Battery diagnosis method and battery diagnosis device

Abstract

The invention provides a battery diagnosis apparatus and a battery diagnosis method. The battery diagnosis method includes: obtaining diagnosis target information including a target full cell curve of a battery cell associated with a first electrical stimulus; correcting the target full cell curve to be associated with a second electrical stimulus different from the first electrical stimulus based on a predetermined overpotential curve; applying diagnostic logic to the corrected target full cell curve to determine first diagnostic result information, the first diagnostic result information being a preliminary diagnostic result of charge / discharge performance of the battery cells; and determining second diagnostic result information based on at least one preliminary diagnostic factor included in the first diagnostic result information using the factor correction model, the second diagnostic result information being a precise diagnostic result of the charge / discharge performance of the battery cell.

Description

TECHNICAL FIELD

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

[0002] This application claims priority to Korean Patent Application No. 10-2023-0165769, filed on November 24, 2023, and Korean Patent Application No. 10-2024-0065906, filed on May 21, 2024, the disclosures of which are incorporated herein by reference. BACKGROUND

[0003] Recently, there is a rapid increase in demand for portable electronic products such as laptop computers, video 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-hydrogen batteries, nickel-zinc batteries, lithium batteries, etc., and among them, lithium batteries have little or no memory effect, and thus they are gaining more attention than nickel-based batteries because of their advantages in that they can be recharged whenever it is convenient, have a very low self-discharge rate, and have a high energy density.

[0005] Generally, due to reasons such as manufacturing defects or deterioration due to use, the actual charge / discharge performance of a battery can not reach the normal charge / discharge performance, and it is necessary to accurately diagnose the charge / discharge performance of the battery in order to improve the lifespan and safety of the battery.

[0006] Conventionally, while a low electrical stimulus (e.g., low-rate charging or discharging) is being applied to a battery, the voltage and capacity of the battery are measured and recorded, and a full cell curve representing the correspondence between the voltage and the capacity is generated based on the recorded measurement values to diagnose the state of the battery. However, since the capacity and voltage of the battery slowly change while the low electrical stimulus (e.g., low-rate charging or discharging) is being applied, there is a limitation that it takes a long time to diagnose the battery.

[0007] In terms of shortening the diagnosis time, a high level of electrical stimulus is naturally more advantageous than a low level of electrical stimulus. However, when a high electrical stimulus (e.g., high-rate charging or discharging) is being applied to a battery, the proportion of overpotential in the battery voltage can excessively increase. More specifically, as the current flowing through the battery is greater, polarization phenomena are generated more, and overpotential is caused by the polarization phenomena. Since the voltage of the battery can be regarded as the sum of the OCV (open circuit voltage) and the overpotential, as the level of the electrical stimulus applied to the battery is higher, the difference between the battery voltage and the actual OCV increases.

[0008] As the voltage of the battery is closer to the actual OCV, the charging / discharging performance of the battery can be more accurately diagnosed, and thus the overpotential acts as a kind of noise that reduces diagnostic accuracy. Accordingly, the diagnostic result of the charging / discharging performance based on the full cell curve obtained using the high electrical stimulus can have a significant gap from the actual charging / discharging performance of the battery. SUMMARY

[0009] TECHNICAL PROBLEM

[0010] The disclosure is designed to solve the problems of the related art, and thus the disclosure relates to providing a battery diagnosis method and a battery diagnosis apparatus that can simultaneously shorten a diagnosis time and ensure diagnostic accuracy by applying a high electrical stimulus to a battery to obtain charging / discharging information ("first target full cell curve" in claims) and removing noise caused by overpotential included in the obtained charging / discharging information using a factor correction model based on machine learning.

[0011] These and other objects and advantages of the disclosure can be understood from the following detailed description, and will become more fully apparent from the exemplary embodiments of the disclosure. Moreover, it will be readily understood to those skilled in the art that the objectives and advantages of the present disclosure can be met by the means shown in the claims and combinations thereof. Accordingly, the disclosure should not be construed as being limited to the embodiments set forth in the claims and / or the following detailed description.

[0012] TECHNICAL SOLUTION

[0013] In one aspect of the disclosure, a battery diagnosis method is provided, including: obtaining diagnosis target information including a target full cell curve of a battery cell associated with a first electrical stimulus; correcting the target full cell curve to be associated with a second electrical stimulus different from the first electrical stimulus based on a predetermined overpotential curve; applying diagnosis logic to the corrected target full cell curve to determine first diagnosis result information, which is a preliminary diagnosis result of charging / discharging performance of the battery cell; and determining second diagnosis result information, which is an accurate diagnosis result of the charging / discharging performance of the battery cell, based on at least one preliminary diagnosis factor included in the first diagnosis result information using a factor correction model.

[0014] The transient voltage change induced in the battery cell when the second electrical stimulus is applied can be less than the transient voltage change induced in the battery cell when the first electrical stimulus is applied.

[0015] The first electrical stimulus can be a charging current greater than or equal to a first current rate, and the second electrical stimulus can be a charging current less than or equal to a second current rate, the second current rate being less than the first current rate.

[0016] The first electrical stimulus can be a discharge current greater than or equal to a first current rate, and the second electrical stimulus can be a discharge current less than or equal to a second current rate, the second current rate being less than the first current rate.

[0017] The overpotential curve can represent a difference between a first reference full monomer curve and a second reference full monomer curve. The first reference full monomer curve can be predetermined as a correspondence between a capacity factor and a voltage of a reference monomer while the first electrical stimulus is being applied. The second reference full monomer curve can be predetermined as a correspondence between a capacity factor and a voltage of the reference monomer while the second electrical stimulus is being applied.

[0018] The step of correcting the target full monomer curve can be subtracting the overpotential curve from the target full monomer curve to generate a corrected target full monomer curve.

[0019] The diagnosis target information can further include temperature information of the battery monomer measured during an application period of the first electrical stimulus. The temperature information can be input into the factor correction model together with the at least one preliminary diagnosis factor.

[0020] The diagnosis target information can further include impedance information of the battery monomer measured during an application period of the first electrical stimulus. The impedance information can be input into the factor correction model together with the at least one preliminary diagnosis factor.

[0021] The factor correction model can be a machine learning model trained entirely by a training data set including pairs of the first diagnosis result information and the second diagnosis result information of each of a plurality of test monomers having different charge / discharge performances.

[0022] The first diagnosis result information of each of the plurality of test monomers can be obtained by applying diagnosis logic to each of a plurality of corrected test full monomer curves. The plurality of corrected test full monomer curves can be obtained by individually correcting a plurality of first test full monomer curves associated with the first electrical stimulus based on the overpotential curve. The second diagnosis result information of each of the plurality of test monomers can be obtained by applying diagnosis logic to a plurality of second test full monomer curves associated with the second electrical stimulus.

[0023] The step of determining the second diagnosis result information can be performed on a condition that a performance index of the factor correction model is evaluated to be greater than or equal to a threshold value.

[0024] The battery diagnosis can further include transmitting a message to a user device to notify that additional training of the factor correction model is needed when the performance index of the factor correction model is evaluated to be less than a threshold value.

[0025] The second diagnosis result information can include at least one type of diagnosis factor among a positive electrode participation start point, a positive electrode participation end point, and a positive electrode scaling factor related to a charge / discharge performance of a positive electrode of the battery cell as an accurate diagnosis factor of the battery cell.

[0026] The second diagnosis result information can include at least one type of diagnosis factor among a negative electrode participation start point, a negative electrode participation end point, and a negative electrode scaling factor related to a charge / discharge performance of a negative electrode of the battery cell as an accurate diagnosis factor of the battery cell.

[0027] The second diagnosis result information can include at least one type of diagnosis factor among a positive electrode load amount related to a charge / discharge performance of a positive electrode of the battery cell, a negative electrode load amount related to a charge / discharge performance of a negative electrode of the battery cell, and an NP ratio related to charge / discharge performances of both the positive electrode and the negative electrode of the battery cell as an accurate diagnosis factor of the battery cell.

[0028] In another aspect of the disclosure, there is provided a battery diagnosis apparatus including a data obtaining unit configured to obtain diagnosis target information including a target full cell curve of a battery cell related to a first electrical stimulation, and a control circuit configured to correct the target full cell curve to be associated with a second electrical stimulation different from the first electrical stimulation based on a predetermined overpotential curve. The control circuit is configured to apply diagnosis logic to the corrected target full cell curve to determine first diagnosis result information which is a preliminary diagnosis result of a charge / discharge performance of the battery cell, and determine second diagnosis result information which is an accurate diagnosis result of the charge / discharge performance of the battery cell based on at least one preliminary diagnosis factor included in the first diagnosis result information using a factor correction model.

[0029] The control circuit can be configured to subtract the overpotential curve from the target full cell curve to generate the corrected target full cell curve.

[0030] The factor correction model can be a machine learning model trained entirely by a training data set including pairs of the first diagnosis result information and the second diagnosis result information of each of a plurality of test cells having different charge / discharge performances.

[0031] In still another aspect of the disclosure, there is provided a battery pack including the battery diagnosis apparatus.

[0032] In still another aspect of the disclosure, there is provided a battery system including the battery diagnosis apparatus.

[0033] Advantageous Effects

[0034] According to at least one of the embodiments of the disclosure, even if the charging / discharging information of the battery cell (‘target full cell curve’ in the claims) is obtained by applying a high-level electrical stimulus to the battery cell to be diagnosed, the charging / discharging performance of the battery can be accurately diagnosed. Accordingly, compared to a diagnosis method using a low-level electrical stimulus (e.g., low-rate charging or discharging), the time required to diagnose the charging / discharging performance of the battery cell can be shortened.

[0035] In addition, according to at least one of the embodiments of the disclosure, by correcting the charging / discharging information such that at least part of the overpotential component caused by the high electrical stimulus is removed and then analyzing the corrected charging / discharging information (‘corrected target full cell curve’ in the claims), the accuracy of the diagnosis of the charging / discharging performance can be improved.

[0036] In addition, according to at least one of the embodiments of the disclosure, by correcting the preliminary diagnosis result information representing the charging / discharging performance determined from the corrected charging / discharging information using a factor correction model based on machine learning, precise diagnosis result information having a high consistency with the actual charging / discharging performance of the battery can be ensured.

[0037] Effects of the disclosure are not limited to the above-mentioned effects, and those skilled in the art will clearly understand from the attached claims these and other effects. BRIEF DESCRIPTION OF DRAWINGS

[0038] The accompanying drawings illustrate preferred embodiments of the disclosure and, together with the foregoing disclosure, provide further understanding of the technical features of the disclosure, and therefore the disclosure is not to be interpreted as being limited to the accompanying drawings.

[0039] Figure 1 FIG. 1 is a diagram exemplarily illustrating a configuration of a battery system according to the disclosure.

[0040] Figure 2 FIG. 2 is a diagram for explaining a relationship between an electrical stimulus and a full cell curve.

[0041] Figure 3 FIG. 3 is a diagram schematically illustrating an overpotential curve that can be obtained from a first reference full cell curve and a second reference full cell curve of FIG. 2. Figure 2

[0042] Figure 4 FIG. 5 is a graph for explaining a relationship between a first target full cell curve, a corrected target full cell curve, and a second target full cell curve.

[0043] Figure 5 FIG. 6 is a graph for explaining an example of each of the corrected target full cell curve, a second reference full cell curve, a reference positive electrode curve, and a reference negative electrode curve.​

[0044] Figures 6 to 8 is a graph referred to for explaining an example of a process for generating a comparative full monomer curve according to diagnostic logic.

[0045] Figures 9 to 11 is a graph referred to for explaining another example of a process for generating a comparative full monomer curve according to diagnostic logic.

[0046] Figure 12 is a graph referred to for explaining a function of a factor correction model.

[0047] Figures 13 to 21 is a graph referred to for explaining a training data set provided for training a factor correction model.

[0048] Figure 22 is a graph showing an example of a neural network structure of a factor correction model of Figure 12

[0049] Figure 23 is a graph showing an example of a correlation coefficient between diagnostic factors that can be obtained by training a factor correction model.

[0050] Figure 24 is a flowchart for schematically showing a battery diagnostic method according to another embodiment of the disclosure.

[0051] Figure 25 is a flowchart for schematically showing an additional battery diagnostic method related to the battery diagnostic method of Figure 24

[0052] Figure 26 is a graph referred to for explaining a method according to Figure 25 DETAILED DESCRIPTION

[0053] Hereinafter, preferred embodiments of the 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 interpreted as being limited to general and dictionary meanings and should be interpreted based on the meanings and concepts corresponding to technical aspects of the present disclosure on the basis of the principle that the inventor is allowed to define terms appropriately for the best explanation of the patent to be granted.

