Battery diagnostic device and operation method thereof

Abstract

A battery diagnostic device, according to one embodiment disclosed in the present document, comprises: a data acquisition unit which acquires first data related to the voltage and capacity of a reference battery, and second data related to the voltage and capacity of a target battery; and a controller which fits the first data and the second data to each other so as to minimize the value of an objective function related to the error between the first data and the second data, and which predicts the degree of degradation of the target battery on the basis of the fitting result.

Description

Battery diagnostic device and its operating method

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority to Korean Patent Application No. 10-2024-0031857, filed March 6, 2024, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] One embodiment disclosed in this document relates to a battery diagnostic device and an operating method thereof.

[0005] Recently, active research and development has been conducted on secondary batteries. The term "secondary battery" refers to a rechargeable battery, encompassing both conventional Ni / Cd and Ni / MH batteries, as well as more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries boast a significantly higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight form, making them a popular power source for mobile devices. Recently, their use has expanded to include power sources for electric vehicles, drawing attention as a next-generation energy storage medium.

[0006] Batteries exhibit gradual performance degradation, such as capacity degradation, over time. Therefore, it is necessary to diagnose their condition and predict their remaining lifespan. Utilizing various factors that indicate battery condition during this process allows for a more detailed understanding of the battery's electrochemical mechanisms.

[0007] One object of the embodiments disclosed in this document is to provide a battery diagnostic device and an operating method thereof for predicting a reliable battery degradation level.

[0008] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which the present invention pertains from the description below.

[0009] A battery diagnostic device according to an embodiment disclosed in the present document may include a data acquisition unit that acquires first data related to voltage and capacity of a reference battery and second data related to voltage and capacity of a target battery; and a controller that fits the first data and the second data to each other so that a value of an objective function related to an error between the first data and the second data is minimized, and predicts a degree of degradation of the target battery based on the fitting result.

[0010] According to one embodiment, each of the first data and the second data includes a first graph and a second graph representing a relationship between a voltage and a capacity of the reference battery and the target battery, respectively, and the controller can fit the first graph to the second graph by shifting and shrinking it.

[0011] According to one embodiment, the shift operation may correspond to an operation of moving the capacity axis of the first graph by a set value, and the shrink operation may correspond to an operation of shrinking the capacity axis of the first graph by a set ratio.

[0012] According to one embodiment, the controller may calculate a first MAPE (Mean Absolute Percent Error) value between the shifted-and-shrinked first graph and the second graph, calculate a second MAPE value between a first derivative graph (dV1 / dQ) obtained by differentiating the shifted-and-shrinked first graph with respect to capacity and a second derivative graph (dV2 / dQ) obtained by differentiating the second graph with respect to capacity, calculate a third MAPE value between the shifted-and-shrinked first graph in a set section and the second graph in the set section, calculate a fourth MAPE value between the first derivative graph (dV1 / dQ) in the set section and the second derivative graph (dV2 / dQ) in the set section, and generate a first objective function representing the degree of error between the shifted-and-shrinked first graph and the second graph based on the first MAPE value, the second MAPE value, the third MAPE value, and the fourth MAPE value.

[0013] According to one embodiment, the first objective function may satisfy Equations 1 and 2 below.

[0014] [Formula 1]

[0015] First objective function = (X * 1st MAPE value) + (Y * 2nd MAPE value) + (Z * (3rd MAPE value + 4th MAPE value))

[0016] [Formula 2]

[0017] X = 1,

[0018] Y = tan(θ), (0<θ< )

[0019] Z =

[0020] According to one embodiment, the controller can calculate the value of the first objective function of each of the shifted and shrunk first graphs by changing the set value and the set ratio.

[0021] According to one embodiment, the controller generates a second objective function related to an error between each of the shifted and shrunk first graphs and the second graph, and the second objective function can satisfy the following equation 3.

[0022] [Formula 3]

[0023] Second objective function = first MAPE value + second MAPE value

[0024] According to one embodiment, the controller can diagnose the degradation degree of the target battery based on the X, Y, Z values, the set value, and the set ratio when the first objective function and the second objective function each have a minimum value.

[0025] According to one embodiment, the set section may correspond to a section in which a sixth phase transition (Ta6) occurring in the charging direction from the negative electrode of the reference battery appears.

[0026] An operating method of a battery diagnosis device according to an embodiment disclosed in the present document may include: acquiring first data related to voltage and capacity of a reference battery and second data related to voltage and capacity of a target battery; fitting the first data and the second data to each other so that a value of an objective function related to an error between the first data and the second data is minimized; and predicting a degree of degradation of the target battery based on a result of the fitting.

[0027] According to one embodiment, each of the first data and the second data may include a first graph and a second graph, respectively, representing a relationship between the voltage and capacity of the reference battery and the target battery.

