Method and apparatus with battery model optimization

Abstract

Disclosed are a method and apparatus having battery model optimization. The apparatus having battery model optimization includes a processor configured to perform optimization on a battery model to determine optimized parameter values of parameters of the battery model, wherein to perform the optimization, the processor is configured to: select target parameters from among the parameters of the battery model; set current boundary conditions for each target parameter; determine optimized parameter values of each target parameter based on the set current boundary conditions; set subsequent boundary conditions reduced from the current boundary conditions based on the determined optimized parameter values; and determine subsequent optimized parameter values of each target parameter based on the subsequent boundary conditions.

Description

[0001] This application claims the benefit of Korean Patent Application No. 10-2021-0129038, filed September 29, 2021, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety by reference for all purposes. TECHNICAL FIELD

[0002] The following description relates to a method and apparatus with battery model optimization. BACKGROUND

[0003] For optimal management of a battery, various methods can be used to estimate a state of the battery. For example, the state of the battery can be estimated by integrating a current of the battery or by using a battery model (e.g., an electrical circuit model or an electrochemical model). The current integration method can calculate a remaining amount of the battery by attaching a current sensor to one end of a battery cell, a battery module, or a battery pack and calculating a sum of amounts of electric charges to be charged or discharged. The electrical circuit model can be a circuit model including a resistor and a capacitor that represents a change in a voltage value as the battery is charged or discharged, and the electrochemical model can be a model that models internal physical phenomena (e.g., a battery ion concentration, an electric potential, etc.) of the battery. SUMMARY

[0004] This summary is provided to introduce a selection of concepts, which are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in determining the scope of the claimed subject matter.

[0005] In one general aspect, an apparatus with battery model optimization includes a processor configured to perform optimization on a battery model to determine optimized parameter values of parameters of the battery model, wherein, to perform the optimization, the processor is configured to select target parameters from among the parameters of the battery model, set current boundary conditions for each of the target parameters, determine the optimized parameter values of each of the target parameters based on the set current boundary conditions, set subsequent boundary conditions that decrease from the current boundary conditions based on the determined optimized parameter values, and determine subsequent optimized parameter values of each of the target parameters based on the subsequent boundary conditions.

[0006] The processor can be configured to perform the optimization for each of a plurality of predefined different state of charge (SOC) intervals corresponding to a degree of progress of charging of the battery.

[0007] The processor can be configured to perform the optimization based on a plurality of predefined different SOC intervals corresponding to a degree of progress of discharging of the battery.

[0008] To set the subsequent boundary condition, the processor can be configured to determine a target change direction of the diffusion parameter based on a voltage error between a voltage of the battery estimated through the battery model and a voltage of the battery based on the profile data of the battery, and set the subsequent boundary condition based on the determined target change direction.

[0009] The processor can be configured to perform the optimization for each of the predefined temperature intervals.

[0010] The processor can be configured to select the target parameter based on a value obtained by performing one or more differentiations on the parameters of the battery model.

[0011] The processor can be configured to iteratively perform the setting of the subsequent boundary condition decreased from the current boundary condition and the determination of the subsequent optimized parameter value of each target parameter based on the subsequent boundary condition until the number of executions of the optimization reaches the set number of times.

[0012] To set the subsequent boundary condition, the processor can be configured to change the current boundary condition for all target parameters based on the optimized parameter value obtained according to the current boundary condition, and the changed boundary condition can correspond to the subsequent boundary condition.

[0013] The processor can be configured to select a point associated with a diffusion characteristic of the battery from among the parameters of the battery model, and to select the target parameter based on the selected point.

[0014] The processor can be configured to determine an estimated state value of the battery model based on the target parameter, determine an optimization loss based on a difference between the estimated state value and an actual state value obtained from the profile data of the battery, and adjust at least one of the target parameters such that the optimization loss decreases.

[0015] The parameters of the battery model can include a diffusion parameter depending on an SOC level of the battery, and the diffusion parameter can include a charge parameter associated with charging of the battery and a discharge parameter associated with discharging of the battery.

[0016] The electronic device can include a memory storing instructions that, when executed by the processor, configure the processor to perform the optimization.

[0017] In another general aspect, a method having battery model optimization includes selecting a target parameter from among parameters of a battery model, and performing an optimization on the target parameter, wherein the step of performing the optimization can include setting a current boundary condition for each target parameter, determining an optimized parameter value of each target parameter based on the set current boundary condition, setting a subsequent boundary condition decreased from the current boundary condition based on the determined optimized parameter value, and determining a subsequent optimized parameter value of each target parameter based on the subsequent boundary condition.

[0018] The step of performing the optimization can include performing the optimization for each of predefined different state of charge (SOC) intervals corresponding to a degree of progress of charging of the battery.

[0019] The step of performing the optimization can include performing the optimization for each of predefined different SOC intervals corresponding to a degree of progress of discharging of the battery.

[0020] The step of setting the subsequent boundary condition can include determining a target change direction of the diffusion parameter based on a voltage error between a voltage of the battery estimated through the battery model and a voltage of the battery based on the profile data of the battery, and setting the subsequent boundary condition based on the determined target change direction.

[0021] The step of performing the optimization can include performing the optimization for each of predefined temperature intervals.

[0022] In another general aspect, one or more embodiments include a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform any one, any combination, or all of the operations and methods described herein.

[0023] In another general aspect, a battery power supply includes a battery configured to supply power to an electronic device, and a battery model optimization apparatus configured to optimize a battery model corresponding to the battery, wherein, to optimize the battery model, the battery model optimization apparatus can be configured to select target parameters from among parameters of the battery model, set a current boundary condition for each of the target parameters, determine an optimized parameter value of each of the target parameters based on the set current boundary condition, set a subsequent boundary condition reduced from the current boundary condition based on the determined optimized parameter value, and determine a subsequent optimized parameter value of each of the target parameters based on the subsequent boundary condition.

[0024] The battery model optimization apparatus can be configured to perform the optimization for each of predefined different state of charge (SOC) intervals corresponding to a degree of progress of charging or discharging of the battery.

[0025] To set the subsequent boundary condition, the battery model optimization apparatus can be configured to determine a target change direction of the diffusion parameter based on a voltage error between a voltage of the battery estimated through the battery model and a voltage of the battery based on the profile data of the battery, and set the subsequent boundary condition based on the determined target change direction.

[0026] In another general aspect, a method of battery model optimization includes setting a boundary condition for a target parameter of a battery model, determining an optimization parameter of the target parameter to be within the boundary condition, setting a subsequent boundary condition having a range reduced from the current boundary condition based on a difference between a state of the battery estimated using the battery model with the optimization parameter and a predetermined state of the battery, and optimizing the battery model by determining a subsequent optimization parameter of the target parameter to be within the subsequent boundary condition.

