Information processing systems, information processing methods, and programs

Abstract

To provide an information processing system.SOLUTION: In an acquisition step, operation history data about an operation history of a power storage device is acquired. The operation history data includes time series data about an electric parameter of the power storage device while the power storage device operates, and the electric parameter includes at least voltage or SOC, and current of the power storage device. In a section specification step, a plurality of specific sections are specified from the operation history data. Each of the specific sections is a section in which the voltage or the SOC of the power storage device reaches an end value from a predetermined start value, and the voltage or the SOC of the power storage device is configured so as to include a first time reaching a predetermined first value. In an output step, a sectional capacity in the specific section is output if the specific section satisfies a first determination condition. The first determination condition is determined on the basis of a set composed of first determination currents being currents when the voltage or the SOC reaches the first value in the specific section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent Document 1 discloses a technology that can calculate degradation characteristics while shortening the battery testing period. Specifically, the server according to the technology described in Patent Document 1 acquires at least one degradation characteristic calculation model of a storage battery based on testing, acquires operating data of the storage battery after testing, acquires the degree of degradation of the storage battery in the operating data, inputs the operating data into at least one degradation characteristic calculation model to calculate at least one degradation coefficient, inputs the operating data into a degradation degree calculation model for calculating the degree of degradation of the storage battery, calculates the degree of degradation using the calculated at least one degradation coefficient, adjusts at least one degradation characteristic calculation model so that the calculated degree of degradation approaches the acquired degree of degradation, calculates at least one degradation characteristic using the adjusted at least one degradation characteristic calculation model, and outputs the calculated at least one degradation characteristic. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-18264 [Overview of the Initiative] [Problems that the invention aims to solve]

[0004] By the way, in order to improve various performance aspects related to battery control, such as improving the reliability of these degradation characteristics, relatively reliable data on segment capacity is necessary. [Means for solving the problem]

[0005] According to one aspect of the present invention, an information processing system is provided. This information processing system comprises at least one processor capable of executing a program so as to perform the following steps: In the acquisition step, operation history data relating to the operation history of a power storage device is acquired. The operation history data includes time-series data relating to the electrical parameters of the power storage device while the power storage device is in operation, the electrical parameters including at least the voltage or SOC and current of the power storage device. In the interval identification step, a plurality of specific intervals are identified from the operation history data. Each specific interval is an interval in which the voltage or SOC of the power storage device ranges from a predetermined start value to an end value, and is configured to include a first time when the voltage or SOC of the power storage device reaches a predetermined first value. In the output step, if the specific interval satisfies a first determination condition, the interval capacity in the specific interval is output. The first determination condition is determined based on a set of first determination currents for each of the plurality of specific intervals, the first determination current being the current when the voltage or SOC reaches a first value in the specific interval.

[0006] With this configuration, it is possible to prioritize obtaining relatively reliable interval capacities from a given operational history data set. [Brief explanation of the drawing]

[0007] [Figure 1] This is a diagram showing the configuration of Information Processing System 1. [Figure 2] This is a block diagram showing the hardware configuration of the information processing device 2. [Figure 3] This is a block diagram showing the hardware configuration of user terminal 3. [Figure 4] This diagram shows the hardware configuration of the drive unit 4. [Figure 5] This figure shows an example of the functional components of the processor 23. [Figure 6] This figure shows an example of the configuration of the operational history data D1. [Figure 7] This figure shows an example of time-series data of the voltage of a certain energy storage device 421 from the operational history data D1. [Figure 8] This figure shows the relationship between the section capacity and the starting current value I1 and the ending current value I2. [Figure 9] This flowchart shows an example of the information processing flow performed in Information Processing System 1. [Figure 10] This figure shows the time-series data of interval capacity obtained from the operational history data D1. [Modes for carrying out the invention]

[0008] Embodiments of the present invention will be described below with reference to the drawings. The various features shown in the embodiments below can be combined with each other.

[0009] Incidentally, the program for implementing the software appearing in this embodiment may be provided as a non-transitory computer-readable medium, or it may be provided so that it can be downloaded from an external server, or it may be provided so that the program is launched on an external computer and its functions are realized on a client terminal (so-called cloud computing).

[0010] Furthermore, in this embodiment, "part" may include, for example, hardware resources implemented by circuits in a broad sense, and the information processing of software that can be specifically realized by these hardware resources. In addition, various types of information are handled in this embodiment, and these types of information can be represented, for example, by the physical values ​​of signal values ​​representing voltage and current, the high or low values ​​of signal values ​​as a set of binary bits composed of 0s or 1s, or by quantum superposition (so-called qubits), and communication and calculations can be performed on circuits in a broad sense.

[0011] In addition, a circuit in a broad sense is a circuit realized by appropriately combining at least a circuit, circuitry, a processor, a memory, etc. That is, it includes an application specific integrated circuit (ASIC), programmable logic devices (for example, a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)), etc.

[0012] 1. Hardware Configuration In this section, the hardware configuration will be described.

[0013] <Information Processing System 1> FIG. 1 is a configuration diagram showing an information processing system 1. The information processing system 1 includes an information processing device 2, a user terminal 3, a driving device 4, and a database DB1. The information processing device 2, the user terminal 3, and the database DB1 are configured to be communicable through a telecommunication line. In one embodiment, the information processing system 1 consists of one or more devices or components. For example, if it consists only of the information processing device 2, the information processing system 1 can be the information processing device 2. Hereinafter, these components will be described.

[0014] <Database DB1> The database DB1 is configured to be able to store various information such as the section capacity output from the information processing device 2.

[0015] <Information Processing Device 2> Figure 2 is a block diagram showing the hardware configuration of the information processing device 2. The information processing device 2 comprises a communication unit 21, a storage unit 22, and a processor 23, and these components are electrically connected within the information processing device 2 via a communication bus 20. Each component will be described in more detail.

[0016] The communication unit 21 preferably uses wired communication methods such as USB, IEEE1394, Thunderbolt®, and wired LAN network communication, but may also include wireless LAN network communication, mobile communication such as 3G / LTE / 5G, and Bluetooth® communication as needed. In other words, it is more preferable to implement it as a collection of these multiple communication methods. That is, the information processing device 2 may communicate various information from the outside via the communication unit 21 and the network.

