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

Abstract

[Problem] To provide an information processing device, an information processing method, an information processing system, and a computer program for estimating the state of a storage battery. [Solution] An information processing device according to present embodiment comprises: a feature calculation unit that calculates first feature data on the basis of measurement data of a target storage battery in a first range; and an estimation unit that, on the basis of the first feature data and association information in which second feature data based on measurement data of at least one storage battery in a second range longer than the first range is associated with state-related data pertaining to the state of the storage battery, estimates the state of the target storage battery in a third range differing from the first range.

Description

[Technical field]

[0001] The present embodiment relates to an information processing device, an information processing method, an information processing system, and a computer program. [Background technology]

[0002] The use of secondary batteries (storage batteries) is increasing for the purpose of stabilizing the power system and reducing exhaust gas emissions in order to achieve decarbonization. Even if a storage battery is operating normally, it gradually deteriorates depending on the frequency or duration of use. Therefore, understanding the degree of deterioration (health) is necessary to operate the storage battery safely. Furthermore, by estimating the progression of deterioration in advance, it is possible to prepare replacement batteries at the appropriate time and achieve stable operation of the storage battery while reducing maintenance costs.

[0003] One method for estimating the health of a storage battery is to use an estimation formula (root rule) based on electrochemical simulation and physical properties. However, with this method, it is difficult to select appropriate parameters that take into account the operating state of the storage battery.

[0004] On the other hand, due to the lower cost and larger capacity of semiconductors, as well as advances in wireless technology, it is now possible to accumulate a large amount of measurement data examples for storage batteries. A method has also been proposed for estimating the progress of the health state by utilizing the measurement data examples. However, while this method takes into account the vehicle model or type of battery, it does not particularly take into account how the storage battery is operated (how the storage battery is used). Therefore, it is difficult to estimate the health state for storage batteries that are used in different ways. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] International Publication No. 2020 / 044713 Summary of the Invention [Problem to be solved by the invention]

[0006] The present embodiment relates to an information processing device, an information processing method, an information processing system, and a computer program that estimate a state of a storage battery. [Means for solving the problem]

[0007] The information processing device of this embodiment includes a feature calculation unit that calculates first feature data based on measurement data of a target storage battery in a first range, and an estimation unit that estimates the state of the target storage battery in a third range different from the first range based on correspondence information that associates the first feature data, second feature data based on measurement data of at least one storage battery in a second range longer than the first range, and status-related data regarding the state of the storage battery. [Brief description of the drawings]

[0008] [Figure 1] FIG. 2 is a block diagram of an example of a storage battery evaluation device which is an information processing device according to the present embodiment. [Diagram 2] FIG. 1 is a diagram showing a configuration example of a power storage system as an example of a storage battery. [Diagram 3] FIG. 2 shows an example of the configuration of one module. [Figure 4] FIG. 13 is a diagram showing an example of an operation DB. [Diagram 5] FIG. 13 is a diagram showing an example of an operation characteristic case DB. [Figure 6] FIG. 13 is a diagram showing an example of an operation characteristics DB. [Figure 7] FIG. 13 is a diagram showing an example of an SoH transition example DB. [Figure 8] 4 is a diagram showing an example of estimated transition data of SoH estimated by an SoH transition estimation unit. [Figure 9] FIG. 13 is a diagram showing an example in which data on SoH transition cases corresponding to similar cases are displayed in addition to estimated SoH transition data. [Figure 10] FIG. 13 is a diagram showing an example in which data on an upper limit SoH transition example and data on a lower limit SoH transition example are displayed in addition to estimated SoH transition data. [Figure 11]5 is a flowchart showing an example of operation in a learning mode in the storage battery evaluation device according to the present embodiment. [Figure 12] 5 is a flowchart showing an example of operation in an estimation mode in the storage battery evaluation device according to the present embodiment. [Figure 13] An example of power distribution for a power conditioning application and an example of power distribution for a power supply application are shown. [Figure 14] FIG. 13 is a block diagram of an example of a storage battery evaluation device as an information processing device according to a fifth modified example of the present embodiment. [Figure 15] FIG. 13 is a diagram showing an example of a graph showing target SoH transition data and each SoH transition example. [Figure 16] FIG. 2 is a diagram showing an example of a hardware configuration of a storage battery evaluation device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0010] FIG. 1 is a block diagram of an example of a storage battery evaluation device 100 which is an information processing device according to the present embodiment. The storage battery evaluation device 100 includes a database (DB) of examples (SoH transition examples) of transitions in state of health (SoH) of a plurality of storage batteries operated in various usage modes (operation types), and a DB of examples (operation characteristic examples) of operation characteristic data including at least one piece of characteristic information of the plurality of storage batteries. The storage battery evaluation device 100 estimates the SoH of the storage battery to be evaluated, more specifically, the transition of the SoH, based on both DBs and the operation characteristic data (first characteristic data) of the storage battery to be evaluated. This makes it possible to estimate the transition of the SoH of the storage battery with high accuracy, regardless of the usage mode (operation type) of the storage battery to be evaluated. The SoH of the storage battery is an index indicating the degree to which the deterioration of the storage battery has progressed.

[0011] Before describing the storage battery evaluation device 100, the storage battery in this embodiment will be described below. The storage battery is a battery that can be charged and discharged. Although the storage battery is also called a secondary battery in contrast to a primary battery that can only be discharged, the term storage battery will be used hereinafter. In this embodiment, the term "charging and discharging" includes at least one of charging and discharging.

[0012] The storage battery is, for example, a battery mounted on a moving object that operates using electric energy as a power source, such as an electric vehicle (EV), an electric bus, a train, a next-generation light rail transit system (LRT), a bus rapid transit system (BRT), an automated guided vehicle (AGV), an airplane, or a ship. Alternatively, the storage battery may be a storage battery mounted on an electric device (such as a smartphone or a personal computer), or a storage battery that stores power for demand response. The storage battery may also be a storage battery for other purposes.

[0013] FIG. 2 shows a configuration example of a power storage system 201 as one form of a storage battery. The power storage system 201 includes battery panels 1, 2, ... N. The multiple battery panels are connected in series, in parallel, or in series and parallel so as to obtain a desired output depending on the application. Each battery panel includes multiple modules. Battery panel 1 includes modules 1-1 to 1-M, battery panel 2 includes modules 2-1 to 2-M, and battery panel N includes modules N-1 to NM. The multiple modules are connected in series, in parallel, or in series and parallel. In this example, each battery panel includes the same number of modules, but this does not have to be the same.

[0014] Fig. 3 shows an example of the configuration of one module. The module includes a plurality of battery cells. The plurality of battery cells are connected in series, in parallel, or in series and in parallel. As an example, two or more battery cells are connected in series and are connected in parallel.

[0015] The storage battery in this embodiment may be any of a cell, a module, a battery panel, and a power storage system. In the following description, it is mainly assumed that the storage battery is a power storage system.

[0016] The battery evaluation device in Figure 1 includes an input unit 102, an SoH estimation unit 103, an SoH transition case DB 104, an operation characteristics calculation unit 105, an operation characteristics DB 106, a similar case selection unit 108, an SoH transition comparison unit 109, an SoH transition estimation unit 112, an output DB 113, and an output unit 114.

