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

Abstract

A battery diagnosis apparatus includes: a data acquisition unit configured to acquire 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 profiles based on a characteristic point of the differential voltage profile, determine a characteristic slope associated with the first profile of interest, and diagnose a state of degradation of the battery cell based on the characteristic slope.

Description

Technical Field

[0001] The present disclosure relates to a technology for diagnosing a degradation state of a battery cell.

[0002] This application is based on and claims the benefit of priority from Korean Patent Application No. 10-2023-0191785 filed on December 26, 2023, and Korean Patent Application No. 10-2024-0129279 filed on September 24, 2024, in the Korean Intellectual Property Office, the disclosures of which are incorporated herein by reference in their entirety. Background Art

[0003] In recent years, as demand for portable electronic products such as laptops, camcorders, and mobile phones has rapidly increased, and as electric vehicles, energy storage batteries, robots, and satellites have begun to develop rapidly, research on high-performance batteries that can be repeatedly charged and discharged has been actively carried out. At the same time, as these high-performance batteries are widely used, research on how to efficiently manage them is also being actively carried out.

[0004] Currently commercially available batteries include, for example, nickel-cadmium batteries, nickel-metal hydride batteries, nickel-zinc batteries, and lithium batteries. Among these, lithium batteries have attracted considerable attention due to their advantages, including minimal (if any) memory effect compared to nickel-based batteries, free charge and discharge, very low self-discharge rate, and high energy density. Summary of the Invention

[0005] Technical issues

[0006] The present disclosure provides a battery diagnosis apparatus and a battery diagnosis method for accurately estimating at least one degradation parameter related to a degradation state of a battery cell without disassembling the battery cell.

[0007] Other aspects and advantages of the present disclosure can be understood from the following description and will be more clearly understood by practicing the present disclosure.In addition, it will be apparent that the aspects and advantages of the present disclosure can be achieved by the features set forth in the claims and combinations thereof.

[0008] Technical Solution

[0009] According to one aspect of the present disclosure, a battery diagnostic device includes: a data acquisition circuit configured to acquire 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 characteristic 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 degradation state of the battery cell based on the characteristic slope.

[0010] The processor may be configured to determine a data point having a minimum differential voltage value within the target capacity range as a characteristic point of the differential voltage profile.

[0011] The processor may be configured to determine a capacity range of interest based on capacity values ​​of characteristic points of the differential voltage profile, and the processor may be configured to determine a portion of the voltage profile corresponding to the capacity range of interest as a first profile of interest.

[0012] The processor may be configured to normalize the capacitance-voltage domain of the first profile of interest to match the reference capacitance-voltage domain to determine the second profile of interest.The processor may be configured to determine a characteristic slope equal to a slope of a tangent at a characteristic point of the second profile of interest.

[0013] The processor may be configured to determine a plurality of comparison values ​​respectively associated with a plurality of data points of the second interest profile. The processor may be configured to determine any one of the plurality of data points associated with a minimum value of the plurality of comparison values ​​as a characteristic point of the second interest profile.

[0014] 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 data point in the plurality of data points. The processor may be configured to determine that a comparison value for each data point in the plurality of data points is equal to a sum of a first error value based on an average voltage value of the first sub-profile and a second error value based on the average voltage value of the second sub-profile.

[0015] The processor may be configured to determine a first degradation parameter representing a capacity reduction ratio caused by degradation of a positive electrode of the battery cell using the characteristic slope as an input variable of a linear regression model.

[0016] The processor may be configured to determine a second degradation parameter representing a capacity reduction ratio caused by loss of available lithium in the battery cell based on the total capacity reduction ratio of the battery cell and the first degradation parameter.

[0017] The capacity-voltage relationship data may include a capacity time series and a voltage time series of the battery cell during charging or discharging of the battery cell.

[0018] According to another aspect of the present disclosure, a battery pack includes the battery diagnostic device.

[0019] According to another aspect of the present disclosure, a battery system includes the battery diagnostic device.

[0020] According to another aspect of the present disclosure, a method for diagnosing a battery pack may include: 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 interest profile from the voltage profile based on characteristic points of the differential voltage profile; determining a characteristic slope associated with the first interest profile; and diagnosing a degradation state of the battery cell based on the characteristic slope.

[0021] Extracting the first interest profile from the voltage profile may include: determining an interest capacity range based on capacity values ​​of characteristic points of the differential voltage profile; and determining a portion of the voltage profile corresponding to the interest capacity range as the first interest profile.

[0022] Determining a characteristic slope associated with the first profile of interest may also include: normalizing the capacity-voltage domain of the first profile of interest to match a reference capacity-voltage domain to determine a second profile of interest; and determining the characteristic slope equal to the slope of a tangent at a characteristic point of the second profile of interest.

[0023] Determining the characteristic slope associated with the first interest profile may also include: determining multiple comparison values ​​respectively associated with multiple data points of the second interest profile; and determining any one data point among the multiple data points that is associated with the minimum value of the multiple comparison values ​​as a characteristic point of the second interest profile.

[0024] According to another aspect of the present disclosure, a non-transitory computer-readable recording medium stores a computer program. The program includes instructions that, when executed by a processor, cause the processor to perform operations including: obtaining capacity-voltage relationship data for a battery cell; generating a voltage profile and a differential voltage profile for the battery cell based on the capacity-voltage relationship data; extracting a first profile of interest from the voltage profile based on characteristic points of the differential voltage profile; determining a characteristic slope associated with the first profile of interest; and diagnosing a degradation state of the battery cell based on the characteristic slope.

[0025] Extracting the first interest profile from the voltage profile may include: determining an interest capacity range based on capacity values ​​of characteristic points of the differential voltage profile; and determining a portion of the voltage profile corresponding to the interest capacity range as the first interest profile.

[0026] Determining a characteristic slope associated with the first profile of interest may also include: normalizing the capacity-voltage domain of the first profile of interest to match a reference capacity-voltage domain to determine a second profile of interest; and determining the characteristic slope equal to the slope of a tangent at a characteristic point of the second profile of interest.

[0027] Determining the characteristic slope associated with the first interest profile may also include: determining multiple comparison values ​​respectively associated with multiple data points of the second interest profile; and determining any one data point among the multiple data points that is associated with the minimum value of the multiple comparison values ​​as a characteristic point of the second interest profile.

[0028] Beneficial effects

[0029] According to at least one embodiment of the present disclosure, at least one degradation parameter related to the degradation state of a battery cell can be accurately estimated without disassembling the battery cell. For example, the present disclosure can achieve non-destructive diagnosis of the degradation state of the positive electrode of a battery cell.