[0027] The set boundary condition can include a lower limit and an upper limit, and the step of determining the optimization parameter can include determining the optimization parameter to be greater than or equal to the lower limit and less than or equal to the upper limit.

[0028] The step of setting the subsequent boundary condition can include, based on whether the state of the battery estimated using the battery model is greater than the predetermined state of the battery, doing either of increasing at least one of the lower limit and the upper limit and decreasing at least one of the lower limit and the upper limit.

[0029] Other features and aspects will be apparent from the following detailed description, the drawings, and the claims. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 Examples showing how a battery model optimization apparatus operates.

[0031] Figure 2 Examples showing classifying parameters of a battery model.

[0032] Figure 3 And Figure 4 Examples showing a battery model optimization method.

[0033] Figure 5 Examples showing optimizing parameters of a battery model.

[0034] Figure 6 Examples showing selecting a target parameter.

[0035] Figure 7 Examples showing a reduced boundary condition.

[0036] Figure 8 Examples showing optimizing parameters of a battery model based on a target change direction of a diffusion coefficient.

[0037] Figure 9 Examples showing setting a boundary condition based on a target change direction of a diffusion coefficient.

[0038] Figure 10 Examples showing optimizing parameters of a battery model for each temperature interval.

[0039] Figure 11 An example of a battery model optimization apparatus is shown.

[0040] Figure 12 An example of a battery management server including a battery model optimization apparatus is shown.

[0041] Figure 13 An example of a battery power supply including a battery model optimization apparatus is shown.

[0042] Throughout the drawings and detailed description, unless otherwise described or provided, like reference numerals refer to like elements, features, and structures. The drawings can not be to scale, and the relative dimensions, proportions, and depiction of elements in the drawings can be exaggerated for clarity, illustration, and convenience. DETAILED DESCRIPTION

[0043] The following detailed description is provided to help the reader obtain a thorough understanding of the methods, devices, and / or systems described herein. However, various changes, modifications, and equivalents of the methods, devices, and / or systems described herein will be clear to those skilled in the art after understanding the present disclosure. For example, the order of the operations described herein is merely an example and is not limited to the order described herein, but can be changed as will be clear after understanding the present disclosure, except for operations that must occur in a specific order. Also, the description of features known after understanding the present disclosure can be omitted for more clarity and conciseness.

[0044] The features described herein can be implemented in different forms and should not be construed as limited to the examples described herein. Rather, the examples described herein have been provided to merely show some of the many possible ways in which the methods, devices, and / or systems described herein can be implemented after understanding the present disclosure.

[0045] The terminology used herein is for the purpose of describing particular examples only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. As used herein, the terms "include," "comprise," and "have" indicate the presence of the stated feature, number, operation, element, component, and / or a combination thereof, but do not preclude the presence or addition of one or more other features, numbers, operations, elements, components, and / or combinations thereof. The use of the term "may" (e.g., about an example or embodiment can include or implement what) with respect to an example or embodiment herein indicates that there is at least one example or embodiment that includes or implements the feature, but no all examples are limited to this.

[0046] Although the terms "first" or "second" are used herein to describe various components, assemblies, regions, layers or sections, these components, assemblies, regions, layers or sections should not be limited by these terms. Instead, these terms are only used to distinguish one component, assembly, region, layer or section from another component, assembly, region, layer or section. Thus, the first component, first assembly, first region, first layer or first section described in the examples described herein could also be termed a second component, a second assembly, a second region, a second layer or a second section without departing from the teachings of the examples.

[0047] Throughout the specification, when an element (such as a layer, region or substrate) is referred to as being "on" another element, "connected to" or "coupled to" another element, it can be directly on, connected or coupled to the other element, or one or more other elements can be interposed therebetween. In contrast, when an element is referred to as being "directly on", "directly connected to" or "directly coupled to" another element, there are no other elements interposed therebetween. Also, expressions such as "between... and..." and "adjacent to..." can also be interpreted in the like manner as described above.

[0048] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs and the same meaning as understood after reading the present disclosure. Terms such as those defined in a generally used dictionary should be interpreted to have meanings consistent with their meanings in the context of the relevant art and the present disclosure, and should not be interpreted in an idealized or overly formal sense.

[0049] Further, in the description of the example embodiments, detailed descriptions of the structures or functions that are considered to be known after understanding the disclosure of the present application can be omitted when it is deemed that such descriptions can cause ambiguous interpretation of the example embodiments. Hereinafter, the examples will be described in detail with reference to the accompanying drawings, and the same reference numerals denote the same elements throughout the drawings.

[0050] Figure 1 An example of an overview showing how the battery model optimization apparatus operates.

[0051] Referring to Figure 1The battery model 111 can estimate the state (e.g., state of charge (SOC), voltage, etc.) of the battery 120 using various parameters and control equations. The battery model 111 can be an electrochemical thermal (ECT) model based on physical information of the battery. The parameters of the battery model 111 can represent, for example, a shape (e.g., thickness, radius, etc.) of the battery 120, an open circuit potential (OCP), and physical characteristics (e.g., electrical conductivity, ionic conductivity, diffusion coefficient, etc.). The control equations can include electrochemical reactions occurring on an interface between an electrode and an electrolyte and physical conservation equations associated with conservation of concentrations and charges of the electrode and the electrolyte based on the parameters. The parameters of the battery model 111 can include constant parameters independent of an SOC level of the battery 120 and diffusion parameters dependent on the SOC level. For example, the constant parameters can remain constant and the diffusion parameters can change as the SOC level gradually decreases as the battery 120 operates.

[0052] The battery model optimization apparatus 100 (an apparatus configured to perform optimization on the battery model 111) can search for optimized values of parameters for operation of the battery model 111 by updating the parameters multiple times. The battery model optimization apparatus 100 can automatically estimate the optimized values of the parameters of the battery model 111 through a multi-stage optimization process to be described below. The battery model optimization apparatus 100 can reduce a calculation time and ensure reliability of a calculation result by changing parameters to be optimized in each stage and changing boundary conditions of each parameter. The battery model optimization apparatus 100 can obtain the optimized values of the parameters used in the battery model 111 using a limited number of data sets.

[0053] The battery model optimization apparatus 100 can perform optimization on the battery model 111 based on profile data for optimizing the battery model 111. The profile data can include measured experimental data on a state of the battery 120 associated with current, temperature, and voltage. Profile data of various currents, temperatures, and voltages can be used to optimize the battery model 111. The battery model optimization apparatus 100 can calculate an optimization loss based on a difference between actual state values of the battery 120 corresponding to the profile data and estimated values obtained using the battery model 111, and can update parameters of the battery model 111 such that the optimization loss decreases.

