Method and apparatus for battery state estimation

Abstract

A method and apparatus for battery state estimation are disclosed. A processor-implemented method having battery state estimation includes estimating a current state of charge (SOC) of a target battery by correcting a first electrochemical model corresponding to the target battery using a first voltage difference between a measured voltage of the target battery and an estimated voltage of the target battery estimated by the first electrochemical model, estimating an end SOC of the target battery by correcting a second electrochemical model using a second voltage difference between a preset voltage and an estimated voltage of a virtual battery estimated by the second electrochemical model, and estimating a relative SOC (RSOC) of the target battery based on the current SOC and the end SOC of the target battery, wherein the second electrochemical model is based on a virtual battery corresponding to the target battery being discharged to the preset voltage.

Description

[0001] This application claims the benefit of Korean Patent Application No. 10-2020-0111026, filed on September 1, 2020, with the Korean Intellectual Property Office, the entire disclosure of which is incorporated herein by reference for all purposes. Technical Field

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

[0003] For optimal battery operation, the battery state can be estimated. Various methods can be used to estimate the battery state. For example, the battery state can be estimated by integrating or summing the battery currents or by using a battery model (e.g., a circuit model or an electrochemical model). Summary of the Invention

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

[0005] In one general aspect, a processor-implemented method for battery state estimation is provided, the method comprising: estimating the current state of charge (SOC) of a target battery by correcting a first electrochemical model corresponding to the target battery using a first voltage difference between a measured voltage of the target battery and an estimated voltage of the target battery estimated by a first electrochemical model; estimating the final SOC of the target battery by correcting a second electrochemical model using a second voltage difference between an estimated voltage of a virtual battery estimated by a second electrochemical model and a preset voltage; and estimating the relative SOC (RSOC) of the target battery based on the current SOC and the final SOC of the target battery, wherein the second electrochemical model is based on a virtual battery corresponding to the target battery being discharged to a preset voltage.

[0006] The steps for estimating the end SOC may include: estimating the current SOC of the virtual cell using an updated second electrochemical model in response to the estimated voltage of the virtual cell corresponding to a preset voltage; and determining the current SOC of the virtual cell as the end SOC of the target cell.

[0007] The first electrochemical model and the second electrochemical model can have the same physical property parameters but different internal state information.

[0008] The SOC (State of Charge) can be the SOC obtained when the target battery is discharged by the current output from the target battery and reaches a preset voltage.

[0009] The steps for estimating the end of SOC may include: determining the state changes of the virtual cell using a second voltage difference; updating the internal state of a second electrochemical model based on the determined state changes of the virtual cell; and estimating the state information of the virtual cell based on the updated internal state of the second electrochemical model.

[0010] The state changes of the virtual cell can be based on a second voltage difference, previous state information estimated by a second electrochemical model, and an open-circuit voltage (OCV) table.

[0011] The state change of the virtual battery can be determined by obtaining the OCV corresponding to the previous state information based on the OCV table and applying a second voltage difference to the obtained OCV.

[0012] The internal state of the second electrochemical model can be updated by correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode based on the state changes of the virtual battery.

[0013] The internal state of the second electrochemical model may include at least one of the following: the positive electrode lithium ion concentration distribution, the negative electrode lithium ion concentration distribution, or the electrolyte lithium ion concentration distribution of the virtual battery.

[0014] The steps for estimating RSOC may include estimating RSOC based on one of the current SOC and the ending SOC estimated in the current time period and the other of the current SOC and the ending SOC estimated in a previous time period.

[0015] After the steps for estimating the current SOC have been executed a certain number of times, the steps for estimating the termination of the SOC can be executed.

[0016] The target battery may include multiple batteries, and the step of estimating the current SOC is performed for each of the multiple batteries, and the step of estimating the end SOC is performed for a representative battery among the multiple batteries, wherein the step of estimating the RSOC may include: estimating the RSOC of each of the multiple batteries based on the estimated current SOC of each of the multiple batteries and the estimated end SOC based on the representative battery.

[0017] The step of estimating the end SOC may include: using multiple virtual batteries to estimate multiple end SOCs, the multiple virtual batteries indicating virtual states in which the target battery is discharged to a preset voltage through different currents, wherein the step of estimating the RSOC may include: estimating multiple RSOCs of the target battery based on the current SOC of the target battery and the estimated multiple end SOCs.

[0018] The preset voltage can be the end-of-discharge voltage (EDV) of the target battery.

[0019] The target battery can be a single battery cell, a battery module, or a battery pack.

[0020] In another general aspect, a processor-implemented method for battery state estimation is provided, the method comprising: determining a state change of a virtual battery using a voltage difference between an estimated voltage estimated by an electrochemical model corresponding to the virtual battery and a preset voltage, the virtual battery corresponding to a target battery discharged to the preset voltage; updating the internal state of the electrochemical model based on the determined state change of the virtual battery; and estimating the state information of the target battery by estimating the state of the virtual battery based on the updated internal state of the electrochemical model, thereby estimating the final state of charge (SOC) of the target battery.

[0021] The steps for determining the state changes of a virtual cell may include: determining the state changes of the virtual cell based on voltage differences, previous state information estimated by an electrochemical model, and an open-circuit voltage (OCV) table.

[0022] The steps for updating the internal state of an electrochemical model may include: updating 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 changes of the virtual cell.

[0023] In another general aspect, an apparatus for battery state estimation is provided, the apparatus comprising: a memory configured to store a first electrochemical model corresponding to a target battery and a second electrochemical model based on a virtual battery corresponding to the target battery being discharged to a preset voltage; and a processor configured to: estimate the current state of charge (SOC) of the target battery by correcting the first electrochemical model using a voltage difference between a measured voltage of the target battery and an estimated voltage of the target battery estimated by the first electrochemical model; estimate the final SOC of the target battery by correcting the second electrochemical model using a voltage difference between an estimated voltage of the virtual battery estimated by the second electrochemical model and a preset voltage; and estimate the relative charge (RSOC) of the target battery based on the current SOC and the final SOC of the target battery.

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

[0025] Figures 1 to 4 An example of a battery system is shown.

[0026] Figure 5 An example of a battery state estimation method is shown.

[0027] Figure 6 An example of an electrochemical model is shown.

[0028] Figure 7 and Figure 8 An example of a graph used to determine the state changes of a battery is shown.

[0029] Figures 9 to 11 An example of a graph used to update the internal state of a battery model is shown.

[0030] Figures 12 to 20 An example of estimating the relative state of charge (RSOC) is shown.

[0031] Figure 21 An example of a battery state estimation method is shown.

[0032] Figure 22 An example of a battery state estimation method is shown.

[0033] Figure 23 An example of a battery state estimation device is shown.

[0034] Figure 24 An example of a mobile device including a battery state estimation device is shown.

[0035] Figure 25 and Figure 26 An example of a vehicle that includes a battery state estimation device is shown.

