Battery state estimation method and device

Abstract

To provide a method and device for estimating the battery state.SOLUTION: A battery state estimation method disclosed herein comprises: determining a state variation of a battery using a voltage difference between a sensed voltage of the battery and an estimated voltage of the battery estimated by an electrochemical model corresponding to the battery; updating the internal state of the electrochemical model based on the state variation of the battery; and estimating state information of the battery based on the updated internal state of the electrochemical model.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a method and apparatus for estimating a battery state. [Background technology]

[0002] There are various ways to estimate the state of a battery. As an example, the state of a battery may be estimated by integrating the current of the battery, or may be estimated using a battery model (e.g., an electrical circuit model or an electrochemical model). Summary of the Invention [Problem to be solved by the invention]

[0003] An object of the method and apparatus for estimating a battery state according to an embodiment is to correct an estimated SOC value of a battery by simply using an electrochemical model with a small amount of calculation. [Means for solving the problem]

[0004] A battery state estimation method according to one embodiment includes the steps of: determining a state change amount of the battery using a voltage difference between a detected voltage of the battery and an estimated voltage of the battery estimated from an electrochemical model corresponding to the battery; updating an internal state of the electrochemical model based on the state change amount of the battery; and estimating state information of the battery based on the internal state of the electrochemical model.

[0005] In an embodiment of the battery state estimation method, the step of determining the amount of change in the state of the battery may determine the amount of change in the state of the battery based on the voltage difference, previous state information previously estimated by the electrochemical model, and an OCV table.

[0006] In one embodiment of the battery state estimating method, the step of determining the amount of change in the state of the battery may include obtaining an open circuit voltage corresponding to the previous state information based on the OCV table, and reflecting the voltage difference in the open circuit voltage to determine the amount of change in the state of the battery.

[0007] In one embodiment of a battery state estimation method, the step of updating the internal state of the electrochemical model 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 electrode based on the amount of change in the state of the battery.

[0008] In one embodiment of a battery state estimation method, the step of updating the internal state of the electrochemical model can update the internal state of the electrochemical model by uniformly correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode according to the amount of change in the battery state.

[0009] In one embodiment of a battery state estimation method, the step of updating the internal state of the electrochemical model may determine a concentration gradient characteristic based on a diffusion characteristic due to a state change amount of the battery, correct an ion concentration distribution of the battery according to the concentration gradient characteristic, and update the internal state of the electrochemical model.

[0010] In one embodiment of a battery state estimation method, the step of updating the internal state of the electrochemical model can calculate a diffusion equation of an active material according to a change in the state of the battery, and correct the ion concentration distribution in the active material particles or the ion concentration distribution in an electrode to update the internal state of the electrochemical model.

[0011] In one embodiment of the battery state estimation method, the internal state of the electrochemical model may include any one or combination of a positive electrode lithium ion concentration distribution, a negative electrode lithium ion concentration distribution, and an electrolyte lithium ion concentration distribution of the battery.

[0012] According to an embodiment, the method for estimating a battery state may further include determining whether a voltage difference between the detected voltage of the battery and an estimated voltage of the battery obtained from the electrochemical model exceeds a threshold voltage difference.

[0013] In one embodiment of the battery state estimation method, the electrochemical model estimates state information of a target battery of which state information is to be estimated among a plurality of batteries, the detected voltage may be a voltage measured at the target battery, and the estimated voltage may be a voltage previously estimated for another battery among the plurality of batteries by the electrochemical model.

[0014] In the battery state estimation method according to an embodiment, the battery may be a battery cell, a battery module, or a battery pack.

[0015] In one embodiment of the battery state estimation method, the estimated state information of the battery may include any one of a State of Charge (SOC), a State of Health (SOH), and abnormal state information, or a combination thereof.

[0016] Another battery state estimation method according to one embodiment includes the steps of acquiring detection data including a detected voltage of a battery, acquiring an estimated voltage of the battery from the detection data using an electrochemical model corresponding to the battery, calculating a first voltage difference between the detected voltage and the estimated voltage of the battery, selecting a correction method for the electrochemical model based on the voltage difference, correcting an internal state of the electrochemical model or a detected current input from the electrochemical model by applying the selected correction method to the electrochemical model, and estimating state information of the battery using the electrochemical model to which the correction method has been applied.

[0017] In one embodiment of a battery state estimation method, the step of correcting the internal state of the electrochemical model or the detected current input from the electrochemical model using the selected correction method may include updating the internal state of the electrochemical model using a state change amount of the battery determined based on a first voltage difference between a detected voltage of the battery in a current period and an estimated voltage of the battery estimated from the electrochemical model, or correcting the detected current of the battery in the current period input from the electrochemical model using a capacity error corresponding to a second voltage difference between the detected voltage and the estimated voltage of the battery in a previous period.

[0018] In one embodiment of a battery state estimation method, the step of determining a correction method for the electrochemical model based on the voltage difference may select a correction method for correcting an internal state of the electrochemical model when the first voltage difference exceeds a predetermined threshold voltage difference, and may select a correction method for correcting a detection current input from the electrochemical model when the voltage difference is equal to or less than the predetermined threshold voltage difference.

[0019] In one embodiment of a battery state estimation method, the step of determining a correction method for the electrochemical model can select a correction method for the electrochemical model such that a correction method for correcting a detection current input from the electrochemical model is performed more frequently than a correction method for correcting an internal state of the electrochemical model.

[0020] A battery state estimation device according to one embodiment includes a processor that determines a state change amount of the battery using a voltage difference between a detected voltage of the battery and an estimated voltage of the battery estimated by a stored electrochemical model corresponding to the battery, updates an internal state of the electrochemical model based on the state change amount of the battery, and estimates state information of the battery based on the internal state of the electrochemical model.

[0021] The battery state estimating apparatus according to an embodiment may further include a memory for storing the electrochemical model.

