Battery state estimation method and apparatus

Abstract

To provide a method and apparatus for battery state estimation.SOLUTION: A battery state estimation method includes the steps of: estimating a current 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 that is 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 an estimated voltage of a virtual battery that is estimated by the second electrochemical model and a preset voltage; and estimating a RSOC of the target battery on the basis of the current SOC and the end SOC of the target battery. The second electrochemical model corresponds to the virtual battery that the target battery is discharged to reach the preset voltage.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The following embodiments relate to a battery state estimation method and apparatus. [Background technology]

[0002] To optimize battery operation, the battery state is estimated, and there are various methods for estimating this state. For example, the battery state can be estimated by integrating the current of the battery in question, or by using a battery model (e.g., an electrical circuit model or an electrochemical model). [Overview of the Initiative] [Problems that the invention aims to solve]

[0003] According to one embodiment, a battery state estimation method and apparatus are provided that can rapidly and accurately estimate RSOC using an electrochemical model. [Means for solving the problem]

[0004] A battery state estimation method according to one embodiment includes the steps of: estimating the current state of charge (SOC) of a target battery by correcting the first electrochemical model using a first voltage difference between the measured voltage of the target battery and the estimated voltage of the target battery estimated by a first electrochemical model corresponding to the target battery; estimating the end state of charge (SOC) of the target battery by correcting the second electrochemical model using a second voltage difference between the estimated voltage of a virtual battery estimated by a second electrochemical model and a preset voltage; and estimating the current state of charge (RSOC) of the target battery based on the current state of charge (SOC) and the end state of charge (SOC) of the target battery, wherein the second electrochemical model is based on the virtual battery corresponding to the target battery that has reached the preset voltage due to discharge.

[0005] One embodiment of the battery state estimation method involves the estimated voltage of a virtual battery estimated by an electrochemical model corresponding to a virtual battery corresponding to a target battery that has reached a preset voltage due to discharge, and the preset voltage. to between of Voltage difference The process includes the steps of determining the amount of state change of the virtual battery using the method, updating the internal state of the electrochemical model based on the amount of state change of the virtual battery, and estimating the end state of the target battery by estimating the state information of the virtual battery based on the internal state of the electrochemical model.

[0006] A battery state estimation device according to one embodiment includes a memory that stores a first electrochemical model corresponding to a target battery and a second electrochemical model based on a virtual battery corresponding to the target battery that has reached a preset voltage through discharge, and a processor that estimates the RSOC of the target battery. The processor estimates the current SOC of the target battery by correcting the first electrochemical model using the voltage difference between the measured voltage of the target battery and the estimated voltage of the target battery estimated by the first electrochemical model, estimates the end SOC of the target battery by correcting the second electrochemical model using the voltage difference between the estimated voltage of the virtual battery estimated by the second electrochemical model and the preset voltage, and estimates the RSOC of the target battery based on the current SOC and end SOC of the target battery. [Effects of the Invention]

[0007] According to the present invention, RSOC can be estimated quickly and accurately even on an electrochemical model basis. [Brief explanation of the drawing]

[0008] [Figure 1] This is a diagram illustrating a battery system according to one embodiment. [Figure 2]It is a diagram for explaining a battery system according to an embodiment. [Figure 3] It is a diagram for explaining a battery system according to an embodiment. [Figure 4] It is a diagram for explaining a battery system according to an embodiment. [Figure 5] It is a diagram for explaining the process of estimating the state of a battery according to an embodiment. [Figure 6] It is a diagram for explaining an electrochemical model according to an embodiment. [Figure 7] It is a diagram for explaining the process of determining the amount of change in the state of a battery according to an embodiment. [Figure 8] It is a diagram for explaining the process of determining the amount of change in the state of a battery according to an embodiment. [Figure 9] It is a diagram for explaining the process of updating the internal state of a battery model according to an embodiment. [Figure 10] It is a diagram for explaining the process of updating the internal state of a battery model according to an embodiment. [Figure 11] It is a diagram for explaining the process of updating the internal state of a battery model according to an embodiment. [Figure 12] It is a diagram showing various examples of estimating RSOC according to an embodiment. [Figure 13] It is a diagram showing various examples of estimating RSOC according to an embodiment. [Figure 14] It is a diagram showing various examples of estimating RSOC according to an embodiment. [Figure 15] It is a diagram showing various examples of estimating RSOC according to an embodiment. [Figure 16] It is a diagram showing various examples of estimating RSOC according to an embodiment. [Figure 17] It is a diagram showing various examples of estimating RSOC according to an embodiment. [Figure 18] It is a diagram showing various examples of estimating RSOC according to an embodiment. [Figure 19] FIG. showing various illustrations for estimating RSOC according to one embodiment. [Figure 20] FIG. showing various illustrations for estimating RSOC according to one embodiment. [Figure 21] FIG. showing a battery state estimation method according to one embodiment. [Figure 22] FIG. showing a battery state estimation method according to another embodiment. [Figure 23] FIG. showing a battery state estimation device according to one embodiment. [Figure 24] FIG. for explaining a mobile device according to one embodiment. [Figure 25] FIG. for explaining a vehicle according to one embodiment. [Figure 26] FIG. for explaining a vehicle according to one embodiment.

MODE FOR CARRYING OUT THE INVENTION

[0009] <SUMMARY OF THE INVENTION> A battery state estimation method according to one embodiment includes estimating a current SOC of the target battery by correcting the first electrochemical model 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 corresponding to the target battery; estimating an end SOC of the target battery by correcting the second electrochemical model using a second voltage difference between an estimated voltage of a virtual battery estimated by the second electrochemical model and a preset voltage; and estimating an 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 the virtual battery corresponding to the target battery that has reached the preset voltage during discharge.

[0010] In a battery state estimation method according to one embodiment, the step of estimating the end SOC can be to estimate the current SOC of the virtual battery using the updated second electrochemical model in response to the estimated voltage of the virtual battery corresponding to the preset voltage, and determine the current SOC of the virtual battery as the end SOC of the target battery.

[0011] In a battery state estimation method according to one embodiment, the first electrochemical model and the second electrochemical model may include the same physical characteristic parameters and different internal state information.

[0012] In a battery state estimation method according to one embodiment, the final SOC may be the SOC when the target battery is discharged by the current output from the target battery and reaches the preset voltage.

[0013] In a battery state estimation method according to one embodiment, the step of estimating the end SOC involves determining the amount of state change of the virtual battery using the second voltage difference, updating the internal state of the second electrochemical model based on the amount of state change of the virtual battery, and estimating the state information of the virtual battery based on the internal state of the second electrochemical model, thereby estimating the end SOC of the target battery.

