JP2007132738A - Storage battery deterioration diagnosis method and deterioration diagnosis apparatus - Google Patents

Abstract

A deterioration diagnosis method and a deterioration diagnosis apparatus for a storage battery capable of detecting the deterioration degree of each storage battery are provided.
A data storage unit 13 includes first characteristic data indicating output characteristics of a new storage battery sample, first capacity data indicating remaining capacity, second characteristic data indicating output characteristics of a deteriorated battery sample, and remaining capacity. Second capacity data indicating the capacity is stored in advance. The computing unit 14 is based on the first and second characteristic data, the first and second capacity data, and the third characteristic data indicating the output characteristics of the storage battery 1 to be diagnosed measured by the measuring unit 12 when it is new. A correlation equation between the output characteristics and the remaining capacity is calculated, and the remaining capacity is calculated from the fourth characteristic data indicating the output characteristics after the storage battery 1 is installed.
[Selection] Figure 1

Description

  The present invention relates to an apparatus and a method for diagnosing capacity deterioration due to the number of years that have elapsed after installation of a storage battery.

  Communication equipment and social infrastructure equipment are normally supplied with power from a power source. However, as a measure against sudden power outages or instantaneous interruptions, power can be supplied for a certain period of time even if the power supply stops. Are equipped with a storage battery (for example, a lead storage battery). The storage battery deteriorates according to the number of years that have passed, and when the storage battery is near the end of its life, it cannot be normally supplied with power in an emergency such as a power failure, and therefore needs to be replaced at an appropriate time.

  Conventionally, in accordance with the service life recommended by the manufacturer of the storage battery, it has been replaced unconditionally after a certain period of use. However, the degree of deterioration of the storage battery varies depending on the type, model, manufacturer, etc. of the storage battery, and also changes depending on the temperature condition of the storage battery installation environment and the charge / discharge conditions. Further, since there are variations in individual characteristics of the storage battery, even the storage batteries of the same type and the same type used in the same place have different degrees of deterioration. For example, it is known that when a lead-acid battery is repeatedly charged and discharged, an inert substance accumulates and a capacity reduction is promoted.

  As a conventional method for measuring the degree of deterioration of a storage battery and knowing an appropriate replacement time, for example, there is a diagnostic method disclosed in Patent Document 1 below. In the diagnostic method, the surface temperature of the storage battery is continuously measured, and when it is equal to or lower than a predetermined reference temperature, the average value of the measured surface temperatures is set as the reference temperature and stored in the memory in advance. With reference to the installation date of the storage battery and the standard life years corresponding to the reference temperature, it is determined whether or not the storage battery is at the end of its lifetime.

Japanese Patent Laid-Open No. 7-312233

  As described above, the deterioration degree of a storage battery varies depending on the inherent factors of each storage battery, such as its type and model, manufacturer, temperature, and charge / discharge conditions. In the conventional deterioration diagnosis method, since the standard deterioration degree is calculated from indirect values such as the elapsed years and the temperature, it is impossible to obtain the individual deterioration degree of the storage battery that is the actual diagnosis target. Therefore, it was difficult to accurately grasp the life time of each storage battery, and it was difficult to replace the storage battery at an appropriate time.

  The present invention has been made to solve the above-described problems, and an object thereof is to provide a deterioration diagnosis method and a deterioration diagnosis apparatus for a storage battery that can detect the deterioration degree of each storage battery.

  In the storage battery deterioration diagnosis method according to the present invention, (a) the computing means includes first characteristic data indicating output characteristics of a new sample of the storage battery, first capacity data indicating a remaining capacity of the new sample, and a deteriorated product sample of the storage battery. Output characteristics of the storage battery based on the second characteristic data indicating the output characteristics of the battery, the second capacity data indicating the remaining capacity of the deteriorated sample, and the third characteristic data indicating the output characteristics of the storage battery when the diagnosis target product is new. A step of deriving a correlation equation between the remaining capacity and (b) a measuring unit measuring an output characteristic of the diagnostic object and obtaining fourth characteristic data indicating the output characteristic; and (c) the calculating unit Comprises a step of calculating a remaining capacity of the diagnostic target product from the fourth characteristic data based on the correlation equation.

