Battery diagnosis device and battery diagnosis method

Abstract

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

Description

Technical Field

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

[0002] This application claims priority to Korean Patent Application No. 10-2023-0165775, filed in Korea on November 24, 2023, the disclosure of which is incorporated herein by reference. Background Art

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

[0004] Currently, commercially available batteries include nickel-cadmium batteries, nickel-metal hydride batteries, nickel-zinc batteries, lithium batteries, etc. Among them, lithium batteries have little or no memory effect, so they receive more attention than nickel-based batteries because of their advantages of being rechargeable whenever convenient, having a very low self-discharge rate, and 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 may 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 battery life and safety.

[0006] Conventionally, while applying a low electrical stimulus (e.g., low-rate charging or discharging) to the battery, the voltage and capacity of the battery are measured and recorded, and a full-cell curve representing the correspondence between voltage and 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 change slowly while applying a low electrical stimulus (e.g., low-rate charging or discharging), there is a limitation in spending a long time diagnosing the battery.

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

[0008] As the voltage of the battery approaches the actual OCV more closely, the charging / discharging performance of the battery can be diagnosed more accurately. Therefore, the overpotential acts as a kind of noise that reduces the diagnostic accuracy. As a result, there may be a significant gap between the diagnostic results of the charging / discharging performance based on the full-cell curve obtained using high electrical stimulation and the actual charging / discharging performance of the battery. SUMMARY OF THE INVENTION

[0009] TECHNICAL PROBLEM

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

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

[0012] TECHNICAL SOLUTION

[0013] In one aspect of the present disclosure, there is provided a battery diagnostic device, including: a data acquisition unit configured to acquire a first target full-cell curve and temperature information of a target cell, the first target full-cell curve representing the correspondence between the voltage and the capacity factor of the target cell while a first electrical stimulation is applied to the target cell as a battery cell to be diagnosed; and a control circuit configured to generate an estimated full-cell curve based on the first target full-cell curve and the overpotential curve. The control circuit is configured to determine a first set of performance factors as a primary estimation result of the charging / discharging performance of the target cell by applying a cell diagnosis logic to the estimated full-cell curve, and to determine a second set of performance factors as a secondary estimation result of the charging / discharging performance of the target cell by applying a factor correction model to the first set of performance factors and the temperature information. The second set of performance factors includes an estimation result of the charging / discharging performance that can be determined by applying the cell diagnosis logic to a second target full-cell curve instead of the estimated full-cell curve while applying a second electrical stimulation different from the first electrical stimulation, wherein the second target full-cell curve represents the correspondence between the voltage and the capacity factor of the target cell.

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

[0015] The first electrical stimulation can be charging using a first current rate, and the second electrical stimulation can be charging using a second current rate less than the first current rate.

[0016] The first electrical stimulation can be discharging using a first current rate, and the second electrical stimulation can be discharging using a second current rate less than the first current rate.

[0017] The overpotential curve can represent the difference between a first reference full-cell curve and a second reference full-cell curve. The first reference full-cell curve can represent the correspondence between the voltage and the capacity factor of the reference monomer while the first electrical stimulation is applied to the reference monomer that is verified as a normal cell monomer. The second reference full-cell curve can represent the correspondence between the voltage and the capacity factor of the reference monomer while the second electrical stimulation is applied to the reference monomer.

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

[0019] The first set of performance factors can include at least one of the following as performance factors: the positive electrode participation start point, which represents the positive electrode voltage and the positive electrode capacity when the voltage of the target monomer matches a first set voltage; the positive electrode participation end point, which represents the positive electrode voltage and the positive electrode capacity when the voltage of the target monomer matches a second set voltage; the positive electrode scaling factor, which represents the ratio of the capacity difference between the positive electrode participation start point and the positive electrode participation end point to the reference positive electrode capacity; the negative electrode participation start point, which represents the negative electrode voltage and the negative electrode capacity when the voltage of the target monomer matches a first set voltage; the negative electrode participation end point, which represents the negative electrode voltage and the negative electrode capacity when the voltage of the target monomer matches a second set voltage; and the negative electrode scaling factor, which represents the ratio of the capacity difference between the negative electrode participation start point and the negative electrode participation end point to the reference negative electrode capacity. The temperature information can include at least one of the starting temperature, the ending temperature, the average temperature, the maximum temperature, and the minimum temperature.

[0020] The factor correction model can be a machine learning model trained by a training data set that includes the first set of performance factors, the temperature information, and the second set of performance factors for each of a plurality of test monomers having different charge / discharge performances

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

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

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

[0024] In yet another aspect of the present disclosure, a battery diagnostic method is also provided, including: obtaining a first target full-cell curve and temperature information of a target cell, the first target full-cell curve representing the correspondence between the voltage and capacity factor of the target cell while a first electrical stimulus is applied to the target cell as the battery cell to be diagnosed; generating an estimated full-cell curve based on the first target full-cell curve and the overpotential curve; determining a first set of performance factors as a primary estimation result of the charge / discharge performance of the target cell by applying cell diagnostic logic to the estimated full-cell curve; and determining a second set of performance factors as a secondary estimation result of the charge / discharge performance of the target cell by applying a factor correction model to the first set of performance factors and the temperature information. The second set of performance factors can include an estimation result of the charge / discharge performance, which can be determined by applying cell diagnostic logic to a second target full-cell curve instead of the estimated full-cell curve, wherein the second target full-cell curve represents the correspondence between the voltage and capacity factor of the target cell while a second electrical stimulus different from the first electrical stimulus is applied.

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

[0026] The factor correction model can be a machine learning model trained by a training data set, the training data set including a first set of performance factors, temperature information, and a second set of performance factors for each of a plurality of test cells with different charge / discharge performances.

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

[0028] Advantageous Effects

[0029] According to at least one of the embodiments of the present disclosure, the charge / discharge performance of a battery can be diagnosed from charge / discharge information (the "first target full-cell curve" of the claims) obtained by applying a high electrical stimulation to the battery. Therefore, compared with a diagnostic method using a low electrical stimulation (e.g., low-rate charging or discharging), the time required to diagnose the charge / discharge performance of the battery can be shortened.

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

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

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

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

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

[0035] Figure 2 It is a figure referred to for explaining the relationship between electrical stimulation and the full cell curve.

[0036] Figure 3 It schematically shows Figure 2 a figure of the overpotential curve that can be obtained from the first reference full cell curve and the second reference full cell curve of

[0037] Figure 4 It is a graph referred to for explaining the relationship between the first target full cell curve, the estimated full cell curve, and the second target full cell curve.

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

[0039] Figures 6 to 8 It is a figure referred to for explaining an example of the process of generating a comparison full cell curve according to the single cell diagnosis logic.

[0040] Figures 9 to 11 It is a figure referred to for explaining another example of the process of generating a comparison full cell curve according to the single cell diagnosis logic.

[0041] Figure 12 It is a figure referred to for explaining the function of the factor correction model.

[0042] Figure 13 It is a figure referred to for explaining the training data set provided for training the factor correction model.

[0043] Figure 14 It shows Figure 12 a figure of an example of the neural network structure of the factor correction model of

[0044] Figure 15 It is a figure showing an example of the correlation coefficient between the performance factors obtained by training the factor correction model.

[0045] Figure 16 It is a flowchart schematically showing a battery diagnosis method according to another embodiment of the present disclosure. Detailed Description of the Embodiment

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

[0047] Therefore, the description presented herein is merely a preferred example for illustrative purposes and is not intended to limit the scope of the present disclosure. Thus, it should be understood that other equivalents and modifications can be made thereto without departing from the scope of the present disclosure.

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

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

[0050] Furthermore, throughout the specification, it should 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 there can be intermediate elements.

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

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

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

[0054] The vehicle controller 2 (e.g., ECU: Electronic Control Unit) is configured to send an ignition on signal to the battery diagnostic device 100 in response to a start button (not shown) provided in the electric vehicle 1 being switched to the on position by the user. The vehicle controller 2 is configured to send an ignition off signal to the battery diagnostic device 100 in response to the start button being switched to the off position by the user. The charger 3 can communicate with the vehicle controller 2 and supply charging power to the battery 11 in a constant current charging mode, a constant voltage charging mode, and / or a constant power charging mode through the charging terminal P+ and the discharging terminal P- of the battery pack 10.

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

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

[0057] 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 may include at least one unit cell. A unit cell is an electrochemical device that can be independently recharged. When the battery cell BC includes multiple unit cells, the multiple unit cells can be connected in series, parallel, or a combination 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 deteriorates after being verified as a good product and is no longer a new product. Hereinafter, the battery cell BC may be referred to as the "target battery cell" or "target cell".

[0058] The relay 20 is serially electrically connected to the battery 11 through the power path connecting the battery 11 and the inverter 30. Figure 1 In, the relay 20 is shown as being connected between the positive terminal of the battery 11 and the charge and discharge terminal P+. The relay 20 is controlled to turn on and off in response to a switching signal from the battery diagnostic device 100. The relay 20 can be a mechanical connector that turns on and off by the magnetic force of a coil, or a semiconductor switch such as a MOSFET (metal oxide semiconductor field effect transistor).

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

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

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

[0062] The sensing unit 110 includes a voltage sensor 111 and a current sensor 112. The sensing unit 110 further includes a temperature sensor 113.

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

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

[0065] The current sensor 112 is serially connected to the battery 11 through the current path between the battery 11 and the inverter 30. The current sensor 112 is configured to detect the battery current that is the 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 of known current detection elements such as a shunt resistor, a Hall effect element, or a combination of two or more of them.

