Method and apparatus for battery state estimation

Abstract

A method and apparatus for battery state estimation are provided. The method includes: determining a battery state change using a voltage difference between a sensed voltage of a battery and an estimated voltage of the battery estimated by an electrochemical model corresponding to the battery; updating an internal state of the electrochemical model based on the determined battery state change; and estimating battery state information based on the updated internal state of the electrochemical model.

Description

[0001] This application claims the benefit of Korean Patent Application No. 10-2019-0131570 filed on October 22, 2019, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety for all purposes. Technical Field

[0002] The following description relates to methods and apparatus for battery state estimation. Background Art

[0003] There are various methods for estimating the state of a battery. For example, such methods may include estimating the state of a battery by integrating the current of the battery or using a battery model (eg, a circuit model or an electrochemical model). Summary of the Invention

[0004] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0005] In one general aspect, a processor-implemented method for battery state estimation includes: determining a state change of a battery using a voltage difference between a sensed voltage of a battery and an estimated voltage of the battery estimated by an electrochemical model corresponding to the battery; updating an internal state of the electrochemical model based on the determined state change of the battery; and estimating state information of the battery based on the updated internal state of the electrochemical model.

[0006] The determining of the state change of the battery may include determining the state change of the battery based on the voltage difference, previous state information previously estimated by an electrochemical model, and an open circuit voltage (OCV) table.

[0007] The determining of the state change of the battery may further include obtaining an OCV corresponding to previous state information based on an OCV table, and applying the voltage difference to the obtained OCV.

[0008] The step of updating the internal state of the electrochemical model may include correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrodes based on the determined state change of the battery.

[0009] The updating of the internal state of the electrochemical model may include uniformly correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrodes based on the determined state change of the battery.

[0010] Updating the internal state of the electrochemical model may include determining a concentration gradient characteristic based on a diffusion characteristic that varies according to the determined state of the battery, and correcting an ion concentration distribution of the battery based on the determined concentration gradient characteristic.

[0011] The step of updating the internal state of the electrochemical model may further include calculating a diffusion equation of the active material based on the determined state change of the battery, and correcting ion concentration distribution in active material particles or ion concentration distribution in the electrode.

[0012] The internal state of the electrochemical model may include any one or any combination of any two or more of the battery's positive electrode lithium ion concentration distribution, the battery's negative electrode lithium ion concentration distribution, and the battery's electrolyte lithium ion concentration distribution.

[0013] The method may further include verifying whether a voltage difference between the sensed voltage of the battery and the estimated voltage of the battery exceeds a threshold voltage difference.

[0014] The electrochemical model may be configured to estimate state information of a target battery among the plurality of batteries. The sensed voltage may be a voltage measured from the target battery. The estimated voltage may be a voltage previously estimated by the electrochemical model from another battery among the plurality of batteries.

[0015] The battery can be a single cell, a module or a pack.

[0016] The estimated state information of the battery may include any one or any combination of any two or more of state of charge (SOC), state of health (SOH), and abnormal state information.

[0017] In another general aspect, a non-transitory computer-readable storage medium stores instructions that, when executed by a processor, cause the processor to perform the method described above.

[0018] In another general aspect, a processor-implemented method for battery state estimation includes: obtaining sensing data including a sensing voltage of a battery; obtaining an estimated voltage of the battery from the sensing data using an electrochemical model corresponding to the battery; calculating a first voltage difference between the sensing voltage of the battery and the estimated voltage of the battery; selecting a correction method for the electrochemical model based on the first voltage difference; correcting an internal state of the electrochemical model or a sensing current to be input to the electrochemical model by applying the selected correction method to the electrochemical model; and estimating state information of the battery using the electrochemical model to which the correction method is applied.

[0019] The step of correcting the internal state of the electrochemical model or the sensed current to be input to the electrochemical model by applying the selected correction method to the electrochemical model may include: updating the internal state of the electrochemical model using a state change of the battery determined by a first voltage difference between the voltage of the battery sensed in the current period and the voltage of the battery estimated by the electrochemical model; or correcting the sensed current of the battery to be input to the electrochemical model in the current period using a capacity error corresponding to a second voltage difference between the sensed voltage of the battery in the previous period and the estimated voltage of the battery in the previous period.

[0020] The step of selecting a correction method for the electrochemical model based on the first voltage difference may include: in response to the first voltage difference being greater than a threshold voltage difference, selecting a correction method for correcting an internal state of the electrochemical model; and in response to the first voltage difference being less than or equal to the threshold voltage difference, selecting a correction method for correcting a sensing current to be input to the electrochemical model.

[0021] The selecting of the correction method of the electrochemical model may include selecting the correction method of the electrochemical model so that a correction method of correcting a sensed current to be input to the electrochemical model is performed more frequently than a correction method of correcting an internal state of the electrochemical model.

[0022] In another general aspect, a non-transitory computer-readable storage medium may store instructions that, when executed by a processor, cause the processor to perform the method described above.

[0023] In another general aspect, an apparatus for battery state estimation includes: a processor configured to: determine a state change of a battery using a voltage difference between a sensed voltage of the battery and an estimated voltage of the battery estimated by a stored electrochemical model corresponding to the battery, update an internal state of the electrochemical model based on the determined state change, and estimate state information of the battery based on the updated internal state of the electrochemical model.

[0024] The processor may be further configured to determine a state change of the battery based on the voltage difference, previous state information previously estimated by the electrochemical model, and an open circuit voltage (OCV) table.

[0025] The processor may be further configured to determine a state change of the battery by obtaining an OCV corresponding to previous state information based on an OCV table and applying the voltage difference to the obtained OCV.

[0026] The processor may be further configured to update an internal state of the electrochemical model by correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrodes based on the determined state change of the battery.

[0027] The processor may be further configured to update the internal state of the electrochemical model by uniformly correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrodes based on the determined state change of the battery.

[0028] The processor may also be configured to update the internal state of the electrochemical model by determining a concentration gradient characteristic based on a diffusion characteristic that changes according to the determined state of the battery, and by correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode based on the determined concentration gradient characteristic.

[0029] The electrochemical model may be configured to estimate state information of a target battery among the plurality of batteries. The sensed voltage may be a voltage measured from the target battery. The estimated voltage may be a voltage previously estimated by the electrochemical model from another battery among the plurality of batteries.

[0030] The device may further include a memory for storing the electrochemical model.

[0031] The estimated state information of the battery may include any one or any combination of any two or more of state of charge (SOC), state of health (SOH), and abnormal state information.

[0032] The apparatus may be a vehicle or a mobile device and may be battery powered.

[0033] In another general aspect, a processor-implemented method for battery state estimation includes: calculating a voltage difference between a sensed voltage of a battery and an estimated voltage of the battery estimated by an electrochemical model corresponding to the battery; updating an internal state of the electrochemical model based on the calculated voltage difference; and estimating state information of the battery based on the updated internal state of the electrochemical model.

[0034] The internal state may include either or both of the potential of the battery and the ion concentration distribution in the active material particles or electrodes.