[0054] The battery model optimization apparatus 100 can be used to quickly and accurately predict various parameters of a battery model applied to a power management integrated circuit (PMIC) or a battery management system (BMS) that manages a battery of an electronic device (e.g., a smartphone, a tablet personal computer (PC), a robot cleaner, a wireless cleaner, a drone, a walking aid, an electric vehicle, etc.) provided with a secondary battery (e.g., a lithium ion battery). The PMIC can estimate a state of the battery based on a voltage, a current, and a temperature of the battery, and estimate the state of the battery using an electrochemical model (e.g., an ECT model) as the battery model. The electrochemical model can include a large number of parameters, and each parameter needs to be optimized.

[0055] When performing optimization on the parameters, the battery model optimization apparatus 100 can perform optimization by dividing a plurality of stages by parameter characteristics of the battery model 111 and reconstructing boundary conditions of a current optimization process based on a result of a previous optimization process. Also, the battery model optimization apparatus 100 can sequentially perform optimization for a plurality of SOC intervals by dividing a plurality of SOC intervals corresponding to a degree of progress of charging or discharging of the battery 120. The optimization process of the battery model will be described in greater detail below. The optimization process of the battery model described herein can be applied to various types of batteries. The optimization process of the battery model can optimize the battery model in a short period of time without an expert, and produce a highly accurate optimization result.

[0056] Figure 2 An example of classifying parameters of a battery model is illustrated.

[0057] Referring to Figure 2 The battery model 200 can include a battery model parameter set 210. Each parameter in the battery model parameter set 210 can be classified as a constant parameter 211 and a diffusion parameter 212. The constant parameter 211 can include, for example, a cell shape (e.g., thickness, radius, etc.) independent of an SOC level of the battery, a resistance, an OCP, etc. The diffusion parameter 212 can include a diffusion parameter (or a diffusion coefficient) dependent on the SOC level. The diffusion parameter 212 can include, for example, a charge parameter associated with charging of the battery and a discharge parameter associated with discharging of the battery.

[0058] As shown, parameter 213 can represent an anode discharge parameter, and parameter 214 can represent a cathode discharge parameter. In addition, parameter 215 can represent an anode charge parameter, and parameter 216 can represent a cathode charge parameter. Parameters 213 to 216 are indicated as points on respective graphs in Figure 2 . In the graphs of parameters 213 to 216, the horizontal axis represents the SOC level, and the vertical axis represents the magnitude of the diffusion coefficient. Diffusion parameters 212 can be defined or determined in a desired interval. For example, although parameter 213 is indicated as about 20 points for the SOC level of 0.0 to 1.0 in Figure 2 , parameter 213 can exist in a greater number of points in a more dense interval, or in a smaller number of points in a more sparse interval.

[0059] Figure 3 and Figure 4 show examples of a battery model optimization method. The battery model optimization method described below with reference to Figure 3 and Figure 4 may be performed by a battery model optimization apparatus (e.g., the battery model optimization apparatus 100 of Figure 1 , the battery model optimization apparatus 1100 of Figure 11 , the battery model optimization apparatus 1211 of Figure 12 , and / or the battery model optimization apparatus 1320 of Figure 13 .

[0060] With reference to Figure 3 , in operation 310, the battery model optimization apparatus can set initial boundary conditions for parameters of a battery model. The initial boundary conditions can correspond to predetermined boundary conditions, and can be determined, for example, by experiment or expert adjustment. The boundary conditions described herein can define a lower limit and an upper limit of a value of a parameter when determining the value of the parameter. In the optimization process, the value of the parameter can be determined as a value between the lower limit and the upper limit defined by the boundary conditions (e.g., a value greater than or equal to the lower limit and less than or equal to the upper limit).

[0061] In operation 320, the battery model optimization device can perform an optimization process on the battery model based on the current boundary condition. The initial boundary condition set in operation 310 can be initially used. The battery model optimization device can use the battery model for each parameter to obtain an estimated value of the state of the battery, and calculate an optimization loss based on a difference between the obtained estimated value and an actual state value based on the profile data of the battery. The battery model optimization device can update the parameter value to reduce the optimization loss. The optimization process can be performed separately for the parameters that affect both charging and discharging of the battery and for the parameters that affect each of charging and discharging based on the parameter characteristics.

[0062] Hereinafter, the optimization process will be described in connection with an example of estimating the voltage of the battery by the battery model based on the current and temperature of the battery. The disclosure is not limited thereto, and the voltage of the battery or other parameters other than the voltage can be estimated by the battery model based on other parameters of the battery. When the battery model estimates the voltage of the battery based on the current and temperature of the battery, the result of estimating the state of the battery by the battery model for a parameter θ for a given current I and temperature T can be expressed as ECT(I, T; θ), and the value of ECT(I, T; θ) can correspond to the voltage value of the battery tracked by the battery model. The battery model optimization device can calculate an error between ECT(I, T; θ), which is the estimated state of the battery, and the actual state of the battery (for example, a predetermined state of the battery based on the profile data). The error can be responsive to the optimization loss. The error can include a sum of squared errors (SSE) between ECT(I, T; θ) and the actual state of the battery. According to an example, the error can be calculated using various calculation methods (for example, using a root mean square error (RMSE) or a maximum absolute error (MAE)).

[0063] The battery model optimization device can use the battery model to estimate the state of the battery for any parameter more than once. By estimating the state more than once, the battery model optimization device can generate a parameter set as a set of parameters used in the battery model. The parameter set can include a parameter applied to the battery model and an error between the state of the battery estimated using the parameter and the actual state of the battery. For example, when the state of the battery is estimated n times using the battery model, the parameter set can be expressed as S = {(θ1, E1), (θ2, E2), …, (θn, En)}. In this example, θ denotes a parameter applied to the battery model, and E denotes an error associated with the parameter.

[0064] The battery model optimization apparatus can use an optimization algorithm to determine the candidate parameter θn+1that most likely improves (e.g., reduces) the error between the state of the battery obtained from the battery model and the actual state of the battery. The optimization algorithm can include, for example, a Bayesian optimization algorithm. The Bayesian optimization algorithm can use a minimum evaluation to search for or determine a global optimum. The Bayesian optimization algorithm can search for an optimal combination by iteratively applying calculations based on probability calculations that can minimize a loss function. The optimization can be performed based on probability, and thus it is less likely to get stuck in a local optimum.

[0065] The battery model optimization apparatus can search for the candidate parameter θn+1based on the current boundary condition to determine the candidate parameter θn+1. The battery model optimization apparatus can select the candidate parameter by performing a gradient-based optimization on the acquisition function. The battery model optimization apparatus can estimate the state of the battery using the battery model to which the selected candidate parameter is applied. For example, the battery model optimization apparatus can estimate the state of the battery ECT(I, T; θn+1) using the candidate parameter θn+1. Subsequently, the battery model optimization apparatus can calculate the error (e.g., SSE) between ECT(I, T; θn+1) and the actual state of the battery. The battery model optimization apparatus can update the parameters to reduce the error.

