Patent Description:
There are various methods to estimate a state of a battery. For example, such methods may include estimating a state of a battery by integrating an electrical current of a battery or using a battery model, for example, an electric circuit model or an electrochemical model. <CIT> relates to estimating battery model parameters to update battery models. A reduced-order battery model is derived from a full electro-chemical battery model. The reduced-order model includes a single ordinary differential equation to emulate slow and medium electro-chemical dynamics and an instantaneous voltage drops by current inputs. Two model parameters, the effective diffusion coefficient and the effective internal resistance, are introduced to model the corresponding dynamics. The battery model parameters are updated over the battery life or depending on the operating conditions by processing the input current profiles and the measured output voltage profiles. Performance variables of the battery include the battery state of charge and the allowable battery power limits. The performance variables are estimated from the estimated state variables expressed by lithium ion concentration representing the electro-chemical dynamics of the positive electrode and the negative electrode. It is the object of the present invention to enable an improved battery state estimation. The object is solved by the subject matter of the independent claims which defined the present invention.

In one general aspect, a processor-implemented method with battery state estimation includes: 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 that is estimated by an electrochemical model corresponding to the battery; updating an internal state of the electrochemical model based on the determined state variation of the battery; and estimating state information of the battery based on the updated internal state of the electrochemical model.

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

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

The updating of the internal state of the electrochemical model may include correcting an ion concentration distribution in an active material particle or an ion concentration distribution in an electrode based on the determined state variation of the battery.

The updating of the internal state of the electrochemical model may include uniformly correcting an ion concentration distribution in an active material particle or an ion concentration distribution in an electrode based on the determined state variation of the battery.

The updating of the internal state of the electrochemical model may include determining a concentration gradient characteristic based on a diffusion characteristic based on the determined state variation of the battery, and correcting an ion concentration distribution of the battery based on the determined concentration gradient characteristic.

The updating of the internal state of the electrochemical model may further include calculating a diffusion equation of an active material based on the determined state variation of the battery, and correcting an ion concentration distribution in an active material particle or an ion concentration distribution in the electrode.

The internal state of the electrochemical model may include any one or any combination of any two or more of a positive electrode lithium-ion concentration distribution of the battery, a negative electrode lithium-ion concentration distribution of the battery, and an electrolyte lithium-ion concentration distribution of the battery.

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

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

The battery may be a battery cell, a battery module, or a battery pack.

The estimated state information of the battery may include any one or any combination of any two or more of a state of charge (SOC), a state of heath (SOH), and abnormality state information.

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

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

The correcting of the internal state of the electrochemical model or the sensed current to be input to the electrochemical model based on the selected correction method may include: updating the internal state of the electrochemical model using a state variation of the battery determined by the first voltage difference between a voltage of the battery sensed in a current time period and a voltage of the battery estimated by the electrochemical model; or correcting a sensed current of the battery in the current time period to be input to the electrochemical model using a capacity error corresponding to a second voltage difference between a sensed voltage of the battery in a previous time period and an estimated voltage of the battery in the previous time period.

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

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

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

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

The processor may be further configured to determine the state variation of the battery based on the voltage difference, previous state information that is previously estimated by the electrochemical model, and an open-circuit voltage (OCV) table.

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

The processor may be further configured to update 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 the determined state variation of the battery.

The processor may be further configured to update the internal state of the electrochemical model by uniformly correcting an ion concentration distribution in an active material particle or an ion concentration distribution in an electrode based on the determined state variation of the battery.

The processor may be further configured to: update the internal state of the electrochemical model by determining a concentration gradient characteristic based on a diffusion characteristic based on the determined state variation of the battery, and by correcting an ion concentration distribution in an active material particle or an ion concentration distribution in an electrode based on the determined concentration gradient characteristic.

The apparatus may further include a memory storing the electrochemical model.

The apparatus may be a vehicle or a mobile device, and may be powered by the battery.

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

The internal state may include either one or both of a potential of the battery and an ion concentration distribution in an active material particle or an electrode.

The method may further include: determining an amount of change in a state of charge (SOC) of the battery using the calculated voltage difference. The updating of the internal state of the electrochemical model based on the calculated voltage difference may include updating the internal state of the electrochemical model based on the determined change in the SOC of the battery.

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

Herein, it is noted that use of the term "may" with respect to an example or embodiment, e.g., as to what an example or embodiment may include or implement, means that at least one example or embodiment exists in which such a feature is included or implemented while all examples and embodiments are not limited thereto.

Unless otherwise defined, all terms, including technical and scientific terms, used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains and based on an understanding of the disclosure of this application. Terms, such as those defined in commonly used dictionaries, are to be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the disclosure of this application and are not to be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Also, in the description of example embodiments, detailed description of structures or functions that are thereby known after an understanding of the disclosure of this application will be omitted when it is deemed that such description will cause ambiguous interpretation of the example embodiments.

<FIG> and <FIG> illustrate an example of a battery system.

Referring to <FIG>, a battery system <NUM> includes, for example, a plurality of batteries <NUM>-<NUM> through <NUM>-n and an apparatus <NUM> with battery state estimation. The apparatus <NUM> with battery state estimation will be hereinafter simply referred to as a battery state estimating apparatus <NUM>.

Each of the batteries <NUM>-<NUM> through <NUM>-n may be a battery cell, a battery module, or a battery pack.

The battery state estimating apparatus <NUM> may sense each of the batteries <NUM>-<NUM> through <NUM>-n using at least one sensor. That is, the battery state estimating apparatus <NUM> may collect sensing data of each of the batteries <NUM>-<NUM> through <NUM>-n. The sensing data may include, for example, voltage data, current data, and/or temperature data.