  The deterioration diagnosis apparatus for a storage battery according to the present invention includes first characteristic data indicating output characteristics of a new sample of the storage battery, first capacity data indicating remaining capacity of the new sample, and second output characteristics of a deteriorated sample of the storage battery. A data holding unit for holding characteristic data and second capacity data indicating the remaining capacity of the deteriorated product sample, a measuring unit for measuring output characteristics of a diagnosis target product of the storage battery, the first and second characteristic data, Based on the first and second capacity data, and the third characteristic data indicating the output characteristics of the diagnostic target product measured by the measuring means when new, the correlation equation between the output characteristics and the remaining capacity of the storage battery is calculated, An arithmetic unit that calculates the remaining capacity of the diagnostic target product from the fourth characteristic data indicating the output characteristics after installation of the diagnostic target product measured by the measurement unit using the correlation equation It is obtain things.

  According to the present invention, the deterioration diagnosis of the storage battery is based on data including the third characteristic data reflected by the inherent factor of the storage battery (storage battery type and model, manufacturer, temperature of installation environment, charging / discharging conditions, etc.) Since the correlation equation is used, the remaining capacity can be calculated more accurately than in the past. Therefore, the accuracy of the deterioration diagnosis is improved, and the storage battery can be replaced at an appropriate time. In addition, the accuracy of the deterioration diagnosis is further improved by adopting the Mahalanobis distance having a voltage characteristic having a high correlation with the remaining capacity as an index of the output characteristic of the storage battery.

<Embodiment 1>
FIG. 1 is a diagram showing a configuration of a storage battery deterioration diagnosis device 10 according to Embodiment 1 of the present invention. As shown in the figure, the degradation diagnosis apparatus 10 includes a charge / discharge circuit 11, a measurement unit 12, a data storage unit 13, a calculation unit 14, a display unit 15, and a control unit 16 that controls the operations thereof. The storage battery 1 is a target (diagnosis target product) for deterioration diagnosis performed by the deterioration diagnosis device 10. The charge / discharge circuit 11 is a circuit that charges and discharges the storage battery 1 connected to the deterioration diagnosis device 10. The measuring unit 12 has a function of measuring voltage and current when the storage battery 1 is discharged. The data storage unit 13 holds various data for determining the criteria for deterioration diagnosis, measurement data of the measurement unit 12, and the like. The calculation unit 14 is a microcomputer, and performs calculation related to deterioration diagnosis of the storage battery 1 using data in the data storage unit 13. The display unit 15 is for displaying the calculation result of the calculation unit 14. In FIG. 1, the calculation unit 14 and the control unit 16 are illustrated separately, but both can be realized by a single microcomputer.

  Prior to the actual deterioration diagnosis, the data storage unit 13 includes sample data that serves as a reference for deterioration diagnosis, and a correlation expression that represents the relationship between the output characteristics and the remaining capacity of the storage battery that is created based on the sample data. Pre-recorded. In the present specification, a correlation formula created in advance based on sample data is referred to as a “first correlation formula”. Hereinafter, the sample data and the first correlation equation will be described.

  The sample data includes first characteristic data indicating output characteristics of a new storage battery sample (new sample), first capacity data indicating remaining capacity of the new sample, and output of a deteriorated storage battery sample (deteriorated product sample). Second characteristic data indicating characteristics and second capacity data indicating the remaining capacity of the deteriorated product sample are included. This output characteristic is an item for evaluating the degree of deterioration of the storage battery, and in this embodiment, the voltage characteristic during pulse discharge of the storage battery is employed. Sample data is acquired by the following procedure.

  First, a plurality of new storage battery samples and deteriorated product samples are prepared (each is preferably about 10 or more). As the deteriorated product sample, a new storage battery that has been intentionally deteriorated by applying a predetermined voltage in an environment (for example, a temperature of 60 ° C.) around the upper limit of the operating temperature may be used. Then, after charging each of the new sample and the deteriorated sample, their output characteristics (here, voltage characteristics during pulse discharge) are measured.

