of Health) drops to a set value. When the usage method or environment of the secondary battery changes, it is expected that the SOH degradation trend will change.

[0003] Patent Document 1 discloses a life expectancy prediction device that predicts the remaining life of a lithium-ion battery. The life expectancy prediction device calculates a second-order difference based on capacity measurement data {tn, C(tn)} and identifies an inflection point from the sign change. If an inflection point exists within the measurement range, a convex function portion is determined by fitting for capacity measurement data in cycles smaller than the inflection point. For capacity measurement data in cycles larger than the inflection point, a concave function portion is determined by fitting. That is, Patent Document 1 discloses a method of extracting an inflection point from the distribution of capacity measurement data and predicting the remaining life by changing the polynomial to be fitted before and after the inflection point.

[0004] JP 2011-208966 A

[0005] However, predicting deterioration using data from before the secondary battery was used or before the environment changed leads to a decrease in prediction accuracy.

[0006] The present disclosure has been made in view of these circumstances, and its purpose is to provide a technology for predicting with high accuracy the progression of deterioration of a secondary battery after a change in the way the battery is used.

[0007] A battery analysis system according to an aspect of the present disclosure includes a data acquisition unit that acquires time-series battery data including a current, temperature, and SOC of a secondary battery; an SOH calculation unit that calculates a plurality of SOHs of the secondary battery based on an SOC difference between two points and an integrated current value obtained by referring to the battery data; and a degradation prediction formula (SOH=m+K×X) for predicting the deterioration of the secondary battery by performing curve regression on the calculated plurality of SOHs. ｆ and a degradation rate statistical value calculation unit that calculates a statistical value of a degradation rate of the secondary battery based on battery data of the secondary battery and degradation characteristics of the secondary battery. The degradation rate statistical value calculation unit calculates a statistical value of a degradation rate of the secondary battery at the (n+1)th use of the secondary battery based on actual or predicted battery data of the secondary battery after the nth (n is a natural number) use of the secondary battery has ended and the (n+1)th use has started, and on the degradation characteristics of the secondary battery. The degradation prediction formula generation unit corrects a degradation rate K in the degradation prediction formula for the nth use of the secondary battery based on a ratio between the statistical value of the degradation rate at the nth use calculated from the degradation characteristics of the secondary battery and the statistical value of the degradation rate at the (n+1)th use calculated from the degradation characteristics, to generate a degradation prediction formula for the (n+1)th use.

[0008] Any combination of the above components, and conversion of the expression of the present disclosure into an apparatus, system, method, computer program, etc., are also valid aspects of the present disclosure.

[0009] According to the present disclosure, it is possible to predict with high accuracy the progression of deterioration of a secondary battery after a change in the way it is used.

[0010] FIG. 1 is a diagram showing a schematic configuration of an electric vehicle according to an embodiment; FIG. 2 is a diagram for explaining a battery analysis system according to an embodiment; FIG. 3 is a diagram showing an example of a configuration of a battery analysis system according to an embodiment; FIG. 4 is a diagram showing an example of a storage deterioration characteristic map; FIG. 5 is a diagram showing an example of a charge deterioration characteristic map; FIG. 6 is a diagram showing an example of a discharge deterioration characteristic map; FIG. 7 is a diagram showing a specific image of an FCC estimation method;

[0011] FIG. 1 is a diagram showing a schematic configuration of an electric vehicle 3 according to an embodiment. In this embodiment, the electric vehicle 3 is assumed to be a pure EV that does not have an internal combustion engine. The electric vehicle 3 shown in FIG. 1 is a rear-wheel drive (2WD) EV that includes a pair of front wheels 31f, a pair of rear wheels 31r, and a motor 34 as a power source. The pair of front wheels 31f are connected by a front wheel shaft 32f, and the pair of rear wheels 31r are connected by a rear wheel shaft 32r. A transmission 33 transmits the rotation of the motor 34 to the rear wheel shaft 32r at a predetermined conversion ratio. Note that the electric vehicle 3 may be a front-wheel drive (2WD) or 4WD electric vehicle.

[0012] The power supply system 40 includes a battery pack 41 and a management unit 42. The battery pack 41 includes a plurality of unit cells or a plurality of parallel cells connected in series. Each parallel cell includes a plurality of unit cells connected in parallel. In the case of the battery pack 41 having a large number of unit cells or parallel cells connected in series, the battery pack 41 may be configured by connecting in series a plurality of assembled batteries each including a plurality of unit cells or a plurality of parallel cells connected in series.

[0013] The cells may be lithium-ion battery cells, nickel-metal hydride battery cells, etc. In the following description, we will assume an example in which lithium-ion battery cells (nominal voltage: 3.6-3.7 V) are used. The number of single cells or parallel cells connected in series is determined according to the voltage of the motor 34.

[0014] The management unit 42 manages the state of the battery pack 41. The management unit 42 monitors the following data items for the battery pack 41: the cell voltage of each of a plurality of single cells or a plurality of parallel cells measured by a voltage sensor (not shown), the current flowing through the battery pack 41 measured by a current sensor (not shown), and the temperatures measured by a plurality of temperature sensors (not shown) installed in the battery pack 41.

[0015] The management unit 42 acquires the monitored cell voltages, currents, and temperatures at a predetermined sampling period (e.g., 10 seconds) and stores them in non-volatile memory (not shown). To reduce the amount of data stored, the management unit 42 may store only the maximum and minimum cell voltages among the cell voltages. Also, to reduce the amount of data stored, the management unit 42 may store only the maximum and minimum temperatures among the temperatures at multiple observation points.

[0016] The management unit 42 estimates the SOC (State Of Charge) by combining the OCV (Open Circuit Voltage) method and the current integration method. The OCV method estimates the SOC based on the measured cell OCV and the cell's SOC-OCV curve. The cell's SOC-OCV curve is created in advance by the battery manufacturer based on characteristic tests and is registered in the management unit 42 at the time of shipment.

[0017] The current integration method is a method for estimating the SOC based on the OCV at the start of charging and discharging the cell and the integrated value of the measured current. In the current integration method, current measurement errors accumulate as the charging and discharging time increases. Therefore, it is preferable to use a weighted average of the SOC estimated by the current integration method and the SOC estimated by the OCV method.

