Method and apparatus for optimizing a battery

Abstract

A method and apparatus for optimizing a battery are provided. The battery optimization method includes: selectively adjusting a set of parameters related to a corresponding SoC range from a plurality of parameters of an electrochemical thermal (ECT) model based on operating data for each SoC range, and updating the plurality of parameters of the ECT model based on the adjusted parameter set.

Description

[0001] This application claims the benefit of Korean Patent Application No. 10-2020-0176975, filed on December 17, 2020, with the Korean Intellectual Property Office, the entire disclosure of which is incorporated herein by reference for all purposes. Technical Field

[0002] The following description relates to a battery optimization method and apparatus. Background Technology

[0003] The state of a battery can be estimated for optimal battery operation, and various methods exist for estimating this state. For example, the state can be estimated by integrating the battery current, or using a battery model (e.g., a circuit model or an electrochemical-thermal model). In the current integration method, the amount of charge remaining in the battery is calculated by using a current sensor attached to the end of the battery cell, module, or pack to calculate the total amount of electricity generated during charging and discharging. A circuit model is a circuit model that includes resistors and capacitors and can represent voltage values ​​that change as the battery charges and discharges. An electrochemical-thermal model models internal battery physics, such as ion concentration and potential. Summary of the Invention

[0004] The present invention is provided to introduce, in a simplified form, the selection of concepts further described in the following detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to help determine the scope of the claimed subject matter.

[0005] In one general aspect, a battery optimization method is provided, comprising: determining a first state of charge (SoC) value corresponding to first operating data of the battery; selecting a first SoC interval from the SoC interval corresponding to the first SoC value; selectively adjusting a first set of parameters related to the first SoC interval from the parameters of an electrochemical thermal (ECT) model configured to simulate the battery based on the first operating data; and updating the parameters of the ECT model based on the adjusted first set of parameters.

[0006] The parameters of the ECT model may include a second set of parameters related to a second SoC region. The step of selectively adjusting the first set of parameters based on the first operational data may include keeping the second set of parameters unadjusted.

[0007] The step of selectively adjusting the first parameter set may include: using first operating data and parameters to determine the estimated voltage of the ECT model; determining the optimization loss based on the measured voltage of the first operating data and the estimated voltage of the ECT model; and adjusting at least one parameter of the first parameter set to reduce the optimization loss.

[0008] The step of adjusting at least one parameter in the first parameter set may include: determining candidate parameters for reducing optimization loss in the first parameter set through Bayesian optimization; and adjusting the candidate parameters.

[0009] The parameters of the ECT model include one of constant parameters independent of the battery's SoC level and one of diffusion parameters dependent on the battery's SoC level. Each parameter in the first parameter set can be a diffusion parameter. The diffusion parameters may also include one of charging parameters related to battery charging and discharging parameters related to battery discharging. The first parameter set may include charging parameters in response to first operating data being measured during battery charging. The first parameter set may include discharging parameters in response to first operating data being measured during battery discharging. Constant parameters may include one of film resistance, volume fraction of active material, and anode-cathode open-circuit potential shift.

[0010] The battery can be configured to supply power to a battery-powered device. First operational data can be generated in response to the battery supplying power to the battery-powered device. The first operational data may include at least one of the battery's voltage, current, and temperature, depending on the battery's operation.

[0011] The battery optimization method may include: determining a new first parameter set by updating the first parameter set in response to the completion of selective adjustment of the first parameter set; and selectively adjusting a second parameter set related to a second SoC interval based on new parameters of the ECT model including the new first parameter set. The step of selectively adjusting the second parameter set may include: determining a second SoC value corresponding to second operating data of the battery; selecting a second SoC interval to which the second SoC value belongs from the SoC intervals; and selectively adjusting the second parameter set related to the second SoC interval from the new parameters of the ECT model based on the first operating data and the second operating data.

[0012] Different adjustment weights can be applied to selectively adjust the first set of parameters and selectively adjust the second set of parameters.

[0013] In another general aspect, a battery optimization device is provided, comprising: a processor configured to: determine a first state of charge (SoC) value corresponding to first operational data of a battery; select a first SoC interval from SoC intervals corresponding to the first SoC value; selectively adjust a first set of parameters related to the first SoC interval among parameters of an electrochemical thermal (ECT) model configured to simulate a battery based on the first operational data; and update the parameters of the ECT model based on the adjusted first set of parameters.

[0014] The parameters of the ECT model also include a second set of parameters related to a second SoC region, and the processor is further configured to: selectively adjust the first set of parameters based on the first operational data while keeping the second set of parameters unadjusted.

[0015] The processor can be configured to: determine a new first parameter set by updating the first parameter set in response to the completion of selective adjustment of the first parameter set; and selectively adjust a second parameter set related to a second SoC region based on new parameters of the ECT model including the new first parameter set.

[0016] In another general aspect, an electronic device is provided, the electronic device comprising: a battery configured to supply power to the electronic device; and a processor configured to: determine a first state of charge (SoC) value corresponding to the first operating data of the battery; select a first SoC interval from SoC intervals corresponding to the first SoC value; selectively adjust a first set of parameters related to the first SoC interval among the parameters of an electrochemical thermal (ECT) model configured to simulate a battery based on the first operating data; and update the parameters of the ECT model based on the adjusted first set of parameters.

[0017] The parameters of the ECT model may also include a second set of parameters related to a second SoC region, and the processor may be configured to selectively adjust the first set of parameters based on the first operational data while keeping the second set of parameters unadjusted.

[0018] The processor can also be configured to: determine a new first parameter set by updating the first parameter set in response to the completion of selective adjustment of the first parameter set; and selectively adjust a second parameter set related to a second SoC region based on new parameters of the ECT model including the new first parameter set.

[0019] In another general aspect, a processor-implemented method for optimizing a battery is provided, the method comprising: dividing a state of charge (SoC) value into two or more segments based on the SoC value; selecting a first SoC segment from the two or more segments corresponding to the first operational data of the battery; selectively adjusting a first set of parameters of an electrochemical thermal (ECT) model related to the first SoC segment based on the first operational data; selecting a second SoC segment from the two or more segments corresponding to the second operational data of the battery; selectively adjusting a second set of parameters of the ECT model related to the second SoC segment based on the first and second operational data; and updating the parameters of the ECT model based on the adjusted first and second parameter sets.

[0020] The selective adjustment of the first parameter set and the steps of the first parameter set can be performed at periodic time intervals.

[0021] The first and second operational data can be generated during the battery's charging cycle.

[0022] The first and second operating data can be generated during the battery's discharge cycle.

[0023] The battery optimization method may further include: applying weights to the SoC segment with the lowest SoC value from the two or more segments.

[0024] Other features and aspects will become clear from the following detailed description, drawings, and claims. Attached Figure Description

[0025] Figure 1 An example of the operation of the battery optimization device is shown.

[0026] Figure 2 An example of the classification of electrochemical thermal (ECT) parameters is shown.