[0030] According to at least one embodiment of the present disclosure, by extracting a portion of the voltage profile of a battery cell within a predetermined voltage range (hereinafter referred to as a "first interest profile") from the entire voltage profile (hereinafter referred to as a "QV profile"), the voltage characteristics of the positive electrode material are better than the voltage characteristics of the negative electrode material within the predetermined voltage range, and the degradation state of the positive electrode material in the battery cell (for example, the capacity degradation rate caused by positive electrode degradation) can be more accurately identified.

[0031] According to at least one embodiment of the present disclosure, by utilizing the relationship between the total capacity reduction ratio of the battery cells, the capacity reduction ratio caused by positive electrode degradation, and the capacity reduction ratio caused by lithium loss, the capacity reduction ratio caused by lithium loss can be easily determined based on the total capacity reduction ratio of the battery cells and the capacity reduction ratio caused by positive electrode degradation.

[0032] The effects of the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned above will be clearly understood by those having ordinary skill in the art from the description of the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The following drawings illustrate exemplary embodiments of the present disclosure and, together with the detailed description to be described later, are used to further understand the technical concept of the present disclosure. Therefore, the present disclosure should not be construed as being limited to the contents shown in the drawings.

[0034] Figure 1 is a diagram illustrating an example configuration of a battery system according to the present disclosure.

[0035] Figure 2 Graph showing an example of a QV cross-sectional line of a battery cell.

[0036] Figure 3 It is shown from Figure 2 A graph of an example of a normalized QV profile obtained from a QV profile.

[0037] Figure 4 is shown with Figure 3Graphs of examples of Q-dV / dQ profiles associated with normalized QV profiles are shown in FIG.

[0038] Figure 5 is a graph showing an example of a first interest profile.

[0039] Figure 6 is a graph showing an example of the second interest profile.

[0040] Figure 7 is referred to for interpretation and determination Figure 6 A graph of the process of the characteristic slope of the second profile of interest is shown in FIG.

[0041] Figure 8 is an example graph referred to to explain the relationship between the positive electrode degradation degree and the second interest profile.

[0042] Figures 9 to 11 is referred to explain Figure 8 Graph of the characteristic slope for each of the three second interest profiles shown in .

[0043] Figure 12 is an example graph referred to for explaining the relationship between the degree of positive electrode degradation and the characteristic slope.

[0044] Figure 13 FIG. 1 is a flow chart schematically illustrating a battery diagnosis method according to an embodiment of the present disclosure.

[0045] Figure 14 is shown to be included in Figure 13 Flowchart of the subroutine in step S1330.

[0046] Figure 15 is shown to be included in Figure 14 Flowchart of the subroutine in step S1410.

[0047] Figure 16 is shown to be included in Figure 13 Flowchart of the subroutine in step S1330.

[0048] Figure 17 is shown to be included in Figure 16 Flowchart of the subroutine in step S1620. DETAILED DESCRIPTION

[0049] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Before this, the terms or words used in the specification and claims should not be interpreted as limited to their ordinary or dictionary meanings, but should be interpreted as meanings and concepts consistent with the technical ideas of the present disclosure based on the principle that inventors can appropriately define the concepts of terms in order to best explain their inventions.

[0050] Therefore, the embodiments described in this specification and the configurations shown in the drawings are merely exemplary embodiments of the present disclosure and do not represent all technical ideas of the present disclosure. Therefore, it should be understood that various equivalents and modifications that can replace these embodiments may exist when this application is filed.

[0051] Terms including ordinal numbers such as “first” and “second” are used to distinguish one component from another among various components, and are not intended to limit or define the components by the terms.

[0052] Throughout the specification, when a part is described as "including" a certain component, it means that unless there is a specific contrary wording, it does not exclude other components, but indicates that other components may be further included. In addition, terms such as "unit" described in this specification refer to a unit that processes at least one function or operation and can be implemented by hardware, software, or a combination of hardware and software.

[0053] Furthermore, throughout the specification, when a component is described as being “connected” to another component, this includes not only a case where the components are “directly connected” but also a case where the components are “indirectly connected” with another element interposed.

[0054] Various degradation factors occur during repeated charge and discharge cycles that reduce the capacity of lithium-ion batteries. This degradation is influenced by a combination of factors and is understood to be primarily caused by, for example, lithium inventory loss (LLI), active material loss (LAM), and conductivity loss (CL).

[0055] Lithium inventory 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, leading to depletion of the lithium ion source due to electrolyte consumption. Simultaneously, as lithium is repeatedly inserted into and removed from the crystal lattices of the negative and positive electrode active materials, the particle structure may deteriorate and the capacity may decrease. This degradation mode is classified as degradation mode caused by active material loss. In addition, when the electrode separates from the current collector or when cracks form between the electrode materials, for example, conductivity loss may occur, resulting in reduced ion and electron mobility. This degradation is classified as degradation due to conductivity loss (CL).

[0056] There are various techniques for monitoring the degradation of battery cells. For example, the differential voltage analysis method, which may also be referred to as “DVA,” is based on time series data of at least one battery parameter (eg, voltage, current, or capacity) that can be observed from outside the battery cell.

[0057] In DVA, the peak that appears in the differential voltage curve (also known as the "Q-dV / dQ profile") is considered a key factor. Some types of battery cells exhibit a voltage plateau characteristic during charge or discharge, where the rate of voltage change remains almost zero. Within the capacity range that exhibits the voltage plateau characteristic, the differential voltage is also close to zero, making it difficult to detect the peak from the Q-dV / dQ profile.

[0058] The present disclosure provides a method capable of accurately and easily diagnosing a degradation state of a battery cell without extracting peak information indicating the degradation state from a Q-dV / dQ profile.

[0059] Figure 1 is a diagram illustrating an example configuration of a battery system according to the present disclosure.

[0060] refer to Figure 1 A battery system 1 according to one embodiment of the present disclosure includes a system controller 2, a battery pack 10, an inverter 30, and a motor 40. The battery pack 10 includes a battery 11 and a relay 20, and may also include a battery diagnostic device 100. The battery diagnostic device 100 includes a sensing circuit 110, a communication circuit 130, and a processor 150 as a control circuit. The sensing circuit 110 may also include a voltage sensor 111 and a current sensor 112. The processor 150 may also include a memory 151.

[0061] The charge / discharge terminals (P+, P-) of the battery pack 10 can be electrically connected to the charger 3 via, for example, a charging cable. The charger 3 can be included in the battery system 1 or provided separately at a charging station. The system controller 2 (e.g., an electronic control unit (ECU)) is configured to transmit a key-on signal to the battery diagnostic device 100 in response to a user switching a start button (not shown) provided in the battery system 1 to the on position. The system controller 2 is also configured to transmit a key-off signal to the battery diagnostic device 100 of the battery pack 10 in response to a user switching the start button to the off position. The charger 3 can communicate with the system controller 2 and supply charging power selected from constant power, constant current, and constant voltage through the charge / discharge terminals (P+, P-) of the battery pack 10.