[0014] In a battery state estimation method according to one embodiment, the amount of change in the state of the virtual battery is based on the second voltage difference, previous state information estimated in advance by the second electrochemical model, and the OCV table.

[0015] In a battery state estimation method according to one embodiment, the amount of change in the state of the virtual battery can be determined by obtaining the open-circuit voltage corresponding to the previous state information based on the OCV table and reflecting the second voltage difference in the open-circuit voltage.

[0016] In a battery state estimation method according to one embodiment, the internal state of the second electrochemical model can be updated by correcting the ion concentration distribution within the active material particles or the ion concentration distribution within the electrodes based on the amount of change in the state of the virtual battery.

[0017] In a battery state estimation method according to one embodiment, the internal state of the second electrochemical model may include one or more of the positive electrode lithium ion concentration distribution, the negative electrode lithium ion concentration distribution, and the electrolyte lithium ion concentration distribution of the virtual battery.

[0018] In a battery state estimation method according to one embodiment, the step of estimating the RSOC can be performed by estimating the RSOC based on one of the current SOC and the end SOC estimated in the current cycle, and the other one estimated in a previous cycle.

[0019] In a battery state estimation method according to one embodiment, the step of estimating the end SOC may be performed after the step of estimating the current SOC has been performed a predetermined number of times.

[0020] In a battery state estimation method according to one embodiment, the target battery is one of a plurality of batteries, the step of estimating the current SOC is performed for each of the plurality of batteries, the step of estimating the end SOC is performed for a representative battery among the plurality of batteries, and the step of estimating the RSOC can estimate the RSOC of each of the plurality of batteries based on the current SOC estimated for each of the plurality of batteries and the end SOC estimated for the representative battery.

[0021] In a battery state estimation method according to one embodiment, the step of estimating the end SOC can be performed by estimating the end SOC using each of a plurality of virtual batteries, each assumed to have been discharged with a different current and reached a preset voltage, and the step of estimating the RSOC can be performed by estimating the RSOC of the target battery based on the current SOC and the end SOC of the target battery, respectively.

[0022] In a battery state estimation method according to one embodiment, the preset voltage may be the lower discharge voltage of the target battery.

[0023] In a battery state estimation method according to one embodiment, the target battery may be a battery cell, a battery module, or a battery pack.

[0024] One embodiment of the battery state estimation method involves the estimated voltage of a virtual battery estimated by an electrochemical model corresponding to a virtual battery corresponding to a target battery that has reached a preset voltage due to discharge, and the preset voltage. to between of Voltage difference The process includes the steps of determining the amount of state change of the virtual battery using the method, updating the internal state of the electrochemical model based on the amount of state change of the virtual battery, and estimating the end state of the target battery by estimating the state information of the virtual battery based on the internal state of the electrochemical model.

[0025] A battery state estimation device according to one embodiment includes a memory that stores a first electrochemical model corresponding to a target battery and a second electrochemical model based on a virtual battery corresponding to the target battery that has reached a preset voltage through discharge, and a processor that estimates the RSOC of the target battery. The processor estimates the current SOC of the target battery by correcting the first electrochemical model using the voltage difference between the measured voltage of the target battery and the estimated voltage of the target battery estimated by the first electrochemical model, estimates the end SOC of the target battery by correcting the second electrochemical model using the voltage difference between the estimated voltage of the virtual battery estimated by the second electrochemical model and the preset voltage, and estimates the RSOC of the target battery based on the current SOC and end SOC of the target battery.

[0026] <Details of the invention> Specific structural or functional descriptions of embodiments are disclosed for illustrative purposes only and may be modified in various ways. Therefore, embodiments are not limited to any particular disclosure, and the scope of this specification includes modifications, equivalents, or substitutions that are part of the technical concept.

[0027] Terms such as "first" or "second" may be used to describe multiple components, but such terms should be interpreted solely for the purpose of distinguishing one component from others. For example, the first component can be named the second component, and similarly, the second component can also be named the first component.

[0028] A singular expression includes plural expressions unless the context clearly indicates otherwise. In this specification, terms such as “includes” or “has” indicate the presence of features, figures, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood not to preemptively exclude the possibility of the presence or addition of one or more other features, figures, steps, actions, components, parts, or combinations thereof.

[0029] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as those generally understood by a person of ordinary skill in the art to which this embodiment belongs. Commonly used, predefined terms should be interpreted as having the meaning consistent with their meaning in the context of the relevant art, and not as ideal or overly formal unless expressly defined herein.

[0030] Furthermore, when explaining with reference to the drawings, the same components will be assigned the same reference numerals regardless of the reference numerals used in the drawings, and redundant explanations will be omitted. In the description of embodiments, if it is determined that a specific explanation of related prior art would unnecessarily obscure the gist of the present invention, such detailed explanation will be omitted.

[0031] Figures 1 to 4 are diagrams illustrating a battery system according to one embodiment.

[0032] Referring to Figure 1, a battery system 100 according to one embodiment includes a battery 110 and a battery state estimation device 120.

[0033] The battery 110 may consist of one or more battery cells, battery modules, or battery packs.

[0034] The battery state estimation device 120 detects the battery 110 using one or more sensors. In other words, the battery state estimation device 120 collects detection data of the battery 110. For example, the detection data may include voltage data, current data, and / or temperature data.

[0035] The battery state estimation device 120 estimates the state information of the battery 110 based on the detected data and outputs the result. The state information may include, for example, SOC (State of Charge), RSOC (Relative State of Charge), SOH (State of Health), and / or abnormal state information. The battery model used when estimating the state information is an electrochemical model, which will be described later with reference to Figure 6.

[0036] Referring to Figure 2, an example illustrating SOC and RSOC according to one embodiment is shown.

[0037] SOC is the current usable capacity relative to the total battery capacity designed on an OCV (open-circuit voltage) basis, and may be expressed by the following formula (1). SOC is determined based on the OCV graph shown in Figure 2. V shown in Figure 2 max This indicates the buffer voltage, which is the voltage when the battery is fully charged, V min This indicates the lower discharge voltage, which is the voltage at which the device is fully discharged according to the OCV standard. min This indicates the voltage that the manufacturer has pre-set to prevent further battery discharge.

[0038]

number

[0039] In one embodiment, the battery may be discharged by applying a current to a connected load. In such an actual use case, RSOC based on the Under Load voltage may be utilized rather than the SOC based on the OCV. RSOC is the ratio of the total available capacity to the currently available capacity based on the voltage under the current application state, and can display the available capacity from the user's perspective. RSOC may be determined based on the Under Load graph shown in FIG. 2.