[0027] Figure 3 An example of manipulating a dataset is shown.

[0028] Figure 4 An example of parameter optimization processing is shown.

[0029] Figure 5 An example of optimization for each state of charge (SoC) interval is shown.

[0030] Figure 6 An example of optimization operations for each SoC region is shown.

[0031] Figure 7 An example of optimization using adjusted weights is shown.

[0032] Figure 8 An example of optimization operation using the degraded state parameter is shown.

[0033] Figure 9 An example of a battery optimization device is shown.

[0034] Figure 10 An example of a battery-powered device including battery optimization equipment is shown.

[0035] Figure 11 An example of a battery management server including battery optimization devices is shown.

[0036] Figure 12 An example of an electronic device is shown.

[0037] Figure 13 An example of battery optimization operation is shown.

[0038] Throughout the accompanying drawings and detailed embodiments, unless otherwise described or provided, the same reference numerals will be understood to denote the same elements, features, and structures. The drawings may not be to scale, and for clarity, illustration, and convenience, the relative dimensions, scale, and depiction of elements in the drawings may be exaggerated. Detailed Implementation

[0039] The following structural or functional descriptions of the examples disclosed in this disclosure are intended only to describe the purpose of the examples, and the examples may be implemented in various forms. The examples are not intended to be limiting, but rather to suggest that various modifications, equivalents, and alternatives are also covered within the scope of the claims.

[0040] Although the terms "first" or "second" are used to explain various components, the components are not limited by the terms. These terms should only be used to distinguish one component from another. For example, within the scope of the claims according to the concept of this disclosure, a "first" component may be referred to as a "second" component, or similarly, a "second" component may be referred to as a "first" component.

[0041] It will be understood that when a component is referred to as being "connected to" another component, the component may be directly connected to or combined with the other component, or there may be an intermediate component.

[0042] As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. It should also be understood that the terms “comprising” and / or “including”, when used in this specification, indicate the presence of the stated features, integrals, steps, operations, elements, components, or combinations thereof, but do not preclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof.

[0043] Unless otherwise defined, all terms used herein (including technical or scientific terms) shall have the same meaning as commonly understood by a person of ordinary skill in the field to which the examples pertain. It will also be understood that, unless expressly defined herein, terms (such as those defined in a general dictionary) shall be interpreted as having a meaning consistent with their meaning in the context of the relevant field and shall not be interpreted in an idealized or overly formalized sense.

[0044] The examples will be described in detail below with reference to the accompanying drawings. Regarding the reference numerals assigned to the elements in the drawings, it should be noted that the same elements will be represented by the same reference numerals, and redundant descriptions will be omitted.

[0045] Figure 1 An example of the operation of the battery optimization device is shown. (Refer to...) Figure 1 The electrochemical thermal (ECT) model 111 can simulate the internal state of the battery 120 using various ECT parameters and governing equations. The battery optimization device 110 can use the ECT model 111 to estimate the state of charge (SoC) of the battery 120. For example, the parameters of the ECT model 111 can represent the shape of the battery (e.g., thickness, radius, etc.), open-circuit potential (OCP), and physical properties (e.g., conductivity, ionic conductivity, diffusion coefficient, etc.). The governing equations can include physical conservation equations related to the electrochemical reactions occurring at the interface between the electrodes and the electrolyte, based on the parameters and concentrations of the electrodes and the electrolyte, as well as charge conservation.

[0046] The battery optimization device 110 can acquire operational data of the battery 120 and use the operational data and the ECT model 111 to estimate the state of the battery 120. For example, the operational data may include at least one of the voltage, current, and temperature of the battery 120 based on its operation. As the battery 120 continues to operate, the cell state may change due to cell degradation or replacement. Changes in cell state may reduce the accuracy of the SoC estimation based on the ECT model 111. The battery optimization device 110 can update the parameters of the ECT model 111 based on changes in cell state to maintain the accuracy of the estimation.

[0047] The battery optimization device 110 can collect operating data of the battery 120 during operation, calculate the ECT model 111 based on the operating data to obtain the optimization loss, and update the ECT parameters to reduce the optimization loss. Since the operating data corresponds to the measured values, the difference between the estimated value of the ECT model 111 and the measured value of the operating data can correspond to the optimization loss.

[0048] The ECT parameters may include constant parameters independent of the SoC level of battery 120 and diffusion parameters dependent on the SoC level. For example, as the SoC level gradually decreases due to the operation of battery 120, the constant parameters can be kept constant while the diffusion parameters can be changed. Battery optimization device 110 may selectively update parameters among the constant parameters that are highly correlated with the degradation of battery 120 (such as at least one of film resistance, volume fraction of active material, and anode-cathode OCP shift). Additionally, battery optimization device 110 may selectively adjust the diffusion parameters based on the SoC range of operating data. The ECT parameter update process will be described in more detail later.

[0049] As described below, selective adjustment of the diffusion parameters allows the error in each parameter to be corrected based on the optimization loss for the corresponding interval. This improves training accuracy. Furthermore, selective updates significantly reduce the operations required for updates and increase update speed. Reduced operations and faster updates enable on-device training.

[0050] Parameter updates can be performed at predetermined intervals (e.g., monthly, quarterly, etc.) or at specific times (e.g., in response to requests from users, developers, etc.). Additionally, parameter updates can be performed in conjunction with changes in the State of Health (SoH) of the battery 120. For example, a parameter update can be performed each time the SoH decreases by 10%. To avoid the inconvenience of the device being unavailable during the update period, the battery optimization device 110 can request prior approval from the user before the update is performed. Furthermore, the battery optimization device 110 can perform parameter updates during times when the device is rarely used (e.g., late at night).

[0051] When the parameters of ECT model 111 are optimized based on parameter updates, the target device can use ECT model 111 with optimized parameters to estimate the SoC of battery 120. The target device can be a device that receives power from battery 120, and can be, for example... Figure 10 The battery-powered device 1000. Based on the parameters of the optimized ECT model 111, the target device can estimate the SoC with high accuracy. The target device can use existing parameters when the parameters of the ECT model 111 are updated. In the new cycle after the update is completed, the target device can use the updated parameters.

[0052] As described below, the parameter set can be updated for each SoC interval. When the update completion times for SoC intervals differ, each updated parameter set can be used individually based on the completion time of the update for its corresponding interval. For example, although the update of the first parameter set for the first SoC interval has been completed, the update of the second parameter set for the second SoC interval may be in progress. In this example, the updated first parameter set can be used to estimate the SoC of the first SoC interval, and the existing second parameter set can be used to estimate the SoC of the second SoC interval.

[0053] Figure 2 An example of ECT parameter classification is shown. (See reference...) Figure 2The ECT model 200 stores an ECT parameter set 210. The ECT parameters of the ECT parameter set 210 can be categorized into constant parameters 211 and diffusion parameters 212. For example, constant parameters 211 may include the shape of the cells (e.g., thickness, radius, etc.) independent of the battery's SoC level, resistance, and OCP. Diffusion parameters 212 may include diffusion coefficients that depend on the SoC level. Diffusion parameters can also be categorized into one of charging parameters related to battery charging and discharging parameters related to battery discharging.