[0018] The management unit 42 converts the SOC of each single cell or each parallel cell into an actual capacity, combines these actual capacities to calculate the actual capacity of the battery pack 41, and can estimate the SOC of the battery pack 41 based on this actual capacity and the current full charge capacity of the battery pack 41.

[0019] The management unit 42 transmits battery data including each cell voltage or maximum and minimum cell voltages, current, each temperature or maximum and minimum temperatures, and SOC of the battery pack 41 to the vehicle control unit 30 via an in-vehicle network. For example, a controller area network (CAN) or a local interconnect network (LIN) can be used as the in-vehicle network.

[0020] In EVs, a three-phase AC motor is generally used for the drive motor 34. During power running, the inverter 35 converts DC power supplied from the battery pack 41 into AC power and supplies it to the motor 34. During regeneration, the inverter 35 converts AC power supplied from the motor 34 into DC power and supplies it to the battery pack 41. During power running, the motor 34 rotates in accordance with the AC power supplied from the inverter 35. During regeneration, the motor 34 converts rotational energy generated by deceleration into AC power and supplies it to the inverter 35.

[0021] The vehicle control unit 30 is a vehicle ECU (Electronic Control Unit) that controls the entire electric vehicle 3, and may be configured as, for example, an integrated VCM (Vehicle Control Module).

[0022] The vehicle speed sensor 36 generates a pulse signal proportional to the rotation speed of the front wheel shaft 32f or the rear wheel shaft 32r, and transmits the generated pulse signal to the vehicle control unit 30. The vehicle control unit 30 detects the speed of the electric vehicle 3 based on the pulse signal received from the vehicle speed sensor 36. The vehicle control unit 30 calculates the traveling distance of the electric vehicle 3 based on the change in the speed of the electric vehicle 3 over time.

[0023] The wireless communication unit 37 has a modem for wirelessly connecting to the network 5 (see FIG. 2) via an antenna 37a, and performs wireless signal processing. For example, a mobile phone network (cellular network), a wireless LAN, V2I (Vehicle-to-Infrastructure), V2V (Vehicle-to-Vehicle), an ETC (Electronic Toll Collection System), or DSRC (Dedicated Short Range Communications) can be used.

[0024] While the electric vehicle 3 is traveling, the vehicle control unit 30 can transmit traveling data in real time from the wireless communication unit 37 to the data server 4 (see FIG. 2 ) via the network 5. The traveling data includes at least the vehicle speed and accumulated traveling distance of the electric vehicle 3, and battery data received from the management unit 42. The vehicle control unit 30 samples this data periodically (for example, every 10 seconds) and transmits it to the data server 4 each time.

[0025] The vehicle control unit 30 may store the driving data of the electric vehicles 3 in an internal memory and transmit the driving data stored in the memory in a batch at a predetermined timing. For example, the vehicle control unit 30 may transmit the driving data stored in the memory in a batch to the operation management terminal device 2 (see FIG. 2) installed at the delivery company base after the end of business for the day. The operation management terminal device 2 transmits the driving data of the multiple electric vehicles 3 to the data server 4 each time at a predetermined timing.

[0026] Furthermore, when charging from a charger equipped with a network communication function, the vehicle control unit 30 may transmit all of the driving data stored in the memory to the charger via the charging cable. The charger then transmits the received driving data to the data server 4. This example is effective for an electric vehicle 3 that is not equipped with a wireless communication function.

[0027] FIG. 2 is a diagram illustrating a battery analysis system 1 according to an embodiment. The battery analysis system 1 according to the embodiment is a system used by at least one delivery company. The battery analysis system 1 may be constructed, for example, on an in-house server installed in the in-house facility or data center of a battery analysis service provider that provides an analysis service for battery packs 41 installed in electric vehicles 3. The battery analysis system 1 may also be constructed on a cloud server used based on a cloud service. The battery analysis system 1 may also be constructed on multiple servers distributed across multiple locations (data centers, in-house facilities). The multiple servers may be a combination of multiple in-house servers, a combination of multiple cloud servers, or a combination of an in-house server and a cloud server. In the example shown in FIG. 2, the battery analysis system 1 is constructed by a calculation server 1a and a degradation characteristic storage server 1b.

[0028] A delivery company owns a plurality of electric vehicles 3 and has a delivery base where the electric vehicles 3 are parked. An operation management terminal device 2 is installed at the delivery base. The operation management terminal device 2 is configured, for example, by a PC. The operation management terminal device 2 is used to manage the plurality of electric vehicles 3 belonging to the delivery base. An operation manager of the delivery company can use the operation management terminal device 2 to create an operation plan for the plurality of electric vehicles 3.

[0029] The operation management terminal device 2 can access the battery analysis system 1 via the network 5. The operation management terminal device 2 acquires the analysis results of the battery packs 41 mounted on each electric vehicle 3 from the battery analysis system 1. The analysis results of the battery packs 41 include the remaining life of the battery packs 41.

[0030] The data server 4 acquires and stores driving data from the fleet management terminal device 2 or the electric vehicle 3. The data server 4 may be an in-house server installed in the facility or data center of the delivery company or battery analysis service provider, or may be a cloud server used by the delivery company or battery analysis service provider. Furthermore, each delivery company and battery analysis service provider may have its own data server 4.

[0031] The network 5 is a general term for communication paths such as the Internet, dedicated lines, and VPNs (Virtual Private Networks), and the communication media and protocols are not important. Examples of communication media that can be used include a mobile phone network (cellular network), wireless LAN, wired LAN, optical fiber network, ADSL network, and CATV network. Examples of communication protocols that can be used include TCP (Transmission Control Protocol) / IP (Internet Protocol), UDP (User Datagram Protocol) / IP, and Ethernet (registered trademark).

[0032] When the electric vehicle 3 is parked at the delivery base, the vehicle control unit 30 and the operation management terminal device 2 can exchange data via a network 5 (for example, a wireless LAN), a CAN cable, etc. The vehicle control unit 30 and the operation management terminal device 2 may be configured to be able to exchange data via the network 5 even while the electric vehicle 3 is traveling.

[0033] 3 is a diagram showing an example of the configuration of a battery analysis system 1 according to an embodiment. The battery analysis system 1 includes a processing unit 11, a storage unit 12, and a communication unit 13. The communication unit 13 is a communication interface (e.g., a network interface card (NIC)) for connecting to a network 5 via a wired or wireless connection.