[0022] The battery state estimation apparatus according to one embodiment may be a vehicle or a mobile device and may be powered by the battery. Effect of the Invention

[0023] According to an embodiment of the present invention, a battery state estimation method and apparatus can estimate battery state information with high accuracy even with a small amount of calculations by using a feedback structure that determines a battery state change amount and updates an internal state of a battery model so that a voltage difference between a detected voltage of the battery and an estimated voltage estimated from an electrochemical model is minimized. [Brief description of the drawings]

[0024] [Figure 1] FIG. 1 is a diagram for explaining a battery system according to an embodiment. [Diagram 2] FIG. 1 is a diagram for explaining a battery system according to an embodiment. [Diagram 3]5 is a diagram illustrating a process of estimating a battery state according to an embodiment; FIG. [Figure 4] FIG. 13 is a diagram illustrating a process of estimating a battery state according to another embodiment. [Diagram 5] FIG. 13 is a diagram illustrating a process of estimating a battery state according to another embodiment. [Figure 6] FIG. 2 is a diagram for explaining a battery model according to an embodiment. [Figure 7] 11 is a diagram for explaining a process of determining a state change amount of a battery according to an embodiment; FIG. [Figure 8] 11 is a diagram for explaining a process of determining a state change amount of a battery according to an embodiment; FIG. [Figure 9] 11 is a diagram illustrating a process of updating an internal state of a battery model according to an embodiment. FIG. [Figure 10A] 1 illustrates an example of correcting for non-uniform ion concentration distribution and updating the internal state of an electrochemical model according to one embodiment. [Figure 10B] 1 illustrates an example of correcting for non-uniform ion concentration distribution and updating the internal state of an electrochemical model according to one embodiment. [Figure 11] 11 is a diagram illustrating a process of updating an internal state of a battery model according to an embodiment. FIG. [Figure 12A] FIG. 13 is a diagram illustrating a battery state estimation method according to another embodiment. [Figure 12B] FIG. 13 is a diagram illustrating a battery state estimation method according to another embodiment. [Figure 13] 1 is a diagram illustrating a battery state estimation device according to an embodiment. [Figure 14] FIG. 1 is a diagram for explaining a vehicle according to an embodiment. [Figure 15] FIG. 1 is a diagram for explaining a vehicle according to an embodiment. [Figure 16] FIG. 1 is a diagram illustrating a mobile device according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] The specific structural or functional description of the embodiment is disclosed for the purpose of illustration only and may be modified in various forms. Therefore, the embodiment is not limited to the specific disclosed form, and the scope of the present specification includes modifications, equivalents, or alternatives within the technical spirit.

[0026] Although terms such as first or second may be used to describe multiple components, such terms should be construed only for the purpose of distinguishing one component from the other components. For example, a first component may be named as a second component, and similarly, a second component may be named as a first component.

[0027] When a component is referred to as being "coupled" or "connected" to another component, it should be understood that the component is directly coupled or connected to the other component, but that there may be other components in between.

[0028] The singular expression includes the plural expression unless the context clearly indicates otherwise. In this specification, the terms "comprise" or "have" and the like indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, and should be understood as not precluding the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

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

[0030] Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. In the description with reference to the accompanying drawings, the same reference numerals will be used to refer to the same components regardless of the reference numerals, and redundant description thereof will be omitted.

[0031] 1 and 2 are diagrams for explaining a battery system according to an embodiment.

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

[0033] Each of the batteries 110-1 to 110-n may be a battery cell, a battery module, or a battery pack.

[0034] The battery state estimation device 120 detects each of the batteries 110-1 to 110-n using one or more sensors. In other words, the battery state estimation device 120 collects detection data for each of the batteries 110-1 to 110-n. For example, the detection data includes voltage data, current data, and / or temperature data.

[0035] The battery state estimation device 120 estimates state information of each of the batteries 110-1 to 110-n based on the detection data, and outputs the result. The state information includes, for example, SOC (State of Charge), SOH (State of Health), and / or abnormality state information. The battery model used when estimating the state information is an electrochemical model, which will be described later with reference to FIG. 2.

[0036] Referring to FIG. 2, an illustration of estimating state information using a battery model according to one embodiment is shown.

[0037] The battery state estimation device 120 estimates state information of the battery 110 using an electrochemical model corresponding to the battery 110. For example, the battery 110 corresponds to any one of the batteries 110-1 to 110-n shown in Fig. 1. The electrochemical model is a model that estimates the state information of the battery by modeling internal physical phenomena such as the potential of the battery, ion concentration distribution, etc.

[0038] The accuracy of estimating the state information of the battery 110 may affect optimal operation and control of the battery 110. When estimating the state information using an electrochemical model, an error may occur between sensor information measuring current, voltage, and temperature data input to the electrochemical model and the state information calculated by the modeling method, so error correction is performed.

[0039] First, a voltage difference between a detected voltage of the battery 110 measured by a sensor and an estimated voltage of the battery 110 estimated from an electrochemical model is determined. Then, the battery state estimation device 120 determines a state change amount of the battery 110 using the voltage difference. Then, the battery state estimation device 120 updates an internal state of the electrochemical model (e.g., a potential of the battery, an ion concentration distribution, etc.) based on the state change amount. Then, the battery state estimation device 120 can estimate state information of the battery 110 based on the updated internal state of the electrochemical model. In this way, the battery state estimation device 120 determines a state change amount of the battery so that a voltage difference between the detected voltage of the battery 110 and an estimated voltage estimated from the electrochemical model is minimized, and a feedback structure that updates the internal state of the electrochemical model allows the battery state information to be estimated with high accuracy without increasing the complexity of the model and the amount of calculation.

[0040] FIG. 3 is a diagram for explaining a process of estimating a battery state according to an embodiment.

[0041] Referring to Fig. 3, a flow chart is shown in which a battery state estimation device determines battery state information according to an embodiment. The battery state is estimated for a number of periods, and the flow chart shown in Fig. 3 shows a process of estimating battery state information for each period. For convenience of explanation, this case is referred to as a single cell model.

[0042] In step S310, the battery state estimating device collects detection data of the battery. The detection data includes a detected voltage, a detected current, and a detected temperature of the battery. For example, the detection data is stored in the form of a profile showing a change in magnitude over time.

