Battery diagnosis apparatus, battery pack, electric vehicle and battery diagnosis method

Abstract

A battery diagnosis apparatus, a battery pack, an electric vehicle, and a battery diagnosis method are provided. The battery diagnosis apparatus comprises a control circuit configured to generate a Q-V profile, a normalized Q-V profile, and a Q-dV / dQ profile on the basis of capacity-voltage relationship data of a battery cell acquired by a data acquisition unit. The control circuit identifies a cut-off reference point located in a reference capacity range from the Q-dV / dQ profile. The control circuit determines a profile fitting factor associated with a Q-V profile of interest, which is a high capacity side portion of the normalized Q-V profile, on the basis of the capacity value of the cut-off reference point. The control circuit determines at least one degradation parameter of the battery cell on the basis of the profile fitting factor.

Description

Battery diagnostic device, battery pack, electric vehicle 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. 10-2023-0119680, filed on September 8, 2023, and all contents disclosed in the specification and drawings of the said application are incorporated herein by reference.

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

[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] Various techniques exist for monitoring battery cell degradation. In particular, differential voltage analysis (DVA) relies on time-series data of at least one externally observable battery parameter (e.g., voltage, current).

[0006] In DVA, peaks appearing in the differential voltage curve (which may be referred to as the 'Q-dV / dQ profile') are considered as a major factor, and some types of battery cells have a voltage flat characteristic in which the voltage change rate is maintained at almost 0 during charging or discharging. Since the differential voltage is also close to 0 in the capacity range where the voltage flat characteristic is expressed, it is difficult to detect peaks from the Q-dV / dQ profile. Therefore, a method is required that can accurately and easily diagnose the deterioration status of a battery cell even without extracting peak information indicating the deterioration status from the Q-dV / dQ profile.

[0007] The present invention has been made to solve the above problems, and its purpose is to provide a battery diagnostic device and a battery diagnostic method for precisely estimating at least one deterioration parameter regarding the deterioration state of a battery cell having voltage flatness characteristics without disassembling the battery cell.

[0008] 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.

[0009] According to one aspect of the present invention, a battery diagnosis device includes: a data acquisition unit for acquiring capacity-voltage relationship data of a battery cell; and a control circuit configured to generate, based on the capacity-voltage relationship data, a QV profile indicating a correspondence between a capacity and a voltage of the battery cell, a normalized QV profile indicating a correspondence between a normalized capacity and a voltage of the battery cell, and a Q-dV / dQ profile indicating a correspondence between a normalized capacity and a differential voltage of the battery cell. The control circuit is configured to identify a cut-off reference point located in a reference capacity range from the Q-dV / dQ profile. The control circuit is configured to determine a profile fitting factor associated with a QV profile of interest, which is a high-capacity portion of the normalized QV profile, based on a capacity value of the cut-off reference point. The control circuit is configured to determine at least one degradation parameter of the battery cell based on the profile fitting factor.

[0010] The control circuit may be configured to normalize the QV profile based on the entire capacity range of the QV profile to generate the normalized QV profile. The control circuit may be configured to differentiate the normalized QV profile to generate the Q-dV / dQ profile.

[0011] The control circuit may be configured to identify a minimum point within the reference capacity range as the cut-off reference point from the Q-dV / dQ profile.

[0012] The control circuit may be configured to generate a corrected QV profile of interest by executing a profile adjustment procedure to match a start point and an end point of the QV profile of interest to a first reference point and a second reference point, respectively. The control circuit may be configured to obtain a polynomial of the corrected QV profile of interest by applying a curve fitting algorithm to the corrected QV profile of interest. The control circuit may be configured to determine the profile fitting factor to be equal to the coefficient of the highest order term of the polynomial.

[0013] The control circuit may be configured to determine a first degradation parameter by using the profile fitting factor as an input variable for a linear regression model. The linear regression model may be prepared in advance as a relationship function between the profile fitting factor and a bipolar degradation state.

[0014] The above first degradation parameter may represent a rate of capacity reduction due to deterioration of the positive electrode of the battery cell.

[0015] The control circuit may be configured to determine a second degradation parameter based on the total capacity degradation rate of the battery cell and the first degradation parameter. The second degradation parameter may represent a capacity degradation rate due to available lithium loss of the battery cell.

[0016] The above capacity-voltage relationship data may represent the capacity change history and voltage change history of the battery while the battery cell is being charged or discharged.

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

[0018] An electric vehicle according to another aspect of the present invention includes the battery pack.

[0019] According to another aspect of the present invention, a battery diagnosis method may include: obtaining capacity-voltage relationship data of a battery cell; generating, based on the capacity-voltage relationship data, a QV profile indicating a correspondence between a capacity and a voltage of the battery cell, a normalized QV profile indicating a correspondence between a normalized capacity and a voltage of the battery cell, and a Q-dV / dQ profile indicating a correspondence between a normalized capacity and a differential voltage of the battery cell; identifying a cut-off reference point located in a reference capacity range from the Q-dV / dQ profile; determining a profile fitting factor associated with a QV profile of interest, which is a high-capacity portion of the normalized QV profile, based on a capacity value of the cut-off reference point; and determining at least one degradation parameter of the battery cell based on the profile fitting factor.

