Information processing method, information processing device, and information processing program

Abstract

This information processing method includes: acquiring a first SOH, which is the SOH of a battery at a first time point at which reuse of the battery is started, and a second SOH, which is the SOH at a second time point after the first time point; estimating, on the basis of the first SOH and the second SOH, a missing period, which is a period in which the battery was being used before the first time point; and outputting the missing period.

Description

Information processing method, information processing device, and information processing program

[0001] The present disclosure relates to a technique for estimating the usable life of a battery.

[0002] Patent Document 1 discloses a technology for predicting the future deterioration state of a battery by assuming that the amount by which the capacity retention rate of the battery has decreased since the start of use of the battery is proportional to the square root of the elapsed time since the start of use of the battery.

[0003] However, Patent Document 1 does not disclose how to calculate the period of time that a battery has been in use before being reused. As a result, it is not possible to properly determine the amount of time that has elapsed since the battery was first used, and there is a risk that the future deterioration state of the battery cannot be properly predicted.

[0004] Patent No. 5849897

[0005] The present disclosure has been made to solve such problems, and aims to provide a technology that can properly determine the period of time that a battery being reused was used before being reused.

[0006] An information processing method in one aspect of the present disclosure is an information processing method in a computer, which includes obtaining a first SOH, which is the SOH of the battery at a first point in time when reuse of the battery begins, and a second SOH, which is the SOH at a second point in time after the first point in time; estimating a missing period, which is a period during which the battery was used before the first point in time, based on the first SOH and the second SOH; and outputting the missing period.

[0007] 1 is an overall configuration diagram of an information processing system; FIG. 2 is an explanatory diagram of a root rule; FIG. 3 is an explanatory diagram of a method for calculating a missing period using the root rule; FIG. 4 is a flowchart showing an example of processing by the information processing system; FIG. 5 is a diagram showing an example of time-series changes in the SOH calculated using the root rule and the SOH indicated by log information; FIG. 6 is a diagram showing another example of time-series changes in the SOH calculated using the root rule and the SOH indicated by log information; FIG. 7 is a diagram showing another example of the relationship between the regression line of the SOH derived using the n-power rule and the SOH indicated by the operation history of the battery; FIG. 8 is a flowchart showing a modified example of processing by the information processing system;

[0008] (Background to the present disclosure) Conventionally, a technology has been known that uses a learning model to estimate the state of a battery, such as its SOH (State of Health). The SOH indicates the ratio of the current full charge capacity of a battery to the full charge capacity of the battery in its initial state (current full charge capacity of the battery / full charge capacity of the battery in its initial state), and is sometimes called the capacity maintenance rate. The SOH is used as an index indicating the degree of battery degradation.

[0009] A learning model for estimating the state of a battery is constructed by performing machine learning based on the battery's operating history from its initial state. However, when reusing resold batteries, it is difficult to obtain the operating history of the battery before it was reused. For this reason, it is difficult to construct a learning model for estimating the state of a battery using the operating history of the battery from its initial state during reuse.

[0010] On the other hand, as mentioned above, Patent Document 1 discloses a technology for predicting the future degradation state of a battery without using a learning model, based on a root law that states that the amount by which a battery's capacity retention rate has decreased since the battery was first used is proportional to the square root of the elapsed time since the battery was first used. However, Patent Document 1 does not disclose calculating the period of time that a reused battery was used before being reused. Therefore, for a reused battery, it is not possible to properly determine the elapsed time since the battery was first used, and there is a risk that the future degradation state of the battery cannot be properly predicted.

[0011] Therefore, the inventors have conducted extensive research into technology that can properly determine the period of time that a battery being reused was used before being reused, and can also interpolate the operating history during that period, and have come up with the present disclosure described below.

[0012] (1) An information processing method in one aspect of the present disclosure is an information processing method in a computer, which includes obtaining a first SOH, which is the SOH of the battery at a first point in time when reuse of the battery begins, and a second SOH, which is the SOH at a second point in time after the first point in time; estimating a missing period, which is a period during which the battery was used before the first point in time, based on the first SOH and the second SOH; and outputting the missing period.

[0013] In this configuration, the missing period, which is the period during which the battery was used before the first time point and is estimated based on the first SOH and the second SOH, is output, so that the period during which the battery was used before being reused can be properly determined.

[0014] (2) In the information processing method described in (1) above, estimating the missing period may include estimating the missing period based on a root law in which the amount by which the SOH has decreased from the initial state of the battery is proportional to the square root of the elapsed time from the initial state of the battery.

[0015] In this case, since the missing period is estimated based on the root rule, the missing period can be estimated appropriately.

[0016] (3) In the information processing method described in (1) or (2) above, estimating the missing period includes estimating the missing period based on an n-th power law in which the amount by which the SOH has decreased from the initial state of the battery is proportional to the nth power of the elapsed time from the initial state of the battery, and n may be a positive number.

[0017] In this case, since the missing period is estimated based on the n-th power law, the missing period can be estimated appropriately.

[0018] (4) In the information processing method described in (3) above, estimating the missing period may include estimating a plurality of missing period candidates according to the value of n by changing the value of n, and outputting the missing period may include determining the missing period to be output from the plurality of missing period candidates.

