JP2021047132A - Rechargeable battery state detector and rechargeable battery state detection method - Google Patents

Abstract

To correctly detect abnormality which occurs on an electrode of a rechargeable battery mounted on a vehicle, regardless of a use environment.SOLUTION: There is provided a rechargeable battery state detector for detecting a state of a rechargeable battery, the rechargeable battery state detector comprises: detection means (CPU 10a) for detecting voltage of a rechargeable battery on the basis of a signal output from a voltage detection unit; optimization means (CPU 10a) for optimizing a coefficient of a prescribed mathematical model which indicates an internal state of the rechargeable battery on the basis of the voltage value detected by the detection means, by adjusting the coefficient; determination means (CPU 10a) for determining presence/absence of pole plate abnormality of a pole plate which exists in an electrolyte of the rechargeable battery, on the basis of a value or a value of the coefficient calculated by the mathematical model whose coefficient is adjusted by the optimization means; and output means (I/F 10e) for outputting a determination result of the determination means.SELECTED DRAWING: Figure 2

Description

The present invention relates to a rechargeable battery state detecting device and a rechargeable battery state detecting method.

Conventionally, as a technique for detecting an abnormality occurring in a electrode plate of a rechargeable battery, there are techniques disclosed in Patent Document 1 and Patent Document 2.

In the technique disclosed in Patent Document 1, disturbance due to repeated rise / fall is generated as compared with a preset voltage or current or a voltage or current determined by learning the charging behavior in a state where no abnormality occurs. If it is large, it is determined that an electrode plate abnormality has occurred.

Further, in the technique disclosed in Patent Document 2, the open circuit voltage of the rechargeable battery after charging is acquired for a predetermined period in a predetermined sampling cycle, the acquired open circuit voltage is approximated by a power approximation formula, and the reduction rate of the convergent voltage is obtained. When exceeds a predetermined reference level, it is determined that an abnormality (cell short) has occurred in the electrode plate.

Japanese Unexamined Patent Publication No. 07-165016 Japanese Unexamined Patent Publication No. 2005-0433339

By the way, in the technique disclosed in Patent Document 1, since the current-limited charging is detected, for example, it is difficult to guarantee the current-limited charging with a rechargeable battery mounted on a vehicle. There is a problem that it is difficult to detect an abnormality in the electrode plate.

Further, in the technique disclosed in Patent Document 2, the voltage of the rechargeable battery may change due to the fluctuation of the current, and in such a case, there is a problem that it is erroneously determined that the electrode plate is abnormal.

The present invention has been made in view of the above circumstances, and is a rechargeable battery capable of accurately detecting an abnormality occurring in an electrode of a rechargeable battery mounted on a vehicle regardless of the usage environment. It is an object of the present invention to provide a state detection device and a rechargeable battery state detection method.

In order to solve the above problems, the present invention is a detecting means for detecting the voltage of the rechargeable battery based on the signal output from the voltage detection unit in the rechargeable battery state detecting device for detecting the state of the rechargeable battery. An optimization means that optimizes by adjusting a coefficient of a predetermined mathematical model indicating the internal state of the rechargeable battery based on the voltage value detected by the detection means, and the optimization means. A determination means for determining the presence or absence of a plate abnormality of the electrode plate existing in the electrolytic solution of the rechargeable battery based on the value calculated by the mathematical model in which the coefficient is adjusted or the value of the coefficient, and the determination means. It is characterized by having an output means for outputting the determination result according to the above.
According to such a configuration, it is possible to accurately detect an abnormality occurring in the electrodes of the rechargeable battery mounted on the vehicle regardless of the usage environment.

Further, the present invention is characterized in that the electrode plate abnormality is an abnormality in which the electrical characteristics of the rechargeable battery change discontinuously.
According to such a configuration, the electrode plate abnormality of the rechargeable battery can be detected based on the electrical characteristics.

Further, the present invention is characterized in that the electrode plate abnormality is a short circuit in a part region of the electrode plate.
With such a configuration, it becomes possible to accurately detect a plate abnormality that causes a voltage drop.

Further, the present invention is characterized in that the mathematical model includes a term indicating a voltage change with the passage of time of the rechargeable battery.
According to such a configuration, it is possible to determine the presence or absence of a plate abnormality based on the voltage change with the passage of time.

Further, the present invention is characterized in that the mathematical model includes a term indicating a voltage change accompanying a change in the current of the rechargeable battery.
According to such a configuration, it is possible to determine the presence or absence of an abnormality in the electrode plate even when there is a change in the current.

Further, the present invention is characterized in that the mathematical model shows a voltage change after charging / discharging of the rechargeable battery is stopped.
According to such a configuration, the presence or absence of an abnormality in the electrode plate can be accurately determined by utilizing the time when the voltage after charging / discharging is stabilized.

Further, in the present invention, the mathematical model showing the voltage change after the charging / discharging of the rechargeable battery is stopped is an exponential attenuation function having one or more terms for calculating the voltage value of the rechargeable battery. It is characterized by being an inverse proportional function.
According to such a configuration, the voltage change can be detected accurately.

Further, the present invention is characterized in that the mathematical model shows a voltage change during charging / discharging of the rechargeable battery.
According to such a configuration, it is possible to determine the presence or absence of an abnormality in the electrode plate even during charging / discharging.

Further, the present invention is characterized in that the mathematical model showing the voltage change during charging / discharging of the rechargeable battery is an equivalent circuit model of the rechargeable battery.
According to such a configuration, it is possible to determine the presence or absence of a plate abnormality even during charging / discharging based on the equivalent circuit model.

Further, in the present invention, the optimization means includes a first mathematical model showing a voltage change after the rechargeable battery has stopped charging / discharging, and a second mathematical model showing a voltage change during charging / discharging of the rechargeable battery. The model and the coefficient are selected by selecting according to the charging state of the rechargeable battery, and the detecting means is based on the first mathematical model or the second mathematical model selected by the optimizing means. It is characterized in that the electrode plate abnormality is detected.
According to such a configuration, it is possible to determine the presence or absence of an abnormality in the electrode plate regardless of the state of charge and discharge.

Further, in the present invention, the determination means determines the presence or absence of the electrode plate abnormality based on the value calculated by the mathematical model and the measured value of the voltage of the rechargeable battery at two or more different times. It is characterized by that.
According to such a configuration, the presence or absence of a plate abnormality can be accurately determined based on the values calculated at different times and the actually measured values.

Further, in the present invention, in the determination means, does the difference value between the value calculated by the mathematical model and the measured value of the voltage of the rechargeable battery become equal to or more than a predetermined threshold value in two or more different times? Or, when the sum of the difference values or the sum of squares of the difference values is equal to or greater than a predetermined threshold value, it is determined that the plate abnormality is present.
According to such a configuration, the presence or absence of an abnormality in the electrode plate can be accurately determined by a simple method.

