WO2023008061A1

WO2023008061A1 - Battery-monitoring device

Info

Publication number: WO2023008061A1
Application number: PCT/JP2022/025958
Authority: WO
Inventors: 福郎北川; 基正飯塚; 裕基堀; 正規内山
Original assignee: 株式会社デンソー
Priority date: 2021-07-29
Filing date: 2022-06-29
Publication date: 2023-02-02
Also published as: CN117730262A; JP2023019349A

Abstract

This battery-monitoring device (53) is applied to a storage battery (40) in which a reaction heat amount changes by a prescribed volume when the electric power storage capacity changes in association with conduction of electric power. The battery-monitoring device comprises: a data storage unit for storing heat amount data that comprise the electric power storage capacity and a parameter related to a reaction heat amount; a division unit for dividing the heat amount data into a first data group pertaining to a capacity lower than a boundary and a second data group pertaining to a capacity higher than the boundary; an approximation calculation unit for calculating a first approximation function that approximates the first data group using a linear function, and a second approximation function that approximates the second data group using a linear function; an error sum calculation unit for calculating an error sum, which is the sum of an approximation error between the first approximation function and the heat amount data in the first data group and an approximation error between the second approximation function and the heat amount data in the second data group; an acquisition unit for modifying the boundary, inducing calculation of the error sum, and acquiring a plurality of error sums; and an identification unit for identifying a prescribed capacity on the basis of the first and second approximation functions that correspond to the lowest error sum among the plurality of error sums.

Description

battery monitor

Cross-reference to related applications

This application is based on Japanese Application No. 2021-124006 filed on July 29, 2021, and the contents thereof are incorporated herein.

The present disclosure relates to a battery monitoring device that monitors storage batteries.

In recent years, lithium-ion batteries, for example, have attracted attention as lightweight and high-energy-density storage batteries. Some lithium-ion batteries have a plateau region, ie, a region in which the change in battery voltage is small as the storage capacity of the storage battery changes. In the plateau region, it is difficult to calculate the storage capacity of the storage battery using the capacity-voltage characteristic that indicates the correlation between the storage capacity and the battery voltage.

As a technology for calculating the storage capacity of a lithium-ion battery, a technology that uses a singular point where the battery voltage changes stepwise as the capacity changes within the plateau region is known. In a storage battery having a peculiar point, the peculiar point appears at a predetermined capacity of the storage battery regardless of deterioration of the storage battery. Therefore, the predetermined capacity of the storage battery can be obtained by detecting the peculiar point. For example, in Patent Literature 1, the battery voltage of a storage battery is detected, and a singular point is detected based on the amount of voltage change, which is the amount of change in the battery voltage over time.

JP 2014-167457 A

The amount of voltage change at the singular point is minute. Therefore, when a large current flows through the storage battery, such as during high-speed charging, noise generated in the battery voltage makes it impossible to detect a singular point based on the amount of voltage change. I am concerned that it is not possible.

The present disclosure has been made in view of the above problems, and an object thereof is to provide a battery monitoring device capable of specifying a predetermined capacity of a storage battery without using the amount of voltage change of the storage battery.

A first means for solving the above problems is applied to a storage battery in which the amount of reaction heat changes at a predetermined capacity when the storage capacity changes with energization, and a parameter that correlates with the amount of reaction heat of the storage battery during energization is determined. a data storage unit for storing time-series calorie data obtained and composed of the parameter and the storage capacity; and a second data group having a larger capacity than the boundary, a first approximation function that approximates the first data group with a linear function, and a second data group that approximates the second data group with a linear function an approximation calculation unit that calculates two approximation functions; an approximation error between the first approximation function and each of the heat quantity data in the first data group; and the second approximation function and each of the heat quantity data in the second data group. and an error sum calculation unit that calculates the sum of errors that are the sum of the approximation errors of an acquisition unit that acquires a plurality of error sums by calculating the function and the second approximation function and calculating the error sum by the error sum calculation unit; and corresponding to the minimum error sum among the plurality of error sums. and a specifying unit that specifies the predetermined capacity of the storage battery based on the first approximation function and the second approximation function.

In a storage battery in which the amount of reaction heat changes at a given capacity with energization, an inflection point occurs at the given capacity in the relationship between the parameter that correlates with the reaction heat amount of the storage battery and the storage capacity. The present inventors have found that in the relationship between the parameter that correlates with the reaction heat amount of the storage battery and the storage capacity, highly accurate linear approximation is possible on the lower capacity side and the higher capacity side than the predetermined capacity, and that the linear approximation is possible at the predetermined capacity. Focusing on the fact that the slope changes, the inventors have found a method of specifying the predetermined capacity of the storage battery based on the relationship between the above parameters and the storage capacity.

Specifically, time-series calorie data consisting of a parameter correlated with the reaction calorie of the storage battery and the storage capacity are divided into a first data group with a lower capacity than a predetermined boundary and a second data group with a higher capacity than the boundary. and calculating a first approximation function that approximates the first data group with a linear function and a second approximation function that approximates the second data group with a linear function, and calculates the first approximation function and the first data group and the approximation error between the second approximation function and each calorie data in the second data group. Then, the boundary is changed to the low-capacity side or the high-capacity side, and a plurality of error sums are obtained by calculating the first approximation function and the second approximation function and calculating the error sum for each of the changed boundaries. Then, the predetermined capacity of the storage battery is specified based on the first approximation function and the second approximation function corresponding to the minimum error sum among the plurality of error sums.

According to the above configuration, the predetermined capacity of the storage battery can be identified based on the correlation between the parameter that correlates with the amount of reaction heat of the storage battery and the storage capacity, and the predetermined capacity of the storage battery can be identified without using the voltage change amount of the storage battery. can do. In addition, the parameter has a characteristic that it increases in a situation where a large current flows through the storage battery. Therefore, even when it is difficult to appropriately specify the predetermined capacity of the storage battery based on the amount of voltage change due to a large current flowing through the storage battery, such as during high-speed charging, the predetermined capacity of the storage battery can be specified.

In the second means, the data storage unit stores the time-series heat amount data during charging of the storage battery, and the acquisition unit gradually lowers the boundary from the storage capacity of the storage battery on the fully charged side. Change to capacity.