[0036] Throughout the accompanying drawings and detailed embodiments, unless otherwise described or provided, the same reference numerals will be understood to denote the same elements, features, and structures. The drawings may not be to scale, and for clarity, illustration, and convenience, the relative sizes, proportions, and depictions of elements in the drawings may be exaggerated. Detailed Implementation

[0037] The following detailed embodiments are provided to aid 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 apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but may be changed as will become clear upon understanding this disclosure, except for operations that must occur in a specific order.

[0038] 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 merely to illustrate some of the many feasible ways of implementing the methods, apparatus, and / or systems described herein that will be clear upon understanding the disclosure of this application.

[0039] It should be noted here that the use of the term “may” (e.g., what may be included or implemented in an example or embodiment) with respect to examples or embodiments indicates that there exists at least one example or embodiment that includes or implements such a feature, but all examples and embodiments are not limited thereto.

[0040] Throughout the specification, when an element (such as a layer, region, or substrate) is described as being "on" another element, "connected to," or "bonded to" another element, the element may be directly "on" said other element, "connected to," or "bonded to" said other element, or one or more other elements may be present in between. Conversely, when an element is described as being "directly on" another element, "directly connected to," or "directly bonded to" another element, no other elements may be present in between.

[0041] As used herein, the term “and / or” includes any one of the relevant listed items and any combination of any two or more.

[0042] Although terms such as A, B, C, (a), (b), (c), "first," "second," and "third" 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. Rather, these terms are used only to distinguish one component, assembly, region, layer, or part from another. Thus, without departing from the teaching of the examples, the first component, first assembly, first region, first layer, or first part referred to in the examples described herein may also be referred to as a second component, second assembly, second region, second layer, or second part.

[0043] 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. The terms “comprising,” “including,” and “having” indicate the presence of the features, quantities, operations, components, elements, and / or combinations thereof stated, but do not exclude the presence or addition of one or more other features, quantities, operations, components, elements, and / or combinations thereof.

[0044] If the specification states that a component is "connected," "joined," or "engaged" to a second component, then the first component may be directly "connected," "joined," or "engaged" to the second component, or the third component may be "connected," "joined," or "engaged" between the first and second components. However, if the specification states that a component is "directly connected," "directly joined," or "directly engaged" to a second component, then the third component is not "connected," "joined," or "engaged" between the first and second components. Similar expressions (e.g., "between" and "immediately between," and "adjacent" and "next to") can also be interpreted in this way.

[0045] Furthermore, in the description of the exemplary embodiments, such descriptions will be omitted when it is believed that a detailed description of the structure or function known therefrom after understanding the disclosure of this application would lead to a vague interpretation of the exemplary embodiments.

[0046] The features of the examples described herein can be combined in various ways, as will become clear upon understanding the disclosure of this application. Furthermore, although the examples described herein have various configurations, other configurations are possible, as will become clear upon understanding the disclosure of this application.

[0047] Figures 1 to 4 An example of a battery system is shown.

[0048] Reference Figure 1 The battery system 100 includes a battery 110 and a battery state estimation device 120. The battery state estimation device 120 may also be referred to herein as a device with battery state estimation.

[0049] Battery 110 can be at least one battery cell, battery module or battery pack.

[0050] The battery state estimation device 120 can use at least one sensor to sense the battery 110. That is, the battery state estimation device 120 can collect sensing data of the battery 110. The sensing data may include, for example, voltage data, current data, and / or temperature data.

[0051] The battery state estimation device 120 can estimate the state information of the battery 110 based on sensing data and output the estimation result. The state information may include information associated with, for example, state of charge (SOC), relative state of charge (RSOC), state of health (SOH), and / or anomalies. To estimate the state information, a battery model may be used, and the battery model may be one that will be referred to below. Figure 6 The described electrochemical model.

[0052] Figure 2 The graphs show examples of SOC and RSOC.

[0053] SOC represents the currently available (or usable) capacity compared to the total battery capacity designed based on open-circuit voltage (OCV), and can be expressed by Equation 1 below. It can be based on... Figure 2 The OCV curve shown in the figure is used to determine the SOC. Refer to... Figure 2 V max The full charge voltage, which indicates the voltage at which the battery is fully charged, and V min The end-of-discharge voltage (EDV) indicates the voltage at which an OCV-based battery is fully discharged. (V) min It also indicates the voltage that is preset by the manufacturer to prevent further battery discharge.

[0054] [Equation 1]

[0055]

[0056] In equation 1 above, Q max This represents the design capacity as the total battery capacity based on the OCV design. Q passed This represents the battery capacity used up to the current point in time. Therefore, Q max -Q passed It indicates the current available capacity based on OCV. Because such a SOC indication does not take into account the absolute SOC of the discharge current, it can also be called absolute SOC (ASOC), or, because it indicates the battery SOC in the current state, it is called current SOC.

[0057] In one example, a battery can be discharged by applying current to a load connected to it. In such a practical application example, RSOC based on under-load voltage can be used instead of SOC based on OCV. RSOC represents the current available capacity compared to the total available capacity based on the voltage under the applied current condition, and indicates the capacity available to the user at the user's end. Figure 2 The underload voltage curve shown in the figure is used to determine RSOC.

[0058] [Equation 2]

[0059]

[0060] In equation 2 above, Q usable This indicates the full charge capacity (FCC), which is the total available capacity based on the voltage when a load is connected to the battery and current is applied. Q usable Q can be used max -Q unusable Confirmed. Q unusable This indicates the capacity that is unavailable when the battery connected to the load reaches its EDV and further discharge is limited. Q unusable It can be changed based on the battery's current, temperature, and / or degradation state.

[0061] For example, when a battery is connected to a load and current is drawn from the battery, the battery's output voltage can become lower than the OCV, therefore... Figure 2 As shown, the underload voltage curve can have a smaller value than the OCV curve. That is, as the current output from the battery increases, the gap between the underload curve and the OCV curve can increase. Furthermore, as the current output from the battery increases, Q... unusable It can also be enlarged.

[0062] To accurately predict the RSOC of a battery, it is necessary to accurately predict the Q. usable and Q passed However, because Q usableBased on Q, which is affected by the magnitude of the current and temperature as described above. unusable Therefore, it may not be easy to accurately predict Q. usable Therefore, as shown in Equation 2, RSOC can be expressed by an equation concerning SOC, not Q. That is, RSOC can be determined based on the current SOC and the end SOC. The end SOC represents the SOC when the battery is discharged by the applied current and subsequently reaches a preset voltage (e.g., EDV). In other words, the end SOC can be the SOC at EDV and can vary based on the magnitude of the current, temperature, and / or the battery's degradation state. The end SOC can also be referred to herein as SOC. EDV .

[0063] In one example, the magnitude of the battery's output current can vary depending on the type of operation of the device equipped with the battery. For example, based on various operation types (e.g., playing music, video, or playing games in a smartphone, or operating in an idle state), the magnitude of the output current can change, and the final SOC can change, and the RSOC can also change accordingly. For example, when playing video, the available operating time of the device can be shorter than when playing music. As mentioned above, the final SOC can be the result of estimating the SOC when the battery discharges at the currently applied current and reaches the EDV and / or the result of predicting future states from the current state. The estimation of RSOC will be described in detail below with reference to the accompanying drawings.