Further, in the present invention, in the determination means, the frequency or frequency rate at which the difference value between the value calculated by the mathematical model and the measured value of the voltage of the rechargeable battery is out of a predetermined range is equal to or higher than a predetermined threshold value. If this is the case, it is characterized in that it is determined that the electrode plate is abnormal.
According to such a configuration, the presence or absence of electrode plate abnormality can be accurately determined based on the frequency or the frequency rate.

Further, in the present invention, when the standard deviation of the difference value between the value calculated by the mathematical model and the measured value of the voltage of the rechargeable battery is equal to or more than a predetermined threshold value, the determination means is the plate. It is characterized in that it is determined to be abnormal.
According to such a configuration, the presence or absence of electrode plate abnormality can be accurately determined based on the standard deviation.

Further, in the present invention, when the determination means has a plurality of peaks in the distribution of the difference value between the value calculated by the mathematical model and the measured value of the voltage of the rechargeable battery, the plate abnormality is determined. It is characterized by making a judgment.
According to such a configuration, the presence or absence of electrode plate abnormality can be accurately determined based on the peak of the distribution.

Further, the present invention is characterized in that the determination means determines the plate abnormality based on at least one coefficient of the mathematical model optimized by the optimization means.
According to such a configuration, the presence or absence of electrode plate abnormality can be accurately determined based on the coefficient.

Further, in the present invention, when the determination means differs from at least one coefficient of the mathematical model optimized by the optimization means and a coefficient when optimized in the past by a predetermined threshold value or more. Is characterized in that it is determined that the electrode plate is abnormal.
According to such a configuration, the presence or absence of electrode plate abnormality can be accurately determined by comparing with the coefficient in the past.

Further, in the present invention, the rechargeable battery is mounted on the vehicle, the output means notifies the determination result by the determination means to a higher-level control device mounted on the vehicle, and the control device that receives the notification is , A warning is issued to the user based on the determination result, and / or the execution of the idling stop that stops the engine at the time of idling is suspended.
According to such a configuration, it is possible to notify the user of the occurrence of a plate abnormality and prevent the engine from being unable to restart.

Further, according to the present invention, in the rechargeable battery state detecting method for detecting the state of the rechargeable battery, the detection step of detecting the voltage of the rechargeable battery based on the signal output from the voltage detection unit and the detection step of the detection step. An optimization step that optimizes by adjusting a coefficient of a predetermined mathematical model indicating the internal state of the rechargeable battery based on the detected voltage value, and the above-mentioned that the coefficient is adjusted in the optimization step. A determination step for determining the presence or absence of an abnormality in the electrode plate existing in the electrolytic solution of the rechargeable battery based on the value calculated by the mathematical model or the value of the coefficient, and the determination result in the determination step are output. It is characterized by having an output step.
According to such a method, it is possible to accurately detect an abnormality occurring in an electrode of a rechargeable battery mounted on a vehicle regardless of the usage environment.

According to the present invention, there is provided a rechargeable battery state detection device and a rechargeable battery state detection method capable of accurately detecting an abnormality occurring in an electrode of a rechargeable battery mounted on a vehicle regardless of the usage environment. can do.

It is a figure which shows the structural example of the power-source system of the vehicle which has the rechargeable battery state detection device which concerns on embodiment of this invention. It is a block diagram which shows the detailed configuration example of the control part of FIG. It is a figure which shows the change of the voltage and the current of the rechargeable battery after the charge / discharge stop when the rechargeable battery is normal. It is a figure which shows the change of the voltage and the current of the rechargeable battery after the charge / discharge stop when the electrode plate abnormality occurs in the rechargeable battery. It is a flowchart for demonstrating an example of the process executed in Embodiment of this invention. It is a flowchart for demonstrating an example of the process of optimizing the coefficient of a mathematical model. It is a flowchart for demonstrating an example of the process for determining the presence or absence of a plate abnormality. It is a figure which shows the relationship between the voltage measurement timing and the number of terms of the exponential decay function. It is a figure which shows the relationship between an error and a current when a current flows through a rechargeable battery. It is a figure which shows the distribution of the difference value with and without the electrode plate abnormality. It is a flowchart for demonstrating an example of the process for determining the presence or absence of a plate abnormality. It is a figure which shows the process of optimizing the coefficient of a healthy rechargeable battery. It is a figure which shows the process of optimizing the coefficient of the rechargeable battery which caused the electrode plate abnormality. It is a figure which shows each value of the coefficient A1 to A6 optimized in FIG. 12 or FIG. It is a flowchart for demonstrating an example of the process for determining the presence or absence of a plate abnormality. It is a figure for demonstrating an example of a time window. It is a figure which shows an example of the equivalent circuit model.

Next, an embodiment of the present invention will be described.

(A) Explanation of Configuration of Embodiment of the Present Invention FIG. 1 is a diagram showing a power supply system of a vehicle having a rechargeable battery state detection device according to the embodiment of the present invention. In this figure, the rechargeable battery state detection device 1 has a control unit 10 as a main component, and a voltage detection unit 11, a current detection unit 12, a temperature detection unit 13, and a discharge circuit 15 are connected to the outside to charge the battery. The state of the possible battery 14 is detected. The control unit 10, the voltage detection unit 11, the current detection unit 12, the temperature detection unit 13, and the discharge circuit 15 may not be configured separately, but may be partially or all of them.

Here, the control unit 10 refers to the outputs from the voltage detection unit 11, the current detection unit 12, and the temperature detection unit 13, detects the state of the rechargeable battery 14, and outputs the detection result information to the outside. At the same time, the charging state of the rechargeable battery 14 is controlled by controlling the generated voltage of the alternator 16. The control unit 10 does not control the charging state of the rechargeable battery 14 by controlling the generated voltage of the alternator 16, but for example, an ECU (Electric Control Unit) (external control unit) (not shown) is the control unit 10. The charging state may be controlled based on the information from.

The voltage detection unit 11 detects the terminal voltage of the rechargeable battery 14 and supplies it to the control unit 10 as a voltage signal. The current detection unit 12 detects the current flowing through the rechargeable battery 14 and supplies it to the control unit 10 as a current signal. The temperature detection unit 13 detects the environmental temperature or the internal temperature of the rechargeable battery 14 and supplies it to the control unit 10 as a temperature signal.

The discharge circuit 15 is composed of, for example, a semiconductor switch and a resistance element connected in series, and by turning on / off the semiconductor switch according to the control of the control unit 10, the rechargeable battery 14 is discharged with a desired waveform. Can be made to. The discharge circuit 15 can be excluded from the configuration.