[0054] Accordingly, the description set forth herein is merely illustrative of preferred examples of the disclosure and is not intended to limit the scope of the disclosure as there are modifications and variations which can be made to the disclosure without departing from the spirit or scope of the disclosure as defined in the claims. Therefore, the present disclosure should not be limited to the specific embodiments described herein but should be given the full scope defined by the appended claims.

[0055] The terms including ordinal numbers such as "first," "second," etc. are used to distinguish one element from another element among various elements but are not intended to limit the elements by the terms.​​​

[0056] The terms "include" and "comprise" when used in this specification designate the presence of stated elements, but do not exclude the presence or addition of one or more other elements. In addition, the term "unit" as used herein means at least one processing unit of a function or operation, and can be implemented by hardware and software, alone or in combination.

[0057] In addition, throughout the specification, it will also 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 an intermediate element can be present.

[0058] Figure 1 FIG. 1 is a diagram exemplarily illustrating a configuration of a battery system according to the present disclosure.

[0059] Reference Figure 1 The battery system 1 includes a system controller 2, a battery pack 10, a relay 20, an inverter 30, and an electrical load 40.

[0060] The charge terminal P+ and the discharge terminal P- of the battery pack 10 can be electrically connected to the inverter 30 and / or a charger 3 through a charging cable or the like. The charger 3 can be included in the battery system 1, or can be provided at a charging station.

[0061] The system controller 2 (for example, an ECU: Electronic Control Unit) is configured to transmit a key-on signal to the battery diagnosis device 100 in response to a start button (not shown) provided in the battery system 1 being switched to an on position by a user. The system controller 2 is configured to transmit a key-off signal to the battery diagnosis device 100 in response to the start button being switched to an off position by the user. The charger 3 can communicate with the system controller 2, and supply charging power to the battery 11 through the charge terminal P+ and the 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.

[0062] The battery pack 10 includes the battery 11. The battery pack 10 can also include the battery diagnosis device 100.

[0063] 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), the plurality of battery cells can be connected in series, in parallel, or in a mixture of series and parallel.

[0064] The type of the battery cell BC is not particularly limited as long as it can be repeatedly charged and discharged, such as a lithium-ion cell. The battery cell BC can include at least one unit cell. The unit cell is an electrochemical device that can be independently recharged. When the battery cell BC includes a plurality of unit cells, the plurality of unit cells can be connected in series, in parallel, or in a mixture of series and parallel. The battery cell BC can be a new battery cell that needs to be verified whether it is a good product, or a battery cell that is deteriorated after being verified as a good product and is no longer a new product. Hereinafter, the battery cell BC can be referred to as a "target battery cell" or a "target cell".

[0065] The relay 20 is electrically connected in series to the battery 11 through a power path connecting the battery 11 and the inverter 30. In Figure 1 The relay 20 is shown as being connected between the positive terminal of the battery 11 and the charge and discharge terminal P+ in the middle. 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 can be a mechanical connector 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).

[0066] The inverter 30 can convert direct current from the battery 11 into alternating current in response to a command from the battery diagnostic device 100 or the system controller 2.

[0067] The alternating current power from the inverter 30 is used to drive the electric load 40. As the electric load 40, for example, a three-phase alternating current motor 40 can be used.

[0068] The battery diagnostic device 100 includes a control circuit 130 and a memory 131. The battery diagnostic device 100 can also include at least one of a sensing unit 110 and a communication circuit 150. The data obtaining unit described in the claims of the present application can refer to at least one of the sensing unit 110 and the communication circuit 150 or collectively refer to the sensing unit 110 and the communication circuit 150.

[0069] The sensing unit 110 includes a voltage sensor 111 and a current sensor 112.

[0070] The voltage sensor 111 is connected in parallel to the battery 11, measures the battery voltage which is the voltage across the two terminals of the battery 11, and is configured to generate a voltage signal representing the measured battery voltage.

[0071] Of course, the voltage sensor 111 can be connected to the positive and negative terminals of each battery cell BC included in the battery 11, measure a cell voltage that is a voltage across the two terminals of each battery cell BC (which can be referred to as a "full cell voltage"), and output an additional voltage signal representing the measured cell voltage (i.e., a measured value of the full cell voltage) to the control circuit 130.

[0072] The current sensor 112 is connected in series to the battery 11 through a current path between the battery 11 and the inverter 30. The current sensor 112 is configured to detect a battery current that is a current flowing through the battery 11, and generate a current signal representing the detected battery current. The current sensor 112 can be implemented as one or a combination of two or more of known current detection elements such as a shunt resistor, a Hall effect element, and the like.

[0073] The temperature sensor 113 is provided to measure a cell temperature that is a temperature of the battery cell BC. The temperature sensor 113 can periodically or aperiodically detect the cell temperature of the target battery cell BC while the first electrical stimulus is being applied to the battery cell BC. The temperature sensor 113 can generate a temperature signal representing the detected cell temperature.

[0074] The communication circuit 150 is configured to support wired or wireless communication between the control circuit 130 and the system controller 2. The wired communication can be, for example, CAN (Controller Area Network) communication, and the wireless communication can be, for example, ZigBee or Bluetooth communication. The type of communication protocol is not particularly limited as long as it supports wired and wireless communication between the control circuit 130 and the system controller 2. The communication circuit 150 can include an output device (e.g., a display, a speaker) that provides information received from the control circuit 130 and / or the system controller 2 in a user-recognizable form.

[0075] The control circuit 130 is operatively coupled to the relay 20, the voltage sensor 111, the current sensor 112, and the communication circuit 150. The operative coupling of two components means that the two components are connected directly or indirectly to enable transmission and reception of signals in one direction or both directions.

[0076] The control circuit 130 can collect the voltage signal from the voltage sensor 111 and / or the current signal from the current sensor 112. The control circuit 130 can 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.

[0077] The control unit 130 can be referred to as a "control unit" or a "battery controller," and can 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.

[0078] The memory 131 can include at least one type of storage medium such as a flash memory type, a hard disk type, a solid state disk (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 can store data and programs required for a computing operation of the control unit 130. The memory 131 can store data representing a result of a computing operation performed by the control unit 130. Although the memory 131 is depicted as being physically independent of the control circuit 130, Figure 1 The memory 131 can be embedded within the control circuit 130.

[0079] The control circuit 130 can turn on the relay 20 in response to a key-on signal. The control circuit 130 can turn off the relay 20 in response to a key-off signal. The key-on signal is a signal requesting a switch from the rest mode to the charging or discharging mode. The key-off signal is a signal that initiates a switch from the cycle state to the rest state. Alternatively, the system controller 2 can be responsible for turning on / off the relay 20 instead of the control circuit 130.

[0080] If the relay 20 is turned on while the inverter 30 or the charger 3 is operating, the battery 11 enters the cycle state. Conversely, if the relay 20 is turned off or the inverter 30 and the charger 3 stop operating, the battery 11 enters the rest state.

[0081] 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 the charging / discharging of the battery 11 is stopped. The fact that the battery 11 is in the cycle state or the rest state means that each battery cell BC included in the battery 11 is also in the cycle state or the rest state.

[0082] The control circuit 130 can determine a voltage detection value and a current detection value based on the voltage signal and the current signal while the battery cell BC is in the cycle state and / or the rest state, 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.

[0083] If the charger 3 is operating in a constant current charging mode, the current rate of the charging current supplied to the battery cell BC (also referred to as a "C-rate") 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.

[0084] The SOC is a ratio of the remaining capacity of the battery cell BC to the full charge capacity (maximum capacity), and is generally processed in the range of 0 to 1 or 0 to 100%. The SOC can be determined using known methods such as ampere counting, an OCV (open circuit voltage)-SOC curve, and / or a Kalman filter.

[0085] The communication circuit 150 can obtain a first target full cell curve associated with the first electrical stimulus (corresponding to "target full cell curve" in the claims) from a separate computing device (e.g., the battery system 1) externally provided via wired communication and / or wireless communication. Alternatively, the sensing unit 110 can directly generate the first target full cell curve of the target cell BC based on the measurement signals including the current signal and the voltage signal of the target cell BC, which is the battery cell to be diagnosed. Alternatively, the control circuit 130 can collect the measurement signals including the current signal and the voltage signal of the target cell BC from the sensing unit 110, and then generate the first target full cell curve of the target cell BC based on the collected measurement signals. The measurement signals can also include a temperature signal.

[0086] The control circuit 130 can generate the first target full cell curve of the target cell BC based on the collected measurement signals.

[0087] The first target full cell curve can represent a correspondence between the voltage and the capacity factor of the target cell BC while the first electrical stimulus is being applied to the target cell BC. The capacity factor can be the residual capacity or the SOC (state of charge).

[0088] The first electrical stimulus is an electrical stimulus whose transient voltage change induced in the target cell BC upon application of the first electrical stimulus exceeds an allowable level, and corresponds to a "high electrical stimulus". The second electrical stimulus is an electrical stimulus whose transient voltage change induced in the target cell BC upon application of the second electrical stimulus is less than the allowable level, and corresponds to a "low electrical stimulus". For example, the first electrical stimulus can be a charging current at a first current rate (e.g., 1.0C, 0.33C, etc.), and the second electrical stimulus can be a charging current at a second current rate (e.g., 0.05C) less than the first current rate. For another example, the first electrical stimulus can be a discharging current at a first current rate, and the second electrical stimulus can be a discharging current at a second current rate.

[0089] The first target full-cell curve can be a curve showing the relationship between the capacity of the target cell BC and the full-cell voltage while being charged or discharged at a constant current within a given voltage range (e.g., 3.0 to 4.0V) or a given SOC range (e.g., 0 to 100% SOC).

[0090] The data acquisition unit can also acquire temperature information of the target monomer BC during the application period of the first electrical stimulation associated with the first target monomer curve. The temperature information of the target monomer BC includes at least one of the following: initial temperature, final temperature, average temperature, maximum temperature, and minimum temperature. The initial temperature represents the temperature at the start time of the first electrical stimulation application period. The final temperature represents the temperature at the end time of the first electrical stimulation application period. The average temperature represents the average temperature during the first electrical stimulation application period. The maximum temperature represents the maximum temperature during the first electrical stimulation application period. The minimum temperature represents the minimum temperature during the first electrical stimulation application period.

[0091] In the following text, before explaining the first target full monomer curve obtained using the target monomer BC of this disclosure, the first reference full monomer curve and the second reference full monomer curve will be explained first.

[0092] Figure 2 This is a graph used to explain the relationship between electrical stimulation and the whole-cell curve.

[0093] Figure 2 The first reference whole cell curve R1 and the second reference whole cell curve R2 shown can be obtained in advance through a pre-experimental process of applying the first and second electrical stimuli separately to the reference cell.

[0094] A reference cell is a cell that has been verified as functioning correctly and can have the same level of positive and negative electrode performance as a new cell that has been verified as a good product. A reference cell can be simply referred to as a "reference cell". A reference cell can be a button cell comprising a positive and a negative half-cell, or a three-electrode cell.

[0095] A new battery cell refers to a battery cell that is in a new state. New state is the same concept as BOL (Start of Life). For example, the period before the accumulated charge / discharge capacity from the time of manufacture reaches the set capacity can be called BOL, and the period from the time the accumulated charge / discharge capacity reaches the set capacity can be called MOL (Mid-Life).

[0096] exist Figure 2 In the graph, the horizontal axis (X-axis) represents capacity (Ah), and the vertical axis (Y-axis) represents voltage (V).

[0097] The first reference full-cell curve R1 shows a relationship between the voltage and the capacity of the reference cell while the first electrical stimulus is being applied (e.g., during charging using the first current rate). The second reference full-cell curve R2 shows a relationship between the voltage and the capacity of the reference cell while the second electrical stimulus is being applied (e.g., during charging using the second current rate). The first reference full-cell curve R1 can be obtained by performing charging using the first current rate in a state where the OCV of the reference cell is set to be equal to the lower limit of the given voltage range (e.g., 3.0 V). The second reference full-cell curve R2 can be obtained by performing charging using the second current rate in a state where the OCV of the reference cell is set to be equal to the lower limit of the given voltage range. Thus, in Figure 2 the first reference full-cell curve R1 and the second reference full-cell curve R2 have their starting points approximately coincide, while their ending points are significantly different.

