Information processing device, information processing method, and program

Abstract

This information processing device comprises a processing unit. The processing unit generates a plurality of pieces of divided data obtained by dividing, for each of a plurality of pieces of identification information, time-series operation data obtained during operation of a second battery, which is a battery including a plurality of first batteries each identified by each of the plurality of pieces of identification information, the operation data including time, a specific attribute satisfying a predetermined condition among attributes of the plurality of first batteries, and identification information identifying the first battery having the specific attribute. The processing unit uses the plurality of pieces of divided data to estimate the state of each of the plurality of first batteries.

Description

Information processing device, information processing method, and program

[0001] Embodiments of the present invention relate to an information processing device, an information processing method, and a program.

[0002] A battery system, for example, is configured by connecting battery units in parallel until the required storage capacity is achieved. A battery unit corresponds to a battery unit in which battery modules are connected in series according to the required voltage. A battery module corresponds to a battery unit in which the smallest unit, the battery cell, is connected in series and parallel.

[0003] For control purposes, moment-by-moment measurements of voltage and temperature at the battery cell level are used. On the other hand, considering the amount of data, the recorded data is often aggregated in units other than the smallest unit, such as the battery unit itself. The collected data may be used to estimate the state of the battery (e.g., whether it is degraded).

[0004] Meanwhile, discussions are progressing on certification rules and traceability, such as battery passports and digital product passports (DPPs), which will also impact international trade. If data is configured to be recorded in smaller units to accommodate traceability, concerns arise about the enormous amount of data to be recorded. If the quality of measurement is reduced to control the amount of data (e.g., by reducing the number of samples), the accuracy of estimating the battery state using the data may decrease.

[0005] Japanese Patent No. 7193678, International Publication No. 2022 / 244572, International Publication No. 2022 / 107536, International Publication No. 2024 / 189850

[0006] The present invention aims to provide an information processing device, an information processing method, and a program that can estimate the state of a battery with higher accuracy while suppressing an increase in the amount of data to be recorded.

[0007] The information processing device of the embodiment includes a processing unit. The processing unit generates multiple divided data by dividing the time-series operation data obtained when a second battery, which is a battery containing multiple first batteries identified by multiple pieces of identification information, is in operation, the operation data includes the time, a specific attribute among the attributes of the multiple first batteries that satisfies predetermined conditions, and identification information that identifies the first battery that has the specific attribute, into multiple divided data for each of the multiple identification information. The processing unit estimates the state of each of the multiple first batteries using the multiple divided data.

[0008] Block diagram of the information processing system according to the embodiment. Diagram showing an example of operational data. Diagram showing an example of segmented data. Flowchart of the estimation process in the embodiment. Diagram showing an example of estimating the coefficients of a reference function. Flowchart showing another example of the estimation process. Flowchart showing another example of the estimation process. Hardware configuration diagram of the information processing device of the embodiment.

[0009] A preferred embodiment of the information processing device according to this invention will be described in detail below with reference to the attached drawings.

[0010] Battery system degradation often occurs due to the degradation of some battery cells, not all of them. Typically, the battery module containing the degraded battery cells is replaced. Therefore, it is desirable to properly identify the degraded battery module (hereinafter referred to as the degraded module).

[0011] As a method for identifying degraded modules, a technique has been proposed that uses operational data (hereinafter referred to as operational data) measured for a larger unit of battery (e.g., a battery unit) to identify degraded batteries among the smaller units of battery (e.g., battery modules) contained within that unit, in order to reduce the amount of data.

[0012] Such technologies do not indicate the status of modules other than the identified degraded module. Therefore, from a traceability standpoint, it is desirable to estimate the status of each small unit of battery. However, in configurations that estimate the status using data measured in small units, for example, the amount of data used can become enormous.

[0013] Therefore, in this embodiment, similar to the technology described above, it is possible to estimate the state of each smaller unit of battery using operational data measured for a larger unit of battery.

[0014] The following describes an example in which aggregated operational data is recorded for a battery unit containing multiple battery modules. Each of the multiple battery modules corresponds to multiple batteries BA (first battery), each functioning as a battery. The battery unit corresponds to a battery BB (second battery), which contains multiple batteries BA and functions as a battery.

[0015] The combinations of battery BA and battery BB, which contains multiple battery BAs, are not limited to those described above. For example, the same procedure can be applied to the following battery units, which are referred to as battery BA and battery BB, respectively: • Battery BA: Battery cell, Battery BB: Battery module • Battery BA: Battery unit, Battery BB: Battery system

[0016] Note that the names of the various battery units, such as battery cell, battery module, battery unit, and battery system, are merely examples, and any other names may be used. For example, a battery unit containing multiple battery modules may be called a battery panel.