[0017] The operation DB101 in FIG. 1 stores measurement data acquired from one or more storage batteries. A storage battery used for learning is called a sample storage battery, and a storage battery to be evaluated (estimated) is called a target storage battery. In the operation DB101, as an example, measurement data from the start of use of the storage battery is stored. The measurement data may be acquired from the storage battery in operation when the storage battery is used for an actual purpose, or may be acquired through an experiment. The measurement data may be acquired as a unit of acquisition of the measurement data for a cell, a module, a battery panel, or a set of a plurality of battery panels connected to each other, or measurement data may be acquired for each of a plurality of acquisition units. The acquisition unit may be different for each storage battery. For example, measurement data for a set of battery panels may be acquired in one storage system, and measurement data for a module unit may be acquired in another storage system. In this embodiment, the storage battery to be evaluated may be any of a cell, a module, a battery panel, and a storage system.

[0018] FIG. 4 shows an example of the operation DB 101. The measurement data includes measurement data (first measurement data) related to the storage battery. More specifically, the measurement data includes a battery ID, a time, a voltage value, a power value, a state of charge (SoC), and a temperature. The measurement data may include other information. For example, the measurement data may include a flag indicating the state of the storage battery when the measurement data is acquired, such as when charging, when discharging, when the system is operating (hereinafter, when operating), and when the system is not operating (hereinafter, when not operating). The measurement data may include other information, such as the use of the storage battery, humidity, or weather. The measurement data may also include a value indicating the state of the storage battery, specifically, the state of health (SoH) of the storage battery. The state of health indicates the degree of deterioration of the storage battery.

[0019] In the example of FIG. 4, the measurement data is acquired at one-second intervals, but the acquisition interval may be another value, such as one minute, five minutes, or one hour. The acquisition interval may not be a fixed time interval, but may vary depending on, for example, the time of day or the usage mode of the storage battery. The voltage value includes a charging voltage or a discharging voltage. A current value may be acquired instead of a power value. In this case, the power value may be calculated by calculation from the current value and the voltage value. Also, a current value may be acquired instead of the SoC value, and the SoC may be calculated by integrating the current. The SoC is an index indicating the charge amount of a battery. The SoC may be calculated by dividing the amount of power (amount of charge) stored in the storage battery by the capacity of the storage battery.

[0020] 1 has a learning mode that accumulates operation characteristic cases and SoH transition cases in an operation characteristic case DB 107 and an SoH transition case DB 104, respectively. The battery evaluation device 100 also has an estimation mode that estimates the SoH (state of degradation) as the state of the storage battery to be evaluated, based on the operation characteristic case DB 107 and the SoH transition case DB 104. Estimation of the SoH also includes estimation (i.e., prediction) of the future SoH.

[0021] The input unit 102 acquires measurement data of the storage battery from the operation DB 101 in the learning mode or the estimation mode, and provides the acquired measurement data to the operation characteristic calculation unit 105. Furthermore, the input unit 102 provides the measurement data to the SoH estimation unit 103 in the learning mode. The operation of acquiring the measurement data may be performed when instruction data is received from at least one of the operation characteristic calculation unit 105 and the SoH estimation unit 103. The acquired measurement data may be, for example, all of the measurement data of the storage battery, or may be measurement data within a predetermined or arbitrarily specified range. The range of the acquired measurement data may be determined, for example, by a period or an index indicating the usage amount of the storage battery. An example of an index for specifying the usage amount of the storage battery is the number of equivalent cycles. The range of the acquired measurement data corresponds to the second range of the measurement data. In the estimation mode, measurement data of a storage battery (target storage battery) different from the storage battery used in the learning mode is acquired. The range of the measurement data of the target storage battery acquired in the estimation mode is, for example, a first range of the measurement data, and it is assumed that the above-mentioned second range is longer than the first range. It should be noted that a case where measurement data of the same storage battery as that used in the learning mode is acquired in the estimation mode is also not excluded.

[0022] In the learning mode or the estimation mode, the operation characteristic calculation unit 105 calculates characteristic data of the storage battery (hereinafter referred to as operation characteristic data) based on the measurement data. The operation characteristic data calculated in the learning mode corresponds to the second characteristic data. The operation characteristic data calculated in the estimation mode (operation characteristic data of the target storage battery) corresponds to the first characteristic data.

[0023] The operation characteristic data includes, as an example, at least one piece of characteristic information (or a feature amount) calculated based on the measurement data. The operation characteristic data acquires a distribution of one or more items in the measurement data (e.g., at least one of voltage, power, SoC, temperature, and current) as an example of the one or more pieces of characteristic information. The distribution may be a set (sequence) of values ​​itself, or a function that approximates the set of values. Each of the one or more pieces of characteristic information included in the first feature data corresponds to the first feature information, and each of the one or more pieces of characteristic information included in the second feature data corresponds to the second feature information.

[0024] Furthermore, the characteristic information included in the operation characteristic data may be a quantified value of each item. Examples of the quantitative values ​​include statistics such as average value, maximum value, minimum value, and standard deviation. For example, the operation characteristic data may be a vector including multiple pieces of characteristic information such as (average voltage, average power, average SoC, average temperature, and average current value).

[0025] Operation characteristic data may be calculated for each operation type. If the measurement data does not include the operation type, the operation type of the storage battery may be estimated from the current value. For example, if the current flows in the charging direction for a certain period of time or more (for example, when the storage battery for an EV is being charged), or if the current flows in the discharging direction for a certain period of time or more (for example, when the storage battery for an EV is being used as a power source), the battery may be estimated as being used as a power source. In addition, if regenerative power is acquired while the EV is running, and if a smoothing process is performed, the current may be considered to flow in the discharging direction for a certain period of time (if the current can be considered to flow in the discharging direction overall, although there is some charging due to the regenerative power), the battery may be designated as being used as a power source. If charging or discharging is repeated for less than a certain period of time (for example, when the storage battery is used to adjust supply and demand in the power system), the battery may be estimated as being used as a power adjustment battery. The battery may be used as a power source and a power adjustment battery.

[0026] In the learning mode, the operation characteristic data (second characteristic data) calculated by the operation characteristic calculation unit 105 is associated with the identification information (ID) of the storage battery and stored in the operation characteristic case DB 107 as an operation characteristic case. For example, an operation characteristic case of (battery ID, operation characteristic data) is stored. Information on the data usage range (second range) used in calculating the operation characteristic data (second characteristic data) may be included in the operation characteristic case. In this case, the data of (battery ID, range, operation characteristic data) is stored in the operation characteristic case DB 107. Note that, hereinafter, the operation characteristic data included in the operation characteristic case may also be referred to as the operation characteristic case.

[0027] 5 shows an example of the operation characteristic case DB 107. A set of a battery ID, a range (data usage range), and operation characteristic data is stored as one entry. For example, for battery 1, operation characteristic data (operation characteristic case) calculated based on measurement data for six months from March 1 to August 31, 2018 is stored. The six months from March 1 to August 31, 2018 correspond to the second range, which is the data usage range (data measurement range) of the measurement data of battery 1.

[0028] On the other hand, in the estimation mode, the operation characteristic data (first characteristic data) calculated by the operation characteristic calculation unit 105 is stored in the operation characteristic DB 106. The operation characteristic data may be associated with identification information (ID) of the target storage battery. In this case, for example, data (battery ID, operation characteristic data) is stored. Information on the data usage range of the measurement data used to calculate the operation characteristic data may further be stored.

[0029] 6 shows an example of the operation characteristic DB 106. A set of the battery ID, range (data usage range), and operation characteristic data of the target storage battery is stored. For example, for battery X, operation characteristic data calculated based on measurement data for two months from March 1 to April 30, 2018 is stored. The two months from March 1 to April 30, 2018 correspond to the first range, which is the data usage range (data measurement range) of the measurement data of the target storage battery.

