Battery diagnosis device and battery diagnosis method

Abstract

The invention provides a battery diagnosis apparatus and a battery diagnosis method. The battery diagnosis apparatus includes: a data obtaining unit configured to obtain a first target total battery cell curve representing a correspondence relationship between a voltage and a capacity factor of a target cell while a first electrical stimulus is applied to the target cell; and a control circuit configured to generate an estimated total cell curve based on the first target total cell curve and the overpotential curve. The control circuit determines a first set of performance factors as a primary estimate of charge / discharge performance of the target cell by applying cell diagnostic logic to the estimated full cell curve. The control circuit determines a second set of performance factors as a secondary estimate of charge / discharge performance of the target cell by applying a factor correction model to the first set of performance factors. The second performance factor group comprises an estimation result of the negative electrode participation starting point of the target monomer.

Description

Technical Field

[0001] The present disclosure relates to a battery diagnosis apparatus and method for non-destructively diagnosing charge / discharge performance of a battery.

[0002] This application claims priority from Korean Patent Application No. 10-2023-0165459 filed in Korea on November 24, 2023, the disclosure of which is incorporated herein by reference. Background Art

[0003] Recently, there has been a rapid increase in demand for portable electronic products such as laptop computers, cameras, and mobile phones, and with the widespread development of electric vehicles, accumulators for energy storage, robots, and satellites, much research is being conducted on high-performance batteries that can be repeatedly recharged.

[0004] Currently, commercially available batteries include nickel-cadmium batteries, nickel-metal hydride batteries, nickel-zinc batteries, lithium batteries, etc., and among them, lithium batteries have little or no memory effect, so they have received more attention than nickel-based batteries because they have advantages in that they can be recharged whenever convenient, have a very low self-discharge rate and have a high energy density.

[0005] Typically, actual charge / discharge performance of a battery may fall short of normal charge / discharge performance due to reasons such as manufacturing defects or degradation due to use, and accurate diagnosis of the charge / discharge performance of the battery is required in order to improve the lifespan and safety of the battery.

[0006] Conventionally, while a low electrical stimulus (e.g., low-rate charge or discharge) is applied to the battery, the battery's voltage and capacity are measured and recorded. Based on the recorded measured values, a full-cell curve representing the corresponding relationship between voltage and capacity is generated to diagnose the battery's condition. However, since the capacity and voltage of a battery change slowly while applying a low electrical stimulus (e.g., low-rate charge or discharge), battery diagnosis is limited by the time it takes.

[0007] High electrical stimulation is naturally more advantageous than low electrical stimulation in shortening diagnostic time. However, when high electrical stimulation (e.g., high-rate charging or discharging) is applied to a battery, the proportion of overpotential in the battery voltage becomes excessively high. More specifically, as the current flowing through the battery increases, polarization increases, and this overpotential is caused by this polarization. Since the battery voltage can be considered the sum of the open-circuit voltage (OCV) and the overpotential, the difference between the battery voltage and the actual OCV increases with higher levels of electrical stimulation applied to the battery.

[0008] As the battery voltage approaches the actual OCV, the battery's charge / discharge performance can be diagnosed more accurately, so the overpotential acts as a noise that reduces diagnostic accuracy. Therefore, the diagnostic results based on the charge / discharge performance of the full-cell curve obtained using high electrical stimulation may have a significant gap with the battery's actual charge / discharge performance. Summary of the Invention

[0009] Technical issues

[0010] The present disclosure is designed to solve the problems of the related art, and therefore the present disclosure is directed to providing a battery diagnostic device and a battery diagnostic method, which can simultaneously shorten the diagnosis time and simultaneously ensure the diagnosis accuracy by applying high electrical stimulation to the battery to obtain charge / discharge information (the first target full-cell curve in the claims) and using a factor correction model based on machine learning to remove noise caused by overpotential included in the obtained charge / discharge information.

[0011] These and other purposes and advantages of the present disclosure can be understood from the following detailed description and will become more fully apparent from the exemplary embodiments of the present disclosure. Moreover, it will be readily understood that the purposes and advantages of the present disclosure can be achieved by the means shown in the appended claims and their combinations.

[0012] Technical Solution

[0013] In one aspect of the present disclosure, a battery diagnostic device is provided, comprising: a data acquisition unit configured to obtain a first target full-cell curve while a first electrical stimulus is applied to a target cell, the first target full-cell curve representing a correspondence between the voltage and capacity factor of the target cell, the target cell being the battery cell to be diagnosed; and a control circuit configured to generate an estimated full-cell curve based on the first target full-cell curve and an overpotential curve. The control circuit is configured to determine a first performance factor group as a primary estimate of the charge / discharge performance of the target cell by applying a cell diagnostic logic to the estimated full-cell curve, and to determine a second performance factor group as a secondary estimate of the charge / discharge performance of the target cell by applying a factor correction model to the first performance factor group. The second performance factor group represents an estimate of the negative electrode participation starting point of the target cell, which can be determined by applying the cell diagnostic logic to the second target full-cell curve. The second target full-cell curve represents a correspondence between the voltage and capacity factor of the target cell while a second electrical stimulus different from the first electrical stimulus is applied.

[0014] The first electrical stimulation may be an electrical stimulation that induces an overpotential exceeding an allowable level in the target cell, and the second electrical stimulation may be an electrical stimulation that induces an overpotential less than the allowable level in the target cell.

[0015] The first electrical stimulus may charge using a first current rate, and the second electrical stimulus may charge using a second current rate that is less than the first current rate.

[0016] The first electrical stimulation may be discharged using a first current rate, and the second electrical stimulation may be discharged using a second current rate that is less than the first current rate.

[0017] The overpotential curve may represent the difference between a first reference all-cell curve and a second reference all-cell curve. The first reference all-cell curve may represent the corresponding relationship between the voltage and capacity factor of a reference cell when a first electrical stimulus is applied to the reference cell, where the reference cell is a battery cell that has been verified to be normal. The second reference all-cell curve may represent the corresponding relationship between the voltage and capacity factor of the reference cell when a second electrical stimulus is applied to the reference cell.

[0018] The control circuit may be configured to generate the estimated full-cell curve by subtracting the overpotential curve from the first target full-cell curve.

[0019] The first performance factor group may include at least one of the following as a performance factor: a positive electrode participation starting point, which represents the positive electrode voltage and the positive electrode capacity when the voltage of the target cell matches the first set voltage; a positive electrode participation end point, which represents the positive electrode voltage and the positive electrode capacity when the voltage of the target cell matches the second set voltage; a positive electrode scaling factor, which represents the ratio of the capacity difference between the positive electrode participation starting point and the positive electrode participation end point relative to the reference positive electrode capacity; a negative electrode participation starting point, which represents the negative electrode voltage and the negative electrode capacity when the voltage of the target cell matches the first set voltage; a negative electrode participation end point, which represents the negative electrode voltage and the negative electrode capacity when the voltage of the target cell matches the second set voltage; and a negative electrode scaling factor, which represents the ratio of the capacity difference between the negative electrode participation starting point and the negative electrode participation end point relative to the reference negative electrode capacity.

[0020] The factor correction model may be a machine learning model trained by a training data set including pairs of a first performance factor group and a second performance factor group for each of a plurality of test cells having different charge / discharge performances.

[0021] A first performance factor set for each of the plurality of test cells can be obtained by applying cell diagnostic logic to each of the plurality of estimated test full-cell curves. A plurality of estimated test full-cell curves can be obtained by subtracting an overpotential curve from each of the plurality of primary test full-cell curves representing a correspondence between a voltage and a capacity factor for each of the plurality of test cells while a first electrical stimulus is applied to each of the plurality of test cells. A second performance factor set for each of the plurality of test cells can be obtained by applying cell diagnostic logic to a plurality of secondary test full-cell curves. The plurality of secondary test full-cell curves can represent a correspondence between a voltage and a capacity factor for each of the plurality of test cells while a second electrical stimulus is applied to each of the plurality of test cells.

[0022] In another aspect of the present disclosure, a battery pack including a battery diagnostic device is further provided.

[0023] In yet another aspect of the present disclosure, an electric vehicle including a battery pack is provided.

[0024] In another aspect of the present disclosure, a battery diagnostic method is provided, comprising: obtaining a first target full-cell curve representing a correspondence between a voltage and a capacity factor of a target cell, which is a battery cell to be diagnosed, while a first electrical stimulus is applied to the target cell; generating an estimated full-cell curve based on the first target full-cell curve and an overpotential curve; determining a first performance factor group as a primary estimate of the charge / discharge performance of the target cell by applying a cell diagnostic logic to the estimated full-cell curve; and determining a second performance factor group as a secondary estimate of the charge / discharge performance of the target cell by applying a factor correction model to the first performance factor group. The second performance factor group may represent an estimate of a negative electrode participation starting point of the target cell that can be determined by applying the cell diagnostic logic to a second target full-cell curve instead of the estimated full-cell curve, wherein the second target full-cell curve represents a correspondence between the voltage and the capacity factor of the target cell while a second electrical stimulus different from the first electrical stimulus is applied.

[0025] The step of generating an estimated full-cell curve may be generating the estimated full-cell curve by subtracting the overpotential curve from the first target full-cell curve.

[0026] The factor correction model may be a machine learning model trained by a training data set including pairs of a first performance factor group and a second performance factor group for each of a plurality of test cells having different charge / discharge performances.

[0027] A first performance factor group for each of the plurality of test cells can be obtained by applying cell diagnostic logic to each of the plurality of estimated test full-cell curves. While a first electrical stimulus is applied to each of the plurality of test cells, a plurality of estimated test full-cell curves can be obtained by subtracting an overpotential curve from each of a plurality of primary test full-cell curves representing a correspondence between voltage and capacity factor for each of the plurality of test cells. A second performance factor group for each of the plurality of test cells can be obtained by applying cell diagnostic logic to a plurality of secondary test full-cell curves. The plurality of secondary test full-cell curves can represent a correspondence between voltage and capacity factor for each of the plurality of test cells while a second electrical stimulus is applied to each of the plurality of test cells.

[0028] Beneficial effects

[0029] According to at least one of the embodiments of the present disclosure, a battery's charge / discharge performance can be diagnosed based on charge / discharge information obtained by applying a high electrical stimulus to the battery (the "first target all-cell curve" in the claims). Consequently, compared to diagnostic methods using low electrical stimulus (e.g., low-rate charge or discharge), the time required to diagnose the battery's charge / discharge performance can be shortened.

[0030] In addition, according to at least one of the embodiments of the present disclosure, the accuracy of the diagnosis of the charge / discharge performance can be improved by estimating the charge / discharge information from which the overpotential component caused by the high electrical stimulus is removed from the charge / discharge information obtained by applying a high electrical stimulus to the battery (the "estimated full-cell curve" in the claims) and analyzing the estimated charge / discharge information to diagnose the charge / discharge performance.

[0031] In addition, according to at least one of the embodiments of the present disclosure, by using a machine learning-based factor correction model to correct the performance factor group representing the charge / discharge performance determined from the estimated charge / discharge information, a diagnostic result with a high degree of consistency with the actual charge / discharge performance of the battery can be ensured.