[0017] Figure 1 is a block diagram showing an example of the configuration of the information processing system 10 according to this embodiment. As shown in Figure 1, the information processing system 10 comprises an information processing device 100, a battery 200, and a monitoring system 300.

[0018] The information processing device 100 and the battery 200, and the information processing device 100 and the monitoring system 300 are connected by a network. The network connecting the information processing device 100 and the battery 200 and the network connecting the information processing device 100 and the monitoring system 300 may be the same network or different networks. The network may be a wireless network, a wired network, or a network that combines wireless and wired connections.

[0019] Battery 200 is a rechargeable battery capable of charging and discharging electrical energy. Battery 200 can have any configuration as long as it is possible to acquire operational data.

[0020] The battery 200 may be a battery mounted on a mobile vehicle that operates using electrical energy as a power source. Examples of mobile vehicles include electric vehicles (EVs), electric buses, trains, next-generation light rail transit (LRT) systems, bus rapid transit (BRT) systems, automated guided vehicles (AGVs), airplanes, and ships. The battery 200 may also be a battery component mounted on an electrical device (such as a smartphone or personal computer), or a battery that supplies and supplies power for demand response. The battery 200 may also be a battery for other applications.

[0021] The information processing device 100 may collect operational data measured by the battery 200 sequentially (in real time), or it may collect operational data from multiple times together. For example, in the case of a battery 200 mounted on a moving object such as an airplane or a ship, the operational data may be stored in a memory unit provided inside the battery 200, and the device may be configured to collect the operational data stored in the memory unit together when it arrives at an airport or port.

[0022] The battery 200 is charged by a charger located at a charging station, on the roadside, in a parking lot, or by a charger connected to an electrical outlet. The power stored in the battery 200 may be discharged (reverse power flow) to the power grid via the charger. The method of transmitting power from the charger to the battery 200 may be either a contact charging method or a non-contact charging method.

[0023] The monitoring system 300 monitors the battery 200 based on information indicating the state of the battery 200 (hereinafter referred to as "state information") provided by the information processing device 100. For example, the monitoring system 300 generates screen data to be used for monitoring and displays the generated screen data on a monitor. The user (monitor) understands the state of the battery 200 to be monitored by referring to the screen displayed on the monitor. The monitoring system 300 may control the operation of the battery 200 according to the monitoring results or according to the user's commands.

[0024] The information processing device 100 includes a storage unit 121, an acquisition unit 101, an output control unit 102, a generation unit 111, a determination unit 112, and an estimation unit 113.

[0025] The memory unit 121 stores various types of information used by the information processing device 100. For example, the memory unit 121 stores operational data acquired (input) from the battery 200, and information (intermediate products) obtained during processing of each unit.

[0026] The storage unit 121 can be composed of any commonly used storage medium, such as flash memory, memory card, RAM (Random Access Memory), HDD (Hard Disk Drive), and optical disc.

[0027] The acquisition unit 101 acquires various types of information used by the information processing device 100. For example, the acquisition unit 101 acquires operating data of the battery 200 from the battery 200. The operating data may be acquired at regular time intervals (for example, every second) or irregularly. The acquisition unit 101 stores the acquired operating data in the storage unit 121.

[0028] As described above, in this embodiment, the operational data includes, in principle, measured values ​​per battery unit. The operational data includes, for example, the following measured values: • SoC (State of Charge) of the entire battery unit • Voltage of the entire battery unit • Current of the entire battery unit • Temperature inside the battery unit (for example, the average temperature of the internal battery module group)

[0029] Power may be obtained instead of current. In this case, the value of current may be calculated from the value of power and the value of voltage. SoC is an indicator of the battery's charge level. For example, SoC is calculated by dividing the amount of energy (in Wh) or charge (in Ah) stored in battery 200 by the rated capacity (energy or charge) of battery 200.

[0030] The operational data includes not only the measured values ​​(aggregated values) for the entire battery unit as described above, but also specific attributes of multiple battery modules that meet predetermined conditions, and identification information (hereinafter referred to as module ID) that identifies the battery module with the specific attribute. This information is recorded, for example, to maintain a control history.