[0030] In the learning mode, the SoH estimation unit 103 (state estimation unit) estimates the SoH, which is the state of the storage battery (sample storage battery), based on the measurement data. The SoH estimation unit 103 divides the measurement data into segments at a predetermined division unit, and estimates the SoH of each segment. The segments may be set by a time width such as one day or one week, or may be divided according to the operation type in addition to the time width. Segmentation may also be performed based on the equivalent cycle number (described later). The SoH estimation unit 103 generates an SoH transition example by associating data (SoH transition data) in which the SoH of each segment is arranged in chronological order with the ID of the storage battery (sample storage battery) and information identifying each segment (for example, date or range). That is, the SoH transition example includes the battery ID, the SoH sequence, and information identifying each segment (range or equivalent cycle number, etc.).

[0031] As an example, the SoH estimation target range may be the same as the data usage range (second range) of the measurement data used in estimating the SoH. The SoH estimation target range may include at least a fourth range that is later than the length of the first range (data usage range of the measurement data of the target storage battery) from the start of the second range. As an example of the fourth range, in the battery 1 of FIG. 5, the range from May 1 to August 31, 2018 corresponds to the length of the first range (see FIG. 6), so that the range from May 1 to August 31 corresponds to the fourth range. The SoH transition example may be state-related data related to the state of the sample storage battery in at least the fourth range of the second range. In the example of this embodiment, the SoH transition example corresponds to state-related data related to the state of the sample storage battery in the second range.

[0032] The SoH estimation unit 103 may use any existing method as a method for estimating the SoH from the measurement data for each segment. For example, based on the measurement data belonging to the segment, values ​​are plotted in a coordinate system with the horizontal axis representing the charge amount (SoC) and the vertical axis representing the voltage, to obtain a QV plot. Voltage range information in a predetermined charge amount range (for example, a range of SoC 60 to 66%) is calculated in the QV plot. For example, a standard deviation is calculated as the voltage range information. Based on teacher data in which the standard deviation and the SoH are previously associated, an SoH corresponding to the calculated standard deviation is obtained. The teacher data in which the standard deviation and the SoH are associated may be a table, or may be a function with the standard deviation as an input variable and the SoH as an output variable. The SoH may be estimated using the method described in Japanese Patent No. 6313502.

[0033] If an SoH is associated with the measurement data stored in the operation DB 101, that SoH may be used. For example, statistics such as the average, maximum, and minimum value of the SoH associated with the measurement data may be used. As a specific example, if the segment for calculating the SoH is in weekly units and the measurement data is associated with an SoH on a daily basis, the average of the one week (7 SoHs) may be calculated as the SoH for the segment. As another method, if a measurement value of the SoH is provided as a separate input by a user, that value may be used.

[0034] The SoH transition cases (state-related data) generated by the SoH estimation unit 103 are stored in the SoH transition case DB 104. The SoH transition cases in the SoH transition case DB 104 and the operation characteristic case (second characteristic data) in the operation characteristic case DB 107 are associated with each other via a battery ID. Alternatively, the SoH transition data and the operation characteristic case are associated with each other via a set of a battery ID and a data usage range. The SoH transition case DB 104 and the operation characteristic case DB 107 correspond to an example of correspondence information that associates a plurality of second characteristic data based on measurement data on a plurality of storage batteries (sample storage batteries) with state-related data on the SoH (state) of a plurality of storage batteries.

[0035] 7 shows an example of the SoH transition example DB 104. A plurality of pairs of a battery ID, an SoH, and a range are stored as one entry. In this example, a segment is a range for each month. As an example, the SoH transition example DB 104 stores the SoH from the start of use of the storage battery.

[0036] In the estimation mode, the similar case selection unit 108 reads out the operation characteristic data (first characteristic data) calculated by the operation characteristic calculation unit 105, i.e., the operation characteristic data of the target storage battery, from the operation characteristic DB 106. The similar case selection unit 108 selects one or more operation characteristic cases (second characteristic data) similar to the operation characteristic data (first characteristic data) of the target storage battery. The selected operation characteristic case is referred to as a similar case. The similar case selection unit 108 acquires the selected similar case from the operation characteristic case DB 107. The similar case selection unit 108 acquires an SoH transition case (state related data) corresponding to the acquired similar case (second characteristic data) from the SoH transition case DB 104. The similar case selection unit 108 corresponds to a first selection unit that selects at least one operation characteristic case from the multiple operation characteristic cases based on the distance between the operation characteristic data (first characteristic data) of the target storage battery and the multiple operation characteristic cases (second characteristic data).

[0037] An SoH transition case corresponding to a similar case is, for example, an SoH transition case having the same battery ID as the similar case.

[0038] In addition, when the operation characteristic data (first characteristic data) of the target storage battery is provided separately from outside by user input, etc., the operation characteristic data provided as input may be used instead of the operation characteristic data calculated by the operation characteristic calculation unit 105.

[0039] An example of obtaining similar cases (second feature data) that are similar to the operation feature data (first feature data) of a target storage battery is shown below. As an example, the distance between the operation feature data of the target storage battery and the operation feature cases is calculated. If the operation feature data is a distribution, the distribution distance is calculated. Examples of distribution distances include the Kullback-Leibler distance, Pearson distance, and relative Pearson distance. Operation feature cases that satisfy a selection criterion (first selection criterion) such as a minimum distance, a threshold or less, or a certain number from the minimum are selected.

[0040] When the operation characteristic data of the target storage battery includes multiple distributions as multiple pieces of characteristic information, the distance is calculated for each distribution, and a distance vector including the calculated multiple distances as elements is obtained. Operation characteristic cases that satisfy a selection criterion such as the magnitude of the distance vector being minimum, being equal to or less than a threshold, or being a certain number from the minimum are selected.

[0041] When the operation characteristic data of the target storage battery includes one or more quantified values ​​(feature values) as one or more pieces of characteristic information, the distance is calculated between vectors (between movement characteristic data and movement characteristic cases) including one or more quantified values. Examples of the distance between vectors include Euclidean distance, Manhattan distance, and Mahalanobis distance. In addition, the distance may be calculated by weighting and adding these distances, or by normalizing and adding these distances.

[0042] When the operation characteristic data of the target battery contains a mixture of distributions and quantification values, the distance between the distributions is calculated, and the distance between the vectors of the quantification values ​​is calculated, and the two distances are added together. This makes it possible to calculate the distance between the operation characteristic data of the target battery and the operation characteristic cases.

[0043] The SoH transition estimation unit 112 (estimation unit) creates a model representing the relationship between the operation characteristic case and the SoH transition case by machine learning, based on one or more pairs of the mutually corresponding similar case and the SoH transition case acquired by the similar case selection unit 108. More specifically, a regression model is generated in which the operation characteristic data or the characteristic information is one or more input variables (explanatory variables) and the SoH or the transition of the SoH is the output variable (objective variable). The coefficients and constants included in the regression model are determined (learned) by optimizing or semi-optimizing according to the type of regression model. Examples of regression models include linear regression, non-regular regression, neural net, random forest, SVR, etc., but other types may be used as long as they are a method for constructing a regression model. The type of the model (function) is predetermined, and only the coefficients of the explanatory variables and the constant terms may be calculated (learned) by optimization or semi-optimization. The function may be a linear function, an exponential function, etc.

