Electronic device and method for battery state estimation

Abstract

An electronic device and a method for battery state estimation are provided. The electronic device, in addition to updating a degradation parameter that can be updated at an estimation time point, updates a degradation parameter that does not satisfy an update condition, determines a parameter value of another parameter based on an updated parameter value of a selected target parameter based on monitoring of a battery, estimates a battery state from the target parameter value and the other parameter value using a battery state estimation model, updates the other parameter value from the estimated battery state, and re-estimates the battery state.

Description

[0001] This application claims the benefit of Korean Patent Application No. 10-2025-0015447, filed on February 6, 2025, 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 electronic devices and methods for battery state estimation. Background Technology

[0003] Conventional battery state estimation algorithms typically explain battery degradation by considering increases in resistance and decreases in capacity. For algorithms that consider increases in resistance, battery degradation is simplified to an increase in a single resistance component. Such algorithms can be implemented by automatically updating a lookup table (LUT) for such resistance. For algorithms that consider decreases in capacity, the total capacity is estimated by accumulating the discharge current across two sufficiently wide ranges of near-fully discharged state of charge (SOC) segments (e.g., from 95% to 5%). However, these methods fail to adequately capture the complexity of battery degradation, which can involve behaviors far more complex than those represented by simple changes in resistance and capacity. Summary of the Invention

[0004] The present invention is provided in a simplified form to introduce the choice of concepts further described in the following detailed embodiments. 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, an electronic device includes: a battery; one or more processors; and a memory storing instructions, wherein, when executed by the one or more processors, the instructions cause the electronic device to: store updated parameter values ​​for at least one target parameter, the at least one target parameter being selected from a plurality of degradation parameters based on monitoring of the battery; determine parameter values ​​for other parameters based on the updated parameter values ​​for the at least one target parameter; estimate the battery state using a battery state estimation model based on the updated parameter values ​​for the at least one target parameter and the determined parameter values ​​for the other parameters; update the parameter values ​​for the other parameters based on the estimated battery state; and determine an updated battery state based on the updated parameter values ​​for the at least one target parameter and the updated parameter values ​​for the other parameters.

[0006] When executed, the instructions may also cause the electronic device to: obtain measurements of one or more of the battery's current, voltage, and temperature for each portion of the battery's operating time; identify the state of charge (SOC) segment to which the battery belongs during each portion of the operating time; and determine, based on the measurements, parameter values ​​of the degradation parameters corresponding to the identified SOC segment.

[0007] When the instruction is executed, the electronic device may also: in response to the cumulative time of a portion of the operating time in the identified SOC segment exceeding a threshold time, select a degradation parameter corresponding to the identified SOC segment as the at least one target parameter; and determine an updated parameter value of the selected target parameter based on the parameter value determined during the portion of the operating time corresponding to the identified SOC segment.

[0008] When the instruction is executed, the electronic device may also: in response to the corresponding cumulative operating time in the first SOC segment and the second SOC segment both exceeding the threshold time, select the degradation parameter corresponding to the first SOC segment and the degradation parameter corresponding to the second SOC segment as target parameters.

[0009] The multiple degradation parameters may include one or more of the following: the capacity ratio of the cathode active material, the resistance of the anode solid electrolyte interface (SEI), and electrode balance.

[0010] When the instruction is executed, the electronic device may also: determine the parameter value of the reference parameter corresponding to the updated parameter value of the at least one target parameter based on deterioration trend information indicating the relationship between the at least one target parameter and the reference parameter; and update the determined parameter values ​​of other parameters based on the parameter value of the reference parameter.

[0011] When the instruction is executed, it can also cause the electronic device to determine the parameter values ​​of other parameters corresponding to the parameter values ​​of the reference parameter, based on information indicating the deterioration trend of the relationship between other parameters and the reference parameter.

[0012] Battery state estimation models may include one or both of electrochemical models and machine learning-based models.

[0013] When the instruction is executed, the electronic device may also update the values ​​of other parameters to values ​​corresponding to the estimated battery state, based on information indicating the deterioration trend of the relationship between the battery state and other parameters.

[0014] When executed, the instruction may also cause the electronic device to determine the updated battery state at a target time point using the battery state estimate, based on one or both the convergence of the error between the estimated battery state values ​​and the convergence of the error between the updated values ​​of other parameters.

[0015] The electronic device may further include a display, wherein, when the instruction is executed, the electronic device also causes the electronic device to: display on the display one or both of a state of health (SOH) corresponding to the updated battery state and a remaining charge amount determined based on the SOH.

[0016] When the instruction is executed, the electronic device may also: in response to the remaining charge determined based on the updated battery state being less than a threshold charge, shut down the electronic device or reduce the power consumption of the electronic device.

[0017] In one general aspect, a processor-implemented method includes: storing updated parameter values ​​of at least one target parameter, the at least one target parameter being selected from a plurality of degradation parameters based on monitoring of the battery; determining parameter values ​​of other parameters based on the updated parameter values ​​of the at least one target parameter; estimating the battery state using a battery state estimation model based on the updated parameter values ​​of the at least one target parameter and the determined parameter values ​​of the other parameters; updating the parameter values ​​of the other parameters based on the estimated battery state; and determining an updated battery state based on the updated parameter values ​​of the at least one target parameter and the updated parameter values ​​of the other parameters.

[0018] The step of storing the updated parameter value of the at least one target parameter may include: obtaining measurements of one or more of the battery's current, voltage, and temperature for each segment of battery operation time; identifying the state of charge (SOC) segment corresponding to each segment of operation time; and determining parameter values ​​of the degradation parameter corresponding to the identified SOC segment based on the measurements.

[0019] The step of storing the updated parameter value of the at least one target parameter may further include: in response to the cumulative time of a portion of the operating time in the identified SOC segment exceeding a threshold time, selecting a degradation parameter corresponding to the identified SOC segment as the at least one target parameter; and determining the updated parameter value of the selected target parameter based on the parameter value determined during the portion of the operating time.

[0020] The step of storing the updated parameter value of the at least one target parameter may further include: in response to the corresponding cumulative operating time in each SOC segment exceeding a threshold time, selecting the degradation parameter corresponding to at least two SOC segments as the target parameter.

[0021] The step of determining the parameter values ​​of other parameters may include: determining the parameter value of the reference parameter corresponding to the updated parameter value of the at least one target parameter based on degradation trend information indicating the relationship between the at least one target parameter and the reference parameter; and updating the determined parameter values ​​of other parameters based on the parameter values ​​of the reference parameter.

[0022] The steps for determining the values ​​of other parameters may include updating the determined values ​​of other parameters based on degradation trend information that indicates the relationship between other parameters and reference parameters.

[0023] The steps to update the values ​​of other parameters may include: updating the values ​​of other parameters to correspond to the estimated battery state based on degradation trend information that indicates the relationship between the battery state and other parameters.

[0024] In one general aspect, a non-transitory computer-readable storage medium is provided that stores instructions which, when executed by one or more processors, cause the processors to perform the methods described herein.

[0025] Other features and aspects will become clear from the following detailed description, drawings, and claims. Attached Figure Description

[0026] Figure 1 An example battery state estimation system according to one or more embodiments is shown.

[0027] Figure 2 An example electrochemical model according to one or more embodiments is shown.

[0028] Figure 3 Examples of battery state degradation are shown according to one or more embodiments.

[0029] Figures 4 to 6 Examples of degradation parameters according to one or more embodiments are shown.

[0030] Figure 7 An example battery state estimation method according to one or more embodiments is shown.

[0031] Figure 8 Examples of updated parameters and battery state for battery state estimation according to one or more embodiments are shown.

[0032] Figure 9 Examples are shown of multiple SOC segments across the entire region of the state of charge (SOC) according to one or more embodiments, and corresponding degradation parameters for each SOC segment.

[0033] Figures 10A to 10D Examples of selecting degradation parameters to be updated based on battery operating mode according to one or more embodiments are shown.

[0034] Figure 11 and Figure 12 Examples are shown of determining the values ​​of other degradation parameters from the target degradation parameter based on degradation trends, according to one or more embodiments.

[0035] Figure 13 Examples of degradation trends are shown according to one or more embodiments.

[0036] Figure 14 Example iterative updates of degradation parameters and battery status according to one or more embodiments are shown.

[0037] Figure 15 An example configuration of an electronic device according to one or more embodiments is shown.

[0038] Throughout the accompanying drawings and detailed embodiments, unless otherwise described or provided, the same reference numerals shall be construed as denoteing the same or identical 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 detailed description is provided to assist the reader in gaining a comprehensive understanding of the methods, apparatus, and / or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become clear upon understanding the disclosure of this application. For example, the order of operations described herein is merely illustrative and is not limited to the order set forth herein, but may be changed as will become clear upon understanding the disclosure of this application, except for operations that must occur in a specific order. Furthermore, for greater clarity and conciseness, descriptions of features known upon understanding the disclosure of this application may be omitted.

[0040] The features described herein may 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 only to illustrate some of the many feasible ways in which the methods, apparatus, and / or systems described herein will be clear upon understanding the disclosure of this application.

[0041] The terminology used herein is for the purpose of describing various examples only and is not intended to limit disclosure. Unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. As used herein, the term "and / or" includes any one or any combination of any two or more of the associated listed items. As a non-limiting example, the terms "comprising," "including," and "having" indicate the presence of the features, quantities, operations, components, elements, and / or combinations thereof stated, but do not preclude the presence or addition of one or more other features, quantities, operations, components, elements, and / or combinations thereof.

[0042] Throughout this specification, when a component or element is described as being "connected to," "joined to," or "attached to" another component or element, that component or element may be directly "connected to," "joined to," or "attached to" that other component or element, or there may reasonably be one or more other components or elements present in between. When a component or element is described as being "directly connected to," "directly joined to," or "directly attached to" another component and element, there may be no other elements present in between. Similarly, expressions such as "between" and "immediately between," and "adjacent to" and "closely adjacent to" may also be interpreted as described above.