The battery state estimating apparatus <NUM> may estimate state information of each of the batteries <NUM>-<NUM> through <NUM>-n and output a result of the estimating. The state information may include, for example, a state of charge (SOC), a state of heath (SOH), and/or abnormality state information. A battery model used to estimate such state information may be, for example, an electrochemical model which will be described hereinafter with reference to <FIG>.

<FIG> illustrates an example of estimating the state information using the battery model.

Referring to <FIG>, the battery state estimating apparatus <NUM> may estimate state information of a battery <NUM> using an electrochemical model corresponding to the battery <NUM>. The battery <NUM> may, for example, correspond to any one of the batteries <NUM>-<NUM> through <NUM>-n described above with respect to <FIG>. The electrochemical model may be configured to model an internal physical phenomenon, such as, for example, a potential of a battery and an ion concentration distribution, and estimate state information of the battery.

A level of accuracy in estimating the state information of the battery <NUM> may affect an optimal operation and control of the battery <NUM>. When estimating the state information using the electrochemical model, there may be an error between sensor information that is obtained by a sensor configured to measure current, voltage, and temperature data and is to be input to the electrochemical model, and state information calculated through a modeling method. The error may need to be compensated for or corrected. The terms "compensated for" and "corrected" may be interchangeably used herein.

In an example, the battery state estimating apparatus <NUM> may determine a voltage difference between a sensed voltage of the battery <NUM> that is measured by a sensor and an estimated voltage of the battery <NUM> that is estimated by the electrochemical model. The battery state estimating apparatus <NUM> may then determine a state variation of the battery <NUM> using the determined voltage difference. The battery state estimating apparatus <NUM> may then update an internal state (e.g., a potential of the battery and/or an ion concentration distribution) of the electrochemical model based on the determined state variation. The battery state estimating apparatus <NUM> may then estimate state information of the battery <NUM> based on the updated internal state of the electrochemical model. As described, the battery state estimating apparatus <NUM> may determine a state variation of a battery such that a voltage difference between a sensed voltage of the battery and an estimated voltage of the battery is to be minimized, and update an internal state of the electrochemical model. Through such a feedback structure, the battery state estimating apparatus <NUM> may estimate accurate state information of the battery <NUM> without increasing a model complexity and an operation or calculation amount.

<FIG> is a flowchart illustrating an example of determining state information of a battery by a battery state estimating apparatus. In an example, a state of a battery may be estimated in a plurality of time periods and the battery state estimating apparatus may estimate state information of the battery in each of the time periods. Such example will be described as applied to a single cell model for the convenience of description.

Referring to <FIG>, in operation <NUM>, the battery state estimating apparatus collects sensing data of a battery. The sensing data may include, for example, a sensed voltage, a sensed current, and a sensed temperature. For example, the sensing data may be stored in a form of a profile indicating a change in magnitude over time.

In operation <NUM>, an estimated voltage of the battery and state information (e.g., SOC) of the battery are determined, for example, by an electrochemical model to which the sensed current and the sensed temperature are input.

In operation <NUM>, the battery state estimating apparatus calculates a voltage difference between the sensed voltage of the battery and the estimated voltage that is estimated by the electrochemical model. For example, the voltage difference may be determined to be a moving average voltage for a latest preset time period.

Although not illustrated in <FIG>, according to an example, the battery state estimating apparatus may determine whether the state information of the battery needs to be corrected, based on whether the calculated voltage difference exceeds a threshold voltage difference. When an error occurs in the electrochemical model, the estimated voltage that is obtained using the electrochemical model may differ from the sensed voltage of the battery by a significant amount or an amount that results in the estimated voltage being excessively inaccurate. Thus, to prevent errors from being accumulated, the battery state estimating apparatus may determine whether the correcting is needed based on the voltage difference.

For example, when the calculated voltage difference exceeds the threshold voltage difference, the battery state estimating apparatus may determine that the state information of the battery needs to be corrected, and perform operation <NUM>. In contrast, when the calculated voltage difference does not exceed the threshold voltage difference, the battery state estimating apparatus may determine that the state information of the battery does not need to be corrected, and return to operation <NUM> without performing operations <NUM>, <NUM>, and <NUM>.

In operation <NUM>, the battery state estimating apparatus determines a state variation of the battery using the calculated voltage difference. For example, the battery state estimating apparatus may determine the state variation of the battery based on the calculated voltage difference, previous state information of the battery, and an open-circuit voltage (OCV) table. The previous state information of the battery may be the state information that is previously estimated using the electrochemical model in operation <NUM>. For example, the state variation may include an amount of change in SOC, hereinafter simply an SOC variation, which will be described in greater detail with reference to <FIG> and <FIG>.

In operation <NUM>, the battery state estimating apparatus updates the electrochemical model by correcting an internal state of the electrochemical model based on the state variation of the battery. For example, the battery state estimating apparatus may update 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 the state variation of the battery. In such an example, an active material may include a positive electrode and a negative electrode of the battery. The battery state estimating apparatus estimates the state information of the battery using the electrochemical model of which the internal state is updated. Thus, through such a feedback structure by which the battery state estimating apparatus determines the state variation of the battery to minimize the voltage difference between the sensed voltage of the battery and the estimated voltage of the battery that is estimated by the electrochemical model, and then updates the internal state of the electrochemical model, it is possible to estimate the state information of the battery more accurately with fewer operations and/or calculations. A more detailed description of an example of updating the electrochemical model will follow hereinafter with reference to <FIG>.

