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

Abstract

This disclosure aims to provide a data processing method, program, data processing device, battery management method, and battery management system that can predict short circuits inside a battery. [Solution] The data processing method involves a processor performing a process that includes acquiring parameters related to the state of the storage battery and calculating feature quantities that affect the lifespan of the storage battery based on specific processing using the parameters.

Description

Technical Field

[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.

Background Art

[0002] In Patent Document 1, in order to prevent power supply interruption, a method of estimating the degree of deterioration of a lead-acid battery is disclosed by using any one of the deterioration factors of corrosion of the positive electrode grid, sludging of the positive electrode material, negative electrode sulfation, and shrinkage of the negative electrode material.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Here, when the charge and discharge of the storage battery are repeated, the positive electrode active material of the storage battery becomes muddy, the muddy positive electrode active material falls off from the positive electrode, and the fallen positive electrode active material can short-circuit the positive electrode and the negative electrode. Therefore, even when the degree of deterioration of the storage battery is small, the storage battery may stop functioning at an unintended timing. That is, even though the degree of deterioration of the capacity of the storage battery (specifically, State of Health: SOH) shows a high value, if the above-mentioned short circuit occurs inside the storage battery, the deterioration of the storage battery proceeds rapidly, and the storage battery may reach the end of its life. The conventional technology only estimates the degree of deterioration of the lead-acid battery by using any one of the corrosion of the positive electrode grid, the sludging of the positive electrode material, the negative electrode sulfation, and the shrinkage of the negative electrode material, and there is room for improvement in predicting a short circuit inside 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. [Means for solving the problem]

[0006] The data processing method of the first embodiment includes a processor performing a process that includes acquiring parameters related to the state of the storage battery and calculating feature quantities that affect the lifespan of the storage battery based on a specific process using the parameters.

[0007] The program of the second embodiment causes the processor to perform a process that includes obtaining parameters related to the state of the storage battery and calculating feature quantities that affect the lifespan of the storage battery based on specific processing using the parameters.

[0008] The data processing device of the third embodiment includes an acquisition unit that acquires parameters related to the state of the storage battery, The system includes a calculation unit that calculates feature quantities that affect the lifespan of the storage battery based on a specific process using the aforementioned parameters.

[0009] The fourth embodiment of the battery management method includes a processor executing a process that includes acquiring parameters relating to the state of a storage battery, calculating feature quantities that affect the lifespan of the storage battery based on a specific process using the parameters, calculating usage condition information indicating the usage conditions of the storage battery that suppress the deterioration of the storage battery's lifespan through a specific process using the feature quantities, and outputting the calculated usage condition information.

[0010] The fifth embodiment of the battery management method includes a processor executing a process that includes acquiring parameters relating to the state of a storage battery, calculating feature quantities 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 operating conditions by a specific process using the feature quantities, and outputting prediction information regarding the predicted occurrence of an internal short circuit.

[0011] The sixth embodiment of the battery management system comprises a parameter acquisition device that acquires parameters relating to the state of a storage battery, and a data processing device that receives the parameters transmitted from the parameter acquisition device and calculates feature quantities that affect the lifespan of the storage battery based on specific processing using the input parameters. [Effects of the Invention]

[0012] According to this disclosure, a data processing method, a program, a data processing device, a battery management method, and a battery management system are provided that can predict short circuits inside a battery. [Brief explanation of the drawing]