[0035] The method may further include determining a change in the battery's state of charge (SOC) using the calculated voltage difference. Updating the internal state of the electrochemical model based on the calculated voltage difference may include updating the internal state of the electrochemical model based on the determined change in the battery's SOC.

[0036] The determining of the amount of change in the SOC of the battery may include determining the amount of change in the SOC of the battery based on the calculated voltage difference, previous SOC information previously estimated by an electrochemical model, and an open circuit voltage (OCV) table.

[0037] In another general aspect, a non-transitory computer-readable storage medium may store instructions that, when executed by a processor, cause the processor to perform the method described above.

[0038] Other features and aspects will be apparent from the following detailed description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 and Figure 2 An example of a battery system is shown.

[0040] Figure 3 An example of estimating the state of a battery is shown.

[0041] Figure 4 and Figure 5 An example of estimating the state of a battery is shown.

[0042] Figure 6 An example of a battery model is shown.

[0043] Figure 7 and Figure 8 An example of determining a change in the state of a battery is shown.

[0044] Figure 9 、 Figure 10a 、 Figure 10b and Figure 11 An example of updating the internal state of a battery model is shown.

[0045] Figure 12a and Figure 12b Another example of a method of estimating a battery state is shown.

[0046] Figure 13 An example of a device for battery state estimation is shown.

[0047] Figure 14 and Figure 15 An example of a vehicle embodiment is shown.

[0048] Figure 16 An example of a mobile device embodiment is shown.

[0049] Throughout the drawings and detailed description, like reference numerals denote like elements. The drawings may not be to scale, and the relative sizes, proportions, and depictions of elements in the drawings may be exaggerated for clarity, illustration, and convenience. DETAILED DESCRIPTION

[0050] The following detailed description is provided to help the reader gain a comprehensive understanding of the methods, devices, and / or systems described herein. However, various changes, modifications, and equivalents of the methods, devices, and / or systems described herein will become apparent upon understanding the disclosure of this application. For example, the order of operations described herein is merely an example and is not limited to the order set forth herein, but may be changed as will become apparent upon understanding the disclosure of this application, except for operations that must occur in a particular order.

[0051] The features described herein can be implemented in different forms and are not to be construed as limited to the examples described herein. Rather, the examples described herein have been provided to illustrate only some of the many possible ways to implement the methods, devices, and / or systems described herein that will be apparent after understanding the disclosure of this application.

[0052] It should be noted here that the use of the term "may" with respect to an example or embodiment (e.g., with respect to what an example or embodiment may include or implement) means that there is at least one example or embodiment that includes or implements such features, but all examples and embodiments are not limited thereto.

[0053] Throughout the specification, when an element such as a layer, a region, or a substrate is described as being “on,” “connected to,” or “coupled to” another element, the element may be directly “on,” “connected to,” or “coupled to” the other element, or one or more intervening elements may be present. Conversely, when an element is described as being “directly on,” “directly connected to,” or “directly coupled to” another element, there may be no intervening elements.

[0054] As used herein, the term "and / or" includes any one of the associated listed items and any combination of any two or more.

[0055] Although terms such as "first," "second," and "third" may be used herein to describe various members, components, regions, layers, or portions, these members, components, regions, layers, or portions are not limited by these terms. Instead, these terms are used solely to distinguish one member, component, region, layer, or portion from another member, component, region, layer, or portion. Thus, what is referred to as a first member, first component, first region, first layer, or first portion in the examples described herein may also be referred to as a second member, second component, second region, second layer, or second portion without departing from the teachings of the examples.

[0056] The terms used herein are intended only to describe various examples and are not intended to limit the disclosure. Unless the context clearly indicates otherwise, the singular is intended to include the plural. The terms "comprise," "include," and "have" indicate the presence of stated features, quantities, operations, components, elements, and / or combinations thereof, but do not preclude the presence or addition of one or more other features, quantities, operations, components, elements, and / or combinations thereof.

[0057] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art to which the present disclosure pertains and generally understood based on an understanding of the disclosure of the present application. Unless expressly defined otherwise herein, terms (such as those defined in general dictionaries) will be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the disclosure of the present application, and should not be interpreted in an idealized or overly formal manner.

[0058] Furthermore, in the description of the exemplary embodiments, when it is considered that a detailed description of a structure or function known therefrom after understanding the disclosure of the present application will lead to an obscure interpretation of the exemplary embodiments, such description will be omitted.

[0059] As will be clear after understanding the disclosure of this application, the features of the examples described herein may be combined in various ways. In addition, although the examples described herein have various configurations, as will be clear after understanding the disclosure of this application, other configurations are feasible.

[0060] Figure 1 and Figure 2 An example of a battery system is shown.

[0061] Reference Figure 1 , the battery system 100 includes, for example, a plurality of batteries 110 - 1 to 110 - n and a device for battery state estimation 120 . Hereinafter, the device for battery state estimation 120 will be simply referred to as the battery state estimation device 120 .

[0062] Each of the batteries 110 - 1 to 110 - n may be a battery cell, a battery module, or a battery pack.

[0063] Battery state estimation device 120 may use at least one sensor to sense each of batteries 110-1 to 110-n. That is, battery state estimation device 120 may collect sensing data of each of batteries 110-1 to 110-n. The sensing data may include, for example, voltage data, current data, and / or temperature data.

[0064] The battery state estimation device 120 may estimate the state information of each battery in the batteries 110-1 to 110-n and output the estimated result. The state information may include, for example, state of charge (SOC), state of health (SOH) and / or abnormal state information. The battery model used to estimate such state information may be, for example, a battery model to be referred to below. Figure 2 Describe the electrochemical model.

[0065] Figure 2 An example of estimating state information using a battery model is shown.

[0066] Reference Figure 2 , the battery state estimation device 120 can use the electrochemical model corresponding to the battery 110 to estimate the state information of the battery 110. For example, the battery 110 may correspond to the above Figure 1 Any one of the batteries 110 - 1 to 110 - n described herein. The electrochemical model may be configured to model internal physical phenomena (eg, potential and ion concentration distribution of the battery) and estimate state information of the battery.

[0067] The level of accuracy in estimating the state information of battery 110 can affect the optimal operation and control of battery 110. When estimating state information using an electrochemical model, there may be errors between sensor information (obtained by sensors configured to measure current, voltage, and temperature data and input into the electrochemical model) and the state information calculated using the modeling method. This error may need to be compensated or corrected. The terms "compensation" and "correction" are used interchangeably herein.

[0068] In one example, the battery state estimation device 120 may determine the voltage difference between the sensed voltage of the battery 110 measured by the sensor and the estimated voltage of the battery 110 estimated by the electrochemical model. The battery state estimation device 120 may then use the determined voltage difference to determine the state change of the battery 110. The battery state estimation device 120 may then update the internal state of the electrochemical model (e.g., the potential and / or ion concentration distribution of the battery) based on the determined state change. The battery state estimation device 120 may then estimate the state information of the battery 110 based on the updated internal state of the electrochemical model. As described, the battery state estimation device 120 may determine the state change of the battery so that the voltage difference between the sensed voltage of the battery and the estimated voltage of the battery will be minimized, and update the internal state of the electrochemical model. Through such a feedback structure, the battery state estimation device 120 can estimate accurate state information of the battery 110 without increasing the complexity of the model and the amount of operation or calculation.

