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

Abstract

A deterioration amount transition identification unit (112) identifies a transition in the amount of deterioration of a secondary battery on the basis of battery data and a deterioration characteristic map for the secondary battery. A maximum / minimum period search unit (113) searches, in a first period, for each of a deterioration maximum period in which the total deterioration amount is at a maximum and a deterioration minimum period in which the total deterioration amount is at a minimum, with a second period being used as a unit for the search. A conversion coefficient calculation unit (114) calculates each of a maximum-side conversion coefficient indicating the ratio of the statistical value of the deterioration amount between the first period and the deterioration maximum period, and a minimum-side conversion coefficient indicating the ratio of the statistical value of the deterioration amount between the first period and the deterioration minimum period. A deterioration prediction formula generation unit (116) performs a curvilinear regression on the time-series state of health (SOH) of the secondary battery to generate a basic deterioration prediction formula, generates a deterioration-maximum-side deterioration prediction formula using the basic deterioration prediction formula and the maximum-side conversion coefficient, and generates a deterioration-minimum-side deterioration prediction formula using the basic deterioration prediction formula and the minimum-side conversion coefficient.

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] When predicting the remaining life of a secondary battery, it is difficult to uniquely determine the period until the end of its life because the usage method and environment of future users are unknown. Therefore, it is possible to predict deterioration when used under high or low loads based on past usage history and output multiple remaining lifespans.

[0003] Patent Literature 1 discloses a method for estimating multiple life spans of a storage battery by calculating the average, maximum, and minimum daily degradation rates from the degradation rates determined by the SOC (State Of Charge) and temperature, and accumulating and regressing each of the degradation rates. It also discloses a method for calculating the average, maximum, and minimum degradation rates based on the degradation rates within a 1σ interval, a 2σ interval, or a 3σ interval, assuming that the distribution of the degradation rates is a normal distribution.

[0004] The above method estimates the degree of degradation using only the degradation rate characteristics, and therefore does not take into account individual differences between cells or the behavior of the period between SOC and temperature measurements, which may result in deviations from the actual degree of degradation. Furthermore, it does not take into account factors that cause degradation other than SOC and temperature, such as current. Furthermore, in cases where it is not possible to assume that the usage pattern that maximizes or minimizes the degradation rate within a day will continue indefinitely into the future, the accuracy of lifespan predictions is low.

[0005] Japanese Patent Application Laid-Open No. 2020-038138

[0006] The present disclosure has been made in view of these circumstances, and its purpose is to provide a technique for improving the accuracy of predicting the life of a secondary battery.

[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 an SOC, current, and temperature of a secondary battery; a degradation amount transition identification unit that identifies a transition of a degradation amount of the secondary battery based on the battery data of the secondary battery and a degradation characteristic map of the secondary battery that is prepared in advance; a maximum / minimum period search unit that searches for a maximum degradation period in which the total degradation amount is maximum and a minimum degradation period in which the total degradation amount is minimum within a first period, using a second period that is shorter than the first period as a search unit; a conversion coefficient calculation unit that calculates a maximum conversion coefficient indicating a ratio of a statistical value of a degradation amount in the maximum degradation period to a statistical value of a degradation amount in the first period, and a minimum conversion coefficient indicating a ratio of a statistical value of a degradation amount in the minimum degradation period to a statistical value of a degradation amount in the first period; and a time-series SOH (State of Health) of the secondary battery estimated based on the battery data in the first period. a degradation prediction formula generation unit that generates a degradation prediction formula for a maximum degradation side using the degradation prediction basic formula and the maximum side conversion coefficient, and generates a degradation prediction formula for a minimum degradation side using the degradation prediction basic formula and the minimum side conversion coefficient; and a life prediction unit that predicts a range of life of the secondary battery using the degradation prediction formula for the maximum degradation side and the degradation prediction formula for the minimum degradation side.

[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 improve the accuracy of predicting the life of a secondary battery.

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

[0011] 1 is a diagram illustrating a battery-equipped device 2 according to an embodiment. The battery-equipped device 2 according to the embodiment is a device equipped with a chargeable and dischargeable battery pack 30. Examples of the battery-equipped device 2 include consumer information devices (e.g., PCs, tablets, and smartphones), home appliances (e.g., cleaning robots), electric cars, electric motorcycles, electric bicycles, electric kick scooters, and multicopters (drones).

[0012] The battery-equipped device 2 includes a control unit 21, a load unit 22, a charging unit 23, a communication unit 24, and a battery pack 30. The control unit 21 controls the entire battery-equipped device 2. The functions of the control unit 21 can be realized by a combination of hardware and software resources, or by hardware resources alone. 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. Software resources that can be used include programs such as an operating system and applications.

[0013] The load unit 22 is a general term for components (excluding the control unit 21, the charging unit 23, and the communication unit 24) that consume power in the battery-equipped device 2. The charging unit 23 is connected to the commercial power grid 4, and converts AC power input from the commercial power grid 4 into DC power of a predetermined voltage or current and outputs it.

[0014] Charging unit 23 includes an AC / DC converter and a DC / DC converter. The DC / DC converter (e.g., a switching regulator) controls the voltage or current of the DC power supplied from the AC / DC converter in accordance with a voltage command value or a current command value supplied from control unit 21, and outputs the voltage or current to at least one of load unit 22 and battery pack 30. When charging is performed from an externally installed quick charger, charging is performed using DC power.

[0015] The battery pack 30 includes a battery assembly 31 and a battery management device 32. The battery assembly 31 includes multiple cells E1-En connected in series. The number of cells connected in series is determined by the specifications of the load unit 22. The cells may be lithium-ion battery cells, nickel-metal hydride battery cells, lead battery cells, or the like. In the following description, an example using lithium-ion battery cells is assumed. Note that, in each series stage of cells, multiple cells may be connected in parallel to increase capacity.

[0016] A switch SW1 that switches between electrical continuity with the load unit 22 or the charging unit 23 is inserted in a power line connecting the battery pack 31 to the load unit 22 or the charging unit 23. A semiconductor switch or a relay can be used as the switch SW1.

[0017] The battery management device 32 includes a measurement unit 33 and a control unit 34. The measurement unit 33 is configured with an AFE (Analog Front End) IC or an ASIC (Application Specific Integrated Circuit), and the control unit 34 is configured with a microcontroller.

[0018] The measurement unit 33 is connected to each node of the multiple cells E1-En connected in series by multiple voltage measurement lines, and measures the voltage of each cell E1-En by measuring the voltage between each two adjacent voltage measurement lines.

[0019] The measurement unit 33 includes a multiplexer and an A / D converter. The multiplexer outputs the voltages of the multiple cells E1-En to the A / D converter in a predetermined order. The A / D converter converts the analog voltages input from the multiplexer into digital values. The measurement unit 33 transmits the voltage values ​​of the cells E1-En, converted into digital values, to the control unit 34 via a serial communication interface.