[0066] In operation 330, the battery model optimization apparatus can store and analyze the results of the optimization process performed in operation 320. In operation 340, the battery model optimization apparatus can determine whether the number of executions of the optimization process (e.g., the number of executions of operation 320) reaches a set number of executions. When the number of executions of the optimization process reaches the set number of times, the optimization process of the battery model can be terminated. In operation 350, when the number of executions of the optimization process does not reach the set number of times, the battery model optimization apparatus can set a reduced boundary condition that is reduced from the previous boundary condition used in the previously performed optimization process. The battery model optimization apparatus can set the reduced boundary condition based on the results of the previously performed optimization process. The reduced boundary condition can be a boundary condition having an "interval reduced from an interval defined by an upper limit and a lower limit of the previous boundary condition." Subsequently, the battery model optimization apparatus can perform the optimization process again based on the reduced boundary condition. The battery model optimization apparatus can add the candidate parameter θn+1to the parameter set and select a new candidate parameter. The battery model optimization apparatus can calculate the error between the state of the battery obtained from the battery model and the actual state of the battery, and iteratively perform the operation of updating the parameter set to minimize the error. In this way, when the optimization process in one stage is terminated, the battery model optimization apparatus can perform the optimization process again by resetting the boundary condition of the subsequent stage based on the results of the optimization process in the stage.

[0067] The battery model optimization apparatus can iteratively perform operation 350, operation 320, and operation 330 until the number of execution of the optimization process reaches a set number of times.

[0068] Figure 4 A detailed example of the optimization process is illustrated.

[0069] Referring to Figure 4 In operation 410, the battery model optimization apparatus can select target parameters from among the parameters of the battery model. The battery model optimization apparatus can select specific points (or anchor points) associated with diffusion characteristics of the battery from among the parameters of the battery model, and determine target parameters based on the selected points. The target parameters can correspond to some of the parameters selected from all the parameters of the battery model. The target parameters can be determined so that the parameter dimension of the battery model is reduced while maintaining the physical diffusion characteristics of the battery model. For example, the battery model optimization apparatus can select the target parameters based on values obtained by performing at least one differentiation on the parameters of the battery model. The battery model optimization apparatus can determine the target parameters based on specific points at which the values obtained by performing at least one differentiation are zero (or the derivative becomes zero). The battery model optimization apparatus can determine parameters in which the diffusion coefficient is significantly changed at the corresponding specific points as the target parameters. The optimization process can be performed based on the target parameters, and the parameter dimension can be reduced through the target parameters. Accordingly, the battery model optimization apparatus of one or more embodiments can reduce the amount of calculation and time for the optimization process.

[0070] The battery model optimization apparatus can perform the optimization process on the target parameters by performing operations 420 to 460. In operation 420, the battery model optimization apparatus can set a current boundary condition for each target parameter. A pre-set initial boundary condition can be initially used as the current boundary condition. In operation 430, the battery model optimization apparatus can search for (e.g., determine) an optimized parameter value of each target parameter based on the set current boundary condition. The battery model optimization apparatus can determine an estimated state value of the battery model (or an estimated value of the state of the battery) based on the target parameters, and determine an optimization loss based on a difference between the estimated state value and an actual state value obtained from profile data of the battery. The battery model optimization apparatus can adjust at least one of the target parameters to reduce the optimization loss. To search for the optimized parameter value, the optimization process described above in connection with operation 320 of Figure 3 may be applied.

[0071] In one example, when performing the optimization process, the battery model optimization apparatus can perform the optimization process for each of pre-defined different SOC intervals corresponding to the degree of progress of charging or discharging of the battery. For example, the battery model optimization apparatus can divide the SOC intervals into, for example, a 0-30% interval, a 30%-70% interval, and a 70%-100% interval, and sequentially optimize the diffusion characteristics of the battery model for the SOC intervals based on the charging or discharging behavior. The optimization process of one or more embodiments can be performed even for an SOC interval for which optimization is not sufficiently performed due to relatively low importance compared to other SOC intervals, and thus the optimization sensitivity of the SOC interval can be improved.

[0072] In another example, when performing the optimization process, the battery model optimization apparatus can perform the optimization process for each temperature interval. For example, the battery model optimization apparatus can set pre-defined temperature intervals respectively corresponding to low temperature, room temperature, and high temperature, and perform the optimization process for each temperature interval. The battery model optimization apparatus can calculate the estimated state value of the battery model for each temperature interval, and determine the optimization loss based on the calculated estimated state value and the profile data of each temperature interval. The battery model optimization apparatus can adjust the parameters to reduce the optimization loss.

[0073] In operation 440, the battery model optimization apparatus can set a subsequent boundary condition reduced from the current boundary condition based on the previous search result of the optimization parameter value. The battery model optimization apparatus can change the boundary condition of all target parameters based on the optimization parameter value acquired according to the current boundary condition.

[0074] In the example of operation 440, the battery model optimization apparatus can calculate a voltage error between the voltage of the battery estimated by the battery model in the previous optimization process and the voltage of the battery based on the profile data of the battery, and determine the target change direction of the diffusion parameter (or diffusion coefficient) based on the calculated voltage error. The battery model optimization apparatus can set the subsequent boundary condition based on the determined target change direction. In the case of the diffusion parameter, the direction to be changed can be known or determined based on the voltage error, and the boundary condition to be used in the subsequent optimization process to be subsequently performed can be set based on the direction.

[0075] In operation 450, the battery model optimization apparatus can search for the optimization parameter value of each target parameter based on the set subsequent boundary condition. Operation 450 can be the same as operation 430 except that the boundary condition is changed, and thus a repetitive description thereof will be omitted here.

[0076] In operation 460, the battery model optimization device can determine whether the number of executions of the optimization process reaches the set number of executions. When the number of executions of the optimization process reaches the set number, the optimization process of the battery model can be terminated. When the number of executions of the optimization process does not reach the set number, the battery model optimization device can set a subsequent boundary condition based on the previous search result and perform the optimization process again in operation 450. The battery model optimization device can iteratively perform the operation of setting a subsequent boundary condition that is decreased from the current boundary condition and the operation of searching for an optimized parameter value of each target parameter based on the subsequent boundary condition until the number of executions of the optimization process reaches the set number.

