阅读说明：本技术 诊断电池组破裂的设备以及包括该设备的电池组和车辆 (Apparatus for diagnosing rupture of battery pack, and battery pack and vehicle including the same ) 是由 宋胜镐 朴正仁 洪明秀 崔容硕 于 2019-03-06 设计创作，主要内容包括：提供了一种用于诊断电池组中的破裂的装置,以及包括该装置的一种电池组和一种车辆。该装置包括：被配置为产生指示电池组的振动随时间的变化的振动信号的感测单元；和处理器。该处理器使用振动信号产生频谱密度数据。该处理器从频谱密度数据检测多个峰,并且基于该多个峰来诊断在电池组中是否存在破裂。(Provided are a device for diagnosing a rupture in a battery pack, and a battery pack and a vehicle including the same. The device includes: a sensing unit configured to generate a vibration signal indicating a change over time in vibration of the battery pack; and a processor. The processor generates spectral density data using the vibration signal. The processor detects a plurality of peaks from the spectral density data and diagnoses whether a crack exists in the battery pack based on the plurality of peaks.)

1. An apparatus for diagnosing rupture of a battery pack, the apparatus comprising:

a sensing unit configured to generate a first temporal vibration signal indicating a change over time in vibration of the battery pack for a first measurement period, a second temporal vibration signal indicating a change over time in vibration of the battery pack for a second measurement period after the first measurement period, and a third temporal vibration signal indicating a change over time in vibration of the battery pack for a third measurement period after the second measurement period; and

a processor operatively coupled to the sensing unit,

wherein the processor is configured to:

generating first spectral density data based on the first temporal vibration signal,

generating second spectral density data based on the second temporal vibration signal,

generating third spectral density data based on the third temporal vibration signal,

detecting a first plurality of peaks from the first spectral density data,

detecting a second plurality of peaks from the second spectral density data,

detecting a third plurality of peaks from the third spectral density data,

determining at least one of the first plurality of peaks as a first characteristic peak by comparing the first plurality of peaks with the second plurality of peaks, and

determining whether the battery pack is broken based on a frequency of one of the third plurality of peaks and a frequency of the first characteristic peak.

2. The device of claim 1, wherein the processor is configured to:

converting the first time vibration signal into a first frequency vibration signal,

generating the first spectral density data using the first frequency vibration signal,

converting the second time vibration signal into a second frequency vibration signal,

generating the second spectral density data using the second frequency vibration signal,

converting the third time vibration signal into a third frequency vibration signal, an

Generating the third spectral density data using the third frequency vibration signal.

3. The device of claim 1, wherein the processor is configured to calculate a first rate of change of frequency at a frequency of a first peak based on a difference between the frequency of the first peak and a frequency of a second peak, wherein the first peak is one of the first plurality of peaks and the second peak is one of the second plurality of peaks.

4. The apparatus of claim 3, wherein the processor is configured to determine the first peak as the first characteristic peak when the first rate of frequency change is within a predetermined first reference range.

5. The device of claim 4, wherein the processor is configured to not determine the first peak as the first characteristic peak when the first rate of frequency change is outside of the predetermined first reference range.

6. The device of claim 3, wherein the processor is configured to calculate a second rate of change of frequency at a frequency of a third peak based on a difference between the frequency of the first characteristic peak and the frequency of the third peak, wherein the third peak is one of the third plurality of peaks.

7. The apparatus of claim 6, wherein the processor is configured to determine that the battery pack is not ruptured when the second rate of frequency change is within a predetermined second reference range.

8. The apparatus of claim 7, wherein the processor is configured to determine that the battery pack is broken when the second rate of frequency change is outside of the predetermined second reference range.

9. The device of claim 1, wherein the sensing unit is configured to generate a fourth temporal vibration signal indicative of a variation over time of vibration of the battery pack for a fourth measurement period subsequent to the third measurement period, and

the processor is configured to:

generating fourth spectral density data based on the fourth temporal vibration signal,

detecting a fourth plurality of peaks from the fourth spectral density data,

determining at least one of the third plurality of peaks as a second characteristic peak by comparing the third plurality of peaks with the fourth plurality of peaks, and

determining whether the battery pack is broken based on the number of the first characteristic peaks and the number of the second characteristic peaks.

