Data processing method, program, data processing device, battery management method and battery management system

Abstract

In this data processing method, a processor executes processing including: acquiring a parameter relating to the state of a storage battery; and calculating a feature amount affecting the lifetime of the storage battery on the basis of specific processing using the parameter.

Description

Data processing method, program, data processing device, battery management method and battery management system

[0001] The present disclosure relates to a data processing method, a program, a data processing device, a battery management method, and a battery management system.

[0002] Patent Document 1 discloses a method for estimating the degree of deterioration of a lead-acid battery, in order to prevent interruption of power supply, by utilizing any one of the following deterioration factors of the lead-acid battery: corrosion of the positive electrode grid, sludge formation of the positive electrode material, sulfation of the negative electrode, and shrinkage of the negative electrode material.

[0003] Patent No. 7310137

[0004] Here, repeated charging and discharging of a storage battery causes the positive electrode active material of the storage battery to turn into a muddy state, which then falls off from the positive electrode. The fallen positive electrode active material can short-circuit the positive and negative electrodes, potentially causing the storage battery to stop functioning unintentionally, even if the degree of deterioration of the storage battery is small. In other words, even if the degree of deterioration of the storage battery's capacity (specifically, its State of Health (SOH)) is high, if such a short circuit occurs within the storage battery, the storage battery's deterioration may progress rapidly, potentially bringing the storage battery to the end of its life. Conventional technologies only estimate the degree of deterioration of a lead-acid battery by utilizing one of the following: corrosion of the positive electrode grid, muddy state of the positive electrode material, sulfation of the negative electrode, and shrinkage of the negative electrode material. Therefore, there is room for improvement in predicting short circuits within the battery.

[0005] An object of the present disclosure is to provide a data processing method, a program, a data processing device, a battery management method, and a battery management system that can predict a short circuit inside a battery.

[0006] In the data processing method of the first aspect, a processor executes processing including acquiring parameters related to the state of a storage battery and calculating features that affect the life of the storage battery based on a specific processing using the parameters.

[0007] The program of the second aspect causes a processor to execute processing including acquiring parameters related to the state of a storage battery and calculating features that affect the life of the storage battery based on a specific processing using the parameters.

[0008] A data processing device of a third aspect includes an acquisition unit that acquires parameters related to the state of a storage battery, and a calculation unit that calculates a feature that affects the life of the storage battery based on a specification process using the parameters.

[0009] A fourth aspect of the battery management method involves a processor performing processes including acquiring parameters related to the state of a storage battery, calculating features that affect the lifespan of the storage battery based on a specific process using the parameters, calculating usage condition information indicating usage conditions of the storage battery that suppress a reduction in the lifespan of the storage battery through the specific process using the features, and outputting the calculated usage condition information.

[0010] A fifth aspect of the battery management method involves a processor performing processes including acquiring parameters related to the state of a storage battery, calculating features that affect the lifespan of the storage battery based on a specific process using the parameters, predicting the occurrence of an internal short circuit in the storage battery under specific usage conditions through the specific process using the features, and outputting prediction information related to the predicted occurrence of the internal short circuit.

[0011] A sixth aspect of the battery management system includes a parameter acquisition device that acquires parameters related to the state of a storage battery, and a data processing device that inputs the parameters transmitted from the parameter acquisition device and calculates features that affect the lifespan of the storage battery based on a specific process using the input parameters.

[0012] According to the present disclosure, a data processing method, a program, a data processing device, a battery management method, and a battery management system are provided that are capable of predicting a short circuit inside a battery.

[0013] FIG. 1 is a diagram illustrating an example of a configuration of a battery management system 100 according to an embodiment of the present disclosure. FIG. 2 is a diagram illustrating an example of main functions of a data processing device 40 according to an embodiment of the present disclosure. FIG. 3A is a diagram illustrating a mechanism by which an internal short circuit occurs. FIG. 3B is a diagram illustrating a mechanism by which an internal short circuit occurs. FIG. 3C is a diagram illustrating a mechanism by which an internal short circuit occurs. FIG. 3D is a diagram illustrating a mechanism by which an internal short circuit occurs. FIG. 4 is a diagram illustrating the relationship between the life product rate and the number of years elapsed of a storage battery 200. FIG. 5A is a diagram illustrating the relationship between the degree of sludge formation of active material and the cumulative discharge amount. FIG. 5B is a diagram illustrating the relationship between the degree of sludge formation of active material and the cumulative discharge amount. FIG. 5C is a diagram illustrating the relationship between the degree of sludge formation of active material and the cumulative discharge amount. FIG. 5D is a diagram illustrating the relationship between the degree of sludge formation of active material and the cumulative discharge amount. FIG. 6A is a diagram illustrating the relationship between the amount of shed active material and the C rate. FIG. 6B is a diagram illustrating the relationship between the amount of shed active material and the temperature. FIG. 6C is a diagram illustrating the relationship between the amount of shed active material and the SOC. Fig. 7 is a diagram showing an example of the falling-off rate of active material calculated using a specific model formula. Fig. 8 is a diagram showing an example of the configuration of the specific processing unit 610. Fig. 9 is a flowchart for explaining a first operation example of the data processing device 40. Fig. 10 is a flowchart for explaining a second operation example of the data processing device 40. Fig. 11 is a flowchart for explaining a third operation example of the data processing device 40. Fig. 12 is a diagram for explaining the update process of the prediction model formula by the update unit 613.