[0066] The temperature sensor 113 is provided to measure the cell temperature that is the temperature of the battery cell BC. While the first electrical stimulation is applied to the battery cell BC, the temperature sensor 113 can periodically or aperiodically detect the cell temperature of the target battery cell BC. The temperature sensor 113 can generate a temperature signal representing the detected cell temperature.

[0067] The communication circuit 150 is configured to support wired or wireless communication between the control circuit 130 and the vehicle 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 vehicle controller 2. The communication circuit 150 can include output devices (e.g., a display, a speaker) that provide the information received from the control circuit 130 and / or the vehicle controller 2 in a user-identifiable form.

[0068] The control circuit 130 is operably coupled to the relay 20, the voltage sensor 111, the current sensor 112, and the communication circuit 150. The operable coupling of two components means that the two components are directly or indirectly connected so that signals can be transmitted and received in one direction or two directions.

[0069] 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 use the ADC (Analog-to-Digital Converter) provided therein to convert each analog signal collected from the sensors 111 and 112 into a digital value and record the digital value.

[0070] The control circuit 130 may be referred to as a "control unit" or "battery controller", and may be implemented in hardware using at least one of an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a microprocessor, or an electrical unit for performing other functions.

[0071] The memory 131 may include at least one type of storage medium such as a flash type, a hard disk type, a solid state drive (SSD) type, a silicon disk drive (SDD) type, a multimedia card micro type, a random access memory (RAM), a static random access memory (SRAM), a read only memory (ROM), an electrically erasable programmable read only memory (EEPROM), or a programmable read only memory (PROM). The memory 131 may store data and programs required for the computational operations of the control circuit 130. The memory 131 may store data representing the results of the computational operations performed by the control circuit 130. Although the memory 131 is depicted as being physically independent of the control circuit 130 in Figure 1 it may be embedded within the control circuit 130.

[0072] The control circuit 130 may turn on the relay 20 in response to an ignition on signal. The control circuit 130 may turn off the relay 20 in response to an ignition off signal. The ignition on signal is a signal requesting a switch from the standby mode to the charge or discharge mode. The ignition off signal is a signal triggering a switch from the cycling state to the standby state. Alternatively, the vehicle controller 2 may be responsible for turning on / off the relay 20 instead of the control circuit 130.

[0073] If the relay 20 is turned on while the inverter 30 or the charger 3 is operating, the battery 11 enters the cycling state. Conversely, if the relay 20 is turned off or the inverter 30 and the charger 3 stop operating, the battery 11 enters the standby state.

[0074] The cycling state refers to the state in which the battery 11 is being charged / discharged, and the standby state refers to the state in which the charging / discharging of the battery 11 stops. The fact that the battery 11 is in the cycling state or the standby state means that each battery cell BC included in the battery 11 is also in the cycling state or the standby state.

[0075] While the battery cell BC is in the cycling state and / or the standby state, the control circuit 130 may determine a voltage detection value and a current detection value based on a voltage signal and a current signal, and then determine (estimate) the SOC (state of charge) of the battery cell BC based on the voltage detection value and / or the current detection value.

[0076] If the charger 3 operates in a constant current charging mode, the current rate (also known as the C-rate) of the charging current supplied to the battery cell BC is a known constant value. Therefore, when estimating the SOC of the battery cell BC, the current value of the constant current output from the charger 3 can be used instead of the current detection value obtained using the current sensor 112.

[0077] SOC is the ratio of the remaining capacity of the battery cell BC to the fully charged capacity (maximum capacity), and is typically processed in the range of 0 to 1 or 0 to 100%. Known methods such as ampere counting, OCV (open circuit voltage)-SOC curve, and / or Kalman filter can be used to determine the SOC.

[0078] The communication circuit 150 can obtain the first target full battery curve from a separate computing device (e.g., the electric vehicle 1) externally set via wired communication and / or wireless communication. Alternatively, the sensing unit 110 can directly generate the first target full battery curve of the target cell BC based on measurement signals including the current signal and voltage signal of the target cell BC to be diagnosed. Alternatively, the control circuit 130 can collect measurement signals including the current signal and voltage signal of the target cell BC from the sensing unit 110, and then generate the first target full battery curve of the target cell BC based on the collected measurement signals. The measurement signals can also include temperature signals.

[0079] The control circuit 130 can generate the first target full battery curve of the target cell BC based on the collected measurement signals.

[0080] The first target full battery curve can represent the correspondence between the voltage of the target cell BC and the capacity factor while a first electrical stimulus is applied to the target cell BC. The capacity factor can be the remaining capacity or SOC (state of charge) of the target cell BC.

[0081] The first electrical stimulus is an electrical stimulus that induces an overpotential exceeding the allowable level in the target cell BC and corresponds to a "high electrical stimulus". The second electrical stimulus is an electrical stimulus that induces an overpotential less than the allowable level in the target cell BC and corresponds to a "low electrical stimulus". For example, the first electrical stimulus can be charging using a first current rate (e.g., 1.0C), and the second electrical stimulus can be charging using a second current rate less than the first current rate (e.g., 0.05C). Another example is that the first electrical stimulus can be discharging using the first current rate, and the second electrical stimulus can be discharging using the second current rate.

[0082] The first target full-cell curve can be a curve representing the correspondence between the capacity of the target single cell BC and the full-cell voltage while charging or discharging at a constant current within a given voltage range (e.g., 3.0 to 4.0 V) or a given SOC range (e.g., 0 to 100% SOC). The data acquisition unit may also acquire the temperature information of the target single cell BC during the application period of the first electrical stimulus used to generate the first target full-cell curve.

[0083] The temperature information of the target single cell BC includes at least one of a starting temperature, an ending temperature, an average temperature, a maximum temperature, and a minimum temperature. The starting temperature represents the single cell temperature of the target single cell BC at the start time of the application period of the first electrical stimulus. The ending temperature represents the single cell temperature of the target single cell BC at the end time of the application period of the first electrical stimulus. The average temperature represents the average single cell temperature of the target single cell BC during the application period of the first electrical stimulus. The maximum temperature represents the maximum single cell temperature of the target single cell BC during the application period of the first electrical stimulus. The minimum temperature represents the minimum single cell temperature of the target single cell BC during the application period of the first electrical stimulus.

[0084] Hereinafter, before explaining the first target full-cell curve obtained using the target single cell BC of the present disclosure, the first reference full-cell curve and the second reference full-cell curve will be first explained.

[0085] Figure 2 is a reference diagram for explaining the relationship between electrical stimuli and full-cell curves.

[0086] Figure 2 The first reference full-cell curve R1 and the second reference full-cell curve R2 shown can be obtained in advance through an experimental pre-process of applying the first electrical stimulus and the second electrical stimulus to the reference cell single body separately.

[0087] The reference cell single body is a cell single body that has been verified as normal and may have the same level of positive electrode performance and negative electrode performance as a new cell single body that has been verified as a good product. The reference single body can be simply referred to as "reference single body". The reference single body can be a coin-type single body including a positive electrode half-cell and a negative electrode half-cell, or a three-electrode single body.

[0088] A new cell single body refers to a cell single body in a new state. The new state is the same concept as BOL (beginning of life). For example, it can be called BOL before the cumulative charge / discharge capacity reaches the set capacity from the time of manufacturing completion, and it can be called MOL (mid-life of life) from the time when the cumulative charge / discharge capacity reaches the set capacity.

[0089] In Figure 2 the graph, the horizontal axis (X-axis) represents the capacity (Ah), and the vertical axis (Y-axis) represents the voltage (V).

[0090] The first reference full-cell curve curve R1 shows the relationship between the voltage and capacity of the reference cell while the first electrical stimulation is applied (e.g., during charging at the first current rate). The second reference full-cell curve curve R2 shows the relationship between the voltage and capacity of the reference cell while the second electrical stimulation is applied (e.g., during charging at the second current rate). The first reference full-cell curve R1 can be obtained by performing charging at the first current rate while the OCV of the reference cell is set to be equal to the lower limit of a given voltage range (e.g., 3.0 V). The second reference full-cell curve R2 can be obtained by performing charging at the second current rate while the OCV of the reference cell is set to be equal to the lower limit of the given voltage range. Therefore, in Figure 2 it, the starting points of the first reference full-cell curve R1 and the second reference full-cell curve R2 approximately coincide, while the ending points are significantly different.

[0091] The first reference full-cell curve R1 and the second reference full-cell curve R2 can represent the correspondence between the capacity of the reference cell and the full-cell voltage within at least the voltage range of interest (e.g., 3.0 to 4.0 V). The lower limit and upper limit of the voltage range of interest can represent the first set voltage ( Figure 2 3.0 V in Figure 2 ) and the second set voltage (

[0092] 4.0 V in

[0093] 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 limit and upper limit of the battery cell voltage corresponding to 0% to 100% SOC (state of charge) of any battery cell including the reference cell.

[0094] The starting capacity (Qi) can refer to the remaining capacity when the full-cell voltage of any battery cell is equal to the first set voltage. The ending capacity (Qf) can refer to the remaining capacity when the full-cell voltage of any battery cell is equal to the second set voltage.

[0095] The first reference full-cell curve R1 can be based on the voltage time series and current time series (or capacity time series) obtained by periodically measuring the current and full-cell voltage of the reference cell while the first electrical stimulation is applied.

[0096] Here, when compared with the second reference full-cell curve R2, the first reference full-cell curve R1 may include an overpotential corresponding to a voltage value at the same capacity value. Thus, for the same capacity value, the voltage difference between the first reference full-cell curve R1 and the second reference full-cell curve R2 can be calculated as the overpotential.

[0097] 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 the voltage difference by capacity), an overpotential curve indicating the overpotential by capacity can be generated.

[0098] Figure 3 is a diagram schematically showing the 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.