10. The apparatus of claim 9, wherein the processor is configured to determine that the battery pack is broken when the number of second characteristic peaks is greater than the number of first characteristic peaks.

11. A battery comprising the apparatus of any one of claims 1 to 10.

12. A vehicle comprising an apparatus according to any one of claims 1 to 10.

Technical Field

The present disclosure relates to an apparatus for diagnosing whether a battery pack is broken, and a battery pack and a vehicle including the same.

This application claims priority to korean patent application No.10-2018-0026445, filed on 6.3.2018 to the korean intellectual property office, the disclosure of which is incorporated herein by reference in its entirety.

Background

In general, secondary (rechargeable) batteries are batteries that can be used semi-permanently because they are charged with electric power generated by electric current supplied from an external power source during oxidation and reduction reactions of materials between a positive electrode and a negative electrode. Primary (disposable) batteries cannot be reused and require a great deal of cost to collect or recycle the batteries, while secondary batteries can be repeatedly recharged. In addition, secondary batteries are not only portable electronic devices such as laptop computers, mobile phones, and camcorders but also the core of electric vehicles, and they are considered as one of the "three major electronic components" of the 21 st century, along with semiconductors and displays, due to high added value. In particular, the global market for secondary batteries has reached $ 200 billion by 2011, and with the growth of the electric vehicle market and the growth of the secondary battery market for medium-and large-sized energy storage, the secondary batteries are expected to rapidly expand the market in the near future.

Secondary batteries are classified into nickel batteries, ion batteries, lithium ion batteries, polymer batteries, lithium polymer batteries, and lithium sulfur batteries according to the type of material used to fill the secondary batteries. The introduction of lithium polymer batteries in the 2000 s led to a new era of secondary batteries, following the advent of nickel-cadmium batteries and nickel-hydrogen batteries in the 1980 s, with the advent of lithium-based secondary batteries in the 1990 s.

With the recent trend toward compact and lightweight design of electronic devices and widespread use of mobile electronic devices, lithium ion batteries currently occupy most of the market of secondary batteries, and are manufactured by filling an organic electrolyte solution or a polymer electrolyte solution between positive and negative electrodes made of a material capable of intercalating and deintercalating lithium ions, and generate electric energy through oxidation and reduction reactions during intercalation and deintercalation of lithium ions at the positive and negative electrodes. Lithium ion batteries have been used in a wide range of applications from low-capacity batteries for mobile phones to high-capacity batteries for electric vehicles due to their advantages of light weight and high capacity.

In addition, lithium polymer batteries are an evolving form of lithium ion batteries, and use a solid or gel-type polymer electrolyte between the positive and negative electrodes to generate electricity. Advantageously, the lithium polymer battery can be manufactured in various shapes, and the minimum thickness is achieved in the secondary batteries developed so far.

The secondary battery generally includes a plurality of battery cells, and each battery cell is thin and thus may be easily broken. Therefore, a rigidity test of the produced battery cells is performed, and the existing test method is performed by sampling some of the battery cells before the produced battery cells are mounted in a vehicle and checking the rupture and rigidity of the battery cells by applying an external force until the sampled battery is broken.

However, since the earlier cell testing method is performed before the battery cell is mounted in the vehicle as described above, the crack inspection is impossible after the battery cell is mounted in the vehicle, and further, since the test is performed in a destructive manner, it is impossible to test the rigidity of all the produced battery cells.

Disclosure of Invention

Technical problem

The present disclosure is directed to an apparatus that converts a vibration signal indicating a change over time in vibration of a battery pack into spectral density data, detects a plurality of peaks from the spectral density data, and diagnoses a rupture of the battery pack based on the plurality of peaks.