[0032] The effects of the present disclosure are not limited to the above-described effects, and those skilled in the art will clearly understand these and other effects from the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings illustrate preferred embodiments of the present disclosure and, together with the foregoing disclosure, serve to provide a further understanding of the technical features of the present disclosure, and therefore, the present disclosure is not to be construed as being limited to the accompanying drawings.

[0034] Figure 1 is a diagram exemplarily showing the configuration of an electric vehicle according to the present disclosure.

[0035] Figure 2This is a graph used to explain the relationship between electrical stimulation and whole monomer curves.

[0036] Figure 3 is a schematic diagram showing that Figure 2 Figure 3 is a graph of overpotential curves obtained from a first reference all-monomer curve and a second reference all-monomer curve.

[0037] Figure 4 It is a graph referred to for explaining the relationship among the first target all-monomer curve, the estimated all-monomer curve, and the second target all-monomer curve.

[0038] Figure 5 is a graph referred to for explaining an example of each of the estimated all-cell curve, the second reference all-cell curve, the reference positive electrode curve, and the reference negative electrode curve.

[0039] Figures 6 to 8 This is a diagram referred to for explaining an example of a process of generating a comparison all-cell curve based on a cell diagnosis logic.

[0040] Figures 9 to 11 FIG. 1 is a diagram referred to for explaining another example of a process of generating a comparison all-cell curve according to a cell diagnosis logic.

[0041] Figure 12 is a graph to refer to for interpreting the functionality of the factor-adjusted model.

[0042] Figure 13 is a graph referenced for explaining the training data set provided for training the factor correction model.

[0043] Figure 14 It shows Figure 12 Figure 1 shows an example of a neural network structure for a factor correction model.

[0044] Figure 15 is a graph showing an example of correlation coefficients between performance factors obtained by training a factor correction model.

[0045] Figure 16 FIG. 1 is a flowchart schematically illustrating a battery diagnosis method according to another embodiment of the present disclosure. DETAILED DESCRIPTION

[0046] Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Before the description, it should be understood that the terms used in the specification and the appended claims should not be construed as limited to general meanings and dictionary meanings, but should be interpreted based on the meanings and concepts corresponding to the technical aspects of the present disclosure, based on the principle that the inventor is allowed to appropriately define the terms for the best interpretation.

[0047] Therefore, the descriptions presented herein are merely preferred examples for illustrative purposes, and are not intended to limit the scope of the present disclosure, and it should be understood that other equivalents and modifications may be employed without departing from the scope of the present disclosure.

[0048] Terms including ordinal numbers such as “first,” “second,” etc. are used to distinguish one element from another among various elements, but are not intended to limit the elements by the terms.

[0049] Unless the context clearly indicates otherwise, the terms "comprise" and "include" when used in this specification specify the presence of the elements described, but do not exclude the presence or addition of one or more other elements. In addition, the term "unit" as used herein refers to a processing unit of at least one function or operation, and can be implemented by hardware and software alone or in combination.

[0050] Furthermore, throughout the specification, it will be understood that when an element is referred to as being “connected to” another element, it can be directly connected to the other element or intervening elements may be present.

[0051] Figure 1 is a diagram exemplarily showing the configuration of an electric vehicle according to the present disclosure.

[0052] refer to Figure 1 , the electric vehicle 1 includes a vehicle controller 2 , a battery pack 10 , a relay 20 , an inverter 30 and a motor 40 .

[0053] The charging terminal P+ and the discharging terminal P− of the battery pack 10 may be electrically connected to the inverter 30 and / or the charger 3 through a charging cable, etc. The charger 3 may be included in the electric vehicle 1 or may be provided at a charging station.

[0054] The vehicle controller 2 (e.g., an ECU: Electronic Control Unit) 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 electric vehicle 1 to the on position. The vehicle controller 2 is also configured to transmit a key-off signal to the battery diagnostic device 100 in response to a user switching the start button to the off position. The charger 3 can communicate with the vehicle controller 2 and supply charging power to the battery 11 via the charge terminal P+ and discharge terminal P− of the battery pack 10 in a constant current charging mode, a constant voltage charging mode, and / or a constant power charging mode.

[0055] The battery pack 10 includes a battery 11 . The battery pack 10 may further include a battery diagnostic device 100 .

[0056] The battery 11 includes at least one battery cell BC. When the battery 11 includes a plurality of battery cells (BC1 to BC N, N is a natural number greater than or equal to 2), multiple battery cells can be connected in series, in parallel, or in a mixture of series and parallel connections.

[0057] There is no particular limitation on the type of battery cell BC, as long as it can be repeatedly charged and discharged, such as a lithium-ion cell. The battery cell BC may include at least one unit cell. A unit battery is an electrochemical device that can be recharged independently. When the battery cell BC includes a plurality of unit cells, the plurality of unit cells may be connected in series, in parallel, or in a mixture of series and parallel. The battery cell BC may be a new battery cell that needs to be verified as a good product, or a battery cell that has deteriorated after being verified as a good product and is no longer a new product. Hereinafter, the battery cell BC may be referred to as a "target battery cell" or a "target cell."

[0058] The relay 20 is electrically connected in series to the battery 11 through a power path connecting the battery 11 and the inverter 30. Figure 1 , a relay 20 is shown connected between the positive terminal of the battery 11 and the charge and discharge terminal P+. The relay 20 is controlled to be turned on and off in response to a switching signal from the battery diagnostic device 100. The relay 20 may be a mechanical connector 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).

[0059] The inverter 30 is configured to convert a DC current from the battery 11 into an AC current in response to a command from the battery diagnosis device 100 or the vehicle controller 2 .

[0060] The motor 40 is driven using the AC current power from the inverter 30. As the motor 40, for example, a three-phase AC current motor 40 can be used.

[0061] The battery diagnostic apparatus 100 includes a control circuit 130 and a memory 131. The battery diagnostic apparatus 100 may further include at least one of a sensing unit 110 and a communication circuit 150. The data acquisition unit described in the claims of the present application includes at least one of the sensing unit 110 and the communication circuit 150.

[0062] The sensing unit 110 includes a voltage sensor 111 and a current sensor 112 .

[0063] The voltage sensor 111 is connected in parallel to the battery 11 , measures a battery voltage, which is a voltage across two terminals of the battery 11 , and is configured to generate a voltage signal representing the measured battery voltage.

[0064] Of course, the voltage sensor 111 can be connected to the positive terminal and the negative terminal of each battery cell BC included in the battery 11, measuring the cell voltage (which can be referred to as the "full cell voltage") as the voltage across the two terminals of each battery cell BC, and outputting an additional voltage signal representing the measured cell voltage (i.e., the measured value of the full cell voltage) to the control circuit 130.

[0065] 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 a battery current flowing through the battery 11 and generate a current signal representing the detected battery current. The current sensor 112 may be implemented as one or a combination of two or more known current detection elements (such as a shunt resistor, a Hall effect element, etc.).

[0066] The communication circuit 150 is configured to support wired or wireless communication between the control circuit 130 and the vehicle controller 2. Wired communication may be, for example, CAN (Controller Area Network) communication, and wireless communication may be, for example, ZigBee or Bluetooth communication. The type of communication protocol is not particularly limited, as long as it supports both wired and wireless communication between the control circuit 130 and the vehicle controller 2. 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 vehicle controller 2 in a user-readable format.

[0067] Control circuit 130 is operatively coupled to relay 20, voltage sensor 111, current sensor 112, and communication circuit 150. Operable coupling of two components means that the two components are directly or indirectly connected to enable transmission and reception of signals in one or both directions.

[0068] The control circuit 130 may collect the voltage signal from the voltage sensor 111 and / or the current signal from the current sensor 112. The control circuit 130 may convert each analog signal collected from the sensors 111 and 112 into a digital value using an ADC (Analog-to-Digital Converter) provided therein and record the digital value.

[0069] The control circuit 130 may be referred to as a “control unit” or 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 an electrical unit for performing other functions.

[0070] The memory 131 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 131 may store data and programs required for the computational operations of the control circuit 130. The memory 131 may store data representing the results of the computational operations performed by the control circuit 130. Although the memory 131 is Figure 1 1 is depicted as being physically independent of the control circuit 130 , but the memory 131 may be embedded within the control circuit 130 .

[0071] 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 requests a switch from the rest mode to the charging or discharging mode. The key-off signal triggers a switch from the cycling state to the rest state. Alternatively, the vehicle controller 2 may be responsible for turning the relay 20 on and off, rather than the control circuit 130.

[0072] If the relay 20 is turned on while the inverter 30 or the charger 3 is operating, the battery 11 enters a circulating state. Conversely, if the relay 20 is turned off or the inverter 30 and the charger 3 stop operating, the battery 11 enters a resting state.

[0073] The cycle state refers to a state in which the battery 11 is being charged / discharged, and the rest state refers to a state in which charging / discharging of the battery 11 is stopped. The fact that the battery 11 is in the cycle state or rest state means that each battery cell BC included in the battery 11 is also in the cycle state or rest state.

[0074] While the battery cell BC is in a cycling state and / or a resting state, the control circuit 130 may determine a voltage detection value and a current detection value based on the voltage signal and the current signal, and then determine (estimate) the SOC (state of charge) of the battery cell BC based on the voltage detection value and / or the current detection value.

[0075] If the charger 3 is operating in a constant current charging mode, the current rate (also referred to as the C-rate) of the charging current supplied to the battery cell BC is a known constant value. Therefore, when estimating the SOC of the battery cell BC, the current value of the constant current output from the charger 3 can be used instead of the current detection value obtained using the current sensor 112.

[0076] SOC is the ratio of the remaining capacity of a battery cell BC to its fully charged capacity (maximum capacity) and is typically expressed in a range of 0 to 1 or 0 to 100%. SOC can be determined using known methods such as ampere counting, OCV (open circuit voltage)-SOC curves, and / or Kalman filters.

[0077] The communication circuit 150 can obtain the first target full-cell curve from a separate external computing device (e.g., the electric vehicle 1) via wired and / or wireless communication. Alternatively, the sensing unit 110 can directly generate the first target full-cell curve for the target cell BC (the battery cell to be diagnosed) based on measurement signals including the current and voltage signals of the target cell BC. Alternatively, the control circuit 130 can collect measurement signals including the current and voltage signals of the target cell BC from the sensing unit 110 and then generate the first target full-cell curve for the target cell BC based on the collected measurement signals.

[0078] The first target full-cell curve may represent a corresponding relationship between the voltage and capacity factor of the target cell BC when the first electrical stimulus is applied to the target cell BC. The capacity factor may be the residual capacity or SOC (state of charge) of the target cell BC.

[0079] The first electrical stimulus is an electrical stimulus that induces an overpotential exceeding the permissible level in the target cell BC and corresponds to a "high electrical stimulus." The second electrical stimulus is an electrical stimulus that induces an overpotential less than the permissible level in the target cell BC and corresponds to a "low electrical stimulus." For example, the first electrical stimulus may be charging at a first current rate (e.g., 1.0C), and the second electrical stimulus may be charging at a second current rate (e.g., 0.05C) that is less than the first current rate. In another example, the first electrical stimulus may be discharging at a first current rate, and the second electrical stimulus may be discharging at a second current rate.