[0014] An example of an embodiment of the present disclosure will be described below with reference to the drawings. In each drawing, identical or equivalent components and parts are designated by the same reference numerals. The dimensional proportions in the drawings are exaggerated for the sake of explanation and may differ from the actual proportions. In the following embodiment, a processor with a reference numeral (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of the computing device include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose Computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit). In the following embodiments, a communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] 1 is a diagram illustrating an example of the configuration of a battery management system 100 according to an embodiment of the present disclosure. The battery management system 100 may include a measurement device 10 and a data processing device 40.

[0016] (Measuring Device 10) The measuring device 10 may be interpreted as a device that measures parameters that indicate the state of the storage battery 200. The parameters will be described in detail below.

[0017] The measurement device 10 may include a computer 30, an input / output unit 21, a communication I / F 22, a control unit 23, and a measurement unit 24. The computer 30 may include an input / output interface 31, a CPU 32, a RAM (Random Access Memory) 33, and a ROM (Read Only Memory) 34. These components may be connected to each other via a control bus 35.

[0018] The CPU 32 is an example of a processor. The CPU 32 executes various programs and controls each part. The RAM 33 temporarily stores the program 36 or data as a work area. The ROM 34 may store the program 36 that causes the processor to execute the operations of the measuring device 10. The CPU 32 reads the program 36 from the ROM 34 and executes the program 36 using the RAM 33 as a work area. By the CPU 32 executing the program 36, various functions that control each part of the measuring device 10 are realized.

[0019] The input / output unit 21 may be considered as a user interface that receives instructions from a user, notifies the CPU 32 of the computer 30 of the received instructions, and further presents information to the user in accordance with the instructions of the CPU 32. The input / output unit 21 may perform at least one of receiving input and displaying information to the user. For example, the input / output unit 21 may be a combination of a device such as a keyboard or switch that receives input and a device such as a display or printer that presents information.

[0020] The communication I / F 22 may be interpreted as a device that communicates with other devices via the network 300. The network 300 may be interpreted as a WAN (Wide Area Network) and / or a LAN (Local Area Network), etc.

[0021] The control unit 23 may be controlled by the computer 30 to control the voltage, current, discharge time, charge time, etc. when the storage battery 200 is charged or discharged.

[0022] The measurement unit 24 may include a sensor that measures the state of the storage battery 200 and transmit parameters that indicate the state of the storage battery 200 measured by the sensor to the computer 30 .

[0023] (Data Processing Device 40) The data processing device 40 may be interpreted as, for example, a server. The data processing device 40 may include a computer 60, a database 50, and a communication I / F 51. The computer 60 may include a processor 61, a RAM 62, and a storage 63. The processor 61, the RAM 62, and the storage 63 may be connected to a bus 64. The database 50 and the communication I / F 51 may also be connected to the bus 64. The communication I / F 51 may be connected to a network 300.

[0024] 2 shows an example of the main functions of the data processing device 40. In the data processing device 40, a specific process may be performed by the processor 61. A specific process program 631 and parameters 632 indicating the state of the storage battery 200 may be stored in the storage 63. The specific process program 631 is an example of a "program" in the present disclosure. The processor 61 may read the specific process program 631 from the storage 63 and execute the read specific process program 631 on the RAM 62. The specific process may be realized by the processor 61 operating as a specific processing unit 610 in accordance with the specific process program 631 executed on the RAM 62. The configuration of the specific processing unit 610 will be described in detail below.

[0025] Next, the prerequisites and problems involved in performing the specific processing by the specific processing unit 610 will be explained, and then a specific example of the specific processing will be explained.

