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 full cell curve representing a correspondence relationship between a voltage and a capacity factor of a target cell while a first electrical stimulation is applied to the target cell; 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 determines a first set of performance factors as a primary estimate of charge / discharge performance of the target cell by applying cell diagnostic logic to the estimated full cell curve. The control circuit determines a second set of performance factors as a secondary estimate of charge / discharge performance of the target cell by applying a factor correction model to the first set of performance factors. The second performance factor group comprises an estimation result of the negative electrode participation end point of the target monomer.

Description

TECHNICAL FIELD

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

[0002] This application claims priority to Korean Patent Application No. 10-2023-0165772, filed on November 24, 2023, in Korea, the disclosure of which is incorporated herein by reference. BACKGROUND

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

[0004] Currently, commercially available batteries include nickel-cadmium batteries, nickel-hydrogen batteries, nickel-zinc batteries, lithium batteries, and the like, and among them, lithium batteries have little or no memory effect, so they are gaining more attention than nickel-based batteries because of their advantages in that they can be recharged whenever it is convenient, have a very low self-discharge rate, and have a high energy density.

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

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

[0007] In terms of shortening the diagnosis time, a high 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 a battery, the proportion of overpotential in the battery voltage is too high. More specifically, as the current flowing through the battery is greater, polarization phenomena are generated more, and overpotential is caused by polarization phenomena. Since the voltage of the battery can be regarded as the sum of the OCV (open circuit voltage) and the overpotential, as the level of the electrical stimulus applied to the battery is higher, the difference between the battery voltage and the actual OCV increases.

[0008] As the battery voltage gets closer to the actual OCV, the battery's charge / discharge performance can be diagnosed more accurately; therefore, overpotential acts as noise that reduces diagnostic accuracy. Consequently, diagnostic results based on charge / discharge performance curves obtained using high electrical stimulation may differ significantly from the battery's actual charge / discharge performance. Summary of the Invention

[0009] Technical issues

[0010] This disclosure is designed to address the problems of related technologies, and therefore relates to providing a battery diagnostic apparatus and a battery diagnostic method that can simultaneously shorten diagnostic time and ensure diagnostic accuracy by applying high electrical stimulation to the battery to obtain charging / discharging information (the first target whole-cell curve in the claims) and using a machine learning-based factor correction model to remove noise caused by overpotentials included in the obtained charging / discharging information.

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

[0012] Technical solution

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

[0014] The first electrical stimulation may be an electrical stimulation that induces an overpotential exceeding an acceptable level in the target monomer, and the second electrical stimulation may be an electrical stimulation that induces an overpotential below an acceptable level in the target monomer.

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

[0016] The first electrical stimulation can discharge at a first current rate, and the second electrical stimulation can discharge at a second current rate lower than the first current rate.

[0017] The overpotential curve represents the difference between the first and second reference full-cell curves. The first reference full-cell curve represents the relationship between the voltage and capacity factor of the reference cell when the first electrical stimulus is applied; the reference cell is a verified normal battery cell. The second reference full-cell curve represents the relationship between the voltage and capacity factor of the reference cell when the second electrical stimulus is applied.

[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 performance factor group may include at least one of the following as performance factors: positive electrode participation start point, which represents the positive electrode voltage and positive electrode capacity when the voltage of the target cell matches a first set voltage; positive electrode participation end point, which represents the positive electrode voltage and positive electrode capacity when the voltage of the target cell matches a second set voltage; 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 a 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 a 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 a second set voltage; and 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 a reference negative electrode capacity.

[0020] The factor correction model can be a machine learning model trained on a training dataset that includes pairs of first and second performance factor groups for each of multiple test cells with different charge / discharge performance.

[0021] A first set of performance factors for each of the multiple test cells can be obtained by applying cell diagnostic logic to each of the multiple estimated full-cell test curves. Multiple estimated full-cell test curves can be obtained by subtracting the overpotential curve from each of the multiple primary full-cell test curves, which represent the correspondence between voltage and capacity factors for each of the multiple test cells simultaneously with the application of a first electrical stimulus. A second set of performance factors for each of the multiple test cells can be obtained by applying cell diagnostic logic to multiple secondary full-cell test curves. The multiple secondary full-cell test curves can represent the correspondence between voltage and capacity factors for each of the multiple test cells simultaneously with the application of a second electrical stimulus.

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

[0023] In another aspect of this disclosure, an electric vehicle including a battery pack is also provided.

[0024] In another aspect of this disclosure, a battery diagnostic method is also provided, comprising: obtaining a first target full-cell curve representing the correspondence between voltage and capacity factor of the target cell while a first electrical stimulus is applied to the target cell, the target cell being the battery cell to be diagnosed; generating an estimated full-cell curve based on the first target full-cell curve and an overpotential curve; determining a first set of performance factors as a primary estimate 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 estimate of the charge / discharge performance of the target cell by applying a factor correction model to the first set of performance factors. The second set of performance factors may include an estimate of the negative electrode participation endpoint of the target cell, which can be determined by applying cell diagnostic logic to the second target full-cell curve instead of the estimated full-cell curve, wherein the second target full-cell curve represents the correspondence between 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 whole monomer curve can be to generate the estimated whole monomer curve by subtracting the overpotential curve from the first target whole monomer curve.

[0026] The factor correction model can be a machine learning model trained on a training dataset that includes pairs of first and second performance factor groups for each of multiple test cells with different charge / discharge performance.

[0027] A first set of performance factors for each of the multiple test cells can be obtained by applying cell diagnostic logic to each of the multiple estimated full-cell test curves. Simultaneously with the application of a first electrical stimulus to each of the multiple test cells, multiple estimated full-cell test curves can be obtained by subtracting the overpotential curve from each of the multiple primary full-cell test curves representing the correspondence between voltage and capacity factors for each of the multiple test cells. A second set of performance factors for each of the multiple test cells can be obtained by applying cell diagnostic logic to multiple secondary full-cell test curves. The multiple secondary full-cell test curves can represent the correspondence between voltage and capacity factors for each of the multiple test cells simultaneously with the application of a second electrical stimulus to each of the multiple test cells.

