Battery diagnostic device and method

Abstract

A battery diagnostic apparatus according to an embodiment of the present disclosure includes: a data acquisition unit configured to acquire a first dataset representing the correspondence between the state of oxygen (SOH) of a plurality of batteries and time, and a second dataset representing the correspondence between the SOH of a target battery and time; and a control unit configured to calculate a first rate of change representing the change of SOH over time based on the first dataset, calculate a second rate of change representing the change of SOH over time based on the second dataset, and diagnose the state of the target battery based on the first rate of change and the second rate of change.

Description

Technical Field

[0001] This application is based on and claims priority to Korean Patent Application No. 10-2024-0142986, filed with the Korean Intellectual Property Office on October 18, 2024, the disclosure of which is incorporated herein by reference in its entirety.

[0002] This disclosure relates to a battery diagnostic apparatus and method capable of diagnosing the state of a battery. Background Technology

[0003] Recently, demand for portable electronic products such as laptops, cameras, and mobile phones has increased dramatically, and electric vehicles, energy storage batteries, robots, and satellites have seen significant development. Therefore, high-performance batteries that allow for repeated charging and discharging are being actively researched.

[0004] Currently available batteries include nickel-cadmium (NiCd), nickel-metal hydride (NiMH), nickel-zinc (NiZn), and lithium-ion batteries. Among them, lithium-ion batteries are particularly noteworthy because, compared to nickel-based batteries, they exhibit almost no memory effect and possess extremely low self-discharge rates and high energy density.

[0005] While extensive research is underway to increase the capacity and density of these batteries, improving their lifespan and safety is also crucial. To enhance battery safety, technologies capable of accurately diagnosing the battery's condition are needed. Summary of the Invention

[0006] Technical issues

[0007] This disclosure is designed to address the problems of related technologies, and therefore aims to provide a battery diagnostic apparatus and method that can more accurately estimate the current state of a battery.

[0008] These and other objects and advantages of this disclosure will become apparent from the following detailed description and will become even more fully apparent from exemplary embodiments of this disclosure. Furthermore, it will be readily understood that the objects and advantages of this disclosure can be achieved by the means and combinations thereof shown in the appended claims.

[0009] Technical solution

[0010] A battery diagnostic apparatus according to one aspect of the present disclosure may include: a data acquisition unit configured to acquire a first dataset representing the correspondence between the state of oxygen (SOH) of a plurality of batteries and time, and a second dataset representing the correspondence between the SOH of a target battery and time; and a control unit configured to calculate a first rate of change representing the change of SOH over time based on the first dataset, calculate a second rate of change representing the change of SOH over time based on the second dataset, and diagnose the state of the target battery based on the first rate of change and the second rate of change.

[0011] The control unit can be configured to derive a linear regression model for the first dataset and calculate a first rate of change based on the derived linear regression model.

[0012] The control unit can be configured to first derive a nonlinear regression model for a first dataset, and then derive a linear regression model based on the derived nonlinear regression model.

[0013] The control unit can be configured to derive a linear regression model for the second dataset and calculate a second rate of change based on the derived linear regression model.

[0014] The control unit can be configured to estimate the relative degradation of the target battery with respect to multiple batteries as the state of the target battery.

[0015] The control unit can be configured to calculate the difference between the first rate of change and the second rate of change, and to estimate the relative degree of degradation by calculating the ratio of the difference in rates of change to the first rate of change.

[0016] The control unit can be configured to classify the relative degree of degradation into one of a set of preset groups and diagnose the state of the target battery based on the classified groups.

[0017] The control unit can be configured to compare a first state of the target battery diagnosed at a previous diagnostic time point with a second state of the target battery diagnosed at the current diagnostic time point, and when the first state and the second state are different, calculate the rate of change between the relative degree of degradation at the current diagnostic time point and the relative degree of degradation at a previous diagnostic time point.

[0018] The control unit can be configured to calculate a first rate of change based on data from a first dataset corresponding to a preset reference time period, and to calculate a second rate of change based on data from a second dataset corresponding to a reference time period.

[0019] The first dataset can be configured to include the second dataset.

[0020] According to another aspect of this disclosure, a battery pack includes a battery diagnostic device according to one aspect of this disclosure.

[0021] A vehicle according to another aspect of this disclosure includes a battery diagnostic device according to one aspect of this disclosure.

[0022] The server according to another aspect of this disclosure includes a battery diagnostic device according to one aspect of this disclosure.

[0023] A battery diagnostic method according to another aspect of this disclosure may include: a data acquisition step, acquiring a first dataset representing the correspondence between SOH and time for multiple batteries and a second dataset representing the correspondence between SOH and time for a target battery; a rate of change calculation step, calculating a first rate of change representing the change of SOH over time based on the first dataset and calculating a second rate of change representing the change of SOH over time based on the second dataset; and a diagnostic step, diagnosing the state of the target battery based on the first rate of change and the second rate of change.