[0080] The first target full-cell curve may be a curve representing a corresponding relationship between the capacity of the target cell BC and the full-cell voltage while charging or discharging at a constant current within a given voltage range (eg, 3.0 to 4.0 V) or a given SOC range (eg, 0 to 100% SOC).

[0081] Hereinafter, before explaining the first target full monomer curve obtained using the target monomer BC of the present disclosure, the first reference full monomer curve and the second reference full monomer curve will be explained first.

[0082] Figure 2 is a reference graph used to explain the relationship between electrical stimulation and whole-unit curves.

[0083] Figure 2The illustrated first reference whole-cell curve R1 and second reference whole-cell curve R2 may be obtained in advance through a pre-experimental process of applying the first electrical stimulus and the second electrical stimulus individually to a reference battery cell.

[0084] A reference battery cell is a battery cell that has been verified to be normal and can have the same level of positive and negative electrode performance as a new battery cell that has been verified to be a good product. A reference battery cell can be simply referred to as a "reference cell." A reference cell can be a coin-shaped cell consisting of a positive electrode half cell and a negative electrode half cell, or a three-electrode cell.

[0085] A new battery cell refers to a battery cell in a new state. This state is the same concept as BOL (Beginning of Life). For example, a cell may be called BOL until the cumulative charge / discharge capacity reaches the set capacity from the time of manufacturing completion, or MOL (Mid-Life) from the time the cumulative charge / discharge capacity reaches the set capacity.

[0086] exist Figure 2 In the graph, the horizontal axis (X-axis) represents capacity (Ah), and the vertical axis (Y-axis) represents voltage (V).

[0087] The first reference full-cell curve R1 shows the relationship between the voltage and capacity of the reference cell while a first electrical stimulus is applied (e.g., during charging using a first current rate). The second reference full-cell curve R2 shows the relationship between the voltage and capacity of the reference cell while a second electrical stimulus is applied (e.g., during charging using a second current rate). The first reference full-cell curve R1 can be obtained by performing charging using a first current rate with the OCV of the reference cell set equal to the lower limit of a given voltage range (e.g., 3.0 V). The second reference full-cell curve R2 can be obtained by performing charging using a second current rate with the OCV of the reference cell set equal to the lower limit of a given voltage range. Therefore, Figure 2 , the starting points of the first reference all-monomer curve R1 and the second reference all-monomer curve R2 are roughly coincident, but the end points are obviously different.

[0088] The first reference full-cell curve R1 and the second reference full-cell curve R2 may represent the corresponding relationship between the capacity of the reference cell and the full-cell voltage within at least the voltage range of interest (e.g., 3.0 to 4.0 V). The lower limit and upper limit of the voltage range of interest may represent the first set voltage ( Figure 2 3.0V) and the second set voltage ( Figure 2 4.0V in the CMOS).

[0089] The SOC when the full cell voltage of any battery cell is equal to a first set voltage may be set to 0%, and the SOC when the full cell voltage is equal to a second set voltage may be set to 100%. In other words, the first set voltage and the second set voltage may be the lower and upper limits of the battery cell voltage corresponding to an SOC (State of Charge) of 0% to 100% for any battery cell including a reference cell.

[0090] The starting capacity (Qi) may refer to the residual capacity when the full cell voltage of any battery cell is equal to a first set voltage, and the ending capacity (Qf) may refer to the residual capacity when the full cell voltage of any battery cell is equal to a second set voltage.

[0091] The first reference all-cell curve R1 may be based on a voltage time series and a current time series (or a capacity time series) acquired by periodically measuring the current of the reference cell and the all-cell voltage while the first electrical stimulation is applied.

[0092] The second reference all-cell curve R2 may be based on a voltage time series and a current time series acquired by periodically measuring the current of the reference cell and the all-cell voltage while the second electrical stimulus is applied.

[0093] Here, when compared with the second reference all-cell curve R2, the first reference all-cell curve R1 may include an overpotential corresponding to a voltage value of the same capacity value. Therefore, for the same capacity value, the voltage difference between the first reference all-cell curve R1 and the second reference all-cell curve R2 may be calculated as the overpotential.

[0094] Specifically, by removing the second reference all-cell curve R2 based on the second electrical stimulation (calculating the voltage difference by capacity) from the first reference all-cell curve R1 based on the first electrical stimulation, an overpotential curve indicating overpotential by capacity may be generated.

[0095] Figure 3 is a schematic diagram showing that Figure 2 Graph of the overpotential curve OP obtained from the first reference all-monomer curve R1 and the second reference all-monomer curve R2.

[0096] The overpotential curve OP may be a curve showing a corresponding relationship between capacity and overpotential, and may be a curve showing a voltage difference according to capacity between the first reference all-cell curve R1 and the second reference all-cell curve R2.

[0097] The capacity range (Qi to Qf) of the overpotential curve OP may be a common capacity range between the first reference all-cell curve R1 and the second reference all-cell curve R2. Figure 2, the capacity range of the first reference all-cell curve R1 is 5 to 47 Ah, and the capacity range of the second reference all-cell curve R2 is 5 to 50 Ah, so Qi may be 5 Ah and Qf may be 47 Ah.

[0098] Figure 4 It is a graph for reference in explaining the relationship between the first target all-monomer curve M, the estimated all-monomer curve E and the second target all-monomer curve N.

[0099] exist Figures 2 to 4 In FIG, Ah is used as the unit of the horizontal axis, but the unit may be expressed in other forms. For example, instead of Ah, a percentage indicating SOC (State of Charge) may be used as the unit of the horizontal axis.

[0100] Please refer to Figure 4 While the first electrical stimulus is applied to the target cell BC, the control circuit 130 may generate a first target full-cell curve M, which represents the correspondence between the full-cell voltage and capacity of the target cell BC. The first target full-cell curve M may represent the correspondence between the capacity and full-cell voltage of the target cell BC, at least within a voltage range of interest.

[0101] Therefore, since the reference cell and the target cell BC have different charge / discharge properties, some differences inevitably exist between the first target all-cell curve M and the first reference all-cell curve R1.

[0102] For example, in the same voltage range of interest (e.g., 3.0 to 4.0 V), Figure 2 The capacity range of the first reference full-cell curve R1 shown in FIG is 5 to 47 Ah, while the capacity range of the first target full-cell curve M is 5 to 45 Ah.

[0103] The control circuit 130 can be based on Figure 3 The overpotential curve OP and Figure 4 The estimated full-cell curve E is generated by using the first target full-cell curve M. Specifically, the control circuit 130 may generate the estimated full-cell curve E by subtracting the overpotential curve OP from the first target full-cell curve M. Therefore, at the same capacity value, the voltage value of the estimated full-cell curve E may be smaller than the voltage value of the first target full-cell curve M.

[0104] The control circuit 130 can obtain the estimated full-cell curve E by subtracting the capacity-specific overpotential of the overpotential curve OP from the capacity-specific voltage of the first target full-cell curve M within the common capacity range of the first target full-cell curve M and the overpotential curve OP. In this case, the capacity range of 45Ah to 47Ah within the entire capacity range of the overpotential curve OP may not be utilized. In other words, the estimated full-cell curve E can be obtained by removing the capacity-specific overpotential of the overpotential curve OP corresponding to the capacity-specific voltage of the first target full-cell curve M.

[0105] Alternatively, the control circuit 130 may generate an adjusted overpotential curve (not shown) by scaling the overpotential curve OP along the horizontal axis so that the capacity range of the overpotential curve OP matches the capacity range of the first target full-cell curve M. Subsequently, the control circuit 130 may generate an estimated full-cell curve E by subtracting the overpotential value of the adjusted overpotential curve from the voltage value of the first target full-cell curve M within the capacity range of the overpotential curve OP. In other words, the estimated full-cell curve E may be obtained by removing the capacity-specific overpotential of the adjusted overpotential curve that corresponds to the capacity-specific voltage of the first target full-cell curve M.

[0106] The second target all-cell curve N is an example of a curve representing the corresponding relationship between the voltage and the capacity factor of the target cell BC expected to be obtained if the second electrical stimulus is applied to the target cell BC instead of the first electrical stimulus.

[0107] The estimated full-cell curve E is an estimation result of the second target full-cell curve N based on the first target full-cell curve M and the overpotential curve OP.

[0108] refer to Figure 4 , the estimated all-cell curve E is a curve obtained by subtracting the overpotential curve OP from the first target all-cell curve M, and the estimated all-cell curve E is more similar to the second target all-cell curve N than the first target all-cell curve M. Therefore, when diagnosing the charge / discharge performance of the target cell BC, using the estimated all-cell curve E rather than the first target all-cell curve M is advantageous in terms of diagnostic accuracy.

[0109] At the same time, since the estimated full-cell curve E does not completely match the second target full-cell curve N, there may still be a considerable difference between the diagnosis result of the charge / discharge performance based on the estimated full-cell curve E and the actual charge / discharge performance. Figure 12 Methods for reducing errors in diagnostic results of charge / discharge performance are described.

[0110] The control circuit 130 may determine a first performance factor set representing the charge / discharge performance of the target cell BC by applying the cell diagnostic logic to the estimated all-cell curve E. The first performance factor set may be considered as a preliminary estimation result of the charge / discharge performance of the target cell BC.

[0111] The first performance factor group may include a performance factor of at least one of a positive pole participation start point, a positive pole participation end point, a positive pole scaling factor, a negative pole participation start point, a negative pole participation end point, and a negative pole scaling factor.

[0112] In this specification, the positive electrode participation starting point on the positive electrode curve of any battery cell indicates the positive electrode voltage and positive electrode capacity (or positive electrode SOC) when the full cell voltage of the corresponding battery cell matches the first set voltage. The positive electrode voltage at the positive electrode participation starting point can be referred to as the "positive electrode starting potential." Furthermore, the negative electrode participation starting point on the negative electrode curve of the corresponding battery cell indicates the negative electrode voltage and negative electrode capacity (or negative electrode SOC) when the full cell voltage of the corresponding battery cell matches the first set voltage. The negative electrode voltage at the negative electrode participation starting point can be referred to as the "negative electrode starting potential." Therefore, the voltage difference between the positive electrode participation starting point and the negative electrode participation starting point can be equal to the first set voltage.

[0113] Furthermore, the positive electrode participation endpoint on the positive electrode curve of any battery cell indicates the positive electrode voltage and positive electrode capacity when the full cell voltage of the corresponding battery cell matches the second set voltage. The positive electrode voltage at the positive electrode participation endpoint can be referred to as the "positive electrode termination potential." Furthermore, the negative electrode participation endpoint on the negative electrode curve of the corresponding battery cell indicates the negative electrode voltage and negative electrode capacity when the full cell voltage of the corresponding battery cell matches the second set voltage. The negative electrode voltage at the negative electrode participation endpoint can be referred to as the "negative electrode termination potential." Therefore, the voltage difference between the positive electrode participation endpoint and the negative electrode participation endpoint can be equal to the second set voltage.

