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

Abstract

To provide an information processing device, an information processing method, a program, and an information processing system that can efficiently select an appropriate battery state estimation method according to an application.SOLUTION: An information processing device includes a processing unit. The processing unit analyzes operation data of a battery, calculates characteristic information that represents characteristics of charging and discharging the battery, selects an estimation method according to the characteristic information from among a plurality of estimation methods for estimating a state of the battery from the operation data, and estimates the state from the operation data using the selected estimation method.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Battery systems are used to improve power quality by stabilizing the power supplied by power grids and suppressing frequency fluctuations in the power grid, as well as to supply power to electric vehicles (EVs) and industrial equipment, etc. In addition, technologies have been proposed that estimate (evaluate) and continuously monitor the state of battery systems (health, state of degradation, etc.) without shutting down the operation of the battery systems.

[0003] The optimal estimation method (estimation algorithm) for estimating the state may differ depending on the various applications described above. Therefore, it is desirable to select an estimation method appropriate for the application. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 6313502 [Patent Document 2] International Publication No. 2021 / 186512 [Patent Document 3] Japanese Patent Publication No. 2020-119712 [Patent Document 4] Japanese Patent Application Laid-Open No. 2017-509867 Summary of the Invention [Problem to be solved by the invention]

[0005] An object of the present invention is to provide an information processing device, an information processing method, a program, and an information processing system that can efficiently select an appropriate method for estimating the state of a battery depending on the application. [Means for solving the problem]

[0006] According to an embodiment, an information processing device includes a processing unit that analyzes operation data of a battery, calculates characteristic information that indicates characteristics of charging and discharging of the battery, selects an estimation method corresponding to the characteristic information from among a plurality of estimation methods for estimating the state of the battery from the operation data, and estimates the state from the operation data using the selected estimation method. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a block diagram of an information processing system according to an embodiment. [Figure 2] FIG. 2 is a diagram showing an example of the configuration of a battery. [Figure 3] FIG. 2 is a diagram showing an example of a module configuration. [Figure 4] FIG. 4 is a diagram showing an example of operation data. [Figure 5] 10 is a flowchart of an estimation process according to an embodiment. [Figure 6] 4 is a flowchart of a state-of-health estimation process according to the embodiment. [Figure 7] 10 is a flowchart of an output process according to an embodiment. [Figure 8] FIG. 10 is a diagram showing an example of a graph showing changes in health level. [Figure 9] FIG. 10 is a diagram showing an example of a graph showing changes in health level. [Figure 10] FIG. 10 is a diagram showing an example of a graph showing changes in health level. [Figure 11] FIG. 10 is a diagram showing an example of a graph showing changes in health level. [Figure 12] FIG. 1 is a hardware configuration diagram of an information processing apparatus according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of an information processing apparatus according to the present invention will be described in detail below with reference to the accompanying drawings.

[0009] In the following, an example of estimating the state of health (SoH) of the batteries (battery components) that constitute the battery system will be mainly described as the state of the battery system. The state of the battery to be estimated is not limited to the state of health, and may be any other information such as the amount of charged / discharged power or the temperature of the battery cell.

[0010] In line with the Sustainable Development Goals (SDGs), carbon neutral implementation, and circular economy trends, there is a demand for battery systems to be operated safely over longer periods of time. To operate battery systems over the long term, it is necessary to estimate the health of the battery system and to properly replace and maintain battery components that no longer meet the battery system specifications. Removed battery components are reinserted into battery systems with different applications and used until they can no longer meet their specifications. When a battery system with specifications (applications) that match the deteriorating battery components no longer exists, the battery components are discarded. In this process, it is necessary to continuously monitor the health of each battery component.

[0011] It is also desirable to estimate the state of health (SOH) of various battery systems with different uses, such as those described above, from operating data without shutting down the system. This would allow the SoH to be estimated based on operational data, and a method could be considered in which tag information indicating a use previously specified by a user or the like is used to select an appropriate SOH estimation method for the use. However, with such a method, it becomes necessary to re-specify the tag information, for example, when a battery component is re-inserted into a battery system with a different use, as described above.

[0012] In this embodiment, operation data obtained from the battery is analyzed, and an appropriate method for estimating the state of health is selected based on the analysis results. This makes it possible to efficiently select an appropriate method for estimating the state of the battery depending on the application. Furthermore, in this embodiment, by managing the state of health for each battery component, the history is tracked even if the application or configuration of the battery system changes, and the state of health is continuously monitored for each battery component. This makes it possible to use up battery components until there are no more battery systems with usable specifications (applications).

[0013] 1 is a block diagram showing an example of the configuration of an information processing system 10 according to this embodiment. As shown in FIG. 1, the information processing system 10 includes an information processing device 100, a battery 200, and a monitoring system 300.

[0014] The information processing device 100 and the battery 200, and the information processing device 100 and the monitoring system 300 are connected via 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 mixed network of wireless and wired networks.