[0099] The overpotential curve OP can be a curve representing the correspondence between capacity and overpotential. The overpotential curve OP can be a curve representing the voltage difference by capacity between the first reference full-cell curve R1 and the second reference full-cell curve R2.

[0100] The capacity range (Qi to Qf) of the overpotential curve OP can be the 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.

[0101] Figure 4 is a graph used as a reference for explaining the relationship between the first target full-cell curve M, the estimated full-cell curve E, and the second target full-cell curve N.

[0102] In Figures 2 to 4 Ah is used as the unit of the horizontal axis, but this unit can be expressed in other forms. For example, instead of Ah, the percentage % indicating the SOC (state of charge) can be used as the unit of the horizontal axis.

[0103] Referring to Figure 4 , the control circuit 130 can generate the first target full-cell curve M, which represents the correspondence between the full-cell voltage and the capacity of the target cell BC while the first electrical stimulation is applied to the target cell BC. The first target full-cell curve M can represent the correspondence between the capacity and the full-cell voltage of the target cell BC at least within the voltage range of interest.

[0104] Therefore, since the reference monomer and the target monomer BC have different charge / discharge performances, there are inevitably some differences between the first target full-cell curve M and the first reference full-cell curve R1.

[0105] For example, in the same voltage range of interest (e.g., 3.0 to 4.0 V), Figure 2 the capacity range of the first reference full-cell curve R1 shown in is 5 to 47 Ah, while the capacity range of the first target full-cell curve M is 5 to 45 Ah.

[0106] The control circuit 130 can be based on Figure 3 the overpotential curve OP and Figure 4 the first target full-cell curve M of to generate an estimated full-cell curve E. Specifically, the control circuit 130 can generate the estimated full-cell curve E by subtracting the overpotential curve OP from the first target full-cell curve M. As a result, at the same capacity value, the voltage value of the estimated full-cell curve E can be less than the voltage value of the first target full-cell curve M.

[0107] The control circuit 130 can obtain the estimated full-cell curve E by subtracting the capacity-specific overpotential of the overpotential curve OP from the capacity-specific voltage of the first target full-cell curve M in the common capacity range of the first target full-cell curve M and the overpotential curve OP. In this case, the capacity range of 45 Ah to 47 Ah in the entire capacity range of the overpotential curve OP can be not utilized. That is, the estimated full-cell curve E can be obtained by removing the capacity-specific overpotential of the overpotential curve OP corresponding to the capacity-specific voltage of the first target full-cell curve M.

[0108] Alternatively, the control circuit 130 can generate an adjusted overpotential curve (not shown in the figure) by scaling the overpotential curve OP along the horizontal axis such that the capacity range of the overpotential curve OP matches the capacity range of the first target full-cell curve M. Subsequently, the control circuit 130 can generate the estimated full-cell curve E by subtracting the overpotential value of the adjusted overpotential curve from the voltage value of the first target full-cell curve M within the capacity range of the overpotential curve OP. That is, the estimated full-cell curve E can be obtained by removing the capacity-specific overpotential of the adjusted overpotential curve corresponding to the capacity-specific voltage of the first target full-cell curve M.

[0109] The second target full-cell curve N is an example of a curve representing the correspondence between the voltage and the capacity factor of the target monomer BC that is expected to be obtained if the second electrical stimulation instead of the first electrical stimulation is applied to the target monomer BC.

[0110] The estimated full-cell curve E is the estimation result of the second target full-cell curve N based on the first target full-cell curve M and the overpotential curve OP.

[0111] Reference Figure 4 , the estimated full-cell curve E is a curve obtained by subtracting the overpotential curve OP from the first target full-cell curve M, and the estimated 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 to use the estimated full-cell curve E instead of the first target full-cell curve M in terms of diagnostic accuracy.

[0112] At the same time, since the estimated full-cell curve E does not exactly match the second target full-cell curve N, there may still be a significant difference between the diagnostic result of the charge / discharge performance based on the estimated full-cell curve E and the actual charge / discharge performance. This will be described later with reference to Figure 12 methods for reducing the error in the diagnostic result of the charge / discharge performance.

[0113] The control circuit 130 can determine a first set of performance factors representing the charge / discharge performance of the target cell BC by applying cell diagnosis logic to the estimated full-cell curve E. The first set of performance factors can be regarded as the primary estimation result of the charge / discharge performance of the target cell BC.

[0114] The first set of performance factors can include performance factors such as the positive electrode participation start point, positive electrode participation end point, positive electrode scaling factor, negative electrode participation start point, negative electrode participation end point, and negative electrode scaling factor.

[0115] In this specification, when the full-cell voltage of the corresponding battery cell matches the first set voltage, the positive electrode participation start point on the positive electrode curve of any battery cell represents the positive electrode voltage and positive electrode capacity (or positive electrode SOC). The positive electrode voltage at the positive electrode participation start point can be referred to as the "positive electrode starting potential". In addition, when the full-cell voltage of the corresponding battery cell matches the first set voltage, the negative electrode participation start point on the negative electrode curve of the corresponding battery cell indicates the negative electrode voltage and negative electrode capacity (or negative electrode SOC). The negative electrode voltage at the negative electrode participation start point can be referred to as the "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.

[0116] In addition, when the full-cell voltage of the corresponding battery cell matches the second set voltage, 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. The positive electrode voltage at the positive electrode participation end point can be referred to as the "positive electrode termination potential". In addition, when the full-cell voltage of the corresponding battery cell matches the second set voltage, 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. The negative electrode voltage at the negative electrode participation end point can be referred to as the "negative electrode termination potential". Therefore, 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.

[0117] In this specification, the positive electrode capacity (capacity value) at a specific point on the positive electrode curve of any battery cell can mean the capacity difference between any one of the two end points of the positive electrode curve and the specific point. The positive electrode SOC at a specific point on the positive electrode curve of any battery cell can mean the ratio of the capacity difference between any one of the two end points of the positive electrode curve (e.g., the low-capacity point) and the specific point to the capacity difference between the two end points of the positive electrode curve.

[0118] Similarly, the negative electrode capacity (capacity value) at a specific point on the negative electrode curve of any battery cell can mean the capacity difference between any one of the two end points of the negative electrode curve (or the positive electrode curve) and the specific point. The negative electrode SOC at a specific point on the negative electrode curve of any battery cell can mean the ratio of the capacity difference between any one of the two end points of the negative electrode curve (or the positive electrode curve) (e.g., the low-capacity point) and the specific point to the capacity difference between the two end points of the negative electrode curve.

[0119] 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.

[0120] In the memory 131, information indicating the voltage and 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, which represents the charge / discharge performance of the reference cell, can be pre-recorded.

[0121] From now on, with reference to Figures 5 to 11 , the diagnostic process included in the cell diagnosis logic will be explained.

[0122] Figure 5 is the graph referred to for explaining the examples of each of the estimated full-cell curve E, the second reference full-cell curve R2, the reference positive electrode curve Rp, and the reference negative electrode curve Rn. In Figure 5In the curve graph, the horizontal axis (X-axis) represents the capacity, and the vertical axis (Y-axis) represents the voltage. The estimated full cell curve E, the second reference full cell curve R2, and Figure 2 are the same as those in

[0123] Reference Figure 5 , the reference positive electrode curve Rp can be a curve representing the correspondence between the positive electrode voltage and the capacity while the second electrical stimulation is applied to the reference monomer. The positive electrode voltage of the reference monomer refers to the potential difference between the potential of the reference electrode (not shown) and the potential of the positive electrode of the reference monomer.

[0124] The reference negative electrode curve Rn can be a curve representing the correspondence between the negative electrode voltage and the capacity while the second electrical stimulation is applied to the reference monomer. The negative electrode voltage of the reference monomer refers to the potential difference between the potential of the reference electrode and the potential of the negative electrode of the reference monomer.

[0125] The potential of the reference electrode can be, for example, the redox potential of lithium. The positive electrode voltage can be abbreviated as the positive electrode potential, and the negative electrode voltage can be abbreviated as the negative electrode potential.

[0126] The reference positive electrode curve Rp and the reference negative electrode curve Rn can be pre-stored in the memory 131.

[0127] At least one of the reference positive electrode curve Rp and the reference negative electrode curve Rn can be aligned along the horizontal axis so that the combined result of a part of the common capacity range ( Figure 5 5 Ah to 50 Ah in

[0128] Figure 5 shows an example in which the reference negative electrode curve Rn is aligned to be shifted to the right based on the starting point (the point corresponding to the capacity 0) of the reference positive electrode curve Rp.

[0129] From Figure 5 it can be seen that the two ends of the reference positive electrode curve Rp and the reference negative electrode curve Rn are offset from each other. In other words, the capacity ranges of the reference positive electrode curve Rp and the reference negative electrode curve Rn do not match and can only partially overlap. Therefore, the second reference full cell curve R2 can indicate the full cell voltage of the reference monomer in a part of the common capacity range of the reference positive electrode curve Rp and the reference negative electrode curve Rn.

[0130] The control circuit 130 may be configured to compare the estimated full cell curve E with at least one comparative full cell curve. The comparative full cell curve may be a result of adjusting each of a reference positive electrode curve Rp and a 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 to generate an adjusted positive electrode curve and an adjusted negative electrode curve.

[0131] In other words, when the second reference full cell curve R2 is a result of subtracting a part of the reference negative electrode curve Rn from a part of the reference positive electrode curve Rp, the comparative full cell curve may be considered as a result of subtracting a part of the adjusted negative electrode curve from a part of the adjusted positive electrode curve.

[0132] The control circuit 130 may 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 may 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 may obtain the comparative full cell curve by accessing the memory 131 and reading the comparative full cell curve.