[0026] (Prerequisites and Issues) Factors that shorten the lifespan of a lead-acid battery, which is an example of the storage battery 200, will be described. (1) The positive electrode active material of the lead-acid battery becomes muddy due to an increase in the cumulative discharge amount, average discharge amount, etc. of the lead-acid battery. (2) When the lead-acid battery is charged, gas is generated at the positive electrode due to decomposition of the electrolyte caused by the SOC (State of Charge), temperature, charge rate, etc. of the lead-acid battery. (3) The muddy positive electrode active material leaks from the positive electrode along with the generated gas. This fallen positive electrode active material can cause a short circuit (internal short circuit) between the positive electrode and the negative electrode, shortening the lifespan of the lead-acid battery. Hereinafter, the positive electrode active material may be simply referred to as the active material.

[0027] The mechanism by which such an internal short circuit occurs will be specifically described with reference to FIGS. 3A to 3D.

[0028] FIG. 3A shows the active material becoming muddy and the muddy active material flowing out of the positive electrode. Specifically, (1) in the initial state of the lead-acid battery, the amorphous phase separates multiple active materials (β-PbO ２ ) particles are bonded to each other. Therefore, the active material particles are not easily moved even when subjected to the flow of the electrolyte. The initial state may be interpreted as the state immediately after the lead-acid battery is manufactured, or the state in which the charge / discharge amount of the manufactured lead-acid battery is small. (2) When the lead-acid battery is charged / discharged, dissolution and precipitation are repeated, and the amorphous phase is formed in the active material (β-PbO ２ ), the bonds between the active material particles gradually weaken. Therefore, some of the active material particles begin to flow in response to the flow of the electrolyte. (3) When the lead-acid battery is further charged and discharged, most of the amorphous phase changes to active material, and the bonds between the active material particles disappear. As a result, the active material particles fall off in response to the flow of the electrolyte, that is, they flow out of the positive electrode.

[0029] FIG. 3B shows the sludge-like active material and the H generated during discharge. ２ Specifically, as shown in FIG. 3B, the discharge reaction of the lead-acid battery causes H ２ O is generated, and the pores shrink as the volume of the active material expands. Due to these effects, the electrolyte is pushed out of the pores, and the active material (β-PbO ２ ) a leaks out from the positive electrode as shown by the arrow in the figure.

[0030] FIG. 3C shows the active material in a sludge state, and the gas (O ２ ) b shows how the positive electrode is swept away by the influence of the standard electrode potential of the positive electrode of a lead-acid battery. ２Because the potential is more noble than the decomposition reaction of O, there is a possibility that oxygen gas will be generated due to decomposition of the electrolyte, particularly during charging, depending on the state of the electrodes or the charging conditions. When gas b is generated due to the decomposition of the electrolyte, the electrolyte in the positive electrode pores and the retainer is pushed out from the positive electrode, and at the same time, active material a falls off from the positive electrode.

[0031] 3D shows how the positive electrode and the negative electrode are short-circuited by the active material a that has fallen off the positive electrode during charging and discharging. When the positive electrode and the negative electrode are short-circuited by the active material a in this way, the lead-acid battery may reach the end of its life prematurely even when the degree of deterioration of the lead-acid battery is small, that is, even when the degree of deterioration of the capacity of the lead-acid battery (specifically, the SOH) is high, such as 93 or 90.

[0032] While examining the conditions of use that are advantageous for effectively suppressing the sludge-like active material from falling off, the applicant focused on parameter 632 related to the state of the storage battery 200 and discovered a method for calculating the characteristic quantities that affect the lifespan of the storage battery 200, in other words, a method for predicting the occurrence of a short circuit inside the battery.

[0033] More specifically, based on the fact that the influence of gas is dominant in the detachment of active material as described above, a method for estimating the amount of gas generated was studied based on actual operation data through simulation. Based on the results of this study, a specific model formula for predicting the lifespan of storage battery 200 was constructed. The specific model formula may be interpreted as a specific model formula including parameter 632. This model formula may be interpreted as a model that assumes that the lifespan of storage battery 200 caused by a short circuit due to detachment of active material after the active material becomes sludge occurs in a stochastic process, for example.

[0034] Here, the predicted life of the storage battery 200 can be interpreted as the period during which the sludge formation progresses as shown in Figure 4, that is, the period (1) until the active material becomes sludge as shown in (2) of Figure 3A due to the start of operation of the storage battery 200 and repeated charging and discharging, plus the period (2) during which a short circuit occurs due to the active material leaking in proportion to the amount of gas generated as described above.