[0019] In this case, a plurality of missing period candidates are estimated according to the value of n, and the missing period determined from among them is output, so that an appropriate missing period can be determined.

[0020] (5) In the information processing method described in (4) above, the method may further include obtaining a positive numerical range input by the user, and estimating the missing period may include changing the value of n within the numerical range.

[0021] In this case, multiple missing period candidates are estimated according to the value of n within the positive numerical range entered by the user, and the missing period determined from among them is output. Therefore, a user who is familiar with batteries can determine the missing period that is suitable for the battery by entering the positive numerical range that is suitable for the battery.

[0022] (6) In the information processing method described in (4) or (5) above, the method may further include acquiring a history of the SOH of the battery from the first time point to the second time point, and estimating the missing period may further include deriving a plurality of regression lines that indicate the relationship between the nth power of the elapsed time from the initial state of the battery and the SOH of the battery, the plurality of regression lines corresponding to the value of n, and calculating an evaluation index for evaluating each of the plurality of regression lines based on the SOH history of the battery, and outputting the missing period may include outputting, as the missing period, the missing period candidate that corresponds to the regression line with the best evaluation index among the plurality of missing period candidates.

[0023] In this case, an evaluation index for evaluating each of the multiple regression lines is calculated based on the battery's SOH history. The missing period candidate corresponding to the regression line with the best evaluation index is output as the missing period. Therefore, based on the battery's SOH history, it is possible to determine the period during which the battery was most likely used before being reused.

[0024] (7) In the information processing method described in (6) above, the evaluation index may indicate the degree of deviation between the battery's SOH history and each of the multiple regression lines, and the regression line may be evaluated as being better the smaller the degree of deviation.

[0025] In this case, the missing period candidate corresponding to the regression line with the smallest deviation from the battery's SOH history is output as the missing period. Therefore, based on the SOH history of the reused battery, it is possible to determine the period during which the battery was most likely used before being reused.

[0026] (8) In the information processing method described in any one of (1) to (7) above, the method may further include obtaining an operation history of the battery from a third time point to a fourth time point prior to the first time point, and estimating the missing period may further include estimating the missing period as the period obtained by excluding the period from the third time point to the fourth time point from the estimated missing period.

[0027] In this case, the missing period is the period obtained by excluding the period from the third time point to the fourth time point from the missing period estimated based on the first SOH and the second SOH, and is output as the missing period. Therefore, it is possible to grasp only the period during which the battery was used before being reused, during which the battery's operation history cannot be obtained.

[0028] (9) In the information processing method described in any one of (1) to (8) above, the method may further include acquiring an operation history of the battery from the first time point to the second time point, and interpolating the operation history of the battery during the missing period based on the acquired operation history of the battery.

[0029] In this case, the operation history of the battery during the missing period is interpolated based on the operation history of the battery from the first time point to the second time point. Therefore, it is possible to use the operation history of the battery from its initial state to the second time point. This makes it possible to build a learning model that can accurately estimate the state of a reused battery through machine learning based on the operation history of the battery from its initial state, for example.

[0030] The present disclosure can be realized not only as an information processing method that executes the characteristic processes described above, but also as an information processing device or the like that has a characteristic configuration corresponding to the characteristic processes executed by the information processing method. Furthermore, the present disclosure can also be realized as a computer program that causes a computer to execute the characteristic processes included in such an information processing method. Therefore, the same effects as those of the above information processing method can also be achieved in the following other aspects.

[0031] (10) In another aspect of the present disclosure, an information processing device includes a processor that executes the following operations: acquiring a first SOH, which is the SOH of the battery at a first point in time when reuse of the battery begins; acquiring a second SOH, which is the SOH of the battery at a second point in time after the first point in time; estimating a missing period, which is a period during which the battery was used before the first point in time, based on the first SOH and the second SOH; and outputting the missing period.

[0032] (11) In yet another aspect of the present disclosure, an information processing program causes a computer to acquire a first SOH, which is the SOH of the battery at a first point in time when reuse of the battery begins, and a second SOH, which is the SOH of the battery at a second point in time after the first point in time; estimate a missing period, which is a period during which the battery was used before the first point in time, based on the first SOH and the second SOH; and output the missing period.

[0033] The present disclosure can also be realized as an information processing system operated by such an information processing program. Needless to say, such a computer program can be distributed on a computer-readable non-transitory recording medium such as a CD-ROM or via a communication network such as the Internet.

[0034] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. Note that each of the embodiments described below represents a specific example of the present disclosure. The numerical values, shapes, components, steps, order of steps, etc. shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Furthermore, among the components in the following embodiments, components that are not described in an independent claim that represents a superordinate concept will be described as optional components. Furthermore, in all embodiments, the respective contents can be combined.

[0035] 1 is a diagram showing the overall configuration of an information processing system 100. The information processing system 100 includes a battery-mounted device 1 and a server 2 (information processing device). The battery-mounted device 1 and the server 2 are connected to each other so as to be able to communicate with each other via a network 4. The network 4 is, for example, the Internet.