When charging the storage battery, it is assumed that charging will start from an arbitrary storage capacity, and then end when the battery reaches a fully charged state. In other words, while the storage capacity at the start of charging is arbitrary, the storage capacity at the end of charging is considered to be a substantially constant storage capacity (full charge capacity). In the above configuration, when changing the boundary between the first data group and the second data group, the storage capacity of the storage battery is gradually changed from the fully charged side to the low capacity side. As a result, when dividing into a first data group having a lower capacity than the boundary and a second data group having a higher capacity than the boundary, the boundary can be set based on a substantially constant storage capacity. It is possible to prevent the occurrence of a situation in which there is no calorie data in the data group, and to appropriately specify the predetermined capacity of the storage battery.

In the third means, the parameter is a battery temperature indicating the temperature of the storage battery, and the approximation calculation unit uses time-series heat amount data including the battery temperature and the storage capacity to calculate the first approximation A function and the second approximation function are calculated.

In a storage battery in which the amount of reaction heat changes at a given capacity with energization, the battery temperature changes with the change in the reaction heat amount, and an inflection point occurs at the given capacity in the relationship between the battery temperature and the storage capacity. According to the above configuration, since the first approximation function and the second approximation function are calculated using the time-series heat amount data consisting of the battery temperature and the storage capacity, the first approximation function and the second approximation function are calculated. can be used to detect the inflection point and to identify the predetermined capacity of the storage battery.

In the fourth means, the parameter is the impedance of the storage battery, and the approximation calculation unit calculates the first approximation function and the second Calculate the approximation function.

In a storage battery in which the amount of reaction heat changes at a given capacity with energization, the impedance changes due to the temperature change when the reaction heat changes, and an inflection point occurs at the given capacity in the relationship between the impedance and the storage capacity. According to the above configuration, since the first approximation function and the second approximation function are calculated using the time-series heat amount data consisting of the impedance and the storage capacity, the first approximation function and the second approximation function are used. can detect the point of inflection and specify the predetermined capacity of the storage battery.

In a fifth means, an impedance calculation unit obtains a response signal of the storage battery to the AC signal while a predetermined AC signal is applied to the storage battery, and calculates the impedance based on the response signal. The frequency of the signal is set to a frequency below the ohmic frequency corresponding to the ohmic resistance of the storage battery.

In the above configuration, when calculating the impedance of the storage battery, the frequency of the AC signal is set to a frequency equal to or lower than the ohmic frequency. Especially at frequencies lower than the ohmic frequency, the calculated impedance is strongly affected by the reaction resistance. Since the reaction resistance is highly dependent on temperature, the amount of change in impedance at a given capacity tends to be large. Therefore, by using the reaction resistance, it is possible to accurately specify the predetermined capacity of the storage battery.

The sixth means comprises a slope determination unit for determining whether the absolute value of the slope of the first approximation function is greater than the absolute value of the slope of the second approximation function, and the error sum calculation unit determines the slope The error sum is calculated on condition that the absolute value of the slope of the first approximation function is determined by the unit to be larger than the absolute value of the slope of the second approximation function.

In the relationship between the parameter that correlates with the reaction heat amount of the storage battery and the storage capacity, when the boundary is set in the vicinity area including the predetermined capacity of the storage battery, the absolute value of the slope of the first approximation function (approximation function on the low capacity side) is greater than the absolute value of the slope of the second approximation function (approximation function on the high capacity side). However, in a wide range of storage capacity, depending on the setting of the boundary, the slope relationship of each approximation function may be reversed. There is a concern that the predetermined capacity may be erroneously specified. In this respect, according to the above configuration, the predetermined capacity can be obtained correctly based on the first approximation function and the second approximation function.

In the seventh means, when the storage battery is charged, a charging current storage unit that stores time-series charging current data until the storage battery reaches a fully charged state; an integrated value calculation unit that calculates an integrated current value of the charging current that has flowed through the storage battery in a period from the specified capacity specified by the above to a fully charged state; and a deterioration state determination unit for determining.

The state of deterioration of the storage battery is determined based on the predetermined capacity and integrated current value. Here, the storage capacity at which the amount of reaction heat changes in the storage battery, that is, the predetermined capacity, does not change even if the storage battery deteriorates, while the storage capacity in the fully charged state changes due to deterioration of the storage battery. In this case, the deterioration state of the storage battery can be properly determined by obtaining the predetermined capacity of the storage battery as described above and calculating the current integrated value up to the full charge capacity based on the predetermined capacity.

In the eighth means, the storage battery contains graphite in the negative electrode. In a storage battery containing graphite in the negative electrode, when the storage capacity reaches a predetermined capacity, the electrode structure changes at the negative electrode, causing a change in the amount of reaction heat. Therefore, the predetermined capacity of the storage battery can be specified by using the change in the amount of reaction heat.

In the ninth means, the storage battery contains lithium iron phosphate in the positive electrode. In an olivine-based storage battery containing lithium iron phosphate in the positive electrode, the amount of change in voltage of the storage battery due to changes in the storage capacity of the storage battery is small, and it is difficult to specify the predetermined capacity of the storage battery based on the amount of change in voltage. In the above configuration, since the predetermined capacity of the storage battery is specified based on the parameter that correlates with the amount of reaction heat of the storage battery, the predetermined capacity of the storage battery can be specified without using the voltage change amount of the storage battery.

The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description with reference to the accompanying drawings. The drawing is
FIG. 1 is a circuit diagram of a power supply system; FIG. 2 is a diagram showing the configuration of a storage battery, FIG. 3 is a diagram showing the correlation between the storage capacity, the battery voltage, and the amount of reaction heat, FIG. 4 is a diagram showing the correlation between the storage capacity and the battery temperature, FIG. 5 is a flowchart of determination processing in the first embodiment, FIG. 6 is a flowchart of the predetermined capacity identification process, FIG. 7 is a diagram showing a plurality of boundary candidates; FIG. 8 is a diagram showing changes in the first approximation function and the second approximation function when the boundary is changed, FIG. 9 is a diagram showing changes in the error sum when the boundary is changed, FIG. 10 is a diagram showing the correlation between the storage capacity and the impedance, FIG. 11 is a flowchart of determination processing in the second embodiment, FIG. 12 is a diagram showing changes in the first approximation function and the second approximation function when the boundary is changed.

(First embodiment)
A first embodiment in which a battery monitoring device is applied to a power supply system of a vehicle (for example, a hybrid vehicle or an electric vehicle) will be described below with reference to the drawings.