[0054] Parameter 213 can be an anode discharge parameter. Parameter 214 can be a cathode discharge parameter. Parameter 215 can be an anode charge parameter. Parameter 216 can be a cathode charge parameter. Parameters 213 to 216 are plotted on each graph. In the graph, the horizontal axis represents the SoC (System-on-Chips), and the vertical axis represents the diffusion coefficient. Diffusion parameter 212 can be defined as the desired spacing. For example, in... Figure 2 In this context, parameter 213 is represented by approximately 20 points along the SoC level from 0.0 to 1.0. However, by definition, a larger number of parameters 213 can be plotted densely, or a smaller number of parameters 213 can be plotted coarsely.

[0055] The initial values ​​of the ECT parameter set 210 can be experimentally determined through processing (such as detailed debugging processing). This processing can include expert-led detailed debugging. When the battery cell state changes in response to device use, the battery optimization device can update the ECT parameter set 210 with new values ​​based on the changed state. Thus, the accuracy of the SoC estimation and the optimized state of the ECT parameter set 210 can be maintained.

[0056] Figure 3 An example of manipulating a dataset is shown. (See reference...) Figure 3 Operational dataset 300 can be generated during battery operation. Operational dataset 300 may include various battery-related data (e.g., at least one of battery voltage, current, and temperature) measured by various sensors connected to the battery.

[0057] Each operational dataset in operational dataset 300 can be collected based on the SoC. For example, an operational dataset can be generated when the SoC decreases from 1.0 to 0.0. Furthermore, another operational dataset can be generated when the SoC decreases from 1.0 to 0.0 again after the battery has been fully charged. Here, 1.0 and 0.0 are merely examples, and operational datasets can also be generated in other SoC ranges. For example, an operational dataset can be generated when the SoC decreases from 1.0 to 0.3, and an operational dataset can also be generated during a charging cycle.

[0058] As an example, the following description gives the first operation dataset 310 in operation dataset 300. Operation dataset 300 may include m operation datasets, where m is a positive integer. The first operation dataset 310 corresponds to one of the m operation datasets. The first operation dataset 310 includes first operation data 311 and second operation data 312. The first operation dataset 310 may include n data values, where n is a positive integer.

[0059] The battery optimization device can adjust each diffusion parameter based on the SoC range. It can obtain an optimization loss based on each element in the operating data and selectively adjust the corresponding diffusion parameter for the corresponding SoC range based on this optimization loss.

[0060] For example, the first operational data 311 may correspond to a SoC value of 0.8, and the second operational data 312 may correspond to a SoC value of 0.5. When the entire SoC range (e.g., 1.0 to 0.0) is divided into multiple sub-ranges, the corresponding range for each of the operational data can be selected from the sub-ranges. For example, when the entire SoC range is divided into three sub-ranges, a SoC value of 0.8 may belong to the first range, and a SoC value of 0.5 may belong to the second range. Therefore, the battery optimization device can use the optimization loss obtained from the first operational data 311 to selectively adjust the parameters related to the first range, and use the optimization loss obtained from the second operational data 312 to selectively adjust the parameters related to the second range.

[0061] Since the SoC level decreases over time in operational dataset 300, operational dataset 300 can represent the battery's discharge. Operational dataset 300 can be used to adjust the discharge parameters among the diffusion parameters. Unlike operational dataset 300, when other operational datasets are generated during battery charging, the SoC level increases over time in the operational datasets, and these operational datasets can be used to adjust the charging parameters among the diffusion parameters.

[0062] Figure 4 An example of parameter optimization processing is shown. Figure 4 An example of a method for training a neural network to generate images is shown. Figure 4 The operations can be performed in the order and manner shown, but without departing from the spirit and scope of the illustrative examples described, the order of some operations may be changed or some operations may be omitted. Figure 4 Many of the operations shown can be performed in parallel or simultaneously. Figure 4 One or more blocks, and combinations thereof, can be implemented by a computer based on dedicated hardware (such as a processor) or a combination of dedicated hardware and computer instructions to perform the specified function. In addition to the following... Figure 4In addition to the description, Figures 1 to 3 The description also applies to Figure 4 And it is included here by reference. Therefore, the above description need not be repeated here.

[0063] Reference Figure 4 In operation 410, the battery optimization device can process the ECT model and determine the estimated voltage. The battery optimization device can process the ECT model based on initial parameters in the initial iteration, and can process the ECT model based on new parameters adjusted according to the parameters from subsequent iterations. The initial parameters can be determined through debugging. The estimated voltage can be determined through processing the ECT model. This processing can be represented by Equation 1 below.

[0064] [Equation 1]

[0065]

[0066] In equation 1, This indicates the estimated voltage; ECT represents the voltage estimation operation using the ECT model; I represents the battery current; T represents the battery temperature; and Θ represents the ECT parameter set. I and T can be obtained from the operation dataset. (See reference...) Figure 3 The operational dataset can include n operational data points, each including the battery's voltage, current, and temperature. For example, this can be represented as V = {v1, v2, ..., v...} n}、I={i1,i2,...,i n} and T = {t1, t2, ..., t n In the initial iteration, the initial parameter set Θ can be used. init Battery optimization devices can be based on I, T, and Θ. init To process the ECT model, thereby estimating

[0067] In operation 420, the battery optimization device can determine the optimization loss. The operation data is V = {v1, v2, ..., v...} n This corresponds to the measured value, and therefore can be called the "measured voltage". Furthermore, according to the processing of the ECT model... This corresponds to the estimated value, and therefore can be called the "estimated voltage". The optimization loss can be corresponding to the difference between the measured voltage and the estimated voltage.

[0068] In operation 430, the battery optimization device can adjust the ECT parameters and determine new parameters. For example, in the initial iteration, the battery optimization device can adjust the initial parameter values ​​and determine new parameters. The battery optimization device can adjust the ECT parameter values ​​in the direction in which the optimization loss is reduced (e.g., the direction in which the optimization loss is minimized). This process can be illustrated by Equation 2 below.

[0069] [Equation 2]

[0070]

[0071] In equation 2, Indicates the new parameter, v i Indicates the measured voltage, and This represents the estimated voltage. In one example, when i = n, one iteration is completed, and new parameters are generated. When optimization is not complete, the battery optimization device can be based on new parameters. (For example, a new parameter generated in a previous iteration) Operation 410 is executed again (for example, operation 410 in the next iteration), and then another new parameter is obtained in operation 430. When optimization is complete, in operation 440, the battery optimization device can apply the new parameters. Determined as the optimal parameter Θ opt and using the optimal parameter Θ opt Update the ECT parameters. For example, optimization can be determined when training on m operational datasets is required, when an operational dataset is trained in one iteration, and when the current iteration corresponds to the m-th iteration. Furthermore, optimization can be determined when a predetermined performance metric (e.g., a very low optimization loss (e.g., below a predetermined threshold)) is achieved.