[0028] Beneficial effects

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

[0030] Furthermore, according to at least one embodiment of this disclosure, the accuracy of the charge / discharge performance diagnosis can be improved by estimating the charge / discharge information obtained by applying high electrical stimulation to the battery, from which the overpotential component caused by the high electrical stimulation has been removed (the "estimated whole cell curve" in the claims), and analyzing the estimated charge / discharge information to diagnose charge / discharge performance.

[0031] Furthermore, according to at least one embodiment of this disclosure, by correcting the set of performance factors representing the charge / discharge performance determined from the estimated charge / discharge information using a machine learning-based factor correction model, it is possible to ensure diagnostic results that are highly consistent with the actual charge / discharge performance of the battery.

[0032] The effects of this disclosure are not limited to those described above, and those skilled in the art will clearly understand these and other effects from the appended claims. Attached Figure Description

[0033] The accompanying 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 to be construed as limited to the drawings.

[0034] Figure 1 This is an exemplary diagram showing the configuration of an electric vehicle according to this disclosure.

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

[0036] Figure 3 It is a schematic representation of what can be seen from... Figure 2 The graph shows the overpotential curves obtained from the first and second reference full monomer curves.

[0037] Figure 4 It is a graph used to interpret the relationship between the first target whole-monomer curve, the estimated whole-monomer curve, and the second target whole-monomer curve.

[0038] Figure 5 It is a graph referenced for interpreting examples of each of the estimated full monomer curve, second reference full monomer curve, reference positive electrode curve, and reference negative electrode curve.

[0039] Figures 6 to 8 This is a diagram used to illustrate an example of the process of generating a comparison full-monopoly curve based on the monopoly diagnostic logic.

[0040] Figures 9 to 11 This is a diagram used to explain another example of the process of generating a comparison full-monopoly curve based on the monopoly diagnostic logic.

[0041] Figure 12 This is a diagram used to explain the function of the factor correction model.

[0042] Figure 13 It is a diagram used to interpret the training dataset provided for the training factor correction model.

[0043] Figure 14 It is shown Figure 12 A diagram illustrating an example of the neural network structure of a factor correction model.

[0044] Figure 15 This is a graph showing an example of the correlation coefficients between performance factors obtained by correcting the model through training factors.

[0045] Figure 16 This is a flowchart illustrating, schematically, a battery diagnostic method according to another embodiment of the present disclosure. Detailed Implementation

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

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

[0048] Ordinal terms such as “first” and “second” are used to distinguish one element from another among various elements, but are not intended to limit elements by terminology.

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

[0050] Furthermore, throughout the specification, it should 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 an intermediate element.

[0051] Figure 1 This is an exemplary diagram showing the configuration of an electric vehicle according to this disclosure.

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

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

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

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

[0056] Battery 11 includes at least one battery cell BC. When battery 11 includes multiple battery cells (BC1 to BC2), the battery is considered to have multiple battery cells (BC1 to BC2). NWhen N is a natural number greater than or equal to 2, multiple battery cells can be connected in series, in parallel, or in a mixture of series and parallel.

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

[0058] Relay 20 is connected in series to battery 11 via a power path connecting battery 11 and inverter 30. Figure 1 In the diagram, relay 20 is shown connected between the positive terminal of battery 11 and the charging / discharging terminal P+. Relay 20 is controlled to turn on and off in response to a switching signal from battery diagnostic device 100. Relay 20 can be a mechanical connector that turns on and off via 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 DC current from battery 11 into AC current in response to commands from battery diagnostic device 100 or vehicle controller 2.

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

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

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

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

[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 to measure the cell voltage (which may be referred to as the "full cell voltage") as the voltage across the two terminals of each battery cell BC, and output an additional voltage signal (i.e., the measured value of the full cell voltage) representing the measured cell voltage to the control circuit 130.

[0065] A current sensor 112 is connected in series to the battery 11 via a current path between the battery 11 and the inverter 30. The current sensor 112 is configured to detect the battery current as 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 or a combination of two or more known current sensing elements, such as a shunt resistor, a Hall effect element, etc.

[0066] Communication circuit 150 is configured to support wired or wireless communication between control circuit 130 and vehicle controller 2. Wired communication may be, for example, CAN (Controller Area Network) communication, and wireless communication may be, for example, ZigBee or Bluetooth communication. The type of communication protocol is not particularly limited, as long as it supports both wired and wireless communication between control circuit 130 and vehicle controller 2. Communication circuit 150 may include output devices (e.g., displays, speakers) that provide information received from control circuit 130 and / or vehicle controller 2 in a user-recognizable format.

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

[0068] Control circuit 130 can collect voltage signals from voltage sensor 111 and / or current signals from current sensor 112. Control circuit 130 can use the ADC (analog-to-digital converter) provided therein to convert each analog signal collected from sensors 111 and 112 into a digital value and record the digital value.

[0069] 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.

[0070] Memory 131 may include at least one type of storage medium, such as flash memory, hard disk, solid-state drive (SSD), silicon disk drive (SDD), multimedia card micro, random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), or programmable read-only memory (PROM). Memory 131 may store data and programs required for the computational operations of control circuitry 130. Memory 131 may store data representing the results of computational operations performed by control circuitry 130. Although memory 131 is... Figure 1 The memory is described as being physically independent of the control circuit 130, but the memory 131 may be embedded within the control circuit 130.

[0071] Control circuit 130 can activate relay 20 in response to a key-on signal. Control circuit 130 can deactivate relay 20 in response to a key-off signal. The key-on signal is a signal requesting a switch from a rest mode to a charging or discharging mode. The key-off signal is a signal triggering a switch from a cycle state to a rest state. Alternatively, vehicle controller 2 can be responsible for activating / deactivating relay 20 instead of control circuit 130.

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

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

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

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

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

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

[0078] The first target monomer curve can be represented as the relationship between the voltage and capacity factor of the target monomer BC at the moment the first electrical stimulation is applied. The capacity factor can be the residual capacity or SOC (state of charge) of the target monomer BC.