Objects of the present disclosure are not limited to those described above, and these and other objects and advantages can be understood by the following description and will be apparent from the embodiments of the present disclosure.

Technical scheme

An apparatus for diagnosing rupture of a battery pack according to an aspect of the present disclosure includes a sensing unit and a processor operatively coupled to the sensing unit. The sensing unit is configured to generate a first temporal vibration signal indicative of a change over time in vibration of the battery pack for a first measurement period, a second temporal vibration signal indicative of a change over time in vibration of the battery pack for a second measurement period subsequent to the first measurement period, and a third temporal vibration signal indicative of a change over time in vibration of the battery pack for a third measurement period subsequent to the second measurement period. The processor is configured to generate first spectral density data based on the first temporal vibration signal. The processor is configured to generate second spectral density data based on the second temporal vibration signal. The processor is configured to generate third spectral density data based on the third temporal vibration signal. The processor is configured to detect a first plurality of peaks from the first spectral density data. The processor is configured to detect a second plurality of peaks from the second spectral density data. The processor is configured to detect a third plurality of peaks from the third spectral density data. The processor is configured to determine at least one of the first plurality of peaks as a first characteristic peak by comparing the first plurality of peaks to the second plurality of peaks. The processor is configured to determine whether the battery pack is broken based on a frequency of one of the third plurality of peaks and a frequency of the first characteristic peak.

The processor may convert the first time vibration signal to a first frequency vibration signal and generate first spectral density data using the first frequency vibration signal. The processor may convert the second time vibration signal to a second frequency vibration signal and generate second spectral density data using the second frequency vibration signal. The processor may convert the third temporal vibration signal to a third frequency vibration signal and generate third spectral density data using the third frequency vibration signal.

The processor may calculate a first rate of change of frequency at the frequency of the first peak based on a difference between the frequency of the first peak and a frequency of a second peak, wherein the first peak is one of the first plurality of peaks and the second peak is one of the second plurality of peaks.

The processor may determine the first peak as a first characteristic peak when the first frequency change rate is within a predetermined first reference range.

The processor may not determine the first peak as the first characteristic peak when the first frequency change rate is outside the predetermined first reference range.

The processor may calculate a second rate of change of frequency at a frequency of a third peak based on a difference between the frequency of the first characteristic peak and the frequency of the third peak, wherein the third peak is one of the third plurality of peaks.

The processor may determine that the battery pack is not broken when the second frequency change rate is within a predetermined second reference range.

The processor may determine that the battery pack is broken when the second frequency change rate is outside a predetermined second reference range.

The sensing unit may be configured to generate a fourth time vibration signal indicating a change over time in vibration of the battery pack for a fourth measurement period subsequent to the third measurement period. The processor may generate fourth spectral density data based on the fourth temporal vibration signal. The processor may detect a fourth plurality of peaks from the fourth spectral density data. The processor may determine at least one of the third plurality of peaks as a second characteristic peak by comparing the third plurality of peaks to the fourth plurality of peaks. The processor may determine whether the battery pack is broken based on the number of the first characteristic peaks and the number of the second characteristic peaks.

The processor may be configured to determine that the battery pack is broken when the number of second characteristic peaks is greater than the number of first characteristic peaks.

A battery pack according to another aspect of the present disclosure includes the apparatus.

A vehicle according to yet another aspect of the present disclosure includes the apparatus.

Advantageous effects

According to the present disclosure, spectral density data is acquired from a vibration signal indicating a change in vibration of a battery pack over time, a plurality of peaks are detected from the spectral density data, and a rupture of the battery pack is diagnosed based on the plurality of peaks. Accordingly, it is possible to accurately diagnose the rupture of the battery pack when the battery pack is coupled to the electrical load without electrically separating the battery pack from the electrical load.

Drawings

Fig. 1 is a diagram illustrating an apparatus for diagnosing rupture of a battery according to an embodiment of the present disclosure, which is included in a vehicle provided with a battery pack.