[0044] The SoH transition estimation unit 112 uses this model and the operation characteristic data of the target storage battery (operation characteristic data used to obtain similar cases) to estimate the SoH of the target storage battery, more specifically, the transition of the SoH. The transition of the SoH includes the SoH at multiple times. The output of the model is obtained by providing one or more pieces of characteristic information included in the operation characteristic data of the target storage battery to one or more explanatory variables of the model. The output of the model represents an estimated value of the SoH, or an estimated transition of the SoH.

[0045] The output DB 113 stores the estimated transition data of SoH estimated by the SoH transition estimation unit 112 in association with information on the target storage battery (e.g., a battery ID). The output DB 113 may also store information such as operation characteristic data of the target storage battery calculated by the operation characteristic calculation unit 105, similar cases, and SoH transition cases corresponding to the similar cases.

[0046] The output unit 114 outputs evaluation result information based on the information stored in the output DB 113. For example, the output unit 114 outputs estimated transition data of the SoH estimated for the target storage battery. The output unit 114 is a display device that displays data or information on a screen. Alternatively, the output unit 114 may be a transmitting device that transmits information to another device by wire or wirelessly. The other device to which the information is transmitted may be a terminal device held by a user of the device, a server that remotely monitors the target storage battery, or a device equipped with a storage battery.

[0047] When a neural network is used as a model as an example, each piece of characteristic information of the operation characteristic data of the target storage battery is assigned to an input node. From one or more output nodes, SoH in an evaluation range including a third range different from the data usage range (first range) of the operation characteristic data is output. The third range is, for example, a range after the first range. The third range is a range equal to or less than the length obtained by subtracting the length of the first range from the length of the second range. The multiple output nodes correspond to SoH at different times, and a collection of these SoHs corresponds to the transition of SoH. The evaluation range may include both the data usage range (first range) of the operation characteristic data and the third range after the data range. The length of the evaluation range may be the same as the data usage range of the measurement data of the sample storage battery used in learning (for example, the longest data usage range among the data usage ranges of the multiple sample storage batteries).

[0048] In addition to the operation characteristic data, at least one of the following may be added to the model as explanatory variables: elapsed time, number of equivalent cycles, total power (Wh), and total current (Ah). The reference point in time, such as the elapsed time, may be the start of use of the storage battery, or may be another point in time. The characteristic information may be information corresponding to an axis (e.g., horizontal axis) other than the SoH axis (e.g., vertical axis) when a graph of SoH transition is displayed in a two-dimensional coordinate system with two axes.

[0049] In other words, the model may be a function with elapsed time (or equivalent cycle, total power, total charge, etc.) as the independent variable and SoH as the dependent variable. In this case, the function itself becomes data that represents the transition of SoH. An example of the function in this case is shown in the following formula (1).

number

[0050] Equation (1) is a function that starts with a gentle slope of 0 and asymptotically approaches y=k*x+b. x is an explanatory variable (number of independent variables) such as elapsed time, equivalent cycle, total power, or total current. k, a, and b are values ​​including explanatory variables (feature information) that are input, or constants (coefficients) determined in model learning. f(x) is the objective variable (dependent variable).

[0051] As a specific example, when similar cases are obtained using feature information other than temperature among the feature information included in the operation feature data, a model like the above formula (1) may be generated using only the feature information related to temperature and the elapsed time as explanatory variables. Instead of the elapsed time, an equivalent cycle, total power amount, total current amount, etc. may be used. In this case, k in formula (1) may be calculated according to, for example, the Arrhenius equation, which shows the relationship between temperature and chemical reaction. An example of the calculation formula for k is shown in formula (2).

number

[0052] In addition, when similar cases are obtained using characteristic information related to temperature among characteristic information included in the operation characteristic data, a model such as the above formula (1) may be generated using only the characteristic information related to temperature and the elapsed time as explanatory variables. Instead of the elapsed time, an equivalent cycle, a total amount of power, a total amount of current, or the like may be used.

[0053] Of the multiple methods for generating the above-mentioned model, the method with the smallest error may be selected. Ensemble estimation may be performed, such as by using the average value of model estimates calculated by multiple methods.

[0054] Instead of generating a model by the SoH transition estimation unit 112, the model may be stored in advance in a storage unit accessible by the SoH transition estimation unit 112. In this case, the SoH transition estimation unit 112 may read the model from the storage unit and estimate the SoH transition based on the read model and the operation characteristic data of the target storage battery.

[0055] When the characteristic information is a sequence of values, the explanatory variable may be an accumulated value of the values. For example, when the characteristic information in the operation characteristic data is time-series data of temperature per unit time, the accumulated value of temperature may be used as the explanatory variable. Alternatively, the average temperature may be calculated at a certain time interval in the time-series data, and the accumulated value of the calculated average may be used as the explanatory variable.

[0056] FIG. 8 shows an example of estimated transition data of SoH (estimated SoH transition data or SoH estimation curve) estimated by the SoH transition estimation unit 112. In this example, the horizontal axis is the equivalent cycle number. The equivalent cycle number is a value obtained by dividing the integrated current amount by the rated capacity value, and is an index showing the number of times full charging or full discharging has been performed. It can be said that the equivalent cycle number represents the usage amount of the storage battery. The horizontal axis is not limited to the equivalent cycle number, and may also be the date, the amount of current [Ah], or the amount of power [Wh]. In the case of a storage battery mounted on a mobile object such as an EV, the horizontal axis may also be the travel distance of the mobile object.

[0057] FIG. 9 shows an example of displaying data of SoH transition cases corresponding to operation characteristic cases (similar cases) acquired by the similar case selection unit 108 in addition to the estimated SoH transition data (estimated SoH curve) estimated by the SoH transition estimation unit 112. In this example, among a plurality of SoH transition cases corresponding to a plurality of acquired similar cases, an SoH transition case corresponding to one similar case whose distance from the operation characteristic data of the target storage battery is the smallest or equal to or less than a threshold is displayed. Two or more similar cases may be selected and SoH transition cases corresponding to each of the selected similar cases may be displayed. In the example of FIG. 9, the similar cases are displayed in association with the SoH transition cases corresponding to the similar cases, and the operation characteristic data of the target storage battery is displayed in association with the estimated estimated SoH transition data. Three pieces of characteristic information, namely, average temperature, average SOC, and average power, are displayed as information based on the similar cases (information based on the second characteristic data) and information based on the operation characteristic data of the target storage battery (information based on the first characteristic data), respectively. In the case where there is a large number of pieces of characteristic information, when the user specifies a graph of an SoH transition case or an SoH estimation curve (for example, when the user places the pointer over the graph using the mouse), information based on similar cases and information based on the operating characteristic data of the target storage battery may be displayed.

[0058] 10 shows an upper limit SoH transition example (upper limit curve) and a lower limit SoH transition example (lower limit curve) in addition to the SoH estimated transition data (SoH estimated curve) estimated by SoH transition estimation unit 112. The upper limit curve is, as an example, an SoH transition example in which deterioration progresses the slowest among the SoH transition examples corresponding to similar cases. The lower limit curve is, as an example, an SoH transition example in which deterioration progresses the fastest among the SoH transition examples corresponding to similar cases.

[0059] For example, the operation characteristic data of the target storage battery includes an average temperature, an average SOC, and an average power. It is known that the higher the temperature, the faster the deterioration progresses, the closer the SoC is to both ends of the SoC usage range, the faster the deterioration progresses, and the higher the power usage, the faster the deterioration progresses. For example, among the similar cases, a similar case in which the average temperature is higher, the average SOC is closer to one of the ends, and the average power is higher than the average temperature, average SOC, and average power indicated by the operation characteristic data of the target storage battery may be identified, and the SoH transition case corresponding to the similar case may be set as the lower limit curve. Similarly, among the similar cases, a similar case in which the average temperature is lower, the average SOC is farther from one of the ends, and the average power is lower than the average temperature, average SOC, and average power indicated by the operation characteristic data of the target storage battery may be identified, and the SoH transition case corresponding to the identified similar case may be set as the upper limit curve.