[0079] The first electrical stimulation is an electrical stimulation that induces an overpotential exceeding an acceptable level in the target monomer BC, and corresponds to "high electrical stimulation". The second electrical stimulation is an electrical stimulation that induces an overpotential below an acceptable level in the target monomer BC, and corresponds to "low electrical stimulation". For example, the first electrical stimulation can be charging using a first current rate (e.g., 1.0C), and the second electrical stimulation can be charging using a second current rate (e.g., 0.05C) lower than the first current rate. Alternatively, 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.

[0080] The first target full-cell curve can be a curve showing the relationship between the capacity of the target cell BC and the full-cell voltage while being charged or discharged at a constant current within a given voltage range (e.g., 3.0 to 4.0V) or a given SOC range (e.g., 0 to 100% SOC).

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

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

[0083] Figure 2The first reference whole cell curve R1 and the second reference whole cell curve R2 shown can be obtained in advance through an experimental pre-process of applying the first and second electrical stimuli separately to the reference cell.

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

[0085] A new battery cell refers to a battery cell in a new state. New state is the same concept as BOL (Start of Life). For example, it can be called BOL before the time when the accumulated charge / discharge capacity from the time of manufacturing completion reaches the set capacity, and it can be called MOL (Mid-Life) from the time when the accumulated charge / discharge capacity reaches the set capacity.

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

[0087] The first reference full-cell curve R1 shows the relationship between the voltage and capacity of the reference cell while the first electrical stimulation is applied (e.g., during charging at the first current rate). The second reference full-cell 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 with the reference cell's OCV set to equal the lower limit of a given voltage range (e.g., 3.0V). The second reference full-cell curve R2 can be obtained by performing charging at the second current rate with the reference cell's OCV set to equal the lower limit of a given voltage range. Therefore, in Figure 2 In the diagram, the starting points of the first reference full monomer curve R1 and the second reference full monomer curve R2 are roughly the same, but their ending points are significantly different.

[0088] The first reference full-cell curve R1 and the second reference full-cell curve R2 can represent the correspondence between the capacitance 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 and upper limits of the voltage range of interest can represent a first set voltage ( Figure 2 3.0V in the middle) and the second set voltage ( Figure 2 (4.0V in the middle).

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

[0090] Initial capacity (Qi) refers to the remaining capacity when the total voltage of any battery cell equals a first set voltage. Final capacity (Qf) refers to the remaining capacity when the total voltage of any battery cell equals a second set voltage.

[0091] The first reference whole cell curve R1 can be based on voltage time series and current time series (or capacity time series) obtained by periodically measuring the current of the reference cell and the whole cell voltage at the same time the first electrical stimulation is applied.

[0092] The second reference whole-cell curve R2 can be based on the voltage time series and current time series obtained by periodically measuring the current of the reference cell and the whole-cell voltage while the second electrical stimulation is applied.

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

[0094] Specifically, by removing the second reference whole-cell curve R2 based on the second electrical stimulation from the first reference whole-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.

[0095] Figure 3 It is a schematic representation of what can be seen from... Figure 2 The graph shows the overpotential curve OP obtained from the first reference full monomer curve R1 and the second reference full monomer curve R2.

[0096] The overpotential curve OP can be a curve representing the relationship between capacitance and overpotential. Alternatively, the overpotential curve OP can be a curve representing the voltage difference in capacitance between the first reference full-cell curve R1 and the second reference full-cell curve R2.

[0097] 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. Figure 2In the first reference full monomer curve R1, the capacity range is 5 to 47 Ah, and the capacity range of the second reference full monomer curve R2 is 5 to 50 Ah. Therefore, Qi can be 5 Ah and Qf can be 47 Ah.

[0098] Figure 4 It is a graph used to interpret the relationship between the first target whole-monomer curve M, the estimated whole-monomer curve E, and the second target whole-monomer curve N.

[0099] exist Figures 2 to 4 In this context, Ah is used as the unit for the horizontal axis, but this unit can be expressed in other forms. For example, instead of Ah, the percentage (%) indicating the state of charge (SOC) can be used as the unit for the horizontal axis.

[0100] Please refer to Figure 4 Simultaneously with the application of the first electrical stimulation to the target cell BC, the control circuit 130 generates a first target whole-cell curve M, which represents the correspondence between the whole-cell voltage and capacity of the target cell BC. The first target whole-cell curve M can represent the correspondence between the capacity and the whole-cell voltage of the target cell BC, at least within the voltage range of interest.

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

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

[0103] The control circuit 130 can be based on Figure 3 overpotential curves OP and Figure 4 The estimated full-cell curve E is generated from the first target full-cell curve M. 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. Therefore, for the same capacitance 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.

[0104] The control circuit 130 can obtain the estimated full-cell curve E by subtracting the capacity-specific overpotential of the overpotential curve OP from the capacity-specific voltage of the first target full-cell curve M within the common capacity range of the first target full-cell curve M and the overpotential curve OP. In this case, the capacity range of 45Ah to 47Ah within the entire capacity range of the overpotential curve OP can be omitted. 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.

[0105] Alternatively, control circuit 130 can generate an adjusted overpotential curve (not shown) 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, control circuit 130 can generate an estimated full-cell curve E by subtracting the overpotential value of the adjusted overpotential curve from the voltage value of the first target full-cell curve M within the capacity range of the overpotential curve OP. In other words, the estimated full-cell curve E 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.

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

[0107] The estimated whole-monomer curve E is the result of estimating the second target whole-monomer curve N based on the first target whole-monomer curve M and the overpotential curve OP.

[0108] refer to Figure 4 The estimated full-cell curve E is 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, using the estimated full-cell curve E instead of the first target full-cell curve M is advantageous in terms of diagnostic accuracy when diagnosing the charge / discharge performance of the target cell BC.

[0109] Meanwhile, because the estimated full-cell curve E does not perfectly match the second target full-cell curve N, there may still be a considerable discrepancy between the diagnostic results of charge / discharge performance based on the estimated full-cell curve E and the actual charge / discharge performance. This will be discussed later. Figure 12 Describe a method for reducing errors in diagnostic results for charge / discharge performance.