[0064] Figure 3 An example of using a battery model to estimate state information is shown.

[0065] Reference Figure 3 The battery state estimation device can use an electrochemical model to estimate the state information of battery 310. An electrochemical model represents a model that models and estimates the battery's state information by modeling internal physical phenomena occurring within the battery (such as the battery's potential and ion concentration distribution). (Refer to...) Figure 6 Describe the electrochemical model in detail.

[0066] The accuracy of estimating the state information of battery 310 can affect the optimal operation and control of battery 310. When estimating state information using an electrochemical model, there may be errors between the sensor information input to the electrochemical model (e.g., current, voltage, and temperature data) and the estimated information obtained through calculations based on the modeling method. Therefore, error correction may be required to correct such errors.

[0067] As described above, in order to estimate RSOC, both the current SOC and the ending SOC are required. The current SOC can be estimated in the real battery unit 320, and the ending SOC can be estimated in the virtual battery unit 330.

[0068] The real battery section 320 may be the part that estimates the current state of charge (SOC) of the battery 310. The battery 310 may also be referred to herein as a real battery or target battery, to distinguish it from the virtual battery described below. The real electrochemical model may be a model that estimates the state information of the battery 310 by modeling the internal physical phenomena of the battery 310. Input data for the real electrochemical model may include information associated with real-time measurements of the voltage, current, and temperature of the battery 310.

[0069] To estimate the current SOC of battery 310, the battery state estimation device can determine the voltage difference between the sensed voltage of battery 310 measured by a sensor and the estimated voltage of battery 310 estimated by a real electrochemical model. In one example, the battery state estimation device can use the determined voltage difference to determine the state change of battery 310. The battery state estimation device can then update the internal state of the real electrochemical model based on the determined state change. The battery state estimation device can then estimate the current SOC of battery 310 based on the updated internal state of the real electrochemical model.

[0070] By correcting the internal state of the real electrochemical model to minimize the voltage difference between the sensed voltage and the estimated voltage of the battery 310, the battery state estimation device can estimate the current SOC of the battery 310 with high accuracy, while minimizing the increase in model complexity and computational cost.

[0071] Furthermore, the virtual battery section 330 can be a part that estimates the final state of charge (SOC) of the battery 310. To estimate the final SOC, a virtual battery indicating a virtual state where the battery 310 has been discharged to a preset voltage (e.g., EDV) can be used. The virtual electrochemical model can be a model that estimates the state information of the virtual battery by modeling the internal physical phenomena of the virtual battery. The estimated SOC of the virtual battery can correspond to the final SOC of the battery 310. The virtual electrochemical model can have the same physical property parameters as the real electrochemical model, but different internal state information. For example, physical property parameters may include characteristics of active material particles (e.g., size, shape, etc.), electrode thickness, electrolyte thickness, and physical property values ​​(e.g., conductivity, ionic conductivity, diffusion coefficient, etc.). Internal state information may include, for example, the potential in the active material particles, the ion concentration distribution in the active material particles, the potential in the electrode, the ion concentration distribution in the electrode, etc. The input data for the virtual electrochemical model may include information associated with the moving average current, moving average temperature, and EDV of the battery 310. For example, the moving average current may be a moving average current over a time period. In one example, the time period can be preset. In some examples, instead of a moving average current, information associated with the currently applied current, the arithmetic mean current over the preset time period, and the weighted average current can be input into the virtual electrochemical model. In another example, instead of a moving average temperature, information associated with the current temperature, the arithmetic mean temperature, and the weighted average temperature can be input into the virtual electrochemical model. In addition to the examples described above, other current and temperature information identified as having various characteristics indicating average values ​​can also be input into the virtual electrochemical model.

[0072] To estimate the final state of charge (SOC) of battery 310, the battery state estimation device can determine the voltage difference between the estimated voltage (EDV) of the virtual battery, as estimated by the virtual electrochemical model. The battery state estimation device can then use the determined voltage difference to determine the state changes of the virtual battery. The battery state estimation device can then update the internal state of the virtual electrochemical model based on the determined state changes. Finally, the battery state estimation device can estimate the SOC of the virtual battery based on the updated internal state of the virtual electrochemical model to determine the final SOC of battery 310.

[0073] By correcting the internal state of the virtual electrochemical model to match the estimated voltage of the virtual battery with a preset voltage (e.g., EDV) through a feedback structure, the battery state estimation device can estimate the final SOC of battery 310 with a high level of accuracy, while minimizing model complexity and computational cost.

[0074] Furthermore, the battery state estimation device can calculate the RSOC of battery 310 based on the current SOC and the final SOC. For a more detailed description of the RSOC calculation, please refer to Equation 2 above.

[0075] In one example, the battery state estimation device can quickly and accurately estimate the RSOC of battery 310 using a dual-monomer model based on two electrochemical models and a corrector configured to correct the respective internal states of the electrochemical models.

[0076] Figure 4 An example of the operation of the dual-monomer model is shown. Figure 4 In the examples provided, detailed numerical values ​​are given solely for ease of description, and therefore the examples are not limited to these. Other numerical values ​​may be used without departing from the spirit and scope of the illustrative examples described.

[0077] Reference Figure 4 The battery state estimation device can determine the RSOC based on the current SOC estimated by the real electrochemical model 410 and the final SOC estimated by the virtual electrochemical model 420. To minimize the time required to predict the RSOC, the time required to estimate both the final and current SOC can be minimized. To minimize this time, assuming a virtual battery exists around the EDV, the method can directly estimate the final SOC by correcting the virtual electrochemical model 420 corresponding to the virtual battery so that the voltage estimated by the virtual electrochemical model 420 reaches a preset voltage (e.g., EDV). Unlike simulation methods that start discharging the battery from the current SOC position, the method of directly estimating the final SOC from the virtual electrochemical model 420 around the EDV can be used to minimize the time required to estimate the final SOC by performing calculations only around the EDV to obtain the final SOC.

[0078] Figure 5 An example of a battery state estimation method is shown. Although Figure 5 The operations can be performed in the order and manner shown, but the order of some operations may be changed or some operations may be omitted without departing from the spirit and scope of the illustrative examples described. Figure 5 Many of the operations shown can be performed in parallel or simultaneously. Figure 5 One or more blocks, and combinations thereof, can be implemented by a computer based on dedicated hardware (such as a processor) or a combination of dedicated hardware and computer instructions to perform specific functions. (Except for the following...) Figure 5 In addition to the description, Figures 1 to 4 The description can also be applied to Figure 5 And it is included here by reference. Therefore, the above description need not be repeated here.

[0079] Reference Figure 5 A battery state estimation device can estimate the state information of a battery. The battery state can be estimated over multiple time periods, and the device can estimate the battery state information within each time period. The estimation of the current state of charge (SOC) in a real battery cell will be described first below.