[0043] Although terms such as “first,” “second,” and “third,” or A, B, (a), (b), etc., may be used herein to describe various components, assemblies, regions, layers, or parts, these components, assemblies, regions, layers, or parts should not be limited by these terms. For example, each of these terms is not used to define the nature, order, or sequence of the corresponding component, assembly, region, layer, or part, but only to distinguish the corresponding component, assembly, region, layer, or part from other components, assemblies, regions, layers, or parts. Therefore, without departing from the teaching of the examples described herein, the first component, first assembly, first region, first layer, or first part referred to as the first component, first assembly, first region, first layer, or first part may also be referred to as the second component, second assembly, second region, second layer, or second part.

[0044] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains, based on an understanding of the disclosure of this application. 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 art and in the disclosure of this application, and shall not be interpreted in an idealized or overly formal sense. The use of the term “may” herein with respect to examples or embodiments (e.g., regarding what an example or embodiment may include or implement) indicates the existence of at least one example or embodiment that includes or implements such a feature, but not all examples are limited thereto.

[0045] Figure 1 An example battery state estimation system according to one or more embodiments is shown.

[0046] A battery state estimation system can estimate a battery state 190, which is an indicator of one or more of the battery's electrical, chemical, and / or physical states. Battery state 190 may include, for example, the battery's state of health (SOH), state of charge (SOC), state of power (SOP), state of energy (SOE), and state of function (SOF). SOH can be expressed as the ratio of the battery's current capacity (e.g., degraded capacity) to its initial capacity and can indicate the battery's remaining lifespan. Although SOH is primarily described herein as an example of battery state 190, the examples are not limited thereto.

[0047] With continued use, battery performance can deteriorate over time. This degradation can be caused by various factors, including a reduction in the amount of cathode or anode active material, lithium plating, an increase in simple resistivity, and electrode imbalance. The degradation trend and level can vary depending on battery usage patterns and environmental factors. As degradation progresses, the battery state of matter (SOH) can change (e.g., the battery's state of harmonics (SOH) can decrease).

[0048] Battery state estimation systems (e.g., electronic devices) can estimate battery state information (e.g., SOH) based on multiple battery degradation parameters. Degradation parameters are parameters used in an electrochemical model of the battery that indicate the battery's degradation state, the factors and / or phenomena leading to battery degradation, and may include, for example, one or a combination of two or more of the following: anode solid electrolyte interface (SEI) resistance, cathode active material capacity, and electrode balance shift. See below for further details. Figures 4 to 6 A more detailed description of the battery degradation parameters (hereinafter referred to as "degradation parameters").

[0049] The electronic device can perform monitoring of measurement data 110. The electronic device can collect multiple measurement data points (such as battery voltage, current, and / or temperature) by monitoring the measurement data. For example, the electronic device can collect multiple measurement data points within a partial operating time interval (or a portion of the operating time). As described below, the electronic device can calculate and record the corresponding degradation parameter value for each of the partial operating time intervals.

[0050] Based on monitoring 110, the electronic device can determine the parameter value of at least one target parameter (e.g., a first degradation parameter 111) selected from a plurality of degradation parameters. As described below, the electronic device can identify the SOC segment to which the battery's SOC value belongs during each partial operating time, and can select the first degradation parameter 111 corresponding to the corresponding SOC segment as the target parameter. For example, the electronic device can update the value of the first degradation parameter 111 using a value obtained during a partial operating time interval for the first degradation parameter 111.

[0051] The electronic device can determine the parameter values ​​of other parameters among a plurality of degradation parameters based on the updated parameter values ​​of the target parameter. The electronic device can determine a reference value 120 corresponding to the parameter value of the first degradation parameter 111, and then derive the parameter values ​​of other parameters (e.g., the (N-1)th degradation parameter 118 and the Nth degradation parameter 119, where N is an integer greater than or equal to 3) based on the reference value.

[0052] The electronic device may determine a reference value 120 (e.g., the SOH value) corresponding to an updated parameter value of the target parameter based on degradation trend information (e.g., a trend line of reference parameter values ​​versus target parameter values) representing the relationship between a reference parameter (e.g., SOH) and a target parameter. The trend line may be a straight line or curve obtained by approximating the relationship between two parameters using data points (e.g., the values ​​of other parameters when any parameter has a predetermined value). For example, the electronic device may determine a reference value 120 corresponding to an updated parameter value of a first degradation parameter 111. Although the reference value 120 is primarily described herein as an example of an SOH value, the examples are not limited thereto.

[0053] The electronic device can determine the parameter values ​​of other deteriorating parameters corresponding to the aforementioned reference value 120 based on the degradation trend information between the reference parameter and other parameters (e.g., a trend line of the reference parameter versus other parameters). For example, the electronic device can determine the (N-1)th degradation parameter 118 and the Nth degradation parameter 119 based on the aforementioned reference value 120.

[0054] Using battery state estimation model 150, an electronic device can estimate battery state 190 based on updated values ​​of the target parameter and other parameters. Battery state estimation model 150 may include one or more of a circuit model, an electrochemical model, and a machine learning-based model.

[0055] An electrochemical model is a model of physical conservation equations and electrochemical reactions, and can be used to simulate the physical properties associated with the electrochemical reactions occurring inside a battery. For example, an electrochemical model is obtained by modeling internal physical phenomena of the battery, such as lithium-ion (Li) concentration, battery potential, etc., and can be used to estimate remaining battery power (e.g., battery lifetime or state 190). An electrochemical model can be represented as the electrochemical reactions occurring at the electrode / electrolyte interface, the concentrations of the electrodes / electrolytes, and the physical conservation equations associated with charge conservation. Electrochemical models can be modeled using a variety of parameters, including multiple degradation parameters. Parameters used in an electrochemical model can include geometric properties (e.g., thickness, radius, etc.), open-circuit potential (OCP), and material properties (e.g., conductivity, ionic conductivity, and diffusion coefficient). To improve the accuracy of estimating the state 190 of the battery under degradation conditions, the degradation parameters must be accurately updated in addition to the initial state of the battery.

[0056] In electrochemical models, various state variables (such as ion concentration and electrode potential) can be interdependent. The estimated voltage of the battery derived from the electrochemical model corresponds to the potential difference between the cathode and anode terminals, and the lithium-ion concentration distribution at the cathode and anode can affect their potentials. Furthermore, the average lithium-ion concentration at the cathode and anode can be used to estimate the state of charge (SOC) of the battery in the electrochemical model.

[0057] In addition to electrochemical models, electronic devices can also use machine learning-based models to estimate battery state information. For example, a trained neural network can be used to predict and output battery state information based on degradation parameter inputs.

[0058] The electronic device can update the parameter values ​​of other parameters 180 based on the estimated battery state 190. For example, the electronic device can store the updated parameter values ​​of other parameters based on degradation trend information (e.g., a trend line) inferred from the estimated battery state 190.

[0059] The electronic device can determine the updated battery state 190 based on the updated parameter values ​​of the target parameter and other updated parameter values. The electronic device can determine the updated battery state 190 at the estimated time point in response to the convergence of parameter values ​​and the update of battery state 190.

[0060] The electronic device for a battery state estimation system can be implemented using an FG integrated circuit (IC) chip for battery charge measurement (FG). The FG IC chip may be equipped with firmware that performs the battery state estimation operations described herein. The electronic device may be implemented as a portable electronic device or a household appliance. However, in addition to the aforementioned firmware, algorithms for estimating the battery state may be installed as software on the electronic device. For example, the battery state estimation system may be implemented as a battery management system (BMS) that includes the function of estimating the battery's state of charge (SOH). In another example, the battery state estimation system may be implemented as a device (e.g., an electronic device) that includes the function of estimating the battery's SOH. The battery state estimation system can also be integrated into battery-powered devices (such as vehicles, electronic devices, or energy storage units). By accurately estimating SOH and remaining charge, the device can enhance the user experience by reliably predicting remaining usage time and charging requirements, enabling continuous and stable operation over the expected duration.

[0061] The battery state estimation system can update the remaining degradation parameters using those parameters that have already been updated from measurement data collected within a specific SOC range. For example, the target parameter could be any of the anode SEI resistance, the capacity of the cathode active material, and the electrode balance offset. The remaining degradation parameters can be parameters other than the selected degradation parameters. Therefore, even if the battery operates only within a portion / range of SOC, the battery state estimation system can accurately predict the values ​​of degradation parameters and the battery state 190 (e.g., SOH).

[0062] Conversely, in the first comparative example, the degradation state can be simplified due to a simple increase in resistance and a decrease in capacity. This method can lead to low accuracy, especially under discharge conditions or harsh operating environments (e.g., fast charge / discharge, low temperatures). In the first comparative example, an approximately one-hour rest period (e.g., a low-current segment) under full charge and discharge levels may be required to accurately estimate capacity degradation. Since full discharge conditions are not easily encountered in real-world use, it can be difficult to obtain values ​​for all degradation parameters of the electrochemical model. Therefore, the model may not provide all the necessary degradation parameters and may result in incomplete or inaccurate battery state estimates.

[0063] In the second comparative example, key internal degradation parameters can be estimated by analyzing the differences between the output values ​​of the electrochemical model (e.g., voltage and SOC) and the measurements of the battery under degradation conditions (e.g., current, voltage, temperature). However, in the second comparative example, under partial discharge conditions, the electrode balance shift value, which is one of the degradation parameters in the electrochemical model, can only be estimated to a limited extent when the discharge is performed to a region with a low SOC of 10%.

[0064] In the third comparative example, under partial discharge conditions, the degradation parameters can be estimated even within a portion of the electrode equilibrium shift value, which is one of the degradation parameters in the electrochemical model. In this third comparative example, some of the three degradation parameters (e.g., the remaining two) may not be updated. Therefore, the electrochemical model may still be incomplete and insufficient for complete degradation estimation.