[0031] The attribute is, for example, at least one of voltage and temperature. The condition is, for example, at least one of the following: a condition indicating that the attribute is one of the top n attributes (n is an integer greater than or equal to 2) in descending order of attribute value, and a condition indicating that the attribute is one of the top m attributes (m is an integer greater than or equal to 2) in ascending order of attribute value. If n=1, it indicates that the attribute has the maximum attribute value. If m=1, it indicates that the attribute has the minimum attribute value.

[0032] For example, if the attribute is voltage and n=1, the specific attribute indicates that the voltage is at its maximum value (hereinafter referred to as maximum voltage). If the attribute is voltage and m=1, the specific attribute indicates that the voltage is at its minimum value (hereinafter referred to as minimum voltage). When the attribute is voltage and maximum voltage and minimum voltage are used as specific attributes, the operational data includes the following measured values: - Maximum voltage and the module ID of the battery module where the maximum voltage was measured - Minimum voltage and the module ID of the battery module where the minimum voltage was measured

[0033] A battery module with the maximum or minimum voltage can be interpreted as being more degraded than other battery modules, for example. In other words, certain attributes such as maximum and minimum voltage can be interpreted as indicating a battery module with a greater degree of degradation.

[0034] The maximum and minimum voltage values ​​may be measured and output as operational data in any way. For example, the maximum and minimum voltage values ​​(representative cell voltages) and the module ID of the battery module containing the cell that takes the maximum and minimum values ​​may be output as operational data, based on the voltage measured for each cell in multiple battery modules within a battery unit.

[0035] When the attribute is temperature, the operation data includes, for example, at least one of the following measured values: - The maximum temperature (the maximum value of the temperature), and the module ID of the battery module at which the maximum temperature was measured. - The minimum temperature (the minimum value of the temperature), and the module ID of the battery module at which the minimum temperature was measured. - The median value of the temperature, and the module ID of the battery module at which the median value of the temperature was measured. - The average value of the temperature

[0036] The attribute may be either voltage or temperature, or both. Also, when the battery 200 is a storage battery, the voltage may be either the voltage during charging or the voltage during discharging, or both.

[0037] FIG. 2 is a diagram showing an example of operation data. As shown in FIG. 2, the operation data includes time, SoC, voltage, current, the module ID (Id) corresponding to the maximum voltage, the module ID corresponding to the minimum voltage, the module ID corresponding to the highest temperature, and the module ID corresponding to the lowest temperature. Thus, the operation data is time-series data obtained during the operation of the battery unit, and is data including time, a specific attribute, and identification information for identifying the battery module having the specific attribute. Note that the data structure of the operation data in FIG. 2 is an example and is not limited thereto.

[0038] The generation unit 111 generates a plurality of data (hereinafter, divided data) obtained by dividing the operation data. The operation data to be processed is, for example, operation data acquired during a period (for example, one day) specified as a period for estimating the state of the battery. For example, the generation unit 111 generates a plurality of divided data obtained by dividing the operation data for each of a plurality of module IDs.

[0039] FIG. 3 is a diagram showing an example of the divided data. FIG. 3 corresponds to an example of the divided data obtained by dividing the operation data of FIG. 2. For example, the divided data 301 is the divided data including records in which the module ID at which the maximum voltage is measured or the module ID at which the minimum voltage is measured is "2" among a plurality of records (data for each of a plurality of times) of the operation data of FIG. 2. The divided data 302 is the divided data including records in which the module ID at which the maximum voltage is measured or the module ID at which the minimum voltage is measured is "3".

[0040] A certain record may be included in both the divided data corresponding to the module ID at which the maximum voltage is measured and the divided data corresponding to the module ID at which the minimum voltage is measured. That is, there may be a case where the operation data is divided such that one record is included in a plurality of divided data. In the example of FIG. 3, the record at time t3 is included in the divided data 301 because the module ID at which the maximum voltage is measured is "2", and is also included in the divided data 302 because the module ID at which the minimum voltage is measured is "3".

[0041] Assuming that the period for acquiring the operation data is one day and the operation data is acquired every second, the number of records of the operation data is 86,400 (= 60 × 60 × 24). Since one record can be included in a maximum of two divided data, the total number of records included in the plurality of divided data obtained by dividing the operation data is at most 86,400 × 2.

[0042] The determination unit 112 determines whether the state of the battery module can be estimated using the corresponding divided data for each of the plurality of module IDs. For example, the determination unit 112 determines that the state cannot be estimated when the number of the corresponding divided data does not satisfy a predetermined estimation condition for each of the plurality of module IDs. The determination unit 112 may determine that the state cannot be estimated when the corresponding divided data does not exist for each of the plurality of module IDs.