[0028] According to one embodiment, the fitting step may include: performing a shift operation for shifting the capacity axis of the first graph by a set value; performing a shrink operation for shrinking the capacity axis of the first graph by a set ratio; and generating a first objective function indicating the degree of error between the shifted and shrunk first graph and the second graph.

[0029] According to one embodiment, the step of generating the first objective function may include: calculating a first MAPE (Mean Absolute Percent Error) value between the shifted-and-shrinked first graph and the second graph; calculating a second MAPE value between a first differential graph (dV1 / dQ) obtained by differentiating the shifted-and-shrinked first graph with respect to capacity and a second differential graph obtained by differentiating the second graph with respect to capacity; calculating a third MAPE value between the first graph in a set section and the second differential graph in the set section; calculating a fourth MAPE value between the first differential graph (dV1 / dQ) in the set section and the second differential graph (dV2 / dQ) in the set section; and generating a first objective function indicating the degree of error between the shifted-and-shrinked first graph and the second graph based on the first MAPE value, the second MAPE value, the third MAPE value, and the fourth MAPE value.

[0030] According to one embodiment, the first objective function may satisfy Equations 1 and 2 below.

[0031] [Formula 1]

[0032] First objective function = (X * 1st MAPE value) + (Y * 2nd MAPE value) + Z * (3rd MAPE value + 4th MAPE value))

[0033] [Formula 2]

[0034] X = 1,

[0035] Y = tan(θ), (0<θ< )

[0036] Z =

[0037] According to one embodiment, the step of predicting the degree of degradation may include the steps of calculating the value of the first objective function of each of the first graphs shifted and shrunk while changing the set value and the set ratio; generating a second objective function related to an error between each of the first graphs shifted and shrunk and the second graph; and diagnosing the degree of degradation of the target battery based on the X, Y, and Z values, the set value, and the set ratio when the first objective function and the second objective function each have a minimum value.

[0038] A battery diagnostic device according to an embodiment disclosed in this document can predict the degree of degradation of a battery by reflecting the electrochemical mechanism of the battery.

[0039] FIG. 1 is a drawing for explaining a battery diagnostic device (1000) according to one embodiment disclosed in this document.

[0040] Figure 2 is a graph illustrating data fitting according to an embodiment disclosed in this document.

[0041] FIG. 3 is a graph illustrating a shift and shrink operation according to an embodiment disclosed in this document.

[0042] Figure 4 is a graph for explaining the results before and after fitting according to one embodiment disclosed in this document.

[0043] Figure 5 is a graph for explaining a differential value according to an embodiment disclosed in this document.

[0044] FIG. 6 is a flowchart illustrating the operation of a battery diagnostic device according to an embodiment disclosed in this document.

[0045] Hereinafter, embodiments disclosed in this document will be described in detail with reference to exemplary drawings. When designating components in each drawing, it should be noted that, where possible, identical components are given identical reference numerals, even if they appear in different drawings. Furthermore, when describing embodiments disclosed in this document, detailed descriptions of related known structures or functions will be omitted if they are deemed to hinder understanding of the embodiments disclosed in this document.

[0046] In describing the components of the embodiments disclosed in this document, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only intended to distinguish the components from other components and do not limit the nature, order, or sequence of the components. In addition, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning they have in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined in this application.

[0047] FIG. 1 is a drawing for explaining a battery diagnostic device (1000) according to one embodiment disclosed in this document.

[0048] The battery (100) may be implemented as a battery pack, a battery module, a battery cell, etc. When the battery (100) is implemented as a battery pack unit, the battery (100) may be configured to include a plurality of battery modules (not shown). In this case, each battery module (not shown) may be configured to include a plurality of battery cells (not shown), and the battery cells (not shown) may be lithium ion (Li-ion) batteries, nickel-hydrogen (Ni-H) batteries, etc., but are not limited to these examples.

[0049] The battery (100) may be configured to supply power to a target device (not shown), and for this purpose, the battery (100) may be electrically connected to the target device (not shown). Here, the target device (not shown) may include an electrical, electronic, or mechanical device that operates by receiving power from the battery (100). For example, the target device (not shown) may be, but is not limited to, a two-wheeled electric vehicle such as an electric vehicle (EV) or an electric scooter. In addition, when the target device is a two-wheeled electric vehicle such as an electric scooter, the battery (100) mounted on the two-wheeled electric vehicle may be replaceable through a battery swapping station (BSS).

[0050] A battery diagnostic device (1000) can diagnose a battery (100) based on data related to the voltage and capacity of the battery (100) and predict the degree of degradation of the battery (100). Referring to FIG. 1, the battery diagnostic device (1000) may include a data acquisition unit (1100), a controller (1200), and a memory (1300).

[0051] The data acquisition unit (1100) can acquire data related to the voltage and capacity of the battery (100). According to one embodiment, the data acquisition unit (1100) can acquire first data related to the voltage and capacity of a reference battery and second data related to the voltage and capacity of a target battery. Here, the target battery may be a battery whose degree of deterioration is to be diagnosed, and the reference battery may be a battery whose degree of deterioration is not progressing and thus whose degree of deterioration is to be determined. For example, the data acquisition unit may be a shipped battery, but is not limited to these examples.

