Battery diagnosis apparatus, battery pack, battery system and battery diagnosis method

Abstract

This battery diagnosis apparatus comprises: a data acquisition unit for acquiring capacity-voltage relationship data of a battery cell; and a processor configured to generate a voltage profile and a differential voltage profile of the battery on the basis of the capacity-voltage relationship data. The processor is configured to extract a first profile of interest from the voltage profile on the basis of a feature point of the differential voltage profile. The processor is configured to determine a characteristic slope associated with the first profile of interest. The processor is configured to diagnose a degradation state of the battery cell on the basis of the characteristic slope.

Description

Battery diagnostic device, battery pack, battery system and battery diagnostic method

[0001] The present invention relates to a technique for diagnosing the deterioration state of a battery cell.

[0002] This application claims priority to Korean Patent Application No. 10-2023-0191785, filed December 26, 2023, and Korean Patent Application No. 10-2024-0129279, filed September 24, 2024, all of which are incorporated herein by reference in their entirety.

[0003] Recently, demand for portable electronic devices such as laptops, video cameras, and mobile phones has rapidly increased, and with the development of electric vehicles, energy storage batteries, robots, and satellites in full swing, research into high-performance batteries capable of repeated charging and discharging is actively underway. Meanwhile, along with the widespread use of these high-performance batteries, research is also actively underway to efficiently manage them.

[0004] Currently commercialized batteries include nickel-cadmium batteries, nickel-hydrogen batteries, nickel-zinc batteries, and lithium batteries. Among these, lithium batteries are receiving attention for their advantages of being able to charge and discharge freely, having a very low self-discharge rate, and having a high energy density, as they have almost no memory effect compared to nickel-based batteries.

[0005] The present invention provides a battery diagnostic device and a battery diagnostic method for precisely estimating at least one degradation parameter regarding the degradation state of a battery cell without disassembling the battery cell.

[0006] Other objects and advantages of the present invention can be understood through the following description and will be more clearly understood through the embodiments of the present invention. Furthermore, it will be readily apparent that the objects and advantages of the present invention can be realized by the means and combinations thereof set forth in the claims.

[0007] According to one aspect of the present invention, a battery diagnosis device includes a data acquisition unit that acquires capacity-voltage relationship data of a battery cell, and a processor configured to generate a voltage profile and a differential voltage profile of the battery based on the capacity-voltage relationship data. The processor is configured to extract a first profile of interest from the voltage profile based on feature points of the differential voltage profile. The processor is configured to determine a characteristic slope associated with the first profile of interest. The processor is configured to diagnose a deterioration state of the battery cell based on the characteristic slope.

[0008] The processor may be configured to determine a data point having a minimum differential voltage value within a target capacity range as the feature point of the differential voltage profile.

[0009] The processor may be configured to determine a capacity range of interest based on the capacity value of the feature point of the differential voltage profile. The processor may be configured to determine a portion of the voltage profile corresponding to the capacity range of interest as the first profile of interest.

[0010] The processor may be configured to determine a second profile of interest by normalizing the capacitance-voltage domain of the first profile of interest to match a reference capacitance-voltage domain. The processor may be configured to determine the characteristic slope to be equal to the slope of a tangent line at a feature point of the second profile of interest.

[0011] The processor may be configured to determine a plurality of comparison values ​​individually associated with a plurality of data points of the second interest profile. The processor may determine any one of the plurality of data points, which is associated with a minimum of the plurality of comparison values, as the feature point of the second interest profile.

[0012] The processor may be configured to divide the second interest profile into a first sub-profile and a second sub-profile based on the capacity value of each of the plurality of data points. The processor may be configured to determine the comparison value of each of the plurality of data points to be equal to the sum of a first error value based on an average voltage value of the first sub-profile and a second error value based on an average voltage value of the second sub-profile.

[0013] The processor may be configured to determine a first degradation parameter representing a rate of capacity reduction due to anode degradation of a cell of the battery by using the characteristic slope as an input variable for a linear regression model.

[0014] The processor may be configured to determine a second degradation parameter representing a capacity degradation rate due to available lithium loss of the battery cell based on the total capacity degradation rate of the battery cell and the first degradation parameter.

[0015] The above capacity-voltage relationship data may include a capacity time series and a voltage time series of the battery cell while the battery cell is being charged or discharged.

[0016] A battery pack according to another aspect of the present invention includes the battery diagnostic device.

[0017] A battery system according to another aspect of the present invention includes the battery diagnostic device.

[0018] A battery diagnosis method according to another aspect of the present invention may include a step of obtaining capacity-voltage relationship data of a battery cell, a step of generating a voltage profile and a differential voltage profile of the battery cell based on the capacity-voltage relationship data, a step of extracting a first interest profile from the voltage profile based on feature points of the differential voltage profile, a step of determining a characteristic slope associated with the first interest profile, and a step of diagnosing a deterioration state of the battery cell based on the characteristic slope.

[0019] The step of extracting the first interest profile from the voltage profile may include the step of determining a capacity range of interest based on the capacity value of the feature point of the differential voltage profile, and the step of determining a portion of the voltage profile corresponding to the capacity range of interest as the first interest profile.

[0020] The step of determining a characteristic slope associated with the first profile of interest may further include the step of determining a second profile of interest by normalizing a capacitance-voltage domain of the first profile of interest to match a reference capacitance-voltage domain, and the step of determining the characteristic slope to be the same as a slope of a tangent line at a feature point of the second profile of interest.

[0021] The step of determining a feature slope associated with the first interest profile may further include the step of determining a plurality of comparison values ​​individually associated with a plurality of data points of the second interest profile, and the step of determining any one of the plurality of data points, which is associated with a minimum of the plurality of comparison values, as the feature point of the second interest profile.

[0022] According to another aspect of the present invention, a non-transitory computer-readable recording medium stores a computer program. The computer program, when executed by a processor, includes instructions for causing the processor to perform operations including: obtaining capacity-voltage relationship data of a battery cell; generating a voltage profile and a differential voltage profile of the battery cell based on the capacity-voltage relationship data; extracting a first profile of interest from the voltage profile based on feature points of the differential voltage profile; determining a characteristic slope associated with the first profile of interest; and diagnosing a deterioration state of the battery cell based on the characteristic slope.

[0023] The step of extracting the first interest profile from the voltage profile may include the step of determining a capacity range of interest based on the capacity value of the feature point of the differential voltage profile; and the step of determining a portion of the voltage profile corresponding to the capacity range of interest as the first interest profile.

[0024] The step of determining a characteristic slope associated with the first profile of interest may further include the step of determining a second profile of interest by normalizing a capacitance-voltage domain of the first profile of interest to match a reference capacitance-voltage domain; and the step of determining the characteristic slope to be the same as a slope of a tangent line at a feature point of the second profile of interest.

[0025] The step of determining a characteristic slope associated with the first interest profile may further include the step of determining a plurality of comparison values ​​individually associated with a plurality of data points of the second interest profile; and the step of determining any one of the plurality of data points, which is associated with a minimum of the plurality of comparison values, as the feature point of the second interest profile.