[0110] 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 diagnostic logic to the estimated full-cell curve E. The first set of performance factors can be regarded as a preliminary estimate of the charge / discharge performance of the target cell BC.

[0111] The first performance factor group may include at least one of the following: 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.

[0112] In this specification, 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) when the total voltage of the corresponding battery cell matches the first set voltage. The positive electrode voltage at the positive electrode participation start point can be referred to as the "positive electrode initiation potential". Similarly, 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) when the total voltage of the corresponding battery cell matches the first set voltage. The negative electrode voltage at the negative electrode participation start point can be referred to as the "negative electrode initiation 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.

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

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

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

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

[0117] In the memory 131, information on the voltage and capacity of each of the reference positive electrode participation start, reference positive electrode participation end, reference negative electrode participation start, and reference negative electrode participation end, which indicate the charging / discharging performance of the reference cell, can be pre-recorded.

[0118] From now on, refer to Figures 5 to 11 This will explain the diagnostic process included in the single-unit diagnostic logic.

[0119] Figure 5 This is a reference graph used to interpret examples of each of the estimated whole-cell curve E, the second reference whole-cell curve R2, the reference positive electrode curve Rp, and the reference negative electrode curve Rn. Figure 5 In the graph, the horizontal axis (X-axis) represents capacity, and the vertical axis (Y-axis) represents voltage. The estimated full-cell curve E and the second reference full-cell curve R2 are compared with... Figure 2 The same as in.

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

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

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

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

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

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

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

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

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

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

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

[0131] The control circuit 130 can specify any one of a plurality of comparative full-cell curves that has the minimum error relative to the estimated full-cell curve E. Then, the control circuit 130 can determine that the adjusted positive and adjusted negative curves mapped to the specified comparative full-cell curve are the positive and negative curves of the target cell BC.

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

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

[0134] Figures 6 to 8 This is a diagram used to explain an example of the process for generating a comparison of full monomer curves.

[0135] Reference Figures 6 to 8 The process for generating the comparative full-cell curve can be performed 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 The second routine used to perform curve shifting (see...) Figure 7 ) and a third routine for performing capacity scaling (see Figure 8 In other words, the process for generating a comparison full monomer curve according to embodiments of this disclosure may include a first routine to a third routine.

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

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

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

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

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

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

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

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

[0144] If 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) are completely determined, then the control circuit 130 will shift at least one of the reference positive electrode curve Rp and the reference negative electrode curve Rn to the left or right along the horizontal axis.

[0145] refer to Figure 6 The control circuit 130 can shift the reference positive curve Rp to the left (towards lower capacity) or shift the reference negative curve Rn to the right (towards higher capacity), or both, so that the capacity values ​​of the positive participation start point (pi) and the negative participation start point (ni) are matched.

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

[0147] Figure 7 This illustrates the case where only the positive electrode curve Rp is shifted to the left to generate an adjusted reference positive electrode curve (Rp'), and thus, the capacity value of the positive electrode participation start point (pi') matches the capacity value of the negative electrode participation start point (ni). The adjusted reference positive electrode curve (Rp') can be the result of applying an adjustment process to the reference positive electrode curve Rp, which is to shift the capacity difference between the positive electrode participation start point (pi) and the negative electrode participation start point (ni) to the left. Therefore, two points (pi, pi') can differ only in capacity value and have the same voltage. Furthermore, two points (pf, pf') can differ only in capacity value and have the same voltage.

[0148] If at least one of the reference positive curve Rp and the reference negative curve Rn is shifted, the adjustment result curve (Rp', Rn) is ensured, then the control circuit 130 can scale the capacity range of at least one of the adjustment result curves (Rp', Rn).

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

[0150] refer to Figure 8The control circuit 130 generates an adjusted reference positive curve (Rp") by shrinking or expanding the adjusted reference positive curve (Rp') so that the capacity range between two points (pi', pf') of the adjusted reference positive curve (Rp') matches the capacity range of the estimated full-cell curve E. In this case, either point (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.

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

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

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

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

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

[0156] The control circuit 130 can calculate the error (curve error) between the comparison value of the full single-unit curve S and the estimated full single-unit curve E.

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

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

[0159] 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).

[0160] In the following text, ps can be used as a symbol to indicate the positive scaling factor, and ns can be used as a symbol to indicate the negative scaling factor.

[0161] Meanwhile, as mentioned above, when the positive voltage range of the reference positive curve Rp is divided into multiple small voltage segments, the boundary point of two adjacent small voltage segments among the multiple small voltage segments can be set as the positive participation start point (pi).

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

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

[0164] The control circuit 130 can identify the minimum curve error among the multiple comparison full-unit curves generated as described above, and then obtain a first performance factor set from the memory 131, which is information mapped to the minimum curve error (e.g., at least one of 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).

[0165] Figures 9 to 11 This is a diagram used as a reference to illustrate another example of the process of generating a comparison of full-unit curves based on unit diagnostic logic. For reference, Figures 9 to 11 The embodiments shown are independent of Figures 6 to 8 The illustrated embodiment. Therefore, it is commonly used to describe Figures 6 to 8 The illustrated embodiments and Figures 9 to 11 The terminology or reference numerals in the embodiments shown should be understood to be limited to each embodiment.

[0166] The generation will refer to Figures 9 to 11 The process of comparing the full single-unit curve U can be explained by following the fourth routine of capacity scaling (see [link]). Figure 9 The fifth routine sets 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 the sixth routine for performing curve shifting (see Figure 11 The process of generating a comparative full monomer curve according to another embodiment of this disclosure may include the fourth to sixth routines.

[0167] refer to Figure 9The control circuit 130 can generate an adjusted reference positive curve (Rp') and an adjusted reference negative curve (Rn') by applying the positive and negative scaling factors selected from the scaling range to the reference positive curve Rp and the reference negative curve Rn, respectively.

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

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

[0170] Since the positive and negative scaling factors 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 ease of understanding, the reference positive curve Rp and the reference negative curve Rn are shown with their starting points fixed and the remaining portions shrunk to the left along the horizontal axis.