[0020] The step of generating the Q-dV / dQ profile may include the step of normalizing the QV profile based on the entire capacity range of the QV profile to generate the normalized QV profile; and the step of differentiating the normalized QV profile to generate the Q-dV / dQ profile.

[0021] The step of determining the profile fitting factor of the battery cell may include the steps of: executing a profile adjustment procedure to match a start point and an end point of the QV profile of interest to predetermined first and second reference points, respectively, to generate a corrected QV profile of interest; applying a curve fitting algorithm to the corrected QV profile of interest to obtain a polynomial of the corrected QV profile of interest; and determining the profile fitting factor to be equal to the coefficient of the highest order term of the polynomial.

[0022] The step of determining at least one degradation parameter of the battery cell may include the step of determining a first degradation parameter by using the profile fitting factor as an input variable for a linear regression model. The linear regression model may be prepared in advance as a relationship function between the profile fitting factor and the positive electrode degradation state.

[0023] The step of determining at least one degradation parameter of the battery cell may further include the step of determining a second degradation parameter based on the total capacity degradation rate of the battery cell and the first degradation parameter. The second degradation parameter may represent a capacity degradation rate due to available lithium loss of the battery cell.

[0024] According to at least one embodiment of the present invention, at least one degradation parameter regarding the degradation state of a battery cell can be precisely estimated without disassembling the battery cell. In particular, the present invention has the advantage of being able to non-destructively diagnose the anode degradation state of an LFP battery cell having voltage flatness characteristics.

[0025] In addition, according to at least one of the embodiments of the present invention, by extracting a portion ('QV profile of interest' described below) in which the deterioration characteristics of the positive electrode material overwhelm the deterioration characteristics of the negative electrode material from the entire voltage curve ('QV profile' described below) of the battery cell for a predetermined voltage range as a target of analysis, the state of the positive electrode deterioration of the battery cell (capacity deterioration rate due to positive electrode deterioration) can be more precisely identified.

[0026] 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 calculated from the total capacity decline rate of the battery cell and the capacity decline rate due to cathode degradation.

[0027] 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.

[0028] The following drawings attached to this specification illustrate preferred 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.

[0029] Figure 1 is a drawing exemplarily showing the configuration of an electric vehicle according to the present invention.

[0030] Fig. 2 is a graph showing an example of a QV profile of a battery cell.

[0031] Figure 3 is an example of a normalized QV profile obtained from the QV profile of Figure 2.

[0032] Figure 4 is an example of a Q-dV / dQ profile associated with the normalized QV profile illustrated in Figure 3.

[0033] Figure 5 is a graph showing an example of a QV profile of interest extracted from the normalized QV profile of Figure 4.

[0034] Figure 6 is an example of a corrected interest QV profile obtained from the interest QV profile of Figure 5.

[0035] Figure 7 is a drawing for reference to exemplarily illustrate the relationship between different bipolar degradation rates and corrected QV profiles of interest.

[0036] Figures 8 to 10 illustrate the results of polynomial fitting of each of the three corrected interest QV profiles shown in Figure 7 by a curve picking algorithm.

[0037] Figure 11 is a drawing for reference to exemplarily explain the relationship between different anode degradation rates and profile fitting factors.

[0038] FIG. 12 is a flowchart schematically illustrating a battery diagnosis method according to one embodiment of the present invention.

[0039] FIG. 13 is a flowchart exemplifying subroutines that may be included in step S1220 of FIG. 12.

[0040] FIG. 14 is a flowchart exemplifying subroutines that may be included in step S1230 of FIG. 12.

[0041] FIG. 15 is a flowchart exemplifying subroutines that may be included in step S1250 of FIG. 12.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] Throughout the specification, when a part is said to "include" a component, this does not exclude other components, unless otherwise specifically stated, but rather implies that other components may be included. Furthermore, terms such as "control circuit (130)" described in the specification refer to a unit that processes at least one function or operation, and may be implemented using hardware, software, or a combination of hardware and software.

[0046] 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.

[0047] Figure 1 is a drawing exemplarily showing the configuration of an electric vehicle according to the present invention.

[0048] Referring to FIG. 1, an electric vehicle (1) includes a system controller (2), a battery pack (10), an inverter (30), and an electric motor (40). The charge / discharge terminals (P+, P-) of the battery pack (10) can be electrically connected to a charger (3) via a charging cable or the like. The charger (3) may be included in the electric vehicle (1) or provided at a charging station. The electric vehicle (1) is an example of a battery system, which is a higher-level concept system including a battery pack (10) for at least one of energy storage and energy supply. Therefore, the following description can be commonly applied to battery systems including the electric vehicle (1).