[0043] The estimation conditions are defined, for example, as conditions under which the estimation unit 113 can appropriately perform state estimation. The estimation conditions are, for example, the following: When determining the voltage distribution FV, the voltage distribution (RMS: Root Mean Square) ≈ 0, the number of charging voltage data > 0, and the number of discharging voltage data > 0 within the range of SoC (SoC band, percentile) set for the battery unit and the application of the battery unit.

[0044] Whether the battery is charging or discharging can be determined by referring to the current value. For example, if the current value > 0, it is considered charging, and if the current value < 0, it is considered discharging. The polarity of charging and discharging follows the definition specified for the applicable battery unit.

[0045] The estimation unit 113 estimates the state of each of the multiple battery modules using multiple divided data. The state is, for example, a State of Health (SoH), where a higher value indicates a healthier battery module. Any method can be used to estimate SoH, but for example, a method can be applied in which feature quantities (voltage distribution, power distribution, representative temperature, etc.) are calculated for each battery module, and SoH is calculated from these feature quantities.

[0046] The estimated state is not limited to State of Heat (SoH); any other indicator representing the state of the battery module may be used. For example, an indicator that shows a better state of the battery module (healthy, not degraded, etc.) as the smaller the value, may be used. The following mainly describes an example in which SoH is used to estimate the state of the battery module.

[0047] For module IDs that the estimation conditions are determined to be met, the estimation unit 113 uses multiple segmented data to estimate the state of the battery module identified by that module ID.

[0048] As described above, the operational data shown in Figure 2 corresponds to data measured for a larger unit, the battery unit. Therefore, if a method for estimating State of Heat (SOH) is applied to such operational data, it becomes possible to estimate the state of the battery unit. On the other hand, this method does not estimate the state of each of the multiple battery modules contained within the battery unit.

[0049] Therefore, in this embodiment, the state (State of Health) is estimated using the divided data obtained by splitting the operational data for each of the multiple battery modules. This makes it possible to estimate the state of each smaller battery (battery module) using the operational data measured for a larger unit of battery (battery unit).

[0050] The segmented data, obtained by dividing the operational data, includes measured values ​​(voltage, current, temperature, etc.) for the entire battery unit, which is a larger unit. However, these measured values ​​are those obtained when the battery module corresponding to the segmented data has reached a specific attribute. Therefore, the segmented data can be used as data to estimate the state of the corresponding battery module.

[0051] If the period for acquiring operational data and the specific attributes are set appropriately, records containing the corresponding module ID will be recorded in the operational data for all or many of the battery modules, indicating them as battery modules corresponding to the specific attributes. As a result, segmented data will be generated for all or many of the battery modules.

[0052] If a battery module's attributes do not match a specific attribute, a record containing the module ID of that battery module may not be included in the operational data. For example, if the specific attributes are maximum and minimum voltage, then for battery modules with little degradation, the maximum and minimum voltages may not be measured, and the corresponding record containing the module ID may not be recorded in the operational data. Furthermore, even if a record containing the corresponding module ID is recorded in the operational data, the status may not be properly estimated from the segmented data containing that record due to factors such as a small number of records.

[0053] Taking these circumstances into consideration, in this embodiment, the determination unit 112 determines whether or not the state of the battery module can be estimated using the segmented data. Then, if it is determined that estimation is possible, the estimation unit 113 performs state estimation using the segmented data.

[0054] For each of the one or more module IDs (hereinafter referred to as "unidentifiable information") that is determined not to satisfy the estimation conditions, the estimation unit 113 estimates the state using the state estimated for the battery module identified by one or more module IDs other than the unidentifiable information. For example, for each of the one or more unidentifiable information, the estimation unit 113 estimates an SoH that is greater than or equal to the maximum value among the SoH estimated for one or more module IDs other than the unidentifiable information.

[0055] This makes it possible to estimate the status of battery modules, for example, even if a record containing the corresponding module ID is not recorded in the operational data.

[0056] The estimation unit 113 may also estimate indicators other than State of Heat (SOH), such as equivalent cycle count (charge / discharge cycles). For example, the estimation unit 113 may use operating data to calculate the equivalent cycle count of a battery unit and output the calculated equivalent cycle count as the equivalent cycle count for each of the multiple battery modules included in the battery unit.

[0057] The output control unit 102 controls the output of various types of information used by the information processing device 100. For example, the output control unit 102 outputs the SoH estimated by the estimation unit 113 to the monitoring system 300. The method of outputting the information can be any method, but for example, it can be displayed on a display device or transmitted to an external device via a network.