[0098] The first reference full-cell curve R1 and the second reference full-cell curve R2 can represent a correspondence between the capacity and the full-cell voltage of the reference cell within at least a voltage range of interest (e.g., 3.0 to 4.0 V). The lower and upper limits of the voltage range of interest can represent the first set voltage (3.0 V in Figure 2 ) and the second set voltage (4.0 V in Figure 2 ).

[0099] The SOC when the full-cell voltage of any battery cell is equal to the first set voltage can be set to 0%, and the SOC when the full-cell voltage is equal to the second set voltage can be set to 100%. That is, the first set voltage and the second set voltage can be the lower and upper limits of the battery cell voltage corresponding to the 0% to 100% SOC (state of charge) of any battery cell including the reference cell.

[0100] The initial capacity (Qi) can refer to the residual capacity when the full-cell voltage of any battery cell is equal to the first set voltage. The final capacity (Qf) can refer to the residual capacity when the full-cell voltage of any battery cell is equal to the second set voltage.

[0101] The first reference full-cell curve R1 can be based on a voltage time series and a current time series (or a capacity time series) acquired by periodically measuring the full-cell voltage and the current of the reference cell while the first electrical stimulus is being applied.

[0102] The second reference full-cell curve R2 can be based on a voltage time series and a current time series acquired by periodically measuring the full-cell voltage and the current of the reference cell while the second electrical stimulus is being applied.

[0103] Here, the first reference full cell curve R1 can include an overpotential of a voltage value corresponding to the same capacity value when compared with the second reference full cell curve R2. Accordingly, a voltage difference between the first reference full cell curve R1 and the second reference full cell curve R2 for the same capacity value can be calculated as an overpotential.

[0104] Specifically, by removing the second reference full cell curve R2 based on the second electrical stimulation from the first reference full cell curve R1 based on the first electrical stimulation (calculating a voltage difference per capacity), an overpotential curve indicating an overpotential per capacity can be generated.

[0105] Figure 3 is a graph schematically showing an overpotential curve OP that can be obtained from Figure 2 the first reference full cell curve R1 and the second reference full cell curve R2 of

[0106] The overpotential curve OP can be a curve representing a correspondence between a capacity and an overpotential. The overpotential curve OP can be a curve representing a voltage difference per capacity between the first reference full cell curve R1 and the second reference full cell curve R2.

[0107] A capacity range (Qi to Qf) of the overpotential curve OP can be a common capacity range between the first reference full cell curve R1 and the second reference full cell curve R2. In Figure 2 , the capacity range of the first reference full cell curve R1 is 5 to 47 Ah, and the capacity range of the second reference full cell curve R2 is 5 to 50 Ah, thus Qi can be 5 Ah and Qf can be 47 Ah.

[0108] Figure 4 is a graph for explaining a reference for a relationship between the first target full cell curve M, the corrected target full cell curve E, and the second target full cell curve N.

[0109] In Figures 2 to 4 , Ah is used as a unit of the horizontal axis, but the unit can be expressed in other forms. For example, instead of Ah, a percentage % indicating an SOC (State of Charge) can be used as a unit of the horizontal axis.

[0110] Referring to Figure 4 , the control circuit 130 can generate the first target full cell curve M representing a correspondence between a full cell voltage and a capacity of the target cell BC while the first electrical stimulation is being applied to the target cell BC. The first target full cell curve M can represent a correspondence between a capacity and a full cell voltage of the target cell BC at least within a voltage range of interest.

[0111] Therefore, since the reference monomer and the target monomer BC have different charge / discharge properties, there are inevitably some differences between the first target full monomer curve M and the first reference full monomer curve R1.

[0112] 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 monomer curve R1 shown in FIG. 1A is 5 to 47 Ah, and the capacity range of the first target full monomer curve M is 5 to 45 Ah.

[0113] The control circuit 130 can generate the corrected target full monomer curve E by correcting the first target full monomer curve M to be associated with the second electrical stimulus based on the overpotential curve OP. Specifically, the control circuit 130 can generate the corrected target full monomer curve E by subtracting the overpotential curve OP from the first target full monomer curve M. Therefore, at the same capacity value, the voltage value of the corrected target full monomer curve E can be less than the voltage value of the first target full monomer curve M.

[0114] The control circuit 130 can obtain the corrected target full monomer curve E by subtracting the capacity-specific overpotential of the overpotential curve OP from the capacity-specific voltage of the first target full monomer curve M in the common capacity range of the first target full monomer curve M and the overpotential curve OP. In this case, the capacity range of 45 Ah to 47 Ah among the entire capacity range of the overpotential curve OP can not be utilized. That is, the corrected target full monomer 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 monomer curve M.

[0115] Alternatively, the control circuit 130 can generate an adjusted overpotential curve (not shown in the drawing) 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 monomer curve M. Subsequently, the control circuit 130 can generate the corrected target full monomer curve E by subtracting the overpotential value of the adjusted overpotential curve from the voltage value of the first target full monomer curve M. That is, the corrected target full monomer curve E can be obtained by removing the capacity-specific overpotential of the adjusted overpotential curve from the capacity-specific voltage of the first target full monomer curve M.

[0116] The second target full monomer curve N is an example of a curve representing a correspondence relationship between the voltage and the capacity factor of the target monomer BC expected to be obtained instead of the first target full monomer curve M if the second electrical stimulus is applied to the target monomer BC instead of the first electrical stimulus.

[0117] The corrected target 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.

[0118] Reference Figure 4 Since the corrected target full-cell curve E is a curve obtained by subtracting the overpotential curve OP from the first target full-cell curve M, the corrected target full-cell curve E is more similar to the second target full-cell curve N than the first target full-cell curve M. Therefore, when diagnosing the charge / discharge performance of the target cell BC, it is advantageous in terms of diagnosis accuracy to utilize the corrected target full-cell curve E rather than the first target full-cell curve M.

[0119] Meanwhile, since the corrected target full-cell curve E does not completely match the second target full-cell curve N, there can still be a considerable difference between the diagnosis result of the charge / discharge performance based on the corrected target full-cell curve E and the actual charge / discharge performance. This will be described later with reference to FIG. 6. Figure 12 A method for reducing an error in a diagnosis result of charge / discharge performance is described.

[0120] The control circuit 130 can determine first diagnosis result information about the charge / discharge performance of the target cell BC by applying diagnosis logic to the corrected target full-cell curve E. The first diagnosis result information can be regarded as a preliminary diagnosis result about the charge / discharge performance of the target cell BC. The first diagnosis result information can include a first diagnosis factor group, and the first diagnosis factor group can include at least one preliminary diagnosis factor.

[0121] The first diagnosis factor group can include at least one of a positive electrode participation start point, a positive electrode participation end point, a positive electrode scaling factor, a negative electrode participation start point, a negative electrode participation end point, a negative electrode scaling factor, a positive electrode load amount, a negative electrode load amount, and an NP ratio as the preliminary diagnosis factor.

[0122] In this specification, the positive electrode participation start point on the positive electrode curve of any battery cell indicates a positive electrode voltage and a positive electrode capacity (or a 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 start point can be referred to as a "positive electrode starting potential". Also, the negative electrode participation start point on the negative electrode curve of the corresponding battery cell indicates a negative electrode voltage and a negative electrode capacity (or a 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 start point can be referred to as a "negative electrode starting potential". Therefore, the voltage difference between the positive electrode participation start point and the negative electrode participation start point can be equal to the first set voltage.

[0123] Further, the positive electrode participation end point on the positive electrode curve of any battery cell indicates the positive electrode voltage and the 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 end point can be referred to as the "positive electrode termination potential". Further, the negative electrode participation end point on the negative electrode curve of the corresponding battery cell indicates the negative electrode voltage and the 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 end point can be referred to as the "negative electrode termination potential". Thus, the voltage difference between the positive electrode participation end point and the negative electrode participation end point can be equal to the second set voltage.

[0124] In the present specification, the positive electrode capacity (capacity value) at a certain point on the positive electrode curve of any battery cell can mean the capacity difference between either of the two end points of the positive electrode curve and the certain point. The positive electrode SOC at a certain point on the positive electrode curve of any battery cell can mean the ratio of the capacity difference between either of the two end points of the positive electrode curve (e.g., the low capacity point) and the certain point to the capacity difference between the two end points of the positive electrode curve.

[0125] Likewise, the negative electrode capacity (capacity value) at a certain point on the negative electrode curve of any battery cell can mean the capacity difference between either of the two end points of the negative electrode curve (or the positive electrode curve) and the certain point. The negative electrode SOC at a certain point on the negative electrode curve of any battery cell can mean the ratio of the capacity difference between either of the two end points of the negative electrode curve (or the positive electrode curve) (e.g., the low capacity point) and the certain point to the capacity difference between the two end points of the negative electrode curve.

[0126] The positive electrode scaling factor of any battery cell can 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 can 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.

[0127] In the memory 131, information indicating the voltage and the capacity of each of the reference positive electrode participation start point, the reference positive electrode participation end point, the reference negative electrode participation start point, and the reference negative electrode participation end point representing the charge / discharge performance of the reference cell can be recorded in advance.

[0128] From now on, referring to Figures 5 to 11 A diagnosis process included in the diagnosis logic will be explained.

[0129] Figure 5 is a graph for explaining an example of each of the corrected target full cell curve E, the second reference full cell curve R2, the reference positive electrode curve Rp, and the reference negative electrode curve Rn referred to. In Figure 5In the curve graph, the horizontal axis (X-axis) represents capacity, and the vertical axis (Y-axis) represents voltage. The corrected target full-cell curve E and the second reference full-cell curve R2 are compared with... Figure 2 Same as above.

[0130] refer to Figure 5 The reference positive electrode curve Rp can be a curve representing the relationship between the positive electrode voltage and capacity while the second electrical stimulation is being applied to the reference cell. The positive electrode voltage of the reference cell refers to the potential difference between the potential of the reference electrode (not shown) and the potential of the positive electrode of the reference cell.

[0131] The reference negative electrode curve Rn can be a curve representing the relationship between the negative electrode voltage and capacity while the second electrical stimulation is being 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.

[0132] The potential of the reference electrode can be, for example, the redox potential of lithium. The positive electrode voltage can be simply referred to as the positive electrode potential, and the negative electrode voltage can be simply referred to as the negative electrode potential.

[0133] The reference positive electrode curve Rp and the reference negative electrode curve Rn can be pre-stored in the memory 131.

[0134] At least one of the reference positive electrode curve Rp and the reference negative electrode curve Rn can be aligned along the horizontal axis such that the common capacity range of the reference positive electrode curve Rp and the reference negative electrode curve Rn ( Figure 5 The synthesis results of a portion of the monomer (5Ah to 50Ah) matched the R2 curve of the second reference whole monomer.

[0135] Figure 5 An example is shown in which the reference negative curve Rn is aligned and shifted to the right based on the starting point of the reference positive curve Rp (corresponding to the point of capacity 0).

[0136] from Figure 5 It can be observed that the two ends of the reference positive curve Rp and the reference negative curve Rn are offset from each other. In other words, the capacity range of the reference positive curve Rp and the capacity range of the reference negative 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 common capacity range of the reference positive curve Rp and the reference negative curve Rn.

[0137] The control circuit 130 can be configured to compare the corrected target full cell curve E with at least one comparative full cell curve. The comparative full cell curve can be a result of adjusting each of the reference positive electrode curve Rp and the reference negative electrode curve Rn stored in the memory 131 and then synthesizing (combining) the adjusted positive electrode curve and the adjusted negative electrode curve.

[0138] In other words, when the second reference full cell curve R2 is a result of subtracting a portion of the reference negative electrode curve Rn from a portion of the reference positive electrode curve Rp, the comparative full cell curve can be considered a result of subtracting a portion of the adjusted negative electrode curve from a portion of the adjusted positive electrode curve.

[0139] The control circuit 130 can generate at least one comparative full cell curve by directly adjusting the reference positive electrode curve Rp and the reference negative electrode curve Rn. Alternatively, at least one comparative full cell curve can be pre-ensured based on the reference positive electrode curve Rp and the reference negative electrode curve Rn and stored in the memory 131. In this case, the control circuit 130 can obtain the comparative full cell curve by accessing the memory 131 and reading the comparative full cell curve.

[0140] The control circuit 130 can generate a plurality of comparative full cell curves from the reference positive electrode curve Rp and the reference negative electrode curve Rn by repeatedly adjusting each of the reference positive electrode curve Rp and the reference negative electrode curve Rn to several levels and then synthesizing their adjustment processes. The comparative full cell curve can also be referred to as an "adjusted reference full cell curve".

