Charging management device and operation method thereof

Abstract

A charging management device according to an embodiment disclosed in the present document may comprise: a parameter extraction unit which extracts a plurality of parameters related to a battery cell; a core parameter selection unit which selects one or more core parameters from the plurality of parameters; and a depth prediction unit which predicts a depth of charge of the battery cell on the basis of the core parameters.

Description

Charge management device and its operating method

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority from Korean Patent Application No. 10-2023-0128266, filed September 25, 2023, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a charge management device and a method of operating the same.

[0005] Recently, research and development on secondary batteries has been actively underway. Here, secondary batteries are defined as rechargeable and dischargeable batteries, encompassing both conventional Ni / Cd and Ni / MH batteries, as well as more recent lithium-ion batteries. Recently, their use has expanded to include power sources for electric vehicles, attracting attention as a next-generation energy storage medium.

[0006] Electric vehicles receive external electricity to charge battery cells, which are then discharged to drive the motor and generate power. Battery cells undergo internal deformation and transformation through various charging and discharging cycles during production and use, altering their physical and chemical properties. For example, internal short circuits, external short circuits, venting due to lithium deposition, or undervoltage failures (where the battery cell voltage drops below a certain level) can occur. These battery degradation and deterioration necessitate technologies to manage battery cell charging behavior.

[0007] One object of the embodiments disclosed in this document is to provide a charge management device and an operating method thereof capable of managing rapid charging by predicting a rapid charge depth that minimizes battery deterioration.

[0008] One purpose of the embodiments disclosed in this document is to provide a charge management device and an operating method thereof capable of managing rapid charging by predicting a rapid charging depth that minimizes battery deterioration by taking into account the degree of battery degradation.

[0009] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0010] A charging management device according to an embodiment disclosed in this document may include a parameter extraction unit for extracting a plurality of parameters related to a battery cell; a key parameter selection unit for selecting one or more key parameters among the plurality of parameters; and a depth prediction unit for predicting a depth of charge of the battery cell based on the key parameters.

[0011] According to an embodiment, the core parameter selection unit may change the value of at least one parameter among the plurality of parameters within a target range in an environment in which the battery cell is charged based on a specific temperature and a specific C-rate, and select the at least one parameter as the core parameter when the amount of change in the state value of the battery cell due to the changed value of the at least one parameter is greater than or equal to a threshold value.

[0012] According to an embodiment, the target range may be a range of values ​​of the at least one parameter corresponding to a first degradation degree of the battery cell.

[0013] According to an embodiment, the core parameter selection unit may determine the first degradation degree based on an increase in resistance or a decrease in voltage of the battery cell in a discharge section of the battery cell.

[0014] According to an embodiment, the key parameter may include at least one of diffusivity, tortuosity and rate constant for the negative electrode of the battery cell.

[0015] According to an embodiment, the depth prediction unit can estimate the value of the key parameter based on the ohmic resistance and polarization resistance of the battery cell in the discharge section of the battery cell, and predict the depth of charge of the battery cell based on the value of the estimated key parameter.

[0016] According to an embodiment, the depth prediction unit can estimate the value of the key parameter by considering a greater weight of the polarization resistance of the battery cell than the ohmic resistance of the battery cell.

[0017] According to an embodiment, the depth prediction unit can calculate a C-rate profile corresponding to the SOC of the battery cell based on the value of the estimated key parameter, and predict the depth of charge of the battery cell based on the profile.

[0018] A method for managing a charge according to an embodiment disclosed in this document may include a step of extracting a plurality of parameters related to a battery cell; a step of selecting at least one key parameter among the plurality of parameters; and a step of predicting a depth of charge of the battery cell based on the key parameter.

[0019] According to an embodiment, the step of selecting at least one core parameter among the plurality of parameters may include: a step of changing a value of at least one parameter among the plurality of parameters within a target range in an environment in which the battery cell is charged based on a specific temperature and a specific C-rate; and a step of selecting the at least one parameter as the core parameter when the amount of change in the state value of the battery cell due to the changed value of the at least one parameter is greater than or equal to a threshold value.