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

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

[0173] In other words, if any one of 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') is set, the remaining three points can be automatically set by the magnitude of the capacity range of the first set voltage, the second set voltage, and / or the estimated full-cell curve E (e.g., Figure 4 (45Ah - 5Ah = 40Ah).

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

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

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

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

[0178] If any one of 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') is determined, the control circuit 130 can additionally determine the remaining three points based on the determined points.

[0179] For example, if the positive electrode participation start point (pi') is determined first, the control circuit 130 can set a point on the adjusted reference positive electrode curve (Rp') with a capacity value that is larger than the estimated capacity range of the full-cell curve E than the positive electrode participation start point (pi') as the positive electrode participation end point (pf'). Furthermore, the control circuit 130 can search for a point on the adjusted reference negative electrode curve (Rn') that is 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'). Additionally, the control circuit 130 can set a point on the adjusted reference negative electrode curve (Rn') with a capacity value that is larger than the estimated capacity range of the full-cell curve E than the negative electrode participation start point (ni') as the negative electrode participation end point (nf').

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

[0181] As another example, when determining the negative electrode participation start point (ni'), the control circuit 130 can set a point on the adjusted reference negative electrode curve (Rn') whose capacity value is larger than the estimated capacity range of the full-cell curve E by the capacity value of the negative electrode participation start point (ni') as the negative electrode participation end point (nf'). Furthermore, the control circuit 130 can search for a point on the adjusted reference positive electrode curve (Rp') that is 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 can set a point on the adjusted reference positive electrode curve (Rp') with a capacity value larger than the estimated capacity range of the full-cell curve E by the capacity value of the positive electrode participation start point (pi') as the positive electrode participation end point (pf').

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

[0183] If 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') are determined entirely based on the pairing of positive and negative electrode scaling factors, then the control circuit 130 can shift at least one of the adjusted reference positive electrode curve (Rp') and the adjusted reference negative electrode curve (Rn') to the left or right along the horizontal axis, such 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.

[0184] Figure 11 The adjusted reference negative electrode curve (Rn") shown is obtained by only using Figure 10 The adjusted reference negative electrode curve (Rn') shown is obtained by shifting it to the right. Therefore, the capacity values ​​of the positive electrode participation start point (pi') and the negative electrode participation start point (ni") are matched on the horizontal axis. Correspondingly, 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'). Therefore, if the capacity values ​​of the positive electrode participation start point (pi') and the negative electrode participation start point (ni'') are matched on the horizontal axis, then the capacity values ​​of the positive electrode participation end point (pf') and the negative electrode participation end point (nf') are also matched on the horizontal axis.

[0185] refer to Figure 11 The control circuit 130 can generate a comparison full-cell curve U by subtracting the portion of the curve between two points (ni" and nf") of the adjusted reference positive curve (Rp') from the portion of the curve between two points (pi' and pf') of the adjusted reference negative curve (Rn").

[0186] The control circuit 130 can calculate and compare the error (curve error) between the full single-unit curve U and the estimated full single-unit curve E.

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

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

[0189] The control circuit 130 can identify the minimum curve error among multiple comparison full-unit curves, and then obtain information mapped to the minimum curve error from the memory 131.

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

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

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

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

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

[0195] Figure 12 This is a reference diagram used to explain the function of the factor correction model.Figure 13 This is a reference diagram used to interpret the training dataset provided for the training factor correction model. Figure 14 It is shown Figure 12 A diagram illustrating an example neural network structure for a factor correction model, and Figure 15 This is a graph showing an example of the correlation coefficients between performance factors obtained by correcting the model through training factors.

[0196] refer to Figure 12 The control circuit 130 can determine the second performance factor set 1220, which is the secondary estimate of the charge / discharge performance of the target single cell BC, by applying the factor correction model 200 to the first performance factor set 1210, which is the primary estimate of the charge / discharge performance of the target single cell BC.

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

[0198] The second performance factor set 1220 can be obtained by correcting the first performance factor set 1210 using the factor correction model 200 to reduce the error between the charge / discharge performance indicated by the first performance factor set 1210 and the actual charge / discharge performance of the target cell BC. The second performance factor set 1220 can represent the estimated charge / discharge performance of the target cell BC if the cell diagnostic logic is applied to the second target full-cell curve N. In other words, a specific performance factor (e.g., negative electrode participation endpoint) of the second performance factor set 1220 can be a specific performance factor of the first performance factor set 1210 that has been corrected to approximate the actual specific performance factor of the target cell.

[0199] The factor correction model 200 can be a machine learning model trained on a training dataset that includes a pair of first and second performance factor groups for each of multiple test individuals.

[0200] For the purpose of training the factor correction model 200, multiple test cells are prepared in advance. At least one of the multiple test cells can be a new battery cell that has been verified as a good product. Each of the remaining test cells can be a test cell in which at least one of the positive and negative electrodes has been forcibly degraded from a new state through charge / discharge cycles different from those of the other test cells.

[0201] The first set of performance factors for a specific test cell can be obtained in advance by applying the individual cell diagnostic logic to the estimated full-cell test curve for the corresponding test cell. The estimated full-cell test curve for a specific test cell can be obtained in advance by subtracting the overpotential curve OP from the primary full-cell test curve, which represents the correspondence between the voltage and capacity factor of the test cell at the moment the first electrical stimulus is applied to the corresponding test cell.

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

[0203] exist Figure 13 In the graph shown, multiple data points included in the training dataset are labeled on a two-dimensional coordinate system. Figure 13 The number of data points marked on the curve can be equal to the number of test units.

[0204] Each data point is defined by two estimates of a specific performance factor. That is, the X-axis coordinate of each data point represents the value included in the first performance factor group as an estimate of the specific performance factor, and the Y-axis coordinate represents the value included in the second performance factor group as another estimate of the specific performance factor. For ease of interpretation, Figure 13 Each of the X and Y axes is shown as the negative SOC representing the endpoint of the negative electrode participation.

[0205] refer to Figure 13 The data points in the training dataset are distributed to have learnable trends. That is, the correlation between the values ​​included in the first set of performance factors, which are estimates of a particular performance factor, and the values ​​included in the second set of performance factors, which are other estimates of the same performance factor, can be trained by the factor correction model 200.