[0080] In operation 510, the battery state estimation device collects sensing data from the battery. This sensing data may include the battery's sensed voltage, sensed current, and sensed temperature. In one example, the sensing data may be stored in the form of a profile indicating how its size changes over time.

[0081] In operation 520, the estimated voltage and state information (e.g., current SOC) of the battery are determined by inputting an electrochemical model of the sensed current and sensed temperature.

[0082] In operation 530, the battery state estimation device calculates the voltage difference between the sensed voltage of the battery and the estimated voltage estimated by the electrochemical model. For example, the voltage difference can be determined as the moving average voltage over the last time period.

[0083] Although not in Figure 5 As shown, however, according to one example, a battery state estimation device can determine whether the battery's state information needs correction based on whether the voltage difference exceeds a threshold voltage difference. When errors occur in the electrochemical model, the estimated voltage (as the voltage estimated using the electrochemical model) may differ from the battery's sensed voltage. Therefore, to prevent such error accumulation, the battery state estimation device can determine whether correction is needed based on the voltage difference.

[0084] For example, when the voltage difference exceeds a threshold voltage, the battery state estimation device can determine that the battery state information needs to be corrected and perform operation 540. In another example, for example, when the voltage difference does not exceed a threshold voltage, the battery state estimation device can determine that the battery state information does not need to be corrected and return to operation 510 without performing operations 540, 550, and 560.

[0085] In operation 540, the battery state estimation device uses the voltage difference to determine the battery's state change. For example, the battery state estimation device may determine the battery's state change based on the voltage difference, previous state information of the battery, and an OCV table. The previous state information of the battery may be state information previously estimated using an electrochemical model in operation 520. For example, the battery's state change may include a change in SOC, which will refer to... Figure 7 and Figure 8 Detailed description.

[0086] In operation 550, the battery state estimation device updates the electrochemical model by correcting the internal state of the electrochemical model based on changes in the battery's state. For example, the battery state estimation device can 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 electrodes based on changes in the battery's state. Here, the active material may include the positive and negative electrodes of the battery. The battery state estimation device can use the electrochemical model with its internal state updated to estimate the battery's state information. Through a feedback structure that updates the internal state of the electrochemical model by determining changes in the battery's state, minimizing the voltage difference between the sensed voltage and the estimated voltage, the battery state estimation device can estimate the battery's state information with high accuracy using fewer computational resources. (Refer to...) Figures 9 to 11 Provide a detailed description.

[0087] In operation 560, the battery state estimation device determines whether to terminate the battery state estimation operation. For example, if the preset operation time has not yet elapsed, operation 510 can be performed in the next time period. Conversely, if the preset operation period has elapsed, the battery state estimation operation can be terminated.

[0088] The estimation of the final SOC in the virtual battery section will be described below. The estimation of the final SOC will be based on the difference between this estimate and the current SOC estimate described above. For example, the electrochemical model used in operation 520 can be a real electrochemical model corresponding to a real battery in the real battery section, while the electrochemical model corresponding to a virtual battery in the virtual battery section can be a virtual electrochemical model. Furthermore, in operation 530, which calculates the voltage difference, the voltage difference between the estimated voltage of the virtual battery and the preset EDV can be calculated in the virtual battery section. For a more detailed description of the remaining operations, please refer to the preceding description.

[0089] Figure 6 An example of an electrochemical model is shown.

[0090] Reference Figure 6 Electrochemical models can estimate the remaining capacity or state of charge (SOC) of a battery by modeling its internal physical phenomena, such as ion concentration, potential, etc. In one example, an electrochemical model can be represented by a physical conservation equation relating to the electrochemical reactions occurring at the electrode / electrolyte interface, the concentrations of the electrodes and electrolyte, and charge conservation. For the physical conservation equation, electrochemical models 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.).

[0091] In an electrochemical model, various state variables (such as concentration and potential, for example) can be coupled to each other. The estimated voltage 610 of the battery, estimated by the electrochemical model, indicates the potential difference between the two ends serving as the positive and negative electrodes. As shown by reference numeral 620, the potential information of each of the positive and negative electrodes can be affected by the ion concentration distribution in each of the positive and negative electrodes. The SOC 630 estimated by the electrochemical model indicates the average ion concentration of the positive and negative electrodes.

[0092] The aforementioned ion concentration distribution can be either an ion concentration distribution 640 within the electrode or an ion concentration distribution 650 within active material particles present at a specific location within the electrode. The ion concentration distribution 640 within the electrode can be a surface ion concentration distribution or an average ion concentration distribution of the active material particles located in the electrode direction. The electrode direction can 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 diaphragm). Furthermore, the ion concentration distribution 650 within the active material particles can be an ion concentration distribution based on the center direction of the active material particles. The center direction of the active material particles can be the direction connecting the center of the active material particles and the surface of the active material particles.

[0093] As described above, in order to reduce the voltage difference between the sensed voltage and the estimated voltage of the battery, or the voltage difference between the preset EDV and the estimated voltage, the battery state estimation device can move or change the ion concentration distribution of each of the positive and negative electrodes while maintaining the physical conservation associated with concentration. Based on the moved ion concentration distribution, it obtains the potential information of each of the positive and negative electrodes, and calculates the voltage based on the obtained potential information. The battery state estimation device can obtain internal state changes with a voltage difference of 0, and ultimately determine the current SOC or the end of the SOC of the battery.

[0094] Figure 7 and Figure 8 A graph showing an example of how battery state changes.

[0095] Figure 7 This illustrates an example of determining the state change of a battery when the sensed voltage or EDV of the battery is greater than the estimated voltage of the battery as estimated by an electrochemical model. The estimated voltage may be the battery voltage estimated over a previous time period.

[0096] In one example, the OCV table indicates the SOC-OCV curve representing the inherent characteristics of the battery. Real electrochemical models and virtual electrochemical models can have the same physical property parameters, thus the same OCV table can be used to obtain the current SOC and the final SOC. When using the OCV table, the ΔSOC to be corrected can vary depending on the SOC value, and information from previous (e.g., the most recent) estimated SOC time periods can be used. The SOC information from previous time periods can be the estimated SOC of the battery in those previous time periods.

[0097] It is possible Figure 7 The characteristic curve of the OCV table shown yields an estimated OCV corresponding to state information (e.g., SOC information) from a previous time period. A previously calculated voltage difference can be applied to the estimated OCV. This example involves a case where the sensed voltage or EDV is greater than the estimated voltage; therefore, the calculated voltage difference can be applied by adding the calculated voltage difference 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.

[0098] Figure 8 An example is shown where the battery's sensed voltage or EDV is less than the estimated voltage of the battery as estimated by the electrochemical model, indicating a change in the battery's state.

[0099] As mentioned above, when using the OCV table, SOC information from previous (e.g., the most recent) estimated time periods can be used. This can be achieved through... Figure 8 The characteristic curve of the OCV table shown yields an estimated OCV corresponding to state information (e.g., SOC information) from a previous time period. A previously calculated voltage difference can be applied to the estimated OCV. This example involves a case where the sensed voltage or EDV is less than the estimated voltage; therefore, the calculated voltage difference can be 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.