[0077] When moving to the next stage of the optimization process, the battery model optimization device can perform optimization while changing the boundary condition based on the optimization result from the previous stage as described above. The battery model optimization device of one or more embodiments can improve optimization efficiency by changing the boundary condition based on the optimization result from the previous stage according to the characteristics of the parameters. The optimization process of one or more embodiments described above can enable generation of a battery model that provides stable performance for different types of batteries and in various battery operating conditions (e.g., temperature, operating conditions, etc.). In one embodiment, the optimized battery model generated through the optimization process described above can be used to estimate the state of the battery. For example, the optimized battery model can be used to track the voltage value of the battery or other parameters other than the voltage value based on the current and temperature (or other parameters) of the battery.

[0078] Figure 5 An example of optimizing parameters of a battery model is shown.

[0079] Referring to Figure 5 In operation 510, the battery model optimization device can perform initial setting. The battery model optimization device can identify various parameters of the battery model during the initial setting and set initial boundary conditions for optimization. The initial boundary conditions can be defined in advance or determined experimentally. In operation 520, the battery model optimization device can perform an optimization process on the parameters of the battery model.

[0080] In operation 522, the battery model optimization device can select a target parameter from among the parameters of the battery model. Accordingly, the battery model optimization device can reduce the number of parameters for optimization. For example, the battery model optimization device can perform optimization by extracting a specific point at which the derivative of the diffusion coefficient is zero.

[0081] In operation 524, the battery model optimization device can obtain estimated state values (e.g., SOC and voltage) of the battery by processing the battery model. For example, the battery model optimization device can obtain an estimated value of the voltage of the battery based on an operation (or calculation) of the battery model based on the current and temperature of the battery and the parameter set of the battery model.

[0082] In operation 526, the battery model optimization device can determine an optimization loss. The battery model optimization device can determine the optimization loss based on a difference between the predefined profile data of the battery and the state values of the battery estimated using the battery model. The profile data can include actually measured state values or reference values of the battery. The battery model optimization device can determine the optimization loss based on a probability of a Bayesian optimization algorithm.

[0083] In operation 528, the battery model optimization device can adjust the parameter values based on the optimization loss. The battery model optimization device can adjust the parameter values to determine new parameters. The battery model optimization device can adjust the parameter values such that the optimization loss is reduced (e.g., minimized).

[0084] In operation 529, the battery model optimization device can set a new boundary condition having a reduced range compared to the previous boundary condition based on the result of operation 528. For example, the battery model optimization device can set a boundary condition having a range with a size smaller than the size of the parameter values determined in operation 528. Subsequently, the battery model optimization device can iteratively perform operation 522 of selecting target parameters, operation 524 of processing the battery model, operation 526 of determining the optimization loss, operation 528 of adjusting the parameter values, and operation 529 of setting a new boundary condition a plurality of times. This optimization process can be iteratively performed until a preset number of iterations is reached or until the optimization loss satisfies a defined condition. In addition, the battery model optimization device can perform the optimization process for each SOC interval or temperature interval according to the examples described above with reference to Figure 4

[0085] In operation 530, when the optimization process of operation 520 is terminated, the battery model optimization device can update the parameters of the battery model based on the optimized parameter values obtained through the iterations of the optimization process. The battery model with the updated parameters can be used to estimate the state of the battery.

[0086] ​To optimize the battery model, an iterative execution of the optimization process can be used. When a typical battery model optimization apparatus uses fixed boundary conditions, a large number of iterative calculations can be performed to minimize the optimization loss (or cost function). As the number of iterative calculations increases, the amount of calculation and time can correspondingly increase. However, as described above, the battery model optimization apparatus of one or more embodiments can perform optimization based on some target parameters and gradually reduce the boundary conditions as the optimization process proceeds, thereby reducing the number of iterations and the calculation time of optimization.

[0087] Figure 6 An example of selecting target parameters is shown.

[0088] Referring to Figure 6 , parameters 610 based on stoichiometry included in a battery model and target parameters 620 selected when the dimension of the parameters is reduced by a battery model optimization apparatus are shown. The battery model optimization apparatus can extract specific points from initial values of the parameters 610 and select the extracted specific points as the target parameters 620. For example, the battery model optimization apparatus can select a specific point at which a value obtained by performing at least one differentiation on the parameters 610 is zero as the target parameter 620. In the example of Figure 6 , the value of the target parameter 620 can correspond to a specific point at which the first derivative is zero. However, a specific point at which a higher-order derivative (e.g., a second derivative or a higher-order derivative) is zero can also be selected as the target parameter. The battery model optimization apparatus can perform optimization only on the target parameters 620 having a reduced dimension. The battery model optimization apparatus can determine a parameter value of another point other than the target parameters 620 by performing interpolation based on the optimized values of the target parameters 620 corresponding to the specific points. For example, the battery model optimization apparatus can perform interpolation using a Gaussian Process Regression (GPR) method as a non-parametric model. In the example of Figure 6 , the battery model optimization apparatus can extract values of the target parameters 620 corresponding to seven specific points, perform optimization only on the extracted values of the target parameters 620, and then determine another parameter value other than the target parameters 620 by interpolation. The battery model optimization apparatus can set boundary conditions for searching for an optimized parameter value based on initial values of the parameters 610. In the graph shown in Figure 6 , the boundary indicated by a dotted line can correspond to the boundary conditions.

[0089] Figure 7 An example of reduced boundary conditions is shown.

[0090] Referring to Figure 7The reduced boundary condition can be applied to the parameter 702 and / or the parameter 704 of the battery model representing a diffusion characteristic. In the illustrated graph, the vertical axis indicates the magnitude of the parameter value, and the horizontal axis indicates the SOC level. When the previous boundary condition 710 is determined for the parameter 702 and then the boundary condition to be used for the subsequent optimization process is determined as the boundary condition 720 according to the boundary condition 710, the subsequent optimization process can be performed based on the boundary condition 720. The optimized parameter value of the parameter 702 can be determined as a value between intervals corresponding to the boundary condition 720 by being constrained to the boundary condition 720.

[0091] Figure 8 An example of optimizing parameters of a battery model based on a target change direction of a diffusion coefficient is illustrated.

[0092] Referring to Figure 8 , the operations 810, 820, 830, and 840 can correspond to the operations 310, 320, 330, and 340 described above with reference to Figure 3 , and thus repetitive descriptions thereof will be omitted here. In operation 850, for a diffusion coefficient corresponding to a diffusion parameter, the battery model optimization device can set a boundary condition reduced from a previous boundary condition based on a target change direction of the diffusion coefficient. Here, the target change direction can indicate a direction in which the diffusion coefficient is expected to change. The battery model optimization device can calculate a voltage error between a voltage of a battery estimated through a battery model in a previous optimization process and a voltage of the battery based on profile data of the battery, and determine the target change direction based on the calculated voltage error. The battery model optimization device can set a subsequent boundary condition based on the boundary condition of the diffusion coefficient having a directionality of the target change direction. The battery model optimization device can iteratively perform an optimization process based on the set subsequent boundary condition. The optimization process can be performed until the number of times of performing the optimization process reaches a set number of times of performing.