[0040] [Number] In the above formula (2), Q usable is the total available capacity based on the voltage under the current application state with a load connected to the battery, indicating the FCC (full charge capacity). Q usable is determined to be "Q max -Q unusable ". Q unusable indicates the capacity that cannot be used as more discharge is restricted as the battery connected to the load reaches the discharge lower limit voltage. Q unusable can vary according to the magnitude of the battery current, temperature, and / or degradation state.

[0041] For example, if a load is connected to the battery and a current is output from the battery, the output voltage of the battery becomes lower than the OCV. Therefore, in FIG. 2, the Under Load graph has a smaller value than the OCV graph. In other words, the greater the current output from the battery, the greater the interval between the Under Load graph and the OCV graph. The greater the current output from the battery, the greater Q unusable also increases.

[0042] To accurately predict the battery's RSOC, Q usable and Q passed This must be predicted accurately. However, as explained earlier, Q usable Q is affected by the magnitude of the current and temperature. unusable Since accurate prediction can be difficult to determine based on this, RSOC can be expressed in a formula using SOC other than Q, as in formula (2). Expressed differently, RSOC may be determined based on the current SOC and the end SOC. Here, the end SOC represents the SOC when the battery is discharged with current applied and reaches the lower discharge voltage. The end SOC, as the SOC at the lower discharge voltage, may vary depending on the magnitude of the battery current, temperature, and / or degradation state. The end SOC may also be referred to as SOCEDV.

[0043] In one embodiment, the magnitude of the battery's output current may vary depending on the operating type of the device to which the battery is installed. For example, depending on the various operating types, such as playing music, playing videos, playing games, or operating in standby mode on a smartphone, the magnitude of the output current changes, which in turn changes the termination SOC, and therefore the RSOC based on this may also change. When playing videos, the operating time of the device may be shorter than when playing music. Thus, termination SOC estimates the SOC when the battery reaches its discharge limit voltage, assuming it is discharged at the current magnitude applied, and is equivalent to predicting the future state from the current state. The process of estimating RSOC will be described in detail below with reference to the drawings.

[0044] Referring to Figure 3, a block diagram is shown illustrating the process of estimating state information using a battery model according to one embodiment.

[0045] In one embodiment, the battery state estimation device estimates the state information of the battery 310 using an electrochemical model. The electrochemical model is a model that estimates the state information of the battery by modeling the internal physical characteristics of the battery, such as its potential and ion concentration distribution. The electrochemical model will be described later with reference to Figure 6.

[0046] The accuracy of the estimated state information of the battery 310 can affect the optimal operation and control of the battery 310. When estimating state information using an electrochemical model, there is a risk of errors occurring between the sensor information measuring current, voltage, and temperature data input to the electrochemical model and the estimated information calculated by the modeling method; therefore, error correction may be performed.

[0047] As explained earlier, in order to estimate RSOC, the current SOC and the end SOC are required, the current SOC may be estimated in the actual battery part 320, and the end SOC may be estimated in the virtual battery part 330.

[0048] The actual battery part 320 is the part that estimates the current state of charge (SOC) of battery 310, and to distinguish it from the virtual battery described below, battery 310 will be referred to as the actual battery or target battery. The actual electrochemical model may also be a model that estimates the state information of battery 310 by modeling the physical development within battery 310. The input data for the actual electrochemical model may include information on the voltage, current, and temperature of battery 310 measured in real time.

[0049] To estimate the current SOC of battery 310, first, the voltage difference between the detected voltage of battery 310 measured by the sensor and the estimated voltage of battery 310 estimated by the actual electrochemical model is determined. Then, the battery state estimation device may use the voltage difference to determine the amount of state change of battery 310. The battery state estimation device then updates the internal state of the actual electrochemical model based on the amount of state change. Finally, the battery state estimation device can estimate the current SOC of battery 310 based on the updated internal state of the actual electrochemical model.

[0050] Thus, 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 load, through a feedback structure that corrects the internal state of the actual electrochemical model so that the voltage difference between the detected voltage of the battery 310 and the estimated voltage estimated by the actual electrochemical model is minimized.

[0051] Furthermore, the virtual battery part 330 is the part that estimates the end state of charge (SOC) of battery 310, and for this purpose, a virtual battery may be used that assumes that battery 310 has reached a preset voltage (e.g., discharge lower limit voltage) through discharge. The virtual electrochemical model is a model that estimates the state information of the virtual battery by modeling the physical development inside the virtual battery, and the estimated SOC of the virtual battery corresponds to the end state of charge of battery 310. The virtual electrochemical model may have the same physical characteristic parameters as the actual electrochemical model and different internal state information. For example, the physical characteristic parameters may include the characteristics of the active material particles (e.g., size, shape, etc.), the thickness of the electrodes, the thickness of the electrolyte, and physical properties (e.g., electrical conductivity, ionic conductivity, diffusion coefficient, etc.). The internal state information may include the potential within the active material particles, the ion concentration distribution within the active material particles, the potential within the electrodes, and the ion concentration distribution within the electrodes. The input data of the virtual electrochemical model may include information on the moving average current, moving average temperature, and discharge lower limit voltage of battery 310. For example, the moving average current is the moving average current over a predetermined specific time period, and depending on the case, information about the currently applied current, the arithmetic mean current over a predetermined specific time period, and the weighted mean current may be input into the virtual electrochemical model instead of the moving average current. Similarly, information about the moving average temperature, the current temperature, and the weighted mean temperature may be input into the virtual electrochemical model. In addition, current and temperature information determined to have various average characteristics may be input into the virtual electrochemical model without limitation.

[0052] To estimate the end-of-life state of charge (SOC) of battery 310, the voltage difference between the estimated voltage of the virtual battery, estimated by the virtual electrochemical model, and the discharge limit voltage is determined. The battery state estimation device can then use this voltage difference to determine the change in the state of the virtual battery. Based on the change in state, the battery state estimation device can update the internal state of the virtual electrochemical model. Finally, based on the updated internal state of the virtual electrochemical model, the battery state estimation device can estimate the SOC of the virtual battery and determine the end-of-life SOC of battery 310.

[0053] In this way, by using a feedback structure that corrects the internal state of the virtual electrochemical model so that the estimated voltage of the virtual battery estimated by the virtual electrochemical model matches the discharge lower limit voltage, the end state of charge (SOC) of the battery 310 can be estimated with high accuracy while minimizing the complexity and computational load of the model.

[0054] The battery state estimation device then calculates the RSOC of battery 310 based on the current SOC and the end SOC. Since the same principles explained based on formula (2) can be applied to this calculation, a more detailed explanation is omitted.

[0055] Thus, the battery state estimation device can quickly and accurately estimate the RSOC of battery 310 through a dual-cell model based on two electrochemical models and correctors that correct the internal states of each.

