State estimation method, state estimation device, state estimation system, and state estimation program

Abstract

To provide a device for estimating a state change in an estimation object, and to provide a state estimation method for increasing estimation precision for the state change in the estimation object.SOLUTION: A state estimation method extracts the maximum value and the minimum value of a main physical quantity and an inter-extreme value time from an extreme value to a next extreme value, from time-series data showing a temporal change in the main physical quantity regarding an estimation object. The state estimation method calculates a state change speed of the estimation object on the basis of relation data showing a relation of state change speeds of the estimation object relative to the maximum and minimum values of the main physical quantity, and the extracted maximum and minimum values of the main physical quantity. The state estimation method calculates an amount of state change in the estimation object for each inter-extreme value time on the basis of the extracted inter-extreme value time and the calculated state change speed.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] Embodiments of the present invention relate to a state estimation method, a state estimation device, a state estimation system, and a state estimation program.

Background Art

[0002] In the estimation of the state change of an estimation target, based on the physical quantity related to the estimation target, the state change speed, the state change amount, etc. of the estimation target are estimated. For example, when estimating the state change of a battery as the estimation target, as physical quantities related to the battery, the SOC of the battery, the temperature of the battery, the current flowing through the battery, etc. are measured, and based on the measured physical quantities as indicators, the deterioration speed of the battery, the degree of deterioration of the battery, etc. are estimated. When estimating the state change of the estimation target based on the physical quantity related to the estimation target as described above, it is required that the configuration of the device and system for performing the estimation is simple and that the device and system are easy to implement. Further, it is required that the estimation accuracy regarding the state change of the estimation target is high.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

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[0003]

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発明の概要

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[0003]

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発明の概要

発明が解決しようとする課題

[0004] The problem to be solved by the present invention is to provide a state estimation method, a state estimation device, a state estimation system, and a state estimation program that have a simple configuration for a device and a system for estimating the state change of an estimation target and that increase the estimation accuracy regarding the state change of the estimation target.

課題を解決するための手段

[0005] It seems there are some redundant and unclear parts in the original text during the description of "

先行技術文献

特許文献

[0006] [Figure 1] Figure 1 is a schematic diagram showing a state estimation system according to the first embodiment. [Figure 2] Figure 2 is a schematic diagram showing an example of processing performed by the processing circuit when executing a state estimation program in the state estimation device according to the first embodiment. [Figure 3] Figure 3 is a schematic diagram showing an example of SOC time-series data used in processing in the state estimation device according to the first embodiment. [Figure 4] Figure 4 is a schematic diagram showing the time-varying SOC flag data generated from the SOC time-series data of the example in Figure 3, in the first embodiment. [Figure 5] Figure 5 is a schematic diagram showing the time evolution of the maximum and minimum values ​​of SOC extracted from the SOC time series data of the example in Figure 3, in the first embodiment. [Figure 6] Figure 6 is a schematic diagram showing the time evolution of the interval between extreme values ​​extracted from the SOC time series data of the example in Figure 3, in the first embodiment. [Figure 7] Figure 7 is a schematic diagram showing an example of relational data (first relational data) stored in the storage medium of the state estimation device in the first embodiment. [Figure 8] Figure 8 is a schematic diagram showing an example of processing performed by the processing circuit when executing a state estimation program in the state estimation device according to the second embodiment. [Figure 9] Figure 9 is a schematic diagram showing an example of second relational data stored in the storage medium of the state estimation device in the second embodiment. [Figure 10] Figure 10 is a schematic diagram showing an example of processing performed by the processing circuit when executing a state estimation program in the state estimation device according to the third embodiment. [Figure 11] Figure 11 is a schematic diagram showing an example of processing performed by a processing circuit when executing a state estimation program in a state estimation device according to a modified example of the third embodiment. [Figure 12A] Figure 12A is a schematic diagram showing a part of an example of the processing performed by the processing circuit when executing the state estimation program in the state estimation device according to the fourth embodiment. [Figure 12B] Figure 12B is a schematic diagram showing an example of the processing performed by the processing circuit when executing the state estimation program in the state estimation device according to the fourth embodiment, excluding the part shown in Figure 12A. [Modes for carrying out the invention]

[0007] The embodiments will be described below with reference to the drawings.

[0008] (First embodiment) First, as an example of an embodiment, a first embodiment will be described. Figure 1 shows a state estimation system 1 according to the first embodiment. In the state estimation system 1, the state changes of the battery 2 are estimated as the target of estimation. As state changes of the battery 2, for example, the degradation rate of the battery 2 and the degree of degradation of the battery 2 from the start of use of the battery 2 are estimated. Examples of the state change rate of the battery 2 that constitutes the degradation rate of the battery 2 include the rate at which the capacity of the battery 2 decreases and the rate at which the resistance of the battery 2 increases. Examples of the amount of state change of the battery 2 that constitutes the degree of degradation of the battery 2 include the amount of decrease in the capacity of the battery 2 from the start of use, etc. and the amount of increase in the resistance of the battery 2 from the start of use, etc.

[0009] The battery 2 whose state change is to be estimated is, for example, a secondary battery such as a lithium-ion secondary battery. The battery 2 may be formed from single cells (single batteries), or it may be a battery module or cell block formed by electrically connecting multiple single cells. When the battery 2 is formed from multiple single cells, the multiple single cells in the battery 2 may be electrically connected in series, or they may be electrically connected in parallel. In addition, the battery 2 may have both a series connection structure in which multiple single cells are connected in series, and a parallel connection structure in which multiple single cells are connected in parallel. Furthermore, the battery 2 may be a battery string, a battery array, or a storage battery in which multiple battery modules are electrically connected. In addition, in a battery module in which multiple single cells are electrically connected, each of the multiple single cells may be the battery 2 whose state change is to be estimated.

[0010] The state estimation system 1 is equipped with a current detection circuit 3, a voltage detection circuit 5, and a temperature sensor 6. The current detection circuit 3 periodically detects and measures the current flowing through the battery 2, and the voltage detection circuit 5 periodically detects and measures the voltage applied to the battery 2. The temperature sensor 6 periodically detects and measures the temperature of the battery 2.

[0011] The state estimation system 1 includes a state estimation device 10. The state estimation device 10 estimates changes in the state of the battery 2, for example, estimating the cumulative value from a certain point in time, such as the start of use of the battery 2, for either the decrease in the capacity of the battery 2 or the increase in the resistance of the battery 2. In an example such as Figure 1, the state estimation device 10 is a processing device (computer) such as a server, and includes a processing circuit 11, a storage medium 12, a communication module 13, and a user interface 15. The processing circuit 11 is composed of a processor or integrated circuit, and the processor etc. that constitutes the processing circuit 11 includes any of the following: CPU (Central Processing Unit), ASIC (Application Specific Integrated Circuit), microcontroller, FPGA (Field Programmable Gate Array), and DSP (Digital Signal Processor). The processing circuit 11 may be composed of one processor etc. or may be composed of multiple processors etc.

[0012] The storage medium 12 is either a main memory device such as a memory, or an auxiliary memory device. Examples of storage mediums 12 include magnetic disks, optical disks (CD-ROM, CD-R, DVD, etc.), magneto-optical disks (MO, etc.), and semiconductor memory. The state estimation device 10 may be provided with only one memory device that serves as the storage medium 12, or it may be provided with multiple devices.

[0013] The processing circuit 11 performs processing by executing programs stored in the storage medium 12. In the example shown in Figure 1, the programs executed by the processing circuit 11 include a data management program 21, a time-series data generation program 22, and a state estimation program 23. The processing circuit 11 writes data to the storage medium 12 and reads data from the storage medium 12 by executing the data management program 21. The processing circuit 11 also performs processing to generate time-series data, which will be described later, by executing the time-series data generation program 22. Finally, the processing circuit 11 performs processing to estimate the state changes of the battery 2, which will be described later, by executing the state estimation program 23.

[0014] In one example, the state estimation device 10 is composed of a plurality of processing devices (computers) such as a plurality of servers, and the processors of the plurality of processing devices cooperate to perform the processes described below. In another example, the state estimation device 10 is composed of a cloud server in a cloud environment. The infrastructure of the cloud environment is composed of virtual processors such as virtual CPUs and cloud memory. Therefore, when the state estimation device 10 is composed of a cloud server, the virtual processor performs the processes described below, including the process of estimating the state change of the battery 2, instead of the processing circuit 11. And the cloud memory has a function of storing programs, data, etc., similar to the storage medium 12.

[0015] In one example, the storage medium 12 that stores the program executed by the processing circuit 11 and the data used in the processing of the processing circuit 11 is provided in a computer separate from the state estimation device 10. In this case, the state estimation device 10 is connected to the computer provided with the storage medium 12 etc. via a network. In another example, the state estimation device 10 is mounted on a battery-mounted device (not shown) on which the battery 2 is mounted. In this case, in the state estimation device 10, a processor etc. mounted on the battery-mounted device performs the processes described below, including the process of estimating the state change of the battery 2, instead of the processing circuit 11.

[0016] The communication module 13 is composed of a communication interface of a processing device that constitutes the state estimation device 10, etc. The processing circuit 11 communicates with external devices, etc. via the communication module 13. The processing circuit 11 receives the measurement results of the current detection circuit 3, the voltage detection circuit 5, and the temperature sensor 6 via the communication module 13. In the user interface 15, operations related to the estimation of the state change of the battery 2 are input by users of the state estimation system 1, etc. For this reason, the user interface 15 is provided with an operation unit, such as a button, a mouse, a touch panel, or a keyboard, etc., on which operations are input by users, etc. Also, in the user interface 15, a notification unit for notifying information related to the estimation of the state change of the battery 2 is provided. In the notification unit, information is notified by any of screen display, sound transmission, etc. Note that the user interface 15 may be provided separately from the processing device that constitutes the state estimation device 10.

[0017] The processing circuit 11 generates time-series data by executing the time-series data generation program 22. In the generation of time-series data, the processing circuit 11 generates current time-series data indicating the time change of the current flowing through the battery 2 based on the measurement result of the current detection circuit 3, and generates voltage time-series data indicating the time change of the voltage applied to the battery 2 based on the measurement result of the voltage detection circuit 5. Then, the processing circuit 11 generates temperature time-series data indicating the time change of the temperature of the battery 2 based on the measurement result of the temperature sensor 6. Regarding the current of the battery 2, for example, the current input to the battery 2, that is, the charging current of the battery 2, is indicated by a positive value, and the current output from the battery 2, that is, the discharge current, is indicated by a negative value.