[0036] The battery-mounted device 1 is, for example, an electric vehicle such as an electric car, electric truck, electric motorcycle, or electric bicycle, which is equipped with a rechargeable battery 11 and moves using power stored in the battery 11. The battery-mounted device 1 is not limited to this, and may also be an electric mobile body such as a drone, a ship, or a robot, which is equipped with a rechargeable battery 11 and moves using power stored in the battery 11. Furthermore, the battery-mounted device 1 may be a stationary power supply device which is equipped with a battery 11 that stores power supplied from a solar power generation device and a power supply company, etc., and supplies the power stored in the battery 11 to a facility.

[0037] The battery mounting device 1 periodically transmits log information (operation history) indicating the operating state of the battery 11 to the server 2. Details of the log information will be described later.

[0038] The server 2 is, for example, a cloud server. The server 2 receives various information, such as log information, from the battery-mounted device 1. The server 2 stores a learning model for estimating the SOH (State of Health) of the battery 11 (hereinafter referred to as the SOH model) and a learning model for estimating the SOC (State of Charge) of the battery 11 (hereinafter referred to as the SOC model). The SOH indicates the ratio of the current full charge capacity of the battery 11 to the full charge capacity of the battery 11 in its initial state (current full charge capacity of the battery 11 / full charge capacity of the battery 11 in its initial state), and is sometimes referred to as the capacity maintenance rate. The SOC indicates the ratio of the current charge capacity (remaining capacity) of the battery 11 to the full charge capacity of the battery 11 (current charge capacity of the battery 11 / current full charge capacity of the battery 11), and is sometimes referred to as the remaining battery capacity.

[0039] The following describes the configurations of the battery mounting device 1 and the server 2. The battery mounting device 1 includes a battery 11, a memory 12, a communication unit 13, an operation unit 14, and a control unit 15.

[0040] The battery 11 is a rechargeable secondary battery such as a lithium-ion battery. The battery 11 discharges (supplies) the power stored therein to various operating units of the battery-mounted device 1. The battery 11 is a second-hand battery that was previously used in a device other than the battery-mounted device 1 and then resold. If the battery-mounted device 1 is a stationary power supply device, the battery 11 discharges (supplies) the power stored therein to an external device via a power cable (not shown).

[0041] The memory 12 is a storage device capable of storing various types of information, such as a random access memory (RAM), a solid state drive (SSD), or a flash memory.

[0042] The communication unit 13 is a communication interface circuit that transmits and receives various information to and from external devices such as the server 2 via the network 4 .

[0043] The operation unit 14 accepts operations of the battery-mounted device 1. The operation unit 14 includes, for example, a liquid crystal display and a touch panel device.

[0044] The control unit 15 is, for example, a microcomputer equipped with a processor, non-volatile memories such as RAM (Random Access Memory) and ROM (Read Only Memory), an input / output circuit, a timer circuit, and various measurement circuits, etc. The various measurement circuits include measurement circuits that measure the current, voltage, and temperature of the battery 11.

[0045] The control unit 15 charges the battery 11 with power supplied via a power cable (not shown).

[0046] The control unit 15 measures the state of the battery 11 periodically, for example, every 10 seconds, and stores log information indicating the measurement results in the memory 12. The log information includes, for example, identification information of the battery-mounted device 1 (hereinafter, device ID), identification information of the battery 11 (hereinafter, battery ID), the date and time when the state of the battery 11 was measured (hereinafter, measurement date and time), and information indicating the measurement results of the state of the battery 11 (hereinafter, state information). The state of the battery 11 includes the current, voltage, and temperature of the battery 11.

[0047] When the communication unit 13 receives the SOC model and the SOH model from the server 2, the control unit 15 stores the SOC model and the SOH model in the memory 12. Furthermore, the control unit 15 periodically requests the server 2 to transmit the SOC model and the SOH model using the communication unit 13, such as once a month. The control unit 15 stores the SOC model and the SOH model received by the communication unit 13 from the server 2 in response to the request in the memory 12. The timing at which the control unit 15 requests the server 2 to transmit the SOC model and the SOH model is not limited to this. For example, the control unit 15 may request the server 2 to transmit the SOC model and the SOH model in response to a user operation of the operation unit 14.

[0048] When log information indicating the current state of the battery 11 is input, the SOC model outputs the current SOC of the battery 11. When log information indicating the state of the battery 11 during a charge / discharge period is input, the SOH model outputs the SOH of the battery 11 during the charge / discharge period. The charge / discharge period is the period from the start to the end of charging or discharging the battery 11.

[0049] The control unit 15 estimates the SOC of the battery 11 periodically, for example, once every 10 seconds. Specifically, the control unit 15 acquires the SOC model stored in the memory 12 and inputs the most recently stored log information into the SOC model. When the SOC model outputs the SOC of the battery 11, the control unit 15 displays the SOC on the liquid crystal display provided in the operation unit 14. The control unit 15 also adds the SOC to the log information stored in the memory 12 that has been input to the SOC model.

[0050] Furthermore, the control unit 15 estimates the SOH of the battery 11 when charging or discharging of the battery 11 is completed. The timing at which the control unit 15 estimates the SOH of the battery 11 is not limited to this. For example, the control unit 15 may estimate the SOH of the battery 11 in response to a user's operation of the operation unit 14. Specifically, the control unit 15 acquires an SOH model stored in the memory 12 and inputs log information stored in the memory 12 for the most recent charging or discharging period into the SOH model. When the SOH model outputs the SOH of the battery 11, the control unit 15 displays the SOH on the liquid crystal display provided in the operation unit 14. The control unit 15 also adds the SOH to the log information stored in the memory 12 and input to the SOH model.