[0093] Figure 9 An example of setting a boundary condition based on a target change direction of a diffusion coefficient is illustrated.

[0094] Referring to Figure 9 , when a voltage error (dV) between a voltage of a battery estimated through a battery model and a voltage of the battery based on profile data of the battery is not zero, a direction in which the diffusion coefficient is to be moved to have a desired value can be determined. A new boundary condition to be applied to the diffusion coefficient can be set based on the voltage error for each SOC level. The direction in which the diffusion coefficient physically changes can be set based on the sign of the voltage error. A change 910 in the value of the voltage error based on the voltage error change in the intervals A, B, and C is illustrated.

[0095] For example, when the voltage error (or, for example, difference) is positive at the battery's state of charge (SOC), the voltage error can be reduced by moving the boundary conditions 920 for the anode diffusion coefficient and 930 for the cathode diffusion coefficient upwards. Conversely, when the voltage error is negative, it can be reduced by moving the boundary conditions 920 for the anode diffusion coefficient and 930 for the cathode diffusion coefficient downwards. New boundary conditions 925 for the anode diffusion coefficient and 935 for the cathode diffusion coefficient based on the sign of the voltage error are also shown. Setting the boundary conditions for the diffusion coefficients used for optimization based on the physical meaning of the battery characteristics improves the efficiency of the optimization process and increases the accuracy of the battery model.

[0096] Figure 10 Examples of optimizing the parameters of the battery model for each temperature range are shown.

[0097] The battery model optimization device can perform optimization processing on the parameters of each pair of battery models within a predefined temperature range. (Refer to...) Figure 10 The system can perform optimization processes including operations 1010, 1015, 1020, 1025, and 1030 for a first temperature range. When the number of executions of the optimization process for the first temperature range in operation 1025 reaches the set number, the optimization process for the first temperature range can be terminated in operation 1035. Subsequently, in operation 1040, boundary conditions for a second temperature range can be set, and optimization processes including operations 1040, 1045, 1050, 1055, and 1060 can be performed for the second temperature range. When the number of executions of the optimization process for the second temperature range in operation 1055 reaches the set number, the optimization process for the second temperature range can be terminated in operation 1065. In the optimization process for each temperature range, optimization can be performed according to the characteristics of each temperature range, and configuration file data or parameters corresponding to each temperature range can be used. (Refer to the above...) Figure 3 and Figure 4 The described optimization process can be applied to the reference. Figure 10 The optimization process described.

[0098] Although the optimization process is described above as being performed separately for the first temperature interval and the second temperature interval, the temperature intervals can be divided into three or more temperature intervals, and the optimization can be performed for each temperature interval. For example, the temperature intervals can be defined as a first temperature interval corresponding to a low temperature, a second temperature interval corresponding to a room temperature, and a third temperature interval corresponding to a high temperature. In this example, the optimization process described above can be performed for each of the first temperature interval, the second temperature interval, and the third temperature interval. As described above, performing the optimization for each temperature interval can enable efficient optimization of parameters specific to each temperature interval, and also enable efficient optimization of a battery model having different parameter sets corresponding to the plurality of temperature intervals.

[0099] Figure 11 An example of a battery model optimization apparatus is illustrated.

[0100] Referring to Figure 11 The battery model optimization apparatus 1100 can include a processor 1110 (e.g., one or more processors) and a memory 1120 (e.g., one or more memories). The memory 1120 can be connected to the processor 1110, and store instructions executable by the processor 1110, data to be processed by the processor 1110, or data processed by the processor 1110. The memory 1120 can include a non-transitory computer readable medium (e.g., a high-speed random access memory (RAM)) and / or a non-volatile computer readable storage medium (e.g., one or more disk storage devices, flash memory devices, or other non-volatile solid state storage devices).

[0101] The processor 1110 can control overall operations of the battery model optimization apparatus 1100, and execute functions and instructions for performing operations in the battery model optimization apparatus 1100. The processor 1110 can execute functions and instructions for performing the operations described above with reference to Figures 1 to 10instructions of one or more or all of the described operations. When the instructions are executed by the processor 1110, the processor 1110 can perform an optimization process for determining an optimized parameter value of a parameter of a battery model. In the optimization process, the processor 1110 can select a target parameter from among the parameters of the battery model. The processor 1110 can select a specific point associated with a diffusion characteristic of the battery from among the parameters of the battery model, and determine the target parameter based on the selected point. For example, the processor 1110 can select the target parameter based on a value obtained by performing at least one differentiation with respect to the parameters of the battery model. The processor 1110 can set a current boundary condition for each target parameter, and search for an optimized parameter value of each target parameter based on the set current boundary condition. The processor 1110 can determine an estimated state value of the battery model based on the target parameter, and determine an optimization loss based on a difference between the estimated state value and an actual state value obtained from profile data of the battery. The processor 1110 can adjust at least one of the target parameters to reduce the optimization loss.

[0102] The processor 1110 can set a subsequent boundary condition decreased from the current boundary condition based on a result of searching for the optimized parameter value. The processor 1110 can change the boundary condition of all target parameters based on the optimized parameter value obtained according to the current boundary condition, and set the changed boundary condition as the subsequent boundary condition. The processor 1110 can determine a target change direction of the diffusion parameter based on a voltage error between a voltage of the battery estimated through the battery model and a voltage of the battery based on the profile data of the battery, and set the subsequent boundary condition based on the determined target change direction. The processor 1110 can perform again the operation of searching for the optimized parameter value of each target parameter based on the set subsequent boundary condition. The processor 1110 can iteratively perform the operation of setting the subsequent boundary condition decreased from the current boundary condition and the operation of searching for the optimized parameter value of each target parameter based on the subsequent boundary condition until the number of executions of the optimization process reaches a set number of executions.

[0103] In one example, the processor 1110 can perform the above-described optimization process for each of different SOC intervals corresponding to a degree of progress of charging of the battery. In addition, the processor 1110 can perform the optimization process based on different SOC intervals corresponding to a degree of progress of discharging of the battery. In addition to the SOC intervals, the processor 1110 can perform the optimization process based on predefined temperature intervals. The processor 1110 can also perform operations related to the optimization process described herein.

[0104] Figure 12 An example of a battery management server including a battery model optimization apparatus is illustrated.

