JP7388103B2 - Estimation device, estimation method and computer program - Google Patents

Description

The present disclosure relates to an estimation device, an estimation method, and a computer program for estimating the risk of drying up a lead-acid battery.

A lead-acid battery includes a positive plate, a negative plate, and a case containing an electrolyte. The positive electrode plate and the negative electrode plate are immersed in the electrolyte in the case. Lead-acid batteries are used for automotive, industrial, and other applications. For example, an in-vehicle lead-acid battery is installed in a vehicle and supplies power to in-vehicle equipment (electrical loads) such as lighting and a car stereo. A lead-acid battery is charged with electric power generated by a generator (alternator) included in a vehicle. For example, industrial lead-acid batteries are used as a power source for emergency power supplies.

Patent Document 1 describes a power source that includes a lead-acid battery that supplies power to an electrical load of a vehicle, an acquisition device that acquires the temperature of the lead-acid battery, and a determination device that determines deterioration of the lead-acid battery based on the acquired temperature. system is disclosed.

Japanese Patent Application Publication No. 2019-78571

The aforementioned power supply system can determine deterioration of the lead-acid battery based on the obtained temperature of the lead-acid battery. The amount of electrolyte in a lead-acid battery decreases through repeated discharging and charging. A lead-acid battery that is in a depleted state where the amount of electrolyte is less than a certain amount is affected by effects such as a decrease in capacity. Conventional technology has a problem in that it is not possible to estimate whether or not there is a possibility that liquid drying up will occur (hereinafter also referred to as liquid drying risk).

The present disclosure aims to provide an estimation device, an estimation method, and a computer program for estimating the risk of liquid drying up.

An estimation device according to the present disclosure includes an acquisition unit that acquires a current, a voltage, and a temperature of a lead-acid battery, the acquired current, the voltage, and the temperature, and the acquired current, the voltage, and a data storage unit that stores a first history of the results calculated from the temperature, and a second history that stores at least a part of the stored first history, and the current, voltage, and temperature of the lead acid battery. a specifying unit that identifies the amount of electrolyte of the lead-acid battery based on the relationship between at least a portion of the amount of electrolyte and the amount of electrolyte; and a determination unit that estimates a risk of liquid drying up of the lead-acid battery based on the specified amount of electrolyte. and an estimation section.

An estimation device according to the present disclosure includes an acquisition unit that acquires a current, a voltage, and a temperature of a lead-acid battery, the acquired current, the voltage, and the temperature, and the acquired current, the voltage, and a data storage unit that stores a history of results calculated from the temperature, and a learning model that outputs the risk of drying up of the lead-acid battery when at least a part of the history is input. and an estimator that inputs at least a portion of the history to estimate a risk of drying up the lead acid battery.

An estimation device according to the present disclosure includes an acquisition unit that acquires the amount of electrolyte of a lead-acid battery, and an estimation unit that estimates a risk of drying up the lead-acid battery based on the acquired amount of liquid.

The estimation method according to the present disclosure acquires the current, voltage, and temperature of a lead-acid battery, and calculates the acquired current, voltage, and temperature, and the acquired current, voltage, and temperature. A first history of the results calculated from is accumulated, at least a part of the accumulated first history, at least a part of a second history formed by accumulating current, voltage, and temperature of the lead-acid battery, and electrolysis. The amount of electrolytic solution of the lead acid battery is specified based on the relationship with the amount of liquid, and the risk of liquid depletion of the lead acid battery is estimated based on the specified amount of electrolyte solution.

A computer program according to the present disclosure acquires a current, a voltage, and a temperature of a lead-acid battery, and acquires the acquired current, voltage, and temperature, and acquires the acquired current, voltage, and temperature. A first history of the results calculated from is accumulated, at least a part of the accumulated first history, at least a part of a second history formed by accumulating current, voltage, and temperature of the lead-acid battery, and electrolysis. A computer is caused to execute a process of specifying the amount of electrolyte of the lead acid battery based on the relationship with the amount of liquid and estimating the risk of drying up of the lead acid battery based on the specified amount of electrolyte.

According to the present disclosure, it is possible to estimate the risk of liquid drying up.

FIG. 1 is a block diagram showing the configuration of a vehicle according to a first embodiment. FIG. 2 is a perspective view showing the external configuration of a battery. 3 is a sectional view taken along line III-III in FIG. 2. FIG. FIG. 2 is a block diagram showing the configuration of a BMU. It is a conceptual diagram which shows the example of the content of a history. It is a flowchart which shows the procedure of the drying-up risk estimation process performed by the control part of BMU. It is a schematic diagram of the graph which shows the relationship between the amount of overcharge and the amount of decrease in electrolyte solution. It is a schematic diagram of a learning model. FIG. 3 is a conceptual diagram showing an example of the contents of teacher data. FIG. 2 is a block diagram showing the configuration of a BMU according to a second embodiment. It is a schematic diagram of a learning model. FIG. 3 is a conceptual diagram showing an example of the contents of teacher data. It is a flowchart which shows the procedure of the drying-up risk estimation process performed by the control part of BMU. FIG. 2 is a block diagram showing the configuration of a vehicle according to a third embodiment. It is a flowchart which shows the procedure of the drying-up risk estimation process performed by the control part of BMU.

(Summary of this embodiment)
The estimation device according to the present embodiment includes an acquisition unit that acquires the current, voltage, and temperature of a lead-acid battery, and the acquired current, voltage, and temperature, and the acquired current and voltage. , a data storage unit that stores a first history of the results calculated from the temperature, and a second data storage unit that stores at least a part of the stored first history, and a current, voltage, and temperature of the lead-acid battery. a specifying unit that identifies the amount of electrolyte of the lead-acid battery based on a relationship between at least a part of the history and the amount of electrolyte; and an estimation of a risk of liquid depletion of the lead-acid battery based on the specified amount of electrolyte. and an estimating unit.

The electrolyte of lead-acid batteries contains dilute sulfuric acid. Water in dilute sulfuric acid is electrolyzed by charging and discharging a lead acid battery. Water in dilute sulfuric acid tends to evaporate when the temperature of the lead-acid battery is high. Due to electrolysis and evaporation of water in dilute sulfuric acid, the amount of electrolyte in a lead-acid battery decreases. According to the above configuration, the identification unit identifies the amount of electrolyte based on the relationship between at least a portion of the accumulated first history and at least a portion of the second history and the amount of electrolyte. For example, when the specified amount of electrolytic solution is below a certain value, the estimating unit can estimate that there is a risk of liquid drying up.