[0051] Control unit 15 periodically transmits the log information stored in memory 12 to server 2 using communication unit 13. The timing at which control unit 15 transmits the log information to server 2 is not limited to this. For example, control unit 15 may transmit the log information stored in memory 12 to server 2 using communication unit 13 when charging / discharging of battery 11 is completed. Alternatively, control unit 15 may transmit the log information stored in memory 12 to server 2 using communication unit 13 when a predetermined number of charging / discharging operations are completed.

[0052] The server 2 includes a memory 22, a communication unit 23, and a control unit 25 (computer).

[0053] The communication unit 23 is a communication interface circuit that transmits and receives various information to and from external devices such as the battery-mounted device 1 via the network 4. For example, when the communication unit 23 receives log information from the battery-mounted device 1, the communication unit 23 outputs the received log information to the control unit 25. Furthermore, under the control of the control unit 25, the communication unit 23 transmits the SOC model and the SOH model to the battery-mounted device 1.

[0054] The memory 22 is configured as a non-volatile rewritable storage device such as a hard disk drive or a solid state drive, etc. The memory 22 stores an information processing program executed by the control unit 25.

[0055] The memory 22 includes an operation history storage unit 221 and a model storage unit 222 .

[0056] The operation history storage unit 221 stores the log information received by the communication unit 23 from the battery mounting device 1 .

[0057] The model storage unit 222 stores an SOH model for estimating the SOH of the battery 11 included in the battery-mounted device 1 and an SOC model for estimating the SOC of the battery 11 included in the battery-mounted device 1. Hereinafter, the SOH model for estimating the SOH of the battery 11 included in the battery-mounted device 1 will be abbreviated as the SOH model. The SOC model for estimating the SOC of the battery 11 included in the battery-mounted device 1 will be abbreviated as the SOC model.

[0058] The control unit 25 is configured with a processor such as a CPU, for example. The control unit 25 functions as an acquisition unit 251, an estimation unit 252, an output unit 253, and an evaluation unit 254 by executing an information processing program stored in the memory 22. The information processing program may be recorded on a non-transitory computer-readable recording medium. The acquisition unit 251, the estimation unit 252, the output unit 253, and the evaluation unit 254 may be configured with dedicated hardware circuits such as an ASIC.

[0059] The acquisition unit 251 acquires the log information (battery operation history) received by the communication unit 23 from the battery mounting device 1 , and stores it in the operation history storage unit 221 .

[0060] The estimation unit 252 refers to the log information stored in the operation history memory unit 221 and estimates the period during which the battery 11 was in use before the first point in time (hereinafter, the missing period) based on the SOH of the battery 11 at the first point in time when reuse of the battery 11 began (hereinafter, the first SOH) and the SOH of the battery 11 at a second point in time after the first point in time (hereinafter, the second SOH).

[0061] FIG. 2 is an explanatory diagram of the root law. The vertical axis of FIG. 2 represents the SOH of the battery 11, and the horizontal axis represents the square root (0.5 power) of the elapsed time from the initial state of the battery 11. Specifically, as shown in FIG. 2, the estimation unit 252 estimates the missing period based on the root law, in which the amount of decrease in the SOH of the battery 11 from the initial state (100%) of the battery 11 is proportional to the square root (0.5 power) of the elapsed time from the initial state of the battery 11. Note that the root law was derived based on the idea that the thickness of the SEI film formed on the negative electrode of a lithium-ion battery is proportional to the amount of capacity degradation of the lithium-ion battery, and that the growth rate of the SEI film is proportional to the reciprocal of the thickness of the SEI film.

[0062] 3 is an explanatory diagram of a method for calculating the missing period. For example, the estimation unit 252 refers to the log information stored in the operation history storage unit 221. As a result, the estimation unit 252 calculates the missing period from the first time t when the reuse of the battery 11 starts from the initial state of the battery 11, as shown in FIG. １ The first SOH, which is the SOH of the battery 11 at ａ (%). The estimation unit 252 determines that the second time point t ２ The second SOH, which is the SOH of the battery 11 at ｂ (%). The estimation unit 252 determines that the first time point t １ From the second time point t ２ It is understood that the time elapsed until is N (days).

[0063] Here, from the initial state of the battery 11 to the first time point t １ The time elapsed until the first time point t １ The missing period in which the battery 11 was used before is x １ (days). ２ The elapsed time until ２ (days).

[0064] In this case, the following formulas (1) and (2) that represent the root rule hold: In formulas (1) and (2), a represents a predetermined coefficient (hereinafter referred to as the deterioration coefficient), and 100 represents the SOH (%) in the initial state of the battery 11. Note that the SOH in the initial state of the battery 11 is not limited to 100(%), and may be greater than 100(%), for example, 103(%).

[0065] Substituting equation (1) into equation (2), the time from the initial state of the battery 11 to the second time point t ２ Time elapsed until x ２ At the first time point t １ From the second time point t ２ and the first time t when reuse of the battery 11 starts from the initial state of the battery 11. １ Elapsed time until (missing period) x １ and the sum (x ２ = N + x １ ), the following equation (3) is obtained.

[0066] Transforming equation (1) yields equation (4) below.