[0052] According to one embodiment, the first data may include a first graph representing the voltage of the reference battery according to the capacity of the reference battery. In addition, the second data may include a second graph representing the voltage of the target battery according to the capacity of the target battery. Here, the first graph may include data obtained through experiments / development of the reference battery, and the second graph may include, but is not limited to, BOL (Beginning of Life) data and / or MOL (Middle of Life) data of the target battery.

[0053] According to one embodiment, the data acquisition unit (1100) may acquire data related to a graph representing the voltage of the battery according to the capacity of the battery (100) by charging and discharging the battery (100). In this case, the data acquisition unit (1100) may further include various circuits for applying voltage and / or current to the battery (100) or performing a charging and discharging operation of the battery (100) and a processor for processing the acquired data.

[0054] According to one embodiment, the data acquisition unit (1100) can indirectly acquire data related to a graph representing different voltages depending on the capacity of the battery (100). In this case, the data acquisition unit (1100) may further include a communication module for communicating with the battery (100) via wires and / or wirelessly.

[0055] The controller (1200) can control the overall operation of the battery diagnostic device (1000). Here, the controller (1200) can execute commands or software to control at least one other component (e.g., hardware or software) of the battery diagnostic device (1000), or perform operations such as processing and / or calculating various data.

[0056] The controller (1200) can predict the degree of degradation of the battery (100) based on data related to the voltage and capacity of the battery (100) acquired by the data acquisition unit (1100).

[0057] According to one embodiment, the controller (1200) can fit data related to the capacity and voltage of a reference battery to data related to the voltage and capacity of a target battery, and predict the degree of degradation of the target battery based on the fitting result.

[0058] Here, the controller (1200) can fit a first graph representing the voltage of the reference battery according to the capacity of the reference battery to a second graph representing the voltage of the target battery according to the capacity of the target battery. In the process of fitting the first graph to the second graph, the controller (1200) can fit the first graph to the second graph so that the value of the first objective function related to the degree of error between the first graph and the second graph is minimized.

[0059] The controller (1200) can shift and shrink a first graph representing the voltage of a reference battery according to the capacity of the reference battery and fit it to a second graph. Specific details related to this will be described later in the description of FIG. 3.

[0060] The controller (1200) can fit the first graph to the second graph by varying a set value, which is the degree to which the first graph is shifted, and a set ratio, which is the rate at which the first graph is shrunk, and then calculate the value of the first objective function related to the error between each of the shifted and shrunk first graphs and the second graph.

[0061] The controller (1200) can calculate the value of the first objective function related to the error between the shifted and shrunk first graph and the second graph by reflecting various variables.

[0062] According to one embodiment, the controller (1200) may calculate a value of an objective function based on a difference between the voltage of the reference battery and the voltage of the target battery by utilizing a first graph representing voltage according to the capacity of a shifted-and-shrinked reference battery and a second graph representing voltage according to the capacity of a target graph. Specifically, the controller (1200) may calculate a first MAPE value, which is a mean absolute percent error (MAPE) between the first graph of the shifted-and-shrinked reference battery and the second graph of the target battery.

[0063] According to one embodiment, the controller (1200) may use a first differential graph (dV1 / dQ) obtained by differentiating a first graph representing voltage according to capacity of a shifted and shrunk reference battery with respect to capacity and a second differential graph (dV2 / dQ) obtained by differentiating a second graph representing voltage according to capacity of a target battery with respect to capacity to calculate a value of an objective function based on the difference between the first differential value obtained by differentiating the voltage of the reference battery with respect to capacity and the second differential value obtained by differentiating the voltage of the target battery with respect to capacity. Specifically, the controller (1200) may calculate a second MAPE value which is an average absolute percentage error between the first differential graph and the second differential graph.

[0064] According to one embodiment, the controller (1200) can calculate a third MAPE value, which is an average absolute percentage error between a first graph representing voltage according to capacity of a shifted and shrunk reference battery in a set interval and a second graph representing voltage according to capacity of a target battery.

[0065] According to one embodiment, the controller (1200) can calculate the value of the objective function based on the difference between the first differential graph (dV1 / dQ) obtained by differentiating the voltage of the shifted and shrunk reference battery with respect to the capacity in the set section and the second differential graph (dV2 / dQ) obtained by differentiating the voltage of the target battery with respect to the capacity. Specifically, the controller (1200) can calculate the fourth MAPE value, which is the mean absolute percentage error between the first differential graph (dV1 / dQ) and the second differential graph (dV2 / dQ) in the set section.