[0017] The memory unit 22 stores various types of information as defined above. This can be implemented, for example, as a storage device such as a solid-state drive (SSD) that stores various programs related to the information processing device 2 executed by the processor 23, or as memory such as random access memory (RAM) that stores temporarily necessary information (arguments, arrays, etc.) related to program calculations. The memory unit 22 stores various programs and variables related to the information processing device 2 executed by the processor 23.

[0018] The processor 23 performs processing and control of the overall operation related to the information processing device 2. The processor 23 is, for example, a central processing unit (CPU) not shown. The processor 23 realizes various functions related to the information processing device 2 by reading predetermined programs stored in the memory unit 22. That is, information processing by software stored in the memory unit 22 can be concretely realized by the processor 23, which is an example of hardware, and executed as each functional unit included in the processor 23. These will be described in more detail in the next section. Note that the processor 23 is not limited to being a single unit, and may be implemented with multiple processors 23 for each function, or a combination thereof.

[0019] <User Terminal 3> Figure 3 is a block diagram showing the hardware configuration of the user terminal 3. The user terminal 3 comprises a communication unit 31, a storage unit 32, a processor 33, a display unit 34, and an HMI device 35, and these components are electrically connected within the user terminal 3 via a communication bus 30. The descriptions of the communication unit 31, storage unit 32, and processor 33 are the same as the descriptions of each part in the information processing device 2, so they are omitted here.

[0020] The display unit 34 may be included in the user terminal 3 housing or it may be an external component. The display unit 34 displays a graphical user interface (GUI) screen that can be operated by the user. This is preferably done by using different display devices such as a CRT display, liquid crystal display, organic EL display, and plasma display, depending on the type of user terminal 3.

[0021] The HMI device 35 is a human-machine interface device. The HMI device 35 may be included in the housing of the user terminal 3 or it may be an external device. For example, the HMI device 35 may be implemented as a touch panel integrated with the display unit 34. If it is a touch panel, the user can input tap operations, swipe operations, etc. Of course, instead of a touch panel, a switch button, mouse, QWERTY keyboard, voice recognition device, gesture detection device, gaze detection device, biosignal detection device, imaging device, etc. may be used. In other words, the HMI device 35 receives operation input made by the user. In response, the HMI device 35 transmits a signal corresponding to the operation input to the processor 33 via the communication bus 30. The processor 33 can perform predetermined controls and calculations as needed. The HMI device 35 can also be said to include an input unit configured to accept input from the user.

[0022] <Drive unit 4> Figure 4 shows the hardware configuration of the drive unit 4. The drive unit 4 is configured to be driven by electricity and is, for example, an electric vehicle (EV). The drive unit 4 comprises a drive unit 41, a power storage unit 42, and a measurement unit 43.

[0023] The drive unit 41 is a drive source for driving the drive device 4 using electric power, and includes, for example, an electric motor that can be operated by electric power.

[0024] The energy storage unit 42 is configured to enable charging and discharging of power. The power released from the energy storage unit 42 is supplied to the drive unit 41, thereby enabling the drive unit 41 to operate. The energy storage unit 42 comprises at least one (more specifically, more) energy storage devices 421 and one battery management system (BMS) 422.

[0025] The energy storage device 421 is a rechargeable battery that can be charged and discharged via its positive and negative electrodes, and can be any type of battery configured to be rechargeable, such as a lead-acid battery, nickel-cadmium battery, lithium-ion battery, or air battery. Multiple energy storage devices 421 are connected in series with each other to be configured to output a specified voltage as a whole.

[0026] The BMS422 is configured to detect the state of the energy storage devices 421. The BMS422 is configured to measure parameters representing the state of each energy storage device 421, such as voltage, current, and temperature, as time-series data. The value of each parameter is associated with time information. Furthermore, the BMS422 is configured to measure or estimate indicators representing the state of the energy storage devices 421, such as state of charge (SOC) and state of health (SOH), based on the measurement results. The information measured or estimated by the BMS422 is transmitted to the information processing device 2, etc.

[0027] The measurement unit 43 is configured to measure environmental information related to the external environment of the energy storage device 421. Environmental information may include, for example, the temperature, humidity, and absolute time (year, month, day, etc.) outside the drive unit 4. For the sake of explanation, the temperature represented by the environmental information will be referred to as the ambient temperature. The measurement unit 43 may be provided independently of the drive unit 4.

[0028] 2. Functional configuration of the information processing device 2 Figure 5 shows an example of the functional units of the processor 23. As shown in Figure 5, the processor 23 includes an acquisition unit 231, a identification unit 232, a calculation unit 233, a determination unit 234, an output unit 235, and an estimation unit 236. This section will explain the outline of these functional units. Details of each functional unit will be explained later in conjunction with the information processing described below.

[0029] The acquisition unit 231 is configured to acquire information from the user terminal 3 or other devices. The acquisition unit 231 is configured to acquire various information by reading various information stored in the storage area, which is at least a part of the memory unit 22, and writing the read information to the work area, which is at least a part of the memory unit 22. The storage area is, for example, the area of ​​the memory unit 22 that is implemented as a storage device such as an SSD. The work area is, for example, the area that is implemented as memory such as RAM. The acquisition by the acquisition unit 231 includes acquiring the output results of each functional unit included in the processor 23.

[0030] The identification unit 232 is configured to identify various types of information based on the acquisition results of the acquisition unit 231.

[0031] The calculation unit 233 is configured to calculate various information based on the acquisition results of the acquisition unit 231.

[0032] The determination unit 234 makes various determinations based on the calculation results of the calculation unit 233, etc.

[0033] The output unit 235 outputs the calculation result of the calculation unit 233 to the database DB1 or the like, based on the determination result of the determination unit 234.

[0034] The estimation unit 236 is configured to estimate a model relating to the degradation characteristics of the energy storage device 421 based on the output results of the output unit 235, etc.

[0035] The processor 23 may be configured to display various types of information. This information can be presented to the user via the display unit 34 of the user terminal 3 or another device. In such a case, for example, the processor 23 controls the display unit 34 of the user terminal 3 to display visual information such as screens, images including still images or videos, icons, and messages. The processor 23 may generate only rendering information for displaying the visual information on the user terminal 3. The processor 23 may also present the outputted information to the user without going through the user terminal 3 or another device user.