[0015] Battery 200 is a battery capable of charging and discharging electrical energy. Battery 200 may have any configuration as long as it is possible to acquire operational data for estimating the state of health. The operational data may include, for example, the voltage, current, temperature, and humidity of each parallel circuit constituting battery 200.

[0016] Battery 200 may be a battery mounted on a mobile object that operates using electric energy as a power source. Examples of mobile objects 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. Battery 200 may be a battery component mounted on an electrical device (such as a smartphone or personal computer), or a battery that inputs and outputs power for demand response. Battery 200 may also be a battery for other purposes.

[0017] Battery 200 is charged by a charger located at a charging stand, roadside, parking lot, etc., or a charger connected to an outlet, etc. The power stored in battery 200 may be discharged (reverse power flow) to the power grid via the charger. The method of transmitting power from the charger to battery 200 may be either a contact charging method or a contactless charging method.

[0018] An example configuration of the battery 200 will be described below. The battery 200 is not limited to this example configuration. The battery 200 includes multiple battery boards. The multiple battery boards are connected in series or parallel. Alternatively, the multiple battery boards are connected in series and parallel. Also, the battery may be a single battery.

[0019] Fig. 2 is a diagram showing an example configuration of a battery 200. The battery 200 includes a plurality of battery panels 31. Each battery panel 31 includes a plurality of modules 32 and a BMU (Battery Management Unit) 33. The plurality of modules 32 are connected in series, in parallel, or in series and parallel. In the example of Fig. 2, each battery panel includes the same number of modules, but this does not have to be the case.

[0020] Fig. 3 is a diagram showing an example of the configuration of one module 32. The module includes a plurality of battery cells 34, a temperature sensor, and a CMU (Cell Monitoring Unit). The plurality of battery cells 34 are connected in series, in parallel, or in series and parallel. In the example of Fig. 3, two or more battery cells 34 connected in series are further connected in parallel.

[0021] Returning to FIG. 1 , the monitoring system 300 monitors the battery 200 based on information indicating the state of the battery 200 (hereinafter, 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. A user (monitor) can understand 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 a command from the user.

[0022] The information processing device 100 includes a storage unit 121 , an acquisition unit 101 , a calculation unit 102 , a selection unit 103 , an estimation unit 104 , and an output control unit 105 .

[0023] The storage unit 121 stores various types of information used by the information processing device 100. For example, the storage unit 121 stores operation data acquired (input) from the battery 200, the health state estimated by the estimation unit 104, and information (intermediate products) obtained during the estimation.

[0024] The storage unit 121 can be configured from any commonly used storage medium such as a flash memory, a memory card, a RAM (Random Access Memory), an HDD (Hard Disk Drive), and an optical disk.

[0025] The acquisition unit 101 acquires various types of information used in the information processing device 100. For example, the acquisition unit 101 acquires operation data of the battery 200 from the battery 200. The operation data is, for example, time-series data including a measurement time (the time when the data was measured), a voltage, and a current. The operation data may be acquired at regular time intervals (for example, one second), or may be acquired irregularly. The acquisition unit 101 stores the acquired operation data in the storage unit 121. The operation data may be acquired for each cell, module, battery panel, or battery 200 (multiple battery panels connected to each other). In the following description, it is assumed that the operation data is acquired for each battery 200.

[0026] The operation data includes, for example, information on time, charge amount (SoC: State of Charge), voltage, current, and temperature. Power may be acquired instead of current. In this case, the current value may be calculated by calculation from the power value and the voltage value. SoC is an index indicating the charge amount of the battery. For example, SoC is calculated by dividing the amount of power (unit: Wh) or amount of charge (unit: Ah) stored in the battery 200 by the rated capacity (amount of power or amount of charge) of the battery 200. FIG. 4 is a diagram showing an example of operation data. As shown in FIG. 4, the operation data includes information on time, SoC, voltage, current, and temperature.

[0027] The calculation unit 102 analyzes the operation data of the battery 200 and calculates characteristic information that indicates the characteristics of charging and discharging by the battery 200. Note that charging and discharging refers to at least one of charging and discharging.

[0028] The calculation unit 102 calculates, for example, some or all of the following multiple pieces of feature information (F1) to (F3). (F1) Average value ratio, which is the ratio between the average charge C-rate value, which is the average during charging at the C-rate, and the average discharge C-rate value, which is the average during discharging at the C-rate. The C-rate is calculated, for example, by normalizing (dividing) the current value by the rated current, or by normalizing (dividing) the power value calculated by multiplying the voltage and current by the rated power value. (F2) Data point ratio, which is the ratio of the number of charges, which is the number of data points for the C rate during charging, to the number of discharges, which is the number of data points for the C rate during discharging. (F3) Reversal rate obtained by dividing the number of reversals between charging and discharging by the number of operational data.

[0029] The calculation unit 102 may perform the calculation process of the characteristic information as described above when the operation data satisfies predetermined conditions. The predetermined conditions are, for example, conditions under which operation data that allows the health degree to be calculated with high accuracy can be obtained, such as the following conditions: The C rate ratio, which is the ratio between the sum of the C rates during charging and the sum of the C rates during discharging, is within a specified range (third specified range).