[0066] Here, the setting section may correspond to the section where the 6th phase transition (Transition anode 6; Ta6) that occurs in the charging direction from the negative electrode of the reference battery appears, but is not limited to this example. That is, the setting section does not necessarily have to be the section where the 6th phase transition appears, and may correspond to the section where the peak of the voltage change due to the phase transition appears the largest in the first differential graph of the reference battery.

[0067] Here, the mean absolute percentage error is an indicator of how much the difference is between the actual value and the estimated value, and can be defined as a value converted into a percentage of the Mean Absolute Error (MAE), which is an average value obtained by adding up the absolute values ​​of the errors. The controller (1200) can calculate the value of the objective function that minimizes the error between the first graph and the second graph by considering the first MAPE value, the second MAPE value, the third MAPE value, and the fourth MAPE value in the capacity ranges of the reference battery and the target battery.

[0068] The controller (1200) may generate a first objective function indicating the degree of error between the shifted and shrunk first graph and the second graph based on the first MAPE value, the second MAPE value, the third MAPE value, and the fourth MAPE value. According to one embodiment, the first objective function may satisfy the following equations.

[0069] [Formula 1]

[0070] First objective function = (X * 1st MAPE value) + (Y * 2nd MAPE value) + (Z * (3rd MAPE value + 4th MAPE value))

[0071] [Formula 2]

[0072] X = 1,

[0073] Y = tan(θ), (0<θ< )

[0074] Z =

[0075] According to one embodiment, the first objective function may include a first weight (X) assigned to a first MAPE value, a second weight (Y) assigned to a second MAPE value, and a third weight (Z) assigned to a third MAPE value and a fourth MAPE value. Here, the second weight and the third weight may vary depending on the value of theta (θ).

[0076] That is, when the first graph is shifted and shrunk by a set value and a set ratio, the first objective function may be a function that uses the value of theta (θ) as a variable. The controller (1200) can calculate the value of the first objective function by changing the value of theta (θ), and obtain the value of theta (θ) when the value of the first objective function becomes minimum.

[0077] The controller (1200) can obtain the value of the first objective function and the theta value when the value of the first objective function becomes minimum by changing the setting ratio and the setting value. That is, the controller (1200) can shift and shrink the first graph by changing the setting ratio and the setting value, and obtain the value of the first objective function between the shifted and shrunk first graph and the second graph and the theta value when the value of the first objective function becomes minimum.

[0078] The controller (1200) can generate a second objective function based on the first MAPE value and the second MAPE value. Here, the second objective function can satisfy the following equation.

[0079] [Formula 3]

[0080] Second objective function = first MAPE value + second MAPE value

[0081] The controller (1200) calculates X, Y, and Z values ​​based on the theta value when each of the first objective function and the second objective function is minimized, and can predict the degradation degree of the target battery based on the calculated X, Y, and Z values ​​and the set value and set ratio.

[0082] The memory (1300) can store various data processed and / or calculated by the battery diagnostic device (1000), for example, the first data and the second data described above, a plurality of graphs and formulas of the graphs, a battery degradation rate corresponding to a set value and a set ratio, a plurality of calculated weights, etc.

[0083] According to one embodiment, it may include a volatile memory device such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), or a non-volatile memory device such as a read only memory (ROM), a programmable ROM (PROM), or a flash memory.

[0084] Referring to FIG. 1, the memory (1300) is illustrated as being included in the battery diagnostic device (1000), but is not limited thereto, and the memory (1300) may be located outside the battery diagnostic device (1000) and configured to store data transmitted by the battery diagnostic device (1000).

[0085] According to one embodiment, the battery diagnostic device (1000) may be configured to be formed integrally with the battery (100) and / or included within the battery (100). In this case, the battery diagnostic device (1000) may be configured to be included in a battery management system (BMS) that controls the battery (100).

[0086] According to one embodiment, the battery diagnostic device (1000) may be formed separately from the battery (100). In this case, the battery diagnostic device (1000) may be configured to diagnose the battery (100) by being connected to the battery (100) via a wired and / or wireless network.

[0087] According to one embodiment, the battery diagnosis device (1000) may be configured to transmit the degradation level of the battery (100) to an external device (e.g., a cloud server or a user terminal) or provide a notification. The cloud server may be configured to provide information related to the degradation level of the battery (100) to the user, and the user terminal may include a terminal such as a personal computer (PC) or a smartphone. When the degradation level of the battery (100) reaches a value lower than a preset reference value, the battery diagnosis device (1000) may transmit a notification to the user terminal instructing the user to replace the battery (100) and / or diagnose the battery (100).

[0088] According to one embodiment, the battery diagnostic device (1000) may be included in a battery swapping station (BSS). Here, the battery swapping station may be an integrated system having a slot into which a battery (100) can be inserted and capable of charging the inserted battery (100).

[0089] Figure 2 is a graph illustrating data fitting according to an embodiment disclosed in this document.

[0090] Referring to FIG. 2, first data related to the voltage and capacity of a reference battery and second data related to the voltage and capacity of a target battery are illustrated.