The alternator 16 is driven by the engine 17, generates AC power, converts it into DC power by a rectifier circuit, and charges the rechargeable battery 14. The alternator 16 is controlled by the control unit 10 so that the generated voltage can be adjusted.

The engine 17 is composed of, for example, a reciprocating engine such as a gasoline engine and a diesel engine, a rotary engine, or the like, is started by a starter motor 18, drives drive wheels via a transmission, gives propulsion to a vehicle, and is an alternator 16. To generate power. The starter motor 18 is composed of, for example, a DC motor, and generates a rotational force by the electric power supplied from the rechargeable battery 14 to start the engine 17. An electric motor may be used instead of the engine 17.

The load 19 is composed of, for example, an electric steering motor, a defogger, a seat heater, an ignition coil, a car audio, a car navigation system, and the like, and is operated by electric power supplied from the rechargeable battery 14. In the example of FIG. 1, only the engine 17 outputs the driving force, but for example, a hybrid vehicle equipped with an electric motor that assists the engine 17 may be used. In the case of a hybrid vehicle, the rechargeable battery 14 activates a high-pressure system (a system for driving an electric motor) composed of a lithium battery or the like, and the high-pressure system starts the engine 17.

FIG. 2 is a diagram showing a detailed configuration example of the control unit 10 shown in FIG. As shown in this figure, the control unit 10 includes a CPU (Central Processing Unit) 10a as a processor, a ROM (Read Only Memory) 10b, a RAM (Random Access Memory) 10c, a communication unit 10d, and an I / F (Interface) 10e. , And a bus 10f. Here, the CPU 10a controls each unit based on the program 10ba stored in the ROM 10b. The ROM 10b is composed of a semiconductor memory or the like and stores the program 10ba or the like. The RAM 10c is composed of a semiconductor memory or the like, and stores data generated when the program 10ba is executed and data 10ca such as a table. The communication unit 10d communicates with the higher-level device such as the ECU, and notifies the higher-level device of the detected information or control information. The I / F 10e converts the voltage signal and the current signal supplied from the voltage detection unit 11, the current detection unit 12, and the temperature detection unit 13 into a digital signal and captures them, and also takes in the discharge circuit 15, the alternator 16, and the alternator 16. , A drive current is supplied to the starter motor 18 and the like to control them. The bus 10f is a group of signal lines for connecting the CPU 10a, the ROM 10b, the RAM 10c, the communication unit 10d, and the I / F 10e to each other and enabling information exchange between them. The control of the alternator 16, the starter motor 18, and the like may be executed by the ECU. The configuration is not limited to that shown in FIG.

In the example of FIG. 2, one CPU 10a is provided, but the distributed processing may be executed by a plurality of CPUs. Further, instead of the CPU 10a, it may be configured by a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or the like. Alternatively, the processing may be performed on the computer on the server by a general-purpose processor or cloud computing that executes a function by loading a software program. Further, although the ROM 10b and the RAM 10c are provided in FIG. 2, for example, a storage device other than these (for example, an HDD (Hard Disk Drive) which is a magnetic storage device) may be used.

(B) Description of Operation of Embodiment of the Present Invention Next, the operation of the embodiment of the present invention will be described. In the following, after explaining the operation of the embodiment of the present invention, the processing of the flowchart for realizing such an operation will be described.

First, the outline of the operation of the embodiment of the present invention will be described. In the case of a lead storage battery, for example, the rechargeable battery 14 has lead dioxide as an anode and lead as a cathode as a electrode plate. Such an electrode plate may have an abnormality (hereinafter, simply referred to as “plate abnormality”) accompanied by a discontinuous change in electrical characteristics (for example, terminal voltage or internal impedance). For example, when the lead sulfate generated by electric discharge is charged while being bridged between the anode and the cathode, the lead sulfate is reduced and changed to lead, which causes a short circuit in a part of the electrode plate. There is. Further, the electrode plate may be peeled off due to vibration of the vehicle or the like, and the anode and the cathode may be conducted by the peeled electrode plate to cause a short circuit in a part of the electrode plate. A short circuit that occurs in a part of the electrode plate is hereinafter referred to as a "partial short circuit".

In the embodiment of the present invention, the voltage of the rechargeable battery 14 is represented by a mathematical model described later, and the coefficient of the mathematical model is optimized so that the value calculated by the mathematical model and the measured value of the voltage match. Then, when the voltage value calculated by the mathematical model obtained in this way and the actually measured voltage value deviate from each other, it is determined that a plate abnormality accompanied by a discontinuous change in the electrical characteristics has occurred. Then, the determination result is notified to the ECU, which is a higher-level control device. The notified ECU or control device may issue a warning to the user based on the determination result. Further, when an abnormality occurs in the electrode plate, for example, the execution of the idling stop may be suspended to prevent the engine 17 from being unable to restart.

Next, the operation of the present invention will be described in detail. In the present embodiment, when the engine 17 of the vehicle is stopped and the rechargeable battery 14 becomes stable, the CPU 10a acquires the quadratic exponential decay function represented by the following equation (1) from the ROM 10b.

・・・（１）

... (1)

Here, VFn (n, In) is a quadratic exponential function, n indicates the number of the sampled data, In is the sampling value of the nth current, and Δt is the sampling period of the data. Yes, exp indicates an exponential function. Further, "A1 · exp (A3 · Δt · n) + A2 · exp (A4 · Δt · n) + A5" is a term indicating a change in voltage with the passage of time, and "A6 · In" is due to the influence of dark current. This item indicates a voltage change.

The CPU 10a initially sets the coefficient of the equation (1) acquired from the ROM 10b. More specifically, the CPU 10a sets a value of Δt (for example, 10 seconds).

Next, the CPU 10a acquires the voltage value and the current value from the voltage detection unit 11 and the current detection unit 12, and sets them as Vn and In, respectively.

Next, the CPU 10a acquires the initial values of the coefficients A1 to A6 of the equation (1) from the ROM 10b and sets them in the equation (1). These coefficients A1 to A6 are used to derive the optimum solution by the least squares method, and the values are sequentially updated in the process of calculation as described later. As the initial value of each coefficient A1 to A6, a predetermined value obtained in advance by an experiment may be used.

Next, the CPU 10a calculates VFn (n, In) by substituting n and the measured value In of the current into the equation (1). As a result, the value of VFn (n, In), which is the calculation result by the mathematical model, is obtained.

Next, the CPU 10a applies the VFn (n, In) obtained by the equation (1) and the voltage value Vn as the measured value acquired from the voltage detection unit 11 to the following equation (2) to obtain the value of Rn. To get.