[0141] The control circuit 130 can designate any one of the plurality of comparative full cell curves, which has the smallest error with respect to the corrected target full cell curve E. Then, the control circuit 130 can determine that the adjusted positive electrode curve and the adjusted negative electrode curve mapped to the designated comparative full cell curve are the positive electrode curve and the negative electrode curve of the target cell BC.

[0142] In relation thereto, various methods known at the time of filing the present application can be employed 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 two curves or the RMSE (Root Mean Square Error) can be used as the error between two curves.

[0143] According to this configuration, various state information about the target cell BC can be obtained based on the finally determined adjusted positive and negative electrode curves. The finally determined adjusted positive and negative electrode curves can be mapped to any one of a plurality of comparative full-cell curves that has the smallest error relative to the corrected target full-cell curve E. Specifically, the comparative full-cell curve obtained through the finally determined adjusted positive and negative electrode curves can be nearly identical in shape to the corrected target full-cell curve E.

[0144] Figures 6 to 8 This is a diagram used to explain an example of the process for generating a comparison of full-unit curves.

[0145] Reference Figures 6 to 8 The process for generating the comparative full-cell curve can be performed in the following order: The first routine (see [link to routine]) sets four points (positive electrode participation start point, positive electrode participation end point, negative electrode participation start point, negative electrode participation end point) to correspond to the voltage range of interest. Figure 6 The second routine used to perform curve shifting (see...) Figure 7 ) and a third routine for performing capacity scaling (see Figure 8 In other words, the process for generating a comparison full monomer curve according to embodiments of this disclosure may include a first routine to a third routine.

[0146] refer to Figure 4 The reference positive electrode curve Rp and the reference negative electrode curve Rn are compared with Figure 6 The same as those shown.

[0147] The control circuit 130 can determine the positive participation start point (pi), positive participation end point (pf), negative participation start point (ni), and negative participation end point (nf) on the reference positive curve Rp and the reference negative curve Rn.

[0148] The positive electrode participation start point (pi) and the negative electrode participation start point (ni) depend on the other.

[0149] As an example, control circuit 130 can divide the positive voltage range from the start point to the end point (or second set voltage) of the reference positive curve Rp into multiple small voltage segments, and then set the boundary point of two adjacent small voltage segments as the positive participation start point (pi). Each small voltage segment can have a predetermined size (e.g., 0.01V). Next, control circuit 130 can set the point on the reference negative curve Rn that is smaller than the positive participation start point (pi) by a first set voltage (e.g., 3V) as the negative participation start point (ni).

[0150] As another example, the control circuit 130 can divide a negative electrode voltage range from the starting point to the ending point of the reference negative electrode curve Rn into a plurality of small voltage sections of a predetermined size, and then set a boundary point between two adjacent small voltage sections among the plurality of small voltage sections as the negative electrode participation starting point (ni). Next, the control circuit 130 can search for a point greater than the negative electrode participation starting point (ni) by a first set voltage (for example, 3 V) from the reference positive electrode curve Rp, and set the searched point as the positive electrode participation starting point (pi).

[0151] Either one of the positive electrode participation ending point (pf) and the negative electrode participation ending point (nf) depends on the other.

[0152] As an example, the control circuit 130 can divide a voltage range from the second set voltage to the ending point of the reference positive electrode curve Rp into a plurality of small voltage sections of a predetermined size, and then set a boundary point between two adjacent small voltage sections among the plurality of small voltage sections as the positive electrode participation ending point (pf). Next, the control circuit 130 can set a point on the reference negative electrode curve Rn smaller than the positive electrode participation ending point (pf) by a second set voltage (for example, 4 V) as the negative electrode participation ending point (nf).

[0153] As another example, the control circuit 130 can divide a negative electrode voltage range from the starting point to the ending point of the reference negative electrode curve Rn into a plurality of small voltage sections of a predetermined size, and then set a boundary point between two adjacent small voltage sections among the plurality of small voltage sections as the negative electrode participation ending point (nf). Next, the control circuit 130 can search for a point greater than the negative electrode participation ending point (nf) by a second set voltage (for example, 4 V) from the reference positive electrode curve Rp, and set the searched point as the positive electrode participation ending point (pf).

[0154] If the positive electrode participation starting point (pi), the positive electrode participation ending point (pf), the negative electrode participation starting point (ni), and the negative electrode participation ending point (nf) are completely determined, the control circuit 130 shifts at least one of the reference positive electrode curve Rp and the reference negative electrode curve Rn leftward or rightward along the horizontal axis.

[0155] With reference to Figure 6 , the control circuit 130 can shift the reference positive electrode curve Rp leftward (toward low capacity) or shift the reference negative electrode curve Rn rightward (toward high capacity) or shift both of them so that the capacity values of the positive electrode participation starting point (pi) and the negative electrode participation starting point (ni) match.

[0156] Alternatively, the control circuit 130 shifts the reference positive electrode curve Rp leftward or shifts the reference negative electrode curve Rn rightward or shifts both of them so that the capacity values of the positive electrode participation ending point (pf) and the negative electrode participation ending point (nf) match.

[0157] Figure 7 The case where only the positive electrode curve Rp is shifted to the left to generate an adjusted reference positive electrode curve (Rp') and thus the capacity value of the positive electrode participation starting point (pi') matches the capacity value of the negative electrode participation starting point (ni) is shown. The adjusted reference positive electrode curve (Rp') can be a result of applying an adjustment process to the reference positive electrode curve Rp, which is to shift the capacity difference between the positive electrode participation starting point (pi) and the negative electrode participation starting point (ni) to the left. Thus, the two points (pi, pi') can differ only in the capacity value and have the same voltage. Also, the two points (pf, pf') can differ only in the capacity value and have the same voltage.

[0158] If the adjustment result curve (Rp', Rn) in which at least one of the reference positive electrode curve Rp and the reference negative electrode curve Rn is shifted is secured, the control circuit 130 can scale the capacity range of at least one of the adjustment result curve (Rp', Rn).

[0159] According to Figure 7 the example shown in

[0160] Referring to Figure 8 , the control circuit 130 can generate an adjusted reference positive electrode curve (Rp") by contracting or expanding the adjusted reference positive electrode curve (Rp') such that the size of the capacity range between the two points (pi', pf') of the adjusted reference positive electrode curve (Rp') matches the size of the capacity range of the corrected target full cell curve E. At this time, any one point (pi') of the two points (pi', pf') can be fixed. Thus, the capacity difference between the two points (pi', pf") of the adjusted reference positive electrode curve (Rp") can match the capacity range of the corrected target full cell curve E.

[0161] In addition, the control circuit 130 can generate an adjusted reference negative electrode curve (Rn') by contracting or expanding the reference negative electrode curve Rn such that the size of the capacity range between the two points (ni, nf) of the reference negative electrode curve Rn matches the size of the capacity range of the corrected target full cell curve E. At this time, any one point (ni) of the two points (ni, nf) can be fixed. Thus, the capacity difference between the two points (ni, nf') of the adjusted reference negative electrode curve (Rn') can match the capacity range of the corrected target full cell curve E.

[0162] In Figure 8 , the adjusted reference positive electrode curve (Rp") is contracted Figure 7the result of the adjusted reference positive electrode curve (Rp') shown in FIG. 6B, and the adjusted reference negative electrode curve (Rn') is the extension Figure 7 the result of the reference negative electrode curve Rn shown in FIG. 6B.

[0163] The positive electrode participation end point (pf") on the adjusted reference positive electrode curve (Rp") corresponds to the positive electrode participation end point (pf) on the adjusted reference positive electrode curve (Rp'). The negative electrode participation end point (nf') on the adjusted reference negative electrode curve (Rn') corresponds to the negative electrode participation end point (nf) on the reference negative electrode curve Rn.

[0164] The capacity difference between the positive electrode participation start point (pi') and the positive electrode participation end point (pf") of the adjusted reference positive electrode curve (Rp") corresponds to the size of the capacity range of the corrected target full cell curve E. Likewise, the capacity difference between the negative electrode participation start point (ni) and the negative electrode participation end point (nf') of the adjusted reference negative electrode curve (Rn') corresponds to the size of the capacity range of the corrected target full cell curve E.

[0165] Further, the capacity range of the two points (pi', pf") of the adjusted reference positive electrode curve (Rp") matches the capacity range of the two points (ni, nf') of the adjusted reference negative electrode curve (Rn'). The control circuit 130 can generate the comparison full cell curve S by subtracting the portion between the two points (pi, pf') of the adjusted reference positive electrode curve (Rp") from the portion between the two points (ni, nf') of the adjusted reference negative electrode curve (Rn').

[0166] The control circuit 130 can calculate an error (curve error) between the comparison value between the comparison full cell curve S and the corrected target full cell curve E.

[0167] The control circuit 130 can map at least two of the adjusted reference positive electrode curve (Rp"), the adjusted reference negative electrode curve (Rn'), the positive electrode participation start point (pi'), the positive electrode participation end point (pf"), the negative electrode participation start point (ni), the negative electrode participation end point (nf'), the positive electrode scaling factor, the negative electrode scaling factor, the comparison full cell curve S, and the curve error to each other and record them in the memory 131.

[0168] The positive scaling factor of the adjusted reference positive electrode curve (Rp") can represent a ratio of a capacity difference between the two points (pi', pf") to a capacity difference between the two points (pi0, pf0). Alternatively, the positive scaling factor of the adjusted reference positive electrode curve (Rp") can represent a ratio of a positive electrode capacity difference between the two points (pi', pf") to a positive electrode capacity difference between the two points (pi0, pf0). Alternatively, the positive scaling factor of the adjusted reference positive electrode curve (Rp") can represent a ratio of a positive electrode SOC difference between the two points (pi', pf") to a positive electrode SOC difference between the two points (pi0, pf0).

[0169] The negative scaling factor of the adjusted reference negative electrode curve (Rn') can represent a ratio of a capacity difference between the two points (ni, nf') to a capacity difference between the two points (ni0, nf0). Alternatively, the negative scaling factor of the adjusted reference negative electrode curve (Rn') can represent a ratio of a negative electrode capacity difference between the two points (ni, nf') to a negative electrode capacity difference between the two points (ni0, nf0). Alternatively, the negative scaling factor of the adjusted reference negative electrode curve (Rn') can represent a ratio of a negative electrode SOC difference between the two points (ni, nf') to a negative electrode SOC difference between the two points (ni0, nf0).

[0170] Hereinafter, ps can be used as a symbol indicating the positive scaling factor, and ns can be used as a symbol indicating the negative scaling factor.

[0171] Meanwhile, as described above, when the positive electrode voltage range of the reference positive electrode curve Rp is divided into a plurality of small voltage sections, a boundary point of two adjacent small voltage sections among the plurality of small voltage sections can be set as the positive electrode participation start point (pi).

[0172] For example, if the positive electrode voltage range of the reference positive electrode curve Rp is divided into 100 small voltage ranges, there can be 100 boundary points that can be set as the positive electrode participation start point (pi). Also, if the voltage range of the reference positive electrode curve Rp that is greater than or equal to the second set voltage is divided into 40 small voltage ranges, there can be 40 boundary points that 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.

[0173] Of course, those skilled in the art will readily appreciate that, as the size of the small voltage sections decreases, the maximum number of comparative full cell curves that can be generated increases, and, conversely, as the size of the small voltage sections increases, the maximum number of comparative full cell curves that can be generated decreases.

[0174] The control circuit 130 can identify a minimum value among the curve errors of the generated plurality of comparison full cell curves as described above, and then obtain a first diagnosis factor group, which is information (e.g., at least one of a positive electrode participation start point, a positive electrode participation end point, a negative electrode participation start point, a negative electrode participation end point, a positive electrode scaling factor, and a negative electrode scaling factor) mapped to the minimum curve error, from the memory 131.

[0175] Figures 9 to 11 is a diagram referred to describe another example of a process of generating a comparison full cell curve according to diagnosis logic. As a reference, Figures 9 to 11 the embodiments shown in Figures 6 to 8 the embodiments shown in Figures 6 to 8 the embodiments shown in Figures 9 to 11 the embodiments shown in

[0176] The process of generating the comparison full cell curve U to be explained with reference to Figures 9 to 11 may proceed in the following order: a fourth routine of performing capacity scaling (see Figure 9 ), a fifth routine of setting four points (a positive electrode participation start point, a positive electrode participation end point, a negative electrode participation start point, and a negative electrode participation end point) (see Figure 10 ), and a sixth routine of performing curve shifting (see Figure 11 ). That is, the process of generating a comparison full cell curve according to another embodiment of the disclosure can include the fourth to sixth routines.