[0035] The vertical axis of the graph shown in Figure 4 represents the life product rate, and the horizontal axis represents time (elapsed years). As shown in Figure 4, the life product rate of storage battery 200 remains approximately constant over the number of years until the sludge-like active material begins to leak. However, if storage battery 200 is further repeatedly charged and discharged after the active material becomes sludge-like, the degree of the impact of active material shedding, i.e., the amount of active material shedding that can short-circuit the positive and negative electrodes, increases rapidly, and the life product rate of storage battery 200 may also increase rapidly.

[0036] The lifespan rate may be interpreted as the ratio of cells that have reached the end of their lifespan to the total number of cells included in the storage battery 200. Cells that have reached the end of their lifespan may be interpreted as cells that cannot be charged or discharged despite the storage battery 200 showing a high state of health (SOH) due to the occurrence of an internal short circuit as described above, or cells in a state where the ratio of the current full charge capacity to the expected SOH is low. The SOH indicates the degree of health or deterioration, and may be interpreted as, for example, the ratio of the current full charge capacity to the initial full charge capacity, expressed on a scale of 0 to 100%. For example, if the total number of battery cells included in the storage battery 200 is six and the number of cells that have reached the end of their lifespan is two, the lifespan rate of the storage battery 200 is 33%.

[0037] The lifespan product rate may also be interpreted as the ratio of the number of storage batteries 200 that have reached the end of their lifespan to the total number of the storage batteries 200. A storage battery 200 that has reached the end of its lifespan may be interpreted as a storage battery 200 that cannot be charged or discharged despite showing a high state of health (SOH) due to an internal short circuit as described above, a storage battery 200 that cannot obtain a desired voltage when discharging, or a storage battery 200 in a state where the ratio of the current full charge capacity to the expected SOH is low. For example, if the total number of storage batteries 200 is 10 and the number of storage batteries 200 that have reached the end of their lifespan is 3, the lifespan product rate of the storage batteries 200 is 30%.

[0038] The graphs in Figures 5A to 5D show the degree of sludge formation of the active material measured in an operational storage battery 200. Specifically, these graphs show the degree of sludge formation of the active material relative to the cumulative discharge amount of the storage battery 200 operated at four different sites (regions). The vertical axis of each graph represents the degree of sludge formation of the active material, and the horizontal axis represents the cumulative discharge amount. The dots in the graphs represent actual measurements, and the solid lines represent predicted values. As can be seen from these graphs, sludge formation progresses rapidly up to an cumulative discharge amount of, for example, around 500 kAh, after which the sludge formation progresses slowly or remains constant. These results, i.e., the degree of sludge formation of the active material relative to the cumulative discharge amount of the storage battery 200, can be used as a sludge formation model to predict the degree of sludge formation. Note that important parameters (mud formation) for predicting the degree of sludge formation of the active material include various elements, such as the rated capacity of the storage battery 200, the cumulative discharge amount of the storage battery 200, the operation period of the storage battery 200, and the charge / discharge rate, but the present applicant has found that the cumulative discharge amount of the storage battery 200 and the operation period of the storage battery 200 are particularly important parameters. Note that the data in each of Figures 5A to 5D may be expressed as the degree of sludge formation of the active material relative to the discharged power amount (kWh) of the storage battery 200.

[0039] Therefore, by constructing a multiple regression model that uses one or more of the above-mentioned parameters as explanatory variables and predicts the degree of progress of the active material becoming muddy, it is possible to predict the degree of progress of the active material becoming muddy.

[0040] The graphs in Figures 6A to 6C show the amount of active material that fell off versus the aforementioned parameter 632. The vertical axis in each graph represents the amount of active material that fell off. The horizontal axis in Figure 6A represents the C rate, the horizontal axis in Figure 6B represents the temperature, and the horizontal axis in Figure 6C represents the SOC. The dots in each graph represent calculated values ​​for the amount of falling off, and the dashed lines represent values ​​obtained by fitting these.

[0041] FIG. 6A shows the amount of active material shed relative to the C-rate of the storage battery 200, i.e., the charge speed and discharge speed. FIG. 6A shows that the amount of active material shed tends to increase as the C-rate increases. This is thought to be because at high C-rates (especially high charge rates), the positive electrode potential increases during charging of the storage battery 200, making gas generation more likely. Thus, the applicant has discovered that the C-rate of the storage battery 200 is an important parameter for predicting the amount of active material shed. In particular, the applicant has discovered a previously unpredictable feature: the amount of active material shed tends to increase sharply once the C-rate of the storage battery 200 exceeds 0.2.