In the above-mentioned estimating device, the specifying unit adds the first history accumulated by the data accumulation unit to a learning model that outputs the amount of electrolyte of the lead acid battery when at least a part of the first history is input. The electrolyte amount may be specified by inputting at least a portion of the amount of the electrolyte.

According to the above configuration, by inputting at least a portion of the first history, the amount of electrolyte of the lead-acid battery is outputted by the learning model. The amount of electrolyte in a lead-acid battery can be determined with high accuracy.

The estimation device according to the present embodiment includes an acquisition unit that acquires the current, voltage, and temperature of a lead-acid battery, and the acquired current, voltage, and temperature, and the acquired current and voltage. and a data storage unit that stores a history of results calculated from the temperature, and a learning model that outputs the risk of drying up of the lead-acid battery when at least a part of the history is input. and an estimating unit that inputs at least a part of the history and estimates a risk of drying up the lead-acid battery.

According to the above configuration, by inputting at least a part of the history of the lead-acid battery, the risk of drying up the lead-acid battery is outputted by the learning model. It is possible to accurately estimate the risk of lead acid battery drying up.

The estimation device according to the present embodiment includes an acquisition unit that acquires the amount of electrolyte of the lead-acid battery, and an estimation unit that estimates the risk of drying up the lead-acid battery based on the acquired amount of liquid.

According to the above configuration, it is possible to accurately estimate the risk of drying up the lead acid battery based on the amount of electrolyte acquired by the acquisition unit.

The above-mentioned estimating device may include a notification section that notifies that the lead-acid battery will run out of fluid according to the risk of running out of fluid estimated by the estimation section.

According to the above configuration, when there is a risk of battery drying up, the notification unit notifies the user of the lead-acid battery that battery drying up will occur. For example, when the lead acid battery is installed in a vehicle, the user of the lead acid battery is the driver of the vehicle. Users of lead-acid batteries can take countermeasures such as replenishing the electrolyte before the battery runs out.

The estimation method according to the present embodiment acquires the current, voltage, and temperature of a lead-acid battery, and calculates the acquired current, voltage, and temperature, as well as the acquired current, voltage, and temperature. Accumulating a first history of results calculated from temperature, at least a part of the accumulated first history, and at least a part of a second history formed by accumulating current, voltage, and temperature of the lead-acid battery; The amount of electrolyte in the lead-acid battery is specified based on the relationship with the amount of electrolyte, and the risk of drying up the lead-acid battery is estimated based on the specified amount of electrolyte.

According to the above configuration, the amount of electrolyte is specified based on the relationship between at least a portion of the accumulated first history and at least a portion of the second history and the amount of electrolyte, and for example, the specified amount of electrolyte is a constant value. In the following cases, it can be assumed that there is a risk of liquid drying up.

The computer program according to the present embodiment acquires the current, voltage, and temperature of a lead-acid battery, and acquires the acquired current, voltage, and temperature; Accumulating a first history of results calculated from temperature, at least a part of the accumulated first history, and at least a part of a second history formed by accumulating current, voltage, and temperature of the lead-acid battery; A computer is caused to execute a process of specifying the amount of electrolyte of the lead-acid battery based on the relationship with the amount of electrolyte and estimating the risk of drying up of the lead-acid battery based on the specified amount of electrolyte.

(First embodiment)
Embodiments of the present invention will be described below based on the drawings. A case will be explained in which the risk of battery drying up of a lead-acid battery installed in a vehicle is estimated. FIG. 1 is a block diagram showing the configuration of a vehicle 1 according to the first embodiment. The vehicle 1 includes a control device 10, an on-vehicle device 11, an engine 12, a generator (alternator) 13, a lead acid battery (hereinafter referred to as a battery) 2, and a BMU (Battery Management Unit) 3. Vehicle 1 is capable of communicating with external server 8 via external network 9 . The in-vehicle equipment 11 is, for example, lighting, a navigation system, and a car stereo. The on-vehicle device 11 is connected to the battery 2. The generator 13 generates electricity by driving the engine 12. Generator 13 is connected to battery 2 .

The control device 10 controls the entire vehicle 1 and includes a control section 101, a storage section 102, a communication section 103, and an operation section 104. The control device 10 is connected to the BMU 3. The external server 8 includes a control section 81 and a communication section 82. The control unit 101 of the control device 10 is connected to the control unit 81 of the external server 8 via the communication unit 103, the external network 9, and the communication unit 82. For example, when the vehicle 1 is equipped with a navigation system having a touch panel, the operation unit 104 is a touch panel.

The control unit 101 and the control unit 81 are configured of, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), etc., and are connected to the control device 10 and external Controls the operation of the server 8. The storage unit 102 is configured with, for example, a nonvolatile semiconductor memory or a hard disk drive (HDD), and stores various programs and data. The communication unit 103, the communication unit 82, and the communication unit 34 included in the BMU 3 (see FIG. 4) have the function of communicating with other devices via the network, and are capable of transmitting and receiving required information. It can be carried out.

2 is a perspective view showing the external structure of the battery 2, and FIG. 3 is a sectional view taken along the line III--III in FIG. As shown in FIGS. 2 and 3, the battery 2 includes a container 20, a positive terminal 28, a negative terminal 29, and a plurality of electrode plate groups 23.

The battery case 20 has a battery case body 201 and a lid 202. The battery case body 201 is a rectangular parallelepiped-shaped container with an open top, and is made of, for example, synthetic resin. For example, the lid 202 made of synthetic resin closes the opening of the battery case body 201. The peripheral edge of the lower surface of the lid 202 and the peripheral edge of the opening of the battery case body 201 are joined by, for example, thermal welding. The space inside the battery case 20 is partitioned by a partition wall 27 into a plurality of cell chambers 21 arranged in the longitudinal direction of the battery case 20 .

Each cell chamber 21 in the battery case 20 accommodates one electrode plate group 23 . Each cell chamber 21 in the battery case 20 contains an electrolytic solution 22 containing dilute sulfuric acid, and the entire electrode plate group 23 is immersed in the electrolytic solution 22. The electrolytic solution 22 is injected into the cell chamber 21 from an inlet (not shown) provided in the lid 202 .

The electrode plate group 23 includes a plurality of positive electrode plates 231, a plurality of negative electrode plates 235, and a separator 239. The plurality of positive electrode plates 231 and the plurality of negative electrode plates 235 are arranged alternately.

