Simulator tuning data derivation method and simulator tuning data derivation apparatus

Abstract

An emulator tuning data derivation method and apparatus is provided for using a battery emulator for a first type of reference cell for a second type of target cell. The method includes the steps of determining a plurality of overpotential values by analyzing a plurality of voltage response curves associated with a plurality of pulse tests performed on the target cell using a plurality of test conditions in which a plurality of SOC conditions are combined with a plurality of current conditions, determining a plurality of target positive overpotential values from the plurality of overpotential values, determining a plurality of positive kinetic rate adjustment ratios corresponding to the plurality of target positive overpotential values, and deriving first tuning data based on the plurality of positive kinetic rate adjustment ratios.

Description

Technical Field

[0001] This disclosure relates to techniques for deriving tuning data necessary to simulate the charging and discharging response characteristics of different types of battery cells using a battery simulator that simulates the charging and discharging response characteristics of a particular type of battery cell.

[0002] This application is based on and claims priority to Korean Patent Application No. 10-2025-0006628, filed with the Korean Intellectual Property Office on January 16, 2025, the disclosure of which is incorporated herein by reference in its entirety. Background Technology

[0003] Recently, the demand for portable electronic products such as laptops, cameras and mobile phones has increased dramatically, and with the widespread development of electric vehicles, batteries for energy storage, robots and satellites, there is a lot of research being done on high-performance batteries that can be recharged repeatedly.

[0004] Currently, many different types of batteries exist on the market, and lithium-ion batteries (LIBs) are one of the most common types. Compared to LIBs, sodium-ion batteries (SIBs) have lower energy density but higher electrochemical stability and are more price-competitive.

[0005] Because different types of battery cells have unique characteristics, chemical composition modeling is required to meet customer requirements or improve performance. In practice, it is necessary to confirm whether the battery cells have the target charge and discharge performance before mass production.

[0006] Battery simulators are widely used to predict battery cell performance. A battery simulator is a collection of equations, algorithms, and / or data that define the two-dimensional or three-dimensional structure of a specific type of battery cell and the particle concentration, current, and electrochemical reactions within the target battery cell, and can be a type of mathematical battery model.

[0007] Battery simulators are typically provided for each battery type and are not easily interchangeable for two or more types of battery cells. For example, because battery simulators for LIBs are designed to output charging and discharging behavior information based on input data reflecting the electrical, chemical, or physical characteristics of the LIB, when input data associated with an SIB is fed into a battery simulator for a LIB, the simulator will output charging and discharging behavior information that is significantly different from the actual charging and discharging behavior of the SIB. Summary of the Invention

[0008] Technical issues

[0009] This disclosure aims to provide a method and apparatus for deriving tuning data, said tuning data being required to simulate the charging and discharging response characteristics of different types of battery cells using a battery simulator that simulates the charging and discharging response characteristics of a particular type of battery cell.

[0010] These and other objects and advantages of this disclosure will be understood from the following description and will become apparent from embodiments of this disclosure. Moreover, it will be readily understood that the objects and advantages of this disclosure can be achieved by the means set forth in the appended claims and combinations thereof.

[0011] Technical solution

[0012] A method for deriving simulator tuning data according to one aspect of this disclosure is provided for using a battery simulator for a first type of reference cell to a second type of target cell. The method includes the steps of: determining multiple overpotential values ​​driven by the charge transfer resistance of the target cell by analyzing multiple voltage response curves associated with multiple pulse tests performed on the target cell using multiple test conditions, the multiple test conditions combining multiple state of charge (SOC) conditions and multiple current conditions; determining multiple target positive electrode overpotential values ​​based on the multiple overpotential values; determining multiple positive electrode kinetic rate regulation ratios corresponding to the multiple target positive electrode overpotential values; and deriving first tuning data indicating the relationship between the SOC conditions, the current conditions, and the positive electrode kinetic rate regulation ratios based on the multiple positive electrode kinetic rate regulation ratios.

[0013] The step of determining the plurality of overpotential values ​​may include: determining a plurality of resistance values ​​of interest based on the plurality of voltage response curves; determining a plurality of charge transfer resistance values ​​by subtracting the ohmic resistance value of the target cell from the plurality of resistance values ​​of interest; and determining the plurality of overpotential values ​​based on the plurality of current conditions and the plurality of charge transfer resistance values.

[0014] The step of determining the plurality of resistor values ​​of interest may include: determining a plurality of voltage change values ​​in the initial period of the plurality of pulse tests based on the plurality of voltage response curves; and determining the plurality of resistor values ​​of interest by dividing the plurality of voltage change values ​​by the plurality of current conditions.

[0015] The k-th positive electrode kinetic rate regulation ratio among the plurality of positive electrode kinetic rate regulation ratios can be determined using the following equation:

[0016] Among them, f pos [k]: The k-th positive electrode kinetic rate regulation ratio αa The charge transfer coefficient of the negative electrode. α c The charge transfer coefficient of the positive electrode. F: Faraday constant, R: gas constant, T: Absolute temperature ηr_ pos [k]: The overpotential value of the k-th reference positive electrode. ηt_ pos [k]: The overpotential value of the kth target positive electrode.

[0017] The steps of deriving the first tuning data may include: dividing the plurality of positive electrode kinetic rate adjustment ratios into plurality of positive electrode subsets according to the plurality of current conditions; deriving a plurality of positive electrode master curves having a predetermined functional form from the plurality of positive electrode subsets; deriving a plurality of positive electrode sub-curves for a plurality of curve parameters of the plurality of positive electrode master curves; and deriving a first composite curve representing the relationship between the SOC condition, the current condition, and the positive electrode kinetic rate adjustment ratio based on the plurality of positive electrode curves. The first tuning data may include the first composite curve.

[0018] The plurality of master curves for the positive electrodes can be multiple exponential curve functions derived by applying curve fitting to the plurality of subsets of positive electrodes. The parameters of the plurality of curves may include intercept, scaling factor, and growth rate.

[0019] The steps of deriving the plurality of positive electrode curves may include: deriving a first positive electrode curve representing the relationship between the current condition and the intercept based on a plurality of intercept values ​​of the plurality of positive electrode master curves; deriving a second positive electrode curve representing the relationship between the current condition and the scale factor based on a plurality of scale factor values ​​of the plurality of positive electrode master curves; and deriving a third positive electrode curve representing the relationship between the current condition and the growth rate based on a plurality of growth rate values ​​of the plurality of positive electrode master curves.

[0020] The simulator tuning data derivation method may further include the following steps: determining multiple target negative electrode overpotential values ​​based on the multiple overpotential values; determining multiple negative electrode dynamic rate adjustment ratios corresponding to the multiple target negative electrode overpotential values; and

[0021] Based on the plurality of negative electrode kinetic rate adjustment ratios, second tuning data indicating the relationship between the SOC condition, the current condition, and the negative electrode kinetic rate adjustment ratios are derived.