[0069] Figure 3This flowchart illustrates an example of determining battery state information by a battery state estimation device. In one example, the battery state may be estimated over multiple time periods, and the battery state estimation device may estimate the battery state information for each time period. For ease of description, this example will be described as applying to a single-cell model.

[0070] Reference Figure 3 In operation 310, the battery state estimation device collects sensing data of the battery. The sensing data may include, for example, sensing voltage, sensing current, and sensing temperature. For example, the sensing data may be stored in the form of a profile indicating changes in size over time.

[0071] In operation 320 , an estimated voltage of the battery and state information (eg, SOC) of the battery are determined, for example, by an electrochemical model inputted with the sensed current and the sensed temperature.

[0072] In operation 330, the battery state estimation apparatus calculates a voltage difference between the sensed voltage of the battery and the estimated voltage estimated by the electrochemical model. For example, the voltage difference may be determined as a moving average voltage over a recent preset period.

[0073] Despite Figure 3 Although not shown, according to one example, the battery state estimation device may determine whether the battery state information needs to be corrected based on whether the calculated voltage difference exceeds a threshold voltage difference. When an error occurs in the electrochemical model, the estimated voltage obtained using the electrochemical model may differ from the battery's sensed voltage by a significant amount or by an amount that causes the estimated voltage to be excessively inaccurate. Therefore, to prevent errors from accumulating, the battery state estimation device may determine whether correction is needed based on the voltage difference.

[0074] For example, when the calculated voltage difference exceeds the threshold voltage difference, the battery state estimation apparatus may determine that the state information of the battery needs to be corrected and perform operation 340. Conversely, when the calculated voltage difference does not exceed the threshold voltage difference, the battery state estimation apparatus may determine that the state information of the battery does not need to be corrected and return to operation 310 without performing operations 340, 350, and 360.

[0075] In operation 340, the battery state estimation device determines a state change of the battery using the calculated voltage difference. For example, the battery state estimation device may determine the state change of the battery based on the calculated voltage difference, previous state information of the battery, and an open circuit voltage (OCV) table. The previous state information of the battery may be state information previously estimated using an electrochemical model in operation 320. For example, the state change may include a change in the SOC, hereinafter referred to as an SOC change, and the SOC change will refer to Figure 7 and Figure 8 Describe in more detail.

[0076] In operation 350, the battery state estimation device updates the electrochemical model by correcting the internal state of the electrochemical model based on the state change of the battery. For example, the battery state estimation device may update the internal state of the electrochemical model by correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode based on the state change of the battery. In such an example, the active material may include the positive electrode and the negative electrode of the battery. The battery state estimation device estimates the state information of the battery using the electrochemical model whose internal state is updated. Therefore, through such a feedback structure (through which the battery state estimation device determines the state change of the battery to minimize the voltage difference between the sensed voltage of the battery and the estimated voltage of the battery estimated by the electrochemical model, and then updates the internal state of the electrochemical model), the state information of the battery can be more accurately estimated with fewer operations and / or calculations. Reference will be made to the following. Figures 9 to 11 An example of updating an electrochemical model is described in more detail.

[0077] In operation 360, the battery state estimation device determines whether to terminate the operation of estimating the battery state. For example, if there is a preset operation period that has not yet elapsed, the battery state estimation device returns to operation 310 to proceed to the next period. Conversely, if the operation period has elapsed, the battery state estimation device terminates the operation of estimating the battery state.

[0078] In one example embodiment, a battery state estimation device may determine state information of a plurality of batteries in each of a plurality of time periods. The battery state estimation device may select a target battery from the plurality of batteries and estimate state information from the target battery using an electrochemical model in each time period. That is, in each time period, the battery state estimation device may determine the state information of the target battery using an electrochemical model and determine the state information of the remaining batteries using a current integration method. The current integration method may be a method for estimating the remaining capacity or SOC of a battery by integrating the amount of current to be charged or discharged via a current sensor arranged at the end of the battery.

[0079] In the next period, the target battery from which state information is estimated based on the electrochemical model may be switched to another battery. For example, a target battery set in the first period of the plurality of periods may be set as a non-target battery in the second period, and a non-target battery in the first period may be set as a target battery in the second period.

[0080] As described above, by sequentially arranging target batteries in a preset order and estimating the state information of the batteries using an electrochemical model, even if an electrochemical model requiring a relatively large amount of operation or calculation is used, the state of the batteries can be estimated efficiently and quickly with a relatively high level of accuracy without the burden of operation or calculation. For ease of description, this will be referred to as a cell switching model. Figure 4 and Figure 5 An example of a single-cell switching model is described in more detail.

[0081] Figure 4 An example of a process in which the battery state estimation apparatus determines state information of a target battery in each of a plurality of time periods is shown.

[0082] Reference Figure 4 Battery 1 is set as the target battery in period T1, and the sensed voltage 410 of Battery 1 is input into the electrochemical model. The electrochemical model then estimates the state information of Battery 1. When the period changes from period T1 to period T2, the target battery switches from Battery 1 to Battery 2, and the electrochemical model receives the sensed voltage 420 of Battery 2 instead of the sensed voltage 410 of Battery 1. That is, at the time of the switch, there may be a discontinuity between sensed voltage 410 and sensed voltage 420 to be input into the electrochemical model. In the presence of this discontinuity, when the electrochemical model derives and outputs the state information of Battery 2 from sensed voltage 420, the output of the electrochemical model may have a discontinuity at the boundary between periods T1 and T2, as shown in graph 430. Because this discontinuity can be applied as an initial error to the electrochemical model when estimating the state information of Battery 2, it can be corrected. The output of the electrochemical model, corrected through the correction described below, may exhibit continuity, as shown in graph 440.

[0083] Figure 5 The flowchart of the example of the process of the battery state estimation device determining the state information of the target battery using the electrochemical model when the target battery is switched to another target battery is shown. However, during the time when the target battery is not switched to another battery in the single cell switching model, the above reference Figure 3 A method of estimating a battery's state of health is described.

[0084] Reference Figure 5In operation 510, the battery state estimation device collects data of the previous battery and the current battery. The current battery may be a battery selected as a target battery in the current period, and the previous battery may represent a battery selected as a target battery in the previous period. For example, the battery state estimation device may collect sensing data of the current battery and collect sensing data and / or estimated voltage of the previous battery. The sensing data may include, for example, a sensed voltage, a sensed current, and a sensed temperature, and may be stored in the form of a profile indicating changes in magnitude over time. The estimated voltage of the previous battery may be the voltage of the previous battery estimated by an electrochemical model in the previous period.

[0085] In operation 520, the battery state estimation apparatus calculates a voltage difference between a sensed voltage of a current battery (or target battery) and a sensed voltage or estimated voltage of a previous battery. For example, the voltage difference may be determined as a moving average voltage over a recent preset period.

[0086] Despite Figure 5 Although not shown, the battery state estimation device determines whether the battery state information needs to be corrected based on whether the calculated voltage difference exceeds a threshold voltage difference. Although the target battery is not switching, the estimated voltage may differ from the sensed voltage of the target battery due to errors in the electrochemical model. Therefore, the need for such correction can be determined based on the calculated voltage difference to prevent errors from accumulating.