The positive electrode plate 231 includes a positive electrode lattice 232 and a positive electrode material 234 supported by the positive electrode lattice 232. The positive electrode lattice 232 is a conductive member having ribs arranged in a substantially lattice-like or mesh-like shape, and is made of, for example, lead or a lead alloy. The positive electrode grating 232 has ears 233 protruding upward near the upper end. Positive electrode material 234 includes lead dioxide. The positive electrode material 234 may further include known additives.

Negative plate 235 includes negative electrode grid 236 and negative electrode material 238 supported by negative electrode grid 236 . The negative electrode grid 236 is a conductive member having ribs arranged in a substantially lattice-like or mesh-like shape, and is made of, for example, lead or a lead alloy. Negative electrode grating 236 has ears 237 near the upper end that protrude upward. Negative electrode material 238 includes lead. Negative electrode material 238 may further include known additives.

The separator 239 is made of an insulating material such as glass or synthetic resin. The separator 239 is interposed between the positive electrode plate 231 and the negative electrode plate 235 that are adjacent to each other. The separator 239 may be configured as an integral member, or may be provided separately between the positive electrode plate 231 and the negative electrode plate 235. The separator 239 may be arranged to enclose either the positive electrode plate 231 or the negative electrode plate 235.

The ears 233 of the plurality of positive electrode plates 231 are connected to a strap 24 made of, for example, lead or a lead alloy. The plurality of positive electrode plates 231 are electrically connected in parallel via straps 24. Similarly, the ears 237 of the plurality of negative electrode plates 235 are connected to a strap 25 made of, for example, lead or a lead alloy. The plurality of negative electrode plates 235 are electrically connected via straps 25.

In the battery 2, the strap 25 in one cell chamber 21 is connected to the strap 24 in one cell chamber 21 adjacent to the one cell chamber 21 via an intermediate pole 26 made of lead or a lead alloy, for example. It is connected. Further, the strap 24 in the one cell chamber 21 is connected to the strap 25 in the other cell chamber 21 adjacent to the one cell chamber 21 via an intermediate pole 26. That is, the plurality of electrode plate groups 23 of the battery 2 are electrically connected in series via the straps 24 and 25 and the intermediate pole 26. As shown in FIG. 3, the strap 24 accommodated in the cell chamber 21 located at one longitudinal end of the battery case 20 is connected not to the intermediate pole 26 but to a positive pole 282, which will be described later. The strap 25 accommodated in the cell chamber 21 located at the other longitudinal end of the battery case 20 is connected not to the intermediate pole 26 but to a negative pole (not shown).

The positive electrode terminal 28 is arranged at one end in the longitudinal direction of the battery case 20, and the negative electrode terminal 29 is arranged near the other end in the longitudinal direction of the battery case 20.

As shown in FIG. 3, the positive electrode terminal 28 includes a bushing 281 and a positive pole 282. The bushing 281 is a substantially cylindrical conductive member, and is made of, for example, a lead alloy. The lower part of the bushing 281 is integrated with the lid 202 by insert molding, and the upper part of the bushing 281 projects upward from the upper surface of the lid 202. The positive electrode column 282 is a substantially cylindrical conductive member, and is made of, for example, a lead alloy. The positive pole 282 is inserted into the hole of the bushing 281. The upper end of the positive pole 282 is located at approximately the same position as the upper end of the bushing 281, and is joined to the bushing 281 by, for example, welding. The lower end of the positive electrode column 282 protrudes below the lower end of the bushing 281 and further below the lower surface of the lid 202, and is housed in the cell chamber 21 located at one longitudinal end of the battery case 20. The strap 24 is connected to the Like the positive terminal 28, the negative terminal 29 includes a bushing 291 and a negative pole 292 (see FIG. 2), and has the same configuration as the positive terminal 28.

The on-vehicle device 11 is connected to the bushing 281 of the positive terminal 28 and the bushing 291 of the negative terminal 29. Power is supplied from the battery 2 to the on-vehicle equipment 11 . That is, the battery 2 is discharged. The generator 13 is connected to the bushing 281 of the positive terminal 28 and the bushing 291 of the negative terminal 29. The battery 2 is charged by power supplied from the generator 13 to the battery 2.

As shown in FIG. 1, the vehicle 1 includes a voltage sensor 5, a current sensor 6, and a temperature sensor 7. The voltage sensor 5 is connected in parallel to the battery 2 and outputs a detection result according to the overall voltage of the battery 2. Current sensor 6 is connected to battery 2 in series. The current sensor 6 outputs a detection result corresponding to the current flowing from the generator 13 to the battery 2 (charging current) and a detection result corresponding to the current flowing from the battery 2 to the on-vehicle device 11 (discharging current). The temperature sensor 7 is placed, for example, near the battery 2 and outputs a detection result according to the temperature of the battery 2.

FIG. 4 is a block diagram showing the configuration of the BMU 3. BMU3 includes a control section 31, a storage section 32, an input section 33, and a communication section 34. These units are communicably connected to each other via a bus. The input unit 33 receives input of detection results from the voltage sensor 5 , current sensor 6 , and temperature sensor 7 . The communication unit 34 communicates with the control device 10 and other devices such as the external server 8.

The storage unit 32 is composed of, for example, a nonvolatile semiconductor memory or an HDD, and stores various programs and data. For example, the storage unit 32 stores (stores) a program 321 for executing a drying-up risk estimation process, which will be described later. The program 321 stored in the storage unit 32 may be a program 321 read from a recording medium 322 readable by the BMU 3. A history 323 is stored in the storage unit 32. FIG. 5 is a conceptual diagram showing an example of the contents of the history 323. Although the details will be described later, the history 323 includes the charging current, discharging current, voltage, and temperature of the battery 2 that the control unit 31 acquired in the battery drying up risk estimation process that will be described later, and the acquisition time. The history 323 further includes the results calculated by the control unit 31 in the liquid drying risk estimation process described below, such as the amount of charged electricity, the amount of discharged electricity, the overcharge amount, the total value of the overcharge amount, and the average value of temperature, which will be described later. (average temperature), the amount of decrease in the electrolyte 22, the integrated value of the amount of decrease, and the amount of electrolyte. The history 323 corresponds to the first history.

The control unit 31 includes, for example, a CPU, a GPU, a ROM, a RAM, and the like, and controls the operation of the BMU 3 by executing a computer program such as the program 321 read from the storage unit 32. For example, the control unit 31 reads and executes the program 321, thereby functioning as a processing unit that executes a drying-up risk estimation process, which will be described later.