[0042] FIG. 6B shows the amount of active material shed with respect to the temperature of the storage battery 200. As can be seen from FIG. 6B, the amount of active material shed tends to increase as the temperature of the storage battery 200 decreases. This is thought to be because the charge acceptance of the storage battery 200 decreases at low temperatures, resulting in a higher positive electrode potential during charging, making gas generation more likely. There are several possible reasons why the charge acceptance of the storage battery 200 decreases at low temperatures. One reason is a decrease in the fluidity of the electrolyte. When the fluidity of the electrolyte decreases, the ionic conductivity of the electrolyte decreases, increasing the polarization resistance. Furthermore, from a reaction kinetics perspective, the lower the temperature, the slower the chemical reaction rate, making it more difficult for the charging reaction to occur. Another reason is the effect on the solubility of the active material. The charging reaction begins when lead sulfate, a discharge product, partially dissolves in the electrolyte. Therefore, if the solubility decreases at low temperatures, the charging reaction does not proceed as easily. Thus, the applicant has discovered that the temperature of the storage battery 200 is an important parameter for predicting the amount of active material shed. In particular, the applicant has discovered a previously unpredictable feature that the amount of active material falling off tends to increase as the temperature of the storage battery 200 decreases.

[0043] FIG. 6C shows the amount of active material shed relative to the SOC of the storage battery 200. As shown in FIG. 6C, the amount of active material shed is small when the SOC is in the range from approximately 0.4 to approximately 0.5. However, at low SOCs (e.g., 0.2 to 0.4) and high SOCs (e.g., 0.5 to 1.0), the amount of active material shed tends to increase rapidly as the SOC decreases or increases. The reason for the increase in the amount of active material shed even at low SOCs is thought to be that at low SOCs, the reaction area of ​​the electrodes decreases, making it easier for the potential to rise locally, thereby facilitating gas generation. Thus, the applicant has discovered that the SOC of the storage battery 200 is an important parameter for predicting the amount of active material shed. In particular, the applicant has discovered a previously unpredictable characteristic: the amount of active material shed tends to increase rapidly at low SOCs.

[0044] In this way, by evaluating the amount of active material falling off for multiple parameters (variables) and then constructing the specific model formula described above, for example by variable separation, it is possible to estimate and predict the amount of active material falling off (falling off rate) under any usage conditions (for example, charge rate, temperature, SOC).

[0045] An example of the rate of active material detachment calculated using a specific model formula is shown in Fig. 7. In the graph shown in Fig. 7, the vertical axis represents the rate of active material detachment (which may be interpreted as the amount of detachment), and the horizontal axis represents the charge rate.

[0046] The dashed line represents the drop speed when the temperature of the storage battery 200 is around 5° C. and the SOC is around 90%.

[0047] The solid line represents the drop rate when the temperature of the storage battery 200 is around 25° C. and the SOC is around 90%.

[0048] The dashed dotted line represents the drop rate when the temperature of the storage battery 200 is around 40° C. and the SOC is around 90%.

[0049] According to the present disclosure, as shown in Figure 7, even with the same charge rate and SOC, the lower the temperature of the storage battery 200, the higher the rate of active material detachment. For example, when the storage battery 200 mounted on a vehicle is continuously operated at a specific SOC and charge rate in an environment where the outside temperature tends to be high, the amount of active material detachment from the storage battery 200 tends to be low, and it is possible to predict that the life of the storage battery 200 will be relatively long. In contrast, even with the storage battery 200 operated at the same SOC and charge rate, when the storage battery 200 is continuously operated (charged and discharged) in an environment where the outside temperature is below 10°C, the amount of active material detachment from the storage battery 200 will increase significantly, and the life product rate of the storage battery 200 will increase sharply, and it is possible to predict that the storage battery 200 is likely to reach the end of its life sooner.

[0050] As described above, this tendency coincides with conditions in which gas is likely to be generated, in other words, when the rate of gas generation tends to be high, specifically, when the temperature is low, when the SOC is high, when the charge rate is high, etc. Therefore, it can be said that gas generation has a significant impact on the life of the storage battery 200, that is, the impact of gas generation is dominant.

[0051] The identification processing unit 610 of the present disclosure is configured to calculate feature quantities (e.g., the amount of dropout, the dropout speed, etc.) that affect the life of the storage battery 200, using parameters 632 related to the state of the storage battery 200, that is, the temperature, SOC, C rate, etc., which are the usage conditions of the storage battery 200, in order to predict the life of the storage battery 200. As shown in FIG. 8 , the identification processing unit 610 may include an acquisition unit 611, a calculation unit 612, an update unit 613, and an output unit 614.