[0087] For example, when the calculated voltage difference exceeds the threshold voltage difference, the battery state estimation apparatus may determine that the state information of the battery needs to be corrected and may perform operation 530. Conversely, when the calculated voltage difference does not exceed the threshold voltage difference, the battery state estimation apparatus may determine that the state information of the battery does not need to be corrected and may estimate the state information of the target battery using the electrochemical model without performing operations 530 and 540.

[0088] In operation 530, the battery state estimation device determines the state change of the current battery (or target battery) using the calculated voltage difference. For example, the battery state estimation device may determine the state change of the current battery based on the calculated voltage difference, the previous state information estimated in the previous period, and the OCV table. The previous state information may be state information estimated from a previous battery selected as a target battery in the previous period by an electrochemical model. The previous battery selected as the target battery in the previous period may be a non-target battery in the current period. The state change of the current battery may include an SOC change, which will be referred to below. Figure 7 and Figure 8 Describe in more detail.

[0089] In operation 540, the battery state estimation device updates the electrochemical model by correcting the internal state of the electrochemical model based on the current battery state change. For example, the battery state estimation device may update the internal state of the electrochemical model by correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode based on the current battery state change. Figures 9 to 11 Describe the update in detail.

[0090] The battery state estimation device may estimate the state information of the current battery using the electrochemical model whose internal state is updated. In addition, the battery state estimation device may estimate the voltage or other properties of the current battery or target battery based on the electrochemical model, such as, but not limited to, properties related to or indicative of the voltage of the current battery or target battery.

[0091] Figure 6 An example of an electrochemical model is shown.

[0092] Reference Figure 6 , the electrochemical model can estimate the remaining capacity or SOC of the battery by modeling the internal physical phenomena of the battery (e.g., the ion concentration, potential, etc. of the battery). That is, the electrochemical model can be represented by physical conservation equations associated with the electrochemical reactions occurring at the electrode / electrolyte interface, the ion concentrations of the electrodes and electrolytes, and charge conservation. For the physical conservation equations, the electrochemical model can use various model parameters, such as shape (e.g., thickness, radius, etc.), open circuit potential (OCP), and physical property values ​​(e.g., conductivity, ionic conductivity, diffusion coefficient, etc.).

[0093] In the electrochemical model, various state variables (e.g., concentration and potential) can be combined with each other. The estimated voltage 610 estimated by the electrochemical model can indicate the potential difference between the two ends of the positive electrode and the negative electrode. As shown in reference numeral 620, the potential information of each electrode in the positive electrode and the negative electrode can be affected by the ion concentration distribution of each electrode in the positive electrode and the negative electrode. The SOC 630 estimated by the electrochemical model can indicate the average ion concentration of the positive electrode and the negative electrode.

[0094] The ion concentration distribution described above may be the ion concentration distribution 640 in the electrode or the ion concentration distribution 650 in the active material particles present at a specific position in the electrode. The ion concentration distribution 640 in the electrode may be the surface ion concentration distribution or the average ion concentration distribution of the active material particles located in the electrode direction. The electrode direction may be the direction connecting one end of the electrode (e.g., the boundary adjacent to the current collector) and the other end of the electrode (e.g., the boundary adjacent to the separator). In addition, the ion concentration distribution 650 in the active material particles may be the ion concentration distribution inside the active material particles based on the center direction of the active material particles. The center direction of the active material particles may be the direction connecting the center of the active material particles and the surface of the active material particles.

[0095] As described above, to reduce the voltage difference between the sensed voltage of the battery and the estimated voltage of the battery, the battery state estimation device may shift or change the ion concentration distribution of each of the positive and negative electrodes while maintaining physical conservation associated with concentration, obtain potential information of each of the positive and negative electrodes based on the shifted ion concentration distribution, and calculate the voltage based on the obtained potential information. The battery state estimation device may calculate the internal state change when the voltage difference is 0 and ultimately determine the SOC of the battery.

[0096] Figure 7 and Figure 8 An example of determining a change in the state of a battery is shown.

[0097] Figure 7 An example of determining a change in the state of a battery when the sensed voltage of the battery is greater than the estimated voltage of the battery estimated by the electrochemical model is shown. Figure 3 In the case of the single cell model described above, the estimated voltage may be the voltage of the battery estimated in the previous period. Figures 4 to 6 In the case of the described cell switching model, the estimated voltage may be an estimated voltage of a previous battery selected as a target battery in a previous period.

[0098] In one example, the OCV table indicates an SOC-OCV curve that indicates inherent characteristics of the battery. When the OCV table is used, the ΔSOC to be corrected may vary according to the SOC value, and the SOC information of the last (eg, most recent) estimated previous period may be used. Figure 3 In the case of the single cell model described above, the SOC information of the previous period may be the estimated SOC of the battery in the previous period. Figures 4 to 6 In the case of the described cell switching model, the SOC information of the previous period may be an estimated SOC of the previous battery selected as the target battery in the previous period.

[0099] The estimated OCV, which is the OCV corresponding to the SOC information of the previous period, can be obtained by Figure 7 The previously calculated voltage difference can be applied to the estimated OCV. In this example, the sensed voltage is greater than the estimated voltage, so the calculated voltage difference can be applied by adding it to the estimated OCV. Using the characteristic curve of the OCV table, a corrected SOC corresponding to the result of applying the calculated voltage difference can be determined, and the difference between the estimated SOC and the corrected SOC can be determined as ΔSOC, indicating a change in state.

[0100] Figure 8 An example of determining a state change of a battery when the sensed voltage of the battery is less than the estimated voltage of the battery estimated by the electrochemical model is shown. Figure 3 In the case of the single cell model described above, the estimated voltage may be the voltage of the battery estimated in the previous period. Figures 4 to 6 In the case of the described cell switching model, the estimated voltage may be an estimated voltage of a previous battery selected as a target battery in a previous period.

[0101] As described above, when the OCV table is used, the SOC information of the last estimated previous period can be used. Figure 3 In the case of the single cell model described above, the SOC information of the previous period may be the estimated SOC of the battery in the previous period. Figures 4 to 6 In the case of the described cell switching model, the SOC information of the previous period may be an estimated SOC of the previous battery selected as the target battery in the previous period.

[0102] The estimated OCV, which is the OCV corresponding to the SOC information of the previous period, can be obtained by Figure 8 The previously calculated voltage difference can be applied. This example relates to a situation where the sensed voltage is less than the estimated voltage, so the calculated voltage difference is applied by subtracting it from the estimated OCV. Using the characteristic curve of the OCV table, a corrected SOC corresponding to the result of applying the calculated voltage difference can be determined, and the difference between the estimated SOC and the corrected SOC can be determined as ΔSOC, indicating a change in state.

[0103] Figures 9 to 11 An example of updating the internal state of the electrochemical model is shown.