[0056] Referring to Figure 4, an example illustrating the operation of a dual-cell model according to one embodiment is shown. The specific numerical values ​​in the graph shown in Figure 4 are embodiments for illustrative purposes only and are not limiting.

[0057] The battery state estimation device determines the RSOC based on the current SOC estimated from the actual electrochemical model 410 and the end SOC estimated from the virtual electrochemical model 420. To minimize the RSOC prediction time, the estimation time for both the current SOC and the end SOC must be minimized. To this end, a method can be used that directly estimates the end SOC by assuming that a virtual battery exists near the discharge limit voltage and correcting the virtual electrochemical model 420 so that the voltage estimated by the virtual electrochemical model 420 corresponding to the virtual battery reaches the discharge limit voltage. In other words, instead of a simulation method that discharges the battery from the current SOC location, a method that directly estimates the end SOC from the virtual electrochemical model 420 near the discharge limit voltage can minimize the estimation time for the end SOC by performing the calculation to determine the end SOC only near the discharge limit voltage.

[0058] Figure 5 is a diagram illustrating the process of estimating the battery state according to one embodiment.

[0059] In the following embodiments, each step may be performed sequentially, but does not necessarily have to be. For example, the order of each step may be changed, and at least two steps may be performed in parallel.

[0060] Referring to Figure 5, a flowchart is shown illustrating how a battery state estimation device estimates battery state information according to one embodiment. The battery state is estimated over multiple periods, and the flowchart shown in Figure 5 illustrates the process of estimating battery state information for each of these periods. First, the process of estimating the current SOC in the actual battery part will be explained.

[0061] In step S510, the battery state estimation device collects battery detection data. The detection data may include the battery's detected voltage, detected current, and detected temperature. For example, the detection data may be stored in a profile format that shows the magnitude change over time.

[0062] In step S520, the estimated voltage and state information (e.g., current SOC) of the battery are determined by an electrochemical model to which the detected current and detected temperature have been input.

[0063] In step S530, the battery state estimation device calculates the voltage difference between the detected battery voltage and the estimated voltage estimated by the electrochemical model. For example, the voltage difference may be determined to be a predetermined moving average voltage over the most recent time period.

[0064] Although not shown separately in Figure 5, in some embodiments, the battery state estimation device determines whether or not the battery state information should be corrected based on whether or not the voltage difference exceeds a threshold voltage. If an error occurs in the electrochemical model, the estimated voltage estimated using the electrochemical model will differ from the battery's detected voltage, and the device determines whether or not correction is necessary based on the voltage difference to prevent the accumulation of errors.

[0065] For example, if the voltage difference exceeds the threshold voltage, the battery state estimation device determines that the battery state information needs to be corrected, and step S540 is then executed. Conversely, if the voltage difference does not exceed the threshold voltage, the battery state estimation device determines that the battery state information does not need to be corrected, and step S510 may be executed again without performing steps S540, S550, and S560.

[0066] In step S540, the battery state estimation device determines the amount of change in the battery state using the voltage difference. For example, the battery state estimation device can determine the amount of change in the battery state based on the voltage difference, the battery's previous state information, and the OCV table. The battery's previous state information may be the state information previously estimated using the electrochemical model in step S520. For example, the amount of change in the battery state may include the change in SOC. Further details will be described later with reference to Figures 7 and 8.

[0067] In step S550, the battery state estimation device updates the electrochemical model by correcting its internal state based on the change in the battery state. For example, the battery state estimation device can update the internal state of the electrochemical model by correcting the ion concentration distribution within the active material particles or within the electrodes based on the change in the battery state. The active material may include the positive and negative electrodes of the battery. The battery state estimation device estimates the battery state information using the electrochemical model with its updated internal state. In this way, a feedback structure that determines the change in the battery state and updates the internal state of the electrochemical model so as to minimize the voltage difference between the detected voltage and the estimated voltage allows for the estimation of battery state information with high accuracy even with limited computational resources. For more detailed information, please refer to Figures 9 to 11 below.

[0068] In step S560, the battery state estimation device determines whether or not to terminate the battery state estimation operation. For example, if the predetermined operating period has not elapsed, step S510 is executed again for the next period. Conversely, if the predetermined operating period has elapsed, the battery state estimation operation is terminated.

[0069] Next, we will explain the process of estimating the final SOC in the virtual battery part. The process of estimating the final SOC will be explained focusing on some differences from the process of estimating the current SOC, which was explained earlier. For example, the electrochemical model used in step S520 is an actual electrochemical model corresponding to an actual battery in the actual battery part, while in the virtual battery part, it is a virtual electrochemical model corresponding to a virtual battery. Also, when calculating the voltage difference in step S530, in the virtual battery part, the voltage difference between the estimated voltage of the virtual battery and the preset discharge lower limit voltage is calculated. The remaining steps can be similarly explained as explained earlier, so a more detailed explanation will be omitted.

[0070] Figure 6 is a diagram illustrating an electrochemical model according to one embodiment.

[0071] Referring to Figure 6, the electrochemical model can estimate the remaining battery capacity by modeling the internal physical developments of the battery, such as the ion concentration and potential. In other words, the electrochemical model is expressed in physical conservation equations relating to the electrochemical reactions that occur at the electrode / electrolyte interface and the conservation of concentration and charge of the electrode / electrolyte, and for this purpose, it uses various model parameters such as shape (e.g., thickness, radius), OCP (Open Circuit Potential), and physical properties (e.g., electrical conductivity, ionic conductivity, diffusion coefficient).

[0072] In an electrochemical model, various state variables such as concentration and potential may be coupled to each other. The estimated battery voltage 610, estimated in the electrochemical model, is the potential difference across the positive and negative electrodes. The potential information for the positive and negative electrodes is influenced by the ion concentration distributions of the positive and negative electrodes, respectively (S620). The SOC630, estimated in the electrochemical model, is the average ion concentration of the positive and negative electrodes.

[0073] Here, the ion concentration distribution refers to either the ion concentration distribution within the electrode 640 or the ion concentration distribution within the active material particles located at a specific position within the electrode 650. The ion concentration distribution within the electrode 640 refers to the surface ion concentration distribution or the average ion concentration distribution of the active material particles located in the direction of the electrode, where the electrode direction refers to 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 separation membrane). Furthermore, the ion concentration distribution within the active material particles 650 refers to the ion concentration distribution inside the active material particles in the direction of the active material particle center, where the direction of the active material particle center refers to the direction connecting the center of the active material particle and the surface of the active material particle.