[0020] The measurement unit 33 measures the current flowing through the battery pack 31. A shunt resistor Rs is connected to a power line connecting the battery pack 31 to the load unit 22 or the charging unit 23. A differential amplifier (not shown) amplifies the voltage across the shunt resistor Rs and outputs it to an A / D converter in the measurement unit 33. The A / D converter converts the analog voltage indicating the current flowing through the battery pack 31, which is input from the differential amplifier, into a digital value. The measurement unit 33 transmits the current value converted into a digital value to the control unit 34 via a serial communication interface.

[0021] A temperature sensor T1 (e.g., a thermistor) is installed on the surface of the battery pack 31. A divided voltage between the temperature sensor T1 and a voltage dividing resistor (not shown) is input to a measurement unit 33. An A / D converter in the measurement unit 33 converts the input analog voltage indicating the temperature into a digital value. The measurement unit 33 transmits the converted digital temperature value to the control unit 34 via a serial communication interface.

[0022] The control unit 34 manages the states of the cells E1-En based on the voltage values ​​of the cells E1-En, the current values ​​flowing through the battery pack 31, and the temperature values ​​of the battery pack 31 received from the measurement unit 33. When the control unit 34 detects overcharge, overdischarge, overcurrent, abnormally high temperature, or abnormally low temperature, it sends a shutoff signal for the switch SW1 to the measurement unit 33 to turn off the switch SW1.

[0023] The control unit 34 executes programs such as firmware within the microcontroller to achieve the following functions. The control unit 34 estimates the SOC by combining the OCV (Open Circuit Voltage) method and the current integration method. The OCV method is a method for estimating 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 control unit 34 at the time of shipment.

[0024] The current integration method is a method for estimating the SOC based on the OCV at the start of cell charging and discharging and the integrated value of the measured current. With 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.

[0025] The control unit 34 transmits battery data including the voltage, current, temperature, and SOC of each cell of the battery pack 30 to the control unit 21 at a predetermined interval (e.g., 10-second intervals, 30-second intervals, or 1-minute intervals) via a network within the device. The communication unit 24 is an external communication interface (e.g., a network interface card (NIC)) for connecting to an external network 5 via a wired or wireless connection. The control unit 21 accesses the network 5 via the communication unit 24 and transmits the battery data received from the control unit 34 of the battery pack 30 to the battery analysis system 1 at a predetermined transmission interval (e.g., 10-second intervals, 30-second intervals, or 1-minute intervals). The control unit 21 may store the received battery data in an internal memory and transmit the battery data stored in the memory all at once at a predetermined timing.

[0026] The network 5 is a general term for communication paths such as the Internet, a dedicated line, and a Virtual Private Network (VPN), and the communication medium and protocol are not important. Examples of communication media that can be used include a wired LAN, a wireless LAN, a mobile phone network, an optical fiber network, an ADSL network, and a 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).

[0027] The battery analysis system 1 is a system for estimating and analyzing the state of a battery pack 30 mounted in a battery-equipped device 2. In this embodiment, the battery analysis system 1 is built on a cloud server installed in a data center managed by a cloud service provider. A battery analysis service provider that provides an analysis service for the battery pack 30 uses the cloud server by entering into a contract with the cloud service provider. Note that the battery analysis system 1 may also be built on the battery analysis service provider's own server installed in its own facility or data center.

[0028] 2 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 control unit 11, a storage unit 12, and a communication unit 13. The communication unit 13 is an external communication interface for connecting to a network 5 via a wired or wireless connection.

[0029] The control unit 11 includes a data acquisition unit 111, a deterioration amount transition identification unit 112, a maximum / minimum period search unit 113, a conversion coefficient calculation unit 114, an SOH estimation unit 115, a deterioration prediction formula generation unit 116, and a life prediction unit 117. The functions of the control 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, NPU, ASIC, FPGA, and other LSIs. Examples of software resources that can be used include programs such as an operating system and applications.

[0030] 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 deterioration characteristics map storage unit 121 and a battery data storage unit 122. The data acquisition unit 111 acquires battery data of the battery pack 30 installed in the battery-equipped device 2 from the battery-equipped device 2 via the network 5. The data acquisition unit 111 saves the acquired battery data in the battery data storage unit 122.

[0031] FIG. 3A shows an image of a storage degradation characteristic map. 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. Generally, 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.

[0032] In the storage degradation characteristic map shown in FIG. 3A, the amount of storage degradation of a secondary battery is defined as the amount of SOH decrease per unit time (ΔSOH / √t). It is generally known that storage degradation progresses approximately linearly with respect to the root law (0.5 power) of elapsed time t (h). Therefore, the unit time is set to √t. The storage degradation characteristic map shown in FIG. 3A is described in a two-dimensional parameter space of temperature (°C) and SOC (%). For example, the unit width of temperature may be set to 1°C, and the unit width of SOC may be set to 1%.

[0033] FIG. 3B shows an image of a charge degradation characteristic map, and FIG. 3C shows an image of a discharge degradation characteristic map. Charge / discharge degradation of a secondary battery 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 degradation rate.

[0034] In the charge deterioration characteristic map shown in FIG. 3B, the charge deterioration amount of the secondary battery is expressed as the SOH decrease amount per unit charge amount (ΔSOH / √Qｃ Generally, charge deterioration is defined as the cumulative charge amount Q ｃ It is known that the charge rate is approximately linear with respect to the root law of (Ah). ｃ The charge degradation characteristic map shown in FIG. 3B is described in a three-dimensional parameter space of temperature (°C), SOC range (%), and charge rate (C). For example, the unit width of the temperature may be set to 1°C, the unit width of the SOC range to 1%, and the unit width of the charge rate to 0.1C.

[0035] In the discharge deterioration characteristic map shown in FIG. 3C, the discharge deterioration amount of the secondary battery is expressed as the SOH decrease amount per unit discharge amount (ΔSOH / √Q ｄ Generally, discharge deterioration is defined as the cumulative discharge amount Q ｄ It is known that the discharge rate is approximately linear with respect to the root law of (Ah). ｄ The discharge degradation characteristic map shown in FIG. 3C is described in a three-dimensional parameter space of temperature (°C), SOC range (%), and discharge rate (C). For example, the unit width of the temperature may be set to 1°C, the unit width of the SOC range to 1%, and the unit width of the discharge rate to 0.1C.

[0036] 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 the battery manufacturer, and are then mapped and stored in the deterioration characteristics map storage unit 121.

[0037] The data acquisition unit 111 acquires time-series battery data including the voltage, current, temperature, and SOC of each cell of the battery pack 30, which is stored in the battery data storage unit 122. The data acquisition unit 111 acquires battery data for a first period (for example, one year).

[0038] The estimation of the cell deterioration amount described below may be performed for all cells constituting the battery pack 31, or may be performed for only the maximum voltage cell and the minimum voltage cell. For example, in a battery pack 31 with a small number of series connections, such as a pedestrian-type battery, the deterioration amount of each cell may be estimated, whereas in a battery pack 31 with a large number of series connections, such as an EV, the deterioration amount may be estimated for only the maximum voltage cell and the minimum voltage cell. The deterioration amount may also be estimated for each battery pack 31. In this case, the sum of the cell voltages is used as the voltage of the battery pack 31, and a converted value obtained by combining the SOCs of the cells is used as the SOC of the battery pack 31.