[0052] 2 . The parameters 632 may include at least one of the temperature, charge rate, and SOC of the storage battery 200. In addition to these, the parameters 632 indicating the state of the storage battery 200 may include the current flowing when the storage battery 200 is charged or discharged, the charge / discharge time of the storage battery 200, the voltage generated when the storage battery 200 is charged or discharged, the accumulated discharge amount of the storage battery 200 (e.g., the discharge amount over a specific period such as several days, several months, one year, or several years), the specific gravity of the storage battery 200, etc.

[0053] (Calculation unit 612) The calculation unit 612 may calculate a feature amount that affects the lifespan of the storage battery 200 based on a specification process using the parameters 632. The calculation unit 612 may calculate the feature amount by combining two or more of the multiple parameters 632. The calculation unit 612 may calculate the feature amount by combining one or more first parameters (e.g., the accumulated discharge amount, operating period, average discharge amount, amount of fallen active material, and falling speed of the storage battery 200) caused by the active material becoming muddy and one or more second parameters (parameters 632) caused by the falling of the active material.

[0054] Specifically, the feature amount may be calculated based on a specific model formula including the parameters 632. By using the specific model formula including the parameters 632, the feature amount can be calculated automatically, and further, even when various parameters 632 are used, the feature amount can be calculated quickly.

[0055] The feature quantity may include at least one of a feature quantity related to the sludge formation of the electrodes of the storage battery 200, a feature quantity related to the detachment of active material from the electrodes of the storage battery 200, and a feature quantity related to gas generation in the storage battery 200. The feature quantity related to the sludge formation may be interpreted as the progress of the sludge formation, the progress rate of the sludge formation, the degree of decrease in the bonding strength between the active materials, etc. The detachment of the active material may be interpreted as the amount of active material detached from the positive electrode, the detachment rate of the active material detached from the positive electrode, the integrated amount of active material detached from the positive electrode, the amount of active material leaking from the positive electrode, the leakage rate of the active material leaking from the positive electrode, the integrated amount of active material leaked from the positive electrode, the amount of gas generated that causes the detachment of the active material, the amount of gas generated that causes the leakage of the active material, the rate of generation of the gas, etc. By combining the features related to the sludge formation, the features related to the detachment of active material, and the features related to the generation of gas to predict the lifespan of a storage battery, it is possible to predict the lifespan more accurately.

[0056] Based on the above-described characteristic quantities, the calculation unit 612 may predict the lifespan of the storage battery 200. For example, when the calculation unit 612 calculates the drop speed based on the temperature, SOC, charge rate, etc. as shown in Fig. 7 , the calculation unit 612 may predict the lifespan of the storage battery 200 by reading out the value of the lifespan corresponding to the drop speed by referring to specific first table information stored in the storage 63, etc.

[0057] (Update Unit 613) The update unit 613 may update the prediction model formula described above based on historical data of the parameters 632. FIG. 12 is a diagram illustrating the process of updating the prediction model formula by the update unit 613. In step S31, the update unit 613 may continuously save and accumulate parameter data (historical data of the parameters 632) and data on feature quantities to be predicted. In step S32, the update unit 613 may periodically analyze the accumulated parameter data. In step S33, the update unit 613 may periodically evaluate the existing prediction model formula using newly analyzed historical data to confirm the accuracy of the prediction model formula. In step S34, the update unit 613 may update the prediction model formula as necessary based on new data and analysis results to improve prediction accuracy.

[0058] (Output Unit 614) The output unit 614 may calculate use condition information by a specification process using the feature amount calculated by the calculation unit 612 and output the calculated use condition information. The use condition information may be interpreted as information indicating use conditions of the storage battery 200 for suppressing a decrease in the lifespan of the storage battery 200. Specifically, the use condition information may be interpreted as information indicating use conditions of the storage battery 200 for suppressing the detachment of active material from the storage battery 200, or as information indicating use conditions of the storage battery 200 for suppressing the generation of the gas described above. For example, as shown in FIG. 7 , even when the ambient temperature of the storage battery 200 tends to be low, such as 5°C, the lifespan of the storage battery 200 tends to be extended by lowering the charge rate. Therefore, the output unit 614 calculates use conditions that are advantageous for suppressing the detachment of active material. Specifically, the output unit 614 refers to specific second table information stored in the storage 63 or the like, and reads (calculates) as the use condition information a charge rate (0.1 to 0.2) at which the drop rate, which is an example of the feature calculated by the calculation unit 612, becomes a low value, such as 0.25 to 0.5 mg / s, when the temperature is, for example, 5° C. The output unit 614 may record the calculated use condition information in the storage 63 or the like in association with the feature at the time of calculating the message, and may simultaneously transmit the calculated use condition information to, for example, a user terminal or the like.