[0043] In step S320, an estimated voltage and state of the battery (eg, SOC) are determined using an electrochemical model that takes into account the sensed current and temperature.

[0044] In step S330, the battery state estimator calculates a voltage difference between the detected voltage of the battery and the estimated voltage estimated from the electrochemical model, for example, the voltage difference is determined to be a running average voltage during a predetermined recent time period.

[0045] Although not shown in FIG 3, in accordance with an embodiment, the battery state estimating device determines whether correction of the battery state information is necessary based on whether the voltage difference exceeds a threshold voltage difference. If an error occurs in the electrochemical model, the estimated voltage estimated using the electrochemical model may vary substantially from the detected voltage of the battery, or may vary to such an extent that the estimated voltage becomes excessively inaccurate. Therefore, in order to prevent accumulation of errors, the necessity of correction is determined based on the voltage difference.

[0046] For example, if the voltage difference exceeds the threshold voltage difference, the battery state estimation device determines that the battery state information needs to be corrected, and step S340 is subsequently executed. Conversely, if the voltage difference does not exceed the threshold voltage difference, the battery state estimation device determines that the battery state information does not need to be corrected, and step S310 is executed again without executing steps S340, S350, and S360.

[0047] In step S340, the battery state estimating device determines the amount of change in the battery state using the voltage difference. For example, the battery state estimating device may determine the amount of change in the battery state 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 step S320. For example, the amount of change in the battery state includes an amount of change in SOC. Details will be described later with reference to FIGS. 7 and 8.

[0048] In step S350, the battery state estimation device updates the electrochemical model by correcting the internal state of the electrochemical model based on the amount of change in the state of the battery. 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 state of the battery. The active material includes the positive electrode and the negative electrode of the battery. The battery state estimation device can estimate the state information of the battery by using the electrochemical model with the updated internal state. In this way, the amount of change in the state of the battery is determined so that the voltage difference between the detected voltage of the battery and the estimated voltage estimated from the electrochemical model is minimized, and the feedback structure that updates the internal state of the electrochemical model allows the battery state information to be estimated with high accuracy even with a small amount of calculation. This will be described in more detail later with reference to Figs. 9 to 11.

[0049] In step S360, the battery state estimation device determines whether to end the battery state estimation operation. For example, if the predetermined operation period has not elapsed, step S310 is next performed for the next period. On the other hand, if the predetermined operation period has elapsed, the battery state estimation operation is ended.

[0050] 4 and 5 are diagrams for explaining a process of estimating a battery state according to another embodiment.

[0051] According to another embodiment, the battery state estimation device may determine state information of a plurality of batteries for each of a plurality of time periods. A target battery for which state information of the battery is to be estimated is selected from among the plurality of batteries for each of a plurality of time periods using an electrochemical model. In other words, in each time period, the battery state estimation device may determine state information of the target battery using the electrochemical model, and determine state information of the remaining non-target batteries using a current integration method. Here, the current integration method may be a method of estimating the remaining capacity of a battery by integrating an amount of current charged or discharged via a current sensor located at the end of the battery.

[0052] In the next period, the target battery whose state information is estimated based on the electrochemical model is also changed to another battery. For example, the target battery set in a first period among the multiple periods may be set as a non-target battery in a second period, and the non-target battery in the first period may be set as the target battery in the second period.

[0053] In this way, the target batteries are sequentially designated according to a predetermined order, and the battery state information is estimated by the electrochemical model, so that the battery state can be estimated efficiently, quickly, and with high accuracy without burdening the calculation amount even when the electrochemical model requiring a relatively large amount of calculation is used. For convenience of explanation, this case is called a cell conversion model. An example of the cell conversion model will be described later with reference to FIG. 4 and FIG. 5.

[0054] Referring to FIG. 4, an illustration is shown of a battery state estimating apparatus determining state information of a target battery at each of a number of time periods according to another embodiment.

[0055] Period T 1 In the period T 1 From period T 2, the target battery is switched from battery 1 to battery 2, and the detected voltage 420 of battery 2, not the detected voltage 410 of battery 1, is input to the electrochemical model. In other words, there is a discontinuity between the detected voltages 410 and 420 input to the electrochemical model at the time of switching. In a state where such a discontinuity exists, if the electrochemical model derives and outputs the state information of battery 2 from the detected voltage 420, the output of the electrochemical model will be, as shown in graph 430, a period T 1 and period T 2 Such discontinuities are applied to the initial error of the electrochemical model when estimating the state information of battery 2, and therefore can be corrected. The corrected output of the electrochemical model becomes continuous as shown in graph 440 by the correction described below.

[0056] 5, a flow chart is shown in which a battery state estimation device determines state information of a converted target battery using an electrochemical model when a target battery is converted to another battery according to another embodiment. In the cell conversion model, the battery state estimation method described with reference to FIG. 3 is also applied while the target battery is not converted to another battery.

[0057] In step S510, the battery state estimation device collects data of the previous battery and the current battery. The current battery is a battery selected as a target battery in the current period, and the previous battery is a battery selected as a target battery in the previous period. For example, the battery state estimation device collects detection data of the current battery and collects detection data and / or estimated voltage of the previous battery. The detection data may include detected voltage and detected current, and may be stored in the form of a profile indicating a change in magnitude over time. The estimated voltage of the previous battery is the voltage of the previous battery estimated by an electrochemical model in the previous period.

[0058] In step S520, the battery state estimator calculates a voltage difference between the detected voltage of the target battery and the detected or estimated voltage of the previous battery, for example, the voltage difference is determined as a running average voltage during a predetermined recent time period.

[0059] Although not shown separately in Fig. 5, according to an embodiment, the battery state estimation device determines whether correction of battery state information is necessary based on whether the voltage difference exceeds a threshold voltage difference. Even if the target battery is not converted, the estimated voltage differs from the detected voltage of the target battery due to an error generated in the electrochemical model, and whether correction is necessary is determined based on the voltage difference to prevent accumulation of errors.

[0060] For example, if the voltage difference exceeds the threshold voltage difference, the battery state estimation device determines that the battery state information needs to be corrected, and step S530 is then performed. Conversely, if the voltage difference does not exceed the threshold voltage difference, the battery state estimation device determines that the battery state information does not need to be corrected, and can estimate the state information of the target battery using the electrochemical model without performing steps S530 and S540.