[0074] As explained earlier, in order to reduce the voltage difference between the detected voltage and the estimated voltage, or the voltage difference between the preset discharge lower limit voltage and the estimated voltage, the ion concentration distributions of the positive and negative electrodes can be shifted while maintaining physical conservation of concentration. Potential information for the positive and negative electrodes can then be derived based on the shifted concentration distributions, and the voltage can be calculated based on the derived potential information for the positive and negative electrodes. The amount of internal state shift at which the voltage difference becomes zero can be derived, and finally, the current SOC or end SOC of the battery can be determined.

[0075] Figures 7 and 8 illustrate the process of determining the amount of change in the state of a battery according to one embodiment.

[0076] Referring to Figure 7, one embodiment shows an example of determining the change in battery state when the detected voltage or discharge limit voltage of the battery is greater than the estimated voltage estimated by the electrochemical model. Here, the estimated voltage may be the battery voltage estimated in a previous period.

[0077] An OCV table according to one embodiment shows the characteristic curve between SOC and open-circuit voltage, which is an intrinsic characteristic of the battery in question. Since the actual electrochemical model and the virtual electrochemical model have the same physical characteristic parameters, the same OCV table may be used when determining the current SOC and the final SOC. When using the OCV table, the ΔSOC that must be corrected changes depending on the SOC value, so previously estimated SOC information from a previous period (e.g., the most recent period) may be used. Here, the SOC information from a previous period may be the estimated SOC of the battery in question for that previous period.

[0078] The estimated OCV, which is the open-circuit voltage corresponding to the SOC information for the previous period, is derived via the characteristic curve of the OCV table. The previously calculated voltage difference is reflected in the estimated OCV, but since the detected voltage or discharge lower limit voltage is greater than the estimated voltage, the voltage difference can be reflected by adding the voltage difference to the estimated OCV. Again, the corrected SOC corresponding to the voltage difference reflection result is determined using the characteristic curve of the OCV table, and the difference between the estimated SOC and the corrected SOC can be determined as the state change amount ΔSOC.

[0079] Referring to Figure 8, one embodiment shows an example of how to determine the amount of change in the battery state when the detected voltage or discharge lower limit voltage of the battery is smaller than the estimated voltage estimated by the electrochemical model.

[0080] As explained earlier, when using the OCV table, previously estimated SOC information for a previous period (e.g., the most recent period) may be used. The estimated OCV, which is the open-circuit voltage corresponding to the SOC information for the previous period, is derived via the characteristic curve of the OCV table. The previously calculated voltage difference is reflected in the estimated OCV, but since the detected voltage or discharge lower limit voltage is smaller than the estimated voltage, the voltage difference can be reflected by subtracting it from the estimated OCV. Again, the corrected SOC corresponding to the voltage difference reflection result is determined using the characteristic curve of the OCV table, and the difference between the estimated SOC and the corrected SOC can be determined as the state change amount ΔSOC.

[0081] Figures 9 to 11 illustrate the process of updating the internal state of a battery model according to one embodiment.

[0082] A battery state estimation device according to one embodiment can update the internal state of an electrochemical model based on the amount of change in the battery state. The electrochemical model is an electrochemical model for estimating battery state information by modeling the internal physical development of the battery. The internal state of the electrochemical model may include the battery 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, and may have a profile form. 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 the amount of change in the battery state, which will be explained with reference to Figures 9 to 11.

[0083] Referring to Figure 9, an example is shown in which the internal state of an electrochemical model is updated by uniformly correcting the ion concentration distribution according to one embodiment. Here, the ion concentration distribution refers to the ion concentration distribution within the active material particles or the ion concentration distribution within the electrodes. When the graph in Figure 9 shows the ion concentration distribution within the active material particles, the horizontal axis of the graph indicates the location within the active material particles, where 0 indicates the center of the active material particles and 1 indicates the surface of the active material particles. Alternatively, when the graph in Figure 9 shows the ion concentration distribution within the electrodes, the horizontal axis of the graph indicates the location within the electrodes, where 0 indicates 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 separation membrane).

[0084] The battery state estimation device converts the change in the battery state into a change in the internal state, and can uniformly reflect the converted change in the internal state of the electrochemical model. Here, the change in the internal state is the change in lithium ion concentration, and the area between the initial internal state and the updated internal state is 910. The method of uniformly updating the internal state assumes that the concentration change is uniform and is applied when the current output from the battery is not large, and is easier to implement than the non-uniform update method described later.

[0085] Furthermore, if the example shown in Figure 9 illustrates an update to the internal state in which the lithium ion concentration increases in the active material of either the positive or negative electrode, the internal state of the other electrode may be updated to decrease the lithium ion concentration by a corresponding amount.

[0086] Referring to Figures 10A and 10B, an example is shown in which the internal state of an electrochemical model is updated by correcting the ion concentration distribution to be non-uniform according to one embodiment.

[0087] Depending on the chemical properties of the battery, if the electrical conductivity is extremely low, the battery current is high, and / or the battery temperature is low, the internal diffusion characteristics become weak, resulting in a characteristic where the gradient of the ion concentration distribution towards the electrodes becomes larger. In this case, the internal state of the electrochemical model may be updated non-uniformly at each position within the active material particles or at each position within the electrodes, taking into account the diffusion characteristics inside the battery.

[0088] In one embodiment, lithium ions may move within the battery based on their diffusion characteristics. Assuming that lithium ions from the positive electrode move to the negative electrode, the lithium ions closest to the negative electrode within the positive electrode will likely move first. If the diffusion characteristics within the battery deteriorate, the movement of lithium ions within the positive electrode will proceed very slowly, and the portion of lithium ions that have escaped to the negative electrode will not be easily filled. As a result, only lithium ions located at the tip of the positive electrode will continuously escape to the negative electrode, and the gradient of the ion concentration distribution will increase, as shown in the graph in Figure 10A. On the other hand, if the diffusion characteristics within the battery improve, lithium ions located within the positive electrode will move quickly to the tip to fill the portion of lithium ions that have escaped to the negative electrode. As a result, the gradient of the ion concentration distribution will decrease, as shown in the graph in Figure 10B. The area 1010,1110 between the initial internal state and the updated internal state represents the change in lithium ion concentration. Since such diffusion characteristics are based on battery state information (e.g., SOC), diffusion characteristics corresponding to the change in battery state are considered, and this will be explained in detail below.