[0013] [Figure 1] Figure 1 shows an example configuration of the battery management system 100 according to an embodiment of the present disclosure. [Figure 2] Figure 2 shows an example of the main functions of the data processing device 40 according to the embodiment of this disclosure. [Figure 3A] Figure 3A is a diagram illustrating the mechanism by which internal short circuits occur. [Figure 3B] Figure 3B is a diagram illustrating the mechanism by which internal short circuits occur. [Figure 3C] Figure 3C is a diagram illustrating the mechanism by which internal short circuits occur. [Figure 3D] Figure 3D is a diagram illustrating the mechanism by which internal short circuits occur. [Figure 4] Figure 4 shows the relationship between the lifespan rate and the number of years elapsed for storage battery 200. [Figure 5A] Figure 5A shows the relationship between the degree of muddying of the active material and the cumulative discharge amount. [Figure 5B] Figure 5B shows the relationship between the degree of muddying of the active material and the cumulative discharge amount. [Figure 5C] Figure 5C shows the relationship between the degree of muddying of the active material and the cumulative discharge amount. [Figure 5D] Figure 5D shows the relationship between the degree of muddying of the active material and the cumulative discharge amount. [Figure 6A] FIG. 6A is a diagram showing the relationship between the amount of active material dropout and the C rate. [Figure 6B] FIG. 6B is a diagram showing the relationship between the amount of active material dropout and the temperature. [Figure 6C] FIG. 6C is a diagram showing the relationship between the amount of active material dropout and the SOC. [Figure 7] FIG. 7 is a diagram showing an example of the dropout rate of the active material calculated using a specific model formula. [Figure 8] FIG. 8 is a diagram showing a configuration example of the specific processing unit �10. [Figure 9] FIG. 9 is a flowchart for explaining the first operation example of the data processing device 40. [Figure 10] FIG. 10 is a flowchart for explaining the second operation example of the data processing device 40. [Figure 11] FIG. 11 is a flowchart for explaining the third operation example of the data processing device 40. [Figure 12] FIG. 12 is a diagram for explaining the update process of the prediction model formula by the update unit �13.

MODE FOR CARRYING OUT THE INVENTION

[0014] Hereinafter, an example of an embodiment of this disclosure will be described with reference to the drawings. In each drawing, the same or equivalent components and parts are given the same reference numerals. Also, the dimensional ratios in the drawings are exaggerated for illustrative purposes and may differ from the actual ratios. In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit). In the following embodiments, the communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to a communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0016] (Measuring device 10) The measuring device 10 can be interpreted as a device that measures parameters indicating the state of the storage battery 200. Details of these parameters will be described later.

[0017] The measuring device 10 may include a computer 30, an input / output unit 21, a communication interface 22, a control unit 23, and a measurement unit 24. The computer 30 may have an input / output interface 31, a CPU 32, RAM (Random Access Memory) 33, and 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 various parts. 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 operation 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 executing the program 36, the CPU 32 realizes various functions that control the various parts of the measuring device 10.

[0019] The input / output unit 21 may be interpreted as a user interface that receives user instructions, notifies the CPU 32 of the computer 30 of the received instructions, and then presents information to the user according to the instructions of the CPU 32. The input / output unit 21 may perform at least one of the following: 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] Communication I / F22 may be interpreted as a device that communicates with other devices via network 300. Network 300 may be interpreted as a WAN (Wide Area Network) and / or LAN (Local Area Network), etc.

[0021] The control unit 23 may be controlled by the computer 30 to control the voltage, current, discharge time, charging time, etc., during the charging and discharging of the storage battery 200.

[0022] The measurement unit 24 includes a sensor for measuring the state of the storage battery 200, and may transmit parameters indicating 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 interface 51. The computer 60 may include a processor 61, RAM 62, and storage 63. The processor 61, RAM 62, and storage 63 may be connected to a bus 64. The database 50 and the communication interface 51 may also be connected to the bus 64. The communication interface 51 may be connected to a network 300.

[0024] Figure 2 shows an example of the main functions of the data processing device 40. In the data processing device 40, a specific processing may be performed by the processor 61. The storage 63 may store a specific processing program 631 and parameters 632 indicating the state of the battery 200. The specific processing program 631 is an example of a "program" in this disclosure. The processor 61 may read the specific processing program 631 from the storage 63 and execute the read specific processing program 631 on the RAM 62. The specific processing may be realized by the processor 61 operating as a specific processing unit 610 according to the specific processing program 631 executed on the RAM 62. Details of the configuration of the specific processing unit 610 will be described later.

[0025] Next, we will explain the prerequisites and challenges for performing the specific processing by the specific processing unit 610, and then describe a specific example of the specific processing.