[0061] In step S530, the battery state estimation device determines a state change amount of the target battery using the voltage difference. For example, the battery state estimation device may determine a state change amount of the target battery based on the voltage difference, previous state information estimated in advance in a previous period, and an OCV table. The previous state information may be state information estimated from an electrochemical model for a previous battery selected as a target battery in a previous period. The previous battery selected as a target battery in a previous period is a non-target battery in a current period. The state change amount of the target battery may include a SOC change amount, and details thereof will be described later with reference to FIGS. 7 and 8.

[0062] In step S540, the battery state estimation device updates the electrochemical model by correcting the internal state of the electrochemical model based on the amount of change in the state of the target battery. For example, the battery state estimation device updates 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 amount of change in the state of the target battery, the details of which will be described later with reference to Figs. 9 to 11.

[0063] The battery state estimation device estimates state information of the target battery using the electrochemical model with the internal state corrected. The battery state estimation device can also estimate the voltage of the target battery or the current battery, or attributes related to or indicating the voltage of the target battery or the current battery, without any restrictions, based on the electrochemical model.

[0064] FIG. 6 is a diagram for explaining an electrochemical model according to one embodiment.

[0065] 6, the electrochemical model can estimate the remaining battery capacity by modeling the internal physical phenomena of the battery, such as the ion concentration and potential of the battery. In other words, the electrochemical model is expressed as a physical conservation equation related to the electrochemical reaction occurring at the electrode / electrolyte interface, the electrode / electrolyte concentration, and charge conservation, and uses various model parameters for this purpose, such as the shape (e.g., thickness, radius, etc.), OCP (Open Circuit Potential), and physical properties (e.g., electrical conductivity, ionic conductivity, diffusion coefficient, etc.).

[0066] In the electrochemical model, various state variables such as concentration, potential, etc. are coupled to each other. The estimated voltage 610 of the battery estimated from the electrochemical model is the potential difference between the positive and negative electrodes. The potential information of each of the positive and negative electrodes is affected by the distribution of the ion concentration of each of the positive and negative electrodes (620). The SOC 630 estimated from the electrochemical model is the average concentration of ions in the positive and negative electrodes.

[0067] Here, the ion concentration distribution indicates an ion concentration distribution 640 in an electrode or an ion concentration distribution 650 in an active material particle present at a specific position in the electrode. The ion concentration distribution 640 in an electrode indicates a surface ion concentration distribution or an average ion concentration distribution of an active material particle located according to an electrode direction, and the electrode direction indicates a direction connecting one end of the electrode (e.g., a boundary adjacent to a current collector) and the other end of the electrode (e.g., a boundary adjacent to a separation membrane). Moreover, the ion concentration distribution 650 in an active material particle indicates an ion concentration distribution inside the active material particle according to a center direction of the active material particle, and the center direction of the active material particle indicates a direction connecting the center of the active material particle and the surface of the active material particle.

[0068] As described above, in order to reduce the voltage difference between the detected voltage and the estimated voltage, the ion concentration distributions of the positive and negative electrodes are moved while maintaining physical storage of the concentrations, potential information of the positive and negative electrodes is derived based on the moved concentration distributions, and the voltages are calculated based on the derived potential information of the positive and negative electrodes. The amount of internal state movement that makes the voltage difference between the detected voltage and the estimated voltage zero is derived, and the SOC of the battery is finally determined.

[0069] 7 and 8 are diagrams illustrating a process of determining a battery state change amount according to an embodiment.

[0070] 7, an example is shown in which a battery state change amount is determined when a detected voltage of the battery is greater than an estimated voltage estimated from an electrochemical model, according to an embodiment. Here, the estimated voltage may be a battery voltage estimated in a previous period in the case of the single cell model described with reference to FIG. 3, or may be an estimated voltage of a previous battery selected as a target battery in a previous period in the case of the cell conversion model described with reference to FIG. 4 to FIG. 6.

[0071] The OCV table according to an embodiment shows a characteristic curve between SOC and open circuit voltage, and indicates the inherent characteristics of the battery. When using the OCV table, ΔSOC, which must be corrected according to the SOC value, changes, so SOC information of the last estimated (e.g., most recently estimated) previous period may be used. Here, the SOC information of the previous period may be the estimated SOC of the battery in the previous period in the case of the single cell model described with reference to FIG. 3, or may be the estimated SOC of the previous battery selected as the target battery in the previous period in the case of the cell conversion model described with reference to FIGS. 4 to 6.

[0072] The estimated OCV, which is the open circuit voltage corresponding to the SOC information of the previous period, is derived from the characteristic curve of the OCV table shown in Figure 7. The voltage difference calculated in advance is reflected in the estimated OCV, but since the detected voltage is greater than the estimated voltage, the voltage difference is reflected by adding it to the estimated OCV. Again, the characteristic curve of the OCV table is used to determine the corrected SOC corresponding to the result of reflecting the voltage difference, and the difference between the estimated SOC and the corrected SOC is determined as the state change amount △SOC.

[0073] 8 shows an example of determining a state change amount of a battery when a detected voltage of the battery is smaller than an estimated voltage estimated from an electrochemical model, according to an embodiment. Here, the estimated voltage may be a voltage of a battery estimated in a previous period in the case of the single cell model described with reference to FIG. 3, or may be an estimated voltage of a previous battery selected as a target battery in a previous period in the case of the cell conversion model described with reference to FIG. 4 to FIG. 6.

[0074] As described above, when using the OCV table, the last estimated SOC information for the previous period is used. Here, the SOC information for the previous period may be the estimated SOC of the battery in the previous period in the case of the single cell model described with reference to FIG. 3, or may be the estimated SOC of the previous battery selected as the target battery in the previous period in the case of the cell conversion model described with reference to FIG. 4 to FIG. 6.