[0030] This condition can be interpreted as meaning that the SoC at the start time of operational data acquisition and the SoC at the end time of operational data acquisition are nearly identical. If the SoC at the start and end times cannot be considered to be identical, for example, because one of the charge amount or discharge amount is greater than the other, an error will occur in the health state estimation. Alternatively, if the SoCs differ, the end time will be advanced (or the start time will be delayed) until they are closer.

[0031] The calculation unit 102 determines the above-mentioned conditions for the plurality of operational data to be subjected to health degree estimation, and if the conditions are not satisfied, stores and accumulates the plurality of operational data in the storage unit 121. If the conditions are satisfied, the calculation unit 102 analyzes the plurality of operational data that have been stored (accumulated) in the storage unit 121 up to that point, and calculates characteristic information.

[0032] The unit of operational data to be used for estimating the health level may be, for example, multiple operational data acquired over a certain period of time (e.g., one day) (such as multiple operational data included in a file output each day), or a specified portion of the multiple operational data (such as specified operational data from the operational data in a file).

[0033] The selection unit 103 selects an estimation method according to the characteristic information from among a plurality of estimation methods for estimating the state of the battery 200 from the operation data. As described above, the characteristic information is information that represents the characteristics of charging and discharging by the battery 200. The characteristics of charging and discharging may vary depending on the application for which the battery 200 is used. Therefore, selecting an estimation method according to the characteristic information corresponds to selecting an estimation method for the state of the battery 200 according to the application.

[0034] Examples of uses of the battery 200 include the following: (U1) Stabilizing the voltage supplied by the power system and suppressing frequency fluctuations in the power system. (U2) Power sources for electrically powered vehicles and industrial equipment (e.g., power sources for automobiles, ships, automated guided vehicles, cranes used in ports, etc.). (U3) An uninterruptible power supply that supplies power to industrial equipment, etc.

[0035] In applications such as (U1), the battery 200 deteriorates due to repeated charging and discharging.

[0036] Applications such as (U2) are characterized by high output for short periods of time (e.g., cranes), high-rate charging and low-rate discharging with regeneration (e.g., automobiles, ships), and long-term, very low-rate charging and discharging (e.g., ships at anchor).

[0037] In applications such as (U3), when a power grid interruption is detected, the stored power is immediately supplied to the connected device. In such applications, the battery is maintained in a fully charged state for a long period of time, and deterioration due to storage or float charging progresses depending on the length of the fully charged state.

[0038] Deterioration of the battery 200 appears in the operation data depending on the characteristics of the battery cells and the charge / discharge state. In other words, the characteristic information obtained by analyzing the operation data varies depending on different applications based on the characteristics of the battery cells. Therefore, even if the same battery cells are used in the battery 200, it is necessary to estimate the state of health using an estimation method appropriate for the application.

[0039] Therefore, in this embodiment, the selection unit 103 selects an estimation method from among a plurality of estimation methods according to the characteristic information, thereby making it possible to efficiently select an appropriate estimation method for the state of the battery 200 according to the intended use.

[0040] The plurality of estimation methods include, for example, the following methods. All of them differ in how they process voltage data included in an arbitrary SoC range in the time-series data of SoC and voltage. (M1) A method for estimating the state using the magnitude of voltage fluctuation as the main characteristic information (first estimation method, hereafter referred to as the Sigma method). (M2) A method of estimating the state using the difference between the average voltage during charging and the average voltage during operation (discharging) of the equipment as the main characteristic information (second estimation method, hereafter referred to as the delta method). (M3) A method for estimating the state using the average value of voltage fluctuations as the main characteristic information (third estimation method, hereafter referred to as the generative OCV method).

[0041] The characteristic information of the voltage data used in the Sigma method is any one of the effective value, standard deviation, variance, and a value calculated based on at least one of them. The Sigma method can be realized, for example, by the technology described in Patent Document 1. The characteristic information of the voltage data used in the Delta method can be realized, for example, by the technology described in Patent Document 2. The generated OCV method can be realized, for example, by the technology described in Patent Document 3.

[0042] For example, the Sigma method is suitable for estimating the state of health of battery 200 based on operational data when used in applications where charge and discharge cycles occur in a relatively short time, such as (U1). The Delta method is suitable for applications where the battery is charged once and then operated, as shown in (U2). The Generated OCV method is suitable for batteries whose main characteristic information of deterioration is reflected in changes in OCV, or for applications where the storage or float time is long, such as (U3).

[0043] The selection unit 103 selects one of a plurality of estimation methods by determining conditions for one or more pieces of feature information.

[0044] For example, in the case of application (U1), charging and discharging are repeated in a short cycle, resulting in a large reversal rate. Therefore, when the condition that the reversal rate of (F3) is greater than a predetermined threshold value TH_a (first threshold value) is satisfied, the selection unit 103 selects the Sigma method suitable for application (U1).