[0024] According to another aspect of this disclosure, a computer-readable recording medium can store a computer program for performing a battery diagnostic method, the battery diagnostic method comprising: a data acquisition step of acquiring a first dataset representing the correspondence between the state of oxygen (SOH) of a plurality of batteries and time, and a second dataset representing the correspondence between the SOH of a target battery and time; a rate of change calculation step of calculating a first rate of change representing the change of SOH over time based on the first dataset, and calculating a second rate of change representing the change of SOH over time based on the second dataset; and a diagnostic step of diagnosing the state of the target battery based on the first rate of change and the second rate of change.

[0025] Beneficial effects

[0026] According to one aspect of this disclosure, the battery diagnostic device has the advantage of being able to more accurately diagnose the relative state of each of the multiple batteries by taking into account the SOH trends of the multiple batteries.

[0027] The effects of this disclosure are not limited to those mentioned above, and those skilled in the art can clearly understand other effects not mentioned from the description of the claims. Attached Figure Description

[0028] The accompanying drawings illustrate preferred embodiments of the present disclosure and, together with the foregoing disclosure, are intended to provide a further understanding of the technical features of the present disclosure; therefore, the present disclosure should not be construed as limited to the drawings.

[0029] Figure 1 This is a schematic diagram illustrating a battery diagnostic apparatus according to an embodiment of the present disclosure.

[0030] Figure 2 This is a schematic diagram illustrating a first dataset according to an embodiment of the present disclosure.

[0031] Figure 3 This is a schematic diagram illustrating a second dataset according to an embodiment of the present disclosure.

[0032] Figure 4 This is a schematic diagram illustrating an example of a linear regression model according to an embodiment of the present disclosure.

[0033] Figure 5 This is a schematic diagram illustrating another example of a linear regression model according to an embodiment of the present disclosure.

[0034] Figure 6 This is a schematic diagram illustrating a battery pack according to another embodiment of the present disclosure.

[0035] Figure 7 This is a schematic diagram illustrating a vehicle according to yet another embodiment of the present disclosure.

[0036] Figure 8 This is a schematic diagram illustrating a server according to yet another embodiment of the present disclosure.

[0037] Figure 9 This is a schematic diagram illustrating a battery diagnostic method according to yet another embodiment of the present disclosure. Detailed Implementation

[0038] Before the description, it should be understood that the terms used in the specification and appended claims should not be construed as limited to their general or dictionary meanings, but rather should be interpreted based on their meanings and concepts corresponding to the technical aspects of this disclosure, on the basis of the principle that the inventors are allowed to define the terms appropriately for the best interpretation.

[0039] Therefore, the description presented herein is merely the best example for illustrative purposes only and is not intended to limit the scope of this disclosure. It should be understood that other equivalents and modifications may be made thereto without departing from the scope of this disclosure.

[0040] Furthermore, in interpreting this disclosure, if a detailed description of a relevant known structure or function is deemed likely to obscure the essential points of this disclosure, such detailed description will be omitted.

[0041] Ordinal terms such as “first” and “second” can be used to distinguish one element from another among various elements, but are not intended to limit elements by these terms.

[0042] Throughout the specification, when a section is referred to as “including” or “containing” any element, it means that the section may further include other elements, rather than excluding other elements, unless otherwise expressly stated.

[0043] Furthermore, throughout the specification, when one part is referred to as being “connected” to another part, it is not limited to the case where they are “directly connected”, but also includes the case where they are “indirectly connected”, in which another element is inserted between them.

[0044] Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0045] Figure 1This is a schematic diagram illustrating a battery diagnostic device 100 according to an embodiment of the present disclosure.

[0046] refer to Figure 1 The battery diagnostic device 100 may include a data acquisition unit 110 and a control unit 120.

[0047] The data acquisition unit 110 can be configured to acquire a first dataset D1 representing the correspondence between the SOH of multiple batteries and time.

[0048] Here, a battery refers to a physically separable, individual cell with a negative and a positive terminal. As an example, a lithium-ion cell or a lithium polymer cell can be considered a battery. Furthermore, the type of battery can be cylindrical, prismatic, or pouch-shaped. Additionally, a battery can also refer to a battery bank, battery module, or battery pack comprising multiple cells connected in series and / or parallel.

[0049] Specifically, time refers to the battery's usage time. Furthermore, SOH (State of Health) refers to the degree of battery degradation. That is, SOH data for each battery at each time point can be included in the first dataset D1. Here, the time at which SOH is recorded can be chosen periodically or non-periodically. That is, the SOH of each battery can be estimated periodically and recorded in the first dataset D1. Additionally, when a non-periodic event occurs for each battery, the corresponding battery's SOH can be estimated and recorded in the first dataset D1.

[0050] Figure 2 This is a schematic diagram illustrating a first dataset D1 according to an embodiment of the present disclosure. Specifically, the first dataset D1 can be represented as an XY chart, where the X-axis is set to time and the Y-axis is set to SOH. However, the first dataset D1 can be represented in various ways (e.g., as a table, etc.), as long as the correspondence between time and SOH can be represented.