In operation <NUM>, the battery state estimating apparatus determines whether to terminate an operation of estimating of a state of the battery. For example, when a preset operation period has not elapsed, the battery state estimating apparatus returns to operation <NUM> for a next period. In contrast, when the operation period has elapsed, the battery state estimating apparatus terminates the operation of estimating a state of the battery.

In an example embodiment, a battery state estimating apparatus may determine state information of a plurality of batteries in each of a plurality of time periods. The battery state estimating apparatus may select a target battery, among the plurality of batteries, from which state information is to be estimated in each of the time periods using an electrochemical model. That is, in each of the time periods, the battery state estimating apparatus may determine state information of a target battery using the electrochemical model, and determine state information of remaining batteries using a current integration method. The current integration method may be a method of estimating a remaining amount of a battery, or a SOC, by integrating a current amount to be charged or discharged through a current sensor disposed at an end of the battery.

In a next time period, the target battery from which the state information is estimated based on the electrochemical model may switch to another battery. For example, a target battery set in a first time period among a plurality of time periods may be set to be a nontarget battery in a second time period, and a nontarget battery in the first time period may be set to be a target battery in the second time period.

As described above, by sequentially setting a target battery in a preset order and estimating state information of a battery using the electrochemical model, it is possible to effectively and rapidly estimate a state of the battery with a relatively greater level of accuracy, without a burden of an amount of operations or calculations, even though using the electrochemical model that requires a relatively greater amount of operations or calculations. This will be referred to as a cell switch model for the convenience of description. An example of the cell switch model will be described in greater detail below, with reference to <FIG> and <FIG>.

<FIG> illustrates an example of a process by which a battery state estimating apparatus determines state information of a target battery in each of a plurality of time periods.

Referring to <FIG>, a battery <NUM> is set to be a target battery in time period T1, and a sensed voltage <NUM> of the battery <NUM> is input to an electrochemical model, and then state information of the battery <NUM> is estimated by the electrochemical model. When a time period changes from time period T1 to time period T2, the target battery switches from the battery <NUM> to the battery <NUM>, and the electrochemical model receives a sensed voltage <NUM> of the battery <NUM> instead of the sensed voltage <NUM> of the battery <NUM>. That is, at a switching time, there may be a discontinuity between the sensed voltage <NUM> and the sensed voltage <NUM> to be input to the electrochemical model. In the presence of such discontinuity, when the electrochemical model derives and outputs state information of the battery <NUM> from the sensed voltage <NUM>, the output of the electrochemical model may have a discontinuity on a boundary between time period T1 and time period T2 as illustrated in a graph <NUM>. This discontinuity may be corrected because it may be applied as an initial error of the electrochemical model when estimating the state information of the battery <NUM>. The output of the electrochemical model that is corrected through correction to be described hereinafter may exhibit a continuity as illustrated in a graph <NUM>.

<FIG> is a flowchart illustrating an example of a process by which a battery state estimating apparatus determines state information of a target battery using an electrochemical model when the target battery switches to another target battery. However, during a time in which the target battery does not switch to another battery in a cell switch model, a method of estimating a state of a battery described above with reference to <FIG> may be applied.

Referring to <FIG>, in operation <NUM>, the battery state estimating apparatus collects data of a previous battery and a current battery. The current battery may be a battery that is selected as a target battery in a current time period, and the previous battery may refer to a battery that was selected as a target battery in a previous time period. For example, the battery state estimating apparatus may collect sensing data of the current battery, and collect sensing data and/or an estimated voltage of the previous battery. The sensing data may include, for example, a sensed voltage, a sensed current, and a sensed temperature, and may be stored in a form of a profile indicating a change in magnitude over time. The estimated voltage of the previous battery may be a voltage of the previous battery that is estimated by an electrochemical model in the previous time period.

In operation <NUM>, the battery state estimating apparatus calculates a voltage difference between the sensed voltage of the current battery (or the target battery) and the sensed voltage or the estimated voltage of the previous battery. For example, the voltage difference may be determined to be a moving average voltage for a latest preset time period.

Although not illustrated in <FIG>, the battery state estimating apparatus determines whether state information of a battery needs to be corrected based on whether a calculated voltage difference exceeds a threshold voltage difference. Although a target battery does not switch, an estimated voltage may differ from a sensed voltage of the target battery due to an error occurring in the electrochemical model. Thus, whether such correction is needed or not may be determined based on the calculated voltage difference to prevent errors from being accumulated.

For example, when the calculated voltage difference exceeds the threshold voltage difference, the battery state estimating apparatus may determine that the state information of the battery needs to be corrected, and may perform operation <NUM>. In contrast, when the calculated voltage difference does not exceed the threshold voltage difference, the battery state estimating apparatus may determine that the state information of the battery does not need to be corrected, and may estimate state information of the target battery using the electrochemical model without performing operations <NUM> and <NUM>.

In operation <NUM>, the battery state estimating apparatus determines a state variation of the current battery (or the target battery) using the calculated voltage difference. For example, the battery state estimating apparatus may determine the state variation of the current battery based on the calculated voltage difference, previous state information that is estimated in the previous time period, and an OCV table. The previous state information may be state information estimated by the electrochemical model from the previous battery that was selected as the target battery in the previous time period. The previous battery selected as the target battery in the previous time period may be a nontarget battery in the current time period. The state variation of the current battery may include an SOC variation, which will be described in greater detail hereinafter with reference to <FIG> and <FIG>.