[0045] For example, application (U2) corresponds to the following conditions: the reversal rate of (F3) is equal to or less than the threshold value TH_a, the data count ratio of (F2) is outside the specified range R_a (first specified range), and the average value ratio of (F1) is outside the specified range R_b (second specified range). Therefore, when these conditions are met, the selection unit 103 selects the delta algorithm suitable for application (U2).

[0046] For example, application (U3) corresponds to the condition that the reversal rate of (F3) is equal to or less than the threshold value TH_a, the data count ratio of (F2) is within the specified range R_a, or the average value ratio of (F1) is within the specified range R_b. Therefore, when these conditions are met, the selection unit 103 selects a generation OCV method suitable for application (U3).

[0047] The above selection methods are merely examples and are not limiting. Any method may be used as long as it selects one of a plurality of estimation methods depending on whether one or more conditions regarding one or more pieces of feature information are satisfied.

[0048] The estimation unit 104 estimates the state from the operational data using the estimation method selected by the selection unit 103.

[0049] The output control unit 105 controls the output of various information used in the information processing device 100. For example, the output control unit 105 outputs the health level, which is the estimation result by the estimation unit 104, to the monitoring system 300.

[0050] When the selection unit 103 does not select an estimation method according to the feature information (for example, when there is no estimation method that satisfies the conditions), the output control unit 105 may output notification information indicating that there is no estimation method according to the feature information. The notification information is output to, for example, the storage unit 121 or the monitoring system 300.

[0051] At least a part of the above-mentioned units (acquisition unit 101, calculation unit 102, selection unit 103, estimation unit 104, and output control unit 105) may be realized by one processing unit. Each of the above-mentioned units is realized, for example, by one or more processors. For example, each of the above-mentioned units may be realized by having a processor such as a CPU (Central Processing Unit) execute a program, that is, by software. Each of the above-mentioned units may be realized by a processor such as a dedicated IC (Integrated Circuit), that is, by hardware. Each of the above-mentioned units may be realized by using a combination of software and hardware. When multiple processors are used, each processor may realize one of the units, or may realize two or more of the units.

[0052] Next, a description will be given of the estimation process performed by the information processing device 100 according to this embodiment. Fig. 5 is a flowchart showing an example of the estimation process according to this embodiment.

[0053] The acquiring unit 101 acquires operation data of the battery 200 (step S101). The operation data may be acquired in any unit, but the acquiring unit 101 acquires a plurality of operation data by inputting, for example, a file including a plurality of operation data for each day.

[0054] The calculation unit 102 determines whether the acquired plurality of pieces of operational data satisfy a predetermined condition. For example, as described above, the calculation unit 102 determines whether the C rate ratio, which is the ratio between the sum of the C rates during charging and the sum of the C rates during discharging, is within a specified range (step S102).

[0055] If this condition is not met (step S102: No), the calculation unit 102 stores the acquired operational data in the storage unit 121 (step S103).

[0056] If this condition is met (step S102: Yes), a state-of-health estimation process is executed using the accumulated operational data (step S104).

[0057] The estimated health level is output to, for example, the monitoring system 300. The monitoring system 300, for example, generates a graph showing changes in the health level and outputs (displays) it to the user. The information processing device 100 may output not only the health level but also information indicating various intermediate products calculated when estimating the health level. The intermediate products are, for example, statistical information on information (such as voltage, current, and temperature) included in the operation data used for the estimation. The monitoring system 300 may output information on the intermediate products together with the health level.

[0058] Next, details of the health level estimation process shown in step S104 in Fig. 5 will be described. Fig. 6 is a flowchart showing an example of the health level estimation process in this embodiment.

[0059] The calculation unit 102 calculates the reversal rate, the number of charges, the number of discharges, the average charge C rate, the average discharge C rate, and the C rate zero rate from the operation data (step S201).

[0060] The number of reversals between charging and discharging is calculated by, for example, the number of times the sign of the product of the current value included in the operational data at a certain time (t-1) and the current value included in the operational data at the next time t is negative. The reversal rate is calculated by dividing the number of reversals by the number of operational data.

[0061] The C rate zero rate is the value obtained by dividing the number of C rates with a value of zero by the number of operational data. A C rate with a value of zero is considered to be no charging or discharging. Therefore, the C rate zero rate can be interpreted as indicating the proportion of operational data when no charging or discharging is occurring. The C rate zero rate is used to determine whether the operational data is suitable for using the Sigma method. If this determination is not necessary, the C rate zero rate does not need to be calculated.

[0062] Thereafter, the selection unit 103 selects one of the estimation methods from the sigma method, the delta method, and the generated OCV method depending on whether the characteristic information based on the information calculated in step S201 satisfies the conditions (steps S202 to S209).

