Patent Description:
The present invention relates to a battery diagnosis method capable of diagnosing the state of a battery including a plurality of battery cells that are connected in parallel, and to a battery diagnosis apparatus and a battery system for providing the method.

Large batteries mounted on electric vehicles, energy storage batteries, robots, satellites, and the like are required to have a higher capacity than small batteries mounted on portable terminals, laptop computers, and the like. A high-capacity battery may be configured by connecting a plurality of batteries in series and/or in parallel. In this case, the plurality of batteries may include a plurality of battery cells connected in parallel.

Meanwhile, when the number of battery cells included in a battery increases, defects may occur in the battery due to problems in the battery cells themselves and/or problems in connection between battery cells. For example, defects, such as disconnection and short circuit, between battery cells may occur. When a defect occurs in a battery, it is necessary to enable a system (for example, a vehicle, and an energy storage device) in which the battery is mounted to operate normally through quick diagnosis and correction of the defect.

However, when a plurality of battery cells is connected in parallel, it is not easy to directly sense a cell voltage or the like of an individual battery cell due to a structural problem in connection and the like. That is, it is difficult to diagnose defects of the entire battery by directly estimating defects of the battery cells themselves.

In addition, a technology for diagnosing a defect in a battery by estimating Direct Current Internal Resistance (DCIR) on a battery basis and comparing the estimated DCIR value with a preset (fixed) reference value has a limitation in not detecting a defect when a plurality of battery cells in a battery is disconnected or shorted at the same time. In addition, a problem of erroneously diagnosing the degree of change in the DC internal resistance value according to aging as occurrence of a defect. Prior art document <CIT> teaches an arrangement that measures and stores battery internal resistance values. Based on previously stored values, a trend line and upper/lower bounds are calculated, to check whether a newly measured resistance value is abnormal or not.

The present invention is conceived to solve the above problems, and provides a battery diagnosis method capable of precisely diagnosing the state of a battery including a plurality of battery cells that are connected in parallel, and a battery system providing the method.

A battery diagnosis apparatus according to an embodiment of the present invention includes: a measuring unit configured to measure a battery voltage at opposite ends of a battery including a plurality of battery cells, and a battery current flowing through the battery; a storage unit configured to store an internal resistance value of the battery that is calculated based on at least one of the battery voltage and the battery current at each diagnosis time point; and a control unit configured to extract a plurality of previous diagnosis time points corresponding to a predetermined number of samples based on a diagnosis time point for the each diagnosis time point, calculate a moving average that is an average of a plurality of internal resistance values corresponding to the plurality of diagnosis time points, respectively, the plurality of internal resistance value including the internal resistance value, and compare the internal resistance value with an upper band threshold that is larger than the moving average by a predetermined value and a lower band threshold that is smaller than the moving average by a predetermined value to diagnose a defect in the battery.

The control unit may calculate an error value by multiplying a standard deviation average value, which is an average of a plurality of standard deviations corresponding to the plurality of diagnosis time points, respectively, by a predetermined multiple, calculate the upper band threshold by adding the error value to the moving average, and calculate the lower band threshold by subtracting the error value from the moving average.

When the internal resistance value exceeds the upper band threshold, the control unit may diagnose that a disconnection defect has occurred in at least one of the plurality of battery cells.

When the internal resistance value is less than the lower band threshold, the control unit may diagnose that a short defect has occurred in at least one of the plurality of battery cells.

When the internal resistance value is within a range that is equal to or greater than the lower band threshold and is equal to or less than the upper band threshold, the control unit may diagnose that the plurality of battery cells is in a normal state.

A battery system according to another embodiment of the present invention includes: the battery diagnosis apparatus as described above and the battery including a plurality of battery cells.

A battery diagnosing method according to still another embodiment of the present invention includes: a battery data collecting operation of collecting a measurement value of each of a battery voltage at opposite ends of a battery including a plurality of battery cells, and a battery current flowing through the battery; a sample group determining operation of extracting, at a predetermined diagnosis time point, a plurality of diagnosis time points previously obtained and corresponding to a number of samples based on the predetermined diagnosis time point; a reference value determining operation of calculating a moving average, which is an average of a plurality of internal resistance values corresponding to the plurality of diagnosis time points, respectively, an upper band threshold larger than the moving average by a predetermined value, and a lower band threshold smaller than the moving average by a predetermined value; and a defect diagnosis operation of diagnosing a defect in the battery by comparing an internal resistance value corresponding to the predetermined diagnosis time point with the upper band threshold and the lower band threshold, from among the plurality of internal resistance values.

The reference value determining operation may include calculating an error value by multiplying a standard deviation average value, which is an average of a plurality of standard deviations corresponding to the plurality of diagnosis time points, respectively, by a predetermined multiple, calculating the upper band threshold by adding the error value to the moving average, and calculating the lower band threshold by subtracting the error value from the moving average.