[0026] According to at least one embodiment of the present invention, at least one degradation parameter regarding the deterioration state of a battery cell can be precisely estimated without disassembling the battery cell. For example, the present invention can non-destructively diagnose the deterioration state of a positive electrode of a battery cell.

[0027] In addition, according to at least one of the embodiments of the present invention, by extracting a portion (the 'first profile of interest' described below) in which the voltage characteristics of the positive electrode material overwhelm the voltage characteristics of the negative electrode material from the entire voltage profile (the 'QV profile' described below) of the battery cell for a predetermined voltage range as a target of analysis, the state of positive electrode degradation of the battery cell (e.g., capacity degradation rate due to positive electrode degradation) can be more precisely identified.

[0028] In addition, according to at least one of the embodiments of the present invention, by using the relationship between the total capacity decline rate of the battery cell, the capacity decline rate due to cathode degradation, and the capacity decline rate due to lithium loss, the capacity decline rate due to lithium loss can be easily determined from the total capacity decline rate of the battery cell and the capacity decline rate due to cathode degradation.

[0029] The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims.

[0030] The following drawings attached to this specification illustrate embodiments of the present invention, and together with the detailed description of the invention described below, serve to further understand the technical idea of ​​the present invention, and therefore, the present invention should not be interpreted as being limited to matters described in such drawings.

[0031] FIG. 1 is a drawing exemplarily showing the configuration of a battery system according to the present invention.

[0032] Fig. 2 is a graph showing an example of a QV profile of a battery cell.

[0033] Figure 3 is a graph illustrating an example of a normalized QV profile obtained from the QV profile of Figure 2.

[0034] FIG. 4 is a graph illustrating an example of a Q-dV / dQ profile associated with the normalized QV profile illustrated in FIG. 3.

[0035] Figure 5 is a graph showing an example of the first interest profile.

[0036] Figure 6 is a graph showing an example of a second interest profile.

[0037] FIG. 7 is a drawing referenced to explain a procedure for determining the characteristic slope of the second interest profile illustrated in FIG. 6.

[0038] Figure 8 is a drawing for reference to exemplarily explain the relationship between the bipolar degradation rate and the second interest profile.

[0039] FIGS. 9 to 11 are drawings for reference in explaining the feature slopes of each of the three second interest profiles illustrated in FIG. 8.

[0040] Figure 12 is a drawing that is referenced to exemplarily explain the relationship between the anode degradation rate and the characteristic slope.

[0041] Figure 13 is a flowchart schematically illustrating a battery diagnosis method according to one embodiment of the present invention.

[0042] FIG. 14 is a flowchart exemplifying subroutines that may be included in step S1330 of FIG. 13.

[0043] FIG. 15 is a flowchart exemplifying subroutines that may be included in step S1410 of FIG. 14.

[0044] FIG. 16 is a flowchart exemplifying subroutines that may be included in step S1330 of FIG. 13.

[0045] FIG. 17 is a flowchart exemplifying subroutines that may be included in step S1620 of FIG. 16.

[0046] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the attached drawings. Prior to this, it should be noted that the terms and words used in this specification and claims should not be construed as limited to their conventional or dictionary meanings. Based on the principle that the inventor can appropriately define the concepts of terms to best explain his or her invention, they should be interpreted in a way that conforms to the technical spirit of the present invention.

[0047] Accordingly, the embodiments described in this specification and the configurations illustrated in the drawings are merely the most preferred embodiments of the present invention and do not represent all of the technical ideas of the present invention. Therefore, it should be understood that there may be various equivalents and modified examples that can replace them at the time of filing this application.

[0048] Terms that include ordinal numbers, such as first, second, etc., are used to distinguish one of the various components from the rest, and are not used to limit the components by such terms.

[0049] Throughout the specification, when a part is said to "include" a component, this does not exclude other components, unless otherwise stated, but rather implies that other components may be included. Furthermore, terms such as "unit" used throughout the specification mean a unit that processes at least one function or operation, and may be implemented using hardware, software, or a combination of hardware and software.

[0050] Additionally, throughout the specification, when we say that a part is "connected" to another part, this includes not only cases where it is "directly connected" but also cases where it is "indirectly connected" with other elements in between.

[0051] Lithium-ion batteries experience various deterioration factors that reduce capacity during repeated charge / discharge reactions. This deterioration is caused by a complex interaction of several factors, and the main factors are understood to be loss of lithium inventory (LLI), loss of active material (LAM), and conductivity loss (CL).

[0052] Lithium loss is a degradation mode in which the solid electrolyte interface (SEI) layer gradually thickens on the surface of the negative electrode due to the decomposition reaction of the electrolyte, and the resulting consumption of electrolyte causes the depletion of the lithium ion source. On the other hand, if lithium is repeatedly inserted and removed into the lattice of the negative and positive active materials, the particle structure may deteriorate and the capacity may decrease, and the deterioration caused by this is classified as a deterioration mode due to active material loss. In addition, if the electrode is separated from the current collector or cracks occur between the electrode materials, which reduces the movement of ions and electrons, this can also cause a loss of conductivity, and the deterioration caused by this is classified as deterioration due to conductivity loss (CL).

[0053] Various techniques exist for monitoring battery cell degradation. For example, Differential Voltage Analysis (DVA) relies on time-series data of at least one externally observable battery parameter (e.g., voltage, current, capacity).

[0054] In DVA, peaks appearing in the differential voltage curve (which can be referred to as the "Q-dV / dQ profile") are considered as a key factor, and some types of battery cells have a voltage plateau characteristic in which the voltage change rate remains nearly zero during charging or discharging. Since the differential voltage is also close to zero in the capacity range where the voltage plateau characteristic is manifested, it is difficult to detect peaks from the Q-dV / dQ profile.

[0055] The present invention provides a method for accurately and easily diagnosing the deterioration status of a battery cell even without extracting peak information indicating the deterioration status from a Q-dV / dQ profile.

[0056] FIG. 1 is a drawing exemplarily showing the configuration of a battery system according to the present invention.

[0057] Referring to FIG. 1, a battery system (1) according to one embodiment of the present invention includes a system controller (2), a battery pack (10), an inverter (30), and an electric motor (40). The battery pack (10) includes a battery (11) and a relay (20), and the battery pack (10) may further include a battery diagnostic device (100). The battery diagnostic device (100) includes a sensing unit (110), a communication circuit (130), and a processor (150) which is a control circuit. The sensing unit (110) may further include a voltage sensor (111) and a current sensor (112). The processor (150) may further include a memory (151).

[0058] The charge / discharge terminals (P+, P-) of the battery pack (10) can be electrically connected to the charger (3) via a charging cable or the like. The charger (3) may be included in the battery system (1) or may be separately provided in the charging station. The system controller (2) (e.g., ECU: Electronic Control Unit) is configured to transmit a key-on signal to the battery diagnosis device (100) in response to a start button (not shown) provided in the battery system (1) being turned to the ON position by a user. The system controller (2) is configured to transmit a key-off signal to the battery diagnosis device (100) of the battery pack (10) in response to a start button being turned to the OFF position by a user. The charger (3) can communicate with the system controller (2) and supply charging power selected from among constant power, constant current, and constant voltage through the charge / discharge terminals (P+, P-) of the battery pack (10).