[0104] In one example, the battery state estimation device may update the internal state of the electrochemical model based on the state change of the battery. The electrochemical model may be configured to model the internal physical phenomena of the battery and estimate the state information of the battery. The internal state of the electrochemical model may be provided in the form of a configuration file and may include, for example, voltage, overpotential, SOC, positive electrode lithium ion concentration distribution, negative electrode lithium ion concentration distribution and / or electrolyte lithium ion concentration distribution. For example, the battery state estimation device may update the internal state of the electrochemical model by correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode based on the state change of the battery. In the following, reference will be made to Figures 9 to 11 Describe in more detail the internal state of the updated electrochemical model.

[0105] Figure 9 An example of updating the internal state of the electrochemical model by uniformly (or consistently) correcting the ion concentration distribution is shown. In this example, the ion concentration distribution may indicate the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode. For example, when Figure 9 When the graph shown in FIG indicates the ion concentration distribution in the active material particle, the horizontal axis of the graph indicates the position in the active material particle. In this embodiment, 0 indicates the center of the active material particle, and 1 indicates the surface of the active material particle. For another example, when Figure 9 When the graph shown in FIG indicates the ion concentration distribution in the electrode, the horizontal axis of the graph indicates the position in the electrode. In this example, 0 indicates one end of the electrode (e.g., the boundary adjacent to the current collector), and 1 indicates the other end of the electrode (e.g., the boundary adjacent to the separator).

[0106] The battery state estimation device can convert the change in the battery state into a change in the internal state and uniformly apply the change to the internal state of the electrochemical model. The change in the internal state can indicate a change in lithium ion concentration corresponding to the region 910 between the initial internal state and the updated internal state. This method of uniformly updating the internal state can be applied when the current output from the battery is not large under the assumption that the concentration change is uniform or consistent. Compared to the non-uniform updating method described below, this method can be simpler to implement.

[0107] Optionally, when the internal state of the increase in the lithium ion concentration in the active material of one of the positive electrode and the negative electrode is updated, the internal state may be updated so that the lithium ion concentration in the active material of the other of the positive electrode and the negative electrode is reduced by the increment of the increase in the lithium ion concentration in the active material of the one electrode.

[0108] Figure 10a and 10bAn example of updating the internal state of the electrochemical model by non-uniformly correcting the ion concentration distribution is shown. In this example, the ion concentration distribution may indicate the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode. For example, when Figure 10a and Figure 10b When the graph shown in FIG indicates the ion concentration distribution in the active material particle, the horizontal axis of the graph indicates the position in the active material particle. In this example, 0 indicates the center of the active material particle, and 1 indicates the surface of the active material particle. For another example, when Figure 10a and 10b When the graph shown in FIG indicates the ion concentration distribution in the electrode, the horizontal axis of the graph indicates the position in the electrode. In this example, 0 indicates one end of the electrode (e.g., the boundary adjacent to the current collector), and 1 indicates the other end of the electrode (e.g., the boundary adjacent to the separator).

[0109] For example, when the conductance is significantly reduced, the battery current is relatively high, and / or the battery temperature is relatively low, the internal diffusion characteristics may be weakened based on the battery's chemical characteristics, and the gradient of the ion concentration distribution may increase in the electrode direction. In this example, based on the battery's internal diffusion characteristics, the internal state of the electrochemical model may be updated non-uniformly at each location in the active material particles or each location in the electrode.

[0110] Lithium ions can move in the battery based on diffusion characteristics. For example, when lithium ions of the positive electrode move to the negative electrode, the lithium ions located closest to the negative electrode among the lithium ions of the positive electrode can move first. In this embodiment, when the internal diffusion characteristics of the battery are worse than before, lithium ions can move very slowly in the positive electrode, and the positions of lithium ions moving out to the negative electrode are not quickly filled, so only lithium ions located at the end of the positive electrode can continuously move out to the negative electrode, and the gradient of the ion concentration distribution can be as follows. Figure 10a On the contrary, when the internal diffusion characteristics of the battery are better than before, the lithium ions in the positive electrode can quickly move to the end to fill the position of the lithium ions moved out to the negative electrode, so the gradient of the ion concentration distribution can be as shown in the graph of . Figure 10b As shown in the graph of Figure 9 The area between the initial internal state and the updated internal state (e.g., Figure 10a Area 1010 and Figure 10b Region 1020 may correspond to a change in lithium ion concentration. Such diffusion characteristics as described above may be based on battery state information (e.g., SOC), and thus, diffusion characteristics based on changes in the battery state may be considered. Consideration of diffusion characteristics based on changes in the battery state will be described in more detail below.

[0111] The battery state estimation device may determine the concentration gradient characteristics based on the diffusion characteristics according to the state change of the battery, and update the internal state of the electrochemical model based on the determined concentration gradient characteristics. The diffusion coefficient may be obtained based on an analysis of the diffusion characteristics of the state direction in which lithium ions will move (for example, the direction in which the lithium ion concentration increases). For example, a diffusion coefficient based on the previous SOC and a diffusion coefficient based on the SOC to be moved may be obtained. In addition, the internal state of the electrochemical model may be updated based on the concentration gradient characteristics pre-set according to the diffusion coefficient. For example, when the diffusion coefficient decreases in the direction of movement, the internal state of the electrochemical model may be updated in the direction in which the concentration gradient increases. Conversely, when the diffusion coefficient increases in the direction of movement, the internal state of the electrochemical model may be updated in the direction in which the concentration gradient decreases.

[0112] In another example, the electrochemical model may be based on the principle that although lithium ions can move between the positive electrode, the negative electrode, and the electrolyte, the total amount of lithium ions remains constant. This movement of lithium ions between the positive electrode, the negative electrode, and the electrolyte can be obtained based on a diffusion equation, which will be described in more detail below.

[0113] The battery state estimation device may calculate the diffusion equation of the active material based on the state change of the battery and update the internal state of the electrochemical model. The battery state estimation device may assign current boundary conditions in the state direction along which the lithium ions will move (for example, the direction in which the lithium ion concentration increases) to calculate the diffusion equation and update the internal state of the electrochemical model. The battery state estimation device may calculate the diffusion equation of the active material with respect to the change in the internal state corresponding to the state change of the battery, and update the internal state of the electrochemical model with the ion concentration distribution calculated by the diffusion equation. The diffusion characteristic is one of multiple physical characteristics, so the battery state estimation device may update the internal state of the electrochemical model non-uniformly by calculating the diffusion equation with respect to the ion concentration distribution.

[0114] Figure 11 is a flowchart illustrating an example of a method of estimating a state of a battery. Hereinafter, the example method of estimating a state of a battery will be referred to simply as a battery state estimation method.

[0115] In the following we will refer to Figure 11 The described battery state estimation method may be executed, for example, by a processor included in a battery state estimation device.

[0116] Reference Figure 11In operation 1110, the battery state estimation device determines a change in the state of the battery using a voltage difference between a sensed voltage of the battery and an estimated voltage of the battery estimated by an electrochemical model. The battery state estimation device may determine the change in the state of the battery based on the voltage difference, previous state information previously estimated by the electrochemical model, and an OCV table. For example, the battery state estimation device may determine the change in the state of the battery by obtaining an OCV corresponding to the previous state information based on the OCV table and applying the voltage difference to the obtained OCV.