[0051] For example, the data acquisition unit 110 can acquire the first dataset D1 by receiving the first dataset D1 from the outside.

[0052] As another example, the data acquisition unit 110 can receive battery information for each of a plurality of batteries. Here, the battery information may include information for estimating SOH and battery time information. The data acquisition unit 110 can 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. In other words, the data acquisition unit 110 can acquire the first dataset D1 by generating the first dataset D1 based on the received battery information.

[0053] In addition, the data acquisition unit 110 can be configured to acquire a second dataset D2 representing the correspondence between the SOH of the target battery and time.

[0054] Here, the target battery is the battery that is being diagnosed. That is, the first dataset D1 is a dataset containing data on the state of health (SOH) of multiple batteries over time, while the second dataset D2 is a dataset containing data on the SOH of the battery undergoing diagnosis (the target battery) over time.

[0055] For example, the data acquisition unit 110 can acquire the second dataset D2 by receiving the second dataset D2 from an external source.

[0056] As another example, the data acquisition unit 110 can receive battery information about the target battery. The data acquisition unit 110 can estimate the state of equilibrium (SOH) of the target battery based on the received battery information, and generate a second dataset D2 based on the 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.

[0057] Figure 3 This is a schematic diagram illustrating a second dataset D2 according to an embodiment of the present disclosure. Specifically, the second dataset D2 can be represented as an XY chart, where the X-axis is set to time and the Y-axis is set to SOH. However, the second dataset D2 can be represented in various ways (e.g., as a table, etc.), as long as the correspondence between time and SOH can be represented.

[0058] The data acquisition unit 110 can be connected to communicate with the control unit 120. For example, the data acquisition unit 110 can be connected to the control unit 120 via wired and / or wireless means. The data acquisition unit can send the acquired first dataset D1 and second dataset D2 to the control unit 120.

[0059] The control unit 120 can be configured to calculate a first rate of change representing the change of SOH over time based on a first dataset D1.

[0060] 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 can be configured to derive a linear regression model for the first dataset D1 and calculate the first rate of change based on the derived linear regression model. For example, the control unit 120 can calculate the slope of the linear regression model as the first rate of change.

[0061] When the first rate of change is used, the correspondence between time and SOH can be clearly and intuitively represented. Therefore, the control unit 120 can calculate the first rate of change based on the first dataset D1, and analyze the first dataset D1 based on the calculated first rate of change.

[0062] Figure 4 This is a schematic diagram illustrating an example of a linear regression model according to an embodiment of the present disclosure. The control unit 120 can derive a first linear regression model P1 based on a first dataset D1. Subsequently, the control unit 120 can calculate the slope of the first linear regression model P1 as a first rate of change.

[0063] The control unit 120 can be configured to calculate a second rate of change representing the change of SOH over time based on a second dataset D2.

[0064] The control unit 120 can calculate the second rate of change of the second dataset D2 in a similar manner to calculating the first rate of change of the first dataset D1.

[0065] 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 can be configured to derive a linear regression model for the second dataset D2 and calculate the second rate of change based on the derived linear regression model.

[0066] exist Figure 4 In this embodiment, the control unit 120 can derive a second linear regression model P2 based on the second dataset D2. Furthermore, the control unit 120 can calculate the slope of the second linear regression model P2 as a second rate of change.

[0067] The control unit 120 can be configured to diagnose the state of the target battery based on a first rate of change and a second rate of change.

[0068] Here, the first rate of change is a value representing the relationship between the state of health (SOH) of the multiple batteries and time, while the second rate of change is a value representing the relationship between the SOH of the target battery and time. Therefore, the control unit 120 can diagnose the state of the target battery relative to the multiple batteries by comparing the first rate of change for the multiple batteries and the second rate of change for the target battery.

[0069] For example, the control unit 120 can compare and analyze the SOH trends of multiple batteries with the SOH trend of the target battery by considering the ratio of the second rate of change to the first rate of change. Therefore, the control unit 120 can diagnose the relative state of the target battery derived from its relationship with the multiple batteries.

[0070] exist Figure 4In one embodiment, the control unit 120 can diagnose the relative state of the target battery with respect to multiple batteries by comparing a first rate of change of a first linear regression model P1 with a second rate of change of a second linear regression model P2.

[0071] According to embodiments of the present disclosure, the battery diagnostic apparatus 100 can diagnose the relative state of a target battery with respect to the multiple batteries based on a first dataset D1 for a plurality of batteries and a second dataset D2 for a target battery.

[0072] Meanwhile, the control unit 120 included in the battery diagnostic device 100 may optionally include processors, application-specific integrated circuits (ASICs), other chipsets, logic circuits, registers, communication modems, data processing devices, etc., known in the art, to execute the various control logics performed in this disclosure. Furthermore, when the control logic is implemented as software, the control unit 120 may be implemented as a collection of program modules. In this case, the program modules may be stored in memory and executed by the control unit 120. The memory may be located internally or externally to the control unit 120 and may be connected to the control unit 120 by various well-known means.