[0059] The battery (11) includes at least one battery cell (BC). In Fig. 1, the battery (11) includes a plurality of battery cells (BC1 to BC) connected in series. N , N is a natural number greater than or equal to 2) is illustrated as an example. Multiple battery cells (BC1 to BC N ) may be provided to have the same electrochemical specifications. Hereinafter, a plurality of battery cells (BC1 to BC N ), the symbol 'BC' is assigned to the battery cell. The charger (3) can execute the charge / discharge cycle necessary to diagnose the deterioration state of the battery cell (BC) through collaboration with an inverter (30) having a discharge function.

[0060] A battery cell (BC) is a target of diagnosis by a battery diagnosis device (100). The type of the battery cell (BC) is not particularly limited as long as it is an electrochemical device capable of repeated charging and discharging. According to one embodiment, the battery cell (BC) may be a lithium iron phosphate battery cell having a voltage plateau characteristic. The voltage plateau characteristic refers to a characteristic in which the voltage change rate is maintained below a predetermined threshold over at least one capacity section (or SOC section). The lithium iron phosphate battery cell may also be referred to as a 'LiFePO4 battery cell', an 'LFP battery cell', or an 'LFP cell'. Hereinafter, it will be assumed that the battery cell (BC) is an LFP battery cell including LFP and graphite as positive and negative electrode materials, respectively.

[0061] The relay (20) is electrically connected in series to the battery (11) via a power path connecting the battery (11) and the inverter (30). In Fig. 1, the relay (20) is illustrated as being connected between the positive terminal of the battery (11) and the charge / discharge terminal (P+). The relay (20) is turned on and off in response to a switching signal from the battery diagnostic device (100). The relay (20) may be a mechanical contactor that is turned on and off by the magnetic force of a coil, or a semiconductor switch such as a MOSFET (Metal Oxide Semiconductor Field Effect transistor).

[0062] An inverter (30) is provided to convert direct current from a battery (11) included in a battery pack (10) into alternating current in response to a command from a battery diagnostic device (100) or a system controller (2). An electric motor (40) is driven using alternating current power from the inverter (30). For example, a three-phase alternating current motor can be used as the electric motor (40). Components within the battery system (1) that receive discharge power from the battery (11), including the inverter (30) and the electric motor (40), can be collectively referred to as an electric load.

[0063] A voltage sensor (111) is connected to the positive and negative terminals of each battery cell (BC), detects a voltage across both ends of the battery cell (BC) (which may be referred to as a 'full cell voltage'), and is configured to generate a voltage signal representing a detected value of the detected voltage. The voltage sensor (111) may be implemented as one or a combination of two or more of known voltage detection elements, such as a voltage measurement IC.

[0064] The current sensor (112) is connected in series to the battery (11) through a current path between the battery (11) and the inverter (30). The current sensor (112) is configured to detect a current flowing through the battery (11) (which may be referred to as a 'charge / discharge current') and generate a current signal representing a detection value of the detected current. A plurality of battery cells (BC1 to BC) N ) are connected in series, the current flowing in the battery (11) is the same as the current flowing in the battery cell (BC). The current sensor (112) can be implemented as one or a combination of two or more of known current detection elements such as a shunt resistor, a Hall effect device, etc.

[0065] The communication circuit (130) is configured to support wired communication or wireless communication between the processor (150) and at least one of the system controller (2), the sensing unit (110), and an external device (not shown). The wired communication may be, for example, CAN (controller area network) communication, and the wireless communication may be, for example, Zigbee or Bluetooth communication. Of course, as long as it supports wired or wireless communication between the processor (150) and the system controller (2), the type of communication protocol is not particularly limited. The communication circuit (130) may include an output device (e.g., a display, a speaker) that provides information received from the processor (150) and / or the system controller (2) in a form recognizable to the user. The 'data acquisition unit' of the present invention may include at least one of the sensing unit (110) and the communication circuit (130).

[0066] The processor (150) may be operably coupled to at least one of a relay (20), a sensing unit (110), and a communication circuit (130) as a control circuit. The fact that two components are operably coupled means that the two components are directly or indirectly connected so as to be capable of transmitting and receiving signals in one direction or both directions.

[0067] The processor (150) may be referred to as a 'battery controller' as a control circuit, and may be implemented in hardware using at least one of ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), microprocessors, and other electrical units for performing functions.

[0068] The memory (151) may include at least one type of storage medium, for example, a flash memory type, a hard disk type, an SSD (Solid State Disk type), an SDD (Silicon Disk Drive type), a multimedia card micro type, a RAM (random access memory), a SRAM (static random access memory), a ROM (read-only memory), an EEPROM (electrically erasable programmable read-only memory), and a PROM (programmable read-only memory). The memory (151) may store data and a program required for an operation by the processor (150). The memory (151) may store data representing a result of an operation by the processor (150). The memory (151) may store data sets and software used to diagnose a deterioration state of a battery cell (BC). The memory (151) may be integrated into the processor (150).

[0069] The processor (150) can collect a voltage signal from the sensing unit (110) and a current signal from the current sensor (112). The term 'detection signal' used in this specification may refer only to the voltage signal or may be a term that refers to both the voltage signal and the current signal. That is, the processor (150) can convert and record each analog signal collected from the sensing unit (110) into a digital value using an ADC (Analog to Digital Converter) provided therein. Alternatively, each of the voltage sensor (111) and the current sensor (112) may include an ADC therein and transmit a digital value generated by the ADC to the processor (150).

[0070] When the relay (20) is turned ON during operation of the electric load inverter (30), motor (40) and / or charger (3), the battery (11) enters the charging mode or the discharging mode. When the relay (20) is turned OFF while the battery (11) is being used in the charging mode or the discharging mode, the battery (11) is switched to the idle mode.

[0071] The processor (150) can turn on the relay (20) in response to a key-on signal. The processor (150) can turn off the relay (20) in response to a key-off signal. The key-on signal is a signal requesting a transition from idle to charging or discharging. The key-off signal is a signal requesting a transition from charging or discharging to idle. Alternatively, the on / off control of the relay (20) may be performed by the system controller (2) instead of the processor (150).

[0072] In this specification, a time series of a parameter represents a temporal change history of the parameter over an arbitrary or specific period. Furthermore, a profile (or curve) representing a correspondence between two parameters obtained at the same timing within the same period may be a mapped time series data of two parameters that can be expressed in the form of a two-dimensional graph, or a polynomial equation obtained by applying a predetermined curve fitting logic to a pair of mapped time series data. Here, the degree of the highest term of the polynomial equation may be predetermined.

[0073] FIG. 2 is a graph illustrating an example of a QV profile of a battery cell, FIG. 3 is a graph illustrating an example of a normalized QV profile obtained from the QV profile of FIG. 2, and FIG. 4 is a graph illustrating an example of a Q-dV / dQ profile, which is a differential profile for capacity related to the normalized QV profile illustrated in FIG. 3.