[0117] In operation 1120, the battery state estimation device updates the internal state of the electrochemical model based on the state change of the battery. The battery state estimation device may update the internal state of the electrochemical model by correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode based on the state change of the battery. For example, the battery state estimation device may update the internal state of the electrochemical model by uniformly correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode to be consistent based on the state change of the battery. In addition, the battery state estimation device may update the internal state of the electrochemical model by determining the concentration gradient characteristics based on the diffusion characteristics according to the state change of the battery, and correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode based on the determined concentration gradient characteristics. In addition, the battery state estimation device may update the internal state of the electrochemical model by calculating the diffusion equation of the active material based on the state change of the battery, and correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode.

[0118] In operation 1130 , the battery state estimation apparatus estimates state information of the battery based on an internal state of the electrochemical model.

[0119] According to one example, before performing operation 1110, the battery state estimation apparatus may verify whether a voltage difference between a sensed voltage of the battery and an estimated voltage of the battery exceeds a threshold voltage difference. When the voltage difference exceeds the threshold voltage difference, the battery state estimation apparatus may perform operations 1110 to 1130.

[0120] For a more detailed description of an example of a battery state estimation method, reference may be made to the above reference. Figure 1 to Figure 1 0 description provided.

[0121] Figure 12a and 12b Another example of a battery state estimation method is shown.

[0122] Figure 12a 1 is a flowchart illustrating another example of a battery state estimation method to be performed by a processor included in a battery state estimation device. The state of the battery can be estimated in a plurality of time periods. Figure 12aIn the example of , the state information of the battery can be estimated in each time period.

[0123] Reference Figure 12a In operation 1210, the battery state estimation apparatus collects sensing data of the battery. The sensing data may include, for example, a sensing voltage, a sensing current, and a sensing temperature of the battery.

[0124] In operation 1220 , an estimated voltage of a battery and state information (eg, SOC) of the battery are determined through an electrochemical model to which the sensed current and the sensed temperature are input.

[0125] In operation 1230, the battery state estimation apparatus calculates a first voltage difference between the voltage of the battery sensed in the current period and the voltage of the battery estimated by the electrochemical model in the current period. For example, the first voltage difference may be determined as a moving average voltage of the most recent preset period.

[0126] Despite Figure 12a Although not shown, according to one example, the battery state estimation device may determine whether the battery state information needs to be corrected based on whether the calculated first voltage difference exceeds a first threshold voltage difference. When an error occurs in the electrochemical model, the estimated voltage, which is the voltage estimated using the electrochemical model, may differ from the sensed voltage of the battery. Therefore, to prevent errors from accumulating, the battery state estimation device may determine whether correction is needed based on the calculated first voltage difference.

[0127] For example, when the calculated first voltage difference exceeds the first threshold voltage difference, the battery state estimation apparatus determines that the state information of the battery needs to be corrected and performs operation 1240. Conversely, for example, when the calculated first voltage difference does not exceed the first threshold voltage difference, the battery state estimation apparatus determines that the state information of the battery does not need to be corrected and returns to operation 1210 in the next period.

[0128] In operation 1240, the battery state estimation device selects at least one correction method from among an ion concentration correction method and a microcurrent correction method. For example, when the calculated first voltage difference exceeds a preset second threshold voltage difference, the battery state estimation device may select the ion concentration correction method; otherwise, the battery state estimation device may select the microcurrent correction method. Alternatively, the battery state estimation device may select at least one correction method from among the ion concentration correction method and the microcurrent correction method, such that the ion concentration correction method is executed for each first time period and the microcurrent correction method is executed for each second time period. In such an example, the first time period may be longer than the second time period. That is, the microcurrent correction method may be executed more frequently than the ion concentration correction method.

[0129] In operation 1250, when the ion concentration correction method is selected, the battery state estimation apparatus uses the state change of the battery determined by the calculated first voltage difference to correct the internal state of the electrochemical model. For a more detailed description of an example of the ion concentration correction method, reference may be made to the above reference. Figures 1 to 11 Provide a description.

[0130] In operation 1260, when the microcurrent correction method is selected, the battery state estimation apparatus corrects the sensed current of the battery to be input to the electrochemical model in the current period using a capacity error corresponding to a second voltage difference between the sensed voltage of the battery in the previous period and the estimated voltage in the previous period. The microcurrent correction method will be described in detail below.

[0131] The battery state estimation device may receive a sensed current of the battery during a current period and determine a correction value using a capacity error corresponding to a voltage difference of the battery during a previous period. The battery state estimation device may then use the correction value to correct the sensed current. The corrected sensed current may then be input into the electrochemical model.

[0132] The voltage difference in the previous period may be a difference between an estimated voltage of the battery in the previous period and a sensed voltage of the battery in the previous period.

[0133] The capacity error may be determined based on the voltage difference in the previous period and the estimated OCV in the previous period. Figure 12b An example of how capacity error may be determined is described using the OCV table shown in FIG.

[0134] Reference Figure 12b , a first SOC 1204 is determined. First SOC 1204 corresponds to a first OCV 1203 obtained by subtracting a value α of a portion of voltage difference ΔV 1202 from an estimated OCV 1201, which is the estimated OCV in the previous period. In addition, a second SOC 1206 is determined. Second SOC 1206 corresponds to a second OCV 1205 obtained by adding a value β of the remaining portion of voltage difference ΔV 1202 to estimated OCV 1201. A state difference ΔSOC 1207 between first SOC 1204 and second SOC 1206 is multiplied by the capacity of the battery, and then a capacity error can be determined.

[0135] The correction value may be determined as a value obtained by applying a weight to the capacity error and dividing the weighted capacity error by a constant value. The weight may be determined based on an average current value, which is calculated based on the sensed current in the current period and / or the sensed current in the previous period. For example, when the average current value is relatively large, the weight may be determined to be relatively small. Conversely, when the average current value is relatively small, the weight may be determined to be relatively large. The constant value may indicate a status information update cycle, for example, the length of a specific cycle.

[0136] Examples of microcurrent correction methods are described in more detail in U.S. patent application publication number 2018-0143254, the entire disclosure of which is incorporated herein by reference.

[0137] In operation 1270, the battery state estimation device determines whether to terminate the operation of estimating the battery state. For example, if the preset operation period has not yet passed, operation 1210 may be performed in the next period. Conversely, if the preset operation period has passed, the operation of estimating the battery state may be terminated.

[0138] Figure 13 An example of a battery state estimation device is shown.

[0139] Reference Figure 13 , the battery state estimation apparatus 1300 may include, for example, a memory 1310 and a processor 1320. The memory 1310 and the processor 1320 may communicate with each other through a bus 1330.

[0140] The memory 1310 may include computer-readable instructions. When the instructions stored in the memory 1310 are executed by the processor 1320, the processor 1320 may perform one or more or all of the operations or methods described above. The memory 1310 may be a volatile memory or a non-volatile memory.

[0141] The processor 1320 may execute instructions or programs, or control the battery state estimation apparatus 1300. The processor 1320 may determine a state change of the battery using a voltage difference between a sensed voltage of the battery and an estimated voltage of the battery estimated by an electrochemical model, update an internal state of the electrochemical model based on the determined state change, and estimate state information of the battery based on the updated internal state of the electrochemical model.