[0049] The system controller (2) (e.g., ECU: Electronic Control Unit) is configured to transmit a key-on signal to the battery diagnostic device (100) in response to a user turning a start button (not shown) provided in the electric vehicle (1) to the ON position. The system controller (2) is configured to transmit a key-off signal to the battery diagnostic device (100) in response to a user turning the start button to the OFF position. 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).

[0050] The battery pack (10) includes a battery (11) and a relay (20). The battery pack (10) may further include a battery diagnostic device (100).

[0051] 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.

[0052] 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. Preferably, 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 range (or SOC range). 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 its positive and negative electrode materials, respectively.

[0053] 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).

[0054] 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 an electric vehicle (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.

[0055] The battery diagnostic device (100) may be implemented in the form of a type of cloud server located remotely from the battery pack (10). The battery diagnostic device (100) includes a control circuit (130). The battery diagnostic device (100) may further include at least one of a sensing unit (110) and a communication circuit (150). The term "data acquisition unit" described in the claims of the present invention may refer to either the sensing unit (110) or the communication circuit (130), or may be a term that collectively refers to both.

[0056] The sensing unit (110) includes a voltage sensor (111). The sensing unit (110) may further include a current sensor (112).

[0057] A voltage sensor (111) is connected to the positive and negative terminals of a battery cell (BC), and is configured to detect a voltage across both ends of the battery cell (BC) (which may be referred to as a 'full cell voltage') and 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.

[0058] 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 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 with one or a combination of two or more of known current detection elements such as a shunt resistor, a Hall effect element, etc.

[0059] The communication circuit (150) is configured to support wired or wireless communication between the control circuit (130) and the system controller (2). 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 control circuit (130) and the system controller (2), the type of communication protocol is not particularly limited. The communication circuit (150) may include an output device (e.g., a display, a speaker) that provides information received from the control circuit (130) and / or the system controller (2) in a form recognizable to a user (driver).

[0060] The control circuit (130) is operably coupled to a relay (20), a voltage sensor (111), and a communication circuit (150). The fact that the 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 or both directions.

[0061] The control circuit (130) can collect a voltage signal from the voltage sensor (111) and a current signal from the current sensor (112). In this specification, the detection signal may refer only to the voltage signal, or may be a term that collectively refers to both the voltage signal and the current signal. That is, the control circuit (130) can convert and record each analog signal collected from the sensors (111, 112) 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 the digital value to the control circuit (130).

[0062] The control circuit (130) may be referred to as a 'battery controller' and may be implemented in hardware using at least one of application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), microprocessors, and other electrical units for performing functions.

[0063] The memory (131) may include at least one type of storage medium among, 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 (131) may store data and a program required for an operation by the control circuit (130). The memory (131) may store data indicating a result of an operation by the control circuit (130). The memory (131) may store data sets and software used to diagnose a deterioration state of a battery cell (BC). The memory (131) can be integrated into the control circuit (130).

[0064] When the relay (20) is turned on during operation of the electric load (30, 40) and / or the 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 in use in the charging mode or the discharging mode, the battery (11) enters the idle mode.

[0065] The control circuit (130) can turn on the relay (20) in response to a key-on signal. The control circuit (130) 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 handled by the system controller (2) instead of the control circuit (130).

[0066] In this specification, time series data of a parameter represents the temporal change history of that parameter. In addition, a profile (or curve) representing the correspondence between two parameters obtained at the same timing during the same period may be a mapping of the time series data of two parameters so that it 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 set of two mapped time series data. Here, the degree of the highest term of the polynomial equation may be predetermined.

[0067] FIG. 2 is a graph illustrating an example of a QV profile of a battery cell, FIG. 3 is an example of a normalized QV profile obtained from the QV profile of FIG. 2, and FIG. 4 is an example of a Q-dV / dQ profile associated with the normalized QV profile illustrated in FIG. 3.

[0068] The graph (200) illustrated in Fig. 2 is a QV profile that represents the correspondence between the capacity and voltage of a battery cell (BC) according to capacity-voltage relationship data to be described later. In the graph (200), the vertical axis represents the voltage of the battery cell (BC), and the horizontal axis represents the capacity (unit: mAh).

[0069] The QV profile (200) may also be referred to as a 'capacity-voltage profile', a 'capacity-voltage curve', or a 'full cell 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).

[0070] It is assumed that the QV profile (200) is obtained through a charging cycle for a battery cell (BC). When performing the charging cycle, constant power or constant current may be utilized.

[0071] In a constant-power charging cycle, as the battery voltage increases, the charging current gradually decreases. Therefore, in addition to voltage time-series data, which represents the temporal history of battery voltage changes, voltage time-series data, which represents the temporal history of current flowing through the battery, are essential.

[0072] In a constant current charge cycle, a charging current having a predetermined current rate is controlled to flow through the battery cell (BC). Therefore, it is safe to assume that the capacity at a specific point in time is equal to 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) multiplied by the predetermined current rate. Of course, even in a constant current charge cycle, since the actual charging current may temporarily be greater or less than the originally intended charging current, the control circuit (130) may directly measure the current flowing through the battery cell (BC) using a current sensor (112), obtain the current measurement value, and repeatedly accumulate the obtained value periodically to generate current time series data by calculating the capacity during charging or discharging in real time.