In operation <NUM>, the battery state estimating apparatus updates the electrochemical model by correcting an internal state of the electrochemical model based on the state variation of the current battery. For example, the battery state estimating apparatus may update 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 the state variation of the current battery. The updating will be described in detail hereinafter with reference to <FIG>.

The battery state estimating apparatus may estimate state information of the current battery using the electrochemical model of which the internal state is updated. In addition, the battery state estimating apparatus may estimate, based on the electrochemical model, a voltage of the current battery or the target battery, or other properties such as, but not limited to, properties related to or indicative of a voltage of the current battery or the target battery.

<FIG> illustrates an example of an electrochemical model.

Referring to <FIG>, an electrochemical model may estimate a remaining amount, or an SOC, of a battery by modeling an internal physical phenomenon of the battery, for example, an ion concentration, a potential, and the like of the battery. That is, the electrochemical model may be represented by a physical conservation equation associated with an electrochemical reaction occurring on an electrode/electrolyte interface, an ion concentration of an electrode and an electrolyte, and conservation of electrical charges. For the physical conservation equation, the electrochemical model may use various model parameters, for example, a shape (e.g., thickness, radius, etc.), an open-circuit potential (OCP), and a physical property value (e.g., electrical conductance, ionic conductance, diffusion coefficient, etc.).

In the electrochemical model, various state variables such as, for example, a concentration and a potential, may be coupled to one another. An estimated voltage <NUM> that is estimated by the electrochemical model may indicate a potential difference between both ends, which are a positive electrode and a negative electrode. As illustrated by reference numeral <NUM>, potential information of each of the positive electrode and the negative electrode may be affected by an ion concentration distribution of each of the positive electrode and the negative electrode. An SOC <NUM> to be estimated by the electrochemical model may indicate an average ion concentration of the positive electrode and the negative electrode.

The ion concentration distribution described above may be an ion concentration distribution <NUM> in an electrode or an ion concentration distribution <NUM> in an active material particle present at a certain position in the electrode. The ion concentration distribution <NUM> in the electrode may be a surface ion concentration distribution or an average ion concentration distribution of an active material particle positioned in an electrode direction. The electrode direction may be a direction connecting one end of the electrode, for example, a boundary adjacent to a current collector, and another end of the electrode, for example, a boundary adjacent to a separator. In addition, the ion concentration distribution <NUM> in the active material particle may be an ion concentration distribution inside the active material particle based on a center direction of the active material particle. The center direction of the active material particle may be a direction connecting a center of the active material particle and a surface of the active material particle.

As described above, to reduce a voltage difference between a sensed voltage of a battery and an estimated voltage of the battery, a battery state estimating apparatus may move or change an ion concentration distribution of each of a positive electrode and a negative electrode while maintaining physical conservation associated with a concentration, obtain potential information of each of the positive electrode and the negative electrode based on the moved ion concentration distribution, and calculate a voltage based on the obtained potential information. The battery state estimating apparatus may calculate an internal state variation at which the voltage difference is to be <NUM> and finally determine an SOC of the battery.

<FIG> and <FIG> illustrate examples of determining a state variation of a battery.

<FIG> illustrates an example of determining a state variation of a battery in a case in which a sensed voltage of the battery is greater than an estimated voltage of the battery that is estimated by an electrochemical model. The estimated voltage may be a voltage of the battery that is estimated in a previous time period in a case of a single cell model described above with reference to <FIG>. Alternatively, the estimated voltage may be an estimated voltage of a previous battery selected as a target battery in a previous time period in a case of a cell switch model described above with reference to <FIG>.

In an example, an OCV table indicates an SOC-OCV curve indicating an intrinsic characteristic of a battery. When using the OCV table, ΔSOC to be corrected may vary according to an SOC value, and SOC information of a previous time period that is lastly (e.g., most recently) estimated may be used. In a case of the single cell model described above with reference to <FIG>, the SOC information of the previous time period may be an estimated SOC of the battery in the previous time period. In a case of the cell switch model described above with reference to <FIG>, the SOC information of the previous time period may be an estimated SOC of a previous battery selected as a target battery in the previous time period.

An estimated OCV, which is an OCV corresponding to the SOC information of the previous time period, may be obtained through the characteristic curve of the OCV table illustrated in <FIG>. A previously calculated voltage difference may be applied to the estimated OCV. This example pertains to a case in which a sensed voltage is greater than an estimated voltage, and thus the calculated voltage difference may be applied by adding the calculated voltage difference to the estimated OCV. Using the characteristic curve of the OCV table, a corrected SOC corresponding to a result of applying the calculated voltage difference may be determined, and a difference between the estimated SOC and the corrected SOC may be determined to be ΔSOC which indicates a state variation.

<FIG> illustrates an example of determining a state variation of a battery in a case in which a sensed voltage of the battery is less than an estimated voltage of the battery that is estimated by an electrochemical model. The estimated voltage may be a voltage of the battery that is estimated in a previous time period in a case of a single cell model described above with reference to <FIG>. Alternatively, the estimated voltage may be an estimated voltage of a previous battery selected as a target battery in a previous time period in a case of a cell switch model described above with reference to <FIG>.

As described above, when using an OCV table, SOC information of a previous time period that is lastly estimated may be used. In a case of the single cell model described above with reference to <FIG>, the SOC information of the previous time period may be an estimated SOC of the battery in the previous time period. In a case of the cell switch model described above with reference to <FIG>, the SOC information of the previous time period may be an estimated SOC of a previous battery selected as a target battery in the previous time period.