[0073] In addition, the battery diagnostic device 100 may also include a storage unit 130. The storage unit 130 may store data necessary for the operation and function of each component of the battery diagnostic device 100, data generated during the execution of operations or functions, etc. The storage unit 130 is not particularly limited in type, as long as it is a known information storage device capable of recording, erasing, updating, and retrieving data. As examples, the information storage device may include RAM, flash memory, ROM, EEPROM, registers, etc. Furthermore, the storage unit 130 may store program code defining processes that can be executed by each component of the battery diagnostic device 100.

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

[0075] Here, relative degradation is an indicator that represents the relative degree of degradation of the target battery when considering the degradation of multiple batteries. That is, the higher the relative degradation, the more the target battery is degraded compared to the other batteries, and the lower the relative degradation, the less the target battery is degraded compared to the other batteries.

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

[0077] Specifically, the control unit 120 can calculate the difference between the first rate of change and the second rate of change using the formula "first rate of change - second rate of change".

[0078] Next, the control unit 120 can be configured to estimate the relative degree of degradation by calculating the ratio of the rate of change difference to the first rate of change.

[0079] Specifically, the control unit 120 can estimate the relative degree of 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 to the first rate of change.

[0080] The battery diagnostic apparatus 100 according to embodiments of the present disclosure 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. Therefore, performance criteria for determining the degree of degradation of the target battery relative to other batteries can be clearly defined. Furthermore, because the state of the target battery is determined by comparing it to the states of other batteries, abnormal degradation patterns or signs of abnormality can be diagnosed at an early stage.

[0081] In a preferred embodiment, the first dataset D1 can be configured to include the second dataset D2. That is, the plurality of batteries corresponding to the first dataset D1 may include the target battery corresponding to the second dataset D2. In other words, the first dataset D1 may include data for the target battery.

[0082] The data acquisition unit 110 may acquire a first dataset D1 and a second dataset D2, or it 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.

[0083] Typically, when the target battery is not included in a group of batteries, the relative states of the multiple batteries to the target battery can significantly reduce the accuracy and reliability of the diagnosis due to the different comparison criteria used between the data. In other words, since the multiple batteries are only indirectly compared to the target battery, diagnostic results considering the relative states of the target battery to the multiple batteries may have slightly lower analytical accuracy and reliability.

[0084] However, according to the battery diagnostic device 100, since the target battery is included as one of multiple batteries, the state of the target battery and other batteries can be directly compared. This allows for a more accurate and reliable diagnosis of abnormal degradation patterns or signs of abnormality in the target battery. In other words, the battery diagnostic device 100 has the advantage of being able to diagnose the relative state of the target battery more accurately and reliably by considering a first dataset D1 that includes a second dataset D2 and comparing it with multiple batteries.

[0085] The control unit 120 can be configured to first derive a nonlinear regression model for the first dataset D1.

[0086] Specifically, the control unit 120 can derive a nonlinear regression model for the first dataset D1. Here, compared to a linear regression model, the nonlinear regression model can better model the complex correlations between variables in the first dataset D1. For example, the nonlinear regression model can better identify high-dimensional relationships between variables in the first dataset D1.

[0087] For example, since the first dataset D1 is a large dataset involving multiple batteries, it is necessary to analyze the high-dimensional correspondence between time and state of equilibrium (SOH). Therefore, the control unit 120 can first derive a nonlinear regression model to identify the SOH trend of the first dataset D1.

[0088] Figure 5 This is a schematic diagram illustrating another example of a linear regression model according to embodiments of the present disclosure. Figure 5 In one embodiment, the control unit 120 may first derive a linear regression model P3 based on the first dataset D1.

[0089] Furthermore, the control unit 120 can be configured to derive a linear regression model based on the derived nonlinear regression model.

[0090] Specifically, the control unit 120 can initially remove unnecessary noise by deriving a nonlinear regression model based on the first dataset D1. Furthermore, the control unit 120 can simplify the relationship between the state of equilibrium (SOH) and time of multiple batteries by deriving a linear regression model based on the noise-removed nonlinear regression model. In other words, the control unit 120 can first analyze the complex patterns in the first dataset D1 using nonlinear regression, and then further simplify the analyzed complex patterns using linear regression.

[0091] Generally, nonlinear regression models offer greater flexibility than linear regression models and can effectively explain various types of correlations in the data. Specifically, nonlinear regression models can explain data correlations more effectively because they capture nonlinear patterns better than linear regression models.

[0092] exist Figure 5 In this embodiment, the control unit 120 can first derive the nonlinear regression model P3 based on the first dataset D1. Subsequently, the control unit 120 can derive the linear regression model P4 based on the nonlinear regression model P3. That is, Figure 4 The linear regression model P1 is directly derived from the first dataset D1, but according to... Figure 5 The difference between the linear regression model P4 in the embodiment is that it is derived from the nonlinear regression model P3 of the first dataset D1.