The defect diagnosing operation may include, when the internal resistance value exceeds the upper band threshold, diagnosing that a disconnection defect has occurred in at least one of the plurality of battery cells. the defect diagnosing operation may include, when the internal resistance value is less than the lower band threshold, diagnosing that a short defect has occurred in at least one of the plurality of battery cells.

According to the present invention, even when a plurality of battery cells is connected in parallel, it is possible to diagnose a battery defect with high precision.

Unlike the prior art of diagnosing a defects in a battery by using a fixed reference value, the present invention diagnoses the battery defect by setting a reference value that reflects the change in the battery's internal resistance value at each diagnosis time point for diagnosing the battery defect, so that the degree of deterioration according to the period of use of the battery is considered, thereby diagnosing the defect in the battery with high precision.

Hereinafter, an exemplary embodiment disclosed the present specification will be described in detail with reference to the accompanying drawings, and the same or similar constituent element is denoted by the same reference numeral regardless of a reference numeral, and a repeated description thereof will be omitted. Suffixes, "module" and and/or "unit" for a constituent element used for the description below are given or mixed in consideration of only easiness of the writing of the specification, and the suffix itself does not have a discriminated meaning or role. Further, in describing the exemplary embodiment disclosed in the present disclosure, when it is determined that detailed description relating to well-known functions or configurations may make the subject matter of the exemplary embodiment disclosed in the present disclosure unnecessarily ambiguous, the detailed description will be omitted. Further, the accompanying drawings are provided for helping to easily understand exemplary embodiments disclosed in the present specification, and the technical spirit disclosed in the present specification is not limited by the accompanying drawings, and it will be appreciated that the present invention includes all of the modifications, equivalent matters, and substitutes included in the spirit and the technical scope of the present invention.

Terms including an ordinary number, such as first and second, are used for describing various constituent elements, but the constituent elements are not limited by the terms. The terms are used only to discriminate one constituent element from another constituent element.

It should be understood that when one constituent element is referred to as being "coupled to" or "connected to" another constituent element, one constituent element can be directly coupled to or connected to the other constituent element, but intervening elements may also be present. By contrast, when one constituent element is referred to as being "directly coupled to" or "directly connected to" another constituent element, it should be understood that there are no intervening elements.

In the present application, it will be appreciated that terms "including" and "having" are intended to designate the existence of characteristics, numbers, operations, operations, constituent elements, and components described in the specification or a combination thereof, and do not exclude a possibility of the existence or addition of one or more other characteristics, numbers, operations, operations, constituent elements, and components, or a combination thereof in advance.

<FIG> is a diagram illustrating a battery diagnosis apparatus according to an exemplary embodiment.

Referring to <FIG>, a battery diagnosis apparatus <NUM> includes a measuring unit <NUM>, a storage unit <NUM>, and a control unit <NUM>.

The measuring unit <NUM> may measure a battery voltage, which is the voltage at both ends of the battery, and a battery current, which is the current flowing through the battery. The battery voltage and the battery current may be battery data required to calculate internal resistance of the battery. For example, the internal resistance may include Direct Current Internal Resistance (DCIR).

The measuring unit <NUM> may include a voltage sensor (not illustrated) electrically connected to both ends of the battery to measure the battery voltage and a current sensor (not illustrated) connected in series to the battery to measure the battery current. For example, the measuring unit <NUM> may measure a battery voltage and a battery current at each diagnosis time point for diagnosing a defect in the battery, and transmit the measurement result to the control unit <NUM>.

The storage unit <NUM> may store an internal resistance value calculated by the control unit <NUM> based on at least one of a battery voltage and a battery current at each diagnosis time point for diagnosing a defect in the battery. In addition, the control unit <NUM> may store the battery voltage value and the battery current value received from the measuring unit <NUM> in the storage unit <NUM> at each diagnosis time point for diagnosing a defect in the battery.

When a diagnosis time point N according to a preset condition arrives, the control unit <NUM> calculates a moving average MA, an upper band threshold UB_Th which is larger than the moving average by a predetermined value, a lower band threshold LB_Th which is smaller than the moving average by a predetermined value, and an internal resistance value corresponding to the diagnosis time point N.

Depending on the exemplary embodiment, a time point at which charging of the battery starts or a time point at which discharging of the battery ends may be a diagnosis time point N for diagnosing a defect in the battery. When the diagnosis time point N arrives, the measuring unit <NUM> may measure each of a battery voltage and a battery current at a predetermined cycle for a predetermined period of time, and transmit the measurement results to the control unit <NUM>.

First of all, the control unit <NUM> may extract a plurality of diagnosis time points included in the preset number of samples SN when counting the diagnosis time points in the direction of the previous diagnosis time point based on the current diagnosis time point N and determine a sample group. In this case, the number of samples SN is the number of a plurality of diagnosis time points included in the sample group, and may be determined as an optimal number based on an experiment or the like. The sample group is a subgroup of a plurality of past diagnosis time points, which is a population, and may be a group for calculating a moving average MA and an standard deviation average value σ_ave, which will be described below.