FIG. 2 is a diagram for explaining a method for measuring voltage characteristics during pulse discharge. In this embodiment, each sample is subjected to pulse discharge with a constant current such as the current waveform of FIG. 2, and the change in output voltage at that time is measured. In this embodiment, the width of the pulse discharge is set to 2 seconds, and the voltage is measured every 200 ms until 3600 ms elapses from the start of the discharge (that is, in FIG. 2, the width W1 = 2000 [ms], time t _n = 200 × n [ms], k = 3600/200 = 18). The current value at the time of pulse discharge shall be 1-3C. This C is a unit defined by C = R / T [A] where C [A] is the current value, R [A · hour] is the rated capacity of the storage battery, and T [hour] is the discharge time. is there. That is, 1C is the amount of current at which discharge ends in 1 hour from full charge, and 3C is the amount of current at which discharge ends in 1/3 hour from full charge.

  In this embodiment, the voltage characteristics during pulse discharge described above are measured for each temperature condition of 0 ° C., 20 ° C., and 40 ° C. Therefore, in the present embodiment, data (voltage characteristic data) indicating voltage characteristics at each measurement point shown in the table of FIG. 3 is obtained. As shown in FIG. 3, the voltage characteristic data of the new sample thus obtained is the first characteristic data, and the voltage characteristic data of the deteriorated sample is the second characteristic data.

  Further, each sample is continuously discharged by a constant current (not pulse discharge), and the remaining capacity of each sample (= current value × duration) is obtained by measuring the duration. Data representing the remaining capacity of the new sample thus obtained is the first capacity data, and data indicating the remaining capacity of the deteriorated sample is the second capacity data.

  Next, the first correlation equation created based on these sample data will be described. The first correlation equation is an equation representing the relationship between the output characteristics of the storage battery and the remaining capacity, but in this embodiment, the first characteristic data is used as a reference as an index of the output characteristics in the first correlation expression. Use the Mahalanobis distance. That is, in the present embodiment, the first correlation equation stored in advance in the data storage unit 13 of the deterioration diagnosis device 10 is the Mahalanobis distance of the voltage characteristic data of the storage battery when the first characteristic data is used as a reference, and the storage battery. This is an expression representing the relationship with the remaining capacity.

  Here, an outline of a Mahalanobis distance calculation method will be described (details of the method are described in, for example, Japanese Patent Application Laid-Open No. 2003-141306).

The number of reference samples for defining a reference space (Mahalanobis space) as a reference for evaluation is n, the number of evaluation items (number of data per sample) is k, and the data obtained from the reference sample is Y _ij (i = 1) , 2,..., K, j = 1, 2,..., N) (data Y _ij is generically referred to as “reference data”).

From first data Y _ij, it calculates the average value m _i and the standard deviation sigma _i, determine the normalized value y _ij for each data Y _ij using them. The normalized value y _ij is
y _ij = (Y _ij −m _i ) / σ _i (1)
Is required. Next, a correlation coefficient between the evaluation items (here, measurement time) in the standardized value y _ij is obtained, and a correlation matrix R having the elements as elements is obtained. Further, a cofactor matrix A of the correlation matrix R is obtained. When the elements of the cofactor matrix A are a _ij , the Mahalanobis distance D (that is, the distance from the reference data group) in the reference space with respect to the evaluation target data X _i is
D ² = (1 / k) Σ (a _ij ((X _i −m _i ) / σ _i ) ((X _j −m _j ) / σ _j )) (2)
Is calculated by

As described above, in the present embodiment, since the reference of the Mahalanobis distance in the first correlation equation is the first characteristic data, the first characteristic data is used as the reference data (Y _ij ) here. Then, the Mahalanobis distance for the first and second characteristic data is calculated using the above equations (1) and (2). In the present embodiment, the measurement of the voltage at the time of pulse discharge, which is an evaluation item, is performed every 200 ms until 3600 ms elapses from the start of discharge, so the evaluation item number k is 18 (the number of evaluation items is at least 2 or more) Further, since measurement is performed under three temperature conditions of 0 ° C., 20 ° C., and 40 ° C., where N is the number of new samples, the reference sample number n is N × 3. Then, from the relationship between the Mahalanobis distance for the obtained first and second characteristic data and the first and second capacity data, a first correlation equation representing the relationship is obtained.