[0059] The output unit 614 may perform a specification process using the feature calculated by the calculation unit 612 to predict the occurrence of an internal short circuit in the storage battery 200 under specific usage conditions, and output prediction information regarding the predicted occurrence of an internal short circuit. For example, as shown in FIG. 7 , even with the same SOC and charge rate, if the temperature of the storage battery 200 tends to be low, such as 5°C, the risk of an internal short circuit, and therefore the lifespan of the storage battery 200, is shorter than when the temperature is high. Therefore, the output unit 614 calculates prediction information indicating when an internal short circuit is likely to occur under certain usage conditions. Specifically, by referencing specific third table information stored in the storage 63, for example, if the drop rate is 2 mg / s when the average temperature is 5°C, the average charge rate is 0.4, and the SOC is 90%, the output unit 614 reads (calculates) the short circuit occurrence timing, the degree of occurrence of an internal short circuit, the probability of occurrence of an internal short circuit, and other information corresponding to the drop rate. The output unit 614 may associate the calculated prediction information with the feature amount at the time the prediction was performed, record the information in the storage 63 or the like, and simultaneously transmit the information to, for example, a user terminal.

[0060] 9 is a flowchart for explaining a first operation of the data processing device 40. In step S1, the data processing device 40 acquires parameters 632 related to the state of the storage battery 200. In step S2, the data processing device 40 calculates feature quantities that affect the life of the storage battery 200. In step S3, the data processing device 40 predicts the life of the storage battery 200 based on the calculated feature quantities.

[0061] 10 is a flowchart for explaining a second operation of the data processing device 40. In step S11, the data processing device 40 acquires parameters 632 related to the state of the storage battery 200. In step S12, the data processing device 40 calculates feature quantities that affect the life of the storage battery 200. In step S13, the data processing device 40 calculates usage condition information that indicates the usage conditions of the storage battery 200 by a specification process using the feature quantities. In step S14, the data processing device 40 outputs the calculated usage condition information.

[0062] 11 is a flowchart illustrating a third operation of the data processing device 40. In step S21, the data processing device 40 acquires parameters 632 related to the state of the storage battery 200. In step S22, the data processing device 40 calculates feature quantities that will affect the life of the storage battery 200. In step S23, the data processing device 40 predicts the occurrence of an internal short circuit in the storage battery 200 due to the detachment of active material from the storage battery 200 under specific usage conditions, based on the calculated feature quantities. In step S24, the data processing device 40 outputs prediction information related to the predicted occurrence of an internal short circuit.

[0063] As described above, the data processing method according to the embodiment of the present disclosure can calculate feature quantities that affect the lifespan of the storage battery 200 based on a specification process using the parameters 632 related to the state of the storage battery 200. This makes it possible to use the feature quantities to calculate, for example, the amount of active material lost from the storage battery 200 under any usage conditions. It is also possible to predict an internal short circuit in a lead-acid battery or the like caused by the loss of active material. It is also possible to provide a user with usage conditions that are effective in suppressing the loss of active material.

[0064] The above describes an embodiment of the present disclosure with reference to the accompanying drawings. However, it is clear that a person with ordinary knowledge in the field of technology to which the present disclosure pertains can conceive of various modifications or applications within the scope of the technical ideas set forth in the claims, and it is understood that these also naturally fall within the technical scope of the present disclosure.

[0065] The following notes are provided regarding the technology of the present disclosure.

[0066] (Supplementary Note 1) A data processing method in which a processor executes a process including: acquiring parameters related to the state of a storage battery; and calculating a feature amount that affects the lifespan of the storage battery based on a specific process using the parameters.

[0067] (Supplementary Note 2) The data processing method according to Supplementary Note 1, wherein the feature amount includes at least one of a feature amount related to sludge formation of electrodes of the storage battery, a feature amount related to detachment of active material from electrodes of the storage battery, and a feature amount related to gas generation of the storage battery.

[0068] (Supplementary Note 3) The data processing method according to Supplementary Note 1 or 2, wherein the processing further includes predicting a lifespan of the storage battery based on the feature amount.

[0069] (Supplementary Note 4) The data processing method according to any one of Supplementary Notes 1 to 3, wherein the parameters include at least one of a temperature, a charge rate, and an SOC of the storage battery.

[0070] (Supplementary Note 5) The data processing method according to any one of Supplementary Notes 1 to 4, further comprising calculating the feature amount based on a specific model formula including the parameter.