[0074] In the graph illustrated in Fig. 2, the vertical axis represents the voltage (V) of the battery cell (BC), and the horizontal axis represents the capacity (unit: mAh). Reference numeral 200 denotes a QV profile based on capacity-voltage relationship data of the battery cell (BC).

[0075] The QV profile (200) may also be referred to as a 'capacity-voltage profile', a 'capacity-voltage curve', or a 'voltage profile'. As described above, the battery cell (BC) has a voltage flat characteristic, and it can be confirmed that the voltage remains almost constant over a capacity range of approximately 20 to 40 mAh in the QV profile (200).

[0076] The QV profile (200) may be based on capacity-voltage relationship data acquired by a data acquisition unit during at least one of a charge cycle and a discharge cycle for a battery cell (BC). In performing either the charge cycle or the discharge cycle, constant power or constant current may be utilized. For convenience of explanation, the QV profile (200) is assumed to be associated with a charge cycle. The following description of the charge cycle may also be common to the discharge cycle, except that discharge current, instead of charge current, flows through the battery cell (BC).

[0077] In a constant-power charging cycle, as the voltage of the battery cell (BC) increases, the charging current gradually decreases. Therefore, in addition to voltage time-series data, which represents the temporal history of changes in the voltage of the battery cell (BC), current time-series data, which represents the temporal history of changes in the current flowing through the battery cell (BC), are also essential.

[0078] In a constant current charge cycle, a charging current having a predetermined current rate (e.g., 0.1 C, 0.5 C, 1.5 C, or 2.0 C) is controlled to flow through the battery cell (BC). Therefore, it is acceptable to assume that the capacity value at a specific point in time is equal to the product of the elapsed time from the start point of the constant current charge cycle to the specific point in time (i.e., the time difference between the start point and the specific point in time) times the current value corresponding to the current rate.

[0079] Of course, even during a constant current charging cycle, the actual charging current may temporarily be greater or less than the originally intended charging current. Therefore, the processor (150) may generate capacity time-series data by repeatedly measuring the current flowing through the battery cell (BC) using a current sensor (112), accumulating, and recording the resulting current measurements.

[0080] A charge cycle may continue until the voltage of the battery cell (BC) changes at least over a predetermined voltage range. The QV profile (200) represents the correspondence between the capacity and voltage of the battery cell (BC), obtained over a period of time during the charge cycle until the voltage of the battery cell (BC) reaches a lower voltage limit of the predetermined voltage range and an upper voltage limit.

[0081] The QV profile (200) illustrated in FIG. 2 illustrates that the voltage of the battery cell (BC) increases from 2.6 V (a predetermined lower limit voltage) to 3.6 V (a predetermined upper limit voltage) while the capacity of the battery cell (BC) increases from 0 mAh to 52 mAh. Here, the range from 0 mAh to 52 mAh can be referred to as the entire capacity range of the QV profile (200), which is a capacity range corresponding to a predetermined voltage range.

[0082] In this regard, the entire capacity range corresponding to a given voltage range may vary depending on the deterioration status of the battery cell (BC), rather than being fixed. In addition, even if the deterioration status of two different battery cells (BC) is the same, the capacity ranges of the two battery cells may be different from each other due to process deviations during manufacturing, etc. Therefore, in order to improve the ease and appropriateness of data processing required to diagnose the deterioration status of the battery cell (BC) and to secure the accuracy of the diagnosis result, it is necessary to normalize the QV profile (200) by applying a normalization procedure to the entire capacity range of the QV profile (200).

[0083] In the graph illustrated in Fig. 3, the vertical axis represents the voltage of the battery cell (BC), similar to Fig. 2, while the horizontal axis represents the normalized capacity (unit: %). Normalized capacity may be a term equivalent to the conventional SOC (State Of Charge). Reference numeral 300 is an example of another QV profile resulting from applying a normalization procedure to the QV profile (200).

[0084] When the capacity-voltage relationship according to the QV profile (200) has a mathematical relationship according to the following Equation 1, the normalized capacity and voltage according to the QV profile (300) have a mathematical relationship according to the following Equation 2. According to one embodiment, the QV profile (300) can be used as the 'voltage profile' of the present invention.

[0085] <Formula 1>

[0086]

[0087] <Formula 2>

[0088]

[0089] In formula 1, Q B is an arbitrary capacity value within the entire capacity range, V B is Q in QV profile (200)B Indicates the voltage value of the corresponding data point. In Equation 2, Q B_normal Silver Q B The normalized capacity value corresponding to Q total represents the size of the entire capacity range (i.e., the upper limit capacity value of the entire capacity range).

[0090] For reference, Figure 3 shows the results of each data point in the full capacity range of Figure 2 normalized to a percentage (ranging from 0 to 100%), but this should be understood as an example only. For example, the data points could be normalized to a different range, such as 0 to 1, instead of the 0 to 100% range.

[0091] Referring to the graph illustrated in FIG. 4, reference numeral 400 is an example of a Q-dV / dQ profile. The Q-dV / dQ profile (400) may also be referred to as a 'capacitance-differential voltage profile', a 'capacitance-differential voltage curve', or a 'differential voltage profile'.

[0092] The processor (150) can generate a Q-dV / dQ profile (400) by differentiating the voltage of the QV profile (300) with respect to the capacity. For example, the processor (150) can determine a differential voltage dV / dQ, which is a ratio of a change amount dV in voltage V to a change amount dQ in normalized capacity Q[%], based on the QV profile (300), and determine a Q-dV / dQ profile (400) as relational data indicating a correspondence between the normalized capacity Q[%] and the differential voltage dV / dQ. The Q-dV / dQ profile (400) can be recorded in the memory (151) of the processor (150).

[0093] The processor (150) can determine a feature point from the Q-dV / dQ profile (400). The feature point of the Q-dV / dQ profile (400) may be a data point on the Q-dV / dQ profile (400) located within a predetermined target capacity range. In Fig. 4, the target capacity range is exemplified as being 50 to 99%. The target capacity range may partially overlap with the capacity range in which the voltage flatness characteristic of the battery cell (BC) is exhibited.

[0094] For example, the processor (150) may, from the Q-dV / dQ profile (400), determine a local maximum point (P) having a maximum differential voltage value within the target capacity range. MAX ) can be identified. Next, the processor (150) identifies the maximum point (P) from the Q-dV / dQ profile (400). MAX ) are data points located on the high-capacity side, and are called feature points (P cut-off ) can be determined. The characteristic point of the Q-dV / dQ profile (400) can also be called a ‘cut-off reference point’.

[0095] If there are two or more local minima within the target capacity range, the largest capacity value (Q) among the two or more local minima cut-off ) is a feature point (P cut-off ) can be set.

[0096] Feature point (P cut-off ) may be the last minimum point on the Q-dV / dQ profile (400) due to the voltage characteristics of the negative electrode material of the battery cell (BC). That is, the characteristic point (P cut-off ) The high capacity side portion may be a capacity range in which the voltage characteristics of the positive electrode material of the battery cell (BC) overwhelm the voltage characteristics of the negative electrode material. Therefore, if only the high capacity side portion of the entire QV profile (300) is specified as the analysis target, it will be easily understood by those skilled in the art that the deterioration parameters due to the deterioration of the positive electrode of the battery cell (BC) can be precisely estimated.