[0093] That is, the battery diagnostic device 100 can first derive a nonlinear regression model for the first dataset D1, and then derive a linear regression model, thereby ensuring that the finally derived linear regression model can maintain sufficient flexibility without oversimplifying the correspondence of the data in the first dataset D1.

[0094] The foregoing has described an embodiment in which a linear regression model can be derived based on a first dataset D1, but depending on the specific embodiment, it can also be applied to derive a linear regression model based on a second dataset D2. For example, the control unit 120 can derive a linear regression model directly based on the second dataset D2, or it can derive a linear regression model based on a nonlinear regression model (not shown) of the second dataset D2.

[0095] In a preferred embodiment, the control unit 120 may be configured to calculate a first rate of change based on data in the first dataset D1 corresponding to a preset reference time period.

[0096] Specifically, the reference period can be set as the period during which the relative state of the target battery is analyzed.

[0097] For example, the reference period can be preset by the user to a specific period (e.g., 600 days).

[0098] As another example, the reference time period can be set to the largest time period included in the second dataset D2. Figure 4 In this embodiment, since the target battery has been used for K days, the maximum time period included in the second dataset D2 is K days. Therefore, the control unit 120 can set the reference time period to K days.

[0099] The control unit 120 can derive a linear regression model from a first dataset D1 using only up to K days of data, and calculate a first rate of change based on the derived linear regression model. For example, even if the maximum time period included in the first dataset D1 exceeds K days, the control unit 120 can derive a linear regression model using only data from K days.

[0100] Furthermore, the control unit 120 can be configured to calculate a second rate of change based on the data in the second dataset D2 corresponding to the reference time period.

[0101] 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 of the first dataset D1 and the second dataset D2 by a preset reference period.

[0102] For example, refer to Figure 2 and 3 Some of the multiple batteries were used for up to approximately 1100 days, but the first rate of change was only used for K days (approximately 750 days). That is, when the time period between the first dataset D1 and the second dataset D2 is not considered, the first rate of change derived from the first dataset D1 and the second rate of change derived from the second dataset D2 have different scales in terms of time period. In other words, since the first dataset D1 and the second dataset D2 are not under the same conditions in terms of time period, the reliability of the relative state of the target battery diagnosed based on the first and second rates of change may be reduced. Therefore, the battery diagnostic device 100 can improve the accuracy and reliability of the diagnosed relative state of the target battery by synchronizing the time periods of the first dataset D1 and the second dataset D2 as reference time periods.

[0103] The control unit 120 can be configured to classify the relative degree of degradation into one of a preset group of multiple groups.

[0104] Specifically, state information corresponding to each of the multiple groups can be preset. In the following text, the multiple groups are described as three groups, but depending on the embodiment, the multiple groups may also be divided into two groups or three or more groups.

[0105] The multiple groups can be classified into Group 1, Group 2, and Group 3.

[0106] Specifically, the first group includes batteries in normal condition, the second group includes batteries in a warning state, and the third group includes batteries in a faulty state. Here, a warning state refers to a battery that is not in a faulty state but requires continuous monitoring, while a faulty state refers to a battery that has already malfunctioned and is advised not to be used.

[0107] Preferably, a degradation range can be set for each of the multiple groups. For example, a first degradation range corresponding to the first group can be set to be less than the first degradation level. Furthermore, a second degradation range corresponding to the second group can be set to be equal to or greater than the first degradation level and less than the second degradation level. Finally, a third degradation range corresponding to the third group can be set to be equal to or greater than the second degradation level.

[0108] The control unit 120 can compare the relative degree of degradation of the target battery with the first to third degree of degradation range to determine the group to which the target battery belongs.

[0109] The control unit 120 can be configured to diagnose the state of a target battery based on the classified groups.

[0110] As previously explained, corresponding state information can be preset for each of the multiple groups. Therefore, the control unit 120 can determine the group to which the target battery belongs and can diagnose the battery's state based on the state information set to correspond to the determined group.

[0111] For example, if the target battery belongs to the first group, the control unit 120 can diagnose the target battery's state as normal. As another example, if the target battery belongs to the second group, the control unit 120 can diagnose the target battery's state as a warning state. As yet another example, if the target battery belongs to the third group, the control unit 120 can diagnose the target battery's state as a fault state.

[0112] The advantage of the battery diagnostic device 100 is that it not only estimates the relative degree of degradation of the target battery relative to multiple batteries, but also can use a preset group to specifically determine the state of the target battery.

[0113] Meanwhile, the control unit 120 can be configured to compare the first state of the target battery diagnosed at a previous diagnostic time point with the second state of the target battery diagnosed at the current diagnostic time point.

[0114] Preferably, the previous diagnosis time point refers to the diagnosis time point immediately preceding the current diagnosis time point. That is, the control unit 120 can determine whether the diagnosis results (first state and second state) at consecutive diagnosis time points are the same.

[0115] The control unit 120 can be configured to calculate the rate of change between the relative degree of deterioration at the current diagnostic time point and the relative degree of deterioration at a previous diagnostic time point if the first state and the second state are different.