[0034] The processing unit 11 includes a data acquisition unit 111, an SOH calculation unit 112, a deterioration prediction equation generation unit 113, a deterioration rate statistical value calculation unit 114, a life prediction unit 115, and a notification unit 116. The functions of the processing unit 11 can be realized by a combination of hardware resources and software resources, or by hardware resources alone. Examples of hardware resources that can be used include a CPU, ROM, RAM, GPU (Graphics Processing Unit), NPU (Neural Network Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), and other LSIs. Examples of software resources that can be used include programs such as an operating system and applications.

[0035] The storage unit 12 includes a non-volatile recording medium such as an HDD or SSD, and stores various data. The storage unit 12 includes a battery deterioration characteristics storage unit 121. The battery deterioration characteristics storage unit 121 stores storage deterioration characteristics, charge deterioration characteristics, and discharge deterioration characteristics for each type of secondary battery.

[0036] The battery degradation characteristics storage unit 121 stores at least one of the following battery information for identifying the type of secondary battery: model number, model type, cell shape, positive electrode material, composition ratio of the positive electrode material, negative electrode material, composition ratio of the negative electrode material, energy weight density, energy volume density, etc. The data in the battery degradation characteristics storage unit 121 is updated every time a new type of secondary battery is registered. In addition, the data in the battery degradation characteristics storage unit 121 is also updated when characteristic information of an already registered secondary battery is updated.

[0037] Storage degradation of a secondary battery is degradation that progresses over time depending on the temperature and SOC of the secondary battery at each point in time. It progresses over time regardless of whether the battery is being charged or discharged. Storage degradation is mainly caused by the formation of a film (SEI (Solid Electrolyte Interphase) film) on the negative electrode. Storage degradation depends on the SOC and temperature at each point in time. In general, the higher the SOC at each point in time and the higher the temperature at each point in time, the faster the storage degradation rate.

[0038] Charge / discharge degradation of secondary batteries progresses as the number of charge / discharge cycles increases. Charge / discharge degradation is mainly caused by cracking or peeling due to expansion or contraction of the active material. Charge / discharge degradation depends on the SOC range, temperature, and current rate used. In general, the wider the SOC range used, the higher the temperature, and the higher the current rate, the faster the charge / discharge degradation rate.

[0039] The storage deterioration characteristics, charge deterioration characteristics, and discharge deterioration characteristics are derived in advance for each type of secondary battery through experiments and simulations by battery manufacturers.

[0040] 4 is a diagram showing an example of a storage deterioration characteristic map, in which the horizontal axis represents SOC [%] and the vertical axis represents the storage deterioration rate [% / T ｆ１ Storage degradation is described by a model in which it progresses linearly with respect to the 0.5 to 1.0 power of the elapsed time T(h). In general, it is often described by a model in which it progresses linearly with respect to the root law (f1=0.5) of the elapsed time T(h).

[0041] For the sake of simplicity, Fig. 4 only illustrates storage degradation characteristics for two temperatures, 25°C and 45°C, but in reality, storage degradation characteristics for a large number of temperatures are generated. Note that the storage degradation characteristics may be defined not by a map, but by a function that uses SOC and temperature as explanatory variables and the storage degradation rate as a response variable.

[0042] FIG. 5A shows an example of a charge deterioration characteristic map, and FIG. 5B shows an example of a discharge deterioration characteristic map. The horizontal axis indicates the usage range of SOC [%]. In FIGS. 5A and 5B, each SOC value indicates the lower limit of the usage range of 10%. For example, an SOC of 10% indicates that charging and discharging occurs in the SOC range of 10 to 20%, and an SOC of 11% indicates that charging and discharging occurs in the SOC range of 11 to 21%. The vertical axis indicates the charge deterioration rate [% / E ｆ2 ] or discharge deterioration rate [% / E ｆ3 ] is shown.

[0043] Charge and discharge deterioration is described by a model in which deterioration progresses linearly with the 0.5 to 1.0 power of the total charge amount E (Ah) or the total discharge amount E (Ah). Generally, it is often described by a model in which deterioration progresses linearly with the root rule (f2, f3 = 0.5) of the total charge amount E (Ah) or the total discharge amount E (Ah). Note that, instead of the cumulative charge current amount (Ah) or the cumulative discharge current amount (Ah), the cumulative charge energy (kWh) or the cumulative discharge energy (kWh) may be used as the total charge amount E or the total discharge amount E.

[0044] For simplicity, Figures 5A and 5B only depict charge / discharge degradation characteristics for two current rates, 0.1 C and 0.8 C. However, in reality, charge / discharge degradation characteristics for many current rates are generated. During charging, as shown in Figure 5A, it can be seen that the degradation rate increases in the low and high SOC usage range. During discharging, as shown in Figure 5B, it can be seen that the degradation rate increases in the low SOC usage range.

[0045] Furthermore, the charge / discharge degradation characteristics are also affected by temperature, although this is not as significant as the current rate. Therefore, to improve the accuracy of estimating the charge / discharge degradation rate, it is preferable to prepare charge / discharge degradation characteristics that define the relationship between the SOC usage range and the charge / discharge degradation rate for each two-dimensional combination of multiple current rates and multiple temperatures. On the other hand, when generating a simple charge / discharge degradation characteristic map, it is sufficient to assume that the temperature is room temperature and to prepare charge / discharge degradation characteristics for each of multiple current rates.

[0046] The charge / discharge degradation characteristics may be defined not by a map but by a function having the SOC range, current rate, and temperature as explanatory variables and the charge / discharge degradation rate as a response variable. The temperature may be a constant.

[0047] The discharge deterioration of the secondary battery included in the battery pack 41 mounted on the electric vehicle 3 can also be described by a model in which the discharge deterioration rate progresses linearly with the 0.5 to 1.0 power of the cumulative travel distance D (km) of the electric vehicle 3. In this case, the unit of the discharge deterioration rate is [% / D ｆ4 In general, it is often described as a model that progresses linearly with respect to the route rule (f4=0.5) of the cumulative travel distance D (km) of the electric vehicle 3.

[0048] Returning to Fig. 3, the data acquisition unit 111 acquires time-series battery data including the current, temperature, and SOC of the battery pack 41 from the data server 4. For example, battery data for the past year or more is acquired.