[0062] The battery 11 includes at least one battery cell (BC). Figure 1 , the battery 11 is shown to include a plurality of battery cells BC1 to BC N , where N is a natural number of 2 or greater. A plurality of battery cells BC1 to BC N In the following, when describing a plurality of battery cells BC1 to BC NWhen the content is shared, the symbol "BC" is assigned to the battery cell. The charger 3 can perform a charge / discharge cycle required for diagnosing the degradation state of the battery cell BC by cooperating with the inverter 30 having a discharge function.

[0063] The battery cell BC is the diagnostic object of the battery diagnostic device 100. The types of battery cells BC are not particularly limited as long as they are electrochemical devices that can be repeatedly charged and discharged. According to one embodiment, the battery cell BC may be a lithium iron phosphate (LiFePO4 or LFP) 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 across at least one capacity range (or SOC range). The lithium iron phosphate battery cell may also be referred to as a "LiFePO4 battery cell", "LFP battery cell" or simply "LFP battery cell". In the following, it is assumed that the battery cell BC is an LFP battery cell including LFP and graphite as the positive electrode material and the negative electrode material, respectively.

[0064] The relay 20 is electrically connected in series with the battery 11 via a power path interconnecting the battery 11 and the inverter 30. Figure 1 , relay 20 is shown connected between the positive terminal of battery 11 and the charge / discharge terminal P+. Relay 20 is controlled on / off in response to a switching signal from battery diagnostic system 100. 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 metal oxide semiconductor field effect transistor (MOSFET).

[0065] The inverter 30 is configured to convert direct current (DC) from the battery 11 included in the battery pack 10 into alternating current (AC) in response to a command from the battery diagnostic device 100 or the system controller 2. The AC power from the inverter 30 is used to drive the motor 40. For example, a three-phase AC motor can be used for the motor 40. The inverter 30, the motor 40, and other components within the battery system 1 that receive discharge power from the battery 11 can be collectively referred to as an electrical load.

[0066] The voltage sensor 111 is connected to the positive and negative terminals of each battery cell BC and is configured to detect the voltage across the battery cell BC (which may also be referred to as the "full-cell voltage") and generate a voltage signal representing the detected voltage value. The voltage sensor 111 may be implemented using one or a combination of two or more known voltage sensing elements, such as a voltage measurement integrated circuit (IC).

[0067] The current sensor 112 is connected in series to the battery 11 via a current path between the battery 11 and the inverter 30. The current sensor 112 is configured to detect the current (which may also be referred to as "charge / discharge current") flowing through the battery 11 and generate a current signal representing the detected current value. N The current flowing through the battery 11 is the same as the current flowing through each battery cell BC because they are connected in series. The current sensor 112 may be implemented using one or a combination of two or more known current detection elements such as a shunt resistor or a Hall effect device.

[0068] The communication circuit 130 is configured to support wired or wireless communication between the processor 150 and at least one of the system controller 2, the sensing circuit 110, and an external device (not shown). The wired communication may be, for example, a controller area network (CAN) communication, and the wireless communication may be, for example, a Zigbee communication. or Bluetooth Communication. The type of communication protocol is not particularly limited, as long as it supports both wired and wireless communication between the processor 150 and the system controller 2. The communication circuit 130 may include an output device (e.g., a display or speaker) that provides information received from the processor 150 and / or the system controller 2 in a user-readable format. The data acquisition unit (data acquisition circuit) of the present disclosure may include at least one of the sensing circuit 110 and the communication circuit 130.

[0069] The processor 150 may be operatively coupled to at least one of the relay 20, the sensing circuit 110, and the communication circuit 130 as a control circuit. The description "two components are operatively coupled" means that the two components are directly or indirectly connected to allow unidirectional or bidirectional signal transmission.

[0070] As a control circuit, the processor 150 may be referred to as a "battery controller" and may be implemented in hardware using at least one of an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a microprocessor, or other electrical units for performing functions.

[0071] The memory 151 may include at least one type of storage medium, such as a flash memory type, a hard disk type, a solid-state drive (SSD) type, a silicon disk drive (SDD) type, a multimedia card micro type, a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), or a programmable read-only memory (PROM). The memory 151 may store data and programs required for computing operations performed by the processor 150. The memory 151 may also store data representing the results of computing operations performed by the processor 150. The memory 151 may also store data sets and software used to diagnose the degradation state of the battery cells BC. The memory 151 may be integrated into the processor 150.

[0072] Processor 150 can collect voltage signals from sensing circuit 110 and current signals from current sensor 112. As used herein, the term "detection signal" can refer solely to the voltage signal, or can be a general term referring to both the voltage and current signals. Processor 150 can use an internally provided analog-to-digital converter (ADC) to convert and record each analog signal collected from sensing circuit 110 into a digital value. Alternatively, each of voltage sensor 111 and current sensor 112 can include an internal ADC and transmit the digital values ​​generated by the ADC to processor 150.

[0073] When the relay 20 is turned on during operation of the inverter 30, the motor 40, and / or the charger 3 as the electric load, 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 or discharging mode, the battery 11 switches to the idle mode.

[0074] Processor 150 can turn on relay 20 in response to a key-on signal. Processor 150 can turn off relay 20 in response to a key-off signal. The key-on signal requests a switch from idle to charging or discharging. The key-off signal requests a switch from charging or discharging to idle. Alternatively, system controller 2 may control the on / off state of relay 20 instead of processor 150.

[0075] In this specification, a time series of a parameter represents the time history of changes in that parameter over an arbitrary or specific time period. Furthermore, a profile (or curve) representing the correspondence between any two parameters obtained at the same timing within the same time period can be a representation obtained by mapping the time series data of the two parameters into a form that can be represented in the form of a two-dimensional graph, or a polynomial equation obtained by applying a predetermined curve fitting logic to the mapped time series data. The degree of the highest-order term in the polynomial equation can be predetermined.

[0076] Figure 2 is a graph showing an example of a QV cross-sectional line of a battery cell, Figure 3 It is shown from Figure 2 A graph of an example of a normalized QV profile obtained from a QV profile, and Figure 4 is a graph showing an example of a Q-dV / dQ profile. The Q-dV / dQ profile is a graph showing an example of a Q-dV / dQ profile. Figure 3 The differential profile of capacity associated with the normalized QV profile shown in .

[0077] exist Figure 2 In the graph shown in , the vertical axis represents the voltage V of the battery cell BC, and the horizontal axis represents the capacity in mAh. Reference numeral 200 denotes a QV profile based on the capacity-voltage relationship data of the battery cell BC.