[0116] Specifically, if the first state and the second state are the same, it can be assumed that the state of the target battery has not changed significantly within consecutive diagnostic time points. Therefore, the control unit 120 may not need to calculate the rate of change of the relative degradation of the target battery. However, if a request for calculating the rate of change of the relative degradation is received from an external source, the control unit 120 may calculate the rate of change between the relative degradation at the current diagnostic time point and the relative degradation at previous diagnostic time points, and provide the calculated rate of change to the external source.

[0117] Conversely, if the first state and the second state are different, it can be assumed that the state of the target battery has changed significantly at consecutive diagnostic time points. Therefore, the control unit 120 can calculate the rate of change between the relative degradation degree at the current diagnostic time point and the relative degradation degree at previous diagnostic time points to provide the extent to which the state of the target battery has changed. In other words, the control unit 120 can quantify how much the relative degradation degree at the current diagnostic time point has changed compared to the relative degradation degree at previous diagnostic time points.

[0118] For example, the relative degree of deterioration relative to a previous diagnosis time point is referred to as the first degree of deterioration, and the relative degree of deterioration relative to the current diagnosis time point is referred to as the second degree of deterioration. The control unit 120 can calculate the rate of change between the relative degrees of deterioration using the formula "(first degree of deterioration - second degree of deterioration) ÷ first degree of deterioration".

[0119] The battery diagnostic device 100 can not only track and diagnose the state of a target battery, but also quantitatively assess the degree of change whenever the state of the target battery changes significantly. Therefore, the battery diagnostic device 100 can provide specific values ​​for the state changes of the target battery, thereby providing significant assistance in battery maintenance.

[0120] The battery diagnostic device 100 according to this disclosure can be applied to a battery management system (BMS). That is, the BMS according to this disclosure may include the battery diagnostic device 100 described above. In this configuration, at least some of the components of the battery diagnostic device 100 can be implemented by supplementing or adding the functionality of components included in a conventional BMS. For example, the data acquisition unit 110 and the control unit 120 of the battery diagnostic device 100 can be implemented as components of the BMS.

[0121] Furthermore, the battery diagnostic device 100 according to this disclosure can be equipped in a battery pack. That is, the battery pack according to this disclosure may include the aforementioned battery diagnostic device 100 and at least one battery cell. In addition, the battery pack may also include electrical components (relays, fuses, etc.) and a housing, etc.

[0122] Figure 6 This is a schematic diagram illustrating a battery pack 10 according to another embodiment of the present disclosure.

[0123] Battery group 11 may include multiple batteries connected in series and / or in parallel. Furthermore, the positive terminal of battery group 11 may be connected to the positive terminal P+ of battery group 10, and the negative terminal of battery group 11 may be connected to the negative terminal P- of battery group 10.

[0124] The measuring unit 12 can be electrically connected to the battery pack 11. Furthermore, the measuring unit 12 can measure the voltage of each of the multiple batteries included in the battery pack 11.

[0125] Furthermore, the measuring unit 12 can be electrically connected to the current measuring unit A. For example, the current measuring unit A can be an ammeter or a shunt resistor capable of measuring the charging current and discharging current of the battery pack 11. The measuring unit 12 can measure the charging current of the battery pack 11 and calculate the charging amount via the current measuring unit A. Furthermore, the measuring unit 12 can measure the discharging current of the battery pack 11 and calculate the discharging amount via the current measuring unit A.

[0126] An external device can be connected to the positive terminal P+ and the negative terminal P- of battery pack 10. For example, the external device can be a charging device or a load. In addition, the positive terminal of battery pack 11, the positive terminal P+ of battery pack 10, the external device, the negative terminal P- of battery pack 10, and the negative terminal of battery pack 11 can be electrically connected.

[0127] Preferably, the data acquisition unit 110 can be configured to receive battery information from the measurement unit, and the control unit 120 can be configured to diagnose the state of multiple batteries included in the battery group 11 based on the battery information. Furthermore, the battery information received by the data acquisition unit 110 and the state of the multiple batteries diagnosed by the control unit 120 can be stored in the storage unit 130.

[0128] Figure 7 The figure schematically illustrates a vehicle 700 according to yet another embodiment of the present disclosure.

[0129] refer to Figure 7 The battery pack 710 according to embodiments of this disclosure can be included in a vehicle 700, such as an electric vehicle (EV) or a hybrid vehicle (HV). Furthermore, the battery pack 710 can drive the vehicle 700 by supplying power to a motor via an inverter disposed in the vehicle 700. Here, the battery pack 710 may include a battery diagnostic device 100. That is, the battery diagnostic device 100 can be included in the vehicle 700. In this case, the battery diagnostic device 100 can be an on-board device included in the vehicle 700.

[0130] Figure 8 This is a schematic diagram illustrating a server 800 according to yet another embodiment of the present disclosure.