[0142] In addition, the battery state estimation apparatus 1300 may perform the above-described operations or methods.

[0143] Figure 14 and Figure 15 An example of a vehicle is shown.

[0144] Reference Figure 14, the vehicle 1400 may include, for example, a battery pack 1410 and a battery management system (BMS) 1420. The vehicle 1400 may use the battery pack 1410 as a power source. For example, the vehicle 1400 may be an electric vehicle or a hybrid vehicle.

[0145] The battery pack 1410 may include a plurality of battery modules, each of which includes a plurality of battery cells.

[0146] BMS 1420 can monitor whether an abnormality occurs in battery pack 1410 and prevent battery pack 1410 from being overcharged or over-discharged. In addition, when the temperature of battery pack 1410 exceeds a first temperature (e.g., 40°C) or is less than a second temperature (e.g., -10°C), BMS 1420 can perform thermal control on battery pack 1410. In addition, BMS 1420 can perform cell balancing to equalize the states of charge of the battery cells included in battery pack 1410.

[0147] In one example, the BMS 1420 may include the battery state estimation device described above and determine state information of each of the battery cells included in the battery pack 1410 or state information of the battery pack 1410. The BMS 1420 may determine a maximum value, a minimum value, or an average value of the state information of the plurality of battery cells as the state information of the battery pack 1410.

[0148] The BMS 1420 may transmit status information of the battery pack 1410 to an electronic control unit (ECU) or a vehicle control unit (VCU) of the vehicle 1400. The ECU or VCU of the vehicle 1400 may output the status information of the battery pack 1410 to a display of the vehicle 1400.

[0149] like Figure 15 As shown in FIG, the ECU or VCU may display the status information of the battery pack 1410 on the instrument panel 1510. Alternatively, the ECU or VCU may display the remaining available driving distance determined based on the estimated status information on the instrument panel 1510. Alternatively or additionally, the ECU or VCU may display the status information, the remaining available driving distance, etc. on a head-up display of the vehicle 1400.

[0150] For a detailed description of example features and operation of the vehicle 1400, reference is made to the above referenced Figures 1 to 13 The description is provided, and for the sake of brevity, more detailed and repeated descriptions will be omitted here.

[0151] Figure 16 An example of a mobile device is shown.

[0152] Reference Figure 16, the mobile device 1600 includes a battery pack 1610. The mobile device 1600 may use the battery pack 1610 as a power source. The mobile device 1600 may be a portable terminal (such as a smart phone). For ease of description, Figure 16 The mobile device 1600 is shown as a smartphone as an example. However, the mobile device 1600 may be another terminal (eg, a laptop computer, a tablet personal computer (PC), a wearable device, etc.). The battery pack 1610 may include a BMS and battery cells (or battery modules).

[0153] In one example, the mobile device 1600 may include the battery state estimation apparatus described above. The battery state estimation apparatus may update the internal state of the electrochemical model based on the state change of the battery pack 1610 or the battery cells included in the battery pack 1610, and may estimate the state information of the battery pack 1610 based on the updated internal state of the electrochemical model.

[0154] For a more detailed description of example features and operations of mobile device 1600, reference may be made to the above referenced Figures 1 to 15 The description is provided, and for the sake of brevity, more detailed and repeated descriptions will be omitted here.

[0155] Perform the operations described in this application Figures 1 to 16The battery device, battery device 100, battery state estimation device, battery state estimation device 120 and battery state estimation device 1300, memory, memory 1310, processor, processor 1320, bus 1330, BMS, BMS 1420, ECU and VCU are implemented by hardware components, and the hardware components are configured to perform the operations described in this application performed by the hardware components. Examples of hardware components that can be used to perform the operations described in this application include, where appropriate: 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 that perform the operations described in this application are implemented by computing hardware (e.g., by one or more processors or computers). The processor or computer can be implemented by one or more processing elements (such as logic gate arrays, controllers and arithmetic logic units, digital signal processors, microcomputers, programmable logic controllers, field programmable gate arrays, programmable logic arrays, microprocessors or any other device or combination of devices configured to respond and execute instructions in a limited manner to achieve the 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. The hardware components implemented by the processor or computer can execute instructions or software (such as operating systems (OS) and one or more software applications running on the OS) for performing the operations described in this application. The hardware components can also access, manipulate, process, create and store data in response to the execution of instructions or software. For simplicity, the singular term "processor" or "computer" can be used in the description of the examples described in this application, but in other examples, multiple processors or computers can be used, or the processor or computer can include multiple processing elements or multiple types of processing elements or both. For example, a single hardware component or two or more hardware components can be implemented by a single processor, or two or more processors, or a processor and a controller. One or more hardware components 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 another processor and another controller. One or more processors, or a processor and a controller, may implement a single hardware component or two or more hardware components. The hardware components may have any one or more of different processing configurations, examples of which include: a single processor, independent processors, parallel processors, single instruction single data (SISD) multiprocessing, single instruction multiple data (SIMD) multiprocessing, multiple instruction single data (MISD) multiprocessing, and multiple instruction multiple data (MIMD) multiprocessing.

[0156] Figures 1 to 16 The method for performing the operations described in the present application shown in the is performed by computing hardware (e.g., by one or more processors or computers), which is implemented as executing instructions or software as described above to perform the operations performed by the method described in the present 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 another processor and another controller. One or more processors, or a processor and a controller may perform a single operation or two or more operations.

[0157] 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, 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 the 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. Instructions or software may be written in any programming language based on the block diagrams and flow charts shown in the accompanying drawings and the corresponding description in the specification, wherein the block diagrams and flow charts shown in the accompanying drawings and the corresponding description in the specification disclose algorithms for performing the operations performed by the hardware components and methods described above.

[0158] The instructions or software for controlling computing hardware (e.g., one or more processors or computers) to implement the hardware components and perform the methods described above, and 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. Examples of non-transitory computer-readable storage media include read-only memory (ROM), random access memory (RAM), flash 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, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state disk, and any other device configured to store the instructions or software and any associated data, data files, and data structures in a non-transitory manner and provide the instructions or software and any associated data, data files, and data structures to one or more processors or computers so that the one or more processors and computers can execute the instructions. In one example, the instructions or software and any associated data, data files, and data structures are distributed over a networked computer system so that the instructions and software and any associated data, data files, and data structures are stored, accessed, and executed in a distributed fashion by one or more processors or computers.

[0159] Although the present disclosure includes specific examples, it will be clear after understanding the disclosure of the present application that various changes in form and detail can be made in these examples without departing from the spirit and scope of the claims and their equivalents. The examples described herein are to be considered merely descriptive and not for purposes of limitation. The description of the features or aspects in each example should be considered applicable to similar features or aspects in other examples. Suitable results can be achieved if the described techniques are performed in a different order, and / or if the components in the described systems, architectures, devices or circuits are combined in different ways, and / or replaced or supplemented by other components or their equivalents. Therefore, the scope of the disclosure is not limited by the specific embodiments, but by the claims and their equivalents, and all changes within the scope of the claims and their equivalents should be interpreted as included in the disclosure.