As shown in FIG. 1, the power supply system 10 is a system that supplies electric power to an electrical load 20 such as a rotating machine (motor generator), and includes a storage battery 40 . A relay switch SMR (system main relay switch) is connected to at least one of a positive power supply path L1 connecting the positive electrode of the storage battery 40 and the electrical load 20 and a negative power supply path L2 connecting the negative electrode of the storage battery 40 and the electrical load 20. is provided, and a relay switch SMR is configured to switch between energization and energization cutoff between the storage battery 40 and the electric load 20 . In addition, in this embodiment, a lithium ion storage battery is used as the storage battery 40 .

A positive charging path L3 is connected to the positive power supply path L1, and a negative charging path L4 is connected to the negative power supply path L2. ing. The storage battery 40 is configured to be connectable to an external charging device outside the power supply system 10 via a pair of connection terminals 21, and is charged by the external charging device.

The configuration of the storage battery 40 will be described based on FIG. The storage battery 40 includes an electrode body 44 , an electrolyte 45 , and a housing case 46 that houses the electrode body 44 and the electrolyte 45 . The electrolyte 45 is a non-aqueous electrolyte such as organic solvent ethylene carbonate or diethyl carbonate. The housing case 46 is made of, for example, an aluminum alloy, and external terminals 47 are provided at both longitudinal ends of the upper surface thereof.

The electrode body 44 is composed of a positive electrode conductive plate 44a as a positive electrode, a negative electrode conductive plate 44b as a negative electrode, and a separator 44c arranged between the positive electrode conductive plate 44a and the negative electrode conductive plate 44b.

The positive electrode conductive plate 44a is composed of a positive electrode metal foil 44d made of a metal foil such as aluminum, and a positive electrode active material 44e applied to the front and rear surfaces of the positive electrode metal foil 44d. The positive electrode active material 44e is olivine-type iron phosphate, that is, lithium iron phosphate. The negative electrode conductive plate 44b is composed of a negative electrode metal foil 44f made of a thin metal such as copper and a negative electrode active material 44g applied to the front and rear surfaces of the negative electrode metal foil 44f. 44 g of negative electrode active materials are graphite, such as graphite and carbon. The separator 44c is a porous insulating film made of polyethylene resin. The electrode body 44 conducts electricity by moving lithium ions between the positive electrode conductive plate 44a and the negative electrode conductive plate 44b via the separator 44c.

In FIG. 2, an equivalent circuit model of the complex impedance Zm of the storage battery 40 is shown. In this equivalent circuit model, the complex impedance Zm of the storage battery 40 is composed of a series connection of an ohmic resistance Rohm, a reaction resistance Rct, and a diffusion resistance Rw. The ohmic resistance Rohm is a current-carrying resistance in the electrodes and the electrolytic solution that constitute the storage battery 40 . The reaction resistance Rct represents the resistance due to the electrode interfacial reaction in the electrode, and is expressed as a parallel connection of the resistance component 42a and the capacitance component 42b. The diffusion resistance Rw represents the resistance associated with the diffusion of lithium ions into the electrode active material applied to the electrode surface.

Also, as shown in FIG. 1 , the power supply system 10 includes a battery measuring device 50 that measures the state of the storage battery 40 and an ECU 60 that controls the electric load 20 . The battery measuring device 50 is a device that measures the storage capacity Q and SOH (state of deterioration) of the storage battery 40 . The battery measuring device 50 includes a current modulation circuit 51, a first voltmeter 52, and a control device 53 as a battery monitoring device.

The current modulation circuit 51 is a circuit that applies a predetermined AC signal to the storage battery 40 to be measured. The current modulation circuit 51 has an oscillator 51 a and a first ammeter 51 b connected in series with the oscillator 51 a and is connected to the storage battery 40 by a first electrical path 81 .

The oscillator 51 a generates an AC signal instructed by the control device 53 and applies it to the storage battery 40 . The AC signal is, for example, a sine wave signal or a square wave signal. The first ammeter 51 b measures the current signal generated in the first electrical path 81 and outputs the measured current signal data to the controller 53 .

The first voltmeter 52 is connected to the storage battery 40 via a second electrical path 82 different from the first electrical path 81 . A response signal (voltage fluctuation) reflecting the complex impedance Zm of the storage battery 40 is generated in the first voltmeter 52 when an AC signal is applied. The first voltmeter 52 measures this response signal and outputs the measured response signal data to the control device 53 .

The battery measuring device 50 also includes a second ammeter 54 , a second voltmeter 55 and a thermometer 56 . The second ammeter 54 is provided in the positive electrode side power supply path L1. The second ammeter 54 measures the battery current I of the storage battery 40 and outputs the measured battery current data to the control device 53 . The second voltmeter 55 is connected in parallel with the electrical load 20 . The second voltmeter 55 measures the battery voltage V of the storage battery 40 and outputs the measured battery voltage data to the control device 53 . The thermometer 56 is, for example, a thermocouple or a thermistor, and is arranged near the storage battery 40 . The thermometer 56 measures the battery temperature T of the storage battery 40 and outputs the measured battery temperature data to the control device 53 .

The control device 53 is mainly composed of a microcomputer, and implements various control functions by executing programs stored in its own storage device. Based on the battery current I and the battery voltage V, the control device 53 grasps the drive state of the electric load 20 and outputs the drive state to the ECU 60 .

The control device 53 also calculates the complex impedance Zm of the storage battery 40, that is, the real part ReZm and the imaginary part ImZm of the complex impedance Zm, based on the response signal and the current signal. The controller 53 creates, for example, a complex impedance plane plot (Cole-Cole plot) based on the calculation results, and grasps the characteristics of the positive conductive plate 44a, the negative conductive plate 44b, the electrolyte 45, and the like.

A complex impedance plane plot of the storage battery 40 is shown in FIG. In the complex impedance plane plot, in order to show the vector trajectory focusing on the capacitance component in the first quadrant, the upper side of the vertical axis is written as "-ImZm", that is, the imaginary part is inverted.

On the complex impedance plane plot, the complex impedance Zm changes according to the frequency ωr of the AC signal indicated by the indication signal, and an arc Cr, which is a semicircular locus, appears in the low frequency region. The imaginary part ImZm of the complex impedance Zm becomes zero at the end point PA on the high frequency side of the arc Cr. The value of the real part ReZm of the complex impedance Zm at this end point PA represents the ohmic resistance Rohm.