[0067] Substituting equation (4) into equation (3) gives the following equation (5).

[0068] Transforming equation (5) yields the following equations (6), (7), and (8).

[0069] Transforming equation (8) results in the following equation (9): ａ , 2nd SOH SOH ｂ , and the value of the elapsed time N from the first time point to the second time point, the missing period x １ Calculate (estimate)

[0070] The output unit 253 stores (outputs) the missing period estimated by the estimation unit 252 in the operation history storage unit 221 in association with the battery ID of the battery 11. The output unit 253 may transmit (output) the missing period estimated by the estimation unit 252 to the battery mounting device 1 using the communication unit 23.

[0071] The output unit 253 interpolates the log information for the missing period estimated by the estimation unit 252 based on the log information acquired from the first time point to the second time point that is stored in the operation history storage unit 221.

[0072] Specifically, the output unit 253 extracts log information for a period of the same length as the missing period estimated by the estimation unit 252 using a predetermined method from the log information acquired from the first point in time to the second point in time stored in the operation history memory unit 221.

[0073] For example, the output unit 253 randomly extracts log information for a period of the same length as the missing period from the log information acquired between the first time point and the second time point. Alternatively, the output unit 253 may extract log information for a period of the same length as the missing period in descending or ascending order of the acquisition date and time included in the log information. Alternatively, the output unit 253 may extract log information for a period of the same length as the missing period from the log information acquired between the first time point and the second time point using another method.

[0074] The output unit 253 stores the extracted log information in the operation history storage unit 221 as log information acquired on a date and time during the missing period from a point in time that is the missing period before the first point in time to the first point in time. Specifically, the output unit 253 changes the acquisition date and time included in the extracted log information to a date and time that goes back by the elapsed time from the first point in time to the second point in time. The output unit 253 stores the log information with the changed acquisition date and time in the operation history storage unit 221.

[0075] The output unit 253 transmits (outputs) the SOH model and the SOC model stored in the model storage unit 222 to the battery mounting device 1 using the communication unit 23. When the communication unit 23 receives a request to transmit the SOH model and the SOC model from the battery mounting device 1, the output unit 253 returns (outputs) the SOH model and the SOC model stored in the model storage unit 222 to the battery mounting device 1 using the communication unit 23.

[0076] The evaluation unit 254 will be described in detail later.

[0077] Next, the processing of the information processing system 100 will be described. This processing estimates the missing period during which the reused battery 11 was used before being reused, and interpolates log information for the estimated missing period. Figure 4 is a flowchart showing an example of the processing of the information processing system 100. This processing is started by the estimation unit 252 at any timing, such as when the communication unit 23 receives an instruction to execute the processing via the network 4.

[0078] First, in step S1 , the estimation unit 252 acquires, from the operation history storage unit 221 , log information acquired during the period from the first time point to the second time point.

[0079] In step S2, the estimation unit 252 refers to the log information acquired in step S1, and calculates the missing period by substituting the SOH of the battery 11 at the first point in time (first SOH), the SOH of the battery 11 at the second point in time (second SOH), and the elapsed time from the first point in time to the second point in time into the above formula (9).

[0080] In step S3, the output unit 253 stores (outputs) the missing period calculated in step S2 in the operation history storage unit 221 in association with the battery ID of the battery 11.

[0081] In step S4, the output unit 253 extracts log information for a period of the same length as the missing period calculated in step S2 from the log information acquired from the first point in time to the second point in time stored in the operation history memory unit 221.

[0082] In step S5, the output unit 253 stores (outputs) the log information extracted in step S4 in the operation history storage unit 221 as log information acquired on the date and time of the missing period from a point in time preceding the first point in time by the missing period to the first point in time. In this way, the output unit 253 interpolates the log information of the battery 11 during the missing period. When the output unit 253 finishes the processing of step S5, it ends the processing shown in FIG. 4.

[0083] In this way, according to the first embodiment, the missing period in which the battery 11 was used before the first time point is stored in the operation history storage unit 221. Therefore, it is possible to appropriately grasp the period in which the battery 11 being reused was used before being reused.

[0084] Furthermore, according to the first embodiment, based on the log information of the battery 11 acquired during the period from the first time point to the second time point, log information for a period of the same length as the missing period is stored in the operation history storage unit 221. This makes it possible to use the log information of the battery 11 from the initial state of the battery 11 to the second time point. This makes it possible to build a learning model that can accurately estimate the state of the battery 11, for example, by machine learning based on the log information from the initial state of the battery 11 to the second time point.

[0085] In the process shown in FIG. 4, steps S4 and S5 may be omitted.

[0086] Second Embodiment FIG. 5 is a diagram showing an example of a time-series change Fy in the SOH of battery 11 according to the root law and an example of a time-series change Fx in the SOH of battery 11 obtained from experimental values. The vertical axis of FIG. 5 represents the SOH of battery 11, and the horizontal axis of FIG. 5 represents the square root (0.5 power) of the time elapsed from the initial state of battery 11. As shown in FIG. 5, the inventors compared the time-series change Fy in the SOH of battery 11 according to the root law described in the first embodiment with the time-series change Fx in the SOH of battery 11 obtained from experimental values. As a result, the inventors found that as the time elapsed from the initial state of battery 11 increases, the rate of decrease in the SOH may become worse than that obtained from the root law.