[0026] (Prerequisites and Issues) This section explains the factors that shorten the lifespan of a lead-acid battery, which is an example of a battery 200. (1) As the cumulative discharge amount and average discharge amount of the lead-acid battery increase, the positive electrode active material of the lead-acid battery becomes muddy. (2) During charging of the lead-acid battery, gas is generated at the positive electrode due to the decomposition of the electrolyte, caused by the State of Charge (SOC), temperature, and charge rate of the lead-acid battery. (3) Along with the generated gas, the muddy positive electrode active material leaks out from the positive electrode. The positive electrode and negative electrode can short-circuit (internal short circuit) due to the detached positive electrode active material, which can shorten the lifespan of the lead-acid battery. In the following, the positive electrode active material may be simply referred to as the active material.

[0027] The mechanism by which this internal short circuit occurs will be explained in detail with reference to Figures 3A to 3D.

[0028] Figure 3A shows the process of the active material turning into a muddy substance and the muddy active material flowing out of the positive electrode. Specifically, (1) In the initial state of a lead-acid battery, multiple active material (β-PbO2) particles are bound together by the amorphous phase. Therefore, the active material particles do not easily flow even when subjected to the flow of the electrolyte. The initial state can be interpreted as the state immediately after the lead-acid battery is manufactured, or the state in which the manufactured lead-acid battery has had little charge and discharge. (2) As the lead-acid battery is charged and discharged, dissolution and extraction are repeated, and the amorphous phase changes into active material (β-PbO2), gradually weakening the bonds between multiple active material particles. Therefore, some of the active material particles begin to flow when subjected to the flow of the electrolyte. (3) As the lead-acid battery is further charged and discharged, most of the amorphous phase changes into active material, and the bonds between multiple active material particles disappear. As a result, the active material particles fall off when subjected to the flow of the electrolyte, that is, the active material particles flow out of the positive electrode.

[0029] Figure 3B shows how the muddy active material is pushed out of the positive electrode due to the influence of H2O generated during discharge. Specifically, as shown in Figure 3B, the discharge reaction of a lead-acid battery generates H2O at the positive electrode, and the pores contract 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-PbO2)a, whose binding force has been reduced due to muddying, leaks out of the positive electrode as indicated by the arrows in the figure.

[0030] Figure 3C shows how the muddy active material is pushed away from the positive electrode by the gas (O2)b generated during charging. The standard electrode potential of the positive electrode of a lead-acid battery is a potential higher than the decomposition reaction of H2O, so especially during charging, depending on the electrode state or charging conditions, oxygen gas may be generated due to the decomposition of the electrolyte. When gas b is generated due to the decomposition of the electrolyte in this way, the electrolyte in the positive electrode pores and retainer is pushed out from the positive electrode, and along with it, the active material a is detached from the positive electrode.

[0031] Figure 3D shows how the positive and negative electrodes are short-circuited by active material a, which detaches from the positive electrode during charging and discharging. When the positive and negative electrodes are short-circuited by active material a in this way, the lead-acid battery may reach the end of its lifespan prematurely, even if the degree of degradation of the lead-acid battery is small, that is, even if the degree of degradation of the lead-acid battery capacity (specifically, SOH) is at a high value such as 93 or 90.

[0032] In investigating usage conditions that are advantageous for effectively suppressing the shedding of muddy active material, the applicant focused on parameter 632 related to the state of the storage battery 200 and found a method for calculating characteristic quantities that affect the lifespan of the storage battery 200, or in other words, a method for predicting the occurrence of short circuits inside the battery.

[0033] More specifically, based on simulations showing that gas is the dominant factor in active material shedding, as mentioned above, we investigated a method for estimating gas generation based on actual operational data. Based on the results of this investigation, we constructed a specific model equation for predicting the lifespan of the battery 200. This specific model equation can be interpreted as a specific model equation that includes parameter 632. This model equation can be interpreted, for example, as a model that assumes that the lifespan of the battery 200, caused by short circuits due to active material shedding after the active material has become muddy, occurs in a stochastic process.

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

[0035] In the graph shown in Figure 4, the vertical axis represents the lifespan rate, and the horizontal axis represents time (elapsed years). As shown in Figure 4, the lifespan rate of the battery 200 remains approximately constant with respect to the elapsed years until the sludgy active material leaks out. However, after the active material has turned sludgy, if the battery 200 is repeatedly charged and discharged, the degree of the effect of active material shedding, that is, the amount of active material that can short-circuit the positive and negative electrodes, increases rapidly, and the lifespan rate of the battery 200 can also increase rapidly.