[0039] The degradation amount transition identifying unit 112 identifies a transition in the degradation amount of the target cell based on the battery data of the target cell during the first period and the cell degradation characteristic map stored in the degradation characteristic map storage unit 121. More specifically, the degradation amount transition identifying unit 112 identifies a transition in the storage degradation amount of the target cell based on the transitions in the temperature and SOC of the target cell during the first period and the storage degradation characteristic map. The degradation amount transition identifying unit 112 identifies a transition in the charge degradation amount of the target cell based on the transitions in the temperature, SOC range, and charge rate of the target cell during the first period and the charge degradation characteristic map. The degradation amount transition identifying unit 112 identifies a transition in the discharge degradation amount of the target cell based on the transitions in the temperature, SOC range, and discharge rate of the target cell during the first period and the discharge degradation characteristic map. The charge rate and discharge rate can be calculated from the full charge capacity and current data of the target cell.

[0040] The maximum / minimum period search unit 113 searches for the maximum degradation period in which the total degradation amount of the target cell is maximum and the minimum degradation period in which the total degradation amount of the target cell is minimum, using the second period as a search unit, within the first period. More specifically, the maximum / minimum period search unit 113 searches for the maximum degradation period in which the total of the storage degradation amount, charge degradation amount, and discharge degradation amount is maximum and the minimum degradation period in which the total of the storage degradation amount, charge degradation amount, and discharge degradation amount is minimum, based on the respective trends in the storage degradation amount, charge degradation amount, and discharge degradation amount of the target cell during the first period.

[0041] The second period is set to a period shorter than the first period, and may be set to a fixed period or a variable period.

[0042] FIG. 4 is a diagram showing an example of how the second period is set. The SOC of a cell fluctuates with charging and discharging. If the second period is a fixed period, it may be set to, for example, 30 days or 90 days. In this case, the maximum / minimum period search unit 113 searches for the maximum degradation period and the minimum degradation period of the target cell within the first period by shifting the first period by a unit time (for example, one day).

[0043] When the second period is a variable period, the second period may be set to, for example, the period from the completion of charging to the completion of the next charging. The completion of charging may be when the battery reaches full charge.

[0044] The conversion coefficient calculation unit 114 calculates a maximum conversion coefficient indicating the ratio of the statistical value of the deterioration amount in the maximum deterioration period to the statistical value of the deterioration amount in the first period, and calculates a minimum conversion coefficient indicating the ratio of the statistical value of the deterioration amount in the minimum deterioration period to the statistical value of the deterioration amount in the first period.

[0045] As shown in the following (Equation 1), the conversion coefficient calculation unit 114 refers to the storage deterioration characteristic map and calculates the average value D of the storage deterioration amount per unit time (ΔSOH / √Δt) in the first period. ｓall The average value D of the storage deterioration amount per unit time during the maximum deterioration period ｓｍａｘ The maximum conversion coefficient R for storage deterioration indicates the ratio of ｓｍａｘ Δt indicates the unit sampling interval of the battery data.

[0046] R ｓｍａｘ =D ｓｍａｘ / D ｓall　 ...(Formula 1) D ｓall is the sum of (ΔSOH / √Δt) × √Δt in the first period / the sum of √Δt in the first period D ｓｍａｘ is the sum of (ΔSOH / √Δt)×√Δt in the maximum degradation period / the sum of √Δt in the maximum degradation period. The conversion coefficient calculation unit 114 refers to the storage degradation characteristic map and calculates the average value D of the storage degradation amount (ΔSOH / √Δt) per unit time in the first period, as shown in the following (Equation 2). ｓall The average value D of the storage deterioration amount per unit time in the minimum deterioration period ｓｍinThe minimum conversion coefficient R for storage deterioration indicates the ratio of ｓｍｉｎ Calculate.

[0047] R ｓｍａｘ =D ｓｍａｘ / D ｓall　 ...(Formula 2) D ｓall is the sum of (ΔSOH / √Δt) × √Δt in the first period / the sum of √Δt in the first period D ｓｍin is the sum of (ΔSOH / √Δt)×√Δt in the minimum degradation period / the sum of √Δt in the minimum degradation period. The conversion coefficient calculation unit 114 refers to the charge degradation characteristic map and calculates the amount of charge degradation (ΔSOH / √ΔQ) per unit charge amount in the first period, as shown in the following (Equation 3). ｃ ) average value D ｃall The average value D of the charge deterioration amount per unit charge amount during the maximum deterioration period ｃｍａｘ The maximum conversion coefficient R for charge deterioration indicates the ratio of ｃｍａｘ Calculate ΔQ ｃ indicates the increase in the cumulative charge amount in a unit sampling interval of the battery data.

[0048] R ｃｍａｘ =D ｃｍａｘ / D ｃall　 ...(Formula 3) D ｃall is (ΔSOH / √ΔQ ｃ ) × √ΔQ ｃ Sum of / √ΔQ in the first period ｃ Total of D ｃｍａｘ is the maximum deterioration period (ΔSOH / √ΔQ ｃ ) × √ΔQ ｃ Total / √ΔQ during maximum degradation period ｃ The conversion coefficient calculation unit 114 refers to the charge degradation characteristic map and calculates the charge degradation amount per unit charge amount in the first period (ΔSOH / √ΔQ ｃ ) average value D ｃall The average value D of the charge deterioration amount per unit charge amount during the minimum deterioration period ｃｍin The minimum conversion coefficient R for charge deterioration indicates the ratio of ｃｍｉｎ Calculate.

[0049] R ｃｍａｘ =D ｃｍａｘ / D ｃall　 ...(Formula 4) D ｃall is (ΔSOH / √ΔQ ｃ ) × √ΔQ ｃ Sum of / √ΔQ in the first period ｃ Total of D ｃｍｉｎ is the (ΔSOH / √ΔQ ｃ ) × √ΔQ ｃ Sum of / √ΔQ in the minimum deterioration period ｃ The conversion coefficient calculation unit 114 refers to the discharge deterioration characteristic map and calculates the discharge deterioration amount per unit discharge amount in the first period (ΔSOH / √ΔQ ｄ ) average value D ｄall The average value D of the discharge deterioration amount per unit discharge amount during the maximum deterioration period ｄｍａｘ The maximum conversion coefficient R for discharge deterioration, which indicates the ratio of ｄｍａｘ Calculate ΔQ ｄ indicates the increase in the cumulative discharge amount in a unit sampling interval of the battery data.