A straight line Pr, which is a linear locus, appears on the low-frequency side of the arc Cr. When the connection point between the arc Cr and the straight line Pr is the low-frequency end point PB of the arc Cr, the value of the real part ReZm of the complex impedance Zm at the end point PB and the real part ReZm of the complex impedance Zm at the end point PA The difference value of the values represents the reaction resistance Rct.

Furthermore, based on the battery current I and the battery voltage V, the control device 53 grasps the storage capacity Q and SOH of the storage battery 40 . FIG. 3 shows the correlation between the storage capacity Q and the battery voltage V in the storage battery 40. As shown in FIG. As shown in FIG. 3, the olivine-based storage battery 40 using lithium iron phosphate as the positive electrode active material 44e has a region in which the change in the battery voltage V due to the change in capacity is small, that is, a plateau region. It is difficult to calculate the storage capacity Q of the storage battery 40 using the correlation between the storage capacity Q and the battery voltage V in the plateau region.

Within the plateau region, there is a singular point where the battery voltage V changes stepwise as the capacity changes. In the storage battery 40, since a singular point appears at the predetermined capacity QA of the storage battery 40 regardless of deterioration of the storage battery 40, the predetermined capacity QA of the storage battery 40 is specified by detecting the singular point using the voltage change amount of the battery voltage V. can do. However, the amount of voltage change at the singular point is very small. Therefore, in a situation where a large current flows in the storage battery 40, such as during high-speed charging, noise generated in the battery voltage V makes it impossible to detect a singular point based on the amount of voltage change. difficult to identify.

FIG. 3 shows the correlation between the storage capacity Q and the amount of reaction heat H in the storage battery 40 . Reaction heat quantity H is the product of battery temperature T, battery current I, and heat generation coefficient dV/dT. Heat generation coefficient dV/dT is the amount of change in open circuit voltage per unit temperature. have a value. In the olivine-based storage battery 40 using graphite as the negative electrode active material 44g, when the storage capacity Q changes with the passage of electricity, the reaction heat quantity H changes at a predetermined capacity QA. Battery temperature T changes at a predetermined capacity QA. As a result, as shown in FIG. 4, in the correlation between the storage capacity Q and the battery temperature T, an inflection point occurs at a predetermined capacity QA.

The present inventors have found that in the correlation between the storage capacity Q and the battery temperature T, highly accurate linear approximation is possible on the lower capacity side and the higher capacity side than the predetermined capacity QA, and the slope of the straight line at the predetermined capacity QA We focused on the change in In the storage battery 40, the slope of the linear approximation is positive both on the lower capacity side and on the higher capacity side than the predetermined capacity QA. The slope of the linear approximation is smaller than the side. The inventors have found a method of specifying the predetermined capacity QA of the storage battery 40 based on the correlation between the storage capacity Q and the battery temperature T. FIG.

Specifically, as shown in FIG. 8, the control device 53 acquires time-series calorie data consisting of the storage capacity Q and the battery temperature T when energized, and spreads the acquired calorie data from a predetermined boundary B. A first data group DA with a smaller capacity than the boundary B and a second data group DB with a higher capacity than the boundary B. A first approximation function F1 that approximates the first data group DA with a linear function and a second approximation function F2 that approximates the second data group DB with a linear function are calculated, and the first approximation function F1 and the first data group are calculated. An error sum G is calculated which is the sum of an approximation error between each calorie data in DA and an approximation error between the second approximation function F2 and each calorie data in the second data group DB. Then, the boundary B is changed to the low-capacity side or the high-capacity side, and the calculation of the first approximation function F1 and the second approximation function F2 and the calculation of the error sum G are performed for each of the changed boundaries B. , and based on the first approximation function F1 and the second approximation function F2 corresponding to the minimum error sum G among the plurality of error sums G, the predetermined capacity QA of the storage battery 40 is specified. Carry out specific processing. According to the predetermined capacity identification process, the predetermined capacity QA of the storage battery 40 can be identified based on the correlation between the storage capacity Q and the battery temperature T, and the predetermined capacity QA of the storage battery 40 can be obtained without using the voltage change amount of the storage battery 40. QA can be specified.

FIG. 5 shows a flowchart of the determination processing of this embodiment. The determination process includes the predetermined capacity identification process described above, and determines the SOH based on the predetermined capacity QA of the storage battery 40 identified by the predetermined capacity identification process. When charging the storage battery 40, the control device 53 repeatedly performs the determination process at each predetermined control cycle.

When the determination process is started, in step S11, it is determined whether charging of the storage battery 40 has been completed. If the storage battery 40 is not fully charged and the charging of the storage battery 40 continues, the process proceeds to step S12. On the other hand, when the storage battery 40 is fully charged, the process proceeds to step S14. Note that the fully charged state of the storage battery 40 is, for example, a state in which the battery voltage V of the storage battery 40 reaches a predetermined full charge voltage, or a state in which the storage battery 40 exits the plateau region as the capacity increases and the battery voltage V changes as the capacity changes. means that the has become larger.

In step S12, the battery current I is measured and stored as charging current data. In the subsequent step S13, the battery temperature T is measured, the storage capacity Q of the storage battery 40 is calculated by current integration using the charging current data, and heat quantity data consisting of the storage capacity Q and the battery temperature T is stored.

The processes of steps S12 and S13 are repeatedly performed while the storage battery 40 is being charged. As a result, in step S12, time-series charging current data until the storage battery 40 reaches the fully charged state is stored, and in step S13, time-series heat amount data until the storage battery 40 reaches the fully charged state is stored. . In this embodiment, the process of step S12 corresponds to the "charging current storage section", and the process of step S13 corresponds to the "data storage section".

In step S14, a predetermined capacity identification process is performed. FIG. 6 shows a flowchart of the predetermined capacity specifying process. When the predetermined capacity specifying process is started, in step S21, an initial boundary B for calculating the first approximation function F1 and the second approximation function F2 is set.

As shown in FIG. 7, in the present embodiment, a plurality of boundary candidate BAs arranged at equal intervals are determined in the range of the storage capacity Q for which the battery temperature data is obtained, and selected from the plurality of boundary candidate BAs. One boundary candidate BA is set as the boundary B. At this time, the calorie data with a capacity lower than the boundary B is the first data group DA, and the calorie data with a capacity higher than the boundary B is the second data group DB. In step S21, the boundary candidate BA located on the highest capacity side among the plurality of boundary candidate BAs is set as the boundary B. FIG. This minimizes the amount of heat data included in the second data group DB.