[0091] According to one embodiment, the first data related to the voltage and capacity of the reference battery may include a first graph (a) representing the voltage (Voltage) of the reference battery according to the capacity (Capacity) of the reference battery. Here, the first graph (a) may be a graph in which the horizontal axis represents the capacity (Ah) of the reference battery and the vertical axis represents the open circuit voltage (OCV) of the reference battery.

[0092] According to one embodiment, the second data related to the voltage and capacity of the target battery may include a second graph (b) representing the voltage of the target battery according to the capacity of the target battery. Here, the second graph (b) may be a graph in which the horizontal axis represents the capacity (Ah) of the target battery and the vertical axis represents the open circuit voltage (OCV) of the target battery. Here, the target battery may correspond to a BOL (Beginning of Life) state in which it is installed in an electric vehicle or a two-wheeled electric vehicle and begins operation, or a battery in which it is actually operated and deteriorates (MOL; Middle of Life), and as deterioration progresses, the voltage of the target battery with respect to the capacity of the target battery may gradually change.

[0093] As described above in the description of FIG. 1, the controller (1200, see FIG. 1) can fit the first graph (a) to the second graph (b). The specific fitting process is described later in the detailed description of FIGS. 3 to 5.

[0094] FIG. 3 is a graph illustrating a shift and shrink operation according to an embodiment disclosed in this document.

[0095] Referring to FIG. 3, the controller (1200, see FIG. 1) can shift and shrink the first graph (a) to fit the first graph (a) to the second graph (b, see FIG. 2).

[0096] According to one embodiment, the shift may be related to a phenomenon in which the capacity axis of the first graph (a), which is a graph of the open circuit voltage (OCV) versus capacity of a battery (100, see FIG. 1), shifts in a positive direction when a loss of lithium inventory (LLI) occurs due to a phenomenon that consumes lithium ions, such as a thickening of the solid electrolyte interphase (SEI) layer formed on the negative surface of the target battery, decomposition of the electrolyte, or lithium plating. That is, the controller (1200) can fit the first graph (a) representing the voltage according to the capacity of the reference battery by a set value to the second graph (b, see FIG. 2) representing the voltage according to the capacity of the target battery, and predict the degree of degradation of the target battery corresponding to the set value. Here, the set value can be set and changed in various ways.

[0097] According to one embodiment, shrink may be related to a phenomenon in which the capacity axis in the second graph (b, see FIG. 2), which is a graph of the open circuit voltage (OCV) versus capacity of the battery (100), is reduced when loss of active material occurs due to causes such as the degree of disorder of the crystal structure inside the battery (100) and / or destruction or loss of particles of the negative and positive electrodes of the battery (100). That is, the controller (1200) shrinks the first graph (a) representing the voltage according to the capacity of the reference battery by a set ratio and fits it to the second graph (b, see FIG. 2) representing the voltage according to the capacity of the target battery, and can predict the degree of degradation of the target battery corresponding to the set ratio. Similarly, the set ratio may also be set and changed to have various values.

[0098] The controller (1200) can generate a shifted and shrunk first graph (c) by variously changing the above-described setting values ​​and setting ratios.

[0099] Figure 4 is a graph for explaining the results before and after fitting according to one embodiment disclosed in this document.

[0100] The graph shown on the left is a graph that plots the first graph (a) and the second graph (b) shown in Fig. 2 on a single coordinate plane. Here, the horizontal axis represents the capacity of the reference battery and the target battery, and the vertical axis represents the open circuit voltage of the reference battery and the target battery. Here, the first graph (a) is a graph before the shift and shrink operation described with reference to Fig. 3 is performed, and there is a large difference in voltage value from the second graph (b), which represents the voltage according to the capacity of the target battery.

[0101] The controller (1200, see FIG. 1) can fit the first graph (a) to the second graph (b) by performing the shift and shrink operation described with reference to FIG. 3. Here, the graph illustrated on the right is a graph that plots the shifted and shrunk first graph (c) illustrated in FIG. 3 and the second graph (b) illustrated in FIG. 2 on a single coordinate plane. Similarly, the horizontal axis represents the capacity of the reference battery and the target battery, and the vertical axis represents the open circuit voltage of the reference battery and the target battery.

[0102] According to one embodiment, the controller (1200) can shift and shrink the first graph (a) to fit the second graph (b). Referring to the graph illustrated on the right, it can be confirmed that the voltage difference between the reference battery and the target battery has decreased compared to the graph illustrated on the left.

[0103] According to one embodiment, the controller (1200) can calculate a first MAPE value, which is an average absolute percentage error of the voltage difference (V1-V2) between the shifted and shrunk first graph (c) and the second graph (b). Here, the controller (1200) can calculate the first MAPE value in the entire range of capacity (e.g., 0 Ah to 100 Ah) and / or in a specific range, and the process of calculating the first MAPE value is related to general mathematical content, so a detailed description thereof is omitted.