[0065] Compared to methods that simply use lookup tables (LUTs) to map degradation information, the proposed estimation embodiments described herein enable more accurate updates to both the target degradation parameter and other parameters. The following sections describe one or more embodiments of automatically estimating and updating battery degradation state variables by analyzing the response characteristics (e.g., voltage response) of a degraded battery based on user-specific usage patterns.

[0066] Figure 2 An example electrochemical model according to one or more embodiments is shown.

[0067] Electrochemical models simulate the internal physical phenomena of a battery (such as ion concentration and potential) or related battery behavior. Electrochemical models can incorporate electrochemical reactions occurring at the electrode / electrolyte interface, electrode / electrolyte concentrations, and physical conservation equations associated with charge conservation. Various model parameters can be used to define an electrochemical model, including geometric properties (e.g., electrode thickness and radius), OCP (Optical Point Content), and physical properties (e.g., conductivity, ionic conductivity, and diffusion coefficient). Electrochemical models can include information (e.g., equations) indicating the relationships between the various model parameters based on the aforementioned internal physical phenomena.

[0068] Electronic devices can use various model parameters of an electrochemical model to estimate the remaining battery capacity. In this context, multiple parameters (e.g., state variables) in the electrochemical model, including concentration and potential, can be interdependent. The electronic device can estimate the potential difference between the anode and cathode based on the parameters of the electrochemical model, which is expressed as an estimated voltage of the battery 210. The lithium-ion concentration distribution at the anode and cathode can influence the corresponding potentials at the anode and cathode 220. Furthermore, the electronic device can estimate the average lithium-ion concentration at the anode and cathode to determine the battery's SOC according to the electrochemical model.

[0069] The lithium-ion concentration distribution can represent the lithium-ion concentration distribution within the electrode 240 or within a predetermined region of the active material particles 250 located in the electrode. The lithium-ion concentration distribution in the electrode 240 can represent the surface lithium-ion concentration distribution or the average lithium-ion concentration distribution of the active material particles along the electrode direction. The electrode direction can represent the direction in which one end of the electrode (e.g., the boundary adjacent to the collector) is connected to the opposite end of the electrode (e.g., the boundary adjacent to the separator). Furthermore, the lithium-ion concentration distribution in the active material particles 250 can represent the lithium-ion concentration distribution in the active material particles along the direction of the center of the active material particles. The direction of the center of the active material particles can represent the direction in which the center of the active material particles is connected to the surface of the active material particles.

[0070] Figure 3 Example graphs depicting the degradation of battery state according to one or more embodiments are shown.

[0071] Figure 3 A degradation curve 300 is shown to represent the battery's condition. The horizontal axis of the degradation curve 300 may represent a time period (e.g., from the time point of full charge to the time point of full discharge). The graph includes curves showing the voltage reduction of both new and degraded batteries during this time period starting from the time point of full charge.

[0072] For example, Figure 3 The diagram shows the open-circuit voltage (OCV) 310 and cell voltage 330 of a brand-new battery, and the OCV 320 and cell voltage 340 of a degraded battery (e.g., the unit of voltage is volts (V)). As the battery degrades, a voltage drop can occur due to changes in the battery's internal resistance R. This can be observed at various time points (e.g., in seconds (s)). Since the OCV decreases due to the change in internal resistance R, the time to reach the minimum voltage Vmin can be shortened. As a result, the battery capacity 390 can be reduced. The degradation of the battery capacity 390 can be expressed as the difference 391 between the OCV 310 of a new battery and the OCV 320 of a degraded battery, or as the difference 392 between the single-cell voltage 330 reaching the minimum voltage of a new battery and the single-cell voltage 340 reaching the minimum voltage of a degraded battery.

[0073] Figures 4 to 6 Examples of degradation parameters according to one or more embodiments are shown.

[0074] Some of the parameters in the aforementioned electrochemical model can be degradation parameters that affect battery degradation. Multiple degradation parameters may include one or more of the following: the capacity ratio of the cathode active material, the anode SEI resistance, and electrode balance.

[0075] Figure 4 The capacity ratio of the cathode active material is shown as an example of a battery degradation parameter.

[0076] The capacity ratio of the cathode active material can be determined based on the ratio between the response characteristics (e.g., voltage slope) in the degraded monomer and the response characteristics (e.g., voltage slope) in the novel monomer. Figure 4 The voltage curve 411 of the cathode of a brand new battery, the voltage curve 421 of the cathode of a degraded (or aged) battery, the voltage curve 412 of the anode of a brand new battery, and the voltage curve 422 of the anode of a degraded battery are shown. Figure 4 As shown, in the SOC segment adjacent to the time point of full charge, differences may appear between the voltage curves 411 and 421 of the cathodes of the new battery and the degraded battery.

[0077] For example, the ratio between the response characteristics at the start time t1 and the end time t2 can be determined from multiple estimated voltages. V 1 Fresh and V 2 Fresh The determined slope 1 and the voltage measured from multiple lines V 1 Aged and V 2 Aged The ratio between the determined slopes 2. When the battery current decreases at a predetermined time point ta due to changes in the operation of the electronic device, the battery voltage may suddenly increase, but the ratio between the response characteristics can be based solely on multiple estimated voltages at the start time point t1 and the end time point t2. V 1 Freshh and V 2 Fresh The determined slope 1 and the voltage measured from multiple lines V 1 Aged and V 2 Aged The ratio between the determined slopes 1 and 2 is used to determine this. The difference between the estimated voltage and the measured voltage can gradually increase over time (e.g., as the battery deteriorates), and the difference in characteristics can be expressed as the ratio between slope 1 and slope 2. The ratio between the response characteristics can be expressed as shown in Equation 1 below.

[0078] Equation 1:

[0079] In Equation 1, as described above, the capacity ratio of the cathode active material... CA ratio This can be expressed as the ratio between the voltage difference (e.g., voltage slope) of a degraded battery at two time points and the voltage difference (e.g., voltage slope) of a brand-new battery at two time points. Electronic devices can reflect the determined capacity ratio of the cathode active material in an electrochemical model. CA ratio To update the degradation parameters corresponding to the capacity of the cathode active material.

[0080] The ratios between defined response characteristics may not be immediately reflected in the degradation parameters, but can instead be stored in memory. When update conditions are met, the degradation parameters of the electrochemical model can be updated based on the stored values ​​(such as averages and / or moving averages).

[0081] Figure 5 The anode SEI resistor is shown as an example of a degradation parameter.

[0082] Figure 5 Examples of estimated voltage 521 and measured voltage 511 are shown, both of which change in response to changes in the battery current. Figure 5 The voltage curve at the top of the graph shown can represent voltage changes over a relatively short time interval. For example, when a battery-powered electronic device performs a high-power task or exits a low-power (e.g., sleep) mode, the battery current can suddenly increase. Figure 5 As shown, the current can change for various reasons. Due to the anode SEI resistance, as mentioned above, the voltage changes of a brand-new battery and the voltage changes of a deteriorated battery can respond differently to sudden changes in current.

[0083] The anode SEI resistance represents the resistance that occurs when the SEI layer stacks on the anode surface due to anodic side reactions. As degradation progresses, the anode SEI resistance can gradually increase. The anode SEI resistance can be updated based on the difference in response characteristics between the estimated voltage 521 and the measured voltage 511. The electronic device can determine the increase in resistance based on changes in the estimated voltage 521, changes in the measured voltage 511, and changes in current, as shown in Equation 2 below.

[0084] Equation 2:

[0085] In equation 2, dV Fresh This represents the change in the estimated voltage 521 estimated using an electrochemical model, and can be calculated for two points where the current change exceeds a predetermined threshold. Here, the electrochemical model can be an initial electrochemical model reflecting a brand-new state of the battery without degradation or an electrochemical model reflecting a previously degraded state. dV Aged This indicates the change in the measured voltage 511 of the actual battery. dI It represents the change in current and can be determined as . F This represents Faraday's constant. This indicates the change in current density. This indicates an increase in resistance, and uses Changes in anode SEI resistance dR SEI It can be determined as shown in Equation 3 below.

[0086] Equation 3:

[0087] In equation 3, a n This represents the specific surface area of ​​the anode active material. eps a,s This indicates the volume fraction of the anode active material. r a This indicates the radius of the anolyte active material. l n Indicates the thickness of the anode electrode. area This represents the area of ​​the anode electrode. The electronic device can determine changes in the anode SEI resistance as changes in degradation. Using these changes in anode SEI resistance, the electronic device can update the degradation parameters corresponding to the anode SEI resistance.

[0088] The determined changes in anodic SEI resistance may not be immediately reflected in the degradation parameters, but can instead be stored in memory. When update conditions are met, the degradation parameters of the electrochemical model can be updated based on the stored values ​​(such as averages and / or moving averages).

[0089] Figure 6 Electrode balance (e.g., electrode balance offset) is shown as a degradation parameter.

[0090] Figure 6 This includes two graphs showing the cell voltage, cathode OCP, and anode OCP for both the brand-new state (without degradation) and the degraded state (with degradation). The cell voltage graph shows the discharge behavior of the battery during use. The voltage difference between the brand-new state 610 and the degraded state 620 can be greater at the low SOC state (the end of the discharge) compared to the high SOC state (the initial stage of discharge).

[0091] For example, at low SOC, a sudden change in voltage can occur between the new state and the deteriorated state. Referring to the OCP curve, the cathode OCP can have a small difference between the new state 611 and the deteriorated state 621, while at low SOC, the anode OCP can have a large difference between the new state 611 and the deteriorated state 621. In the deteriorated state 621, the anode OCP can be shifted to the left relative to the new state. This shift can be referred to as the electrode balance shift 690.

[0092] Electrode balance shift 690° indicates the degree to which the balance between the anode and cathode is altered due to the phenomenon where lithium ions are chemically coupled to the anode via side reactions, thus preventing lithium from returning to the cathode. The more severe the degradation, the larger the electrode balance shift 690° can be.