[0060] When the operation characteristic data of the target storage battery includes an average value of any item such as temperature, the operation characteristic data A may be generated by replacing the average value included in the operation characteristic data with a maximum value, and the operation characteristic data B may be generated by replacing the average value included in the operation characteristic data with a minimum value. Using each of the operation characteristic data A and B (using at least the maximum value or the minimum value), SoH estimation transition data (SoH estimation curve) is acquired. Each of the acquired SoH estimation curves is used as a lower limit curve or an upper limit curve. Note that instead of the maximum value and the minimum value, values ​​higher and lower than the "N sigma average" may be used. The N sigma average refers to the value obtained by adding N times the standard deviation (sigma) to the average value, and the value obtained by subtracting N times the standard deviation (sigma) from the average value. N is an integer of, for example, 1, 2, or 3.

[0061] If the operation characteristic data corresponding to the SoH estimation curve (i.e., the operation characteristic data of the target storage battery) is not included in the similar case, the estimation accuracy of the model may be reduced. For this reason, the SoH transition estimation unit 112 may determine whether the operation characteristic data corresponding to the SoH estimation curve is included in the similar case, and if not, may display a message (warning) regarding the estimation accuracy on the output unit 114. For example, the message may include information indicating that the estimation accuracy may be low. The determination of whether the operation characteristic data is included in the similar case can be confirmed by checking whether the value of the characteristic information in the operation characteristic data is included between the minimum and maximum values ​​of the characteristic information in the similar case. Alternatively, an outlier detection method such as Hotelling's theory or one-class SVM (Sport Vector Machine) may be used to detect whether the operation characteristic data is included.

[0062] FIG. 11 is a flowchart showing an example of operation in the learning mode of the storage battery evaluation device 100 according to this embodiment.

[0063] In step S11, the input unit 102 acquires measurement data of a plurality of storage batteries (sample storage batteries) from the operation DB 101. As an example, the data usage range of the measurement data of the sample storage batteries is a second range that is longer than the data usage range (first range) of the measurement data of the target storage battery. The length of the second range may be the same for all sample storage batteries, or may be different. When the length of the second range differs between sample storage batteries, the measurement data may be divided into ranges of the same length from the start of use, and the measurement data of the same length may be used. Alternatively, all of the measurement data of each sample storage battery may be used regardless of the length of the second range.

[0064] In step S12, the operation characteristic calculation unit 105 calculates operation characteristic data (second characteristic data) of each storage battery based on the measurement data of each storage battery acquired in step S11.

[0065] In step S13, the SoH estimation unit 103 calculates SoH transition data (state-related data) for each storage battery based on the measurement data of each storage battery. The range of the SoH transition data is, for example, a second range that is the same as the data usage range of the measurement data of the storage batteries. The range of the SoH transition data may include at least a part of a fourth range of the second range that is from the start of the second range to the end of the length of the first range. It is also possible to configure the measurement data to be associated with an SoH in advance. In this case, the SoH estimation unit 103 may acquire the SoH transition data based on the SoH.

[0066] In step S14, the operation characteristic calculation unit 105 associates the operation characteristic data (second characteristic data) calculated in step S12 with the ID, etc. of each storage battery, and stores the data as an operation characteristic case in the operation characteristic case DB 107. The SoH estimation unit 103 associates the SoH transition data with the ID, data usage range, etc. of the storage battery, and stores the data as an SoH transition case in the SoH transition case DB 104.

[0067] FIG. 12 is a flowchart showing an example of operation in the estimation mode in the battery evaluation device 100 according to this embodiment.

[0068] In step S21, the input unit 102 acquires measurement data of the target storage battery from the operation DB 101.

[0069] In step S22, the operation characteristic calculation unit 105 calculates operation characteristic data (first characteristic data) of the target storage battery based on the measurement data of the storage battery.

[0070] In step S23, the operation characteristic calculation unit 105 stores the calculated operation characteristic data in the operation characteristic DB 106 in association with the ID of the storage battery and the like.

[0071] In step S24, the similar case selection unit 108 reads out operation characteristic data of the target storage battery from the operation characteristic DB 106. The similar case selection unit 108 acquires, as similar cases, one or more operation characteristic cases that satisfy a selection criterion for the operation characteristic data (similar to the operation characteristic data) from the operation characteristic case DB 107. The similar case selection unit 108 acquires an SoH transition case corresponding to the similar case from the SoH transition case DB 104 based on the battery ID.

[0072] In step S25, the SoH transition estimation unit 112 generates a model by learning the relationship between the similar case selected in step S24 and the SoH transition case corresponding to the similar case. The SoH transition estimation unit 112 calculates SoH estimated transition data (SoH estimation curve) of the target storage battery based on the generated model and the operation characteristic data of the target storage battery read out in step S23. The range of the SoH estimation curve is, as an example, an evaluation range that includes a data usage range (first range) of the measurement data of the target storage battery and a third range after the first range. The evaluation range includes at least the third range of the third range and the first range.

[0073] In step S266, the output unit 114 outputs the SoH estimation transition data (SoH estimation curve) of the target storage battery.

[0074] In this embodiment, a model is generated by learning the relationship between the similar case selected in step S24 and the SoH transition case corresponding to the similar case, and the model is used to generate the SoH estimated transition data for the target storage battery.As a simpler method, one similar case may be selected, and the SoH transition case corresponding to the selected similar case may be used as the SoH estimated transition data for the target storage battery.

[0075] As described above, according to the present embodiment, the SoH transition of the storage battery to be evaluated is estimated from operation characteristic examples and SoH transition examples of various storage batteries used in various types of battery operation and the operation characteristic data of the target storage battery, thereby improving the estimation accuracy of the SoH transition of the target storage battery.

[0076] (Variation 1) The content of the operation characteristic data calculated from the measurement data of the storage battery may be changed depending on the type of usage (operation type) of the storage battery. For example, the usage of the storage battery is broadly divided into cases where it is used for power adjustment such as for stabilizing power in a grid, and cases where it is used as a power source such as for EVs.

[0077] FIG. 13(A) shows an example of power distribution in the case of power conditioning use. In the case of power conditioning use, charging and discharging are repeated at short time intervals, and the average power value is close to 0. The standard deviation of the power value is an appropriate index to represent the strength of the power. Therefore, in the case of a storage battery for power conditioning use, the standard deviation of the power value may be used as one of the characteristic information to be included in the operation characteristic data.

[0078] FIG. 13(B) shows an example of power distribution in the case of power source application. In the case of power source application, the storage battery is used so as to be charged at once and slowly discharged. Therefore, two peaks are formed, one for charging and one for discharging (i.e., two unimodal distributions are formed). Therefore, the characteristic information of each of the two unimodal distributions (e.g., the standard deviation or peak value of each distribution) may be used as the characteristic information to be included in the operation characteristic data. Alternatively, when the operation characteristic data is calculated using only the measurement data of charging, the characteristic information of one unimodal distribution may be included as one piece of characteristic information in the operation characteristic data. Similarly, when the operation characteristic data is calculated using only the measurement data of discharging, the characteristic information of one unimodal distribution may be included as one piece of characteristic information in the operation characteristic data.