[0018] Furthermore, in battery 2, the charge amount (amount of charge) and SOC (state of charge) are defined as parameters indicating the charge state of battery 2. In generating time-series data, the processing circuit 11 generates SOC time-series data that shows the time change of the SOC of battery 2. For example, in battery 2, the state in which the charge amount becomes a first charge amount is defined as the SOC being 0%, and the state in which the charge amount becomes a second charge amount greater than the first charge amount is defined as the SOC being 100%. The ratio of the subtraction value obtained by subtracting the first charge amount from the real-time charge amount to the subtraction value obtained by subtracting the first charge amount from the second charge amount becomes the real-time SOC of battery 2.

[0019] Furthermore, the real-time charge of battery 2 can be calculated based on the charge of battery 2 at a predetermined reference point and the time change of the current from that predetermined point. For example, the processing circuit 11 calculates the real-time charge of battery 2 by adding the time-integrated value of the current from a predetermined reference point to the charge of battery 2 at that predetermined point. Then, the processing circuit 11 calculates the real-time SOC of battery 2 using the calculation result of the real-time charge of battery 2 as described above. Therefore, the processing circuit 11 can calculate SOC time-series data showing the time change of the SOC of battery 2.

[0020] Furthermore, in the generated time-series data, only the measured values ​​at the most recent measurement point may be shown for the physical quantities related to battery 2, such as SOC, current, and temperature, as mentioned above. In this case, the physical quantities related to battery 2 are measured periodically. Each time a new measurement is taken for the physical quantities related to battery 2, the values ​​(measured values) in the time-series data are sequentially overwritten with the newly measured values ​​from the measured values ​​of the previous measurement.

[0021] Furthermore, for battery 2, a lower voltage limit Vmin and an upper voltage limit Vmax are specified. For battery 2, the charging capacity (amount of charge) from the lower voltage limit Vmin to the upper voltage limit Vmax during charging under specified conditions, or the discharge capacity (amount of charge) from the upper voltage limit Vmax to the lower voltage limit Vmin during discharging under specified conditions, is defined as the capacity of battery 2. The capacity of battery 2 decreases with repeated charging and discharging. Also, even when battery 2 is not being charged or discharged, its capacity decreases over time.

[0022] Furthermore, the processing circuit 11 estimates the state change of the battery 2 to be estimated by executing the state estimation program 23. The measurement data generated as described above is used to estimate the state change of the battery 2. In the following explanation, the decrease in the capacity of the battery 2 is estimated as the amount of state change that indicates the state change of the battery 2. Note that the amount of state change to be estimated is not limited to the decrease in capacity, as long as it is an amount of state change that indicates the degree of degradation of the battery 2. The process for estimating the decrease in the capacity of the battery 2 shown below can also be applied to estimating other amounts of state change that indicate the degree of degradation of the battery 2, such as the increase in the resistance of the battery 2.

[0023] In the process of estimating the decrease in capacity of battery 2, the processing circuit 11 uses the State of Charge (SOC) of battery 2, which is a physical quantity related to battery 2, as the primary physical quantity. The processing circuit 11 then extracts the maximum and minimum values ​​of SOC, which are the extreme values ​​of SOC, and the inter-extreme time, which is the time between one extreme value of SOC and the next, from the SOC time series data (first time series data) J1. When battery 2 is used, charging and discharging of battery 2 are repeated. When battery 2 is charged, the SOC increases, and when battery 2 is discharged, the SOC decreases. Therefore, in the SOC time series data J1, the SOC of battery 2 reaches a maximum value when switching from charging to discharging, and the SOC of battery 2 reaches a minimum value when switching from discharging to charging. Thus, extreme values ​​of SOC exist in the SOC time series data J1.

[0024] Figure 2 shows an example of the processing performed by the processing circuit 11 when executing the state estimation program 23 in this embodiment. In the example in Figure 2, the processing circuit 11 calculates the time derivative of the SOC of battery 2, which is the principal physical quantity, in the SOC time series data J1 (S101). This calculates the time change of the derivative value of SOC, and generates derivative value time series data showing the time change of the derivative value of SOC. Then, the processing circuit 11 determines whether the derivative value of SOC has switched from positive to negative based on the derivative value time series data (S102). The processing circuit 11 also determines whether the derivative value of SOC has switched from negative to positive based on the derivative value time series data (S103). In the determinations in S102 and S103, the derivative value time series data may be converted into time series data of the time change flag of SOC. The time-series data for the SOC time-varying flag is generated in the differential time-series data by, for example, converting positive derivative values ​​to 1 and negative derivative values ​​to -1.

[0025] Then, when the processing circuit 11 determines that the derivative of SOC has switched from positive to negative, it extracts the value of SOC at the time the derivative of SOC switched from positive to negative as the local maximum of SOC (principal physical quantity) based on the SOC time series data (S104). Also, when the processing circuit 11 determines that the derivative of SOC has switched from negative to positive, it extracts the value of SOC at the time the derivative of SOC switched from negative to positive as the local minimum of SOC (principal physical quantity) based on the SOC time series data (S105). The extraction of the local maximum and local minimum of SOC is performed, for example, by sampling and holding the value of SOC at the time the derivative switches from positive to negative and at the time the derivative switches from negative to positive.

[0026] Then, when the processing circuit 11 determines that the derivative value of SOC has switched from positive to negative, it extracts the time at which the derivative value of SOC switched from positive to negative as the time when SOC (the principal physical quantity) reaches its maximum value, based on the time information ε (S106). Also, when the processing circuit 11 determines that the derivative value of SOC has switched from negative to positive, it extracts the time at which the derivative value of SOC switched from negative to positive as the time when SOC (the principal physical quantity) reaches its minimum value, based on the time information ε (S107). The extraction of the times when SOC reaches its maximum and minimum values ​​is performed, for example, by sampling and holding the time at the timing when the derivative value switches from positive to negative, and at the timing when the derivative value switches from negative to positive.

[0027] Then, the processing circuit 11 extracts the interval between extreme values ​​of SOC from the SOC time series data (first time series data) J1 based on the times extracted by the processes in S106 and S107 (S108). In this case, for example, the absolute value of the difference between the time extracted in the process in S106 and the time extracted in the process in S107 is calculated as the interval between extreme values. By extracting the interval between extreme values ​​in this way, the time from the moment when the derivative of SOC switches from positive to negative to the moment when the derivative next switches from negative to positive, and the time from the moment when the derivative of SOC switches from negative to positive to the moment when the derivative next switches from positive to negative are each extracted as the interval between extreme values.

[0028] Figure 3 shows an example of SOC time series data. Figure 4 shows the time-varying flag data of SOC generated from the example SOC time series data in Figure 3. Figure 5 shows the time evolution of the maximum and minimum values ​​of SOC extracted from the example SOC time series data in Figure 3. Figure 6 shows the time evolution of the time between extreme values ​​extracted from the example SOC time series data in Figure 3. In each of Figures 3 to 6, the horizontal axis represents time. In Figures 3 and 5, the vertical axis represents SOC, in Figure 4, the vertical axis represents the value of the SOC time-varying flag, and in Figure 6, the vertical axis represents the time between extreme values. In Figure 5, the time evolution of the extracted maximum value of SOC is shown by a solid line, and the time evolution of the extracted minimum value of SOC is shown by a dashed line.

[0029] In the example shown in Figures 3 to 6, at time t1, the time change flag for SOC switches from 1 to -1. Therefore, the SOC value η1 at time t1 is extracted as the local maximum of SOC by the process in S104, and time t1 is extracted as the time when SOC reached its local maximum by the process in S106. Furthermore, at time t2, which is after time t1, the time change flag for SOC switches from -1 to 1. Therefore, the SOC value η2 at time t2 is extracted as the local minimum of SOC by the process in S105, and time t2 is extracted as the time when SOC reached its local minimum by the process in S107. In addition, the time interval β1 from time t1 to time t2 is extracted as the time between extreme values ​​by the process in S108. Note that between time t1 and time t2, the SOC value η3 at time t3, which is before time t1, is extracted as the local minimum of SOC.

[0030] Furthermore, as shown in Figure 1, the storage medium 12 stores relational data D1 in addition to the program and other data mentioned above. The relational data (first relational data) D1 shows the relationship between the maximum and minimum values ​​of the main physical quantity, SOC, and the rate of decrease in the capacity of battery 2, which is the rate of change of state. For example, relational data D1 shows calculation formulas, functions, graphs, and tables for calculating the rate of decrease in the capacity of battery 2 using the maximum and minimum values ​​of SOC. For example, a function for calculating the rate of decrease in capacity using the maximum and minimum values ​​of SOC as arguments is shown. When estimating the increase in the resistance of battery 2 as the amount of change of state, relational data D1 shows the relationship between the maximum and minimum values ​​of SOC and the rate of increase in the resistance of battery 2, which is the rate of change of state. The rate of change of state is the amount of change of state per unit time, such as the amount of change of state per day.

[0031] The relational data D1 is generated, for example, based on the test results of a cycle test previously performed on a battery similar to battery 2. In the cycle test, a lower limit ηmin and an upper limit ηmax are set for the State of Charge (SOC), and charging from the lower limit ηmin to the upper limit ηmax and discharging from the upper limit ηmax to the lower limit ηmin are repeated to check the degree of battery degradation, including the decrease in battery capacity. Then, cycle tests are performed under multiple conditions in which at least one of the lower limit ηmin and upper limit ηmax differs from one another, and relational data D1 is generated based on the degree of battery degradation in each of the cycle tests under multiple conditions.

[0032] Figure 7 shows an example of relational data D1. In the example relational data D1 in Figure 7, the response surface α1 is shown in a three-axis coordinate system in which the maximum value of SOC is shown on the first axis, the minimum value of SOC is shown on the second axis, and the rate of capacity decrease is shown on the third axis. The relationship between the rate of capacity decrease of battery 2 and the maximum and minimum values ​​of SOC is shown by the response surface α1.

[0033] In cycle testing, even among multiple conditions where the lower limit ηmin and upper limit ηmax of SOC are the same, the degree of degradation will differ if at least one of the battery temperature and the magnitude of the current flowing through the battery differs from one another. For this reason, it is preferable that relational data D1 shows the relationship between the rate of decrease in the capacity of battery 2 and the maximum and minimum values ​​of SOC for each of the multiple conditions where at least one of the temperature of battery 2 and the absolute value of the current (magnitude of the current) differs from one another. However, in this embodiment, for the sake of explanation, it is assumed that the temperature of battery 2 and the magnitude of the current flowing through battery 2 during charging and discharging do not change. Then, relational data D1 shows the relationship between the rate of decrease in the capacity of battery 2 and the maximum and minimum values ​​of SOC under the condition that the temperature of battery 2 reaches a predetermined temperature and the magnitude of the current flowing through battery 2 (absolute value of the current) reaches a predetermined magnitude.