[0022] A simulator tuning data derivation apparatus according to another aspect of this disclosure is provided for using a battery simulator for a first type of reference cell to serve a second type of target cell. The simulator tuning data derivation apparatus includes: a battery tester configured to perform multiple pulse tests on the target cell using multiple test conditions, the multiple test conditions combining multiple SOC conditions and multiple current conditions; and a processor configured to analyze the multiple voltage response curves. The processor is further configured to determine multiple overpotential values ​​driven by the charge transfer resistance of the target cell by analyzing the multiple voltage response curves. The processor is further configured to determine multiple target positive electrode overpotential values ​​based on the multiple overpotential values. The processor is further configured to determine multiple positive electrode kinetic rate regulation ratios corresponding to the multiple target positive electrode overpotential values. The processor is further configured to derive first tuning data indicating the relationship between the SOC conditions, the current conditions, and the positive electrode kinetic rate regulation ratios based on the multiple positive electrode kinetic rate regulation ratios.

[0023] The processor can also be configured to determine multiple resistance values ​​of interest based on the multiple voltage response curves, and to determine multiple charge transfer resistance values ​​by subtracting the ohmic resistance value of the target cell from the multiple resistance values ​​of interest. The processor can also be configured to determine multiple overpotential values ​​based on the multiple current conditions and the multiple charge transfer resistance values.

[0024] The processor can also be configured to determine the kth positive electrode kinetic rate regulation ratio among the plurality of positive electrode kinetic rate regulation ratios using the following equation:

[0025] Among them, f pos [k]: The k-th positive electrode kinetic rate regulation ratio α a The charge transfer coefficient of the negative electrode. α c The charge transfer coefficient of the positive electrode. F: Faraday constant, R: gas constant, T: Absolute temperature ηr_ pos [k]: The overpotential value of the k-th reference positive electrode. ηt_ pos [k]: The overpotential value of the kth target positive electrode.

[0026] The processor can also be configured to group the plurality of positive electrode kinetic rate adjustment ratios into a plurality of positive electrode subsets based on the plurality of current conditions. The processor can also be configured to derive a plurality of principal positive electrode curves having a predetermined functional form from the plurality of positive electrode subsets. The processor can also be configured to derive a plurality of sub-positive electrode curves for a plurality of curve parameters of the plurality of principal positive electrode curves. The processor can also be configured to derive a first composite curve representing the relationship between the SOC condition, the current condition, and the positive electrode kinetic rate adjustment ratio based on the plurality of positive electrode curves. The first tuning data includes the first composite curve.

[0027] The processor is further configured to determine a plurality of target negative electrode overpotential values ​​based on the plurality of overpotential values. The processor is also configured to determine a plurality of negative electrode kinetic rate adjustment ratios corresponding to the plurality of target negative electrode overpotential values. The processor is further configured to derive second tuning data indicating the relationship between the SOC condition, the current condition, and the negative electrode kinetic rate adjustment ratios based on the plurality of negative electrode kinetic rate adjustment ratios.

[0028] According to another aspect of this disclosure, the battery simulation system includes a simulator tuning data derivation device.

[0029] Beneficial effects

[0030] According to at least one embodiment of this disclosure, simulator tuning data required to simulate the charging and discharging response characteristics of a specific type of battery cell using a battery simulator can be derived to simulate the charging and discharging response characteristics of different types of battery cells.

[0031] 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 through the appended claims. Attached Figure Description

[0032] The accompanying drawings illustrate exemplary embodiments of the present disclosure and are used, together with the following detailed description, to provide a better understanding of the technical aspects of the present disclosure; therefore, the present disclosure should not be construed as being limited to any of the drawings.

[0033] Figure 1 This is a diagram illustrating a battery simulation system according to the present disclosure.

[0034] Figure 2 The diagrams are shown for reference when briefly describing the multiple voltage response curves corresponding to multiple pulse tests.

[0035] Figure 3a and Figure 3b This is a diagram used to describe the differences in voltage response characteristics between a reference cell and a target cell.

[0036] Figure 4 and Figures 5a to 5c This is a diagram referenced during the derivation of the first tuning data.

[0037] Figure 6 and Figures 7a to 7c This is a diagram referenced in the process of describing the derivation of the second tuning data.

[0038] Figure 8 This is a flowchart schematically illustrating a simulator tuning data derivation method according to another embodiment of the present disclosure.

[0039] Figure 9 This is an illustrative representation of what may be included in accordance with Figure 8 The flowchart shows an example of a subroutine in step S810 of the method.

[0040] Figure 10 This is an illustrative representation that may be included in accordance with Figure 8 The flowchart shows an example of a subroutine in step S828 of the method.

[0041] Figure 11 This is an illustrative representation of what may be included in accordance with Figure 8 The flowchart shows an example of a subroutine in step S838 of the method. Detailed Implementation

[0042] In the following, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Before the description, it should be understood that the terms or words used in the specification and appended claims should not be construed as limited to their general and dictionary meanings, but should be interpreted based on the principle that the inventor is allowed to appropriately define the terms for the best interpretation, and based on the meanings and concepts corresponding to the technical aspects of the present disclosure.

[0043] Therefore, the embodiments described herein and the illustrations shown in the accompanying drawings are embodiments of this disclosure to describe the technical aspects of this disclosure, but are not intended to be limiting. It should be understood that various other equivalents and modifications may be made thereto when this application is filed.

[0044] Terms including ordinal numbers such as “first” and “second” are used to distinguish one element from another among various elements, but are not intended to limit these elements by these terms.

[0045] Unless the context clearly indicates otherwise, the terms "comprising" and "including" as used in this specification specify the presence of the stated element, but do not exclude the presence or addition of one or more other elements. Additionally, the term "unit" as used herein refers to a processing unit having at least one function or operation, and may be implemented by hardware and software, individually or in combination.

[0046] Furthermore, throughout the specification, it should be understood that when a component is referred to as being “connected to” another component, it can be directly connected to the other component, or there can be an intermediate component.

[0047] Figure 1 This is a diagram illustrating a battery simulation system according to the present disclosure.

[0048] Reference Figure 1 The battery simulation system 1 includes a battery tester 10 and a simulator tuning data derivation device 100.

[0049] The battery tester 10 is configured to selectively perform one of the charging, resting, and discharging functions according to the test plan.

[0050] A battery tester 10 is provided to repeatedly perform pulse tests on battery cells BC of a specific type (e.g., SIB). Each time a pulse test is performed, voltage response data reflecting the charging and discharging response characteristics of the battery cell BC can be obtained as a result of the pulse test.