[0079] (Variation 2) The operation type of the storage battery may be provided as an input by an external device or a user's operation device. Alternatively, the storage battery evaluation device may determine the operation type from the measurement data. Methods for determining the operation type from the measurement data include determining the operation type from the number of peaks in a power histogram, the position of the peaks, and the change pattern of increase and decrease in the charge amount. Examples of change patterns include repeated increase and decrease in the case of power adjustment use, and continuous charging or discharging over a certain range or more in the case of power supply use.

[0080] Furthermore, a user may use an operation device to set an operation type to the operation characteristic case stored in the operation characteristic case DB 107. The similar case selection unit 108 may select a similar case from among operation characteristic cases in the operation characteristic case DB 107 that have the same operation type as the operation type of the target storage battery.

[0081] (Variation 3) The operation type may be classified in more detail. For example, if the operation type is for power source use, it may be classified according to the motif of the power storage system, such as EV, EV bus, ship, etc. If the storage battery operates by switching between multiple operation types, the ratio of operation time of each operation type may be calculated, and SoH estimated transition data for each operation type may be generated. Then, the SoH estimated transition data may be weighted according to the ratio of each operation type and added to finally generate the SoH estimated transition data of the target storage battery. In this case, the SoH transition cases to be stored in the SoH transition case DB 104 may also be generated for each operation type, and the generated SoH transition cases may be weighted and added to generate the SoH transition cases.

[0082] (Variation 4) Factors that affect the progression of deterioration of a storage battery include, for example, temperature, charge amount (SoC), power value, etc. For this reason, operation characteristic data including only characteristic information related to at least one of temperature, charge amount (SoC), power value, etc. may be used as the operation characteristic data of the target storage battery. Alternatively, the operation characteristic data may include characteristic information related to at least one of temperature, charge amount (SoC), power value, etc., and may further include other characteristic information. In this way, the accuracy of estimating the SoH can be improved.

[0083] Furthermore, with regard to the temperature of the storage battery, the way in which deterioration of the storage battery progresses (e.g., the speed of deterioration) differs between high and low temperatures. For this reason, characteristic information indicating how long the storage battery was in each of the high and low temperatures may be used. For example, characteristic information indicating the proportion of the time spent at 0°C or below and characteristic information indicating the proportion of the time spent at 40°C or above may be used. This can improve the accuracy of estimating the SoH. At least one of the high and low temperatures corresponds to a first temperature range of the storage battery, and the other corresponds to a second temperature range of the storage battery.

[0084] Furthermore, with regard to the state of charge (SoC) of the storage battery, the way in which deterioration of the storage battery progresses (e.g., the speed of progression) differs between a fully discharged state and a fully charged state. For this reason, feature information indicating how long the storage battery has been in a low SoC and how long it has been in a high SoC may be used. For example, feature information indicating the proportion of time spent in an SoC of 10% or less and feature information indicating the proportion of time spent in an SoC of 90% or more may be used. This makes it possible to improve the accuracy of estimating the SoH. One of SoC 10% or less and SoC 90% or more corresponds to a first charge amount range of the charge amount of the storage battery, and the other corresponds to a second charge amount range.

[0085] (Variation 5) 14 is a block diagram of an example of a battery evaluation device 100A as an information processing device according to Modification 5 of the present embodiment. An SoH transition comparison unit 109 (second selection unit) is added to the battery evaluation device 100 of FIG.

[0086] The SoH transition comparison unit 109 calculates the distance between the SoH transition data up to the present time of the target storage battery and each SoH transition case in the SoH transition case DB 104. The SoH transition data indicates the SoH transition according to any one of elapsed time, power amount (Wh), current amount (Ah), etc. The SoH (state) or SoH (state) transition data up to the present time of the target storage battery is calculated by the SoH estimation unit 103 based on, for example, the measurement data of the storage battery. Hereinafter, the SoH transition data up to the present time of the target storage battery is referred to as target SoH transition data. The present time corresponds to, for example, the end time of the data usage range (first range) of the measurement data of the target storage battery.

[0087] FIG. 15 shows an example of a graph G1 of target SoH transition data and graphs G11-G13 of each SoH transition case in the SoH transition case DB 104. Graph G1 corresponds to the SoH transition data of battery X in FIG. 6. Graphs G11-G13 correspond to the SoH transition cases of batteries 1-3 in FIG. 5. For simplicity, in this example, three graphs of SoH transition cases are displayed, but graphs of other SoH transition cases may be displayed. The horizontal axis is time, and represents the elapsed time from the start month of use. The horizontal axis may be the equivalent cycle number, as in FIGS. 8-10.

[0088] The target SoH transition data of the graph G1 represents the transition of the SoH for two months between the start of use and the following month, with the elapsed time H from the start of use of the target storage battery. The graphs G11 to G13 represent the SoH for June, December, and June, respectively, from the start of use (however, the notation for the fifth month and after is omitted). In the graphs G11 to G13 of the SoH transition examples, a distance (written as SoH distance) is calculated between the part from the start of use of the storage battery to the elapsed time H (two months from the start of use in this example) and the graph G1 of the SoH transition data. The SoH distance may be calculated using a distance measure between time series data such as Euclidean distance or DTW (Dynamic Time Warping). The target SoH transition data and each SoH transition example may be subjected to a smoothing process, and the SoH distance may be calculated based on the data after the smoothing process. Alternatively, the target SoH transition data and the SoH transition examples may be fitted to a specific type of function, and the SoH distance may be calculated between the fitted functions.

[0089] The SoH transition estimation unit 112 selects an SoH transition case in which the SoH distance satisfies a selection criterion (second selection criterion). The SoH transition estimation unit 112 corresponds to a second selection unit that selects at least one of each SoH transition case based on the distance between the target SoH transition data (state-related data of the target storage battery) and a data portion of a range corresponding to the first range in each SoH transition case (state-related data). The range corresponding to the first range in each SoH transition case (state-related data) is, as one example, a range of the SoH transition case that corresponds to the length of the first range from the start of use of the storage battery.

[0090] The selection criterion (second selection criterion) may be to select an SoH transition case with the smallest SoH distance, to select an SoH transition case with an SoH distance equal to or less than a threshold, or to select a certain number of SoH transition cases with the smallest SoH distance. In the selected SoH transition case, the portion after the range in which the SoH distance between the target SoH transition data is calculated is used as estimated SoH transition data from the current time of the target battery. For example, when the SoH transition case of graph G12 in FIG. 15 is selected, the data portion of graph G12 from the third month onwards becomes estimated data of the SoH transition of the target battery from the third month onwards. When multiple SoH transition cases are selected, estimated SoH transition data of the target battery may be obtained based on an average of the multiple SoH transition cases, etc.

[0091] The SoH transition estimation unit 112 may estimate the SoH transition of the target storage battery by using both the target SoH transition data and the SoH distance between each SoH transition case, and the operation characteristic data of the target storage battery and the distance between each operation characteristic case in the operation characteristic case DB 107. Specifically, the SoH transition estimation unit 112 selects an SoH transition case that satisfies the above-mentioned selection criterion (second selection criterion) and an SoH transition case that corresponds to an operation characteristic case (similar case) that satisfies the selection criterion (first selection criterion) described in the explanation of the first embodiment. All the selected SoH transition cases are set as multiple estimated transition candidates of the SoH of the target storage battery. The multiple estimated transition candidates may be displayed on the output unit 114. Alternatively, an average or the like of all the selected SoH transition cases may be acquired as estimated transition data of the SoH of the target storage battery, and the acquired estimated transition data may be displayed on the output unit 114.