[0114] In this specification, the positive electrode capacity (capacity value) at a specific point on the positive electrode curve of any battery cell may refer to the capacity difference between either of the two endpoints of the positive electrode curve and the specific point. The positive electrode SOC at a specific point on the positive electrode curve of any battery cell may refer to the ratio of the capacity difference between either of the two endpoints of the positive electrode curve (e.g., the low-capacity point) and the specific point to the capacity difference between the two endpoints of the positive electrode curve.

[0115] Similarly, the negative electrode capacity (capacity value) at a specific point on the negative electrode curve of any battery cell may refer to the capacity difference between either of the two endpoints of the negative electrode curve (or the positive electrode curve) and the specific point. The negative electrode SOC at a specific point on the negative electrode curve of any battery cell may refer to the ratio of the capacity difference between either of the two endpoints (e.g., the low-capacity point) of the negative electrode curve (or the positive electrode curve) and the specific point to the capacity difference between the two endpoints of the negative electrode curve.

[0116] The positive electrode scaling factor of any battery cell may represent the ratio of the capacity difference between the positive electrode participation start point and the positive electrode participation end point of the corresponding battery cell to the reference positive electrode capacity of the reference cell. The negative electrode scaling factor of any battery cell may represent the ratio of the capacity difference between the negative electrode participation start point and the negative electrode participation end point of the corresponding battery cell to the reference negative electrode capacity of the reference cell.

[0117] In the memory 131 , information indicating the voltage and capacity of each of a reference positive electrode participation start point, a reference positive electrode participation end point, a reference negative electrode participation start point, and a reference negative electrode participation end point representing charge / discharge performance of a reference cell may be recorded in advance.

[0118] From now on, refer to Figures 5 to 11 , the diagnostic process included in the single-body diagnostic logic will be explained.

[0119] Figure 5 is a graph referred to for explaining an example of each of the estimated all-cell curve E, the second reference all-cell curve R2, the reference positive electrode curve Rp, and the reference negative electrode curve Rn. Figure 5 In the graph, the horizontal axis (X axis) represents capacity, and the vertical axis (Y axis) represents voltage. The estimated full-cell curve E and the second reference full-cell curve R2 are Figure 2 The same as in .

[0120] refer to Figure 5 The reference positive electrode curve Rp may be a curve representing the corresponding relationship between the positive electrode voltage and the capacity when the second electrical stimulus is applied to the reference cell. The positive electrode voltage of the reference cell refers to the potential difference between the potential of a reference electrode (not shown) and the potential of the positive electrode of the reference cell.

[0121] The reference negative electrode curve Rn may be a curve representing the corresponding relationship between the negative electrode voltage and the capacity when the second electrical stimulus is applied to the reference cell. The negative electrode voltage of the reference cell refers to the potential difference between the potential of the reference electrode and the potential of the negative electrode of the reference cell.

[0122] The potential of the reference electrode may be, for example, the redox potential of lithium. The positive electrode voltage may be simply referred to as the positive electrode potential, and the negative electrode voltage may be simply referred to as the negative electrode potential.

[0123] The reference positive polarity curve Rp and the reference negative polarity curve Rn may be pre-stored in the memory 131 .

[0124] At least one of the reference positive curve Rp and the reference negative curve Rn may be aligned along a horizontal axis such that a common capacity range ( Figure 5 The synthesis results of a portion of the 5Ah to 50Ah (in the figure) match the second reference full-monomer curve R2.

[0125] Figure 5 An example is shown in which the reference negative electrode curve Rn is aligned so as to be shifted rightward based on the starting point (the point corresponding to the capacity 0) of the reference positive electrode curve Rp.

[0126] from Figure 5 As can be seen, the ends of the reference positive electrode curve Rp and the reference negative electrode curve Rn are offset from each other. In other words, the capacity range of the reference positive electrode curve Rp and the capacity range of the reference negative electrode curve Rn do not match and may only partially overlap. Therefore, the second reference full-cell curve R2 can indicate the full-cell voltage of the reference cell within a portion of the capacity range shared by the reference positive electrode curve Rp and the reference negative electrode curve Rn.

[0127] The control circuit 130 may be configured to compare the estimated full-cell curve E with at least one comparative full-cell curve. The comparative full-cell curve may be a result of adjusting each of the reference positive curve Rp and the reference negative curve Rn stored in the memory 131 to generate an adjusted positive curve and an adjusted negative curve, and then synthesizing (combining) the adjusted positive curve and the adjusted negative curve.

[0128] In other words, when the second reference full-cell curve R2 is the result of subtracting a portion of the reference negative curve Rn from a portion of the reference positive curve Rp, the comparative full-cell curve can be considered the result of subtracting a portion of the adjusted negative curve from a portion of the adjusted positive curve.

[0129] Control circuit 130 can generate at least one comparative full-cell curve by directly adjusting reference positive electrode curve Rp and reference negative electrode curve Rn. Alternatively, at least one comparative full-cell curve can be pre-secured based on reference positive electrode curve Rp and reference negative electrode curve Rn and stored in memory 131. In this case, control circuit 130 can obtain the comparative full-cell curve by accessing memory 131 and reading the comparative full-cell curve.

[0130] The control circuit 130 can generate multiple comparative full-cell curves from the reference positive curve Rp and the reference negative curve Rn by repeatedly adjusting each of the reference positive curve Rp and the reference negative curve Rn to several levels and then synthesizing their adjustment processes. The comparative full-cell curves can also be referred to as "adjusted reference full-cell curves."

[0131] The control circuit 130 may specify any one comparative full-cell curve among the plurality of comparative full-cell curves that has the smallest error relative to the estimated full-cell curve E. The control circuit 130 may then determine that the adjusted positive electrode curve and the adjusted negative electrode curve mapped to the specified comparative full-cell curve are the positive electrode curve and the negative electrode curve of the target cell BC.

[0132] In this regard, various methods known at the time of filing this application can be used to determine the error between two curves as a set of data points, each of which can be expressed in a two-dimensional coordinate system. For example, the integral of the absolute value of the area between the two curves or the RMSE (root mean square error) can be used as the error between the two curves.

[0133] According to this configuration of the present disclosure, various state information about the target cell BC can be obtained based on the finalized adjusted positive and negative curves. The finalized adjusted positive and negative curves can be mapped to any one of the multiple comparative full-cell curves that has the smallest error relative to the estimated full-cell curve E. In particular, the comparative full-cell curve formed by the finalized adjusted positive and negative curves can be nearly identical in shape to the estimated full-cell curve E.

[0134] Figures 6 to 8 is a figure referenced for explaining an example of a process for generating comparative all-monomer curves.

[0135] Will refer to Figures 6 to 8 The explained process for generating comparative all-monomer curves can be performed in the following order: a first routine for setting four points (positive electrode participation start, positive electrode participation end, negative electrode participation start, negative electrode participation end) corresponding to the voltage range of interest (see Figure 6 ), a second routine for performing the curve shift (see Figure 7 ) and a third routine for performing capacity scaling (see Figure 8 ). That is, the process for generating a comparative full monomer curve according to an embodiment of the present disclosure may include first to third routines.

[0136] Figure 6 The reference positive electrode curve Rp and the reference negative electrode curve Rn shown are Figure 5 Same as those shown.

[0137] The control circuit 130 may determine a positive electrode participation starting point (pi), a positive electrode participation end point (pf), a negative electrode participation starting point (ni), and a negative electrode participation end point (nf) on the reference positive electrode curve Rp and the reference negative electrode curve Rn.

[0138] The positive electrode participation starting point (pi) or the negative electrode participation starting point (ni) depends on the other.

[0139] As an example, the control circuit 130 may divide the positive electrode voltage range (or the second set voltage) from the starting point to the end point of the reference positive electrode curve Rp into multiple small voltage segments, and then set the boundary points of two adjacent small voltage segments among the multiple small voltage segments as the positive electrode participation starting point (pi). Each small voltage segment may have a predetermined value (e.g., 0.01V). Next, the control circuit 130 may set a point on the reference negative electrode curve Rn that is lower than the positive electrode participation starting point (pi) by a first set voltage (e.g., 3V) as the negative electrode participation starting point (ni).

[0140] As another example, the control circuit 130 may divide the negative voltage range from the start point to the end point of the reference negative curve Rn into multiple small voltage segments of predetermined sizes, and then set the boundary points of two adjacent small voltage segments among the multiple small voltage segments as the negative electrode participation starting point (ni). Next, the control circuit 130 may search for a point on the reference positive curve Rp that is greater than the negative electrode participation starting point (ni) by a first set voltage (e.g., 3V), and set the searched point as the positive electrode participation starting point (pi).

[0141] The positive pole participation endpoint (pf) or the negative pole participation endpoint (nf) depends on the other.

[0142] As an example, the control circuit 130 may divide the voltage range from the second set voltage to the end point of the reference positive electrode curve Rp into multiple small voltage segments of predetermined sizes, and then set the boundary points of two adjacent small voltage segments among the multiple small voltage segments as the positive electrode participation end point (pf). Next, the control circuit 130 may set a point on the reference negative electrode curve Rn that is lower than the positive electrode participation end point (pf) by a second set voltage (e.g., 4V) as the negative electrode participation end point (nf).

[0143] As another example, the control circuit 130 may divide the negative electrode voltage range from the start point to the end point of the reference negative electrode curve Rn into multiple small voltage segments of predetermined sizes, and then set the boundary point between two adjacent small voltage segments among the multiple small voltage segments as the negative electrode participation end point (nf). Next, the control circuit 130 may search for a point on the reference positive electrode curve Rp that is a second set voltage (e.g., 4V) greater than the negative electrode participation end point (nf), and set the searched point as the positive electrode participation end point (pf).

[0144] If the positive pole participation starting point (pi), the positive pole participation end point (pf), the negative pole participation starting point (ni), and the negative pole participation end point (nf) are completely determined, the control circuit 130 shifts at least one of the reference positive pole curve Rp and the reference negative pole curve Rn to the left or right along the horizontal axis.

[0145] refer to Figure 6 , the control circuit 130 may shift the reference positive electrode curve Rp to the left (towards low capacity) or the reference negative electrode curve Rn to the right (towards high capacity), or both, so that the capacity values of the positive electrode participation starting point (pi) and the negative electrode participation starting point (ni) match.

[0146] Alternatively, the control circuit 130 shifts the reference positive electrode curve Rp to the left or the reference negative electrode curve Rn to the right or both so that the capacity values of the positive electrode participation endpoint (pf) and the negative electrode participation endpoint (nf) match.

[0147] Figure 7 The diagram shows a case where only the reference positive electrode curve Rp is shifted leftward to generate an adjusted reference positive electrode curve (Rp'). Consequently, the capacity value at the positive electrode participation starting point (pi') matches the capacity value at the negative electrode participation starting point (ni). The adjusted reference positive electrode curve (Rp') may be the result of applying an adjustment process to the reference positive electrode curve Rp, shifting the capacity difference between the positive electrode participation starting point (pi) and the negative electrode participation starting point (ni) to the left. Therefore, the two points (pi, pi') may differ only in capacity value and have the same voltage. Furthermore, the two points (pf, pf') may differ only in capacity value and have the same voltage.