In step S22, a first approximation function F1 and a second approximation function F2 are calculated. The first approximation function F1 and the second approximation function F2 are calculated using, for example, the method of least squares. In subsequent step S23, it is determined whether or not the absolute value of the first slope θ1, which is the slope of the first approximation function F1, is greater than the absolute value of the second slope θ2, which is the slope of the second approximation function F2. If the absolute value of the first slope θ1 is greater than the absolute value of the second slope θ2, the process proceeds to step S24. On the other hand, if the absolute value of the first slope θ1 is smaller than the absolute value of the second slope θ2, the error sum G is not calculated and the process proceeds to step S27. In the present embodiment, the process of step S22 corresponds to the "approximation calculation unit", and the process of step S23 corresponds to the "inclination determination unit".

In step S24, the error sum G is calculated. The error sum G is calculated using residuals, for example. As shown in FIG. 8A, in the time-series heat quantity data, the storage capacity Q and the battery temperature T indicated by the heat quantity data stored for the i-th time (i is a natural number) are assumed to be the storage capacity Qi and the battery temperature Ti. . In the first approximation function F1 and the second approximation function F2, the value corresponding to the storage capacity Qi is assumed to be the approximation value Fi. In this case, assuming that the number of time-series heat quantity data is n, the error sum G can be expressed by the following formula (1). It should be noted that in the present embodiment, the process of step S24 corresponds to the "error sum calculation unit".

In step S25, it is determined whether or not the error sum G calculated in step S24 is the minimum. At this time, if the error sum G calculated this time is smaller than the minimum value of the error sums G up to the previous time, it is assumed that the error sum G this time is the minimum. When the error sum G is the minimum, in step S26, the first approximation function F1 and the second approximation function F2 calculated in step S22 are stored, and the process proceeds to step S27. On the other hand, if the error sum G is not the minimum, the process proceeds to step S27 without storing the first approximation function F1 and the second approximation function F2 calculated in step S22.

In step S27, it is determined whether or not to move the boundary B to the low capacity side. In this embodiment, the setting range of the boundary B is determined in advance, and the boundary B is gradually changed from the storage capacity Q of the storage battery 40 on the fully charged side to the low capacity side. Specifically, the boundary candidate BA set as the boundary B is sequentially changed from the boundary candidate BA positioned on the highest capacity side to the boundary candidate BA positioned on the lowest capacity side. In step S27, it is determined whether or not the current boundary B for which the sum of errors G has been calculated is the boundary candidate BA on the lowest capacity side among the plurality of boundary candidate BAs in the set range of the boundary B. If it is not the boundary candidate BA, it is determined that the boundary B can be moved to the low capacity side, and the process proceeds to step S28. In step S28, the boundary B is moved to the low capacity side, and the process returns to step S22. On the other hand, if the current boundary B is the boundary candidate BA on the lowest capacity side, the process proceeds to step S29. It should be noted that in the present embodiment, the processing of steps S21 and S28 corresponds to the "dividing section".

That is, in the predetermined capacity identification process, each boundary candidate BA is set as the boundary B once. Every time each boundary candidate BA is set as the boundary B, the first approximation function F1 and the second approximation function F2 are calculated, and the error sum G is acquired. Then, the smallest error sum G is selected from among the plurality of error sums G obtained. Note that, in the present embodiment, repetition of the processing of steps S22 to S26 corresponds to the "acquisition unit".

In step S29, the predetermined capacity QA of the storage battery 40 is identified based on the first approximation function F1 and the second approximation function F2 stored in step S26, and the predetermined capacity identification process ends. If a plurality of first approximation functions F1 and second approximation functions F2 are stored in step S26, the last stored first approximation function among the plurality of first approximation functions F1 and second approximation functions F2 A predetermined capacity QA of the storage battery 40 is specified based on F1 and the second approximation function F2. In this embodiment, the storage capacity Q at the intersection X of the first approximation function F1 and the second approximation function F2 is specified as the predetermined capacity QA. It should be noted that in the present embodiment, the process of step S29 corresponds to the "specification unit".

Returning to FIG. 6, when the predetermined capacity specifying process ends, the process proceeds to step S15. In step S15, in the first approximation function F1 and the second approximation function F2 used to calculate the predetermined capacity QA in step S29, the slope that is the difference between the absolute value of the first slope θ1 and the absolute value of the second slope θ2 It is determined whether or not the difference Δθ is greater than the threshold θth. When the predetermined capacity QA is not included in the range of the storage capacity Q for which the battery temperature data is acquired and the slope difference Δθ is smaller than the threshold θth, the determination process is terminated. On the other hand, when the predetermined capacity QA is included in the range of the storage capacity Q for which the battery temperature data is acquired and the slope difference Δθ is larger than the threshold θth, the process proceeds to step S16.

In step S16, using the time-series charging current data stored in step S12, the battery current I that flowed through the storage battery 40 during the period from the specified capacity QA specified in step S29 until the storage battery 40 reached a fully charged state is calculated. to calculate the integrated current value ΔQ. In the subsequent step S17, SOH is determined based on the current integrated value ΔQ calculated in step S17, and the determination process ends. In this embodiment, the process of step S16 corresponds to the "integrated value calculation unit", and the process of step S17 corresponds to the "degradation state determination unit".

Explain how to determine SOH. The fully charged storage capacity Q when the storage battery 40 is new can be expressed as Q=QA+ΔQN using the integrated current value ΔQN when the storage battery 40 is new. Therefore, the SOH of the storage battery 40 can be represented by the following formula (2). When the predetermined capacity QA is specified by the predetermined capacity specifying process and the integrated current value ΔQN for the case where the storage battery 40 is new is acquired in advance, the SOH can be determined based on the integrated current value ΔQ. can.

Next, FIG. 8 shows an example of the predetermined capacity specifying process. 8A to 8C show a first approximation function F1 and a second approximation function F1 and a second approximation function when three different boundary candidates BA among the plurality of boundary candidates BA shown in FIG. 7 are set as the boundary B. F2 is shown.

In FIG. 8(A), the boundary B is set on the higher capacity side than the predetermined capacity QA. In this case, the approximation accuracy of the second approximation function F2 is good, but the approximation accuracy of the first approximation function F1 is poor. Therefore, although the approximation error between the second approximation function F2 and each calorie data in the second data group DB is small, the approximation error between the first approximation function F1 and each calorie data in the first data group DA is large, and the error sum G increases.