[0036] The lifespan percentage can be interpreted as the ratio of the number of cells that have reached the end of their lifespan to the total number of cells contained in the battery 200. Cells that have reached the end of their lifespan can be interpreted as cells that cannot be charged or discharged despite showing a high degree of degradation in the battery 200's capacity (SOH) due to the internal short circuit described above, or cells where the ratio of the current full charge capacity to the expected SOH is low. SOH indicates the degree of health or degradation and can 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 contained in the battery 200 is 6 and the number of cells that have reached the end of their lifespan is 2, then the lifespan percentage of the battery 200 is 33%.

[0037] Furthermore, the percentage of batteries that have reached the end of their lifespan can be interpreted as the ratio of the number of batteries that have reached the end of their lifespan to the total number of batteries 200. Batteries that have reached the end of their lifespan can be interpreted as batteries that cannot be charged or discharged despite showing a high degree of capacity degradation (SOH) due to an internal short circuit as described above, batteries that cannot obtain the desired voltage during discharge, or batteries that have a low ratio of current full charge capacity to the expected SOH. For example, if the total number of batteries 200 is 10 and the number of batteries that have reached the end of their lifespan is 30%, then the percentage of batteries that have reached the end of their lifespan is 30%.

[0038] The graphs in Figures 5A to 5D show the degree of active material sludge formation measured in the operational battery 200. Specifically, these show the degree of active material sludge formation in relation to the cumulative discharge amount of battery 200 operating at four different sites (regions). In each figure, the vertical axis represents the degree of active material sludge formation, and the horizontal axis represents the cumulative discharge amount. The points in the figures represent measured values, and the solid lines represent predicted values. As can be seen from these figures, sludge formation progresses rapidly up to, for example, an cumulative discharge amount of around 500 [kAh], and thereafter the progression of sludge formation becomes more moderate or remains constant. By using these results, that is, the degree of active material sludge formation in relation to the cumulative discharge amount of battery 200, as a sludge formation model, the degree of sludge formation can be predicted. The parameters important for predicting the progression of muddying of the active material (muddying) include various elements such as the rated capacity of the battery 200, the cumulative discharge amount of the battery 200, the operating period of the battery 200, and the charge / discharge rate. However, the applicant has found that the cumulative discharge amount of the battery 200 and the operating period of the battery 200 are particularly important parameters. The data in Figures 5A to 5D may also be expressed as the progression of muddying of the active material in relation to the discharge power amount kWh of the battery 200.

[0039] Therefore, by constructing a multiple regression model that uses one or more of the aforementioned parameters as explanatory variables to predict the progression of muddying of the active material, it is possible to predict the progression of muddying of the active material.

[0040] The graphs in Figures 6A to 6C show the amount of active material shedding relative to the parameter 632 mentioned above. The vertical axis in each figure represents the amount of active material shedding. 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 points in each figure represent the calculated values ​​of shedding, and the dashed lines represent the fitted values.

[0041] Figure 6A shows the C-rate of the battery 200, i.e., the amount of active material lost in relation to the charging and discharging speeds. Figure 6A shows that the amount of active material lost tends to increase as the C-rate increases. This is thought to be because, at high C-rates (especially high charging rates), the positive electrode potential becomes higher during charging of the battery 200, making it easier for gas to be generated. Thus, the applicant has found that the C-rate of the battery 200 is an important parameter for predicting the amount of active material lost. In particular, the applicant has found a previously unpredictable characteristic: the amount of active material lost shows a sharp increase when the C-rate of the battery 200 exceeds around 0.2.