Fig. 2 is a schematic diagram showing a configuration of the apparatus of fig. 1.

Fig. 3 is a graph exemplarily showing a difference between the first spectral density data and the second spectral density data.

Fig. 4 is a graph showing first characteristic peak and third Power Spectral Density (PSD) data.

Fig. 5 is a graph exemplarily illustrating a difference between the first PSD data and the third PSD data.

Detailed Description

Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Before the description, it should be understood that the terms or words used in the specification and the appended claims should not be construed as limited to general and dictionary meanings, but interpreted based on the meanings and concepts corresponding to technical aspects of the present invention on the basis of the principle that the inventor is allowed to define terms appropriately for the best explanation.

Therefore, the embodiments described herein and the illustrations shown in the drawings are only the most preferred embodiments of the present disclosure and are not intended to fully describe the technical aspects of the present disclosure, so it should be understood that various other equivalent substitutions and modifications can be made thereto at the time of filing the application.

In addition, in describing the present disclosure, when it is determined that certain detailed description of related known elements or functions makes the key subject matter of the present disclosure unclear, the detailed description is omitted herein.

Ordinal terms such as "first," "second," etc., are used to distinguish one element from another element among the various elements and are not intended to limit the elements by the terms.

Unless the context clearly dictates otherwise, it is to be understood that the term "comprising" or "comprises", when used in this specification, specifies the presence of the stated elements but does not preclude the presence or addition of one or more other elements. In addition, the term < control unit > as used herein refers to at least one processing unit of function or operation, and this may be implemented by hardware or software, alone or in combination.

In addition, throughout the specification, it will be further understood that when an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may be present.

Fig. 1 is a diagram showing an apparatus for diagnosing rupture of a battery according to an embodiment of the present disclosure, which is included in a vehicle provided with a battery pack, fig. 2 is a schematic diagram showing a configuration of the apparatus of fig. 1, and fig. 3 is a graph exemplarily showing a difference between first spectral density data and second spectral density data.

Referring to fig. 1 and 2, the apparatus 100 may be included in a vehicle C provided with a battery pack B. The device 100 may be coupled to the battery B to diagnose whether the battery B is broken.

The device 100 may be included in a battery management system provided in the battery pack B.

The device 100 may include a sensing unit 110, a storage unit 120, a processor 130, and a notification unit 140.

The battery pack B includes a plurality of unit cells electrically connected in series and/or parallel. Of course, the present disclosure is not limited to the battery pack B including one unit cell.

The unit monomer is not limited to a specific type, and includes any monomer type capable of being repeatedly recharged. For example, the unit cell may be a pouch type lithium polymer battery.

The battery pack B may be electrically coupled to various types of external devices through external terminals. The external device may be a device driven using electricity, and may be, for example, an electric vehicle, a hybrid electric vehicle, a flying object such as a drone, a mass Energy Storage System (ESS) included in a power grid, or a mobile device. In this case, the battery pack B may include some or all of the unit cells included in the modular battery pack mounted in the external device.

The external terminal of the battery pack B may be couplable to the charging device. The charging device may be selectively electrically coupled to the battery pack B by the control of an external device mounted in the battery pack B.

The sensing unit 110 is operatively coupled to the processor 130. That is, the sensing unit 110 may transmit an electrical signal to the processor 130 or receive an electrical signal from the processor 130.

The sensing unit 110 may be configured to measure vibration of the battery pack B and generate a vibration signal indicating the measured vibration. In detail, the sensing unit 110 may include a vibration sensor (e.g., an acceleration sensor) attached to a surface of the battery pack B. The sensing unit 110 may generate a vibration signal at a predetermined period using a vibration sensor and provide the vibration signal to the processor 130.

The vibration signal may include a vibration component derived from the resonance characteristic of the battery B and a vibration component derived from an external condition (e.g., driving of the vehicle C).

When the processor 130 receives the vibration signal from the sensing unit 110, the processor 130 may convert the vibration signal into a digital value through signal processing and store the digital value in the storage unit 120.