[0097] In the present invention, the voltage characteristics of the negative electrode material are characterized by a characteristic point (P) compared to other parts of the QV profile (300). cut-off ) was relatively very small in the high-capacity side portion. Therefore, it was also recognized through the present invention that, through analysis of the high-capacity side portion among the entire QV profile (300), parameters related to cathode deterioration among various deterioration parameters related to the deterioration state of the battery cell (BC) can be accurately diagnosed.

[0098] In this specification, feature points (P cut-off ) capacity value (Q) cut-off ), when the QV profile (300) is divided into a low-capacity side part and a high-capacity side part as in Fig. 3, the high-capacity side part is referred to as the 'first interest profile' (see symbol 500 in Fig. 5).

[0099] FIG. 5 is a graph showing an example of a first interest profile, FIG. 6 is a graph showing an example of a second interest profile, and FIG. 7 is a drawing referenced in explaining a procedure for determining a characteristic slope of the second interest profile shown in FIG. 6.

[0100] Referring to FIG. 5, the first interest profile (500) is characterized by a feature point (P cut-off ) is an enlarged view of a portion of the QV profile (300) corresponding to the capacity range of interest (e.g., 92-99%), with the lower and upper limits being the capacity value (e.g., 92%) and the upper limit capacity value of the target capacity range (e.g., 99%), respectively.

[0101] In this regard, even if the positive electrode degradation state of the battery cell (BC) is the same, if other degradation factors, such as the negative electrode degradation state of the battery cell (BC) or the available lithium amount, are different, the starting point, ending point, and / or shape (e.g., curvature) of the first interest profile (500) may be different. Therefore, similar to the normalization procedure applied to the QV profile (200), it is necessary to apply a normalization procedure to the first interest profile (500).

[0102] Referring to FIG. 6, the second interest profile (600) can be confirmed as a result of applying the normalization procedure to the first interest profile (500).

[0103] The processor (150) can determine the second profile of interest (600) by normalizing the capacity-voltage domain of the first profile of interest (500) to match a predetermined reference capacity-voltage domain. The reference capacity-voltage domain can be a rectangular area defined by a reference capacity range and a reference voltage range.

[0104] For example, the processor (150) may start at the starting point (P) of the first interest profile (500). S ) and endpoint (P E ) are each a predetermined first reference point (P R1 ) and the second reference point (P R2 ), a normalization procedure for the first interest profile (500) can be applied to generate a second interest profile (600). The reference capacity range is the first reference point (P R1 ) and the second reference point (P R2 ) is the capacity range, and the reference voltage range is the first reference point (P R1 ) and the second reference point (P R2 ) can be a voltage range between .

[0105] Starting point (P) of the first interest profile (500) S ) may be a data point having the minimum capacity value of the first interest profile (500). The end point (P) of the first interest profile (500)E ) may be a data point having the maximum capacity value of the first interest profile (500).

[0106] First reference point (P R1 ) is the capacity value of the starting point (P S ) may be less than the capacity value of the first reference point (P R1 ) voltage value is the starting point (P S ) may be less than the voltage value of the second reference point (P R2 ) is the capacity value of the end point (P E ) can be greater than the capacity value of the second reference point (P R2 ) is the voltage value at the end point (P E ) may be greater than the voltage value.

[0107] The processor (150) has a starting point (P S ) is the first reference point (P R1 ) to match the end point (P E ) is the second reference point (P R2 ), a first operation for shifting the first interest profile (500) along the horizontal axis (capacity axis) and the vertical axis (voltage axis) and a second operation for scaling the first interest profile (500) along the capacity axis and the voltage axis can be performed. The first operation can include at least one of horizontal movement (leftward movement or rightward movement with respect to the horizontal axis) and vertical movement (upward movement or downward movement with respect to the vertical axis). The second operation can include at least one of reduction and enlargement with respect to at least one of the horizontal axis and the vertical axis.

[0108] Starting point (P S ), endpoint (P E ), first reference point (P R1 ) and the second reference point (P R2 ) are the two-dimensional coordinates of (Q S , V S ), (Q E , V E ), (Q R1 , V R1 ), (Q R2 , VR2 ) Let's say. In Fig. 6, the first reference point (P R1 ) 2D coordinates (Q R1 , V R1 ) is (90%, 0V), and the second reference point (P R2 ) 2D coordinates (Q R2 , V R2 ) is exemplified as (100%, 1V).

[0109] The processor (150) Q the first interest profile (500) to the low capacity side. S - Q R1 Shift as much as V to the low voltage side S - V R1 can be shifted by as much. Accordingly, the starting point (P S ) is the first reference point (P R1 ), so now the endpoint (P E ) as the second reference point (P R2 ) needs to be matched. Therefore, the processor (150) processes the first interest profile (500) along the capacity axis (Q R2 - Q R1 ) / (Q E - Q S ) and scaled along the voltage axis (V R2 - V R1 ) / (V E - V S ) can be scaled as a ratio. Accordingly, the endpoint (P E ) is the second reference point (P R2 ), thereby completing the normalization procedure for determining the second interest profile (600) from the first interest profile (500). That is, the second interest profile (600) may be the result of the first interest profile (500) being projected onto the reference capacity-voltage domain.

[0110] When the capacity and voltage according to the first interest profile (500) have a mathematical relationship according to the following equation 3, the capacity and voltage according to the second interest profile (600) have a mathematical relationship according to the following equation 4.

[0111] <Formula 3>

[0112]

[0113] <Formula 4>

[0114]

[0115] In formula 3, Q B_normal is the capacity value of any data point on the first interest profile (500), V B is Q in the first interest profile (500) B_normal Indicates the voltage value mapped to .

[0116] In formula 4, Q B_normal_2 Silver Q B_normal The capacity value of the second interest profile (600) corresponding to FIG. 6 is a diagram showing the result of each data point of the entire voltage range of FIG. 5 being normalized to a range of 0 to 1 V, but this should be understood as an example only.

[0117] The processor (150) can determine the characteristic slope of the second interest profile (600). Since the second interest profile (600) is based on the first interest profile (500), the characteristic slope can be said to be related to the first interest profile (500).

[0118] The second interest profile (600) can be said to be a set of multiple data points, each representing a normalized capacity and a normalized voltage.

[0119] Hereinafter, it is assumed that the second interest profile (600) includes the first to nth data points. Here, n is a natural number greater than or equal to 3. The difference in capacity between two adjacent data points among the first to nth data points may be constant at a predetermined value. Alternatively, the difference in voltage between two adjacent data points among the first to nth data points may be constant at a predetermined value.

[0120] The first data point is the first reference point (P R1 ) may be the same as the nth data point, and the second reference point (P R2 ) may be the same as the first reference point (P R1 ) has a capacity value that is greater than the capacity value of the second reference point (P R2 ) may have a capacity value that is smaller by a predetermined value than the capacity value of the first data point. Alternatively, the first data point may have a capacity value that is smaller than the capacity value of the first reference point (P R1 ) has a voltage value greater than the voltage value of the second reference point (P R2 ) can have a voltage value that is lower by a predetermined value than the voltage value.