[0051] The battery tester 10 may include a battery monitoring unit BM, a power supply circuit 11, a charging / discharging circuit 12, and a test controller 13.

[0052] The power supply circuit 11 is configured to convert power from an alternating current (AC) power source and / or a direct current (DC) power source into DC with a predetermined voltage level suitable for the input specifications of the charging / discharging circuit 12 in response to a command from the test controller 13. The power supply circuit 11 may include one or a combination of AC-DC converters and DC-DC converters commonly used in the art.

[0053] The charging / discharging circuit 12 may have a pair of power terminals (+, -) respectively connected to the positive and negative terminals of the battery cell BC, to apply charging or discharging pulses to the battery cell BC in response to a command from the test controller 13. The charging / discharging circuit 12 may include one or a combination of constant current circuits and constant voltage circuits commonly used in the art.

[0054] The test controller 13 has an internal memory 121 for pre-storing test plans. The memory 121 may be equipped with a battery simulator (BSM) to simulate the charging and discharging response characteristics of a different type of battery cell (e.g., LIB) than the battery cell BC.

[0055] The test controller 13 controls the charging / discharging circuit 12 to sequentially perform pulse tests under different test conditions according to the test plan.

[0056] Pulse testing can be an electrical stimulation procedure that involves continuously applying a predetermined current rate (in C) for a predetermined time period.

[0057] The charging / discharging circuit 12 can not only have a charging pulse application function, but also a discharging pulse application function, and each time a specific pulse test is completed, it performs a rest or state of charge (SOC) adjustment process for the next pulse test.

[0058] The battery monitoring unit BM includes a voltage sensor 21 and a current sensor 22.

[0059] Voltage sensor 21 is connected between the positive and negative terminals of battery cell BC to detect the voltage response of battery cell BC to pulse testing and to generate (output) a signal indicating the detected voltage response.

[0060] A current sensor 22 is mounted on the power path connecting the battery cell BC to the charging / discharging circuit 12 to detect the current response of the battery cell BC to a pulse test and to generate (output) a signal indicating the detected current response. As an example, the current sensor 22 may include known current sensing devices, such as a shunt resistor and / or a Hall sensor. The voltage sensor 21 and the current sensor 22 may be integrated into a single chip.

[0061] The battery monitoring unit BM can be included as a component of the simulator tuning data derivation device 100, rather than as a component of the battery tester 10.

[0062] The test controller 13 and / or data acquisition unit 110 can generate a battery response dataset that includes voltage response data and / or current response data for each pulse test by repeatedly recording the voltage response and / or current response received from the battery monitoring unit BM in chronological order. For example, the pulse test can use Hybrid Pulse Power Characterization (HPPC).

[0063] The simulator tuning data derivation device 100 includes a data acquisition unit 110 and a processor 120. The simulator tuning data derivation device 100 may also include an information interface unit 130.

[0064] The simulator tuning data derivation device 100 provides tuning data that reflects the charging and discharging response characteristics of the battery cell BC based on multiple battery response datasets that have a one-to-one correspondence with multiple pulse tests already completed by the battery tester 10.

[0065] The data acquisition unit 110 may include at least one communication circuit that supports wired and / or wireless communication with the battery tester 10. Wired communication may include, for example, Controller Area Network (CAN) communication, while wireless communication may include, for example, Zigbee or Bluetooth communication. The communication protocol is not limited to the types described above and may include, but is not limited to, any type of communication protocol that supports wired or wireless communication.

[0066] Each time a pulse test of battery cell BC is completed, data acquisition unit 110 can acquire time-series data of the voltage and / or current response of each pulse test from battery tester 10. Alternatively, data acquisition unit 110 can acquire time-series data of the voltage and / or current response of each pulse test via communication with battery monitoring unit BM.

[0067] The processor 120 is operatively coupled to the battery tester 10, the data acquisition unit 110, and / or the information interface unit 130. As used herein, operatively coupled means an indirect or direct connection that enables signal transmission and reception in one or both directions.

[0068] The processor 120 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.

[0069] Processor 120 may include memory 121. Memory 121 may include at least one type of storage medium, such as flash memory, hard disk, solid-state drive (SSD), silicon disk drive (SDD), micro multimedia card, 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 121 may store data and programs required for the following operations that can be performed by processor 120: Memory may store data indicating the results of operations performed by processor 120.

[0070] An information interface unit 130 is provided to output information related to pulse testing in a recognizable form. For example, the information interface unit 130 may include a monitor, a touch screen, a speaker, and / or a vibrator.

[0071] The first to pth pulse tests can be performed on the battery cell BC.

[0072] Specifically, each of the first to p-th test conditions can be a combination of one of the first to m-th current conditions and one of the first to n-th SOC conditions. Each of m and n is a natural number of 2 or greater, and they can be the same or different. p can be equal to the product of m and n.

[0073] Each of the first to the m-th current conditions represents the amplitude and direction of the current pulse applied to the battery cell BC during the pulse test. For example, when m=4, the first current condition can be a 1 C discharge current, the second current condition can be a 1.2 C discharge current, the third current condition can be a 5 C discharge current, and the fourth current condition can be a 10 C discharge current.

[0074] Each of the first to nth SOC conditions indicates the SOC of the battery cell BC at the start of the pulse test. For example, when n=8, the first to eighth SOC conditions could be 90%, 80%, 70%, 60%, 50%, 40%, 30%, and 20% in sequence.

[0075] Assume i is a natural number equal to or less than m, and j is a natural number equal to or less than n. In the first through p-th test conditions, the {(i-1)n+j}-th test condition can be a combination of the i-th current condition and the j-th SOC condition. As an example, the combination of the first current condition and the first SOC condition is the first test condition. As another example, the combination of the m-th current condition and the n-th SOC condition is the p-th test condition. As yet another example where m=4 and n=8, the combination of the second current condition and the third SOC condition is the eleventh test condition.

[0076] The first to the pth pulse tests can be matched with the first to the pth test conditions respectively. When k=(i-1)n+j, under the condition that the SOC of the battery cell BC meets the jth SOC condition, the kth pulse test can apply the current pulse of the ith current condition to the battery cell BC within a predetermined time period.

[0077] After completing the kth pulse test according to the kth test condition, the (k+1)th pulse test according to the (k+1)th test condition can be executed.

[0078] Figure 2 A graph is shown for reference when briefly describing multiple voltage response curves corresponding to multiple pulse tests, respectively. To aid in better understanding, Figure 2 Eight voltage response curves are shown under a fixed current condition of 1 C discharge current, according to eight SOC conditions (90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%).

[0079] exist Figure 2 In the chart, the X-axis (horizontal axis) indicates the duration of the pulse test, and the Y-axis (vertical axis) indicates the voltage of the battery cell BC.