An estimated OCV, which is an OCV corresponding to the SOC information of the previous time period, may be obtained through the characteristic curve of the OCV table shown in <FIG>. A previously calculated voltage difference may be applied. This example pertains to a case in which a sensed voltage is less than an estimated voltage, and thus the calculated voltage difference may be applied by subtracting the calculated voltage difference from the estimated OCV. Using the characteristic curve of the OCV table, a corrected SOC corresponding to a result of applying the calculated voltage difference may be determined, and a difference between the estimated SOC and the corrected SOC may be determined to be ΔSOC which indicates a state variation.

<FIG> illustrate examples of updating an internal state of an electrochemical model.

In an example, a battery state estimating apparatus may update an internal state of an electrochemical model based on a state variation of a battery. The electrochemical model may be configured to model an internal physical phenomenon of the battery and estimate state information of the battery. The internal state of the electrochemical model may be provided in a form of a profile and may include, for example, a voltage, an overpotential, an SOC, a positive electrode lithium ion concentration distribution, a negative electrode lithium ion concentration distribution, and/or an electrolyte lithium ion concentration distribution. For example, the battery state estimating apparatus may update 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 the state variation of the battery. A more detailed description of updating an internal state of an electrochemical model will follow hereinafter with reference to <FIG>.

<FIG> illustrates an example of updating an internal state of an electrochemical model by uniformly correcting an ion concentration distribution. In this example, the ion concentration distribution may indicate an ion concentration distribution in an active material particle or an ion concentration distribution in an electrode. For example, when a graph illustrated in <FIG> indicates the ion concentration distribution in the active material particle, a horizontal axis of the graph indicates a location in the active material particle. In this example, <NUM> indicates a center of the active material particle and <NUM> indicates a surface of the active material particle. For another example, when the graph illustrated in <FIG> indicates the ion concentration distribution in the electrode, a horizontal axis of the graph indicates a location in the electrode. In this example, <NUM> indicates one end of the electrode (e.g., a boundary adjacent to a collector) and <NUM> indicates another end of the electrode (e.g., a boundary adjacent to a separator).

The battery state estimating apparatus may convert a state variation of a battery to a variation of an internal state, and uniformly apply the variation to the internal state of the electrochemical model. The variation of the internal state may indicate a lithium ion concentration variation which corresponds to an area <NUM> between an initial internal state and an updated internal state. Such a method of uniformly updating the internal state may be applied when a current output from the battery is not large under the assumption that a concentration variation is uniform or consistent. The method may be simpler in implementation compared to a nonuniform updating method to be described hereinafter.

Alternatively, when updating the internal state in which a lithium ion concentration increases in an active material of one of a positive electrode and a negative electrode, the internal state may be updated such that a lithium ion concentration in an active material of the other one of the positive electrode and the negative electrode decreases by an increment of the increase in the lithium ion concentration in the active material in the one electrode.

<FIG> and <FIG> illustrate an example of updating an internal state of an electrochemical model by nonuniformly correcting an ion concentration distribution. In this example, the ion concentration distribution may indicate an ion concentration distribution in an active material particle or an ion concentration distribution in an electrode. For example, when graphs illustrated in <FIG> and <FIG> indicate the ion concentration distribution in the active material particle, a horizontal axis of the graphs indicates a location in the active material particle. In this example, <NUM> indicates a center of the active material particle, and <NUM> indicates a surface of the active material particle. For another example, when the graphs illustrated in <FIG> and <FIG> indicate the ion concentration distribution in the electrode, the horizontal axis of the graphs indicates a location in the electrode. In this example, <NUM> indicates one end of the electrode (e.g., a boundary adjacent to a collector) and <NUM> indicates another end of the electrode (e.g., a boundary adjacent to a separator).

For example, when a conductance is substantially lowered, a current of a battery is relatively great, and/or a temperature of the battery is relatively low, an internal diffusion characteristic may be weakened based on a chemical characteristic of the battery, and, accordingly, a gradient of the ion concentration distribution may increase in an electrode direction. In this example, based on the internal diffusion characteristic of the battery, the internal state of the electrochemical model may be nonuniformly updated at each location in the active material particle or each location in the electrode.

A lithium ion may move in the battery based on the diffusion characteristic. For example, when a lithium ion of the positive electrode moves to the negative electrode, a lithium ion that is located nearest to the negative electrode among lithium ions of the positive electrode may move first. In this example, when the internal diffusion characteristic of the battery is worse than before, lithium ions may move considerably slowly in the positive electrode and a spot of the lithium ion moved out to the negative electrode may not be rapidly filled, and thus only a lithium ion located at an end of the positive electrode may continuously move out to the negative electrode and a gradient of the ion concentration distribution may increase as illustrated in the graph of <FIG>. In contrast, when the internal diffusion characteristic of the battery is better than before, a lithium ion located in the positive electrode may rapidly move to an end to fill the spot of the lithium ion moved out to the negative electrode, and thus the gradient of the ion concentration distribution may decrease as illustrated in the graph of <FIG>. As described above with reference to <FIG>, an area between an initial internal state and an updated internal state, for example, an area <NUM> of <FIG> and an area <NUM> of <FIG>, may correspond to a lithium ion concentration variation. Such diffusion characteristic as described above may be based on state information (e.g., SOC) of a battery, and thus a diffusion characteristic based on a state variation of the battery may be considered. A more detailed description of consideration of a diffusion characteristic based on a state variation of the battery will follow.

