Apparatus and method for diagnosing battery

Abstract

A battery diagnosis apparatus according to an embodiment of the present invention may comprise: a data acquisition unit configured to acquire a first dataset representing a correspondence relationship between time and state of health (SOH) for a plurality of batteries and a second dataset representing a correspondence relationship between time and SOH for a target battery; and a processor configured to calculate, from the first dataset, a first rate of change representing a change in the SOH over the time, calculate, from the second dataset, a second rate of change representing a change in the SOH over the time, and diagnose the state of the target battery on the basis of the first rate of change and the second rate of change.

Description

Battery diagnostic device and method

[0001] This application is a priority application for Korean Patent Application No. 10-2024-0142986 filed on October 18, 2024, and all contents disclosed in the specification and drawings of said application are incorporated into this application by reference.

[0002] The present invention relates to a battery diagnostic device and method capable of diagnosing the condition of a battery.

[0003] Recently, as the demand for portable electronic products such as laptops, video cameras, and mobile phones has increased rapidly, and the development of electric vehicles, energy storage batteries, robots, and satellites has accelerated, research on high-performance batteries capable of repeated charging and discharging is actively underway.

[0004] Currently commercialized batteries include nickel-cadmium, nickel-hydrogen, nickel-zinc, and lithium batteries. Among these, lithium batteries are gaining attention for their advantages, such as the ability to freely charge and discharge with almost no memory effect compared to nickel-based batteries, a very low self-discharge rate, and high energy density.

[0005] While much research is being conducted on these batteries in terms of increasing capacity and density, improving lifespan and safety is also important. To enhance battery safety, technology capable of accurately diagnosing the battery's current state is required.

[0006] The present invention was devised to solve the above-mentioned problems and aims to provide a battery diagnostic device and method that more accurately estimate the current state of a battery.

[0007] Other objects and advantages of the present invention may be understood from the following description and will become more clearly apparent from the embodiments of the present invention. Furthermore, it will be readily apparent that the objects and advantages of the present invention can be realized by the means and combinations thereof set forth in the claims.

[0008] A battery diagnostic device according to one aspect of the present invention may be configured to acquire a first dataset representing a correspondence between time and SOH for a plurality of batteries and a second dataset representing a correspondence between the time and SOH for a target battery; and to calculate a first rate of change representing a change in SOH according to time in the first dataset and calculate a second rate of change representing a change in SOH according to time in the second dataset, and to diagnose the state of the target battery based on the first rate of change and the second rate of change.

[0009] The control unit may be configured to derive a linear regression model for the first dataset and to calculate the first rate of change from the derived linear regression model.

[0010] The control unit may be configured to first derive a non-linear regression model for the first dataset and to derive the linear regression model from the derived non-linear regression model.

[0011] The control unit may be configured to derive a linear regression model for the second dataset and to calculate the second rate of change from the derived linear regression model.

[0012] The control unit above may be configured to estimate the relative degradation degree of the target battery with respect to the plurality of batteries as the state of the target battery.

[0013] The control unit may be configured to calculate the difference in the rate of change between the first rate of change and the second rate of change, and to estimate the relative degree of degeneration by calculating the ratio of the difference in the rate of change to the first rate of change.

[0014] The control unit may be configured to classify the relative degradation degree into one of a plurality of preset groups and to diagnose the state of the target battery based on the classified group.

[0015] The control unit may be configured to compare a first state of the target battery diagnosed at a previous diagnosis time with a second state of the target battery diagnosed at a current diagnosis time, and if the first state and the second state are different, to calculate the rate of change between the relative degradation degree with respect to the current diagnosis time and the relative degradation degree with respect to the previous diagnosis time.

[0016] The control unit may be configured to calculate the first rate of change from data corresponding to a preset reference period in the first dataset and to calculate the second rate of change from data corresponding to the reference period in the second dataset.

[0017] The first dataset above may be configured to include the second dataset above.

[0018] A battery pack according to another aspect of the present invention may include a battery diagnostic device according to one aspect of the present invention.

[0019] An automobile according to another aspect of the present invention may include a battery diagnostic device according to one aspect of the present invention.

[0020] A server according to another aspect of the present invention may include a battery diagnostic device according to one aspect of the present invention.

[0021] A battery diagnostic method according to another aspect of the present invention may include: a data acquisition step of acquiring a first dataset representing a correspondence between time and SOH for a plurality of batteries and a second dataset representing a correspondence between said time and said SOH for a target battery; a change rate calculation step of calculating a first change rate representing a change in said SOH according to said time in the first dataset and calculating a second change rate representing a change in said SOH according to said time in the second dataset; and a diagnostic step of diagnosing the state of said target battery based on said first change rate and said second change rate.

[0022] A computer-readable recording medium according to another aspect of the present invention may store a computer program for executing a battery diagnosis method comprising: a data acquisition step of acquiring a first dataset representing a correspondence relationship between time and SOH for a plurality of batteries and a second dataset representing a correspondence relationship between said time and said SOH for a target battery; a change rate calculation step of calculating a first change rate representing a change in said SOH according to said time in the first dataset and calculating a second change rate representing a change in said SOH according to said time in the second dataset; and a diagnosis step of diagnosing the state of said target battery based on said first change rate and said second change rate.