[0034] As shown in Figure 2, in the process of estimating the decrease in capacity of battery 2, the processing circuit 11 calculates the rate of decrease in capacity of battery 2 for each interval between extreme values ​​(S109), based on the maximum and minimum values ​​of SOC extracted by the processes in S104 and S105, and the aforementioned relational data D1. At this time, the rate of decrease in capacity of battery 2 is calculated as the state change rate of battery 2 by substituting the extracted maximum and minimum values ​​of SOC into a function that takes the maximum and minimum values ​​as arguments. The state change rate calculated based on the relational data (first relational data) D1 is also called the "first state change rate," and the rate of decrease in capacity calculated by the process in S109 is also called the "first decrease rate."

[0035] Then, the processing circuit 11 calculates the amount of capacity reduction of the battery 2 for each extreme value interval, based on the extreme value interval time extracted by the processing in S108 and the capacity reduction rate calculated by the processing in S109 (S110). In this case, the amount of capacity reduction, which is the change in state of the battery 2, is calculated, for example, by multiplying the extreme value interval time by the capacity reduction rate.

[0036] Here, we will calculate the rate and amount of capacity decrease of battery 2 for each extreme value interval time from the SOC time series data of the example in Figure 3. In this case, as mentioned above, for the extreme value interval time β1 between time t1 and time t2, value η1 is extracted as the maximum value of SOC, and value η3 is extracted as the minimum value of SOC. Therefore, using values ​​η1, η3 and related data D1, the rate of capacity decrease is calculated, for example, the rate of decrease v1 is calculated. Then, for example, the value obtained by multiplying the rate of decrease v1 by the extreme value interval time β1 is calculated as the amount of capacity decrease of battery 2 during the extreme value interval time β1 between time t1 and time t2.

[0037] Furthermore, in the process of estimating the decrease in capacity of battery 2, the processing circuit 11 calculates a cumulative value of the decrease in capacity, which is the change in state of battery 2, based on the decrease in capacity of battery 2 for each interval between extreme values. The cumulative value of the decrease in capacity may be calculated from the start of use of battery 2, or from the start of measurement of the current of battery 2, etc. As shown in Figure 2, in calculating the cumulative value of the decrease in capacity, the processing circuit 11 determines the switching between positive and negative differential values ​​of SOC based on the differential value time series data generated by the processing in S101 (S111). That is, it determines the switching from positive to negative differential values ​​of SOC, and the switching from negative to positive differential values.

[0038] Then, when the processing circuit 11 determines that the derivative value has switched between positive and negative, it calculates the cumulative value of the capacity decrease C1cal based on the amount of capacity decrease during the extreme value interval calculated by the processing in S110 (S112). At the timing when the derivative value of SOC switches from positive to negative, and at the timing when the derivative value switches from negative to positive, the processing circuit 11 adds the amount of capacity decrease during the extreme value interval, with the timing of the switch as the end point, to the real-time cumulative value of the capacity decrease C1real. In this way, each time the derivative value of the principal physical quantity SOC switches from positive to negative, and each time the derivative value switches from negative to positive, the amount of capacity decrease during the extreme value interval is added to the real-time cumulative value C1real. Therefore, each time the extreme value interval ends, the amount of capacity decrease during the extreme value interval is successively added to the real-time cumulative value C1real.

[0039] In one example, post-processing is performed not each time an extreme interval ends, but each time a certain period containing multiple extreme intervals ends, and the cumulative value of the capacity decrease C1cal is calculated. In this case, the processing circuit 11 stores the SOC time-series data from the start of the certain period to the end of the certain period in the storage medium 12. Then, when the certain period ends, the processing circuit 11 calculates the amount of capacity decrease at each of the multiple extreme intervals included in the certain period as described above. Then, the processing circuit 11 calculates the amount of capacity decrease for the certain period from the amount of capacity decrease at each of the multiple extreme intervals. Then, the processing circuit 11 adds the amount of capacity decrease, which is the change in state of battery 2 during the certain period, to the real-time cumulative value C1real. Therefore, in this example, each time a certain period containing multiple extreme intervals ends, the amount of state change during the certain period is added to the real-time cumulative value of the state change.

[0040] The addition of the capacity reduction to the real-time cumulative value C1real may be performed each time the interval between extreme values ​​ends, or each time the aforementioned fixed period ends. However, by adding the capacity reduction to the real-time cumulative value C1real each time the interval between extreme values ​​ends, it becomes possible to reduce the amount of data stored in the storage medium 12, thereby reducing the capacity of the storage medium 12.

[0041] Furthermore, in this embodiment, noise may be removed using a low-pass filter or the like. This allows the aforementioned processing, such as extracting the maximum and minimum values ​​of SOC from the SOC time-series data, to be performed more appropriately. In addition, in this embodiment, the processing circuit 11 may calculate the remaining lifespan of the battery 2 based on the cumulative value C1cal of the decrease in the capacity of the battery 2 calculated as described above, i.e., the cumulative value of the change in the state of the battery 2. Furthermore, the processing circuit 11 may issue a warning via the user interface 15 when the cumulative value of the change in the state of the battery 2 exceeds a threshold, or when the remaining lifespan of the battery 2 falls below a threshold.

[0042] In this embodiment, the maximum and minimum values ​​of SOC, and the interval between extreme values ​​(the time between one extreme value and the next) of SOC are extracted from the SOC time series data (first time series data) J1, which shows the time change of SOC of the battery 2, which is the main physical quantity. Then, based on the relationship data (first relationship data) D1, which shows the relationship between the maximum and minimum values ​​of SOC and the rate of change of state, such as the rate of decrease in the capacity of the battery 2, and the extracted maximum and minimum values ​​of SOC, the rate of change of state of the battery (first rate of change of state) is calculated for each interval between extreme values. Then, based on the extracted interval between extreme values ​​and the calculated rate of change of state, the amount of change of state, such as the amount of decrease in the capacity of the battery 2, is calculated for each interval between extreme values. As a result, the amount of change of state of the battery 2 for each interval between extreme values ​​is calculated with high accuracy, and the estimation accuracy of the change of state of the battery 2 to be estimated is improved.

[0043] Furthermore, as mentioned above, since the amount of change in the state of battery 2 is estimated, the configuration of the state estimation device 10 and state estimation system 1 for estimating the change in the state of battery 2 becomes simpler. This makes it easier to implement the state estimation device 10 and state estimation system 1.

[0044] Furthermore, in this embodiment, the time variation of the derivative of SOC is calculated by differentiating SOC with respect to time in the SOC time series data J1. The value of SOC at the moment when the derivative of SOC switches from positive to negative is extracted as a local maximum, and the value of SOC at the moment when the derivative of SOC switches from negative to positive is extracted as a local minimum. In addition, the time from the moment when the derivative of SOC switches from positive to negative to the moment when it next switches from negative to positive, and the time from the moment when the derivative of SOC switches from negative to positive to the moment when it next switches from positive to negative, are each extracted as the time interval between extreme values. As a result, the local maximum and minimum values ​​of SOC, as well as the time interval between extreme values, are appropriately extracted from the SOC time series data.

[0045] (Second embodiment) Next, a second embodiment will be described as a modification of the first embodiment. In this embodiment, in order to estimate the decrease in capacity, which is the change in state of the battery 2, in addition to the aforementioned SOC time series data (first time series data) J1, current time series data (second time series data) J2 showing the change in current flowing through the battery 2 over time, and temperature time series data (third time series data) J3 showing the change in temperature of the battery 2 over time are used. Furthermore, in this embodiment, in the process of estimating the decrease in capacity of the battery 2, the processing circuit 11 processes the data with the SOC of the battery 2 as the principal physical quantity, the current of the battery 2 (absolute value of the current) as the first dependent physical quantity, and the temperature of the battery 2 as the second dependent physical quantity.

[0046] Figure 8 shows an example of the processing performed by the processing circuit 11 when executing the state estimation program 23 in this embodiment. In the example in Figure 8, the processing circuit 11 determines whether the absolute value of the current of the battery 2 is less than or equal to a threshold value based on the current time series data J2 (S121). The threshold value is set to a value such that the state of affairs (SOC) of the battery 2 does not change even if current flows through the battery 2 at the magnitude of the threshold. Therefore, by determining whether the absolute value of the current of the battery 2 is less than or equal to the threshold value, it is determined whether the SOC of the battery 2 is changing. In one example, the threshold value may be set to 0.

[0047] If the absolute value of the current, which is the first dependent physical quantity, is greater than the threshold, the processing circuit 11 performs the S101 to S112 processes described above using the SOC time series data J1 and the relational data (first relational data) D1, similar to the first embodiment (S122). Therefore, for the period when the absolute value of the current of the battery 2 is greater than the threshold, the processing circuit 11 extracts the maximum and minimum values ​​of SOC and the interval between extreme values ​​from the SOC time series data J1 as described above. Then, based on the extracted maximum and minimum values ​​of SOC (principal physical quantity) and the relational data D1, the processing circuit 11 calculates the rate of decrease in the capacity of the battery 2 for each interval between extreme values. Then, similar to the embodiments described above, the amount of decrease in the capacity of the battery 2 is calculated for each interval between extreme values, and similar to the embodiments described above, the cumulative value C1cal of the amount of decrease in the capacity of the battery 2 is calculated. As a result, the first cumulative value C1cal is calculated as the cumulative value of the amount of decrease in capacity during the period when the absolute value of the current is greater than the threshold.

[0048] Furthermore, in this embodiment, in addition to the first relational data corresponding to the relational data D1 described above, a second relational data D2 is stored in the storage medium 12. The second relational data D2 shows the relationship between the primary physical quantity, SOC, and the second dependent physical quantity, the rate of decrease in the capacity of the battery 2, which is the rate of change of state of the battery 2, with respect to the temperature of the battery 2. For example, the second relational data D2 shows calculation formulas, functions, graphs, and tables for calculating the rate of decrease in the capacity of the battery 2 using the value of SOC (holding value) and temperature. For example, a function for calculating the rate of decrease in capacity with respect to the value of SOC and temperature is shown. When estimating the increase in the resistance of the battery 2 as the amount of change of state, the second relational data D2 shows the relationship between the value of SOC and the rate of increase in the resistance of the battery 2, which is the rate of change of state, with respect to temperature.