The control unit 31 obtains the charging current and the discharging current from the current sensor 6 via the input unit 33. The control unit 31 obtains the voltage of the battery 2 from the voltage sensor 5 via the input unit 33 . The control unit 31 acquires the temperature of the battery 2 from the temperature sensor 7. The control unit 31 performs calculations on the acquired charging current, discharging current, and temperature. Although details will be described later, the control unit 31 calculates the amount of charging electricity and the amount of discharging electricity, for example, from the acquired charging current and discharging current. The acquired charging current, discharging current, temperature, and calculation results are accumulated in the storage unit 32 as a history 323.

Since the electrolyte 22 of the battery 2 contains dilute sulfuric acid, water contained in the dilute sulfuric acid is electrolyzed by charging and discharging the battery 2. Water contained in dilute sulfuric acid tends to evaporate when the temperature of the battery 2 is high. Electrolysis or evaporation of water in dilute sulfuric acid reduces the amount of electrolyte in lead-acid batteries. The control unit 31 specifies the amount of electrolyte in the battery 2 based on at least a portion of the accumulated history 323 and the relationship between at least a portion of the history 323 and the amount of electrolyte. A method for specifying the amount of electrolyte in the battery 2 will be described later.

The control unit 31 estimates the risk of battery 2 running out based on the specified amount of electrolyte 22 . For example, when the specified amount of electrolytic solution 22 is below a predetermined value, the control unit 31 estimates that there is a risk of liquid drying up (liquid drying up may occur). When the control unit 31 estimates that there is a risk of liquid drying up, it notifies the person inside the vehicle 1, for example, the driver of the vehicle 1, that there is a risk of liquid drying up.

BMU3 functions as an estimation device. The BMU 3 functions as an acquisition section, a data storage section, a specification section, an estimation section, and a notification section. The control device 10 or the external server 8 may function as the estimation device. When the external server 8 does not function as an estimation device, the external server 8 does not need to be connected to the control device 10.

FIG. 6 is a flowchart showing the procedure of the liquid drying risk estimation process performed by the control unit 31 of the BMU 3. Hereinafter, step will be abbreviated as S. For example, when the ignition switch (not shown) of the vehicle 1 is turned on, the control unit 31 executes the following processing according to the program 321.

The control unit 31 acquires the charging current and discharging current of the battery 2, the voltage of the battery 2, and the temperature of the battery 2 at the same time at a constant period, for example, every minute (S1). In FIG. 6, the charging current and the discharging current are collectively expressed as current. The acquired charging current and discharging current of the battery 2, voltage of the battery 2, and temperature of the battery 2 are stored in the storage unit 32 as a history 323 in association with the acquired time.

The control unit 31 calculates the amount of electricity charged and the amount of electricity discharged from the battery 2 from the acquired charging current and discharging current (S2). For example, when the period for acquiring the charging current and the discharging current is every 1 minute (1/60 hour), the control unit 31 adds the acquired charging current [A] and discharging current [A] to the acquisition period 1/60 [ h], the amount of charged electricity [Ah] and the amount of discharged electricity [Ah] are calculated. The calculated amount of charging electricity and the amount of discharging electricity are stored in the storage unit 32 as a history 323 in association with the time when the charging current and discharging current used in the calculation were acquired.

The control unit 31 calculates the difference between the calculated amount of charged electricity and the calculated amount of discharged electricity (amount of charged electricity - amount of discharged electricity) (S3). Hereinafter, the difference between the amount of charged electricity and the amount of discharged electricity will be referred to as an overcharge amount. The calculated amount of overcharge is stored in the storage unit 32 as a history 323 in association with the time when the amount of charged electricity and the amount of discharged electricity used for the calculation were acquired. As shown in FIG. 5, the amount of charged electricity, the amount of discharged electricity, the overcharged amount, the voltage, and the temperature are sequentially accumulated in the history 323 every minute.

The control unit 31 specifies the amount of electrolyte in the battery 2 based on at least a portion of the accumulated history 323 and the relationship between at least a portion of the history 323 and the amount of electrolyte (S4). An example of a method by which the control unit 31 specifies the amount of the electrolytic solution 22 will be described below. In the history 323, the initial amount of electrolyte in the battery 2 is recorded. In FIG. 5, the initial liquid amount is the electrolytic liquid amount at acquisition time 0, and is 500 g. For example, when the battery 2 is mounted on the vehicle 1, the initial fluid amount is input to the BMU 3 via the communication unit 34 by operating the operation unit 104, and is recorded in the history 323.

The storage unit 32 stores relational data 324. The relational data 324 is data representing the relationship between the amount of overcharge and the amount of decrease in the electrolytic solution 22, and is determined in advance through experiments. The relational data 324 is input to the BMU 3 via the communication unit 34 and stored in the storage unit 32. FIG. 7 is a schematic diagram of a graph showing the relationship between the amount of overcharge and the amount of decrease in the electrolytic solution 22. The horizontal axis of the graph indicates the overcharge amount [Ah], and the vertical axis indicates the decrease amount [g] of the electrolytic solution 22. Obtaining multiple data on the amount of overcharging when charging current and discharging current flow through the battery 2 at a certain temperature for a certain period of time, for example, 30 minutes, and the amount of decrease in the electrolytic solution 22 over a certain period of time through an experiment. As a result, the graph in FIG. 7 is obtained. In FIG. 7, graphs are shown for temperatures of 25°C, 50°C, and 75°C. When the amount of overcharge is large, the amount of decrease in the electrolytic solution 22 is large. When the temperature is high, the amount of electrolyte 22 decreases is large. When the amount of overcharge is large and the temperature is high, the amount of decrease in electrolyte 22 is larger. From the data acquired in the above experiment, for example, by the least squares method, the amount of decrease in the electrolyte 22 with respect to the amount of overcharge when charging current and discharging current flow through the battery 2 at a certain temperature for a certain period of time is shown. Functions can be found. The function is obtained for a plurality of temperatures, for example, 25° C., 50° C., and 75° C., and is recorded in the storage unit 32 as relational data 324. The relational data 324 is, for example, a data table showing, for each temperature, the relationship between the amount of overcharge when charging current and discharging current flow through the battery 2 for a certain period of time, and the amount of decrease in the electrolytic solution 22 for a certain period of time. Good too. Among the data acquired in the experiment, data excluding the amount of decrease in the electrolytic solution 22 corresponds to the second history.