[0104] Figure 5 is a graph for explaining a differential value according to an embodiment disclosed in this document.

[0105] The controller (1200, see FIG. 1) can differentiate the shifted and shrunk first graph (c). The controller (1200) can generate a first differential graph by differentiating the shifted and shrunk first graph (c) with respect to capacity.

[0106] The controller (1200) can differentiate the second graph (b). The controller (1200) can generate a second differential graph by differentiating the second graph (b) with respect to capacity.

[0107] Referring to Figure 5, the horizontal axis represents capacity, and the vertical axis represents the differential value (dV / dQ) of the open circuit voltage of each of the reference battery and the target battery with respect to capacity.

[0108] According to one embodiment, the controller (1200) can calculate a second MAPE value, which is an average absolute percentage error of the difference (dV1 / dQ-dV2 / dQ) between the first differential graph and the second differential graph. Here, the controller (1200) can calculate the second MAPE value in the entire range of capacity (e.g., 0 Ah to 100 Ah) and / or in a specific range, and the process of calculating the second MAPE value is related to general mathematical content, so a detailed description thereof is omitted.

[0109] The controller (1200) can calculate a third MAPE value, which is the average absolute percentage error of the difference between the first graph and the second graph in the set section. Here, the process of calculating the third MAPE value is related to general mathematical content, so a detailed description is omitted.

[0110] The controller (1200) can calculate the fourth MAPE value, which is the average absolute percentage error of the difference (dV1 / dQ-dV2 / dQ) between the first and second differential graphs in the set section. Similarly, the process of calculating the root mean square error is related to general mathematical content, so a detailed description thereof will be omitted.

[0111] According to one embodiment, the set section may be identified based on a differential graph obtained by differentiating the voltage versus capacity graph of the battery (100, see FIG. 1) with respect to the capacity. Specifically, the set section may be identified based on a plurality of peaks appearing in the differential graph of the battery (100) in the charging direction of the battery (100), i.e., in the direction in which the capacity of the battery (100) increases. Each of the plurality of peaks appearing in the differential graph of the battery (100) may correspond to points at which a phase transition occurring in the battery (100) appears, and according to one embodiment, the set section may be a section including a peak (Transition Anode 6; Ta6) at which a sixth phase transition occurring in the negative electrode of the battery (100) appears in the charging direction of the battery (100), but is not limited to this example.

[0112] According to one embodiment, the controller (1200) can identify a point having the highest peak in the differential graph of the battery (100). Here, the highest peak has the greatest intensity among a plurality of peaks identified based on the differential graph of the battery (100). The controller (1200) can estimate a voltage change due to degradation of the positive and negative electrodes of the battery (100) and / or irreversible lithium loss based on the voltage of the battery (100) at the peak having the greatest intensity, the differential value (dV / dQ) obtained by differentiating the voltage with respect to the capacity, etc.

[0113] According to one embodiment, the controller (1200) can identify a capacity interval including the maximum and minimum values ​​of the identified highest peak. The controller (1200) can identify a peak (1) having the strongest intensity in the first differential graph of the reference battery, and can identify a set interval including the maximum and minimum values ​​of the peak (1). The controller (1200) can calculate a third MAPE value and a fourth MAPE value based on the first graph, the second graph, the first differential graph, and the second differential graph of the reference battery in the set interval illustrated in FIG. 5.

[0114] The controller (1200) can calculate the values ​​of the first objective function and the second objective function by changing the set value, the set ratio, and theta value based on the calculated first MAPE value, the second MAPE value, the third MAPE value, and the fourth MAPE value until the values ​​of the first objective function and the second objective function described above become minimum. The controller (1200) can store X, Y, and Z values ​​according to the set value, the set ratio, and theta value when the values ​​of the first objective function and the second objective function become minimum in the memory (1300, see FIG. 1).

[0115] The controller (1200) can predict the degree of degradation of the target battery based on the X, Y, and Z values ​​when the value of the first objective function becomes minimum and the set value and set ratio when the value of the second objective function becomes minimum. Here, the X, Y, and Z values ​​and the set value and set ratio may vary depending on, for example, the type of vehicle.

[0116] A battery diagnosis device (1000, see FIG. 1) according to an embodiment disclosed in this document can fit data by considering not only the first root mean square error, but also the second root mean square error and the third root mean square error. That is, the battery diagnosis device (1000) can diagnose the degree of degradation of the battery (100) by considering not only the change in voltage relative to the capacity of the battery (100), but also the amount of change in voltage relative to the capacity and the amount of change in voltage relative to the capacity in a specific section. Since the amount of change in voltage relative to the capacity of the battery (100) and the amount of change in voltage relative to the capacity in a specific section of the battery (100) can represent data regarding the electrical and chemical mechanism of the battery (100), more advanced data fitting is possible when various variables are reflected in the objective function.