[0072] The battery optimization device can perform optimization processes 410 to 430 based on machine learning. In this process, the machine learning model can learn from the operational data to optimize the parameters Θ. opt The goal is to obtain the desired value. In one example, the battery optimization device could employ Bayesian optimization. Bayesian optimization is a technique used to optimize the value of an unknown objective function using a surrogate model and a retrieval function. The surrogate model performs a probabilistic estimate of the objective function based on the function values ​​obtained so far and the input values. The retrieval function uses the results of the surrogate model to recommend useful candidates for finding the optimal solution. In one example, the battery optimization device could use Bayesian optimization to determine candidate parameter values ​​that reduce the optimization loss and adjust at least one parameter in the parameter set to a candidate parameter value. In another example, the battery optimization device could use Bayesian optimization to (e.g., from the parameter set) determine candidate parameters that reduce the optimization loss and adjust the candidate parameters.

[0073] According to Equation 2, the optimization loss for the entire SoC interval can be applied to the entire ECT parameter set. Alternatively, the battery optimization device can selectively adjust the parameter set for each SoC interval. Thus, the error of each parameter can be corrected based on the optimization loss of the corresponding interval, which can improve training accuracy. Furthermore, selective parameter updates can significantly reduce the operations required for updates and increase update speed. For example, when the entire SoC interval is divided into three sub-intervals, the battery optimization device can perform optimization using Equations 3 through 5. Equation 3 is associated with the first SoC interval, Equation 4 with the second SoC interval, and Equation 5 with the third SoC interval. However, this is merely an example, and the battery optimization device can also perform optimization by dividing the SoC interval into different numbers of intervals (e.g., dividing the SoC interval into two or four or more intervals).

[0074] [Equation 3]

[0075]

[0076] According to Equation 3, the optimized loss is obtained from the interval 1 to j. Indices 1 to j can correspond to a first SoC interval (e.g., in the case of discharge, the interval from 1.0 to 0.7). In this case, the battery optimization device can obtain the optimized loss while selectively adjusting the parameters in the parameter set Θ that are related to the first SoC interval (hereinafter also referred to as the "first parameter set"). Thus, a new parameter set (e.g., parameter set...) is obtained. The new parameter set can be estimated. This can correspond to adjusting a version of the first parameter set associated with the first SoC interval across the entire parameter set Θ. For example, when the operating data corresponds to discharge data, the first parameter set can correspond to the parameters belonging to the first SoC interval among the discharge parameters of parameter set Θ. Subsequently, the values ​​of the first parameter set can be fixed or updated, so that optimization for the second SoC interval is performed continuously using Equation 4 based on multiple new parameters of the ECT model including the fixed first parameter set.

[0077] [Equation 4]

[0078]

[0079] According to Equation 4, the optimized loss is obtained in the interval from 1 to k, where k is an integer greater than j. The indices j+1 to k can correspond to a second SoC interval (e.g., 0.7 to 0.4 in the case of discharge). The reason for using indices 1 to k in Equation 4 instead of j+1 to k is due to the continuity of the operating data. Battery charging and discharging data can have the characteristic that previously applied current also has an effect thereafter. Therefore, if information about an intermediate time point (e.g., the time point i = j+1) is used to estimate the SoC of subsequent time points (e.g., the time points i = j+2 to i = k), errors may occur. Therefore, an initial time point (e.g., the time point i = 1) can be used instead of an intermediate time point (e.g., the time point i = j+1).

[0080] Battery optimization equipment can optimize losses while selectively adjusting the parameter set. The parameters related to the second SoC range (hereinafter also referred to as the "second parameter set"). Thus, a new parameter set (e.g., parameter set) The new parameter set can be estimated. It can correspond to the entire parameter set. The version of the second parameter set related to the second SoC range is adjusted. At this time, the first parameter set can be fixed during the process of obtaining candidates for optimization. Thus, selective adjustment can significantly reduce the operations required for optimization. For example, when the operating data corresponds to discharge data, the second parameter set can correspond to the parameter set... The discharge parameters belong to the second SoC range. Subsequently, the values ​​of the first and second parameter sets can be fixed or updated, allowing the optimization of the third SoC range to be performed continuously using Equation 5 based on multiple new parameters of the ECT model, including the fixed first and second parameter sets.

[0081] [Equation 5]

[0082]

[0083] According to Equation 5, the optimization loss is obtained in the interval from 1 to n, where n is an integer greater than k. Indices k+1 to n can correspond to the third SoC interval (e.g., in the case of discharge, the interval from 0.4 to 0.0). Due to the aforementioned continuity of the operating data, the optimization loss can be obtained using indices 1 to n instead of indices k+1 to n.

[0084] Battery optimization equipment can optimize losses while selectively adjusting the parameter set. The parameters related to the third SoC range (hereinafter also referred to as the "third parameter set"). Therefore, a new parameter set (e.g., parameter set) is created. The new parameter set can be estimated. It can correspond to the entire parameter set. The version of the third parameter set related to the third SoC range is adjusted. At this time, the first and second parameter sets can be fixed during the process of obtaining candidates for optimization. For example, when the operating data corresponds to discharge data, the third parameter set can correspond to the parameter set. The discharge parameters belong to the third SoC range. Subsequently, the parameter set can be... Determined as the new parameter for the current iteration

[0085] The description of selective adjustment given above can be similarly applied to charging parameters. When operational data based on battery charging is available, each operational data value can be categorized into multiple SoC ranges, and the charging parameters for the corresponding SoC range can be selectively adjusted. For example, the entire SoC range can be divided into a first SoC range of 0.0 to 0.4, a second SoC range of 0.4 to 0.7, and a third SoC range of 0.7 to 1.0.

[0086] Since the constant parameters have constant values ​​and are independent of the SoC range, the above description of selective adjustment can be disregarded. However, because SoC estimation errors are more likely to occur in the low SoC range compared to the high SoC range, battery optimization devices can employ techniques for adjusting the constant parameters in the low SoC range. For example, when performing optimization using operational data during battery discharge, the constant parameters can be optimized together with a third set of discharge parameters. According to the explanation of Equation 5 above, the optimization loss can be calculated, while the third set of parameters is within the parameter set... The parameters are adjusted. In this case, the optimization loss can be calculated, while the third parameter set and constant parameters are adjusted within the parameter set. The middle part was adjusted.

[0087] Figure 5 An example of optimizations for each SoC region is shown. (See reference...) Figure 5 The graph 520 shows the battery voltage of the operational dataset 510 over time. Since the voltage decreases over time, it can be known that the operational dataset 510 was measured during battery discharge. The entire SoC range 500 can be divided into a first SoC range 501, a second SoC range 502, and a third SoC range 503 based on the SoC level or size. For example, the first SoC range 501 can correspond to an SoC level of 1.0 to 0.7, the second SoC range 502 can correspond to an SoC level of 0.7 to 0.4, and the third SoC range 503 can correspond to an SoC level of 0.4 to 0.0.