The control unit 31 calculates a total value (integrated value) of the overcharge amount accumulated in the history 323 over a certain period of time, for example, every 30 minutes. The calculated total value of the overcharge amount for each fixed period of time is sequentially accumulated in the storage unit 32 as a history 323. The control unit 31 calculates the average value of the temperatures accumulated in the history 323 at regular intervals. The average value of the calculated temperature for each fixed period of time is sequentially accumulated in the storage unit 32 as a history 323. The control unit 31 calculates the amount of decrease in the electrolytic solution 22 based on the calculated total value of the overcharge amount, the average value of the temperature, and the function recorded in the relational data 324. The amount of decrease in the electrolytic solution 22 is calculated at regular intervals and sequentially stored in the storage unit 32 as a history 323. The control unit 31 integrates the amount of decrease in the electrolytic solution 22 calculated at regular time intervals, and sequentially stores the accumulated value (accumulated value of the decrease amount) in the storage unit 32 as a history 323. By subtracting the integrated value of the amount of decrease from the initial amount of electrolyte 22, the amount of electrolyte in battery 2 can be determined. The specified amount of electrolytic solution is stored in the storage unit 32 as a history 323.

The control unit 31 estimates the risk of battery 2 running out of liquid based on the specified amount of electrolyte (S5). When the specified amount of electrolytic solution is larger than a predetermined value, for example 400 g, the control unit 31 estimates that there is no risk of liquid depletion (there is no possibility that liquid depletion will occur). When the control unit 31 estimates that there is no risk of liquid drying up (S6: NO), the control unit 31 performs the process of S1. When the specified amount of electrolytic solution is less than or equal to a predetermined value, the control unit 31 estimates that there is a risk of liquid drying up (liquid drying up may occur). When the control unit 31 estimates that there is a risk of liquid drying up (S6: YES), it notifies the person inside the vehicle 1 that there is a risk of liquid drying up (S7), and ends the process. For example, the control unit 31 causes a message such as "The electrolyte may run out. Please replenish the electrolyte." to be displayed on the display of the navigation system included in the vehicle 1 via the communication unit 34. The message is stored in the storage unit 32, for example. The message may be included in the program 321.

When the electrolytic solution 22 is rehydrated, for example, the person who rehydrated the electrolytic solution 22 inputs to the BMU 3 via the communication unit 34 a message that the electrolyte 22 has been rehydrated by operating the operation unit 104 . For example, if the navigation system is equipped with a touch panel, the person who rehydrated presses a "rehydration complete" button displayed on the touch panel. Furthermore, the person who refilled the water inputs the amount of water refilled (water refill amount) into the BMU 3 by operating the operation unit 104. The control unit 31 of the BMU 3 updates the initial liquid amount in the history 323 to the sum of the input water replenishment amount and the latest electrolytic solution amount in the history 323. The control unit 31 of the BMU 3 deletes the data in the history 323 except for the initial liquid amount.

Alternatively, the control unit 31 may estimate the amount of water replenishment from changes in voltage. The decrease in the electrolytic solution 22 is caused by a decrease in dilute sulfuric acid water contained in the electrolytic solution 22. When the electrolytic solution 22 decreases, the specific gravity (concentration of dilute sulfuric acid) of the electrolytic solution 22 changes. The voltage of the battery 2 changes as the specific gravity of the electrolytic solution 22 changes, that is, the proportion of water contained in the electrolytic solution 22 changes. Since the amount of liquid in the battery 2 changes significantly before and after refilling water, the control unit 31 determines that refilling has been performed if the difference between the obtained voltage and the voltage obtained immediately before is equal to or greater than a predetermined value. judge. The relationship between the amount of water replenishment and the amount of change in the voltage of the battery 2, for example, a function, is determined in advance through experiments and is stored in the storage unit 32. When determining that water replenishment has been performed, the control unit 31 determines the amount of water replenishment using the amount of change between the acquired voltage and the previously acquired voltage, and the relationship between the amount of water replenishment and the amount of change in the voltage of the battery 2. Estimate. The control unit 31 updates the initial liquid amount in the history 323 to the sum of the estimated water replenishment amount and the electrolyte amount at the time corresponding to the voltage acquired immediately before the history 323. The control unit 31 deletes the data in the history 323 except for the initial liquid amount.

Alternatively, the control unit 31 may specify the amount of electrolyte in the battery 2 using the learning model 325 in S4. When at least part of the history 323, such as overcharge amount, voltage, and temperature, is input, the learning model 325 outputs a plurality of electrolyte amounts and the probability that the battery 2 has the electrolyte amounts. For example, a plurality of electrolyte amounts, such as a probability that the electrolyte amount is 475 g and a probability that the electrolyte amount is 450 g, and a probability that the battery 2 has the electrolyte amount are output. The learning model 325 can use, for example, a multilayer convolutional neural network (CNN) learned by deep learning. The learning model 325 may use a neural network other than CNN, for example, a recurrent neural network (RNN). The learning model 325 may be one learned by other machine learning.

FIG. 8 is a schematic diagram of the learning model 325. The learning model 325 includes an intermediate layer between the input layer and the output layer. The intermediate layer includes a convolution layer and a pooling layer consisting of multiple stages, and a fully connected layer at the final stage. The numbers of convolution layers, pooling layers, and fully connected layers can be determined as appropriate. One or more nodes exist in each of the input layer, intermediate layer, and output layer. The nodes of each layer are connected in one direction with the nodes existing in the previous and subsequent layers with desired weights and biases. The data input to each node of the input layer is input to the first intermediate layer. In this middle class,
An output is calculated using an activation function that includes weights and biases. The calculated output is input to the next intermediate layer. In the same manner, the output of the output layer is successively transmitted to subsequent layers until the output is determined.

The learning model 325 inputs at least a portion of the history 323, such as overcharge amount, voltage, and temperature, and outputs a plurality of electrolyte amounts and the probability that the battery 2 has the electrolyte amounts. The probability that each output node of the output layer outputs is a value between 0 and 1.0.
The output layer is
For example, the amount of electrolyte 475g...0.91
Electrolyte amount 450g...0.08
...
Output like this.

The learning model 325 uses the teacher data 326 to determine, when at least part of the history 323, such as overcharge amount, voltage, and temperature, is input, a plurality of electrolyte amounts and the battery 2 have the electrolyte amount. This is a model trained to output probability. Hereinafter, the probability that the battery 2 has this amount of electrolyte will also be referred to as the probability of the amount of electrolyte.