[0023] According to one aspect of the present invention, the battery diagnostic device has the advantage of being able to more accurately diagnose the relative state of each of the plurality of batteries by considering the SOH trend of the plurality of batteries.

[0024] The effects of the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art from the description in the claims.

[0025] The following drawings attached to this specification serve to further enhance understanding of the technical concept of the invention in conjunction with the detailed description of the invention set forth below; therefore, the invention should not be interpreted as being limited only to the matters described in such drawings.

[0026] FIG. 1 is a schematic diagram illustrating a battery diagnostic device according to one embodiment of the present invention.

[0027] FIG. 2 is a schematic diagram illustrating a first dataset according to an embodiment of the present invention.

[0028] FIG. 3 is a schematic diagram illustrating a second dataset according to an embodiment of the present invention.

[0029] FIG. 4 is a schematic diagram illustrating an embodiment of a linear regression model according to an embodiment of the present invention.

[0030] FIG. 5 is a schematic diagram illustrating another embodiment of a linear regression model according to one embodiment of the present invention.

[0031] FIG. 6 is a schematic diagram illustrating a battery pack according to another embodiment of the present invention.

[0032] FIG. 7 is a schematic drawing illustrating an automobile according to another embodiment of the present invention.

[0033] FIG. 8 is a schematic drawing illustrating an automobile according to another embodiment of the present invention.

[0034] FIG. 9 is a schematic diagram illustrating a battery diagnostic method according to another embodiment of the present invention.

[0035] Terms and words used in this specification and claims should not be interpreted as being limited to their ordinary or dictionary meanings, but should be interpreted in a meaning and concept consistent with the technical spirit of the invention, based on the principle that the inventor can appropriately define the concept of the terms to best describe his invention.

[0036] Therefore, the embodiments described in this specification and the configurations illustrated in the drawings are merely the most preferred embodiments of the present invention and do not represent all of the technical ideas of the present invention; thus, it should be understood that various equivalents and modifications that can replace them may exist at the time of filing this application.

[0037] In addition, in describing the present invention, if it is determined that a detailed description of related known components or functions may obscure the essence of the invention, such detailed description is omitted.

[0038] Terms including ordinal numbers, such as first, second, etc., are used for the purpose of distinguishing one of the various components from the rest, and are not used to limit the components by such terms.

[0039] Throughout the specification, when a part is described as "including" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0040] Additionally, throughout the specification, when it is said that a part is "connected" to another part, this includes not only cases where they are "directly connected," but also cases where they are "indirectly connected" with other components in between.

[0041]

[0042] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the attached drawings.

[0043] FIG. 1 is a schematic diagram illustrating a battery diagnostic device (100) according to one embodiment of the present invention.

[0044] Referring to FIG. 1, the battery diagnostic device (100) may include a data acquisition unit (110) and a control unit (120).

[0045] The data acquisition unit (110) may be configured to acquire a first dataset (D1) representing the correspondence between time and SOH for a plurality of batteries.

[0046] Here, a battery refers to a single independent cell that is physically separable and equipped with a negative terminal and a positive terminal. For example, a lithium-ion battery or a lithium-polymer battery may be considered a battery. Additionally, the type of battery may be cylindrical, prismatic, or pouch type. Furthermore, a battery may refer to a battery bank, battery module, or battery pack in which a plurality of cells are connected in series and / or parallel.

[0047] Specifically, time refers to the usage time of the battery. And, SOH (state of health) refers to the degree of degradation of the battery. That is, data regarding the time-dependent SOH of each of the multiple batteries can be included in the first dataset (D1). Here, the time at which the SOH is recorded can be selected to be periodic or non-periodic. That is, the SOH for each battery can be estimated periodically and recorded in the first dataset (D1). Additionally, if a non-periodic event occurs for each battery, the SOH for the corresponding battery can be estimated and recorded in the first dataset (D1).

[0048] FIG. 2 is a schematic diagram illustrating a first dataset (D1) according to an embodiment of the present invention. Specifically, the first dataset (D1) can be represented as an XY graph where the X-axis is set to time and the Y-axis is set to SOH. However, as long as a correspondence between time and SOH can be shown, the first dataset (D1) can be represented in various ways (e.g., a table, etc.).

[0049] For example, the data acquisition unit (110) can acquire the first dataset (D1) by receiving the first dataset (D1) from the outside.

[0050] As another example, the data acquisition unit (110) may receive battery information for each of a plurality of batteries. Here, the battery information may include information for estimating SOH and time information of the batteries. The data acquisition unit (110) may estimate the SOH of each of the plurality of batteries based on the received battery information and generate a first dataset (D1) based on the time information and the estimated SOH. That is, the data acquisition unit (110) may acquire the first dataset (D1) by generating the first dataset (D1) based on the received battery information.

[0051] Additionally, the data acquisition unit (110) may be configured to acquire a second dataset (D2) representing the correspondence between time and SOH for the target battery.