[0089] The battery state estimation device can determine the concentration gradient characteristics based on the diffusion characteristics corresponding to the change in the battery state, and update the internal state of the electrochemical model according to the concentration gradient characteristics. Diffusion coefficients may be derived from an analysis of the diffusion characteristics in the direction in which lithium ions must move (e.g., the direction in which lithium ion concentration increases). For example, a diffusion coefficient between the previous state of affairs (SOC) base diffusion coefficient and the moving SOC may be derived. Then, the internal state of the electrochemical model is updated according to the concentration gradient characteristics predetermined by the diffusion coefficients. For example, if the diffusion coefficient in the direction in which movement must occur decreases, the internal state of the electrochemical model may be updated in the direction in which the concentration gradient increases. Conversely, if the diffusion coefficient in the direction in which movement must occur increases, the internal state of the electrochemical model may be updated in the direction in which the concentration gradient decreases.

[0090] In another embodiment, the electrochemical model is based on the premise that lithium ions simply move between the positive electrode, negative electrode, and electrolyte, while the total amount of lithium ions remains constant. The movement of lithium ions between the positive electrode, negative electrode, and electrolyte is determined based on the diffusion equation, which will be explained in more detail below.

[0091] The battery state estimation device can calculate the diffusion equation of the active material in response to the change in the battery state and update the internal state of the electrochemical model. The internal state of the electrochemical model is updated by calculating the diffusion equation by applying current boundary conditions in the direction in which lithium ions must move (for example, the direction in which the lithium ion concentration increases). The battery state estimation device can calculate the diffusion equation of the active material for the change in the internal state corresponding to the change in state, and update the internal state of the electrochemical model with the ion concentration distribution calculated via the diffusion equation. Since diffusion characteristics are also physical characteristics, the battery state estimation device can non-uniformly update the internal state of the electrochemical model by solving the diffusion spinning equation for the ion concentration distribution.

[0092] Figures 12 to 20 show various examples of estimating RSOC according to one embodiment.

[0093] Referring to Figure 12, the current SOC and end SOC are estimated periodically, and the RSOC may also be estimated periodically based on these. For example, one period may range from several hundred milliseconds (ms) to several seconds (s). To obtain the RSOC, the following steps are performed sequentially: "Estimate current SOC with actual electrochemical model," "Correct current SOC," "Estimate end SOC with virtual electrochemical model," "Correct end SOC," and "Calculate RSOC." The execution time of these five steps is the time it takes to estimate the RSOC. For example, the current SOC is estimated with an actual electrochemical model, where the initial value of the current SOC in the actual electrochemical model may be set from the battery's starting voltage (e.g., buffer voltage). The end SOC is estimated with a virtual electrochemical model, where the initial value of the end SOC in the virtual electrochemical model is the OCV state, so it may be set from 0V.

[0094] Referring to Figure 13, the current SOC and end SOC are not fully calculated in one cycle, but are calculated alternately each cycle. The RSOC is calculated each cycle by using the SOC values ​​calculated in the previous cycle for any SOC values ​​not calculated in the previous cycle. As explained in Figure 12, the execution process-compared computation time was reduced by 50%.

[0095] Referring to Figure 14, by utilizing the characteristic that the end SOC gradually changes compared to the current SOC, the current SOC is calculated N times, and then the end SOC is calculated once. This updates both the current SOC and the end SOC, allowing the RSOC to be calculated at each cycle. As explained in Figure 12, even if the computation time compared to the execution process is reduced by 50%, the accuracy of the current SOC can be maintained.

[0096] Referring to Figure 15, if the device contains multiple batteries, the current SOC and end SOC can be calculated for each of the batteries for each period, and the RSOC can also be calculated for each period.

[0097] Referring to Figure 16, the end-of-life (SOC) for each of the multiple batteries is calculated for each cycle, but the current SOC is calculated for only one of the multiple batteries in each cycle. For the remaining batteries, the RSOC is calculated for each cycle using the previously updated current SOC. The current SOC may be calculated once per cycle, and the end-of-life (SOC) may be calculated N times.

[0098] Referring to Figure 17, the current SOC for each of the multiple batteries is calculated for each cycle, but the end SOC for one cycle is calculated for only one of the multiple batteries, and the RSOC for the remaining batteries is calculated for each cycle using the previously updated end SOC. The current SOC may be calculated N times and the end SOC may be calculated once per cycle.

[0099] Referring to Figure 18, the current SOC and end SOC are calculated for only one of the multiple batteries in a single cycle, and the RSOC is calculated only for that battery. The RSOC of the remaining batteries may be the previously updated RSOC. The current SOC may be calculated once and the end SOC may be calculated once per cycle.

[0100] Referring to Figure 19, the current SOC for each of the multiple batteries is calculated for each cycle, but the final SOC for one cycle is calculated only for a representative battery among the multiple batteries, and the remaining batteries use the final SOC of the representative battery to calculate the RSOC for each cycle. Here, the multiple batteries correspond to batteries with the same cell characteristics, and since there is not a significant difference in the final SOC from battery to battery, the final SOC may be calculated only for the representative battery. The battery with the average current SOC (or final SOC) among the multiple batteries may be selected as the representative battery. The current SOC may be calculated N times and the final SOC may be calculated once per cycle.

[0101] Referring to Figure 20, in a single-battery structure, the current SOC may be calculated once per cycle, the end SOC may be calculated N times, and the RSOC may be calculated N times per cycle. As explained earlier, the end SOC varies depending on the current applied to the battery, and the applied current varies depending on the operating type of the device. Through such an execution process, the remaining usable time for various cases such as music playback, video playback, gameplay, and standby operation can be calculated and provided to the user based on the current battery state. The current SOC may be calculated once per cycle, and the end SOC may be calculated N times.

[0102] Figure 21 shows a battery state estimation method according to one embodiment.

[0103] In the following embodiments, each step is performed sequentially, but it does not necessarily have to be done sequentially. For example, the order of each step may be changed, and at least two steps may be performed in parallel.

[0104] Referring to Figure 21, a battery state estimation method is shown that is performed by a processor provided in a battery state estimation device according to one embodiment.

[0105] In step S2110, the battery state estimation device estimates the current state of charge (SOC) of the target battery by correcting the first electrochemical model using a first voltage difference between the measured voltage of the target battery and the estimated voltage of the target battery estimated from the first electrochemical model corresponding to the target battery. In step S2120, the battery state estimation device estimates the end state of charge (SOC) of the target battery by correcting the second electrochemical model using a second voltage difference between the estimated voltage of the virtual battery estimated by the second electrochemical model and a preset voltage. The second electrochemical model corresponds to a virtual battery that assumes the target battery has reached a preset voltage through discharge. In step S2130, the battery state estimation device estimates the rate of charge (RSOC) of the target battery based on the current SOC and end state of charge of the target battery.

[0106] Since the aforementioned points can be directly applied to each step shown in Figure 21 by referring to Figures 1 to 20, a more detailed explanation will be omitted.

[0107] Figure 22 shows a battery state estimation method according to another embodiment.