[0063] First, the selection unit 103 determines whether the reversal rate is greater than a threshold TH_a (e.g., 0.2) (step S202). If the reversal rate is greater than the threshold TH_a (step S202: Yes), the selection unit 103 determines whether the C rate zero rate is less than a predetermined threshold TH_b (e.g., 0.05) (step S208).

[0064] If the C-rate zero rate is smaller than the threshold TH_b (step S208: Yes), the selection unit 103 selects the Sigma method as the method for estimating the state of health (step S209).

[0065] If the reversal rate is equal to or less than the threshold value TH_a (step S202: No), the selection unit 103 determines whether the ratio between the number of charges and the number of discharges, that is, the ratio of the number of data, is within a specified range R_a (for example, 0.8 to 1.2) (step S203).

[0066] If the data number ratio is not within the specified range R_a (step S203: No), the selection unit 103 determines whether the average value ratio, which is the ratio between the average charge C rate and the average discharge C rate, is within a specified range R_b (for example, 1 / 5 to 5) (step S204). The condition that the specified range is 1 / 5 to 5 can be rephrased as a condition indicating that the larger of the average charge C rate and the average discharge C rate is 5 times or less the smaller of the two.

[0067] If the average value ratio is not within the specified range R_b (step S204: No), the selection unit 103 selects the delta method as the method for estimating the state of health (step S205).

[0068] If the data count ratio is within the specified range R_a (step S203: Yes), or if the average value ratio is within the specified range R_b (step S204: Yes), the selection unit 103 determines whether the SoC range of the operational data is greater than the specified range (step S206). This determination corresponds to determining whether operational data within the SoC range required for estimation using the generated OCV method is obtained. For example, the specified range is an SoC range of 70% to 100%.

[0069] If the range of the SoC of the operation data is greater than the specified range (step S206: Yes), the selection unit 103 selects the generated OCV method as the estimation method (step S207).

[0070] If the C rate zero rate is equal to or greater than the threshold value TH_b (step S208: No) and if the SoC range of the operational data is equal to or smaller than the specified range (step S206: No), the calculation unit 102 determines that the health level cannot be estimated. In this case, the output control unit 105 may output notification information indicating that there is no estimation method corresponding to the feature information (step S211). For example, by referring to the notification information, the user can understand that there is operational data that does not fit any of the multiple assumed estimation methods.

[0071] When an estimation method is selected in step S205, step S207, or step S209, the estimation unit 104 estimates the health state using the operation data by the selected estimation method (step S210).

[0072] As described above, the output control unit 105 may output information (hereinafter, output information) for the monitoring system 300 to generate a graph showing changes in the health level, for example. The output information may include not only output information related to the battery 200 but also output information related to a system that uses the battery 200.

[0073] The output information about the battery 200 includes, in addition to the state of health (SoH) obtained every fixed period (for example, every day), at least some of the following information obtained every fixed period: Equivalent cycle count (number of times charge / discharge has been performed) Average Power Rate ·Representative temperature Charge / discharge efficiency Selected estimation method

[0074] The output information regarding the system that uses the battery 200 includes, for example, at least some of the following information obtained at regular intervals: ·temperature ·Humidity The travel distance (travel distance, cruising distance, etc.) of the mobile object equipped with the battery 200 Operating area ·Sunshine

[0075] The monitoring system 300 can generate a graph (SoH run chart) showing changes in the health level using output information obtained at regular intervals, and output (display) it to the user. Fig. 7 is a flowchart showing an example of output processing for generating and outputting an SoH run chart. Note that the output processing may be configured to be executed by the output control unit 105.

[0076] The monitoring system 300 determines whether or not output information output at regular intervals from the information processing device 100 (output control unit 105) has been acquired (step S301). As described above, the output information may include not only output information related to the battery 200 but also output information related to the system that uses the battery 200.

[0077] The monitoring system 300 stores the acquired output information in, for example, an internal storage device (step S302). The monitoring system 300 calculates cumulative data, which is a cumulative value for at least a portion of the information included in the output information (step S303).

[0078] For example, since the number of equivalent cycles included in the output information is a value per fixed period, the monitoring system 300 calculates the cumulative number of equivalent cycles, which is a value obtained by accumulating the number of equivalent cycles since the service started operating, as cumulative data. If the output information includes the travel distance of the mobile object equipped with the battery 200, the monitoring system 300 may calculate the cumulative value of the travel distance as cumulative data.

[0079] The monitoring system 300 uses the output information and cumulative data to update time series data that can be used to generate an SoH run chart (step S304). For example, the monitoring system 300 adds the operation date to the data output daily, replaces the number of equivalent cycles with the cumulative number of equivalent cycles, and generates the following time series data: {Date, health status, cumulative equivalent cycle count, average power rate, representative temperature, selected estimation method, information indicating the characteristics of the system using the battery (travel distance, etc.)}

[0080] The selected estimation method may be replaced with a corresponding application (for example, any of (U1) to (U3) above).

[0081] For example, when a user instructs the display of an SoH run chart, the monitoring system 300 generates and outputs display information for displaying an SoH run chart showing changes in the state of health from the generated time-series data (step S305). The SoH run chart is, for example, a graph with information showing the passage of time on the horizontal axis and the state of health (SoH) on the vertical axis.