[0042] Figure 6B shows the amount of active material lost from the battery 200 with respect to temperature. Figure 6B shows that the amount of active material lost tends to increase as the temperature of the battery 200 decreases. This is thought to be because, at low temperatures, the charge acceptance of the battery 200 decreases, causing the positive electrode potential to increase during charging, which makes it easier for gas to be generated. There are several possible reasons why the charge acceptance of the battery 200 is poor at low temperatures. One reason is that the fluidity of the electrolyte decreases. When the fluidity of the electrolyte decreases, the ionic conductivity of the electrolyte decreases, and the polarization resistance increases. Also, from a reaction kinetics perspective, the rate of chemical reactions slows down at lower temperatures, so it is thought that the charging reaction becomes less likely to occur. Another reason is the effect on the solubility of the active material. The starting point of the charging reaction is the partial dissolution of lead sulfate, a discharge product, in the electrolyte, so if the solubility decreases at low temperatures, the charging reaction becomes less likely to proceed. Thus, the applicant has found that the temperature of the battery 200 is an important parameter in predicting the amount of active material lost. In particular, the applicant discovered a previously unpredictable characteristic: the amount of active material detached tends to increase as the temperature of the battery 200 decreases.

[0043] Figure 6C shows the amount of active material shedding in the battery 200 with respect to its State of Charge (SOC). According to Figure 6C, in the range of SOC from around 0.4 to around 0.5, the amount of active material shedding is small, while at low SOC (e.g., 0.2-0.4) and high SOC (e.g., 0.5-1.0), the amount of active material shedding tends to increase sharply as the SOC decreases or increases. The increase in active material shedding even at low SOC is thought to be because at low SOC, the reaction area of ​​the electrodes decreases, making it easier for the potential to rise locally, thus making it easier for gas to be generated. Thus, the applicant has found that the SOC of the battery 200 is an important parameter for predicting the amount of active material shedding. In particular, the applicant has found a previously unpredictable characteristic: that the amount of active material shedding tends to increase sharply at low SOC.

[0044] In this way, by evaluating the amount of active material shedding for multiple parameters (variables), and then constructing the aforementioned specific model equation using, for example, variable separation, it is possible to estimate and predict the amount (shedding rate) of active material shedding under arbitrary operating conditions (e.g., charge rate, temperature, SOC).

[0045] Figure 7 shows an example of the active material shedding rate calculated using a specific model equation. In the graph shown in Figure 7, the vertical axis represents the active material shedding rate (which can be interpreted as the amount of shedding), and the horizontal axis represents the charging rate.

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

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

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

[0049] According to this disclosure, as shown in Figure 7, even with the same charge rate and SOC, the rate of active material shedding tends to increase as the temperature of the battery 200 decreases. For example, if a battery 200 installed in a vehicle is continuously operated in an environment with a tendency for high ambient temperatures at a specific SOC and charge rate, the amount of active material shedding from the battery 200 tends to be low, and therefore, it can be predicted that the lifespan of the battery 200 will be relatively long. In contrast, even if a battery 200 is operated at the same SOC and charge rate, if it is continuously operated (charged and discharged) in an environment where the ambient temperature is below 10°C, the amount of active material shedding from the battery 200 will increase significantly, and it can be predicted that the lifespan of the battery 200 will increase sharply, meaning that there is a high possibility that the battery 200 will reach the end of its lifespan prematurely.

[0050] As mentioned earlier, this trend is consistent with conditions that make gas generation more likely, or in other words, conditions that increase the rate of gas generation, specifically such as low temperatures, high SOC (State of Charge), and high charge rates. Therefore, it can be said that gas generation has a significant impact on the lifespan of the battery 200; in other words, the effect of gas generation is dominant.

[0051] The identification processing unit 610 of this disclosure is configured to calculate characteristic quantities that affect the lifespan of the battery 200 (e.g., depletion amount, depletion rate, etc.) using parameters 632 related to the state of the battery 200, i.e., temperature, SOC, C rate, etc., which are the operating conditions of the battery 200, in order to predict the lifespan of the battery 200. As shown in Figure 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] (Acquisition part 611) The acquisition unit 611 may acquire the parameter 632 and store the acquired parameter 632 in the storage 63 shown in Figure 2. The parameter 632 may include at least one of the temperature, charge rate, and state of charge (SOC) of the battery 200. In addition to these, the parameter 632 indicating the state of the battery 200 may also include the current that flows when the battery 200 is charged and discharged, the charge and discharge time of the battery 200, the voltage generated when the battery 200 is charged and discharged, the cumulative discharge amount of the battery 200 (for example, the discharge amount over a specific period such as several days, several months, one year, or several years), and the specific gravity of the battery 200.