[0073] The 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 when the voltage of the battery cell (BC) reaches a lower voltage limit of a predetermined voltage range from an upper voltage limit by a constant current charge cycle.

[0074] The QV profile (200) illustrated in FIG. 2 illustrates that the voltage of the battery cell (BC) increases from 2.6 V (lower voltage limit) to 3.6 V (upper voltage limit) 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 corresponds to a predetermined voltage range.

[0075] In this regard, at least the entire capacity range corresponding to a given voltage range may vary depending on the deterioration status of the battery cell (BC). Furthermore, even if the deterioration status of two different battery cells (BC) is the same, the capacity ranges of the two battery cells (BC) may differ due to manufacturing process variations, etc. In order to improve the ease and appropriateness of data processing required to diagnose the deterioration status of battery cells (BC) and to ensure the accuracy of the diagnosis results, it is necessary to apply a normalization procedure for the entire capacity range.

[0076] The graph (300) illustrated in FIG. 3 is an example of a normalized QV profile obtained by applying a normalization procedure to the QV profile (200). In the graph (300), the vertical axis represents the voltage of the battery cell (BC), similar to FIG. 2 , while the horizontal axis represents the normalized capacity (unit: %). The normalized capacity may be a term equivalent to the conventional SOC (State Of Charge).

[0077] When the voltage and capacity according to the QV profile (200) have a mathematical relationship according to the following equation 1, the voltage and normalized capacity according to the QV profile (300) have a mathematical relationship according to the following equation 2.

[0078] <Formula 1>

[0079]

[0080] <Formula 2>

[0081]

[0082] In formula 1, Q B is an arbitrary capacity value within the entire capacity range, V B is Q in QV profile (200) B Each represents a voltage value mapped to Q. In Equation 2, Q B_normal Silver Q B The normalized capacity value corresponding to Q total Each represents the size of the entire capacity range (i.e., the upper limit of the entire capacity range). For reference, although Fig. 3 shows the results of each data point of the entire capacity range of Fig. 2 normalized to a percentage (ranging from 0 to 100%), this should be understood as an example only. For example, instead of the range of 0 to 100%, the data point could be normalized to a different range, such as 0 to 1.

[0083] The graph (400) illustrated in Fig. 4 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' or a 'capacitance-differential voltage curve'.

[0084] The control circuit (130) can generate a Q-dV / dQ profile (400) by differentiating the voltage of the normalized QV profile (300) with respect to the normalized capacity. Specifically, the control circuit (130) can determine a differential voltage dV / dQ, which is a ratio of a change amount dV in the voltage V to a change amount dQ in the normalized capacity Q [%], and record the Q-dV / dQ profile (400) as relational data indicating a correspondence between the normalized capacity Q and the differential voltage dV / dQ in a memory.

[0085] The control circuit (130) can set a cut-off reference point located within a predetermined reference capacity range from the Q-dV / dQ profile (400). The reference capacity range may partially overlap with the capacity range in which the voltage flatness characteristic of the battery cell (BC) is expressed.

[0086] Specifically, the control circuit (130) determines from the Q-dV / dQ profile (400) the maximum point (P) having the maximum differential voltage within the reference capacity range. MAX ) can be identified. Next, the control circuit (130) identifies the maximum point (P) from the Q-dV / dQ profile (400). MAX ) is located on the high-capacity side of the cut-off reference point (P cut-off ) can be identified (detected). Cut-off reference point (P cut-off ) is the capacity value (Q) within the standard capacity range. cut-off ) may be a minimum point.

[0087] If there are two or more local minimum points within the reference capacity range, the local maximum point (P MAX ) is the cut-off reference point (P cut-off ) can be identified as the cut-off reference 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 cut-off reference point (P cut-off) can 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, those skilled in the art will easily understand that if only the high-capacity portion of the entire normalized QV profile (300) is analyzed, the positive electrode deterioration state of the battery cell (BC) can be precisely estimated.

[0088] The inventor of the present invention has found that the voltage characteristics of the negative electrode material are, compared to other parts of the normalized QV profile (300), at a cut-off reference point (P cut-off ) is reflected relatively very little in the high-capacity side part than the normalized QV profile (300). Therefore, the inventor of the present invention has also recognized that, among various degradation parameters related to the degradation state of the battery cell (BC), the parameter related to the anode degradation can be accurately diagnosed through analysis of the high-capacity side part among the entire normalized QV profile (300). In this specification, the cut-off reference point (P cut-off ), when the normalized QV profile (300) is divided into a low-capacity portion and a high-capacity portion as in Fig. 3, the high-capacity portion of the normalized QV profile (300) is referred to as the 'QV profile of interest' (see reference numeral 500 in Fig. 5). In Fig. 4, 50 to 99% is shown as an example set as the reference capacity range.