[0020] According to an embodiment, the target range may be a range of values ​​of the at least one parameter corresponding to a first degradation degree of the battery cell.

[0021] According to an embodiment, the step of selecting one or more core parameters among the plurality of parameters may further include the step of determining the first degradation degree based on an increase in resistance or a decrease in voltage of the battery cell in a discharge section of the battery cell.

[0022] According to an embodiment, the key parameter may include at least one of diffusivity, tortuosity and rate constant for the negative electrode of the battery cell.

[0023] According to an embodiment, the step of predicting the depth of charge of the battery cell may include the step of estimating the value of the key parameter based on the ohmic resistance and polarization resistance of the battery cell in the discharge section of the battery cell; and the step of predicting the depth of charge of the battery cell based on the value of the estimated key parameter.

[0024] According to an embodiment, the step of predicting the depth of charge of the battery cell may include the step of estimating the value of the key parameter by giving a greater weight to the polarization resistance of the battery cell than to the ohmic resistance of the battery cell.

[0025] According to an embodiment, the step of predicting the depth of charge of the battery cell may further include the step of calculating a C-rate profile corresponding to the SOC of the battery cell based on the values ​​of the estimated key parameters.

[0026] According to an embodiment, the step of predicting the depth of charge of the battery cell based on the value of the estimated key parameter may predict the depth of charge based on the profile.

[0027] The charge management device and its operating method according to the embodiment disclosed in this document can manage rapid charging by predicting a rapid charging depth that minimizes deterioration of a battery.

[0028] A charge management device and its operating method according to an embodiment disclosed in this document can manage rapid charging by predicting a rapid charging depth that minimizes deterioration of a battery by considering the degree of degradation of the battery.

[0029] In addition, various effects may be provided, either directly or indirectly, through this document.

[0030] FIG. 1 is a drawing showing a charging management system according to one embodiment disclosed in this document.

[0031] FIG. 2 is a block diagram illustrating the configuration of a charging management device according to one embodiment disclosed in this document.

[0032] FIG. 3 is a flowchart illustrating the operation of a charging management device according to one embodiment disclosed in this document.

[0033] FIG. 4 is a flowchart showing the operation of a core parameter selection unit according to one embodiment disclosed in this document.

[0034] FIGS. 5A to 5C are diagrams showing the operation of a core parameter selection unit according to one embodiment disclosed in this document.

[0035] FIG. 6 is a diagram showing the operation of a core parameter selection unit according to one embodiment disclosed in this document.

[0036] FIG. 7 is a flowchart illustrating the operation of a depth prediction unit according to one embodiment disclosed in this document.

[0037] FIG. 8 is a flowchart illustrating the operation of a depth prediction unit according to one embodiment disclosed in this document.

[0038] Hereinafter, various embodiments of the present invention will be described with reference to the accompanying drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that the present invention encompasses various modifications, equivalents, and / or alternatives of the embodiments.

[0039] The various embodiments and terminology used in this document are not intended to limit the technical features described in this document to specific embodiments, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiments. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise.

[0040] In this document, the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used merely to distinguish the corresponding element from other corresponding elements, and do not limit the corresponding elements in any other respect (e.g., importance or order) unless specifically stated otherwise.

[0041] In this document, whenever a component (e.g., a first component) is referred to as being “connected,” “coupled,” or “connected,” with or without the terms “functionally” or “communicatively,” or “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or via a third component.

[0042] According to one embodiment, the method according to various embodiments disclosed in this document may be provided as a computer program product. The computer program product may be traded as a commodity between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) through an application store or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0043] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0044] FIG. 1 is a drawing showing a charging management system according to one embodiment disclosed in this document.

[0045] Referring to FIG. 1, a charge management system (1) may include a battery pack (1000) and a charge management device (2000).

[0046] According to an embodiment, a battery pack (1000) may include an upper battery unit (100) and a battery management device (300).

[0047] The upper battery unit (100) may include a plurality of battery units (110, 120, …, 130). For example, when the upper battery unit (100) is a battery module, the plurality of battery units (110, 120, …, 130) may be a plurality of battery cells. For example, the plurality of battery units (110, 120, …, 130) may be, but are not limited to, lithium-ion (Li-ion) batteries, lithium-ion polymer (Li-ion polymer) batteries, nickel-cadmium (Ni-Cd) batteries, nickel-metal hydride (Ni-MH) batteries, etc.