・・・（２）

... (2)

Next, the CPU 10a calculates the partial differential term corresponding to each coefficient A1 to A6 based on the following equation (3) in order to apply the least squares method.

・・・（３）

... (3)

Next, the CPU 10a calculates the matrix B to be applied to the simultaneous equations of the least squares method using each partial differential term obtained by the equation (3) based on the following equation (4).

・・・（４）

... (4)

The matrix B in Eq. (4) is a 6 × 6 square matrix, and is a symmetric matrix in which B (x, y) = B (y, x).

Next, the CPU 10a calculates dR represented by the following equation (5) by using the Rn obtained by the equation (2) and the partial differential term obtained by the equation (4).

・・・（５）

... (5)

Next, the CPU 10a calculates the difference dd represented by the following equation (6) by using the matrix B obtained by the equation (4) and the dR obtained by the equation (5).

・・・（６）

... (6)

As described above, since the six differences dd1 to dd6 corresponding to the coefficients A1 to A6 can be obtained, the CPU 10a satisfies the following equation (7) for these six differences dd1 to dd6. Judge whether or not.

・・・（７）

... (7)

The Th on the right side of the equation (7) can be ^{, for example, 10-12.} Note that ^10-12 is an example, and other values that can be determined as values close to zero may be used. As a result, when it is determined that the equation (7) is not satisfied, the coefficients A1 to A6 are updated based on the following equation (8), and the same processing as described above is repeated.

・・・（８）

... (8)

On the other hand, when it is determined that the equation (7) is satisfied, it is determined that the optimum solution of the least squares method is obtained at that time because the differences dd1 to dd6 are sufficiently close to zero.

By the above processing, a mathematical model having optimized coefficients A1 to A6 can be obtained.

In the mathematical model obtained as described above, the voltage actually measured in the normal state of the electrode plate and the voltage estimated by the mathematical model are in good agreement. FIG. 3 shows a change in the measured voltage of the rechargeable battery 14 (indicated by a circle) after the engine 17 is stopped, a change in the voltage calculated by the mathematical model (indicated by a diamond), and a change in the current (indicated by a triangle). ) Is shown. As shown in FIG. 3, when the electrodes of the rechargeable battery 14 are in a normal state, the circle indicating the measured voltage and the rhombus indicating the voltage calculated by the mathematical model are in good agreement.

On the other hand, when a plate abnormality occurs in the rechargeable battery 14, a voltage fluctuation due to the plate abnormality occurs, and a discrepancy occurs between the voltage calculated by the mathematical model and the measured voltage. FIG. 4 shows the measured voltage change (circled) of the rechargeable battery 14 and the voltage change calculated by the mathematical model (circled) when a cell short circuit (partial short circuit), which is an abnormality of the electrode, occurs in the vicinity of 1000 seconds. The relationship between the change in current (indicated by a triangle) and the change in current (indicated by a diamond) is shown. In FIG. 4, there is a discrepancy between the measured voltage of the rechargeable battery 14 and the calculated voltage of the mathematical model after 1000 seconds when the cell short occurs. By detecting such a dissociation, it is possible to detect an abnormality in the electrode plate of the rechargeable battery 14.

More specifically, the CPU 10a obtains the model voltage VFn (n, In) by substituting the measured value of the current In into the equation (1) for which the optimum solution of the coefficients A1 to A6 is obtained by the above processing. .. Next, the CPU 10a obtains the absolute value of the difference value between VFn (n, In) and the measured value Vn of the voltage, and when the absolute value of the difference value is larger than the predetermined threshold value Th, an electrode plate abnormality occurs. In other cases, it is determined that no electrode abnormality has occurred. In addition, since there is a possibility that an erroneous determination may occur if only one determination is made, it may be determined that an electrode plate abnormality has occurred when an abnormality is determined in a plurality of determinations.

Next, the details of the processing executed in the embodiment of the present invention will be described with reference to FIGS. 5 to 7.

FIG. 5 is a diagram for explaining the main processing executed in the present embodiment. The process shown in FIG. 5 is executed with a predetermined time window (for example, a window of 1 hour) as a repeating cycle, and the mathematical model shown in the equation (1) is optimized in the time window. When the processing of the flowchart shown in FIG. 5 is started, the following steps are executed.

In step S1, the CPU 10a executes a process of optimizing the coefficients A1 to A6 of the mathematical model shown in the equation (1). The details of the process in step S1 will be described later with reference to FIG.

In step S2, the CPU 10a uses the mathematical model shown in the equation (1) in which the coefficients A1 to A6 are optimized in step S1 to VFn (n, In) (n = 1, 2, ..., Ns). Executes the process of calculating. As a result, Ns data (for example, 60 data) can be obtained.

In step S3, the CPU 10a refers to the mathematical model calculated in step S2 and executes a plate abnormality determination process for determining whether or not a plate abnormality has occurred. The details of the process in step S3 will be described later with reference to FIG. 7.

In step S4, the CPU 10a proceeds to step S5 when it is determined that an electrode plate abnormality has occurred in the plate abnormality determination process shown in step S3 (step S4: Y), and proceeds to step S5 in other cases (step S4). : N) proceeds to step S7. For example, if it is determined in step S4 that an electrode plate abnormality has occurred, it is determined to be Y and the process proceeds to step S5.

In step S5, the CPU 10a determines that the electrode plate abnormality has occurred, and notifies, for example, the occurrence of the electrode plate abnormality to an ECU (not shown).

In step S6, the ECU executes the processing when a plate abnormality occurs. More specifically, the ECU can suspend the execution of a so-called idling stop that temporarily stops the engine 17 at the time of idling in order to suppress fuel consumption. As a result, it is possible to prevent the engine 17 from being unable to restart when recovering from the idling stop. Further, since the electrode plate abnormality has occurred, the ECU can display a message prompting the prompt replacement of the rechargeable battery 14 on a display unit (not shown) to notify the user. Of course, processing other than these may be executed.

In step S7, the CPU 10a determines whether or not to repeat the process, and if it is determined to repeat the process (step S7: Y), the CPU 10a returns to step S1 and repeats the same process as described above, and other than that. In the case (step S7: N), the process ends. The repetition in step S7 is executed according to the time window described above.

FIG. 6 is a flowchart for explaining the details of the process of step S1 of FIG. More specifically, FIG. 6 is a flowchart for explaining a flow of processing executed for optimizing the coefficients A1 to A6 of the mathematical model shown in the equation (1). The process shown in FIG. 6 is performed when the engine 17 is stopped and the voltage of the rechargeable battery 14 is stable (for example, when a predetermined time (for example, several hours) has elapsed since the engine 17 was stopped). Will be executed. When the processing of the flowchart shown in FIG. 6 is started, the following steps are executed.