FIG. 9 is a conceptual diagram showing an example of the contents of the teacher data 326. The teacher data 326 is created, for example, by an experiment conducted in advance. The experimenter obtains a plurality of overcharge amounts and voltages of the battery 2 by changing the amount and temperature of the electrolyte of the battery 2, for example. Overcharge amounts and voltages are obtained for a plurality of electrolyte amounts and temperatures. In the teacher data 326, the overcharge amount and voltage obtained in the experiment, the temperature corresponding to the obtained overcharge amount and voltage, and the amount of electrolyte 22 are recorded for each ID (Identification) number. has been done. The overcharge amount and voltage acquired in the experiment, and the temperature corresponding to the acquired overcharge amount and voltage correspond to the second history.

An example of learning by the learning model 325 will be described. The learning model 325 is a model learned by a learning device configured with a computer such as a PC (Personal Computer). The learning device may be the control device 10 or the external server 8. A learning model 325 and teacher data 326 are stored in this learning device. In the teacher data 326, a correct value (correct value) of the probability of the amount of electrolyte is given for each ID number. For example, in ID number (1) in FIG. 9, the amount of electrolyte is 475 g. ID number (1) is assigned 1.0 as the correct value of the probability that the amount of electrolyte is 475 g. For ID numbers other than ID number (1) where the electrolyte amount is 475 g, 1.0 is assigned as the correct value of the probability that the respective electrolyte amount is 475 g. ID number (1) includes the probability of the amount of electrolyte other than the probability that the amount of electrolyte is 475 g, for example, the probability that the amount of electrolyte is 450 g, and the probability that the amount of electrolyte is Xg. 0 is assigned. X is a positive number. For ID numbers other than ID number (1) in which the electrolyte amount is 475 g, 0 is assigned as the correct value of the probability of the electrolyte amount other than the probability that the respective electrolyte amount is 475 g. For ID number (N), the amount of electrolyte is Xg. N is a natural number. 1.0 is assigned to the ID number (N) as the correct value of the probability that the amount of electrolyte is Xg. For ID numbers other than ID number (N) where the electrolyte amount is Xg, 1.0 is assigned as the correct value of the probability that the respective electrolyte amount is Xg. 0 is given to the ID number (N) as the correct value of the probability of the electrolyte amount other than the probability that the electrolyte amount is Xg. For ID numbers other than the ID number (N) where the electrolyte amount is Xg, 0 is assigned as the correct value of the probability of the electrolyte amount other than the probability that the respective electrolyte amount is Xg.

The learning device inputs the temperature, overcharge amount, and voltage of ID number (1) into the input layer of the learning model 325. After the arithmetic processing in the intermediate layer, the output layer outputs a plurality of electrolyte amounts and the probability that the battery 2 has the electrolyte amount (probability of the electrolyte amount). The learning device compares each of the probabilities of the output electrolyte amount with the correct value of ID number (1). The learning device optimizes various parameters such as weights and biases that connect nodes used for arithmetic processing in the intermediate layer so that the probability of the amount of electrolyte output from the output layer approaches the correct value. Regarding the information after ID number (2), the learning device similarly inputs the temperature, overcharge amount, and voltage into the input layer. The learning device optimizes the parameters by comparing each of the output probabilities of the amount of electrolyte with the correct value of the same ID number as the input. Although the parameter optimization method is not particularly limited, the learning device optimizes various parameters using, for example, an error backpropagation method. The learning model 325 is trained by the learning device optimizing the parameters.

The learning model 325 is learned by a learning device, for example, and then recorded on a recording medium readable by the BMU 3. The learning model 325 read from the storage medium by the BMU 3 is stored in the storage unit 32. The learning model 325 may be downloaded from the external server 8 and stored in the storage unit 32, for example.

In S4, the control unit 31 inputs at least part of the history 323, such as the overcharge amount, voltage, and temperature, to the learning model 325. The learning model 325 outputs a plurality of amounts of electrolyte and the probability that the battery 2 has the amount of electrolyte. For example, the probability that the electrolytic solution amount is 475 g, the probability that the electrolytic solution amount is 450 g, and the probability that the electrolytic solution amount is Xg are output. The control unit 31 selects the electrolyte amount with the highest probability from each of the probabilities that the battery 2 has the electrolyte amount, and assumes that the selected electrolyte amount is the electrolyte amount of the battery 2. Determine the fluid volume. For example, when the probability that the amount of liquid is 450 g is highest, the control unit 31 specifies that the amount of electrolyte in the battery 2 is 450 g.

The amount of electrolyte in the battery 2 can be specified based on at least a portion of the history 323, such as the overcharge amount and temperature, and the relationship between at least a portion of the history 323 and the amount of electrolyte. Based on the specified amount of electrolytic solution, it is possible to estimate the risk of the battery 2 running out of fluid, that is, whether or not there is a possibility that the battery 2 will run out of fluid.

The amount of electrolyte in battery 2 is calculated based on the amount of electrolyte in battery 2, for example, the amount of electrolyte in battery 2 when at least part of the history 323, for example, overcharge amount, voltage, and temperature, is input. It may be specified using the learning model 325 that outputs a probability that is a liquid amount. The amount of electrolyte in the battery 2 can be determined with high accuracy.

If there is a risk of drying up the battery, the person inside the vehicle 1 will be notified that there is a risk of running out of the battery, so the person can take countermeasures such as refilling the battery 2 with water before the battery 2 runs out. , it is possible to prevent the effects of liquid drying up. The effects of the liquid drying up include, for example, a decrease in the capacity of the battery 2 and deterioration (corrosion) of the positive electrode plate 231 and negative electrode plate 235 exposed from the electrolytic solution 22. Another problem is that hydrogen gas generated in the battery case 20 by electrolysis of dilute sulfuric acid water contained in the electrolytic solution 22 explodes due to sparks generated in the deteriorated portions of the positive electrode plate 231 and the negative electrode plate 235.

(Second embodiment)
FIG. 10 is a block diagram showing the configuration of the BMU 3 according to the second embodiment. Among the configurations according to the second embodiment, the same components as those in the first embodiment are denoted by the same reference numerals, and detailed explanation thereof will be omitted. A learning model 327 is stored in the storage unit 32 of the BMU 3. The learning model 327 differs from the learning model 325 of the first embodiment in that it reduces the risk of battery 2 running out of fluid when at least part of the history 323, for example, time series data of overcharge amount, voltage, and temperature, is input. Output. The control unit 31 uses the learning model 327 to estimate the risk of battery 2 running out.