[0093] The SOC correction corresponding to a determined electrode equilibrium shift may not be immediately reflected in the degradation parameters, but can instead be stored in memory. When update conditions are met, the degradation parameters of the electrochemical model can be updated based on the stored values ​​(e.g., average and moving average).

[0094] Although it has been referenced Figures 4 to 6 Examples of degradation parameters are described, but these examples are not limiting. Various other degradation parameters may be considered depending on system design and application requirements.

[0095] Figure 7 An example battery state estimation method according to one or more embodiments is shown.

[0096] In operation 710, the electronic device can store the parameter value of a target parameter selected from a plurality of degradation parameters. For example, based on battery monitoring, the electronic device can determine an updated parameter value for at least one target parameter selected from a plurality of degradation parameters. The electronic device can store the updated parameter value in a memory (e.g., Figure 15 In memory 1530). See reference. Figure 9 The selection of the target parameters will be described in further detail.

[0097] In operation 730, the electronic device can determine the parameter values ​​of other parameters based on the updated parameter values ​​of the target parameter, and store the determined parameter values ​​of the other parameters in memory based on the degradation trend among the target parameter, reference parameter, and other parameters. For example, the electronic device can determine a reference value (e.g., a reference SOH value) corresponding to the updated parameter values ​​in operation 730, and can determine the parameter values ​​of other parameters corresponding to the reference values. (Refer to...) Figure 12 The determination of reference values ​​and the determination of parameter values ​​for other parameters that use reference values ​​are further described.

[0098] In operation 750, the electronic device can estimate the battery state based on the values ​​of the target parameter and other parameters. For example, the electronic device can use a battery state estimation model (e.g., electrochemical model or machine learning-based model) to estimate the battery state (e.g., SOH) from the updated values ​​of the target parameter and other parameters. The electronic device can store the estimated battery state in memory.

[0099] In operation 770, the electronic device can update the parameter values ​​of other parameters. For example, the electronic device can update the parameter values ​​of other parameters based on the estimated battery state, and can store the updated parameter values ​​of other parameters in memory. The electronic device can determine the parameter values ​​corresponding to the estimated battery state with respect to other parameters based on the degradation trend (e.g., trend line) between the battery state and other parameters.

[0100] In operation 790, the electronic device can update the battery state based on the updated parameter values ​​of the target parameter and other parameters. For example, the electronic device can iteratively update the battery state and degradation parameter values ​​until the error between the estimated battery state (e.g., SOH value) in operation 750 and the updated battery state in operation 790 converges, thereby generating a final updated battery state. The electronic device can store the final updated battery state in memory.

[0101] Figure 8 Examples of updated parameters and battery state for battery state estimation according to one or more embodiments are shown.

[0102] Under operating conditions where only a subset (e.g., one or more) of the degradation parameters (e.g., target parameters) in the electrochemical model can be updated, the electronic device can update other degradation parameters accordingly.

[0103] The electronic device can collect multiple battery information points by detecting the battery. These multiple battery information points can include voltage, current, and temperature measurements at various points in time. For example, in operation 801, the electronic device can measure the battery's current, voltage, and temperature.

[0104] In operation 803, the electronic device can calculate and identify the degradation parameter values ​​corresponding to the SOC segment based on the measured values. For example, the electronic device can determine which degradation parameter shows a major change at a given time point among the battery's degradation parameters based on monitored battery information.

[0105] For example, the degradation parameter exhibiting a major change can vary depending on the SOC range. When the battery operates in the first SOC range, the value of the first degradation parameter can change significantly in the corresponding battery. When the first degradation parameter is the capacity ratio of the cathode active material, the electronic device can be referenced as follows. Figure 4 The capacity ratio of the cathode active material is calculated as a degradation parameter value, as described above. Similarly, the value of the second degradation parameter can be changed when the battery operates in the second SOC region, and the value of the third degradation parameter can be changed when the battery operates in the third SOC region. When the second degradation parameter is the anode SEI resistance, the electronic device can be used as described above. Figure 5The anode SEI resistance value is calculated as described above. When the third degradation parameter is electrode balance, the electronic device can be used as described in reference... Figure 6 Calculate the electrode balance offset value as described above.

[0106] In operation 805, the electronic device may determine whether to update the degradation parameters. For example, the electronic device may determine whether multiple pieces of information collected at a time point when the battery state is to be estimated (e.g., the estimation time point) meet predetermined update conditions. When the time length in at least one SOC segment exceeds a threshold time, the electronic device may determine that update conditions for the degradation parameters are met, within which the calculated values ​​for the degradation parameters exist in at least one of the multiple SOC segments. The electronic device may select the degradation parameters that meet the update conditions as target parameters.

[0107] In operation 810, the electronic device can determine the parameter value of the target parameter based on the parameter values ​​calculated during monitoring. For example, in operations 801, 803, and 805, the electronic device can update the parameter value of the target parameter to a statistical value (e.g., average, median, minimum, or maximum) of the parameter values ​​collected and calculated for the target parameter.

[0108] In operation 830, for other degradation parameters, the electronic device may determine parameter values ​​based on the parameter value of the target parameter and according to the degradation trend. For example, the electronic device may determine the parameter values ​​of other degradation parameters based on a first degradation trend (e.g., a first trend line) between the target parameter and a reference parameter and a second degradation trend (e.g., a second trend line) between the reference parameter and other parameters. The electronic device may determine a reference value (e.g., a SOH value) corresponding to the parameter value of the target parameter along the first trend line. The electronic device may determine the parameter values ​​of other parameters corresponding to the reference value (e.g., a SOH value) of the reference parameter along the second trend line.

[0109] In operation 850, the electronic device can use a battery state estimation model to estimate the battery state based on a set of degradation parameters, including target parameters and other parameters. For example, the electronic device can estimate the battery state value using simulations of an electrochemical model that utilizes the set of degradation parameters as input. In another example, the electronic device can output the battery state using inferences from a machine learning-based model that utilizes model parameters including the set of degradation parameters. See below. Figure 14 Describe the inference of battery state using simulations based on electrochemical models or machine learning-based models.

[0110] For reference, when the reference parameter in operation 830 is SOH, the reference value (e.g., SOH value) determined based on the parameter value of the target parameter and the degradation trend may differ from the battery state (e.g., SOH value) estimated in operation 850. For example, because the values ​​of the remaining parameters are not considered in the trend line, the degradation trend may be a trend line between the target parameter and the reference parameter (e.g., SOH) (e.g., a line obtained by approximating the trend between the reference parameter and the target parameter). The trend line may indicate an approximate relationship between the two parameters, and the SOH value determined in operation 830 may be a value that appears statistically (e.g., on average) among the predetermined parameter values ​​of the target parameter. That is, even with the same SOH value, the parameter value of the predetermined degradation parameter may vary depending on the combination of parameter values ​​in the degradation parameter set. Therefore, because the parameter values ​​are approximately determined based on the degradation trend without considering the values ​​of the remaining parameters, the parameter values ​​of other parameters determined in operation 830 may also be inaccurate. As a result, errors may occur in the battery state estimated in operation 850.

[0111] In operation 871, the electronic device can determine whether the error has converged. The error can be determined based on the estimated battery state and / or the values ​​of the degradation parameters. For example, the electronic device can determine the error based on the difference between the estimated battery state obtained from the updated set of degradation parameters in the first iteration at the estimation time point and the estimated battery state obtained from the updated set of degradation parameters in the second iteration. However, the example is not limited to this, and the error can also be determined based on the difference between the updated parameter values ​​in two iterations for each of the degradation parameters.

[0112] In operation 873, when the error fails to converge, the electronic device may change the parameter values ​​of other degradation parameters. The electronic device may iteratively update the battery state and other degradation parameters using operations 873 and 850 until the error based on at least one of the battery state and degradation parameters converges. The error is the difference between any iteration (e.g., the i-th iteration) and a previous iteration (e.g., the (i-1)-th iteration, where i is an integer greater than or equal to 2), and may be, for example, a value corresponding to the difference between the estimated battery state in two iterations. In another example, the error may be the error between updated parameters in adjacent iterations (e.g., the error between the updated degradation parameter value in the i-th iteration and the updated degradation parameter value in the (i-1)-th iteration). However, the error is not limited to this, and the electronic device may also determine the error based on the updated battery state and degradation parameter values ​​in the aforementioned iterations. When the error determined in the i-th iteration is within the convergence tolerance, the electronic device may terminate the updating of the battery state and degradation parameters at the estimated time point (e.g., the current time point).

[0113] In operation 890, when the error converges, the electronic device can determine the estimated battery state and degradation parameter values. For example, the electronic device can determine the final updated battery state and degradation parameter values ​​for the estimated time point (e.g., the current time point). The electronic device can then present the remaining battery capacity or control the operation of the electronic device based on the determined battery state and degradation parameter values ​​for the estimated time point.

[0114] Figure 8 An example is shown where the values ​​of other degradation parameters are updated while maintaining the values ​​of the target parameter. However, the example is not limited to this. When the error fails to converge within a set number of iterations or remains above a convergence tolerance threshold, the electronic device may update the value of the target parameter, in addition to other degradation parameters. In such a case, the electronic device may re-estimate the battery state using the newly updated target parameter values ​​and other parameter values, based on a battery state estimation model (e.g., an electrochemical model or a machine learning-based model).

[0115] Figure 9 Examples are shown of multiple SOC segments within the entire area of ​​a SOC according to one or more embodiments, and corresponding degradation parameters for each SOC segment.

[0116] The SOC of a battery at 900 can vary based on battery operation (such as charging and / or discharging). The portion of the SOC value maintained at 950 can vary within the electronic device, depending on the usage scenario. This variation can occur based on changes in degradation parameters according to the electrochemical model, depending on the portion of the SOC value maintained at 950.