[0121] In this specification, it is assumed that the symbol i used as an ordinal number is a natural number less than or equal to n.

[0122] Referring to FIG. 7, the processor (150) selects the i-th data point (P) among the first to n-th data points. C_i ) can be divided into a first sub-profile and a second sub-profile based on the capacity value of the i-th data point (P C_i ) may be a part of a second interest profile (600) corresponding to a capacity range below the capacity value of the i data point (P C_i ) may be a part of a second interest profile (600) corresponding to a capacity range greater than or equal to the capacity value of the first sub-profile. For example, when n = 1000 and i = 700, the first sub-profile may include the first to 700th data points, and the second sub-profile may include the 700th to 1000th data points.

[0123] Next, the processor (150) can determine the average voltage value of the first sub-profile and the average voltage value of the second sub-profile. The average voltage value of the first sub-profile may be the average of the voltage values ​​of the data points included in the first sub-profile. The average voltage value of the second sub-profile may be the average of the voltage values ​​of the data points included in the second sub-profile.

[0124] Next, the processor (150) calculates the i-th data point (P) equal to the sum of the first error value based on the average voltage value of the first sub-profile and the second error value based on the average voltage value of the second sub-profile. C_i ) can determine the i comparison value. In Fig. 7, the voltage line (L 1_i ) represents the average voltage of the first sub-profile, and the voltage line (L 1_2 ) represents the average voltage of the second sub-profile.

[0125] The processor (150) may determine (i) a first error value of the first sub-profile based on the average voltage of the first sub-profile and (ii) a second error value of the second sub-profile based on the average voltage of the second sub-profile. Each error value may be determined using at least one of known algorithms that quantify the level of difference between two data sets, such as, for example, MSE (Mean Square Error), RMSE (Root Mean Square Error), etc. As an example, the first error value may be a value obtained by summing the squares of the voltage differences of all data points of the first sub-profile with respect to the average voltage value of the first sub-profile. As another example, the second error value may be a value obtained by summing the squares of the voltage differences of all data points of the second sub-profile with respect to the average voltage value of the second sub-profile.

[0126] Next, the processor (150) can determine the i-th comparison value, which is equal to the sum of the first error value and the second error value. When the above-described process is repeated n times by individually setting natural numbers less than or equal to n as i, a plurality of comparison values ​​individually associated with a plurality of data points can be determined.

[0127] The processor (150) can determine any one of the plurality of data points associated with the minimum of the plurality of comparison values ​​as a feature point of the second interest profile (600).

[0128] Next, the processor (150) can determine the characteristic slope equal to the slope of the tangent line at the feature point of the second interest profile (600). In Fig. 7, the symbol T C_i is the i data point (P C_i ) is an example of a tangent line at the i data point (P C_i ) is identified as a feature point of the second interest profile (600), then the tangent (T C_i ) can be determined by the characteristic slope.

[0129] The processor (150) can determine a first degradation parameter by using the characteristic slope as an input variable in a linear regression model. The first degradation parameter can represent a capacity reduction rate due to the deterioration of the positive electrode of the battery cell. 2

[0130] The rate of capacity reduction due to positive electrode degradation can be referred to as the "positive electrode degradation degree" or "positive electrode degradation-derived capacity reduction ratio." The linear regression model will be described in more detail below with reference to FIGS. 8 to 12.

[0131] According to one embodiment of the present invention, by sequentially performing a first process of forcibly deteriorating a plurality of battery cells prepared as experimental subjects to have different degrees of anode deterioration, a second process of determining a characteristic slope associated with each forcibly deteriorated battery cell, a third process of disassembling each forcibly deteriorated battery cell to produce anode half-cell, and a fourth process of measuring and recording the available capacity (e.g., maximum capacity) of each anode half-cell, relational data that can be generated using a linear regression model are secured.

[0132] FIG. 8 is a drawing for reference to exemplarily explain the relationship between the bipolar degradation degree and the second interest profile, FIGS. 9 to 11 are drawings for reference to explain the characteristic slope of each of the three second interest profiles illustrated in FIG. 8, and FIG. 12 is a drawing for reference to exemplarily explain the relationship between the bipolar degradation degree and the characteristic slope.

[0133] Figure 8 exemplarily illustrates how the shape of the second interest profile changes as the degree of anode degradation increases. The degree of anode degradation may refer to the rate of capacity reduction due to anode degradation.

[0134] Referring to FIG. 8, curve (810) illustrates a second profile of interest when the anode is in a new state with no deterioration at all, curve (820) illustrates a second profile of interest when the anode deterioration degree is 1.75%, and curve (830) illustrates a second profile of interest when the anode deterioration degree is 9.40%.

[0135] That is, as the polarization deterioration increases, the second interest profile gradually decreases relative to the first reference point (P R1 ) and the second reference point (P R2 ) is changed to a shape close to a straight line connecting the two, and accordingly, it can be confirmed from Figures 9 to 11 that the characteristic points of the second interest profile are changed to the low-capacity side.

[0136] Referring to FIGS. 9 to 11, each of the curves (810, 820, 830) is illustrated as a polynomial equation with a highest degree of 3. In addition, the characteristic slopes of the curves (810, 820, 830) are 4.35, 3.45, and 2.05, respectively, and it can be seen from this that as the degree of anode degradation increases, the characteristic slopes decrease.

[0137] Referring to Fig. 12, a linear regression model (1200) is prepared in advance as relational data representing the correspondence between the characteristic slope and the bipolar degradation state. In the graph of Fig. 12, data point (1210) is related to curve (810) of Fig. 8, data point (1220) is related to curve (820) of Fig. 8, and data point (1230) is related to curve (830) of Fig. 8. Although not all are shown, in the present invention, additional data points other than data points (1210, 1220, 1230) were acquired as a result of the above-described experiment and then utilized to obtain a linear regression model (1200) through linear regression analysis. The linear regression model (1200) may be stored in advance in the memory (151) of the processor (150). Equation 5 below is an example of a linear regression model (1200).

[0138] <Formula 5>

[0139]

[0140] In Equation 5, A and B are two coefficients representing the slope and y-axis intercept of the straight line according to the linear regression model (1200), respectively. x represents the characteristic slope as an input variable (see FIG. 7), and y represents the degree of cathode degradation as an output variable. A and B may vary depending on the type and composition ratio of the cathode and anode materials, respectively. Therefore, A and B can also be appropriately tuned according to the type and manufacturing information of the battery cell (BC) provided as the target of diagnosis (e.g., the type and composition ratio of the cathode and anode materials, respectively). For example, A and B of the linear regression model (1200) illustrated in FIG. 12 are -4.29 and 17.66, respectively.

[0141] The processor (150) can input the characteristic slope determined based on the capacity-voltage relationship data of a battery cell (BC) having an as-yet-unknown positive electrode deterioration state as an input variable x into a linear regression model (1200) to obtain the positive electrode deterioration degree as an output variable y. For example, when the characteristic slope of the battery cell (BC) is determined to be 3.45, the positive electrode deterioration degree (y = -4.29 × 3.45 + 17.66) of the battery cell (BC) can be determined to be approximately 2.86% by the linear regression model (1200). In FIG. 12, reference numeral 1250 represents a data point corresponding to the characteristic slope = 3.45 and the positive electrode deterioration degree = 2.86%.