[0087] Fig. 6 shows an example of the time series change Fya of the SOH of the battery 11 according to the n-th power law when n is 0.3, and the time series change Fy of the SOH of the battery 11 according to the root law. The vertical axis of Fig. 6 represents the SOH of the battery 11, and the horizontal axis of Fig. 6 represents the 0.3 power of the elapsed time from the initial state of the battery 11. The n-th power law states that the amount of decrease in the SOH of the battery 11 from the initial state (100%) of the battery 11 is proportional to the n-th power of the elapsed time from the initial state of the battery 11. n is a positive number (0<n).

[0088] The inventors have found that the time-series change Fy in the SOH of battery 11 obtained from the experimental values ​​of battery 11 over a long period of time from the initial state, according to the root law, i.e., the n-power law when n is 0.5, may be substantially similar to the time-series change Fya in the SOH of battery 11 over a long period of time from the initial state, according to the n-power law when n is 0.3. However, in this case, as shown in FIG. 6 , the inventors have found that, although the time-series change Fya when n is 0.3 and the time-series change Fy when n is 0.5 are substantially similar in a long-time situation, there is a large difference between the time elapsed from the initial state (100%) of battery 11 estimated based on these changes. Therefore, the inventors have found that, assuming that the SOH of battery 11 decreases according to the n-power law, it is necessary to determine an appropriate value of n in order to properly calculate the missing period.

[0089] In the second embodiment, the present inventors have conceived based on the above findings, and a description will be given of a configuration in which a plurality of missing period candidates according to the value of n are estimated based on the n-th power law, and a missing period to be output is determined from among them. In this description, the details of the evaluation unit 254 will be described.

[0090] Specifically, the acquiring unit 251 acquires the numerical range information received by the communication unit 23 from the external device via the network 4. The numerical range information is information indicating the range of positive numerical values ​​input by the user to the external device. The acquiring unit 251 stores the acquired numerical range information in the memory 22.

[0091] The estimation unit 252 estimates a plurality of missing period candidates according to the value of n by changing the value of n within the numerical range indicated by the numerical range information. More specifically, the estimation unit 252 refers to the log information stored in the operation history storage unit 221, as in the first embodiment. As a result, the estimation unit 252 estimates a first time t when reuse of the battery 11 starts from the initial state of the battery 11, as shown in FIG. １ The first SOH, which is the SOH of the battery 11 at ａ (%). The estimation unit 252 determines that the second time point t ２ The second SOH, which is the SOH of the battery 11 at ｂ (%). The estimation unit 252 determines that the first time point t１ From the second time point t ２ It is understood that the time elapsed until is N (days).

[0092] Here, from the initial state of the battery 11 to the first time point t １ The time elapsed until the first time point t １ The missing period in which the battery 11 was used before is x １ (days). ２ The elapsed time until ２ (days).

[0093] In this case, the following equations (11) and (12) showing the n-th power law hold true. In equations (11) and (12), a represents the deterioration coefficient, as in the first embodiment. 100 represents the SOH (%) in the initial state of the battery 11, as in the first embodiment. Note that the SOH in the initial state of the battery 11 is not limited to 100(%), and may be greater than 100(%), such as 103(%).

[0094] Substituting equation (11) into equation (12), the time from the initial state of the battery 11 to the second time point t ２ Time elapsed until x ２ At the first time point t １ From the second time point t ２ and the first time t when reuse of the battery 11 starts from the initial state of the battery 11. １ Elapsed time until (missing period) x １ and the sum (x ２ = N + x １ ), the following equation (13) is obtained.

[0095] Transforming equation (11) yields equation (14) below.

[0096] Substituting equation (14) into equation (13) gives the following equation (15).

[0097] Transforming equation (15) yields the following equations (16), (17), and (18).

[0098] Transforming equation (18) results in the following equation (19): ａ , 2nd SOH SOH ｂ , and the elapsed time N from the first time point to the second time point, and by substituting the value of n, a missing period candidate x １ Calculate (estimate)

[0099] The estimation unit 252 further derives a plurality of regression lines that indicate the relationship between the nth power of the time elapsed from the initial state of the battery 11 and the SOH of the battery 11, the regression lines corresponding to the value of n.

[0100] Specifically, the estimation unit 252 estimates the missing period candidate x １ The first SOH and the value of n substituted into equation (19) are substituted into equation (14) to calculate the deterioration coefficient a. Then, the estimation unit 252 uses the deterioration coefficient a to derive a regression line according to the value of n, which indicates the relationship between the nth power of the elapsed time x from the initial state of the battery 11 and the SOH of the battery 11, as shown in the following equation (20).

[0101] The estimation unit 252 further calculates an evaluation index for evaluating each of the plurality of regression lines according to the value of n, based on the SOH history of the battery 11 from the first time point to the second time point. Specifically, the estimation unit 252 calculates, as the evaluation index, the degree of deviation between the SOH history of the battery 11 from the first time point to the second time point and each of the plurality of regression lines.

[0102] 7 is a diagram showing an example of the relationship between the SOH history Px of the battery 11 from the first time point to the second time point and the regression line Fyc when the value of n is 0.45. The vertical axis of Fig. 7 represents the SOH of the battery 11, and the horizontal axis represents the 0.45 power of the elapsed time from the initial state of the battery 11.