[0117] A battery's State of Charge (SOC) region can be divided into multiple SOC segments (also known as charging regions). When the battery operates within a predetermined range of an SOC segment, the values ​​of some of the multiple degradation parameters may change. For example, within a predetermined SOC segment, the change in one of the multiple degradation parameters (e.g., a target parameter) may be greater than the change in other parameters. Compared to the changes in other parameters, the target parameter may exhibit a major change in its corresponding SOC segment. For each of the multiple degradation parameters, there may be a SOC segment where the change occurs. Therefore, the battery's SOC region may include an SOC segment corresponding to each of the degradation parameters.

[0118] At least one SOC segment may partially overlap with other SOC segments. For example, the first SOC segment 910 may range from 70% to 100% SOC, and the second SOC segment 920 may range from 30% to 80% SOC, thus the two SOC segments may have an overlapping area from 70% to 80% SOC. Furthermore, at least one SOC segment may not overlap with other SOC segments. For example, the third SOC segment 930 may range from 0% to 40% SOC, and the third SOC segment 930 may not overlap with the first SOC segment 910. Within the SOC segments, the first SOC segment 910 may partially overlap with the second SOC segment 920, but may not overlap with the third SOC segment 930. Figure 9 An example is shown in which the first degradation parameter param1 is mapped to the first SOC segment 910, the second degradation parameter param2 is mapped to the second SOC segment 920, and the third degradation parameter param3 is mapped to the third SOC segment 930.

[0119] The electronic device can identify target parameters among the degradation parameters of an electrochemical model that meet update conditions. Update conditions can be determined based on one or more combinations of the following: the number of cycles the battery has operated in a portion of its SOC corresponding to the predetermined parameter, cumulative usage capacity, cumulative usage time, and the amount of degradation changes stored in memory. For example, to update degradation parameters using degradation changes accumulated over a number of charge and discharge cycles, one or more combinations of the number of battery cycles, cumulative usage capacity, cumulative usage time, and the amount of degradation changes stored in memory can be used as update conditions. Update conditions can be set independently for each of the anode SEI resistance, cathode active material capacity, and electrode balance shift, allowing the predetermined degradation parameter to be updated more frequently than other degradation parameters. However, examples of update conditions are not limited to this.

[0120] Figures 10A to 10D Examples are shown of selecting degradation parameters to be updated based on battery operating mode according to one or more embodiments.

[0121] Figure 10A Example 1000a shows the duration during which the battery operates in the first SOC segment 910 for a period exceeding a threshold time.

[0122] The electronic device can obtain one or more measurements of battery current, voltage, and temperature for each segment of battery operation time. The electronic device can collect the measured values ​​for each segment of operation time and store the collected values ​​in memory. The electronic device can identify the applicable State of Charge (SOC) segment of the battery for each segment of operation time. Figure 10ADuring the majority of the operating time in the first SOC segment 910, the battery operates.

[0123] Based on the measured values ​​of the degradation parameters corresponding to the identified SOC section, the electronic device can determine the parameter values ​​for the corresponding partial operating time. For example, in Figure 10A In the process, when k partial operating times 1060a are identified as belonging to the first SOC segment 910, the electronic device can use the measurements collected from the k partial operating times 1060a to individually determine the k parameter values ​​of the first degradation parameter param1. (Refer to the above...) Figures 4 to 6 The electronic device can determine the values ​​of degradation parameters. The calculated k parameter values ​​can be stored in memory, where k is an integer equal to or greater than 1.

[0124] Furthermore, for a portion of the operating time belonging to a SOC segment other than the first SOC segment 910, the electronic device can determine and store parameter values ​​of degradation parameters corresponding to other SOC segments. Additionally, a portion of the operating time is within the first SOC segment 910, and may overlap to belong to different SOC segments. In such cases, the electronic device can determine and store parameter values ​​of degradation parameters corresponding to the respective SOC segments to which the battery belongs during the portion of the operating time.

[0125] For reference, the determination of parameter values ​​based on each obtained measurement during a portion of the operating time can be performed by the power management integrated circuit (PMIC) of the electronic device. However, the example is not limited to this, and the determination can be performed by additional processing circuitry (e.g., a processor) of the electronic device. Here, the determined parameter values ​​can be used to define, as will be referred to below. Figures 11 to 13 The data points described (e.g., the points corresponding to the parameter values ​​and reference parameter values).

[0126] When the cumulative time 1090a of the k partial operating times 1060a corresponding to the identified SOC segment exceeds a threshold time, the electronic device can select a degradation parameter corresponding to the identified SOC segment as a target parameter. Partial operating time can be, for example, partial charge cycles and / or partial discharge cycles. The cumulative time 1090a of the k partial operating times 1060a can represent the sum of the time lengths spanning the k partial operating times 1060a. As described above, when the battery's SOC value 1050a remains within the first SOC segment 910 for more than the threshold time, the change in the first degradation parameter param1 can be primary. In such a case, the parameter value of the first degradation parameter param1 can be an update target.

[0127] The electronic device can determine an updated parameter value for the selected target parameter based on parameter values ​​determined over k partial operating times 1060a corresponding to the identified SOC segment. For example, before the SOC value 1050a remains in the first SOC segment 910 for more than a threshold time, the electronic device can determine the updated parameter value of the target parameter as a statistical value (e.g., the average value) of the parameter values ​​(e.g., the k parameter values) determined based on measurements of the k partial operating times 1060a identified as the first SOC segment 910.

[0128] Although the first degradation parameter param1 is referenced Figure 10A This is the primary description, but the examples are not limited to this. Similarly, when the battery's SOC value of 1050a remains in the second SOC segment 920 for more than a threshold time, the impact on the second degradation parameter param2 can be primary. In such a case, the parameter value of the second degradation parameter param2 can be the update target. (See reference...) Figure 10A The provided description can be similarly applied to Figures 10B to 10D .

[0129] Figure 10B Example 1000b is shown in which the battery operates for more than a threshold time in the third SOC segment 930.

[0130] like Figure 10B As shown, when the battery's SOC value of 1050b remains within the third SOC segment 930 for more than a threshold time, the change in the third degradation parameter param3 can be significant. In such a case, the parameter value of the third degradation parameter param3 can be an update target. The threshold time can be, but is not limited to, a predetermined time length, and can be a time length corresponding to a threshold ratio in a predetermined reference time length. The threshold time can be set to be the same for each of the degradation parameters, but is not limited to this, and different threshold times can be set.

[0131] Figure 10C Example 1000c is shown in which the battery operates for more than a threshold time in the first SOC segment 910 and the second SOC segment 920.

[0132] The battery's SOC value of 1050c may exceed a threshold time in two or more SOC segments (e.g., the first SOC segment 910 and the second SOC segment 920). When, within the SOC segments, the first cumulative operating time corresponding to the first SOC segment 910 and the second cumulative operating time corresponding to the second SOC segment 920 respectively exceed the threshold time, the electronic device may select a degradation parameter (e.g., the first degradation parameter param1) corresponding to the first SOC segment 910 and a degradation parameter (e.g., the second degradation parameter param2) corresponding to the second SOC segment 920 as target parameters.

[0133] Figure 10D Example 1000d shows where the battery operates for more than a threshold time in the first SOC segment 910 and the third SOC segment 930.

[0134] The battery's SOC value of 1050d can exceed a threshold time in both the first SOC segment 910 and the third SOC segment 930. When the first cumulative operating time corresponding to the first SOC segment 910 and the third cumulative operating time corresponding to the third SOC segment 930 exceed the threshold time, the electronic device can select a degradation parameter (e.g., the first degradation parameter param1) corresponding to the first SOC segment 910 and a degradation parameter (e.g., the third degradation parameter param3) corresponding to the third SOC segment 930 as target parameters.

[0135] Examples of cumulative operation time exceeding the threshold time for each of the degradation parameters are not limited to the reference. Figures 10A to 10D The provided description indicates that at predetermined time points, there may be three or more degradation parameters that satisfy update conditions (e.g., when the cumulative operating time exceeds a threshold time). It can be independently determined whether the update condition is met for each degradation parameter. Once the cumulative operating time in a portion of the SOC segment corresponding to each degradation parameter exceeds the threshold time, the determined value for the corresponding parameter can be obtained and stored, the cumulative operating time can be initialized, and the measurement value of the corresponding portion of the SOC segment can be monitored again.

[0136] Figure 11 and Figure 12 Examples are shown of determining the values ​​of other degradation parameters from the target degradation parameter based on degradation trends, according to one or more embodiments.

[0137] In operation 1131, the electronic device may use the degradation trend between the target degradation parameter and a reference parameter (e.g., first degradation trend information) to determine a reference value (e.g., a parameter value of the reference parameter, such as a SOH value) corresponding to a determined parameter value of the target degradation parameter. The first degradation trend information is information indicating the relationship between the target parameter and the reference parameter, and may include a trend line obtained by approximating the trend between the target parameter and the reference parameter.

[0138] Figure 12 This illustrates an example of selecting the first degradation parameter, param1, as the target parameter. The electronic device can then execute the above-mentioned reference... Figures 9 to 10DThe described operation determines a target parameter value p1_val_CAL (e.g., a parameter value of a target degradation parameter). The electronic device can determine a reference parameter value refSOH_val_EST corresponding to the first degradation parameter value p1_val_CAL in a trend line 1210 between the first degradation parameter and the SOH parameter. The trend line 1210 can be a line obtained by approximating data points 1211 (or graphs corresponding to the data points) corresponding to the values ​​of the first degradation parameter and the reference parameter. Each data point can be represented as a determined and / or updated parameter value based on measurements collected while monitoring battery information. Each data point can be one axis (e.g., the vertical axis) mapped to the determined parameter value and another axis (e.g., the horizontal axis) mapped to the parameter value as described below. Figure 14 The operation determines the point of battery state (e.g., SOH value). Figure 12 Arrows ① and ② illustrate the process of determining the reference parameter value refSOH_val_EST from the first degradation parameter value p1_val_CAL. For reference, and to aid in intuitive understanding, in Figure 12 In this context, the SOH on the horizontal axis is described as decreasing from 1 to 0. This is because SOH can deteriorate or decrease over time.