Hereinafter, Table <NUM> is an example of the internal resistance DCIR value, the moving average MA, the upper band threshold UB_Th, and the lower band threshold LB_Th calculated at each of a plurality of diagnosis time points. It is assumed that the number of samples SN is <NUM>.

For reference, in Table <NUM>, it may be difficult to directly calculate a moving average MA, a standard deviation σ, an standard deviation average value σ_ave, an upper band threshold UB_Th, and a lower band threshold LB_Th of the first diagnosis time point <NUM> (therefore, the corresponding values in Table <NUM> are marked with blanks). In addition, it may also be difficult to directly calculate moving averages MA, standard deviations σ, standard deviation average values σ_ave, upper band thresholds UB_Th, and lower band thresholds LB_Th at diagnosis time points <NUM>, <NUM>,. adjacent to the first diagnosis time point <NUM> because there are no or insufficient past diagnostic values for calculation. In this case, the values calculated on average according to the experiment may be replaced with the moving averages MA, standard deviations σ, the standard deviation average value values σ_ave, the upper band thresholds UB_Th, and the lower band thresholds LB_Th at the first diagnosis time points <NUM>, <NUM>, <NUM>,.

When the control unit <NUM> counts the diagnosis time point in the direction of the previous diagnosis time point based on the current diagnosis time point N, the control unit <NUM> may extract an N-<NUM>th diagnosis time point, an N-<NUM>th diagnosis time point, an N-<NUM>th diagnosis time point, an N-<NUM>th diagnosis time point, and an N-<NUM>th diagnosis time point corresponding to the sample number SN of <NUM>, and determine the sample group.

The control unit <NUM> may determine a sample group by extracting a plurality of diagnosis time points (N-<NUM>, N-<NUM>, N-<NUM>, N-<NUM>, and N-<NUM>), and determine reference values (upper and lower band thresholds to be described below) used for defect diagnosis based on the internal resistance value calculated at each of the plurality of diagnosis time points belonging to the sample group. Then, it is possible to solve the problem of misdiagnosing the degree of aging due to long-term use of the battery and/or the temporary change in internal resistance value as a defect of the battery.

Next, the control unit <NUM> determines the reference values (upper and lower band thresholds) for diagnosing defects in the battery at the Nth diagnosis time point based on the internal resistance value calculated in each of the plurality of diagnosis time points N-<NUM>, N-<NUM>, N-<NUM>, N-<NUM>, and N-<NUM> belonging to the sample group.

According to the exemplary embodiment, the control unit <NUM> compares a value of the internal resistance DCIRN corresponding to the Nth diagnosis time point N with the upper band threshold UBN_Th and the lower band threshold LBN_Th to diagnose a defect in the battery. For example, referring to Table <NUM>, at the Nth diagnosis time point N, the control unit <NUM> calculates the internal resistance value (①), the upper band threshold (⑤) and the lower band threshold (⑥), and compare the calculated internal resistance value (①) with the upper band threshold (⑤) and the lower band threshold (⑥) to diagnose the defect of the battery. In this case, in order to calculate the upper band threshold (⑤) and the lower band threshold (⑥), the moving average (②) and the standard deviation average value (④) are required. However, the standard deviation (③) is not a value necessary for defect diagnosis at the Nth diagnosis time point N, but is necessary for defect diagnosis at the next diagnosis time points N+<NUM>, N+<NUM>,. , so that the standard deviation (③) may be calculated at the Nth diagnosis time point and stored in the storage unit <NUM>.

Hereinafter, the internal resistance value (①), the moving average (②), the standard deviation (③), the standard deviation average value (④), the upper band threshold (⑤), and the lower band threshold (⑥) calculated by the control unit <NUM> at the Nth diagnosis time point N in Table <NUM> will be described.

The control unit <NUM> may calculate the internal resistance DCIRN value corresponding to the Nth diagnosis time point N based on the battery voltage, which is the voltage of the both ends of the battery, and the battery current, which is the current flowing through the battery. For example, the value of the internal resistance DCIRN (①) may be calculated by Equation (<NUM>) below.

For example, the control unit <NUM> may calculate a voltage difference (ΔV = |V1 - V2|) between a battery voltage V1 corresponding to a first time point when charging starts and a battery voltage V2 corresponding to a second time point after a predetermined time has elapsed from the first time point. The control unit <NUM> may calculate the internal resistance DCIRN value based on the charging current I flowing through the battery and the voltage difference ΔV. For example, it is assumed that the internal resistance DCIRN value corresponding to the Nth diagnosis time point N is calculated as 30Ω.

Referring to Table <NUM>, the control unit <NUM> may calculate an moving average MAN (②) corresponding to the diagnosis time point N by averaging (23Ω+ 24Ω+ 20Ω+ 21Ω+ 23Ω/<NUM> = <NUM>Ω) the plurality of internal resistance values (23Ω, 24Ω, 20Ω, 21Ω, and 23Ω) corresponding to the plurality of diagnosis time points (N-<NUM>, N-<NUM>, N-<NUM>, N-<NUM>, and N-<NUM>), respectively, belonging to the sample group. That is, the internal resistance DCIRN value corresponding to the Nth diagnosis time point may be <NUM>.