[0133] The control circuit 130 may 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 a plurality of levels and then synthesizing their adjustment processes. The comparative full cell curve may also be referred to as an "adjusted reference full cell curve".

[0134] The control circuit 130 may specify any one of the plurality of comparative full cell curves that has the minimum error relative to the estimated full cell curve E. Then, the control circuit 130 may determine that the adjusted positive electrode curve and the adjusted negative electrode curve mapped to the specified comparative full cell curve are the positive electrode curve and the negative electrode curve of the target cell BC.

[0135] In this regard, various methods known at the time of filing of the present application may be adopted to determine the error between two curves as a set of data points, and each data point may be expressed in a two - dimensional coordinate system. For example, the integral of the absolute value of the area between the two curves or the RMSE (root mean square error) may be used as the error between the two curves.

[0136] According to this configuration of the present disclosure, various state information about the target cell BC can be obtained based on the finally determined adjusted positive electrode curve and adjusted negative electrode curve. The finally determined adjusted positive electrode curve and adjusted negative electrode curve can be mapped to any one of the plurality of comparative full-cell curves that has the smallest error relative to the estimated full-cell curve E. In particular, the comparative full-cell curve of the finally determined adjusted positive electrode curve and adjusted negative electrode curve can be almost equal in shape to the estimated full-cell curve E.

[0137] Figures 6 to 8 FIG. is a diagram referred to for explaining an example of the process for generating a comparative full-cell curve.

[0138] Reference will be made to Figures 6 to 8 The process for generating a comparative full-cell curve to be explained can be carried out in the following order: a first routine for setting four points (positive electrode participation start point, positive electrode participation end point, negative electrode participation start point, negative electrode participation end point) corresponding to the voltage range of interest (see Figure 6 ), a second routine for performing curve shifting (see Figure 7 ), and a third routine for performing capacity scaling (see Figure 8 ). That is, the process for generating a comparative full-cell curve according to an embodiment of the present disclosure can include the first routine to the third routine.

[0139] Reference Figure 4 , the reference positive electrode curve Rp and the reference negative electrode curve Rn are the same as those shown in Figure 6 .

[0140] The control circuit 130 can determine the positive electrode participation start point (pi), positive electrode participation end point (pf), negative electrode participation start point (ni), and negative electrode participation end point (nf) on the reference positive electrode curve Rp and the reference negative electrode curve Rn.

[0141] One of the positive electrode participation start point (pi) or the negative electrode participation start point (ni) depends on the other.

[0142] As an example, the control circuit 130 can divide the positive electrode voltage range (or the second set voltage) from the start point to the end point of the reference positive electrode curve Rp into a plurality of small voltage segments, and then set the boundary point between two adjacent small voltage segments among the plurality of small voltage segments as the positive electrode participation start point (pi). Each small voltage segment can have a predetermined size (for example, 0.01V). Next, the control circuit 130 can set the point on the reference negative electrode curve Rn that is smaller than the positive electrode participation start point (pi) by a first set voltage (for example, 3V) as the negative electrode participation start point (ni).

[0143] As another example, the control circuit 130 may divide the negative voltage range from the starting point to the ending point of the reference negative curve Rn into a plurality of small voltage segments of a predetermined size, and then set the boundary point between two adjacent small voltage segments among the plurality of small voltage segments as the negative participation starting point (ni). Next, the control circuit 130 may search the reference positive curve Rp for a point greater than the negative participation starting point (ni) by a first set voltage (e.g., 3V), and set the searched point as the positive participation starting point (pi).

[0144] One of the positive participation ending point (pf) or the negative participation ending point (nf) depends on the other.

[0145] As an example, the control circuit 130 may divide the voltage range from the second set voltage to the ending point of the reference positive curve Rp into a plurality of small voltage segments of a predetermined size, and then set the boundary point between two adjacent small voltage segments among the plurality of small voltage segments as the positive participation ending point (pf). Next, the control circuit 130 may set the point on the reference negative curve Rn that is smaller than the positive participation ending point (pf) by a second set voltage (e.g., 4V) as the negative participation ending point (nf).

[0146] As another example, the control circuit 130 may divide the negative voltage range from the starting point to the ending point of the reference negative curve Rn into a plurality of small voltage segments of a predetermined size, and then set the boundary point between two adjacent small voltage segments among the plurality of small voltage segments as the negative participation ending point (nf). Next, the control circuit 130 may search the reference positive curve Rp for a point greater than the negative participation ending point (nf) by a second set voltage (e.g., 4V), and set the searched point as the positive participation ending point (pf).

[0147] If the positive participation starting point (pi), the positive participation ending point (pf), the negative participation starting point (ni), and the negative participation ending point (nf) are completely determined, the control circuit 130 shifts at least one of the reference positive curve Rp and the reference negative curve Rn left or right along the horizontal axis.

[0148] Reference Figure 6 , the control circuit 130 may shift the reference positive curve Rp to the left (towards low capacity) or shift the reference negative curve Rn to the right (towards high capacity), or shift both, so that the capacity values of the positive participation starting point (pi) and the negative participation starting point (ni) match.

[0149] Alternatively, the control circuit 130 shifts the reference positive curve Rp to the left or shifts the reference negative curve Rn to the right or shifts both, so that the capacity values of the positive participation ending point (pf) and the negative participation ending point (nf) match.

[0150] Figure 7 Shows a case where only the reference positive curve Rp is shifted to the left to generate an adjusted reference positive curve (Rp'), and thus, the capacity value of the positive electrode participation start point (pi') matches the capacity value of the negative electrode participation start point (ni). The adjusted reference positive curve (Rp') can be the result of applying an adjustment process to the reference positive curve Rp, and the adjustment process is to shift the capacity difference between the positive electrode participation start point (pi) and the negative electrode participation start point (ni) to the left. Therefore, the two points (pi, pi') can only differ in capacity value and have the same voltage. Moreover, the two points (pf, pf') can only differ in capacity value and have the same voltage.

[0151] If an adjustment result curve (Rp', Rn) in which at least one of the reference positive curve Rp and the reference negative curve Rn is shifted is ensured, the control circuit 130 can scale the capacity range of at least one of the adjustment result curves (Rp', Rn).

[0152] According to Figure 7 the example shown, the control circuit 130 can perform an additional adjustment process to contract or expand at least one of the adjusted reference positive curve (Rp') and the reference negative curve Rn along the horizontal axis.

[0153] Reference Figure 8 , the control circuit 130 can generate an adjusted reference positive curve (Rp") by contracting or expanding the adjusted reference positive curve (Rp') such that the magnitude of the capacity range between the two points (pi', pf') of the adjusted reference positive curve (Rp') matches the magnitude of the capacity range of the estimated full-cell curve E. At this time, any one of the two points (pi', pf') (pi') can be fixed. Therefore, the capacity difference between the two points (pi', pf") of the adjusted reference positive curve (Rp") can match the capacity range of the estimated full-cell curve E.

[0154] In addition, the control circuit 130 can generate an adjusted reference negative curve (Rn') by contracting or expanding the reference negative curve Rn such that the magnitude of the capacity range between the two points (ni, nf) of the reference negative curve Rn matches the magnitude of the capacity range of the estimated full-cell curve E. At this time, any one of the two points (ni, nf) (ni) can be fixed. Therefore, the capacity difference between the two points (ni, nf') of the adjusted reference negative curve (Rn') can match the capacity range of the estimated full-cell curve E.

[0155] In Figure 8 it, the adjusted reference positive curve (Rp") is contracted Figure 7The results of the adjusted reference positive electrode curve (Rp'), and the adjusted reference negative electrode curve (Rn') is an extension Figure 7 of the results of the reference negative electrode curve Rn shown.

[0156] 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).

[0157] 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 magnitude of the capacity range of the estimated full cell curve E. Similarly, 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 magnitude of the capacity range of the estimated full cell curve E.

[0158] In addition, the capacity ranges of the two points (pi', pf") of the adjusted reference positive electrode curve (Rp") match the capacity ranges of the two points (ni, nf') of the adjusted reference negative electrode curve (Rn'). The control circuit 130 can generate a 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').

[0159] The control circuit 130 can calculate the error (curve error) between the comparison values between the comparison full cell curve S and the estimated full cell curve E.

[0160] 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.

[0161] The positive electrode scaling factor of the adjusted reference positive electrode curve (Rp") can represent the ratio of the capacity difference between two points (pi', pf) to the capacity difference between two points (pi0, pf0). Alternatively, the positive electrode scaling factor of the adjusted reference positive electrode curve (Rp) can represent the ratio of the positive electrode capacity difference between two points (pi', pf") to the positive electrode capacity difference between two points (pi0, pf0). Alternatively, the positive electrode scaling factor of the adjusted reference positive electrode curve (Rp") can represent the ratio of the positive electrode SOC difference between two points (pi', pf") to the positive electrode SOC difference between two points (pi0, pf0).

[0162] The negative electrode scaling factor of the adjusted reference negative electrode curve (Rn') can represent the ratio of the capacity difference between two points (ni, nf') to the capacity difference between two points (ni0, nf0). Alternatively, the negative electrode scaling factor of the adjusted reference negative electrode curve (Rn') can represent the ratio of the negative electrode capacity difference between two points (ni, nf') to the negative electrode capacity difference between two points (ni0, nf0). Alternatively, the negative electrode scaling factor of the adjusted reference negative electrode curve (Rn') can represent the ratio of the negative electrode SOC difference between two points (ni, nf') to the negative electrode SOC difference between two points (ni0, nf0).

[0163] Hereinafter, ps can be used as a symbol indicating the positive electrode scaling factor, and ns can be used as a symbol indicating the negative electrode scaling factor.