[0139] In operation 1135, the electronic device can determine the parameter values ​​of other deteriorated parameters corresponding to the reference values ​​by using the deterioration trend between other deteriorated parameters and the reference parameter (e.g., second deterioration trend information). For example, the electronic device can determine the parameter values ​​of other parameters based on the parameter values ​​of the reference parameter. The second deterioration trend information can indicate the relationship between the other parameters and the reference parameter.

[0140] For example, the electronic device can also determine the parameter values ​​of the remaining degradation parameters (e.g., the second degradation parameter param2 and the third degradation parameter param3) based on the corresponding degradation trend. The electronic device can determine the second degradation parameter value p2_val_TRD in the trend line 1220 between the second degradation parameter and the reference parameter, which corresponds to the reference parameter value refSOH_val_EST. The trend line 1220 can be a line obtained by approximating the data points 1221 corresponding to the values ​​of the second degradation parameter param2 and the reference parameter. Figure 12 Arrow ③ illustrates the process of determining the second degradation parameter value p2_val_TRD from the reference parameter value refSOH_val_EST. Similarly, the electronic device can determine the third degradation parameter value p3_val_TRD corresponding to the reference parameter value refSOH_val_EST in the trend line 1230 between the third degradation parameter and the reference parameter. The trend line 1230 can be a line obtained by approximating the data points 1231 corresponding to the values ​​of the third degradation parameter and the reference parameter. Figure 12Arrow ④ illustrates the process of determining the third degradation parameter value p3_val_TRD from the reference parameter value refSOH_val_EST.

[0141] Since the degradation trend is approximate information, therefore from Figure 12 The obtained first degradation parameter value p1_val_CAL, second degradation parameter value p2_val_TRD, and third degradation parameter value p3_val_TRD can be a preliminary parameter set. This preliminary parameter set can be used to estimate the preliminary battery state, such as... Figure 14 As shown, the initial battery state can be corrected through iterative updates.

[0142] Figure 13 Examples of degradation trends are shown according to one or more embodiments.

[0143] exist Figure 12 The text primarily describes an example where the degradation trend information is a reference parameter and indicates the trend of each degradation parameter relative to SOH. However, the examples are not limited to this. Figure 13 In the middle, as a reference parameter, the degradation trend information 1300, which indicates the trend of each degradation parameter (e.g., the first degradation parameter) compared to the cumulative cycle (e.g., the horizontal axis), can also be used to determine the values ​​of other parameters.

[0144] Figure 14 An example of iterative updates of degradation parameters and battery status according to one or more embodiments is shown.

[0145] An electronic device can estimate a battery state 1460 from parameters 1410 using a battery state estimation model 1450 including an electrochemical model 1451. The electronic device can estimate the battery state 1460 from parameters 1410, including an updated set of parameters (or a set of degradation parameters) 1420, at an estimation time point. The updated set of parameters 1420 at the estimation time point may include an updated first degradation parameter value p1_val_CAL, an updated second degradation parameter value p2_val_TRD, and an updated third degradation parameter value p3_val_TRD. As described above, the first degradation parameter value p1_val_CAL may be a value determined and updated based on at least a portion of measurements collected up to the estimation time point, and the second degradation parameter value p2_val_TRD and the third degradation parameter value p3_val_TRD may be values ​​determined based on a degradation trend based on the first degradation parameter value p1_val_CAL. For example, the battery state estimation model 1450 may include one or both of an electrochemical model 1451 and a machine learning-based model 1452.

[0146] For reference, Figure 14In this context, parameter set 1420 can represent different states within the battery's life cycle. For example, p1_val_1, p2_val_1, and p3_val_1 can represent degradation parameter values ​​for a brand-new battery, while p1_val_CAL, p2_val_TRD, and p3_val_TRD can represent degradation parameter values ​​at estimated time points, and p1_val_T, p2_val_T, and p3_val_T can represent degradation parameter values ​​at predetermined time points (e.g., the final degradation time point when the battery becomes unusable).

[0147] When the battery state estimation model 1450 is an electrochemical model 1451, the electronic device can perform simulations based on the electrochemical model 1451. For example, the electronic device can determine the voltage (e.g., OCV or cell voltage) corresponding to parameters 1410 (e.g., a set of parameters) for each time point based on the electrochemical model 1451. A time point can be defined as the time elapsed from the battery's fully charged state to its fully discharged state. The electronic device can determine the battery state 1460 for each time point based on the model parameters 1410, which includes a degradation parameter set 1420, until the determined voltage reaches a cutoff voltage (e.g., Vcut-off). The time point at which the determined voltage reaches the cutoff voltage can be converted into the battery state 1460 (e.g., the estimated SOH value SOH_val_EST).

[0148] When the battery state estimation model 1450 is based on the machine learning model 1452, the electronic device can use the machine learning model 1452 to perform inferences about the battery state 1460. For example, the electronic device can determine the battery state 1460 by applying various model parameters 1410, including a set of degradation parameters 1420, to the machine learning model 1452. The machine learning model 1452 is a model designed and trained to output the battery state 1460 (e.g., SOH value) from parameter values ​​defined using the electrochemical model 1451, and may include, for example, a neural network. The electronic device can use the machine learning model 1452 to estimate the battery state 1460 (e.g., the estimated SOH value SOH_val_EST).

[0149] Electronic devices can update the reference value based on the estimated SOH value SOH_val_EST. Figure 14In this context, the reference parameter is the SOH parameter, and the updated reference parameter value refSOH_val_UPD is shown. The updated reference parameter value refSOH_val_UPD can be equal to, but is not limited to, the estimated SOH value SOH_val_EST. A value obtained by correcting the estimated SOH value SOH_val_EST (e.g., by multiplying the weights by the estimated SOH value SOH_val_EST) can also be used as the updated reference parameter value refSOH_val_UPD.

[0150] The electronic device can update the values ​​of other parameters to correspond to the estimated values ​​of the battery state 1460 based on degradation trend information indicating the relationship between the battery state 1460 and other parameters. For example, the electronic device can determine the updated second parameter value p2_val_UPD corresponding to the reference parameter value refSOH_val_UPD in the trend line 1220 indicating the relationship between the SOH parameter and the second degradation parameter param2. Similarly, the electronic device can determine the updated third parameter value p3_val_UPD corresponding to the reference parameter value refSOH_val_UPD in the trend line 1230 indicating the relationship between the SOH parameter and the third degradation parameter param3. The electronic device can obtain the updated degradation parameter set 1420 by replacing the values ​​of the second degradation parameter param2 and the third degradation parameter param3 in the degradation parameter set 1420 corresponding to the estimated time point in parameter 1410 with the second parameter value p2_val_UPD and the third parameter value p3_val_UPD, respectively. The updated degradation parameter set 1420 can be used for new estimations of battery state 1460.

[0151] The electronic device can determine the updated battery state 1460 for a target time point by using the estimated battery state 1460, based on at least one of the convergence of the error between the estimated values ​​of battery state 1460 (e.g., SOH_val_EST and SOH_val_EST') and the convergence of the error between the updated values ​​of other parameters. For example, the electronic device can determine whether the error has converged. The electronic device can update the battery state 1460 and parameter 1410 through multiple iterations. The electronic device can determine whether the difference between the updated battery state 1460 (e.g., SOH value) and / or the difference between parameter 1410 (e.g., degradation parameter value) in multiple iterations (e.g., the i-th iteration and the (i-1)-th iteration as adjacent iterations) is within the convergence tolerance.

[0152] For example, in Figure 14In the first iteration, the electronic device can estimate a first SOH value, SOH_val_EST, based on a degradation parameter set 1420 (p1_val_CAL, p2_val_TRD, and p3_val_TRD). In the second iteration, the electronic device can estimate a second SOH value, SOH_val_EST', based on a degradation parameter set 1420 of (p1_val_CAL, p2_val_UPD, and p3_val_UPD). The electronic device can calculate the error based on the difference between the first SOH value, SOH_val_EST, and the second SOH value, SOH_val_EST'. The electronic device can also calculate the error based on the difference between the degradation parameter set 1420 of (p1_val_CAL, p2_val_TRD, and p3_val_TRD) and the degradation parameter set 1420 of (p1_val_CAL, p2_val_UPD, and p3_val_UPD). The error between parameter sets can be calculated based on, but is not limited to, the difference between corresponding parameter values ​​(e.g., p2_val_TRD and p2_val_UPD). The electronic device can also determine whether the error has converged by calculating the total error, which includes the error from the difference between battery states 1460 in the iteration and the error from the difference between parameter sets.

[0153] The electronic device can determine the final degradation parameter set 1420 and the final battery state 1460 (e.g., the determined SOH value for the estimated time point) by iteratively updating the parameter set 1420 and estimating the battery state 1460 until a convergence condition (e.g., degradation parameters and battery state 1460) is met. The convergence condition (or termination condition) can be a condition where the changes in the degradation parameter values ​​and / or battery state values ​​updated through the above iterations are within a convergence tolerance. When the error converges, the electronic device can finally determine the updated battery state 1460 (e.g., the SOH value) for the estimated time point (e.g., the current time point).

[0154] Figure 15 An example configuration of an electronic device according to one or more embodiments is shown.

[0155] Electronic device 1500 may include a display 1510, a battery 1520, a memory 1530, one or more processors 1540, a camera 1550, a communicator 1560, and a speaker 1570. Electronic device 1500 may be implemented as a mobile terminal, a vehicle, an aircraft, a head-mounted display (HMD) device, and a wearable device, and may also be implemented as various devices equipped with battery 1520.