[0088] Operational dataset 510 includes first operational data 515 and second operational data 516. First operational data 515 may correspond to a first SoC value. Second operational data 516 may correspond to a second SoC value. ECT parameters 530 may correspond to discharge parameters (e.g., anodic discharge parameters). ECT parameters 530 are plotted on a graph. In the graph, the horizontal axis represents SoC (e.g., 1.0 to 0.0), and the vertical axis represents diffusion concentration. ECT parameters 530 can be categorized into a first parameter set 531 belonging to a first SoC range 501, a second parameter set 532 belonging to a second SoC range 502, and a third parameter set 533 belonging to a third SoC range 503.

[0089] Since the first SoC value of the first operation data 515 belongs to the first SoC interval 501, the first parameter set 531 can be selectively adjusted based on the first operation data 515. Thus, the first parameter set 531 can be selectively adjusted based on the first operation data group 511 of the first SoC interval 501. In this process, the second parameter set 532 and the third parameter set 533 can be maintained without adjustment. When all operation data in the first operation data group 511 is processed, the first parameter set 531 can be fixed. Since the second SoC value belongs to the second SoC interval 502, the second parameter set 532 can be selectively adjusted based on the first operation data 515 and the second operation data 516. Thus, the second parameter set 532 can be selectively adjusted based on the second operation data group 512 of the first SoC interval 501 and the second SoC interval 502. At this time, although the first parameter set 531 is fixed, the estimation candidates for the second parameter set 532 can be determined. When all operation data in the second operation data group 512 is processed, the second parameter set 532 can be fixed. Similarly, the third parameter set 533 can be selectively adjusted based on the third operating data group 513 of the first SoC interval 501, the second SoC interval 502, and the third SoC interval 503.

[0090] The target device can use the updated parameters in a new cycle after the parameter update of ECT model 111 is completed. In this case, when there are differences in the completion times for updates of different SoC intervals, each updated parameter set can be used individually based on the completion time of the corresponding interval. For example, although the update of the first parameter set 531 of the first SoC interval 501 has been completed, the update of the second parameter set 532 of the second SoC interval 502 may be in progress. In this example, the target device can use the updated first parameter set 531 to estimate the SoC of the first SoC interval 501 and use the existing second parameter set 532 to estimate the SoC of the second SoC interval 502.

[0091] Figure 6 An example of optimization operations for each SoC region is shown. Figure 6 An example of a method for training a neural network to generate images is shown. Figure 6 The operations shown can be performed in the order and manner illustrated, but without departing from the spirit and scope of the illustrative examples described, the order of some operations may be changed or some operations may be omitted. Many of the operations shown in Figure 6 can be performed in parallel or simultaneously. Figure 6 One or more blocks, and combinations thereof, can be implemented by a computer based on dedicated hardware (such as a processor) or a combination of dedicated hardware and computer instructions to perform the specified function. In addition to the following... Figure 6 In addition to the description, Figure 1 Of Figure 5 The description also applies to Figure 6 And it is included here by reference. Therefore, the above description need not be repeated here.

[0092] Reference Figure 6 In operation 610, the battery optimization device obtains operational data OD. i , where i is an integer value between 1 and n. In operation 620, the battery optimization device determines the operation data OD. i SoC i .

[0093] In operation 631, the battery optimization device uses operation data OD. i To optimize the first parameter set. For example, an optimization search based on machine learning (such as Bayesian optimization) can be performed. The first parameter set can represent the parameters that belong to the first SoC interval in the entire ECT parameter set. Operational data OD with indices 1 to j. i It can be used for the optimization search of the first parameter set. When the optimization of the first parameter set is completed, in operation 632, the battery optimization device can fix the optimization result of the first parameter set.

[0094] In operation 641, the battery optimization device uses operation data OD. i To optimize the second parameter set. The second parameter set can represent parameters belonging to the second SoC range within the entire ECT parameter set. It has operational data OD with indices 1 to k. i It can be used for the optimization search of the second parameter set. When the optimization of the second parameter set is completed, in operation 642, the battery optimization device can fix the optimization result of the second parameter set.

[0095] In operation 651, the battery optimization device uses operation data OD. iTo optimize the third parameter set. The third parameter set can represent parameters belonging to the third SoC range in the entire ECT parameter set. It has operational data OD with indices 1 to n. n This can be used for optimizing the search of the third parameter set. When the optimization of the third parameter set is complete, in operation 652, the battery optimization device can fix the optimization result of the third parameter set. The battery optimization device can merge the parameter sets in operation 661 and update the ECT parameter set with the new merged parameter set in operation 662. When the update of the ECT parameters is complete, the target device can use the updated ECT parameters to estimate the battery SoC. However, it should be understood that although this application describes an example of estimating the battery SoC by dividing the entire SoC range into three SoC ranges, it is also feasible to estimate the battery SoC by dividing the entire SoC range into fewer or more SoC ranges. For example, when the entire SoC range is divided into one SoC range, operations 641 to 652 can be omitted; when the entire SoC range is divided into two SoC ranges, operations 651 to 652 can be omitted; when the entire SoC range is divided into four or more SoC ranges, new operations similar to operations 651 and 652 can be added between operations 652 and 661.

[0096] Figure 7 An example of optimization using adjusted weights is shown. (See reference...) Figure 7 The battery optimization device can selectively adjust a first parameter set 731 using a first operating data set 711 of a first SoC range, selectively adjust a second parameter set 732 using a second operating data set 712 of a second SoC range, and selectively adjust a third parameter set 733 using a third operating data set 713 of a third SoC range. The battery optimization device can apply different adjustment weights to adjust the corresponding parameter sets. The battery optimization device can increase the update intensity by applying higher adjustment weights to intervals corresponding to higher probabilities of estimation errors. For example, typically, SoC estimation errors may be high at low SoC levels. Reflecting this, an adjustment weight of 0.3 can be applied to the first parameter set 731, an adjustment weight of 0.7 can be applied to the second parameter set 732, and an adjustment weight of 1.0 can be applied to the third parameter set 733.

[0097] The battery optimization device can apply adjustment weights by multiplying the optimization loss by an adjustment weight. For example, when the first optimization loss is obtained from the first operating data of the first operating data set 711, the battery optimization device can reduce the update effect of the first optimization loss to 0.3 times by multiplying it by an adjustment weight of 0.3. Similarly, each optimization loss of the second operating data set 712 can be multiplied by an adjustment weight of 0.7, and each optimization loss of the third operating data set 713 can be multiplied by an adjustment weight of 1.0. Thus, the third operating data set 713 can perform updates with the highest intensity. Furthermore, as described above, constant parameters can be adjusted together in the adjustment process of the third parameter set 733. This configuration of adjustment weights can improve the accuracy of the constant parameters.