[0052] Here, the target battery is the battery subject to condition diagnosis. That is, the first dataset (D1) is a dataset containing data regarding the hourly SOH of multiple batteries, and the second dataset (D2) is a dataset containing data regarding the hourly SOH of the battery subject to diagnosis (target battery).

[0053] For example, the data acquisition unit (110) can acquire the second dataset (D2) by receiving the second dataset (D2) from the outside.

[0054] As another example, the data acquisition unit (110) can receive battery information regarding a target battery. The data acquisition unit (110) can estimate the SOH of the target battery based on the received battery information and generate a second dataset (D2) based on time information and the estimated SOH. That is, the data acquisition unit (110) can acquire the second dataset (D2) by generating the second dataset (D2) based on the received battery information.

[0055] FIG. 3 is a schematic diagram illustrating a second dataset (D2) according to an embodiment of the present invention. Specifically, the second dataset (D2) can be represented as an XY graph where the X-axis is set to time and the Y-axis is set to SOH. However, as long as a correspondence between time and SOH can be shown, the second dataset (D2) can be represented in various ways (e.g., a table, etc.).

[0056] The data acquisition unit (110) may be connected to the control unit (120) to enable communication. For example, the data acquisition unit (110) may be connected to the control unit (120) via wired and / or wireless connections. The data acquisition unit may transmit the acquired first dataset (D1) and second dataset (D2) to the control unit (120).

[0057] The control unit (120) may be configured to calculate a first rate of change representing the change in SOH over time in the first dataset (D1).

[0058] Specifically, the first rate of change is an indicator representing the trend of the data included in the first dataset (D1). To this end, the control unit (120) may be configured to derive a linear regression model for the first dataset (D1) and to calculate the first rate of change from the derived linear regression model. For example, the control unit (120) may calculate the slope of the linear regression model as the first rate of change.

[0059] When using the first rate of change, the correspondence between time and SOH can be clearly and intuitively displayed. Accordingly, the control unit (120) can calculate the first rate of change from the first dataset (D1) and analyze the first dataset (D1) based on the calculated first rate of change.

[0060] FIG. 4 is a schematic diagram illustrating an embodiment of a linear regression model according to an embodiment of the present invention. A control unit (120) can derive a first linear regression model (P1) from a first dataset (D1). And, the control unit (120) can calculate the slope of the first linear regression model (P1) as a first rate of change.

[0061] The control unit (120) may be configured to calculate a second rate of change representing the change in SOH over time in the second dataset (D2).

[0062] The control unit (120) can calculate a second change rate for the second dataset (D2) similarly to how it calculated a first change rate for the first dataset (D1).

[0063] Specifically, the second rate of change is an indicator representing the trend of the data included in the second dataset (D2). To this end, the control unit (120) may be configured to derive a linear regression model for the second dataset (D2) and to calculate the second rate of change from the derived linear regression model.

[0064] In the embodiment of FIG. 4, the control unit (120) can derive a second linear regression model (P2) from a second dataset (D2). And, the control unit (120) can calculate the slope of the second linear regression model (P2) as a second rate of change.

[0065] The control unit (120) may be configured to diagnose the state of the target battery based on the first rate of change and the second rate of change.

[0066] Here, the first rate of change is a value representing the correspondence between the time and SOH of a plurality of batteries, and the second rate of change is a value representing the correspondence between the time and SOH of a target battery. Accordingly, the control unit (120) can diagnose the state of the target battery in relation to the plurality of batteries by comparing the first rate of change for the plurality of batteries and the second rate of change for the target battery.

[0067] For example, the control unit (120) can compare and analyze the SOH trend of a plurality of batteries and the SOH trend of a target battery by considering the ratio of the second change rate to the first change rate, etc. Accordingly, the control unit (120) can diagnose the relative state of the target battery derived from the relationship with the plurality of batteries.

[0068] In the embodiment of FIG. 4, the control unit (120) can diagnose the relative state of a target battery for a plurality of batteries by comparing the first rate of change of the first linear regression model (P1) and the second rate of change of the second linear regression model (P2).

[0069] A battery diagnostic device (100) according to one embodiment of the present invention can diagnose the relative state of a target battery with respect to a plurality of batteries based on a first dataset (D1) for a plurality of batteries and a second dataset (D2) for a target battery.

[0070]

[0071] Meanwhile, the control unit (120) provided in the battery diagnostic device (100) may optionally include a processor, an ASIC (application-specific integrated circuit), another chipset, a logic circuit, a register, a communication modem, a data processing device, etc., known in the art, to execute various control logics performed in the present invention. Additionally, when the control logic is implemented in software, the control unit (120) may be implemented as a set of program modules. At this time, the program modules may be stored in memory and executed by the control unit (120). The memory may be located inside or outside the control unit (120) and may be connected to the control unit (120) by various well-known means.

[0072] Additionally, the battery diagnostic device (100) may further include a storage unit (130). The storage unit (130) may store data or programs necessary for each component of the battery diagnostic device (100) to perform operations and functions, or data generated during the process of performing operations and functions. The storage unit (130) is not subject to any special restrictions on its type as long as it is a known information storage means capable of recording, erasing, updating, and reading data. As an example, the information storage means may include RAM, flash memory, ROM, EEPROM, registers, etc. Additionally, the storage unit (130) may store program codes that define processes executable by each component of the battery diagnostic device (100).