[0080] In each graph, the solid line indicates the actual voltage response curve of the battery cell BC (pulse test result), and the dashed line indicates the voltage response curve output from the battery simulator (BSM) under specific test conditions (simulation result). For each graph, there are slight differences between the two voltage response curves because the battery simulator (BSM) is programmed to accommodate the characteristic information of a different type of battery cell than the battery cell BC.

[0081] The inventors have discovered that when a battery simulator (BSM) is tuned using tuning data derived through the following process to compensate for differences in characteristic information between different types of battery cells, the error level (e.g., RSME) between the voltage response curve (indicated by the dashed line) output from the battery simulator (BSM) and the actual voltage response curve (indicated by the solid line) is lower than a predetermined value.

[0082] Processor 120 can determine the k-th target overpotential value driven by the charge transfer resistance of battery cell BC by analyzing the k-th voltage response curve. The k-th voltage response curve can be data indicating the actual voltage response of battery cell BC in the k-th pulse test. Since k = 1 to p, the first to p-th target overpotential values ​​can be determined based on the first to p-th voltage response curves. Specifically, processor 120 can determine the k-th resistance value of interest based on the k-th voltage response curve, and then determine the k-th charge transfer resistance value by subtracting the ohmic resistance value from the k-th resistance value of interest. Subsequently, processor 120 can determine the k-th target overpotential value based on the current conditions of the k-th pulse test and the k-th charge transfer resistance value.

[0083] The k-th voltage response curve and the k-th overpotential value can have a relationship according to Equations 1 and 2 below.

[0084] Equation 1

[0085] Equation 2

[0086] In Equations 1 and 2, I[i] represents the current condition for the k-th pulse test ( Figure 2 IC[C-rate]), ΔV drop R represents the voltage change of battery cell BC during the initial cycle of the k-th pulse test. drop [k] represents the k-th resistor value of interest, R CT[k] represents the k-th charge transfer resistance value of battery cell BC, R ohm This represents the ohmic resistance value of battery cell BC, and η t [k] represents the k-th overpotential value. R ohm It can be a preset value obtained through electrochemical impedance spectroscopy (EIS) analysis, and it remains constant regardless of the test conditions.

[0087] The initial period of the k-th pulse test can be the time period from the start time of the k-th pulse test until the elapsed time of interest. The time of interest can be a very short preset time, such as 0.1 seconds. The initial period of the k-th pulse test can be so short that the effect of the diffusion resistance on the voltage change of the battery cell BC can be ignored. For example, ΔV drop =V a [k]-V b [k]. V a [k] represents the voltage of battery cell BC at the start of the k-th pulse test, and V b [k] represents the voltage of battery cell BC at the moment of interest elapsed since the start of the k-th pulse test.

[0088] In determining ΔV drop Then, processor 120 passes ΔV drop Divide by I[i] to determine R drop [k]. Subsequently, processor 120 can R CT [k] is determined to be equal to R. drop [k] and R ohm The product of the difference between them and I[i].

[0089] Unlike ohmic resistance, which is almost unaffected by test conditions, charge transfer resistance is highly dependent on them. Furthermore, during the initial cycle of a pulse test, charge transfer resistance has a more significant impact on the voltage response than diffusion resistance. Therefore, tuning data extracted by analyzing the voltage response during the initial cycle of each pulse test can adequately compensate for the characteristic differences caused by charge transfer resistance between the two different battery types.

[0090] In the following text, a specific type of battery cell programmed into a battery simulator (BSM) to simulate charge and discharge response characteristics will be referred to as a 'reference cell', and will be different from a standard reference cell. Figure 1 Another type of battery cell (BC) will be referred to as the 'target cell'.

[0091] Figure 3a and Figure 3b This is a chart used to describe the differences in voltage response characteristics between a reference cell and a target cell.

[0092] Figure 3a The graph shows the relationship between current conditions, SOC conditions, and voltage changes for the reference cell, while Figure 3b The chart illustrates the relationship between the target cell's current conditions, SOC conditions, and voltage variations.

[0093] In each chart, data points can be matched with the first to thirty-two pulse tests using the first to fourth current conditions and the first to eighth SOC conditions. As shown in the charts, data points with a common SOC condition are connected by the same line.

[0094] As can be seen by comparing these two charts, Figure 3a The chart was drawn as lines representing the first through eighth SOC conditions that are close to each other, while Figure 3b The graphs are plotted as lines representing the first through eighth SOC conditions that are relatively far apart. That is, the reference cell can have lower sensitivity to voltage changes in SOC, while the target cell can have higher sensitivity to voltage changes in SOC.

[0095] Therefore, in order to simulate the charging and discharging response characteristics of a target cell using a battery simulator (BSM), it is necessary to apply tuning data to the battery simulator (BSM) to replace or approximate the voltage response characteristics of a reference cell programmed into the battery simulator (BSM) with the voltage response characteristics of the target cell.

[0096] Equations 3 and 4 below are used in the control equations of the pseudo-two-dimensional (P2D) model used for the reference cell. The control equations of the P2D model used for the reference cell can be programmed into the battery simulator (BSM).

[0097] Equation 3

[0098] Equation 4

[0099] In Equations 3 and 4, the symbols indicating different parameters of the reference cell are defined as follows.

[0100] j r : Reaction current density

[0101] a s Active surface area per unit volume of the electrode (specific surface area)

[0102] i r Exchange current density

[0103] α aCharge transfer coefficient of the negative electrode (e.g., 0.5)

[0104] α c : Charge transfer coefficient of the positive electrode (e.g., 0.5)

[0105] η r Overpotential value

[0106] R: Gas constant

[0107] T: Absolute temperature

[0108] F: Faraday constant

[0109] k r : Reaction rate constant

[0110] c e Concentration of working ions in the electrolyte

[0111] c s,max Maximum concentration of working ions in solids

[0112] c s,e Concentration of working ions at the interface between the electrolyte and the solid.

[0113] When the reference cell is a lithium-ion cell (LIB), the working ion refers to lithium ions; when the reference cell is a sodium-ion cell (SIB), the working ion can refer to sodium ions.

[0114] Based on, except j r η r i r and k r The remaining parameters are preset constants independent of battery type, or remain substantially equal during the initial cycle of the pulse test. The inventors have discovered that this can be mitigated by tuning k. r The process is used to fully suppress the remaining parameters.

[0115] The tuning k will be described in detail below. r The process. First, the parameters j of the target cell. t i r k t η r It can be defined as follows.

[0116] j t : Target cell reaction current density

[0117] i t Exchange current density

[0118] k t : The reaction rate constant of the target battery cell

[0119] ηt Overpotential value

[0120] Subsequently, assuming j in the initial period of the pulse test r and j t When they are equal, the relationship in Equation 5 below can be derived from Equations 3 and 4.