[0098] Figure 8 An example of optimization operation using the degraded state parameter is shown. Figure 8 An example of a method for training a neural network to generate images is shown. Figure 8 The operations can be performed in the order and manner shown, but without departing from the spirit and scope of the illustrative examples described, the order of some operations may be changed or some operations may be omitted. Figure 8 Many of the operations shown can be performed in parallel or simultaneously. Figure 8 One or more blocks, and combinations thereof, can be implemented by a computer based on dedicated hardware (such as a processor) or a combination of dedicated hardware and computer instructions to perform the specified function. In addition to the following... Figure 8 In addition to the description, Figures 1 to 7 The description also applies to Figure 8 And it is included here by reference. Therefore, the above description need not be repeated here.

[0099] Reference Figure 8 In operation 810, the battery optimization device defines degradation state parameters. These degradation state parameters may include at least one of, for example, film resistance, volume fraction of active material, and anode-cathode OCP offset. These parameters may be constant and best reflect the battery's degradation state among the ECT parameters.

[0100] In operation 820, the battery optimization device acquires operational data; in operation 830, it optimizes the degradation state parameters; and in operation 840, it updates the ECT parameters using the new optimized degradation state parameters. When adjusting constant parameters and diffusion parameters together, the battery optimization device can selectively adjust degradation state parameters instead of adjusting the entire constant parameter, or selectively adjust the degradation state parameters of the constant parameter without adjusting the diffusion parameter. Thus, when optimizing constant parameters, the battery optimization device can selectively optimize degradation state parameters, thereby reducing the computational load for optimization. For example, selective optimization of degradation state parameters can be performed when processor computing power is insufficient or rapid updates are required.

[0101] Figure 9 An example of a battery optimization device is shown. (See reference) Figure 9 The battery optimization device 900 includes a processor 910 and a memory 920. The memory 920 can be connected to the processor 910 and stores instructions to be executed by the processor 910, data to be calculated by the processor 910, or data already processed by the processor 910. The memory 920 can be volatile memory or non-volatile memory.

[0102] In one example, the volatile memory device may be, for example, high-speed random access memory and / or non-volatile computer-readable storage media, dynamic random access memory (DRAM), static RAM (SRAM), thyristor RAM (T-RAM), zero-capacitance RAM (Z-RAM), dual-transistor RAM (TTRAM), one or more disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. Further description of memory 920 is given below.

[0103] In one example, the non-volatile memory device can be, for example, electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic RAM (MRAM), spin-transfer torque (STT) MRAM (STT-MRAM), conductive bridged RAM (CBRAM), ferroelectric RAM (FeRAM), phase-change RAM (PRAM), resistive RAM (RRAM), nanotube RRAM, polymer RAM (PoRAM), nanofloating gate memory (NFGM), holographic memory, molecular electronic memory device, or insulator resistance-changing memory. Further description of memory 920 is given below.

[0104] Processor 910 can execute instructions to perform according to Figures 1 to 8 and Figures 10 to 13The described operation. Processor 910 may be hardware including circuitry with a physical structure for performing the desired operation. For example, the desired operation may include instructions or code contained in a program. For example, a hardware-implemented tracing device may include, for example, a microprocessor, a central processing unit (CPU), a single processor, a standalone processor, a parallel processor, a single-instruction single-data (SISD) multiprocessor, a single-instruction multiple-data (SIMD) multiprocessor, a multiple-instruction single-data (MISD) multiprocessor, a multiple-instruction multiple-data (MIMD) multiprocessor, a controller and arithmetic logic unit (ALU), a DSP, a microcomputer, a processor core, a multi-core processor, a multiprocessor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a programmable logic unit (PLU), a central processing unit (CPU), a graphics processing unit (GPU), a neural processor (NPU), or any other device capable of responding to and executing instructions in a defined manner.

[0105] For example, processor 910 can determine a first SoC value corresponding to first operating data of the battery, select a first SoC range to which the first SoC value belongs from the entire SoC range, selectively adjust a first set of parameters related to the first SoC range among multiple parameters of the ECT model simulating the battery based on the first operating data, and update multiple parameters of the ECT model based on the adjusted first set of parameters. Additionally, Figures 1 to 8 and Figures 10 to 13 The description can be applied to the battery optimization device 900. Further description of the processor 910 is given below.

[0106] Figure 10 An example of a battery-powered device including battery optimization features is shown. (See reference) Figure 10 The battery-powered device 1000 includes a battery 1010 and a battery optimization device 1020. The battery optimization device 1020 can be implemented as at least a part of the battery management system (BMS) of the battery-powered device 1000. The battery 1010 can supply power to the battery-powered device 1000, enabling the battery optimization device 1020 to collect operational data from the battery 1010 and maintain the battery 1010 in an optimized state. For example, the operational data can be generated based on the operation of the battery 1010 when a user uses the battery-powered device 1000, or the operational data can be generated in response to the battery 1010 supplying power to the battery-powered device 1000.

[0107] The battery optimization device 1020 can perform optimization based on on-device methods. In this case, the battery optimization device 1020 can significantly reduce the computational operations used for optimization through selective optimization. Alternatively, the battery optimization device 1020 can use a cloud server to perform optimization. The battery optimization device 1020 can synchronously store the ECT parameter set and operational data in the cloud server. When optimization is needed, the cloud server can process the optimization operation and provide the results to the battery optimization device 1020. The battery optimization device 1020 can use the optimization results received from the cloud server to update the ECT parameter set. The battery-powered device 1000 can be driven by power supplied from the battery 1010. In this case, the SoC of the battery 1010 can be accurately estimated using an ECT model based on the ECT parameters optimized by the battery optimization device 1020. Furthermore, Figures 1 to 9 and Figures 11 to 13 The description can be applied to battery-powered device 1000.

[0108] Figure 11 An example of a battery management server including battery optimization equipment is shown. (See reference...) Figure 11 ,and Figure 10 Unlike other battery management systems, battery optimization device 1111 is included in battery management server 1110. Battery optimization device 1111 can receive and store operational data from battery power supply device 1121. When optimization is needed, battery optimization device 1111 can process optimization operations based on the operational data and provide the results of the optimization operations to battery power supply device 1120. At this time, communication module and BMS can perform operations between battery optimization device 1111 and battery 1121. Battery power supply device 1120 can be driven by power supplied from battery 1121. In this case, the SoC of battery 1121 can be accurately estimated using an ECT model based on the ECT parameters optimized by battery optimization device 1111. Furthermore, Figures 1 to 10 and Figures 11 to 13 The description can be applied to battery-powered device 1000.

[0109] Figure 12 An example of an electronic device is shown. (See reference) Figure 12Electronic device 1200 includes a processor 1210, a memory 1220, a camera 1230, a storage device 1240, an input device 1250, an output device 1260, and a network interface 1270. The processor 1210, memory 1220, camera 1230, storage device 1240, input device 1250, output device 1260, and network interface 1270 can communicate via a communication bus 1280. For example, electronic device 1200 can be implemented as part of a mobile device (such as a mobile phone, smartphone, PDA, netbook, tablet computer, and laptop computer), a wearable device (such as a smartwatch, smart bracelet, and smart glasses), a computing device (such as a desktop computer and server), a home appliance (such as a television (TV), smart TV, vacuum cleaner, and refrigerator), a security device (such as a door lock), or a vehicle (such as a smart car). Electronic device 1200 may include... Figure 1 Battery optimization equipment 110 Figure 9 Battery optimization equipment 900, Figure 10 Battery-powered device 1000, and Figure 11 Either the battery management server 1110 and / or the battery power supply device 1120 may be used as a structural and / or functional component.