[0103] For example, the estimation unit 252 refers to log information acquired during a period from a first time point to a second time point, which is stored in the operation history storage unit 221. For each of the multiple time points indicated by the log information, the estimation unit 252 calculates a sum of squares error SSE, which indicates the degree of deviation between the SOH of the battery 11 at each time point and the SOH of the battery 11 at each time point on the regression line Fyc when the value of n is 0.45. The estimation unit 252 calculates the sum of the sum of squares error SSE calculated for each of the multiple time points as an evaluation index for the regression line Fyc when the value of n is 0.45.

[0104] Note that the estimation unit 252 may calculate, as the deviation, a mean squared error (Mean Squared Error), which is the result of dividing the sum of the squared sum errors SSE calculated for each of the multiple time points, by the number of the multiple time points, instead of the sum of the squared sum errors SSE calculated for each of the multiple time points.

[0105] The evaluation unit 254 evaluates the multiple regression lines derived by the estimation unit 252 based on the evaluation index calculated by the estimation unit 252. Specifically, the evaluation unit 254 evaluates that the smaller the evaluation index of a regression line, the better the regression line. In other words, the evaluation unit 254 evaluates the regression line with the smallest evaluation index, among the multiple regression lines derived by the estimation unit 252, as the regression line with the best evaluation index.

[0106] 8 is a diagram showing an example of the relationship between the SOH history Px of the battery 11 from the first time point to the second time point and the regression line Fyd when the value of n is 0.55. For example, the degree of deviation between the SOH history Px of the battery 11 and the regression line Fyd, which is an evaluation index for the regression line Fyd in FIG. 8, is smaller than the degree of deviation between the SOH history Px of the battery 11 and the regression line Fyc, which is an evaluation index for the regression line Fyc in FIG. 7. Therefore, the evaluation unit 254 evaluates that the regression line Fyd in FIG. 8 is a regression line with a better evaluation index than the regression line Fyc in FIG. 7.

[0107] The output unit 253 determines a missing period to be output from among a plurality of missing period candidates, and stores (outputs) the determined missing period in the operation history storage unit 221 in association with the battery ID of the battery 11, as in the first embodiment. Also, as in the first embodiment, the output unit 253 interpolates log information for the missing period based on log information acquired from the first time point to the second time point that is stored in the operation history storage unit 221.

[0108] Specifically, the output unit 253 identifies the value of n used to derive the regression line for which the evaluation unit 254 has evaluated the evaluation index to be the best. The output unit 253 determines the missing period candidate according to the value of n as the missing period to be output.

[0109] Next, the processing of the information processing system 100 in the second embodiment will be described. Fig. 9 is a flowchart showing another example of the processing of the information processing system 100. This processing is started by the estimation unit 252, for example, when the communication unit 23 receives an instruction to execute the processing, including the numerical range information, from an external device via the network 4, and the acquisition unit 251 acquires the numerical range information. Note that if the numerical range information is stored in the memory 22, the processing shown in Fig. 9 may be started by the estimation unit 252 at any timing.

[0110] First, the estimation unit 252 performs step S1, similarly to the first embodiment. As a result, the estimation unit 252 acquires, from the operation history storage unit 221, log information acquired during the period from the first time point to the second time point.

[0111] Next, in step S21, the estimation unit 252 sets the value of n to the lower limit of the numerical range indicated by the numerical range information stored in the memory 22. When the processing of step S21 ends, the processing proceeds to step S2a.

[0112] Next, in step S2a, the estimation unit 252 refers to the log information acquired in step S1, and substitutes the SOH of the battery 11 at the first point in time (first SOH), the SOH of the battery 11 at the second point in time (second SOH), the elapsed time N from the first point in time to the second point in time, and the value of n into the above formula (19), to calculate a candidate missing period according to the value of n.

[0113] Next, in step S22, the estimation unit 252 derives a regression line that indicates the relationship between the nth power of the elapsed time from the initial state of the battery 11 and the SOH of the battery 11, depending on the value of n.

[0114] Next, in step S23, the estimation unit 252 calculates an evaluation index for evaluating the regression line derived in step S22 based on the history of the SOH of the battery 11 from the first time point to the second time point.

[0115] Next, in step S24 , the estimation unit 252 determines whether n is equal to or greater than the upper limit of the numerical range indicated by the numerical range information stored in the memory 22 .

[0116] In step S24, if the estimation unit 252 determines that n is less than the upper limit value (NO in step S24), the process proceeds to step S25, and if the estimation unit 252 determines that n is equal to or greater than the upper limit value (YES in step S24), the process proceeds to step S26. In other words, the estimation unit 252 changes the value of n within the numerical range indicated by the numerical range information.

[0117] In step S25, the estimation unit 252 increases n by a predetermined amount Δn. When the process of step S25 ends, the process proceeds to step S2a again.

[0118] In step S26, the evaluation unit 254 evaluates the multiple regression lines derived in step S22 based on the evaluation index calculated in step S23. The output unit 253 identifies the value of n used to derive the regression line for which the evaluation unit 254 has evaluated the evaluation index to be the best. The output unit 253 determines, from the missing period candidates calculated in step S2a, the missing period candidate that corresponds to the value of n identified in step S26 as the missing period to be output.