[0050] R ｄｍａｘ =D ｃｍａｘ / D ｃall　 ...(Formula 5) D ｄall is (ΔSOH / √ΔQ ｄ ) × √ΔQ ｄ Sum of / √ΔQ in the first period ｄ Total of D ｄｍａｘ is the maximum deterioration period (ΔSOH / √ΔQ ｄ ) × √ΔQ ｄ Total / √ΔQ during maximum degradation period ｄ The conversion coefficient calculation unit 114 calculates the discharge deterioration amount per unit discharge amount in the first period (ΔSOH / √ΔQ ｄ ) average value D ｄall The average value D of the discharge deterioration amount per unit discharge amount in the minimum deterioration period ｄｍｉｎ The minimum conversion coefficient R for discharge deterioration, which indicates the ratio of ｄｍｉｎ Calculate.

[0051] R ｄｍｉｎ =D ｃｍａｘ / D ｃall　 ...(Formula 6) Dｄall is (ΔSOH / √ΔQ ｄ ) × √ΔQ ｄ Sum of / √ΔQ in the first period ｄ Total of D ｄｍｉｎ is the (ΔSOH / √ΔQ ｄ ) × √ΔQ ｄ Sum of / √ΔQ in the minimum deterioration period ｄ The sum of the above values. As the statistical value of each deterioration amount, the median or mode may be used instead of the average value.

[0052] The SOH estimation unit 115 calculates the SOH of the target cell in a time series manner during the first period using a two-point OCV method that uses the SOC difference between two points and the current integrated value obtained by referring to the battery data of the target cell during the first period. Specifically, the SOH estimation unit 115 calculates the SOC difference (ΔSOC) between the SOC in the first resting state and the SOC in the second resting state based on the voltage in the first resting state and the voltage in the second resting state of the battery pack 30 and the SOC-OCV curve. The SOH estimation unit 115 calculates the current integrated value Q for the period from the first resting state to the second resting state based on the current value included in the battery data. The SOH estimation unit 115 calculates the current full charge capacity (FCC) of the target cell based on the current integrated value Q and ΔSOC. The SOH estimation unit 115 estimates the SOH of the target cell based on the ratio between the current FCC of the target cell and the initial FCC.

[0053] 5 is a diagram showing a specific image of the FCC estimation method. The SOH estimation unit 115 identifies two points, OCV1 and OCV2, in the first and second resting states, and sets these as the two OCV points. The SOH estimation unit 115 references the SOC-OCV curve to identify two points, SOC1 and SOC2, corresponding to the two points, OCV1 and OCV2, and calculates ΔSOC between the two SOC points.

[0054] The SOH estimation unit 115 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 estimation unit 115 calculates the following (Equation 7) to estimate the FCC.

[0055] FCC=Q / ΔSOC (Equation 7) 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 estimation unit 115 calculates the following (Equation 8) to estimate the SOH.

[0056] SOH = current FCC / initial FCC × 100 (Equation 8) The SOH estimation unit 115 uses the battery data for the target cell in the first period and the above-mentioned two-point OCV method to calculate the SOH at regular intervals (for example, every month, every two weeks, or every week) and generate a time-series SOH.

[0057] In the case of a battery pack 30 that has undergone periodic maintenance charging to check its capacity, the SOH estimation unit 115 can estimate the SOH based on battery data obtained during the maintenance charging. During maintenance charging, the battery pack 30 is charged at a constant current from 0% SOC to 100% SOC, and the FCC can be estimated by integrating the current during the maintenance charging period.

[0058] The degradation prediction equation generator 116 performs curve regression on the time-series SOH of the target cell in the first period to generate the degradation prediction basic equation shown in Equation 9 below. For example, the least squares method can be used for the curve regression. The initial SOH is basically set to 100%, but may also be set to (100-α)%.

[0059] SOH = initial SOH - w ｓ √t-w ｃ √Q ｃ -w ｄ √Q ｄ　 ...(Formula 9) (t: time, Q ｃ : Charge amount, Q ｄ : Discharge amount, w ｓ , w ｃ , w ｄ : regression coefficient) The deterioration prediction formula generating unit 116 generates a deterioration prediction basic formula and a maximum side conversion coefficient R for storage deterioration. ｓｍａｘ , the maximum conversion coefficient R for charge deterioration ｃｍａｘ , the maximum conversion coefficient R for discharge deterioration ｄｍａｘ Using the above, the deterioration prediction formula for the maximum deterioration side shown in the following (Formula 10) is generated.

[0060] SOH=Initial SOH-R ｓｍａｘ ・w ｓ √t-R ｃｍａｘ ・w ｃ √Q ｃ -R ｄｍａｘ ・w ｄ √Q ｄ　 ...(Equation 10) The deterioration prediction equation generator 116 generates a deterioration prediction basic equation and a minimum conversion coefficient R ｓｍｉｎ , the minimum conversion coefficient R for charge deterioration ｃｍｉｎ , the minimum conversion coefficient R for discharge deterioration ｄｍｉｎ Using the above, a deterioration prediction formula on the minimum deterioration side shown in the following (Formula 11) is generated.

[0061] SOH=Initial SOH-R ｓｍｉｎ ・w ｓ √t-R ｃｍｉｎ ・w ｃ √Q ｃ -R ｄｍｉｎ ・w ｄ √Q ｄ　 ...(Equation 11) The life prediction unit 117 predicts the range of life of the target cell using the deterioration prediction equation for the maximum deterioration side and the deterioration prediction equation for the minimum deterioration side. Because the deterioration prediction equations shown in (Equation 9) to (Equation 11) above are three-dimensional hypersurfaces, when calculating the remaining life, it is necessary to determine the transition of the cumulative charge amount and cumulative discharge amount over the operation period.

[0062] The life prediction unit 117 calculates R ｃｍａｘ ・w ｃ √Q ｃ Q in the section ｃ It is assumed that the change in the charge amount during the maximum deterioration period is repeated. Similarly, the life prediction unit 117 calculates R ｄｍａｘ ・w ｄ √Q ｄ Q in the section ｄ It is assumed that the transition of the discharge amount during the maximum degradation period is repeated.

[0063] The life prediction unit 117 calculates R ｃｍｉｎ ・w ｃ √Q ｃ Q in the section ｃIt is assumed that the transition of the charge amount in the minimum deterioration period is repeated. Similarly, the life prediction unit 117 calculates R ｄｍｉｎ ・w ｄ √Q ｄ Q in the section ｄ It is assumed that the transition of the discharge amount during the minimum deterioration period is repeated.

[0064] As a result, the deterioration prediction equations shown above in (Equation 9) to (Equation 11) become functions of only the elapsed time t, and the life prediction unit 117 can calculate the range of remaining life until the SOH (e.g., 70%) set as the life is reached in advance.

[0065] 6 is a flowchart illustrating a process for predicting a lifespan of a target cell performed by the battery analysis system 1 according to the embodiment. The data acquisition unit 111 acquires time-series battery data including the voltage, current, temperature, and SOC of the target cell for a first period stored in the battery data storage unit 122 (S10). The deterioration amount transition identification unit 112 identifies the transition of the deterioration amount of the target cell based on the battery data for the first period of the target cell and each deterioration characteristic map of the same cell (S11).