[0048] Meanwhile, although FIG. 1 illustrates a single upper battery unit (100), this is not a limitation, and the battery pack (1000) may be configured to include n (n is a natural number greater than or equal to 2) upper battery units. In addition, some configurations may be excluded from the battery pack (1000), or other general-purpose configurations may be further included in the battery pack (1000).

[0049] The upper battery unit (100) can supply power to a target device (not shown). To this end, the upper battery unit (100) can be electrically connected to the target device. Here, the target device can include an electrical, electronic, or mechanical device that operates by receiving power from a battery pack (1000) including the upper battery unit (100). For example, the target device can be, but is not limited to, an electric vehicle (EV) or an energy storage system (ESS).

[0050] A battery management system (BMS) (300) can manage and / or control the status and / or operation of the upper battery unit (100) and / or each of a plurality of battery units (110, 120, …, 130).

[0051] Hereinafter, for convenience of explanation, the upper battery unit (100) and / or each of the plurality of battery units (110, 120, …, 130) is referred to as a battery unit (110), but the same may be applied substantially to other battery units (120, …, and 130) or the upper battery unit (100).

[0052] According to an embodiment, the battery management device (300) can determine the state of the battery unit (110) and manage the state of the battery unit (110) based on the determination. According to an embodiment, the battery management device (300) can control the operation of the battery unit (110) and manage the operation of the battery unit (110) based on the determination.

[0053] According to an embodiment, the battery management device (300) can monitor the voltage, current, and / or temperature of the battery unit (110). In addition, for monitoring, sensors or various measurement modules not shown may be additionally installed at any location, such as the upper battery unit (110), the charging / discharging path, or the plurality of battery units (110, 120, ..., 130) included in the upper battery unit (110).

[0054] According to an embodiment, the battery management device (300) may calculate battery information regarding the state of the battery unit (110) based on measured values ​​such as monitored voltage, current, and temperature. For example, the battery information regarding the state of the battery unit (110) may include at least one of a State of Charge (SOC), a State of Health (SOH), resistance, a current cycle, a remaining predicted cycle, and a C-rate.

[0055] According to an embodiment, the battery management device (300) may include an On-Board Diagnostic (OBD) device. Such OBD devices may include not only OBD-I, OBD 1.5, and OBD-II, but also various devices that output information related to the status of the battery unit (110) to another device (e.g., a charging management device (2000)). In addition, the operation of the battery management device (300) may be performed in various devices such as a server, cloud, charger, or charger / discharger connected to the battery management device (300) or a vehicle equipped with the battery management device (300).

[0056] According to an embodiment, the charge management device (2000) may receive various information obtained through the battery management device (300) and manage the charging of the battery unit (110) based on the received information. For example, the charge management device (2000) may predict a charging depth associated with optimized rapid charging of the battery unit (110). Here, the optimized rapid charging may mean charging that minimizes deterioration of the battery unit (110).

[0057] The battery unit (110) may undergo deterioration and deterioration as its physicochemical composition changes through repeated charging and discharging. In addition, the battery unit (110) may undergo rapid deterioration and deterioration during a rapid charging process of the battery unit (110). For example, on the positive electrode side of the battery unit (110), the electrolyte may be oxidized or the crystal structure may be destroyed, causing the battery unit (110) to deteriorate / deteriorate, and on the negative electrode side, metallic lithium ions may be precipitated (i.e., lithium plating) and causing the battery unit (110) to deteriorate / deteriorate. Therefore, the charge management device (2000) can predict charging conditions that minimize the deterioration and deterioration of the battery unit (110). For example, the charge management device (2000) can predict the rapid charge depth that minimizes the deterioration and deterioration of the battery unit (110). Here, the charge depth may mean the point in time at which lithium plating occurs. Through this, the charge management device (2000) can rapidly charge the battery unit (110) to a limit where lithium plating does not occur.