[0089] FIG. 5 is a graph showing an example of a QV profile of interest extracted from the normalized QV profile of FIG. 4, and FIG. 6 is an example of a corrected QV profile of interest obtained from the QV profile of interest of FIG. 5.

[0090] Referring to Figure 5, the QV profile of interest (500) is a cut-off reference point (P cut-off) is an enlarged view of a portion of a normalized QV profile (300) corresponding to a capacity range of interest (e.g., 92-99%), where the lower and upper capacity values ​​are the capacity value (e.g., 92%) and the upper capacity value (e.g., 99%) of the reference capacity range, respectively.

[0091] 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 start point, end point, and / or shape (e.g., curvature) of the QV profile (500) of interest extracted from the QV profile of interest (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 QV profile of interest (500).

[0092] Referring to FIG. 6, a corrected QV profile of interest (600) obtained through a normalization procedure (profile adjustment procedure) performed on the QV profile of interest (500) can be confirmed.

[0093] Specifically, the control circuit (130) determines the starting point (P) of the QV profile (500) of interest. S ) and endpoint (P E ) are each a predetermined first reference point (P R1 ) and the second reference point (P R2 ), at least one of shifting or scaling can be performed on the QV profile of interest (500) to generate a corrected QV profile of interest (600). The starting point (P) of the QV profile of interest (500) S ) may be a point having the minimum capacity value of the QV profile (500) of interest. The end point (P) of the QV profile (500) of interest E ) may be a point having the maximum capacity value of the QV profile of interest (500).

[0094] First reference point (P R1 ) is the capacity value of the starting point (P S) is smaller than the capacity value of the first reference point (P R1 ) is the voltage value at the starting point (P S ) is less than the voltage value of the second reference point (P R2 ) is the capacity value of the end point (P E ) is greater than the capacity value of the second reference point (P R2 ) is the voltage value at the end point (P E ) is greater than the voltage value.

[0095] The control circuit (130) is 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 ) can be performed a first operation for shifting the QV profile of interest (500) along at least one of the capacity axis or the voltage axis and a second operation for scaling the QV profile of interest (500) along at least one of the capacity axis or the voltage axis. The first operation can include at least one of horizontal movement (leftward movement or rightward movement with respect to the horizontal axis) or vertical movement (upward movement or downward movement with respect to the vertical axis). The second operation can include at least one of reduction or enlargement with respect to at least one of the horizontal axis or the vertical axis.

[0096] 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 , V R2 ) 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).

[0097] The control circuit (130) is configured to QV the interest QV 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 ) is required to match. Therefore, the control circuit (130) controls the QV profile (500) of interest along the capacity axis (Q R2 - Q R1 ) / (Q E - Q S ) and scaled along the capacity axis (V R2 - V R1 ) / (V E - V S ) can be scaled as a ratio. Accordingly, the process of generating a corrected QV profile of interest (600) from the QV profile of interest (500) is completed. That is, the corrected QV profile of interest (600) can be a result of the QV profile of interest (500) being projected into a normalized voltage range of 0 to 1 V and a normalized capacity range of 90 to 100%.

[0098] When the voltage and normalized capacity according to the QV profile of interest (500) have a mathematical relationship according to Equation 3 below, the normalized voltage and normalized capacity according to the corrected QV profile of interest (600) have a mathematical relationship according to Equations 4-1 and 4-2 below.

[0099] <Formula 3>

[0100]

[0101] <Formula 4-1>

[0102]

[0103] <Formula 4-2>

[0104]

[0105] In formula 3, Q B_normal is the normalized capacity value of any point on the QV profile of interest (500), V B is the QV profile of interest (500) B_normal Each voltage value mapped to is represented.

[0106] In Equations 4-1 and 4-2, Q B_normal_2 Silver Q B_normal The normalized capacity value of the corrected interest QV profile (600) corresponding to the corresponding voltage is shown in FIG. 6. For reference, FIG. 6 shows the result of each data point of the entire voltage range of FIG. 5 being normalized to the range of 0 to 1 V, but this should be understood as an example only.

[0107] The control circuit (130) can determine a profile fitting factor of the battery cell (BC) based on the corrected QV profile of interest (600). Specifically, the control circuit (130) can determine a polynomial of the corrected QV profile of interest (600) by applying a curve fitting algorithm to the corrected QV profile of interest (600). Then, the control circuit (130) can determine a profile fitting factor equal to the coefficient of the highest order term of the polynomial. The order of the highest order term may be predetermined.

[0108] The control unit (130) can input the profile fitting factor as an input variable to a linear regression model to determine a first degradation parameter associated with the degradation state of a battery cell (BC). The first degradation parameter can represent a capacity reduction rate due to positive electrode degradation of a battery cell. The capacity reduction rate due to positive electrode degradation can be referred to as a "positive electrode degradation degree" or a "positive electrode degradation-derived capacity reduction ratio." Hereinafter, the linear regression model will be described in more detail with reference to FIGS. 7 to 11.