[0121] Equation 5

[0122] When the common c on both sides is eliminated from equation 5 e c s,max and c s,e When, equation 5 can be simplified to the following equation 6.

[0123] Equation 6

[0124] Then, equation 7 can be derived from equation 6.

[0125] Equation 7

[0126] In Equation 7, f can be a parameter indicating the kinetic rate adjustment ratio used for tuning from the reference cell to the target cell, i.e., the ratio between the reaction rate constant of the reference cell and the reaction rate constant of the target cell. The processor 120 can program k into Equation 3 in the battery simulator (BSM). r Replace with (k) r ×f), and therefore, the battery simulator (BSM) can be tuned to output charging and discharging response curves that fit the voltage response characteristics of the target cell.

[0127] Equation 8 below is a special form of Equation 7 used for the test of the k-th pulse.

[0128] Equation 8

[0129] In Equation 8, the parameter marked with the symbol [k] indicates its relevance to the k-th pulse test. f[k] is the k-th kinetic rate adjustment ratio used in the k-th pulse test to compensate for the difference in reaction rates between the reference and target cells. η r [k] represents the overpotential value of the reference cell in the initial cycle of the k-th pulse test, η t [k] represents the overpotential value of the target cell in the initial cycle of the k-th pulse test. η r [k] can be a value automatically calculated when the k-th test condition is input into the battery simulator (BSM), and ηt [k] can be a value obtained as the result of actually performing the kth pulse test on the target cell (see Equations 1 and 2).

[0130] Equation 9 below represents the dynamic rate regulation ratio of the positive electrode of the target cell derived from Equation 8, and Equation 9 represents the dynamic rate regulation ratio of the positive electrode of the target cell derived from Equation 8.

[0131] Equation 9

[0132] Equation 10

[0133] In equations 9 and 10, η r_pos [k] and η r_neg [k] is a given value, and can be referred to as the k-th reference positive electrode overpotential and the k-th reference negative electrode overpotential, respectively. Furthermore, f pos [k] and f neg [k] can be referred to as the k-th positive electrode kinetic rate regulation ratio and the k-th negative electrode kinetic rate regulation ratio, respectively. η r_pos [k]、η r_neg [k]、η t_pos [k] and η t_neg [k] can have a relationship according to Equations 11 and 12 below.

[0134] Equation 11

[0135] Equation 12

[0136] In equations 11 and 12, η t_pos [k] represents the total overpotential of the entire battery cell based on the target cell in the initial cycle of the k-th pulse test (i.e., η). t The overpotential value at the positive electrode of the target cell in [k]), and η t_neg [k] represents the overpotential value at the negative electrode of the target cell. R r_pos This represents the overpotential at the positive electrode and the total overpotential of the entire cell based on the reference cell during the initial period of the k-th pulse test (i.e., η). r The ratio of [k]). R r_neg It can be (1-R) r_pos ).

[0137] By substituting natural numbers from 1 to p into k in Equation 9, the kinetic rate regulation ratios of the first to p-th positive electrodes can be determined. Similarly, by substituting natural numbers from 1 to p into k in Equation 10, the kinetic rate regulation ratios of the first to p-th negative electrodes can be determined.

[0138] The processor 120 can derive first tuning data indicating the relationship between the SOC condition, the current condition, and the positive kinetic rate regulation ratio based on the first to the p-th positive kinetic rate regulation ratios.

[0139] Figure 4 and Figures 5a to 5c This is a diagram referenced during the derivation of the first tuning data.

[0140] The processor 120 groups the first to the p-th positive electrode kinetic rate regulation ratios into first to the m-th positive electrode subsets based on the first to the m-th current conditions. Therefore, each of the first to the m-th positive electrode subsets may include n positive electrode kinetic rate regulation ratios.

[0141] Subsequently, the processor 120 derives the first to the m-th positive electrode principal curves having a predetermined functional form from the first to the m-th positive electrode subsets. The i-th positive electrode principal curve can be an exponential curve function, a polynomial function, or a linear function derived by applying curve fitting to the i-th positive electrode subset.

[0142] Figure 4 The three main positive electrode curves MC are shown, respectively, for 2.5 C, 5 C, and 10 C as the first to third current conditions. pos_1 MC pos_2 MC pos_3 .

[0143] Equation 13 below is an example of the i-th positive main curve expressed in the form of an exponential curve function.

[0144] Equation 13

[0145] In equation 13, f pos_i This corresponds to the target positive electrode kinetic rate regulation ratio under the i-th current condition. The three curve parameters a of the exponential curve function are... i b i and c i In this order, the order is intercept, scaling factor, and growth rate.

[0146] When deriving the first to mth positive electrode master curves, determine the first to mth intercept values ​​a1 to a2. m The first to the m-th scaling factor values ​​b1~b m and the first to mth growth rate values ​​c1~c mThe i-th intercept value, the i-th scaling factor value, and the i-th growth rate value can be associated with the i-th current condition.

[0147] The processor 120 can derive the positive sub-curve for each curve parameter by applying curve fitting to the values ​​of curve parameters of the same type as the positive master curve.

[0148] Specifically, the processor 120 can be based on the first to the m-th intercept values ​​a1 to a2. m To derive the first positive electrode curve representing the relationship between the current condition and the intercept (the first curve parameter of the main positive electrode curve). Figure 5a The first positive pole curve SC, fitted as a linear function, is shown. pos_1 Examples. For instance, when x and y are the current condition and intercept, respectively, the first positive pole curve SC pos_1 The equation is y = (-0.047 × x) - 0.076.

[0149] Processor 120 can be based on the first to the m-th scaling factor values ​​b1~b m To derive the second positive electrode curve representing the relationship between the current condition and the scaling factor (the second curve parameter of the main positive electrode curve). Figure 5b The second positive pole curve SC, fitted as a linear function, is shown. pos_2 Examples. For instance, when x and y are the current condition and the scaling factor, respectively, the second positive pole curve SC... pos_2 The equation is y = (-0.47 × x) - 1.24.

[0150] Processor 120 can be based on the first to the mth growth rate values ​​c1 to c2. m To derive the third positron curve (the third curve parameter of the main positron curve) representing the relationship between current conditions and growth rate. Figure 5c The third positive pole curve SC, fitted as an exponential curve function, is shown. pos_3 Examples. For instance, when x and y are the current condition (i.e., C-rate) and growth rate, respectively, the third positron curve SC... pos_3 The equation is y = -0.04 - (0.22 × exp[-0.55 × x]).

[0151] Processor 120 can be based on the first to third positron curves SC pos_1 SC pos_2 SC pos_3 This is used to derive a first composite curve representing the relationship between SOC conditions, current conditions, and positive electrode kinetic rate regulation ratio. The first tuning data may include the first composite curve.