[0058] According to an embodiment, the charge management device (2000) can predict the rapid charge depth by reflecting the deterioration degree of the battery unit (110). For example, the charge management device (2000) can predict the rapid charge depth that reflects the deterioration degree of the battery unit (110) based on the deterioration state of the battery unit (110) according to charge and discharge. Here, the charge management device (2000) can predict the rapid charge depth based on a plurality of parameters related to the physical / chemical characteristics of the battery unit (110).

[0059] According to an embodiment, the charge management device (2000) can analyze parameters affecting the charge depth or rapid charge depth of the battery unit (110). Thus, the charge management device (2000) can calculate an optimal temperature that minimizes deterioration of the battery unit (110) during the charge section of the battery unit (110) and an optimal C-rate for the SOC of the battery unit (110).

[0060] According to an embodiment, parameters affecting the rapid charging depth of the battery unit (110) may be acquired by the battery management device (300) and transmitted to and stored in the charging management device (2000). The operation of the charging management device (2000) may be performed in various devices such as a server, cloud, charger, or charger / discharger connected to the battery management device (300) or a vehicle equipped with the battery management device. Details related to the charging depth prediction of the charging management device (2000) according to the embodiment may be described in detail through the drawings below.

[0061] FIG. 2 is a block diagram illustrating the configuration of a charging management device according to one embodiment disclosed in this document.

[0062] Referring to FIG. 2, the charging management device (2000) may include a communication circuit (510), a memory (520), an output device (530), and a processor (540). However, this is merely exemplary, and various embodiments are not limited thereto. For example, at least one of the components of the charging management device (2000) described above may be omitted, or one or more other components (e.g., an input device, at least one sensor, a power management device, etc.) may be added to the configuration of the charging management device (2000). In addition, at least one of the components described above may be integrated with another component.

[0063] According to an embodiment, the communication circuit (510) can establish a wired communication channel and / or a wireless communication channel between the charge management device (2000) and another device, and transmit and receive data through the established communication channel. According to an embodiment, the communication circuit (510) can receive data from a communication module (not shown) included in the battery management device (300). For example, the communication circuit (510) of the charge management device (2000) can receive data related to the battery unit (110). Here, the data related to the battery unit (110) can include at least one of type data, usage cycle data, voltage data, current data, temperature data, resistance data, charge depth data, SOC data, and C-rate data of the battery unit (110).

[0064] According to an embodiment, the connection between the communication circuit (510) of the charge management device (2000) and the communication module (not shown) of the battery management device (300) may be a communication connection via a wired and / or wireless network. In one embodiment, the wired network may be based on a local area network (LAN) communication or a power line communication. In one embodiment, the wireless network may be based on a short-range communication network (e.g., Bluetooth, wireless fidelity (WiFi), or infrared data association (IrDA)) or a long-range communication network (cellular network, 4G network, 5G network).

[0065] According to an embodiment, the connection between the communication circuit (510) of the charge management device (2000) and the communication module (not shown) of the battery management device (300) may be a connection via a device-to-device communication method (e.g., a bus, a general purpose input and output (GPIO), a serial peripheral interface (SPI), or a mobile industry processor interface (MIPI)).

[0066] According to an embodiment, the memory (520) may store data used by at least one component (e.g., the processor (540)) of the charging management device (2000). For example, the data may include software (or instructions related thereto), input data, or output data. In one embodiment, the instructions, when executed by the processor (540), may cause the charging management device (2000) to perform operations defined by the instructions.

[0067] According to an embodiment, the memory (520) may store data obtained from the communication circuit (510). According to an embodiment, the memory (520) may store one or more programs used in the mathematical or logical operation process of the charge management device (2000). For example, the memory (520) may include a program related to an Analysis of Variance (ANOVA) analysis method or a Simulator of Battery Dynamics (SimBD) analysis method.

[0068] According to an embodiment, the memory (520) may include a memory of a flash memory type, a hard disk type, a micro type, and a card type (e.g., a Secure Digital Card (SD card) or an eXtream Digital Card (XD card)), and a storage medium of at least one type of memory of a Random Access Memory (RAM), a Static RAM (SRAM), a Read-Only Memory (ROM), a Programmable ROM (PROM), an Electrically Erasable PROM (EEPROM), a Magnetic RAM (MRAM), a magnetic disk, and an optical disk, and the various embodiments are not limited thereto.