[0156] Electronic device 1500 may use battery 1520 as a power source. For example, battery 1520 may have a single-cell reference capacity of 10 ampere-hours (Ah) or less, and may be a pouch cell, but is not limited thereto. Electronic device 1500 may be a portable terminal (e.g., a smartphone). A display 1510 disposed in electronic device 1500 may display information about battery 1520 and / or the operation screen of electronic device 1500. Figure 15 For ease of description, the electronic device 1500 is described as a smartphone, but various terminals such as laptop computers, tablet PCs and wearable devices can be used without restriction.

[0157] Display 1510 may display data processed by one or more processors 1540 or the operation of electronic device 1500. Furthermore, display 1510 may be a touchscreen display for detecting touch gestures input from a user. Touch gestures detected on the touchscreen display may be sent to one or more processors 1540 and processed.

[0158] Battery 1520 can power the operation of electronic device 1500. For example, battery 1520 can power display 1510, memory 1530, one or more processors 1540, camera 1550, cover, communicator 1560, and speaker 1570. Battery 1520 may have a capacity of 10 Ah or less per cell. Electronic device 1500 may include a power management system (PMIC) for battery 1520 (e.g., individual battery cells and / or battery packs).

[0159] Memory 1530 may store at least one of a battery state estimation model, parameters based on an electrochemical model (e.g., connection weights of a neural network and coefficients of the electrochemical model), parameter values ​​for degradation parameters, measurements monitored for battery 1520, and estimated battery state (e.g., SOH value). Memory 1530 may store parameter values ​​calculated for a portion of the operating time for each degradation parameter (see [reference]). Figures 4 to 6 ), the updated parameter values ​​of the selected target parameters (see Figures 10A to 10D ), reference parameter values, and parameter values ​​for other parameters determined based on the degradation trend (see...). Figures 11 to 13 The updated parameter values ​​for ) and other parameters (see Figure 14 At least one of the above parameter values. The electronic device 1500 may separately store the above parameter values ​​in one or more memories 1530, but is not limited thereto, and for memory efficiency, it may only store values ​​for the same estimated time point. Figure 14 The parameter values ​​are determined by the iterative operation. The memory 1530 can store the state values ​​(e.g., SOH value) and various parameter values ​​described herein.

[0160] Furthermore, memory 1530 may include volatile and non-volatile memory. Memory 1530 may store electrochemical models and / or machine learning-based models as state estimation models for battery 1520.

[0161] One or more processors 1540 may use an electrochemical model to estimate the voltage of battery 1520. Furthermore, one or more processors 1540 may control the overall operation of electronic device 1500. For example, one or more processors 1540 may display the estimated voltage of battery 1520 on display 1510. Each of the one or more processors 1540 may be a microcontroller unit (MCU).

[0162] Electronic device 1500 may also include a PMIC. Memory 1530 and one or more processors 1540 may be included in the PMIC, and electronic device 1500 may have a separate application processor. In this case, one or more processors 1540 of the PMIC may provide the application processor with an estimated state of battery 1520 (e.g., SOH value and / or SOC value) as described above. The application processor may also output via display 1510 the results obtained by processing data received from the PMIC regarding the state of battery 1520 (e.g., remaining battery capacity and / or expected remaining operating time). However, the example is not limited to this, and in another example, memory 1530 and one or more processors 1540 may not be included in the PMIC. In this case, one or more processors 1540 of electronic device 1500 may perform state estimation of battery 1520 and presentation of information derived from the state of battery 1520.

[0163] One or more processors 1540 can use a state estimation model (e.g., an electrochemical model or a machine learning-based model) corresponding to the battery 1520 to estimate the state information of the battery 1520. Electronic device 1500 can collect multiple measurements of voltage, current, and temperature by monitoring the battery 1520. Electronic device 1500 can update the parameter value of a target parameter among multiple degradation parameters that can be updated to the collected measurements. Electronic device 1500 can also update the parameter values ​​of the remaining degradation parameters based on the target parameter and a reference parameter. Electronic device 1500 can use the updated parameter values ​​to estimate the state of the battery 1520 based on the state estimation model of the battery 1520. One or more processors 1540 can iteratively update the degradation parameter values ​​based on the state of the battery 1520 and estimate the state of the battery 1520 based on the degradation parameter values. As described above, when the parameters and estimates of the state of the battery 1520 converge, one or more processors 1540 can determine the state of the battery 1520 at the corresponding estimated time point.

[0164] Here, the state estimation model of battery 1520 can be stored in the memory 1530 of electronic device 1500, but is not limited thereto. For example, the state estimation model of battery 1520 (e.g., a machine learning-based model) can also be stored in an external server separate from electronic device 1500. In this case, one or more processors 1540 of electronic device 1500 can request an estimate of the state of battery 1520 from the external server via communicator 1560. For example, one or more processors 1540 can send updated degradation parameter values ​​to the external server. The external server can estimate the state of battery 1520 (e.g., SOH value) based on the received degradation parameter values ​​using a machine learning-based model. Electronic device 1500 can receive the estimated state of battery 1520 from the external server. Whenever the update conditions of a predetermined degradation parameter are met, electronic device 1500 can request an estimate of the state of battery 1520 from the external server, for example, having the state estimation model of battery 1520.

[0165] However, the examples are not limited to this, and the electronic device 1500 can collaborate with an external server to update the parameter values ​​of degradation parameters and estimate the state of the battery 1520. For example, whenever measurements of the battery 1520 are collected, the electronic device 1500 can send the measurements to the external server. The external server can also select a target parameter to be updated based on the collected measurements, calculate the parameter value of the target parameter, and iteratively estimate the state of the battery 1520. In another example, the electronic device 1500 can also update the target parameter at the estimation time point based on the measurements collected from the battery 1520. The external server can estimate the state of the battery 1520 at the estimation time point based on the updated target parameter value and can respond to the electronic device 1500. In another example, when the electronic device 1500 sends the target parameter value to the external server, the external server can determine the remaining parameter values ​​based on degradation trend information and can respond to the electronic device 1500. The electronic device 1500 can also estimate the state of the battery 1520 based on parameter values ​​received from the external server.

[0166] One or more processors 1540 may display on display 1510 one or both of the State of Emergency (SOH) corresponding to the updated state of the battery 1520 and the remaining charge determined based on the SOH. Furthermore, when the remaining charge determined based on the updated state of the battery 1520 is less than a threshold charge, one or more processors 1540 may shut down the electronic device 1500 or reduce the power consumption of the electronic device 1500. The electronic device 1500 can mitigate and / or prevent sudden changes in the remaining charge representation of the battery 1520 and / or accidental shutdown of the electronic device 1500 by using a more accurate estimate of the state of the battery 1520 (e.g., SOH value and / or SOC value). Therefore, the electronic device 1500 can provide more predictable and stable operation of the battery 1520.

[0167] Camera 1550 may be configured to capture a user viewing display 1510. For example, camera 1550 may be positioned on the same side as display 1510 in electronic device 1500, but is not limited thereto, and may capture images and / or videos in various directions within electronic device 1500. Electronic device 1500 may include multiple cameras 1550.

[0168] The communicator 1560 may include communication circuitry for performing communication with an external device. The communicator 1560 may transmit data received from an external device to one or more processors 1540, or transmit data processed by one or more processors 1540 to an external device.

[0169] The speaker 1570 may be arranged to output sound according to the operation of the electronic device 1500. For example, the speaker 1570 may be disposed on the same side surface as the display 1510 to output sound to a user viewing the display 1510, but is not limited thereto, and may be disposed in various directions in the electronic device 1500 to output sound.

[0170] Electronic device 1500 can accurately estimate the state of battery 1520 for each state of degradation that may occur due to all users and different operating environments. Furthermore, because the size of the LUT required for the FG algorithm mounted on electronic device 1500 is reduced, the code size and the size of the chips (e.g., FG IC chips and PMIC chips) are reduced, thereby lowering manufacturing costs.

[0171] Reference Figures 1 to 14 The provided description can also be applied to Figure 15 Therefore, repeated descriptions are omitted.

[0172] The processors, memory, displays, interfaces, batteries, and other devices, apparatuses, units, and components described herein (including those related to...) Figures 1 to 15The description of the hardware components (as described above) is implemented by or represents hardware components. Examples of hardware components that can be used to perform the operations described in this application, as appropriate, 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 the operations described in this application are implemented by computing hardware (e.g., by one or more processors or computers). A processor or computer may be implemented by one or more processing elements (such as logic gate arrays, controllers and arithmetic logic units (ALUs), digital signal processors (DSPs), microcomputers, programmable logic controllers, field-programmable gate arrays (FPGAs), programmable logic arrays (PLUs), microprocessors, or any other means or combination of means configured to respond to and execute instructions (e.g., code or encoding) in a defined manner to achieve a desired result. In one example, the processor or computer includes or is connected to one or more memories storing instructions or software executed by the processor or computer. Hardware components implemented by a processor or computer can execute instructions or software (such as an operating system (OS) and one or more software applications running on the OS) to perform the operations described in this application. Hardware components can also access, manipulate, process, create, and store data in response to the execution of instructions or software. For brevity, the singular terms "processor" or "computer" may be used in the description of the examples described in this application; however, in other examples, multiple processors or computers may be used, or a processor or computer may include multiple processing elements, or multiple types of processing elements, or both, and therefore while some references may be made to a single processor or computer, such references are also intended to refer to multiple processors or computers. For example, a single hardware component, or two or more hardware components, may be implemented by a single processor, or two or more processors, or a processor and a controller. One or more hardware components may be implemented by one or more processors, or a processor and a controller, and one or more other hardware components may be implemented by one or more other processors, or additional processors and additional controllers. One or more processors, or a processor and a controller, may implement a single hardware component, or two or more hardware components. As described above, or in addition to the above description, the example hardware component may have any one or more different processing configurations, examples of which include: a single processor, a discrete processor, a parallel processor, a single instruction single data (SISD) multiprocessing, a single instruction multiple data (SIMD) multiprocessing, multiple instruction single data (MISD) multiprocessing, and multiple instruction multiple data (MIMD) multiprocessing.Therefore, reference to a processor herein refers to a processing circuit system (e.g., a circuit system including one or more processing element circuits). One or more processors including a processing circuit system also refers to each processor including a processing circuit system, and some or all of one or more processors including the same processing circuit system. Furthermore, processor(s) and controller(s) as non-limiting examples do not represent human processing or human control, but rather represent hardware components as described herein as non-limiting examples.