[0066] The maximum / minimum period search unit 113 searches for the maximum degradation period in which the total degradation amount of the target cells is maximum and the minimum degradation period in which the total degradation amount of the target cells is minimum within the first period, using the second period width, and identifies them (S12). The conversion coefficient calculation unit 114 calculates a maximum conversion coefficient indicating the ratio of the statistical value of the degradation amount in the maximum degradation period to the statistical value of the degradation amount in the first period (S13). The conversion coefficient calculation unit 114 calculates a minimum conversion coefficient indicating the ratio of the statistical value of the degradation amount in the minimum degradation period to the statistical value of the degradation amount in the first period (S14).

[0067] The SOH estimation unit 115 calculates the SOH of the target cell in a time series during the first period using the two-point OCV method based on the battery data of the target cell during the first period (S15). The degradation prediction formula generation unit 116 generates a basic degradation prediction formula by performing curve regression on the time series SOH of the target cell during the first period (S16). The degradation prediction formula generation unit 116 generates a maximum degradation prediction formula based on the basic degradation prediction formula and the maximum conversion coefficient (S17). The degradation prediction formula generation unit 116 generates a minimum degradation prediction formula based on the basic degradation prediction formula and the minimum conversion coefficient (S18). The life prediction unit 117 predicts the range of life of the target cell using the maximum degradation prediction formula and the minimum degradation prediction formula (S19).

[0068] 7 is a diagram showing an image of lifespan prediction using a deterioration prediction formula. The SOH estimated based on the past usage history of the battery pack 30 includes a mixture of data from normal load, low load, and high load. The deterioration prediction basic formula, which is regressed based on the past time series SOH, reflects the progression of the deterioration amount over the entire past usage history of the battery pack 30.

[0069] In this embodiment, the conversion coefficient calculation unit 114 refers to each deterioration characteristic map to calculate the average deterioration rate for the entire period, the average deterioration rate for the period of low-load use, and the average deterioration rate for the period of high-load use. The conversion coefficient calculation unit 114 calculates a conversion coefficient f for converting the average deterioration rate for the entire period to the average deterioration rate for the period of low-load use, and calculates a conversion coefficient g for converting the average deterioration rate for the entire period to the average deterioration rate for the period of high-load use.

[0070] The deterioration prediction formula generation unit 116 corrects the basic deterioration prediction formula with a conversion coefficient f to generate a deterioration prediction formula for when the battery pack 30 is used under low load, and corrects the basic deterioration prediction formula with a conversion coefficient g to generate a deterioration prediction formula for when the battery pack 30 is used under high load.

[0071] In this embodiment, it is assumed that the deterioration amount estimated from the time series SOH generated by the two-point OCV method and the deterioration amount estimated by referring to the deterioration characteristic map are in a proportional relationship. ｃ (Ah), cumulative discharge amount Q ｄThe value of each index of (Ah) depends on the battery characteristics and does not change basically depending on the load of the battery pack 30. ｓ , w ｃ , w ｄ The regression coefficient w depends on the load of the battery pack 30, and the higher the load, the larger the value. ｓ , w ｃ , w ｄ By multiplying by a conversion coefficient, the influence of the usage load of the battery pack 30 can be reflected in the deterioration prediction formula.

[0072] In the deterioration prediction formulas shown in (Equation 9) to (Equation 11) above, the elapsed time t (h) and the cumulative charge amount Q ｃ (Ah), cumulative discharge amount Q ｄ Although each exponent of (Ah) is set to 0.5, each exponent may be set to a value other than 0.5 (specifically, a value between 0.5 and 1.0) depending on the type of cell.

[0073] 8 is a diagram showing an image of the remaining life of a target cell predicted from the basic deterioration prediction formula, the maximum deterioration prediction formula, and the minimum deterioration prediction formula. The period from the present to the SOH set as the life is the shortest when predicted from the maximum deterioration prediction formula, and the longest when predicted from the minimum deterioration prediction formula. The life prediction unit 117 predicts the range from the end of the life estimated from the maximum deterioration prediction formula to the end of the life estimated from the minimum deterioration prediction formula as the remaining life range.

[0074] The life prediction unit 117 predicts the range of remaining life of all cells constituting the battery pack 31, or of the maximum voltage cell and the minimum voltage cell, and presents to the user the range of remaining life of the cell whose range of remaining life is closest to the present as the range of remaining life of the battery pack 30. Note that when the deterioration amount is estimated and the range of remaining life is predicted for each battery pack 31, the life prediction unit 117 presents to the user the range of remaining life of the battery pack 30.

[0075] The life prediction unit 117 may generate a message for the user to improve usage based on the difference between the statistical value of the storage deterioration amount of the target cell in the first period and the statistical value of the storage deterioration amount in the minimum deterioration period, the difference between the statistical value of the charge deterioration amount of the target cell in the first period and the statistical value of the charge deterioration amount in the minimum deterioration period, and the difference between the statistical value of the discharge deterioration amount of the target cell in the first period and the statistical value of the discharge deterioration amount in the minimum deterioration period. The life prediction unit 117 presents the remaining life range of the battery pack 30 to the user together with the message for improving usage.

[0076] For example, if storage degradation accounts for the largest contribution to the reduction in degradation during the minimum degradation period, the life prediction unit 117 selects a message recommending that the user adjust the temperature of the storage environment when the battery-equipped device 2 is not in use. For example, if charging degradation accounts for the largest contribution to the reduction in degradation during the minimum degradation period, the life prediction unit 117 selects a message recommending that the user reduce the use of rapid charging. For example, if discharge degradation accounts for the largest contribution to the reduction in degradation during the minimum degradation period, the life prediction unit 117 selects a message recommending that the user reduce the load when using the battery-equipped device 2. For example, if the battery-equipped device 2 is an EV, the life prediction unit 117 selects a message recommending that the user reduce the frequency of sudden acceleration. For example, if the battery-equipped device 2 is an information device, the life prediction unit 117 selects a message recommending that the user reduce the screen brightness.

[0077] For example, if the largest contribution to the decrease in the amount of deterioration during the minimum deterioration period is storage deterioration, the life prediction unit 117 may instruct the control unit 21 of the battery-equipped device 2 to lower the target SOC value at the end of charging. For example, if the largest contribution to the decrease in the amount of deterioration during the minimum deterioration period is charge deterioration, the life prediction unit 117 may instruct the control unit 21 of the battery-equipped device 2 to lower the charge rate during charging. For example, if the largest contribution to the decrease in the amount of deterioration during the minimum deterioration period is discharge deterioration, the life prediction unit 117 may instruct the control unit 21 of the battery-equipped device 2 to reduce the load on the battery-equipped device 2 during use. For example, if the battery-equipped device 2 is an EV, the life prediction unit 117 may instruct the control unit 21 of the battery-equipped device 2 to impose an acceleration limit. For example, if the battery-equipped device 2 is an information device, the life prediction unit 117 may instruct the control unit 21 of the battery-equipped device 2 to lower the screen brightness.