[0148] If an adjustment result curve (Rp′, Rn) in which at least one of the reference positive polarity curve Rp and the reference negative polarity curve Rn is shifted is ensured, the control circuit 130 may scale the capacity range of at least one of the adjustment result curves (Rp′, Rn).

[0149] according to Figure 7 In the example shown, the control circuit 130 may perform an additional adjustment process to shrink or expand at least one of the adjusted reference positive polarity curve (Rp′) and the reference negative polarity curve Rn along the horizontal axis.

[0150] refer to Figure 8, the control circuit 130 can generate an adjusted reference positive curve (Rp") by shrinking or expanding the adjusted reference positive curve (Rp') so that the size of the capacity range between the two points (pi', pf') of the adjusted reference positive curve (Rp') matches the size of the capacity range of the estimated full-cell curve E. At this time, any one point (pi') of the two points (pi', pf') can be fixed. Therefore, the capacity difference between the two points (pi', pf") of the adjusted reference positive curve (Rp") can match the capacity range of the estimated full-cell curve E.

[0151] Furthermore, the control circuit 130 can generate an adjusted reference negative electrode curve (Rn') by shrinking or expanding the reference negative electrode curve Rn so that the capacity range between two points (ni, nf) on the reference negative electrode curve Rn matches the capacity range of the estimated full-cell curve E. In this case, either point (ni) of the two points (ni, nf) can be fixed. Therefore, the capacity difference between the two points (ni, nf') of the adjusted reference negative electrode curve (Rn') can match the capacity range of the estimated full-cell curve E.

[0152] exist Figure 8 The adjusted reference positive curve (Rp") is the contraction Figure 7 The results of the adjusted reference positive curve (Rp') are shown, and the adjusted reference negative curve (Rn') is extended Figure 7 Results for the reference negative electrode curve Rn are shown.

[0153] The positive electrode participation endpoint (pf") on the adjusted reference positive electrode curve (Rp") corresponds to the positive electrode participation endpoint (pf) on the adjusted reference positive electrode curve (Rp'). The negative electrode participation endpoint (nf') on the adjusted reference negative electrode curve (Rn') corresponds to the negative electrode participation endpoint (nf) on the reference negative electrode curve Rn.

[0154] The capacity difference between the positive electrode participation start (pi') and the positive electrode participation end (pf") of the adjusted reference positive electrode curve (Rp") corresponds to the size of the capacity range of the estimated full-cell curve E. Similarly, the capacity difference between the negative electrode participation start (ni) and the negative electrode participation end (nf') of the adjusted reference negative electrode curve (Rn') corresponds to the size of the capacity range of the estimated full-cell curve E.

[0155] Furthermore, the capacity range of the two points (pi', pf") of the adjusted reference positive curve (Rp") matches the capacity range of the two points (ni, nf') of the adjusted reference negative curve (Rn'). The control circuit 130 may generate a comparative full-cell curve S by subtracting the portion between the two points (pi, pf') of the adjusted reference positive curve (Rp") from the portion between the two points (ni, nf') of the adjusted reference negative curve (Rn').

[0156] The control circuit 130 may calculate an error (curve error) between comparison values between the all-cell curve S and the estimated all-cell curve E.

[0157] The control circuit 130 can map at least two of the adjusted reference positive curve (Rp"), the adjusted reference negative curve (Rn'), the positive participation starting point (pi'), the positive participation end point (pf"), the negative participation starting point (ni), the negative participation end point (nf'), the positive scaling factor, the negative scaling factor, the comparison full-cell curve S and the curve error to each other, and record them in the memory 131.

[0158] The adjusted positive scaling factor of the reference positive curve (Rp") may represent a ratio of a capacity difference between two points (pi', pf") to a capacity difference between two points (pi0, pf0). Alternatively, the adjusted positive scaling factor of the reference positive curve (Rp") may represent a ratio of a positive capacity difference between two points (pi', pf") to a positive capacity difference between two points (pi0, pf0). Alternatively, the adjusted positive scaling factor of the reference positive curve (Rp") may represent a ratio of a positive SOC difference between two points (pi', pf") to a positive SOC difference between two points (pi0, pf0).

[0159] The adjusted negative scaling factor of the reference negative electrode curve (Rn') may represent the ratio of the capacity difference between the two points (ni, nf') to the capacity difference between the two points (ni0, nf0). Alternatively, the adjusted negative scaling factor of the reference negative electrode curve (Rn') may represent the ratio of the negative electrode capacity difference between the two points (ni, nf') to the negative electrode capacity difference between the two points (ni0, nf0). Alternatively, the adjusted negative scaling factor of the reference negative electrode curve (Rn') may represent the ratio of the negative electrode SOC difference between the two points (ni, nf') to the negative electrode SOC difference between the two points (ni0, nf0).

[0160] Hereinafter, ps may be used as a sign indicating a positive scaling factor, and ns may be used as a sign indicating a negative scaling factor.

[0161] Meanwhile, as described above, when the positive voltage range of the reference positive curve Rp is divided into a plurality of small voltage segments, boundary points of two adjacent small voltage segments among the plurality of small voltage segments may be set as positive participation starting points (pi).

[0162] For example, if the positive electrode voltage range of the reference positive electrode curve Rp is divided into 100 small voltage ranges, 100 boundary points can be set as the positive electrode participation starting point (pi). Furthermore, if the voltage range of the reference positive electrode curve Rp greater than or equal to the second set voltage is divided into 40 small voltage ranges, 40 boundary points can be set as the positive electrode participation end point (pf). In this case, at least 4,000 different comparative full-cell curves can be generated.

[0163] Of course, those skilled in the art will readily understand that as the size of the small voltage segment decreases, the maximum number of comparative full-cell curves that can be generated increases, and conversely, as the size of the small voltage segment increases, the maximum number of comparative full-cell curves that can be generated decreases.

[0164] The control circuit 130 can identify the minimum value among the curve errors of multiple comparative full-monomer curves generated as described above, and then obtain a first performance factor group from the memory 131, which is information mapped to the minimum curve error (for example, at least one of the positive pole participation starting point, the positive pole participation end point, the negative pole participation starting point, the negative pole participation end point, the positive pole scaling factor, and the negative pole scaling factor).

[0165] Figures 9 to 11 is a diagram that is referenced to describe another example of a process for generating a comparative full-cell curve according to a cell diagnostic logic. For reference, Figures 9 to 11 The embodiment shown is independent of Figures 6 to 8 Therefore, it is usually used to describe Figures 6 to 8 The embodiment shown and Figures 9 to 11 The terms or reference signs of the illustrated embodiments should be understood to be limited to each embodiment.

[0166] Generate reference Figures 9 to 11 The process of comparing the full monomer curve U can be explained by following the fourth routine for performing capacity scaling (see Figure 9 ), set the fourth point (positive electrode participation start point, positive electrode participation end point, negative electrode participation start point and negative electrode participation end point) of the fifth routine (see Figure 10 ) and a sixth routine that performs the curve shift (see Figure 11 That is, the process of generating a comparison full monomer curve according to another embodiment of the present disclosure may include the fourth to sixth routines.

[0167] refer to Figure 9, the control circuit 130 may generate an adjusted reference positive polarity curve (Rp′) and an adjusted reference negative polarity curve (Rn′) by applying a positive polarity scaling factor and a negative polarity scaling factor selected from the scaling value range to the reference positive polarity curve Rp and the reference negative polarity curve Rn, respectively.

[0168] The scaling value range can be predetermined or vary depending on the ratio of the capacity range of the estimated full-cell curve E to the capacity range of the second reference full-cell curve R2. As an example, assuming that the positive and negative scaling factors can be selected from values within a scaling value range (e.g., 90% to 99%) at intervals of 0.1% (i.e., 90%, 90.1%, 90.2%, ..., 98.9%, 99%), 91 values can be selected as the positive and negative scaling factors, respectively. In this case, based on 91 × 91 = 8,281 adjustment levels (combinations of positive and negative scaling factors), a maximum of 8,281 adjusted curve pairs (Rp', Rn') can be generated. An adjusted curve pair refers to a combination of an adjusted positive curve (Rp') and an adjusted negative curve (Rn').

[0169] refer to Figure 9 , the adjusted reference positive curve (Rp') and the adjusted reference negative curve (Rn') show the results of applying the positive scaling factor and the negative scaling factor to the reference positive curve Rp and the reference negative curve Rn, respectively.

[0170] Since the positive and negative scaling factors are less than 100%, the reference positive curve Rp is contracted along the horizontal axis to obtain an adjusted reference positive curve (Rp'), and the reference negative curve Rn is contracted along the horizontal axis to obtain an adjusted reference negative curve (Rn'). For ease of understanding, the reference positive curve Rp and the reference negative curve Rn are shown with their starting points fixed and their remaining portions contracted to the left along the horizontal axis.

[0171] refer to Figure 10 The control circuit 130 may determine the positive electrode participation starting point (pi'), the positive electrode participation end point (pf'), the negative electrode participation starting point (ni'), and the negative electrode participation end point (nf') on the adjusted reference positive electrode curve (Rp') and the adjusted reference negative electrode curve (Rp').

[0172] The positive electrode participation starting point (pi') or the negative electrode participation starting point (ni') may depend on the other. Furthermore, the positive electrode participation end point (pf') or the negative electrode participation end point (nf') may depend on the other. Furthermore, the positive electrode participation starting point (pi') or the positive electrode participation end point (pf') may be set based on the other.

[0173] That is, if any one of the positive electrode participation starting point (pi'), the positive electrode participation end point (pf'), the negative electrode participation starting point (ni'), and the negative electrode participation end point (nf') is set, the remaining three points can be automatically set by the first set voltage, the second set voltage, and / or the size of the estimated capacity range of the full-cell curve E (e.g., Figure 4 45Ah-5Ah=40Ah).

[0174] As an example, the control circuit 130 may divide the positive electrode voltage range (or the second set voltage) from the start point to the end point of the adjusted reference positive electrode curve (Rp') into multiple small voltage segments, and then set the boundary points of two adjacent small voltage segments in the multiple small voltage segments as the positive electrode participation starting point (pi'). Next, the control circuit 130 may set a point on the adjusted reference negative electrode curve (Rn') that is lower than the positive electrode participation starting point (pi') by the first set voltage as the negative electrode participation starting point (ni').

[0175] As another example, the control circuit 130 may divide the negative electrode voltage range from the start point to the end point of the adjusted reference negative electrode curve (Rn') into multiple small voltage segments of predetermined sizes, and then set the boundary points of two adjacent small voltage segments in the multiple small voltage segments as the negative electrode participation starting point (ni'). Next, the control circuit 130 may search for a point on the adjusted reference positive electrode curve (Rp') that is greater than the negative electrode participation starting point (ni') by a first set voltage, and select the searched point as the positive electrode participation starting point (pi').

[0176] As another example, the control circuit 130 may divide the voltage range from the second set voltage to the endpoint of the adjusted reference positive polarity curve (Rp') into multiple small voltage segments of predetermined sizes, and then set the boundary points of two adjacent small voltage segments in the multiple small voltage segments as the positive polarity participation endpoint (pf'). Next, the control circuit 130 may search the adjusted reference negative polarity curve (Rn') for a point that is lower than the positive polarity participation endpoint (pf') by a second set voltage (e.g., 4V), and set the searched point as the negative polarity participation endpoint (nf').