[0078] 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 plateau characteristic, and it can be observed from the QV profile 200 that the voltage remains almost constant within a capacity range of approximately 20 to 40 mAh.

[0079] QV profile 200 may be based on capacity-voltage relationship data acquired by a data acquisition unit while performing at least one of a charge cycle and a discharge cycle of a battery cell BC. During the charge cycle or the discharge cycle, a constant power or constant current may be utilized. For ease of explanation, it will be assumed that QV profile 200 is associated with the charge cycle. The details described later regarding the charge cycle also apply to the discharge cycle, except that a discharge current, rather than a charge current, flows through the battery cell BC.

[0080] During a constant power charging cycle, the charging current gradually decreases as the voltage of the battery cell BC increases. Therefore, in addition to voltage time-series data representing the time history of changes in the battery cell BC voltage, current time-series data representing the time history of changes in the current flowing through the battery cell BC is also generally required.

[0081] In a constant current charging cycle, a charging current having a predetermined current rate (e.g., 0.1C, 0.5C, 1.5C, or 2.0C) is controlled to flow through the battery cell BC. Therefore, it can be assumed that the capacity value at a specific time point is equal to the value obtained by multiplying the time elapsed from the start of the constant current charging cycle to the specific time point (e.g., the time difference between the start of the constant current charging cycle and the specific point) by the current value corresponding to the current rate.

[0082] Even in a constant current charging cycle, the actual charging current may temporarily become greater or less than the initially intended charging current. For this reason, the processor 150 can generate capacity time series data by repeating a process in which the current flowing through the battery cell BC is periodically measured using the current sensor 112, and integrating and recording the obtained current measurement values.

[0083] The charging cycle may continue until the voltage of the battery cell BC varies across at least the predetermined voltage range. QV profile 200 represents the relationship between the capacity and voltage of the battery cell BC obtained during a period in which the voltage of the battery cell BC rises from a lower limit voltage to an upper limit voltage of the predetermined voltage range due to the charging cycle.

[0084] Figure 2 The QV profile 200 shown in FIG. 1 illustrates an example in which 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 may be referred to as the total capacity range of the QV profile 200, which corresponds to the predetermined voltage range.

[0085] In this regard, the total capacity range corresponding to a predetermined voltage range can vary depending on factors such as the degradation state of the battery cells BC, rather than being fixed. Furthermore, even when two different battery cells BC have the same degradation state, their capacity ranges may differ due to, for example, process variations during manufacturing. Therefore, to enhance the ease and appropriateness of the data processing required to diagnose the degradation state of the battery cells BC and ensure the accuracy of the diagnostic results, it is necessary to normalize the QV profile 200 by applying a normalization process to the total capacity range of the QV profile 200.

[0086] exist Figure 3 In the graph shown in FIG, the vertical axis represents Figure 2 The horizontal axis represents the voltage of the battery cell BC shown in FIG, while the horizontal axis represents the normalized capacity (unit: %). The normalized capacity can be a term equivalent to the conventional state of charge (SOC). Reference numeral "300" represents an example of another QV profile obtained by applying the normalization process to the QV profile 200.

[0087] When the capacity-voltage relationship according to the QV profile 200 has a mathematical relationship as represented by the following Equation 1, the normalized capacity and voltage according to the QV profile 300 have a mathematical relationship as represented by the following Equation 2. According to one embodiment, the QV profile 300 may be used as a “voltage profile” of the present disclosure.

[0088] <Equation 1>

[0089]

[0090] <Equation 2>

[0091]

[0092] In Equation 1, Q B Indicates any capacity value within the total capacity range, and V B Indicates that the QV section line 200 corresponds to Q B The voltage value of the data point. In Equation 2, Q B_normal Indicates that it corresponds to Q B The normalized capacity value, and Q total Indicates the size of the total capacity range (that is, the upper capacity value of the total capacity range).

[0093] For reference, Figure 3 express Figure 2 Each data point in the total capacity range is normalized to a percentage (ranging from 0 to 100%), but this should be understood as an example. For example, normalization can be performed on a different range, such as from 0 to 1, rather than from 0 to 100%.

[0094] refer to Figure 4 In the graph shown in FIG, reference numeral 400 represents an example of a Q-dV / dQ profile. The Q-dV / dQ profile 400 may also be referred to as a "capacity-differential voltage profile," a "capacity-differential voltage curve," or a "differential voltage profile."

[0095] The processor 150 can generate a Q-dV / dQ profile 400 by differentiating the voltage of the QV profile 300 relative to the capacity. For example, the processor 150 can determine a differential voltage dV / dQ, which is the ratio of the change dV in the voltage V to the change dQ in the normalized capacity Q [%], based on the QV profile 300, and can determine the Q-dV / dQ profile 400 as relationship data indicating the corresponding relationship 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.

[0096] The processor 150 may determine a characteristic point based on the Q-dV / dQ profile 400. The characteristic point of the Q-dV / dQ profile 400 may be a data point on the Q-dV / dQ profile 400 that is within a predetermined target capacity range. Figure 4 , the target capacity range is shown as 50% to 99%. The target capacity range may partially overlap with the capacity range representing the voltage plateau characteristic of the battery cell BC.

[0097] For example, the processor 150 may identify a local maximum point (P ) having the highest differential voltage value within the target capacity range from the Q-dV / dQ profile 400. MAX ). The processor 150 may then convert the Q-dV / dQ profile 400 relative to the local maximum point (P MAX ) The data point located on the higher capacity side is determined as the characteristic point (P cut-off The characteristic point of the Q-dV / dQ profile 400 may also be referred to as a “cutoff reference point”.

[0098] When there are two or more local minimum points within the target capacity range, the one with the largest capacity value Q can be selected cut-off Any local minimum point of is set as the characteristic point P cut-off .

[0099] Due to the voltage characteristics of the negative electrode material of the battery cell BC, the characteristic point P cut-off It may be the final local minimum point on the Q-dV / dQ profile 400. For example, beyond the characteristic point P on the higher capacity side cut-off The portion may correspond to a capacity range in which the voltage characteristics of the positive electrode material of the battery cell BC are superior to those of the negative electrode material. Therefore, those skilled in the art will readily understand that if only the higher capacity side portion of the entire QV profile 300 is designated as an analysis target, it will be possible to accurately estimate degradation parameters caused by degradation of the positive electrode of the battery cell BC.

[0100] In the present disclosure, it has been confirmed through many experiments that the voltage characteristic of the negative electrode material exceeds the characteristic point P on the higher capacity side compared to other parts of the QV profile 300. cut-off Therefore, it has also been recognized through the present disclosure that analyzing the higher capacity side portion of the entire QV profile 300 enables accurate diagnosis of parameters related to positive electrode degradation and various degradation parameters associated with the degradation state of the battery cell BC.