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

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

[0208] In the factor correction model 200, the number of nodes in each layer, the connections between nodes, and the function of each node in the intermediate layer 2000 can be predetermined. Furthermore, the weights of each connection between nodes can be automatically determined using a machine learning process with the training dataset.

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

[0210] The i-th input node Ii can be provided with the i-th input dataset Xi, which is the performance factor data associated therewith. For example, the first input node I1 can be provided with a first input dataset X1. The first input dataset X1 can include values ​​related to the positive electrode participation start point of multiple test cells (e.g., positive electrode initiation potential and capacity value).

[0211] 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 participation start point, positive participation end point, positive scaling factor, negative participation start point, negative participation end point, and negative scaling factor. Figure 14 For ease of explanation, it is assumed that the first output node O1 to the sixth output node O6 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, respectively. The positive electrode participation start point, the positive electrode participation end point, the positive electrode scaling factor, the negative electrode participation start point, the negative electrode participation end point, and the negative electrode scaling factor can be referred to as the first to sixth performance factors in this order.

[0212] When the first to sixth input datasets X1 to X6 are input to the first to sixth input nodes I1 to I6, the j-th output dataset Zj can be output from the j-th output node Oj. For example, when the first output node O1 is associated with the positive electrode participation start point, the third output dataset Z3 may include the values ​​of the first input dataset X1 with correction.

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

[0214] The output dataset from the first output dataset Z1 to the sixth output dataset Z6 that will be output by the factor correction model 200 can be determined by the connections between nodes, the weight of each connection between nodes, and the function of each node included in the intermediate layer 2000, and it is not subject to any particular restrictions.

[0215] The intermediate layer 2000 may include first to m-th intermediate nodes F1 to Fm (where 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 at least one of the first to sixth input nodes I1 to I6 and at least one of the first to sixth output nodes O1 to O6. The k-th intermediate node Fk may have the form of a function determined by the learning process and may send an estimate calculated based on the input values ​​from each input node connected to it to each output node connected to it. The j-th output node Oj may output an estimate equal to the sum of the estimates received from each intermediate node connected to it, as a correction result for the first performance factor set.

[0216] The function of the k-th intermediate node Fk can 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, with a correlation coefficient closer to -1 indicating a negative correlation between the two factors and a correlation coefficient closer to +1 indicating a positive correlation between the two factors.

[0217] The function of each intermediate node in the intermediate layer 2000 can be a weighted average function. In this case, the correlation coefficient, which indicates the 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, can be used as the weight of the function of each intermediate node in the intermediate layer 2000.

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

[0219] When the target unit BC is in the MOL state, the control circuit 130 can determine at least one degradation parameter based on the second performance factor set. 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 set when the target unit BC is in the new state may have already been recorded in the memory 131.

[0220] Table 1

[0221] Each variable listed in Table 1 is a diagnostic factor that can be included in the second performance factor group mentioned above. The definitions of the degradation parameters and variables in Table 1 can be as follows.

[0222] <Degradation Parameters>

[0223] P SOH : The positive electrode SOH (health status) of the target monomer BC

[0224] N SOH : The negative electrode SOH of the target monomer BC

[0225] L SOH Available lithium SOH for target monomer BC

[0226] F SOH : The total monomer SOH of target monomer BC

[0227] P LOSS : Cathode loss rate of target single cell BC

[0228] N LOSS Negative electrode loss rate of target monomer BC

[0229] L LOSS Available lithium loss rate of target monomer BC

[0230] F LOSS Total monomer loss rate of target monomer BC

[0231] P loading_MOL : Positive electrode loading of target cell BC

[0232] N loading_MOL The negative electrode loading of the target single cell BC

[0233] N / P _MOL : NP ratio of target monomer BC

[0234] As any battery cell deteriorates, at least one of the following: total positive electrode capacity, total negative electrode capacity, available lithium content, and total total cell capacity, will gradually decrease from its value at BOL (Start of Life). Total total cell capacity can be represented as the capacity difference between the two endpoints of the total cell curve. For example, total total cell capacity can refer to the full charge capacity (FCC). Available lithium content represents the total amount of lithium that can contribute to the charging and discharging of the battery cell. SOH It can represent the retention rate of the total positive electrode capacity. N SOH It can represent the retention rate of the total negative electrode capacity. L SOH This can indicate the retention rate of available lithium content. F SOH It can represent the maintenance rate of total monomer capacity.

[0235] P SOH and P LOSS The sum of N SOH and N LOSS The sum, L SOH and L LOSS The sum and F SOH and F LOSS The sum of each can be equal to 1. F LOSS It can be equal to P LOSS and L LOSS sum.

[0236] The positive electrode loading of any battery cell represents the amount of positive electrode active material (or usable capacity) per unit area of ​​the positive electrode of the battery cell. The negative electrode loading of any battery cell represents the amount of negative electrode active material (or usable capacity) per unit area of ​​the negative electrode of the battery cell. The unit of loading can be mAh / cm². 2 or mg / cm 2 In Table 1, P loading_ref This indicates the reference positive electrode load, and N loading_ref This indicates the reference negative electrode loading. The reference positive electrode loading is a predetermined value representing the amount (or available capacity) of positive electrode active material per unit area of ​​the reference monomer's positive electrode. The reference positive electrode loading can be expressed as the reference positive electrode capacity (Q). P_ref The reference positive electrode capacity is the value obtained by dividing the reference positive electrode capacity 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 monomer. The reference positive electrode area can be a value preset as the area of ​​the positive electrode of the reference monomer. The reference negative electrode loading is a predetermined value representing the amount (or available capacity) of negative electrode active material per unit area of ​​the negative electrode of the reference monomer. The reference negative electrode loading can be obtained by dividing the reference negative electrode capacity (Q) by the reference negative electrode area. N_ref The value is obtained by dividing the reference negative electrode area by the total negative electrode area. Here, the reference negative electrode capacity can be the value of the total negative electrode capacity of the reference cell, which is preset to be the reference cell. The reference negative electrode area can be the value of the area of ​​the negative electrode of the reference cell, which is preset to be the reference cell.