In step S10, the CPU 10a executes a process of initializing the coefficient of the equation (1). More specifically, the CPU 10a sets a value of Δt (for example, 10 seconds). In addition to the above-mentioned values, the initial setting values include, for example, the number of sample acquisitions Ns and the stabilization time Tx required for the voltage of the rechargeable battery 14 to stabilize. For example, initial setting values such as ΔTs = 10 (seconds), Ns = 60 (pieces), and Tx = 100,000 (seconds) may be used. In this case, it is possible to set an appropriate fixed initial setting value in advance according to the characteristics of the rechargeable battery 14, but the initial setting location may be appropriately changed according to the operating condition or the like.

In step S11, the CPU 10a executes a process of measuring the voltage Vn and the current In of the rechargeable battery 14. More specifically, the CPU 10a sequentially reads the output signals of the voltage detection unit 11 and the current detection unit 12 to acquire a plurality of voltage values and current values of the rechargeable battery 14 on the time axis. As a result, Ns voltage and current values measured in the sampling period ΔTs are sequentially acquired. The CPU 10a sequentially stores the acquired voltage value and current value in the RAM 10c as data 10ca, and reads them out as needed. In the following, it is assumed that the nth (n = 1, 2, 3 ... Ns) voltage value acquired in step S11 is V (n) and the current value is I (n).

In step S12, the CPU 10a executes a process of initializing the coefficients A1 to A6 of the equation (1). More specifically, the CPU 10a reads out the initial values acquired in advance by the experiment and stored in the ROM 10b, and sets the coefficients A1 to A6 in the equation (1).

In step S13, the CPU 10a calculates VFn (n, In) by substituting n and In with respect to the equation (1). As a result, the value of VFn (n, In), which is the calculation result by the mathematical model, is obtained.

In step S14, the CPU 10a applies the VFn (n, In) obtained in the formula (1) in step S13 and the voltage value Vn obtained from the voltage detection unit 11 in step S11 to the above-mentioned formula (2). Obtain the value of Rn.

In step S15, the CPU 10a calculates the partial differential term corresponding to each coefficient A1 to A6 based on the above-mentioned equation (3).

In step S16, the CPU 10a calculates the matrix B based on the above-mentioned equation (4).

In step S17, the CPU 10a calculates dR represented by the equation (5) by using the Rn obtained by the equation (2) in the step S15 and the partial differential term obtained by the equation (3) in the step S16. ..

In step S18, the CPU 10a applies the matrix B obtained by the equation (4) in step S16 and the dR obtained by the equation (5) in step S17 to the equation (6) to calculate the difference dd.

In step S19, the CPU 10a determines whether or not the equation (7) is satisfied for the six differences dd1 to dd6 corresponding to the coefficients A1 to A6 obtained in step S18, and satisfies the equation (7). If it is determined (step S19: Y), the process returns (returns) to the process of FIG. 5 assuming that the optimum solution has been obtained, and in other cases (step S19: N), the process proceeds to step S20. The Th on the right side of the equation (7) can be ^{, for example, 10-12.} Of course, other values may be used.

In step S20, the CPU 10a updates the coefficients A1 to A6 based on the above-mentioned equation (8), returns to step S13, and repeats the same process as described above.

By the above processing, the coefficients A1 to A6 of the equation (1) can be optimized.

Next, with reference to FIG. 7, the details of the plate abnormality determination process shown in step S3 of FIG. 5 will be described. When the processing of the flowchart shown in FIG. 7 is started, the following steps are executed.

In step S30, the CPU 10a calculates the absolute value ΔVn of the difference between the value VFn (n, In) of the mathematical model shown in the formula (1) and the measured value Vn of the voltage based on the following formula (9). .. As a result, for example, the absolute value ΔVn of the difference of Ns (for example, 60) can be obtained.

・・・（９）

... (9)

In step S31, the CPU 10a sets ΔVn1, which is the largest of the ΔVn obtained in step S30, as ΔVn1.

In step S32, the CPU 10a sets ΔVn2 as the second largest ΔVn among the ΔVn obtained in step S30.

In step S33, the CPU 10a determines whether or not ΔVn1 is larger than a predetermined threshold value Th2, proceeds to step S34 if ΔVn1> threshold value Th2 is satisfied, and returns to the original process in other cases (return). ). The threshold Th2 is a value that cannot be taken when the rechargeable battery 14 is normal, and a value that can be taken when an electrode plate abnormality has occurred can be obtained, for example, by actual measurement or the like.

In step S34, the CPU 10a determines whether or not ΔVn2 is larger than a predetermined threshold value Th2, proceeds to step S35 if ΔVn2> threshold value Th2 is satisfied, and returns to the process of FIG. 5 in other cases (in other cases). Return). The threshold value used in step S34 may be a threshold value smaller than the threshold value used in step S33.

In step S35, the CPU 10a returns to the process of FIG. 5 after determining that the electrode plate abnormality has occurred.

According to the above processing, it is possible to detect the occurrence of a plate abnormality of the rechargeable battery 14 by using the mathematical model in which the coefficients A1 to A6 are optimized by the processing of FIG.

In the process of FIG. 7, the determination is made based on two values ΔVn1 and ΔVn2, but the determination may be made based on three or more values. According to such a method, the occurrence of erroneous determination can be reduced.

(C) Description of Modified Embodiment The above embodiment is an example, and it goes without saying that the present invention is not limited to the cases described above. For example, in the above embodiments, a quadratic function is used as the exponential decay function, but an exponential decay function of first order or lower or third order or higher may be used. For example, the following equation (10) can be used as the fourth-order exponential decay function.

・・・（１０）

... (10)

Further, the order of the exponential decay function may be changed according to the timing at which the measurement is started. Specifically, as shown in FIG. 8, when the measurement start timing is 0 to 10 seconds, an exponential decay function including all of the first to fourth terms is applied. In such an initial stage, the influence of each term of the exponential decay function is relatively large, so in order to ensure sufficient calculation accuracy, all four terms are used and the original fourth-order exponential decay function is applied for calculation. This is because it is necessary to do.

On the other hand, at the timing when 10 seconds have passed from the start of the arithmetic processing, the first term of the exponential decay function is attenuated to a negligible extent. Therefore, the exponential decay function including the second to fourth terms excluding the first term. To apply. In addition, at the timing when 60 seconds have passed from the start of the arithmetic processing, the second term in addition to the first term of the exponential decay function is attenuated to a negligible degree. Apply the exponential decay function of the including form. Furthermore, at the timing when 600 seconds have passed from the start of the arithmetic processing, the third term in addition to the first and second terms of the exponential decay function is attenuated to a negligible extent, so only the fourth term except the first to third terms is used. Apply an exponential decay function that includes.