[0101] In this specification, when the QV section line 300 is as shown Figure 3 Based on the characteristic point P shown in cut-off Capacity value Q cut-off When divided into a lower capacity side portion and a higher capacity side portion, the higher capacity side portion will be referred to as the "first interest profile" (see Figure 5 500 in the figure).

[0102] Figure 5 is a graph showing an example of a first interest profile, Figure 6 is a graph showing an example of a second interest profile, and Figure 7 is referred to for the purpose of explaining Figure 6A graph of the process of the characteristic slope of the second profile of interest is shown in FIG.

[0103] refer to Figure 5 , the first interest profile 500 is an enlarged view of a portion of the QV profile 300 corresponding to an interest capacity range (eg, 92% to 99%), wherein the characteristic point P cut-off The capacity value (e.g., 92%) and the upper capacity value (e.g., 99%) of the target capacity range are the lower limit and the upper limit, respectively.

[0104] In this regard, even when the positive electrode degradation state of the battery cells BC is the same, when other degradation factors such as the negative electrode degradation state or the amount of available lithium are different, the starting point, end point, and / or shape (e.g., curvature) of the first interest profile 500 may vary. Therefore, a normalization process similar to the normalization process applied to the QV profile 200 needs to be applied to the first interest profile 500.

[0105] refer to Figure 6 , a second interest profile 600 can be identified, which is a result obtained by applying the normalization process to the first interest profile 500 .

[0106] The processor 150 may determine the second interest profile 600 by normalizing the capacity-voltage domain of the first interest profile 500 to match a predetermined reference capacity-voltage domain. The reference capacity-voltage domain may be a rectangular area defined by a reference capacity range and a reference voltage range.

[0107] For example, the processor 150 may generate the second interest section 600 by applying a normalization process to the first interest section 500 so that the starting point P of the first interest section 500 is equal to S and the end point P E Match the predetermined first reference point P respectively R1 and the second reference point P R2 The reference capacity range can be the first reference point P R1 With the second reference point P R2 The capacity range between, and the reference voltage range can be the first reference point P R1 With the second reference point P R2 voltage range between .

[0108] The starting point P of the first interest profile 500 S It can be the data point with the minimum capacity value of the first interest profile 500. The end point P of the first interest profile 500 E It may be the data point of the first interest profile 500 having the largest capacity value.

[0109] First reference point P R1 The capacity value can be less than the starting point P SThe capacity value, and the first reference point P R1 The voltage value can be less than the starting point P S In addition, the second reference point P R2 The capacity value can be greater than the end point P E The capacity value, and the second reference point P R2 The voltage value can be greater than the end point P E voltage value.

[0110] The processor 150 may shift the first interest profile 500 along the horizontal axis (capacity axis) and the vertical axis (voltage axis) so that the starting point P S Match the first reference point P R1 or end point P E Match the second reference point P R2 , and a second operation of scaling the first interest profile 500 along the capacity axis and the voltage axis. The first operation may include at least one of horizontal movement (leftward or rightward movement along the horizontal axis) and vertical movement (upward or downward movement along the vertical axis). The second operation may include at least one of scaling down or scaling up along at least one of the horizontal axis and the vertical axis.

[0111] Assume that the starting point P S , end point P E , first reference point P R1 and the second reference point P R2 The two-dimensional coordinates are (Q S , V S )、(Q E , V E )、(Q R1 , V R1 ) and (Q R2 , V R2 ).exist Figure 6 In the first reference point P R1 The two-dimensional coordinates (Q R1 , V R1 ) is shown as (90%, 0V), and the second reference point P R2 The two-dimensional coordinates (Q R2 , V R2 ) is shown as (100%, 1V).

[0112] The processor 150 may shift the first interest profile 500 toward the low capacity side by a certain amount Q S -Q R1 And shift a certain amount V toward the low voltage side S -V R1 Therefore, since the starting point P S With the first reference point P R1 Match, so the end point PE With the second reference point P R2 Therefore, the processor 150 can match the ratio (Q R2 -Q R1 ) / (Q E -Q S ) along the capacity axis and as a ratio (V R2 -V R1 ) / (V E -V S ) scales the first interest profile 500 along the voltage axis. As a result, the end point P E With the second reference point P R2 The matching is performed, thereby completing the normalization process of determining the second interest profile 600 according to the first interest profile 500. Moreover, the second interest profile 600 can be the result of projecting the first interest profile 500 onto the reference capacitance-voltage domain.

[0113] When the capacity and the voltage according to the first interest profile 500 have a mathematical relationship as expressed in Equation 3 below, the capacity and the voltage according to the second interest profile 600 have a mathematical relationship as expressed in Equation 4 below.

[0114] <Equation 3>

[0115]

[0116] <Equation 4>

[0117]

[0118] In Equation 3, Q B_normal represents the capacity value of any data point on the first interest profile 500, and V B Indicates the mapping of the first interest profile 500 to Q B_normal voltage value.

[0119] In Equation 4, Q B_normal_2 The second interest profile 600 corresponds to Q B_normal For reference, Figure 6 Represents the range normalized to 0 to 1 V Figure 5 The total voltage range of each data point is shown as a result, but this should be understood as an example.

[0120] The processor 150 may determine a 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 may be associated with the first interest profile 500 .

[0121] The second profile of interest 600 can be viewed as a collection of multiple data points, each data point representing a normalized capacity and a normalized voltage.

[0122] Hereinafter, it is assumed that the second interest profile 600 includes the first to nth data points. Here, n is a natural number of 3 or greater. The capacitance difference between any two adjacent data points from the first to nth data points can be constant at a predetermined value. Alternatively, the voltage difference between any two adjacent data points from the first to nth data points can be constant at a predetermined value.

[0123] The first data point can be compared with the first reference point P R1 The same, and the nth data point can be compared with the second reference point P R2 Alternatively, the first data point may have a value greater than that of the first reference point P. R1 The capacity value of the nth data point may be greater than the capacity value of the second reference point P R2 Alternatively, the first data point may have a capacity value smaller than the first reference point P R1 The voltage value of the nth data point may be greater than the voltage value of the second reference point P R2 The voltage value is smaller than the predetermined value.

[0124] In the present disclosure, it is assumed that a symbol i used as an ordinal number is a natural number equal to or smaller than n.

[0125] refer to Figure 7 , the processor 150 may be based on the i-th data point P among the first to n-th data points C_i The capacity value of the second interest profile 600 is divided into a first sub-profile and a second sub-profile. The first sub-profile can be the second interest profile 600 and is less than or equal to the i-th data point P C_i The second sub-profile may be a portion of the second interest profile 600 that is greater than or equal to the i-th data point P. C_i For example, when n=1,000 and i=700, the first sub-section line may include the first to 700th data points, and the second sub-section line may include the 700th to 1000th data points.