[0069] According to an embodiment, the output device (530) can output information related to the operation of the charge management device (2000) to the outside of the charge management device (2000). According to one embodiment, the output device (530) can include a display configured to output visual information. For example, the display can include one or more of a liquid crystal display, a thin film transistor-liquid crystal display, an organic light-emitting diode, a flexible display, a 3D display, and a transparent display. In addition, the display can also include a touch sensor configured to detect a touch.

[0070] However, this is merely an example and various embodiments are not limited thereto. For example, information related to the operation of the charging management device (2000) may be provided as auditory information. In this case, the output device (530) may further include an audio output device (e.g., a speaker) configured to output an audio signal to the outside of the charging management device (2000).

[0071] According to an embodiment, the processor (540) may control at least one other component of the charge management device (2000) (e.g., communication circuit (510), memory (520), and output device (530)) and perform various data processing or operations.

[0072] According to an embodiment, the processor (540) may execute software stored in the memory (520) to control at least one other component (e.g., hardware or software component) of the charging management device (2000) connected to the processor (540) and perform operations of the charging management device (2000).

[0073] In an embodiment, the processor (540) may manage the depth of charge of the battery unit (110) based on various information obtained through the battery management device (300). For example, the processor (540) may predict the depth of rapid charge by reflecting the degree of degradation of the battery unit (110). In an embodiment, the depth of charge may indicate the point in time at which lithium plating occurs.

[0074] According to an embodiment, the processor (540) may include a parameter extraction unit (541), a core parameter selection unit (542), and a depth prediction unit (543). The operations of the parameter extraction unit (541), the core parameter selection unit (542), and the depth prediction unit (543) may be described with reference to FIG. 3.

[0075] FIG. 3 is a flowchart illustrating the operation of a charging management device according to one embodiment disclosed in this document.

[0076] Referring to FIG. 3, the charging management device (2000) may include a step (S101) of extracting a plurality of parameters related to the battery unit (110) in a parameter extraction unit (541), a step (S102) of selecting one or more core parameters among the plurality of parameters in a core parameter selection unit (542), and a step (S103) of predicting the charging depth of the battery unit (110) based on the core parameters in a depth prediction unit (543).

[0077] In step S101, the parameter extraction unit (541) can extract a plurality of parameters related to the battery unit (110). For example, the parameter extraction unit (541) can extract parameters used to build a model for predicting the state and / or operation of the battery unit (110). According to an embodiment, when the charging management device (2000) predicts the rapid charging depth of the battery unit (110), the parameter extraction unit (541) can extract parameters used in the rapid charging depth prediction model.

[0078] According to an embodiment, the parameter may be a characteristic value of the battery unit (110). For example, the parameter of the charge depth prediction model according to the degradation of the battery unit (110) may include a characteristic value of the battery unit (110) that affects lithium plating, which is a phenomenon in which lithium is precipitated from an electrode of the battery unit (110). According to an embodiment, the parameter related to lithium plating may include at least one of anode diffusivity, cathode diffusivity, anode tortuosity, cathode tortuosity, anode rate constant, cathode rate constant, anode conductivity, cathode conductivity, and electrolyte initial concentration of the battery unit (110).

[0079] In step S102, the core parameter selection unit (542) may select a plurality of core parameters related to the battery unit (110). For example, the core parameter selection unit (542) may include parameters related to the accuracy of prediction in predicting the rapid charging depth of the battery unit (110). Here, the core parameter may include one or more parameters among the plurality of parameters extracted by the parameter extraction unit (541).

[0080] According to an embodiment, the core parameter selection unit (542) may select parameters highly correlated with the predicted operation among the parameters used in the prediction model of the charge management device (2000) as core parameters. For example, the core parameter selection unit (542) may select major parameters that affect the accuracy or performance of the charge depth prediction model among a plurality of parameters as core parameters. According to an embodiment, the core parameters may include parameters that have a large degree of influence on lithium plating. In another aspect, the core parameters may include parameters associated with the deterioration of the battery unit (110).