[0177] As another example, the control circuit 130 may divide the negative electrode voltage range from the start point to the end point of the adjusted second reference negative electrode curve (Rn') into multiple small voltage segments of predetermined sizes, and then set the boundary points of two adjacent small voltage segments among the multiple small voltage segments as the negative electrode participation end point (nf'). Next, the control circuit 130 may search for a point on the adjusted reference positive electrode curve (Rp') that is greater than the negative electrode participation end point (nf') by a second set voltage, and set the searched point as the positive electrode participation end point (pf').

[0178] If any one of the positive pole participation starting point (pi'), the positive pole participation end point (pf'), the negative pole participation starting point (ni'), and the negative pole participation end point (nf') is determined, the control circuit 130 may additionally determine the remaining three points based on the determined point.

[0179] For example, if the positive electrode participation starting point (pi') is first determined, the control circuit 130 may set a point on the adjusted reference positive electrode curve (Rp') having a capacity value greater than the capacity value of the positive electrode participation starting point (pi') by an amount equivalent to the estimated capacity range of the full cell curve E as the positive electrode participation end point (pf'). Furthermore, the control circuit 130 may search for a point on the adjusted reference negative electrode curve (Rn') that is lower than the positive electrode participation starting point (pi') by a first set voltage and set the searched point as the negative electrode participation starting point (ni'). Furthermore, the control circuit 130 may set a point on the adjusted reference negative electrode curve (Rn') having a capacity value greater than the capacity value of the negative electrode participation starting point (ni') by an amount equivalent to the estimated capacity range of the full cell curve E as the negative electrode participation end point (nf').

[0180] As another example, when first determining the positive electrode participation endpoint (pf'), the control circuit 130 may set a point on the adjusted reference positive electrode curve (Rp') having a capacity value that is smaller than the capacity value at the positive electrode participation endpoint (pf') by an amount equivalent to the estimated capacity range of the full-cell curve E as the positive electrode participation starting point (pi'). Furthermore, the control circuit 130 may search for a point on the adjusted reference negative electrode curve (Rn') that is lower than the positive electrode participation endpoint (pf') by a second set voltage and set the searched point as the negative electrode participation endpoint (nf'). Furthermore, the control circuit 130 may set a point on the adjusted reference negative electrode curve (Rn') having a capacity value that is smaller than the capacity value at the negative electrode participation endpoint (nf') by an amount equivalent to the estimated capacity range of the full-cell curve E as the negative electrode participation starting point (ni').

[0181] As another example, when determining the negative electrode participation starting point (ni'), the control circuit 130 may set a point on the adjusted reference negative electrode curve (Rn') whose capacity value is greater than the capacity value at the negative electrode participation starting point (ni') by an amount equivalent to the estimated capacity range of the full cell curve E as the negative electrode participation end point (nf'). Furthermore, the control circuit 130 may search for a point on the adjusted reference positive electrode curve (Rp') that is higher than the negative electrode participation starting point (ni') by a first set voltage and set the searched point as the positive electrode participation starting point (pi'). Furthermore, the control circuit 130 may set a point on the adjusted reference positive electrode curve (Rp') that has a capacity value greater than the capacity value at the positive electrode participation starting point (pi') by an amount equivalent to the estimated capacity range of the full cell curve E as the positive electrode participation end point (pf').

[0182] As another example, when determining the negative electrode participation endpoint (nf'), the control circuit 130 may set a point on the adjusted reference negative electrode curve (Rn') whose capacity value is smaller than the capacity value at the negative electrode participation endpoint (nf') by the estimated capacity range of the full-cell curve E as the negative electrode participation starting point (ni'). Furthermore, the control circuit 130 may search for a point on the adjusted reference positive electrode curve (Rp') that is higher than the negative electrode participation endpoint (nf') by a second set voltage and set the searched point as the positive electrode participation endpoint (pf'). Furthermore, the control circuit 130 may set a point on the adjusted reference positive electrode curve (Rp') that has a capacity value smaller than the capacity value at the positive electrode participation endpoint (pf') by the estimated capacity range of the full-cell curve E as the positive electrode participation starting point (pi').

[0183] If the positive electrode participation starting point (pi'), the positive electrode participation end point (pf'), the negative electrode participation starting point (ni'), and the negative electrode participation end point (nf') are determined entirely based on the pairing of the positive electrode scaling factor and the negative electrode scaling factor, the control circuit 130 can shift at least one of the adjusted reference positive electrode curve (Rp') and the adjusted reference negative electrode curve (Rn') to the left or right along the horizontal axis so that the capacity values of the positive electrode participation starting point (pi') and the negative electrode participation starting point (ni') match or the capacity values of the positive electrode participation end point (pf') and the negative electrode participation end point (nf') match.

[0184] Figure 11 The adjusted reference negative curve (Rn") shown is obtained by only Figure 10 The adjusted reference negative electrode curve (Rn') shown is shifted to the right. Therefore, the capacity values at the positive electrode participation starting point (pi') and the negative electrode participation starting point (ni") match each other on the horizontal axis. Correspondingly, the capacity difference between the positive electrode participation starting point (pi') and the positive electrode participation end point (pf') is equal to the capacity difference between the negative electrode participation starting point (ni') and the negative electrode participation end point (nf'). Therefore, if the capacity values at the positive electrode participation starting point (pi') and the negative electrode participation starting point (ni'') match each other on the horizontal axis, then the capacity values at the positive electrode participation end point (pf') and the negative electrode participation end point (nf') also match each other on the horizontal axis.

[0185] refer to Figure 11 , the control circuit 130 may generate the comparative full-cell curve U by subtracting the portion of the curve between two points (pi′, pf′) of the adjusted reference positive curve (Rp′) from the portion of the curve between two points (ni″, nf″) of the adjusted reference negative curve (Rn″).

[0186] The control circuit 130 may calculate an error (curve error) between the comparison full-cell curve U and the estimated full-cell curve E.

[0187] The control circuit 130 can map at least two of the adjusted reference positive curve (Rp'), the adjusted reference negative curve (Rn"), the positive participation starting point (pi'), the positive participation end point (pf'), the negative participation starting point (ni"), the negative participation end point (nf"), the positive scaling factor, the negative scaling factor, the comparison full-cell curve U and the curve error, and record them in the memory 140.

[0188] As described above, the control circuit 130 can generate a comparison full monomer curve U corresponding to each pair of positive and negative scaling factors selected from the scaling value range. Since the pair of positive and negative scaling factors is complex, it is obvious that the comparison curve U will also be generated in complex form.

[0189] The control circuit 130 may identify a minimum value among the curve errors of the plurality of compared full-cell curves and then obtain information mapped to the minimum curve error from the memory 131 .

[0190] As described above, the control circuit 130 may execute the cell diagnostic logic to generate a comparative full-cell curve having a minimum error with the estimated full-cell curve E based on the reference positive curve Rp and the reference negative curve Rn.

[0191] The control circuit 130 can determine a first performance factor group, which includes performance factors that are respectively mapped to at least one of the positive pole participation start point, the positive pole participation end point, the positive pole scaling factor, the negative pole participation start point, the negative pole participation end point and the negative pole scaling factor of the minimum curve error.

[0192] At the same time, since the first performance factor group is the result of applying the cell diagnostic logic to the estimated full-cell curve E, it can more accurately represent the actual charge / discharge performance of the target cell BC than the result of applying the cell diagnostic logic to the first target full-cell curve M.

[0193] However, since the overpotential curve OP is related to the reference cell rather than the target cell BC, there may still be a considerable difference between the charge / discharge performance indicated by the first performance factor group and the actual charge / discharge performance of the target cell BC.

[0194] Therefore, it is desirable to perform a process for correcting the first performance factor group to narrow the gap between the charge / discharge performance indicated by the first performance factor group and the actual charge / discharge performance, and this can be achieved by a factor correction model explained later. The correction process for the first performance factor group is performed to determine a second performance factor group as a secondary estimation result of the charge / discharge performance of the target cell BC.

[0195] Figure 12 is the referenced plot used to explain the functionality of the factor-adjusted model, Figure 13 is a referenced graph for explaining the training data set provided for training the factor correction model, Figure 14 It shows Figure 12 Figure 1 shows an example of a neural network structure for a factor correction model, and Figure 15 is a graph showing an example of correlation coefficients between performance factors obtained by training a factor correction model.

[0196] refer to Figure 12 , the control circuit 130 may determine a second performance factor group 1220 as a secondary estimation result of the charge / discharge performance of the target cell BC by applying the factor correction model 200 to the first performance factor group 1210 as a primary estimation result of the charge / discharge performance of the target cell BC.

[0197] The first performance factor group 1210 may include a performance factor of at least one of a positive electrode participation start point, a negative electrode participation start point, a positive electrode participation end point, a negative electrode participation end point, a positive electrode scaling factor, and a negative electrode scaling factor of a target cell BC determined based on the estimated full cell curve E.

[0198] Second performance factor group 1220 may be the result of correcting first performance factor group 1210 using factor correction model 200 so that the error between the charge / discharge performance indicated by first performance factor group 1210 and the actual charge / discharge performance of the target cell BC is reduced. Second performance factor group 1220 may represent an estimated result of the charge / discharge performance of the target cell BC that would be determined if the cell diagnostic logic were applied to the second target all-cell curve N. In other words, the specific performance factors (e.g., the negative electrode participation starting point) of second performance factor group 1220 may be obtained by correcting the specific performance factors of first performance factor group 1210 to approximate the actual specific performance factors of the target cell.

[0199] The factor correction model 200 may be a machine learning model trained by a training data set that includes a pair of a first performance factor group and a second performance factor group for each of a plurality of test cells.

[0200] To train the factor correction model 200, a plurality of test cells are prepared in advance. At least one of the plurality of test cells may be a new battery cell that has been verified as a good product. Each of the remaining test cells may have at least one of its positive and negative electrodes forcibly degraded from a new state through a different charge / discharge cycle than that of the other test cells.

[0201] The first performance factor set for a specific test cell can be pre-obtained by applying the cell diagnostic logic to the estimated test full-cell curve for the corresponding test cell. The estimated test full-cell curve for the specific test cell can be pre-obtained by subtracting the overpotential curve OP from a primary test full-cell curve representing the corresponding relationship between the voltage and the capacity factor of the test cell while the first electrical stimulus is applied to the corresponding test cell.

[0202] The second performance factor group for a specific test cell can be pre-obtained by applying the cell diagnostic logic to the secondary test full-cell curve for the specific test cell. The secondary test full-cell curve for the specific test cell can represent the corresponding relationship between the voltage and the capacity factor of the corresponding test cell when the second electrical stimulus is applied to the corresponding test cell.

[0203] exist Figure 13 In the graph shown, a plurality of data points included in the training data set are marked on two-dimensional coordinates. Figure 13 The number of data points marked on the graph may be equal to the number of test units.