[0126] Next, the processor 150 may determine an average voltage value of the first sub-section and an average voltage value of the second sub-section. The average voltage value of the first sub-section may be an average of the voltage values ​​of the data points included in the first sub-section. The average voltage value of the second sub-section may be an average of the voltage values ​​of the data points included in the second sub-section.

[0127] Then, the processor 150 may convert the ith comparison value P of the ith data point into C_iDetermined to be equal to the sum of a first error value based on the average voltage value of the first sub-section line and a second error value based on the average voltage value of the second sub-section line. Figure 7 In the middle, the voltage line L 1_i represents the average voltage of the first sub-section line, and the voltage line L 1_2 represents the average voltage of the second sub-section line.

[0128] The processor 150 may (i) determine a first error value for the first sub-section based on the average voltage of the first sub-section, and (ii) determine a second error value for the second sub-section based on the average voltage of the second sub-section. Each error value may be determined using at least one of well-known algorithms that quantify the level of difference between two data sets, such as a mean square error (MSE) or a root mean square error (RMSE). For 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-section relative to the average voltage value of the first sub-section. 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-section relative to the average voltage value of the second sub-section.

[0129] Next, the processor 150 may determine that the i-th comparison value is equal to the sum of the first error value and the second error value. By setting a natural number equal to or less than n as i and repeating the above process n times, multiple comparison values ​​individually associated with multiple data points may be determined.

[0130] The processor 150 may determine one of the plurality of data points associated with the minimum value among the plurality of comparison values ​​as a characteristic point of the second interest profile 600 .

[0131] Then, the processor 150 may determine that the characteristic slope is equal to the slope of the tangent line at the characteristic point of the second interest profile 600. Figure 7 In the symbol T C_i Shows that at the i-th data point P C_i The tangent line at . When the i-th data point P C_i When the point is identified as a characteristic point of the second interest profile 600, the tangent line T C_i The slope of can be determined as the characteristic slope.

[0132] The processor 150 may determine the first degradation parameter by using the characteristic slope as an input variable in a linear regression model. The first degradation parameter may indicate a capacity reduction ratio caused by degradation of the positive electrode of the battery cell.

[0133] The capacity reduction ratio due to positive electrode degradation may be referred to as the "positive electrode degradation degree" or the "positive electrode degradation-induced capacity reduction ratio". Figures 8 to 12 Provides a more detailed description of the linear regression model.

[0134] According to one embodiment of the present disclosure, relationship data that can be generated using a linear regression model is obtained by sequentially performing the following processes: a first process of forcibly degrading a plurality of battery cells prepared as experimental samples to have different degrees of positive electrode degradation; a second process of determining a characteristic slope associated with each of the forcibly degraded battery cells; a third process of disassembling each of the forcibly degraded battery cells to manufacture positive electrode half cells; and a fourth process of measuring and recording the available capacity (e.g., maximum capacity) of each positive electrode half cell.

[0135] Figure 8 is an example diagram referred to to explain the relationship between the positive electrode degradation degree and the second interest profile, Figures 9 to 11 are referred to for explanation Figure 8 A graph of the characteristic slopes of the three second interest profiles shown in , and Figure 12 is an example graph referred to for explaining the relationship between the degree of positive electrode degradation and the characteristic slope.

[0136] Figure 8 The second profile of interest changes as the degree of positive electrode degradation increases. The degree of positive electrode degradation may refer to a capacity reduction ratio caused by positive electrode degradation.

[0137] refer to Figure 8 , curve 810 represents the second interest profile when the positive electrode is in a new product state without degradation, curve 820 represents the second interest profile when the degree of degradation of the positive electrode is 1.75%, and curve 830 represents the second interest profile when the degree of degradation of the positive electrode is 9.40%.

[0138] For example, as the degradation degree of the positive electrode increases, the second interest profile gradually changes to be similar to the line connecting the first reference point P R1 and the second reference point P R2 straight line, and therefore, can be obtained from Figures 9 to 11 It is confirmed that the characteristic point of the second interest profile is shifted toward the low-capacity side.

[0139] refer to Figures 9 to 11 , each of the curves 810 , 820 , and 830 is shown as a polynomial of the highest degree 3. In addition, the characteristic slopes of the curves 810 , 820 , and 830 are 4.35, 3.45, and 2.05, respectively, from which it can be seen that the characteristic slope decreases as the degree of positive electrode degradation increases.

[0140] refer to Figure 12 , the linear regression model 1200 has been prepared in advance as relational data representing the corresponding relationship between the characteristic slope and the positive electrode degradation state. Figure 12 In the curve graph, data point 1210 and Figure 8 The curve 810 in FIG. 1 is associated with the data point 1220. Figure 8 The curve 820 in FIG is associated with the data point 1230. Figure 8 . Although not all data points are shown, in the present disclosure, additional data points other than data points 1210, 1220, and 1230 are obtained as a result of the aforementioned experiments and are used to derive a linear regression model 1200 through linear regression analysis. The linear regression model 1200 may be pre-stored in the memory 151 of the processor 150. Equation 5 below provides an example of the linear regression model 1200.

[0141] <Equation 5>

[0142]

[0143] In Equation 5, "A" and "B" are two coefficients representing the slope and y-intercept of the line, respectively, according to the linear regression model 1200. The symbol "x" represents the characteristic slope as an input variable (see Figure 7 ), and the symbol "y" represents the degree of positive electrode degradation as an output variable. The coefficients A and B may vary depending on factors such as the type and composition ratio of each of the positive and negative electrode materials. Therefore, the coefficients A and B may be appropriately adjusted according to the type and manufacturing information of the battery cell BC provided as the diagnosis target (for example, the type and composition ratio of each of the positive and negative electrode materials). For example, Figure 12 The coefficients A and B of the linear regression model 1200 shown in FIG. 1 are -4.29 and 17.66, respectively.

[0144] The processor 150 can obtain the positive electrode degradation degree as the output variable y by inputting the characteristic slope determined based on the capacity-voltage relationship data of the battery cell BC with an unknown positive electrode degradation state into the linear regression model 1200 as the input variable x. For example, when the characteristic slope of the battery cell BC is determined to be 3.45, the positive electrode degradation degree of the battery cell BC can be determined to be approximately 2.86% through the linear regression model 1200 (y=-4.29×3.45+17.66). Figure 12 , reference numeral 1250 denotes a data point corresponding to a characteristic slope of 3.45 and a positive electrode degradation degree of 2.86%.