[0073]

[0074] In a preferred embodiment, the control unit (120) may be configured to estimate the relative degradation of the target battery for a plurality of batteries as the state of the target battery.

[0075] Here, relative degradation refers to an indicator representing the relative degree of degradation of a target battery when considering the degradation of multiple batteries. In other words, the higher the relative degradation, the more the target battery has degraded compared to multiple batteries, and the lower the relative degradation, the less the target battery has degraded compared to multiple batteries.

[0076] First, the control unit (120) may be configured to calculate the difference in the rate of change between the first rate of change and the second rate of change.

[0077] Specifically, the control unit (120) can calculate the formula “first change rate - second change rate” to calculate the difference in change rates between the first change rate and the second change rate.

[0078] Next, the control unit (120) may be configured to estimate the relative degree of deterioration by calculating the ratio of the difference in the rate of change to the first rate of change.

[0079] Specifically, the control unit (120) can estimate the relative degradation of the target battery by calculating the formula “difference in rate of change ÷ first rate of change”. That is, the control unit (120) can express the relative difference between the first rate of change and the second rate of change as a ratio by calculating the ratio of the difference between the first rate of change and the second rate of change with respect to the first rate of change.

[0080] A battery diagnostic device (100) according to one embodiment of the present invention can diagnose the relative state of a target battery by estimating the relative degree of degradation of the target battery relative to a plurality of batteries. Accordingly, performance criteria regarding how much the target battery has degraded compared to other batteries can be clarified. In addition, since the state of the target battery is judged by comparison with the state of other batteries, abnormal degradation patterns or signs of abnormality can be diagnosed early.

[0081]

[0082] In a preferred embodiment, the first dataset (D1) may be configured to include a second dataset (D2). That is, the first dataset (D1) may include a plurality of batteries corresponding to the second dataset (D2), and a target battery corresponding to the second dataset (D2). In other words, the first dataset (D1) may include data regarding the target battery.

[0083] The data acquisition unit (110) may acquire the first dataset (D1) and the second dataset (D2) respectively, or may acquire only the first dataset (D1). In the latter case, the data acquisition unit (110) may generate the second dataset (D2) by extracting only the data for the target battery from the first dataset (D1).

[0084] Generally, when a target battery is not included among multiple batteries, the accuracy and reliability of the diagnosis can be significantly reduced because the comparison criteria between the data regarding the relative status of the multiple batteries and the target battery differ. In other words, since the multiple batteries and the target battery are compared only indirectly, the accuracy and reliability of the analysis may be somewhat lower when considering the diagnostic results based on the relative status of the target battery relative to the multiple batteries.

[0085] However, according to the battery diagnostic device (100), since the target battery is included as one of a plurality of batteries, the state between the target battery and other batteries can be directly compared. As a result, abnormal degradation patterns or signs of abnormality of the target battery can be diagnosed more accurately and reliably. That is, the battery diagnostic device (100) has the advantage of being able to diagnose the relative state of the target battery more accurately and reliably through comparison with a plurality of batteries by considering the first dataset (D1) which includes the second dataset (D2).

[0086]

[0087] The control unit (120) may be configured to first derive a non-linear regression model for the first dataset (D1).

[0088] Specifically, the control unit (120) can derive a non-linear regression model for the first dataset (D1). Here, the non-linear regression model can model complex correlations between variables of the first dataset (D1) better than the linear regression model. For example, using the non-linear regression model allows for a better understanding of high-dimensional relationships between variables of the first dataset (D1).

[0089] For example, since the first dataset (D1) is big data regarding multiple batteries, analysis of high-dimensional correspondence relationships with time and SOH is required. Therefore, the control unit (120) can first derive a non-linear regression model to identify the SOH trend for the first dataset (D1).

[0090] FIG. 5 is a schematic diagram illustrating another embodiment of a linear regression model according to one embodiment of the present invention. In the embodiment of FIG. 5, the control unit (120) can first derive a non-linear regression model (P3) from a first dataset (D1).

[0091] And, the control unit (120) can be configured to derive a linear regression model from the derived non-linear regression model.

[0092] Specifically, the control unit (120) can first remove unnecessary noise by deriving a non-linear regression model from the first dataset (D1). Then, the control unit (120) can simplify the correspondence relationship between time and SOH for multiple batteries by deriving a linear regression model from the non-linear regression model from which noise has been removed. That is, the control unit (120) can first analyze the complex pattern of the first dataset (D1) through non-linear regression and then simplify the analyzed complex pattern again through linear regression.

[0093] In general, nonlinear regression models offer greater flexibility than linear regression models and can effectively explain various forms of correlation in data. In particular, because nonlinear regression models can capture nonlinear patterns compared to linear regression models, they can explain data correlations more effectively.

[0094] In the embodiment of FIG. 5, the control unit (120) can first derive a non-linear regression model (P3) from the first dataset (D1). Then, the control unit (120) can derive a linear regression model (P4) from the non-linear regression model (P3). That is, while the linear regression model (P1) of FIG. 4 was derived directly from the first dataset (D1), there is a difference in that the linear regression model (P4) according to the embodiment of FIG. 5 is derived from the non-linear regression model (P3) of the first dataset (D1).