[0108] In the following embodiments, each step may be performed sequentially, but does not necessarily have to be. For example, the order of each step may be changed, and at least two steps may be performed in parallel.

[0109] Referring to Figure 22, a battery state estimation method is shown that is performed by a processor provided in a battery state estimation device according to another embodiment.

[0110] In step S2210, the battery state estimation device determines the change in state of the virtual battery using the voltage difference between the estimated voltage of the virtual battery estimated from an electrochemical model corresponding to a virtual battery (assuming the target battery has reached a preset voltage due to discharge) and the preset voltage. In step S2220, the battery state estimation device updates the internal state of the electrochemical model based on the change in state of the virtual battery. In step S2230, the battery state estimation device estimates the end state of the target battery by estimating the state information of the virtual battery based on the internal state of the electrochemical model.

[0111] Since the aforementioned points can be directly applied to each step shown in Figure 22 by referring to Figures 1 to 20, a more detailed explanation will be omitted.

[0112] Figure 23 shows a battery state estimation device according to one embodiment.

[0113] Referring to Figure 23, a battery state estimation device 2300 according to one embodiment includes a memory 2310 and a processor 2320. The memory 2310 and the processor 2320 communicate via a bus 2330.

[0114] Memory 2310 may contain instruction words that can be read by the computer. The processor 2320 can perform the operations described above by executing the instruction words stored in memory 2310. Memory 2310 may be volatile memory or non-volatile memory.

[0115] The processor 2320 may be a device that executes instructions or programs, or controls the battery state estimation device 2300. The processor 2320 estimates the current state of charge (SOC) of the target battery by correcting the first electrochemical model using the 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 processor 2320 then estimates the end state of charge (SOC) of the target battery by correcting the second electrochemical model using the voltage difference between the estimated voltage of the virtual battery estimated by the second electrochemical model and a preset voltage. The processor 2320 then estimates the rate of return (RSOC) of the target battery based on the current SOC and end state of charge (SOC) of the target battery.

[0116] In one embodiment, the battery state estimation device 2300 may be applied to a BMS (Battery Management System) that includes a function for estimating the State of Charge (SOC) of a secondary battery, electronic equipment using a secondary battery, means of transport, or power storage devices based on a secondary battery. Furthermore, the battery state estimation device 2300 efficiently shortens the time required to calculate the end SOC of a battery through an end SOC estimation method based on a virtual battery model, and may be installed in low-spec equipment such as a PMIC (Power Management Integrated circuit). In addition, the battery state estimation device 2300 may be applied to rapid charging that reflects the internal state information of an electrochemical model, automatic updating of the degradation basis of the electrochemical model, prediction of internal short circuits in batteries, and a battery fuel gauge.

[0117] Furthermore, the battery state estimation device 2300 may be applied to various computing devices such as smartphones, tablets, laptops, and personal computers; various wearable devices such as smartwatches and smart glasses; various home appliances such as smart speakers, smart TVs, and smart refrigerators; smart automobiles, robots, drones, WADs (Walking Assist Devices), and IoT (Internet of Things) devices.

[0118] In addition, the battery state estimation device 2300 can process the operations described above.

[0119] Figure 24 is a diagram illustrating a mobile device according to one embodiment.

[0120] Referring to Figure 24, the mobile device 2400 includes a battery pack 2410. The mobile device 2400 is a device that uses the battery pack 2410 as a power source. The mobile device 2400 is a portable terminal, and may be, for example, a smartphone. In Figure 24, for the sake of explanation, the mobile device 2400 is shown as a smartphone, but various other terminals such as notebook computers, tablet PCs, and wearable devices may also be applied without limitation. The battery pack 2410 includes a BMS and battery cells (or battery modules).

[0121] According to one embodiment, the mobile device 2400 includes a battery state estimation device. The battery state estimation device estimates the RSOC of the battery pack 2410 based on the current SOC and end SOC of the battery pack 2410 (or the battery cells within the battery pack 2410).

[0122] Since the matters described with reference to Figures 1 to 23 can also be applied to the matters described based on Figure 24, a detailed explanation will be omitted.

[0123] Figures 25 and 26 are diagrams illustrating a vehicle according to one embodiment.

[0124] Referring to Figure 25, the vehicle 2500 includes a battery pack 2510 and a battery management system 2520. The vehicle 2500 can use the battery pack 2510 as a power source. The vehicle 2500 may be, for example, an electric vehicle or a hybrid vehicle.

[0125] The battery pack 2510 may include multiple battery modules. Each battery module may include multiple battery cells.

[0126] The battery management system 2520 monitors the battery pack 2510 for any abnormalities and prevents it from being overcharged or over-discharged. The battery management system 2520 may also perform thermal control on the battery pack 2510 if its temperature exceeds a first temperature (e.g., 40°C) or falls below a second temperature (e.g., -10°C). Furthermore, the battery management system 2520 performs cell balancing to ensure that the charge levels of the battery cells within the battery pack 2510 are evenly distributed.

[0127] According to one embodiment, the battery management system 2520 may include the battery state estimation device described above, and the state information of each battery cell contained in the battery pack 2510 or the state information of the battery pack 2510 can be determined via the battery state estimation device. The battery management system 2520 may determine the maximum, minimum, or average value of the state information of each battery cell as the state information of the battery pack 2510.

[0128] The battery management system 2520 transmits the status information of the battery pack 2510 to the ECU (Electronic Control Unit) or VCU (Vehicle Control Unit) of the vehicle 2500. The ECU or VCU of the vehicle 2500 outputs the status information of the battery pack 2510 to the vehicle 2500's display. As shown in Figure 26, the ECU or VCU may display the status information of the battery pack 2510 on the instrument panel 2610 inside the vehicle 2500. Alternatively, the ECU or VCU may display the remaining mileage, etc., determined based on the estimated status information, on the instrument panel 2610. Although not shown in Figure 26, the ECU or VCU can display the status information of the battery pack 2510, the remaining mileage, etc., on the vehicle 2500's head-up display.

[0129] Since the matters described with reference to Figures 1 to 23 may also apply to the matters explained with reference to Figures 25 and 26, a detailed explanation will be omitted.

[0130] The embodiments described above are embodied in hardware components, software components, or combinations of hardware and software components. For example, the devices and components described in these embodiments are embodied using one or more general-purpose or special-purpose computers, such as a processor, controller, ALU (arithmetic logic unit), digital signal processor, microcomputer, FPA (field programmable array), PLU (programmable logic unit), microprocessor, or different devices that execute and respond to instructions. The processing device executes an operating system (OS) and one or more software applications that run on the OS. The processing device also accesses, stores, manipulates, processes, and generates data in response to the execution of the software. For convenience of understanding, the processing device may sometimes be described as being used as a single unit, but a person with ordinary skill in the art will understand that the processing device includes multiple processing elements and / or multiple types of processing elements. For example, the processing device includes multiple processors or one processor and one controller. Other processing configurations, such as parallel processors, are also possible.