[0049] The second relational data D2 shows the influence of the State of Charge (SOC) and temperature of battery 2 on the degradation rate and degree of battery 2 when no current flows through battery 2. Degradation of battery 2 when no current flows through it is also called "calendar degradation." The second relational data D2 is generated, for example, based on the test results of a calendar test previously performed on a battery similar to battery 2. In a calendar test, retention values ​​are set for SOC and temperature, and the SOC and temperature are held at the set retention values ​​for a predetermined time to check the degree of battery degradation, including the decrease in battery capacity. In one example, the battery is held for a predetermined time with an SOC of 100% and a temperature of 20°C. Then, calendar tests are performed under multiple conditions in which at least one of the retention values ​​for SOC and temperature differs from one another, and the second relational data D2 is generated based on the degree of battery degradation in each of the calendar tests under multiple conditions.

[0050] Figure 9 shows an example of the second relational data D2. In the example of the second relational data D2 in Figure 9, the response surface α2 is shown in a three-axis coordinate system in which the first axis represents the SOC retention value, the second axis represents temperature, and the third axis represents the rate of capacity decrease. The response surface α2 then shows the relationship between the rate of capacity decrease of battery 2 and the SOC retention value and temperature.

[0051] As shown in Figure 8, if the absolute value of the current, which is the first dependent physical quantity, is less than or equal to a threshold, the processing circuit 11 extracts the SOC retention value of the battery 2 for each period in which the absolute value of the current is less than or equal to the threshold, based on the SOC time series data J1 (S123). The processing circuit 11 also calculates the temperature of the battery 2 for each period in which the absolute value of the current is less than or equal to the threshold, based on the temperature time series data (third time series data) J3 (S124). In this case, for example, the temperature is measured multiple times for each period in which the absolute value of the current is less than or equal to the threshold, and the average value or RMS value of the multiple temperature measurements is calculated as the temperature for each period in which the absolute value of the current is less than or equal to the threshold.

[0052] Then, the processing circuit 11 calculates the rate of decrease in the capacity of the battery 2 for each period during which the SOC is retained, based on the retained SOC value extracted by the processing in S123, the temperature calculated by the processing in S124, and the second relational data D2 mentioned above (S125). At this time, the rate of decrease in the capacity of the battery 2 is calculated as the state change rate of the battery 2 by substituting the extracted retained SOC value and the calculated temperature into a function that takes the retained SOC value and temperature as arguments. The state change rate calculated based on the second relational data is also called the "second state change rate," and the rate of decrease in capacity calculated by the processing in S125 is also called the "second decrease rate."

[0053] Then, the processing circuit 11 calculates the amount of capacity reduction of the battery 2 for each period during which the SOC is maintained, that is, for each SOC retention period, based on the capacity reduction rate calculated by the processing in S125 (S126). In this case, the amount of capacity reduction, which is the change in state of the battery 2, is calculated, for example, by multiplying the duration of the period during which the SOC is maintained by the capacity reduction rate.

[0054] Then, the processing circuit 11 calculates a second cumulative value C2cal as the cumulative value of the decrease in capacity during periods when the absolute value of the current is below a threshold, based on the decrease in capacity of the battery 2 for each period during which the SOC is maintained (S127). At this time, for example, the decrease calculated by the processing in S126 is added to the second cumulative value C2real in real time, thereby updating the second cumulative value. Then, the processing circuit 11 adds the first cumulative value C1cal calculated by the processing in S122 and the second cumulative value C2cal calculated by the processing in S127 (S128). As a result, a third cumulative value C3cal is calculated as the cumulative value of the decrease in capacity of the battery 2, including both periods when the absolute value of the current is greater than the threshold and periods when the absolute value of the current is below the threshold. As a result, the cumulative value of the decrease in capacity from the start of use of the battery 2 or the start of measurement of current, etc., is calculated as the third cumulative value C3cal.

[0055] This embodiment also provides the same functions and effects as the first embodiment. Furthermore, in this embodiment, even during periods when no current flows through the battery 2, the rate of change in the state of the battery 2 (second rate of change in state), such as the rate of decrease in capacity, is calculated. Then, even during periods when no current flows through the battery 2, the amount of change in state, such as the amount of decrease in the capacity of the battery 2, is calculated based on the calculated rate of change in state. Therefore, the estimation accuracy of the change in state of the battery 2 to be estimated is further improved.

[0056] (Third embodiment) Next, a third embodiment will be described as a modification of the first embodiment. In this embodiment, in order to estimate the decrease in capacity, which is the change in state of the battery 2, in addition to the aforementioned SOC time series data (first time series data) J1, temperature time series data (third time series data) J3, which shows the change in temperature of the battery 2 over time, is used. Furthermore, in this embodiment, in the process of estimating the decrease in capacity of the battery 2, the processing circuit 11 processes with the SOC of the battery 2 as the primary physical quantity and the temperature of the battery 2 as the dependent physical quantity.

[0057] In this embodiment, relational data (first relational data) D1 shows the relationship between the maximum and minimum values ​​of the principal physical quantity, SOC, and the rate of decrease in the capacity of battery 2, which is the rate of change of state of battery 2 with respect to temperature, which is a dependent physical quantity. For example, relational data D1 shows calculation formulas, functions, graphs, and tables for calculating the rate of decrease in the capacity of battery 2 using the maximum and minimum values ​​of SOC and temperature. For example, a function is shown that calculates the rate of decrease in capacity using the maximum and minimum values ​​of SOC and temperature as arguments. Therefore, relational data D1 shows response surfaces similar to the response surface α1 in the example in Figure 7 for each of several different temperatures.

[0058] In this embodiment, for example, response surfaces for each of several different temperatures are generated by performing cycle tests under multiple conditions with different temperatures relative to each other, and relational data D1 is generated. In one example, the response surface at a predetermined temperature is generated by cycle testing, and the response surfaces at temperatures other than the predetermined temperature are generated by performing calculations using the response surface at the predetermined temperature and appropriate mathematical formulas. Furthermore, interpolation between data with different temperatures relative to each other is performed by appropriate interpolation methods such as linear interpolation, nearest neighbor interpolation, and spline interpolation.

[0059] Figure 10 shows an example of the processing performed by the processing circuit 11 when it executes the state estimation program 23 in this embodiment. In the example in Figure 10, similar to the example in Figure 2 of the first embodiment, the processing circuit 11 performs the processing from S101 to S112. Also, in the example in Figure 10, if it is determined by the processing in S102 that the differential value of SOC has switched from positive to negative, or if it is determined by the processing in S103 that the differential value of SOC has switched from negative to positive, the processing circuit 11 extracts the time change of temperature during a period corresponding to the extreme value interval time, with the timing of the switch as the end point, from the temperature time series data (S131).

[0060] Then, the processing circuit 11 calculates the temperature value of the battery 2 for the extreme value interval, which is obtained by the processing in S131 (S132). In one example, the processing circuit 11 samples and holds the temperature of the battery 2 at the start and end of the extreme value interval. The average of the two sampled and held temperatures is then calculated as the temperature value of the battery 2 during the extreme value interval. In another example, the processing circuit 11 obtains the temperature of the battery 2 for each of several points in time between the start and end of the extreme value interval. The average value or RMS value of the temperatures at these multiple points in time is then calculated as the temperature value of the battery 2 during the extreme value interval.

[0061] Furthermore, in the example shown in Figure 10, in S109, the processing circuit 11 calculates the rate of decrease in the capacity of battery 2 for each interval between extreme values, based on the maximum and minimum values ​​of SOC extracted by the processing in S104 and S105, the related data (first related data) D1, and the temperature value calculated by the processing in S132. At this time, the rate of decrease in the capacity of battery 2 (first rate of decrease) is calculated as the rate of change of state of battery 2 by substituting the extracted maximum and minimum values ​​of SOC and the calculated temperature value into a function that takes the maximum and minimum values ​​of SOC and the temperature as arguments.

[0062] This embodiment also provides the same functions and effects as the first embodiment. Furthermore, in this embodiment, in addition to the maximum and minimum values ​​of the State of Charge (SOC) of the battery 2, the influence of the temperature of the battery 2 is also considered, and the rate of change in the state of the battery 2 (first rate of change in state), such as the rate of capacity decrease, is calculated for the time between extreme values. Therefore, in addition to the maximum and minimum values ​​of the SOC of the battery 2, the influence of the temperature of the battery 2 is also considered, and the amount of change in state, such as the amount of capacity decrease of the battery 2, is calculated. Consequently, the estimation accuracy of the change in state of the battery 2 to be estimated is further improved.

[0063] (Modified version of the third embodiment) In one modified version of the third embodiment, in addition to the aforementioned SOC time series data (first time series data) J1 and temperature time series data (third time series data) J3, current time series data (second time series data) J2, which shows the time change of the current of the battery 2, is used. Furthermore, in this modified version, in the process of estimating the decrease in the capacity of the battery 2, the processing circuit 11 processes with the SOC of the battery 2 as the principal physical quantity and the temperature and current (absolute value of the current) of the battery 2 as dependent physical quantities.

[0064] In this modified example, the relationship between the maximum and minimum values ​​of the principal physical quantity, SOC, and the dependent physical quantities, the temperature of battery 2 and the absolute value of the current (magnitude of the current), and the rate of decrease in the capacity of battery 2, which is the rate of change of state, is shown in relational data (first relational data) D1. For example, relational data D1 shows calculation formulas, functions, graphs, and tables for calculating the rate of decrease in the capacity of battery 2 using the maximum and minimum values ​​of SOC, temperature, and the absolute value of the current including the C rate, etc. For example, a function is shown that calculates the rate of decrease in capacity with the maximum and minimum values ​​of SOC, temperature, and the absolute value of the current as arguments. Therefore, in relational data D1, a response surface similar to the response surface α1 in the example in Figure 7 is shown for each of several conditions in which at least one of the absolute values ​​of temperature and current differs from each other.

[0065] In this modified example, for instance, a response surface is generated for each of several conditions where at least one of the absolute values ​​of temperature and current differs from the other, such as by performing cycle tests under multiple conditions where at least one of the absolute values ​​of temperature and current differs from the other, and relational data D1 is generated. In one example, a response surface for a predetermined condition where a predetermined temperature and a predetermined current value are obtained is generated by cycle testing, and the response surface for conditions other than the predetermined condition is generated by performing calculations using the response surface for the predetermined condition and appropriate mathematical formulas. Furthermore, interpolation between data where the absolute values ​​of current differ from each other is performed by appropriate interpolation methods such as linear interpolation, nearest neighbor interpolation, and spline interpolation.