FIG. 4 shows the relationship between the Mahalanobis distance of the voltage characteristic data (first and second characteristic data) and the remaining capacity (that is, the first and second capacity data) corresponding to the first characteristic data. FIG. As shown in the figure, there is a difference in the Mahalanobis distance between the new sample and the deteriorated sample, and it can be seen that there is a high correlation between the remaining capacity and the Mahalanobis distance. On the other hand, FIG. 5 uses the same data as FIG. 4 and simply outputs the output voltage value at the time of pulse discharge (specifically, the minimum value V _min shown in FIG. 2) and the corresponding remaining capacity (that is, the first and first capacitances). It is the figure which showed the relationship with (2 capacity | capacitance data). In this case, the difference in output voltage value between the new sample and the deteriorated sample is not clear, and it can be seen that the correlation between the remaining capacity and the output voltage value is relatively small.

  Thus, since the Mahalanobis distance of the voltage characteristic data of the storage battery and its remaining capacity are highly correlated, by using the first correlation equation showing the relationship between them, the Mahalanobis distance of the voltage characteristic data of any storage battery It can be expected that the remaining capacity with high accuracy is calculated. As shown in FIG. 4, sample data has variations due to differences in temperature conditions (0 ° C., 20 ° C., 40 ° C.) and differences in the electrical characteristics of individual sample products. An approximate expression such as a least square method is used.

  With reference to FIG. 1 again, the operation of the deterioration diagnosis of the storage battery performed by the deterioration diagnosis apparatus 10 using the microcomputer will be described. FIG. 6 is a flowchart showing the operation.

  First, the control unit 16 determines whether correction of the correlation equation is necessary (step S1). Here, when the storage battery 1 is new (during initial installation or subsequent replacement), it is determined that the correlation formula correction step (step S2) is performed, and if the correlation formula has already been corrected, the storage battery 1 It is determined to shift to the deterioration diagnosis process (steps S3 to S6).

  As described above, the data storage unit 13 of the degradation diagnosis apparatus 10 stores sample data and the first correlation equation. The first correlation equation is effective in calculating the remaining capacity of the storage battery, but is only created based on sample data. Therefore, the inherent factor of the storage battery 1 that is the diagnosis target product (type and model of storage battery) , Manufacturer, installation environment temperature, charge / discharge conditions, etc.). The control unit 16 corrects the first correlation equation using the calculation unit 14 and recreates the correlation equation reflecting the intrinsic factor of the storage battery 1 that is the diagnosis target product. Hereinafter, the corrected correlation formula is referred to as a “second correlation formula”.

  FIG. 7 is a flowchart showing specific contents of the correlation equation correction process (step S2 in FIG. 6). The deterioration diagnosis device 10 collects third characteristic data indicating voltage characteristics when the storage battery 1 to be diagnosed is new (step S11). This step is repeatedly performed, for example, every 3 hours during a period in which the storage battery 1 is regarded as new (for example, about 1 to 6 months from the time of installation). The 3rd characteristic data is performed by the same method as collection of the 1st and 2nd characteristic data which is sample data.

  That is, in the present embodiment, the charging / discharging circuit 11 causes the storage battery 1 to pulse-discharge at a constant current, and the measurement unit 12 measures the output voltage value of the storage battery 1 every 200 ms until 3600 ms elapses from the start of discharge. Characteristic data is collected. The third characteristic data collected in this way is data representing the output characteristics of the storage battery 1 itself that is the diagnosis target, and is collected under the actually installed environmental conditions and charging / discharging conditions. Therefore, it can be said that the intrinsic factor of the storage battery 1 is included. The collected third characteristic data is stored in the data storage unit 13.

In the first correlation formula, the reference data (Y _ij ) for obtaining the Mahalanobis distance is only the first characteristic data. In the second correlation formula, the third characteristic data is further added to the reference data ( Step S12). That is, in the second correlation equation, the reference space serving as a reference for the Mahalanobis distance is defined by a set of the first characteristic data and the third characteristic data.

The calculation unit 14 uses the first and third characteristic data stored in the data storage unit 13 as reference data (Y _ij ), and the first and second characteristic data according to the above equations (1) and (2). The Mahalanobis distance for is calculated (step S13). Then, a second correlation equation is created that indicates the relationship between the obtained Mahalanobis distance and the first and second capacity data stored in the data storage unit 13 (step S14). Since the second correlation equation thus obtained is created based on the reference data including the third characteristic data, the inherent factor of the storage battery 1 is reflected. Thus, the correlation equation correction process is completed.