Referring to Table <NUM> below, the control unit <NUM> may calculate a standard deviation σN (③) corresponding to the diagnosis time point N based on the internal resistance value DCIR and moving average MA corresponding to each of the plurality of diagnosis time points N-<NUM>, N-<NUM>, N-<NUM>, N-<NUM>, and N-<NUM> belonging to the sample group.

As described above, the standard deviation σN (③) corresponding to the Nth diagnosis time point N is not a value necessary for defect diagnosis at the Nth diagnosis time point N, but is necessary when defects are diagnosed at the next diagnosis time points N+<NUM>, N+<NUM>, and. Accordingly, the standard deviation σN (③) corresponding to the Nth diagnosis time point N may be calculated at the Nth diagnosis time point N and stored in the storage unit <NUM>.

Referring to Table <NUM> below, the control unit <NUM> may calculate an standard deviation average value σN_ave (④) corresponding to the Nth diagnosis time point N based on the plurality of standard deviations σN-<NUM>, σN-<NUM>, σN-<NUM>, σN-<NUM>, and σN-<NUM> corresponding to the plurality of diagnosis time points N-<NUM>, N-<NUM>, N-<NUM>, N-<NUM>, and N-<NUM>, respectively, belonging to the sample group.

The control unit <NUM> may calculate an upper band threshold UBN_Th that is larger than the moving average MAN by a predetermined value and a lower band threshold LBN_Th that is smaller than the moving average MA by a predetermined value. According to the exemplary embodiment, the control unit <NUM> may calculate a first error value by multiplying the standard deviation average value σN_ave by a predetermined first multiple, and calculate the upper band threshold UBN_Th by adding the first error value to the moving average MAN. In addition, the control unit <NUM> may calculate a second error value by multiplying the standard deviation average value σN_ave by a predetermined second multiple, and calculate the lower band threshold LBN_Th by subtracting the second error value from the moving average MAN. In this case, the first multiple and the second multiple may be the same, but are not limited thereto, and the error value may be calculated as various multiples.

According to the exemplary embodiment, the control unit <NUM> may calculate an error value (E = σN_ave × Q) by multiplying the standard deviation average value σN_ave by a predetermined multiple Q. In this case, the multiple Q is a value for reflecting a predetermined error, and may be determined by various values through experiments. For example, it is assumed that the multiple Q is the natural number <NUM>.

The control unit <NUM> may calculate the upper band threshold UBN_Th of <NUM> by adding the error value ((E = σN_ave × Q = <NUM> × <NUM> = <NUM>) to the moving average (MAN = <NUM>) of the sample group as illustrated in Equation <NUM> below. In addition, the control unit <NUM> may calculate the lower band threshold LBN_Th of <NUM> by subtracting the error value (E = σN_ave × Q = <NUM> × <NUM> = <NUM>) from the moving average (MAN = <NUM>) of the sample group as illustrated in Equation <NUM> below. <MAT> <MAT>.

Next, the control unit <NUM> may compare the value of the internal resistance DCIRN corresponding to the Nth diagnosis time point N with the upper band threshold UBN_Th and the lower band threshold LBN_Th corresponding to the Nth diagnosis time point to diagnose a defect in the battery.

According to the exemplary embodiment, when the internal resistance DCIRN value exceeds the upper band threshold UBN_Th, the control unit <NUM> may diagnose that a disconnection defect (DD) has occurred in at least one of a plurality of battery cells included in the battery. When the value of the internal resistance DCIRN value is less than the lower band threshold LBN_Th, the control unit <NUM> may diagnose that a short defect (SD) has occurred in at least one of a plurality of battery cells included in the battery. That is, when the value of the internal resistance DCIRN value is out of the normal range corresponding to the lower band threshold LBN_Th or more and the upper band threshold UBN_Th or less, the control unit <NUM> may diagnose that a defect (disconnection defect or short defect) has occurred in the battery. In addition, when the value of the internal resistance DCIRN value falls within the normal range, the control unit <NUM> may diagnose the state of the battery as normal.

For example, as described above through Tables <NUM> and <NUM> and Equations <NUM> to <NUM>, the internal resistance DCIRN value, the upper band threshold UBN_Th, and the lower band threshold LBN_Th corresponding to the Nth diagnosis time point N may be calculated as 30Ω, <NUM>, and <NUM>, respectively. In this case, the control unit <NUM> may diagnose a battery defect (disconnection defect) based on the fact that the internal resistance value (DCIRN = <NUM>) exceeds the upper band threshold (UBN_Th = <NUM>).

<FIG> is a diagram illustrating a battery system according to another exemplary embodiment.

Referring to <FIG>, a battery system <NUM> includes a battery <NUM>, a relay <NUM>, a current sensor <NUM>, and a battery management system (BMS) <NUM>.