[0095] That is, the battery diagnostic device (100) first derives a non-linear regression model of the first dataset (D1) and then derives a linear regression model, so that the finally derived linear regression model can maintain sufficient flexibility without excessively simplifying the correspondence relationship of the data of the first dataset (D1).

[0096] In the above description, an embodiment in which a linear regression model can be derived from the first dataset (D1) has been explained, but depending on the embodiment, it may also be applied to deriving a linear regression model from the second dataset (D2). For example, the control unit (120) may directly derive a linear regression model from the second dataset (D2), or may derive a linear regression model from a non-linear regression model (not shown) of the second dataset (D2).

[0097]

[0098] In a preferred embodiment, the control unit (120) may be configured to calculate a first rate of change from data corresponding to a preset reference period in the first dataset (D1).

[0099] Specifically, the reference period can be set as the period during which the relative state of the target battery is analyzed.

[0100] For example, the reference period can be pre-set by the user to a specific period (e.g., 600 days).

[0101] As another example, the reference period can be set to the maximum period included in the second dataset (D2). In the embodiment of FIG. 4, since the target battery was used up to K days, the maximum period included in the second dataset (D2) is K days. Therefore, the control unit (120) can set the reference period to K days.

[0102] The control unit (120) can derive a linear regression model using only the data up to day K in the first dataset (D1) and calculate a first rate of change from the derived linear regression model. For example, even if the maximum period included in the first dataset (D1) has elapsed to day K, the control unit (120) can derive a linear regression model using only the data up to day K.

[0103] And, the control unit (120) may be configured to calculate a second rate of change from data corresponding to a reference period in the second dataset (D2).

[0104] That is, the control unit (120) can more accurately diagnose the relative state of the target battery up to the reference period by limiting the analysis period for the first dataset (D1) and the second dataset (D2) through a preset reference period.

[0105] For example, referring to FIGS. 2 and 3, some of the multiple batteries were used for up to about 1100 days, but the first change rate was used only up to K days (about 750 days). That is, if the period between the first dataset (D1) and the second dataset (D2) is not considered, the first change rate derived from the first dataset (D1) and the second change rate derived from the second dataset (D2) are on different scales in terms of the period. That is, since the first dataset (D1) and the second dataset (D2) are not under the same conditions in terms of the period, the reliability of the relative state of the target battery diagnosed according to the first change rate and the second change rate may be lowered. Therefore, the battery diagnostic device (100) can improve the accuracy and reliability of the relative state of the target battery diagnosed by synchronizing the periods of the first dataset (D1) and the second dataset (D2) with a reference period.

[0106]

[0107] The control unit (120) may be configured to classify the relative degeneration degree into one of a plurality of preset groups.

[0108] Specifically, state information corresponding to each of the plurality of groups can be pre-set. In the following description, the plurality of groups are described as three groups, but depending on the embodiment, the plurality of groups may be divided into two groups or three or more groups.

[0109] Multiple groups can be divided into a first group, a second group, and a third group.

[0110] Specifically, Group 1 includes batteries in a normal state, Group 2 includes batteries in a warning state, and Group 3 includes batteries in a defective state. Here, a warning state refers to a condition where the battery is not defective but requires continuous monitoring, and a defective state refers to a condition where a defect has occurred in the battery and discontinuation of use is recommended.

[0111] Preferably, a degradation interval corresponding to each of the plurality of groups may be set. For example, a first degradation interval corresponding to the first group may be set to a value less than the first degradation. And, a second degradation interval corresponding to the second group may be set to a value greater than the first degradation and less than the second degradation. Finally, a third degradation interval corresponding to the third group may be set to a value greater than or equal to the second degradation.

[0112] The control unit (120) can determine the group to which the target battery belongs by comparing the relative degradation level of the target battery with the first to third degradation level ranges.

[0113] The control unit (120) may be configured to diagnose the condition of the target battery based on the classified group.

[0114] As previously explained, corresponding state information can be pre-set for each of the multiple groups. Accordingly, the control unit (120) can determine the group to which the target battery belongs and diagnose the state of the battery based on the state information set to correspond to the determined group.

[0115] For example, if the target battery belongs to the first group, the control unit (120) can diagnose the state of the target battery as normal. As another example, if the target battery belongs to the second group, the control unit (120) can diagnose the state of the target battery as warning. As yet another example, if the target battery belongs to the third group, the control unit (120) can diagnose the state of the target battery as defective.

[0116] The battery diagnostic device (100) has the advantage of not only estimating the relative degradation of a target battery for a plurality of batteries, but also being able to specifically determine the state of the target battery using a preset group.

[0117]

[0118] Meanwhile, the control unit (120) may be configured to compare the first state of the target battery diagnosed at the previous diagnosis time with the second state of the target battery diagnosed at the current diagnosis time.

[0119] Preferably, the previous diagnosis point refers to the diagnosis point immediately preceding the current diagnosis point. That is, the control unit (120) can determine whether the diagnosis results (first state and second state) at consecutive diagnosis points are the same.