[0110] Processor 1210 executes functions and instructions for use in electronic device 1200. For example, processor 1210 may process instructions stored in memory 1220 or storage device 1240. Processor 1210 may execute references... Figures 1 to 11 and Figure 13 One or more operations are described. Memory 1220 may store data for battery optimization. Memory 1220 may include a computer-readable storage medium or a computer-readable storage device. Memory 1220 may store instructions to be executed by processor 1210, and store related information when software and / or applications are executed by electronic device 1200.

[0111] Camera 1230 can capture images and / or video. Camera 1230 may be a 3D camera that includes depth information about objects. Storage device 1240 includes a computer-readable storage medium or a computer-readable storage device. Compared to memory 1220, storage device 1240 can store a larger amount of information and store information for a longer period of time. Storage device 1240 may include, for example, a magnetic hard disk, optical disk, flash memory, floppy disk, or other types of non-volatile memory known in the art.

[0112] Input device 1250 can receive input from a user based on conventional input methods using a keyboard and mouse, as well as newer input methods such as touch input, voice input, gesture input, and image input. For example, input device 1250 may include any device that detects input from a keyboard, mouse, touchscreen, microphone, or user and transmits the detected input to electronic device 1200. Output device 1260 can provide output from electronic device 1200 to a user through visual, auditory, or tactile channels. Output device 1260 may include, for example, a display, touchscreen, speaker, vibration generating device, or any device for providing output to a user. For example, network interface 1270 can communicate with external devices via a wired or wireless network.

[0113] Figure 13 An example of battery optimization operation is shown. Figure 13 The operations can be performed in the order and manner shown, but without departing from the spirit and scope of the illustrative examples described, the order of some operations may be changed or some operations may be omitted. Figure 13 Many of the operations shown can be performed in parallel or simultaneously. Figure 13 One or more blocks, and combinations thereof, can be implemented by a computer based on dedicated hardware (such as a processor) or a combination of dedicated hardware and computer instructions to perform the specified function. In addition to the following... Figure 13 In addition to the description, Figures 1 to 12 The description also applies to Figure 13 And it is included here by reference. Therefore, the above description need not be repeated here.

[0114] Reference Figure 13 In operation 1310, the battery optimization device determines a first SoC value corresponding to first operational data of the battery. In operation 1320, a first SoC interval to which the first SoC value belongs is selected from the entire SoC interval. In operation 1330, based on the first operational data, a first set of parameters related to the first SoC interval among multiple parameters of the ECT model simulating the battery is selectively adjusted. In operation 1340, multiple parameters of the ECT model are updated based on the adjusted first set of parameters. Additionally, Figures 1 to 12 The description can be applied to battery optimization operations.

[0115] The examples described herein can be implemented using hardware and software components. For example, hardware components may include microphones, amplifiers, bandpass filters, audio-to-digital converters, and processing devices. Processing devices may be implemented using one or more hardware devices configured to implement and / or execute program code by performing arithmetic, logical, and input / output operations. One or more processing devices may include processors, controllers, arithmetic logic units, digital signal processors, microcomputers, field-programmable arrays, programmable logic units, microprocessors, or any other device capable of responding to and executing instructions in a defined manner. The processing device may run an operating system (OS) and one or more software applications running on the OS. The processing device may also access, store, manipulate, process, and create data in response to the execution of software. For simplicity, the description of processing devices is used as the singular; however, those skilled in the art will understand that processing devices may include multiple processing elements and various types of processing elements. For example, a processing device may include multiple processors or a processor and a controller. Additionally, different processing configurations (such as parallel processors) are feasible.

[0116] Software may include computer programs, code segments, instructions, or combinations thereof, to independently or jointly instruct and / or configure a processing device to operate as needed, thereby transforming the processing device into a dedicated processor. Software and data may be permanently or temporarily implemented in any type of machine, component, physical or virtual device, computer storage medium, or apparatus, or as propagated signal waves capable of providing instructions or data to or being interpreted by the processing device. Software may also be distributed across networked computer systems, enabling it to be stored and executed in a distributed manner. Software and data may be stored on one or more non-transitory computer-readable recording media.

[0117] The methods described in the examples above can be recorded in a non-transitory computer-readable medium, which includes program instructions for implementing the various operations described in the examples. The medium may also include data files, data structures, etc., alone or in combination with the program instructions. The program instructions recorded on the medium may be program instructions specifically designed and constructed for the purposes of the examples, or they may be of types known and available to those skilled in the art of computer software. Examples of non-transitory computer-readable media include magnetic media (such as hard disks, floppy disks, and magnetic tapes), optical media (such as CD-ROMs, DVDs, and / or Blu-ray discs), magneto-optical media (such as optical discs), and hardware devices specifically configured to store and execute program instructions (such as read-only memory (ROM), random access memory (RAM), flash memory (e.g., USB flash drives, memory cards, memory sticks, etc.)). Examples of program instructions include machine code (such as machine code generated by a compiler) and files containing higher-level code that can be executed by a computer using an interpreter.

[0118] The aforementioned hardware device can be configured to function as one or more software modules to perform the operations described above, and vice versa.

[0119] While this disclosure includes specific examples, it will be apparent to those skilled in the art that various changes in form and detail may be made in these examples without departing from the spirit and scope of the claims and their equivalents. The examples described herein are to be considered in a descriptive sense only and not for purposes of limitation. The description of a feature or aspect in each example will be considered applicable to similar features or aspects in other examples. Suitable results may be achieved if the described techniques are performed in a different order, and / or if the components in the described system, architecture, apparatus, or circuit are combined in a different manner, and / or replaced or supplemented by other components or their equivalents.

[0120] Therefore, the scope of the disclosure is not limited by the specific embodiments, but by the claims and their equivalents, and all variations within the scope of the claims and their equivalents shall be interpreted as included in the disclosure.

Claims

1. A battery optimization method, the battery optimization method comprising: Determine a first state of charge value corresponding to first operating data of the battery, wherein the first operating data includes at least one of the battery's voltage, current, and temperature according to the operation of the battery; Select the first charge state interval that corresponds to the first charge state value from the entire charge state interval; Based on the first operational data, selectively adjust a first set of parameters of an electrochemical thermal model that are related to a first state of charge interval, thereby reducing the optimization loss, wherein the electrochemical thermal model is configured to simulate a battery, and wherein the optimization loss corresponds to the difference between the estimated value of the electrochemical thermal model and the measured value of the first operational data; and The multiple parameters of the electrochemical thermal model are updated based on the adjusted first set of parameters.