[0105] Referring to Figure 12The battery model optimization device 1211 may be included in the battery management server 1210. The battery model optimization device 1211 may correspond to the battery model optimization device described herein (e.g., Figure 1 Battery model optimization device 100 and Figure 11 The battery model optimization device 1211 can receive and store the operating data of the battery 1221 from the battery power supply 1220. When optimization of the battery model corresponding to the battery 1221 is to be performed, the battery model optimization device 1211 can optimize the parameters of the battery model based on the operating data and provide the updated parameters obtained as a result of the optimization to the battery power supply 1220. The battery model optimization device 1211 can optimize the parameters based on the optimization process described herein. The battery power supply 1220 can provide power supplied from the battery 1221 to an external device and estimate the SOC of the battery 1221 using the battery model based on the parameters optimized by the battery model optimization device 1211.

[0106] Figure 13 An example of a battery power supply including a battery model optimization device is shown.

[0107] Reference Figure 13 The battery power source 1300 may include a battery 1310 and a battery model optimization device 1320. The battery model optimization device 1320 may correspond to the battery model optimization device described herein (e.g., Figure 1 Battery model optimization device 100 and Figure 11 The battery model optimization device 1100. The battery power supply 1300 can provide power supplied from the battery 1310 to external devices and can be implemented as part of the BMS. The battery 1310 can power electronic devices connected to the battery power supply 1300, and the battery model optimization device 1320 can optimize the battery model corresponding to the battery 1310.

[0108] When optimizing the battery model, the battery model optimization device 1320 can perform the following operations: an operation of selecting target parameters from among parameters of the battery model, an operation of setting a current boundary condition for each target parameter, an operation of searching for an optimized parameter value of each target parameter based on the set current boundary condition, an operation of setting a subsequent boundary condition that is reduced from the current boundary condition based on a result of the search, and an operation of searching for an optimized parameter value of each target parameter based on the subsequent boundary condition. The battery model optimization device 1320 can determine a target change direction of a diffusion parameter based on a voltage error between a voltage of the battery 1310 estimated through the battery model and a voltage based on profile data of the battery 1310, and set a subsequent boundary condition based on the determined target change direction. The battery model optimization device 1320 can perform the optimization process for each of different SOC intervals corresponding to a degree of progress of charging or discharging of the battery 1310. In addition, the battery model optimization device 1320 can perform the optimization process for each of pre-defined temperature intervals. Furthermore, the battery model optimization device 1320 can perform other operations for the optimization process described herein.

[0109] Herein with respect to Figures 1 to 13The described battery model optimization apparatus, battery, processor, memory, battery management server, battery power supply, battery model optimization apparatus 100, battery 120, battery model optimization apparatus 1100, processor 1110, memory 1120, battery management server 1210, battery model optimization apparatus 1211, battery power supply 1220, battery 1221, battery power supply 1300, battery 1310, battery model optimization apparatus 1320, and other apparatuses, devices, apparatuses, units, modules, and components are implemented by or represent hardware components. Examples of hardware components that can be used to perform the operations described in this application include controllers, sensors, generators, drivers, memories, comparators, arithmetic logic units, adders, subtractors, multipliers, dividers, integrators, and any other electronic components configured to perform the operations described in this application. In other examples, one or more of the hardware components performing operations described in this application are implemented by computing hardware (e.g., by one or more processors or computers). A processor or computer can be implemented by one or more processing elements, such as logic gates arrays, controllers and arithmetic logic units, digital signal processors, microcomputers, programmable logic controllers, field programmable gate arrays, programmable logic arrays, microprocessors, or any other device or combination of devices configured to respond to and perform instructions in a defined manner to achieve a desired result. In one example, a processor or computer includes or is connected to one or more memories that store instructions or software executed by the processor or computer. The hardware components implemented by the processor or computer can execute instructions or software (such as an operating system (OS) and one or more software applications running on the OS) for performing the operations described in this application. The hardware components can also access, manipulate, process, create, and store data in response to the execution of the instructions or software. For simplicity, the singular term "processor" or "computer" can be used in the description of the examples described in this application, but in other examples, multiple processors or computers can be used, or a processor or computer can include multiple processing elements, or multiple types of processing elements, or both. For example, a single hardware component or two or more hardware components can be implemented by a single processor, or two or more processors, or a processor and a controller. One or more hardware components can be implemented by one or more processors, or a processor and a controller, and one or more other hardware components can be implemented by one or more other processors, or additional processors and additional controllers. The one or more processors or a processor and a controller can implement a single hardware component, or two or more hardware components.The hardware components can have any one or more of various processing configurations, examples of which include a single processor, multiple processors, a parallel processing processor, a single instruction single data (SISD) multiprocessor, a single instruction multiple data (SIMD) multiprocessor, a multiple instruction single data (MISD) multiprocessor, and a multiple instruction multiple data (MIMD) multiprocessor.

[0110] Figures 1 to 13 The methods of performing the operations described in this application are performed by computing hardware (e.g., by one or more processors or computers) implemented to execute instructions or software as described above to perform the operations described in this application as performed by the methods. For example, a single operation or two or more operations can be performed by a single processor, or two or more processors, or a processor and a controller. One or more operations can be performed by one or more processors, or a processor and a controller, and one or more other operations can be performed by one or more other processors, or further processors and further controllers. The one or more processors, or a processor and a controller, can perform a single operation, or two or more operations.

[0111] The instructions or software for controlling computing hardware (e.g., one or more processors or computers) to implement the hardware components and perform the methods as described above can be written in a computer program, a code segment, instructions, or any combination thereof, to individually or collectively instruct or configure one or more processors or computers as a machine or special purpose computer to operate to perform the operations performed by the hardware components and methods as described above. In one example, the instructions or software include machine code (such as produced by a compiler) directly executable by the one or more processors or computers. In another example, the instructions or software include high-level code to be executed by the one or more processors or computers using an interpreter. The instructions or software can be written in any programming language based on the block diagrams and flowcharts shown in the drawings and corresponding descriptions in the specification, which disclose algorithms for performing the operations performed by the hardware components and methods as described above, using any programming language.

[0112] Instructions or software for controlling computing hardware (e.g., one or more processors or computers) to implement the hardware components and perform the methods as described above, as well as any associated data, data files, and data structures, can be recorded, stored, or fixed in one or more non-transitory computer-readable storage media, or on one or more non-transitory computer-readable storage media. Examples of non-transitory computer-readable storage media include read-only memory (ROM), programmable read-only memory (PROM), electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), dynamic random-access memory (DRAM), static random- access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disk storage, hard disk drive (HDD), solid-state drive (SSD), flash memory, card memory such as a multimedia card or a micro card (e.g., a secure digital (SD) or extreme digital (XD)), magnetic tape, a floppy disk, a magneto-optical data storage device, an optical data storage device, a hard disk, a solid state disk, and any other device configured to store instructions or software and any associated data, data files, and data structures in a non-transitory manner and provide the instructions or software and any associated data, data files, and data structures to one or more processors or computers so that the one or more processors or computers can execute the instructions. In one example, the instructions or software and any associated data, data files, and data structures are distributed over a networked computer system so that the instructions and software and any associated data, data files, and data structures are stored, accessed, and executed by one or more processors or computers in a distributed manner.