[0204] Each data point is defined by two estimated values of a particular performance factor. That is, the X-axis coordinate of each data point represents the value included in the first performance factor group as the estimated value of the particular performance factor, and the Y-axis coordinate represents the value included in the second performance factor group as the other estimated value of the particular performance factor. For ease of explanation, Figure 13 Each of the X-axis and the Y-axis in is shown as representing the negative electrode SOC at the starting point of negative electrode participation.

[0205] refer to Figure 13 The data points of the training data set are distributed so as to have a learnable trend. That is, the correlation between the values included in the first performance factor group, which are estimated values of a specific performance factor, and the values included in the second performance factor group, which are other estimated values of the specific performance factor, can be trained by the factor correction model 200.

[0206] The factor correction model 200 can be trained based on the correlation between two estimated values of a specific performance factor, and the correlation information between the two estimated values obtained through learning can be expressed as Figure 15 This will be explained in detail later.

[0207] refer to Figure 14 , the neural network of the factor correction model 200 may include an input layer 1000 , an intermediate layer 2000 and an output layer 3000 .

[0208] In the factor correction model 200, the number of nodes included in each layer, the connections between the nodes, the function of each node included in the middle layer 2000, etc. can be predetermined. In addition, the weight of each connection between the nodes can be automatically determined through a machine learning process using a training data set.

[0209] The input layer 1000 may include first to sixth input nodes I1 to I6. When i is a natural number less than or equal to 6, the i-th input node Ii may be associated with one performance factor of the first performance factor group 1210. Figure 14 In the figure, for ease of explanation, it is assumed that the first input node I1 to the sixth input node I6 are respectively associated with the positive participation starting point, the positive participation end point, the positive scaling factor, the negative participation starting point, the negative participation end point and the negative scaling factor that can be included in the first performance factor group.

[0210] The i-th input node Ii may be provided with an i-th input data set Xi, which is performance factor data associated therewith. For example, the first input node I1 may be provided with a first input data set X1. The first input data set X1 may include values associated with the positive electrode participation starting point of multiple test cells (e.g., positive electrode starting potential and capacity values).

[0211] The output layer 3000 may include at least one of the first to sixth output nodes O1 to O6. When j is a natural number less than or equal to 6, the j-th output node Oj may be associated with any one of a positive pole participation start point, a positive pole participation end point, a positive pole scaling factor, a negative pole participation start point, a negative pole participation end point, and a negative pole scaling factor. Figure 14 For ease of explanation, it is assumed that the first to sixth output nodes O1 to O6 are associated with a positive pole participation start point, a positive pole participation end point, a positive pole scaling factor, a negative pole participation start point, a negative pole participation end point, and a negative pole scaling factor, respectively. The positive pole participation start point, the positive pole participation end point, the positive pole scaling factor, the negative pole participation start point, the negative pole participation end point, and the negative pole scaling factor may be referred to as the first to sixth performance factors in this order.

[0212] When the first to sixth input data sets X1 to X6 are input to the first to sixth input nodes I1 to I6, the j-th output data set Zj can be output from the j-th output node Oj. For example, when the first output node O1 is associated with the positive participation starting point, the third output data set Z3 can include values of the first input data set X1 with corrections.

[0213] Figure 14Although input layer 1000 is shown to include first to sixth input nodes I1 to I6, and output layer 2000 includes first to sixth output nodes O1 to O6, this is merely an example. That is, it is sufficient for input layer 1000 to include at least one of first to sixth input nodes I1 to I6, and it is also sufficient for output layer 3000 to include at least one of first to sixth output nodes O1 to O6. For example, when all first to sixth input data sets X1 to X6 are provided to input layer 1000, output layer 3000 may output only one of first to sixth output data sets Z1 to Z6.

[0214] Which output dataset among the first output dataset Z1 to the sixth output dataset Z6 will be output by the factor correction model 200 can be determined by the connections between nodes, the weight of each connection between nodes, the function of each node included in the intermediate layer 2000, etc., and it is not particularly limited.

[0215] The intermediate layer 2000 may include first to mth intermediate nodes F1 to Fm (m is a natural number greater than or equal to 2). When k is a natural number less than or equal to m, the kth intermediate node Fk may be connected to at least one of the first to sixth input nodes I1 to I6 and at least one of the first to sixth output nodes O1 to O6. The kth intermediate node Fk may have the form of a function determined by a learning process and may transmit an estimated value calculated based on the input value from each input node connected thereto to each output node connected thereto. The jth output node Oj may output an estimated value equal to the sum of the estimated values received from each intermediate node connected thereto as a correction result of the first performance factor group.

[0216] The function of the k-th intermediate node Fk can be generated based on the correlation coefficient between the performance factor associated with each node of the input layer 1000 connected to the k-th intermediate node Fk and the performance factor associated with each node of the output layer 3000 connected to the k-th intermediate node Fk. For reference, the correlation coefficient is a real number between -1 and +1, and a correlation coefficient closer to -1 indicates a negative correlation between the two factors, and a correlation coefficient closer to +1 indicates a positive correlation between the two factors.

[0217] The function of each intermediate node of the intermediate layer 2000 may be a weighted average function. In this case, a correlation coefficient indicating the degree of correlation between the estimated values of the first to sixth performance factors included in the first performance factor group and the estimated value of at least one of the first to sixth performance factors included in the second performance factor group may be used as the weight of the function of each intermediate node of the intermediate layer 2000.

[0218] Therefore, the factor correction model 200 includes at least one of the first to sixth machine learning models. The first to sixth machine learning models may be models that provide secondary estimation results for the first to sixth performance factors in that order.

[0219] When the target cell BC is in the MOL state, the control circuit 130 may determine at least one degradation parameter based on the second performance factor set. Table 1 below summarizes the degradation parameters and the formulas used to determine each degradation parameter. For reference, the second performance factor set for the target cell BC when it is in the new state may already be recorded in the memory 131.

[0220] Table 1

[0221]

[0222] Each variable listed in Table 1 is a diagnostic factor that may be included in the second performance factor group described above. The definitions of the degradation parameters and variables in Table 1 may be as follows.

[0223] <degradation parameter>

[0224] P SOH : Cathode SOH (healthy state) of target cell BC

[0225] N SOH : Negative electrode SOH of target monomer BC

[0226] L SOH : Available lithium SOH of target monomer BC

[0227] F SOH : All monomer SOH of target monomer BC

[0228] P LOSS : Positive electrode loss rate of target monomer BC

[0229] N LOSS : Negative electrode loss rate of target monomer BC

[0230] L LOSS : Available lithium loss rate of target monomer BC

[0231] F LOSS : Total monomer loss rate of target monomer BC

[0232] P loading_MOL : cathode loading amount of target monomer BC

[0233] N loading_MOL : Negative electrode loading of target monomer BC

[0234] N / P _MOL : NP ratio of target monomer BC

[0235] As any battery cell degrades, at least one of the total positive electrode capacity, total negative electrode capacity, available lithium content, and total full cell capacity of the battery cell may gradually decrease from the value at the BOL (Beginning of Life) state. The total full cell capacity may represent the capacity difference between the two end points of the full cell curve. For example, the total full cell capacity may refer to the full charge capacity (FCC). The available lithium content may represent the total amount of lithium that can contribute to the charging and discharging of the battery cell. SOH It can express the maintenance rate of total positive electrode capacity. SOH It can express the maintenance rate of the total negative electrode capacity. SOH It can express the maintenance rate of available lithium content. SOH It can express the maintenance rate of the total monomer capacity.

[0236] P SOH and P LOSS The sum of N SOH and N LOSS The sum of L SOH and L LOSS The sum of F SOH and F LOSS The sum of can each be equal to 1. LOSS Can be equal to P LOSS and L LOSS sum.

[0237] The positive electrode loading of any battery cell represents the amount of positive electrode active material (or available capacity) per unit area of the positive electrode of the battery cell. The negative electrode loading of any battery cell represents the amount of negative electrode active material (or available capacity) per unit area of the negative electrode of the battery cell. The unit of loading can be mAh / cm 2 or mg / cm 2 In Table 1, P loading_ref represents the reference positive electrode loading, and N loading_ref The reference positive electrode loading is a predetermined value representing the amount of positive electrode active material per unit area (or available capacity) of the positive electrode of a reference monomer. The reference positive electrode loading can be obtained by dividing the reference positive electrode capacity (Q P_ref ) divided by the reference positive electrode area. Here, the reference positive electrode capacity may be a value preset as the total positive electrode capacity of the reference cell. The reference positive electrode area may be a value preset as the area of the positive electrode of the reference cell. The reference negative electrode loading is a predetermined value representing the amount of negative electrode active material (or available capacity) per unit area of the negative electrode of the reference cell. The reference negative electrode loading may be obtained by dividing the reference negative electrode capacity (Q N_ref ) divided by the reference negative electrode area. Here, the reference negative electrode capacity may be a value preset to the total negative electrode capacity of the reference cell. The reference negative electrode area may be a value preset to the area of the negative electrode of the reference cell.

[0238] <variable>

[0239] pi BOL : The positive electrode capacity (positive electrode SOC) at the starting point of positive electrode participation when the target monomer BC is in the BOL state

[0240] pi MOL : The current cathode participation starting point of the target monomer BC (e.g., Figure 8 The positive electrode capacity (positive electrode SOC) of pi') shown in

[0241] pf BOL : The positive electrode capacity (positive electrode SOC) at the end point when the target monomer BC is in the BOL state

[0242] pf MOL : The current cathode participation endpoint of the target monomer BC (e.g., Figure 8 The positive electrode capacity (positive electrode SOC) of the pf shown in

[0243] ni BOL : Negative electrode capacity (negative electrode SOC) at the starting point of negative electrode participation when the target monomer BC is in the BOL state

[0244] ni MOL : The current negative electrode participation starting point of the target monomer BC (e.g., Figure 8 The negative electrode capacity (negative electrode SOC) of ni) is shown in

[0245] nf BOL : When the target monomer BC is in the BOL state, the negative electrode capacity of the negative electrode at the end point (negative electrode SOC)

[0246] nf MOL : The current negative electrode participation endpoint of the target monomer BC (e.g., Figure 8 The negative electrode capacity (negative electrode SOC) of nf') is shown in

[0247] ps BOL : positive electrode scaling factor when the target monomer BC is in the BOL state

[0248] ps MOL : Current positive scaling factor of the target monomer BC

[0249] ns BOL : Negative scaling factor when the target monomer BC is in the BOL state

[0250] ns MOL : Current negative scaling factor of target cell BC

[0251] The process of determining the second set of performance factors may be repeated periodically or aperiodically during the life of the target cell BC.

[0252] Figure 15 An example of correlation information between the first performance factor group and the second performance factor group obtained by learning the factor correction model 200 in matrix form is shown.

[0253] Figure 15 The matrix shown is a 6×6 matrix. The six rows represent the first to sixth performance factors of the first performance factor group provided in that order as a training data set. The six columns represent the first to sixth performance factors of the second performance factor group provided in that order as a training data set. Figure 15 In the above, pi_A[1], pf_A[2], ps_A[3], ni_A[4], nf_A[5], and ns_A[6] represent the positive pole participation starting point, positive pole participation end point, positive pole scaling factor, negative pole participation starting point, negative pole participation end point, and negative pole scaling factor included in the first performance factor group of the training data set in that order. In addition, pi_B[1], pf_B[2], ps_B[3], ni_B[4], nf_B[5], and ns_B[6] represent the positive pole participation starting point, positive pole participation end point, positive pole scaling factor, negative pole participation starting point, negative pole participation end point, and negative pole scaling factor included in the second performance factor group of the training data set in that order, respectively.