[0036] 3. Regarding information processing This section describes the information processing performed in the information processing system 1 mentioned above. In one embodiment, this information processing is used, for example, to calculate the interval capacity of at least one energy storage device 421 based on the operation history data D1 while the energy storage device 421 is operating to drive the drive device 4, and to output a reliable interval capacity. In another embodiment, this information processing is used to obtain a model for estimating the capacity degradation rate based on the calculated interval capacity output.

[0037] 3.1. About Operation History Data D1 First, an example of the operation history data D1 will be described. Figure 6 shows an example of the configuration of the operation history data D1. The operation history data D1 includes information on voltage, SOC, current, battery temperature, and ambient temperature. This information is associated with time information (HH:MM:SS) in the operation history data D1. Time, voltage, SOC, current, and battery temperature are measured by the BMS422. Ambient temperature is measured by the measurement unit 43. The time may be a relative time counted by the BMS422, or an absolute time specified by an external element such as the measurement unit 43. When absolute time is used as the time, the relative time counted by the BMS422 is associated with the absolute time specified by the measurement unit 43, etc., and stored in the operation history data D1. The BMS422 may be configured to measure absolute time. In other words, the operation history data D1 includes time-series data on the electrical parameters of the energy storage device 421 while the energy storage device 421 is in operation. The electrical parameters include at least the voltage or state of charge (SOC) and current of the energy storage device 421.

[0038] Figure 7 shows an example of time-series data of the voltage of a certain energy storage device 421 from the operational history data D1. The vertical axis in Figure 7 is voltage V (more specifically, the closed-circuit voltage of the energy storage device 421), and the horizontal axis is time t. As shown in Figure 7, the voltage V in the operational history data D1 decreases monotonically due to the discharge of the energy storage device 421 before the charging start time t0, and increases monotonically due to the charging of the energy storage device 421 from the charging start time t0 to the charging end time t3. Thus, the interval included in the operational history data D1 is composed of a discharge interval Td and a charging interval Tc. The discharge interval Td is the interval in which the energy storage device 421 is discharging, for example, supplying power to the drive unit 41, and is the interval in Figure 7 before the charging start time t0 and after the charging end time t3. The charging interval Tc is the interval in which the energy storage device 421 is charging, and is the interval in Figure 7 from the charging start time t0 to the charging end time t3. In other words, among the time intervals included in the operating history data D1, a continuous interval in which the voltage V decreases monotonically (especially strictly monotonically) is identified as one discharge interval Td, and a continuous interval in which the voltage V increases monotonically (especially strictly monotonically) is identified as one charge interval Tc.

[0039] In this embodiment, at least one of the charging intervals Tc may include a specific interval Ts. The specific interval Ts is an interval within the charging interval Tc used for calculating the interval capacity of the energy storage device 421, and in this embodiment, it is specified as the interval within the charging interval Tc from the first time t1 when the voltage V becomes a first value V1 to the second time t2 when the voltage V becomes a second value V2. The first value V1 and the second value V2 can each be set arbitrarily. In this embodiment, the second value V2 is greater than the first value V1. Hereinafter, for the sake of explanation, the value of the current at the first time t1 will be called the starting current value I1, and for example, the value of the current at the second time t2 will be called the ending current value I2. The starting current value I1 is the current when the voltage reaches the first value V1 in the specific interval Ts, and is an example of a first determination current. The ending current value I2 is the current when the voltage reaches the second value V2 in the specific interval Ts, and is an example of a second determination current.

[0040] Furthermore, the current value in the operation history data D1 can be arbitrarily changed depending on the charging and discharging pattern of the energy storage device 421. This means that the operation history data D1 of this embodiment is not obtained under predetermined conditions of a fixed charge and discharge test, but rather represents the charge and discharge history of the energy storage device 421 obtained under the actual operating conditions of the drive unit 4, that is, while the energy storage device 421 is actually being used. Therefore, this information processing is not limited to charge and discharge history obtained in charge and discharge tests conducted under predetermined conditions, for example, but can output a section capacity with acceptable reliability for estimating a model of the capacity degradation rate using charge and discharge history obtained from the energy storage device 421 used under any environment.

[0041] The interval capacity (more specifically, the change in capacity) can be calculated by integrating the current I from the first time t1 to the second time t2 over time. In this way, as the charging and discharging of the energy storage device 421 is repeated, at least one charging interval Tc is identified from a series of operating history data D1, and a specific interval Ts is identified from within that charging interval Tc. Then, the interval capacity of the energy storage device 421 can be obtained from this specific interval Ts. To summarize, each specific interval Ts is an interval in which the voltage of the energy storage device 421 goes from a predetermined start value to an end value, and is configured to include a first time t1 when the voltage V of the energy storage device 421 reaches a predetermined first value V1. For example, a specific interval Ts is identified such that the first time t1 is the start or end point of the specific interval Ts. For example, each specific interval Ts is further configured to include a second time t2 when the voltage V of the energy storage device 421 becomes a second value V2 that is different from the first value V1.

[0042] 3.2. Correlation between section capacity and current Next, the relationship between the interval capacity and the current (for example, the start current value I1, the end current value I2, etc.) will be described. FIG. 8 is a diagram showing the respective relationships of the start current value I1 and the end current value I2 with respect to the interval capacity. As described above, the interval capacity is calculated as the capacity change from the first time t1 to the second time t2. Each data point P1 (interval capacity, start current value I1, and end current value I2) included in FIG. 8 is specified or calculated from one specific interval Ts. T in FIG. 8 true is an interval where the interval capacity has a predetermined reliability, and T false is an interval where the interval capacity does not have such reliability. The reliability can be set as appropriate according to the use of the interval capacity. Here, for the sake of convenience of explanation, it is equivalent to "having reliability" that it can be used as data in the estimation of the degradation characteristics model of the power storage device 421, and it is equivalent to "not having reliability" that it is difficult to use.

[0043] As shown in FIG. 8, between T true , as time t elapses, the interval capacity of the power storage device 421 tends to monotonically decrease. This tendency means that the chargeable capacity decreases from the first value V1 to the second value V2 due to leaving the power storage device 421 unattended, repeated charge and discharge, etc., suggesting that the degradation of the power storage device 421 is progressing. On the other hand, when entering from T true to T false , although the degradation is progressing, the interval capacity temporarily increases. From this, the interval capacity in the interval of T false may have lower reliability than the interval capacity in the interval of T true .