Claims

1. A method for battery state estimation, comprising: determining a change in state of the battery using a voltage difference between a sensed voltage of the battery and an estimated voltage of the battery estimated by an electrochemical model corresponding to the battery; updating an internal state of the electrochemical model based on the determined state change of the battery; and Estimate the battery's state information based on the internal state of the updated electrochemical model, Among them, the step of determining the state change of the battery includes: obtaining an estimated open circuit voltage corresponding to previous state information previously estimated by an electrochemical model based on an open circuit voltage table, applying the voltage difference to the estimated open circuit voltage, using the open circuit voltage table to determine corrected state information corresponding to a result of applying the voltage difference, and determining the difference between the previous state information and the corrected state information as the state change of the battery.

2. The method according to claim 1, wherein Applying the voltage difference to the estimated open circuit voltage includes adding the voltage difference to the estimated open circuit voltage when the sensed voltage is greater than the estimated voltage and subtracting the voltage difference from the estimated open circuit voltage when the sensed voltage is less than the estimated voltage.

3. The method according to claim 1, wherein The step of updating the internal state of the electrochemical model includes correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrodes based on the determined change in the state of the battery.

4. The method according to claim 1, wherein The step of updating the internal state of the electrochemical model includes uniformly correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrodes based on the determined state change of the battery.

5. The method according to claim 1, wherein The step of updating the internal state of the electrochemical model includes determining a concentration gradient characteristic based on a diffusion characteristic that changes according to the determined state of the battery, and correcting an ion concentration distribution of the battery based on the determined concentration gradient characteristic.

6. The method according to claim 1, wherein The step of updating the internal state of the electrochemical model further includes calculating a diffusion equation of the active material based on the determined state change of the battery, and correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode.

7. The method according to claim 1, wherein The internal state of the electrochemical model includes any one or any combination of any two or more of the positive electrode lithium ion concentration distribution of the battery, the negative electrode lithium ion concentration distribution of the battery, and the electrolyte lithium ion concentration distribution of the battery.

8. The method according to claim 1, further comprising: It is verified whether a voltage difference between the sensed voltage of the battery and the estimated voltage of the battery exceeds a threshold voltage difference.

9. The method according to claim 1, wherein The electrochemical model is configured to estimate state information of a target battery among the plurality of batteries, where the sense voltage is the voltage measured from the target battery, and The estimated voltage is a voltage previously estimated from another battery among the plurality of batteries by an electrochemical model.

10. The method according to claim 1, wherein The battery is a battery cell, a battery module or a battery pack.

11. The method according to claim 1, wherein The estimated battery status information includes any one or any combination of any two or more of the state of charge, state of health, and abnormal state information.

12. A non-transitory computer-readable storage medium storing instructions, which, when executed by a processor, cause the processor to perform the method of claim 1.

13. A method for battery state estimation, comprising: obtaining sensed data including a sensed voltage of the battery; obtaining an estimated voltage of the battery from the sensed data using an electrochemical model corresponding to the battery; calculating a first voltage difference between a sensed voltage of the battery and an estimated voltage of the battery; selecting a calibration method for the electrochemical model based on the first voltage difference; correcting an internal state of the electrochemical model or a sensed current to be input to the electrochemical model by applying the selected correction method to the electrochemical model; and Use an electrochemical model with applied correction methods to estimate the state of the battery, The step of correcting the internal state of the electrochemical model by applying the selected correction method to the electrochemical model comprises: An estimated open circuit voltage corresponding to previous state information previously estimated by an electrochemical model is obtained based on an open circuit voltage table, a first voltage difference is applied to the estimated open circuit voltage, corrected state information corresponding to a result of applying the first voltage difference is determined using the open circuit voltage table, a difference between the previous state information and the corrected state information is determined as a state change of the battery, and an internal state of the electrochemical model is updated based on the determined state change of the battery.

14. The method according to claim 13, wherein: The step of correcting the sensed current to be input to the electrochemical model by applying the selected correction method to the electrochemical model includes: A sensed current of the battery to be input to the electrochemical model in a current period is corrected using a capacity error corresponding to a second voltage difference between a sensed voltage of the battery in a previous period and an estimated voltage of the battery in the previous period.

15. The method according to claim 13, wherein The step of selecting a correction method for an electrochemical model based on the first voltage difference includes: In response to the first voltage difference being greater than a threshold voltage difference, selecting a correction method that corrects an internal state of the electrochemical model; and In response to the first voltage difference being less than or equal to the threshold voltage difference, a correction method of correcting a sense current to be input to the electrochemical model is selected.

16. The method according to claim 13, wherein: The step of selecting the correction method of the electrochemical model includes selecting the correction method of the electrochemical model so that a correction method of correcting a sense current to be input to the electrochemical model is performed more frequently than a correction method of correcting an internal state of the electrochemical model.

17. A non-transitory computer-readable storage medium storing instructions, which, when executed by a processor, cause the processor to perform the method of claim 13.

18. A device for battery state estimation, comprising: a processor configured to: determine a state change of the battery using a voltage difference between a sensed voltage of the battery and an estimated voltage of the battery estimated by a stored electrochemical model corresponding to the battery, update an internal state of the electrochemical model based on the determined state change, and estimate state information of the battery based on the updated internal state of the electrochemical model, The processor is configured to obtain an estimated open-circuit voltage corresponding to previous state information previously estimated by an electrochemical model based on an open-circuit voltage table, apply the voltage difference to the estimated open-circuit voltage, determine corrected state information corresponding to a result of applying the voltage difference using the open-circuit voltage table, and determine the difference between the previous state information and the corrected state information as a state change of the battery.

19. The apparatus according to claim 18, wherein The processor is configured to: add the voltage difference to the estimated open-circuit voltage when the sensed voltage is greater than the estimated voltage; and subtract the voltage difference from the estimated open-circuit voltage when the sensed voltage is less than the estimated voltage.

20. The apparatus of claim 18, wherein The processor is further configured to update an internal state of the electrochemical model by correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrodes based on the determined state change of the battery.

21. The apparatus of claim 18, wherein The processor is further configured to update an internal state of the electrochemical model by uniformly correcting an ion concentration distribution in the active material particles or an ion concentration distribution in the electrodes based on the determined state change of the battery.

22. The apparatus of claim 18, wherein The processor is also configured to: The internal state of the electrochemical model is updated by determining concentration gradient characteristics based on diffusion characteristics that change according to the determined state of the battery, and by correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode based on the determined concentration gradient characteristics.

23. The apparatus of claim 18, wherein: The electrochemical model is configured to estimate state information of a target battery among the plurality of batteries, where the sense voltage is the voltage measured from the target battery, and The estimated voltage is a voltage previously estimated from another battery among the plurality of batteries by an electrochemical model.

24. The apparatus of claim 18, further comprising: Memory for storing electrochemical models.

25. The apparatus of claim 18, wherein The estimated battery status information includes any one or any combination of any two or more of the state of charge, state of health, and abnormal state information.

26. The apparatus of claim 18, wherein The device is a vehicle or mobile apparatus and is battery powered.

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