Further, in the above embodiment, the exponential decay function is used, but in addition to this, for example, an inverse proportional curve or the like may be used.

Further, in the flowchart shown in FIG. 7, when both the first largest ΔVn1 and the second largest ΔVn2 exceed a predetermined threshold Th2, it is determined that the plate is abnormal. However, the absolute value of the first largest ΔVn1 and the absolute value of the second largest ΔVn2 may be calculated, the obtained values may be added, and the value obtained by the addition may be compared with the threshold value. Further, it may be added as a sum of squares instead of an absolute value. Further, values obtained by selecting and adding values having the same sign of the difference value between VFn (n, In) and Vn are added so that the obtained value and the threshold value are compared or weighted and added. And the threshold value may be compared. In short, it is possible to detect a plate abnormality by detecting a case where the calculated value obtained by the mathematical model changes discontinuously as compared with the normal state.

Further, in the flowchart shown in FIG. 7, the plate abnormality cannot be detected until a time has elapsed until the voltage disturbance due to the plate abnormality can be clearly distinguished from the influence of the current. In this case, the abnormality is detected at a sufficiently early stage. There is a risk that it will not be possible or that detection omission will occur. Therefore, in order to avoid such a situation, for example, a first threshold value may be set for the deviation or error, and the frequency of points exceeding the first threshold value or the frequency rate may be used for determination. ..

This is because, as shown in FIG. 9, the error due to the influence of the current tends to occur in a short period of time at the moment when the current suddenly changes. In the example of FIG. 9, the horizontal axis represents time, the vertical axis represents the error as the difference value between VFn (n, In) and Vn, and the current suddenly increases in the vicinity of 500 seconds and 1000 seconds surrounded by the broken line. The error increases accordingly. On the other hand, in the case of a plate abnormality, the state of deviation from the mathematical model tends to continue for a certain period of time, so even if the deviation due to the influence of the current change is about the same as the deviation due to the plate abnormality, the threshold value is above a certain level. The frequency of those exceeding is increased.

FIG. 10 is a diagram in which the difference value between VFn (n, In) and Vn is measured as data over a predetermined period, and the measured data is stratified according to the voltage. In FIG. 10, the hatched rectangle shows the case where the electrode plate is abnormal, and the non-hatched rectangle shows the case where there is no electrode plate abnormality. As shown in FIG. 10, most of the data fall within the range of the first thresholds of -2V and + 2V and have one peak when there is no plate abnormality, but when there is a plate abnormality, there is one peak. Most of the data does not fall within the range of the first threshold and has a plurality of peaks.

Therefore, if the frequency of the data within the range of the first threshold value is equal to or higher than the predetermined threshold value, it is determined that there is no plate abnormality, and in other cases, it is determined that there is a plate abnormality. Good. Alternatively, if the number of data peaks is one, it may be determined that there is no electrode plate abnormality, and if the number of data peaks is multiple, it may be determined that there is an electrode plate abnormality. Further, instead of the frequency indicating the number of data, the frequency rate indicating the ratio of the data existing outside the predetermined range (range of -2 to +2) among all the data may be used for the determination. ..

FIG. 11 is a flowchart showing an example of an abnormality determination process using the frequency rate f indicating the ratio of the data existing in a predetermined range (range of −Th3 to +Th3) among all the data. When the processing of the flowchart shown in FIG. 11 is started, the following steps are executed.

In step S50, the CPU 10a calculates ΔVn = VFn (n, In) −Vn, which is a difference value between VFn (n, In) and Vn.

In step S51, the CPU 10a stratifies ΔVn calculated in step S50 for each value. As a result, for example, as shown in FIG. 10, data stratified for each value of ΔVn is obtained.

In step S52, the CPU 10a calculates the frequency f in which ΔVn stratified in step S51 is within a predetermined range, that is, within the range of −Th3 <ΔVn <+ Th3. For example, as shown in FIG. 10, the frequency f of ΔVn within the range of -2 <ΔVn <+ 2 is calculated.

In step S53, the CPU 10a determines whether or not the frequency f calculated in step S52 is less than the predetermined threshold value Th4, and if f <Th4 is satisfied (step S53: Y), the process proceeds to step S54, and in other cases. In (step S53: N), the process returns to the process shown in FIG. The determination is made based on f1 / f2, which is the ratio of the frequency f1 that is "inside" the range of -Th3 <ΔVn <+ Th3 and the frequency f2 that is "outside" the range of -Th3 <ΔVn <+ Th3. May be good.

In step S54, the CPU 10a returns to the process of FIG. 5 after determining that the electrode plate abnormality has occurred. For example, when an electrode plate abnormality has occurred, as shown in FIG. 10, the frequency f as the number of data within a predetermined range (range of -2 to +2 in FIG. 10) is less than the predetermined threshold value Th4. Therefore, in that case, it is determined that an electrode plate abnormality has occurred, and the process returns to the process shown in FIG.

Further, as another embodiment for improving detection delay and detection omission, a method of detecting a plate abnormality depending on whether or not the standard deviation of the difference value between VFn (n, In) and Vn exceeds a predetermined threshold value. There is. More specifically, when the difference value between VFn (n, In) and Vn is x _i and the average value of x _{i is x av} , the standard deviation s can be obtained by the following equation (11).

・・・（１１）

... (11)

When the standard deviation s thus obtained is equal to or greater than a predetermined threshold value, it may be determined that an electrode plate abnormality has occurred.

Further, as another embodiment for improving the detection delay and the detection omission, the electrode plate abnormality is detected by whether or not the coefficient of the optimized mathematical model exceeds the range of the value that can be taken by the healthy rechargeable battery 14. There is a way to do it. For example, taking the mathematical model of the equation (1) as an example, the coefficients A1 to A5 are the coefficients of the mathematical model expressing the voltage that changes with the passage of time, and A6 is the coefficient of the mathematical model expressing the influence of the current. It is a coefficient. More specifically, A1 and A2 correspond to the amount of change in voltage over time, A3 and A4 correspond to the rate of change, and A5 corresponds to the base absolute voltage. Further, the coefficient related to the influence of the current of A6 corresponds to the internal resistance of the rechargeable battery 14.