  When the correction of the correlation formula (creation of the second correlation formula) is completed, the process proceeds to an actual deterioration diagnosis process for the storage battery 1 thereafter. First, 4th characteristic data which shows the voltage characteristic of the storage battery 1 are acquired (step S3). The 4th characteristic data is performed by the same method as collection of the 1st and 2nd characteristic data which is sample data. That is, in this embodiment, the charging / discharging circuit 11 performs pulse discharge of the storage battery 1 with a constant current, and measures the output voltage of the storage battery 1 every 200 ms until 3600 ms elapses from the start of the measurement unit 12 discharge.

In order to apply the obtained fourth characteristic data to the second correlation equation, the calculation unit 14 uses the first and third characteristic data as reference data (Y _ij ) and calculates the first characteristic data by the above equations (1) and (2). The Mahalanobis distance of the four characteristic data is calculated (step S4). Then, the remaining capacity of the storage battery 1 is calculated from the Mahalanobis distance of the fourth characteristic data using the second correlation equation (step S5).

  Finally, the control unit 16 displays the calculated remaining capacity on the display unit 15. At this time, when the remaining capacity becomes smaller than a predetermined value, the control unit 16 determines that the storage battery 1 has deteriorated and is approaching the end of its life, and the storage battery 1 is installed on the display unit 15. Processing such as displaying a message prompting the administrator of the user to exchange the information may be performed.

  According to the present embodiment, the second correlation equation in which the deterioration diagnosis of the storage battery 1 is reflected by the inherent factors of the storage battery 1 (such as the type and model of the storage battery, the manufacturer, the temperature of the installation environment, and the charge / discharge conditions). Therefore, the remaining capacity can be calculated more accurately than in the past. Therefore, the accuracy of the deterioration diagnosis is improved, and the storage battery 1 can be replaced at an appropriate time. In addition, as in the present embodiment, the accuracy of the deterioration diagnosis is remarkably improved by adopting the Mahalanobis distance of the voltage characteristic having a high correlation with the remaining capacity as the index of the output characteristic of the storage battery 1.

Although only one storage battery 1 is illustrated in FIG. 1, a plurality of storage batteries 1 may be diagnosed using one deterioration diagnosis device 10. In that case, the output characteristics (third characteristic data) of the plurality of storage batteries 1 when they are new (for example, about 1 to 6 months from the time of installation) are acquired, and all the acquired third characteristic data is acquired. _May be included in the reference data (Y _ij ) to correct the correlation equation (create the second correlation equation).

<Embodiment 2>
In the second embodiment, a modification of the first embodiment will be described.

  In the first embodiment, sample data is collected for a plurality of temperature conditions (0 ° C., 20 ° C., 40 ° C.), but the installation location of the diagnosis target product (storage battery 1) is adjusted to a constant temperature. If it is known, it is not necessary to take temperature changes into consideration, so sample data may be collected only under the same temperature conditions. For example, when the installation location of the diagnostic object is constant at 20 ° C., sample data may be acquired only under the condition of 20 ° C. and used to create a correlation equation. Since less sample data is required, it is easier to collect sample data.

  Further, in the first embodiment, an example is shown in which correction of the correlation formula (creation of the second correlation formula) is always executed when the diagnosis target product (storage battery 1) is new (during installation or replacement). However, if the sample product used for collecting sample data and the product to be diagnosed have the same factors such as the type and model of the storage battery, the manufacturer, the temperature of the installation environment, and the charge / discharge conditions, the correlation formula Even if the first correlation equation is used as it is without performing the above correction and the deterioration diagnosis is performed, it is considered that the calculation accuracy of the remaining capacity is almost the same as that in the case of the correction. Therefore, if this is known, it may be determined in step S1 in FIG. 6 that the correlation formula correction process (step S2) is not performed even when the diagnostic target product is new. By doing so, the deterioration diagnosis can be performed immediately after the installation / replacement of the diagnosis target product, and therefore, for example, an initial failure of the diagnosis target product can be detected immediately.

  In the first embodiment, “capacity [A · hour]” itself is used as an index representing the remaining capacity. For example, “discharge time [hour]” (= capacity [A [Hour] / discharge current [A]) or “capacity ratio [%]” (= {capacity [A · hour] / rated capacity [A · hour]} × 100) representing the ratio to the rated capacity Also good. In particular, when the rated capacity of the sample product used for collecting the sample data and the product to be diagnosed (storage battery 1) are different, the method of the first embodiment cannot be applied as it is, but the “capacity ratio” is used. For example, the remaining capacity index can be made common between the sample product and the diagnosis target product, and the deterioration diagnosis method according to the present invention can be applied.