In FIG. 8(B), the boundary B is set near the predetermined capacity QA. In this case, the approximation accuracy of the first approximation function F1 and the second approximation function F2 is improved. Therefore, both the approximation error between the first approximation function F1 and each calorie data in the first data group DA and the approximation error between the second approximation function F2 and each calorie data in the second data group DB are small, and the error sum G becomes smaller.

In FIG. 8(C), the boundary B is set on the lower capacity side than the predetermined capacity QA. In this case, the approximation accuracy of the first approximation function F1 is good, but the approximation accuracy of the second approximation function F2 is poor. Therefore, although the approximation error between the first approximation function F1 and each calorie data in the first data group DA is small, the approximation error between the second approximation function F2 and each calorie data in the second data group DB becomes large, and the error sum G increases.

As a result, as shown in FIG. 9, when the boundary B is gradually changed from the high-capacity side to the low-capacity side, the error sum G decreases and then increases. A minimum error sum G appears in between. In this embodiment, the boundary B corresponding to the minimum error sum G is the boundary B in FIG. 8(B). In this case, the predetermined capacity QA is specified by the storage capacity Q at the intersection X of the first approximation function F1 and the second approximation function F2 in FIG. 8(B).

According to the present embodiment detailed above, the following effects can be obtained.

According to this embodiment, the predetermined capacity QA of the storage battery 40 can be specified based on the correlation between the battery temperature T, which correlates with the amount of reaction heat H of the storage battery 40, and the storage capacity Q. Therefore, the predetermined capacity QA of the storage battery 40 can be specified without using the voltage change amount of the storage battery 40 . Moreover, the battery temperature T has a characteristic that it becomes high when a large current flows through the storage battery 40 . Therefore, even when it is difficult to appropriately specify the predetermined capacity QA of the storage battery 40 based on the amount of voltage change due to a large current flowing through the storage battery 40, such as during high-speed charging, the predetermined capacity QA of the storage battery 40 can be specified. can do.

When charging the storage battery 40, charging starts from an arbitrary storage capacity Q, and then ends when the battery reaches a fully charged state. That is, while the storage capacity Q at the start of charging is arbitrary, the storage capacity Q at the end of charging is considered to be a substantially constant storage capacity (full charge capacity). In this embodiment, when changing the boundary B between the first data group DA and the second data group DB, the storage capacity Q of the storage battery 40 is gradually changed from the full charge side to the low capacity side. As a result, when dividing into a first data group DA having a capacity lower than that of the boundary B and a second data group DB having a capacity higher than that of the boundary B, the boundary B is set based on a generally constant storage capacity Q. Thus, it is possible to make it difficult for the situation in which the heat quantity data of the first data group DA does not exist, and to appropriately specify the predetermined capacity QA of the storage battery 40 .

In the storage battery 40 in which the amount of reaction heat H changes with a predetermined capacity QA as electricity is supplied, the battery temperature T changes with the change in the amount of reaction heat H, and the relationship between the battery temperature T and the storage capacity Q varies at the predetermined capacity QA. A point occurs. According to the present embodiment, since the first approximation function F1 and the second approximation function F2 are calculated using the time-series heat amount data including the battery temperature T and the storage capacity Q, the first approximation function F1 and the second approximation function F2 are calculated. The inflection point can be detected using the second approximation function F2. Therefore, even if it is difficult to appropriately specify the predetermined capacity QA of the storage battery 40 based on the amount of voltage change, the predetermined capacity QA of the storage battery 40 can be specified.

Also, the degree of change in the battery temperature T associated with the change in the amount of reaction heat H varies depending on factors outside the storage battery 40, such as the amount of heat released to the outside air, and the degree of change may not be determined unconditionally. In this embodiment, the predetermined capacity QA is specified from the minimum boundary B of the error sum G regardless of the degree of change in battery temperature T. FIG. Therefore, resistance to factors outside the storage battery 40 can be enhanced.

In the relationship between the battery temperature T and the storage capacity Q, when the boundary B is set in the vicinity area including the predetermined capacity QA of the storage battery 40, the absolute value of the first slope θ1 of the first approximation function F1 is the second approximation function It is larger than the absolute value of the second slope θ2 of F2. However, in the wide range of the storage capacity Q, depending on the setting of the boundary B, it is possible that the relationship between the slopes of each approximation function is reversed. There is a concern that the predetermined capacity QA may be erroneously specified based on . In this regard, according to the present embodiment, the predetermined capacity QA can be obtained correctly based on the first approximation function F1 and the second approximation function F2.

The SOH of the storage battery 40 is determined based on the predetermined capacity QA and the integrated current value ΔQ. Here, the storage capacity Q at which the amount of reaction heat H changes in the storage battery 40, that is, the predetermined capacity QA, does not change even if the storage battery 40 deteriorates. do. In this case, the SOH of the storage battery 40 can be properly determined by obtaining the predetermined capacity QA of the storage battery 40 as in the present embodiment and calculating the integrated current value ΔQ up to the full charge capacity based on the predetermined capacity QA. can.

Since the storage battery 40 contains graphite in the negative electrode, when the storage capacity Q reaches the predetermined capacity QA, the electrode structure changes in the negative electrode, and the reaction heat amount H changes. On the other hand, since the storage battery 40 contains lithium iron phosphate in the positive electrode, the voltage change amount of the storage battery 40 due to the change in the storage capacity Q of the storage battery 40 is small, and the predetermined capacity QA of the storage battery 40 is specified based on the voltage change amount. difficult. In this embodiment, since the predetermined capacity QA of the storage battery 40 is specified based on the battery temperature T that correlates with the amount of reaction heat H of the storage battery 40, the predetermined capacity QA of the storage battery 40 is specified without using the voltage change amount of the storage battery 40. be able to.

(Second embodiment)
The second embodiment will be described below with reference to FIGS. 10 to 12, focusing on differences from the first embodiment. This embodiment differs from the first embodiment in that the parameter correlated with the amount of reaction heat H is the complex impedance Zm.

FIG. 10 shows the correlation between the storage capacity Q and the real part ReZm of the complex impedance Zm. In the olivine-based storage battery 40 using graphite as the negative electrode active material 44g, when the storage capacity Q changes with energization, the complex impedance Zm changes with the temperature change that accompanies the change in the reaction heat quantity H at a predetermined capacity QA. As a result, as shown in FIG. 10, an inflection point occurs at a predetermined capacitance QA in the correlation between the storage capacitance Q and the real part ReZm of the complex impedance Zm.