[0173] Performing the operations described in this application Figures 1 to 15 Shown and about Figures 1 to 15 The methods discussed are executed by computing hardware (e.g., by one or more processors or a computer), which is implemented as described above to implement instructions (e.g., computer or processor / processing device readable instructions) or software to perform the operations performed by the methods described in this application. For example, a single operation, or two or more operations, may be performed by a single processor, or two or more processors, or a processor and a controller. One or more operations may be performed by one or more processors, or a processor and a controller, and one or more other operations may be performed by one or more other processors, or additional processors and additional controllers. One or more processors, or a processor and a controller, may perform a single operation, or two or more operations. References to processors, or one or more processors, configured to perform two or more operations as non-limiting examples indicate that processors, or two or more processors, are configured to jointly perform all of the two or more operations, and that two or more processors are configured to each perform any corresponding one of the two or more operations (e.g., corresponding one or more processors are configured to perform each of the two or more operations, or any corresponding combination of one or more processors is configured to perform any corresponding combination of the two or more operations). Similarly, a reference to a processor-implemented method is a reference to a method executed by one or more processors or other processing or computing hardware of a device or system.

[0174] Instructions or software for controlling computing hardware (e.g., one or more processors or computers) to implement hardware components and perform the methods described above may be written as computer programs, code segments, or other executable instructions or any combination thereof to individually or collectively instruct or configure one or more processors or computers to operate as a machine or special-purpose computer to perform operations performed by the hardware components and methods described above. In one example, the instructions or software include machine code (such as machine code generated by a compiler) that is directly executed by one or more processors or computers. In another example, the instructions or software include high-level code that is executed by one or more processors or computers using an interpreter. The instructions or software may be written in any programming language based on the block diagrams and flowcharts shown in the accompanying drawings and the corresponding description herein, which disclose algorithms for performing operations performed by the hardware components and methods described above.

[0175] Instructions or software used to control computing hardware (e.g., one or more processors or computers) to implement hardware components and perform the methods described above, along with any associated data, data files, and data structures, may be recorded, stored, or fixed in, or on, one or more non-transitory computer-readable storage media, and are therefore not the signal itself. Thus, references to storage media herein refer to storage media hardware and not to transient media, not the signal itself. As described above, or in addition to the above description, examples of non-transitory computer-readable storage media include any one or more of the following: read-only memory (ROM), random access programmable read-only memory (PROM), 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 disc storage devices, hard disk drives (HDDs), solid-state drives (SSDs), card storage devices (such as multimedia cards or microcards (e.g., Secure Digital (SD) or Extreme Digital (XD))), magnetic tape, floppy disks, magneto-optical data storage devices, optical data storage devices, hard disks, solid-state drives, and / or any other device configured to store instructions or software and any associated data, data files, and data structures in a non-transitory manner, and to provide instructions or software and any associated data, data files, and data structures to one or more processors or computers, such that 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 across a networked computer system, such that the instructions and software and any associated data, data files, and data structures are stored, accessed, and executed in a distributed manner by one or more processors or computers.

[0176] While this disclosure includes specific examples, it will be clear upon understanding this disclosure 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 should be considered descriptive only and not for limiting purposes. The description of features or aspects in each example should 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 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.

[0177] Therefore, in addition to the above and all the accompanying drawings, the scope of the disclosure also includes the claims and their equivalents (i.e., all variations within the scope of the claims and their equivalents should be interpreted as included in the disclosure).

Claims

1. An electronic device comprising: Battery; One or more processors; as well as Memory, stored instructions Wherein, when the instruction is executed by the one or more processors, the one or more processors cause the one or more processors to: Store the updated parameter value of at least one target parameter, which is selected from a plurality of degradation parameters based on battery monitoring; The parameter values ​​of other parameters are determined based on the updated parameter values ​​of the at least one target parameter; The battery state is estimated using a battery state estimation model based on the updated parameter value of the at least one target parameter and the determined parameter values ​​of other parameters. The determined parameter values ​​of other parameters are updated based on the estimated battery state; and The updated battery state is determined based on the updated parameter values ​​of the at least one target parameter and the updated parameter values ​​of other parameters.

2. The electronic device as claimed in claim 1, wherein, When the instruction is executed, it also causes the one or more processors to: For each segment of the battery's operation time, obtain measurements of one or more of the battery's current, voltage, and temperature; Identify the state of charge segment of the battery during each partial operation time; as well as The parameter values ​​of the degradation parameters corresponding to the identified state-of-charge segments are determined based on the measured values.

3. The electronic device as claimed in claim 2, wherein, When the instruction is executed, it also causes the one or more processors to: In response to the cumulative time of a portion of the operation time in the identified state of charge segment exceeding a threshold time, a degradation parameter corresponding to the identified state of charge segment is selected as the at least one target parameter. as well as The updated parameter value of the selected at least one target parameter is determined based on the parameter value determined during a portion of the operating time in the identified state-of-charge segment.

4. The electronic device as claimed in claim 2, wherein, When the instruction is executed, it also causes the one or more processors to: In response to the cumulative operating time in both the first state of charge segment and the second state of charge segment exceeding a threshold time, a degradation parameter corresponding to the first state of charge segment and a degradation parameter corresponding to the second state of charge segment are selected as the at least one target parameter.

5. The electronic device as claimed in claim 1, wherein, The plurality of degradation parameters include one or more of the following: the capacity ratio of the cathode active material, the interfacial resistance of the anode solid electrolyte, and electrode balance.

6. The electronic device as claimed in claim 1, wherein, When the instruction is executed, it also causes the one or more processors to: Based on the degradation trend information indicating the relationship between the at least one target parameter and the reference parameter, the parameter value of the reference parameter corresponding to the updated parameter value of the at least one target parameter is determined. as well as The values ​​of other parameters are determined based on the values ​​of the reference parameters.

7. The electronic device as claimed in claim 1, wherein, When the instruction is executed, it also causes the one or more processors to determine the parameter values ​​of other parameters corresponding to the parameter values ​​of the reference parameter, based on degradation trend information indicating the relationship between other parameters and the reference parameter.

8. The electronic device as claimed in claim 1, wherein, Battery state estimation models include one or both of electrochemical models and machine learning-based models.

9. The electronic device as claimed in claim 1, wherein, When the instruction is executed, it also causes the one or more processors to: Based on the degradation trend information indicating the relationship between the battery state and other parameters, the determined parameter values ​​of other parameters are updated to values ​​corresponding to the estimated battery state.

10. The electronic device of claim 1, wherein, When the instruction is executed, it also causes the one or more processors to: Based on the convergence of the error between the battery state estimates and the convergence of the error between the updated values ​​of other parameters, or both, the battery state estimates are used to determine the updated battery state at the target time point.

11. The electronic device according to any one of claims 1 to 10, further comprising: monitor, When the instruction is executed, it also causes the one or more processors to: display on a screen a health status corresponding to the updated battery status and a remaining charge amount determined based on the health status, or both.

12. The electronic device according to any one of claims 1 to 10, wherein, When the instruction is executed, it also causes the one or more processors to: In response to the remaining charge determined based on the updated battery state being less than a threshold charge, the electronic device is turned off or the power consumption of the electronic device is reduced.

13. A method for battery state estimation, the method comprising: Store the updated parameter value of at least one target parameter, which is selected from a plurality of degradation parameters based on battery monitoring; The parameter values ​​of other parameters are determined based on the updated parameter values ​​of the at least one target parameter; The battery state is estimated using a battery state estimation model based on the updated parameter values ​​of at least one target parameter and the determined parameter values ​​of other parameters. The values ​​of other parameters are updated based on the estimated battery state. The updated battery state is determined based on the updated parameter values ​​of the at least one target parameter and the updated parameter values ​​of other parameters.

14. The method of claim 13, wherein, The step of storing the updated parameter value of the at least one target parameter includes: For each segment of the battery's operation time, obtain measurements of one or more of the battery's current, voltage, and temperature; Identify the state-of-charge segments corresponding to the operating time of each part; and The parameter values ​​of the degradation parameters corresponding to the identified state-of-charge segments are determined based on the measured values.

15. The method of claim 14, wherein, The step of storing the updated parameter value of the at least one target parameter further includes: In response to the cumulative time of a portion of the operating time in the identified state of charge segment exceeding a threshold time, a degradation parameter corresponding to the identified state of charge segment is selected as the at least one target parameter; and The updated parameter value of the selected at least one target parameter is determined based on the parameter value determined during a portion of the operating time in the identified state-of-charge segment.

16. The method of claim 14, wherein, The step of storing the updated parameter value of the at least one target parameter further includes: In response to the cumulative operating time in each of the at least two state-of-charge segments exceeding a threshold time, a degradation parameter corresponding to the at least two state-of-charge segments is selected as the at least one target parameter.

17. The method of claim 13, wherein, The steps to determine the values ​​of other parameters include: Based on degradation trend information indicating the relationship between the at least one target parameter and a reference parameter, determine the parameter value of the reference parameter corresponding to the updated parameter value of the at least one target parameter; and The values ​​of other parameters are determined based on the values ​​of the reference parameters.

18. The method of claim 13, wherein, The steps for determining the values ​​of other parameters include: determining the values ​​of other parameters based on degradation trend information that indicates the relationship between other parameters and reference parameters.

19. The method of claim 13, wherein, The steps for updating the values ​​of other parameters include: updating the determined values ​​of other parameters to values ​​corresponding to the estimated battery state based on information about the deterioration trend that indicates the relationship between the battery state and other parameters.

20. A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform the method according to any one of claims 13 to 19.

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