[0164] 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, the boundary points of two adjacent small voltage sections among the plurality of small voltage sections can be set as the positive electrode participation start point (pi).

[0165] For example, if the positive electrode voltage range of the reference positive electrode curve Rp is divided into 100 small voltage ranges, then 100 boundary points can be set as the positive electrode participation start point (pi). In addition, if the voltage range greater than or equal to the second set voltage in the reference positive electrode curve Rp is divided into 40 small voltage ranges, then 40 boundary points can be set as the positive electrode participation end point (pf). In this case, at least 4,000 different comparative full cell curves can be generated.

[0166] Of course, those skilled in the art will easily understand that as the size of the small voltage section decreases, the maximum number of comparative full cell curves that can be generated increases, and conversely, as the size of the small voltage section increases, the maximum number of comparative full cell curves that can be generated decreases.

[0167] The control circuit 130 can identify the minimum value among the curve errors of the multiple comparative full-cell curves generated as described above, and then obtain from the memory 131 a first set of performance factors, which is information mapped to the minimum curve error (e.g., at least one of the positive electrode participation start point, positive electrode participation end point, negative electrode participation start point, negative electrode participation end point, positive electrode scaling factor, and negative electrode scaling factor).

[0168] Figures 9 to 11 is a diagram for another example that is referred to describe the process of generating a comparative full-cell curve according to the single-cell diagnostic logic. For reference, Figures 9 to 11 The illustrated embodiment is independent of Figures 6 to 8 the illustrated embodiment. Therefore, the terms or reference numerals commonly used to describe Figures 6 to 8 the illustrated embodiment and Figures 9 to 11 the illustrated embodiment should be understood to be limited to each embodiment.

[0169] The process of generating the comparative full-cell curve U to be referred to Figures 9 to 11 for explanation can be carried out in the order of a fourth routine for performing capacity scaling (see Figure 9 ), a fifth routine for setting four points (positive electrode participation start point, positive electrode participation end point, negative electrode participation start point, and negative electrode participation end point) (see Figure 10 ), and a sixth routine for performing curve shifting (see Figure 11 ). That is, the process of generating a comparative full-cell curve according to another embodiment of the present disclosure can include the fourth to sixth routines.

[0170] 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.

[0171] The scaling value range can be predetermined or can vary depending on the ratio of the size of the capacity range of the estimated full-cell curve E to the size of the capacity range of the second reference full-cell curve R2. As an example, assuming 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%) (i.e., 90%, 90.1%, 90.2%,... 98.9%, 99%), 91 values can be selected as the positive electrode scaling factor and the negative electrode scaling factor, respectively. In this case, up to 8,281 pairs of adjusted curves (Rp', Rn') can be generated according to 91×91 = 8,281 adjustment levels (combinations of the positive electrode scaling factor and the negative electrode scaling factor). A pair of adjusted curves refers to a combination of an adjusted positive electrode curve (Rp') and an adjusted negative electrode curve (Rn').

[0172] Reference Figure 9 The adjusted reference positive curve (Rp') and the adjusted reference negative curve (Rn') respectively show the results of applying the positive scaling factor and the negative scaling factor to the reference positive curve Rp and the reference negative curve Rn.

[0173] Since the positive scaling factor and the negative scaling factor are less than 100%, the adjusted reference positive curve (Rp') is obtained by shrinking the reference positive curve Rp along the horizontal axis, and the adjusted reference negative curve (Rn') is also obtained by shrinking the reference negative curve Rn along the horizontal axis. For the sake of understanding, the reference positive curve Rp and the reference negative curve Rn are shown in a form where their starting points are fixed respectively and the remaining parts shrink leftward along the horizontal axis.

[0174] Reference Figure 10 The control circuit 130 can determine the positive participation starting point (pi'), the positive participation ending point (pf'), the negative participation starting point (ni'), and the negative participation ending point (nf') on the adjusted reference positive curve (Rp') and the adjusted reference negative curve (Rp').

[0175] One of the positive participation starting point (pi') or the negative participation starting point (ni') can depend on the other. Also, one of the positive participation ending point (pf') or the negative participation ending point (nf') can depend on the other. Also, one of the positive participation starting point (pi') or the positive participation ending point (pf') can be set based on the other.

[0176] That is to say, if any one of the positive participation starting point (pi'), the positive participation ending point (pf'), the negative participation starting point (ni'), and the negative participation ending point (nf') is set, the remaining three points can be automatically set by the first set voltage, the second set voltage, and / or the magnitude of the capacity range of the estimated full-cell curve E (for example, Figure 4 45 Ah - 5 Ah = 40 Ah in

[0177] As an example, the control circuit 130 can divide the positive voltage range (or the second set voltage) from the starting point to the ending point of the adjusted reference positive curve (Rp') into multiple small voltage segments, and then set the boundary point between two adjacent small voltage segments in the multiple small voltage segments as the positive participation starting point (pi'). Next, the control circuit 130 can set the point on the adjusted reference negative curve (Rn') that is smaller than the positive participation starting point (pi') by the first set voltage as the negative participation starting point (ni').

[0178] As another example, the control circuit 130 may divide the negative voltage range from the starting point to the ending point of the adjusted reference negative curve (Rn') into a plurality of small voltage segments of a predetermined size, and then set the boundary point between two adjacent small voltage segments among the plurality of small voltage segments as the negative participation starting point (ni'). Next, the control circuit 130 may search the adjusted reference positive curve (Rp') for a point that is greater than the negative participation starting point (ni') by a first set voltage, and set the searched point as the positive participation starting point (pi').

[0179] As yet another example, the control circuit 130 may divide the voltage range from a second set voltage to the ending point of the adjusted reference positive curve (Rp') into a plurality of small voltage segments of a predetermined size, and then set the boundary point between two adjacent small voltage segments among the plurality of small voltage segments as the positive participation ending point (pf'). Next, the control circuit 130 may search the adjusted reference negative curve (Rn') for a point that is smaller than the positive participation ending point (pf') by a second set voltage (e.g., 4V), and set the searched point as the negative participation ending point (nf').

[0180] As yet another example, the control circuit 130 may divide the negative voltage range from the starting point to the ending point of the adjusted second reference negative curve (Rn') into a plurality of small voltage segments of a predetermined size, and then set the boundary point between two adjacent small voltage segments among the plurality of small voltage segments as the negative participation ending point (nf'). Next, the control circuit 130 may search the adjusted reference positive curve (Rp') for a point that is greater than the negative participation ending point (nf') by a second set voltage, and set the searched point as the positive participation ending point (pf').

[0181] If any one of the positive participation starting point (pi'), the positive participation ending point (pf'), the negative participation starting point (ni'), and the negative participation ending point (nf') is determined, the control circuit 130 may additionally determine the remaining three points based on the determined point.

[0182] For example, if the positive electrode participation start point (pi') is determined first, the control circuit 130 may set, on the adjusted reference positive electrode curve (Rp'), a point having a capacity value larger than the size of the capacity range of the full cell curve E estimated from the capacity value of the positive electrode participation start point (pi') as the positive electrode participation end point (pf'). In addition, the control circuit 130 may search, from the adjusted reference negative electrode curve (Rn'), for a point lower than the positive electrode participation start point (pi') by a first set voltage, and set the searched point as the negative electrode participation start point (ni'). Further, the control circuit 130 may set, on the adjusted reference negative electrode curve (Rn'), a point having a capacity value larger than the size of the capacity range of the full cell curve E estimated from the capacity value of the negative electrode participation start point (ni') as the negative electrode participation end point (nf').

[0183] As another example, when the positive electrode participation end point (pf') is determined first, the control circuit 130 may set, on the adjusted reference positive electrode curve (Rp'), a point having a capacity value smaller than the size of the capacity range of the full cell curve E estimated from the capacity value of the positive electrode participation end point (pf') as the positive electrode participation start point (pi'). In addition, the control circuit 130 may search, from the adjusted reference negative electrode curve (Rn'), for a point lower than the positive electrode participation end point (pf') by a second set voltage, and set the searched point as the negative electrode participation end point (nf'). Further, the control circuit 130 may set, on the adjusted reference negative electrode curve (Rn'), a point having a capacity value smaller than the size of the capacity range of the full cell curve E estimated from the capacity value of the negative electrode participation end point (nf') as the negative electrode participation start point (ni').

[0184] As yet another example, when the negative electrode participation start point (ni') is determined, the control circuit 130 may set, on the adjusted reference negative electrode curve (Rn'), a point having a capacity value larger than the size of the capacity range of the full cell curve E estimated from the capacity value of the negative electrode participation start point (ni') as the negative electrode participation end point (nf'). In addition, the control circuit 130 may search, from the adjusted reference positive electrode curve (Rp'), for a point higher than the negative electrode participation start point (ni') by a first set voltage, and set the searched point as the positive electrode participation start point (pi'). Additionally, the control circuit 130 may set, on the adjusted reference positive electrode curve (Rp'), a point having a capacity value larger than the size of the capacity range of the full cell curve E estimated from the capacity value of the positive electrode participation start point (pi') as the positive electrode participation end point (pf').

[0185] As another example, when determining the negative electrode participation end point (nf'), the control circuit 130 may set, on the adjusted reference negative electrode curve (Rn'), a point whose capacity value is smaller than the size of the capacity range of the estimated full cell curve E of the capacity value of the negative electrode participation end point (nf') as the negative electrode participation start point (ni'). In addition, the control circuit 130 may search, from the adjusted reference positive electrode curve (Rp'), for a point that is higher than the negative electrode participation end point (nf') by a second set voltage, and set the searched point as the positive electrode participation end point (pf'). Further, the control circuit 130 may set, on the adjusted reference positive electrode curve (Rp'), a point having a capacity value smaller than the size of the capacity range of the estimated full cell curve E of the capacity value of the positive electrode participation end point (pf') as the positive electrode participation start point (pi').