These are, of course, mathematical models that assume a healthy state (a state in which no plate abnormality has occurred), and are rechargeable batteries when the coefficients are optimized for the observed values of normal voltage and current. The value is within the normal range reflecting the 14 states. On the other hand, when a plate abnormality occurs or a voltage fluctuation due to a plate abnormality occurs, if the coefficients are optimized for the observed values of voltage and current, it will be caused by the plate abnormality. The coefficient is out of the normal range due to the influence of voltage fluctuations. By utilizing this, it is possible to detect the occurrence of an abnormality in the electrode plate of the rechargeable battery 14.

FIG. 12 shows the process of optimizing the coefficients A1 to A6 of the healthy rechargeable battery 14, and FIG. 14 (A) shows the respective values of the optimized coefficients A1 to A6. In FIG. 12, the circles indicate the measured voltages of the rechargeable battery 14, and the diamonds indicate the calculated values of the mathematical model.

Further, FIG. 13 shows the process of optimizing the coefficients A1 to A6 of the rechargeable battery 14 in which the electrode plate abnormality has occurred, and FIG. 14B shows the respective values of the optimized coefficients A1 to A6. ing. In FIG. 13, the circles indicate the measured voltages of the rechargeable battery 14, and the diamonds indicate the calculated values of the mathematical model. From the comparison of FIGS. 14 (A) and 14 (B), the hatched coefficients (A1 to A3, A5) of FIG. 14 (B) are significantly different. Therefore, after optimizing the coefficients A1 to A6 by the optimization process of FIG. 6, when the values of the coefficients are different from a predetermined threshold value or more as compared with at least one of the coefficients A1 to A6 optimized in the normal state. May determine that an electrode plate abnormality has occurred.

FIG. 15 is a flowchart showing an example of abnormality determination processing based on the coefficients A1 to A6. When the processing of the flowchart shown in FIG. 15 is started, the following steps are executed.

In step S70, the CPU 10a executes a process of acquiring the coefficients A1 to A6 obtained by the previous optimization process (optimization process in the normal state). More specifically, the CPU 10a acquires the coefficients A1 to A6 obtained by the previous optimization process and stored in the RAM 10c.

In step S71, the CPU 10a executes a process of acquiring the coefficients A1 to A6 obtained by the optimization process this time. More specifically, the CPU 10a acquires the coefficients A1 to A6 obtained by the optimization process this time and stored in the RAM 10c.

In step S72, the CPU 10a compares each of the coefficients A1 to A6 obtained by the previous optimization process with each of the coefficients A1 to A6 obtained by the current optimization process.

In step S73, the CPU 10a determines whether or not a plurality of coefficients having a predetermined value or more differ from each other based on the comparison result in step S72, and if it is determined that there are a plurality of coefficients (step S73: Y), the step S74 is performed. The process proceeds, and in other cases (step S73: N), the process returns to the process shown in FIG. It should be noted that Y may be determined when one is present instead of a plurality and when the dissociation is equal to or greater than a predetermined threshold value.

In step S74, the CPU 10a returns to the process of FIG. 5 after determining that the electrode plate abnormality has occurred. For example, when an electrode plate abnormality has occurred, as shown in FIG. 14, when a plurality of coefficients differ by a predetermined value or more, it is determined that an electrode plate abnormality has occurred, and the process returns to the process of FIG. ..

In the process of FIG. 15, the presence or absence of an abnormality is determined by comparing the measured values of the coefficients A1 to A6 in the normal state with the measured values of the coefficients A1 to A6 in the present time. Instead of the measured value of A6, the values of typical coefficients A1 to A6 measured in advance and stored in the ROM 10b may be used. Further, when the value obtained by adding a predetermined weighting to each of the difference values of the coefficients A1 to A6 becomes equal to or more than a predetermined threshold value, it may be determined that the plate is abnormal.

Further, in the above embodiment, as shown in FIG. 16A, the determination of the electrode plate abnormality is executed with a predetermined time window (for example, a one-hour window) as a delimiter. However, for example, as shown in FIG. 16B, a part of the time windows may be set to overlap each other, and the plate abnormality may be determined in each time window. In FIG. 16, adjacent time windows are shown by solid lines and broken lines in order to improve visibility. Further, as shown in FIG. 16C, time windows that do not overlap each other are set, and for example, poles are set based on the results obtained by the optimization process in a plurality of (for example, two adjacent) time windows. The plate abnormality may be determined. More specifically, the electrode plate abnormality can be determined by comparing the coefficients A1 to A6 optimized in each of the plurality of time windows, or the difference value between VFn (n, In) and Vn can be determined temporally. The electrode plate abnormality may be determined based on the change. Further, in the example shown in FIG. 16A, a plurality of time windows are provided, but one time window may be provided.

The above is an embodiment using a mathematical model assuming a state in which a current is not substantially flowing through the rechargeable battery 14 after the engine 17 is stopped. However, a mathematical model expressing a state in which a current is flowing is used. May be good. For example, the mathematical model disclosed in Japanese Patent No. 4532416 can be used. As shown in FIG. 17A, this mathematical model has nine types of element constants RΩ, Ra1, Ra2, Ra3, Ra4, Ra5, Rb1, Rb2, Rb3 representing resistance and eight types of element constants representing capacitors. This is an equivalent circuit model of the rechargeable battery 14 having Ca1, Ca2, Ca3, Ca4, Ca5, Cb1, Cb2, and Cb3. As a method of optimizing such an equivalent circuit model, the rechargeable battery 14 is, for example, pulse-discharged by the discharge circuit 15 shown in FIG. 1, and based on the voltage and current during discharge, for example, the least squares method. The element constant is optimized by using an extended Kalman filter, a neural network, or the like. Then, the electrode plate abnormality can be detected by comparing with the element constant of the rechargeable battery 14 in a healthy state. As a comparison method, as in the case described above, the value of the voltage or current at the time of discharging by the discharge circuit 15 and the value of the voltage or current obtained by the equivalent circuit model can be compared, or an element in a healthy state. By comparing with a constant, the occurrence of electrode plate abnormality can be detected. Further, instead of pulse-discharging by the discharge circuit 15, when a current is flowing through the load 19 or the starter motor 18, the voltage and the current are measured, and the elements constituting the equivalent circuit shown in FIG. 17 (A) and the like. May be optimized.

The equivalent circuit shown in FIG. 17A is an example, and other equivalent circuits may be used. For example, as shown in FIG. 17B, an equivalent circuit including RΩ, Ra1, and Ca1 may be used. Alternatively, as shown in FIG. 17C, an equivalent circuit consisting of only RΩ and Ra1 may be used. Alternatively, at least one element of the equivalent circuit shown in FIGS. 17 (A) to 17 (C) may be used. Furthermore, it is desirable to change the mathematical model according to the equivalent circuit.