[0053] (Calculation section 612) The calculation unit 612 may calculate a feature that affects the lifespan of the battery 200 based on a specific process using the parameter 632. The calculation unit 612 may calculate the feature by combining two or more of the multiple parameters 632. The calculation unit 612 may calculate the feature by combining one or more first parameters (e.g., cumulative discharge amount of the battery 200, operating period, average discharge amount, amount of active material detachment, detachment rate, etc.) caused by the muddying of the active material with one or more second parameters (parameter 632) caused by the detachment of the active material.

[0054] Specifically, features may be calculated based on a particular model equation that includes parameter 632. By using a specific model equation that includes parameter 632, features can be calculated automatically, and even when various parameters 632 are used, features can be calculated quickly.

[0055] The feature quantities may include at least one of the following: a feature quantity related to the muddying of the electrodes of the battery 200, a feature quantity related to the shedding of active material from the electrodes of the battery 200, and a feature quantity related to the generation of gas in the battery 200. The feature quantity related to muddying may be interpreted as the degree of muddying progression, the rate of muddying progression, the degree of decrease in the bonding force between active materials, etc. The shedding of active material may be interpreted as the amount of active material shedding from the positive electrode, the rate of shedding of active material that has detached from the positive electrode, the cumulative amount of active material that has detached from the positive electrode, the amount of active material leaking out from the positive electrode, the rate of leakage of active material that has leaked out from the positive electrode, the cumulative amount of active material that has leaked out from the positive electrode, the amount of gas generated that causes the shedding of active material, the amount of gas generated that causes the leakage of active material, the rate of said gas generation, etc. By combining features related to muddying, features related to the shedding of active material, and features related to gas generation, the lifespan of a battery can be predicted with greater accuracy.

[0056] The calculation unit 612 may predict the lifespan of the battery 200 based on the above-mentioned features. For example, if the calculation unit 612 calculates the deactivation rate based on temperature, SOC, charge rate, etc., as shown in Figure 7, it may predict the lifespan of the battery 200 by referring to specific first table information stored in storage 63 or the like to read out the lifespan value corresponding to the deactivation rate.

[0057] (Update section 613) The update unit 613 may update the aforementioned prediction model formula based on the historical data of parameter 632. Figure 12 is a diagram illustrating the update process of 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 parameter 632) and data of the feature 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 the newly analyzed historical data and confirm the accuracy of the prediction model formula. In step S34, the update unit 613 may update the prediction model formula to improve prediction accuracy as needed, based on new data and analysis results.

[0058] (Output section 614) The output unit 614 may calculate usage condition information by performing a specific processing using the feature quantities calculated by the calculation unit 612, and output the calculated usage condition information. The usage condition information may be interpreted as information indicating the usage conditions of the battery 200 in order to suppress the reduction in the lifespan of the battery 200. Specifically, the usage condition information may be interpreted as information indicating the usage conditions of the battery 200 in order to suppress the shedding of the active material of the battery 200, or as information indicating the usage conditions of the battery 200 in order to suppress the generation of the aforementioned gas. For example, as shown in Figure 7, even when the ambient temperature around the battery 200 tends to be low, such as 5°C, the lifespan of the battery 200 tends to be extended by lowering the charge rate. Therefore, the output unit 614 calculates usage conditions that are advantageous for suppressing the shedding of the active material. Specifically, the output unit 614 refers to specific second table information stored in storage 63 or the like, and reads out (calculates) as usage condition information a charging rate (0.1 to 0.2) at which the drop rate, an example of a feature calculated by the calculation unit 612, becomes a low value such as 0.25 to 0.5 [mg / s] when the temperature is 5°C. The output unit 614 associates the calculated usage condition information with the feature at the time the message was calculated and records it in storage 63 or the like, and at the same time may transmit it to, for example, a user terminal.