[0100] Figures 9 to 11 A graph showing an example of the internal state of an updated battery model.

[0101] In one example, a battery state estimation device can update the internal state of an electrochemical model based on changes in the battery's state. The electrochemical model models the internal physics of the battery and estimates its state information. The internal state of the electrochemical model can be provided in the form of a configuration file and may include, for example, voltage, overpotential, state of charge (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 can update the internal state of the electrochemical model by correcting for ion concentration distributions in the active material particles or in the electrodes based on changes in the battery's state. References will be made below. Figures 9 to 11 The internal states of the updated electrochemical model are described in more detail.

[0102] Figure 9 An example is shown where the internal state of an electrochemical model is updated by uniformly correcting the ion concentration distribution. In this example, the ion concentration distribution could indicate the ion concentration distribution within the active material particles or within the electrode. For example, when Figure 9 The graph shown indicates the ion concentration distribution within the active material particles, with the horizontal axis indicating the location within the particles. In this example, 0 represents the center of the active material particle, and 1 represents the surface of the particle. For another example, when... Figure 9 When the graph shown indicates the ion concentration distribution in the electrode, the horizontal axis of the graph indicates the location in the electrode. In this example, 0 indicates one end of the electrode (e.g., the boundary adjacent to the collector), and 1 indicates the other end of the electrode (e.g., the boundary adjacent to the membrane).

[0103] The battery state estimation device converts changes in the battery's state into changes in its internal state and applies these changes uniformly to the internal state of the electrochemical model. Changes in the internal state indicate changes in lithium-ion concentration corresponding to the region 910 between the initial and updated internal states. This uniform update method can be applied when the current output from the battery is small, assuming a uniform or consistent concentration change. This method is simpler to implement than the non-uniform update method described below.

[0104] In another example, when updating the internal state of an increase in lithium-ion concentration in the active material of one of the positive and negative electrodes, the internal state can be updated such that the lithium-ion concentration in the active material of the other electrode is reduced by the increment of the increase in lithium-ion concentration in the active material of the first electrode.

[0105] Figure 10 and Figure 11 An example is shown of updating the internal state of an electrochemical model by non-uniformly correcting the ion concentration distribution.

[0106] For example, when conductivity decreases significantly, battery current is relatively high, and / or battery temperature is relatively low, internal diffusion characteristics can be weakened based on the battery's chemical properties, thus increasing the gradient of ion concentration distribution along the electrode direction. In this example, based on the battery's internal diffusion characteristics, the internal state of the electrochemical model can be updated non-uniformly at each location in the active material particles or at each location in the electrode.

[0107] Lithium ions can move within a battery based on their diffusion properties. For example, when lithium ions move from the positive electrode to the negative electrode, the lithium ions closest to the negative electrode from the positive electrode will move first. In this example, as... Figure 10 As shown in the graph, when the internal diffusion characteristics of the battery are worse than before, lithium ions can move quite slowly in the positive electrode, and the spots of lithium ions moving to the negative electrode may not be quickly filled. Therefore, only lithium ions located at the positive electrode can continuously move to the negative electrode, and the gradient of ion concentration distribution can increase. In another example, such as Figure 11 As shown in the graph, when the internal diffusion characteristics of the battery are better than before, lithium ions located in the positive electrode can quickly move to the end to fill the positions of lithium ions that have moved to the negative electrode, thus reducing the gradient of ion concentration distribution. The region between the initial internal state and the updated internal state (e.g., Figure 10 Area 1010 and Figure 11 Region 1110) can correspond to changes in lithium-ion concentration. Such diffusion characteristics, as described above, can be based on battery state information (e.g., SOC), and therefore diffusion characteristics based on battery state changes can be considered. Diffusion characteristics considering battery state changes will be described in more detail below.

[0108] A battery state estimation device can determine a concentration gradient characteristic based on diffusion properties based on changes in the battery's state, and update the internal state of an electrochemical model based on the determined concentration gradient characteristic. The diffusion coefficient can be obtained based on analysis of the diffusion characteristics of lithium ions along the direction in which they will move (e.g., the direction of increasing lithium ion concentration). For example, a diffusion coefficient based on the previous state of charge (SOC) and the SOC of the lithium ions to be moved (e.g., the changing SOC or the updated SOC) can be obtained. Furthermore, the internal state of the electrochemical model can be updated based on a concentration gradient characteristic pre-set according to the diffusion coefficient. For example, when the diffusion coefficient decreases along the direction of movement, the internal state of the electrochemical model can be updated along the direction of increasing concentration gradient. In another example, when the diffusion coefficient increases along the direction of movement, the internal state of the electrochemical model can be updated along the direction of decreasing concentration gradient.

[0109] In another example, although lithium ions can move between the positive electrode, negative electrode, and electrolyte, the electrochemical model can be based on the principle that the total amount of lithium ions is always conserved. This movement of lithium ions between the positive electrode, negative electrode, and electrolyte can be derived from diffusion equations, which will be described in more detail below.

[0110] Battery state estimation devices can calculate diffusion equations for active materials based on changes in the battery's state and update the internal state of the electrochemical model. The device can assign current boundary conditions along the direction in which lithium ions will move (e.g., the direction of increasing lithium ion concentration) to calculate the diffusion equations and update the internal state of the electrochemical model. Furthermore, the device can calculate diffusion equations for active materials based on changes in the internal state corresponding to changes in the battery's state and update the internal state of the electrochemical model using the ion concentration distribution calculated from the diffusion equations. Since diffusion is one of several physical properties, the battery state estimation device can non-uniformly update the internal state of the electrochemical model by calculating diffusion equations related to the ion concentration distribution.

[0111] Figures 12 to 20 An example of estimating the relative SOC (RSOC) is shown.

[0112] Reference Figure 12 It can estimate the current SOC and the ending SOC in each time period (e.g., T1 and T2), and can also estimate the RSOC based on the current SOC and the ending SOC in each time period. For example, a time period can be from hundreds of milliseconds (ms) to seconds (s). To obtain the RSOC, one can... Figure 12 The following five steps are performed in the order shown: current SOC estimation in the real electrochemical model, current SOC correction, final SOC estimation in the virtual electrochemical model, final SOC correction, and RSOC calculation. The amount of time used to perform these five steps can be the amount of time used to estimate the RSOC. For example, the current SOC can be estimated in the real electrochemical model. In this example, the initial value of the current SOC in the real electrochemical model can be set according to the battery's starting voltage (e.g., FCC). Furthermore, the final SOC can be estimated in the virtual electrochemical model. The initial value of the final SOC in the virtual electrochemical model can be in the OCV state, and therefore set from 0V.

[0113] Reference Figure 13 Instead of calculating the current SOC and the ending SOC in a single time period, the current SOC and the ending SOC can be calculated alternately in each time period. For example, for a SOC that is not calculated in each time period, a value calculated in a previous time period can be used, and that value can be used to calculate the RSOC in each time period. (See above for reference.) Figure 12Compared to the processing described above, operation or computation time can be reduced by 50%.