[0152] In one embodiment, the first composite curve can be the first to third positive pole curves SC. pos_1 SC pos_2 SC pos_3 Replace the curve parameter a of the i-th positive master curve according to Equation 13. i b i and c i Therefore, Equation 13 can be modified into Equation 14 below.

[0153] Equation 14

[0154] According to Equation 14, those skilled in the art will readily understand that the positive kinetic rate regulation ratio for a specific current condition and a specific SOC condition can be obtained from the first composite curve, regardless of the test conditions used in the pulse test.

[0155] Processor 120 can also derive second tuning data.

[0156] Figure 6 and Figures 7a to 7c This is a diagram referenced in the process of describing the derivation of the second tuning data.

[0157] The processor 120 groups the first to the p-th negative electrode kinetic rate regulation ratios into first to the m-th negative electrode subsets based on the first to the m-th current conditions. Therefore, each of the first to the m-th negative electrode subsets may include n negative electrode kinetic rate regulation ratios.

[0158] Subsequently, the processor 120 derives the first to the m-th negative pole principal curves having a predetermined functional form from the first to the m-th negative pole subsets. The i-th negative pole principal curve can be an exponential curve function, a polynomial function, or a linear function derived by applying curve fitting to the i-th negative pole subset.

[0159] Figure 6 The three negative electrode master curves MC are shown, respectively, associated with the first to third current conditions of 2.5 C, 5 C, and 10 C. neg_1 MC neg_2 MC neg_3 .

[0160] Equation 15 below is an example of the i-th negative principal curve expressed in the form of an exponential curve function.

[0161] Equation 15

[0162] In equation 15, f neg_iThis corresponds to the target negative electrode kinetic rate regulation ratio under the i-th current condition. The three curve parameters A of the exponential curve function are... i B i and C i These are the intercept, the scaling factor, and the growth rate.

[0163] When deriving the first to mth negative electrode principal curves, determine the first to mth intercept values ​​A1 to A2. m The first to the m-th scaling factor values ​​B1 to B m and the first to mth growth rate values ​​C1~C m The i-th intercept value, the i-th scaling factor value, and the i-th growth rate value can be associated with the i-th current condition.

[0164] The processor 120 can derive the negative pole curve for each curve parameter by applying curve fitting to the values ​​of curve parameters of the same type as the negative pole master curve.

[0165] Specifically, the processor 120 can be based on the first to the m-th intercept values ​​A1 to A2. m To derive the first negative pole curve representing the relationship between the current condition and the intercept (the first curve parameter of the main negative pole curve). Figure 7a The first negative pole curve SC, fitted as a linear function, is shown. neg_1 Examples. For instance, when x and y are the current condition and intercept, respectively, the first negative pole curve SC neg_1 The equation is y = (-0.047 × x) - 0.076.

[0166] The processor 120 can derive a second negative pole curve representing the relationship between the current condition and the scaling factor (the second curve parameter of the main negative pole curve) based on the first to the m-th scaling factor values ​​B1 to Bm. Figure 7b The second negative pole curve SC, fitted as a linear function, is shown. neg_2 Examples. For instance, when x and y are the current condition and the scaling factor, respectively, the second negative pole curve SC neg_2 The equation is y = (-0.47 × x) - 1.24.

[0167] Processor 120 can be based on the first to mth growth rate values ​​C1 to C2. m To derive the third negative pole curve (the third curve parameter of the main negative pole curve) representing the relationship between current conditions and growth rate. Figure 7c The third negative pole curve SC, fitted in the form of an exponential curve function, is shown. neg_3 Examples. For instance, when x and y are the current condition (i.e., C-rate) and growth rate, respectively, the third negative pole curve SC... neg_3The equation is y = -0.04 – (0.22 × exp[-0.55 × x]).

[0168] Processor 120 can be based on the first to third negative pole curves SC neg_1 SC neg_2 SC neg_3 A second composite curve representing the relationship between SOC conditions, current conditions, and negative electrode dynamic rate regulation ratio is derived. Second tuning data may include this second composite curve.

[0169] In one embodiment, the second composite curve can be the first to third negative pole curves SC. neg_1 SC neg_2 SC neg_3 Replace the curve parameter A of the i-th negative pole principal curve according to Equation 15. i B i C i Therefore, Equation 15 can be modified into Equation 16 below.

[0170] Equation 16

[0171] According to Equation 16, those skilled in the art will readily understand that the negative electrode kinetic rate regulation ratio for a specific current condition and a specific SOC condition can be obtained from the second composite curve, regardless of the test conditions used in the pulse test.

[0172] Figure 8 This is a flowchart schematically illustrating a simulator tuning data derivation method according to another embodiment of the present disclosure. Figure 8 The method includes steps S810, S820, S824 and S828.

[0173] Reference Figure 8 In step S810, the processor 120 determines multiple overpotential values ​​η driven by the charge transfer resistance of the target battery cell by analyzing multiple voltage response curves associated with multiple pulse tests performed on the target battery cell. t [1]~η t [p] (see also) Figure 2 (Equations 1 and 2). Each of the multiple pulse tests can be a process for applying a charging pulse or a discharging pulse to the target cell under test conditions through any combination of multiple SOC conditions and multiple current conditions. Multiple voltage response curves can be obtained from the battery tester 10 by the data acquisition unit 110.

[0174] In step S820, the processor 120 obtains multiple overpotential values ​​η t [1]~ηt [p] determines the overpotential values ​​η of multiple target positive electrodes. t_pos [1]~η t_pos [p] (See Equation 11).

[0175] In step S824, the processor 120 determines the overpotential value η of the multiple target positive electrodes. t_pos [1]~η t_pos [p] Corresponding to multiple positive electrode kinetic rate regulation ratios f pos [1]~f pos [p] (See Equation 9).

[0176] In step S828, the processor 120 adjusts the positive electrode kinetic rate based on multiple positive electrode kinetic rate regulation ratios f. pos [1]~f pos [p] is used to derive the first tuning data representing the relationship between SOC conditions, current conditions, and positive electrode kinetic rate regulation ratio.

[0177] Figure 8 The methods may also include S830, S834 and S838.

[0178] In step S830, the processor 120 obtains multiple overpotential values ​​η t [1]~η t [p] determines the overpotential values ​​η of multiple target negative electrodes. t_neg [1]~η t_neg [p] (See Equation 12).

[0179] In step S834, the processor 120 determines the overpotential value η of the multiple target negative electrodes. t_neg [1]~η t_neg [p] Corresponding to multiple negative electrode kinetic rate regulation ratios f neg [1]~f neg [p] (See Equation 10).