[0117] FIG. 6 is a flowchart illustrating the operation of a battery diagnostic device according to an embodiment disclosed in this document.

[0118] In step S101, the battery diagnostic device (1000, see FIG. 1) can obtain first data related to the voltage and capacity of the reference battery and second data related to the voltage and capacity of the target battery.

[0119] According to one embodiment, the first data may include a first graph (a, see FIG. 2) representing a relationship between the voltage and capacity of a reference battery, and the second data may include a second graph (b, see FIG. 2) representing a relationship between the voltage and capacity of a target battery. That is, the first graph (a) may be a graph in which the horizontal axis represents the capacity (Ah) of the reference battery and the vertical axis represents the open circuit voltage (OCV) of the reference battery, and the second graph (b) may be a graph in which the horizontal axis represents the capacity (Ah) of the target battery and the vertical axis represents the open circuit voltage (OCV) of the target battery. Here, the first graph may include data acquired through experiments / development of the reference battery, and the second graph may include, but is not limited to, BOL (Beginning of Life) data and / or MOL (Middle of Life) data of the target battery.

[0120] In step S102, the battery diagnostic device (1000) can fit the first data to the second data so that the value of the first objective function indicating the degree of error between the first data and the second data is minimized.

[0121] According to one embodiment, the battery diagnostic device (1000) may shift and shrink the first graph to fit the first data to the second data. Here, the shift may be related to an operation of moving the capacity axis of the first graph by a set value, and the shrink may be related to an operation of shrinking the capacity axis of the first graph by a set ratio.

[0122] According to one embodiment, the battery diagnostic device (1000) may calculate a first MAPE (Mean Absolute Percent Error) value between a shifted and shrunk first graph and the second graph, calculate a second MAPE value between a first differential graph (dV1 / dQ) obtained by differentiating the shifted and shrunk first graph with respect to capacity and a second differential graph obtained by differentiating the second graph with respect to capacity, calculate a third MAPE value between the first graph in a set section and the second differential graph in the set section, and calculate a fourth MAPE value between the first differential graph (dV1 / dQ) in the set section and the second differential graph (dV2 / dQ) in the set section.

[0123] According to one embodiment, the battery diagnostic device (1000) can generate a first objective function indicating the degree of error between the shifted and shrunk first graph and the second graph based on the first MAPE value, the second MAPE value, the third MAPE value, and the fourth MAPE value.

[0124] According to one embodiment, the first objective function can satisfy Equations 1 and 2 described above.

[0125] At step S103, the battery diagnostic device (1000) can predict the degree of degradation of the target battery based on the fitting result.

[0126] According to one embodiment, the battery diagnostic device (1000) can calculate the value of the first objective function of each shifted and shrunk first graph by changing the set value and the set ratio.

[0127] According to one embodiment, the battery diagnostic device (1000) can generate a second objective function. Here, the second objective function can satisfy the above-described Equation 3.

[0128] According to one embodiment, the battery diagnostic device (1000) can predict the degradation degree of the target battery based on the X, Y, Z values, the set value, and the set ratio when the first objective function and the second objective function each have a minimum value.

[0129] In the above, all components constituting the embodiments have been described as being combined or operating in combination as one. However, this is not necessarily limited to such embodiments, and within the scope of the purpose, all components may be selectively combined and operated in one or more combinations. Furthermore, terms such as "include," "comprise," or "have" described above, unless specifically stated to the contrary, imply that the corresponding component may be inherent, and therefore should be interpreted to include other components rather than excluding other components.

[0130] The above description is merely an example of the technical idea disclosed in this document, and those skilled in the art to which the embodiments disclosed in this document pertain may make various modifications and variations without departing from the essential characteristics of the embodiments disclosed in this document.

[0131] Accordingly, the embodiments disclosed in this document are intended to illustrate, rather than limit, the technical concepts disclosed in this document, and the scope of the technical concepts disclosed in this document is not limited by these embodiments. The scope of protection of the technical concepts disclosed in this document should be interpreted by the claims below, and all technical concepts within the equivalent scope should be interpreted as being included within the scope of the rights of this document.

[0132] [Explanation of symbols]

[0133] 100: Battery

[0134] 1000: Battery Diagnostic Device

[0135] 1100: Data Acquisition Department

[0136] 1200: Controller

[0137] 1300: Memory

Claims

1. A data acquisition unit that acquires first data related to the voltage and capacity of a reference battery and second data related to the voltage and capacity of a target battery; and A battery diagnostic device comprising a controller that fits the first data and the second data to each other so that the value of an objective function related to the error between the first data and the second data is minimized, and predicts the degree of deterioration of the target battery based on the fitting result.

2. In paragraph 1, The first data and the second data each include a first graph and a second graph representing a relationship between the voltage and capacity of the reference battery and the target battery, respectively, The above controller, A battery diagnostic device that shifts and shrinks the first graph and fits it to the second graph.