[0177] Referring to Figure 9 , the control circuit 130 can generate an adjusted reference positive electrode curve (Rp') and an adjusted reference negative electrode curve (Rn') by applying a positive electrode scaling factor and a negative electrode scaling factor selected from a scaling value range to the reference positive electrode curve Rp and the reference negative electrode curve Rn, respectively.

[0178] The scaling value range can be predetermined or can vary depending on a ratio of a size of a capacity range of the corrected target full monomer curve E to a size of a capacity range of the second reference full monomer curve R2. As an example, assume that the positive electrode scaling factor and the negative electrode scaling factor can be selected among values at intervals of 0.1% in a scaling value range (e.g., 90 to 99%), that is, 90%, 90.1%, 90.2%,..., 98.9%, 99%. In this case, 91 values can be selected as the positive electrode scaling factor and the negative electrode scaling factor, respectively. According to 91 x 91 = 8281 adjustment levels (combinations of the positive electrode scaling factor and the negative electrode scaling factor), a maximum of 8281 adjusted curve pairs (Rp', Rn') can be generated. The adjusted curve pair refers to a combination of the adjusted positive electrode curve (Rp') and the adjusted negative electrode curve (Rn').

[0179] Referring to Figure 9 , the adjusted reference positive electrode curve (Rp') and the adjusted reference negative electrode curve (Rn') respectively show results of applying the positive electrode scaling factor and the negative electrode scaling factor to the reference positive electrode curve Rp and the reference negative electrode curve Rn.

[0180] Since the positive electrode scaling factor and the negative electrode scaling factor are less than 100%, the adjusted reference positive electrode curve (Rp') is obtained by contracting the reference positive electrode curve Rp along the horizontal axis, and the adjusted reference negative electrode curve (Rn') is also obtained by contracting the reference negative electrode curve Rn along the horizontal axis. For ease of understanding, the reference positive electrode curve Rp and the reference negative electrode curve Rn are shown in a form in which their starting points are fixed and the remaining portions are contracted to the left along the horizontal axis, respectively.

[0181] Referring to Figure 10 , the control circuit 130 can determine the positive electrode participation starting point (pi'), the positive electrode participation ending point (pf'), the negative electrode participation starting point (ni'), and the negative electrode participation ending point (nf') on the adjusted reference positive electrode curve (Rp') and the adjusted reference negative electrode curve (Rp').

[0182] Either one of the positive electrode participation starting point (pi') and the negative electrode participation starting point (ni') can depend on the other. Also, either one of the positive electrode participation ending point (pf') and the negative electrode participation ending point (nf') can depend on the other. Also, either one of the positive electrode participation starting point (pi') and the positive electrode participation ending point (pf') can be set based on the other.

[0183] That is, if any one of the positive electrode participation start point (pi'), the positive electrode participation end point (pf'), the negative electrode participation start 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 capacity range of the corrected target full-cell curve E (for example, Figure 4 45 Ah - 5 Ah = 40 Ah in

[0184] As an example, the control circuit 130 can divide the positive electrode voltage range from the start point to the end point (or the second set voltage) of the adjusted reference positive electrode curve (Rp') into a plurality of small voltage sections, and then set the boundary point of two adjacent small voltage sections among the plurality of small voltage sections as the positive electrode participation start point (pi'). Next, the control circuit 130 can set a point on the adjusted reference negative electrode curve (Rn') that is smaller than the positive electrode participation start point (pi') by the first set voltage as the negative electrode participation start point (ni').

[0185] As another example, the control circuit 130 can divide the negative electrode voltage range from the start point to the end point of the adjusted reference negative electrode curve (Rn') into a plurality of small voltage sections of a predetermined size, and then set the boundary point of two adjacent small voltage sections among the plurality of small voltage sections as the negative electrode participation start point (ni'). Next, the control circuit 130 can search for a point on the adjusted reference positive electrode curve (Rp') that is greater than the negative electrode participation start point (ni') by the first set voltage, and select the searched point as the positive electrode participation start point (pi').

[0186] As yet another example, the control circuit 130 can divide the voltage range from the second set voltage to the end point of the adjusted reference positive electrode curve (Rp') into a plurality of small voltage sections of a predetermined size, and then set the boundary point of two adjacent small voltage sections among the plurality of small voltage sections as the positive electrode participation end point (pf'). Next, the control circuit 130 can search for a point on the adjusted reference negative electrode curve (Rn') that is smaller than the positive electrode participation end point (pf') by the second set voltage (for example, 4 V), and set the searched point as the negative electrode participation end point (nf').

[0187] As yet another example, the control circuit 130 can divide the negative electrode voltage range from the start point to the end point of the adjusted second reference negative electrode curve (Rn') into a plurality of small voltage sections of a predetermined size, and then set the boundary point of two adjacent small voltage sections among the plurality of small voltage sections as the negative electrode participation end point (nf'). Next, the control circuit 130 can search for a point on the adjusted reference positive electrode curve (Rp') that is greater than the negative electrode participation end point (nf') by the second set voltage, and set the searched point as the positive electrode participation end point (pf').

[0188] If any one of the positive electrode participation start point (pi'), the positive electrode participation end point (pf'), the negative electrode participation start point (ni'), and the negative electrode participation end point (nf') is determined, the control circuit 130 can additionally determine the remaining three points based on the determined point.

[0189] For example, if the positive electrode participation start point (pi') is first determined, the control circuit 130 can 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 start point (pi') by the size of the capacity range of the corrected target full cell curve E as the positive electrode participation end point (pf'). In addition, the control circuit 130 can search for a point lower than the positive electrode participation start point (pi') by the first set voltage from the adjusted reference negative electrode curve (Rn'), and set the searched point as the negative electrode participation start point (ni'). Furthermore, the control circuit 130 can 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 start point (ni') by the size of the capacity range of the corrected target full cell curve E as the negative electrode participation end point (nf').

[0190] As another example, when the positive electrode participation end point (pf') is first determined, the control circuit 130 can set a point on the adjusted reference positive electrode curve (Rp') having a capacity value smaller than the capacity value of the positive electrode participation end point (pf') by the size of the capacity range of the corrected target full cell curve E as the positive electrode participation start point (pi'). In addition, the control circuit 130 can search for a point lower than the positive electrode participation end point (pf') by the second set voltage from the adjusted reference negative electrode curve (Rn'), and set the searched point as the negative electrode participation end point (nf'). Furthermore, the control circuit 130 can set a point on the adjusted reference negative electrode curve (Rn') having a capacity value smaller than the capacity value of the negative electrode participation end point (nf') by the size of the capacity range of the corrected target full cell curve E as the negative electrode participation start point (ni').

[0191] As still another example, when the negative electrode participation start point (ni') is determined, the control circuit 130 can 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 start point (ni') by the size of the capacity range of the corrected target full cell curve E as the negative electrode participation end point (nf'). In addition, the control circuit 130 can search for a point higher than the negative electrode participation start point (ni') by the first set voltage from the adjusted reference positive electrode curve (Rp'), and set the searched point as the positive electrode participation start point (pi'). Furthermore, the control circuit 130 can 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 start point (pi') by the size of the capacity range of the corrected target full cell curve E as the positive electrode participation end point (pf').

[0192] As yet another example, when the negative electrode participation end point (nf') is determined, the control circuit 130 can set a point on the adjusted reference negative electrode curve (Rn') having a capacity value smaller than a capacity value of the negative electrode participation end point (nf') by a size of the capacity range of the corrected target full cell curve E to the negative electrode participation start point (ni'). Further, the control circuit 130 can search for a point on the adjusted reference positive electrode curve (Rp') having a second set voltage higher than the negative electrode participation end point (nf') and set the searched point to the positive electrode participation end point (pf'). In addition, the control circuit 130 can set a point on the adjusted reference positive electrode curve (Rp') having a capacity value smaller than a capacity value of the positive electrode participation end point (pf') by a size of the capacity range of the corrected target full cell curve E to the positive electrode participation start point (pi').

[0193] If the positive electrode participation start point (pi'), the positive electrode participation end point (pf'), the negative electrode participation start point (ni'), and the negative electrode participation end point (nf') are completely determined based on a pair 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') along the horizontal axis leftward or rightward so that the capacity values of the positive electrode participation start point (pi') and the negative electrode participation start point (ni') match or the capacity values of the positive electrode participation end point (pf') and the negative electrode participation end point (nf') match.

[0194] Figure 11 The adjusted reference negative electrode curve (Rn") shown in FIG. 12A is obtained by shifting the adjusted reference negative electrode curve (Rn') shown in FIG. 11A rightward by a size of the capacity range of the corrected target full cell curve E. Figure 10 The adjusted reference negative electrode curve (Rn') shown in FIG. 11A is obtained by shifting the adjusted reference negative electrode curve (Rn) shown in FIG. 10A rightward by a size of the capacity range of the corrected target full cell curve E. Thus, the capacity values of the positive electrode participation start point (pi') and the negative electrode participation start point (ni") match each other on the horizontal axis. Relatedly, a capacity difference between the positive electrode participation start point (pi') and the positive electrode participation end point (pf') is equal to a capacity difference between the negative electrode participation start point (ni') and the negative electrode participation end point (nf'). Thus, if the capacity values of the positive electrode participation start point (pi') and the negative electrode participation start point (ni") match each other on the horizontal axis, the capacity values of the positive electrode participation end point (pf') and the negative electrode participation end point (nf') also match each other on the horizontal axis.

[0195] Referring to FIG. 12B, Figure 11 The control circuit 130 can generate the comparison full cell curve U by subtracting a partial curve between the two points (pi', pf') of the adjusted reference positive electrode curve (Rp') from a partial curve between the two points (ni", nf") of the adjusted reference negative electrode curve (Rn").

[0196] The control circuit 130 can calculate an error (a curve error) between the full cell comparison curve U and the corrected target full cell curve E.

[0197] The control circuit 130 can map at least two of the adjusted reference positive electrode curve (Rp'), the adjusted reference negative electrode curve (Rn"), the positive electrode participation start point (pi'), the positive electrode participation end point (pf'), the negative electrode participation start point (ni"), the negative electrode participation end point (nf"), the positive electrode scaling factor, the negative electrode scaling factor, the full cell comparison curve U, and the curve error to each other and record them in the memory 140.

[0198] As described above, the control circuit 130 can generate the full cell comparison curve U corresponding to each pair of the positive electrode scaling factor and the negative electrode scaling factor selected from the scaling value range. Since the pairs of the positive electrode scaling factor and the negative electrode scaling factor are plural, it is obvious that the comparison curve U will also be generated in a plural number.

[0199] The control circuit 130 can identify a minimum value among the curve errors of the plural full cell comparison curves, and then obtain information mapped to the minimum curve error from the memory 131.

[0200] As described above, the control circuit 130 can perform the diagnosis logic to generate the full cell comparison curve having the minimum error from the corrected target full cell curve E based on the reference positive electrode curve Rp and the reference negative electrode curve Rn.

[0201] The control circuit 130 can determine a first diagnosis factor group including at least one of the positive electrode participation start point, the positive electrode participation end point, the positive electrode scaling factor, the negative electrode participation start point, the negative electrode participation end point, and the negative electrode scaling factor mapped to the minimum curve error, respectively, as a preliminary diagnosis factor.

[0202] Meanwhile, since the first diagnosis factor group is a result of applying the diagnosis logic to the corrected target full cell curve E, it can more accurately represent the actual charge / discharge performance of the target cell BC than a result of applying the diagnosis logic to the first target full cell curve M.

[0203] However, since the overpotential curve OP is related to the reference cell, not the target cell BC, there can still be a considerable difference between the charge / discharge performance indicated by the first diagnosis result information and the actual charge / discharge performance of the target cell BC.

[0204] Accordingly, it is desirable to perform a process for correcting at least one preliminary diagnosis factor of the first diagnosis result information to narrow the gap between the charge / discharge performance indicated by the first diagnosis result information and the actual charge / discharge performance, and this can be achieved by a factor correction model explained later. The correction process of the first diagnosis result information is performed to determine the second diagnosis result information as an accurate diagnosis result of the charge / discharge performance of the target monobloc BC.

[0205] Figure 12 is a graph for explaining a function of the factor correction model, Figures 13 to 21 is a graph for explaining a training data set provided for training the factor correction model, Figure 22 is a graph showing Figure 12 an example of a neural network structure of the factor correction model of Figure 23 is a graph showing an example of a correlation coefficient between diagnosis factors that can be obtained by training the factor correction model.