[0186] 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 determined entirely based on the pairing of the positive electrode proportion factor and the negative electrode proportion factor, the control circuit 130 may shift at least one of the adjusted reference positive electrode curve (Rp') and the adjusted reference negative electrode curve (Rn') left or right along the horizontal axis 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.

[0187] Figure 11 The shown adjusted reference negative electrode curve (Rn") is obtained by only shifting Figure 10 the shown adjusted reference negative electrode curve (Rn') to the right. Accordingly, 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. Correlatively, the capacity difference between the positive electrode participation start point (pi') and the positive electrode participation end point (pf') is equal to the capacity difference between the negative electrode participation start point (ni') and the negative electrode participation end point (nf''). Accordingly, 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.

[0188] Refer to Figure 11 , the control circuit 130 may generate a comparison full cell curve U by subtracting the partial curve between two points (pi', pf') of the adjusted reference positive electrode curve (Rp') from the partial curve between two points (ni", nf") of the adjusted reference negative electrode curve (Rn").

[0189] The control circuit 130 may calculate the error (curve error) between the comparison full cell curve U and the estimated full cell curve E.

[0190] The control circuit 130 may map at least two of the adjusted reference positive curve (Rp'), the adjusted reference negative curve (Rn"), the positive participation start point (pi'), the positive participation end point (pf'), the negative participation start point (ni"), the negative participation end point (nf"), the positive proportionality factor, the negative proportionality factor, the comparison of the full cell curve U, and the curve error, and record them in the memory 131.

[0191] As described above, the control circuit 130 may generate a comparison full cell curve U corresponding to each pair of the positive scaling factor and the negative scaling factor selected from the scaling value range. Since the pairs of the positive scaling factor and the negative scaling factor are plural, it is obvious that the comparison curves U will also be generated in plural.

[0192] The control circuit 130 may identify the minimum value among the curve errors of the plurality of comparison full cell curves, and then obtain from the memory 131 the information mapped to the minimum curve error.

[0193] As described above, the control circuit 130 may execute the cell diagnosis logic to generate a comparison full cell curve having the minimum error with the estimated full cell curve E based on the reference positive curve Rp and the reference negative curve Rn.

[0194] The control circuit 130 may determine a first performance factor group including performance factors of at least one of the positive participation start point, the positive participation end point, the positive scaling factor, the negative participation start point, the negative participation end point, and the negative scaling factor, which are respectively mapped to the minimum curve error.

[0195] Meanwhile, since the first performance factor group is the result of applying the cell diagnosis logic to the estimated full cell curve E, it can represent the actual charge / discharge performance of the target cell BC more accurately than the result of applying the cell diagnosis logic to the first target full cell curve M.

[0196] However, since the overpotential curve OP is related to the reference cell rather than the target cell BC, there may still be a significant difference between the charge / discharge performance indicated by the first performance factor group and the actual charge / discharge performance of the target cell BC.

[0197] Therefore, it is desirable to execute a process for correcting the first performance factor group to narrow the gap between the charge / discharge performance indicated by the first performance factor group and the actual charge / discharge performance, and this can be achieved by the factor correction model explained later. Execute the correction process of the first performance factor group to determine a second performance factor group as a secondary estimation result of the charge / discharge performance of the target cell BC.

[0198] Figure 12is a reference diagram for functions explaining a factor correction model, Figure 13 is a reference diagram for explaining a training data set provided for training a factor correction model, Figure 14 is a diagram showing Figure 12 an example of a neural network structure of a factor correction model, and Figure 15 is a diagram showing an example of a correlation coefficient between performance factors obtained by training a factor correction model.

[0199] Referring to Figure 12 , the control circuit 130 can determine a second performance factor group 1220 that is a secondary estimation result of the charge / discharge performance of the target cell BC by applying the factor correction model 200 to a first performance factor group 1210 that is a primary estimation result of the charge / discharge performance of the target cell BC.

[0200] The first performance factor group 1210 may include performance factors of at least one of a positive electrode participation start point, a negative electrode participation start point, a positive electrode participation end point, a negative electrode participation end point, a positive electrode scaling factor, and a negative electrode scaling factor of the target cell BC, which are determined based on an estimated full cell curve E.

[0201] The second performance factor group 1220 may be a result obtained by correcting the first performance factor group 1210 by the factor correction model 200 so as to reduce an error between the charge / discharge performance indicated by the first performance factor group 1210 and the actual charge / discharge performance of the target cell BC. The second performance factor group 1220 may represent an estimation result of the charge / discharge performance of the target cell BC that would be determined if the cell diagnosis logic were applied to a second target full cell curve N. That is, a specific performance factor (e.g., a positive electrode participation start point) of the second performance factor group 1220 may be a specific performance factor of the first performance factor group 1210 that is corrected to be close to the actual specific performance factor of the target cell.

[0202] The factor correction model 200 may be a machine learning model trained by a training data set including a first performance factor group, temperature information, and a second performance factor group for each of a plurality of test cells.

[0203] For the purpose of training the factor correction model 200, a plurality of test cells are prepared in advance. At least one of the plurality of test cells may be a new battery cell verified as a good product. Each of the remaining test cells may be a test cell in which at least one of a positive electrode and a negative electrode is forced to degrade from a new state through a charge / discharge cycle different from the charge / discharge cycles of other test cells.

[0204] A first set of performance factors for a specific test cell can be obtained in advance by applying cell diagnosis logic to an estimated test full-cell curve corresponding to the test cell. The estimated test full-cell curve for a specific test cell can be obtained in advance by subtracting an overpotential curve OP from a primary test full-cell curve representing the correspondence between the voltage and capacity factor of the test cell while a first electrical stimulus is applied to the corresponding test cell. Similar to the first set of performance factors, temperature information for a specific test cell can be obtained in advance. The temperature information for the specific test cell also includes at least one of a starting temperature, an ending temperature, an average temperature, a maximum temperature, and a minimum temperature during the application period of the first electrical stimulus.

[0205] A second set of performance factors for a specific test cell can be obtained in advance by applying cell diagnosis logic to a secondary test full-cell curve of the specific test cell. The secondary test full-cell curve of the specific test cell can represent the correspondence between the voltage and capacity factor of the corresponding test cell while a second electrical stimulus is applied to the corresponding test cell in a temperature environment where the cell temperature of the test cell is maintained at a constant reference temperature.

[0206] In Figure 13 the shown graph, a plurality of data points included in the training data set are marked on a two-dimensional coordinate. In Figure 13 the graph, the number of data points marked can be equal to the number of test cells.

[0207] Each data point is defined by two estimated values of a specific performance factor. That is, the X-axis coordinate of each data point represents a value included in the first set of performance factors as an estimated value of the specific performance factor, and the Y-axis coordinate represents a value included in the second set of performance factors as another estimated value of the specific performance factor. For ease of explanation, Figure 13 each of the X-axis and Y-axis in

[0208] is shown as representing the negative electrode SOC at the negative electrode participation end point. Figure 13 Referring to

[0209] Figure 15 the data points in the training data set are distributed to have a learnable trend. That is, the correlation between the value included in the first set of performance factors as an estimated value of the specific performance factor and the value included in the second set of performance factors as another estimated value of the specific performance factor can be trained by a factor correction model 200.

[0210] Figure 14 Referring to Figure 14, the neural network of the factor correction model 200 may include an input layer 1000, an intermediate layer 2000, and an output layer 3000.

[0211] In the factor correction model 200, the number of nodes included in each layer, the connections between the nodes, the function of each node included in the intermediate layer 2000, etc. can be determined in advance. In addition, the weight of each connection between the nodes can be automatically determined through a machine learning process using a training data set.

[0212] The input layer 1000 may include first to sixth input nodes I1 to I6 and an additional input node I7. When i is a natural number less than or equal to 6, the i-th input node Ii may be associated with one performance factor of the first performance factor group 1210. In Figure 14 For ease of explanation, it is assumed that the first input node I1 to the sixth input node I6 are 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 may be included in the first performance factor group, respectively. In addition, it is assumed that the additional input node (I TEMP ) is associated with temperature information.

[0213] The i-th input node Ii may be provided with the i-th input data set Xi, which is the performance factor data associated with it. For example, the first input node I1 may be set with the first input data set X1. The first input data set X1 may include values related to the positive electrode participation start point of multiple test monomers (e.g., the positive electrode starting potential and the capacity value).

[0214] The additional input data set (X TEMP ) associated with the additional input node (I TEMP ) may be provided to the additional input node (I TEMP ). The additional input data set (X IMP ) may include temperature information of multiple test monomers (e.g., at least one of the starting temperature, the ending temperature, the average temperature, the maximum temperature, and the minimum temperature).

[0215] The output layer 3000 may include at least one of the first output node O1 to the sixth output node O6. When j is a natural number less than or equal to 6, the j-th output node Oj may be associated with any one of 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. In Figure 14In this case, for the sake of explanation, it is assumed that the first output node O1 to the sixth output node O6 are respectively associated with the positive participation start point, the positive participation end point, the positive scaling factor, the negative participation start point, the negative participation end point, and the negative scaling factor. The positive participation start point, the positive participation end point, the positive scaling factor, the negative participation start point, the negative participation end point, and the negative scaling factor may be referred to as the first to sixth performance factors in this order.

[0216] When the first to sixth input data sets X1 to X6 and the additional input data set (X TEMP ) are input to the first to sixth input nodes I1 to I6 and the additional input node (I TEMP ), the j-th output data set Zj can be output from the j-th output node Oj. For example, when the first output node O1 is associated with the positive participation start point, the third output data set Z3 may include the value of the first input data set X1 with correction.