[0049] The SOH calculation unit 112 calculates multiple SOHs of the battery pack 41 using a two-point OCV method that uses the SOC difference between two points and the current integrated value obtained by referring to time-series battery data. Specifically, the SOH calculation unit 112 calculates the SOC difference (ΔSOC) between the SOC in the first rest state and the SOC in the second rest state based on the voltage in the first rest state and the voltage in the second rest state of the battery pack 41 and the SOC-OCV curve. The SOH calculation unit 112 calculates the current integrated value Q for the period between the first rest state and the second rest state based on the current value included in the battery data. The SOH calculation unit 112 calculates the current FCC (Full Charge Capacity) of the battery pack 41 based on the current integrated value Q and ΔSOC. The SOH calculation unit 112 calculates the SOH based on the ratio between the current FCC of the battery pack 41 and the initial FCC.

[0050] FIG. 6 is a diagram showing a specific image of the FCC estimation method. The SOH calculation unit 112 identifies two voltages, a first rest state and a second rest state, and sets these as two OCVs. The SOH calculation unit 112 references the SOC-OCV curve to identify two SOCs corresponding to the two OCVs, and calculates a ΔSOC between the two SOCs. In the example shown in FIG. 6, the two SOCs are 20% and 75%, and the ΔSOC is 55%.

[0051] The SOH calculation unit 112 calculates the integrated current amount (=charge / discharge capacity) Q between the two points where the OCVs at the two points have been acquired. The SOH calculation unit 112 calculates the following (Equation 1) to estimate the FCC.

[0052] FCC=Q / ΔSOC (Equation 1) SOH is defined as the ratio of the current FCC to the initial FCC, and the lower the value (closer to 0%), the more advanced the deterioration. The SOH calculation unit 112 calculates the following (Equation 2) to estimate the SOH.

[0053] SOH = current FCC / initial FCC × 100 (Equation 2) The SOH calculation unit 112 calculates the SOH at regular intervals (for example, every month, every two weeks, or every week) using the above-mentioned two-point OCV method based on the time-series battery data.

[0054] The deterioration prediction formula generation unit 113 performs curve regression using the sample data of the plurality of SOHs in time series calculated by the SOH calculation unit 112, and generates a deterioration prediction formula (SOH=m+K×X ｆ ) For example, the least squares method can be used for the curve regression.

[0055] 7 is a graph showing an example of a deterioration prediction formula for the battery pack 41. Storage deterioration of the battery pack 41 progresses linearly with respect to the f1 power of the elapsed time T (h), as shown in the following (Formula 3).

[0056] SOHs=m+Ks×T ｆ１ ... (Equation 3) m is the initial value, and Ks is the storage deterioration rate.

[0057] The parameter m is common and is usually set in the range of 1.0 to 1.1. When the actual initial capacity matches the nominal value, m is set to 1.0. When the nominal value is set to the minimum guaranteed amount and is set lower than the actual initial capacity, a value greater than 1.0 is set.

[0058] The deterioration prediction equation generator 113 performs fitting of a plurality of time-series SOHs to determine the storage deterioration rate Ks and the power coefficient f1 in the above (Equation 3). Note that a fixed value (e.g., 0.5) may be used for the power coefficient f1.

[0059] As shown in the following (Equation 4), the deterioration of the charging of the battery pack 41 progresses linearly with respect to the f-th power of the charge amount E (Ah).

[0060] SOHc=m+Kc×E ｆ2 ... (Equation 4) m is the initial value, and Kc is the charge deterioration rate.

[0061] The degradation prediction equation generator 113 performs fitting of multiple time-series SOHs to determine the charge degradation rate Kc and the power coefficient f2 in the above (Equation 4). Note that a fixed value (e.g., 0.5) may be used for the power coefficient f2.

[0062] Discharge deterioration of the battery pack 41 progresses linearly with the f³ power of the discharge amount E (Ah) as shown in the following (Equation 5), or with the f⁴ power of the cumulative traveling distance D (km) of the electric vehicle 3 as shown in the following (Equation 6).

[0063] SOHd=m+Kd×E ｆ3 ...(Formula 5) SOHd=m+Kd×D ｆ4 ... (Equation 6) m is the initial value, and Kd is the discharge deterioration rate.

[0064] The degradation prediction equation generator 113 performs fitting of a plurality of time-series SOHs to determine the discharge degradation rate Kd and the power coefficient f3 or f4 in the above (Equation 5) or (Equation 6). Note that a fixed value (e.g., 0.5) may be used for the power coefficient f3 or f4.

[0065] The life prediction unit 115 can predict the period, number of charge times, and number of discharge times until the SOH at which the battery pack 41 should be used to the end is reached by substituting the SOH (e.g., 70%) into the generated deterioration prediction formula. The SOH at which the battery pack 41 should be used to the end may be defined as a weighted average of the SOHs obtained from the storage deterioration prediction formula, the SOHc obtained from the charge deterioration prediction formula, and the SOHd obtained from the discharge deterioration prediction formula, as shown in the following (Formula 7).

[0066] SOH=SOHs×r1+SOHc×r2+SOHd×r3 (Equation 7) r1+r2+r3=1 The contributions r1, r2, and r3 depend on the intended use of the battery pack 41. For example, the contributions r2 and r3 are set large for a battery pack 41 mounted on an electric vehicle 3 or a power storage system for FR (Frequency Regulation) use, and the contribution r1 is set large for a battery pack 41 mounted on a power storage system for backup use.

[0067] The notification unit 116 notifies the analysis results of the battery pack 41, including the remaining life of the battery pack 41 predicted by the life prediction unit 115, to the delivery company's operation management terminal device 2 or the terminal device of the vehicle management department via the network 5.

[0068] The deterioration rate statistical value calculation unit 114 calculates the statistical value of the deterioration rate of the battery pack 41 based on the time-series battery data of the battery pack 41 and the deterioration characteristics of the cells used in the battery pack 41 .

[0069] The deterioration rate statistical value calculation unit 114 calculates a statistical value of the storage deterioration rate of the battery pack 41 based on the SOC and temperature included in the time-series battery data of the battery pack 41 and the storage deterioration characteristics of the cells. The deterioration rate statistical value calculation unit 114 derives the storage deterioration rate for each unit time, and calculates any one of the mean value, median value, mode value, standard deviation value, and variance value as a statistical value of the derived storage deterioration rates. The unit time is set to, for example, one day or a time less than one day.