  Furthermore, in Embodiment 1, when collecting the first to fourth characteristic data, the charge / discharge circuit 11 caused the storage battery 1 to perform pulse discharge with a constant current, and the measurement unit 12 measured the voltage characteristics. There may be a case where the pulse discharge is not performed at a constant current. In this case, the measurement unit 12 measures the current value (I) and the voltage value (V) during pulse discharge, and the voltage value divided by the current value (V / I) is normalized (V / I). You may use as 4 characteristic data. According to this method, although the diagnostic accuracy is lowered, the first to fourth characteristic data can be easily collected. Moreover, it becomes possible to simplify the structure of the charging / discharging circuit 11 of the deterioration diagnostic apparatus 10, and it can contribute to the cost reduction of an apparatus.

It is a figure which shows the deterioration diagnostic apparatus which concerns on Embodiment 1. FIG. FIG. 5 is a diagram for explaining a method for measuring voltage characteristics during pulse discharge with a constant current in the first embodiment. 6 is a diagram for explaining sample data in the first embodiment. FIG. It is a figure which shows the correlation with the Mahalanobis distance of the voltage characteristic data of a storage battery, and a remaining capacity in the deterioration diagnostic method which concerns on Embodiment 1. FIG. It is a figure which shows the correlation with the voltage value at the time of the pulse discharge of a storage battery, and remaining capacity. 3 is a flowchart showing a deterioration diagnosis method according to the first embodiment. 5 is a flowchart showing correlation equation correction processing in the degradation diagnosis method according to Embodiment 1;

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Livestock battery, 10 deterioration diagnostic apparatus, 11 charging / discharging circuit, 12 measuring part, 13 data storage part, 14 calculating part, 15 display part, 16 control part.

Claims (6)

(A) The calculation means includes first characteristic data indicating output characteristics of a new battery sample, first capacity data indicating remaining capacity of the new battery sample, second characteristic data indicating output characteristics of a deteriorated battery sample, and A step of deriving a correlation equation between the output characteristics and the remaining capacity of the storage battery based on the second capacity data indicating the remaining capacity of the deteriorated product sample and the third characteristic data indicating the output characteristics of the storage battery diagnosis target product when new. When,
(B) a step of measuring an output characteristic of the diagnostic object and obtaining fourth characteristic data indicating the measurement characteristic;
(C) The calculation means includes a step of calculating a remaining capacity of the diagnosis target product from the fourth characteristic data based on the correlation equation. A deterioration diagnosis method for a storage battery according to claim 1,
The method for diagnosing deterioration of a storage battery, wherein the correlation equation is an expression showing a relationship between a Mahalanobis distance and a remaining capacity based on the first and third characteristic data indicating output characteristics of the storage battery. A storage battery deterioration diagnosis method according to claim 2,
The method for diagnosing deterioration of a storage battery, wherein the output characteristic is a voltage characteristic during pulse discharge. First characteristic data indicating output characteristics of a new battery sample, first capacity data indicating remaining capacity of the new battery sample, second characteristic data indicating output characteristics of the deteriorated battery sample, and remaining capacity of the deteriorated sample A data holding unit for holding the second capacity data shown;
A measurement unit for measuring the output characteristics of the storage battery diagnosis target product;
Based on the first and second characteristic data, the first and second capacity data, and the third characteristic data indicating the output characteristic of the diagnostic target product when it is new, which is measured by the measuring means, A correlation formula with the remaining capacity is derived, and using the correlation formula, the remaining capacity of the diagnostic target product is calculated from the fourth characteristic data indicating the output characteristics of the diagnostic target product after installation measured by the measurement unit. A deterioration diagnosis apparatus for a storage battery, comprising: an arithmetic unit. A deterioration diagnosis device for a storage battery according to claim 4,
The storage battery deterioration diagnosis apparatus, wherein the correlation expression is an expression showing a relationship between a Mahalanobis distance and a remaining capacity based on the first and third characteristic data indicating output characteristics of the storage battery. A deterioration diagnosis device for a storage battery according to claim 5,
The deterioration diagnosis device for a storage battery, wherein the output characteristic is a voltage characteristic during pulse discharge.

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