FIG. 11 is a schematic diagram of the learning model 327. Similar to the learning model 325, an RNN can be used for the learning model 327. The learning model 327 may use CNN. The learning model 327 inputs at least part of the history 323, for example, time series data of the overcharge amount, voltage, and temperature of the battery 2. The learning model 327 outputs the risk of battery 2 running out, for example, the probability that battery 2 will run out and the probability that battery 2 will not run out.
The output layer of the learning model 327 is
For example, the probability that battery 2 will run out of fluid is 0.92
Probability that battery 2 will not dry up...0.08
Output like this.

The learning model 327 uses the teacher data 328 to determine that when at least part of the history 323, such as time series data of the overcharge amount, voltage, and temperature of the battery 2, is input, battery 2 will run out of fluid. This is a model that has been trained to output the probability and the probability that the battery 2 will not run out of fluid.

FIG. 12 is a conceptual diagram showing an example of the contents of the teacher data 328. The teacher data 328 is created, for example, through an experiment, and records time-series data on the amount of overcharge, voltage, and temperature of the battery 2, and whether or not the battery 2 has run out of liquid, for each ID number. For each ID number in FIG. 12, time-series data of the overcharge amount, voltage, and temperature of the battery 2 every minute and whether or not the battery 2 has dried up at the time of acquiring the time-series data are recorded. There is.

An example of learning by the learning model 327 will be explained. The learning model 327 is a model learned by a learning device configured with a computer such as a PC. The learning device may be the control device 10 or the external server 8. A learning model 327 and teacher data 328 are stored in this learning device. The teacher data 328 is provided with correct values (correct values) for the probability that battery 2 will run out of fluid and the probability that battery 2 will not run out of fluid, for each ID number. If the teacher data 328 indicates whether or not battery 2 will run out of fluid, 1.0 will be assigned as the correct value for the probability that battery 2 will run out, and 1.0 will be assigned as the correct value for the probability that battery 2 will not run out. 0 is assigned. If there is no determination as to whether or not battery 2 will run out of fluid, 0 is assigned as the correct value for the probability that battery 2 will run out, and 1.0 is assigned as the correct value for the probability that battery 2 will not run out. ing.

The learning device inputs time series data of the overcharge amount, voltage, and temperature of the battery 2 with ID number (1) recorded in the teacher data 328 to the input layer of the learning model 327. After the arithmetic processing in the intermediate layer, the output layer outputs the probability that the battery 2 will run out of fluid and the probability that the battery 2 will not run out of fluid. The learning device compares the probability that battery 2 will run out of fluid and the probability that battery 2 will not run out of fluid with the correct value of each ID number (1). The learning device connects the nodes used for arithmetic processing in the intermediate layer so that the probability that battery 2 will run out of battery output from the output layer and the probability that battery 2 will not run out of battery 2 will approach the correct value. Optimize various parameters such as weights and biases. Regarding the information after ID number (2) recorded in the teacher data 328, the learning device similarly inputs time series data of the overcharge amount, voltage, and temperature of the battery 2 to the input layer. The learning device optimizes the parameters by comparing the output probability that the battery 2 will run out of fluid and the probability that the battery 2 will not run out of fluid with the correct value of the same ID number as the input. Although the parameter optimization method is not particularly limited, the learning device optimizes various parameters using, for example, an error backpropagation method. The learning model 327 is trained by the learning device optimizing the parameters.

BMU3 functions as an estimation device. BMU3 functions as an acquisition part, a data accumulation part, an estimation part, and a notification part. The control device 10 or the external server 8 may function as the estimation device. When the external server 8 does not function as an estimation device, the external server 8 does not need to be connected to the control device 10.

FIG. 13 is a flowchart showing the procedure of the liquid drying risk estimation process performed by the control unit 31 of the BMU 3. Hereinafter, step will be abbreviated as S. For example, when the ignition switch of the vehicle 1 is turned on, the control unit 31 executes the following processing according to the program 321.

The processing from S11 to S13 is the same as the processing from S1 to S3 in the first embodiment, so the description thereof will be omitted. The control unit 31 inputs at least part of the history 323, for example, time series data of the overcharge amount, voltage, and temperature of the battery 2, to the learning model 327 (S14). The learning model 327 outputs the probability that battery 2 will run out of fluid and the probability that battery 2 will not run out of fluid.

The control unit 31 estimates the risk of battery 2 running out based on the probability that battery 2 will run out and the probability that battery 2 will not run out, output by the learning model 327 (S15). For example, if the probability that the output battery 2 will run out of fluid is greater than the probability that the output battery 2 will not run out of fluid, the control unit 31 determines that there is a possibility that the battery 2 will run out of fluid, In other words, it is estimated that there is a risk of liquid drying up. When the control unit 31 estimates that there is a risk of liquid drying up (S16: YES), the control unit 31 performs the process of S17 and ends the process. The process in S17 is the same as the process in S7 in the first embodiment, so a description thereof will be omitted. If the probability that the output battery 2 will not run out of fluid is greater than the probability that the output battery 2 will run out of fluid, the control unit 31 determines that there is no possibility that the battery 2 will run out of fluid, that is, the battery 2 has no chance of running out of fluid. It is estimated that there is no risk of withering. When the control unit 31 estimates that there is no risk of liquid drying up (S16: NO), the control unit 31 performs the process of S11.

By using a learning model 327 that outputs the risk of battery 2 draining when at least part of the history 323, for example, time series data of overcharge amount, voltage, and temperature of battery 2, is input, battery 2 can be accurately calculated. The risk of drying up can be estimated.

(Third embodiment)
FIG. 14 is a block diagram showing the configuration of a vehicle 1 according to the third embodiment. Among the configurations according to the third embodiment, the same components as those in the first embodiment are denoted by the same reference numerals, and detailed explanation thereof will be omitted.

The vehicle 1 includes a liquid level sensor 14. The liquid level sensor 14 is attached, for example, to the inside of the battery case body 201 of the battery 2. The liquid level sensor 14 outputs a detection result according to the liquid level of the electrolytic solution 22 of the battery 2. The output result of the liquid level sensor 14, that is, the liquid level of the electrolytic solution 22, is expressed, for example, as a percentage. The input unit 33 of the BMU 3 receives input of the detection result of the liquid level sensor 14 . The control unit 31 of the BMU 3 acquires the liquid level of the electrolytic solution 22 from the liquid level sensor 14 via the input unit 33.