[0237] <variable>

[0238] pi BOL When the target single cell BC is in the BOL state, the positive electrode capacity (positive electrode SOC) at the starting point of the positive electrode participation.

[0239] pi MOL The current positive electrode participation starting point of the target monomer BC (e.g., Figure 8 The positive electrode capacity (positive electrode SOC) of pi' shown in the figure.

[0240] pf BOL When the target single cell BC is in the BOL state, the positive electrode participates in the final positive electrode capacity (positive electrode SOC).

[0241] pf MOL The current positive electrode of the target cell BC participates in the endpoint (e.g., Figure 8 The positive electrode capacity (positive electrode SOC) shown is pf".

[0242] ni BOL When the target monomer BC is in the BOL state, the negative electrode capacity (negative electrode SOC) at the negative electrode participation starting point.

[0243] ni MOL The current negative electrode participation starting point of the target monomer BC (e.g., Figure 8 The negative electrode capacity (negative electrode SOC) of ni shown in the figure.

[0244] nf BOL When the target monomer BC is in the BOL state, the negative electrode participates in the final negative electrode capacity (negative electrode SOC).

[0245] nf MOL The current negative electrode of the target monomer BC participates in the endpoint (e.g., Figure 8 The negative electrode capacity (negative electrode SOC) of nf' shown in the figure.

[0246] ps BOL : Positive scaling factor when the target single cell BC is in the BOL state

[0247] ps MOL : Current positive scaling factor of target cell BC

[0248] ns BOL Negative scaling factor when the target single unit BC is in the BOL state.

[0249] ns MOL : Current negative pole scaling factor of target single cell BC

[0250] The process of determining the second set of performance factors can be repeated periodically or non-periodically during the lifetime of the target monomer BC.

[0251] Figure 15 An example is shown of the correlation information between the first and second performance factor groups obtained by learning a factor correction model 200 in matrix form.

[0252] Figure 15 The matrix shown is a 6×6 matrix. The six rows represent the first through sixth performance factors of the first performance factor group provided in this order as part of the training dataset. The six columns represent the first through sixth performance factors of the second performance factor group provided in this order as part of the training dataset. Figure 15 In this context, pi_A[1], pf_A[2], ps_A[3], ni_A[4], nf_A[5], and ns_A[6] represent the positive participation start point, positive participation end point, positive scaling factor, negative participation start point, negative participation end point, and negative scaling factor included in the first performance factor group of the training dataset in this order. Furthermore, pi_B[1], pf_B[2], ps_B[3], ni_B[4], nf_B[5], and ns_B[6] represent the positive participation start point, positive participation end point, positive scaling factor, negative participation start point, negative participation end point, and negative scaling factor included in the second performance factor group of the training dataset in this order, respectively.

[0253] When p and q are natural numbers less than or equal to 6, the values ​​of the p-th row (pi_A[p]) and q-th column (pi_B[q]) represent the correlation coefficients 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.

[0254] 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.

[0255] Figure 16 This is a flowchart illustrating, schematically, a battery diagnostic method according to another embodiment of the present disclosure. Figure 16 The method can be performed by the battery diagnostic device 100.

[0256] Reference Figures 1 to 16 In step S1610, the control circuit 130 collects measurement signals from the sensing unit 110 representing the measured values ​​of the voltage and current of the target cell BC, which is the battery cell to be diagnosed.

[0257] In step S1620, the control circuit 130 generates a first target whole cell curve M based on the measurement signal collected in step S1610. The first target whole cell curve M represents the correspondence between the voltage and capacity factor of the target cell BC at the same time that the first electrical stimulation is applied to the target cell BC.

[0258] Steps S1610 and S1620 can be replaced by the process of the data acquisition unit directly obtaining the first target full monomer curve M or obtaining the first target full monomer curve M from the outside.

[0259] 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.

[0260] In step S1640, the control circuit 130 applies the individual cell diagnostic logic to the estimated full-cell curve E to determine the first performance factor group 1210 as a preliminary estimate of the charge / discharge performance of the target cell BC.

[0261] In step S1650, the control circuit 130 determines a second performance factor set 1220 as a secondary estimate of the charge / discharge performance of the target cell BC by applying the factor correction model 200 to the first performance factor set 1210. Here, the second performance factor set includes the estimate of the negative electrode participation endpoint of the target cell BC. If the cell diagnostic logic is applied to the second target full cell curve N instead of the estimated full cell curve E, the estimate of the negative electrode participation endpoint can be determined.

[0262] The second target whole-cell curve N represents the correspondence between the voltage and capacity factor of target cell BC when a second electrical stimulus, different from the first electrical stimulus, is applied to target cell BC. The second target whole-cell curve N is not obtained by actually applying the second electrical stimulus to target cell BC. In other words, the second target whole-cell curve N represents the expected correspondence between the voltage and capacity factor of target cell BC if the second electrical stimulus, instead of the first, is applied.

[0263] Control circuit 130 can limit at least one of the allowable voltage range, allowable SOC range, and allowable charge / discharge current of the target cell BC based on at least one degradation parameter. Memory 131 can pre-store relational data indicating the correspondence between at least one limit (i.e., allowable voltage range, allowable SOC range, and / or allowable charge / discharge current) and at least one degradation parameter. For example, when a specific type of degradation parameter (e.g., P...) is present... LOSS N LOSS L LOSS F LOSSWhen the increase from the BOL state is greater and / or when another type of degradation parameter (e.g., P) is greater. SOH N SOH L SOH F SOH When the reduction from the BOL state is greater, the limits on the permissible voltage range, permissible SOC range, and / or permissible charge / discharge current can be greater.

[0264] The embodiments of the present disclosure described above are not implemented solely by means of apparatus and methods, but can be implemented by a program that performs functions corresponding to the configuration of the embodiments of the present disclosure or by a recording medium on which the program is recorded, and such implementation can be readily achieved by those skilled in the art from the disclosure of the previously described embodiments.