[0119] When the process of step S26 is completed, the output unit 253 performs the processes of steps S3, S4, and S5, as in the first embodiment, and then ends the process shown in FIG.

[0120] As described above, according to the second embodiment, an evaluation index for evaluating each of the multiple regression lines is calculated based on the SOH history of the battery 11 from the first time point to the second time point. Furthermore, the missing period candidate corresponding to the regression line with the best evaluation index is stored as the missing period in the operation history storage unit 221. Therefore, based on the SOH history of the battery 11 being reused, it is possible to determine the period during which the battery 11 is most likely to have been used before being reused.

[0121] 9, steps S4 and S5 may be omitted. The numerical range indicated by the numerical range information may be a single positive numerical value. The numerical range information may be input by an administrator of the information processing system 100 via an external device, or may be stored in advance in the memory 22.

[0122] Furthermore, for example, when the battery 11 has been reused in a different battery-mounted device 1 by the same user in the past, log information of the battery 11 from a third time point to a fourth time point prior to the first time point may be stored (acquired) in the operation history storage unit 221. In this case, the estimation unit 252 may estimate, as the missing period, a period obtained by excluding the period from the third time point to the fourth time point from the missing period estimated in step S2. Similarly, the estimation unit 252 may estimate, as the missing period candidate, a period obtained by excluding the period from the third time point to the fourth time point from the missing period candidate estimated in step S2a.

[0123] The present disclosure can estimate the missing period during which a battery was used before being reused and output the missing period. Furthermore, it can also interpolate log information indicating the operating state of the battery during the missing period. Therefore, after a used battery is reused, when the used battery is resold, information indicating the missing period can be added. Furthermore, it can also add the interpolated log information for the missing period. This makes it possible to build a learning model for accurately estimating the condition of the used battery based on the added information when the used battery is next purchased and reused.

[0124] Furthermore, the present disclosure is not limited to being applicable to second-hand batteries, but can also be used to estimate missing periods in batteries that are exchanged and shared between battery-equipped devices that can acquire battery log information and battery-equipped devices that cannot acquire battery log information, and to interpolate log information during those missing periods.

Claims

1. An information processing method in a computer, comprising: acquiring a first SOH, which is the SOH of the battery at a first point in time when reuse of the battery begins, and a second SOH, which is the SOH at a second point in time after the first point in time; estimating a missing period, which is a period during which the battery was used before the first point in time, based on the first SOH and the second SOH; and outputting the missing period.

2. The information processing method according to claim 1, wherein estimating the missing period includes estimating the missing period based on a root law in which the amount by which the SOH has decreased from the initial state of the battery is proportional to the square root of the elapsed time from the initial state of the battery.

3. The information processing method according to claim 1, wherein estimating the missing period includes estimating the missing period based on an n-th power law in which the amount by which the SOH has decreased from the initial state of the battery is proportional to the nth power of the elapsed time from the initial state of the battery, where n is a positive number.

4. The information processing method according to claim 3, wherein estimating the missing period includes estimating a plurality of missing period candidates according to the value of n by changing the value of n, and outputting the missing period includes determining the missing period to be output from among the plurality of missing period candidates.

5. The information processing method according to claim 4, further comprising: acquiring a positive numerical range input by a user; and estimating the missing period comprising changing the value of n within the numerical range.

6. The information processing method according to claim 4, further comprising acquiring a history of the SOH of the battery from the first time point to the second time point, wherein estimating the missing period further comprises deriving a plurality of regression lines showing the relationship between the nth power of the elapsed time from the initial state of the battery and the SOH of the battery, the plurality of regression lines corresponding to the value of n, and calculating an evaluation index for evaluating each of the plurality of regression lines based on the SOH history of the battery, and wherein outputting the missing period comprises outputting, as the missing period, the missing period candidate corresponding to the regression line with the best evaluation index among the plurality of missing period candidates.

7. The information processing method according to claim 6, wherein the evaluation index indicates the degree of deviation between the battery's SOH history and each of the plurality of regression lines, and the regression line is evaluated as being better the smaller the degree of deviation.

8. The information processing method of claim 1, further comprising obtaining an operation history of the battery from a third time point to a fourth time point prior to the first time point, and estimating the missing period further comprises estimating the missing period as a period obtained by excluding the period from the third time point to the fourth time point from the estimated missing period.

9. An information processing method described in any one of claims 1 to 8, further comprising: acquiring an operation history of the battery from the first time point to the second time point; and interpolating an operation history of the battery during the missing period based on the acquired operation history of the battery.

10. An information processing device including a processor that performs the following operations: acquiring a first SOH, which is the SOH of the battery at a first point in time when reuse of the battery begins, and a second SOH, which is the SOH of the battery at a second point in time after the first point in time; estimating a missing period, which is a period during which the battery was used before the first point in time, based on the first SOH and the second SOH; and outputting the missing period.

11. An information processing program that causes a computer to: acquire a first SOH, which is the SOH of the battery at a first point in time when reuse of the battery begins, and a second SOH, which is the SOH of the battery at a second point in time after the first point in time; estimate a missing period, which is a period during which the battery was used before the first point in time, based on the first SOH and the second SOH; and output the missing period.

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