[0180] In step S838, the processor 120 adjusts the multiple negative electrode kinetic rate ratios f. neg [1]~f neg [p]Derivation of the second tuning data representing the relationship between SOC conditions, current conditions, and negative electrode dynamic rate regulation ratio.

[0181] In step S828, the simulator tuning data may include first tuning data, and in step S838, the simulator tuning data may further include second tuning data. The user may input an operation to change the target battery type of the battery simulator (BSM) from a first type to a second type into the information interface unit 130, and in response to this input, the processor 120 may apply the simulator tuning data to the battery simulator (BSM). When the simulator tuning data is applied, the reaction rate constant in the control equation programmed into the battery simulator (BSM) may be replaced by a function based on the first tuning data (see Equation 14) and / or a function based on the second tuning data (see Equation 16).

[0182] Figure 9 This is an illustrative representation that may be included in accordance with Figure 8 The flowchart shows an example of a subroutine in step S810 of the method.

[0183] Reference Figure 9 In step S910, the processor 120 determines multiple resistance values ​​R of interest based on multiple voltage response curves (see Equation 1). drop [1]~R drop [p]. In step S910, the processor 120 can determine the k-th voltage change value ΔV based on the k-th voltage response curve. drop [k], then by the kth voltage change value ΔV drop [k] is divided by the current condition I[i] to determine the k-th resistance value of interest, R. drop [k].

[0184] In step S920, the processor 120 selects from the plurality of resistor values ​​R of interest drop [1]~R drop [p] Subtract the ohmic resistance value of the target cell from the value of [p] to determine the multiple charge transfer resistance values ​​R. CT [1]~R CT [p] (see Equation 2).

[0185] In step S930, the processor 120 bases its decisions on multiple current conditions I[1] to I[m] and multiple charge transfer resistance values ​​R. CT [1]~R CT [p] to determine multiple overpotential values ​​η t [1]~η t [p] (see Equation 2).

[0186] Figure 10 This is an illustrative representation that may be included in accordance with Figure 8 The flowchart shows an example of a subroutine in step S828 of the method.

[0187] Reference Figure 10 In step S1010, the processor 120 groups multiple positive electrode dynamic rate adjustment ratios into multiple positive electrode subsets according to multiple current conditions.

[0188] In step S1020, the processor 120 derives multiple positive master curves having a predetermined functional form from multiple positive subsets (see...). Figure 4 MC in pos_1 MC pos_2 MC pos_3 ).

[0189] In step S1030, the processor 120 derives multiple positive electrode sub-curves for multiple curve parameters of multiple positive electrode master curves (see...). Figures 5a to 5c SC in pos_1 SC pos_2 SC pos_3 ).

[0190] In step S1040, the processor 120 derives a first composite curve (see Equation 14) representing the relationship between the SOC condition, the current condition, and the positive electrode kinetic rate regulation ratio based on multiple positive electrode curves. The first composite curve is included in the first tuning data.

[0191] Figure 11 This is an illustrative representation that may be included in accordance with Figure 8 The flowchart shows an example of a subroutine in step S838 of the method.

[0192] Reference Figure 11 In step S1110, the processor 120 adjusts the multiple negative electrode dynamic rate ratios according to multiple current conditions to form multiple negative electrode subsets.

[0193] In step S1120, processor 120 derives multiple negative pole master curves having a predetermined functional form from multiple negative pole subsets (see...). Figure 6 MC in neg_1 MC neg_2 MC neg_3 ).

[0194] In step S1130, processor 120 derives multiple negative pole sub-curves of multiple curve parameters of multiple negative pole master curves (see...). Figures 7a to 7c SC in neg_1 SC neg_2 SC neg_3 ).

[0195] In step S1140, processor 120 derives a second composite curve (see Equation 16) representing the relationship between SOC conditions, current conditions, and negative electrode kinetic rate regulation ratio based on multiple negative electrode curves. The second composite curve is included in the second tuning data.

[0196] Another embodiment of this disclosure may provide a computer-readable medium having a program thereon for causing a computer to execute the embodiments described above.

[0197] The program can be implemented by hardware components, software components, and / or combinations thereof. The program can be executed by any system capable of executing computer-readable instructions.

[0198] Software may include computer programs, code, instructions, or combinations thereof, and may be configured to operate or command the processing device as needed, either independently or jointly.

[0199] The software can be implemented as a computer program comprising instructions stored on a computer-readable storage medium. Computer-readable storage media include, for example, magnetic storage media (e.g., read-only memory (ROM), random access memory (RAM), floppy disk, hard disk) and optical storage media (e.g., CD-ROM, DVD). The computer-readable storage medium can be distributed across a network-connected computer system to store and execute computer-readable code in a distributed manner. The storage medium can be read by a computer (at least one processor), stored in a memory (at least one memory device), and executed by a processor (at least one processor).

[0200] The computer-readable medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory storage medium' refers to a tangible device and excludes signals (e.g., electromagnetic waves), and the term includes both semi-permanent and temporary data storage on the storage medium. For example, 'non-transitory storage medium' may include buffers for temporarily storing data.

[0201] Alternatively, the program may be provided in the form of a computer program product that includes the program. The computer program product may be a product traded between a seller and a buyer.

[0202] The computer program product may include a software program and a computer-readable storage medium storing the software program. For example, the computer program product may include a product in the form of a software program (e.g., a downloadable application) distributed electronically by an electronics manufacturer or electronic marketplace. For electronic distribution, at least a portion of the software program may be stored on a storage medium or temporarily created. In this case, the storage medium may be the storage medium of a server belonging to an electronics manufacturer, an electronic marketplace, or an intermediate server temporarily storing the software program.

[0203] The embodiments of this 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 exemplary configurations of this disclosure or by a storage medium storing a program, and such implementation can be readily achieved by those skilled in the art from the disclosure of the embodiments described above.

[0204] Although the invention has been described above with respect to certain embodiments and figures, the invention is not limited thereto, and it will be apparent to those skilled in the art that various modifications and changes can be made to the technical aspects of the invention and to the scope of the appended claims and their equivalents.

[0205] Furthermore, since those skilled in the art can make many substitutions, modifications and changes to this disclosure without departing from the technical aspects of this disclosure, this disclosure is not limited to the above-described embodiments and drawings, and some or all of the embodiments can be selectively combined to allow for various modifications.

Claims

1. A method for deriving simulator tuning data for using a battery simulator of a first type of reference cell for a second type of target cell, the method comprising the following steps: Multiple overpotential values ​​driven by the charge transfer resistance of the target battery cell are determined by analyzing multiple voltage response curves associated with multiple pulse tests performed on the target battery cell using multiple test conditions, which combine multiple state of charge (SOC) conditions and multiple current conditions. Multiple target positive electrode overpotential values ​​are determined based on the multiple overpotential values; Determine multiple positive electrode kinetic rate adjustment ratios corresponding to the multiple target positive electrode overpotential values; as well as Based on the plurality of positive electrode kinetic rate regulation ratios, first tuning data indicating the relationship between the SOC condition, the current condition, and the positive electrode kinetic rate regulation ratios are derived.