[0217] Figure 14 It is shown that the input layer 1000 includes the first input node I1 to the sixth input node I6 and the additional input node (I TEMP ), 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, it is sufficient that the input layer 1000 includes at least one of the first input node I1 to the sixth input node I6 and the additional input node (I TEMP ), and it is also sufficient that the output layer 3000 includes at least one of the first output node O1 to the sixth output node O6. For example, when all the first to sixth input data sets X1 to X6 and the additional input data set (X TEMP ) are provided to the input layer 1000, the output layer 3000 may output only one of the first to sixth output data sets Z1 to Z6.

[0218] 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 the connections between the nodes, the weights of each connection between the nodes, the functions of each node included in the intermediate layer 2000, etc., and it is not particularly limited.

[0219] The intermediate layer 2000 may include the first to the m-th intermediate nodes F1 to Fm (m is a natural number greater than or equal to 2). When k is a natural number less than or equal to m, the k-th intermediate node Fk may be connected to the input nodes (I1 to I6, I TEMPAt least one of the above and at least one of output nodes O1 to O6. The k-th intermediate node Fk may have the form of a function determined through a learning process, and may send an estimated value calculated based on input values from each input node connected thereto to each output node connected thereto. The j-th output node Oj may output an estimated value equal to the sum of the estimated values received from each intermediate node connected thereto as a correction result of the first performance factor group.

[0220] The function of the k-th intermediate node Fk may be generated based on the correlation coefficient between the performance factors associated with each node of the input layer 1000 connected to the k-th intermediate node Fk and the performance factors associated with each node of the output layer 3000 connected to the k-th intermediate node Fk. For reference, the correlation coefficient is a real number between -1 and +1, and a correlation coefficient closer to -1 indicates a negative correlation between the two factors, and a correlation coefficient closer to +1 indicates a positive correlation between the two factors.

[0221] The function of each intermediate node of the intermediate layer 2000 may be a weighted average function. In this case, the correlation coefficient indicating the degree of correlation between the estimated values of the first to sixth performance factors included in the first performance factor group and the estimated values of at least one of the first to sixth performance factors included in the second performance factor group may be used as the weight of the function of each intermediate node of the intermediate layer 2000.

[0222] Therefore, the factor correction model 200 includes at least one of the first to sixth machine learning models. The first to sixth machine learning models may be models that provide secondary estimation results for the first to sixth performance factors in this order.

[0223] When the target monomer BC is in the MOL state, the control circuit 130 may determine at least one degradation parameter based on the second performance factor group. Table 1 below summarizes the degradation parameters and the formulas that can be used to determine each degradation parameter. For reference, the second performance factor group when the target monomer BC is in the new state may have been recorded in the memory 131.

[0224] Table 1

[0225] Deterioration parameter Formula <![CDATA[P SOH > #timg# <![CDATA[N SOH > #timg# <![CDATA[L SOH > #timg# <![CDATA[F SOH > #timg# <![CDATA[P LOSS > #timg# <![CDATA[N LOSS > #timg# <![CDATA[L LOSS > #timg# <![CDATA[F LOSS > #timg# <![CDATA[P loading_MOL > #timg# <![CDATA[N loading_MOL > #timg# <![CDATA[N / P _MOL > #timg#

[0226] Each variable listed in Table 1 is a diagnostic factor that can be included in the above-mentioned second performance factor group. The definitions of the degradation parameters and variables in Table 1 may be as follows.

[0227] <Degradation parameter>

[0228] P SOH : Positive electrode SOH (State of Health) of the target monomer BC

[0229] NSOH : State of Health (SOH) of the negative electrode of the target cell BC

[0230] L SOH : State of Health (SOH) of the available lithium of the target cell BC

[0231] F SOH : State of Health (SOH) of the full cell of the target cell BC

[0232] P LOSS : Positive electrode loss rate of the target cell BC

[0233] N LOSS : Negative electrode loss rate of the target cell BC

[0234] L LOSS : Available lithium loss rate of the target cell BC

[0235] F LOSS : Full cell loss rate of the target cell BC

[0236] P loading_MOL : Positive electrode loading of the target cell BC

[0237] N loading_MOL : Negative electrode loading of the target cell BC

[0238] N / P _MOL : N / P ratio of the target cell BC

[0239] As any battery cell deteriorates, at least one of the total positive electrode capacity, total negative electrode capacity, available lithium content, and total full cell capacity of the battery cell can gradually decrease from the value at the BOL (beginning of life) state. The total full cell capacity can represent the capacity difference between the two end points of the full cell curve. For example, the total full cell capacity can mean the full charge capacity (FCC). The available lithium content can represent the total amount of lithium that can contribute to the charging and discharging of the battery cell. P SOH can represent the maintenance rate of the total positive electrode capacity. N SOH can represent the maintenance rate of the total negative electrode capacity. L SOH can represent the maintenance rate of the available lithium content. F SOH can represent the maintenance rate of the total full cell capacity.

[0240] P SOH and P LOSS 's sum, N SOH and N LOSS 's sum, L SOH and L LOSS 's sum, and F SOH and F LOSS 's sum can each be equal to 1. F LOSS can be equal to P LOSS and LLOSS The sum

[0241] The positive electrode loading amount of any battery cell represents the amount of positive electrode active material (or available capacity) per unit area of the positive electrode of the battery cell. The negative electrode loading amount of any battery cell represents the amount of negative electrode active material (or available capacity) per unit area of the negative electrode of the battery cell. The unit of the loading amount can be mAh / cm 2 or mg / cm 2 . In Table 1, P loading_ref represents the reference positive electrode loading amount, and N loading_ref represents the reference negative electrode loading amount. The reference positive electrode loading amount is a predetermined value representing the amount of positive electrode active material (or available capacity) per unit area of the positive electrode of the reference cell. The reference positive electrode loading amount 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 as the total positive electrode capacity of the reference cell. The reference positive electrode area can be a value preset as the area of the positive electrode of the reference cell. The reference negative electrode loading amount is a predetermined value representing the amount of negative electrode active material (or available capacity) per unit area of the negative electrode of the reference cell. The reference negative electrode loading amount 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 as the total negative electrode capacity of the reference cell. The reference negative electrode area can be a value preset as the area of the negative electrode of the reference cell.

[0242] At least one of the degradation parameters in Table 1 can be included in the second performance factor group as an estimation result of the additional performance factor of the target cell BC.

[0243] <Variable>

[0244] pi BOL : The positive electrode capacity (positive electrode SOC) at the starting point of positive electrode participation when the target cell BC is in the BOL state

[0245] pi MOL : The positive electrode capacity (positive electrode SOC) at the current starting point of positive electrode participation of the target cell BC (e.g., pi' shown in Figure 8 )

[0246] pf BOL : The positive electrode capacity (positive electrode SOC) at the ending point of positive electrode participation when the target cell BC is in the BOL state

[0247] pf MOL : The positive electrode capacity (positive electrode SOC) at the current ending point of positive electrode participation of the target cell BC (e.g., pf" shown in Figure 8 )

[0248] niBOL : The negative electrode capacity (negative electrode SOC) at the starting point of the negative electrode participation when the target monomer BC is in the BOL state

[0249] ni MOL : The negative electrode capacity (negative electrode SOC) of the current negative electrode participation starting point of the target monomer BC (for example, Figure 8 ni shown in

[0250] nf BOL : The negative electrode capacity (negative electrode SOC) at the end point of the negative electrode participation when the target monomer BC is in the BOL state

[0251] nf MOL : The negative electrode capacity (negative electrode SOC) of the current negative electrode participation end point of the target monomer BC (for example, Figure 8 nf' shown in

[0252] ps BOL : The positive electrode scaling factor when the target monomer BC is in the BOL state

[0253] ps MOL : The current positive electrode scaling factor of the target monomer BC

[0254] ns BOL : The negative electrode scaling factor when the target monomer BC is in the BOL state

[0255] ns MOL : The current negative electrode scaling factor of the target monomer BC

[0256] The NP ratio can also be expressed as the N / P ratio, the N:P ratio, etc. The NP ratio of the target monomer BC can be a value representing (i) the ratio of the negative electrode loading amount (N loading_MOL ) to the positive electrode loading amount (P loading_MOL ), or (ii) the ratio of the total negative electrode capacity to the total positive electrode capacity of the target monomer BC. The control circuit 130 can determine the total positive electrode capacity of the target monomer BC to be equal to the product of ps MOL and Q P_ref . The control circuit 130 can determine the total negative electrode capacity of the target battery BC to be equal to the product of ns MOL and Q N_ref .

[0257] The process of determining the second set of performance factors can be repeated periodically or aperiodically during the lifetime of the target monomer BC.

[0258] Figure 15 Shows an example of the correlation information between the first set of performance factors, the temperature information, and the second set of performance factors obtained through the factor correction model 200 in matrix form. In Figure 15Among them, the temperature information is exemplified as including the starting temperature (T_ini), the ending temperature (T_end), and the maximum temperature (T_max).

[0259] Figure 15 The matrix shown is a 9×6 matrix. Six of the nine rows represent the first to sixth performance factors of the first performance factor group provided in this order as the training data set, and the remaining three rows represent the starting temperature (T_ini), the ending temperature (T_end), and the maximum temperature (T_max) of the temperature information. The six columns represent the first to sixth performance factors of the second performance factor group provided in this order as the training data set. In Figure 15 Among them, pi_A[1], pf_A[2], ps_A[3], ni_A[4], nf_A[5], and ns_A[6] represent 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 included in the first performance factor group of the training data set in this order. In addition, pi_B[1], pf_B[2], ps_B[3], ni_B[4], nf_B[5], and ns_B[6] respectively represent 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 included in the second performance factor group of the training data set in this order.