[0092] As in the first embodiment, the SoH estimated transition data may be calculated using a model, and the calculated SoH estimated transition data and an SoH transition example selected based on the above-mentioned SoH distance may be displayed on the output unit 114 as estimated transition candidates of the SoH of the target storage battery.

[0093] The temperature of the storage battery significantly affects the progression of deterioration. When selecting operation characteristic cases (similar cases) that satisfy the selection criteria (first selection criteria), operation characteristic cases in which the distance between characteristic information related to temperature is close may be preferentially selected. Alternatively, similar to the above-described embodiment, operation characteristic cases (similar cases) may be obtained based on operation characteristic data including characteristic information other than temperature, and estimated transition data of SoH may be calculated using a model including characteristic information related to temperature as an explanatory variable.

[0094] (Hardware configuration) Fig. 16 shows an example of a hardware configuration of a storage battery evaluation device 100 according to an embodiment of the present invention. This hardware configuration can be used for the storage battery evaluation device 100 according to each of the embodiments described above. The hardware configuration of Fig. 16 is configured as a computer 150. The computer 150 includes a CPU 151, an input interface 152, a display device 153, a communication device 154, a main storage device 155, and an external storage device 156, which are connected to each other via a bus 157 so as to be able to communicate with each other.

[0095] The input interface 152 acquires measurement data of the storage battery via wiring or the like. The input interface 152 may be an operation means by which a user gives instructions to the device. Examples of the operation means include a keyboard, a mouse, and a touch panel. The communication device 154 includes wireless or wired communication means and performs wired or wireless communication with the EV 200. The measurement data may be acquired via the communication device 154. The input interface 152 and the communication device 154 may each be configured as separate circuits such as integrated circuits, or may be configured as a single circuit such as an integrated circuit. The display device 153 is, for example, a liquid crystal display device, an organic EL display device, a CRT display device, or the like. The display device 153 corresponds to the output unit 114 in FIG. 1.

[0096] The external storage device 156 includes, for example, storage media such as an HDD, an SSD, a memory device, a CD-R, a CD-RW, a DVD-RAM, and a DVD-R. The external storage device 156 stores a program for causing the CPU 151, which is a processor, to execute the functions of each processing unit of the storage battery evaluation device 100. Each DB provided in the storage battery evaluation device 100 is also included in the external storage device 156. Although only one external storage device 156 is shown here, multiple external storage devices may be present.

[0097] The main memory device 155, under the control of the CPU 151, deploys a control program stored in the external memory device 156 and stores data required when executing the program, data generated by the execution of the program, etc. The main memory device 155 includes any memory or storage unit, for example, a volatile memory (DRAM, SRAM, etc.) or a non-volatile memory (NAND flash memory, MRAM, etc.). The control program deployed in the main memory device 155 is executed by the CPU 151, thereby executing the functions of each processing unit of the storage battery evaluation device 100. Each DB provided in the storage battery evaluation device 100 may also be included in the main memory device 155.

[0098] The present invention is not limited to the above-described embodiments as they are, and the components can be modified and embodied in the implementation stage without departing from the gist of the invention. Various inventions can be formed by appropriately combining multiple components disclosed in the above-described embodiments. For example, a configuration in which some components are deleted from all the components shown in each embodiment can also be considered. Furthermore, the components described in different embodiments may be appropriately combined. [Explanation of symbols]

[0099] 100 Battery evaluation device 100A Battery Evaluation Device 102 Input section 103 SoH estimation unit (state estimation unit) 104 SoH Transition Case DB (Corresponding Information) 105 Operation characteristic calculation unit (characteristic calculation unit) 106 Operational Characteristics DB 107 Operational Characteristics Case DB (Corresponding Information) 108 Similar Case Selection Section (First Selection Section) 109 SoH transition comparison unit (second selection unit) 112 SoH transition estimation section (estimation section) 113 Output DB 114 Output section 150 Computers 152 Input Interface 153 Display device 154 Communication Equipment 155 Main storage 156 External storage device 157 Bus 201 Energy Storage System

Claims

1. First characteristic data based on measurement data of a target storage battery in a first range; Correspondence information associating second characteristic data based on measurement data of at least one storage battery in a second range longer than the first range with status-related data regarding a status of the storage battery; and an estimation unit that estimates a state of the target storage battery in a third range different from the first range based on the Equipped with The state-related data is data regarding a state of the storage battery in at least a fourth range of the second range that is from a start of the second range to a length of the first range. Information processing device.

2. First characteristic data based on measurement data of a target storage battery in a first range; Correspondence information associating second characteristic data based on measurement data of at least one storage battery in a second range longer than the first range with status-related data regarding a status of the storage battery; and an estimation unit that estimates a state of the target storage battery in a third range different from the first range based on the Equipped with The third range is equal to or less than the length of the second range minus the length of the first range. Information processing device.

3. First characteristic data based on measurement data of a target storage battery in a first range; Correspondence information associating second characteristic data based on measurement data of at least one storage battery in a second range longer than the first range with status-related data regarding a status of the storage battery; and an estimation unit that estimates a state of the target storage battery in a third range different from the first range based on the a first selection unit that selects at least one piece of second feature data from the plurality of second feature data based on distances between the first feature data and the plurality of second feature data, The estimation unit estimates a state of the target storage battery based on at least one state-related data corresponding to the selected at least one second characteristic data. Information processing device.

4. The state-related data represents a transition of the state of the storage battery over time, The estimation unit estimates a transition of a state of the target storage battery according to time. The information processing device according to any one of claims 1 to 3.

5. The correspondence information corresponds to information regarding an operation type of the at least one storage battery; The first selection unit selects the at least one second characteristic data based on an operation type of the target storage battery. The information processing device according to claim 3 .

6. the at least one state-related data corresponding to the at least one selected second feature data is two or more state-related data; The estimation unit estimates a range in which the state of the target storage battery varies from the estimated state based on the two or more state-related data.

6. The information processing device according to claim 3 or 5.

7. The first characteristic data includes a plurality of first characteristic information items that are a plurality of pieces of characteristic information related to the target storage battery, The second characteristic data includes a plurality of second characteristic information items that are a plurality of pieces of characteristic information related to the storage battery. The information processing device according to any one of claims 1 to 6.

8. The estimation unit generates a model having at least one piece of feature information as an input variable and a state as an output variable based on the at least one piece of second feature data and the at least one piece of state-related data, and estimates the state of the target storage battery based on the model and the first feature data. The information processing device according to claim 7, which cites any one of claims 3, 5 and 6.

9. the selected at least one second feature data is two or more second feature data; the estimation unit determines whether a value of the first feature information included in the first feature data is within a range between a lower limit value and an upper limit value of the second feature information included in the two or more second feature data; The method further includes the steps of: outputting a message regarding the estimation accuracy of the state of the target storage battery when the value of the first characteristic information is not included in the range; 9. The information processing apparatus according to claim 7 or claim 8, which cites any one of claims 3, 5 and 6.

10. The plurality of first characteristic information include characteristic information related to a temperature of the target storage battery, characteristic information related to a power value of the target storage battery, and characteristic information related to a charge amount of the target storage battery. The plurality of second characteristic information include characteristic information related to a temperature of the storage battery, characteristic information related to a power value of the storage battery, and characteristic information related to a charge amount of the storage battery. The information processing device according to any one of claims 7 to 9.