[0109] The inventor of the present invention has secured relationship data that can be generated using a linear regression model by sequentially performing a process of forcibly deteriorating a plurality of battery cells (BC) prepared as experimental subjects so that the battery cells (BC) have different degrees of anode deterioration, a process of calculating a profile fitting factor associated with each forcibly deteriorated battery cell (BC), a process of disassembling each forcibly deteriorated battery cell (BC) to produce anode half-cell, and a process of measuring and recording the available capacity of each anode half-cell.

[0110] FIG. 7 is a drawing for reference to exemplarily explain the relationship between different anode degradation degrees and corrected QV profiles of interest, FIGS. 8 to 10 illustrate the results of polynomial fitting of each of the three corrected QV profiles of interest shown in FIG. 7 by a curve picking algorithm, and FIG. 11 is a drawing for reference to exemplarily explain the relationship between different anode degradation degrees and profile fitting factors.

[0111] Figure 7 shows how the corrected QV profile of interest changes in a pattern as the degree of anode degradation increases. The degree of anode degradation can refer to the rate of capacity reduction due to anode degradation.

[0112] Referring to FIG. 7, curve (710) illustrates a corrected QV profile of interest acquired in a new state where the anode is not deteriorated at all, curve (720) illustrates a corrected QV profile of interest acquired in a state where the anode deterioration is 1.75% after 200 charge / discharge cycles, and curve (730) illustrates a corrected QV profile of interest acquired in a state where the anode deterioration is 9.40% after 600 charge / discharge cycles.

[0113] That is, as the degree of polarization increases, the corrected QV profile of interest gradually decreases towards the first reference point (P R1 ) and the second reference point (P R2 ) changes closer to a straight line shape, and accordingly, it can be confirmed from Figure 7 that the profile fitting factor decreases.

[0114] According to FIGS. 8 to 10, each of the curves (710, 720, and 730) is illustrated as a polynomial with a highest degree of 3. In addition, the coefficients of the highest degree terms of the polynomials of the curves (710, 720, and 730) are 0.0056, 0.0036, and 0.0004, respectively, and it can be seen from this that as the degree of anode degradation increases, the coefficients of the highest degree terms of the polynomials decrease. The pattern of a certain polynomial depends most greatly on the coefficient of the highest degree term among the terms of the polynomial.

[0115] Referring to Fig. 11, a linear regression model (1100) is prepared in advance as a relationship function representing the correspondence between the profile fitting factor (coefficient of the highest order term) and the bipolar deterioration state. Point (1110) is related to curve (710) of Fig. 7, point (1120) is related to curve (720) of Fig. 7, and point (1130) is related to curve (730) of Fig. 7. Although not all are shown, the inventors obtained additional points other than points (1110, 1120, 1130) as a result of the above-described experiment and then utilized them to obtain a linear regression model (1100) through linear regression analysis. The linear regression model (1100) may be stored in advance in memory (131). Equation 5 below is an example of a linear regression model (1100).

[0116] <Formula 5>

[0117]

[0118] 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 (1100), respectively. x represents a profile fitting factor as an input variable, 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 each of the cathode and anode materials. 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 each of the cathode and anode materials, etc.). For example, A and B of the linear regression model (1100) illustrated in FIG. 11 are -1898 and 9.88, respectively.

[0119] The control circuit (130) can input a profile fitting factor obtained from a battery cell (BC) having an as-yet-unknown positive electrode deterioration state as an input variable x into a linear regression model (1100) to obtain a positive electrode deterioration degree as an output variable y.

[0120] Table 1 below summarizes the relationship between the number of cycles (number of cycles) of the aforementioned constant-current charging cycles, the total capacity decline rate, the profile fitting factor, the first degradation parameter (capacity decline rate due to anode degradation), and the second degradation parameter (capacity decline rate due to available lithium loss). Here, available lithium may refer to lithium ions that can participate in the charge / discharge reaction of the battery cell (BC).

[0121]

[0122] According to Table 1, as the number of cycles increases, the total capacity decline rate, profile fitting factor, capacity decline rate due to cathode degradation, and capacity decline rate due to available lithium loss all increase simultaneously. Note that the number of cycles can be incremented by one each time a charge (or discharge) by constant power (or constant current) is completed.

[0123] The total capacity decline rate can be the ratio of the decrease in the buffer capacity due to deterioration to the buffer capacity when the battery cell (BC) is in a new state. For example, let the buffer capacity in a new state = P, the buffer capacity in a deteriorated state = U, and the decrease in buffer capacity = W. Then, W = PU, and the total capacity decline rate = (W / P) ס¿ 100%.

[0124] The inventors of the present invention have recognized that the capacity decline rate due to available lithium loss added to the capacity decline rate due to cathode degradation is substantially equal to the total capacity decline rate. Accordingly, the control circuit (130) can determine the capacity decline rate due to available lithium loss as a second degradation parameter by subtracting the cathode degradation rate determined through the aforementioned Equation 5 from the total capacity decline rate.