[0082] The information indicating the passage of time is, for example, the number of days elapsed since the start of service operation or the number of cumulative equivalent cycles. If the battery 200 is mounted on a mobile object, for example, the cumulative value of the travel distance may be used as information indicating the passage of time. Using the number of days elapsed since the start of operation makes it easier to understand the relationship with events such as reconfiguration of the battery system and changes in use. Using the number of cumulative equivalent cycles makes it easier to understand the transition in health relative to the cumulative amount of power processed, making it possible to predict future transitions.

[0083] 8 to 11 are diagrams showing examples of SoH run charts. 8 to 11 show examples of SoH run charts generated using output information output daily. The upper part of each figure is an example of an SoH run chart in which the horizontal axis represents the number of days elapsed since the start of service operation. The lower part of each figure is an example of an SoH run chart in which the horizontal axis represents the cumulative equivalent cycle count (FIGS. 8 to 10) or the cumulative travel distance (FIG. 11). The dots (circles) in each figure indicate the estimated state of health (SoH) value.

[0084] The horizontal lines in Fig. 8 represent the number of equivalent cycles per fixed period. Although the number of equivalent cycles is not displayed in Figs. 9 to 11, the number of equivalent cycles may be displayed in the same manner as in Fig. 8.

[0085] The SoH run chart may output information indicating the estimated use along with the health level. For example, FIG. 9 shows an example in which dots are displayed in different display modes for each "use" replaced by the selected estimation method. The display mode may be, for example, shape or color. In FIG. 9, for example, the white circle corresponds to use (U1), the black circle corresponds to use (U2), and the hatched circle corresponds to use (U3).

[0086] 10 shows an example of an SoH run chart including lines 1001-1004 that represent the time points at which the use changed. Along with the lines, information (such as text data) indicating how the use changed may be displayed.

[0087] This allows the user to visually check from the SoH run chart when and for what purpose the battery was used. In the example of Figure 9, it can be seen that the use of a certain battery changed in the order (U1), (U2), and (U3).

[0088] To understand the state of the battery, one or both of the average power rate and the representative temperature may be added to the vertical axis of the run chart.

[0089] In this way, the information processing device according to this embodiment can estimate the state of health of the battery 200 using an estimation method according to a plurality of pieces of characteristic information obtained by analyzing the operation data of the battery 200. This makes it possible to efficiently select an appropriate estimation method for the state of the battery 200 according to the application.

[0090] According to this embodiment, for example, when the use of the battery 200 is changed, the state of health can be estimated by selecting an estimation method suitable for the use by analyzing operation data continuously acquired from the battery 200, without explicitly specifying the change of use. Therefore, for example, even if a certain battery 200 is switched to be used in a battery system with a different use, the state of health can be continuously monitored for that battery 200.

[0091] Next, the hardware configuration of the information processing device according to this embodiment will be described with reference to Fig. 12. Fig. 12 is an explanatory diagram showing an example of the hardware configuration of the information processing device according to this embodiment.

[0092] The information processing device of this embodiment includes a control device such as a CPU 51, a storage device such as a ROM (Read Only Memory) 52 and a RAM 53, a communication I / F 54 that connects to a network and communicates, and a bus 61 that connects each part.

[0093] The programs executed by the information processing device according to this embodiment are provided in advance in the ROM 52 or the like.

[0094] The program executed by the information processing device according to this embodiment may be configured to be provided as a computer program product by being recorded in an installable or executable file format on 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 according to this embodiment may be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network. Also, the program executed by the information processing device according to this embodiment may be provided or distributed via a network such as the Internet.

[0096] The program executed by the information processing device according to this embodiment can cause a computer to function as each unit of the information processing device described above. In this computer, the CPU 51 can read the program from a computer-readable storage medium onto a main storage device and execute the program.