[0142] Table 1 below summarizes the relationship between the number of charge cycles (cycle count), total capacity decline rate, characteristic slope, first degradation parameter (capacity decline rate due to anode degradation), and second degradation parameter (capacity decline rate due to available lithium loss) described above. Here, available lithium may refer to lithium ions that can participate in the charge / discharge reaction of the battery cell (BC).

[0143] Table 1

[0144]

[0145] According to Table 1, as the number of cycles increases, the characteristic slope decreases, while the total capacity degradation rate, the first degradation parameter, and the second degradation parameter each increase. Note that the number of cycles may be incremented by 1 each time a charge or discharge cycle is completed.

[0146] The total capacity degradation rate can be the ratio of the reduction in the buffer capacity due to deterioration to the design capacity (e.g., the buffer capacity when the battery cell (BC) was new). For example, let the design capacity = P, the current buffer capacity = U, and the reduction in buffer capacity = W. Then, W = PU, and the total capacity degradation rate = (W / P) × 100%.

[0147] In the present invention, it has been recognized that the sum of the first and second degradation parameters is substantially equal to the total capacity degradation rate. Accordingly, the processor (150) can determine the second degradation parameter, which represents the capacity degradation rate due to the loss of available lithium, by subtracting the capacity degradation rate indicated by the first degradation parameter from the total capacity degradation rate.

[0148] Fig. 13 is a flowchart schematically illustrating a battery diagnosis method according to one embodiment of the present invention. The method according to Fig. 13 includes steps S1310 to S1360. The method according to Fig. 13 may further include step S1370.

[0149] In step S1310, the data acquisition unit acquires capacity-voltage relationship data of a battery cell (BC). In this specification, acquisition of any data or information may mean generation through software processing, input by a user or input device, and / or reception through a communication channel.

[0150] For example, when the data acquisition unit includes a sensing unit (110), the data acquisition unit can generate a voltage time series and a capacity time series based on a detection signal generated by the sensing unit (110). The capacity-voltage relationship data can include a voltage time series and a capacity time series.

[0151] As another example, if the data acquisition unit includes a communication circuit (130), the data acquisition unit can receive capacity-voltage relationship data from an external device using the communication circuit (130).

[0152] In step S1320, the processor (150) generates a voltage profile (300) and a differential voltage profile (400) of the battery cell (BC) based on the capacity-voltage relationship data acquired in step S1310.

[0153] In step S1330, the processor (150) determines the feature point (P) of the differential voltage profile (400). cut-off ), a first interest profile (500) is extracted from the voltage profile (300).

[0154] In step S1340, the processor (150) determines the characteristic slope of the first interest profile (500).

[0155] In step S1350, the degradation state of the battery cell (BC) is diagnosed based on the characteristic slope of the first interest profile (500). In step S1350, the processor (150) can determine a first degradation parameter y associated with the degradation state of the battery cell (BC) by using the characteristic slope determined in step S1340 as an input variable x for the linear regression model (1200) (see Equation 5). The linear regression model (1200) may be prepared in advance as relationship data indicating a correspondence between the characteristic slope of the battery cell (BC) and the cathode degradation state. Accordingly, a first degradation parameter indicating a capacity decline rate (%) due to cathode degradation is determined, and a second degradation parameter, which is a capacity decline rate (%) due to available lithium loss, can be additionally determined by subtracting the first degradation parameter from the total capacity decline rate (%).

[0156] In step S1360, the processor (150) may determine at least one protection parameter for the battery cell (BC) based on the results of the diagnosis performed in step S1350. For example, at least one of a maximum charging voltage, a minimum discharging voltage, a maximum allowable current, and a maximum allowable power may be determined as the protection parameter.

[0157] In step S1370, the processor (150) controls charging and discharging of the battery cell based on at least one protection parameter. For example, the processor (150) may turn off the relay (20) or transmit an operation stop request to the inverter (30) and / or the charger (3) when (i) the voltage of the battery cell (BC) is higher than the maximum charging voltage or lower than the minimum discharging voltage, (ii) the current flowing through the battery cell (BC) is higher than the maximum allowable current, and / or (iii) the charging power or discharging power of the battery cell (BC) is higher than the maximum allowable power.

[0158] In the method of FIG. 13, at least one of steps S1360 and S1370 can be omitted.

[0159] FIG. 14 is a flowchart exemplifying subroutines that may be included in step S1330 of FIG. 13.

[0160] Referring to FIG. 14, in step S1410, the processor (150) derives a feature point (P) from a differential voltage profile (400). cut-off ) is decided.

[0161] In step S1420, the processor (150) detects a feature point (P cut-off ) capacity value (Q) cut-off ), based on the capacity range of interest (P S ~P E ) is determined.

[0162] In step S1430, the processor (150) determines the capacity range of interest (P S ~P E ) is determined as a first interest profile (500) of a portion of the voltage profile (300) corresponding to the feature point (P cut-off ) capacity value (Q) cut-off ) may be the high-capacity side portion when dividing the voltage profile (300) based on the reference voltage.

[0163] FIG. 15 is a flowchart exemplifying subroutines that may be included in step S1410 of FIG. 14.

[0164] Referring to FIG. 15, in step S1510, the processor (150) determines from the differential voltage profile (400) a local maximum point (P) having the maximum differential voltage within the target capacity range. MAX ) is detected.

[0165] In step S1520, the processor (150) determines the maximum point (P) from the differential voltage profile (400). MAX ) is located on the high-capacity side, and the characteristic point (P) of the differential voltage profile (400) cut-off ) is decided.

[0166] FIG. 16 is a flowchart exemplifying subroutines that may be included in step S1330 of FIG. 13.

[0167] Referring to FIG. 16, in step S1610, the processor (150) normalizes the capacity-voltage domain of the first interest profile (500) to determine the second interest profile (600). The second interest profile (600) is determined by the starting point (P) of the first interest profile (500). S ) and the end point (P E ) are each a predetermined first reference point (P R1 ) and the second reference point (P R2 ), the shifting operation and the scaling operation for the first interest profile (500) may be performed as a result.

[0168] In step S1620, the processor (150) determines the feature points of the second interest profile (600). The feature points of the second interest profile (600) (e.g., T C_i ) is any one of the multiple data points of the second interest profile (600).

[0169] In step S1630, the processor (150) determines a characteristic slope equal to the slope of the tangent line at the feature point of the second interest profile (600).

[0170] FIG. 17 is a flowchart exemplifying subroutines that may be included in step S1620 of FIG. 16.

[0171] Referring to FIG. 17, in step S1710, the processor (150) selects the i-th data point (P) among the first to n-th data points of the second interest profile (600). C_i ) is divided into a first sub-profile and a second sub-profile based on the capacity value (see Fig. 7).

[0172] In step S1720, the processor (150) determines the average voltage value of the first sub-profile and the average voltage value of the second sub-profile.

[0173] In step S1730, the processor (150) determines a first error value of the first sub-profile based on the average voltage value of the first sub-profile.