[0070] The deterioration rate statistical value calculation unit 114 calculates a statistical value of the charge deterioration rate of the battery pack 41 based on the SOC, the current-based charge rate, the temperature, and the charge deterioration characteristics of the cells included in the time-series battery data of the battery pack 41. The deterioration rate statistical value calculation unit 114 derives a charge deterioration rate for each unit charge amount and calculates statistical values ​​of the derived multiple charge deterioration rates. The unit charge amount may be set to an amount equivalent to the full charge capacity of the battery pack 41, for example.

[0071] The deterioration rate statistical value calculation unit 114 calculates a statistical value of the discharge deterioration rate of the battery pack 41 based on the SOC, current-based discharge rate, temperature, and cell discharge deterioration characteristics included in the time-series battery data of the battery pack 41. The deterioration rate statistical value calculation unit 114 derives the discharge deterioration rate for each unit discharge amount or each unit mileage, and calculates statistical values ​​of the derived multiple discharge deterioration rates. The unit discharge amount may be set to an amount equivalent to the full charge capacity of the battery pack 41, for example. The unit mileage may be set to 100 to 500 km, for example.

[0072] As secondary batteries become longer-lasting, the usage of secondary batteries is increasingly being changed before reaching the end of their final lifespan (e.g., from in-vehicle use to stationary energy storage use). Primary use of a secondary battery is use according to its initial usage, while secondary use of a secondary battery is use according to a different usage from its initial usage. For example, when the SOH reaches the SOH (e.g., 70%) that indicates the end of primary use of the battery pack 41, the primary use is changed to secondary use. While primary use and secondary use generally have different uses, there are also cases where the same use is changed to a usage that increases or reduces the burden. For example, a change from high-speed EV use to low-speed EV use is an example of a change to a usage that reduces the burden. The example shown in FIG. 7 illustrates a case where the secondary use puts a greater burden on the battery pack 41 than the primary use.

[0073] The deterioration rate statistical value calculation unit 114 calculates a statistical value of the deterioration rate of the battery pack 41 during secondary use based on the actual or predicted time series battery data of the battery pack 41 after the primary use of the battery pack 41 has ended and secondary use has begun, and the deterioration characteristics of the cells used in the battery pack 41.

[0074] When actual battery data measured after the start of secondary use is used as time-series battery data of battery pack 41 after the start of secondary use, data for a shorter period (e.g., one month from the start of secondary use) than the time-series battery data during primary use is used for the time-series battery data after the start of secondary use. Since the deterioration rate of battery pack 41 usually differs between primary use and secondary use, it is necessary to update the deterioration prediction formula as soon as possible after the start of secondary use, and the deterioration prediction formula is updated before long-term battery data is collected.

[0075] When using predicted battery data after the start of secondary use as the time-series battery data of battery pack 41 after the start of secondary use, it is possible to use actual measured battery data of another battery pack undergoing the same life cycle. Note that if the full charge capacity of the other battery pack differs from that of battery pack 41, the battery data of the other battery pack needs to be converted according to the capacity ratio of the two. For the actual measured battery data of the other battery pack, statistical values ​​of battery data of multiple battery packs undergoing the same life cycle may be used. Furthermore, instead of using actual measured battery data of another battery pack undergoing the same life cycle, simulation data during secondary use may be used.

[0076] The deterioration prediction formula generation unit 113 corrects the deterioration rate K of the deterioration prediction formula for the primary use of the battery pack 41 based on the ratio between the statistical value of the deterioration rate during primary use calculated from the deterioration characteristics of the cells used in the battery pack 41 and the statistical value of the deterioration rate during secondary use calculated from the deterioration characteristics, and generates a deterioration prediction formula for secondary use.

[0077] The power coefficient f depends on the battery characteristics and does not change basically depending on the usage method of the battery pack 41, so the power coefficient f of the deterioration prediction formula for primary use is used as it is for the power coefficient f of the deterioration prediction formula for secondary use. The power coefficient f determined by fitting when generating the deterioration prediction formula for primary use may be used, or a fixed value (e.g., 0.5) common to the deterioration prediction formula for primary use and the deterioration prediction formula for secondary use may be used.

[0078] 8 is a flowchart for explaining the degradation prediction process of the battery pack 41 during secondary use according to the embodiment. The data acquisition unit 111 acquires time-series battery data (SOC, temperature, current) during primary use of the battery pack 41 from the data server 4 (S10). For example, battery data older than one year from the time of change to secondary use is acquired. The SOH calculation unit 112 calculates the SOH of the battery pack 41 for a fixed period (for example, every month) using the two-point OCV method based on the time-series battery data (S11). The degradation prediction formula generation unit 113 generates a degradation prediction formula (SOH1=m+K1×X) during primary use of the battery pack 41 based on a plurality of sample data of SOH during primary use of the battery pack 41. ｆ ) is fitted (S12).

[0079] The deterioration rate statistical value calculation unit 114 derives the deterioration rate per unit time or per unit amount of electricity (S13) based on the time-series battery data during primary use of the battery pack 41 and the deterioration characteristics of the cells used in the battery pack 41. The deterioration rate statistical value calculation unit 114 calculates a statistical value a (for example, an average value or a standard deviation value) of the derived deterioration rates (S14).

[0080] The data acquisition unit 111 acquires time-series battery data (SOC, temperature, current) of the battery pack 41 after secondary use (S15). The time-series battery data after secondary use may be actual measured values ​​for a short period (e.g., one month) after the start of secondary use, or may be short-term or long-term predicted values. The deterioration rate statistical value calculation unit 114 derives a deterioration rate per unit time or per unit amount of electricity based on the time-series battery data of the battery pack 41 after secondary use and the deterioration characteristics of the cells used in the battery pack 41 (S16). The deterioration rate statistical value calculation unit 114 calculates a statistical value b (e.g., average value, standard deviation value) of the derived multiple deterioration rates (S17).

[0081] The deterioration prediction formula generation unit 113 generates a deterioration prediction formula (SOH2=m+K2×X ｆ ) into the deterioration prediction formula for primary use (SOH1 = m + K1 × X ｆ) and the ratio between the statistical value a of the deterioration rate during primary use calculated from the deterioration characteristics and the statistical value b of the deterioration rate during secondary use (S18). The deterioration prediction formula generation unit 113 calculates the deterioration prediction formula for secondary use (SOH2=m+K2×X ｆ ) and calculate the deterioration rate K2.