[0125] FIG. 12 is a flowchart schematically illustrating a battery diagnosis method according to one embodiment of the present invention. The method according to FIG. 12 includes steps S1210 to S1260. The method according to FIG. 12 may further include step S1270.

[0126] Referring to FIGS. 1 to 12, in step S1210, the control circuit (130) acquires capacity-voltage relationship data of a battery cell (BC) using a data acquisition unit. 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.

[0127] For example, when the data acquisition unit includes a sensing unit (110), the control circuit (130) can generate a voltage time series and a capacity time series based on the detection signal generated by the sensing unit (110). The capacity-voltage relationship data can include a voltage time series and a capacity time series. The data points of the voltage time series and the data points of the current time series can be mapped one-to-one with each other.

[0128] A voltage time series can represent a temporal history of changes in the voltage of a battery cell (BC) while the battery cell (BC) is charged (or discharged) with a constant voltage (or constant current) over a predetermined voltage range. A current time series can represent a temporal history of changes in the current flowing through the battery cell (BC) over a period of time equal to the acquisition period of the voltage time series.

[0129] As another example, if the data acquisition unit includes a communication circuit (150), the control circuit (130) can receive capacity-voltage relationship data from an external device using the communication circuit (150).

[0130] In step S1220, the control circuit (130) generates a QV profile (200), a normalized QV profile (300), and a Q-dV / dQ profile (400) of the battery cell (BC) based on the capacity-voltage relationship data.

[0131] In step S1230, the control circuit (130) determines a cut-off reference point (P) from the Q-dV / dQ profile (400). cut-off ) is identified (detected).

[0132] In step S1240, the control circuit (130) sets a cut-off reference point (P cut-off ) capacity value (Q) cut-off ) is used as a reference, the QV profile of interest (500), which is a high-capacity portion of the normalized QV profile (300).

[0133] In step S1250, the control circuit (130) determines a profile fitting factor associated with the QV profile of interest (500).

[0134] In step S1260, the control circuit (130) determines at least one degradation parameter associated with the degradation state of the battery cell (BC) based on the profile fitting factor. Accordingly, at least one of the first degradation parameter and the second degradation parameter can be determined.

[0135] In step S1270, the control circuit (130) may determine at least one protection parameter for the battery cell (BC) based on at least one degradation parameter determined in step S1260. 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.

[0136] The control circuit (130) can switch the relay (20) to an OFF state 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 charge voltage or lower than the minimum discharge voltage, (ii) the current flowing through the battery cell (BC) is higher than the maximum allowable current, and / or (iii) the charge power or discharge power of the battery cell (BC) is higher than the maximum allowable power.

[0137] FIG. 13 is a flowchart exemplifying subroutines that may be included in step S1220 of FIG. 12.

[0138] In step S1310, the control circuit (130) generates a QV profile (200) from the capacity-voltage relationship data obtained in step S1210.

[0139] In step S1320, the control circuit (130) normalizes the QV profile (200) based on the entire capacity range of the QV profile (200) to generate a normalized QV profile (300).

[0140] In step S1330, the control circuit (130) differentiates the normalized QV profile (300) to generate a Q-dV / dQ profile (400) representing the correspondence between the normalized capacity of the battery cell (BC) and the differential voltage.

[0141] FIG. 14 is a flowchart exemplifying subroutines that may be included in step S1230 of FIG. 12.

[0142] Referring to FIG. 14, in step S1410, the control circuit (130) determines from the Q-dV / dQ profile (400) a local maximum point (P) having a maximum differential voltage within the reference capacity range. MAX ) is identified.

[0143] In step S1420, the control circuit (130) determines the maximum point (P) from the Q-dV / dQ profile (400). MAX) is the cut-off reference point (P cut-off ) is identified.

[0144] FIG. 15 is a flowchart exemplifying subroutines that may be included in step S1250 of FIG. 12.

[0145] Referring to FIG. 15, in step S1510, the control circuit (130) determines the starting point (P) of the QV profile (500) of interest. S ) and the end point (P E ) are each a predetermined first reference point (P R1 ) and the second reference point (P R2 ), a profile adjustment operation is performed on the QV profile of interest (500) to generate a corrected QV profile of interest (600).

[0146] In step S1520, the control circuit (130) applies a curve fitting algorithm to the corrected QV profile of interest (600) to obtain a polynomial of the corrected QV profile of interest (600).

[0147] In step S1530, the control circuit (130) determines a profile fitting factor equal to the coefficient of the highest order term of the polynomial.

[0148] In step S1260, the control circuit (130) can determine a first degradation parameter y associated with the degradation state of the battery cell (BC) by using the profile fitting factor determined in step S1530 as an input variable x for the linear regression model (1100) (see Equation 5). The linear regression model (1100) may be prepared in advance as a relationship function between the profile fitting factor and the positive electrode degradation state. Optionally, the control circuit (130) can additionally determine a second degradation parameter by subtracting the first degradation parameter from the total capacity degradation rate.