[0058] The output control unit 102 may output information indicating that no corresponding segmented data exists for each of the multiple module IDs if such data does not exist. By referring to the outputted information, the following processing becomes possible, for example: - Identify battery modules that do not contain records corresponding to operational data. - For battery modules that do not contain records corresponding to operational data, obtain data in a different format from operational data, which is used to estimate the state.

[0059] At least a portion of each of the above-mentioned parts (acquisition unit 101, output control unit 102, generation unit 111, determination unit 112, and estimation unit 113) may be implemented by one or more processing units. Each of the above-mentioned parts may be implemented by, for example, one or more processors. For example, each of the above-mentioned parts may be implemented by having a processor such as a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit) execute a program, i.e., by software. Each of the above-mentioned parts may be implemented by a processor such as a dedicated IC (Integrated Circuit), i.e., by hardware. Each of the above-mentioned parts may be implemented by using both software and hardware. When multiple processors are used, each processor may implement one of the above-mentioned parts, or two or more of the above-mentioned parts.

[0060] The information processing device 100 may be composed of one physical device or multiple physical devices. For example, the information processing device 100 may be built on a cloud environment. Furthermore, each part of the information processing device 100 may be distributed and provided on multiple devices.

[0061] Next, the estimation process by the information processing device 100 of this embodiment will be described. Figure 4 is a flowchart showing an example of the estimation process in this embodiment.

[0062] The acquisition unit 101 acquires operational data (step S101). For example, the acquisition unit 101 acquires operational data for a period (e.g., one day) that is specified as the period for estimating the state of the battery.

[0063] When estimating the equivalent cycle count, the estimation unit 113 calculates the equivalent cycle count using the operation data (step S102).

[0064] The generation unit 111 generates segmented data by dividing the operating data for each module ID (Id) that has the maximum voltage and each module ID that has the minimum voltage (step S103).

[0065] For each of the generated divided data, the following processes (steps S104 to S111) are executed.

[0066] First, the determination unit 112 obtains the module IDs of the multiple battery modules that have not been processed, and obtains segmented data for the obtained module IDs (step S104). The determination unit 112 determines whether the obtained segmented data satisfies the estimation conditions (step S105).

[0067] If the estimation conditions are met (step S105: Yes), the estimation unit 113 estimates the State of Health (SoH) using the segmented data (step S106). The output control unit 102 outputs the estimated SoH (step S107).

[0068] If the estimation conditions are not met (step S105: No), the determination unit 112 further determines whether the number of divided data for the module ID currently being processed is 0 (step S108).

[0069] If the number of divided data is not zero (step S108: No), the estimation unit 113 outputs that it is waiting for SoH analysis (step S109). Waiting for SoH analysis means, for example, that the battery module with the module ID has a history of being recorded in the operational data as a battery module with specific attributes (corresponding to the number of divided data being not zero), but it is in a state of waiting until it can analyze (calculate) SoH by satisfying the estimation conditions.

[0070] If the number of divided data is 0 (step S108: Yes), the estimation unit 113 outputs that it is waiting for data (step S110). Waiting for data means, for example, that the battery module with the module ID has no history recorded in the operation data (corresponding to a number of divided data being 0), and is in a state of waiting to acquire data until it can analyze (calculate) the State of Health (SoH).

[0071] After steps S107, S109, and S110, the determination unit 112 determines whether or not all module IDs have been processed (step S111). If all module IDs have not been processed (step S111: No), the determination unit 112 returns to step S104 and repeats the processing for the unprocessed module IDs. If all module IDs have been processed (step S111: Yes), the estimation process ends.

[0072] Here, we will explain an example of a method for estimating State of Heat (SOH). Below, we will explain an example of a method that calculates SoH from the calculated features (voltage distribution, power distribution, representative temperature, etc.) for each battery module.

[0073] For example, the estimation unit 113 calculates feature quantities for calculating SoH from segmented data corresponding to time-series operating data classified for each battery module (each module ID). These feature quantities include, for example, the voltage distribution FV and the power distribution P. ｆｖ , and representative temperature T ｆｖ That is the case.

[0074] For example, the estimation unit 113 calculates the voltage distribution FV by following the procedure below: - Create a QV plot, which is a scatter plot of the amount of charge (SoC) and the voltage V of the battery module. - For example, divide the data of the QV plot into 1% widths of SoC and find the RMS of the voltage V included in each 1% width range. - The average value of the RMS for all ranges is taken as the voltage distribution FV.