[0097] A configuration example of the embodiment will be described below. (Configuration example 1) Analyzing operation data of a battery and calculating characteristic information representing characteristics of charging and discharging of the battery; selecting an estimation method corresponding to the characteristic information from among a plurality of estimation methods for estimating the state of the battery from the operation data; using the selected estimation method to estimate the state from the operational data; Processing section, An information processing device comprising: (Configuration example 2) The operation data is time-series data including a measurement time, a voltage, and a current, The processing unit The characteristic information is calculated as follows: an average value ratio, which is the ratio between a charge C-rate average value, which is the average during charging at a C-rate normalized by a rated power value, and a discharge C-rate average value, which is the average during discharging at the C-rate; a data number ratio, which is the ratio between the number of charge data points, which is the number of data points at the C-rate during charging, and the number of discharge data points, which is the number of data points at the C-rate during discharging; and a reversal rate, which is the number of reversals between charging and discharging divided by the number of operating data. The information processing device according to configuration example 1. (Configuration example 3) the plurality of estimation methods include a first estimation method that estimates the state using a magnitude of a fluctuation in the voltage; The processing unit calculating the characteristic information including the reversal rate; selecting the first estimation method when the reversal rate is greater than a first threshold; The information processing device according to configuration example 2. (Configuration example 4) the plurality of estimation methods include a second estimation method that estimates the state using a difference between an average value of the voltage during charging and an average value of the voltage during discharging; The processing unit calculating the characteristic information including the average value ratio, the data number ratio, and the reversal rate; selecting the second estimation method when the reversal rate is equal to or less than a first threshold, the data number ratio is outside a first specified range, and the average value ratio is outside a second specified range; The information processing device according to configuration example 2. (Configuration Example 5) the plurality of estimation methods includes a third estimation method that estimates the state using an average value of the voltage fluctuation; The processing unit calculating the characteristic information including the average value ratio, the data number ratio, and the reversal rate; selecting the third estimation method when the reversal rate is equal to or less than a first threshold, the data number ratio is within a first specified range, or the average value ratio is within a second specified range; The information processing device according to configuration example 2. (Configuration Example 6) The operation data is time-series data including a measurement time, a voltage, and a current, The processing unit a C rate is calculated by normalizing the power value calculated by multiplying the voltage and the current by a rated power value, and if a C rate ratio, which is the ratio between the sum of the C rates during charging and the sum of the C rates during discharging, is outside a third specified range, the operation data is stored in a storage unit, and if the C rate ratio is within the third specified range, the operation data stored in the storage unit is analyzed and the characteristic information is calculated. 6. The information processing device according to any one of configuration examples 1 to 5. (Configuration Example 7) The processing unit When the estimation method according to the feature information is not selected, outputting notification information indicating that the estimation method according to the feature information does not exist. 7. The information processing device according to any one of configuration examples 1 to 6. (Configuration Example 8) The processing unit outputting output information including at least some of the number of times charging and discharging was performed during the period in which the state was estimated, the average power rate during the period, the representative temperature during the period, the charging and discharging efficiency of the battery, the selected estimation method, and information indicating characteristics of a system in which the battery is used, as well as the estimated state; 8. The information processing device according to any one of configuration examples 1 to 7. (Configuration Example 9) The processing unit a calculation unit that calculates the feature information; a selection unit for selecting the estimation method; an estimation unit that estimates the state from the operation data, 10. The information processing device according to any one of configuration examples 1 to 8. (Configuration Example 10) An information processing method executed by an information processing device, a calculation step of analyzing operation data of a battery and calculating characteristic information representing characteristics of charging and discharging of the battery; a selection step of selecting an estimation method according to the characteristic information from among a plurality of estimation methods for estimating the state of the battery from the operation data; an estimation step of estimating the state from the operation data using the selected estimation method; An information processing method including: (Configuration Example 11) On the computer, a calculation step of analyzing operation data of a battery and calculating characteristic information representing characteristics of charging and discharging of the battery; a selection step of selecting an estimation method according to the characteristic information from among a plurality of estimation methods for estimating the state of the battery from the operation data; an estimation step of estimating the state from the operation data using the selected estimation method; A program to execute. (Configuration Example 12) Batteries and Analyzing operation data of the battery and calculating characteristic information representing characteristics of charging and discharging of the battery; selecting an estimation method corresponding to the characteristic information from among a plurality of estimation methods for estimating the state of the battery from the operation data; using the selected estimation method to estimate the state from the operational data; a processing unit; An information processing system comprising:

[0098] Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, and are also included in the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0099] 10 Information Processing Systems 100 Information processing device 101 Acquisition Department 102 Calculation section 103 Selection section 104 Estimation part 105 Output control section 121 Storage section 200 batteries 300 Surveillance System

Claims

1. Analyzing operation data of a battery and calculating characteristic information representing characteristics of charging and discharging of the battery; selecting an estimation method corresponding to the characteristic information from among a plurality of estimation methods for estimating the state of the battery from the operation data; using the selected estimation method to estimate the state from the operational data; a processing unit; The operation data is time-series data including a measurement time, a voltage, and a current, The processing unit The characteristic information is calculated as follows: an average value ratio, which is the ratio between a charge C-rate average value, which is the average during charging at a C-rate obtained by normalizing the power value calculated by multiplying the voltage and the current by a rated power value, and a discharge C-rate average value, which is the average during discharging at the C-rate; a data number ratio, which is the ratio between the number of charge data points, which is the number of data points at the C-rate during charging, and the number of discharge data points, which is the number of data points at the C-rate during discharging; and a reversal rate, which is the number of reversals between charging and discharging divided by the number of operating data. Information processing device.

2. the plurality of estimation methods include a first estimation method that estimates the state using a magnitude of a fluctuation in the voltage; The processing unit calculating the characteristic information including the reversal rate; selecting the first estimation method when the reversal rate is greater than a first threshold; The information processing device according to claim 1 .