[0066] Figure 11 shows an example of the processing performed by the processing circuit 11 when it executes the state estimation program 23 in this modified example. In the example in Figure 11, similar to the example in Figure 10 of the third embodiment, the processing circuit 11 performs the processes S101 to S112, S131, and S132. Also, in the example in Figure 10, if it is determined by the process in S102 that the differential value of SOC has switched from positive to negative, or if it is determined by the process in S103 that the differential value of SOC has switched from negative to positive, the processing circuit 11 extracts the time change of the current during a period corresponding to the extreme value interval time, with the timing of the switch as the end point, from the current time series data J2 (S133).

[0067] Then, the processing circuit 11 calculates the value of the current of the battery 2 for the extreme value interval, for which the time change of current was extracted by the processing in S133 (S134). In this case, whether charging or discharging of the battery 2 is occurring during the extreme value interval, the value of the current is calculated as the absolute value of the current (magnitude of the current), and the calculated value of the current is a positive value. In one example, the processing circuit 11 obtains the absolute value of the current of the battery 2 for each of several time points between the start and end of the extreme value interval. Then, the average value or RMS value of the absolute values ​​of the current at the multiple time points is calculated as the value of the current of the battery 2 during the extreme value interval.

[0068] Furthermore, in the example shown in Figure 10, in S109, the processing circuit 11 calculates the rate of decrease in the capacity of battery 2 for each interval between extreme values, based on the maximum and minimum values ​​of SOC extracted by processing S104 and S105, the relational data (first relational data) D1, and the value of temperature (second dependent physical quantity) calculated by processing S132, in addition to the value of current (first dependent physical quantity) calculated by processing S134. At this time, the rate of decrease in the capacity of battery 2 (first rate of decrease) is calculated as the rate of change of state of battery 2 by substituting the extracted maximum and minimum values ​​of SOC, the temperature, and the absolute value of the current into a function that takes the maximum and minimum values ​​of SOC, the temperature, and the current as arguments.

[0069] This modified version also produces the same functions and effects as the third embodiment, etc. Furthermore, in this modified version, in addition to the maximum and minimum values ​​of SOC in battery 2 and the temperature of battery 2, the influence of the current flowing through battery 2 is also taken into consideration, and the rate of change of state of battery 2 (first rate of change of state), such as the rate of capacity decrease, is calculated for the time between extreme values. Therefore, the amount of change of state, such as the amount of capacity decrease of battery 2, is calculated by taking into consideration the maximum and minimum values ​​of SOC in battery 2 and the temperature of battery 2, as well as the influence of the current flowing through battery 2. Consequently, the estimation accuracy of the change of state of battery 2 to be estimated is further improved.

[0070] Furthermore, in one modified example, the relationship between the maximum and minimum values ​​of the principal physical quantity, SOC, and the absolute value of the current of battery 2 (magnitude of current), which is the dependent physical quantity (first dependent physical quantity), and the rate of decrease in the capacity of battery 2, which is the rate of state change, is shown in relational data (first relational data) D1. In this case, the processes S101 to S112, S133, and S134 shown in the example in Figure 11 are performed, and the processes S131 and S132 are not performed. Then, in S109, the processing circuit 11 calculates the rate of decrease in the capacity of battery 2 for each interval between extreme values, based on the maximum and minimum values ​​of SOC extracted by the processes in S104 and S105, the relational data (first relational data) D1, and the current value calculated by the process in S134. This modified example also produces the same functions and effects as the embodiments described above.

[0071] Furthermore, in one modified example, similar to the third embodiment and its modified examples, the relational data (first relational data) D1 shows the relationship between the maximum and minimum values ​​of the primary physical quantity, SOC, and the rate of decrease in the capacity of battery 2, which is the rate of change of state. It also shows the relationship between the rate of decrease in the capacity of battery 2 and at least one of the dependent physical quantities, the temperature of battery 2 and the absolute value of the current (magnitude of the current). That is, the relational data D1 shows the relationship between the maximum and minimum values ​​of SOC, and at least one of the temperature of battery 2 (second dependent physical quantity) and the absolute value of the current (first dependent physical quantity) and the rate of decrease in the capacity of battery 2. Then, similar to the second embodiment, the processing circuit 11 determines whether the absolute value of the current of battery 2 is below a threshold based on the current time series data (second time series data) J2. For periods when the absolute value of the current of battery 2 (the first dependent physical quantity) is greater than a threshold, the processing circuit 11 calculates the rate of decrease in the capacity of battery 2 as the rate of state change (the first rate of state change) at each interval between extreme values, in the same manner as in the third embodiment. At this time, the rate of decrease in the capacity of battery 2 is calculated based on the maximum and minimum values ​​of the SOC of battery 2, which are principal physical quantities extracted from the SOC time series data (the first time series data), at least one of the absolute value of the current of battery 2 (the first dependent physical quantity) and temperature (the second dependent physical quantity) related to battery 2 during the interval between extreme values, and the related data D1.

[0072] As described above, in this modified example, the dependent physical quantity whose relationship to the capacity degradation rate is shown in the relational data (first relational data) D1 includes at least one of the absolute value of the current of battery 2 (first dependent physical quantity) and temperature (second dependent physical quantity). In this modified example as well, the amount of capacity decrease of battery 2 is calculated for each interval between extreme values, similar to the third embodiment. Then, the first cumulative value C1cal is calculated as the cumulative value of the capacity decrease during the period in which the absolute value of the current is greater than the threshold.

[0073] Furthermore, in this modified example, similar to the second embodiment, a second relational data D2, which shows the relationship between the state of charge (SOC) (principal physical quantity) and the temperature of the battery 2 (second dependent physical quantity), is stored in the storage medium 12. Then, for periods when the absolute value of the current of the battery 2 (first dependent physical quantity) is below a threshold, the processing circuit 11 calculates the rate of decrease in the capacity of the battery 2 as the state change rate (second state change rate) for each period during which the SOC is maintained, in the same manner as in the second embodiment. At this time, the rate of decrease in the capacity of the battery 2 is calculated based on the SOC retention value extracted from the SOC time series data J1, the temperature value of the battery 2 based on the temperature time series data J3, and the second relational data D2.

[0074] In this modified example, similar to the second embodiment, the processing circuit 11 calculates the decrease in capacity of the battery 2 for each period during which the SOC is maintained. A second cumulative value C2cal is calculated as the cumulative value of the decrease in capacity during periods when the absolute value of the current is below the threshold. The processing circuit 11 then adds the first cumulative value C1cal and the second cumulative value C2cal, similar to the second embodiment. As a result, a third cumulative value C3cal is calculated as the cumulative value of the decrease in capacity of the battery 2, including both periods when the absolute value of the current is greater than the threshold and periods when the absolute value of the current is below the threshold. This modified example also produces the same functions and effects as the embodiments described above.

[0075] (Fourth embodiment) Next, a fourth embodiment will be described as a modification of the third embodiment. In this embodiment, a reference value is set for the absolute value of the current of battery 2, which is a dependent physical quantity. The relational data (first relational data) D1 shows the first relation K1, which is the relationship between the maximum and minimum values ​​of the primary physical quantity, SOC, and the rate of decrease in the capacity of battery 2, which is the rate of change of state of battery 2 with respect to temperature, when the absolute value of the current is less than the reference value. The relational data D1 also shows the second relation K2, which is the relationship between the maximum and minimum values ​​of SOC and the rate of decrease in the capacity of battery 2 with respect to temperature, when the absolute value of the current is greater than or equal to the reference value.

[0076] The first relation K1 and the second relation K2 are, respectively, formulas, functions, graphs, and tables for calculating the rate of capacity decrease of battery 2 using the maximum and minimum values ​​of SOC and temperature. For example, they are functions that calculate the rate of capacity decrease using the maximum and minimum values ​​of SOC and temperature as arguments. In the functions corresponding to the first relation K1 and the second relation K2, the absolute value of the current is not an argument. Therefore, in relation data D1, a response surface similar to the response surface α1 in the example in Figure 7 is shown for both the case where the absolute value of the current is less than the reference value and the case where the absolute value of the current is greater than or equal to the reference value.

[0077] Figures 12A and 12B show an example of the processing performed by the processing circuit 11 when it executes the state estimation program 23 in this embodiment. In the example in Figures 12A and 12B, similar to the example in Figure 10 of the third embodiment, the processing circuit 11 performs the processing S101 to S108, S131, and S132. Also in the example in Figures 12A and 12B, when the extreme value interval is extracted by the processing in S108, the processing circuit 11 calculates a first time for each extracted extreme value interval, based on the current time series data (second time series data) J2, at which the absolute value of the dependent physical quantity, the current, is less than the reference value (S141). Furthermore, the processing circuit 11 calculates a second time for each extracted extreme value interval, based on the current time series data J2, at which the absolute value of the current is greater than or equal to the reference value (S142).

[0078] Furthermore, in the example shown in Figures 12A and 12B, the processing circuit 11 performs the processes of S109A and S109B instead of the process of S109, and performs the processes of S110A, S110B, and S143 instead of the process of S110. That is, the processing circuit 11 calculates the rate of decrease in the capacity of battery 2 when the absolute value of the current is less than the reference value for each interval between extreme values, based on the maximum and minimum values ​​of SOC extracted by the processes of S104 and S105, the temperature value calculated by the process of S132, and the first relation K1 shown by relation data (first relation data) D1 (S109A). At this time, for example, the rate of decrease in the capacity of battery 2 (first rate of decrease) is calculated as the rate of change of state of battery 2 by substituting the extracted maximum and minimum values ​​of SOC and the calculated temperature value into a function of the first relation K1 which takes the maximum and minimum values ​​of SOC and temperature as arguments.

[0079] Furthermore, the processing circuit 11 calculates the rate of decrease in the capacity of battery 2 when the absolute value of the current is greater than or equal to a reference value for each interval between extreme values, based on the maximum and minimum values ​​of SOC extracted by the processing in S104 and S105, the temperature value calculated by the processing in S132, and the second relation K2 shown by the relation data (first relation data) D1 (S109B). In this case, for example, the rate of decrease in the capacity of battery 2 (first rate of decrease) is calculated as the rate of change of state of battery 2 by substituting the extracted maximum and minimum values ​​of SOC and the calculated temperature value into a function of the second relation K2 which takes the maximum and minimum values ​​of SOC and temperature as arguments.

[0080] Then, the processing circuit 11 calculates a first decrease in capacity A1 as the amount of decrease in the capacity of battery 2 in the first time, for each interval between extreme values, based on the first time calculated by the processing in S141 and the rate of decrease in capacity based on the first relationship K1 calculated by the processing in S109A (S110A). At this time, the first decrease in capacity (first change in state) A1, which is the amount of change in the state of battery 2, is calculated, for example, by multiplying the rate of decrease in capacity based on the first relationship K1 by the first time.