[0131] A server 800 according to yet another embodiment of the present disclosure may include a battery diagnostic device 100 according to an embodiment of the present disclosure.

[0132] Specifically, server 800 can connect via wired and / or wireless means to communicate with multiple BMS 810. Here, the BMS can be installed in a vehicle, ESS (Energy Storage System), or diagnostic equipment. Preferably, a BMS can be used without restriction as long as it is capable of measuring and diagnosing the state of electrically connected batteries.

[0133] Furthermore, server 800 can store battery information received from multiple BMS 810s. Preferably, server 800 can store battery information for the same type of battery separately.

[0134] 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, server 800 can diagnose the state of the battery included in each of the multiple vehicles A based on the multiple battery information for multiple vehicles A. Similarly, server 800 can diagnose the state of the battery included in each of the multiple vehicles B based on the multiple battery information for multiple vehicles B.

[0135] That is, the state of vehicle A can be diagnosed based on a first dataset D1 of multiple batteries equipped in multiple vehicles A, and the state of vehicle B can be diagnosed based on a first dataset D1 of multiple batteries equipped in multiple vehicles B.

[0136] In addition, server 800 can provide the diagnostic results to the corresponding BMS. That is, by receiving the battery status diagnostic results from server 800, the BMS can determine the relative status of the corresponding battery with respect to multiple corresponding batteries.

[0137] Figure 9 This is a schematic diagram illustrating a battery diagnostic method according to yet another embodiment of the present disclosure.

[0138] refer to Figure 9 The battery diagnostic method may include a data acquisition step (S100), a rate of change calculation step (S200), and a diagnostic step (S300).

[0139] Preferably, each step of the battery diagnostic method can be performed by the battery diagnostic device 100. In the following text, for ease of explanation, content overlapping with the foregoing will be briefly described or omitted.

[0140] The data acquisition step (S100) is to acquire a first dataset D1 representing the correspondence between SOH and time for multiple batteries and a second dataset D2 representing the correspondence between SOH and time for a target battery, and can be executed by the data acquisition unit 110.

[0141] For example, the data acquisition unit 110 can directly receive the first dataset D1 and / or the second dataset D2 from the outside.

[0142] As another example, the data acquisition unit 110 can receive battery information from an external source and generate a first dataset D1 and / or a second dataset D2 based on the received battery information.

[0143] The rate of change calculation step (S200) is a step of calculating a first rate of change representing the change of SOH over time based on a first dataset D1 and a second rate of change representing the change of SOH over time based on a second dataset D2, which can be executed by the control unit 120.

[0144] Control unit 120 can derive a linear regression model based on the first dataset D1 and calculate the slope of the derived linear regression model as the first rate of change. For example, in Figure 4 In one embodiment, the control unit 120 can directly derive the linear regression model P1 based on the first dataset D1. As another example, in... Figure 5 In one embodiment, the control unit 120 can derive a nonlinear regression model P3 based on the first dataset D1, and derive a linear regression model P4 based on the derived nonlinear regression model P3.

[0145] Furthermore, the control unit 120 can derive a linear regression model based on the second dataset D2, and calculate the slope of the derived linear regression model as a second rate of change.

[0146] The diagnostic step (S300) is a step to diagnose the state of the target battery based on the first rate of change and the second rate of change, which can be executed by the control unit 120.

[0147] For example, the control unit 120 can diagnose the state of the target battery by calculating the ratio of the difference between the first rate of change and the second rate of change to the first rate of change. Specifically, the control unit 120 can diagnose the relative state of the target battery with respect to multiple batteries.

[0148] The embodiments of this disclosure described above can be implemented not only by apparatus and methods, but also by a program that implements functions corresponding to the configuration of the embodiments of this disclosure, or a recording medium on which the program is recorded. Those skilled in the art can readily implement the program or recording medium from the above description of the embodiments.

[0149] Another embodiment of this disclosure may provide a computer-readable storage medium having programs recorded thereon for executing the various embodiments described above on a computer.

[0150] A program can be implemented as hardware components, software components, and / or a combination of hardware and software components. The program can be executed by any system capable of executing computer-readable instructions.

[0151] Software may include computer programs, code, instructions, or combinations thereof, which may configure processing equipment to perform desired operations or may independently or jointly command processing equipment.

[0152] Software can be implemented as a computer program that includes instructions stored on a computer-readable storage medium. Examples of computer-readable storage media include magnetic storage media (e.g., read-only memory (ROM), random access memory (RAM), floppy disks, hard disks, etc.) and optically readable media (e.g., CD-ROM, DVD: digital versatile optical disc). Computer-readable storage media can be distributed across network-connected computer systems, allowing computer-readable code to be stored and executed in a distributed manner. The storage medium can be read by a computer, stored in memory, and executed by a processor.

[0153] Computer-readable storage media may be provided in the form of non-transitory storage media. Here, the term "non-transitory storage media" simply means that it is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently on the storage medium and cases where data is temporarily stored. For example, a "non-transitory storage medium" may include a buffer in which data is temporarily stored.