[0044] Here, when comparing the start current value I1 and the end current value I2 in each of the intervals of T true and T false , the start current value I1 hardly varies between T true and T false . In contrast, the end current value I2 in the interval of T false is in the interval of T trueThe difference in the termination current value I2 between each interval is clearly smaller than that of the intervals mentioned above, and the difference in termination current value I2 between each interval is at least 1σ interval. Note that 1σ interval is obtained from the standard deviation of the starting current value I1 in a population whose elements are all data points P1. Thus, it was found that there is a certain degree of correlation between the current value at a given voltage V and the reliability of the interval capacitance. The judgment conditions in the information processing described later are configured to determine whether or not the interval capacitance has a predetermined reliability by utilizing this correlation between the current value and the reliability of the interval capacitance.

[0045] Note T false Factors contributing to the increase in interval capacity include a decrease in polarization due to a reduction in charging current, which increases the charging capacity until the voltage V reaches its second value V2.

[0046] Furthermore, a similar correlation may exist between the starting current value I1 and the reliability of the section capacitance, depending on factors that reduce reliability. Since the first value V1 and the second value V2 are arbitrary, a similar correlation may also exist between the current value at a specific voltage V included in a specific section Ts and the reliability of the section capacitance.

[0047] 3.3. Flow of Information Processing This section describes an example of information processing performed in the information processing system 1. Figure 9 is a flowchart showing an example of the flow of information processing performed in the information processing system 1. Note that this information processing may include arbitrary exception handling not shown. Exception handling includes interruption of the information processing or omission of each process. The selection or input performed in this information processing may be based on user operation or may be performed automatically without user operation. Note that the following information processing may be performed for each energy storage device 421.

[0048] [Step S1] First, in step S1, the acquisition unit 231 acquires operation history data D1 relating to the operation history of the energy storage device 421. The acquisition unit 231 acquires such time-series data from the BMS 422. The operation history data D1 may further include environmental information (e.g., ambient temperature). The acquisition unit 231 acquires environmental information from, for example, the measurement unit 43. By integrating the information acquired from the BMS 422 and the measurement unit 43 according to the time series, the acquisition unit 231 can acquire operation history data D1 including the above time-series data and environmental information.

[0049] [Step S2] Next, the process proceeds to step S2, where the processor 23 divides the operational history data D1 based on environmental information. For example, the processor 23 divides the operational history data D1 into multiple time-series data corresponding to a given range, depending on which of a predetermined ranges the moving average value of the ambient temperature over a certain period falls into. "Dividing the operational history data D1" is not limited to generating multiple data from a single operational history data D1, but may also include virtually dividing the operational history data D1 into multiple data by classifying each data point contained in the operational history data D1 according to the attribute corresponding to the range. The processor 23 may also divide the operational history data D1 based on absolute time as environmental information. For example, the processor 23 divides the operational history data D1 according to absolute time to correspond to each season (e.g., spring, summer, autumn, winter, rainy season, dry season, etc.). This makes it easier to consider the influence of external environmental factors such as season and external temperature when analyzing the relationship between changes in battery temperature and the degree of capacity degradation. For the sake of explanation, each of the divided operational history data D1 will be referred to as "divided data D2" below. In this embodiment, operational history data D1 is divided into four divided data D2 corresponding to spring, summer, autumn, and winter.

[0050] [Step S3] Next, the process proceeds to step S3, where the processor 23 sets the variable i to 1. The variable i is a number that identifies each of the multiple divided data D2. In this embodiment, each of the four divided data D2 is assigned a different variable i (=1 to 4). The following processes from steps S4 to S10 are performed on the i-th divided data D2.

[0051] [Step S4] First, in step S4, the identification unit 232 identifies the charging section Tc from the i-th (in this case, the 1st) divided data D2. The charging section Tc is the period during which the energy storage device 421 is being charged from an external power source while it is in operation. The charging method is arbitrary, but here a constant current method is used. The method for identifying the charging section Tc is arbitrary, but for example, the identification unit 232 identifies the charging section Tc as a continuous section in the operation history data D1 in which the voltage V increases monotonically.

[0052] [Step S5] Next, the process proceeds to step S5, where the identification unit 232 identifies a plurality of specific intervals Ts from the operation history data D1. Specifically, the identification unit 232 identifies a specific interval Ts from among at least one charging interval Tc included in the i-th divided data D2. In this embodiment, the identification unit 232 identifies a specific interval Ts such that the first time t1 is the starting point of the specific interval Ts and the second time t2, when the voltage of the energy storage device 421 becomes the second value V2, is the ending point of the specific interval Ts. Therefore, there is no specific interval Ts in a charging interval Tc where neither the first value V1 nor the second value V2 is included in the range of voltage V. The first time t1 and the second time t2 correspond to the timings that serve as the basis for calculating the interval capacity. With this configuration, by aligning the timing that serves as the basis for calculating the interval capacity with the timing of the specific interval Ts, the reliability of the interval capacity and the correlation with the first determination current can be further strengthened. Furthermore, with this configuration, the section capacity is output based on the reliability of the section capacity, which reflects the state of both the start and end points of the charging section Tc, where the state of the energy storage device 421 is particularly prone to variation. Therefore, the reliability of the output section capacity can be further enhanced.

[0053] [Step S6] Next, the process proceeds to step S6, where the calculation unit 233 calculates the interval capacity of each specific interval Ts identified in step S5. An example of the method for calculating the interval capacity is as described above.

[0054] [Step S7] Next, the process proceeds to step S7, where the identification unit 232 identifies the start current value I1 and the end current value I2 for each specific section Ts.

[0055] [Step S8] Next, the process proceeds to step S8, where the determination unit 234 sets determination conditions based on the currents of each specific section Ts belonging to the same divided data D2. The determination conditions are for determining whether the section capacity calculated in step S6 is reliable, or in other words, whether the error inherent in the calculated section capacity is within an acceptable range. The determination conditions are determined based on the value of the current at the timing when a certain voltage is reached in each specific section Ts belonging to the same divided data D2. The determination conditions in this embodiment include a first determination condition and a second determination condition.