[0075] The estimated OCV, which is the open circuit voltage corresponding to the SOC information of the previous period, is derived from the characteristic curve of the OCV table shown in Figure 8. The voltage difference calculated in advance is reflected in the estimated OCV, but since the detected voltage is smaller than the estimated voltage, the voltage difference is reflected by subtracting it from the estimated OCV. Again, the characteristic curve of the OCV table is used to determine the corrected SOC corresponding to the result of reflecting the voltage difference, and the difference between the estimated SOC and the corrected SOC is determined as the state change amount △SOC.

[0076] 9 to 11 are diagrams for explaining a process of updating the internal state of an electrochemical model according to an embodiment.

[0077] A battery state estimation device according to an embodiment updates an internal state of an electrochemical model based on a change in the state of the battery. The electrochemical model is an electrochemical model for estimating state information of the battery by modeling an internal physical phenomenon of the battery, and the internal state of the electrochemical model may include the voltage, overpotential, SOC, positive electrode lithium ion concentration distribution, negative electrode lithium ion concentration distribution, and / or electrolyte lithium ion concentration distribution of the battery, and has a profile form. For example, the battery state estimation device corrects the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode based on the change in the state of the battery, and updates the internal state of the electrochemical model, which will be described with reference to FIGS. 9 to 11.

[0078] Referring to FIG. 9, an example of updating the internal state of an electrochemical model by uniformly correcting the ion concentration distribution according to an embodiment is shown. Here, the ion concentration distribution indicates an ion concentration distribution in an active material particle or an ion concentration distribution in an electrode. When the graph shown in FIG. 9 indicates an ion concentration distribution in an active material particle, the horizontal axis of the graph indicates a location in the active material particle, for example, 0 indicates the center of the active material particle and 1 indicates the surface of the active material particle. Alternatively, when the graph shown in FIG. 9 indicates an ion concentration distribution in an electrode, the horizontal axis of the graph indicates a location in the electrode, for example, 0 indicates one end of the electrode (e.g., a boundary adjacent to a current collector) and 1 indicates the other end of the electrode (e.g., a boundary adjacent to a separator).

[0079] The battery state estimation device converts a state change amount of the battery into a change amount of the internal state, and can uniformly reflect the converted change amount in the internal state of the electrochemical model. Here, the change amount of the internal state is a change amount of lithium ion concentration, and corresponds to an area 910 between the initial internal state and the updated internal state. The method of uniformly updating the internal state assumes that the concentration change is uniform, is applied when the current output from the battery is not large, and is easier to implement than the non-uniform update method described below.

[0080] In addition, if the example shown in FIG. 9 shows an update of the internal state in which the lithium ion concentration increases in one of the electrode active materials of the positive and negative electrodes, the internal state is updated in the other electrode active material of the positive and negative electrodes such that the lithium ion concentration decreases by the amount of the increase in the lithium ion concentration.

[0081] 10A and 10B show an example of correcting the ion concentration distribution to be non-uniform and updating the internal state of the electrochemical model according to an embodiment. Here, the ion concentration distribution indicates the ion concentration distribution in the active material particle or the ion concentration distribution in the electrode. When the graphs of FIG. 10A and FIG. 10B show the ion concentration distribution in the active material particle, the horizontal axis of the graph indicates the position in the active material particle, for example, 0 indicates the center of the active material particle and 1 indicates the surface of the active material particle. Alternatively, when the graphs of FIG. 10A and FIG. 10B show the ion concentration distribution in the electrode, the horizontal axis of the graph indicates the position in the electrode, for example, 0 indicates one end of the electrode (e.g., the boundary adjacent to the current collector) and 1 indicates the other end of the electrode (e.g., the boundary adjacent to the separator).

[0082] Depending on the chemical characteristics of the battery, if the electrical conductivity is extremely low, the battery current is large, and / or the battery temperature is low, the internal diffusion characteristics are weakened, which indicates that the gradient of the ion concentration distribution increases toward the electrode. In this case, the internal state of the electrochemical model is updated non-uniformly at each position within the active material particle or each position within the electrode, taking into account the diffusion characteristics within the battery.

[0083] According to an embodiment, lithium ions may move within a battery based on the diffusion characteristics. If it is assumed that lithium ions in the positive electrode move to the negative electrode, the lithium ions located closest to the negative electrode among the lithium ions in the positive electrode will move first. Here, if the diffusion characteristics within the battery become worse than before, the movement of lithium ions within the positive electrode becomes much slower, and the lithium ions that have moved to the negative electrode are not easily filled, so only the lithium ions located at the end of the positive electrode continue to move to the negative electrode, and the gradient of the ion concentration distribution becomes large as shown in the graph of FIG. 10A. On the other hand, if the diffusion characteristics within the battery become better than before, the lithium ions located within the positive electrode move quickly to the end to fill the lithium ions that have moved to the negative electrode, so the gradient of the ion concentration distribution becomes small as shown in the graph of FIG. 10B. As described with reference to FIG. 9, the areas 1010 and 1020 between the initial internal state and the updated internal state correspond to the amount of change in lithium ion concentration. Such a diffusion characteristic is based on the state information (eg, SOC) of the battery, so that the diffusion characteristic according to the amount of change in the state of the battery is taken into consideration, which will be described in more detail below.

[0084] The battery state estimation device may determine the concentration gradient characteristics based on the diffusion characteristics according to the amount of change in the battery state, and update the internal state of the electrochemical model according to the concentration gradient characteristics. A diffusion coefficient is derived based on an analysis of the diffusion characteristics of a state direction in which lithium ions must move (e.g., a direction in which the lithium ion concentration increases). For example, a diffusion coefficient between a previous SOC-based diffusion coefficient and a moving SOC is derived. Then, the internal state of the electrochemical model is updated according to the concentration gradient characteristics predetermined by the diffusion coefficient. For example, if the diffusion coefficient in the direction in which the movement must occur decreases, the internal state of the electrochemical model is updated in a direction in which the concentration gradient increases. Conversely, if the diffusion coefficient in the direction in which the movement must occur increases, the internal state of the electrochemical model is updated in a direction in which the concentration gradient decreases.