3. In paragraph 2, The above shift operation corresponds to an operation of moving the capacity axis of the first graph by a set value, The above shrink operation is a battery diagnostic device corresponding to an operation of reducing the capacity axis of the first graph by a set ratio.

4. In the second paragraph, the controller, Compute a first MAPE (Mean Absolute Percent Error) value between the shifted and shrunk first graph and the second graph, Compute the second MAPE value between the first differential graph (dV1 / dQ) obtained by differentiating the shifted and shrunk first graph with respect to the capacity and the second differential graph (dV2 / dQ) obtained by differentiating the second graph with respect to the capacity, Compute the third MAPE value between the first graph shifted and shrunk in the set section and the second graph in the set section, Compute the fourth MAPE value between the first differential graph (dV1 / dQ) in the above-described set section and the second differential graph (dV2 / dQ) in the above-described set section, A battery diagnostic device that generates a first objective function indicating the degree of error between the shifted and shrunk first graph and the second graph based on the first MAPE value, the second MAPE value, the third MAPE value, and the fourth MAPE value.

5. In paragraph 4, The above first objective function is a battery diagnostic device that satisfies the following equations 1 and 2. [Formula 1] First objective function = (X * 1st MAPE value) + (Y * 2nd MAPE value) + (Z * (3rd MAPE value + 4th MAPE value)) [Formula 2] X = 1, Y = tan(θ), (0<θ< ) Z = 6. In the fourth paragraph, the controller, A battery diagnostic device that calculates the value of the first objective function of each of the first graphs shifted and shrunk by changing the above-mentioned setting value and the above-mentioned setting ratio.

7. In the 6th paragraph, the controller, Generate a second objective function related to the error between each of the shifted and shrunk first graphs and the second graph, The second objective function is a battery diagnostic device that satisfies the following equation 3. [Formula 3] Second objective function = first MAPE value + second MAPE value 8. In the 7th paragraph, the controller, A battery diagnostic device that diagnoses the degree of degradation of the target battery based on the X, Y, and Z values, the set value, and the set ratio when the first objective function and the second objective function each have a minimum value.

9. In the fourth paragraph, the setting section is: A battery diagnostic device corresponding to a section in which the 6th phase transition (Ta6) occurring in the charging direction from the negative pole of the above-mentioned reference battery appears.

10. A step of acquiring first data related to the voltage and capacity of a reference battery and second data related to the voltage and capacity of a target battery; A step of fitting the first data and the second data to each other so that the value of the objective function related to the error between the first data and the second data is minimized; and A method for operating a battery diagnosis device, comprising: a step of predicting the degree of degradation of the target battery based on the fitting result.

11. In paragraph 10, A method of operating a battery diagnostic device, wherein each of the first data and the second data includes a first graph and a second graph representing a relationship between the voltage and capacity of the reference battery and the target battery, respectively.

12. In the 11th paragraph, the fitting step is: A step of performing a shift operation to move the capacity axis of the first graph by a set value; A step of performing a shrink operation to reduce the capacity axis of the first graph by a set ratio; and A method of operating a battery diagnostic device, comprising: generating a first objective function representing the degree of error between the shifted and shrunk first graph and the second graph.

13. In the 12th paragraph, the step of generating the first objective function is: A step of calculating a first MAPE (Mean Absolute Percent Error) value between the shifted and shrunk first graph and the second graph; A step of calculating a second MAPE value between a first differential graph (dV1 / dQ) obtained by differentiating the shifted and shrunk first graph with respect to capacity and a second differential graph obtained by differentiating the second graph with respect to capacity; A step of calculating a third MAPE value between the first graph in the set section and the second differential graph in the set section; A step of calculating a fourth MAPE value between the first differential graph (dV1 / dQ) in the above-described set section and the second differential graph (dV2 / dQ) in the above-described set section; and A method of operating a battery diagnosis device, comprising: generating a first objective function indicating the degree of error between the shifted and shrunk first graph and the second graph based on the first MAPE value, the second MAPE value, the third MAPE value, and the fourth MAPE value.

14. In paragraph 13, The above first objective function is an operating method of a battery diagnosis device that satisfies the following equations 1 and 2. [Formula 1] First objective function = (X * 1st MAPE value) + (Y * 2nd MAPE value) + (Z * (3rd MAPE value + 4th MAPE value)) [Formula 2] X = 1, Y = tan(θ), (0<θ< ) Z = 15. In the 13th paragraph, the step of predicting the degree of degeneration is: A step of calculating the value of the first objective function of each of the first graphs shifted and shrunk by changing the above setting value and the above setting ratio; generating a second objective function related to the error between each of the shifted and shrunk first graphs and the second graph; and A method for operating a battery diagnosis device, comprising: a step of diagnosing the degree of degradation of the target battery based on the X, Y, and Z values, the set value, and the set ratio when the first objective function and the second objective function each have a minimum value;

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