The storage unit 120 is a semiconductor storage device, and records, deletes, and updates data generated by the processor 130, and stores a plurality of program codes for diagnosing whether the battery pack B is broken. In addition, the storage unit 120 may store preset values of predetermined parameters used when practicing the present disclosure.

The storage unit 120 is not limited to a specific type, and includes any type of semiconductor storage device known to be capable of recording, deleting, and updating data. For example, the storage unit 120 may include DRAM, SDRAM, flash memory, ROM, EEPROM, and registers. The memory unit 120 may further include a storage medium storing program code defining control logic of the processor 130. The storage medium includes a nonvolatile storage device such as a flash memory or a hard disk. The storage unit 120 may be physically separated from the processor 130, and the storage unit 120 and the processor 130 may be integrated.

The processor 130 may use the vibration signal from the sensing unit 110 to generate spectral density data. That is, the vibration signal generated by the sensing unit 110 is a kind of function or data represented in the time domain, and is converted into spectral density data by the processor 130. In detail, the processor 130 may convert a vibration signal in a time domain (hereinafter, referred to as a "time vibration signal") generated by the sensing unit 110 into a vibration signal in a frequency domain (hereinafter, referred to as a "frequency vibration signal"). For example, the processor 130 may convert the time vibration signal generated by the sensing unit 110 into a frequency vibration signal using a Fast Fourier Transform (FFT). The processor 130 may then use the frequency vibration signal to generate spectral density data.

The spectral density data may be data indicating the energy magnitude of each frequency component of the frequency vibration signal, i.e., Power Spectral Density (PSD).

Processor 130 may generate first spectral density data (hereinafter referred to as "first PSD data" or "first PSD curve") using a temporal vibration signal indicative of a variation over time of the vibration measured by sensing unit 110 for a first measurement period.

Processor 130 may detect a plurality of peaks from the first PSD data. In detail, the processor 130 may detect a frequency at which a variation of the first PSD data increases and then decreases with an infinitesimal variation of the frequency based on the first PSD data, and detect that the PSD at the frequency is a peak of the first PSD data.

To this end, the processor 130 may calculate a first derivative by differentiating the approximation function corresponding to the first PSD data, and calculate a second derivative by differentiating the first derivative of the approximation function again. Processor 130 may detect each position determined by a frequency at which the first order differential coefficient of the approximation function corresponding to the first PSD data is 0 and the second order differential coefficient is negative and the PSD value at the corresponding frequency as a peak of the first PSD data.

After the first measurement period has elapsed, processor 130 may calculate second spectral density data (hereinafter referred to as "second PSD data" or "second PSD curve") using a temporal vibration signal indicative of a change over time of vibration measured by sensing unit 110 for a second measurement period. The second measurement period may be a period in an external condition different from the first measurement period.

Processor 130 may detect multiple peaks from the second PSD data in the same manner as the process of detecting multiple peaks from the first PSD data.

Processor 130 may determine at least one of the plurality of peaks detected from the first PSD data as a first characteristic peak by comparing the plurality of peaks detected from the first PSD data to the plurality of peaks detected from the second PSD data.

In detail, the processor 130 may determine at least one of the plurality of peaks from the first PSD data as a first characteristic peak based on a first frequency change rate at a frequency of the at least one of the plurality of peaks from the first PSD data. In detail, the processor 130 may calculate the first frequency change rate based on a difference between a frequency of one of a plurality of peaks detected from the first PSD data and a frequency of one of a plurality of peaks detected from the second PSD data.

Processor 130 may calculate the first rate of frequency change using equation 1 below.

< equation 1>

V_f1＝(f2-f1)/f1×100

Here, V_f1Representing a first rate of change of frequency, f1 representing detection from first PSD dataThe frequency of one of the plurality of peaks, and f2 represents the frequency of one of the plurality of peaks detected from the second PSD data.

As shown in fig. 3, a plurality of peaksCan be detected from the first PSD data and a plurality of peaksMay be detected from the second PSD data.