3. the plurality of estimation methods include a second estimation method that estimates the state using a difference between an average value of the voltage during charging and an average value of the voltage during discharging; The processing unit calculating the characteristic information including the average value ratio, the data number ratio, and the reversal rate; selecting the second estimation method when the reversal rate is equal to or less than a first threshold, the data number ratio is outside a first specified range, and the average value ratio is outside a second specified range; The information processing device according to claim 1 .

4. the plurality of estimation methods includes a third estimation method that estimates the state using an average value of the voltage fluctuation; The processing unit calculating the characteristic information including the average value ratio, the data number ratio, and the reversal rate; selecting the third estimation method when the reversal rate is equal to or less than a first threshold, the data number ratio is within a first specified range, or the average value ratio is within a second specified range; The information processing device according to claim 1 .

5. Analyzing battery operation data and calculating characteristic information representing characteristics of charging and discharging by the battery; selecting an estimation method corresponding to the characteristic information from among a plurality of estimation methods for estimating the state of the battery from the operation data; using the selected estimation method to estimate the state from the operational data; a processing unit; The operation data is time-series data including a measurement time, a voltage, and a current, The processing unit a C rate is calculated by normalizing the power value calculated by multiplying the voltage and the current by a rated power value, and if a C rate ratio, which is the ratio between the sum of the C rates during charging and the sum of the C rates during discharging, is outside a third specified range, the operation data is stored in a storage unit, and if the C rate ratio is within the third specified range, the operation data stored in the storage unit is analyzed and the characteristic information is calculated. Information processing device.

6. The processing unit When the estimation method according to the feature information is not selected, outputting notification information indicating that the estimation method according to the feature information does not exist. The information processing device according to claim 1 or 5.

7. The processing unit outputting output information including at least some of the number of times charging and discharging was performed during the period in which the state was estimated, the average power rate during the period, the representative temperature during the period, the charging and discharging efficiency of the battery, the selected estimation method, and information indicating characteristics of a system in which the battery is used, as well as the estimated state; The information processing device according to claim 1 or 5.

8. The processing unit a calculation unit that calculates the feature information; a selection unit for selecting the estimation method; an estimation unit that estimates the state from the operation data, The information processing device according to claim 1 or 5.

9. An information processing method executed by an information processing device, a calculation step of analyzing operation data of a battery and calculating characteristic information representing characteristics of charging and discharging of the battery; a selection step of selecting an estimation method according to the characteristic information from among a plurality of estimation methods for estimating the state of the battery from the operation data; an estimation step of estimating the state from the operation data using the selected estimation method, The operation data is time-series data including a measurement time, a voltage, and a current, The calculation step The characteristic information is calculated as follows: an average value ratio, which is the ratio between a charge C-rate average value, which is the average during charging at a C-rate obtained by normalizing the power value calculated by multiplying the voltage and the current by a rated power value, and a discharge C-rate average value, which is the average during discharging at the C-rate; a data number ratio, which is the ratio between the number of charge data points, which is the number of data points at the C-rate during charging, and the number of discharge data points, which is the number of data points at the C-rate during discharging; and a reversal rate, which is the number of reversals between charging and discharging divided by the number of operating data. Information processing methods.

10. On the computer, a calculation step of analyzing operation data of a battery and calculating characteristic information representing characteristics of charging and discharging of the battery; a selection step of selecting an estimation method according to the characteristic information from among a plurality of estimation methods for estimating the state of the battery from the operation data; an estimation step of estimating the state from the operation data using the selected estimation method; The operation data is time-series data including a measurement time, a voltage, and a current, The calculation step The characteristic information is calculated as follows: an average value ratio, which is the ratio between a charge C-rate average value, which is the average during charging at a C-rate obtained by normalizing the power value calculated by multiplying the voltage and the current by a rated power value, and a discharge C-rate average value, which is the average during discharging at the C-rate; a data number ratio, which is the ratio between the number of charge data points, which is the number of data points at the C-rate during charging, and the number of discharge data points, which is the number of data points at the C-rate during discharging; and a reversal rate, which is the number of reversals between charging and discharging divided by the number of operating data. program.

11. Batteries and Analyzing operation data of the battery and calculating characteristic information representing characteristics of charging and discharging of the battery; selecting an estimation method corresponding to the characteristic information from among a plurality of estimation methods for estimating the state of the battery from the operation data; using the selected estimation method to estimate the state from the operational data; a processing unit, The operation data is time-series data including a measurement time, a voltage, and a current, The processing unit The characteristic information is calculated as follows: an average value ratio, which is the ratio between a charge C-rate average value, which is the average during charging at a C-rate obtained by normalizing the power value calculated by multiplying the voltage and the current by a rated power value, and a discharge C-rate average value, which is the average during discharging at the C-rate; a data number ratio, which is the ratio between the number of charge data points, which is the number of data points at the C-rate during charging, and the number of discharge data points, which is the number of data points at the C-rate during discharging; and a reversal rate, which is the number of reversals between charging and discharging divided by the number of operating data. Information processing system.

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