[0085] In another embodiment, the electrochemical model is based on the assumption that the total amount of lithium ions remains constant while lithium ions move between the positive electrode, the negative electrode, and the electrolyte, and the lithium ion movement between the positive electrode, the negative electrode, and the electrolyte is determined based on a diffusion equation, which will be described in more detail below.

[0086] The battery state estimation device calculates a diffusion equation of the active material according to the amount of change in the state of the battery, and updates the internal state of the electrochemical model. The internal state of the electrochemical model is updated by calculating the diffusion equation while applying a current boundary condition to the state direction in which the lithium ions must move (for example, the direction in which the lithium ion concentration increases). The battery state estimation device calculates a diffusion equation of the active material for the amount of change in the internal state corresponding to the amount of change in the state, and updates the ion concentration distribution calculated via the diffusion equation to the internal state of the electrochemical model. Since the diffusion characteristic is also one of the physical characteristics, the battery state estimation device can non-uniformly update the internal state of the electrochemical model by calculating the diffusion equation for the ion concentration distribution.

[0087] FIG. 11 is a diagram illustrating a battery state estimation method according to an embodiment.

[0088] Referring to FIG. 11, there is shown a battery state estimation method performed by a processor included in a battery state estimation device according to an embodiment.

[0089] In step S1110, the battery state estimation device determines the amount of change in the battery state using a voltage difference between the detected voltage of the battery and an estimated voltage of the battery estimated from the electrochemical model. The battery state estimation device determines the amount of change in the battery state based on the voltage difference, previous state information estimated in advance by the electrochemical model, and an OCV table. For example, the battery state estimation device may obtain an open circuit voltage corresponding to the previous state information based on the OCV table, and determine the amount of change in the battery state by reflecting the voltage difference in the open circuit voltage.

[0090] In step S1120, the battery state estimation device updates the internal state of the electrochemical model based on the amount of change in the state of the battery. 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 electrode based on the amount of change in the state of the battery. For example, the battery state estimation device can uniformly correct the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode according to the amount of change in the state of the battery, and update the internal state of the electrochemical model. In addition, the battery state estimation device can determine the characteristics of the concentration gradient based on the diffusion characteristics due to the amount of change in the state of the battery, correct the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode according to the characteristics of the concentration gradient, and update the internal state of the electrochemical model. In addition, the battery state estimation device can calculate a diffusion equation of the active material according to the amount of change in the state of the battery, and correct the ion concentration distribution in the active material particles or the ion concentration distribution in the electrode to update the internal state of the electrochemical model.

[0091] In step S1130, the battery state estimating device estimates state information of the battery based on the internal state of the electrochemical model.

[0092] According to an embodiment, before step S1110 is performed, the battery state estimation device checks whether a voltage difference between the detected voltage of the battery and the estimated voltage of the battery obtained from the electrochemical model exceeds a threshold voltage difference. If the voltage difference exceeds the threshold voltage difference, steps S1110 to S1130 described above are performed.

[0093] The steps shown in FIG. 11 are the same as those described above with reference to FIGS. 1 to 10, and therefore will not be described in detail.

[0094] 12A and 12B are diagrams illustrating a battery state estimation method according to another embodiment.

[0095] 12A shows a method for estimating a battery state performed by a processor included in a battery state estimation device according to another embodiment. The battery state is estimated for a plurality of periods, and the flowchart shown in FIG. 12A shows a process of estimating battery state information for each period.

[0096] In step S1210, the battery state estimating device collects detection data of the battery, including a detected voltage, a detected current, and a detected temperature of the battery.

[0097] In step S1220, an estimated voltage and state of the battery (eg, SOC) are determined using an electrochemical model that receives the sensed current and temperature.

[0098] In step S1230, the battery state estimating device calculates a first voltage difference between the detected voltage of the battery in the current period and the estimated voltage estimated from the electrochemical model, for example, the first voltage difference is determined to be a moving average voltage during a predetermined recent time period.

[0099] Although not separately illustrated in Fig. 12A, the battery state estimating device according to the embodiment determines whether correction of battery state information is necessary based on whether the first voltage difference exceeds a first threshold voltage difference. If an error occurs in the electrochemical model, the estimated voltage estimated using the electrochemical model differs from the detected voltage of the battery, and whether correction is necessary is determined based on the first voltage difference to prevent accumulation of the error.

[0100] For example, if the first voltage difference exceeds the first threshold voltage difference, the battery state estimation device determines that the battery state information needs to be corrected, and step S1240 is then performed. Conversely, if the first voltage difference does not exceed the first threshold voltage difference, the battery state estimation device determines that the battery state information does not need to be corrected, and step S1210 is performed again in the next period.

[0101] In step S1240, the battery state estimation device selects one or more correction methods to be executed from among the concentration correction method and the fine current correction method. For example, if the first voltage difference exceeds a predetermined second threshold criterion, the battery state estimation device selects the concentration correction method, and if not, the fine current correction method. Alternatively, the battery state estimation device selects one or more correction methods to be executed from the concentration correction method and the fine current correction method, such that the concentration correction method is executed every first period and the fine current correction method is executed every second period, where the first period may be longer than the second period. In other words, the fine current correction method is executed more frequently than the concentration correction method.

[0102] If the concentration correction method is selected, in step S1250, the battery state estimating device corrects the internal state of the electrochemical model using the amount of change in the state of the battery determined by the first voltage difference. Since the concentration correction method is the same as that described above with reference to Figures 1 to 11, a detailed description thereof will be omitted.

[0103] If the fine current correction method is selected, in step S1260, the battery state estimating device corrects the detected current of the battery in the current period input from the electrochemical model using a capacity error corresponding to a second voltage difference between the detected voltage and the estimated voltage of the battery in the previous period. The fine current correction method will be described in detail below.

[0104] The battery state estimating device may receive a detected current of the battery in a current section, determine a correction value using a capacity error corresponding to a voltage difference in a previous section of the battery, and correct the detected current using the correction value. The detected current thus corrected is input to an electrochemical model.

[0105] The voltage difference of the previous interval is the difference between the estimated voltage of the battery in the previous interval and the detected voltage of the battery in the previous interval.