The battery <NUM> may include a plurality of battery cells connected in series and/or in parallel. In <FIG>, three battery cells connected in parallel are illustrated, but the present invention is not limited thereto, and the battery <NUM> may include various numbers of battery cells connected in series and/or in parallel. In some exemplary embodiments, the battery cell may be a rechargeable secondary battery.

For example, in the battery <NUM>, a predetermined number of battery cells is connected in parallel to form a battery bank, and a predetermined number of battery banks is connected in series to form a battery pack to supply desired power to an external device. For another example, in the battery <NUM>, a predetermined number of battery cells is connected in parallel to form a battery bank, and a predetermined number of battery banks is connected in parallel to form a battery pack to supply desired power to an external device. However, the present invention is not limited to this connection, and the battery <NUM> may include a plurality of battery banks including a plurality of battery cells connected in series and/or parallel, and the plurality of battery banks may also be connected in series and/or parallel.

In <FIG>, the battery <NUM> is connected between the two output terminals OUT1 and OUT2 of the battery system <NUM>. In addition, the relay <NUM> is connected between a positive electrode of the battery system <NUM> and the first output terminal OUT1, and the current sensor <NUM> is connected between a negative electrode of the battery system <NUM> and the second output terminal OUT2. The configurations illustrated in <FIG> and the connection relationship between the configurations are examples, but the invention is not limited thereto.

The relay <NUM> controls electrical connection between the battery system <NUM> and an external device. When the relay <NUM> is turned on, the battery system <NUM> and the external device are electrically connected to perform charging or discharging, and when the relay <NUM> is turned off, the battery system <NUM> and the external device are electrically separated. In this case, the external device may be a charger in a charging cycle in which power is supplied to the battery <NUM> for charging, and may be a load in a discharging cycle in which the battery <NUM> discharges power to the external device.

The current sensor <NUM> is connected in series to a current path between the battery <NUM> and an external device. The current sensor <NUM> may measure the battery current flowing through the battery <NUM>, that is, a charging current and a discharging current, and transmit the measurement result to the BMS <NUM>.

The BMS <NUM> includes a measuring unit <NUM>, a storage unit <NUM>, and a control unit <NUM>. The battery diagnosis apparatus <NUM> illustrated in <FIG> may correspond to the BMS <NUM> illustrated in <FIG>. Specifically, the functions performed by the measuring unit <NUM>, the storage unit <NUM>, and the control unit <NUM> of the battery diagnosis apparatus <NUM> may correspond to the functions performed by the measuring unit <NUM>, the storage unit <NUM>, and the control unit <NUM> of the BMS <NUM>. For example, the battery diagnosis apparatus <NUM> may be configured separately from the battery system <NUM>. For another example, in the battery system <NUM> as illustrated in <FIG>, the BMS <NUM> may perform the function of the battery diagnosis apparatus <NUM>.

The measuring unit <NUM> is electrically connected to both ends of the battery <NUM> to measure a battery current and a battery voltage. For example, the measuring unit <NUM> may be implemented as an Application Specific Integrated Circuit (ASIC) that monitors the battery <NUM> and measures battery data (voltage, current, and the like) corresponding to the state of the battery <NUM>.

For example, the measuring unit <NUM> may collect the battery voltage by sensing voltage values of both ends of the battery <NUM>. The measuring unit <NUM> may receive a battery current value from the current sensor <NUM>. The measuring unit <NUM> may transmit the battery voltage value and the battery current value to the control unit <NUM>.

The storage unit <NUM> may store an internal resistance value calculated by the control unit <NUM> based on at least one of a battery voltage and a battery current at each diagnosis time point for diagnosing a defect in the battery <NUM>. In addition, the control unit <NUM> may store the battery voltage value and the battery current value received from the measuring unit <NUM> in the storage unit <NUM> at each diagnosis time point for diagnosing a defect in the battery.

When the diagnosis time point N according to a preset condition arrives, the control unit <NUM> calculates a moving average MAN, an upper band threshold UBN_Th, a lower band threshold LBN_Th, and internal resistance DCIRN. Also, the control unit <NUM> may diagnose the state of the battery <NUM> by comparing the value of the internal resistance DCIRN with the upper band threshold UBN_Th and the lower band threshold LBN_Th.

For example, it is assumed that the number of samples SN is <NUM>. In Table <NUM> above, when the control unit <NUM> counts the diagnosis time point in the direction of the previous diagnosis time point based on the current diagnosis time point N, the control unit <NUM> may extract an N-<NUM>th diagnosis time point, an N-<NUM>th diagnosis time point, an N-<NUM>th diagnosis time point, an N-<NUM>th diagnosis time point, and an N-<NUM>th diagnosis time point corresponding to the sample number SN of <NUM>, and determine the sample group.