[0149] 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.

[0150] 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.

[0151] 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 that acquires capacity-voltage relationship data of a battery cell; and A control circuit configured to generate a QV profile indicating a correspondence between the capacity and voltage of the battery cell, a normalized QV profile indicating a correspondence between the normalized capacity and voltage of the battery cell, and a Q-dV / dQ profile indicating a correspondence between the normalized capacity and differential voltage of the battery cell based on the capacity-voltage relationship data, The above control circuit, Identify a cut-off reference point located within the reference capacity range from the above Q-dV / dQ profile, Determine the profile fitting factor associated with the QV profile of interest, which is the high-capacity portion of the normalized QV profile, based on the capacity value of the cut-off reference point, A battery diagnostic device configured to determine at least one degradation parameter of the battery cell based on the profile fitting factor.

2. In paragraph 1, The above control circuit, Normalizing the QV profile based on the entire capacity range of the QV profile to generate the normalized QV profile, A battery diagnostic device configured to generate the Q-dV / dQ profile by differentiating the normalized QV profile.

3. In paragraph 1, The above control circuit, A battery diagnostic device configured to identify a minimum point within the reference capacity range as the cut-off reference point from the above Q-dV / dQ profile.

4. In paragraph 1, The above control circuit, A profile adjustment procedure is executed to match the start and end points of the QV profile of interest to the first and second reference points, respectively, to generate a corrected QV profile of interest, By applying a curve fitting algorithm to the above-mentioned corrected QV profile of interest, a polynomial of the above-mentioned corrected QV profile of interest is obtained, A battery diagnostic device configured to determine the profile fitting factor equal to the coefficient of the highest order term of the above polynomial.

5. In paragraph 1, The above control circuit, The above profile fitting factor is configured to determine the first degradation parameter by using it as an input variable for a linear regression model, A battery diagnostic device, wherein the linear regression model is prepared in advance as a relationship function between the profile fitting factor and the cathode deterioration state.

6. In paragraph 5, The above first degradation parameter is, A battery diagnostic device that indicates the rate of capacity reduction due to deterioration of the positive electrode of the cell of the above battery.

7. In paragraph 5, The above control circuit, Based on the total capacity degradation rate of the above battery cell and the first degradation parameter, a second degradation parameter is determined, A battery diagnostic device, wherein the second degradation parameter indicates a rate of capacity reduction due to loss of available lithium of the battery cell.

8. In paragraph 1, The above capacity-voltage relationship data is, A battery diagnostic device that displays the capacity change history and voltage change history of the battery while the battery cell is being charged or discharged.

9. A battery pack comprising a battery diagnostic device according to any one of claims 1 to 8.

10. An electric vehicle comprising a battery pack according to Article 9.

11. Step of obtaining capacity-voltage relationship data of a battery cell; A step of generating a QV profile representing a correspondence between the capacity and voltage of the battery cell, a normalized QV profile representing a correspondence between the normalized capacity and voltage of the battery cell, and a Q-dV / dQ profile representing a correspondence between the normalized capacity and differential voltage of the battery cell based on the capacity-voltage relationship data; A step of identifying a cut-off reference point located within a reference capacity range from the above Q-dV / dQ profile; A step of determining a profile fitting factor associated with the QV profile of interest, which is a high-capacity portion of the normalized QV profile, based on the capacity value of the cut-off reference point; and A step of determining at least one degradation parameter of the battery cell based on the profile fitting factor; A battery diagnostic method comprising:

12. In paragraph 11, The steps of generating the above Q-dV / dQ profile are: A step of normalizing the QV profile based on the entire capacity range of the QV profile to generate the normalized QV profile; and A step of differentiating the normalized QV profile to generate the Q-dV / dQ profile; A battery diagnostic method comprising:

13. In paragraph 11, The step of determining the profile fitting factor of the above battery cell is: A step of generating a corrected QV profile of interest by executing a profile adjustment procedure to match the start point and the end point of the QV profile of interest to a first reference point and a second reference point, respectively; A step of obtaining a polynomial of the corrected QV profile of interest by applying a curve fitting algorithm to the corrected QV profile of interest; and A step of determining the profile fitting factor to be the same as the coefficient of the highest term of the above polynomial; A battery diagnostic method comprising:

14. In paragraph 13, The step of determining at least one degradation parameter of the battery cell comprises: A step of determining a first degradation parameter by using the above profile fitting factor as an input variable for a linear regression model; Including, but not limited to, A battery diagnosis method, wherein the linear regression model is prepared in advance as a relationship function between the profile fitting factor and the cathode deterioration state.

15. In paragraph 14, The step of determining at least one degradation parameter of the battery cell comprises: A step of determining a second degradation parameter based on the total capacity degradation rate of the battery cell and the first degradation parameter; Including more than, The above second degradation parameter is, A battery diagnosis method, which indicates a capacity reduction rate due to available lithium loss of the above battery cell.

Images