[0056] The first determination condition is defined by whether the difference between the starting current value I1 corresponding to a certain interval capacity and the representative value of the starting current value I1 of the population consisting of all data points P1 belonging to the same divided data D2 is less than or equal to a predetermined tolerance value. The representative value is the mean, median, or mode of the starting current value I1 in a subset that constitutes at least a part of the set, and in this embodiment it is the mean. The tolerance value is determined based on the variance or standard deviation in the subset, and in this embodiment it is a constant multiple of the standard deviation σ of the population. With this configuration, it is possible to output an interval capacity that is more statistically reliable. This constant can be arbitrarily set according to the reliability desired by the user, and when the constant is 1, the first determination condition means whether or not the starting current value I1 is included in the 1σ interval. In other words, the first determination condition is determined based on the set consisting of the starting current values ​​I1 of each of a plurality of specific intervals Ts. The first determination condition is configured to be satisfied when the difference between the starting current value I1 in a specific interval Ts and the representative value of the set formed by the starting current values ​​I1 of multiple specific intervals Ts is less than or equal to an allowable value. With such a configuration, statistically reliable interval capacities can be output.

[0057] The second criterion, like the first criterion, is determined by whether the difference between the termination current value I2 corresponding to a certain interval capacity and the representative value of the termination current value I2 of the population consisting of all data points P1 belonging to the same divided data D2 is less than or equal to a predetermined allowable value. In other words, the second criterion is determined based on the set of termination current values ​​I2 for each of multiple specific intervals Ts (in other words, the termination current values ​​I2 of the set of data points P1).

[0058] [Step S9] Next, the process proceeds to step S9, where the determination unit 234 executes a determination process and determines whether or not to output the interval capacity to the database DB1 or the like.

[0059] [Step S10] Next, the process proceeds to step S10, where the output unit 235 outputs the section capacity that satisfies the judgment conditions to the database DB1. Specifically, if the start current value I1 of each specific section Ts satisfies the first judgment condition and the end current value I2 of the specific section Ts satisfies the second judgment condition, the output unit 235 outputs the section capacity of the specific section Ts in association with the original operation history data D1. On the other hand, if the judgment unit 234 does not satisfy the first or second judgment condition of the specific section Ts, it discards the section capacity of the specific section Ts without outputting it to the database DB1. The judgment unit 234 may also output the section capacity, etc., after adding a label indicating that the specific section Ts is unreliable.

[0060] [Step S11] Next, the process proceeds to step S11, where the determination unit 234 determines whether the variable i has reached 4, or in other words, whether the above process has been performed for all of the divided data D2.

[0061] [Step S12] If the result of the determination in step S11 is negative, the process proceeds to step S12, where the processor 23 increments the variable i by 1 and performs the processing in steps S4 to S10 for the next divided data D2. In other words, the identification unit 232 identifies multiple specific intervals Ts from each of the divided operating history data D1. The output unit 235 also outputs the interval capacity in each of the divided operating history data D1 (i.e., divided data D2) in association with environmental information, if the specific interval Ts satisfies the first and second determination conditions. With this configuration, since the operating status of the energy storage device 421 differs depending on the external environment such as temperature, the determination conditions for outputting a highly reliable interval capacity may also change depending on the external environment. Therefore, by dividing the operating history data D1 based on environmental information regarding the external environment and setting the first determination condition according to the divided data, it is possible to output a highly reliable interval capacity that takes into account the differences in the external environment.

[0062] Figure 10 shows the time-series data of interval capacity obtained from the operational history data D1. As shown in Figure 10, each interval capacity is associated with one of the four divided data D2 obtained from the operational history data D1. Each of the four divided data D2 corresponds to one of the intervals T1 to T4 in Figure 10. The processor 23 can define different populations corresponding to each of the intervals T1 to T4 from the data points P1 contained in each of these intervals T1 to T4, and set the above determination conditions for each population. This makes it possible to reflect the differences in the operating conditions of the energy storage device 421 when calculating the interval capacity, and thus enable more accurate reliability determination.

[0063] In summary, the output unit 235 outputs the section capacity in a specific section Ts when the specific section Ts satisfies the first determination condition. With this configuration, the section capacity can be obtained from each of the different charging sections Tc when the energy storage device 421 is in operation. As a result, by outputting the section capacity in a specific section Ts when the specific section Ts satisfies the first determination condition, it is possible to preferentially obtain a section capacity that is relatively reliable within a given operating history data D1. Furthermore, the output unit 235 outputs the section capacity in a specific section Ts when the specific section Ts satisfies both the first and second determination conditions. With this configuration, it is possible to output a section capacity of a specific section Ts that is even more reliable, as it satisfies both the first and second determination conditions.

[0064] [Step S13] Returning to Figure 9, on the other hand, if the result of the determination in step S11 is positive, that is, if the processing in steps S4 to S10 has been performed for all the divided data D2, the process proceeds to step S13. In step S13, the identification unit 232 calculates the degree of capacity degradation for each section corresponding to the divided data D2 based on the section capacity output and stored in the database DB1. The identification unit 232 may further subdivide the sections of the divided data D2 and then calculate the degree of capacity degradation for each subdivided section.

[0065] [Step S14] Next, the process proceeds to step S14, where the acquisition unit 231 acquires reference information regarding multiple candidate models relating to the degradation characteristics of the energy storage device 421. The reference information is pre-stored in, for example, the database DB1. In this embodiment, the reference information includes a first candidate model and a second candidate model. Each candidate model represents the relationship between parameters representing the environment related to the degradation of the energy storage device 421, such as temperature, voltage, and time, and the degradation rate of the energy storage device 421.

[0066] The first candidate model represents the contribution of degradation due to storage of the energy storage device 421, in other words, the degradation due to factors that occur regardless of whether or not power is supplied. Each of the first candidate models can be expressed by multiplying by basic equations that represent, for example, temperature dependence, voltage dependence, and time dependence. Specifically, the first candidate model can be expressed by equation (1) below as an example.

number

[0067] However, in equation (1), T represents temperature (battery temperature), V represents voltage (closed-circuit voltage), and t represents elapsed time (change in relative time). These parameters for the capacity degradation rate are output in step S10 and obtained from the values ​​stored in database DB1. In equation (1), α, β, γ, δ, and ε are unknown constants (parameters to be optimized). g This is the gas constant.