The control unit <NUM> may calculate a moving average MAN, which is an average of internal resistance values of a sample group. For example, referring to Table <NUM> and Equation <NUM> above, the control unit <NUM> may calculate the moving average MAN of <NUM> corresponding to the current diagnosis time point N by averaging multiple internal resistance values 23Ω, 24Ω, 20Ω, 21Ω, and 23Ω corresponding to a plurality of diagnosis time points N-<NUM>, N-<NUM>, N-<NUM>, N-<NUM>, and N-<NUM>, respectively, belonging to the sample group.

For example, referring to Table <NUM> and Table <NUM> above, the control unit <NUM> may calculate an standard deviation average value σN_ave (<NUM>) corresponding to the current diagnosis time point N based on standard deviations σN-<NUM>, σN-<NUM>, σN-<NUM>, σN-<NUM>, σN-<NUM> corresponding to the plurality of diagnosis time points N-<NUM>, N-<NUM>, N-<NUM>, N-<NUM>, and N-<NUM>, respectively, belonging to the sample group.

The control unit <NUM> may determine a reference value for diagnosing a battery defect at the current diagnosis time point N, that is, the Nth diagnosis time point, based on the internal resistance DCIRN value calculated at each of a plurality of diagnosis time points N-<NUM>, N-<NUM>, N-<NUM>, N-<NUM>, and N-1belonging to the sample group. In this case, the reference value may include an upper band threshold UBN_Th and a lower band threshold LBN_Th.

According to the exemplary embodiment, the control unit <NUM> may calculate an error value (E = σN_ave × Q) by multiplying the standard deviation average value σN_ave that is the average of the standard deviations of the sample group by a predetermined multiple Q. For example, it is assumed that the multiple Q is the natural number <NUM>. The control unit <NUM> may calculate the upper band threshold UBN_Th of <NUM> by adding the error value (E = σN_ave × Q = <NUM> × <NUM> = <NUM>)) to the moving average (MAN = <NUM>) of the sample group as illustrated in Equation <NUM> above. In addition, the control unit <NUM> may calculate the lower band threshold LBN_Th of <NUM> by subtracting the error value (E = σN_ave × Q = <NUM> × <NUM> = <NUM>) from the moving average (MAN = <NUM>) of the sample group as illustrated in Equation <NUM> above. In this case, the multiple Q is a value for reflecting a predetermined error, and may be determined by various values through experiments.

Next, the control unit <NUM> may compare the value of the internal resistance DCIRN corresponding to the Nth diagnosis time point N with the upper band threshold UBN_Th and the lower band threshold LBN_Th corresponding to the Nth diagnosis time point to diagnose a defect in the battery <NUM>.

According to the exemplary embodiment, when the internal resistance DCIRN value exceeds the upper band threshold UBN_Th, the control unit <NUM> may diagnose that a disconnection defect (DD) has occurred in at least one of a plurality of battery cells included in the battery <NUM>. When the value of the internal resistance DCIRN value is less than the lower band threshold LBN_Th, the control unit <NUM> may diagnose that a short defect (SD) has occurred in at least one of a plurality of battery cells included in the battery <NUM>. That is, when the value of the internal resistance DCIRN value is out of the normal range corresponding to the lower band threshold LBN_Th or more and the upper band threshold UBN_Th or less, the control unit <NUM> may diagnose that a defect (disconnection defect or short defect) has occurred in the battery <NUM>. In addition, when the value of the internal resistance DCIRN value falls within the normal range, the control unit <NUM> may diagnose the state of the battery as normal.

For example, as described above through Tables <NUM> and <NUM> and Equations <NUM> to <NUM>, the internal resistance DCIRN value, the upper band threshold UBN_Th, and the lower band threshold LBN_Th corresponding to the Nth diagnosis time point N may be calculated as 30Ω, <NUM>, and <NUM>, respectively. In this case, the control unit <NUM> may diagnose a defect (disconnection defect) of the battery <NUM> based on the fact that the internal resistance value (DCIRN = <NUM>) exceeds the upper band threshold (UBN_Th = <NUM>).

<FIG> is an exemplary diagram in which a moving average, an upper band threshold, and a lower band threshold calculated for each of a plurality of diagnosis time points are accumulated and displayed.

Hereinafter, an example of calculating the moving average MAN, the upper band threshold UBN_Th, and the lower band threshold LBN_Th will be described based on <FIG> and Tables <NUM> and <NUM>.

The BMS <NUM> may determine a sample group by extracting a plurality of diagnosis time points which is adjacent to the diagnosis time point N while being in an environment similar to the predetermined diagnosis time point N. The BMS <NUM> may calculate an upper band threshold UBN_Th and a lower band threshold LBN_Th corresponding to the diagnosis time point N based on the moving average MAN, which is the average of a plurality of internal resistance values belonging to the sample group, and the standard deviation average value σN_ave, which is the average of a plurality of standard deviations.