[0260] When p and q are natural numbers less than or equal to 6, the value of the p-th row (pi_A[p]) and the q-th column (pi_B[q]) represents the correlation coefficient between the p-th performance factor included in the first performance factor group and the q-th performance factor included in the second performance factor group. For example, the correlation coefficient between the first performance factor (pi_A[1]) in the first row and the second performance factor (pf_B[2]) in the second column is -0.52. As another example, the correlation coefficient between the fifth performance factor (nf_A[5]) in the fifth row and the fourth performance factor (ni_B[4]) in the fourth column is 0.46.

[0261] The value of the q-th column in the seventh row represents the correlation coefficient between the starting temperature (T_ini) and the q-th performance factor. The value of the q-th column in the eighth row represents the correlation coefficient between the ending temperature (T_end) and the q-th performance factor. The value of the q-th column in the ninth row represents the correlation coefficient between the maximum temperature (T_max) and the q-th performance factor.

[0262] Figure 16 is a flowchart for schematically showing a battery diagnosis method according to another embodiment of the present disclosure. Figure 16 The method of

[0263] can be executed by the battery diagnosis device 100. Figures 1 to 16, in step S1610, while the first electrical stimulation is applied to the target cell BC, the control circuit 130 collects measurement signals representing the voltage, current, and temperature measurement values of the target cell BC from the sensing unit 110, and the target cell BC is the battery cell to be diagnosed.

[0264] In step S1620, the control circuit 130 generates a first target full-cell curve M and temperature information of the target cell BC based on the measurement signals collected in step S1610. The first target full-cell curve M represents the correspondence between the voltage and the capacity factor of the target cell BC while the first electrical stimulation is applied to the target cell BC.

[0265] Steps S1610 and S1620 can be replaced by a process in which the data acquisition unit directly obtains the first target full-cell curve M and temperature information or obtains the first target full-cell curve M and temperature information from the outside.

[0266] In step S1630, the control circuit 130 generates an estimated full-cell curve E based on the first target full-cell curve M and the overpotential curve OP.

[0267] In step S1640, the control circuit 130 applies the cell diagnosis logic to the estimated full-cell curve E to determine the first performance factor group 1210 as the primary estimation result of the charge / discharge performance of the target cell BC.

[0268] In step S1650, the control circuit 130 determines a second performance factor group 1220 as the secondary estimation result of the charge / discharge performance of the target cell BC by applying the factor correction model 200 to the first performance factor group 1210 and the temperature information. Here, the second performance factor group represents the estimation result of the charge / discharge performance that can be determined if the cell diagnosis logic is applied to the second target full-cell curve N instead of the estimated full-cell curve E.

[0269] The second target full-cell curve N represents the correspondence between the voltage and the capacity factor of the target cell BC while a second electrical stimulation different from the first electrical stimulation is applied to the target cell BC. The second target full-cell curve N cannot be obtained by actually applying the second electrical stimulation to the target cell BC. That is, the second target full-cell curve N represents the correspondence between the voltage and the capacity factor of the target cell BC that is expected to be obtained if the second electrical stimulation is applied to the target cell BC instead of the first electrical stimulation.

[0270] The control circuit 130 may limit at least one of an allowable voltage range, an allowable SOC range, and an allowable charge / discharge current of the target cell BC based on at least one degradation parameter. The memory 131 may pre-store relationship data indicating a correspondence between at least one limiting item (i.e., an allowable voltage range, an allowable SOC range, and / or an allowable charge / discharge current) and at least one degradation parameter. For example, when the increase amount of a specific type of degradation parameter (e.g., P LOSS , N LOSS , L LOSS , F LOSS ) from the BOL state is larger and / or when the decrease amount of another type of degradation parameter (e.g., P SOH , N SOH , L SOH , F SOH ) from the BOL state is larger, the limiting amount of the allowable voltage range, the allowable SOC range, and / or the allowable charge / discharge current may be larger.

[0271] The embodiments of the present disclosure described above are not only implemented by devices and methods, but may be implemented by a program that executes functions corresponding to the configurations of the embodiments of the present disclosure or a recording medium on which the program is recorded, and those skilled in the art can easily achieve such implementation from the disclosure of the embodiments described above.

[0272] 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 aspects of the present disclosure and the equivalent scope of the appended claims.

[0273] In addition, since those skilled in the art can make many substitutions, modifications, and changes to the present disclosure described above without departing from the technical aspects of the present disclosure, the present disclosure is not limited by the above embodiments and drawings, and some or all of the embodiments can be selectively combined to allow various modifications.

Claims

1. A battery diagnosis device, comprising: a data acquisition unit configured to acquire a first target full-cell curve and temperature information of a target cell, the first target full-cell curve representing the correspondence between the voltage and the capacity factor of the target cell while a first electrical stimulation is applied to the target cell being the battery to be diagnosed; and a control circuit configured to generate an estimated full-cell curve based on the first target full-cell curve and an overpotential curve, wherein the control circuit is configured to: determine a first set of performance factors as a primary estimation result of the charge / discharge performance of the target cell by applying a cell diagnosis logic to the estimated full-cell curve, and determine a second set of performance factors as a secondary estimation result of the charge / discharge performance of the target cell by applying a factor correction model to the first set of performance factors and the temperature information, wherein the second set of performance factors includes an estimation result of the charge / discharge performance, the estimation result being determinable by applying the cell diagnosis logic to a second target full-cell curve instead of the estimated full-cell curve while a second electrical stimulation different from the first electrical stimulation is applied, wherein the second target full-cell curve represents the correspondence between the voltage and the capacity factor of the target cell.

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

3. The battery diagnosis device according to claim 1, wherein, The first electrical stimulation is charging using a first current rate, and wherein the second electrical stimulation is charging using a second current rate less than the first current rate.

4. The battery diagnostic device according to claim 1, wherein, The first electrical stimulation is discharging using a first current rate, and wherein the second electrical stimulation is discharging using a second current rate less than the first current rate.

5. The battery diagnosis device according to claim 1, wherein, The overpotential curve represents the difference between a first reference full-cell curve and a second reference full-cell curve, wherein the first reference full-cell curve represents the correspondence between the voltage and the capacity factor of the reference cell while the first electrical stimulation is applied to the reference cell being a reference cell verified as a normal battery cell, and wherein the second reference full-cell curve represents the correspondence between the voltage and the capacity factor of the reference cell while the second electrical stimulation is applied to the reference cell.

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

7. The battery diagnosis device according to claim 1, wherein, The first set of performance factors includes at least one of the following as performance factors: a positive electrode participation start point, the positive electrode participation start point representing the positive electrode voltage and the positive electrode capacity when the voltage of the target cell matches a first set voltage; a positive electrode participation end point, the positive electrode participation end point representing the positive electrode voltage and the positive electrode capacity when the voltage of the target cell matches a second set voltage; Positive scaling factor, which represents the ratio of the capacity difference between the positive electrode participation start point and the positive electrode participation end point to the reference positive electrode capacity; Negative electrode participation start point, which represents the negative electrode voltage and negative electrode capacity when the voltage of the target cell matches the first set voltage; Negative electrode participation end point, which represents the negative electrode voltage and negative electrode capacity when the voltage of the target cell matches the second set voltage; and Negative scaling factor, which represents the ratio of the capacity difference between the negative electrode participation start point and the negative electrode participation end point to the reference negative electrode capacity, wherein the temperature information includes at least one of a starting temperature, an ending temperature, an average temperature, a maximum temperature, and a minimum temperature.

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

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

10. A battery pack including the battery diagnosis device according to any one of claims 1-9.

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

12. A battery diagnosis method, comprising: obtaining a first target full-cell curve and the temperature information of the target cell, where the first target full-cell curve represents the correspondence between the voltage and the capacity factor of the target cell while a first electrical stimulus is applied to the target cell as the battery cell to be diagnosed; generating an estimated full-cell curve based on the first target full-cell curve and the overpotential curve; determining a first performance factor group as a primary estimation result of the charge / discharge performance of the target cell by applying cell diagnosis logic to the estimated full-cell curve; and determining a second performance factor group as a secondary estimation result of the charge / discharge performance of the target cell by applying a factor correction model to the first performance factor group and the temperature information, Among them, the second performance factor group includes the estimation results of charging / discharging performance, and the estimation results can be determined by applying the single-cell diagnosis logic to the second target full-cell curve instead of the estimated full-cell curve, where the second target full-cell curve represents the correspondence between the voltage and the capacity factor of the target single cell while a second electrical stimulation different from the first electrical stimulation is applied.

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

14. The battery diagnosis method according to claim 12, wherein, The factor correction model is a machine learning model trained by a training data set, and the training data set includes the first performance factor group, temperature information, and the second performance factor group of each of a plurality of test single cells with different charging / discharging performances.

15. The battery diagnosis method according to claim 14, wherein, The first performance factor group of each of the plurality of test single cells is obtained by applying the single-cell diagnosis logic to each of the plurality of estimated test full-cell curves. Among them, the plurality of estimated test full-cell curves are obtained by subtracting the overpotential curve from each of the plurality of primary test full-cell curves, and the plurality of primary test full-cell curves represent the correspondence between the voltage and the capacity factor of each of the plurality of test single cells while the first electrical stimulation is applied to each of the plurality of test single cells. Among them, the second performance factor group of each of the plurality of test single cells is obtained by applying the single-cell diagnosis logic to a plurality of secondary test full-cell curves, and among them, the plurality of secondary test full-cell curves represent the correspondence between the voltage and the capacity factor of each of the plurality of test single cells while the second electrical stimulation is applied to each of the plurality of test single cells.

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