[0081] Furthermore, the processing circuit 11 calculates a second decrease in capacity A2 for each extreme time interval, based on the second time calculated by the process in S142 and the rate of capacity decrease based on the second relationship K2 calculated by the process in S109B (S110B). In this case, the second decrease in capacity (second state change amount) A2, which is the change in state of the battery 2, is calculated, for example, by multiplying the rate of capacity decrease based on the second relationship K2 by the second time.

[0082] Then, the processing circuit 11 calculates the sum of the first decrease in capacity A1 calculated by the processing in S110A and the second decrease in capacity A2 calculated by the processing in S110B (A1 + A2) as the net decrease in capacity during the inter-extreme time (S143). In this way, for each inter-extreme time, the net decrease in capacity for the entire inter-extreme time, including the first time and the second time, is calculated.

[0083] In the examples shown in Figures 12A and 12B, the processing circuit 11 performs the S111 and S112 processes, similar to the example in Figure 10 of the third embodiment. In this embodiment, in S112, the net decrease in capacity during the inter-extreme time (A1+A2) calculated by the processing in S143 is added to the cumulative value of the real-time decrease in capacity C1real to calculate the cumulative value of the decrease in capacity C1cal.

[0084] This modified version also produces the same functions and effects as the third embodiment, etc. Furthermore, in this modified version, in addition to the maximum and minimum values ​​of SOC in battery 2 and the temperature of battery 2, the influence of the current flowing through battery 2 is also taken into consideration, and the rate of change of state of battery 2 (first rate of change of state), such as the rate of capacity decrease, is calculated for the time between extreme values. Therefore, the amount of change of state, such as the amount of capacity decrease of battery 2, is calculated by taking into consideration the maximum and minimum values ​​of SOC in battery 2 and the temperature of battery 2, as well as the influence of the current flowing through battery 2. Consequently, the estimation accuracy of the change of state of battery 2 to be estimated is further improved.

[0085] (Modification of the fourth embodiment) In the fourth embodiment, only one reference value is set for the absolute value of the current, but in one modified example, two or more reference values ​​are set for the absolute value of the current. Then, in relational data D1, the absolute value of the current is classified into three or more ranges based on the two or more reference values, and for each of the three or more ranges classified for the absolute value of the current, the relationship between the maximum and minimum values ​​of the SOC and the rate of decrease in the capacity of the battery 2, which is the rate of change of state of the battery 2 with respect to temperature, is shown.

[0086] In this case, the processing circuit 11 classifies the time between extreme values ​​into three or more time periods based on two or more reference values ​​for the absolute value of the current. Then, for each of the three or more classified time periods, the rate of capacity decrease and the amount of capacity decrease are calculated based on one of the corresponding relationships shown in relational data D1, in the same manner as in the fourth embodiment. The sum of the amounts of capacity decrease for the three or more time periods is calculated as the net amount of capacity decrease during the time between extreme values.

[0087] Furthermore, in one modified example, the relationship between the maximum and minimum values ​​of SOC and the rate of decrease in the capacity of battery 2, which is the rate of state change, is shown for each of the first relationship K1 and the second relationship K2 shown in relation data D1, but the relationship between the rate of decrease in the capacity of ionization 2 and temperature is not shown. In this case, in the example in Figures 12A and 12B, the processes in S131 and S132 are not performed. Then, in S109A, based on the maximum and minimum values ​​of SOC extracted by the processes in S104 and S105, and the first relationship K1 shown in relation data D1, the rate of decrease in the capacity of battery 2 when the absolute value of the current is less than the reference value is calculated for each interval between extreme values. Similarly, in S109B, based on the maximum and minimum values ​​of SOC extracted by the processes in S104 and S105, and the second relationship K2 shown in relation data D1, the rate of decrease in the capacity of battery 2 when the absolute value of the current is greater than or equal to the reference value is calculated for each interval between extreme values.

[0088] Furthermore, in one modified example, similar to the fourth embodiment and its modified examples, the relational data (first relational data) D1 shows a first relation K1, which is the relationship between the rate of decrease in the capacity of battery 2, which is the rate of state change, and the maximum and minimum values ​​of the primary physical quantity, SOC, when the absolute value of the current (first dependent physical quantity) is less than a reference value, and a second relation K2, which is the relationship between the rate of decrease in the capacity of battery 2, and the maximum and minimum values ​​of SOC, when the absolute value of the current is equal to or greater than the reference value. Then, similar to the second embodiment, the processing circuit 11 determines whether the absolute value of the current of battery 2 is less than or equal to a threshold value based on the current time series data (second time series data) J2. In this modified example, the threshold value set for the absolute value of the current is smaller than any of the one or more reference values.

[0089] In this modified example, for periods when the absolute value of the current of battery 2 (the first dependent physical quantity) is greater than a threshold, the processing circuit 11 calculates the rate of decrease in the capacity of battery 2 as the rate of change of state (first rate of change of state) for each extreme value interval, in the same manner as in the fourth embodiment, etc. At this time, the rate of decrease in the capacity of battery 2 when the absolute value of the current is less than a reference value is calculated based on the maximum and minimum values ​​of SOC extracted from the SOC time series data (first time series data) J1, and the first relationship K1 shown in the relationship data (first relationship data) D1. Then, the rate of decrease in the capacity of battery 2 when the absolute value of the current is greater than or equal to a reference value is calculated based on the maximum and minimum values ​​of SOC extracted from the SOC time series data J1, and the second relationship K2 shown in the relationship data D1. In this modified example as well, in the same manner as in the fourth embodiment, etc., the net decrease in capacity for the entire extreme value interval, including the first and second intervals, is calculated for each extreme value interval. Then, a first cumulative value C1cal is calculated as the cumulative value of the decrease in capacity during the period when the absolute value of the current is greater than the threshold.

[0090] Furthermore, in this modified example, similar to the second embodiment, a second relational data D2, which shows the relationship between the state of charge (SOC) (principal physical quantity) and the temperature of the battery 2 (second dependent physical quantity), is stored in the storage medium 12. Then, for the period during which the absolute value of the current of the battery 2 is below a threshold, the processing circuit 11 calculates the rate of decrease in the capacity of the battery 2 as the state change rate (second state change rate) for each period during which the SOC is maintained, in the same manner as in the second embodiment. At this time, the rate of decrease in the capacity of the battery 2 is calculated based on the SOC retention value extracted from the SOC time series data J1, the temperature value of the battery 2 based on the temperature time series data (third time series data) J3, and the second relational data D2.

[0091] In this modified example, similar to the second embodiment, the processing circuit 11 calculates the decrease in capacity of the battery 2 for each period during which the SOC is maintained. A second cumulative value C2cal is calculated as the cumulative value of the decrease in capacity during periods when the absolute value of the current is below the threshold. The processing circuit 11 then adds the first cumulative value C1cal and the second cumulative value C2cal, similar to the second embodiment. As a result, a third cumulative value C3cal is calculated as the cumulative value of the decrease in capacity of the battery 2, including both periods when the absolute value of the current is greater than the threshold and periods when the absolute value of the current is below the threshold. This modified example also produces the same functions and effects as the embodiments described above.

[0092] (Other variations) In the embodiments described above, a battery was used as an example of the target for estimating the change of state, but the target for estimating the change of state is not limited to this. In one modified example, the change of state is estimated using a solder joint as the target. In this case, the temperature of the joint is used as the primary physical quantity instead of the State of Temperature (SOC) described above to estimate the change of state. The related data D1 shows the relationship between the maximum and minimum temperatures of the joint and the cleave rate (strain rate) of the joint. In this modified example, the processing circuit extracts the maximum and minimum temperatures and the time between extreme values ​​from the temperature time series data showing the change in temperature of the joint over time. The processing circuit then calculates the cleave rate of the joint based on the extracted maximum and minimum temperatures and the related data D1. The processing circuit then calculates the amount of cleave (strain) of the joint for each time between extreme values ​​based on the calculated cleave rate and the extracted time between extreme values.

[0093] In at least one embodiment or example described above, the maximum and minimum values ​​of the principal physical quantity, and the interval between extreme values ​​(from one extreme to the next) are extracted from time-series data showing the time evolution of the principal physical quantity. Based on relational data showing the relationship between the maximum and minimum values ​​of the principal physical quantity and the rate of state change of the target to be estimated, and the extracted maximum and minimum values ​​of the principal physical quantity, the rate of state change of the target to be estimated is calculated. Then, based on the extracted interval between extreme values ​​and the calculated rate of state change, the amount of state change of the target to be estimated is calculated for each interval between extreme values. This makes it possible to provide a state estimation method, state estimation device, state estimation system, and state estimation program that have a simple configuration for the device and system for estimating the state change of the target to be estimated, and that have high estimation accuracy for the state change of the target to be estimated.