[0206] Referring to Figure 12 , the control circuit 130 can determine the second diagnosis result information as an accurate diagnosis result of the charge / discharge performance of the target monobloc BC by applying the factor correction model 200 to at least one preliminary diagnosis factor of the first diagnosis factor group 1210 that is a preliminary diagnosis result of the charge / discharge performance of the target monobloc BC.

[0207] The first diagnosis factor group 1210 can include at least one type of diagnosis factor among the positive electrode participation start point, the negative electrode participation start point, the positive electrode participation end point, the negative electrode participation end point, the positive electrode scaling factor, the negative electrode scaling factor, the positive electrode load amount, the negative electrode load amount, and the NP ratio of the target monobloc BC, which are determined based on the corrected target full monobloc curve E, as a preliminary diagnosis factor.

[0208] The second diagnosis factor group 1220 can include at least one type of diagnosis factor among the positive electrode participation start point, the negative electrode participation start point, the positive electrode participation end point, the negative electrode participation end point, the positive electrode scaling factor, the negative electrode scaling factor, the positive electrode load amount, the negative electrode load amount, and the NP ratio of the target monobloc BC as an accurate diagnosis factor.

[0209] At least one precise diagnostic factor of the second diagnostic factor group 1220 can be a result obtained by correcting at least one preliminary diagnostic factor of the first diagnostic factor group 1210 through the factor correction model 200 such that an error between the charge / discharge performance indicated by the first diagnostic factor group 1210 and the actual charge / discharge performance of the target monomer BC is reduced. The second diagnostic factor group 1220 can represent an estimated result of the charge / discharge performance of the target monomer BC to be determined in the case where the diagnostic logic is applied to the second target full monomer curve N. For example, a specific diagnostic factor (e.g., a positive electrode participation start point) of the second diagnostic factor group 1220 can be a specific diagnostic factor of the first diagnostic factor group 1210 corrected to be close to the actual charge / discharge performance of the target monomer BC.

[0210] The factor correction model 200 can be a machine learning model trained by a training data set including pairs of the first diagnostic result information and the second diagnostic result information of each of a plurality of test monomers. The training data set can further include temperature information and / or impedance information of each of the plurality of test monomers.

[0211] For the purpose of training the factor correction model 200, a plurality of test monomers are prepared in advance. At least one of the plurality of test monomers can be a new battery monomer verified as a good product. Each of the remaining test monomers can have a test monomer in which at least one of a positive electrode and a negative electrode is forced to be deteriorated from a new state by a charge / discharge cycle different from that of the other test monomers.

[0212] The first diagnostic result information of a specific test monomer can include a first diagnostic factor group, which can be obtained in advance by applying diagnostic logic to a corrected test full monomer curve of the corresponding test monomer. The corrected test full monomer curve of the specific test monomer can be a first test full monomer curve of the corresponding test monomer corrected based on the overpotential curve OP. The first test full monomer curve can represent a corresponding relationship between a voltage and a capacity factor of the corresponding test monomer while the first electrical stimulus is being applied to the corresponding test monomer. The first diagnostic factor group of the specific test monomer can include at least one type of diagnostic factor among 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 as a preliminary diagnostic factor of the specific test monomer.

[0213] The temperature information of the specific test monomer includes at least one of a start temperature, an end temperature, an average temperature, a maximum temperature, and a minimum temperature of the corresponding test monomer during an application period of the first electrical stimulus.

[0214] The second diagnosis result information of the specific test cell is obtained in advance by applying the diagnosis logic to the second test full cell curve of the corresponding test cell, and can include a second diagnosis factor group. The second test full cell curve of the specific test cell can represent a corresponding relationship between the voltage of the corresponding test cell and the capacity factor while the second electrical stimulus is being applied to the corresponding test cell under a specific external condition. The second diagnosis factor group of the specific test cell can include at least one type of diagnosis factor among the positive electrode participation start point, the negative electrode participation start point, the positive electrode participation end point, the negative electrode participation end point, the positive electrode scaling factor, the negative electrode scaling factor, the positive electrode load amount, the negative electrode load amount, and the NP ratio as an accurate diagnosis factor.

[0215] Figures 13 to 21 The graphs shown in FIGS. 1 to 9 are respectively associated with nine types of diagnosis factors, and a plurality of data points representing a pair of the same type of diagnosis factors included in the training data set are marked on a two-dimensional coordinate. The number of data points marked in each graph can be equal to the number of test cells.

[0216] Each data point is defined by two estimated values of a specific type of diagnosis factor. That is, the X-axis coordinate of each data point represents the value of the preliminary diagnosis factor included in the first diagnosis result information as an estimated value of a specific diagnosis factor, and the Y-axis coordinate represents the value of the accurate diagnosis factor included in the second diagnosis result information as another estimated value of the specific diagnosis factor.

[0217] Figure 13 Each of the X-axis and the Y-axis of FIG. 1 is exemplarily shown as representing the positive electrode SOC indicating the positive electrode participation start point. Figure 14 Each of the X-axis and the Y-axis of FIG. 2 is exemplarily shown as representing the positive electrode SOC indicating the positive electrode participation end point. Figure 15 Each of the X-axis and the Y-axis of FIG. 3 is exemplarily shown as representing the positive electrode scaling factor. Figure 16 Each of the X-axis and the Y-axis of FIG. 4 is exemplarily shown as representing the positive electrode load amount. For reference, the positive electrode load amount included in the first diagnosis factor group can be equal to the product of the positive electrode scaling factor included in the first diagnosis factor group and a reference positive electrode load amount.

[0218] Figure 17 Each of the X-axis and the Y-axis of FIG. 5 is exemplarily shown as representing the negative electrode SOC indicating the negative electrode participation start point. Figure 18 Each of the X-axis and the Y-axis of FIG. 6 is exemplarily shown as representing the negative electrode SOC indicating the negative electrode participation end point. Figure 19 Each of the X-axis and the Y-axis of FIG. 7 is exemplarily shown as representing the negative electrode scaling factor. Figure 20 Each of the X-axis and the Y-axis of FIG. 8 is exemplarily shown as representing the negative electrode load amount. Figure 21 Each of the X-axis and the Y-axis of FIG. 9 is exemplarily shown as representing the NP ratio.

[0219] Referring to Figures 13 to 21 , data points of the training data set are distributed to have a trainable tendency. That is, a correlation between a value included in the first diagnosis result information as an estimated value of each type of diagnosis factor and a value included in the second diagnosis result information can be trained by the factor correction model 200.

[0220] The factor correction model 200 can be trained based on a correlation between two estimated values of a specific diagnosis factor, and the correlation information between the two estimated values obtained by learning can be expressed as a correlation coefficient in Figure 23 . This will be explained in detail later.

[0221] Among the nine types of diagnosis factors described above, three types of diagnosis factors, i.e., the positive electrode load amount, the negative electrode load amount, and the NP ratio, can be regarded as depending on the remaining six types of diagnosis factors. Therefore, the following explanation will focus on the six types of diagnosis factors.

[0222] Referring to Figure 22 , the neural network of the factor correction model 200 can include an input layer 1000, an intermediate layer 2000, and an output layer 3000.

[0223] In the factor correction model 200, the number of nodes included in each layer, connections between nodes, a function of each node included in the intermediate layer 2000, etc. can be determined in advance. In addition, the weight of each connection between nodes can be determined automatically using a training data set through a machine learning process.

[0224] The input layer 1000 can include first to sixth input nodes I1 to I6. The input layer 1000 can further include at least one additional input node (I TEMP , I IMP ).

[0225] When i is a natural number less than or equal to 6, the i-th input node Ii can be associated with one diagnosis factor of the first diagnosis factor group 1210. In Figure 22 , for convenience of explanation, it is assumed that the first input node I1 to the sixth input node I6 are respectively associated with the positive electrode participation start point, the positive electrode participation end point, the positive electrode scaling factor, the negative electrode participation start point, the negative electrode participation end point, and the negative electrode scaling factor, which are six types of diagnosis factors that can be included in the first diagnosis factor group.

[0226] The i-th input node Ii can be provided with an i-th input data set Xi, which is diagnostic factor data associated therewith. For example, the first input node I1 can be provided with a first input data set X1. The first input data set X1 can include values related to the positive electrode participation starting point of a plurality of test cells (e.g., positive electrode starting potential and capacity values).

[0227] The additional input node (I TEMP ) can be provided with an additional input data set (X TEMP ) associated therewith. The additional input data set (X TEMP ) can include a plurality of temperature information (e.g., at least one of a starting temperature, an ending temperature, an average temperature, a maximum temperature, and a minimum temperature) of the test cell.

[0228] The additional input node (I IMP ) can be provided with an additional input data set (X IMP ) associated therewith. The additional input data set (X IMP ) can include impedance information of a plurality of test batteries. The impedance information of each test battery can include at least one of a first to third impedance.

[0229] The first impedance can be an impedance at the time of high-rate charging as the first electrical stimulus. The second impedance can be an impedance at the time of high-rate discharging as the first electrical stimulus. The third impedance can be a result obtained by correcting the first impedance or the second impedance via a cell temperature. Each impedance can be an impedance at a starting time point or an ending time point of an application period of the first electrical stimulus. Alternatively, each impedance can be an average impedance in the application period of the first electrical stimulus.

[0230] For reference, it is well known that the impedance of a battery cell is highly dependent on temperature. Accordingly, the first impedance (or the second impedance) can be corrected based on a temperature difference between a cell temperature measured at the same timing as the determined time of the first impedance (or the second impedance) and a reference temperature. Predetermined relationship data between a correction amount for the impedance and the temperature difference can be recorded in a memory. Meanwhile, it is well known that the impedance of a battery cell can be determined using Ohm's law, EIS (Electrochemical Impedance Spectroscopy), etc., and thus will not be described in detail in the present specification.

[0231] The output layer 3000 can include at least one of a first output node O1 to a sixth output node O6. When j is a natural number less than or equal to 6, the j-th output node Oj can be associated with any one of the positive electrode participation starting point, the positive electrode participation ending point, the positive electrode scaling factor, the negative electrode participation starting point, the negative electrode participation ending point, and the negative electrode scaling factor. In Figure 22In the present embodiment, for convenience of explanation, it is assumed that the first output node O1 to the sixth output node O6 are associated with a positive electrode participation start point, a positive electrode participation end point, a positive electrode scaling factor, a negative electrode participation start point, a negative electrode participation end point, and a negative electrode scaling factor, which are six types of diagnosis factors. The positive electrode participation start point, the positive electrode participation end point, the positive electrode scaling factor, the negative electrode participation start point, the negative electrode participation end point, and the negative electrode scaling factor can be referred to as first to sixth diagnosis factors in this order.

[0232] When the input data sets (X1 to X6, X TEMP , X IMP ) are input to the input nodes (I1 to I6, I TEMP , I IMP ), the jth output data set Zj can be output from the jth output node Oj. For example, when the first output node O1 is associated with the positive electrode participation start point, the third output data set Z3 can include a correction result of the value of the first input data set X1.

[0233] Figure 22 It is shown that the input layer 1000 includes the first input node I1 to the sixth input node I6, and the output input layer 2000 includes the first output node O1 to the sixth output node O6, but this is only an example. That is, the input layer 1000 can include at least one of the first input node I1 to the sixth input node I6, and the output layer 3000 can include at least one of the first output node O1 to the sixth output node O6. For example, when all of the input data sets (X1 to X6, X TEMP , X IMP ) are provided to the input layer 1000, the output layer 3000 can output only one of the first output data set Z1 to the sixth output data set Z6.

[0234] Which of the first output data set Z1 to the sixth output data set Z6 will be output by the factor correction model 200 can be determined by a connection between nodes, a weight of each connection between nodes, a function of each node included in the intermediate layer 2000, etc., and is not particularly limited.

[0235] The intermediate layer 2000 can include the first intermediate node F1 to the mth intermediate node 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 can be connected to the input nodes (I1 to I6, I TEMP , I IMPat least one of the first to sixth output nodes O1 to O6. The k-th intermediate node Fk can have a form of a function determined through a learning process, and can transmit an estimated value calculated based on an input value from each input node connected thereto to each output node connected thereto. The j-th output node Oj can output an estimated value equal to a sum of estimated values received from each intermediate node connected thereto, as a correction result of the first diagnostic factor group.

[0236] The function of the k-th intermediate node Fk can be generated based on a correlation coefficient between a diagnostic factor associated with each node of the input layer 1000 connected to the k-th intermediate node Fk and a diagnostic 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 two factors, and a correlation coefficient closer to +1 indicates a positive correlation between two factors.