[0068] Furthermore, the first candidate model can be expressed by the following equation (2) as an alternative example.

number

[0069] For the sake of explanation, variables and constants in equation (2) that have properties common to those in equation (1) are represented by the same symbols. In equation (2), F represents the Faraday constant, and V refV is the voltage when SOC is a predetermined reference value. The reference value is specifically, for example, 40%, 45%, 50%, 55%, or 60%, and may be within the range of any two of the values ​​exemplified here.

[0070] The second candidate model represents the contribution of degradation based on factors caused by the energization (in other words, charging and discharging) of the energy storage device 421. The second candidate model can be expressed, for example, by multiplying the basic equations that represent temperature dependence, current dependence, and energization dependence, respectively. Specifically, the second candidate model can be expressed, as an example, by the following equation (3).

number

[0071] In equation (3), T is the temperature (battery temperature), I is the current (charge / discharge current), and Q is the amount of current flowing. These parameters for the capacity degradation rate are output in step S10 and obtained from the values ​​stored in database DB1. α, β, γ, and δ are unknown constants (parameters to be optimized). R g This is the gas constant.

[0072] Furthermore, the second candidate model can be expressed by the following equation (4) as an alternative example.

number

[0073] For the sake of clarity, variables and constants in equation (4) that share properties with those in equation (3) are represented using the same symbols.

[0074] The processor 23 generates candidate capacity degradation models to be optimized based on these candidate models. The capacity degradation model represents a model of degradation characteristics that reflects the degradation factors of the first candidate model and the second candidate model, respectively, relating to the energy storage device 421 corresponding to the operational history data D1. Candidate capacity degradation models can be generated in any way, but for example, one of the first candidate models and one of the second candidate models described above can be selected, and a weighted linear sum of these two candidate models can be generated as the capacity degradation model. The parameters included in the first candidate model and the second candidate model are treated as independent parameters. For example, if the capacity degradation model is represented by a linear combination of equations (1) and (3), the capacity degradation model has a total of 10 unknown variables: five unknown variables included in equation (1), four unknown variables included in equation (3), and weighting variables for equations (1) and (3). The processor 23 generates a capacity degradation model defined by a combination of multiple candidate models by appropriately setting these unknown variables. In this embodiment, the processor 23 generates candidate capacity degradation models for all combinations (i.e., 4 combinations) of the first candidate model and the second candidate model, and optimizes the parameters included in each capacity degradation model through the processing described later.

[0075] [Step S15] Next, the process proceeds to step S15, where the estimation unit 236 optimizes the parameters included in each candidate capacity degradation model based on the capacity degradation degree stored in the database DB1. This allows the estimation unit 236 to estimate the capacity degradation model of the energy storage device 421 represented by the operation history data D1 from among the candidate capacity degradation models. The capacity degradation degree is associated with various information contained in the operation history data D1. Therefore, the database DB1 stores the input parameters and the output capacity degradation degree in a related state. In other words, the estimation unit 236 estimates a model relating to degradation characteristics (in other words, a capacity degradation model) by optimizing a combination of multiple candidate models based on the operation history data D1 and the output interval capacity. This configuration allows for obtaining a capacity degradation model with higher accuracy than when estimated based on a single candidate model, thereby improving the estimation accuracy of the degradation rate of the energy storage device 421 corresponding to the operation history data D1. For example, the processor 23 optimizes each parameter included in the capacity degradation model so that the error between the estimated capacity degradation value obtained by inputting inputs (e.g., temperature, voltage, current, time) from the operating history data D1 into the latest model and the capacity degradation value (in other words, the measured value) obtained in step S13 is minimized. The specific optimization method is arbitrary, but examples include exhaustive search, Bayesian optimization, and genetic algorithms. In this way, the estimation unit 236 estimates a model for degradation characteristics based on the output interval capacity for each interval divided based on environmental information in the operating history data D1. With this configuration, it is possible to estimate a capacity degradation model that reflects differences in the external environment and is more suited to the actual operating environment. Subsequently, the estimation unit 236 identifies the capacity degradation model with the smallest error among the four capacity degradation models with optimized parameters.

[0076] [Step S16] Subsequently, the process proceeds to step S16, where the output unit 235 outputs the capacity degradation model with the smallest error, which was identified in step S15. The output capacity degradation model may be presented to the user as an optimization result, or it may be stored in a BMS 422 or the like to enable more accurate management of the energy storage device 421.

[0077] According to this information processing, reliable interval capacities can be selectively extracted from the operation history data D1 relating to the operation history of the energy storage device 421 in an uncontrolled environment. Therefore, the estimation unit 236 can estimate a model for degradation characteristics regardless of whether or not there is test data relating to the operation history of the energy storage device 421 during degradation testing conducted in a controlled environment. The output unit 235 may output interval capacities that satisfy the judgment conditions from the test data if test data is stored in the database DB1. In this case, the estimation unit 236 may estimate the model based on interval capacities obtained from the test data in addition to the interval capacities obtained from the operation history data D1. In other words, the operation history data D1 may include test data.

[0078] 4. Others The above embodiment is merely an example and is not limited thereto.

[0079] In the above embodiment, the determination condition only needs to include at least one of the first determination condition and the second determination condition. The first determination condition and the second determination condition are names used for convenience of explanation, and the same principle holds true even if the names are swapped.

[0080] The criteria may further include a third criterion. The third criterion is determined based on the set of currents at the time when the voltage V reaches a third value V3 between a first value V1 and a second value V2. The third criterion is defined in the same way as the first criterion.

[0081] The first value V1 does not have to be specified to coincide with the first time t1, which is the start time of the specific interval Ts. Similarly, the second value V2 does not have to be specified to coincide with the second time t2, which is the end time of the specific interval Ts. In other words, the first value V1 and the second value V2 can be any values ​​that can be taken in the interval for calculating the interval capacity.

[0082] The estimation unit 236 may estimate a model for the degradation characteristics from a list of candidate capacity degradation models represented by a single candidate model. In other words, the estimation unit 236 estimates a model based on the operational history data D1 and the output interval capacity. With this configuration, a model for degradation characteristics can be estimated based on highly reliable interval capacity, thereby improving the accuracy of the capacity degradation rate estimation.