[0059] The output unit 614 may predict the occurrence of an internal short circuit in the battery 200 under specific operating conditions by performing specific processing using the feature quantities calculated by the calculation unit 612, and output prediction information regarding the predicted occurrence of the internal short circuit. For example, as shown in Figure 7, even with the same SOC and charge rate, if the temperature of the battery 200 tends to be low, such as 5°C, the risk of an internal short circuit, i.e., the lifespan of the battery 200, will be shorter than at high temperatures. Therefore, the output unit 614 calculates prediction information indicating when an internal short circuit is likely to occur under arbitrary operating conditions. Specifically, the output unit 614 refers to specific third table information stored in storage 63 or the like, and for example, if the depletion rate is 2 [mg / s] when the average temperature is 5°C, the average charge rate is 0.4, and the SOC is 90%, it reads out (calculates) prediction information such as the timing of the short circuit, the degree of the internal short circuit, and the probability of the internal short circuit occurring, corresponding to this depletion rate. The output unit 614 may record the calculated prediction information in storage 63 or the like, associating it with the feature quantities at the time the prediction was performed, and simultaneously transmit it to, for example, a user terminal.

[0060] Figure 9 is a flowchart illustrating the 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 battery 200. In step S2, the data processing device 40 calculates features that affect the lifespan of the battery 200. In step S3, the data processing device 40 predicts the lifespan of the battery 200 based on the calculated features.

[0061] Figure 10 is a flowchart illustrating the 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 battery 200. In step S12, the data processing device 40 calculates feature quantities that affect the lifespan of the battery 200. In step S13, the data processing device 40 calculates usage condition information indicating the usage conditions of the battery 200 through specific processing using the feature quantities. In step S14, the data processing device 40 outputs the calculated usage condition information.

[0062] Figure 11 is a flowchart illustrating the 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 battery 200. In step S22, the data processing device 40 calculates feature quantities that affect the lifespan of the battery 200. In step S23, based on the calculated feature quantities, the data processing device 40 predicts the occurrence of an internal short circuit in the battery 200 due to the shedding of the active material under specific operating conditions. In step S24, the data processing device 40 outputs prediction information regarding the predicted occurrence of the internal short circuit.

[0063] As described above, the data processing method according to the embodiment of this disclosure can calculate feature quantities that affect the lifespan of the battery 200 based on a specific process using a parameter 632 related to the state of the battery 200. This allows for the calculation of, for example, the amount of active material loss in the battery 200 under arbitrary operating conditions. Furthermore, it can predict internal short circuits in lead-acid batteries and the like caused by active material loss. It can also provide users with operating conditions that are effective in suppressing active material loss.

[0064] While embodiments of this disclosure have been described above with reference to the attached drawings, it is clear that any person with ordinary skill in the art to which this disclosure belongs could conceive of various modifications or applications within the scope of the technical idea described in the claims, and these too are naturally understood to fall within the technical scope of this disclosure.

[0065] The following additional information is disclosed regarding the technology described herein.

[0066] (Note 1) To obtain parameters related to the battery status, Based on a specific process using the aforementioned parameters, the characteristic quantities that affect the lifespan of the storage battery are calculated. A data processing method in which the processor performs processing that includes [specific actions].

[0067] (Note 2) The data processing method according to Appendix 1, wherein the feature quantity includes at least one of the following: a feature quantity related to the muddying of the electrodes of the battery; a feature quantity related to the shedding of active material from the electrodes of the battery; and a feature quantity related to gas generation in the battery.

[0068] (Note 3) The data processing method according to Appendix 1 or 2, further comprising predicting the lifespan of the storage battery based on the features.

[0069] (Note 4) The data processing method described in any one of Appendix 1 to 3, wherein the parameters include at least one of the temperature, charge rate, and state of charge of the storage battery.

[0070] (Note 5) A data processing method according to any one of the appendices 1 to 4, comprising calculating the feature quantities based on a specific model formula including the aforementioned parameters.

[0071] (Note 6) The data processing method described in Appendix 5, which includes updating the model equation based on historical data of the parameters.

[0072] (Note 7) To obtain parameters related to the battery status, Based on a specific process using the aforementioned parameters, the characteristic quantities that affect the lifespan of the storage battery are calculated. A program that causes the processor to execute a process that includes [a specific element / task].