[0114] Reference Figure 14 Taking advantage of the characteristic that the ending SOC changes more slowly than the current SOC, the current SOC can (for example, in time periods T1, T2...T) change more slowly. N The SOC is calculated N times, where N can be an integer greater than 1, and then the SOC ends (e.g., in time period T). N+1 The current SOC and the end SOC are calculated once, and the current SOC and end SOC can be updated to calculate the RSOC for each time period. (See above for reference.) Figure 12 Compared to the processing described, operation or computation time can be reduced by 50% while maintaining the accuracy of the current SOC.

[0115] Reference Figure 15 In a device that includes multiple batteries (e.g., cell 1, cell 2... cell N, where N can be an integer greater than 1), the current SOC and the end SOC can be calculated for each battery in each time period to calculate the RSOC in each time period.

[0116] Reference Figure 16 The system can calculate the final SOC for each of the multiple batteries in each time period, and can calculate the current SOC for one of the multiple batteries in a time period. For the remaining batteries, the previously updated current SOC can be used to calculate the RSOC in each time period. In each time period, the current SOC calculation can be performed once, and the final SOC calculation can be performed N times.

[0117] Reference Figure 17 The system can calculate the current SOC for each of the multiple batteries in each time period, and can calculate the end SOC for one of the multiple batteries in a time period. For the remaining batteries, the previously updated end SOC can be used to calculate the RSOC in each time period. In each time period, the current SOC calculation can be performed N times, and the end SOC calculation can be performed once.

[0118] Reference Figure 18 It can calculate the current SOC and the end SOC for one of multiple batteries within a time period, and can also calculate the RSOC for that battery. For the remaining batteries, the previously updated RSOC can be used. Within each time period, the current SOC calculation can be performed once, and the end SOC calculation can be performed once.

[0119] Reference Figure 19The system can calculate the current SOC for each of the multiple batteries in each time period, and can calculate the final SOC for a representative battery in a time period. For the remaining batteries, the final SOC of the representative battery can be used to calculate the RSOC in each time period. Multiple batteries can have the same individual characteristics, so the final SOC will not be significantly different for each battery. Therefore, the final SOC can be calculated only for the representative battery. Among the multiple batteries, the battery with the average current SOC (or final SOC) can be selected as the representative battery. In each time period, the current SOC calculation can be performed N times, and the final SOC calculation can be performed once.

[0120] Reference Figure 20 In a single battery configuration, the current State of Charge (SOC) can be calculated once per time period, and the final SOC can be calculated N times per time period, resulting in N RSOC calculations per time period. As mentioned above, the final SOC can vary depending on the applied current of the battery, and the applied current can vary depending on the operating type of the device. In various situations (e.g., playing music, playing video, playing games, and operating in an idle state), these processes calculate the remaining available time based on the current battery state and provide the calculated remaining time to the user. The current SOC calculation can be performed once per time period, and the final SOC calculation can be performed N times per time period.

[0121] Figure 21 An example of a battery state estimation method is shown. Although Figure 21 The operations can be performed in the order and manner shown, but the order of some operations may be changed or some operations may be omitted without departing from the spirit and scope of the illustrative examples described. Figure 21 Many of the operations shown can be performed in parallel or simultaneously. Figure 21 One or more blocks, and combinations thereof, can be implemented by a computer based on dedicated hardware (such as a processor) or a combination of dedicated hardware and computer instructions to perform specific functions. (Except for the following...) Figure 21 In addition to the description, Figures 1 to 20 The description can also be applied to Figure 21 And it is included here by reference. Therefore, the above description need not be repeated here.

[0122] The following will refer to Figure 21 The battery state estimation method described herein can be executed by a processor included in the battery state estimation device described herein.

[0123] Reference Figure 21In operation 2110, the battery state estimation device estimates the current SOC of the target battery by correcting the first electrochemical model corresponding to the target battery using a first voltage difference between the measured voltage of the target battery and the estimated voltage of the target battery estimated by the first electrochemical model. In operation 2120, the battery state estimation device estimates the final SOC of the target battery by correcting the second electrochemical model using a voltage difference between the estimated voltage of the virtual battery estimated by the second electrochemical model and a preset voltage. The second electrochemical model may correspond to a virtual battery indicating a virtual state in which the target battery has been discharged to a preset voltage. In operation 2130, the battery state estimation device estimates the RSOC of the target battery based on the current SOC and the final SOC of the target battery.

[0124] Figure 22 An example of a battery state estimation method is shown. Although Figure 22 The operations can be performed in the order and manner shown, but the order of some operations may be changed or some operations may be omitted without departing from the spirit and scope of the illustrative examples described. Figure 22 Many of the operations shown can be performed in parallel or simultaneously. Figure 22 One or more blocks, and combinations thereof, can be implemented by a computer based on dedicated hardware (such as a processor) or a combination of dedicated hardware and computer instructions to perform specific functions. (Except for the following...) Figure 22 In addition to the description, Figures 1 to 21 The description can also be applied to Figure 22 And it is included here by reference. Therefore, the above description need not be repeated here.

[0125] The following will refer to Figure 22 The battery state estimation method described herein can be executed by a processor included in the battery state estimation device described herein.

[0126] Reference Figure 22 In operation 2210, the battery state estimation device uses the voltage difference between the estimated voltage of the virtual battery, estimated by the electrochemical model corresponding to the virtual battery, and a preset voltage to determine the state change of the virtual battery. Here, the virtual battery may indicate a virtual state assuming the target battery is discharged to the preset voltage. In operation 2220, the battery state estimation device updates the internal state of the electrochemical model based on the determined state change of the virtual battery. In operation 2230, the battery state estimation device estimates the final state of charge (SOC) of the target battery by estimating the state information of the virtual battery based on the updated internal state of the electrochemical model.

[0127] Figure 23 An example of a battery state estimation device is shown.

[0128] Reference Figure 23 The battery state estimation device 2300 includes a memory 2310 and a processor 2320. The memory 2310 and the processor 2320 can communicate with each other via a bus 2330.

[0129] Memory 2310 may include computer-readable instructions. When the instructions stored in memory 2310 are executed by processor 2320, processor 2320 may perform one or more or all of the operations or methods described above. Memory 2310 may be volatile memory or non-volatile memory. In one embodiment, memory 2310 may store a real electrochemical model corresponding to a target battery or a real battery (e.g., battery 310) and a virtual electrochemical model based on a virtual battery.

[0130] In one example, the non-volatile memory device may be, for example, dynamic random access memory (DRAM), static RAM (SRAM), thyristor RAM (T-RAM), zero-capacitor RAM (Z-RAM), or dual-transistor RAM (TTRAM).

[0131] In one example, the volatile memory device may be, for example, an electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic RAM (MRAM), spin-transfer torque (STT) MRAM (STT-MRAM), conductive bridged RAM (CBRAM), ferroelectric RAM (FeRAM), phase-change RAM (PRAM), resistive RAM, nanotube RAM, polymer RAM (PoRAM), nanofloating gate memory (NFGM), holographic memory, molecular electronic memory device, or insulator resistance-changing memory. Furthermore, a description of memory 2310 is given below.