[0083] "Outputting the interval capacity that satisfies the judgment condition" in step S10 is not limited to outputting the interval capacity that satisfies the judgment condition to the database DB1 and storing it, but may also include outputting the interval capacity that satisfies the judgment condition from the existing interval capacity stored in the database DB1, etc., to any system that uses that interval capacity. An example of such a system is a system that estimates a model related to the degradation characteristics of the energy storage device 421. Such a system is configured to perform the processing in steps S13 to S16, for example.

[0084] The output of segment capacity based on operational history data D1 and the estimation of the model based on the degree of capacity degradation may be performed independently.

[0085] The above information processing may be performed based on SOC, not limited to voltage V, for the identification of a specific interval Ts. This is because SOC generally has a positive correlation with the voltage V (closed-circuit voltage or open-circuit voltage) of the energy storage device 421.

[0086] The above-mentioned specific section Ts is not limited to those included in the charging section Tc, but may also include those included in the discharging section Td.

[0087] The information processing device 2 may be on-premise or in a cloud-based configuration. In the case of a cloud-based information processing device 2, for example, the above-mentioned functions and processing may be provided in the form of SaaS (Software as a Service) or cloud computing.

[0088] In the above embodiment, the information processing device 2 performed various storage and control functions, but instead of the information processing device 2, multiple external devices may be used. That is, various information and programs may be stored in a distributed manner across multiple external devices using blockchain technology or the like.

[0089] The above embodiment is not limited to the information processing system 1, but may also be an information processing method or an information processing program. The information processing method includes each step of the information processing system 1. The information processing program causes at least one computer to execute each step of the information processing system 1.

[0090] The above-mentioned information processing system 1, etc., may be provided in any of the following embodiments.

[0091] (1) An information processing system comprising at least one processor capable of executing a program such that the following steps are performed, wherein in the acquisition step, operation history data relating to the operation history of a power storage device is acquired, the operation history data includes time-series data relating to the electrical parameters of the power storage device while the power storage device is in operation, the electrical parameters include at least the voltage or SOC and current of the power storage device; in the interval identification step, a plurality of specific intervals are identified from the operation history data, each of which is an interval from a predetermined start value to an end value of the voltage or SOC of the power storage device, and includes a first time when the voltage or SOC of the power storage device reaches a predetermined first value; and in the output step, if the specific interval satisfies a first determination condition, the interval capacity in the specific interval is output, where the first determination condition is determined based on a set of first determination currents for each of the plurality of specific intervals, and the first determination current is the current when the voltage or SOC reaches the first value in the specific interval.

[0092] With this configuration, the section capacity can be obtained from each of the different charging or discharging sections while the energy storage device is in operation. The internal resistance of the energy storage device in operation may vary depending on various factors such as its operating conditions and inherent polarization. Therefore, the reliability of the section capacity in a given section is affected by these factors. Accordingly, the inventors of this application have found that the difference between the current when the voltage or SOC reaches a first value in a particular section and the current when the voltage or SOC reaches a first value in another particular section correlates with the reliability of the section capacity. As a result, by outputting the section capacity in a particular section when that section satisfies the first determination condition in the output step, it is possible to preferentially obtain a section capacity that is relatively reliable within a given operating history data.

[0093] (2) An information processing system as described in (1) above, wherein in the section identification step, the information processing system identifies the specified section such that the first time is the start or end point of the specified section.

[0094] With this configuration, by aligning the timing used as the basis for calculating the section capacity with the timing of a specific section, the reliability of the section capacity and the correlation with the first judgment current can be further strengthened.

[0095] (3) An information processing system as described in (1) or (2) above, wherein each of the specified intervals is configured to include a second time when the voltage or SOC of the energy storage device becomes a second value different from the first value, and in the output step, if the specified interval satisfies the first determination condition and the second determination condition, the interval capacity in the specified interval is output, wherein the second determination condition is determined based on a set composed of a second determination current for each of the plurality of specified intervals, and the second determination current is the current when the voltage or SOC in the specified interval reaches the second value.

[0096] With this configuration, it is possible to output a more reliable section capacity for a specific section that satisfies both the first and second criteria.

[0097] (4) The information processing system described in (3) above, wherein in the section identification step, the information processing system identifies the specified section such that the first time and the second time when the voltage of the energy storage device becomes the second value are the start and end points of the specified section, respectively.

[0098] With this configuration, the section capacity is output based on the reliability of the section capacity, which reflects the state of both the start and end points of the charging or discharging section, where the state of the energy storage device is particularly prone to variation. Therefore, the reliability of the output section capacity can be further enhanced.

[0099] (5) An information processing system described in any one of (1) to (4) above, wherein the first determination condition is satisfied when the difference between the first determination current in a certain specific interval and the representative value of the set formed by the first determination currents of each of the plurality of specific intervals is less than or equal to an allowable value.

[0100] This configuration allows for the output of statistically reliable interval capacities.

[0101] (6) An information processing system as described in (5) above, wherein the representative value is the mean, median, or mode of the first determination current in a subset that constitutes at least a part of the set, and the tolerance value is determined based on the variance or standard deviation in the subset.

[0102] This configuration allows for the output of more statistically reliable interval capacities.

[0103] (7) An information processing system according to any one of (1) to (6) above, wherein the operation history data includes environmental information relating to the external environment of the energy storage device, further comprising: a division step, which divides the operation history data based on the environmental information; a section identification step, which identifies a plurality of specific sections from each of the divided operation history data; and an output step, which outputs, for each of the divided operation history data, the section capacity in the specific section in association with the environmental information if the specific section satisfies the first determination condition.

[0104] With this configuration, the operating status of the energy storage device varies depending on external environmental factors such as temperature. Therefore, the criteria for determining a reliable interval capacity may also change depending on the external environment. To address this, the operating history data is divided based on environmental information related to the external environment, and the first determination criteria are set according to the divided data. This allows for the output of a reliable interval capacity that takes into account the differences in the external environment.

[0105] (8) An information processing system according to any one of (1) to (7) above, wherein in the estimation step, an information processing system estimates a model relating to the degradation characteristics of the energy storage device based on the operation history data and the output section capacity.

[0106] With this configuration, it is possible to estimate a model of degradation characteristics based on highly reliable section capacity, thereby improving the accuracy of estimating the capacity degradation rate.