[0106] The capacity error is determined based on the voltage difference in the previous section and the estimated open circuit voltage of the battery in the previous section. For convenience of explanation, the process of determining the capacity error will be described with reference to the OCV table shown in FIG. 12B. First, a first SOC 1204 corresponding to a first OCV 1203 obtained by subtracting a part of the voltage difference ΔV 1202, a value α, from an estimated OCV 1201, which is an estimated open circuit voltage in the previous section, is determined, and a second SOC 1206 corresponding to a second OCV 1205 obtained by adding the remaining value β of the voltage difference ΔV 1202 to the estimated OCV 1201 is determined. Then, a state difference ΔSOC 1207 between the first SOC 1204 and the second SOC 1206 is multiplied by the capacity of the battery to determine the capacity error.

[0107] The compensation value is determined by applying a weight to the capacitance error and dividing the weighted capacitance error by a constant value. The weight is determined based on an average current value calculated based on a detected current in a current section and / or a detected current in a previous section. For example, if the average current value is large, the weight is determined to be small, and conversely, if the average current value is small, the weight is determined to be large. The constant value may be the length of a specific period as an update cycle of the status information.

[0108] The fine current correction method is described in U.S. Patent Application Publication No. 2018-0143254, the disclosure of which is incorporated herein by reference.

[0109] In step S1270, the battery state estimation device determines whether to end the battery state estimation operation. For example, if a predetermined operation period has not elapsed, step S1210 is next performed for the next period. On the other hand, if a predetermined operation period has elapsed, the battery state estimation operation is ended.

[0110] FIG. 13 is a diagram illustrating a battery state estimating device according to an embodiment.

[0111] 13, a battery state estimation device 1300 according to an embodiment includes a memory 1310 and a processor 1320. The memory 1310 and the processor 1320 communicate with each other via a bus 1330.

[0112] The memory 1310 includes computer readable instructions. The processor 1320 performs the above-mentioned operations by executing the instructions stored in the memory 1310. The memory 1310 may be a volatile memory or a non-volatile memory.

[0113] The processor 1320 is a device that executes commands or programs and controls the battery state estimation device 1300. The processor 1320 can determine a battery state change amount using a voltage difference between a detected voltage of the battery and an estimated voltage of the battery estimated from an electrochemical model, update an internal state of the electrochemical model based on the battery state change amount, and estimate battery state information based on the internal state of the electrochemical model.

[0114] In addition, the battery state estimating device 1300 processes the above-mentioned operations.

[0115] 14 and 15 are diagrams for explaining a vehicle according to one embodiment.

[0116] 14, a vehicle 1400 includes a battery pack 1410 and a battery management system 1420. The vehicle 1400 uses the battery pack 1410 as a power source. The vehicle 1400 may be, for example, an electric vehicle or a hybrid vehicle.

[0117] The battery pack 1410 includes a plurality of battery modules. The battery module includes a plurality of battery cells.

[0118] The battery management system 1420 monitors whether an abnormality occurs in the battery pack 1410 and prevents the battery pack 1410 from being over-charged or over-discharged. In addition, the battery management system 1420 performs thermal control on the battery pack 1410 if the temperature of the battery pack 1410 exceeds a first temperature (e.g., 40° C.) or is lower than a second temperature (e.g., −10° C.). In addition, the battery management system 1420 performs cell balancing to equalize the charge states of the battery cells included in the battery pack 1410.

[0119] According to an embodiment, the battery management system 1420 may include the battery state estimation device described above, and may determine state information of each battery cell included in the battery pack 1410 or state information of the battery pack 1410 through the battery state estimation device. The battery management system 1420 may determine a maximum value, a minimum value, or an average value of the state information of each battery cell as the state information of the battery pack 1410.

[0120] The battery management system 1420 transmits status information of the battery pack 1410 to an ECU (Electronic Control Unit) or a VCU (Vehicle Control Unit) of the vehicle 1400. The ECU or VCU of the vehicle 1400 outputs the status information of the battery pack 1410 to a display of the vehicle 1400. As in the example shown in FIG. 15, the ECU or VCU displays the status information of the battery pack 1410 on an instrument panel 1510 in the vehicle 1400. Alternatively, the ECU or VCU may display the remaining mileage, etc., determined based on the estimated status information, on the instrument panel 1510. Although not shown in FIG. 15, the ECU or VCU displays the status information of the battery pack 1410, the remaining mileage, etc., on a head-up display of the vehicle 1400.

[0121] 1 to 13 can be applied to the matters described with reference to FIGS. 14 and 15, and therefore detailed description thereof will be omitted.

[0122] FIG. 16 is a diagram illustrating a mobile device according to an embodiment.

[0123] Referring to FIG. 16, a mobile device 1600 includes a battery pack 1610. The mobile device 1600 is a device that uses the battery pack 1610 as a power source. The mobile device 1600 is a portable terminal, and may be, for example, a smartphone. In FIG. 16, for convenience of explanation, the mobile device 1600 is described as a smartphone, but various other terminals such as a notebook computer, a tablet PC, and a wearable device may be applied without limitation. The battery pack 1610 may include a BMS and a battery cell (or a battery module).

[0124] According to an embodiment, the mobile device 1600 includes a battery charging device that can update an internal state of an electrochemical model based on a change in state of the battery pack 1610 (or a battery cell in the battery pack 1610) and estimate state information of the battery based on the internal state of the electrochemical model.

[0125] The matters described with reference to FIGS. 1 to 15 can be applied to the matters described with reference to FIG. 16, and therefore detailed description thereof will be omitted.

[0126] The above-described embodiments may be implemented with hardware components, software components, or a combination of hardware and software components. For example, the devices and components described herein may be implemented using one or more general-purpose or special-purpose computers, such as a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), a programmable logic unit (PLU), a microprocessor, or other 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 operating system. The processing device also accesses, stores, manipulates, processes, and generates data in response to the execution of the software. For ease of understanding, the processing device may be described as being one in which one is used, but one skilled in the art will appreciate that the processing device may include multiple processing elements and / or multiple types of processing elements. For example, the processing device may include multiple processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

[0127] Software includes computer programs, codes, instructions, or any combination of one or more thereof, capable of configuring or instructing a processing device to operate as desired, either independently or in combination. The software and / or data may be embodied, permanently or temporarily, in any type of machine, component, physical device, virtual device, computer storage medium or device, or transmitted signal wave, to be interpreted by the processing device or to provide instructions or data to the processing device. The software may be distributed across computer systems coupled to a network, and may be stored and executed in a distributed manner. The software and data may be stored on one or more computer readable storage media.