[0082] K2=b / a×K1 (Equation 8) The life prediction unit 115 uses the calculated deterioration prediction equation for secondary use (SOH2=m+K2×X ｆ ) is substituted with the SOH at which secondary use of the battery pack 41 should be terminated, and the period, number of charges, and number of discharges until the SOH at which secondary use should be terminated is predicted (S19).

[0083] As described above, according to this embodiment, it is possible to accurately predict the progression of deterioration after a change in the method of use of the battery pack 41. Since sample data of the SOH during primary use is not used to generate a deterioration prediction formula for secondary use, it is possible to generate a deterioration prediction formula for secondary use with high accuracy.

[0084] To generate a deterioration prediction formula for the battery pack 41, typically, a minimum of about 25 to 40 SOH sample data are required. Therefore, it takes a long time to reach a state where a deterioration prediction formula for secondary use can be generated from the SOH sample data after secondary use begins. In particular, if the frequency of use of the device equipped with the battery pack 41 is low, it takes a long time to collect about 25 to 40 SOH sample data. For example, if the usage method is changed to a backup power storage system, it takes a long time to collect the required number of SOH sample data after secondary use begins.

[0085] In contrast to this, in this embodiment, the deterioration rate during secondary use is calculated based on the SOC, current, and temperature that can be sampled at any time, and the storage deterioration characteristic map, charge deterioration characteristic map, and discharge deterioration characteristic map, and the deterioration rate of the deterioration prediction formula for primary use is updated, thereby making it possible to quickly generate a deterioration prediction formula for secondary use. For example, even if the method of use of the power storage system for backup use is changed, the deterioration rate can be quickly updated based on the SOC, temperature, and storage deterioration characteristic map after secondary use begins.

[0086] The present disclosure has been described above based on the embodiments. The embodiments are merely examples, and it will be understood by those skilled in the art that various modifications are possible in the combination of the respective components and processing steps, and that such modifications are also within the scope of the present disclosure.

[0087] In the above embodiment, an example has been described in which a deterioration prediction formula for after the primary use of the battery pack 41 has ended and the secondary use has begun has been generated. In this regard, the present disclosure is also applicable to a case in which a deterioration prediction formula for after the secondary use of the battery pack 41 has ended and the tertiary use has begun has been generated. That is, the present disclosure is applicable to a case in which a deterioration prediction formula for after the nth (n is a natural number)th use of the battery pack 41 has ended and the (n+1)th use has begun has been generated.

[0088] In the above embodiment, the storage degradation characteristic map, the charge degradation characteristic map, and the discharge degradation characteristic map are not divided by application, but the storage degradation characteristic map, the charge degradation characteristic map, and the discharge degradation characteristic map may be generated separately for each application. For example, a storage degradation characteristic map, a charge degradation characteristic map, and a discharge degradation characteristic map for an in-vehicle storage system, a storage degradation characteristic map, a charge degradation characteristic map, and a discharge degradation characteristic map for an electric storage system used in an FR, and a storage degradation characteristic map, a charge degradation characteristic map, and a discharge degradation characteristic map for an electric storage system used in a backup may be generated respectively.

[0089] The functions executed by the battery analysis system 1 described above may be incorporated into the management unit 42 of the power supply system 40 in the electric vehicle 3 .

[0090] In the above embodiment, the electric vehicle 3 is assumed to be a four-wheeled electric vehicle. In this regard, it may also be an electric motorcycle (electric scooter), an electric bicycle, or an electric kick scooter. Furthermore, electric vehicles include not only full-scale electric vehicles but also low-speed electric vehicles such as golf carts and land cars. Furthermore, the objects on which the battery pack 41 is mounted are not limited to the electric vehicle 3. The objects on which the battery pack 41 is mounted also include electric vehicles such as electric ships, railway vehicles, construction machinery, and multicopters (drones), stationary power storage systems that are charged and discharged under high loads, and medical equipment.

[0091] The embodiment may be specified by the following items.

[0092] [Item 1] A data acquisition unit (111) acquires time-series battery data including a current, a temperature, and a SOC (State Of Charge) of a secondary battery (41); an SOH calculation unit (112) calculates a plurality of SOHs (State Of Health) of the secondary battery (41) based on an SOC difference between two points and an integrated current value obtained by referring to the battery data; and a degradation prediction formula (SOH=m+K×X) for the secondary battery (41) by performing a curve regression of the calculated plurality of SOHs. ｆ and a degradation rate statistical value calculation unit (114) that calculates a statistical value of the degradation rate of the secondary battery (41) based on battery data of the secondary battery (41) and degradation characteristics of the secondary battery (41), wherein the degradation rate statistical value calculation unit (114) calculates a statistical value of the degradation rate of the secondary battery (41) at the time of the (n+1)th use of the secondary battery (41) based on actual measured or predicted battery data of the secondary battery (41) after the nth (n is a natural number) use of the secondary battery (41) has ended and the (n+1)th use has started, and the degradation characteristics of the secondary battery (41), The deterioration prediction formula generation unit (113) corrects the deterioration rate K of the deterioration prediction formula for the nth use of the secondary battery (41) based on a ratio between a statistical value of the deterioration rate for the nth use calculated from the deterioration characteristics of the secondary battery (41) and a statistical value of the deterioration rate for the (n+1)th use calculated from the deterioration characteristics, to generate a deterioration prediction formula for the (n+1)th use.

[0093] This makes it possible to predict with high accuracy the progression of deterioration of the secondary battery (41) after a change in the way it is used.

[0094] [Item 2] The deterioration rate statistical value calculation unit (114) derives a storage deterioration rate of the secondary battery (41) per unit time based on an SOC and a temperature included in battery data of the secondary battery (41) and storage deterioration characteristics of the secondary battery (41), and calculates statistical values ​​of the derived storage deterioration rates; and the deterioration prediction formula generation unit (113) generates a storage deterioration prediction formula (SOH=m+K×period) of the secondary battery (41). ｆ2. The battery analysis system (1) according to item 1,

[0095] This makes it possible to predict with high accuracy the progression of storage deterioration after a change in the method of use of the secondary battery (41).