The control unit 31 specifies and acquires the amount of electrolyte solution based on the obtained liquid level of the electrolyte solution 22 . For example, the storage unit 32 stores a data table showing the relationship between the liquid level of the electrolyte 22 of the battery 2 and the amount of the electrolyte of the battery 2. The control unit 31 specifies and acquires the amount of electrolyte from the obtained liquid level of the electrolyte 22 of the battery 2 and the above-mentioned data table.

The control unit 31 estimates the risk of battery 2 running out based on the obtained amount of electrolyte 22 . For example, if the specified amount of electrolytic solution 22 is below a predetermined value, the control unit 31 estimates that there is a risk of the electrolytic solution running out. When the control unit 31 estimates that there is a risk of liquid drying up, it notifies people in the vehicle 1 that there is a risk of liquid drying up. BMU3 functions as an estimation device. BMU3 functions as an acquisition part, an estimation part, and a notification part. The control device 10 or the external server 8 may function as the estimation device. When the external server 8 does not function as an estimation device, the external server 8 does not need to be connected to the control device 10.

FIG. 15 is a flowchart showing the procedure of the liquid drying risk estimation process performed by the control unit 31 of the BMU 3. Hereinafter, step will be abbreviated as S. For example, when the ignition switch of the vehicle 1 is turned on, the BMU 3 control unit 31 executes the following processing according to the program 321.

The control unit 31 acquires the liquid level of the electrolyte 22 of the battery 2 (S21). The control unit 31 specifies and acquires the amount of electrolyte in the battery 2 based on the obtained liquid level of the electrolyte 22 in the battery 2 (S22). The electrolytic solution amount is specified based on, for example, the obtained liquid level of the electrolytic solution 22 of the battery 2 and the above-mentioned data table.

The control unit 31 estimates the risk of battery 2 running out of liquid based on the obtained amount of electrolyte (S23). If the obtained amount of electrolytic solution is larger than a predetermined value, the control unit 31 estimates that there is no risk of the electrolyte running out. When the control unit 31 estimates that there is no risk of liquid drying up (S24: NO), the control unit 31 performs the process of S21. The control unit 31 estimates that there is a risk of liquid drying up when the specified amount of electrolyte is less than a predetermined value. When the control unit 31 estimates that there is a risk of liquid drying up (S24: YES), the control unit 31 performs the process of S25 and ends the process. The process in S25 is the same as the process in S7 of the first embodiment, so a description thereof will be omitted.

Since it is obvious that there is a correlation between the liquid level of the electrolytic solution 22 in the battery 2 and the amount of the electrolytic solution in the battery 2, S22 may be omitted. In this case, in S23, the control unit 31 estimates the risk of the battery 2 running out of liquid based on the liquid level of the electrolyte 22 of the battery 2 acquired in S21. For example, when the obtained liquid level of the electrolytic solution 22 is a certain value, for example, 80% or less, the control unit 31 estimates that there is a risk of liquid drying up. If the obtained liquid level of the electrolytic solution 22 of the battery 2 is larger than a certain value, the control unit 31 estimates that there is no risk of liquid drying up.

The control unit 31 obtains the liquid level of the electrolytic solution 22 using the liquid level sensor 14, and estimates the risk of liquid drying up based on the amount of electrolytic solution specified based on the obtained liquid level or the obtained liquid level. It is possible to estimate the risk of drying up with high accuracy.

The embodiments disclosed herein are illustrative in all respects and should not be considered restrictive. The technical features described in each example can be combined with each other, and the scope of the present invention is intended to include all changes within the scope of the claims and the range of equivalents to the scope of the claims. be done.

2 Battery (lead acid battery)
3 BMU
31 Control unit 32 Storage unit 33 Input unit 323 History 325 Learning model 327 Learning model

Claims (6)

an acquisition unit that acquires the current, voltage, and temperature of the lead-acid battery;
a data storage unit that accumulates the acquired current, the voltage, and the temperature, and a first history of results calculated from the acquired current, the voltage, and the temperature;
Among the calculated results of the accumulated first history, the overcharge amount calculated based on the average value of temperature, the amount of charged electricity and the amount of discharged electricity, and the relationship between the overcharge amount and the amount of decrease in the electrolyte solution. a specifying unit that specifies the amount of electrolyte in the lead-acid battery based on relational data shown for each temperature ;
An estimating unit that estimates a risk of drying up the lead-acid battery based on the specified amount of electrolyte. The identification unit inputs at least a part of the first history accumulated by the data accumulation unit to a learning model that outputs the amount of electrolyte of the lead-acid battery when at least a part of the first history is input. The estimating device according to claim 1, wherein the amount of electrolyte is specified. an acquisition unit that acquires the current, voltage, and temperature of the lead-acid battery;
a data storage unit that accumulates a history of the acquired current, the voltage, and the temperature, and the results of calculations from the acquired current, the voltage, and the temperature;
When the overcharge amount, voltage, and temperature are input, the acquired voltage and temperature, and the calculated results of the history accumulated by the data storage unit are applied to a learning model that outputs the risk of drying up of the lead-acid battery. An estimation device comprising: an estimating unit that inputs an overcharge amount calculated based on a charging amount of electricity and a discharging amount of electricity to estimate a risk of drying up the lead-acid battery. The estimating device according to any one of claims 1 to 3 , further comprising a notification section that notifies that the lead-acid battery will run out of fluid according to the risk of running out of fluid estimated by the estimation section. Obtaining the current, voltage, and temperature of the lead-acid battery,
accumulating a first history of the acquired current, the voltage, and the temperature, and the results of calculations from the acquired current, the voltage, and the temperature;
Among the calculated results of the accumulated first history, the overcharge amount calculated based on the average value of temperature, the amount of charged electricity and the amount of discharged electricity, and the relationship between the overcharge amount and the amount of decrease in the electrolyte solution. Identifying the amount of electrolyte of the lead-acid battery based on the relational data shown for each temperature ,
An estimation method for estimating a risk of liquid drying up of the lead-acid battery based on the specified amount of electrolyte. Obtaining the current, voltage, and temperature of the lead-acid battery,
accumulating the acquired current, the voltage, and the temperature, and a first history of results calculated from the acquired current, the voltage, and the temperature;
Among the calculated results of the accumulated first history, the overcharge amount calculated based on the average value of temperature, the amount of charged electricity and the amount of discharged electricity, and the relationship between the overcharge amount and the amount of decrease in the electrolyte solution. Identifying the amount of electrolyte of the lead-acid battery based on the relational data shown for each temperature ,
A computer program that causes a computer to execute a process of estimating a risk of drying up the lead-acid battery based on the specified amount of electrolyte.

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