[0094] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims of the invention and its equivalents. The following are additional notes. [1] Extracting the maximum and minimum values ​​of the principal physical quantity, which are the extreme values ​​of the principal physical quantity, and the time interval between extreme values, from a first time series data showing the time change of the principal physical quantity related to the object of estimation, A first relationship data showing the relationship between the maximum and minimum values ​​of the principal physical quantity and the rate of change of state of the estimated target, and a first rate of change of state of the estimated target calculated as the rate of change of state of the estimated target for each time interval between extreme values, based on the extracted maximum and minimum values ​​of the principal physical quantity. Based on the extracted interval between extreme values ​​and the calculated first rate of change in state, the amount of change in state of the estimated target is calculated for each interval between extreme values. A state estimation method comprising the following: [2] In extracting the extreme values ​​of the principal physical quantity from the first time series data, By differentiating the principal physical quantity with respect to the first time series data, the time change of the derivative of the principal physical quantity is calculated. The value of the principal physical quantity at the moment when the derivative of the principal physical quantity changes from positive to negative is extracted as the local maximum value. The value of the principal physical quantity at the moment when its derivative changes from negative to positive is extracted as the local minimum. [1] State estimation method. [3] In extracting the time interval between extreme values ​​from the first time series data, By differentiating the principal physical quantity with respect to the first time series data, the time change of the derivative of the principal physical quantity is calculated. The time interval between extreme values ​​is defined as the time from when the derivative of the principal physical quantity switches from positive to negative to when it next switches from negative to positive, and the time interval between when the derivative of the principal physical quantity switches from negative to positive to when it next switches from positive to negative. [1] State estimation method. [4] The state estimation method of [1], further comprising calculating a cumulative value of the state change amount of the estimated target from the state change amount of the estimated target for each time interval between extreme values. [5] In calculating the cumulative value of the state change amount of the estimated target, By differentiating the principal physical quantity with respect to the first time series data, the time change of the derivative of the principal physical quantity is calculated. Each time the derivative of the principal physical quantity switches from positive to negative, and each time the derivative switches from negative to positive, the amount of state change of the estimated target during the time between extreme values ​​is added to the cumulative value in real time. [4] State estimation method. [6] In calculating the cumulative value of the state change amount of the estimated target, For each set of periods that include multiple intervals between extreme values, the amount of change in the state of the estimated target during the set of periods is calculated from the amount of change in the state of the estimated target during each of the multiple intervals between extreme values. Each time the aforementioned fixed period ends, the amount of state change during the aforementioned fixed period is added to the cumulative value in real time. [4] State estimation method. [7] The principal physical quantity is the SOC of the battery to be estimated, The state change rate of the estimated target, including the first state change rate, is either the rate of decrease in the battery capacity or the rate of increase in resistance. [1] to [6], one of the state estimation methods. [8] Based on second time series data showing the time change of the first dependent physical quantity relating to the estimation target, it is determined whether the first dependent physical quantity is below a threshold, For the period during which the first dependent physical quantity is greater than the threshold, the rate of change of the first state of the target to be estimated is calculated for each interval of time between extreme values, based on the maximum and minimum values ​​of the principal physical quantity extracted from the first time series data, and the first relational data. For the period during which the first dependent physical quantity is less than or equal to the threshold, the second state change rate of the estimated target is calculated based on second relational data showing the relationship between the primary physical quantity and the second dependent physical quantity relating to the estimated target and the state change rate of the estimated target, the retained value of the primary physical quantity in the first time series data, and the value of the second dependent physical quantity based on third time series data showing the time change of the second dependent physical quantity, A state estimation method further comprising any one of [1] to [6]. [9] The principal physical quantity is the SOC of the battery to be estimated, The first dependent physical quantity is the absolute value of the current flowing through the battery, The second dependent physical quantity is the temperature of the battery, The estimated state change rate, including the first state change rate and the second state change rate, is either the rate at which the battery capacity decreases or the rate at which the resistance increases. [8] State estimation method.

[10] The method further comprises at least one of the following: calculating the value of the first dependent physical quantity during the time interval between extreme values ​​based on a second time series data showing the time change of the first dependent physical quantity relating to the object to be estimated; and calculating the value of the second dependent physical quantity during the time interval between extreme values ​​based on a third time series data showing the time change of the second dependent physical quantity relating to the object to be estimated. In the first relational data, in addition to the relationship between the state change rate of the estimated target and the maximum and minimum values ​​of the principal physical quantity, the relationship between the state change rate of the estimated target and at least one of the first dependent physical quantity and the second dependent physical quantity is shown. In calculating the first state change rate, the first state change rate is calculated based on the first relational data, the maximum and minimum values ​​of the principal physical quantity extracted from the first time series data, and the value of at least one of the first dependent physical quantity and the second dependent physical quantity during the time between the extreme values. [1] to [6], one of the state estimation methods.

[11] Based on the second time series data, determine whether the first dependent physical quantity is below a threshold, For the period during which the first dependent physical quantity is greater than the threshold, the maximum and minimum values ​​of the principal physical quantity extracted from the first time series data, the value of at least one of the first dependent physical quantity and the second dependent physical quantity during the time between the extreme values, and the first relational data are used to calculate the first state change rate of the target to be estimated for each time between the extreme values. For the period during which the first dependent physical quantity is less than or equal to the threshold, the second state change rate of the estimated object is calculated based on the value of the second dependent physical quantity based on the third time series data, second relational data showing the relationship between the principal physical quantity and the second dependent physical quantity related to the estimated object and the state change rate of the estimated object, and the value of the principal physical quantity held in the first time series data. A state estimation method

[10] further comprising:

[12] The main physical quantity is the SOC of the battery to be estimated, The first dependent physical quantity is the absolute value of the current flowing through the battery, The second dependent physical quantity is the temperature of the battery, The state change rate of the estimated target, including the first state change rate, is either the rate of decrease in the battery capacity or the rate of increase in resistance.

[10] State estimation method.

[13] Based on second time series data showing the time change of the first dependent physical quantity relating to the estimation target, the method further comprises calculating, for each interval between extreme values, a first time when the first dependent physical quantity is less than a reference value and a second time when the first dependent physical quantity is equal to or greater than the reference value, The first relational data shows a first relationship, which is the relationship between the rate of change of state of the estimated target and the maximum and minimum values ​​of the principal physical quantity when the first dependent physical quantity is less than the reference value, and a second relationship, which is the relationship between the rate of change of state of the estimated target and the maximum and minimum values ​​of the principal physical quantity when the first dependent physical quantity is equal to or greater than the reference value. In calculating the first state change rate, In the case where the first dependent physical quantity is less than the reference value, the first state change rate is calculated based on the first relationship shown in the first relational data and the maximum and minimum values ​​of the principal physical quantity extracted from the first time series data. When the first dependent physical quantity is greater than or equal to the reference value, the first state change rate is calculated based on the second relationship shown in the first relational data, and the maximum and minimum values ​​of the principal physical quantity extracted from the first time series data. In calculating the amount of state change of the target to be estimated during the time interval between extreme values, Based on the first relationship and the first calculated time, a first state change amount is calculated as the state change amount of the estimated target in the first time. Based on the first state change rate derived from the second relationship and the calculated second time, a second state change amount is calculated as the state change amount of the estimated target at the second time. The sum of the first state change amount and the second state change amount is calculated as the state change amount of the estimated target during the time between extreme values. [1] to [6], one of the state estimation methods.

[14] Based on the second time series data, determine whether the first dependent physical quantity is less than or equal to a threshold smaller than the reference value, For the period during which the first dependent physical quantity is greater than the threshold, the first state change rate of the target to be estimated is calculated for each interval of the extreme value time, based on the maximum and minimum values ​​of the principal physical quantity extracted from the first time series data and the first relationship shown in the first relationship data, when the first dependent physical quantity is less than the reference value, and the first state change rate of the target to be estimated is calculated for when the first dependent physical quantity is greater than or equal to the reference value, based on the maximum and minimum values ​​of the principal physical quantity extracted from the first time series data and the second relationship shown in the first relationship data. For the period during which the first dependent physical quantity is less than or equal to the threshold, the second state change rate of the estimated target is calculated based on second relational data showing the relationship between the primary physical quantity and the second dependent physical quantity relating to the estimated target and the state change rate of the estimated target, the retained value of the primary physical quantity in the first time series data, and the value of the second dependent physical quantity based on third time series data showing the time change of the second dependent physical quantity, The state estimation method of

[13] further comprises the following:

[15] The main physical quantity is the SOC of the battery to be estimated, The first dependent physical quantity is the absolute value of the current flowing through the battery, The state change rate of the estimated target, including the first state change rate, is either the rate of decrease in the battery capacity or the rate of increase in resistance.

[13] State estimation method.

[16] From time series data showing the time evolution of the principal physical quantity related to the object of estimation, the local maximum and local minimum values ​​which are the extrema of the principal physical quantity, and the time interval between extrema from one extrema to the next, Based on the relationship data showing the relationship between the maximum and minimum values ​​of the principal physical quantities and the rate of change of state of the target to be estimated, and the extracted maximum and minimum values ​​of the principal physical quantities, the rate of change of state of the target to be estimated is calculated for each time interval between extreme values. Based on the extracted interval between extreme values ​​and the calculated rate of change in state for each interval between extreme values, the amount of change in state of the estimated target is calculated for each interval between extreme values. A state estimation device equipped with a processor.

[17]

[16] state estimation device, The state estimation device estimates the state change of the target to be estimated, A state estimation system equipped with the following features.

[18] The subject of the estimation is a battery, The primary physical quantity is the State of Charge (SOC) of the battery. The rate of change of state of the estimated target is either the rate of decrease in the battery capacity or the rate of increase in resistance.

[17] State estimation system.

[19] Computers, From time-series data showing the time evolution of the principal physical quantity related to the target of estimation, the local maximum and local minimum values, which are the extreme values ​​of the principal physical quantity, and the time interval between extreme values, from one extreme value to the next, are extracted. Relationship data showing the relationship between the maximum and minimum values ​​of the principal physical quantities and the rate of change of state of the estimated target, and based on the extracted maximum and minimum values ​​of the principal physical quantities, the rate of change of state of the estimated target is calculated for each time interval between extreme values. Based on the extracted interval between extreme values ​​and the calculated rate of change in state for each interval between extreme values, the amount of change in state of the estimated target is calculated for each interval between extreme values. State estimation program. [Explanation of Symbols]

[0095] 1...State estimation system, 2...Battery, 10...State estimation device, 11...Processing circuit, 12...Storage medium, 23...State estimation program, D1...Relational data (first relational data), D2...Second relational data.

Claims

1. From a first time series data showing the time evolution of the principal physical quantity related to the object of estimation, the local maximum and local minimum values, which are the extrema of the principal physical quantity, and the time interval between extrema from one extrema to the next, are extracted. A first relationship data showing the relationship between the maximum and minimum values ​​of the principal physical quantity and the rate of change of state of the estimated target, and a first rate of change of state of the estimated target calculated as the rate of change of state of the estimated target for each time interval between extreme values, based on the extracted maximum and minimum values ​​of the principal physical quantity. Based on the extracted interval between extreme values ​​and the calculated first rate of change in state, the amount of change in state of the estimated target is calculated for each interval between extreme values. It is equipped with, In extracting the extreme values ​​of the principal physical quantity from the first time series data, By differentiating the principal physical quantity with respect to the first time series data, the time change of the derivative of the principal physical quantity is calculated. The value of the principal physical quantity at the moment when the derivative of the principal physical quantity changes from positive to negative is extracted as the local maximum value. The value of the principal physical quantity at the moment when the derivative of the principal physical quantity changes from negative to positive is extracted as the local minimum. The main physical quantity is the state of temperature (SOC) of the battery to be estimated, or the temperature of the junction portion to be estimated. The estimated state change rate, including the first state change rate, is either the rate of decrease in the battery capacity or the rate of increase in resistance, or the cleave rate of the junction portion. The first relational data shows the relationship between the rate of decrease of the capacitance and the rate of increase of the resistance with respect to the maximum and minimum values ​​of the SOC, or the relationship between the cleave rate and the maximum and minimum values ​​of the temperature of the junction portion. State estimation method.