[0174] In step S1740, the processor (150) determines a second error value of the second sub-profile based on the average voltage value of the second sub-profile.

[0175] In step S1750, the processor (150) determines the i-th comparison value to be equal to the sum of the first error value and the second error value.

[0176] When the method according to Fig. 17 is repeated with natural numbers less than or equal to n as i, the first to nth comparison values ​​are determined. The processor (150) can determine any one data point associated with the minimum comparison value among the first to nth comparison values ​​as a feature point of the second interest profile (600).

[0177] The embodiments of the present invention described above are not implemented only through devices and methods, but may also be implemented through a program that realizes a function corresponding to the configuration of the embodiments of the present invention or a recording medium on which the program is recorded, and such implementation can be easily implemented by an expert in the technical field to which the present invention belongs based on the description of the embodiments described above.

[0178] Although the present invention has been described above with reference to limited embodiments and drawings, the present invention is not limited thereto, and it is obvious that various modifications and variations are possible within the scope of the technical idea of ​​the present invention and the equivalent scope of the patent claims to be described below by a person having ordinary skill in the art to which the present invention pertains.

[0179] In addition, the present invention described above is not limited to the above-described embodiments and the attached drawings, and all or part of each embodiment may be selectively combined and configured so that various modifications can be made, as those skilled in the art can make various substitutions, modifications, and changes within the scope of the technical idea of ​​the present invention.

Claims

1. A data acquisition unit for acquiring capacity-voltage relationship data of a battery cell; and A processor configured to generate a voltage profile and a differential voltage profile of the battery based on the capacity-voltage relationship data, The above processor, Based on the feature points of the above differential voltage profile, a first interest profile is extracted from the voltage profile, Determine the characteristic slope associated with the first interest profile, A battery diagnostic device configured to diagnose a deterioration state of the battery cell based on the above characteristic slope.

2. In paragraph 1, The above processor, A battery diagnostic device configured to determine a data point having a minimum differential voltage value within a target capacity range as the feature point of the differential voltage profile.

3. In paragraph 1, The above processor, Based on the capacity value of the above characteristic point of the above differential voltage profile, the capacity range of interest is determined, A battery diagnostic device configured to determine a portion of the voltage profile corresponding to the above-mentioned interest capacity range as the first interest profile.

4. In paragraph 1, The above processor, A second profile of interest is determined by normalizing the capacitance-voltage domain of the first profile of interest to match the reference capacitance-voltage domain, A battery diagnostic device configured to determine the characteristic slope to be the same as the slope of the tangent line at the feature point of the second interest profile.

5. In paragraph 4, The above processor, determining a plurality of comparison values ​​individually associated with a plurality of data points of the second interest profile; A battery diagnostic device configured to determine any one of the plurality of data points, associated with a minimum of the plurality of comparison values, as the feature point of the second profile of interest.

6. In paragraph 5, The above processor, Based on the capacity value of each of the plurality of data points, the second interest profile is divided into a first sub-profile and a second sub-profile, A battery diagnostic device configured to determine the comparison value of each of the plurality of data points as being equal to the sum of a first error value based on the average voltage value of the first sub-profile and a second error value based on the average voltage value of the second sub-profile.

7. In paragraph 1, The above processor, A battery diagnostic device configured to determine a first degradation parameter representing a capacity reduction rate due to positive electrode degradation of a cell of the battery by using the above characteristic slope as an input variable for a linear regression model.

8. In paragraph 7, The above processor, A battery diagnostic device configured to determine a second degradation parameter representing a capacity degradation rate due to available lithium loss of the battery cell based on the total capacity degradation rate of the battery cell and the first degradation parameter.

9. In paragraph 1, The above capacity-voltage relationship data is, A battery diagnostic device, comprising a capacity time series and a voltage time series of the battery cell while the battery cell is being charged or discharged.

10. A battery pack comprising a battery diagnostic device according to any one of claims 1 to 9.

11. A battery system comprising a battery diagnostic device according to any one of claims 1 to 9.

12. Step of obtaining capacity-voltage relationship data of battery cells; A step of generating a voltage profile and a differential voltage profile of the battery cell based on the capacity-voltage relationship data; A step of extracting a first interest profile from the voltage profile based on the feature points of the above differential voltage profile; a step of determining a characteristic slope associated with the first interest profile; and A step of diagnosing the deterioration state of the battery cell based on the above characteristic slope; A battery diagnostic method comprising:

13. In paragraph 12, The step of extracting the first interest profile from the above voltage profile is: A step of determining a capacity range of interest based on the capacity value of the feature point of the differential voltage profile; and A step of determining a portion of the voltage profile corresponding to the above interest capacity range as the first interest profile; A battery diagnostic method comprising:

14. In paragraph 12, The step of determining the characteristic slope associated with the first interest profile is: A step of determining a second profile of interest by normalizing the capacity-voltage domain of the first profile of interest to match the reference capacity-voltage domain; and A step of determining the characteristic slope to be the same as the slope of the tangent line at the feature point of the second interest profile; A battery diagnostic method further comprising:

15. In paragraph 14, The step of determining the characteristic slope associated with the first interest profile is: A step of determining a plurality of comparison values ​​individually associated with a plurality of data points of the second interest profile; and A step of determining any one of the plurality of data points, which is associated with a minimum value among the plurality of comparison values, as the feature point of the second interest profile; A battery diagnostic method further comprising:

16. A non-transitory computer-readable recording medium storing a computer program, wherein the computer program, when executed by a processor, A step of obtaining capacity-voltage relationship data of a battery cell; A step of generating a voltage profile and a differential voltage profile of the battery cell based on the capacity-voltage relationship data; A step of extracting a first interest profile from the voltage profile based on the feature points of the above differential voltage profile; a step of determining a characteristic slope associated with the first interest profile; and A step of diagnosing the deterioration state of the battery cell based on the above characteristic slope; A non-transitory computer-readable recording medium comprising instructions for causing the processor to perform an operation, including:

17. In paragraph 16, The step of extracting the first interest profile from the above voltage profile is: A step of determining a capacity range of interest based on the capacity value of the feature point of the differential voltage profile; and A step of determining a portion of the voltage profile corresponding to the above interest capacity range as the first interest profile; A non-transitory computer-readable recording medium comprising instructions for causing the processor to perform an operation, including:

18. In paragraph 16, The step of determining the characteristic slope associated with the first interest profile is: A step of determining a second profile of interest by normalizing the capacity-voltage domain of the first profile of interest to match the reference capacity-voltage domain; and A step of determining the characteristic slope to be the same as the slope of the tangent line at the feature point of the second interest profile; A non-transitory computer-readable recording medium comprising instructions for causing the processor to perform an operation, further comprising:

19. In Article 18, The step of determining the characteristic slope associated with the first interest profile is: A step of determining a plurality of comparison values ​​individually associated with a plurality of data points of the second interest profile; and A step of determining any one of the plurality of data points, which is associated with a minimum value among the plurality of comparison values, as the feature point of the second interest profile; A non-transitory computer-readable recording medium comprising instructions for causing the processor to perform an operation, further comprising:

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