[0128] The method according to the present invention may be embodied in the form of program instructions that may be executed by various computer means and recorded on a computer readable storage medium. The storage medium may include program instructions, data files, data structures, and the like, alone or in combination. The storage medium and the program instructions may be specially designed and constructed for the purposes of the present invention, or may be known and available to those skilled in the art of computer software. Examples of computer readable storage media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs, DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine code, such as produced by a compiler, but also high-level language code that is executed by a computer using an interpreter, and the like. The hardware devices may be configured to operate as one or more software modules to perform the operations shown in the present invention, and vice versa.

[0129] As described above, even if the embodiments are described by limited drawings, those having ordinary skill in the art can apply various technical modifications and variations based on the above description. For example, the described techniques can be performed in a different order from the described method, and / or the components of the described system, structure, device, circuit, etc. can be combined or combined in a different form from the described method, or replaced or substituted by other components or equivalents to achieve appropriate results.

[0130] Therefore, the scope of the present invention should not be limited to the disclosed embodiment, but should be determined by the appended claims and their equivalents. [Explanation of symbols]

[0131] 110 Battery 120 Battery state estimation device 1310 Memory 1320 Processor 1410 Battery Pack 1420 Battery Management System

Claims

1. 1. A method for estimating a battery state performed by a processor, comprising: determining, as a state of charge (ΔSOC) of the battery, a voltage difference between a detected voltage of the battery and an estimated voltage of the battery estimated from an electrochemical model corresponding to the battery; updating an internal state of the electrochemical model based on a change in the state of the battery; estimating state information of the battery based on an internal state of the electrochemical model; A battery state estimation method comprising:

2. The step of updating the internal state of the electrochemical model includes updating the internal state of the electrochemical model by correcting an ion concentration distribution in an active material particle or an ion concentration distribution in an electrode based on a state change amount of the battery. The method of claim 1 .

3. The step of updating the internal state of the electrochemical model includes uniformly correcting the ion concentration distribution in the active material particles or the ion concentration distribution in the electrodes in accordance with the change in the state of the battery, and updating the internal state of the electrochemical model. The method of estimating a battery state according to claim 2 .

4. The step of updating the internal state of the electrochemical model includes determining a characteristic of a concentration gradient based on a diffusion characteristic due to a change in state of the battery, correcting an ion concentration distribution of the battery according to the characteristic of the concentration gradient, and updating the internal state of the electrochemical model. The method of estimating a battery state according to claim 2 .

5. The step of updating the internal state of the electrochemical model includes calculating a diffusion equation of an active material according to a change in state of the battery, and correcting an ion concentration distribution in the active material particles or an ion concentration distribution in an electrode to update the internal state of the electrochemical model. The method of estimating a battery state according to claim 2 .

6. The internal state of the electrochemical model includes any one of a positive electrode lithium ion concentration distribution, a negative electrode lithium ion concentration distribution, and an electrolyte lithium ion concentration distribution of the battery, or a combination thereof; The method of claim 1 .

7. The battery state estimation method further comprises: determining whether a voltage difference between the detected voltage of the battery and an estimated voltage of the battery obtained from the electrochemical model exceeds a threshold voltage difference; The method of claim 1 , comprising:

8. The electrochemical model estimates state information of a target battery for which state information is to be estimated among a plurality of batteries; the detected voltage is a voltage measured on the target battery; the estimated voltage is a voltage previously estimated for another battery from among the plurality of batteries by the electrochemical model; The method of claim 1 .

9. The battery is a battery cell, a battery module, or a battery pack. The method of claim 1 .

10. The estimated state information of the battery includes any one of SOC (State of Charge), SOH (State of Health), and abnormal state information, or a combination thereof; The method of claim 1 .

11. A computer readable storage medium having recorded thereon a program for executing the method according to any one of claims 1 to 10.

12. A ΔSOC (State of Charge) is calculated as a voltage difference between a detected voltage of a battery and an estimated voltage of the battery estimated by a stored electrochemical model corresponding to the battery, and is determined as a state change amount of the battery; updating an internal state of the electrochemical model based on a change in the state of the battery; estimating state information of the battery based on an internal state of the electrochemical model; a processor, Battery state estimator.

13. The processor updates an internal state of the electrochemical model by correcting an ion concentration distribution in an active material particle or an ion concentration distribution in an electrode based on an amount of change in the state of the battery. The battery state estimating device according to claim 12.

14. The processor uniformly corrects the ion concentration distribution in the active material particles or the ion concentration distribution in the electrodes according to the amount of change in the state of the battery, and updates the internal state of the electrochemical model. The battery state estimating device according to claim 13.

15. The processor determines a characteristic of a concentration gradient based on a diffusion characteristic due to a change in the state of the battery, corrects an ion concentration distribution in the active material particles or an ion concentration distribution in the electrode in accordance with the characteristic of the concentration gradient, and updates an internal state of the electrochemical model. The battery state estimating device according to claim 13.

16. The electrochemical model estimates state information of a target battery for which state information is to be estimated among a plurality of batteries; the detected voltage is a voltage measured on the target battery; the estimated voltage is a voltage previously estimated for another battery from among the plurality of batteries by the electrochemical model; The battery state estimating device according to claim 12.

17. The battery state estimating device further comprises: a memory for storing said electrochemical model; The battery state estimating device according to claim 12.

18. The estimated state information of the battery includes any one of SOC (State of Charge), SOH (State of Health), and abnormality state information, or a combination thereof; The battery state estimating device according to claim 12.

19. the battery state estimation apparatus is a vehicle or a mobile device and is powered by the battery; The battery state estimating device according to claim 12.

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