2. The battery optimization method of claim 1, wherein, The electrochemical thermal model's parameters also include a second set of parameters related to a second charge state interval over the entire charge state interval, and The step of selectively adjusting the first parameter set based on the first operational data includes keeping the second parameter set unadjusted.

3. The battery optimization method as described in claim 1, wherein, The steps for selectively adjusting the first parameter set include: The estimated voltage of the electrochemical thermal model is determined using the first operating data and the plurality of parameters; The optimized loss is determined based on the measured voltage from the first operating data and the estimated voltage from the electrochemical-thermal model; and Adjust at least one parameter in the first parameter set to reduce the optimization loss.

4. The battery optimization method as described in claim 3, wherein, The steps of adjusting at least one parameter of the first parameter set include: Candidate parameters for reducing the optimization loss in the first parameter set are determined through Bayesian optimization; and Adjust the candidate parameters.

5. The battery optimization method as described in claim 1, wherein, The multiple parameters of the electrochemical thermal model include one of a constant parameter independent of the battery's state of charge (SOC) level and a diffusion parameter dependent on the SOC level of the battery. Each parameter in the first parameter set is a diffusion parameter.

6. The battery optimization method as described in claim 5, wherein, The diffusion parameters include one of the charging parameters related to battery charging and the discharging parameters related to battery discharging.

7. The battery optimization method as described in claim 6, wherein, In response to the measurement of first operational data during battery charging, the first parameter set includes charging parameters, and In response to the measurement of first operational data during battery discharge, the first parameter set includes discharge parameters.

8. The battery optimization method as described in claim 5, wherein, The constant parameters include one of the following: membrane resistance, volume fraction of active material, and anode-cathode open-circuit potential offset.

9. The battery optimization method as described in claim 1, wherein, The battery is configured to supply power to battery-powered devices, and The first operational data is generated in response to the battery supplying power to the battery-powered device.

10. The battery optimization method according to any one of claims 1 to 9, further comprising: In response to the completion of the selective adjustment of the first parameter set, a new first parameter set is determined by updating the first parameter set; as well as Based on multiple new parameters of the electrochemical thermal model including the new first parameter set, the second parameter set associated with the second charge state interval across the entire charge state interval is selectively adjusted.

11. The battery optimization method as described in claim 10, wherein, The steps for selectively adjusting the second parameter set include: Determine the second state of charge value corresponding to the second operating data of the battery; Select the second charge state interval to which the second charge state value belongs from the entire charge state interval; and Based on the first and second operating data, the second set of parameters related to the second state of charge interval among the plurality of new parameters of the electrochemical thermal model is selectively adjusted.

12. The battery optimization method as described in claim 11, wherein, Different adjustment weights are applied to selectively adjust the first set of parameters and selectively adjust the second set of parameters.

13. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform the method according to claims 1 to 12.

14. A battery optimization device, the battery optimization device comprising: The processor is configured as follows: Determine a first state of charge value corresponding to first operating data of the battery, wherein the first operating data includes at least one of the battery's voltage, current, and temperature according to the operation of the battery; Select the first charge state interval that corresponds to the first charge state value from the entire charge state interval; Based on the first operational data, selectively adjust a first set of parameters of an electrochemical thermal model that are related to a first state of charge interval, thereby reducing the optimization loss, wherein the electrochemical thermal model is configured to simulate a battery, and wherein the optimization loss corresponds to the difference between the estimated value of the electrochemical thermal model and the measured value of the first operational data; and The multiple parameters of the electrochemical thermal model are updated based on the adjusted first set of parameters.

15. The battery optimization device as described in claim 14, wherein, The electrochemical thermal model's parameters also include a second set of parameters related to a second charge state interval over the entire charge state interval, and The processor is also configured to: selectively adjust the first parameter set based on the first operational data, while keeping the second parameter set unadjusted.

16. The battery optimization device as claimed in claim 14 or claim 15, wherein, The processor is also configured as follows: In response to the completion of the selective adjustment of the first parameter set, a new first parameter set is determined by updating the first parameter set; and Based on multiple new parameters of the electrochemical thermal model including the new first parameter set, the second parameter set associated with the second charge state interval across the entire charge state interval is selectively adjusted.

17. An electronic device, the electronic device comprising: A battery is configured to power the electronic device; as well as The processor is configured as follows: Determine a first state of charge value corresponding to first operating data of the battery, wherein the first operating data includes at least one of the battery's voltage, current, and temperature according to the operation of the battery; Select the first charge state interval that corresponds to the first charge state value from the entire charge state interval; Based on the first operational data, a first set of parameters related to the first state of charge interval from among multiple parameters of the electrochemical thermal model is selectively selected to reduce the optimization loss, wherein the electrochemical thermal model is configured to simulate a battery, and wherein the optimization loss corresponds to the difference between the estimated value of the electrochemical thermal model and the measured value of the first operational data; and The multiple parameters of the electrochemical thermal model are updated based on the adjusted first set of parameters.

18. The electronic device of claim 17, wherein, The electrochemical thermal model's parameters also include a second set of parameters related to a second charge state interval over the entire charge state interval, and The processor is also configured to: selectively adjust the first parameter set based on the first operational data, while keeping the second parameter set unadjusted.

19. The electronic device as claimed in claim 17 or claim 18, wherein, The processor is also configured as follows: In response to the completion of the selective adjustment of the first parameter set, a new first parameter set is determined by updating the first parameter set; and Based on multiple new parameters of the electrochemical thermal model including the new first parameter set, the second parameter set associated with the second charge state interval across the entire charge state interval is selectively adjusted.

20. A processor-implemented battery optimization method, the battery optimization method comprising: The charge state segment is divided into two or more segments based on the charge state value; Select a first state of charge segment corresponding to the first operating data of the battery from the two or more segments, wherein the first operating data includes at least one of the battery voltage, current and temperature according to the operation of the battery; Based on the first operating data, the first set of parameters of the electrochemical thermal model that are related to the first state of charge segment are selectively adjusted to reduce the optimization loss, wherein the optimization loss corresponds to the difference between the estimated value of the electrochemical thermal model and the measured value of the first operating data. Select a second state of charge segment corresponding to the second operating data of the battery from the two or more segments; Based on the first and second operating data, selectively adjust the second set of parameters among the plurality of parameters of the electrochemical thermal model that are related to the second state of charge interval; and The multiple parameters of the electrochemical thermal model are updated based on the adjusted first and second parameter sets.

21. The battery optimization method as described in claim 20, wherein, The steps of selectively adjusting the first parameter set and selectively adjusting the second parameter set are performed at periodic time intervals.

22. The battery optimization method as described in claim 20, wherein, The first and second operation data are generated during the battery's charging cycle.

23. The battery optimization method as described in claim 20, wherein, The first and second operating data are generated during the battery's discharge cycle.

24. The battery optimization method as described in claim 20, further comprising: Weights are applied to the charge state segment from the two or more segments that has the lowest charge state value.

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