[0154] Furthermore, programs can be provided as part of a computer program product. Computer program products can be traded as goods between sellers and buyers.

[0155] Computer program products may include software programs and computer-readable storage media storing the software programs. For example, a computer program product may include a product distributed electronically in the form of a software program (e.g., a downloadable application) by a manufacturer of an electronic device or through an electronic marketplace. For electronic distribution, at least a portion of the software program may be stored on a storage medium or temporarily generated. In this case, the storage medium may be the storage medium of a server belonging to the manufacturer of the electronic device, the storage medium of a server belonging to an electronic marketplace, or the storage medium of a relay server temporarily storing the software program.

[0156] Although this disclosure has been described in detail, it should be understood that while the detailed description and specific examples point to preferred embodiments of this disclosure, they are given by way of illustration only, as various changes and modifications within the scope of this disclosure will become apparent to those skilled in the art based on this detailed description.

[0157] Furthermore, without departing from the technical aspects of this disclosure, those skilled in the art can make many substitutions, modifications and changes to the disclosure described above, and this disclosure is not limited to the above embodiments and drawings, and each embodiment can be selectively combined in part or in whole to allow various modifications.

[0158] [Explanation of reference numerals in the attached figures]

[0159] 10: Battery Pack

[0160] 11: Battery Grouping

[0161] 12: Measurement Unit

[0162] 100: Battery diagnostic device

[0163] 110: Data Acquisition Unit

[0164] 120: Control Unit

[0165] 130: Storage unit

[0166] 700: Vehicles

[0167] 710: Battery Pack

[0168] 800: Server

[0169] 810: Multiple BMS

Claims

1. A battery diagnostic device, comprising: A data acquisition unit is configured to acquire a first dataset representing the correspondence between the state of oxygen (SOH) of multiple batteries and time, and a second dataset representing the correspondence between the SOH of a target battery and time. as well as A control unit is configured to calculate a first rate of change representing the change of SOH over time based on a first dataset, calculate a second rate of change representing the change of SOH over time based on a second dataset, and diagnose the state of the target battery based on the first rate of change and the second rate of change.

2. The battery diagnostic device according to claim 1, in, The control unit is configured to derive a linear regression model for the first dataset and calculate the first rate of change based on the derived linear regression model.

3. The battery diagnostic device according to claim 2, in, The control unit is configured to first derive a nonlinear regression model for the first dataset, and then derive a linear regression model based on the derived nonlinear regression model.

4. The battery diagnostic device according to claim 1, in, The control unit is configured to derive a linear regression model for the second dataset and calculate the second rate of change based on the derived linear regression model.

5. The battery diagnostic device according to claim 1, in, The control unit is configured to estimate the relative degree of degradation of the target battery with respect to the plurality of batteries as the state of the target battery.

6. The battery diagnostic device according to claim 5, in, The control unit is configured to: Calculate the difference in rate of change between the first rate of change and the second rate of change, and The relative degree of degradation is estimated by calculating the ratio of the difference in the rate of change to the first rate of change.

7. The battery diagnostic device according to claim 5, in, The control unit is configured to: The relative degradation level is classified into one of a set of preset groups, and The state of the target battery is diagnosed based on the classified groups.

8. The battery diagnostic device according to claim 7, in, The control unit is configured to: The first state of the target battery diagnosed at a previous diagnostic time point is compared with the second state of the target battery diagnosed at the current diagnostic time point, and... When the first state and the second state are different, calculate the rate of change between the relative deterioration at the current diagnosis time point and the relative deterioration at the previous diagnosis time point.

9. The battery diagnostic device according to claim 1, in, The control unit is configured to: The first rate of change is calculated based on the data in the first dataset corresponding to the preset reference time period, and The second rate of change is calculated based on the data in the second dataset corresponding to the reference time period.

10. The battery diagnostic device according to claim 1, in, The first dataset is configured to include the second dataset.

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

12. A vehicle comprising a battery diagnostic device according to any one of claims 1 to 10.

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

14. A battery diagnostic method, comprising: The data acquisition steps involve acquiring a first dataset representing the correspondence between SOH and time for multiple batteries, and a second dataset representing the correspondence between SOH and time for the target battery. The rate of change calculation step involves calculating a first rate of change representing the change of SOH over time based on the first dataset, and calculating a second rate of change representing the change of SOH over time based on the second dataset. as well as The diagnostic step involves diagnosing the state of the target battery based on the first rate of change and the second rate of change.

15. A computer-readable recording medium storing a computer program for performing a battery diagnostic method, the battery diagnostic method comprising: The data acquisition steps involve acquiring a first dataset representing the correspondence between SOH and time for multiple batteries, and a second dataset representing the correspondence between SOH and time for the target battery. The rate of change calculation step involves calculating a first rate of change representing the change of SOH over time based on the first dataset, and calculating a second rate of change representing the change of SOH over time based on the second dataset. as well as The diagnostic step involves diagnosing the state of the target battery based on the first rate of change and the second rate of change.

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