[0132] Processor 2320 can execute instructions or programs, or control battery state estimation device 2300. Processor 2320 can estimate the current state of charge (SOC) of the target battery by correcting the first electrochemical model using the voltage difference between a measured voltage of the target battery and an estimated voltage of the target battery estimated by a first electrochemical model. Processor 2320 can estimate the final state of charge (SOC) of the target battery by correcting the second electrochemical model using the voltage difference between an estimated voltage of a virtual battery estimated by a second electrochemical model and a preset voltage. Then, processor 2320 can estimate the relative state of charge (RSOC) of the target battery based on the current SOC and the final SOC.

[0133] In one example, the battery state estimation device 2300 can be applied to a battery management system (BMS) with the function of estimating the SOC of a secondary battery, electronic devices using secondary batteries, vehicles using secondary batteries, and energy storage devices based on secondary batteries. Furthermore, by effectively reducing the amount of time spent calculating the battery's end SOC through the use of a virtual battery model-based end SOC estimation method, the battery state estimation device 2300 can be installed in low-specification devices (such as power management integrated circuits (PMICs)). The battery state estimation device 2300 can also be applied to fast charging based on internal state information from electrochemical models, automatic degradation updates based on electrochemical models, prediction of internal short circuits in batteries, and battery fuel metering.

[0134] The battery state estimation device 2300 can be applied to various computing devices (e.g., smartphones, tablets, laptops, PCs, etc.), various wearable devices (e.g., smartwatches, smart glasses, etc.), various home appliances (e.g., smart speakers, smart TVs, smart refrigerators, etc.) and other smart devices (e.g., smart vehicles, robots, drones, walking aids (WADs), Internet of Things (IoT) devices, etc.).

[0135] In addition to the foregoing, the battery state estimation device 2300 may perform the operations described herein.

[0136] Figure 24 An example of a mobile device embodiment is shown.

[0137] Reference Figure 24 Mobile device 2400 includes a battery pack 2410. Mobile device 2400 can use the battery pack 2410 as a power source. Mobile device 2400 can be a portable terminal such as a smartphone. For ease of description, Figure 24 The mobile device 2400 shown is an example as a smartphone. However, the mobile device 2400 can be other terminals (e.g., laptop computers, tablet PCs, wearable devices, etc.). The battery pack 2410 may include a BMS and individual battery cells (or battery modules).

[0138] In one example, mobile device 2400 may include the battery state estimation device described above. The battery state estimation device may estimate the RSOC of battery pack 2410 based on the current SOC and the end SOC of battery pack 2410 (or the individual battery cells in battery pack 2410).

[0139] To describe the example features and operation of the mobile device 2400 in more detail, the above references... Figures 1 to 23 The provided descriptions can be referenced, and for the sake of brevity, more detailed and repetitive descriptions will be omitted here.

[0140] Figure 25 and Figure 26 An example of a vehicle embodiment is shown.

[0141] Reference Figure 25 Vehicle 2500 includes battery pack 2510 and BMS 2520. Vehicle 2500 can use battery pack 2510 as a power source. For example, vehicle 2500 can be an electric vehicle or a hybrid vehicle.

[0142] The battery pack 2510 may include multiple battery modules, each of which includes multiple battery cells.

[0143] BMS 2520 can monitor for abnormalities in battery pack 2510 and prevent overcharging or over-discharging of battery pack 2510. Furthermore, BMS 2520 can perform thermal control on battery pack 2510 when the temperature of battery pack 2510 exceeds a first temperature (e.g., 40°C) or falls below a second temperature (e.g., -10°C). Additionally, BMS 2520 can perform cell balancing to equalize the state of charge of the battery cells included in battery pack 2510.

[0144] In one example, BMS 2520 may include the battery state estimation device described herein, and use the battery state estimation device to determine state information for each battery cell included in battery pack 2510 or state information for battery pack 2510. BMS 2520 may determine the maximum, minimum, or average value of the state information of the battery cells as the state information for battery pack 2510.

[0145] The BMS 2520 can send the status information of the battery pack 2510 to the electronic control unit (ECU) or vehicle control unit (VCU) of the vehicle 2500. The ECU or VCU of the vehicle 2500 can then output the status information of the battery pack 2510 to the display of the vehicle 2500. For example... Figure 26 As shown, the ECU or VCU can display the status information of the battery pack 2510 on the instrument panel 2610 of the vehicle 2500. In another example, the ECU or VCU can display the remaining available driving range determined based on the estimated status information on the instrument panel 2610. In yet another example, the ECU or VCU can display status information, remaining available driving range, etc., on a head-up display of the vehicle 2500.

[0146] To describe the example features and operation of vehicle 2500 in more detail, the above refers to... Figures 1 to 23 The provided descriptions can be referenced, and for the sake of brevity, more detailed and repetitive descriptions will be omitted here.

[0147] In this regard Figure 1 , Figure 23 , Figure 24 , Figure 25 and Figure 26The described battery state estimation devices, battery state estimation device 120, battery state estimation device 2300, BMS 2520, and other devices, units, modules, apparatuses, and components are implemented by 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 hardware components performing the operations described in this application are implemented by computing hardware (e.g., by one or more processors or computers). The processor or computer may 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 means or combination of means configured to respond to and execute instructions 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) 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 terms "processor" or "computer" are 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. 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.The hardware components may be any one or more with different processing configurations. Examples of different processing configurations include: a single processor, a standalone processor, a parallel processor, a single instruction single data (SISD) multiprocessor, a single instruction multiple data (SIMD) multiprocessor, multiple instruction single data (MISD) multiprocessor, and multiple instruction multiple data (MIMD) multiprocessor, a controller and arithmetic logic unit (ALU), a DSP, a microcomputer, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a programmable logic unit (PLU), a central processing unit (CPU), a graphics processing unit (GPU), a neural processing unit (NPU), or any other device capable of responding to and executing instructions in a defined manner.

[0148] Figures 1 to 26 The methods for performing the operations described in this application, as shown, are executed by computing hardware (e.g., one or more processors or a computer), which is implemented to execute instructions or software as described above to perform the operations performed by the methods described in this application. For example, a single operation, or two or more operations, may be executed by a single processor, or two or more processors, or a processor and a controller. One or more operations may be executed by one or more processors, or a processor and a controller, and one or more other operations may be executed by one or more other processors, or additional processors and additional controllers. One or more processors, or a processor and a controller, may execute a single operation, or two or more operations.

[0149] 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 can 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 machines or special-purpose computers 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 one example, the instructions or software include at least one of applets, dynamic link libraries (DLLs), middleware, firmware, device drivers, and applications that store methods for estimating battery state. 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 can be written using any programming language based on the block diagrams and flowcharts shown in the accompanying drawings and the corresponding descriptions in the specification, which disclose algorithms for performing operations performed by the hardware components and methods described above.