2. The simulator tuning data derivation method according to claim 1, in, The steps for determining the plurality of overpotential values ​​include: Multiple resistance values ​​of interest are determined based on the multiple voltage response curves; Multiple charge transfer resistance values ​​are determined by subtracting the ohmic resistance value of the target battery cell from the multiple resistance values ​​of interest; and The multiple overpotential values ​​are determined based on the multiple current conditions and the multiple charge transfer resistance values.

3. The simulator tuning data derivation method according to claim 2, in, The steps for determining the plurality of resistor values ​​of interest include: Based on the multiple voltage response curves, determine multiple voltage change values ​​during the initial period of the multiple pulse tests; and The plurality of resistance values ​​of interest are determined by dividing the plurality of voltage change values ​​by the plurality of current conditions.

4. The simulator tuning data derivation method according to claim 1, in, The kth positive electrode kinetic rate regulation ratio among the plurality of positive electrode kinetic rate regulation ratios is determined using the following equation: Among them, f pos [k]: The k-th positive electrode kinetic rate regulation ratio α a The charge transfer coefficient of the negative electrode. α c The charge transfer coefficient of the positive electrode. F: Faraday constant, R: gas constant, T: Absolute temperature ηr_ pos [k]: The overpotential value of the k-th reference positive electrode. ηt_ pos [k]: The overpotential value of the kth target positive electrode.

5. The simulator tuning data derivation method according to claim 1, in, The steps for deriving the first tuning data include: Based on the multiple current conditions, the multiple positive electrode dynamic rate adjustment ratios are divided into multiple positive electrode subsets; Multiple positive pole master curves with predetermined functional forms are derived from the multiple positive pole subsets; Derivation of multiple cathode sub-curves for multiple curve parameters of the multiple master cathode curves; and Based on the multiple positive electrode curves, a first comprehensive curve representing the relationship between the SOC condition, the current condition, and the positive electrode kinetic rate regulation ratio is derived, and The first tuning data includes the first composite curve.

6. The simulator tuning data derivation method according to claim 5, in, The plurality of positive electrode master curves are multiple exponential curve functions derived by applying curve fitting to the plurality of positive electrode subsets, and The multiple curve parameters include intercept, scaling factor, and growth rate.

7. The simulator tuning data derivation method according to claim 6, in, The steps for deriving the plurality of positive pole curves include: Based on the multiple intercept values ​​of the multiple positive electrode master curves, a first positive electrode sub-curve representing the relationship between the current condition and the intercept is derived; Based on the multiple scaling factor values ​​of the multiple positive electrode master curves, a second positive electrode curve representing the relationship between the current condition and the scaling factor is derived; and Based on the multiple growth rate values ​​of the multiple positive electrode master curves, a third positive electrode curve representing the relationship between the current condition and the growth rate is derived.

8. The simulator tuning data derivation method according to claim 1, further comprising the following steps: Multiple target negative electrode overpotential values ​​are determined based on the multiple overpotential values; Determine multiple negative electrode kinetic rate adjustment ratios corresponding to the multiple target negative electrode overpotential values; as well as Based on the plurality of negative electrode kinetic rate adjustment ratios, second tuning data indicating the relationship between the SOC condition, the current condition, and the negative electrode kinetic rate adjustment ratios are derived.

9. A simulator tuning data derivation device for using a battery simulator of a first type of reference cell for a second type of target cell, the simulator tuning data derivation device comprising: A data acquisition unit is configured to use multiple test conditions to acquire multiple voltage response curves associated with multiple pulse tests performed on the target cell, the multiple test conditions combining multiple state of charge (SOC) conditions and multiple current conditions. as well as A processor configured to determine multiple overpotential values ​​driven by the charge transfer resistance of the target cell by analyzing the plurality of voltage response curves. The processor is further configured as follows: Multiple target cathode overpotential values ​​are determined based on the multiple overpotential values; and Determine multiple cathode kinetic rate adjustment ratios corresponding to the multiple target cathode overpotential values; and Based on the plurality of positive electrode kinetic rate regulation ratios, first tuning data indicating the relationship between the SOC condition, the current condition, and the positive electrode kinetic rate regulation ratios are derived.

10. The simulator tuning data derivation device according to claim 9, in, The processor is also configured to: Multiple resistance values ​​of interest are determined based on the multiple voltage response curves; as well as Multiple charge transfer resistance values ​​are determined by subtracting the ohmic resistance value of the target cell from the multiple resistance values ​​of interest; as well as The plurality of overpotential values ​​are determined based on the plurality of current conditions and the plurality of charge transfer resistance values.

11. The simulator tuning data derivation device according to claim 9, in, The processor is also configured to: The kth positive electrode kinetic rate regulation ratio among the plurality of positive electrode kinetic rate regulation ratios is determined using the following equation: Among them, f pos [k]: The k-th positive electrode kinetic rate regulation ratio α a The charge transfer coefficient of the negative electrode. α c The charge transfer coefficient of the positive electrode. F: Faraday constant, R: gas constant, T: Absolute temperature ηr_ pos [k]: The overpotential value of the k-th reference positive electrode. ηt_ pos [k]: The overpotential value of the kth target positive electrode.

12. The simulator tuning data derivation device according to claim 9, in, The processor is also configured to: Based on the multiple current conditions, the multiple positive electrode dynamic rate adjustment ratios are divided into multiple positive electrode subsets. Multiple principal curves with predetermined functional forms are derived from the multiple positive pole subsets. Derivation of multiple positive electrode sub-curves for multiple curve parameters of the multiple positive electrode master curves, and Based on the multiple positive electrode curves, a first comprehensive curve representing the relationship between the SOC condition, the current condition, and the positive electrode kinetic rate regulation ratio is derived, and The first tuning data includes the first composite curve.

13. The simulator tuning data derivation device according to claim 9, in, The processor is also configured to: Multiple target negative electrode overpotential values ​​are determined based on the aforementioned multiple overpotential values. Determine multiple negative electrode kinetic rate adjustment ratios corresponding to the multiple target negative electrode overpotential values, and Based on the plurality of negative electrode kinetic rate adjustment ratios, second tuning data indicating the relationship between the SOC condition, the current condition, and the negative electrode kinetic rate adjustment ratios are derived.

14. A battery simulation system, the battery simulation system comprising the simulator tuning data derivation device according to any one of claims 9 to 13.

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