[0254] When p and q are natural numbers less than or equal to 6, the value of the p-th row (pi_A[p]) and the q-th column (pi_B[q]) represents a correlation coefficient between the p-th performance factor included in the first performance factor group and the q-th performance factor included in the second performance factor group.

[0255] For example, the correlation coefficient between the first performance factor (pi_A[1]) in the first row and the second performance factor (pf_B[2]) in the second column is -0.52. As another example, the correlation coefficient between the fifth performance factor (nf_A[5]) in the fifth row and the fourth performance factor (ni_B[4]) in the fourth column is 0.46.

[0256] Figure 16 FIG. 1 is a flowchart schematically illustrating a battery diagnosis method according to another embodiment of the present disclosure. Figure 16 The method may be executed by the battery diagnosis device 100 .

[0257] Reference Figures 1 to 16 In step S1610 , the control circuit 130 collects measurement signals representing measured values of voltage and current of a target cell BC, which is a battery cell to be diagnosed, from the sensing unit 110 .

[0258] In step S1620 , the control circuit 130 generates a first target all-cell curve M based on the measurement signal collected in step S1610 , which represents the corresponding relationship between the voltage and the capacity factor of the target cell BC when the first electrical stimulus is applied to the target cell BC.

[0259] Steps S1610 and S1620 may be replaced with a process in which the data obtaining unit directly obtains the first target all-monomer curve M or obtains the first target all-monomer curve M from the outside.

[0260] In step S1630 , the control circuit 130 generates an estimated full-cell curve E based on the first target full-cell curve M and the overpotential curve OP.

[0261] In step S1640 , the control circuit 130 applies the cell diagnostic logic to the estimated all-cell curve E to determine a first performance factor group 1210 as a preliminary estimate of the charge / discharge performance of the target cell BC.

[0262] In step S1650, the control circuit 130 determines a second performance factor group 1220 as a secondary estimate of the charge / discharge performance of the target cell BC by applying the factor correction model 200 to the first performance factor group 1210. Here, the second performance factor group represents an estimate of the negative electrode participation starting point of the target cell BC that would be determined if the cell diagnostic logic were applied to the second target full-cell curve N instead of the estimated full-cell curve E.

[0263] The second target all-cell curve N represents the corresponding relationship between the voltage and capacity factor of the target cell BC when a second electrical stimulus different from the first electrical stimulus is applied to the target cell BC. The second target all-cell curve N is not obtained by actually applying the second electrical stimulus to the target cell BC. In other words, the second target all-cell curve N represents the corresponding relationship between the voltage and capacity factor of the target cell BC that would be expected if the second electrical stimulus was applied to the target cell BC instead of the first electrical stimulus.

[0264] The control circuit 130 may limit at least one of the permissible voltage range, permissible SOC range, and permissible charge / discharge current of the target cell BC based on at least one degradation parameter. The memory 131 may pre-store relationship data indicating the corresponding relationship between at least one restriction item (i.e., permissible voltage range, permissible SOC range, and / or permissible charge / discharge current) and at least one degradation parameter. For example, when a specific type of degradation parameter (e.g., P LOSS 、N LOSS 、L LOSS 、F LOSS) is greater from the BOL state and / or when another type of degradation parameter (e.g., P SOH 、N SOH 、L SOH 、F SOH ) When the reduction amount from the BOL state is larger, the limitation amount of the allowable voltage range, the allowable SOC range and / or the allowable charge / discharge current can be larger.

[0265] The embodiments of the present disclosure described above are not implemented solely by devices and methods, but can be implemented by programs that execute functions corresponding to the configurations of the embodiments of the present disclosure or by recording media on which the programs are recorded, and those skilled in the art can easily reach such implementation from the disclosure of the previously described embodiments.

[0266] While the present disclosure has been described above with respect to a limited number of embodiments and drawings, it is not limited thereto, and it will be apparent to those skilled in the art that various modifications and changes may be made thereto within the technical aspects of the present disclosure and the equivalent scope of the appended claims.

[0267] In addition, since those skilled in the art can make many substitutions, modifications and changes to the disclosure described above without departing from the technical aspects of the disclosure, the disclosure is not limited to the above-mentioned embodiments and drawings, and some or all of the embodiments can be selectively combined to allow various modifications.

Claims

1. A battery diagnostic device comprising: a data acquisition unit configured to obtain a first target full-cell curve while a first electrical stimulus is applied to a target cell, wherein the first target full-cell curve represents a correspondence between a voltage and a capacity factor of the target cell, the target cell being a battery cell to be diagnosed; as well as a control circuit configured to generate an estimated full-cell curve based on the first target full-cell curve and the overpotential curve, Wherein, the control circuit is configured as follows: determining a first set of performance factors as a preliminary estimate of the charge / discharge performance of the target cell by applying cell diagnostic logic to the estimated all-cell curve, and determining a second performance factor group as a secondary estimate of the charge / discharge performance of the target cell by applying a factor correction model to the first performance factor group, Among them, the second performance factor group represents the estimated result of the negative electrode participation starting point of the target cell that can be determined by applying the cell diagnostic logic to a second target full-cell curve, and the second target full-cell curve represents the correspondence between the voltage of the target cell and the capacity factor when a second electrical stimulus different from the first electrical stimulus is applied.

2. The battery diagnostic device according to claim 1, wherein: The first electrical stimulus is an electrical stimulus that induces an overpotential exceeding an allowable level in the target cell, and The second electrical stimulation is an electrical stimulation that induces an overpotential in the target monomer that is less than the allowable level.

3. The battery diagnostic device according to claim 1, wherein: The first electrical stimulation uses a first current rate to charge, and The second electrical stimulation is performed by charging using a second current rate that is less than the first current rate.

4. The battery diagnostic device according to claim 1, wherein: The first electrical stimulus discharges using a first current rate, and The second electrical stimulation is performed by discharging at a second current rate that is smaller than the first current rate.

5. The battery diagnostic device according to claim 1, wherein: The overpotential curve represents the difference between a first reference all-monomer curve and a second reference all-monomer curve, The first reference full-cell curve represents the corresponding relationship between the voltage and capacity factor of the reference cell when the first electrical stimulus is applied to the reference cell, and the reference cell is a battery cell that is verified to be normal, and The second reference full-cell curve represents the corresponding relationship between the voltage of the reference cell and the capacity factor when the second electrical stimulus is applied to the reference cell.

6. The battery diagnostic device according to claim 1, wherein: The control circuit is configured to generate the estimated full-cell curve by subtracting the overpotential curve from the first target full-cell curve.

7. The battery diagnostic device according to claim 1, wherein: The first performance factor group includes at least one of the following as a performance factor: a positive electrode participation starting point, wherein the positive electrode participation starting point represents the positive electrode voltage and positive electrode capacity when the voltage of the target cell matches the first set voltage; a positive electrode participation endpoint, wherein the positive electrode participation endpoint represents the positive electrode voltage and positive electrode capacity when the voltage of the target cell matches the second set voltage; A positive electrode scaling factor, wherein the positive electrode scaling factor represents a ratio of a capacity difference between the positive electrode participation starting point and the positive electrode participation end point relative to a reference positive electrode capacity; a negative electrode participation starting point, wherein the negative electrode participation starting point represents the negative electrode voltage and negative electrode capacity when the voltage of the target cell matches the first set voltage; a negative electrode participation endpoint, the negative electrode participation endpoint representing the negative electrode voltage and negative electrode capacity when the voltage of the target cell matches the second set voltage; and A negative electrode scaling factor represents a ratio of a capacity difference between the negative electrode participation starting point and the negative electrode participation end point relative to a reference negative electrode capacity.

8. The battery diagnostic device according to claim 1, wherein: The factor correction model is a machine learning model trained by a training data set including pairs of a first performance factor group and a second performance factor group for each of a plurality of test cells having different charge / discharge performances.

9. The battery diagnostic device according to claim 8, wherein: obtaining the first set of performance factors for each of the plurality of test cells by applying the cell diagnostic logic to each of a plurality of estimated test full-cell curves, wherein the plurality of estimated test full-cell curves are obtained by subtracting the overpotential curve from each of a plurality of primary test full-cell curves representing the corresponding relationship between the voltage and the capacity factor of each of the plurality of test cells while the first electrical stimulus is applied to each of the plurality of test cells, wherein the second performance factor group for each of the plurality of test cells is obtained by applying the cell diagnostic logic to a plurality of secondary test full cell curves, and The plurality of secondary test full-cell curves represent a corresponding relationship between the voltage and the capacity factor of each of the plurality of test cells when the second electrical stimulus is applied to each of the plurality of test cells.

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

11. An electric vehicle comprising the battery pack according to claim 10.

12. A battery diagnosis method comprising: While a first electrical stimulus is applied to a target cell, a first target full-cell curve is obtained, wherein the first target full-cell curve represents a corresponding relationship between a voltage and a capacity factor of the target cell, the target cell being a battery cell to be diagnosed; generating an estimated full-cell curve based on the first target full-cell curve and the overpotential curve; determining a first set of performance factors as a preliminary estimate of charge / discharge performance of the target cell by applying cell diagnostic logic to the estimated all-cell curve; as well as determining a second performance factor group as a secondary estimate of the charge / discharge performance of the target cell by applying a factor correction model to the first performance factor group, The second performance factor group represents an estimated result of the negative electrode participation starting point of the target cell that can be determined by applying the cell diagnostic logic to a second target full-cell curve instead of the estimated full-cell curve, wherein the second target full-cell curve represents the correspondence between the voltage of the target cell and the capacity factor when a second electrical stimulus different from the first electrical stimulus is applied.

13. The battery diagnosis method according to claim 12, wherein: The step of generating an estimated full-cell curve generates the estimated full-cell curve by subtracting the overpotential curve from the first target full-cell curve.

14. The battery diagnosis method according to claim 12, wherein: The factor correction model is a machine learning model trained by a training data set including pairs of a first performance factor group and a second performance factor group for each of a plurality of test cells having different charge / discharge performances.

15. The battery diagnosis method according to claim 14, wherein: obtaining the first set of performance factors for each of the plurality of test cells by applying the cell diagnostic logic to each of a plurality of estimated test full-cell curves, wherein the plurality of estimated test full-cell curves are obtained by subtracting the overpotential curve from each of a plurality of primary test full-cell curves representing the corresponding relationship between the voltage and the capacity factor of each of the plurality of test cells while the first electrical stimulus is applied to each of the plurality of test cells, wherein the second performance factor group for each of the plurality of test cells is obtained by applying the cell diagnostic logic to a plurality of secondary test full cell curves, and The plurality of secondary test full-cell curves represent a corresponding relationship between the voltage and the capacity factor of each of the plurality of test cells when the second electrical stimulus is applied to each of the plurality of test cells.

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