For example, processor 130 may utilize a plurality of peaks detected from the first PSD data

The frequency of the peak (e.g., P1-1) in a particular order (e.g., first) of the frequencies in (b) is taken as f1 of equation 1, and a plurality of peaks detected from the second PSD data are utilized

The frequency of peaks (e.g., P2-1) in a specific order in (b) is taken as f 2.

In another example, processor 130 may utilize a plurality of peaks detected in the second PSD dataThe frequency having the smallest difference from among the frequencies selected as f1 is taken as f 2.

The processor 130 may calculate the first frequency change rate based on a difference between the frequency of the peak P1-1 and the frequency of the peak P2-1. The processor 130 may calculate the first frequency change rate using a difference between the frequency of the peak P1-2 and the frequency of the peak P2-2. The processor 130 may calculate the first frequency change rate using a difference between the frequency of the peak P1-3 and the frequency of the peak P2-3. The processor 130 may calculate the first frequency change rate using a difference between the frequency of the peak P1-4 and the frequency of the peak P2-4.

For example, processor 130 may calculate 69.2% as the first rate of frequency change using the difference between 13Hz, which is the frequency of peak P1-1, and 22Hz, which is the frequency of peak P2-1. In another example, processor 130 may calculate 1.25% as the first rate of frequency change using the difference between 40Hz, which is the frequency of peak P1-2, and 40.5Hz, which is the frequency of peak P2-2.

Processor 130 may determine whether the first frequency change rate is within a predetermined first reference range and may then detect a plurality of peaks from the first PSD data based on the determination

Is determined as the first characteristic peak.

In detail, when a plurality of peaks are usedThe processor 130 may determine a specific peak as the first characteristic peak when the first frequency change rate obtained as f1 of equation 1 is within a predetermined first reference range. In contrast, when multiple peaks are used

The processor 130 may not determine the specific peak as the first characteristic peak when the first frequency change rate obtained as f1 of equation 1 is outside the predetermined first reference range.

Assuming that the predetermined first reference range is

Since the first frequency change rate of 1.25% calculated using the difference between the frequency of the peak P1-2 and the frequency of the peak P2-2 is within the predetermined first reference range, the processor130 may determine peak P1-2 as the first characteristic peak. In contrast, since the first frequency change rate of 69.2%, calculated using the difference between the frequency of the peak P1-1 and the frequency of the peak P2-1, is outside the predetermined first reference range, the processor 130 may not determine the peak P1-1 as the first characteristic peak.

Thus, processor 130 may be on multiple peaks

Only the second peak P1-2 was selected as the first characteristic peak. The first characteristic peak P1-2 can be derived from a vibration component occurring due to the resonance characteristic of the battery B itself. Hereinafter, for convenience of description, it is assumed that a plurality of peaks detected from the first PSD data

The middle-only peak P1-2 was determined as the first characteristic peak.

At a plurality of peaks

The remaining peaks P1-1, P1-3, P1-4, which are not determined as the first characteristic peak P1-2, may be treated as those resulting from the external environment (e.g., the running of the vehicle C) other than the resonance characteristics of the battery B itself.

Fig. 4 is a graph showing a first characteristic peak and third PSD data, and fig. 5 is a graph exemplarily showing a difference between the first PSD data and the third PSD data.

Referring to fig. 4, after the second measurement period has elapsed, the processor 130 may generate third spectral density data (hereinafter referred to as "third PSD data" or "third PSD curve") using a temporal vibration signal indicative of a change in vibration over time measured by the sensing unit 110 for a third measurement period. The third measurement period may be a period in an external condition different from the second measurement period.

To detect a plurality of peaks from the first PSD data

Is the same as that ofIn this manner, processor 130 may detect a plurality of peaks from the third PSD data

In detail, the processor 130 converts the temporal vibration signal indicating the change over time of the vibration measured for the third measurement period into a frequency vibration signal. Processor 130 may then use the frequency vibration signal associated with the third measurement period to generate third PSD data. Processor 130 may then detect a plurality of peaks from the third PSD data

The first and second measurement periods may be periods required to determine the first characteristic peak, and the third measurement period may be a period required to diagnose whether battery B is broken.