In the correlation between the storage capacity Q and the real part ReZm of the complex impedance Zm, highly accurate linear approximation is possible on the lower capacity side and the higher capacity side than the predetermined capacity QA, and the slope of the straight line changes at the predetermined capacity QA. do. In the storage battery 40, the slopes of the linear approximation are both negative on the lower capacity side and on the higher capacity side than the predetermined capacity QA. The slope of the linear approximation becomes larger than the side. In this embodiment, the predetermined capacity QA of the storage battery 40 is specified based on the correlation between the storage capacity Q and the real part ReZm of the complex impedance Zm.

FIG. 11 shows a flowchart of the determination processing of this embodiment. In FIG. 11, the same steps as those shown in FIG. 6 are denoted by the same step numbers for convenience, and description thereof will be omitted.

In the determination process of the present embodiment, after storing the charging current data in step S12, the process proceeds to step S31. In step S31, the complex impedance Zm is calculated. In this embodiment, the frequency of the AC signal applied to the storage battery 40 when calculating the complex impedance Zm is set to a constant frequency equal to or lower than the ohmic frequency corresponding to the ohmic resistance Rohm of the storage battery 40 indicated by the end point PA in FIG. Specifically, the frequency is set to correspond to the end point PB in FIG. It should be noted that in the present embodiment, the process of step S31 corresponds to the "impedance calculator".

In the following step S13, heat quantity data consisting of the storage capacity Q and the complex impedance Zm calculated in step S31 is stored. In the predetermined capacitance identification process of the present embodiment, of the complex impedance Zm stored in step S13, the real part ReZm of the complex impedance Zm is used to calculate the first approximation function F1 and the second approximation function F2, and the error sum G is calculated to specify the predetermined capacity QA.

Next, FIG. 12 shows an example of the predetermined capacity specifying process. In FIGS. 12A to 12C, three different boundary candidates BA among the plurality of boundary candidates BA defined in the range of the storage capacity Q from which the complex impedance Zm is obtained are set as boundaries B. A first approximation function F1 and a second approximation function F2 are shown for the case.

In FIG. 12(A), the boundary B is set on the higher capacity side than the predetermined capacity QA, and the error sum G becomes large. In FIG. 12B, the boundary B is set near the predetermined capacity QA, and the error sum G is small. In FIG. 12C, the boundary B is set on the lower capacity side than the predetermined capacity QA, and the error sum G becomes large.

As a result, as shown in FIG. 9, when the boundary B is gradually changed from the high capacity side to the low capacity side, the minimum error sum G appears. In this embodiment, the boundary B corresponding to the minimum error sum G is the boundary B in FIG. 12(B). In this case, the predetermined capacity QA is specified by the storage capacity Q at the intersection X of the first approximation function F1 and the second approximation function F2 in FIG. 12(B).

In the storage battery 40 in which the amount of reaction heat H changes with a predetermined capacity QA as electricity is supplied, the complex impedance Zm changes due to temperature changes when the amount of reaction heat H changes, and the relationship between the complex impedance Zm and the storage capacity Q An inflection point occurs at the capacitance QA. According to the present embodiment, since the first approximation function F1 and the second approximation function F2 are calculated using the time-series calorie data including the complex impedance Zm and the storage capacity Q, the first approximation function F1 and the second approximation function F2 are calculated. The inflection point can be detected using the second approximation function F2. Therefore, even if it is difficult to appropriately specify the predetermined capacity QA of the storage battery 40 based on the amount of voltage change, the predetermined capacity QA of the storage battery 40 can be specified.

In this embodiment, when calculating the complex impedance Zm of the storage battery 40, the frequency of the AC signal is set to a frequency equal to or lower than the ohmic frequency. In particular, at frequencies lower than the ohmic frequency, the calculated complex impedance Zm is strongly influenced by the reaction resistance. Since the reaction resistance strongly depends on the temperature, the amount of change in the complex impedance Zm at the predetermined capacitance QA tends to increase. Therefore, by using the reaction resistance, the predetermined capacity QA of the storage battery 40 can be specified with high accuracy.

(Other embodiments)
The present disclosure is not limited to the description of the above embodiments, and may be implemented as follows.

- In the above embodiment, an example of storing the time-series heat amount data when charging the storage battery 40 was shown, but the time-series heat amount data may be stored when the storage battery 40 is discharged. However, the polarity of the reaction heat quantity H is reversed during discharge. This reversal changes the tendency of the temperature change in the reaction heat amount H at the predetermined capacity QA, and the direction of the inequality sign in step S23 is reversed.

· In the above embodiment, an example of changing the plurality of boundaries B at equal intervals was shown, but the intervals between the boundaries B may be uneven. For example, the storage capacity range defined as the setting range of boundary B is divided into a low capacity range, an intermediate range, and a high capacity range in order from the low capacity side, and the interval of boundary B is set in the low capacity range and high capacity range. It may be relatively large and in the intermediate range the spacing of the boundaries B may be relatively small. Alternatively, the predetermined capacity QA calculated in the past may be referred to, the interval of the boundary B may be made relatively small in the neighborhood including the predetermined capacity QA, and the interval of the boundary B may be made relatively large in the area other than the neighborhood.

· In the above embodiment, an example of calculating the sum of errors G using residuals was shown, but the present invention is not limited to this. For example, the error sum G may be calculated as Further, for example, in FIGS. 8 and 12, the area between the graph indicated by the first approximation function F1 and the graph indicated by the first data group DA, and the area indicated by the graph indicated by the second approximation function F2 and the second data group DB The sum of the areas between the graphs may be calculated as the error sum G.

In the above embodiment, when the boundary B is gradually changed from the storage capacity Q on the fully charged side of the storage battery 40 to the low capacity side, the boundary candidate BA set as the boundary B is positioned on the highest capacity side. An example of sequentially changing from the boundary candidate BA to the boundary candidate BA located on the lowest capacity side has been shown, but the present invention is not limited to this. For example, the predetermined capacity QA calculated in the past may be referenced, and instead of the boundary candidate BA located on the highest capacity side, the boundary candidate BA based on the predetermined capacity QA may be changed to the low capacity side.

Also, the boundary candidate BA located on the lowest capacity side may be changed as follows. According to the predetermined capacity specifying process of FIG. 6, as the boundary B is gradually changed from the fully charged storage capacity Q to the low capacity side, the error sum G gradually decreases and then increases. In this case, in step S27, it may be determined whether or not to move the boundary B to the low capacity side based on the error sum G turning from decreasing to increasing.