[0120] The control unit (120) may be configured to calculate the rate of change between the relative deterioration at the current diagnosis time and the relative deterioration at the previous diagnosis time when the first state and the second state are different.

[0121] Specifically, if the first state and the second state are identical, it can be said that the state of the target battery has not changed significantly at consecutive diagnostic points. Therefore, the control unit (120) may not calculate the rate of change of the relative degradation of the target battery. However, if a request to calculate the rate of change of the relative degradation is received from the outside, the control unit (120) may calculate the rate of change between the relative degradation at the current diagnostic point and the relative degradation at the previous diagnostic point, and provide the calculated rate of change to the outside.

[0122] Conversely, if the first state and the second state are different, it can be said that the state of the target battery has changed significantly at consecutive diagnostic points. Accordingly, the control unit (120) can calculate the rate of change between the relative degradation at the current diagnostic point and the relative degradation at the previous diagnostic point in order to provide the degree to which the state of the target battery has changed. That is, the control unit (120) can quantify how much the relative degradation at the current diagnostic point has changed compared to the relative degradation at the previous diagnostic point.

[0123] For example, the relative deterioration level with respect to the previous diagnosis time is called the first deterioration level, and the relative deterioration level with respect to the current diagnosis time is called the second deterioration level. The control unit (120) can calculate the rate of change between relative deterioration levels by calculating the formula “(first deterioration level - second deterioration level) ÷ first deterioration level”.

[0124] The battery diagnostic device (100) can not only track and diagnose the condition of the target battery, but also quantitatively evaluate the degree of change whenever the condition of the target battery changes significantly. Therefore, the battery diagnostic device (100) can provide significant assistance in battery maintenance by providing specific numerical values ​​regarding the change in the condition of the target battery.

[0125]

[0126] The battery diagnostic device (100) according to the present invention may be applied to a Battery Management System (BMS). That is, the BMS according to the present invention may include the battery diagnostic device (100) described above. In this configuration, at least some of the components of the battery diagnostic device (100) may be implemented by supplementing or adding the functions of the components included in a conventional BMS. For example, the data acquisition unit (110) and the control unit (120) of the battery diagnostic device (100) may be implemented as components of the BMS.

[0127] In addition, the battery diagnostic device (100) according to the present invention may be provided in a battery pack. That is, the battery pack according to the present invention may include the battery diagnostic device (100) described above and one or more battery cells. In addition, the battery pack may further include electrical components (relays, fuses, etc.) and a case, etc.

[0128] FIG. 6 is a schematic diagram illustrating a battery pack (10) according to another embodiment of the present invention.

[0129] A battery assembly (11) may include a plurality of batteries electrically connected in series and / or parallel. Additionally, the positive terminal of the battery assembly (11) may be connected to the positive terminal (P+) of the battery pack (10), and the negative terminal of the battery assembly (11) may be connected to the negative terminal (P-) of the battery pack (10).

[0130] The measuring unit (12) can be electrically connected to the battery assembly (11). The measuring unit (12) can measure the voltage of each of the plurality of batteries included in the battery assembly (11).

[0131] Additionally, the measuring unit (12) may be electrically connected to a current measuring unit (A). For example, the current measuring unit (A) may be an ammeter or a shunt resistor capable of measuring the charging current and discharging current of the battery assembly (11). The measuring unit (12) can calculate the charging amount by measuring the charging current of the battery assembly (11) through the current measuring unit (A). Furthermore, the measuring unit (12) can calculate the discharging amount by measuring the discharging current of the battery assembly (11) through the current measuring unit (A).

[0132] An external device may be connected to the positive terminal (P+) and the negative terminal (P-) of the battery pack (10). For example, the external device may be a charging device or a load. Also, the positive terminal of the battery assembly (11), the positive terminal (P+) of the battery pack (10), the external device, the negative terminal (P-) of the battery pack (10), and the negative terminal of the battery assembly (11) may be electrically connected.

[0133] Preferably, the data acquisition unit (110) may be configured to receive battery information from the measurement unit, and the control unit (120) may be configured to diagnose the status of a plurality of batteries included in the battery set (11) based on the battery information. The battery information received by the data acquisition unit (110) and the status of the plurality of batteries diagnosed by the control unit (120) may be stored in the storage unit (130).

[0134]

[0135] FIG. 7 is a schematic drawing illustrating a vehicle (700) according to another embodiment of the present invention.

[0136] Referring to FIG. 7, a battery pack (710) according to an embodiment of the present invention may be included in a vehicle (700), such as an electric vehicle (EV) or a hybrid vehicle (HV). The battery pack (710) can drive the vehicle (700) by supplying power to a motor through an inverter provided in the vehicle (700). Here, the battery pack (710) may include a battery diagnostic device (100). That is, the vehicle (700) may include a battery diagnostic device (100). In this case, the battery diagnostic device (100) may be an on-board device included in the vehicle (700).

[0137]

[0138] FIG. 8 is a schematic diagram illustrating a server (800) according to another embodiment of the present invention.