Further, different mathematical models may be used when the engine 17 is stopped and when the engine is operating, and the plate abnormality may be determined based on the respective mathematical models. For example, when the engine 17 is stopped (when the rechargeable battery 14 is not charged), it is determined based on the above equation (1), and when the engine 17 is operating (when the rechargeable battery 14 is charged), it is determined. The electrode plate abnormality may be determined based on the above-mentioned mathematical model of Japanese Patent No. 4532416.

In the example of FIG. 1, the control unit 10 controls charge / discharge, but an ECU (not shown) receives data such as SOC or OCV from the control unit 10, and the ECU controls based on these values. You may try to do it.

Further, in the above-described embodiment, the influence of temperature is not described, but the voltage may be measured in consideration of the temperature detected by the temperature detection unit 13. For example, the measured voltage may be corrected to the voltage at the standard temperature, and the voltage may be calculated based on the voltage obtained as a result of the correction.

Further, in the above embodiment, the rechargeable battery state detection device 1 has the discharge circuit 15 and the temperature detection unit 13, but the discharge circuit 15 and the temperature detection unit 13 may not be provided.

Further, the flowcharts shown in FIGS. 5, 6, 7, 11, and 15 are examples, and the present invention is not limited to the processing of these flowcharts.

1 Rechargeable battery status detector 10 Control unit 10a CPU
10b ROM
10c RAM
10d communication unit 10e I / F
11 Voltage detector 12 Current detector 13 Temperature detector 14 Rechargeable battery 15 Discharge circuit 16 Alternator 17 Engine 18 Starter motor 19 Load

Claims (19)

In the rechargeable battery status detector that detects the status of the rechargeable battery,
A detection means that detects the voltage of the rechargeable battery based on the signal output from the voltage detection unit, and
An optimization means that optimizes by adjusting a coefficient of a predetermined mathematical model indicating the internal state of the rechargeable battery based on the voltage value detected by the detection means.
Judgment of determining the presence or absence of a plate abnormality of the plate existing in the electrolytic solution of the rechargeable battery based on the value calculated by the mathematical model whose coefficient is adjusted by the optimization means or the value of the coefficient. Means and
An output means for outputting the determination result by the determination means and
A rechargeable battery state detector characterized by having. The rechargeable battery state detecting device according to claim 1, wherein the electrode plate abnormality is an abnormality in which the electrical characteristics of the rechargeable battery change discontinuously. The rechargeable battery state detecting device according to claim 2, wherein the electrode plate abnormality is a short circuit in a part of the electrode plate. The rechargeable battery state detecting device according to any one of claims 1 to 3, wherein the mathematical model includes a term indicating a voltage change with the passage of time of the rechargeable battery. The rechargeable battery state detecting device according to any one of claims 1 to 4, wherein the mathematical model includes a term indicating a voltage change accompanying a change in the current of the rechargeable battery. The rechargeable battery state detecting device according to claim 4, wherein the mathematical model shows a voltage change after charging / discharging of the rechargeable battery is stopped. That the mathematical model showing the voltage change after the charge / discharge of the rechargeable battery is stopped is an exponential decay function or an inverse proportional function having one or more terms for calculating the voltage value of the rechargeable battery. The rechargeable battery state detection device according to claim 6, wherein the rechargeable battery state detection device is characterized. The rechargeable battery state detecting device according to claim 4, wherein the mathematical model shows a voltage change during charging / discharging of the rechargeable battery. The rechargeable battery state detecting device according to claim 8, wherein the mathematical model showing a voltage change during charging / discharging of the rechargeable battery is an equivalent circuit model of the rechargeable battery. The optimization means charges the first mathematical model showing the voltage change after the rechargeable battery has stopped charging / discharging and the second mathematical model showing the voltage change during charging / discharging of the rechargeable battery. Select according to the state of charge of the possible battery, select the above coefficient,
The detecting means detects the plate abnormality based on the first mathematical model or the second mathematical model selected by the optimizing means.
The rechargeable battery state detecting device according to claim 4 or 5. The claim means that the determination means determines the presence or absence of the electrode plate abnormality based on the value calculated by the mathematical model and the measured value of the voltage of the rechargeable battery at two or more different times. Item 1. The rechargeable battery state detection device according to item 1. In the determination means, at two or more different times, each of the difference values between the value calculated by the mathematical model and the measured value of the voltage of the rechargeable battery becomes or more than a predetermined threshold value, or the difference value The rechargeable battery state detecting device according to claim 11, wherein when the sum of the above or the sum of the squares of the difference values is equal to or greater than a predetermined threshold value, it is determined that the plate is abnormal. When the frequency or frequency rate at which the difference value between the value calculated by the mathematical model and the measured value of the voltage of the rechargeable battery is out of the predetermined range is equal to or higher than the predetermined threshold value, the determination means is described. The rechargeable battery state detecting device according to claim 11, wherein the electrode plate abnormality is determined. When the standard deviation of the difference value between the value calculated by the mathematical model and the measured value of the voltage of the rechargeable battery is equal to or more than a predetermined threshold value, the determination means determines that the electrode plate is abnormal. The rechargeable battery state detection device according to claim 11. The determination means is characterized in that when there are a plurality of peaks in the distribution of the difference value between the value calculated by the mathematical model and the measured value of the voltage of the rechargeable battery, it is determined that the electrode plate is abnormal. The rechargeable battery state detecting device according to claim 11. The rechargeable battery state detecting device according to claim 1, wherein the determination means determines the electrode plate abnormality based on at least one coefficient of the mathematical model optimized by the optimization means. When the determination means differs from at least one coefficient of the mathematical model optimized by the optimization means and a coefficient when optimized in the past by a predetermined threshold value or more, the plate The rechargeable battery state detecting device according to claim 16, further comprising determining an abnormality. The rechargeable battery is mounted on the vehicle and
The output means notifies the upper control device mounted on the vehicle of the determination result by the determination means.
Any of claims 1 to 17, wherein the control device that has received the notification issues a warning to the user based on the determination result, and / or suspends execution of the idling stop that stops the engine when idling. The rechargeable battery state detection device according to item 1. In the rechargeable battery status detection method for detecting the status of a rechargeable battery,
A detection step that detects the voltage of the rechargeable battery based on the signal output from the voltage detection unit, and
An optimization step that optimizes by adjusting a coefficient of a predetermined mathematical model indicating the internal state of the rechargeable battery based on the voltage value detected in the detection step.
Judgment of determining the presence or absence of a plate abnormality of the electrode plate existing in the electrolytic solution of the rechargeable battery based on the value calculated by the mathematical model in which the coefficient is adjusted in the optimization step or the value of the coefficient. Steps and
An output step that outputs the determination result in the determination step, and
A rechargeable battery state detection method comprising.

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