According to the exemplary embodiment, first, When the BMS <NUM> counts the diagnosis time point in the direction of the previous diagnosis time point based on the predetermined diagnosis time point N, the BMS <NUM> may extract an N-<NUM>th diagnosis time point, an N-<NUM>th diagnosis time point, an N-<NUM>th diagnosis time point, an N-<NUM>th diagnosis time point, and an N-<NUM>th diagnosis time point corresponding to the sample number SN of <NUM>.

Next, the BMS <NUM> may calculate a moving average ((23Ω+ 24Ω+ 20Ω+ 21Ω+ 23Ω)/<NUM> = <NUM>. 2Ω) corresponding to the diagnosis time point N by averaging the plurality of internal resistance values (23Ω, 24Ω, 20Ω, 21Ω, and 23Ω) corresponding to the plurality of extracted diagnosis time points N-<NUM>, N-<NUM>, N-<NUM>, N-<NUM>, and N-<NUM>, respectively.

Through Table <NUM>, Equation <NUM>, and Equation <NUM> described above, the BMS <NUM> may calculate the upper band threshold <NUM> and the lower band threshold <NUM>.

Next, the BMS <NUM> may diagnose a defect in the battery <NUM> by comparing the value of the internal resistance DCIRN with the upper band threshold UBN_Th and the lower band threshold LBN_Th. In this case, it is assumed that the internal resistance DCIRN is 30Ω, for example. The BMS <NUM> may diagnose a battery defect (disconnection defect) based on the fact that the internal resistance value (DCIRN = <NUM>) exceeds the upper band threshold (UBN_Th = <NUM>).

The internal resistance band (DCIR Band) illustrated in <FIG> may be derived by connecting the moving average MA, the upper band threshold UB_Th, and the lower band threshold LB_Th calculated at each diagnosis time point. The internal resistance band (DCIR Band) may exhibit a trend of an internal resistance value that changes as the battery <NUM> is used.

<FIG> is a flowchart illustrating a battery diagnosis method according to an exemplary embodiment.

Hereinafter, a battery diagnosis method, a battery diagnosis apparatus and a battery system for providing the method will be described with reference to <FIG>. A battery diagnosis method performed in the battery system <NUM> described below may be equally applied to the battery diagnosis apparatus <NUM>.

First, the BMS <NUM> collects battery data (S100). In this case, the battery data may include a battery voltage, which is a voltage of both ends of the battery <NUM>, and a battery current, which is a current flowing through the battery <NUM>.

For example, the battery voltage and the battery current may be battery data required to calculate Direct Current Internal Resistance (DCIR) of the battery.

Next, the BMS <NUM> determines a sample group by extracting a plurality of diagnosis time points adjacent to a predetermined diagnosis time point N (S200).

When the BMS <NUM> counts the diagnosis time point in the direction of the previous diagnosis time point based on a predetermined diagnosis time point N, that is, the Nth diagnosis time point, the BMS <NUM> may extract a plurality of diagnosis time points corresponding to the preset number of samples SN to determine a sample group.

For example, it is assumed that the number of samples SN is <NUM>. In Table <NUM> above, when the BMS <NUM> counts the diagnosis time point in the direction of the previous diagnosis time point based on the Nth diagnosis time point, the BMS <NUM> may extract an N-<NUM>th diagnosis time point, an N-<NUM>th diagnosis time point, an N-<NUM>th diagnosis time point, an N-<NUM>th diagnosis time point, and an N-<NUM>th diagnosis time point corresponding to the sample number SN of <NUM>, and determine the sample group.

Next, the BMS <NUM> determines a reference value for the diagnosis of a defect of the battery <NUM> (S300). According to the exemplary embodiment, the reference value may include an upper band threshold UBN_Th and a lower band threshold LBN_Th.

In operation S300, referring to <FIG>, the BMS <NUM> averages internal resistance values corresponding to a plurality of diagnosis time points belonging to the sample group to calculate a moving average MAN of the sample group (S310).

Referring to Table <NUM> and Equation <NUM> above, the BMS <NUM> may calculate the moving average MAN of <NUM> corresponding to the diagnosis time point N by averaging multiple internal resistance values 23Ω, 24Ω, 20Ω, 21Ω, and 23Ω corresponding to a plurality of diagnosis time points N-<NUM>, N-<NUM>, N-<NUM>, N-<NUM>, and N-<NUM>, respectively, belonging to the sample group.

In operation S300, the BMS <NUM> calculates an error value E based on the standard deviation average value σN_ave of the sample group (S320).

For example, the standard deviation average value σN_ave of the sample group may be calculated by averaging the plurality of standard deviations σN-<NUM>, σN-<NUM>, σN-<NUM>, σN-<NUM>, and σN-<NUM> corresponding to the plurality of diagnosis time points N-<NUM>, N-<NUM>, N-<NUM>, N-<NUM>, and N-<NUM>, respectively, belonging to the sample group.