[0150] 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, as well as any associated data, data files, and data structures, may be recorded, stored, or fixed in, or may be recorded, stored, or fixed 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 programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), magnetic RAM (MRAM), spin-transfer torque (STT)-MRAM, static random access memory (SRAM), thyristor RAM (T-RAM), zero-capacitor RAM (Z-RAM), dual-transistor RAM (TTRAM), conductive bridged RAM (CBRAM), ferroelectric RAM (FeRAM), and phase-change RAM (PRA). M), Resistive RAM (RRAM), Nanotube RRAM, Polymer RAM (PoRAM), Nanofloating Gate Memory (NFGM), Holographic Memory, Molecular Electronic Memory Device, Insulator Resistance Variation Memory, 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), flash memory, card storage (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 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 the instructions or software and any associated data, data files, and data structures to a processor or computer, enabling the processor or computer to 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 or 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.

[0151] 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.

[0152] Therefore, the scope of the disclosure is not limited by the specific embodiments, but by the claims and their equivalents, and all variations within the scope of the claims and their equivalents should be interpreted as included in the disclosure.

Claims

1. A processor-implemented method for battery state estimation, comprising: The current state of charge of the target battery is estimated by correcting the first electrochemical model corresponding to the target battery with the first voltage difference between the measured voltage of the target battery and the estimated voltage of the target battery estimated by the first electrochemical model. The target battery's final state of charge is estimated by correcting the second electrochemical model using a second voltage difference between the estimated voltage estimated by the second electrochemical model of the virtual battery and a preset voltage. and The relative state of charge of the target battery is estimated based on its current state of charge and its final state of charge. The second electrochemical model is based on a virtual battery corresponding to the target battery being discharged to a preset voltage. The step of estimating the current state of charge includes: updating the internal state of the first electrochemical model based on the state change of the target battery corresponding to the first voltage difference, and estimating the current state of charge of the target battery based on the updated internal state of the first electrochemical model. The step of estimating the final state of charge includes: updating the internal state of the second electrochemical model based on the state change of the virtual battery corresponding to the second voltage difference, and estimating the state information of the virtual battery based on the updated internal state of the second electrochemical model. The first electrochemical model and the second electrochemical model have the same physical property parameters but different internal state information.

2. The method of claim 1, wherein, The steps for estimating the end of the state of charge include: In response to the estimated voltage of the virtual cell corresponding to a preset voltage, an updated second electrochemical model is used to estimate the current state of charge of the virtual cell; and The current state of charge of the virtual battery is determined as the final state of charge of the target battery.

3. The method of claim 1, wherein, The end of the state of charge is the state of charge obtained when the target battery is discharged by the current output from the target battery and reaches a preset voltage.

4. The method of claim 1, wherein, The state changes of the virtual battery are based on a second voltage difference, previous state information estimated by a second electrochemical model, and an open-circuit voltmeter.

5. The method of claim 4, wherein, The state change of the virtual battery is determined by obtaining the open-circuit voltage corresponding to the previous state information based on the open-circuit voltmeter, and applying a second voltage difference to the obtained open-circuit voltage.

6. The method of claim 1, wherein, The internal state of the second electrochemical model is updated by correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode based on the state changes of the virtual battery.

7. The method of claim 1, wherein, The internal state of the second electrochemical model includes at least one of the following: the lithium-ion concentration distribution at the positive electrode, the lithium-ion concentration distribution at the negative electrode, and the lithium-ion concentration distribution in the electrolyte of the virtual battery.

8. The method of claim 1, wherein, The steps for estimating the relative state of charge include: The relative state of charge is estimated based on one of the current state of charge and the end of the state of charge estimated in the current period, and the other of the current state of charge and the end of the state of charge estimated in a previous period.

9. The method of claim 1, wherein, After the step of estimating the current state of charge is performed a certain number of times, the step of estimating the end of the state of charge is performed.

10. The method of claim 1, wherein, The target battery comprises multiple batteries. For each of the multiple batteries, a step of estimating the current state of charge is performed, and for a representative battery among the multiple batteries, a step of estimating the final state of charge is performed. The steps for estimating the relative state of charge include: The relative state of charge of each of the plurality of batteries is estimated based on the estimated current state of charge of each of the plurality of batteries and the estimated final state of charge of a representative battery.

11. The method of claim 1, wherein, The steps for estimating the end of the state of charge include: Multiple virtual cells are used to estimate multiple states of charge, which indicate virtual states in which the target battery is discharged to a preset voltage by different currents. The steps for estimating the relative state of charge include: Multiple relative states of charge of the target battery are estimated based on the current state of charge of the target battery and multiple estimated final states of charge.

12. The method of any one of claims 1 to 11, wherein, The preset voltage is the discharge end voltage of the target battery.

13. The method of any one of claims 1 to 11, wherein, The target battery is a single battery cell, a battery module, or a battery pack.

14. A processor-implemented method for battery state estimation, comprising: The state changes of the virtual battery are determined by the voltage difference between the estimated voltage estimated by the electrochemical model corresponding to the virtual battery and the preset voltage. The virtual battery corresponds to the target battery that has been discharged to the preset voltage. The internal state of the electrochemical model is updated based on the determined state changes of the virtual battery. and The final state of charge of the target battery is estimated by estimating the state information of the virtual battery through an updated internal state estimation based on an electrochemical model.

15. The method of claim 14, wherein, The steps to determine the state changes of a virtual battery include: The state changes of the virtual cell are determined based on the voltage difference, previous state information estimated by the electrochemical model, and the open-circuit voltmeter.

16. The method of claim 14, wherein, The steps for updating the internal state of the electrochemical model include: The internal state of the electrochemical model is updated by correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode based on the state changes of the virtual battery.

17. A non-transitory computer-readable storage medium for storing instructions, which, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 16.

18. An apparatus for battery state estimation, comprising: The memory is configured to store a first electrochemical model corresponding to the target battery and a second electrochemical model based on a virtual battery, wherein the virtual battery corresponds to the target battery being discharged to a preset voltage. and The processor is configured as follows: The current state of charge of the target battery is estimated by correcting the first electrochemical model using the first voltage difference between the measured voltage of the target battery and the estimated voltage of the target battery estimated by the first electrochemical model. The target battery's final state of charge is estimated by correcting the second electrochemical model using a second voltage difference between the estimated voltage estimated by the second electrochemical model of the virtual battery and a preset voltage. and The relative state of charge of the target battery is estimated based on its current state of charge and its final state of charge. The process of estimating the current state of charge includes: updating the internal state of the first electrochemical model based on the state change of the target battery corresponding to the first voltage difference, and estimating the current state of charge of the target battery based on the updated internal state of the first electrochemical model. The process of estimating the final state of charge includes: updating the internal state of the second electrochemical model based on the state changes of the virtual battery corresponding to the second voltage difference, and estimating the state information of the virtual battery based on the updated internal state of the second electrochemical model. The first electrochemical model and the second electrochemical model have the same physical property parameters but different internal state information.

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