[0131] Software includes computer programs, code, instructions, or a combination of one or more of these, which can configure a processing unit to operate as desired, or instruct the processing unit independently or in combination. Software and / or data can be permanently or temporarily embodied in any type of machine, component, physical device, virtual device, computer storage medium or device, or transmitted signal wave, for interpretation by a processing unit or for providing instructions or data to a processing unit. Software can be distributed across a network of computer systems and stored and executed in a distributed manner. Software and data can be stored on a recording medium readable by one or more computers.

[0132] The method according to this embodiment is embodied in the form of program instructions that are implemented via various computer means and recorded on a computer-readable recording medium. The recording medium includes program instructions, data files, data structures, etc., individually or in combination. The recording medium and program instructions may be specifically designed and configured for the purposes of the present invention, or they may be known and usable by those skilled in the art who have technology in the field of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floppy disks, and hardware devices specifically configured to store and execute program instructions, such as ROMs, RAMs, and flash memory. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform the operations shown in the present invention, and vice versa.

[0133] As described above, although embodiments have been illustrated with limited drawings, a person with ordinary skill in the art can apply various technical modifications and variations based on the above description. For example, the described techniques may be performed in a different order than described, and / or the components of the described systems, structures, devices, circuits, etc. may be combined or assembled in a different manner than described, or replaced or substituted with other components or equivalents, and still achieve suitable results.

Claims

1. The steps include: estimating the current state of charge (SOC) of the target battery by correcting the first electrochemical model 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 corresponding to the target battery; The steps include: estimating the end SOC of the target battery by correcting the second electrochemical model using a second voltage difference between the estimated voltage of the virtual battery estimated by the second electrochemical model and a preset voltage; The steps include: estimating the RSOC of the target battery based on the current SOC and end SOC of the target battery; Includes, The second electrochemical model is based on the virtual battery corresponding to the target battery that has reached the preset voltage by discharge, Battery status estimation method.

2. The battery state estimation method according to claim 1, wherein the first electrochemical model and the second electrochemical model include the same physical characteristic parameters and different internal state information.

3. The battery state estimation method according to claim 1 or 2, wherein the termination SOC is the SOC when the target battery is discharged by the current output from the target battery and reaches the preset voltage.

4. The battery state estimation method according to claim 1, wherein the step of estimating the termination SOC involves determining the amount of state change of the virtual battery using the second voltage difference, updating the internal state of the second electrochemical model based on the amount of state change of the virtual battery, and estimating the state information of the virtual battery based on the internal state of the second electrochemical model to estimate the termination SOC of the target battery.

5. The battery state estimation method according to claim 4, wherein the amount of state change of the virtual battery is based on the second voltage difference, previous state information estimated in advance by the second electrochemical model, and the OCV table.

6. The battery state estimation method according to claim 5, wherein the amount of change in the state of the virtual battery is determined by obtaining an open-circuit voltage corresponding to the previous state information based on the OCV table and reflecting the second voltage difference in the open-circuit voltage.

7. The battery state estimation method according to any one of claims 4 to 6, 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 electrodes based on the amount of change in the state of the virtual battery.

8. The battery state estimation method according to any one of claims 4 to 6, wherein the internal state of the second electrochemical model includes one or more of the positive electrode lithium ion concentration distribution, the negative electrode lithium ion concentration distribution, and the electrolyte lithium ion concentration distribution of the virtual battery.

9. The battery state estimation method according to any one of claims 1 to 8, wherein the step of estimating the RSOC is to estimate the RSOC based on one of the current SOC and the end SOC estimated in the current cycle and the other one estimated in a previous cycle.

10. The battery state estimation method according to any one of claims 1 to 9, wherein the step of estimating the termination SOC is performed after the step of estimating the current SOC has been performed a predetermined number of times.

11. The aforementioned target battery is one of several batteries, The step of estimating the current SOC is performed for each of the plurality of batteries, The step of estimating the termination SOC is performed on a representative battery among the plurality of batteries, The battery state estimation method according to any one of claims 1 to 10, wherein the step of estimating the RSOC is to estimate the RSOC of each of the plurality of batteries based on the current SOC estimated for each of the plurality of batteries and the end SOC estimated for the representative battery.

12. The step of estimating the termination SOC involves estimating the termination SOC using each of a plurality of virtual batteries, each of which is assumed to have been discharged at a different current and reached the predetermined voltage. The battery state estimation method according to claim 1, wherein the step of estimating the RSOC is to estimate the RSOC of the target battery based on the current SOC and the end SOC of the target battery, respectively.

13. The battery state estimation method according to any one of claims 1 to 12, wherein the preset voltage is the lower discharge voltage of the target battery.

14. The battery state estimation method according to any one of claims 1 to 13, wherein the target battery is a battery cell, a battery module, or a battery pack.

15. A step of determining the amount of state change of the virtual battery using the voltage difference between the estimated voltage of the virtual battery estimated by an electrochemical model corresponding to the virtual battery corresponding to the target battery that has reached a preset voltage through discharge and the preset voltage, The steps include updating the internal state of the electrochemical model based on the amount of change in the state of the virtual battery, The steps include: estimating the end state of care (SOC) of the target battery by estimating the state information of the virtual battery based on the internal state of the electrochemical model; A battery state estimation method including the following.

16. The battery state estimation method according to claim 15, wherein the step of determining the amount of state change of the virtual battery is to determine the amount of state change of the virtual battery based on the voltage difference, previous state information estimated in advance by the electrochemical model, and the OCV table.

17. The battery state estimation method according to claim 15 or 16, wherein the step of updating the internal state of the electrochemical model is to 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 the amount of change in the state of the virtual battery.

18. A computer-readable storage medium on which a program for performing the method described in any one of claims 1 to 17 is recorded.

19. A memory that stores a first electrochemical model corresponding to a target battery and a second electrochemical model based on a virtual battery corresponding to the target battery that has reached a preset voltage during discharge, A processor for estimating the RSOC of the target battery, Includes, The aforementioned processor, The current SOC of the target battery is estimated by correcting the first electrochemical model using the 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 end SOC of the target battery is estimated by correcting the second electrochemical model using the voltage difference between the estimated voltage of the virtual battery estimated by the second electrochemical model and the preset voltage. Based on the current SOC and end SOC of the target battery, the RSOC of the target battery is estimated. Battery status estimation device.

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