[0078] As described above, according to this embodiment, the current SOH of the target cell is calculated based on actual measurements without referencing the degradation characteristic map, and the future SOH is calculated using a degradation prediction formula corrected using the degradation characteristic map, thereby improving the accuracy of predicting the target cell's lifespan. The current SOH fully reflects individual cell differences. The future SOH reflects individual cell differences, including their relative relationship with the degradation characteristic map. Because not only the storage degradation characteristic map but also the charge degradation characteristic map and the discharge degradation characteristic map are used, the impact of the charge rate and discharge rate on cell degradation can be reflected in the lifespan prediction. Furthermore, by searching for the maximum degradation period and the minimum degradation period within the second time span, the accuracy of predicting the cell lifespan when the battery pack 30 is used under high load and when it is used under low load can be improved.

[0079] 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.

[0080] In the above-described embodiment, the storage degradation characteristic, charge degradation characteristic, and discharge degradation characteristic are derived in advance for each cell type through experiments and simulations by the battery manufacturer and are then mapped. In this regard, a degradation characteristic map update unit (not shown) of the battery analysis system 1 may update the storage degradation characteristic map, charge degradation characteristic map, and discharge degradation characteristic map for each cell type based on battery data for each type of cell collected by the battery analysis system 1. This makes it possible to maintain the proportionality coefficient between the degradation amount calculated from the actual measurement data and the degradation amount calculated from the degradation characteristic map as close to 1 as possible.

[0081] The functions executed by the battery analysis system 1 described above may be incorporated into the control unit 34 in the battery pack 30 or the control unit 21 in the battery-equipped device 2 .

[0082] The embodiment may be specified by the following items.

[0083] [Item 1] A data acquisition unit (111) acquires time-series battery data including SOC, current, and temperature of a secondary battery (E1); a degradation amount transition identification unit (112) that identifies a transition of a degradation amount of the secondary battery (E1) based on the battery data of the secondary battery (E1) and a degradation characteristic map of the secondary battery (E1) prepared in advance; a maximum / minimum period search unit (113) that searches, within a first period, for a maximum degradation period in which the total degradation amount is maximum and a minimum degradation period in which the total degradation amount is minimum, using a second period shorter than the first period as a search unit; and a conversion coefficient calculation unit (114) that calculates a maximum conversion coefficient indicating a ratio of a statistical value of a degradation amount in the maximum degradation period to a statistical value of a degradation amount in the first period, and a minimum conversion coefficient indicating a ratio of a statistical value of a degradation amount in the minimum degradation period to a statistical value of a degradation amount in the first period, a deterioration prediction formula generation unit (116) that generates a deterioration prediction formula by performing curve regression on a time-series SOH of the secondary battery (E1) estimated based on battery data for the first period, generates a deterioration prediction formula on the maximum degradation side using the deterioration prediction formula and the maximum side conversion coefficient, and generates a deterioration prediction formula on the minimum degradation side using the deterioration prediction formula and the minimum side conversion coefficient; and a life prediction unit (117) that predicts a life range of the secondary battery (E1) using the deterioration prediction formula on the maximum degradation side and the deterioration prediction formula on the minimum degradation side.

[0084] This makes it possible to improve the accuracy of predicting the life of the secondary battery (E1).

[0085] [Item 2] The battery analysis system (1) according to Item 1, wherein the following degradation characteristic maps are prepared for the secondary battery (E1): a storage degradation characteristic map that specifies the amount of storage degradation per unit time depending on the temperature and SOC of the secondary battery (E1); a charge degradation characteristic map that specifies the amount of charge degradation per unit charge amount depending on the temperature, SOC range, and charge rate of the secondary battery (E1); and a discharge degradation characteristic map that specifies the amount of discharge degradation per unit discharge amount depending on the temperature, SOC range, and discharge rate of the secondary battery (E1).

[0086] This allows the influence of the charge rate and discharge rate on the deterioration of the secondary battery (E1) to be reflected in the life prediction.

[0087] [Item 3] The conversion coefficient calculation unit (114) refers to the storage degradation characteristic map and calculates a maximum side conversion coefficient R for storage degradation, which indicates a ratio of an average value or a median value of the storage degradation amount per unit time in the maximum degradation period to an average value or a median value of the storage degradation amount per unit time in the first period. ｓｍａｘ and a minimum side conversion coefficient R for storage deterioration, which indicates the ratio of the average value or median value of the storage deterioration amount per unit time in the minimum deterioration period to the average value or median value of the storage deterioration amount per unit time in the first period. ｓｍｉｎ and calculating a maximum side conversion coefficient R for charge degradation, which indicates the ratio of the average value or median value of the amount of charge degradation per unit charge amount during the maximum degradation period to the average value or median value of the amount of charge degradation per unit charge amount during the first period, by referring to the charge degradation characteristic map. ｃｍａｘ and a minimum conversion coefficient R for charge degradation indicating the ratio of the average or median value of the amount of charge degradation per unit charge amount in the minimum degradation period to the average or median value of the amount of charge degradation per unit charge amount in the first period. ｃｍｉｎ and calculating a maximum conversion coefficient R for discharge deterioration, which indicates the ratio of the average value or median value of the discharge deterioration amount per unit discharge amount in the maximum deterioration period to the average value or median value of the discharge deterioration amount per unit discharge amount in the first period, by referring to the discharge deterioration characteristic map. ｄｍａｘ and a minimum conversion coefficient R for discharge deterioration, which indicates the ratio of the average value or median value of the discharge deterioration amount per unit discharge amount in the minimum deterioration period to the average value or median value of the discharge deterioration amount per unit discharge amount in the first period. ｄｍｉｎ 3. The battery analysis system (1) according to item 2, wherein

[0088] According to this, the magnitude of the load when the secondary battery (E1) is in use can be quantified as a coefficient for correcting the basic formula for predicting deterioration.

[0089] [Item 4] The battery analysis system (1) according to Item 1, wherein the second period is set to a fixed period or a period from the completion of charging to the next completion of charging.

[0090] This allows the designer to arbitrarily adjust or optimize the width between the maximum deterioration period and the minimum deterioration period.

[0091] [Item 5] The battery analysis system (1) according to Item 1, further comprising an SOH estimation unit (115) that calculates a time-series SOH of the secondary battery (E1) during the first period based on a difference in SOC between two points and an integrated current value obtained by referring to battery data during the first period, or based on battery data during maintenance charging.

[0092] This allows the past SOH of the secondary battery (E1) to be generated as data that completely reflects individual differences.

[0093] [Item 6] The deterioration prediction formula generating unit (116) uses the following deterioration prediction basic formula: SOH=initial SOH−w ｓ √t-w ｃ √Q ｃ -w ｄ √Q ｄ (t: time, Q ｃ : Charge amount, Q ｄ : Discharge amount, w ｓ , w ｃ , w ｄ 3. The battery analysis system (1) according to item 2, wherein the battery analysis system (1) generates a regression coefficient (

[0094] This makes it possible to generate a highly accurate SOH prediction formula.