[0096] [Item 3] The deterioration rate statistical value calculation unit (114) derives a charge deterioration rate for each unit charge amount of the secondary battery (41) based on an SOC, a charge rate based on a current, and a temperature included in battery data of the secondary battery (41) and charge deterioration characteristics of the secondary battery (41), and calculates statistical values ​​of the derived multiple charge deterioration rates; and the deterioration prediction formula generation unit (113) generates a charge deterioration prediction formula (SOH=m+K×accumulated charge amount) of the secondary battery (41). ｆ 2. The battery analysis system (1) according to item 1,

[0097] This makes it possible to predict with high accuracy the progress of charge deterioration after a change in the way the secondary battery (41) is used.

[0098] [Item 4] The deterioration rate statistical value calculation unit (114) derives a discharge deterioration rate for each unit discharge amount of the secondary battery (41) based on an SOC, a discharge rate based on a current, and a temperature included in battery data of the secondary battery (41) and discharge deterioration characteristics of the secondary battery (41), and calculates statistical values ​​of the derived multiple discharge deterioration rates; and the deterioration prediction formula generation unit (113) generates a discharge deterioration prediction formula (SOH=m+K×accumulated discharge amount) of the secondary battery (41). ｆ 2. The battery analysis system (1) according to item 1,

[0099] This makes it possible to predict with high accuracy the progress of discharge deterioration after a change in the method of use of the secondary battery (41).

[0100] [Item 5] The data acquisition unit (111) acquires time-series battery data including a current, a temperature, and an SOC of the secondary battery (41) and accumulated mileage data of a vehicle equipped with the secondary battery (41), the deterioration rate statistical value calculation unit (114) derives a discharge deterioration rate of the secondary battery (41) per unit mileage based on the SOC, a discharge rate based on a current, and a temperature included in the battery data of the secondary battery (41) and on the discharge deterioration characteristics of the secondary battery (41), and calculates statistical values ​​of the derived multiple discharge deterioration rates, and the deterioration prediction formula generation unit (113) generates a discharge deterioration prediction formula (SOH=m+K×accumulated mileage) of the secondary battery (41). ｆ 2. The battery analysis system (1) according to item 1,

[0101] This makes it possible to predict with high accuracy the progress of discharge deterioration after a change in the method of use of the secondary battery (41).

[0102] [Item 6] The battery analysis system (1) according to any one of Items 1 to 5, wherein the actual measured battery data of the secondary battery (41) after the start of the (n+1)th use is data for a shorter period than the battery data of the secondary battery (41) at the nth use.

[0103] This allows for the early generation of a deterioration prediction formula for the secondary battery (41) after a change in the way it is used.

[0104] [Item 7] A step of acquiring time-series battery data including a current, a temperature, and a SOC (State Of Charge) of a secondary battery (41); A step of calculating a plurality of SOH (State Of Health) values ​​of the secondary battery (41) based on an SOC difference between two points and an integrated current value obtained by referring to the battery data; and A step of performing curve regression on the calculated plurality of SOH values ​​to obtain a deterioration prediction formula (SOH=m+K×X ｆand calculating a statistical value of a deterioration rate of the secondary battery (41) based on battery data of the secondary battery (41) and deterioration characteristics of the secondary battery (41), wherein the step of calculating the statistical value of the deterioration rate calculates a statistical value of a deterioration rate of the secondary battery (41) at the (n+1)th use of the secondary battery (41) based on actual or predicted battery data of the secondary battery (41) after the nth (n is a natural number) use of the secondary battery (41) has ended and the (n+1)th use has started, and the step of generating the deterioration prediction formula corrects a deterioration rate K of the deterioration prediction formula at the nth use of the secondary battery (41) based on a ratio between the statistical value of the deterioration rate at the nth use calculated from the deterioration characteristics of the secondary battery (41) and the statistical value of the deterioration rate at the (n+1)th use calculated from the deterioration characteristics, thereby generating the deterioration prediction formula at the (n+1)th use.

[0105] This makes it possible to predict with high accuracy the progression of deterioration of the secondary battery (41) after a change in the way it is used.

[0106] [Item 8] A process of acquiring time-series battery data including a current, a temperature, and a SOC (State Of Charge) of a secondary battery (41); A process of calculating a plurality of SOH (State Of Health) values ​​of the secondary battery (41) based on an SOC difference between two points and an integrated current value obtained by referring to the battery data; and A process of performing curve regression on the calculated plurality of SOH values ​​to obtain a deterioration prediction formula (SOH=m+K×X ｆand calculating a statistical value of a deterioration rate of the secondary battery (41) based on battery data of the secondary battery (41) and deterioration characteristics of the secondary battery (41), wherein the process of calculating the statistical value of the deterioration rate calculates a statistical value of a deterioration rate of the secondary battery (41) at the (n+1)th use of the secondary battery (41) based on actual or predicted battery data of the secondary battery (41) after the nth (n is a natural number) use of the secondary battery (41) has ended and the (n+1)th use has started, and based on the deterioration characteristics of the secondary battery (41), and the process of generating the deterioration prediction formula corrects a deterioration rate K of the deterioration prediction formula at the nth use of the secondary battery (41) based on a ratio between the statistical value of the deterioration rate at the nth use calculated from the deterioration characteristics of the secondary battery (41) and the statistical value of the deterioration rate at the (n+1)th use calculated from the deterioration characteristics, to generate a deterioration prediction formula at the (n+1)th use.

[0107] This makes it possible to predict with high accuracy the progression of deterioration of the secondary battery (41) after a change in the way it is used.

[0108] DESCRIPTION OF SYMBOLS 1 Battery analysis system, 1a Calculation server, 1b Deterioration characteristic holding server, 2 Operation management terminal device, 3 Electric vehicle, 4 Data server, 5 Network, 11 Processing unit, 111 Data acquisition unit, 112 SOH calculation unit, 113 Deterioration prediction formula generation unit, 114 Deterioration rate statistical value calculation unit, 115 Life prediction unit, 116 Notification unit, 12 Memory unit, 121 Battery deterioration characteristic holding unit, 13 Communication unit, 30 Vehicle control unit, 31f Front wheels, 31r Rear wheels, 32f Front wheel axle, 32r Rear wheel axle, 33 Transmission, 34 Motor, 35 Inverter, 36 Vehicle speed sensor, 37 Wireless communication unit, 37a Antenna, 40 Power supply system, 41 Battery pack, 42 ​​Management unit.