The processor 130 may determine the first characteristic peak P1-2 before a predetermined period of time has elapsed from the time when the battery B is mounted in the vehicle C (e.g., a first measurement period, a second measurement period), and diagnose whether the battery B is broken based on a change over time in the vibration of the battery B measured for a third measurement period of time after the predetermined period of time has elapsed from the time when the battery B is mounted in the vehicle C.

Processor 130 may calculate a second rate of frequency change based on a difference between the frequency of the first characteristic peak P1-2 and the frequency of one of the plurality of peaks detected from the third PSD data. In detail, processor 130 may detect a plurality of peaks from the third PSD data

One peak (e.g., P3-2) having a frequency closest to that of the first characteristic peak P1-2 is selected.

Subsequently, the processor 130 may calculate a second frequency rate of change based on a difference between the frequency of the first characteristic peak P1-2 and the frequency of the selected peak (e.g., P3-2).

Processor 130 may calculate the second rate of frequency change using equation 2 below.

< equation 2>

V_f2＝(f3-fc)/fc×100

Here, V_f2Representing the second rate of change of frequency, fc representing the frequency of the first characteristic peak P1-2, and f3 representing the frequency at a plurality of peaks from the third PSD data

The frequency of the selected peak (e.g., P3-2).

For example, processor 130 may be configured to detect multiple peaks

The peak P3-2 having a 45Hz frequency closest to the 40Hz frequency of the first characteristic peak P1-2 among the frequencies of each of is selected as f3 of equation 2.

Subsequently, the processor 130 may use the frequency at 40Hz corresponding to the first characteristic peak P1-2 and at a plurality of peaks

The difference between the 45Hz frequencies of the selected peak P3-2 to calculate a second rate of change of frequency of 12.5%.

The processor 130 may determine whether the second frequency change rate is within a predetermined second reference range, and diagnose whether the battery B is broken based on the determination result. In detail, when the second frequency change rate is outside the predetermined second reference range, the processor 130 may diagnose that the battery B is broken. In contrast, when the second frequency change rate is within the predetermined second reference range, processor 130 may diagnose that battery B is not broken.

Assuming that the predetermined second reference range is

Since the calculated second frequency change rate-2.5% is within the predetermined second reference range, the processor 130 may diagnose that the battery B is not broken.

After the third measurement period has elapsed, processor 130 may generate fourth spectral density data (hereinafter referred to as "fourth PSD data" or "fourth PSD curve") using a temporal vibration signal indicative of a change over time of vibration measured by sensing unit 110 for a fourth measurement period. The fourth measurement period may be a period required to diagnose whether the battery B is broken, similar to the third measurement period. For convenience of description, illustration of the fourth PSD data is omitted here.

Processor 130 may detect a plurality of peaks from the fourth PSD data in the same manner as the process of detecting a plurality of peaks from each of the first PSD data, the second PSD data, and the third PSD data. In detail, the processor 130 converts the temporal vibration signal indicating the change over time of the vibration measured for the fourth measurement period into a frequency vibration signal. Processor 130 may then generate fourth PSD data using the frequency vibration signal associated with the fourth measurement period. Processor 130 may then detect a plurality of peaks from the fourth PSD data.

Processor 130 may then determine at least one of the plurality of peaks detected from the third PSD data as a second characteristic peak by comparing the third PSD data to the fourth PSD data. In detail, the processor 130 may calculate the third frequency change rate using equation 1. In this case, V of equation 1_f1Is the third frequency rate of change, f1 is the frequency of one of the plurality of peaks detected from the third PSD, and f2 is the frequency of one of the plurality of peaks detected from the fourth PSD data. In this case, f2 may be the frequency of one peak having the smallest difference from f1 among a plurality of peaks detected from the fourth PSD data.