[0095] [Item 7] The deterioration prediction formula generating unit (116) uses the following deterioration prediction basic formula: SOH=initial SOH−w ｓ √t-w ｃ √Q ｃ -w ｄ √Q ｄ (t: time, Q ｃ : Charge amount, Q ｄ : Discharge amount, w ｓ , w ｃ , w ｄ: regression coefficient), and the deterioration prediction formula for the maximum deterioration side is: SOH=initial SOH−R ｓｍａｘ ・w ｓ √t-R ｃｍａｘ ・w ｃ √Q ｃ -R ｄｍａｘ ・w ｄ √Q ｄ is generated, and the deterioration prediction formula for the minimum deterioration side is: SOH=initial SOH−R ｓｍｉｎ ・w ｓ √t-R ｃｍｉｎ ・w ｃ √Q ｃ -R ｄｍｉｎ ・w ｄ √Q ｄ 4. The battery analysis system (1) according to Item 3,

[0096] This makes it possible to generate highly accurate prediction formulas for SOH at high load and low load.

[0097] [Item 8] The life prediction unit (117) is a deterioration prediction formula R on the maximum deterioration side. ｃｍａｘ ・w ｃ √Q ｃ Q in the section ｃ Assuming that the change in the charge amount during the maximum deterioration period is repeated, R ｄｍａｘ ・w ｄ √Q ｄ Q in the section ｄ Assuming that the change in the discharge amount during the maximum deterioration period is repeated, R ｃｍｉｎ ・w ｃ √Q ｃ Q in the section ｃ Assuming that the change in the charge amount during the minimum deterioration period is repeated, R ｄｍｉｎ ・w ｄ √Q ｄ Q in the section ｄ 8. The battery analysis system (1) according to item 7, wherein the range of the life of the secondary battery (E1) is predicted on the assumption that the transition of the discharge amount in the minimum deterioration period is repeated.

[0098] According to this, the deterioration prediction formula can be treated as a function of only the parameter t.

[0099] [Item 9] A step of acquiring time-series battery data including SOC, current, and temperature of a secondary battery (E1); A step of identifying a transition of the deterioration amount of the secondary battery (E1) based on the battery data of the secondary battery (E1) and a deterioration characteristic map of the secondary battery (E1) prepared in advance; A step of searching, within a first period, for a maximum deterioration period in which the total deterioration amount is maximum and a minimum deterioration period in which the total deterioration amount is minimum, using a second period shorter than the first period as a search unit; A step of calculating a maximum conversion coefficient indicating a ratio of a statistical value of the deterioration amount in the maximum deterioration period to a statistical value of the deterioration amount in the first period, and a minimum conversion coefficient indicating a ratio of a statistical value of the deterioration amount in the minimum deterioration period to a statistical value of the deterioration amount in the first period, a step of generating a deterioration prediction formula by performing curve regression on a time-series SOH of the secondary battery (E1), which is estimated based on battery data for the first period, generating a deterioration prediction formula for a maximum deterioration side using the deterioration prediction formula and the maximum side conversion coefficient, and generating a deterioration prediction formula for a minimum deterioration side using the deterioration prediction formula and the minimum side conversion coefficient; and a step of predicting a range of a life of the secondary battery (E1) using the deterioration prediction formula for a maximum deterioration side and the deterioration prediction formula for a minimum deterioration side.

[0100] This makes it possible to improve the accuracy of predicting the life of the secondary battery (E1).

[0101] [Item 10] A process of acquiring time-series battery data including SOC, current, and temperature of a secondary battery (E1); A process of identifying a transition of the deterioration amount of the secondary battery (E1) based on the battery data of the secondary battery (E1) and a deterioration characteristic map of the secondary battery (E1) prepared in advance; A process of searching, within a first period, for a maximum deterioration period in which the total deterioration amount is maximum and a minimum deterioration period in which the total deterioration amount is minimum, using a second period shorter than the first period as a search unit; A process of calculating a maximum conversion coefficient indicating a ratio of a statistical value of the deterioration amount in the maximum deterioration period to a statistical value of the deterioration amount in the first period, and a minimum conversion coefficient indicating a ratio of a statistical value of the deterioration amount in the minimum deterioration period to a statistical value of the deterioration amount in the first period; a process of generating a deterioration prediction formula by performing curve regression on a time-series SOH of the secondary battery (E1), which is estimated based on battery data for the first period, a process of generating a deterioration prediction formula on the maximum side using the deterioration prediction formula and the maximum side conversion coefficient, and a process of generating a deterioration prediction formula on the minimum side using the deterioration prediction formula and the minimum side conversion coefficient; and a process of predicting a range of a lifespan of the secondary battery (E1) using the deterioration prediction formula on the maximum side and the deterioration prediction formula on the minimum side.

[0102] This makes it possible to improve the accuracy of predicting the life of the secondary battery (E1).

[0103] 2 Battery-equipped device, 4 Commercial power system, 5 Network, 21 Control unit, 22 Load unit, 23 Charging unit, 24 Communication unit, 30 Battery pack, 31 Assembled battery, 32 Battery management device, 33 Measurement unit, 34 Control unit, E1-En Cell, Rs Shunt resistor, SW1 Switch, 1 Battery analysis system, 11 Control unit, 111 Data acquisition unit, 112 Deterioration amount transition identification unit, 113 Maximum / minimum period search unit, 114 Conversion coefficient calculation unit, 115 SOH estimation unit, 116 Deterioration prediction formula generation unit, 117 Life prediction unit, 12 Memory unit, 121 Deterioration characteristic map storage unit, 122 Battery data storage unit, 13 Communication unit.

Claims

1. A data acquisition unit acquires time-series battery data including SOC (State Of Charge), current, and temperature of a secondary battery; a degradation amount transition identification unit that identifies a transition in the amount of degradation of the secondary battery based on the battery data of the secondary battery and a degradation characteristic map of the secondary battery that is prepared in advance; a maximum / minimum period search unit that searches within a first period for a maximum degradation period in which the total amount of degradation is maximum and a minimum degradation period in which the total amount of degradation is minimum, using a second period that is shorter than the first period as a search unit; a conversion coefficient calculation unit that calculates a maximum conversion coefficient indicating the ratio of a statistical value of the amount of degradation in the maximum degradation period to a statistical value of the amount of degradation in the first period, and a minimum conversion coefficient indicating the ratio of a statistical value of the amount of degradation in the minimum degradation period to a statistical value of the amount of degradation in the first period; and a time-series SOH (State Of Charge) of the secondary battery estimated based on the battery data in the first period. a deterioration prediction formula generation unit that generates a deterioration prediction formula for a maximum deterioration side using the deterioration prediction basic formula and the maximum side conversion coefficient, and generates a deterioration prediction formula for a minimum deterioration side using the deterioration prediction basic formula and the minimum side conversion coefficient; and a life prediction unit that predicts a life range of the secondary battery using the deterioration prediction formula for the maximum deterioration side and the deterioration prediction formula for the minimum deterioration side.