[0145] Table 1 below summarizes the relationship between the number of completed charge cycles (cycle number), total capacity reduction ratio, characteristic slope, first degradation parameter (capacity reduction ratio caused by positive electrode degradation), and second degradation parameter (capacity reduction ratio caused by loss of available lithium). Here, "available lithium" may refer to lithium ions that can participate in the charge and discharge reactions of the battery cell BC.

[0146] [Table 1]

[0147]

[0148] According to Table 1, it can be understood that as the number of cycles increases, the characteristic slope decreases, while the total capacity reduction ratio, the first degradation parameter, and the second degradation parameter each increase accordingly. For reference, the number of cycles can be increased by one each time a charge cycle or a discharge cycle is completed.

[0149] The total capacity reduction ratio may be the ratio of the reduction in full charge capacity due to degradation of the design capacity of the battery cell BC (e.g., the full charge capacity in a new product state). As an example, assuming the design capacity is P, the current full charge capacity is U, and the reduction in full charge capacity is W, W = PU, and the total capacity reduction ratio = (W / P) × 100%.

[0150] In the present disclosure, it has been recognized that the sum of the first degradation parameter and the second degradation parameter is substantially equal to the total capacity reduction ratio. Therefore, the processor 150 can determine the second degradation parameter representing the capacity reduction ratio due to the loss of available lithium by subtracting the capacity reduction ratio represented by the first degradation parameter from the total capacity reduction ratio.

[0151] Figure 13 FIG. 1 is a flow chart schematically illustrating a battery diagnosis method according to an embodiment of the present disclosure. Figure 13 The method includes steps S1310 to S1360. Figure 13 The method may further include step S1370.

[0152] In step S1310 , the data acquisition unit acquires capacity-voltage relationship data of the battery cell BC. In this specification, acquisition of any data or information may refer to generation through software processing, input via a user or input device, and / or reception through a communication channel.

[0153] As an example, when the data acquisition unit includes the sensing circuit 110, the data acquisition unit may generate a voltage time series and a capacity time series based on a detection signal generated by the sensing circuit 110. The capacity-voltage relationship data may include the voltage time series and the capacity time series.

[0154] As another example, when the data acquisition unit includes the communication circuit 130 , the data acquisition unit may receive the capacity-voltage relationship data from the external device using the communication circuit 130 .

[0155] 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 .

[0156] In step S1330, the processor 150 calculates the differential voltage profile 400 based on the characteristic point P cut-off A first profile of interest 500 is extracted from the voltage profile 300 .

[0157] In step S1340 , the processor 150 determines a characteristic slope of the first interest profile 500 .

[0158] In step S1350, the degradation state of the battery cell BC is diagnosed based on the characteristic slope of the first profile of interest 500. In step S1350, the processor 150 may 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 of the linear regression model 1200 (see, for example, Equation 5). The linear regression model 1200 may be pre-prepared with relational data representing the corresponding relationship between the characteristic slope of the battery cell BC and the degradation state of the positive electrode. Therefore, a second degradation parameter representing the capacity reduction ratio (%) due to available lithium loss may be additionally determined by determining a first degradation parameter indicating the capacity reduction ratio (%) due to positive electrode degradation and subtracting the first degradation parameter from the total capacity reduction ratio (%).

[0159] In step S1360, the processor 150 may determine at least one protection parameter of the battery cell BC based on the result of the diagnosis performed in step S1350. For example, at least one of the maximum charge voltage, the minimum discharge voltage, the maximum allowable current, and the maximum allowable power may be determined as the protection parameter.

[0160] In step S1370, the processor 150 controls the charging and discharging of the battery cells based on at least one protection parameter. For example, when (i) the voltage of the battery cell BC is greater than or equal to the maximum charging voltage or less than or equal to the minimum discharging voltage, (ii) the current flowing through the battery cell BC is equal to or higher than the maximum allowable current, and / or (iii) the charging power or discharging power of the battery cell BC is greater than or equal to the maximum allowable power, the processor 150 may switch the relay 20 to an off state or send an operation stop request to the inverter 30 and / or the charger 3.

[0161] exist Figure 13 In the method, at least one of steps 1360 and S1370 may be omitted.

[0162] Figure 14 is shown to be included in Figure 13 Flowchart of the subroutine in step S1330.

[0163] refer to Figure 14 In step S1410, the processor 150 determines the characteristic point P according to the differential voltage profile 400. cut-off .

[0164] In step S1420, the processor 150 calculates the value of the characteristic point P based on the characteristic point P. cut-off Capacity value Q cut-off To determine the interest capacity range P S To P E .

[0165] In step S1430, the processor 150 compares the voltage profile 300 with the capacity range of interest P S To P E The corresponding portion is determined as the first interest profile 500. The first interest profile 500 may be the voltage profile 300 relative to the characteristic point P cut-off Capacity value Q cut-off High capacity side portion when divided.

[0166] Figure 15 is shown to be included in Figure 14 Flowchart of the subroutine in step S1410.

[0167] refer to Figure 15 In step S1510, the processor 150 detects a local maximum point P having a maximum differential voltage within the target capacity range from the differential voltage profile 400. MAX .

[0168] In step S1520, the processor 150 converts the differential voltage profile 400 relative to the local maximum point P MAX The local minimum point located on the higher capacity side is determined as the characteristic point P of the differential voltage profile 400. cut-off .

[0169] Figure 16 is shown to be included in Figure 13 Flowchart of the subroutine in step S1330.

[0170] refer to Figure 16 In step S1610, the processor 150 determines a second interest profile 600 by normalizing the capacity-voltage domain of the first interest profile 500. The second interest profile 600 may be the result of performing a shift and scaling operation on the first interest profile 500, so that the starting point P of the first interest profile 500 is S and the end point P E Corresponding to the predetermined first reference point P R1 and the second reference point P R2 .

[0171] In step S1620, the processor 150 determines a characteristic point of the second interest profile 600. The characteristic point of the second interest profile 600 (eg, T C_i) is any one of the multiple data points of the second interest profile 600.

[0172] In step S1630 , the processor 150 determines that the characteristic slope is the same as the slope of the tangent line at the characteristic point of the second interest profile 600 .

[0173] Figure 17 is shown to be included in Figure 16 Flowchart of the subroutine in step S1620.

[0174] refer to Figure 17 In step S1710, the processor 150 calculates the i-th data point P among the first to n-th data points of the second interest profile 600. C_i The capacity value of the second interest section 600 is divided into a first sub-section and a second sub-section (see Figure 7 ).

[0175] In step S1720, the processor 150 determines an average voltage value of the first sub-section line and an average voltage value of the second sub-section line.