[0071] (Supplementary Note 6) The data processing method according to Supplementary Note 5, further comprising updating the model formula based on historical data of the parameters.

[0072] (Supplementary Note 7) A program that causes a processor to execute a process including: acquiring parameters related to a state of a storage battery; and calculating a feature that affects the lifespan of the storage battery based on a specific process using the parameters.

[0073] (Supplementary Note 8) A data processing device comprising: an acquisition unit that acquires parameters related to a state of a storage battery; and a calculation unit that calculates a feature amount that affects a lifespan of the storage battery based on a specification process using the parameters.

[0074] (Supplementary Note 9) A battery management method in which a processor executes a process including: acquiring parameters related to the state of a storage battery; calculating a feature amount that affects the lifespan of the storage battery based on a specification process using the parameters; calculating, by the specification process using the feature amount, usage condition information indicating usage conditions of the storage battery that suppress a decrease in the lifespan of the storage battery, and outputting the calculated usage condition information.

[0075] (Supplementary Note 10) A battery management method in which a processor executes processes including: acquiring parameters related to the state of a storage battery; calculating feature quantities that affect the lifespan of the storage battery based on a specification process using the parameters; and predicting the occurrence of an internal short circuit in the storage battery under specific usage conditions by the specification process using the feature quantities, and outputting prediction information related to the predicted occurrence of the internal short circuit.

[0076] (Supplementary Note 11) A battery management system comprising: a parameter acquisition device that acquires parameters related to a state of a storage battery; and a data processing device that inputs the parameters transmitted from the parameter acquisition device and calculates feature quantities that affect the lifespan of the storage battery based on a specification process using the input parameters.

[0077] The disclosure of Japanese Patent Application No. 2024-151511, filed on September 3, 2024, is incorporated herein by reference in its entirety.

[0078] REFERENCE SIGNS LIST 10 Measuring device 21 Input / output unit 22 Communication I / F 23 Control unit 24 Measuring unit 30 Computer 31 Input / output interface 32 CPU 33 RAM 34 ROM 35 Control bus 36 Program 40 Data processing device 50 Database 51 Communication I / F 60 Computer 61 Processor 62 RAM 63 Storage 64 Bus 100 Battery management system 200 Storage battery 300 Network 610 Specification processing unit 611 Acquisition unit 612 Calculation unit 613 Update unit 614 Output unit 631 Specification processing program 632 Parameter

Claims

1. A data processing method in which a processor executes a process including: acquiring parameters related to the state of a storage battery; and calculating features that affect the lifespan of the storage battery based on a specific process using the parameters.

2. The data processing method according to claim 1, wherein the feature quantity includes at least one of a feature quantity related to the sludge formation of the electrodes of the storage battery, a feature quantity related to the detachment of active material from the electrodes of the storage battery, and a feature quantity related to the generation of gas in the storage battery.

3. The data processing method according to claim 1, wherein the processing further includes predicting a lifespan of the storage battery based on the feature amount.

4. The data processing method according to claim 1, wherein the parameters include at least one of the temperature, charge rate, and SOC of the storage battery.

5. The data processing method according to claim 1, further comprising calculating the feature amount based on a specific model formula including the parameter.

6. The data processing method according to claim 5, further comprising updating said model formula based on historical data of said parameters.

7. A program that causes a processor to execute a process including: acquiring parameters related to the state of a storage battery; and calculating features that affect the lifespan of the storage battery based on a specific process using the parameters.

8. A data processing device comprising: an acquisition unit that acquires parameters related to the state of a storage battery; and a calculation unit that calculates a feature that affects the life of the storage battery based on a specification process using the parameters.

9. A battery management method in which a processor executes processes including: acquiring parameters related to the state of a storage battery; calculating feature values ​​that affect the lifespan of the storage battery based on a specification process using the parameters; calculating usage condition information indicating usage conditions of the storage battery that suppress a decrease in the lifespan of the storage battery through a specification process using the feature values; and outputting the calculated usage condition information.

10. A battery management method in which a processor executes processes including: acquiring parameters related to the state of a storage battery; calculating features that affect the lifespan of the storage battery based on a specific process using the parameters; predicting the occurrence of an internal short circuit in the storage battery under specific usage conditions through the specific process using the features; and outputting prediction information related to the predicted occurrence of the internal short circuit.

11. A battery management system comprising: a parameter acquisition device that acquires parameters related to the state of a storage battery; and a data processing device that inputs the parameters transmitted from the parameter acquisition device and calculates features that affect the lifespan of the storage battery based on a specific process using the input parameters.

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