[0075] For example, the estimation unit 113 calculates the RMS of the power values ​​included in the divided data and determines the power distribution P ｆｖLet it be so. For example, the estimation unit 113 sets the representative temperature T to the average value of the temperature values output by a plurality of temperature sensors installed in the battery module. ｆｖ Let it be so.

[0076] The estimation unit 113 calculates the SoH by inputting the feature amounts {FV, P ｆｖ , T ｆｖ} calculated as described above into a reference function f(x) as follows. SoH = f(FV, P ｆｖ , T ｆｖ ) = a(1) × FV + a(2), where a(i) = b(i, 1) × P ｆｖ + b(i, 2) × T ｆｖ + b(i, 3), (i = 1, 2)

[0077] The reference function f(x) can be derived using a plurality of prepared teacher data. Each of the plurality of teacher data includes, for example, voltage distribution FV, power distribution P ｆｖ , representative temperature T ｆｖ , and data including SoH (corresponding to correct answer data) for each battery module. The representative temperature T ｆｖ of the teacher data may be specified at a predetermined width (for example, every 5°C) within the temperature range used.

[0078] The coefficients a(1) and a(2) of the reference function f(FV, P ｆｖ , T ｆｖ ) are derived as parameters of meta-functions 501 and 502 as shown in FIG. 5 using the teacher data as described above.

[0079] In FIG. 4, steps S107, step S109, and step S110 correspond to processes for outputting the states of a plurality of battery modules. In step S109 and step S110, the values of the SoH of the battery modules determined not to satisfy the estimation conditions are not output.

[0080] The system may be configured to output the SoH value for battery modules that are determined not to meet the estimation conditions. Figures 6 and 7 are flowcharts illustrating an example of the estimation process when configured in this way. In this example, Figure 6 first estimates and outputs the SoH for battery modules that meet the estimation conditions. Then, Figure 7 estimates and outputs the SoH for battery modules that are determined not to meet the estimation conditions.

[0081] Steps S201 to S205 in Figure 6 are the same as steps S101 to S105 in Figure 4.

[0082] In the example shown in Figure 6, if the estimation conditions are not met (step S205: No), the process in step S208 is executed. In other words, in this example, for battery modules where the estimation conditions are not met, no processing such as state estimation is performed.

[0083] Steps S206 to S207 in Figure 6 are the same as steps S106 to S107 in Figure 4.

[0084] After step S207, and if it is determined that the estimation conditions are not met (step S205: No), the determination unit 112 determines whether or not all module IDs have been processed (step S208). Step S208 is the same as step S111 in Figure 4.

[0085] Let's explain Figure 7. Figure 7 represents the process of estimating and outputting SoH for battery modules that do not meet the estimation conditions, using the SoH estimated for battery modules that meet the estimation conditions according to Figure 6.

[0086] The estimation unit 113 obtains the State of Health (SoH) calculated for the segmented data that satisfies the estimation conditions (step S301). The estimation unit 113 calculates an SoH that is greater than or equal to the maximum value among the obtained SoH (step S302). The maximum value among the calculated SoH corresponds to the SoH of the healthiest battery module among the one or more battery modules corresponding to the segmented data that satisfies the estimation conditions. Therefore, an SoH that is greater than or equal to the maximum value means an SoH that is even healthier than the battery module estimated to be the healthiest.

[0087] The output control unit 102 outputs the calculated SoH to the battery module corresponding to the segmented data that does not satisfy the estimation conditions (step S303), and terminates the estimation process.

[0088] In step S303, the estimation unit 113 may estimate and output a state indicating that SoH is waiting for analysis or waiting for data, depending on whether the number of divided data is 0 or not, similar to steps S108 to S110 in Figure 4.

[0089] As described above, according to the embodiment, the state of each smaller battery (e.g., battery module) can be estimated using operational data measured for a larger battery unit (e.g., battery unit). Therefore, even when considering traceability, the data to be recorded can be operational data acquired (measured) for a larger battery unit. In other words, the state of the batteries can be estimated with higher accuracy while suppressing an increase in the amount of data to be recorded.

[0090] For example, it is possible to estimate the battery state with higher accuracy while suppressing the amount of data recorded, and to perform processes such as battery replacement based on the estimation results. Furthermore, in this embodiment, for battery modules whose state cannot be calculated because the conditions are not met, information indicating the basis for this, or the state estimated from the State of Health (SOH) of other battery modules, can be output. In other words, the requirement of traceability, which involves outputting the state for each of multiple battery modules, can be satisfied.