[0176] In step S1730, the processor 150 determines a first error value of the first sub-section line based on the average voltage value of the first sub-section line.

[0177] In step S1740, the processor 150 determines a second error value of the second sub-section line based on the average voltage value of the second sub-section line.

[0178] In step S1750 , the processor 150 determines whether the i-th comparison value is the same as the sum of the first error value and the second error value.

[0179] When repeated Figure 17 When the method shown in is used, when i is a natural number up to n, the first to nth comparison values ​​are determined. The processor 150 can determine any data point associated with the minimum comparison value among the first to nth comparison values ​​as a characteristic point of the second interest profile 600.

[0180] The above-mentioned embodiments of the present disclosure are not limited to being implemented only by devices and methods, but can also be implemented by programs that implement functions corresponding to the configurations of the embodiments of the present disclosure, or by recording media on which such programs are recorded. Based on the description of the above-mentioned embodiments, ordinary technicians in the technical field to which the present disclosure belongs can easily implement this.

[0181] Although the present disclosure has been described above with reference to several embodiments and drawings, the present disclosure is not limited thereto, and various changes and modifications may be made by a person skilled in the art to which the present disclosure belongs without departing from the technical spirit of the present disclosure and the equivalent scope of the claims to be described below.

[0182] In addition, since ordinary technicians in the field to which the present disclosure belongs can make various replacements, modifications and changes to the present disclosure as described above without departing from the technical spirit of the present disclosure, the present disclosure is not limited to the above-mentioned embodiments and drawings, but can selectively combine all or some of the corresponding embodiments to make various modifications.

Claims

1. A battery diagnostic device comprising: a data acquisition circuit configured to acquire capacity-voltage relationship data of a battery cell; as well as a processor configured to generate a voltage profile and a differential voltage profile of the battery based on the capacity-voltage relationship data, Wherein, the processor is configured to: extracting a first profile of interest from the voltage profile based on characteristic points of the differential voltage profile; determining a characteristic slope associated with the first profile of interest; and A degradation state of the battery cell is diagnosed based on the characteristic slope.

2. The battery diagnostic device according to claim 1, wherein: The processor is configured to: A data point having a minimum differential voltage value within the target capacity range is determined as the characteristic point of the differential voltage profile.

3. The battery diagnostic device according to claim 1, wherein: The processor is configured to: determining a capacity range of interest based on the capacity values ​​of the characteristic points of the differential voltage profile; and A portion of the voltage profile corresponding to the capacity range of interest is determined as the first profile of interest.

4. The battery diagnostic device according to claim 1, wherein: The processor is configured to: Normalizing the capacity-voltage domain of the first profile of interest to match a reference capacity-voltage domain to determine a second profile of interest; as well as The characteristic slope is determined to be equal to the slope of a tangent line at a characteristic point of the second profile of interest.

5. The battery diagnostic device according to claim 4, wherein: The processor is configured to: determining a plurality of comparison values ​​respectively associated with a plurality of data points of the second profile of interest; and Any one of the plurality of data points that is associated with a minimum value among the plurality of comparison values ​​is determined as the characteristic point of the second interest profile.

6. The battery diagnostic device according to claim 5, wherein: The processor is configured to: dividing the second interest profile into a first sub-profile and a second sub-profile based on a capacity value of each data point in the plurality of data points; as well as A comparison value of each of the plurality of data points is determined to be equal to a sum of a first error value based on an average voltage value of the first sub-section line and a second error value based on an average voltage value of the second sub-section line.

7. The battery diagnostic device according to claim 1, wherein: The processor is configured to: A first degradation parameter representing a capacity reduction ratio caused by degradation of a positive electrode of the battery cell is determined using the characteristic slope as an input variable of a linear regression model.

8. The battery diagnostic device according to claim 7, wherein: The processor is configured to: A second degradation parameter indicating a capacity reduction ratio caused by loss of available lithium in the battery cell is determined based on the total capacity reduction ratio of the battery cell and the first degradation parameter.

9. The battery diagnostic device according to claim 1, wherein: The capacity-voltage relationship data includes a capacity time series and a voltage time series of the battery cell during charging or discharging of the battery cell. 10 . A battery pack comprising the battery diagnostic device according to claim 1 . 11 . A battery system comprising the battery diagnosis device according to claim 1 .

12. A method for diagnosing a battery pack, the method comprising: Obtain capacity-voltage relationship data of battery cells; 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 characteristic points of the differential voltage profile; determining a characteristic slope associated with the first profile of interest; and A degradation state of the battery cell is diagnosed based on the characteristic slope.

13. The method according to claim 12, wherein: Extracting the first interest profile from the voltage profile includes: determining a capacity range of interest based on the capacity values ​​of the characteristic points of the differential voltage profile; and A portion of the voltage profile corresponding to the capacity range of interest is determined as the first profile of interest.

14. The method according to claim 12, wherein: Determining the characteristic slope associated with the first profile of interest further comprises: Normalizing the capacity-voltage domain of the first profile of interest to match a reference capacity-voltage domain to thereby determine a second profile of interest; and The characteristic slope is determined to be equal to the slope of a tangent line at a characteristic point of the second profile of interest.

15. The method according to claim 14, wherein Determining the characteristic slope associated with the first profile of interest further comprises: determining a plurality of comparison values ​​respectively associated with a plurality of data points of the second profile of interest; and Any one of the plurality of data points that is associated with a minimum value among the plurality of comparison values ​​is determined as the characteristic point of the second interest profile.

16. A non-transitory computer-readable recording medium storing a computer program including instructions that, when executed by a processor, cause the processor to perform operations comprising: Obtain capacity-voltage relationship data of battery cells; 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 characteristic points of the differential voltage profile; determining a characteristic slope associated with the first profile of interest; and A degradation state of the battery cell is diagnosed based on the characteristic slope.

17. The recording medium according to claim 16, wherein Extracting the first interest profile from the voltage profile includes: determining a capacity range of interest based on the capacity values ​​of the characteristic points of the differential voltage profile; and A portion of the voltage profile corresponding to the capacity range of interest is determined as the first profile of interest.

18. The recording medium according to claim 16, wherein Determining the characteristic slope associated with the first profile of interest further comprises: Normalizing the capacity-voltage domain of the first profile of interest to match a reference capacity-voltage domain to thereby determine a second profile of interest; and The characteristic slope is determined to be equal to the slope of a tangent line at a characteristic point of the second profile of interest.

19. The recording medium according to claim 18, wherein Determining the characteristic slope associated with the first profile of interest further comprises: determining a plurality of comparison values ​​respectively associated with a plurality of data points of the second profile of interest; and Any one of the plurality of data points that is associated with a minimum value among the plurality of comparison values ​​is determined as the characteristic point of the second interest profile.

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