[0107] (9) An information processing system as described in (8) above, wherein in the acquisition step, reference information relating to a plurality of candidate models that are candidates for the model is acquired, and in the estimation step, an information processing system that estimates a model relating to the degradation characteristics by optimizing the combination of the plurality of candidate models based on the operation history data and the output interval capacity.

[0108] This configuration allows for obtaining a model with higher accuracy than when estimated based on a single candidate model, thereby improving the accuracy of estimating the degradation rate of the corresponding energy storage device based on operational history data.

[0109] (10) An information processing system according to (8) or (9) above, wherein the operation history data includes environmental information relating to the external environment of the energy storage device, further comprising: a division step, which divides the operation history data based on the environmental information; a section identification step, which identifies a plurality of specific sections from each of the divided operation history data; an output step, which outputs the section capacity in each of the divided operation history data in association with the environmental information if the specific section satisfies the first determination condition; and an estimation step, which estimates a model relating to the degradation characteristics based on the section capacity output in association with the environmental information.

[0110] This configuration allows for the estimation of a model that reflects differences in the external environment and is more closely suited to the actual operating environment.

[0111] (11) An information processing method comprising each step of an information processing system described in any one of (1) to (10) above.

[0112] With this configuration, it is possible to prioritize obtaining relatively reliable interval capacities from a given operational history data set.

[0113] (12) A program that causes at least one computer to perform each step of the information processing system described in any one of (1) to (10) above.

[0114] With this configuration, it is possible to prioritize obtaining relatively reliable interval capacities from a given operational history data set. Of course, this is not always the case.

[0115] Finally, while various embodiments relating to this disclosure have been described, these are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be implemented 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. [Explanation of symbols]

[0116] 1: Information Processing System 2: Information Processing Device 20: Communications bus 21: Communications Department 22: Storage section 23: Processor 3: User terminal 30: Communications bus 31: Communications Department 32: Storage section 33: Processor 34: Display section 35: HMI devices 4: Drive unit 41: Drive unit 42: Energy storage unit 421: Energy storage device 422: BMS 43: Measurement Unit 231: Acquisition Department 232: Specific part 233: Calculation Unit 234: Judgment section 235: Output section 236: Estimation part D1: Operation history data D2: Split data DB1: Database I1: Starting current value I2: Termination current value P1: Data point T1~T4: Section Tc: Charging section Td: Discharge section Ts: Specific section V: Voltage V1: First value V2: Second value V3: Third value t0: Charging start time t1: 1st time t2: 2nd time t3: Charging completion time

Claims

1. An information processing system, The system comprises at least one processor capable of executing a program so that each of the following steps is performed, In the acquisition step, operational history data regarding the operation history of the energy storage device is acquired. The aforementioned operating history data includes time-series data relating to the electrical parameters of the energy storage device while the energy storage device is in operation, and the electrical parameters include at least the voltage or SOC and current of the energy storage device. In the section identification step, multiple specific sections are identified from the operation history data, Each of the aforementioned specific intervals is an interval during the charging or discharging of the energy storage device in which the voltage or SOC of the energy storage device reaches a predetermined starting value and an predetermined ending value, and is configured to include a first time when the voltage or SOC of the energy storage device reaches a predetermined first value. In the output step, only if the specific section satisfies the first determination condition, the section capacity of the energy storage device in the specific section, calculated by integrating the current in the specific section over time, is output, where An information processing system in which the first determination condition is determined based on a set of first determination currents for each of the plurality of specific intervals, the first determination current being the current when the voltage or SOC reaches the first value in the specific interval.

2. In the information processing system described in claim 1, An information processing system that, in the section identification step, identifies the specified section such that the first time point is the start or end point of the specified section.

3. In the information processing system described in claim 1, Each of the aforementioned specific intervals is further configured to include a second time point in which the voltage or SOC of the energy storage device becomes a second value different from the first value. In the output step, the section capacity in the specified section is output only if the specified section satisfies the second determination condition in addition to the first determination condition, and here, The second determination condition is determined based on a set of second determination currents for each of the plurality of specific intervals, The information processing system wherein the second determination current is the current when the voltage or SOC reaches the second value in the specified interval.

4. In the information processing system described in claim 3, An information processing system that, in the section identification step, identifies the specified section such that the first time and the second time when the voltage or SOC of the energy storage device becomes the second value are the start and end points of the specified section, respectively.

5. In the information processing system described in claim 1, An information processing system configured such that the first determination condition is satisfied when the difference between the first determination current in a specific interval and the representative value of the set formed by the first determination currents of each of the plurality of specific intervals is less than or equal to an allowable value.

6. In the information processing system described in claim 5, An information processing system in which the representative value is the mean, median, or mode of the first determination current in a subset that constitutes at least a part of the set, and the tolerance value is determined based on the variance or standard deviation in the subset.

7. In the information processing system described in claim 1, The aforementioned operating history data includes environmental information relating to the external environment of the energy storage device, Furthermore, in the division step, the operation history data is divided based on the environmental information, In the aforementioned section identification step, the plurality of specific sections are identified from each of the divided operation history data, In the output step, for each of the divided operational history data, if the specific section satisfies the first determination condition, the information processing system outputs the section capacity in that specific section in association with the environmental information.

8. In the information processing system described in claim 1, An information processing system that estimates a model of the degradation characteristics of the energy storage device based on the time-series changes in the output interval capacity in the estimation step.

9. In the information processing system described in claim 8, An information processing system that estimates a model relating to the degradation characteristics by optimizing a combination of multiple candidate models that are candidates for the model based on the operation history data and the output section capacity in the estimation step.

10. In the information processing system described in claim 8, The aforementioned operating history data includes environmental information relating to the external environment of the energy storage device, Furthermore, in the division step, the operation history data is divided based on the environmental information, In the aforementioned section identification step, the plurality of specific sections are identified from each of the divided operation history data, In the output step, for each of the divided operational history data, if the specific section satisfies the first determination condition, the section capacity in that specific section is output in association with the environmental information. An information processing system that, in the estimation step, estimates a model relating to the degradation characteristics based on the section capacity output in association with the environmental information.

11. Information processing method, A method comprising each step of the information processing system described in any one of claims 1 to 10.

12. It is a program, A program that causes at least one computer to perform each step of the information processing system described in any one of claims 1 to 10.

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