Claims

1. A data acquisition unit that acquires time-series battery data including the current, temperature, and SOC (State Of Charge) of a secondary battery, an SOH calculation unit that calculates a plurality of SOHs (State Of Health) of the secondary battery based on the SOC difference and the current integration value between two points obtained by referring to the battery data, and a deterioration prediction formula generation unit that performs curve regression on the calculated plurality of SOHs to generate a deterioration prediction formula (SOH = m + K × X ｆ ), and a deterioration rate statistical value calculation unit that calculates a statistical value of the deterioration rate of the secondary battery based on the battery data of the secondary battery and the deterioration characteristics of the secondary battery. The deterioration rate statistical value calculation unit calculates a statistical value of the deterioration rate of the secondary battery during the (n + 1)th use based on the actually measured or predicted battery data of the secondary battery after the nth (n is a natural number) use of the secondary battery ends and the start of the (n + 1)th use, and the deterioration characteristics of the secondary battery. The deterioration prediction formula generation unit corrects the deterioration rate K of the deterioration prediction formula during the nth use of the secondary battery based on the ratio of the statistical value of the deterioration rate during the nth use calculated from the deterioration characteristics of the secondary battery and the statistical value of the deterioration rate during the (n + 1)th use calculated from the deterioration characteristics, and generates a deterioration prediction formula during the (n + 1)th use. A battery analysis system.

2. The deterioration rate statistical value calculation unit derives the storage deterioration rate per unit time of the secondary battery based on the SOC, temperature included in the battery data of the secondary battery, and the storage deterioration characteristics of the secondary battery, and calculates the statistical value of the plurality of derived storage deterioration rates. The deterioration prediction formula generation unit generates a storage deterioration prediction formula (SOH = m + K × period) of the secondary battery. The battery analysis system according to claim 1. ｆ ).

3. The degradation rate statistical value calculation unit derives the charging degradation rate per unit charge amount of the secondary battery based on the SOC, charging rate based on current, temperature included in the battery data of the secondary battery, and the charging degradation characteristics of the secondary battery, and calculates the statistical value of the plurality of derived charging degradation rates. The degradation prediction formula generation unit generates a charging degradation prediction formula (SOH = m + K × cumulative charge amount) of the secondary battery. The battery analysis system according to claim 1. ｆ ).

4. The deterioration rate statistical value calculation unit derives the discharge deterioration rate per unit discharge amount of the secondary battery based on the SOC, discharge rate based on current, temperature included in the battery data of the secondary battery, and the discharge deterioration characteristics of the secondary battery, and calculates the statistical value of the plurality of derived discharge deterioration rates. The deterioration prediction formula generation unit generates a discharge deterioration prediction formula (SOH = m + K × cumulative discharge amount) of the secondary battery. The battery analysis system according to claim 1. ｆ ).

5. The data acquisition unit acquires time-series battery data including the current, temperature, and SOC of the secondary battery, and cumulative mileage data of the vehicle on which the secondary battery is mounted. The deterioration rate statistical value calculation unit derives the discharge deterioration rate per unit mileage of the secondary battery based on the SOC, discharge rate based on the current, and temperature included in the battery data of the secondary battery, and the discharge deterioration characteristics of the secondary battery, and calculates the statistical value of the plurality of derived discharge deterioration rates. The deterioration prediction formula generation unit generates a discharge deterioration prediction formula (SOH = m + K × cumulative mileage) of the secondary battery. The battery analysis system according to claim 1. ｆ ).

6. The actually measured battery data of the secondary battery after the start of the (n + 1)-th use is data for a shorter period than the battery data of the secondary battery during the n-th use. The battery analysis system according to any one of claims 1 to 5.

7. A step of obtaining time-series battery data including the current, temperature, and SOC (State Of Charge) of the secondary battery; a step of calculating a plurality of SOHs (State Of Health) of the secondary battery based on the SOC difference and the current integration value between two points obtained by referring to the battery data; a step of performing curve regression on the calculated plurality of SOHs to generate a degradation prediction formula for the secondary battery (SOH = m + K×X ｆ ); a step of calculating a statistical value of the degradation rate of the secondary battery based on the battery data of the secondary battery and the degradation characteristics of the secondary battery. The step of calculating the statistical value of the degradation rate calculates the statistical value of the degradation rate of the secondary battery during the (n + 1)-th use based on the actually measured or predicted battery data of the secondary battery after the n-th (n is a natural number) use of the secondary battery is completed and the start of the (n + 1)-th use, and the degradation characteristics of the secondary battery. The step of generating the degradation prediction formula corrects the degradation rate K of the degradation prediction formula for the n-th use of the secondary battery based on the ratio of the statistical value of the degradation rate during the n-th use calculated from the degradation characteristics of the secondary battery and the statistical value of the degradation rate during the (n + 1)-th use calculated from the degradation characteristics, and generates a degradation prediction formula for the (n + 1)-th use. Battery analysis method.

8. A process of acquiring time-series battery data including the current, temperature, and SOC (State Of Charge) of a secondary battery, a process of calculating a plurality of SOHs (State Of Health) of the secondary battery based on the SOC difference and the current integrated value between two points obtained by referring to the battery data, a process of performing curve regression on the calculated plurality of SOHs to generate a degradation prediction formula for the secondary battery (SOH = m + K × X ｆ ), a process of calculating a statistical value of the degradation rate of the secondary battery based on the battery data of the secondary battery and the degradation characteristics of the secondary battery, and causing a computer to execute the process, wherein the process of calculating the statistical value of the degradation rate is based on the actually measured or predicted battery data of the secondary battery after the nth (n is a natural number) use of the secondary battery is completed and the start of the (n + 1)th use, and the degradation characteristics of the secondary battery, and calculating a statistical value of the degradation rate during the (n + 1)th use of the secondary battery, and the process of generating the degradation prediction formula corrects the degradation rate K of the degradation prediction formula during the nth use of the secondary battery based on the ratio of the statistical value of the degradation rate during the nth use calculated from the degradation characteristics of the secondary battery and the statistical value of the degradation rate during the (n + 1)th use calculated from the degradation characteristics, and generates a degradation prediction formula during the (n + 1)th use, a battery analysis program.

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