In other words, when the control device 53 determines that the error sum G has changed from decreasing to increasing as the boundary B is changed to the lower capacity side, it is not necessary to change the boundary B to the lower capacity side. As such, it may be determined that the boundary B is not moved to the low capacity side (NO in step S27).

- In the above embodiment, an example in which all the stored heat quantity data is used has been shown, but the present invention is not limited to this. For example, if part of the calorie data includes a portion where the battery temperature T does not monotonically increase with respect to the storage capacity Q, or a portion where the complex impedance Zm does not monotonically decrease with respect to the storage capacity Q, these calorie data It may contain disturbances. In this case, it is also possible to extract and use only the heat amount data that monotonously increases or monotonically decreases.

In the above embodiment, when specifying the predetermined capacity QA of the storage battery 40 based on the first approximation function F1 and the second approximation function F2 corresponding to the minimum error sum G, the first approximation function F1 and the second approximation function Although an example in which the storage capacity Q at the intersection X of F2 is specified as the predetermined capacity QA has been shown, the present invention is not limited to this. The storage capacity Q at the boundary B corresponding to the minimum error sum G may be specified as the predetermined capacity QA.

- In the above embodiment, an example of using a storage battery having a plateau region as the storage battery 40 was shown, but the technology of the present embodiment may be used for a storage battery that does not have a plateau region.

- In the above embodiment, an example in which the storage battery 40 has one peculiar point (predetermined capacity QA) is shown, but the present invention is not limited to this, and the storage battery 40 may have a plurality of peculiar points (predetermined capacity QA). .

- In the above-described second embodiment, when specifying the predetermined capacity QA of the storage battery 40, an example was shown in which the real part ReZm of the complex impedance Zm was used. Both the part ImZm may be used.

· In the above-described second embodiment, a form in which an AC signal is applied by the oscillator 51a when calculating the complex impedance Zm has been shown, but the present invention is not limited to this. For example, the complex impedance Zm may be calculated by dividing the change in the battery voltage V by the change in the battery current I when the frequency characteristics and the like of the electric load 20 change during energization.

In the above embodiment, the predetermined capacity identification process is performed when the storage battery 40 is in a fully charged state. The predetermined capacity specifying process may be performed on the condition that it is stored.

- The controller and method described in the present disclosure can be performed by a dedicated computer provided by configuring a processor and memory programmed to perform one or more functions embodied by a computer program; may be implemented. Alternatively, the controls and techniques described in this disclosure may be implemented by a dedicated computer provided by configuring the processor with one or more dedicated hardware logic circuits. Alternatively, the control units and techniques described in this disclosure can be implemented by a combination of a processor and memory programmed to perform one or more functions and a processor configured by one or more hardware logic circuits. It may also be implemented by one or more dedicated computers configured. The computer program may also be stored as computer-executable instructions on a computer-readable non-transitional tangible recording medium.

Although the present disclosure has been described with reference to examples, it is understood that the present disclosure is not limited to those examples or structures. The present disclosure also includes various modifications and modifications within the equivalent range. In addition, various combinations and configurations, as well as other combinations and configurations, including single elements, more, or less, are within the scope and spirit of this disclosure.

Claims

Applied to a storage battery (40) in which the amount of reaction heat changes with a predetermined capacity when the storage capacity changes with energization,
a data storage unit that acquires a parameter that correlates with the amount of reaction heat of the storage battery when energized, and that stores time-series heat amount data consisting of the parameter and the storage capacity;
a dividing unit that divides the calorie data stored by the data storage unit into a first data group having a capacity lower than a predetermined boundary and a second data group having a capacity higher than the boundary;
an approximation calculator that calculates a first approximation function that approximates the first data group with a linear function and a second approximation function that approximates the second data group with a linear function;
An error sum that is the sum of an approximation error between the first approximation function and each of the heat quantity data in the first data group and an approximation error between the second approximation function and each of the heat quantity data in the second data group an error sum calculation unit for calculating;
changing the boundary to the low-capacity side or the high-capacity side, calculating the first approximation function and the second approximation function by the approximation calculation unit, and calculating the error sum by the error sum calculation unit for each of the changed boundaries an acquisition unit that performs calculation and acquires a plurality of sums of errors;
a battery monitoring device (53) comprising: a specifying unit that specifies the predetermined capacity of the storage battery based on the first approximation function and the second approximation function corresponding to the minimum error sum among the plurality of error sums. .
The data storage unit stores the time-series calorie data during charging of the storage battery,
2. The battery monitoring device according to claim 1, wherein said acquisition unit gradually changes said boundary from a fully charged side of said storage battery to a lower capacity side.
The parameter is a battery temperature indicating the temperature of the storage battery,
3. The battery according to claim 1, wherein the approximation calculation unit calculates the first approximation function and the second approximation function using time-series heat amount data including the battery temperature and the storage capacity. surveillance equipment.
the parameter is the impedance of the storage battery,
3. The battery monitoring system according to claim 1, wherein said approximation calculator calculates said first approximation function and said second approximation function using time-series heat amount data including said impedance and said storage capacity. Device.
an impedance calculation unit that acquires a response signal of the storage battery to the AC signal while a predetermined AC signal is applied to the storage battery, and calculates the impedance based on the response signal;
5. The battery monitoring device according to claim 4, wherein the frequency of said AC signal is set to a frequency equal to or lower than an ohmic frequency corresponding to the ohmic resistance of said storage battery.
a slope determination unit that determines whether the absolute value of the slope of the first approximation function is greater than the absolute value of the slope of the second approximation function;
The error sum calculation unit calculates the error sum on condition that the slope determination unit determines that the absolute value of the slope of the first approximation function is larger than the absolute value of the slope of the second approximation function. The battery monitoring device according to any one of claims 1 to 5.
a charging current storage unit that stores time-series charging current data until the storage battery reaches a fully charged state when the storage battery is charged;
an integrated value calculation unit that uses the charging current data to calculate an integrated current value of the charging current that has flowed through the storage battery in a period from the predetermined capacity specified by the specifying unit to a fully charged state;
The battery monitoring device according to any one of claims 1 to 6, further comprising a deterioration state determination unit that determines a deterioration state of the storage battery based on the integrated current value.
The battery monitoring device according to any one of claims 1 to 7, wherein the storage battery contains graphite in the negative electrode.
The battery monitoring device according to claim 8, wherein the storage battery contains lithium iron phosphate in the positive electrode.