[0091] Next, the hardware configuration of the information processing device of the embodiment will be described using Figure 8. Figure 8 is an explanatory diagram showing an example of the hardware configuration of the information processing device of the embodiment.

[0092] The information processing device of this embodiment includes a control device such as a CPU (Central Processing Unit) 51, a storage device such as a ROM (Read Only Memory) 52 or RAM (Random Access Memory) 53, a communication interface 54 that connects to a network for communication, and a bus 61 that connects each part.

[0093] The program to be executed in the information processing device of this embodiment is provided pre-loaded into ROM 52 or the like.

[0094] The program executed by the information processing device of this embodiment may be configured to be provided as a computer program product by recording it in an installable or executable file format onto a computer-readable recording medium such as a CD-ROM (Compact Disk Read Only Memory), a flexible disk (FD), a CD-R (Compact Disk Recordable), or a DVD (Digital Versatile Disk).

[0095] Furthermore, the program executed by the information processing device of the embodiment may be stored on a computer connected to a network such as the Internet and provided by downloading it via the network. Alternatively, the program executed by the information processing device of the embodiment may be provided or distributed via a network such as the Internet.

[0096] The program executed in the information processing device of this embodiment can cause the computer to function as a component of the information processing device described above. This computer can read the program from a computer-readable storage medium onto the main memory and execute it using the CPU 51.

[0097] Examples of the configurations of the embodiments are described below. (Configuration Example 1) An information processing apparatus comprising a processing unit that generates a plurality of divided data by dividing the time-series operation data obtained when a second battery is in operation, which is a battery containing a plurality of first batteries each identified by a plurality of identification information, the operation data which includes a time, a specific attribute among the attributes of the plurality of first batteries that satisfies predetermined conditions, and the identification information that identifies the first battery which is the specific attribute, into a plurality of divided data for each of the plurality of identification information, and estimates the state of each of the plurality of first batteries using the plurality of divided data. (Configuration Example 2) The information processing apparatus according to Configuration Example 1, wherein the processing unit determines whether the state can be estimated for each of the plurality of identification information using the corresponding divided data, estimates the state of each of the plurality of first batteries using the plurality of divided data for the identification information for which it has been determined that the state can be estimated, and estimates the state for each of the one or more unidentifiable pieces of information which is the identification information for which it has been determined that the state cannot be estimated, using the state estimated for one or more of the identification information other than the one or more unidentifiable pieces of information. (Configuration Example 3) The state is a health degree in which a larger value indicates that the first battery is in good condition, and the processing unit estimates the health degree for each of the one or more unidentifiable pieces of information, which is greater than or equal to the maximum value among the health degrees estimated for one or more identification pieces of information other than the one or more unidentifiable pieces of information, as described in Configuration Example 2. (Configuration Example 4) The processing unit determines that the state cannot be estimated if the number of corresponding divided data for each of the multiple identification pieces of information does not satisfy a predetermined estimation condition, as described in Configuration Example 2. (Configuration Example 5) The processing unit determines that the state cannot be estimated if there is no corresponding divided data for each of the multiple identification pieces of information, as described in Configuration Example 2. (Configuration Example 6) The condition is at least one of the following: the top n attributes (n is an integer of 2 or more) in descending order of attribute value, and the top m attributes (m is an integer of 2 or more) in descending order of value. as described in Configuration Example 1 to 5.(Configuration Example 7) An information processing device according to any one of Configuration Examples 1 to 6, wherein the attribute is at least one of voltage and temperature. (Configuration Example 8) An information processing device according to Configuration Example 7, wherein the attribute is temperature, and the condition is at least one of the maximum value, minimum value, median value, and average value of the temperatures of a plurality of first batteries. (Configuration Example 9) An information processing device according to any one of Configuration Examples 1 to 8, wherein the processing unit calculates the equivalent number of cycles of the second battery using the operation data, and outputs the equivalent number of cycles as the equivalent number of cycles for each of the plurality of first batteries. (Configuration Example 10) An information processing device according to any one of Configuration Examples 1 to 9, wherein the processing unit outputs information indicating that the divided data does not exist for each of the plurality of identification information when the corresponding divided data does not exist. (Configuration Example 11) An information processing method to be executed by an information processing device, comprising the steps of: generating a plurality of divided data by dividing the time-series operation data obtained when a second battery is in operation, which is a battery containing a plurality of first batteries each identified by a plurality of identification pieces of information, the operation data comprising a time, a specific attribute among the attributes of the plurality of first batteries that satisfies predetermined conditions, and the identification piece of information that identifies the first battery that has the specific attribute, for each of the plurality of identification pieces of information; and estimating the state of each of the plurality of first batteries using the plurality of divided data. (Configuration Example 12) A program to cause a computer to execute the steps of: generating a plurality of divided data by dividing the time-series operation data obtained when a second battery is in operation, which is a battery containing a plurality of first batteries each identified by a plurality of identification pieces of information, the operation data comprising a time, a specific attribute among the attributes of the plurality of first batteries that satisfies predetermined conditions, and the identification piece of information that identifies the first battery that has the specific attribute, for each of the plurality of identification pieces of information; and estimating the state of each of the plurality of first batteries using the plurality of divided data.