[0139] A server (800) according to another embodiment of the present invention may include a battery diagnostic device (100) according to one embodiment of the present invention.

[0140] Specifically, the server (800) may be connected to communicate with a plurality of BMSs (810) via wired and / or wireless means. Here, the BMSs may be equipped in a vehicle, an Energy Storage System (ESS), or a diagnostic device, etc. Preferably, the BMSs may be applied without limitation as long as they can measure and diagnose the condition of electrically connected batteries.

[0141] Additionally, the server (800) can store battery information received from a plurality of BMSs (810). Preferably, the server (800) can store battery information for batteries of the same type separately.

[0142] For example, battery information received from the BMS of vehicle A and battery information received from the BMS of vehicle B can be stored separately. Then, the server (800) can diagnose the state of the battery included in each of the multiple vehicles A based on multiple battery information for multiple vehicles A. Similarly, the server (800) can diagnose the state of the battery included in each of the multiple vehicles B based on multiple battery information for multiple vehicles B.

[0143] That is, the condition of vehicle A can be diagnosed based on a first dataset (D1) of multiple batteries equipped in multiple vehicles A, and the condition of vehicle B can be diagnosed based on a first dataset (D1) of multiple batteries equipped in multiple vehicles B.

[0144] And, the server (800) can provide the diagnosis results to the corresponding BMS. That is, by receiving the battery status diagnosis results from the server (800), the BMS can determine the relative status of the battery in relation to the corresponding multiple batteries.

[0145]

[0146] FIG. 9 is a schematic diagram illustrating a battery diagnostic method according to another embodiment of the present invention.

[0147] Referring to FIG. 9, the battery diagnostic method may include a data acquisition step (S100), a change rate calculation step (S200), and a diagnostic step (S300).

[0148] Preferably, each step of the battery diagnostic method can be performed by a battery diagnostic device (100). For convenience of explanation, details that overlap with previously described content will be omitted or briefly explained below.

[0149] The data acquisition step (S100) is a step of acquiring a first dataset (D1) representing the correspondence relationship between time and SOH for a plurality of batteries and a second dataset (D2) representing the correspondence relationship between time and SOH for a target battery, and can be performed by a data acquisition unit (110).

[0150] For example, the data acquisition unit (110) can directly receive the first dataset (D1) and / or the second dataset (D2) from the outside.

[0151] As another example, the data acquisition unit (110) may receive battery information from the outside and generate a first dataset (D1) and / or a second dataset (D2) based on the received battery information.

[0152] The change rate calculation step (S200) is a step of calculating a first change rate representing the change in SOH over time in a first dataset (D1) and calculating a second change rate representing the change in SOH over time in a second dataset (D2), and can be performed by a control unit (120).

[0153] The control unit (120) may derive a linear regression model from the first dataset (D1) and calculate the slope of the derived linear regression model as the first rate of change. For example, in the embodiment of FIG. 4, the control unit (120) may directly derive a linear regression model (P1) from the first dataset (D1). As another example, in the embodiment of FIG. 5, the control unit (120) may derive a non-linear regression model (P3) from the first dataset (D1) and derive a linear regression model (P4) from the derived non-linear regression model (P3).

[0154] Additionally, the control unit (120) can derive a linear regression model from the second dataset (D2) and calculate the slope of the derived linear regression model as the second rate of change.

[0155] The diagnosis step (S300) is a step of diagnosing the state of the target battery based on the first change rate and the second change rate, and can be performed by the control unit (120).

[0156] For example, the control unit (120) can diagnose the state of the target battery by calculating the ratio of the difference between the first change rate and the second change rate with respect to the first change rate. Specifically, the control unit (120) can diagnose the relative state of the target battery among a plurality of batteries.

[0157]

[0158] The embodiments of the present invention described above are not limited to implementation through devices and methods, but may also be implemented through a program that realizes a function corresponding to the configuration of the embodiments of the present invention or a recording medium on which such a program is recorded. Such implementation can be easily achieved by a person skilled in the art to which the present invention pertains, based on the description of the embodiments described above.

[0159] Another embodiment of the present invention may provide a computer-readable recording medium having a program recorded thereon for executing the various embodiments described above on a computer.

[0160] A program may be implemented as hardware components, software components, and / or a combination of hardware and software components. A program may be executed by any system capable of executing computer-readable instructions.

[0161] Software may include computer programs, code, instructions, or a combination thereof, and may configure a processing unit to operate as desired or command the processing unit independently or collectively.

[0162] Software can be implemented as a computer program containing instructions stored on a computer-readable storage media. Examples of computer-readable storage media include magnetic storage media (e.g., ROM (read-only memory), RAM (random-access memory), floppy disks, hard disks, etc.) and optical reading media (e.g., CD-ROMs, DVDs (Digital Versatile Discs)). Computer-readable storage media can be distributed across networked computer systems, allowing computer-readable code to be stored and executed in a distributed manner. The storage media can be read by a computer, stored in memory, and executed by a processor.

[0163] Computer-readable recording media may be provided in the form of non-transitory recording media. Here, 'non-transitory storage media' simply means that it is a tangible device and does not contain a signal (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily. For example, 'non-transitory storage media' may include a buffer in which data is stored temporarily.