2. The battery analysis system of claim 1, wherein the following degradation characteristic maps are prepared for the secondary battery: a storage degradation characteristic map that specifies the amount of storage degradation per unit time depending on the temperature and SOC of the secondary battery; a charge degradation characteristic map that specifies the amount of charge degradation per unit charge amount depending on the temperature, SOC range, and charge rate of the secondary battery; and a discharge degradation characteristic map that specifies the amount of discharge degradation per unit discharge amount depending on the temperature, SOC range, and discharge rate of the secondary battery.

3. The conversion coefficient calculation unit refers to the storage deterioration characteristic map and calculates a maximum side conversion coefficient R for storage deterioration, which indicates the ratio of the average value or median value of the storage deterioration amount per unit time in the maximum deterioration period to the average value or median value of the storage deterioration amount per unit time in the first period. ｓｍａｘ and a minimum side conversion coefficient R for storage deterioration, which indicates the ratio of the average value or median value of the storage deterioration amount per unit time in the minimum deterioration period to the average value or median value of the storage deterioration amount per unit time in the first period. ｓｍｉｎ and calculating a maximum side conversion coefficient R for charge degradation, which indicates the ratio of the average value or median value of the amount of charge degradation per unit charge amount during the maximum degradation period to the average value or median value of the amount of charge degradation per unit charge amount during the first period, by referring to the charge degradation characteristic map. ｃｍａｘ and a minimum conversion coefficient R for charge degradation indicating the ratio of the average or median value of the amount of charge degradation per unit charge amount in the minimum degradation period to the average or median value of the amount of charge degradation per unit charge amount in the first period. ｃｍｉｎ and calculating a maximum conversion coefficient R for discharge deterioration, which indicates the ratio of the average value or median value of the discharge deterioration amount per unit discharge amount in the maximum deterioration period to the average value or median value of the discharge deterioration amount per unit discharge amount in the first period, by referring to the discharge deterioration characteristic map. ｄｍａｘ and a minimum conversion coefficient R for discharge deterioration, which indicates the ratio of the average value or median value of the discharge deterioration amount per unit discharge amount in the minimum deterioration period to the average value or median value of the discharge deterioration amount per unit discharge amount in the first period. ｄｍｉｎ The battery analysis system according to claim 2 , wherein the battery analysis system calculates:

4. The battery analysis system according to claim 1, wherein the second period is set to a fixed period or a period from the completion of charging to the completion of the next charging.

5. The battery analysis system of claim 1, further comprising an SOH estimation unit that calculates the SOH of the secondary battery in the first period in a time series manner based on the SOC difference between two points and the current integrated value obtained by referring to the battery data for the first period, or based on battery data during maintenance charging.

6. The deterioration prediction formula generation unit uses the following formula as the deterioration prediction basic formula: SOH = initial SOH - w ｓ √t-w ｃ √Q ｃ -w ｄ √Q ｄ (t: time, Q ｃ : Charge amount, Q ｄ : Discharge amount, w ｓ , w ｃ , w ｄ The battery analysis system according to claim 2 , wherein the battery analysis system generates a regression coefficient ( ) ( ).

7. The deterioration prediction formula generation unit uses the following formula as the deterioration prediction basic formula: SOH = initial SOH - w ｓ √t-w ｃ √Q ｃ -w ｄ √Q ｄ (t: time, Q ｃ : Charge amount, Q ｄ : Discharge amount, w ｓ , w ｃ , w ｄ : regression coefficient), and the deterioration prediction formula for the maximum deterioration side is: SOH=initial SOH−R ｓｍａｘ ・w ｓ √t-R ｃｍａｘ ・w ｃ √Q ｃ -R ｄｍａｘ ・w ｄ √Q ｄ is generated, and the deterioration prediction formula for the minimum deterioration side is: SOH=initial SOH−R ｓｍｉｎ ・w ｓ √t-R ｃｍｉｎ ・w ｃ √Q ｃ -R ｄｍｉｎ ・w ｄ √Q ｄ The battery analysis system according to claim 3 , wherein 8. The life prediction unit is R of the deterioration prediction formula on the maximum deterioration side ｃｍａｘ ・w ｃ √Q ｃ Q in the section ｃ Assuming that the change in the charge amount during the maximum deterioration period is repeated, R ｄｍａｘ ・w ｄ √Q ｄ Q in the section ｄ Assuming that the change in the discharge amount during the maximum deterioration period is repeated, R ｃｍｉｎ ・w ｃ √Q ｃ Q in the section ｃ Assuming that the change in the charge amount during the minimum deterioration period is repeated, R ｄｍｉｎ ・w ｄ √Q ｄ Q in the section ｄ The battery analysis system according to claim 7 , wherein the range of the life of the secondary battery is predicted by assuming that the transition of the discharge amount in the minimum deterioration period is repeated.

9. A step of acquiring time-series battery data including SOC (State Of Charge), current, and temperature of the secondary battery; a step of identifying a transition in the deterioration amount of the secondary battery based on the battery data of the secondary battery and a deterioration characteristic map of the secondary battery prepared in advance; a step of searching, within a first period, for a maximum deterioration period in which the total deterioration amount is maximum and a minimum deterioration period in which the total deterioration amount is minimum, using a second period shorter than the first period as a search unit; a step of calculating a maximum conversion coefficient indicating the ratio of a statistical value of the deterioration amount in the maximum deterioration period to a statistical value of the deterioration amount in the first period, and a minimum conversion coefficient indicating the ratio of a statistical value of the deterioration amount in the minimum deterioration period to a statistical value of the deterioration amount in the first period; and a step of calculating a time-series SOH (State Of Charge) of the secondary battery estimated based on the battery data in the first period. generating a deterioration prediction formula for a maximum deterioration side using the deterioration prediction formula and the maximum side conversion coefficient, and generating a deterioration prediction formula for a minimum deterioration side using the deterioration prediction formula and the minimum side conversion coefficient; and predicting a range of a life of the secondary battery using the deterioration prediction formula for the maximum deterioration side and the deterioration prediction formula for the minimum deterioration side.

10. A process of acquiring time-series battery data including SOC (State Of Charge), current, and temperature of a secondary battery; a process of identifying a transition in the amount of deterioration of the secondary battery based on the battery data of the secondary battery and a deterioration characteristic map of the secondary battery prepared in advance; a process of searching, within a first period, for a maximum deterioration period in which the total amount of deterioration is maximum and a minimum deterioration period in which the total amount of deterioration is minimum, using a second period shorter than the first period as a search unit; a process of calculating a maximum conversion coefficient indicating the ratio of a statistical value of the amount of deterioration in the maximum deterioration period to a statistical value of the amount of deterioration in the first period, and a minimum conversion coefficient indicating the ratio of a statistical value of the amount of deterioration in the minimum deterioration period to a statistical value of the amount of deterioration in the first period; and a process of calculating a time-series SOH (State Of Charge) of the secondary battery estimated based on the battery data in the first period. a degradation prediction formula for a maximum side using the degradation prediction formula and the maximum side conversion coefficient; and a degradation prediction formula for a minimum side using the degradation prediction formula and the minimum side conversion coefficient.

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