The battery state estimating apparatus may determine a concentration gradient characteristic based on the diffusion characteristic based on the state variation of the battery, and update the internal state of the electrochemical model based on the determined concentration gradient characteristic. A diffusion coefficient may be derived based on an analysis of a diffusion characteristic of a state direction in which a lithium ion is to move, for example, a direction in which a lithium ion concentration increases. For example, a diffusion coefficient based on a previous SOC and a diffusion coefficient of an SOC to move may be derived. In addition, the internal state of the electrochemical model may be updated based on a concentration gradient characteristic set in advance based on the diffusion coefficient. For example, when the diffusion coefficient decreases in a direction for the movement, the internal state of the electrochemical model may be updated in a direction in which the concentration gradient increases. In contrast, when the diffusion coefficient increases in the direction for the movement, the internal state of the electrochemical model may be updated in a direction in which the concentration gradient decreases.

In another example, the electrochemical model may be a model based on a principle that a total amount of lithium ions is constantly conserved, although the lithium ions may move among the positive electrode, the negative electrode, and an electrolyte. Such movement of the lithium ions among the positive electrode, the negative electrode, and the electrolyte may be obtained based on a diffusion equation, which will be described in greater detail hereinafter.

The battery state estimating apparatus may calculate a diffusion equation of an active material based on the state variation of the battery, and update the internal state of the electrochemical model. The battery state estimating apparatus may assign a current boundary condition in a state direction to which a lithium ion is to move, for example, a direction in which a lithium ion concentration increases, to calculate the diffusion equation, and update the internal state of the electrochemical model. The battery state estimating apparatus may calculate the diffusion equation of the active material with respect to a variation of the internal state corresponding to the state variation of the battery, and update the internal state of the electrochemical model with an ion concentration distribution that is calculated through the diffusion equation. The diffusion characteristic is one physical characteristic among a plurality of physical characteristics, and thus the battery state estimating apparatus may nonuniformly update the internal state of the electrochemical model by calculating the diffusion equation with respect to the ion concentration distribution.

<FIG> is a flowchart illustrating an example of a method of estimating a state of a battery. The example method of estimating a state of a battery will be hereinafter simply referred to as a battery state estimating method.

The battery state estimating method to be described hereinafter with reference to <FIG> may be performed, for example, by a processor included in a battery state estimating apparatus.

Referring to <FIG>, in operation <NUM>, the battery state estimating apparatus determines a state variation of a battery using a voltage difference between a sensed voltage of the battery and an estimated voltage of the battery that is estimated by an electrochemical model. The battery state estimating apparatus may determine the state variation of the battery based on the voltage difference, previous state information that is previously estimated by the electrochemical model, and an OCV table. For example, the battery state estimating apparatus may determine the state variation of the battery by obtaining an OCV corresponding to the previous state information based on the OCV table, and applying the voltage difference to the obtained OCV.

In operation <NUM>, the battery state estimating apparatus updates an internal state of the electrochemical model based on the state variation of the battery. The battery state estimating apparatus may update 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 the state variation of the battery. For example, the battery state estimating apparatus may update the internal state of the electrochemical model by uniformly correcting the ion concentration distribution in the active material particle or the ion concentration distribution in the electrode to be consistent based on the state variation of the battery. In addition, the battery state estimating apparatus may update the internal state of the electrochemical model by determining a concentration gradient characteristic based on a diffusion characteristic based on the state variation of the battery, and correcting the ion concentration distribution in the active material particle or the ion concentration distribution in the electrode based on the determined concentration gradient characteristic. In addition, the battery state estimating apparatus may update the internal state of the electrochemical model by calculating a diffusion equation of an active material based on the state variation of the battery, and correcting the ion concentration distribution in the active material particle or the ion concentration distribution in the electrode.

In operation <NUM>, the battery state estimating apparatus estimates state information of the battery based on the internal state of the electrochemical model.

According to an example, before performing operation <NUM>, the battery state estimating apparatus may verify whether the voltage difference between the sensed voltage of the battery and the estimated voltage of the battery exceeds a threshold voltage difference. When the voltage difference exceeds the threshold voltage difference, the battery state estimating apparatus may perform operations <NUM> through <NUM>.

For a more detailed description of examples of the battery state estimation method, reference may be made to the descriptions provided above with reference to <FIG>.

<FIG> and <FIG> illustrate another example of a battery state estimating method.

<FIG> is a flowchart illustrating another example of a battery state estimating method to be performed by a processor included in a battery state estimating apparatus. A state of a battery may be estimated in a plurality of time periods. In the example of <FIG>, state information of the battery may be estimated in each of the time periods.

Referring to <FIG>, in operation <NUM>, the battery state estimating apparatus collects sensing data of a battery. The sensing data may include, for example, a sensed voltage, a sensed current, and a sensed temperature of the battery.

In operation <NUM>, an estimated voltage of the battery and state information, for example, an SOC, of the battery is determined by an electrochemical model to which a sensed current and a sensed temperature are input.

In operation <NUM>, the battery state estimating apparatus calculates a first voltage difference between a voltage of the battery sensed in a current time period and a voltage of the battery estimated by the electrochemical model in the current time period. For example, the first voltage difference may be determined to be a moving average voltage for a latest preset time period.

Although not illustrated in <FIG>, according to an example, the battery state estimating apparatus may determine whether state information of the battery needs to be corrected based on whether the calculated first voltage difference exceeds a first threshold voltage difference. When an error occurs in the electrochemical model, the estimated voltage, which is a voltage estimated using the electrochemical model, may differ from the sensed voltage of the battery. Thus, to prevent errors from being accumulated, the battery state estimating apparatus may determine whether the correcting is needed based on the calculated first voltage difference.