2. The state estimation method of claim 1, wherein, in extracting the interval between extreme values ​​from the first time series data, the time from the moment when the derivative of the principal physical quantity switches from positive to negative to the moment when the derivative next switches from negative to positive, and the time from the moment when the derivative of the principal physical quantity switches from negative to positive to the moment when the derivative next switches from positive to negative, are each extracted as the interval between extreme values.

3. The state estimation method of claim 1, further comprising calculating a cumulative value of the state change amount of the estimated target from the state change amount of the estimated target for each time interval between extreme values.

4. The state estimation method of claim 3, wherein, in calculating the cumulative value of the state change amount of the estimated target, each time the differential value of the principal physical quantity switches from positive to negative, and each time the differential value switches from negative to positive, the state change amount of the estimated target during the extreme value interval is added to the cumulative value in real time.

5. In calculating the cumulative value of the state change amount of the target to be estimated, For each set of periods that include multiple intervals between extreme values, the amount of change in the state of the estimated target during the set of periods is calculated from the amount of change in the state of the estimated target during each of the multiple intervals between extreme values. Each time the aforementioned fixed period ends, the amount of state change during the aforementioned fixed period is added to the cumulative value in real time. The state estimation method of claim 3.

6. Based on second time-series data showing the time change of the first dependent physical quantity relating to the estimation target, it is determined whether the first dependent physical quantity is below a threshold. For the period during which the first dependent physical quantity is greater than the threshold, the rate of change of the first state of the target to be estimated is calculated for each interval of the time between extreme values, based on the maximum and minimum values ​​of the principal physical quantity extracted from the first time series data, and the first relational data. For the period during which the first dependent physical quantity is less than or equal to the threshold, the second state change rate of the estimated target is calculated based on second relational data showing the relationship between the primary physical quantity and the second dependent physical quantity relating to the estimated target and the state change rate of the estimated target, the retained value of the primary physical quantity in the first time series data, and the value of the second dependent physical quantity based on third time series data showing the time change of the second dependent physical quantity, A method for estimating a state according to any one of claims 1 to 5, further comprising the above.

7. The main physical quantity is the SOC of the battery, The first dependent physical quantity is the absolute value of the current flowing through the battery, The second dependent physical quantity is the temperature of the battery, The state change rate of the estimated target, including the first state change rate and the second state change rate, is either the rate of decrease of the capacity of the battery or the rate of increase of the resistance. The state estimation method of claim 6.

8. The method further comprises at least one of the following: calculating the value of the first dependent physical quantity during the time interval between extreme values ​​based on a second time series data showing the time change of the first dependent physical quantity relating to the estimation target; and calculating the value of the second dependent physical quantity during the time interval between extreme values ​​based on a third time series data showing the time change of the second dependent physical quantity relating to the estimation target. In the first relational data, in addition to the relationship between the state change rate of the estimated target and the maximum and minimum values ​​of the principal physical quantity, the relationship between the state change rate of the estimated target and at least one of the first dependent physical quantity and the second dependent physical quantity is shown. In calculating the first state change rate, the first state change rate is calculated based on the first relational data, the maximum and minimum values ​​of the principal physical quantity extracted from the first time series data, and the value of at least one of the first dependent physical quantity and the second dependent physical quantity during the time between the extreme values. A method for estimating a state according to any one of claims 1 to 5.

9. Based on the second time-series data, it is determined whether the first dependent physical quantity is below a threshold, For the period during which the first dependent physical quantity is greater than the threshold, the maximum and minimum values ​​of the principal physical quantity extracted from the first time series data, the value of at least one of the first dependent physical quantity and the second dependent physical quantity during the time between the extreme values, and the first relational data are used to calculate the first state change rate of the target to be estimated for each time between the extreme values. For the period during which the first dependent physical quantity is less than or equal to the threshold, the second state change rate of the estimated target is calculated based on the value of the second dependent physical quantity based on the third time series data, second relational data showing the relationship between the principal physical quantity and the second dependent physical quantity related to the estimated target and the state change rate of the estimated target, and the value of the principal physical quantity held in the first time series data. The state estimation method of claim 8 further comprises the following:

10. The main physical quantity is the SOC of the battery, The first dependent physical quantity is the absolute value of the current flowing through the battery, The second dependent physical quantity is the temperature of the battery, The state change rate of the estimated target, including the first state change rate, is either the rate of decrease of the capacity of the battery or the rate of increase of the resistance. The state estimation method of claim 8.

11. The method further comprises calculating, for each interval between extreme values, a first time when the first dependent physical quantity is less than a reference value and a second time when the first dependent physical quantity is equal to or greater than the reference value, based on a second time series data showing the time change of the first dependent physical quantity relating to the estimation target. The first relational data shows a first relationship, which is the relationship between the rate of change of state of the estimated target and the maximum and minimum values ​​of the principal physical quantity when the first dependent physical quantity is less than the reference value, and a second relationship, which is the relationship between the rate of change of state of the estimated target and the maximum and minimum values ​​of the principal physical quantity when the first dependent physical quantity is equal to or greater than the reference value. In calculating the first state change rate, In the case where the first dependent physical quantity is less than the reference value, the first state change rate is calculated based on the first relationship shown in the first relational data and the maximum and minimum values ​​of the principal physical quantity extracted from the first time series data. When the first dependent physical quantity is greater than or equal to the reference value, the first state change rate is calculated based on the second relationship shown in the first relational data, and the maximum and minimum values ​​of the principal physical quantity extracted from the first time-series data. In calculating the amount of state change of the target to be estimated during the time interval between extreme values, Based on the first relationship and the first calculated time, the first amount of state change is calculated as the amount of state change of the estimated target in the first time. Based on the first state change rate derived from the second relationship and the calculated second time, a second state change amount is calculated as the state change amount of the estimated target at the second time. The sum of the first state change amount and the second state change amount is calculated as the state change amount of the estimated target during the extreme time interval. A method for estimating a state according to any one of claims 1 to 5.

12. Based on the second time-series data, it is determined whether the first dependent physical quantity is less than or equal to a threshold smaller than the reference value, For the period during which the first dependent physical quantity is greater than the threshold, the first state change rate of the target to be estimated is calculated for each interval of the extreme value time, based on the maximum and minimum values ​​of the principal physical quantity extracted from the first time series data and the first relationship shown in the first relationship data, when the first dependent physical quantity is less than the reference value, and the first state change rate of the target to be estimated is calculated for when the first dependent physical quantity is greater than or equal to the reference value, based on the maximum and minimum values ​​of the principal physical quantity extracted from the first time series data and the second relationship shown in the first relationship data. For the period during which the first dependent physical quantity is less than or equal to the threshold, the second state change rate of the estimated target is calculated based on second relational data showing the relationship between the primary physical quantity and the second dependent physical quantity relating to the estimated target and the state change rate of the estimated target, the retained value of the primary physical quantity in the first time series data, and the value of the second dependent physical quantity based on third time series data showing the time change of the second dependent physical quantity, The state estimation method of claim 11 further comprises the following:

13. The main physical quantity is the SOC of the battery, The first dependent physical quantity is the absolute value of the current flowing through the battery, The state change rate of the estimated target, including the first state change rate, is either the rate of decrease of the capacity of the battery or the rate of increase of the resistance. The state estimation method of claim 11.

14. From time-series data showing the time evolution of the principal physical quantity related to the target of estimation, the local maximum and local minimum values, which are the extreme values ​​of the principal physical quantity, and the time interval between extreme values, from one extreme value to the next, are extracted. Based on the relationship data showing the relationship between the maximum and minimum values ​​of the principal physical quantities and the rate of change of state of the target to be estimated, and the extracted maximum and minimum values ​​of the principal physical quantities, the rate of change of state of the target to be estimated is calculated for each time interval between extreme values. Based on the extracted interval between extreme values ​​and the calculated rate of change in state for each interval between extreme values, the amount of change in state of the estimated target is calculated for each interval between extreme values. Equipped with a processor, In extracting the extreme values ​​of the principal physical quantity from the time-series data, the processor By differentiating the principal physical quantity with respect to the time series data, the time change of the derivative of the principal physical quantity is calculated. The value of the principal physical quantity at the moment when the derivative of the principal physical quantity changes from positive to negative is extracted as the local maximum value. The value of the principal physical quantity at the moment when the derivative of the principal physical quantity changes from negative to positive is extracted as the local minimum. The main physical quantity is the state of temperature (SOC) of the battery to be estimated, or the temperature of the junction portion to be estimated. The estimated rate of change of state is either the rate of decrease in the battery capacity or the rate of increase in resistance, or the cleavage rate of the junction portion. The aforementioned relationship data shows the relationship between the rate of decrease of the capacitance and the rate of increase of the resistance with respect to the maximum and minimum values ​​of the SOC, or the relationship between the cleave rate and the maximum and minimum values ​​of the temperature of the junction portion. State estimation device.

15. The state estimation device of claim 14, The state estimation device estimates the state change of the target to be estimated, A state estimation system equipped with the following features.

16. On the computer, From time-series data showing the time evolution of the principal physical quantity related to the target of estimation, the local maximum and local minimum values, which are the extreme values ​​of the principal physical quantity, and the time interval between extreme values, from one extreme value to the next, are extracted. Relationship data showing the relationship between the maximum and minimum values ​​of the principal physical quantities and the rate of change of state of the target to be estimated, and based on the extracted maximum and minimum values ​​of the principal physical quantities, the rate of change of state of the target to be estimated is calculated for each time interval between extreme values. Based on the extracted interval between extreme values ​​and the calculated rate of change in state for each interval between extreme values, the amount of change in state of the estimated target is calculated for each interval between extreme values. In extracting the extreme values ​​of the principal physical quantity from the time-series data, By differentiating the principal physical quantity with respect to the time series data, the time change of the derivative value of the principal physical quantity is calculated. The value of the principal physical quantity at the moment when the derivative of the principal physical quantity changes from positive to negative is extracted as the local maximum value. The value of the principal physical quantity at the moment when the derivative of the principal physical quantity changes from negative to positive is extracted as the local minimum. The main physical quantity is the state of temperature (SOC) of the battery to be estimated, or the temperature of the junction portion to be estimated. The estimated rate of change of state is either the rate of decrease in the battery capacity or the rate of increase in resistance, or the cleavage rate of the junction portion. The aforementioned relationship data shows the relationship between the rate of decrease of the capacitance and the rate of increase of the resistance with respect to the maximum and minimum values ​​of the SOC, or the relationship between the cleave rate and the maximum and minimum values ​​of the temperature of the junction portion. State estimation program.

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