11. the plurality of first characteristic information include characteristic information regarding a time during which the target storage battery is within a first temperature range and characteristic information regarding a time during which the target storage battery is within a second temperature range different from the first temperature range, The plurality of second characteristic information include characteristic information regarding a time during which the storage battery is in the first temperature range and characteristic information regarding a time during which the storage battery is in the second temperature range. The information processing device according to any one of claims 7 to 10.

12. The plurality of first characteristic information include characteristic information regarding a time when the target storage battery is included in a first charge amount range, and characteristic information regarding a time when the target storage battery is included in a second charge amount range different from the first charge amount range, The plurality of second characteristic information includes characteristic information regarding a time when the storage battery is within the first charge amount range, and characteristic information regarding a time when the storage battery is within the second charge amount range. The information processing device according to any one of claims 7 to 11.

13. The at least one first characteristic data includes characteristic information related to a temperature of the target storage battery and characteristic information different from the temperature, The at least one second characteristic data includes characteristic information related to a temperature of the storage battery and characteristic information different from the temperature, The first selection unit selects the second characteristic data based on the characteristic information different from the temperature among the at least one first characteristic data, The estimation unit estimates a state of the target storage battery based on the characteristic information related to the temperature among the at least one first characteristic data. The information processing device according to claim 3 , 5 , or 6 .

14. A state estimation unit that estimates a state of the target battery in the first range based on the measurement data of the target battery in the first range and acquires state-related data representing a state of the target battery; a second selection unit that selects at least one of the status-related data included in the correspondence information based on a distance between the status-related data of the target storage battery and a data portion of a range corresponding to the first range of the status-related data included in the correspondence information, The estimation unit estimates a state of the target storage battery in the third range based on the at least one state-related data selected by the second selection unit. The information processing device according to any one of claims 1 to 13.

15. an output unit for displaying estimated transition data representing a transition of the SoH of the target storage battery estimated by the estimation unit; The information processing device according to claim 4 .

16. The output unit displays information based on the first feature data together with the estimated transition data. The information processing device according to claim 15.

17. the state is SoH, The state-related data represents a transition of the SoH of the storage battery, The estimation unit estimates a transition of the SoH of the target storage battery, the estimation unit selects, from the at least one state-related data corresponding to the selected at least one second feature data, a first state-related data in which a deterioration of the storage battery progresses slowest and a second state-related data in which a deterioration of the storage battery progresses fastest; and an output unit that displays estimated transition data indicating a transition of the SoH of the target storage battery estimated by the estimation unit, the first status-related data, and the second status-related data. The information processing device according to claim 3 .

18. the output unit displays information based on the first feature data together with the estimated transition data; displaying information based on the second characteristic data corresponding to the first status-related data together with the first status-related data; Displaying information based on the second characteristic data corresponding to the second status-related data together with the second status-related data The information processing device according to claim 17.

19. The first range is a period during which measurement data of the target storage battery is acquired, the second range is a period during which measurement data of the storage battery is acquired, The third range is a period during which the state of the target storage battery is estimated. The information processing device according to any one of claims 1 to 18.

20. The first range is a range of values ​​of an index indicating a usage amount of the target storage battery from which measurement data of the target storage battery is acquired, the second range is a range of values ​​of an index indicating a usage amount of the storage battery for which measurement data of the storage battery is acquired, The third range is a range of values ​​of an index indicating a usage amount of the storage battery within which the state of the target storage battery is estimated. The information processing device according to any one of claims 1 to 18.

21. The index indicating the usage amount of the target storage battery is the equivalent cycle number of the target storage battery, The index indicating the usage of the storage battery is the equivalent cycle number of the storage battery. The information processing device according to claim 20.

22. First characteristic data based on measurement data of a target storage battery in a first range; Correspondence information associating second characteristic data based on measurement data of at least one storage battery in a second range longer than the first range with status-related data regarding a status of the storage battery; and Estimating a state of the target storage battery in a third range after the first range based on the The state-related data is data regarding a state of the storage battery in at least a fourth range of the second range that is from a start of the second range to a length of the first range. Information processing methods.

23. First characteristic data based on measurement data of a target storage battery in a first range; Correspondence information associating second characteristic data based on measurement data of at least one storage battery in a second range longer than the first range with status-related data regarding a status of the storage battery; and and estimating a state of the target storage battery in a third range different from the first range based on the The third range is equal to or less than the length of the second range minus the length of the first range. Information processing methods.

24. First characteristic data based on measurement data of a target storage battery in a first range; Correspondence information associating second characteristic data based on measurement data of at least one storage battery in a second range longer than the first range with status-related data regarding a status of the storage battery; and and estimating a state of the target storage battery in a third range different from the first range based on the selecting at least one second feature data from the plurality of second feature data based on distances between the first feature data and the plurality of second feature data; Estimating the state of the target battery includes estimating the state of the target battery based on at least one state-related data corresponding to the selected at least one second characteristic data. Information processing methods.

25. First characteristic data based on measurement data of a target storage battery in a first range; Correspondence information associating second characteristic data based on measurement data of at least one storage battery in a second range longer than the first range with status-related data regarding a status of the storage battery; and estimating a state of the target storage battery in a third range different from the first range based on the Run the following on your computer: The state-related data is data regarding a state of the storage battery in at least a fourth range of the second range that is from a start of the second range to a length of the first range. Computer program.

26. First characteristic data based on measurement data of a target storage battery in a first range; Correspondence information associating second characteristic data based on measurement data of at least one storage battery in a second range longer than the first range with status-related data regarding a status of the storage battery; and estimating a state of the target storage battery in a third range different from the first range based on the Run the following on your computer: The third range is equal to or less than the length of the second range minus the length of the first range. Computer program.

27. ​​First characteristic data based on measurement data of a target storage battery in a first range; Correspondence information associating second characteristic data based on measurement data of at least one storage battery in a second range longer than the first range with status-related data regarding a status of the storage battery; and estimating a state of the target storage battery in a third range different from the first range based on the selecting at least one second feature data from the plurality of second feature data based on distances between the first feature data and the plurality of second feature data; Run the following on your computer: The step of estimating the state of the target battery includes estimating the state of the target battery based on at least one state-related data corresponding to the selected at least one second characteristic data. Computer program.

28. A target storage battery; First characteristic data based on measurement data of the target storage battery in a first range; Correspondence information associating second characteristic data based on measurement data of at least one storage battery in a second range longer than the first range with status-related data regarding a status of the storage battery; and an estimation unit that estimates a state of the target storage battery in a third range different from the first range based on the Equipped with The state-related data is data regarding a state of the storage battery in at least a fourth range of the second range that is from a start of the second range to a length of the first range. Information processing system.

29. A target storage battery; First characteristic data based on measurement data of the target storage battery in a first range; Correspondence information associating second characteristic data based on measurement data of at least one storage battery in a second range longer than the first range with status-related data regarding a status of the storage battery; and an estimation unit that estimates a state of the target storage battery in a third range different from the first range based on the Equipped with The third range is equal to or less than the length of the second range minus the length of the first range. Information processing system.

30. A target storage battery; First characteristic data based on measurement data of the target storage battery in a first range; Correspondence information associating second characteristic data based on measurement data of at least one storage battery in a second range longer than the first range with status-related data regarding a status of the storage battery; and an estimation unit that estimates a state of the target storage battery in a third range different from the first range based on the a first selection unit that selects at least one piece of second feature data from the plurality of second feature data based on distances between the first feature data and the plurality of second feature data, The estimation unit estimates a state of the target storage battery based on at least one state-related data corresponding to the selected at least one second characteristic data. Information processing system.

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