[0098] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents.

[0099] 10 Information processing system 100 Information processing device 101 Acquisition unit 102 Output control unit 111 Generation unit 112 Determination unit 113 Estimation unit 121 Storage unit 200 Battery 300 Monitoring system

Claims

1. An information processing device comprising a processing unit that generates multiple divided data by dividing the time-series operation data obtained when a second battery is in operation, which is a battery containing multiple first batteries each identified by multiple pieces of identification information, the operation data which includes the time, a specific attribute among the attributes of the multiple first batteries that satisfies predetermined conditions, and the identification information that identifies the first battery that has the specific attribute, into multiple pieces of identification information, and estimates the state of each of the multiple first batteries using the multiple divided data.

2. The information processing apparatus according to claim 1, wherein the processing unit determines whether the state can be estimated for each of the plurality of identification information using the corresponding divided data, estimates the state for each of the plurality of first batteries using the plurality of divided data for the identification information for which it has been determined that the state can be estimated, and estimates the state for each of the one or more unidentifiable pieces of identification information for which it has been determined that the state cannot be estimated, using the state estimated for one or more of the identification information other than the one or more unidentifiable pieces of identification information.

3. The state is a health degree, the larger the value, the healthier the first battery is, and the processing unit estimates a health degree for each of the one or more unidentifiable information, which is greater than or equal to the maximum value among the health degrees estimated for one or more identification information other than the one or more unidentifiable information.

4. The information processing apparatus according to claim 2, wherein the processing unit determines that the state cannot be estimated if the number of corresponding divided data for each of the plurality of identification information does not satisfy a predetermined estimation condition.

5. The information processing apparatus according to claim 2, wherein the processing unit determines that the state cannot be estimated if there is no corresponding segmented data for each of the multiple pieces of identification information.

6. The information processing apparatus according to claim 1, wherein the conditions are at least one of the following: the attributes are the top n (where n is an integer of 2 or more) in descending order of attribute value, and the attributes are the top m (where m is an integer of 2 or more) in ascending order of value.

7. The information processing apparatus according to claim 1, wherein the attribute is at least one of voltage and temperature.

8. The information processing apparatus according to claim 7, wherein the attribute is temperature, and the condition is at least one of the maximum, minimum, median, and average temperatures of a plurality of first batteries.

9. The information processing apparatus according to claim 1, wherein the processing unit calculates the equivalent cycle count of the second battery using the operation data and outputs the equivalent cycle count as the equivalent cycle count of each of the plurality of first batteries.

10. The information processing apparatus according to claim 1, wherein the processing unit outputs information indicating that no corresponding divided data exists for each of the multiple pieces of identification information when no such divided data exists.

11. An information processing method executed by an information processing device, comprising the steps of: generating a plurality of divided data by dividing the time-series operation data obtained when a second battery is in operation, which is a battery containing a plurality of first batteries identified by a plurality of identification pieces of information, the operation data comprising a time, a specific attribute among the attributes of the plurality of first batteries that satisfies predetermined conditions, and the identification piece that identifies the first battery that has the specific attribute, into a plurality of divided data for each of the plurality of identification pieces of information; and estimating the state of each of the plurality of first batteries using the plurality of divided data.

12. A program for a computer to perform the following steps: generate a plurality of divided data by dividing the time-series operation data obtained when a second battery is in operation, which is a battery containing a plurality of first batteries each identified by a plurality of identification pieces of information, the operation data comprising a time, a specific attribute among the attributes of the plurality of first batteries that satisfies predetermined conditions, and the identification piece that identifies the first battery that has the specific attribute, into a plurality of divided data; and estimate the state of each of the plurality of first batteries using the plurality of divided data.

Images