For example, when the calculated first voltage difference exceeds the first threshold voltage difference, the battery state estimating apparatus determines that the state information of the battery needs to be corrected and performs operation <NUM>. In contrast, for example, when the calculated first voltage difference does not exceed the first threshold voltage difference, the battery state estimating apparatus determines that the state information of the battery does not need to be corrected and returns to operation <NUM> in a next time period.

In operation <NUM>, the battery state estimating apparatus selects at least one correction method from an ion concentration correction method and a microcurrent correction method. For example, when the calculated first voltage difference exceeds a preset second threshold voltage difference, the battery state estimating apparatus may select the ion concentration correction method, and, otherwise, may select the microcurrent correction method. Alternatively, the battery state estimating apparatus may select at least one correction method from among the ion concentration correction method and the microcurrent correction method such that the ion concentration correction method is performed for each first period and the microcurrent correction method is performed for each second period. In such an example, the first period may be longer than the second period. That is, the microcurrent correction method may be more frequently performed than the ion concentration correction method.

In operation <NUM>, when the ion concentration correction method is selected, the battery state estimating apparatus corrects an internal state of the electrochemical model using a state variation of the battery that is determined by the calculated first voltage difference. For a more detailed description of an example of the ion concentration correction method, reference may be made to the descriptions provided above with reference to <FIG>.

In operation <NUM>, when the microcurrent correction method is selected, the battery state estimating apparatus corrects a sensed current of the battery in the current time period that is to be input to the electrochemical model, using a capacity error corresponding to a second voltage difference between a sensed voltage of the battery in a previous time period and an estimated voltage in the previous time period. The microcurrent correction method will be described in detail hereinafter.

The battery state estimating apparatus may receive the sensed current of the battery in the current time period, and may determine a correction value using a capacity error corresponding to a voltage difference of the battery in the previous time period. The battery state estimating apparatus may then correct the sensed current using the correction value. The corrected sensed current may then be input to the electrochemical model.

The voltage difference in the previous time period may be a difference between the estimated voltage of the battery in the previous time period and the sensed voltage of the battery in the previous time period.

The capacity error may be determined based on the voltage difference in the previous time period and an estimated OCV in the previous time period. An example of a manner by which the capacity error may be determined will be described hereinafter by referring to an OCV table illustrated in <FIG>, for convenience of description.

Referring to <FIG>, a first SOC <NUM> is determined. The first SOC <NUM> corresponds to a first OCV <NUM> obtained by subtracting a value α of a portion of a voltage difference ΔV <NUM> from an estimated OCV <NUM>, which is an estimated OCV in a previous time period. In addition, a second SOC <NUM> is determined. The second SOC <NUM> corresponds to a second OCV <NUM> obtained by adding a value β of a remaining portion of the voltage difference ΔV <NUM> to the estimated OCV <NUM>. A state difference ΔSOC <NUM> between the first SOC <NUM> and the second SOC <NUM> is multiplied by a capacity of the battery, and then the capacity error may be determined.

The correction value may be determined as a value obtained by applying a weight to the capacity error and dividing, by a constant value, the capacity error to which the weight is applied. The weight may be determined based on an average current value to be calculated based on a sensed current in a current time period and/or a sensed current in a previous time period. For example, when the average current value is relatively great, the weight may be determined to be relatively small. In contrast, when the average current value is relatively small, the weight may be determined to be relatively great. The constant value may indicate a state information updating period, for example, a length of a certain period.

An example of the microcurrent correction method is described in greater detail in <CIT>.

In operation <NUM>, the battery state estimating apparatus determines whether to terminate an operation of estimating a state of the battery. For example, when a preset operation period has not elapsed, operation <NUM> may be performed in a next time period. In contrast, when the preset operation period has elapsed, the operation of estimating a state of the battery may be terminated.

<FIG> illustrates an example of a battery state estimating apparatus.

Referring to <FIG>, a battery state estimating apparatus <NUM> may include, for example, a memory <NUM> and a processor <NUM>. The memory <NUM> and the processor <NUM> may communicate with each other through a bus <NUM>.

The memory <NUM> may include computer-readable instructions. When an instruction stored in the memory <NUM> is executed by the processor <NUM>, the processor <NUM> may perform one or more, or all, of operations or methods described above. The memory <NUM> may be a volatile or nonvolatile memory.

The processor <NUM> may execute instructions or programs, or control the battery state estimating apparatus <NUM>. The processor <NUM> may determine a state variation of a battery using a voltage difference between a sensed voltage of the battery and an estimated voltage of the battery that is estimated by an electrochemical model, update an internal state of the electrochemical model based on the determined state variation, and estimate state information of the battery based on the updated internal sate of the electrochemical model.

In addition, the battery state estimating apparatus <NUM> may perform the operations or methods described above.

<FIG> and <FIG> illustrate an example of a vehicle.

Referring to <FIG>, a vehicle <NUM> may include, for example, a battery pack <NUM> and a battery management system (BMS) <NUM>. The vehicle <NUM> may use the battery pack <NUM> as a power source. The vehicle <NUM> may be an electric vehicle or a hybrid vehicle, for example.

The battery pack <NUM> may include a plurality of battery modules each including a plurality of battery cells.

The BMS <NUM> may monitor whether an abnormality occurs in the battery pack <NUM>, and prevent the battery pack <NUM> from being over-charged or over-discharged. In addition, when a temperature of the battery pack <NUM> exceeds a first temperature, for example, <NUM> or is less than a second temperature, for example, -<NUM>, the BMS <NUM> may perform thermal control on the battery pack <NUM>. In addition, the BMS <NUM> may perform cell balancing to equalize charging states of the battery cells included in the battery pack <NUM>.