When the third frequency change rate is within the predetermined third reference range, the processor 130 may determine a specific peak as the third PSD data whose frequency has f1 used for calculating the third frequency change rate as the second characteristic peak. In contrast, when the third frequency change rate obtained using the frequency of the specific peak of the third PSD data as f1 is outside the predetermined third reference range, the processor 130 may not determine the specific peak of the third PSD data as the second characteristic peak. The predetermined third reference range may be equal to or different from the predetermined first reference range.

Processor 130 may diagnose whether battery B is broken based on the number of first characteristic peaks calculated by comparing the first PSD data with the second PSD data and the number of second characteristic peaks calculated by comparing the third PSD data with the fourth PSD data.

When the number of first characteristic peaks and the number of second characteristic peaks are not equal (e.g., the number of second characteristic peaks is greater than the number of first characteristic peaks), processor 130 may diagnose that battery B is broken. In contrast, when the number of first characteristic peaks and the number of second characteristic peaks are equal, processor 130 may diagnose that battery B is not broken.

For example, as shown in fig. 5, the processor 130 may diagnose the rupture of the battery B in the case of one first characteristic peak P1-2 and two second characteristic peaks P3-2, P3-5. Each characteristic peak is derived from the resonance characteristic of the battery B itself. Therefore, a change in the number of characteristic peaks (e.g., an increase in the number of characteristic peaks over time) indicates a change in the resonance characteristic of battery B itself due to a crack in battery B.

Thus, the processor 130 can diagnose whether the battery B is broken when the vehicle C with the mounted battery B is stopped and while the vehicle C is running.

The processor 130 may transmit a message indicating the rupture diagnosis result of the battery B to an external device through the communication terminal.

Processor 130 may optionally include an Application Specific Integrated Circuit (ASIC), a chipset, a logic circuit, a register, a communications modem, and a data processing device known in the art to execute various control logic. At least one of various control logics capable of being executed by the processor 130 may be combined, and the combined control logic may be written in a computer readable encoding system and stored in a computer readable recording medium. The recording medium is not limited to a specific type, and includes any type that can be accessed by the processor 130 included in the computer. For example, the recording medium may include at least one selected from the group consisting of ROM, RAM, registers, CD-ROM, magnetic tape, hard disk, floppy disk, and optical data recording apparatus. In addition, the encoding system may be modulated into a carrier signal and included in a communication carrier at a specific point in time, and may be stored and executed in computers connected in a distributed manner via a network. In addition, functional programs, codes, and segments for implementing the combined control logic may be easily inferred by programmers in the technical fields related to the present disclosure.

The notification unit 140 may output the result of the diagnosis performed by the processor 130 to an external device. In more detail, the notification unit 140 may include at least one of a display unit displaying the diagnosis result using at least one of a symbol, a graphic, and a code, and a speaker outputting the diagnosis result using audio.

A battery management apparatus according to the present disclosure may include the apparatus 100 described above. Thereby, the battery management apparatus can diagnose whether or not the battery pack B managed by the battery management apparatus is broken.

A vehicle C according to the present disclosure may include the apparatus 100.

The embodiments of the present disclosure described above are not only implemented by the apparatuses and methods, but also can be implemented by a program that executes functions corresponding to the configuration of the embodiments of the present disclosure or a recording medium on which the program is recorded, and those skilled in the art can easily implement such an implementation from the disclosure of the previously described embodiments.

Although the present disclosure has been described above with respect to a limited number of embodiments and drawings, the present disclosure is not limited thereto, and it will be apparent to those skilled in the art that various modifications and changes may be made thereto within the technical scope of the present disclosure and the equivalent scope of the appended claims.

In addition, since many substitutions, modifications and changes may be made to the disclosure described above by those skilled in the art without departing from the technical aspects of the disclosure, the disclosure is not limited to the embodiments and the drawings described above, and some or all of the embodiments may be selectively combined to allow various modifications.