Referring to Table <NUM> and Table <NUM> above, the BMS <NUM> may calculate an standard deviation average value σN_ave of <NUM> corresponding to the Nth diagnosis time point N based on the plurality of standard deviations σN-<NUM>, σN-<NUM>, σN-<NUM>, σN-<NUM>, and σN-<NUM> corresponding to the plurality of diagnosis time points N-<NUM>, N-<NUM>, N-<NUM>, N-<NUM>, and N-<NUM>, respectively, belonging to the sample group. In addition, the BMS <NUM> may calculate an error value (E= σN_ave × Q = <NUM> × <NUM> = <NUM>) by multiplying the standard deviation average value σN_ave by a predetermined multiple Q. In this case, the multiple Q is a value for reflecting a predetermined error, and may be determined by various values through experiments. For example, it is assumed that the multiple Q is the natural number <NUM>.

In operation S300, the BMS <NUM> calculates an upper band threshold UBN_Th and a lower band threshold LBN_Th based on the moving average MAN and the error value E (S330).

Referring to Equation <NUM>, the BMS <NUM> may calculate the upper band threshold UBN_Th of <NUM> by adding the error value (E = σN_ave × Q = <NUM> × <NUM> = <NUM>) to the moving average (MAN = <NUM>) of the sample group. Further, referring to Equation <NUM>, the BMS <NUM> may calculate the lower band threshold LBN_Th of <NUM> by subtracting the error value (E = σN_ave × Q = <NUM> × <NUM> = <NUM>) from the moving average (MAN = <NUM>) of the sample group.

Next, the BMS <NUM> compares the value of the internal resistance DCIRN corresponding to the current diagnosis time point N with the upper band threshold UBN_Th and the lower band threshold LBN_Th corresponding to the current diagnosis time point N, so that the battery <NUM> diagnoses a defect of the battery <NUM> (S400).

The BMS <NUM> may calculate the internal resistance DCIRN value corresponding to the Nth diagnosis time point based on the battery voltage, which is the voltage of the both ends of the battery, and the battery current, which is the current flowing through the battery. Also, the value of the internal resistance DCIRN may be calculated in operation S200 or operation S300.

For example, the BMS <NUM> may calculate a voltage difference (ΔV = |V1 - V2|) between a battery voltage V1 corresponding to a first time point when charging starts and a battery voltage V2 corresponding to a second time point after a predetermined time has elapsed from the first time point. The BMS <NUM> may calculate the internal resistance DCIRN value based on the charging current I flowing through the battery <NUM> and the voltage difference ΔV. For example, it is assumed that the internal resistance DCIRN value corresponding to the Nth diagnosis time point is calculated as 30Ω.

In operation S400, the BMS <NUM> determines whether the value of the internal resistance DCIRN exceeds the upper band threshold UBN_Th (S410).

In operation S400, when the determination result is exceeded (S410, YES), the BMS <NUM> diagnoses that a disconnection defect has occurred in at least one of a plurality of battery cells included in the battery <NUM> (S420).

For example, when the parallel connection of some battery cells among a plurality of battery cells connected in parallel is disconnected, the internal resistance value of the battery <NUM> may increase.

In operation S400, when the determination result does not exceed (S410, NO), the BMS <NUM> determines whether the internal resistance DCIRN value is less than the lower band threshold LBN_Th (S430).

In operation S400, when the internal resistance value is less than the lower band threshold as a result of the determination result (S430, YES), the BMS <NUM> diagnoses that a short defect has occurred in at least one of a plurality of battery cells included in the battery <NUM> (S440).

For example, when the parallel connection of some battery cells among a plurality of battery cells connected in parallel has short, the internal resistance value that is the entire resistance of the battery <NUM> may increase.

In operation S400, when the internal resistance value is equal to or greater than the lower band threshold as a result of the determination result (S430, NO), the BMS <NUM> diagnoses the state of the battery <NUM> as normal (S450).

When the value of the internal resistance DCIRN is out of the normal range corresponding to the lower band threshold LBN_Th or more and the upper band threshold UBN_Th or less, the BMS <NUM> may diagnose the state of the battery <NUM> as a defect (disconnection defect or short defect). In addition, when the value of the internal resistance DCIRN value falls within the normal range, the BMS <NUM> may diagnose the state of the battery as normal.

Claim 1:
A battery diagnosis apparatus, comprising:
a measuring unit (<NUM>, <NUM>) configured to measure a battery voltage at opposite ends of a battery including a plurality of battery cells, and a battery current flowing through the battery;
a storage unit (<NUM>, <NUM>) configured to store an internal resistance value of the battery that is calculated based on at least one of the battery voltage and the battery current at each diagnosis time point; and a control unit (<NUM>, <NUM>) configured to extract a plurality of previous diagnosis time points corresponding to a predetermined number of samples based on a diagnosis time point for the each diagnosis time point, calculate a moving average that is an average of a plurality of internal resistance values corresponding to the plurality of diagnosis time points, respectively, the plurality of internal resistance value including the internal resistance value, and compare the internal resistance value with an upper band threshold that is larger than the moving average by a predetermined value and a lower band threshold that is smaller than the moving average by a predetermined value to diagnose a defect in the battery.