In an example, the BMS <NUM> may include a battery state estimating apparatus described above, and determine state information of each of the battery cells included in the battery pack <NUM> or state information of the battery pack <NUM>. The BMS <NUM> may determine, to be the state information of the battery pack <NUM>, a maximum value, a minimum value, or an average value of the state information the battery cells.

The BMS <NUM> may transmit the state information of the battery pack <NUM> to an electronic control unit (ECU) or a vehicle control unit (VCU) of the vehicle <NUM>. The ECU or the VCU of the vehicle <NUM> may output the state information of the battery pack <NUM> to a display of the vehicle <NUM>.

As illustrated in <FIG>, the ECU or the VCU may display the state information of the battery pack <NUM> on a dashboard <NUM>. Alternatively, the ECU or the VCU may display, on the dashboard <NUM>, a remaining available travel distance determined based on the estimated state information. Alternatively or additionally, the ECU or the VCU may display the state information, the remaining available travel distance, and the like on a head-up display of the vehicle <NUM>.

For a more detailed description of example features and operations of the vehicle <NUM>, reference may be made to the descriptions provided above with reference to <FIG>, and a more detailed and repeated description will be omitted here for brevity.

<FIG> illustrates an example of a mobile device.

Referring to <FIG>, a mobile device <NUM> includes a battery pack <NUM>. The mobile device <NUM> may use the battery pack <NUM> as a power source. The mobile device <NUM> may be a portable terminal such as a smartphone. <FIG> illustrates the mobile device <NUM> as a smartphone as an example for the convenience of description. However, the mobile device <NUM> may be other terminals, for example, a laptop computer, a tablet personal computer (PC), a wearable device, and the like. The battery pack <NUM> may include a BMS and battery cells (or battery modules).

In an example, the mobile device <NUM> may include a battery state estimating apparatus described above. The battery state estimating apparatus may update an internal state of an electrochemical model based on a state variation of the battery pack <NUM> or the battery cells included in the battery pack <NUM>, and may estimate state information of the battery pack <NUM> based on the updated internal state of the electrochemical model.

For a more detailed description of example features and operations of the mobile device <NUM>, reference may be made to the descriptions provided above with reference to <FIG> and a more detailed and repeated description will be omitted here for brevity.

The battery apparatuses, the battery apparatus <NUM>, the battery state estimating apparatuses, the battery state estimating apparatuses <NUM> and <NUM>, the memories, the memory <NUM>, the processors, the processor <NUM>, the bus <NUM>, the BMSs, the BMS <NUM>, the ECU, and the VCU in <FIG> that perform the operations described in this application are implemented by hardware components configured to perform the operations described in this application that are performed by the hardware components. Examples of hardware components that may be used to perform the operations described in this application where appropriate include controllers, sensors, generators, drivers, memories, comparators, arithmetic logic units, adders, subtractors, multipliers, dividers, integrators, and any other electronic components configured to perform the operations described in this application. In other examples, one or more of the hardware components that perform the operations described in this application are implemented by computing hardware, for example, by one or more processors or computers. A processor or computer may be implemented by one or more processing elements, such as an array of logic gates, a controller and an arithmetic logic unit, a digital signal processor, a microcomputer, a programmable logic controller, a field-programmable gate array, a programmable logic array, a microprocessor, or any other device or combination of devices that is configured to respond to and execute instructions in a defined manner to achieve a desired result. In one example, a processor or computer includes, or is connected to, one or more memories storing instructions or software that are executed by the processor or computer. Hardware components implemented by a processor or computer may execute instructions or software, such as an operating system (OS) and one or more software applications that run on the OS, to perform the operations described in this application. The hardware components may also access, manipulate, process, create, and store data in response to execution of the instructions or software. For simplicity, the singular term "processor" or "computer" may be used in the description of the examples described in this application, but in other examples multiple processors or computers may be used, or a processor or computer may include multiple processing elements, or multiple types of processing elements, or both. For example, a single hardware component or two or more hardware components may be implemented by a single processor, or two or more processors, or a processor and a controller. One or more hardware components may be implemented by one or more processors, or a processor and a controller, and one or more other hardware components may be implemented by one or more other processors, or another processor and another controller. One or more processors, or a processor and a controller, may implement a single hardware component, or two or more hardware components. A hardware component may have any one or more of different processing configurations, examples of which include a single processor, independent processors, parallel processors, single-instruction single-data (SISD) multiprocessing, single-instruction multiple-data (SIMD) multiprocessing, multiple-instruction single-data (MISD) multiprocessing, and multiple-instruction multiple-data (MIMD) multiprocessing.

Claim 1:
A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the steps of:
determining (<NUM>) a state variation of a battery using a voltage difference between a sensed voltage of the battery and an estimated voltage of the battery that is estimated by an electrochemical model corresponding to the battery;
updating (<NUM>) an internal state of the electrochemical model based on the determined state variation of the battery; and
estimating (<NUM>) state information of the battery based on the updated internal state of the electrochemical model,
wherein the determining (<NUM>) of the state variation of the battery comprises obtaining, from an open-circuit voltage, OCV, table indicating a state information - OCV curve, an OCV corresponding to previous state information previously estimated by the electrochemical model, obtaining a corrected OCV by adding or subtracting the voltage difference, obtaining, from the OCV table, a corrected state information corresponding to the corrected OCV, and determining the state variation of the battery based on the difference between the corrected state information and the previous state information.