[0265] While this disclosure has been described above with respect to a limited number of embodiments and accompanying drawings, this disclosure is not limited thereto, and it will be apparent to those skilled in the art that various modifications and changes can be made to it within the technical aspects of this disclosure and within the equivalent scope of the appended claims.

[0266] Furthermore, since those skilled in the art can make many substitutions, modifications and changes to the present disclosure without departing from its technical aspects, the present disclosure is not limited to the above embodiments and drawings, and some or all of the embodiments can be selectively combined to allow for various modifications.

Claims

1. A battery diagnostic apparatus comprising: a data obtaining unit configured to obtain a first target full-cell curve representing a correspondence between a voltage and a capacity factor of a target cell while a first electrical stimulus is applied to the target cell, the target cell being 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 an overpotential curve, wherein the control circuit is configured to: determine a first performance factor group as a primary estimation result of charge / discharge performance of the target cell by applying a cell diagnostic logic to the estimated full-cell curve, and determine 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, wherein the second performance factor group includes an estimation result of a negative electrode participation end point of the target cell, the estimation result of the negative electrode participation end point being determinable by applying the cell diagnostic logic to a second target full-cell curve representing a correspondence between the voltage and the capacity factor of the target cell while a second electrical stimulus different from the first electrical stimulus is applied. the first electrical stimulus is an electrical stimulus that induces an overpotential exceeding an allowable level in the target cell, and 2. The battery diagnostic apparatus according to claim 1, wherein wherein the second electrical stimulus is an electrical stimulus that induces an overpotential less than the allowable level in the target cell. the first electrical stimulus is charging using a first current rate, and 3. The battery diagnostic apparatus according to claim 1, wherein wherein the second electrical stimulus is charging using a second current rate less than the first current rate. the first electrical stimulus is discharging using a first current rate, and 4. The battery diagnostic apparatus according to claim 1, wherein wherein the second electrical stimulus is discharging using a second current rate less than the first current rate. the overpotential curve represents a difference between a first reference full-cell curve and a second reference full-cell curve, 5. The battery diagnostic apparatus according to claim 1, wherein wherein the first reference full-cell curve represents a correspondence between a voltage and a capacity factor of a reference cell while the first electrical stimulus is applied to the reference cell, the reference cell being a battery cell verified to be normal, and wherein the second reference full-cell curve represents a correspondence between the voltage and the capacity factor of the reference cell while the second electrical stimulus is applied to the reference cell. the control circuit is configured to generate the estimated full-cell curve by subtracting the overpotential curve from the first target full-cell curve.

6. The battery diagnostic apparatus according to claim 1, wherein the first performance factor group includes at least one of the following as a performance factor:

7. The battery diagnostic apparatus according to claim 1, wherein a positive electrode participation start point representing a positive electrode voltage and a positive electrode capacity when a voltage of the target cell matches a first set voltage; a positive electrode participation end point representing a positive electrode voltage and a positive electrode capacity when the voltage of the target cell matches a second set voltage; ​ a positive electrode scaling factor representing a ratio of a capacity difference between the positive electrode engagement start point and the positive electrode engagement end point to a reference positive electrode capacity; a negative electrode engagement start point representing a negative electrode voltage and a negative electrode capacity when a voltage of the target cell matches the first set voltage; a negative electrode engagement end point representing a negative electrode voltage and a negative electrode capacity when the voltage of the target cell matches the second set voltage; and a negative electrode scaling factor representing a ratio of a capacity difference between the negative electrode engagement start point and the negative electrode engagement end point to a reference negative electrode capacity.

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

9. The battery diagnostic apparatus according to claim 8, wherein The first performance factor group of each of the plurality of test cells is obtained by applying the cell diagnostic logic to each of a plurality of estimated test full cell curves, wherein the plurality of estimated test full cell curves is obtained by subtracting the overpotential curve from each of a plurality of primary test full cell curves representing a 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 of each of the plurality of test cells is obtained by applying the cell diagnostic logic to a plurality of secondary test full cell curves, and wherein the plurality of secondary test full cell curves represents a 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 comprising the battery diagnostic apparatus according to any one of claims 1 to 9.

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

12. A battery diagnostic method comprising: obtaining a first target full cell curve representing a correspondence between a voltage and a capacity factor of a target cell while a first electrical stimulus is applied to the target cell, the target cell being a battery cell to be diagnosed; generating an estimated full cell curve based on the first target full cell curve and an overpotential curve; determining a first performance factor group as a primary estimation result of charge / discharge performance of the target cell by applying cell diagnostic 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, ​ The second set of performance factors includes an estimate of a negative end-of-participation point for the target monobloc, which can be determined by applying the monobloc diagnostic logic to a second target full monobloc curve instead of the estimated full monobloc curve, where the second target full monobloc curve represents a correspondence between the voltage and the capacity factor for the target monobloc while a second electrical stimulus different from the first electrical stimulus is applied.

13. The battery diagnostic method of claim 12, wherein, The step of generating the estimated full monobloc curve is performed by subtracting the overpotential curve from the first target full monobloc curve to generate the estimated full monobloc curve.

14. The battery diagnostic method of claim 12, wherein, The factor correction model is a machine learning model trained from a training dataset including pairs of the first set of performance factors and the second set of performance factors for each of a plurality of test monoblocs having different charge / discharge performance.

15. The battery diagnostic method of claim 14, wherein, The first set of performance factors for each of the plurality of test monoblocs is obtained by applying the monobloc diagnostic logic to each of a plurality of estimated test full monobloc curves, where the plurality of estimated test full monobloc curves is obtained by subtracting the overpotential curve from each of a plurality of primary test full monobloc curves representing a correspondence between the voltage and the capacity factor for each of the plurality of test monoblocs while the first electrical stimulus is applied to each of the plurality of test monoblocs, where the second set of performance factors for each of the plurality of test monoblocs is obtained by applying the monobloc diagnostic logic to a plurality of secondary test full monobloc curves, and where the plurality of secondary test full monobloc curves represents a correspondence between the voltage and the capacity factor for each of the plurality of test monoblocs while the second electrical stimulus is applied to each of the plurality of test monoblocs.

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