[0237] The function of each intermediate node of the intermediate layer 2000 can be a weighted average function. In this case, a correlation coefficient indicating a degree of correlation between an estimated value of the first to sixth diagnostic factors included in the first performance diagnostic group and an estimated value of at least one of the first to sixth diagnostic factors included in the second performance diagnostic group can be used as a weight of the function of each intermediate node of the intermediate layer 2000.

[0238] Accordingly, the factor correction model 200 can include at least one of the first to sixth machine learning models. The first to sixth machine learning models can be models that provide accurate diagnostic results for the first to sixth diagnostic factors in that order.

[0239] When the target monobloc BC is in the MOL state, the control circuit 130 can determine at least one degradation parameter based on the second diagnostic factor group. Table 1 below summarizes the degradation parameters and formulas that can be used to determine each degradation parameter. For reference, the second diagnostic factor group when the target monobloc BC is in the new state can have been recorded in the memory 131.

[0240] Table 1

[0241]

[0242] Each variable listed in Table 1 is a diagnostic factor that can be included in the above-described second diagnostic factor group. The definitions of the degradation parameters and variables in Table 1 can be as follows.

[0243] < Degradation Parameter >

[0244] P SOH : Positive electrode SOH (State of Health) of the target monobloc BC

[0245] N SOH : negative SOH of target monobloc BC

[0246] L SOH : available lithium SOH of target monobloc BC

[0247] F SOH : full monobloc SOH of target monobloc BC

[0248] P LOSS : positive loss rate of target monobloc BC

[0249] N LOSS : negative loss rate of target monobloc BC

[0250] L LOSS : available lithium loss rate of target monobloc BC

[0251] F LOSS : full monobloc loss rate of target monobloc BC

[0252] P loading_MOL : positive loading of target monobloc BC

[0253] N loading_MOL : negative loading of target monobloc BC

[0254] N / P _MOL : NP ratio of target monobloc BC

[0255] As any battery monobloc degrades, at least one of the total positive capacity, total negative capacity, available lithium content, and total full monobloc capacity of the battery monobloc can gradually decrease from a value at BOL (beginning of life). The total full monobloc capacity can represent a capacity difference between two endpoints of a full monobloc curve. For example, the total full monobloc capacity can mean a full charge capacity (FCC). The available lithium content can represent a total amount of lithium that can contribute to charging and discharging of the battery monobloc.P SOH may represent a retention rate of the total positive capacity.N SOH may represent a retention rate of the total negative capacity.L SOH may represent a retention rate of the available lithium content.F SOH may represent a retention rate of the total full monobloc capacity.

[0256] P SOH may represent a retention rate of the positive loading of the target monobloc BC.P LOSS may represent a retention rate of the negative loading of the target monobloc BC.N SOH may represent a retention rate of the NP ratio of the target monobloc BC.N / P LOSS may represent a retention rate of the total positive capacity of the target monobloc BC.P SOH may represent a retention rate of the total negative capacity of the target monobloc BC.N LOSS may represent a retention rate of the available lithium content of the target monobloc BC.L SOH may represent a retention rate of the total full monobloc capacity of the target monobloc BC.F LOSS may represent a retention rate of the full monobloc SOH of the target monobloc BC.F LOSS may equal P LOSSand L LOSS .

[0257] The positive electrode load of any battery cell represents the amount of positive electrode active material per unit area (or available capacity) of the positive electrode of the battery cell. The negative electrode load of any battery cell represents the amount of negative electrode active material per unit area (or available capacity) of the negative electrode of the battery cell. The unit of the load can be mAh / cm 2 or mg / cm 2 In Table 1, P loading_ref represents the reference positive electrode load, and N loading_ref represents the reference negative electrode load.

[0258] The reference positive electrode load is a predetermined value representing the amount of positive electrode active material per unit area (or available capacity) of the positive electrode of the reference cell. The reference positive electrode load can be a value obtained by dividing the reference positive electrode capacity (Q P_ref ) by the reference positive electrode area. Here, the reference positive electrode capacity can be a value preset to the total positive electrode capacity of the reference cell. The reference positive electrode area can be a value preset to the area of the positive electrode of the reference cell.

[0259] The reference negative electrode load is a predetermined value representing the amount of negative electrode active material per unit area (or available capacity) of the negative electrode of the reference cell. The reference negative electrode load can be a value obtained by dividing the reference negative electrode capacity (Q N_ref ) by the reference negative electrode area. Here, the reference negative electrode capacity can be a value preset to the total negative electrode capacity of the reference cell. The reference negative electrode area can be a value preset to the area of the negative electrode of the reference cell.

[0260] At least one of the deterioration parameters in Table 1 can be included in the second diagnosis factor group as an estimated result of an additional diagnosis factor of the target cell BC.

[0261] <Variables>

[0262] pi BOL : positive electrode capacity (positive electrode SOC) of the positive electrode participation starting point when the target cell BC is in the BOL state

[0263] pi MOL : positive electrode capacity (positive electrode SOC) of the current positive electrode participation starting point (e.g., pi') of the target cell BC Figure 8

[0264] pf BOL : positive electrode capacity (positive electrode SOC) of the positive electrode participation end point when the target cell BC is in the BOL state

[0265] pf MOL : positive electrode capacity (positive electrode SOC) of the current positive electrode participation end point (e.g., pf') of the target cell BC Figure 8 ​positive capacity (positive SOC) of the target monobloc BC at the positive participation start point (e.g., ni shown in FIG. 3)

[0266] ni BOL : positive capacity (positive SOC) of the target monobloc BC at the negative participation end point (e.g., nf shown in FIG. 3)

[0267] ni MOL : current negative participation start point (e.g., ni' shown in FIG. 3) of the target monobloc BC Figure 8 positive capacity (positive SOC) of the target monobloc BC at the positive participation start point (e.g., ni shown in FIG. 3)

[0268] nf BOL : positive capacity (positive SOC) of the target monobloc BC at the negative participation end point (e.g., nf shown in FIG. 3)

[0269] nf MOL : current negative participation end point (e.g., nf' shown in FIG. 3) of the target monobloc BC Figure 8 positive capacity (positive SOC) of the target monobloc BC at the negative participation end point (e.g., nf shown in FIG. 3)

[0270] ps BOL : positive scaling factor of the target monobloc BC at BOL

[0271] ps MOL : current positive scaling factor of the target monobloc BC

[0272] ns BOL : negative scaling factor of the target monobloc BC at BOL

[0273] ns MOL : current negative scaling factor of the target monobloc BC

[0274] The NP ratio can also be denoted as N / P ratio, N:P ratio, etc. The NP ratio of the target monobloc BC can be a value that represents (i) a ratio of the negative load (Nl oading_MOL ) to the positive load (P loading_MOL ) of the target monobloc BC or (ii) a ratio of the total negative capacity to the total positive capacity of the target monobloc BC. The control circuit 130 can determine the total positive capacity of the target monobloc BC to be equal to the product of ps MOL and Q P_ref . The control circuit 130 can determine the total positive capacity of the target monobloc BC to be equal to the product of ns MOL and Q N_ref .

[0275] The process of determining the second set of diagnostic factors can be repeated periodically or aperiodically throughout the lifetime of the target monobloc BC.

[0276] Figure 23An example of the correlation information between the first diagnostic factor group and the second diagnostic factor group obtained by the learning factor correction model 200 is shown in matrix form.

[0277] Referring to Figure 23 , the first to sixth rows indicate the first to sixth diagnostic factors of the first diagnostic factor group, which are provided as training data sets in order. The six lists indicate the first to sixth diagnostic factors of the second diagnostic factor group, which are provided as training data sets in order. Figure 23 The seventh to ninth rows are exemplarily shown to correspond to the initial temperature (T_ini), the end temperature (T_end), and the maximum temperature (T_max) included in the temperature information, and the tenth to twelfth rows correspond to the first impedance (z_c), the second impedance (z_d), and the third impedance (z_temp) included in the impedance information.

[0278] In Figure 23 , pi_A[1], pf_A[2], ps_A[3], ni_A[4], nf_A[5], and ns_A[6] indicate the positive electrode participation start point, the positive electrode participation end point, the positive electrode scaling factor, the negative electrode participation start point, the negative electrode participation end point, and the negative electrode scaling factor, which are six types of preliminary diagnostic factors included in the first diagnostic 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] indicate the positive electrode participation start point, the positive electrode participation end point, the positive electrode scaling factor, the negative electrode participation start point, the negative electrode participation end point, and the negative electrode scaling factor, respectively, which are six types of accurate diagnostic factors included in the second diagnostic factor group of the training data set in that order.

[0279] 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]) indicates the correlation coefficient between the p-th diagnostic factor included in the first diagnostic factor group and the q-th diagnostic factor included in the second diagnostic factor group.

[0280] For example, the correlation coefficient between the first diagnostic factor (pi_A[1]) in the first row and the second diagnostic factor (pf_B[2]) in the second column is -0.52. As another example, the correlation coefficient between the fifth diagnostic factor (nf_A[5]) in the fifth row and the fourth diagnostic factor (ni_B[4]) in the fourth column is 0.46.

[0281] The value in the 7th row and the qth column represents a correlation coefficient between the initial temperature (T_ini) and the qth diagnosis factor. The value in the 8th row and the qth column represents a correlation coefficient between the end temperature (T_end) and the qth diagnosis factor. The value in the 9th row and the qth column represents a correlation coefficient between the maximum temperature (T_max) and the qth diagnosis factor.

[0282] The value in the 10th row and the qth column represents a correlation coefficient between the first impedance (z_c) and the qth diagnosis factor. The value in the 11th row and the qth column represents a correlation coefficient between the second impedance (z_d) and the qth diagnosis factor. The value in the 12th row and the qth column represents a correlation coefficient between the third impedance (z_temp) and the qth diagnosis factor.

[0283] Meanwhile, Figure 23 Six types of diagnosis factors are involved, but the present disclosure is not limited thereto. For example, the second diagnosis factor group of the training data set can further include three types of accurate diagnosis factors corresponding to the positive electrode load amount, the negative electrode load amount, and the NP ratio. In this case, the related information between the first diagnosis factor group and the second diagnosis factor group can be expressed as a 6x9 matrix.

[0284] Figure 24 is a flowchart for schematically illustrating a battery diagnosis method according to another embodiment of the present disclosure. Figure 24 The method of can be performed by the battery diagnosis device 100.

[0285] Referring to Figures 1 to 24 In step S2410, the control circuit 130 collects a measurement signal representing a measurement value of the voltage and the current of the target monomer BC from the sensing unit 110 during the application period of the first electrical stimulus to the target monomer BC. The measurement signal can also represent the temperature of the target monomer BC during the application period of the first electrical stimulus.

[0286] In step S2420, the control circuit 130 generates a target full monomer curve M associated with the first electrical stimulus based on the measurement signal collected in step S2410. The target full monomer curve M can represent a correspondence relationship between the voltage and the capacity factor of the target monomer BC while the first electrical stimulus is being applied to the target monomer BC. In step S2420, at least one of the temperature information and the impedance information of the target monomer BC can also be generated. In the present specification, the diagnosis target information can include the target full monomer curve M, and can also include at least one of the temperature information and the impedance information.

[0287] Steps S2410 and S2420 can be replaced with a process in which the data obtaining unit directly or externally obtains the diagnosis target information.

[0288] In step S2430, the control circuit 130 corrects the target full-cell curve M to be associated with the second electric stimulus based on the overpotential curve OP. As a result, a corrected target full-cell curve E is generated.

[0289] In step S2440, the control circuit 130 applies the diagnostic logic (see Figures 4 to 11 ) to the corrected target full-cell curve E to determine first diagnostic result information as a preliminary diagnostic result of the charge / discharge performance of the target cell BC.

[0290] In step S2450, the control circuit 130 determines second diagnostic result information as an accurate diagnostic result of the charge / discharge performance of the target cell BC based on at least one preliminary diagnostic factor included in the first diagnostic result information by using the factor correction model 200. Referring to Figure 22 , the second diagnostic result information is information output from the factor correction model 200 when correction target information is input to the factor correction model 200. The correction target information includes at least one preliminary diagnostic factor, and can further include at least one of temperature information and impedance information. The factor correction model 200 can determine at least one accurate diagnostic factor by correcting at least one preliminary diagnostic factor with at least one of temperature information and impedance information as reference information.

[0291] The second diagnostic factor group 1220 included in the second diagnostic result information indicates an estimation result of the charge / discharge performance that can be determined if the diagnostic logic is applied to the target full-cell curve N instead of the corrected target full-cell curve E.