[0073] (Note 8) An acquisition unit that acquires parameters related to the state of the storage battery, A calculation unit that calculates feature quantities that affect the lifespan of the storage battery based on a specific process using the aforementioned parameters, A data processing device equipped with the following features.

[0074] (Note 9) To obtain parameters related to the battery status, Based on a specific process using the aforementioned parameters, the characteristic quantities that affect the lifespan of the storage battery are calculated. By performing a specific processing using the aforementioned feature quantities, usage condition information indicating the usage conditions of the storage battery that suppress the reduction in the storage battery's lifespan is calculated, and the calculated usage condition information is output. A battery management method in which the processor performs processing that includes [specific actions].

[0075] (Note 10) To obtain parameters related to the battery status, Based on a specific process using the aforementioned parameters, the characteristic quantities that affect the lifespan of the storage battery are calculated. By performing a specific processing using the aforementioned feature quantities, the occurrence of an internal short circuit in the storage battery under specific operating conditions is predicted, and prediction information regarding the predicted occurrence of the internal short circuit is output. A battery management method in which the processor performs processing that includes [specific actions].

[0076] (Note 11) A parameter acquisition device that acquires parameters related to the state of the storage battery, A data processing device that receives the parameters transmitted from the parameter acquisition device and calculates feature quantities that affect the lifespan of the storage battery based on a specific process using the input parameters, A battery management system equipped with this feature. [Explanation of symbols]

[0077] 10 Measuring device 21 Input / output section 22 Communication I / F 23 Control Unit 24 Measurement Unit 30 Computers 31 Input / Output Interfaces 32 CPU 33 RAM 34 ROM 35 Control bus 36 Programs 40 Data Processing Devices 50 Databases 51 Communication I / F 60 Computer 61 processors 62 RAM 63 storage 64 bus 100 Battery Management System 200 Battery 300 Networks 610 Specific Processing Unit 611 Acquisition Department 612 Calculation Unit 613 Update Department 614 Output section 631 Specific Processing Program 632 parameters

Claims

1. To obtain parameters related to the battery status, Based on a specific process using the aforementioned parameters, the characteristic quantities that affect the lifespan of the storage battery are calculated. A data processing method in which the processor performs processing that includes [specific actions].

2. The data processing method according to claim 1, wherein the feature quantity includes at least one of the following: a feature quantity related to the muddying of the electrodes of the battery; a feature quantity related to the shedding of active material from the electrodes of the battery; and a feature quantity related to gas generation in the battery.

3. The data processing method according to claim 1, further comprising predicting the lifespan of the storage battery based on the features.

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

5. The data processing method according to claim 1, comprising calculating the feature quantities based on a specific model equation including the aforementioned parameters.

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

7. To obtain parameters related to the battery status, Based on a specific process using the aforementioned parameters, the characteristic quantities that affect the lifespan of the storage battery are calculated. A program that causes the processor to execute a process that includes [a specific element / task].

8. An acquisition unit that acquires parameters related to the state of the storage battery, A calculation unit that calculates feature quantities that affect the lifespan of the storage battery based on a specific process using the aforementioned parameters, A data processing device equipped with the following features.

9. To obtain parameters related to the battery status, Based on a specific process using the aforementioned parameters, the characteristic quantities that affect the lifespan of the storage battery are calculated. By performing a specific processing using the aforementioned feature quantities, usage condition information indicating the usage conditions of the storage battery that suppress the reduction in the storage battery's lifespan is calculated, and the calculated usage condition information is output. A battery management method in which the processor performs processing that includes [specific actions].

10. To obtain parameters related to the battery status, Based on a specific process using the aforementioned parameters, the characteristic quantities that affect the lifespan of the storage battery are calculated. By performing a specific processing using the aforementioned feature quantities, the occurrence of an internal short circuit in the storage battery under specific operating conditions is predicted, and prediction information regarding the predicted occurrence of the internal short circuit is output. A battery management method in which the processor performs processing that includes [specific actions].

11. A parameter acquisition device that acquires parameters related to the state of the storage battery, A data processing device that receives the parameters transmitted from the parameter acquisition device and calculates feature quantities that affect the lifespan of the storage battery based on a specific process using the input parameters, A battery management system equipped with this feature.

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