[0164] In addition, the program may be provided by being included in a computer program product. A computer program product may be traded between a seller and a buyer as a product.

[0165] A computer program product may include a software program or a computer-readable recording medium on which the software program is stored. For example, a computer program product may include a product in the form of a software program that is distributed electronically through a manufacturer of an electronic device or an electronic market (e.g., a downloadable application). For electronic distribution, at least a portion of the software program may be stored on a recording medium or temporarily created. In this case, the recording medium may be a server of the manufacturer of the electronic device, a server of the electronic market, or a recording medium of a relay server that temporarily stores the software program.

[0166] Although the present invention has been described above by limited embodiments and drawings, the present invention is not limited thereto, and it is obvious that various modifications and variations are possible within the scope of the technical spirit of the present invention and the equivalent scope of the claims described below by those skilled in the art to which the present invention belongs.

[0167] Furthermore, since the present invention described above allows for various substitutions, modifications, and changes within the scope of the technical concept of the present invention to those skilled in the art without departing from the technical spirit of the present invention, it is not limited by the aforementioned embodiments and attached drawings, but rather all or part of each embodiment may be selectively combined to allow for various modifications.

[0168] (Explanation of symbols)

[0169] 10: Battery pack

[0170] 11: Battery assembly

[0171] 12: Measurement section

[0172] 100: Battery Diagnostic Device

[0173] 110: Data acquisition unit

[0174] 120: Control unit

[0175] 130: Storage section

[0176] 700: Car

[0177] 710: Battery pack

[0178] 800: Server

[0179] 810: Multiple BMS

Claims

1. A data acquisition unit configured to acquire a first dataset representing a correspondence relationship between time and SOH for a plurality of batteries and a second dataset representing a correspondence relationship between said time and said SOH for a target battery; and A battery diagnostic device comprising a control unit configured to calculate a first rate of change representing a change in SOH over time in the first dataset, calculate a second rate of change representing a change in SOH over time in the second dataset, and diagnose the state of the target battery based on the first rate of change and the second rate of change.

2. In Paragraph 1, The above control unit is, A battery diagnostic device configured to derive a linear regression model for the first dataset and to calculate the first rate of change from the derived linear regression model.

3. In Paragraph 2, The above control unit is, A battery diagnostic device configured to first derive a non-linear regression model for the first dataset and to derive a linear regression model from the derived non-linear regression model.

4. In Paragraph 1, The above control unit is, A battery diagnostic device configured to derive a linear regression model for the second dataset and to calculate the second rate of change from the derived linear regression model.

5. In Paragraph 1, The above control unit is, A battery diagnostic device configured to estimate the relative degradation degree of the target battery with respect to the plurality of batteries as the state of the target battery.

6. In Paragraph 5, The above control unit is, Calculate the difference in the rate of change between the first rate of change and the second rate of change, and A battery diagnostic device configured to estimate the relative degree of degradation by calculating the ratio of the difference in the rate of change to the first rate of change.

7. In Paragraph 5, The above control unit is, The above relative degeneration degree is classified into one of a plurality of preset groups, and A battery diagnostic device configured to diagnose the condition of the target battery based on classified groups.

8. In Paragraph 7, The above control unit is, Compare the first state of the target battery diagnosed at the previous diagnosis point with the second state of the target battery diagnosed at the current diagnosis point, A battery diagnostic device configured to calculate the rate of change between the relative degradation degree with respect to the current diagnostic time and the relative degradation degree with respect to the previous diagnostic time when the first state and the second state are different.

9. In Paragraph 1, The above control unit is, Calculate the first rate of change from the data corresponding to a preset reference period in the first dataset, and A battery diagnostic device configured to calculate the second rate of change from data corresponding to the reference period in the second dataset.

10. In Paragraph 1, The above first dataset is, A battery diagnostic device configured to include the above-mentioned second dataset.

11. A battery pack comprising a battery diagnostic device according to any one of claims 1 to 10.

12. An automobile comprising a battery diagnostic device according to any one of paragraphs 1 through 10.

13. A server comprising a battery diagnostic device according to any one of paragraphs 1 through 10.

14. A data acquisition step of acquiring a first dataset representing the correspondence relationship between time and SOH for a plurality of batteries and a second dataset representing the correspondence relationship between the time and SOH for a target battery; A change rate calculation step of calculating a first change rate representing the change in SOH over time in the first dataset and calculating a second change rate representing the change in SOH over time in the second dataset; and A battery diagnosis method comprising a diagnosis step of diagnosing the state of the target battery based on the first change rate and the second change rate.

15. A data acquisition step of acquiring a first dataset representing the correspondence relationship between time and SOH for a plurality of batteries and a second dataset representing the correspondence relationship between the time and SOH for a target battery; A change rate calculation step of calculating a first change rate representing the change in SOH over time in the first dataset and calculating a second change rate representing the change in SOH over time in the second dataset; and A computer-readable recording medium storing a computer program for executing a battery diagnostic method comprising a diagnostic step of diagnosing the state of the target battery based on the first rate of change and the second rate of change.

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