[0292] The target full-cell curve N indicates a correspondence between the capacity factor of the target cell BC and the voltage at the application period of the second electric stimulus different from the first electric stimulus. The target full-cell curve N can not be obtained by actually applying the second electric stimulus to the target cell BC. That is, the second target full-cell curve N can indicate a correspondence between the capacity factor of the target cell BC and the voltage expected to be obtained if the second electric stimulus is applied to the target cell BC instead of the first electric stimulus.

[0293] After step S2450, the control circuit 130 can apply a mathematical operation to the second diagnostic result information to determine at least one deterioration parameter of the target cell BC (see Table 1). The control circuit 130 can limit at least one of the allowable voltage range, the allowable SOC range, and the allowable charge / discharge current of the target cell BC based on the at least one deterioration parameter. The memory 131 can pre-store relationship data indicating a correspondence between at least one limitation item (i.e., the allowable voltage range, the allowable SOC range, and / or the allowable charge / discharge current) and at least one deterioration parameter. For example, as a certain type of deterioration parameter (e.g., PLOSS , N LOSS , L LOSS , F LOSS ) is larger than that of the BOL state and / or the amount of decrease of another type of degradation parameter (e.g., P SOH , N SOH , L SOH , F SOH ) is larger than that of the BOL state, the allowable voltage range, the allowable SOC range, and / or the limit amount of the allowable charge / discharge current can be larger.

[0294] Meanwhile, if the number of training data sets used to train the factor correction model 200 is insufficient, the correction performance of the factor correction model 200 can be poor. If the correction performance of the factor correction model 200 is low, the second diagnostic factor group output from the factor correction model 200 can not correctly represent the actual charge / discharge performance of the target cell BC.

[0295] Further, if the above-described degradation parameters are determined based on output data from the factor correction model 200 whose correction performance is lower than a certain level, incorrect control can be performed on the allowable voltage range, the allowable SOC range, and / or the allowable current range of the target cell BC. As a result, the degradation of the life of the target cell BC can be accelerated, or it can be difficult to sufficiently utilize the charge / discharge performance of the target cell BC.

[0296] Figure 25 is a flowchart for schematically showing an additional battery diagnostic method related to the battery diagnostic method of Figure 24 , and Figure 26 is a drawing for explaining the drawings referred to in the method according to Figure 25 . The method of Figure 25 is for evaluating the performance of the factor correction model 200, and can be performed by the battery diagnostic device 100 before performing the method according to Figure 24 .

[0297] Referring to Figure 25 , in step S2510, the control circuit 130 evaluates the performance index of the factor correction model 200. The evaluation of the performance index can be based on an evaluation data set. The evaluation data set can be received from the outside by the data obtaining unit, or can be pre-stored in the memory 131. The evaluation data set can include a plurality of first diagnostic factor groups and a plurality of comparison factor groups paired in a one-to-one relationship. The evaluation data set can be prepared in advance by the same method as the method of obtaining the training data set described above.

[0298] The performance evaluation information indicating the evaluated performance index can indicate the classification performance when the factor correction model 200 is used as a classification model. The performance evaluation information can include the value of at least one type of performance index.

[0299] The control circuit 130 can compare each of the second diagnostic factor groups output from the factor correction model 200 when each of the first diagnostic factor groups of the evaluation data set is input to the factor correction model 200 and each of the comparison factor groups of the evaluation data set related to the input first diagnostic factor groups, and determine the difference therebetween. At this time, two estimates of the same type of diagnostic factor can be compared with each other. For example, the difference between the value of the first diagnostic factor included in the comparison factor group of the evaluation data set and the value of the first diagnostic factor included in the second diagnostic factor group output from the factor correction model 200 can be determined.

[0300] If the difference between the two values included in the second diagnostic factor group and the comparison factor group as a specific type of diagnostic factor is within a predetermined allowable error range, it can be determined that the factor correction model 200 has output a correct answer. Meanwhile, if the difference between the two values exceeds the allowable error range, it can be determined that the factor correction model 200 has output an incorrect answer. The allowable error range can be determined separately for each type of diagnostic factor in advance.

[0301] Figure 26 The performance evaluation information is shown in the form of a data table. Referring to Table 1, Figure 26 Precision, recall, accuracy, and F1 score, which are widely used for performance evaluation of machine learning models, are shown as four performance indicators.

[0302] In step S2520, the control circuit 130 determines whether the performance indicators of the factor correction model 200 are equal to or greater than a threshold value. The threshold value can be preset separately for each performance indicator. For example, if the threshold value for precision is 0.70, Figure 26 The precision shown in Table 1 is 0.76923, and thus the value of step S2520 can be output as "Yes". The value of step S2520 being "Yes" indicates that each of the performance indicators of the performance evaluation information is equal to or greater than the threshold value. If the value of step S2520 is "Yes", the process can proceed to step S2530. If the value of step S2520 is "Yes", the process can proceed to step S2540.

[0303] The performance indicators being greater than or equal to the threshold value indicates that the factor correction model 200 has been sufficiently trained to estimate the charge / discharge performance of the target single body BC with a certain level or higher level of accuracy. The performance indicators being less than the threshold value indicates that the factor correction model 200 is not sufficiently trained.

[0304] In step S2530, the control circuit 130 generates a first evaluation result value. If the first evaluation result value is generated, it can be allowed to perform step S2450 according to Figure 24 ​

[0305] In step S2540, the control circuit 130 generates a second evaluation result value. If the second evaluation result value is generated, it is possible to prohibit execution of step S2450 according to Figure 24 the above-described embodiments of the present disclosure are not realized only by the apparatus and the method, but can be realized by a program that performs a function corresponding to the configuration of the embodiments of the present disclosure or a recording medium on which the program is recorded, and a person skilled in the art can easily realize such a realization from the disclosure of the previously described embodiments.

[0306] Although the present disclosure has been described above with respect to a limited number of embodiments and drawings, the present disclosure is not limited thereto, and it is obvious to those skilled in the art that various modifications and changes can be made within the technical scope of the present disclosure and the appended claims.

[0307] In addition, since a person skilled in the art can make many substitutions, modifications, and changes to the above-described embodiments of the present disclosure without departing from the technical aspects of the present disclosure, the present disclosure is not limited by the above-described embodiments and drawings, and some or all of the embodiments can be selectively combined to allow various modifications.

[0308] In addition, since a person skilled in the art can make many substitutions, modifications, and changes to the above-described embodiments of the present disclosure without departing from the technical aspects of the present disclosure, the present disclosure is not limited by the above-described embodiments and drawings, and some or all of the embodiments can be selectively combined to allow various modifications.

Claims

1. A battery diagnosis method, comprising: obtaining diagnosis target information including a target full-cell curve of a battery cell associated with a first electrical stimulus; correcting the target full-cell curve to be associated with a second electrical stimulus different from the first electrical stimulus based on a predetermined overpotential curve; applying diagnosis logic to the corrected target full-cell curve to determine first diagnosis result information, the first diagnosis result information being a preliminary diagnosis result of charge / discharge performance of the battery cell; and determining second diagnosis result information based on at least one preliminary diagnosis factor included in the first diagnosis result information using a factor correction model, the second diagnosis result information being an accurate diagnosis result of the charge / discharge performance of the battery cell.

2. The battery diagnostic method of claim 1, wherein, a transient voltage change induced in the battery cell when the second electrical stimulus is applied is less than a transient voltage change induced in the battery cell when the first electrical stimulus is applied.

3. The battery diagnostic method of claim 1, wherein, the first electrical stimulus is a charge current greater than or equal to a first current rate, and wherein the second electrical stimulus is a charge current less than or equal to a second current rate, the second current rate being less than the first current rate.

4. The battery diagnostic method of claim 1, wherein, the first electrical stimulus is a discharge current greater than or equal to a first current rate, and wherein the second electrical stimulus is a discharge current less than or equal to a second current rate, the second current rate being less than the first current rate.

5. The battery diagnostic method of claim 1, wherein, the overpotential curve represents a difference between a first reference full-cell curve and a second reference full-cell curve, wherein the first reference full-cell curve is predetermined as a correspondence between a capacity factor of a reference cell and a voltage while the first electrical stimulus is being applied, and wherein the second reference full-cell curve is predetermined as the correspondence between the capacity factor of the reference cell and the voltage while the second electrical stimulus is being applied.

6. The battery diagnostic method of claim 1, wherein, the step of correcting the target full-cell curve is subtracting the overpotential curve from the target full-cell curve to generate a corrected target full-cell curve.

7. The battery diagnostic method of claim 1, wherein, the diagnosis target information further includes temperature information of the battery cell measured during an application period of the first electrical stimulus, and wherein the temperature information is input into the factor correction model together with the at least one preliminary diagnosis factor.

8. The battery diagnostic method of claim 1, wherein, the diagnosis target information further includes impedance information of the battery cell measured during the application period of the first electrical stimulus, and wherein the impedance information is input into the factor correction model together with the at least one preliminary diagnosis factor.

9. The battery diagnostic method of claim 1, wherein, the factor correction model is a machine learning model trained entirely by a training data set including pairs of the first diagnosis result information and the second diagnosis result information of each of a plurality of test cells having different charge / discharge performances.

10. The battery diagnostic method of claim 9, wherein, the first diagnosis result information of each of the plurality of test cells is obtained by applying the diagnosis logic to each of a plurality of corrected test full-cell curves, the second diagnosis result information of each of the plurality of test cells is obtained by inputting the first diagnosis result information of each of the plurality of test cells into the factor correction model, and wherein the plurality of corrected test full-cell curves are obtained by individually correcting a plurality of first test full-cell curves associated with the first electrical stimulus based on the overpotential curve, and wherein the second diagnostic result information of each of the plurality of test cells is obtained by applying the diagnostic logic to a plurality of second test full-cell curves associated with the second electrical stimulus.

11. The battery diagnostic method of claim 1, wherein, The step of determining the second diagnostic result information is performed on a condition that a performance index of the factor correction model is evaluated to be greater than or equal to a threshold value. 12.The battery diagnostic method of claim 11, further comprising: sending a message to a user device to inform that additional training of the factor correction model is needed when the performance index of the factor correction model is evaluated to be less than the threshold value.

13. The battery diagnostic method of claim 1, wherein, The second diagnostic result information includes at least one type of diagnostic factor among a positive electrode participation start point, a positive electrode participation end point, and a positive electrode scaling factor related to a charge / discharge performance of a positive electrode as an accurate diagnostic factor of the battery cell.

14. The battery diagnostic method of claim 1, wherein, The second diagnostic result information includes at least one type of diagnostic factor among a negative electrode participation start point, a negative electrode participation end point, and a negative electrode scaling factor related to a charge / discharge performance of a negative electrode of the battery cell as an accurate diagnostic factor of the battery cell.

15. The battery diagnostic method of claim 1, wherein, The second diagnostic result information includes at least one type of diagnostic factor among a positive electrode load amount related to a charge / discharge performance of a positive electrode of the battery cell, a negative electrode load amount related to a charge / discharge performance of a negative electrode of the battery cell, and an NP ratio related to the charge / discharge performances of both the positive electrode and the negative electrode of the battery cell as an accurate diagnostic factor of the battery cell. 16.A battery diagnostic apparatus comprising: a data obtaining unit configured to obtain diagnostic target information including a target full-cell curve of a battery cell associated with a first electrical stimulus; and a control circuit configured to correct the target full-cell curve to be associated with a second electrical stimulus different from the first electrical stimulus based on a predetermined overpotential curve, wherein the control circuit is configured to: apply diagnostic logic to the corrected target full-cell curve to determine first diagnostic result information which is a preliminary diagnostic result of a charge / discharge performance of the battery cell; and use a factor correction model to determine second diagnostic result information based on at least one preliminary diagnostic factor included in the first diagnostic result information, the second diagnostic result information being an accurate diagnostic result of the charge / discharge performance of the battery cell. The control circuit is configured to subtract the overpotential curve from the target full-cell curve to generate the corrected target full-cell curve.

17. The battery diagnostic apparatus of claim 16, wherein, The factor correction model is a machine learning model trained entirely from a training data set including pairs of first diagnostic result information and second diagnostic result information of each of a plurality of test cells having different charge / discharge performances.

18. The battery diagnostic apparatus of claim 16, wherein, 19.A battery pack comprising the battery diagnostic apparatus according to any one of claims 16 to 18. ​ 20. A battery system comprising the battery diagnostic apparatus of any one of claims 16 to 18.

Images