[0113] While the present disclosure includes certain examples, it will be clear to those skilled in the art that various changes can be made without departing from the spirit and scope of the claims and their equivalents. The examples described herein are to be considered illustrative only and are not intended to limit the scope of the disclosure. The description of features or aspects within each example should be considered applicable to similar features or aspects within other examples. Suitable results can be achieved if the described techniques are performed in a different order, and / or if the components within the described systems, architectures, devices, or circuits are combined in a different manner, and / or replaced or supplemented by other components or equivalents thereof.

Claims

1. An apparatus with battery model optimization, the apparatus comprising: a processor configured to perform optimization on a battery model to determine optimized parameter values of parameters of the battery model, wherein, to perform the optimization, the processor is configured to: select target parameters from among the parameters of the battery model; set current boundary conditions for each of the target parameters; determine optimized parameter values of each of the target parameters based on the set current boundary conditions; set subsequent boundary conditions that are reduced from the current boundary conditions based on the determined optimized parameter values; and determine subsequent optimized parameter values of each of the target parameters based on the subsequent boundary conditions, wherein, to set the subsequent boundary conditions, the processor is configured to: determine a target direction of change of a diffusion parameter based on a voltage error between a voltage of the battery estimated by the battery model and a voltage of the battery based on profile data of the battery; and set the subsequent boundary conditions based on the determined target direction of change.

2. The apparatus of claim 1, wherein, The processor is configured to perform the optimization for each of different state of charge intervals corresponding to a degree of progress of charging of the battery.

3. The apparatus of claim 1, wherein, The processor is configured to perform the optimization based on different state of charge intervals corresponding to a degree of progress of discharging of the battery.

4. The apparatus of claim 1, wherein, The processor is configured to perform the optimization for each of different temperature intervals.

5. The apparatus of claim 1, wherein, The processor is configured to select the target parameters based on values obtained by performing one or more differentiations on the parameters of the battery model.

6. The apparatus of claim 1, wherein, The processor is configured to iteratively perform setting the subsequent boundary conditions that are reduced from the current boundary conditions and determining the subsequent optimized parameter values of each of the target parameters based on the subsequent boundary conditions until a number of times of performance of the optimization reaches a set number of times.

7. The apparatus according to claim 1, wherein, to set the subsequent boundary conditions, the processor is configured to change the current boundary conditions for all of the target parameters based on the optimized parameter values obtained according to the current boundary conditions, and the changed boundary conditions correspond to the subsequent boundary conditions.

8. The apparatus of claim 1, wherein, To select the target parameters, the processor is configured to: select points associated with diffusion characteristics of the battery from among the parameters of the battery model; and determine the target parameters based on the selected points.

9. The apparatus of claim 1, wherein, The processor is configured to: determine estimated state values of the battery model based on the target parameters; determine an optimization loss based on a difference between the estimated state values and actual state values obtained from profile data of the battery; and adjust at least one of the target parameters such that the optimization loss is reduced.

10. The apparatus according to any one of claims 1 to 9, wherein, the parameters of the battery model include diffusion parameters that depend on a state of charge level of the battery, and the diffusion parameters include charging parameters associated with charging of the battery and discharging parameters associated with discharging of the battery.

11. The apparatus according to any one of claims 1 to 9, the processor is configured to: estimate a state of the battery using the optimized battery model.

12. A method with battery model optimization, the method comprising: selecting target parameters from among parameters of a battery model; and performing optimization on the target parameters, wherein the step of performing the optimization includes: setting current boundary conditions for each of the target parameters; ​ determine an optimization parameter value for each target parameter based on the set current boundary condition; set a subsequent boundary condition reduced from the current boundary condition based on the determined optimization parameter value; and determine a subsequent optimization parameter value for each target parameter based on the subsequent boundary condition, wherein the step of setting the subsequent boundary condition comprises: determining a target change direction of the diffusion parameter based on a voltage error between a voltage of the battery estimated by the battery model and a voltage of the battery based on the profile data of the battery; and setting the subsequent boundary condition based on the determined target change direction.

13. The method of claim 12, wherein, The step of performing the optimization comprises performing the optimization for each of predefined different state-of-charge intervals corresponding to a degree of progress of charging of the battery.

14. The method of claim 12, wherein, The step of performing the optimization comprises performing the optimization for each of predefined different state-of-charge intervals corresponding to a degree of progress of discharging of the battery.

15. The method of claim 12, wherein, The step of performing the optimization comprises performing the optimization for each of predefined temperature intervals.

16. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the method of any one of claims 12 to 15.

17. A battery power source comprising: a battery configured to supply power to an electronic device; and a battery model optimization device configured to optimize a battery model corresponding to the battery, wherein, to optimize the battery model, the battery model optimization device is configured to: select target parameters from among parameters of the battery model; set a current boundary condition for each target parameter; determine an optimization parameter value for each target parameter based on the set current boundary condition; set a subsequent boundary condition reduced from the current boundary condition based on the determined optimization parameter value; and determine a subsequent optimization parameter value for each target parameter based on the subsequent boundary condition, wherein, to set the subsequent boundary condition, the battery model optimization device is configured to: determine a target change direction of the diffusion parameter based on a voltage error between a voltage of the battery estimated by the battery model and a voltage of the battery based on the profile data of the battery; and set the subsequent boundary condition based on the determined target change direction.

18. The battery power supply of claim 17, wherein, The battery model optimization device is configured to perform the optimization for each of predefined different state-of-charge intervals corresponding to a degree of progress of charging or discharging of the battery.

19. A method with battery model optimization, the method comprising: setting a boundary condition for a target parameter of a battery model; determining an optimization parameter value for the target parameter to be within the boundary condition; determining a target change direction of a diffusion parameter based on a difference between a state of the battery estimated using the battery model with the optimization parameter value and a predetermined state of the battery, and setting a subsequent boundary condition with a range reduced from the current boundary condition based on the determined target change direction; and optimizing the battery model by determining a subsequent optimization parameter value for the target parameter to be within the subsequent boundary condition.

20. The method of claim 19, wherein the set boundary condition comprises a lower limit and an upper limit, and the step of determining the optimization parameter value comprises determining the optimization parameter value to be greater than or equal to the lower limit and less than or equal to the upper limit. ​ 21. The method of claim 20, wherein, The step of setting the subsequent boundary condition includes performing either one of: increasing at least one of the lower limit and the upper limit, and decreasing at least one of the lower limit and the upper limit, based on whether the state of the battery estimated using the battery model is greater than a predetermined state of the battery.

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