[0081] According to an embodiment, the key parameters may include electrochemical parameters associated with the battery unit (110). For example, the key parameters may include at least one of diffusivity, tortuosity, conductivity, rate constant, and initial concentration of the electrolyte for the positive or negative electrode.

[0082] In step S103, the depth prediction unit (543) can predict the depth of charge of the battery unit (110) based on the selected key parameters. Here, the depth of charge may refer to the point in time at which lithium plating occurs in the battery unit (110). According to an embodiment, the depth prediction unit (543) can predict charging conditions that minimize degradation in the charging section of the battery unit (110) based on the key parameters. For example, the depth prediction unit (543) can calculate a C-rate corresponding to a specific SOC at each temperature of the battery unit (110). Through this, the depth prediction unit (543) can minimize degradation of the battery unit (110) when the battery unit (110) is rapidly charged.

[0083] FIG. 4 is a flowchart illustrating the operation of a core parameter selection unit according to one embodiment disclosed in this document. FIGS. 5A to 5C are diagrams illustrating the operation of a core parameter selection unit according to one embodiment disclosed in this document. FIG. 6 is a diagram illustrating the operation of a core parameter selection unit according to one embodiment disclosed in this document.

[0084] Referring to FIG. 4, the core parameter selection unit (542) may include a step (S201) of changing the value of at least one parameter among a plurality of parameters within a target range in an environment in which the battery unit (110) is charged based on a specific temperature and a specific C-rate, a step (S202) of comparing the amount of change in the state value of the battery unit (110) due to the changed value of the at least one parameter with a threshold value, and a step (S203) of selecting the at least one parameter as a core parameter when the amount of change in the state value of the battery unit (110) is greater than or equal to the threshold value.

[0085] In step S201, the core parameter selection unit (542) may change the value of at least one parameter among the plurality of parameters within a target range in an environment in which the battery unit (110) is charged based on a specific temperature and a specific C-rate. Here, the plurality of parameters may be a plurality of parameters extracted by the parameter extraction unit (541). According to an embodiment, the core parameter selection unit (542) may change the value of at least one parameter among the plurality of parameters within a target range in various environments. Here, the various environments may be any environments in which the battery unit (110) is charged and discharged. For example, the various environments may include a specific temperature (e.g., 10°C, 25°C, and 35°C, etc.), a specific C-rate (e.g., 0.5C, 1C, and 2C, etc.), a specific SOC (e.g., 0%, 5%, 10%, 15%, …, 100%, etc.), and various combinations thereof in a charging section or a discharging section.

[0086] According to an embodiment, the core parameter selection unit (542) may change the value of at least one parameter among the parameters within a target range in an environment in which the battery unit (110) is charged based on a specific temperature and a specific C-rate. Through this, the core parameter selection unit (542) may select core parameters based on information about the parameters of the battery unit (110) in various charging and discharging environments.

[0087] According to an embodiment, the target range may be a range in which the value of a parameter changes depending on the degree of degradation of the battery unit (110). A description related to the target range is provided with reference to FIGS. 5A to 5C.

[0088] Referring to FIGS. 5A to 5C, the correspondence between parameters and voltages is graphically illustrated. FIG. 5A may be a correspondence between parameter E and voltage, FIG. 5B may be a correspondence between parameter F and voltage, and FIG. 5C may be a correspondence between parameter G and voltage. According to an embodiment, parameters E, F, and G may be key parameters. For example, parameter E may be cathode diffusivity, parameter F may be cathode tortuosity, and parameter G may be a rate constant.

[0089] According to an embodiment, the core parameter selection unit (542) can determine the first degradation level based on the degree to which the resistance or voltage of the battery unit (110) changes by a predetermined value, and can determine the range in which the value of the parameter corresponding to the first degradation level changes as the target range.

[0090] According to an embodiment, the core parameter selection unit (542) may determine the degradation degree of the battery unit (110) based on a change in voltage, current, or resistance of the battery unit (110). According to an embodiment, the core parameter selection unit (542) may determine the first degradation degree based on an increase in resistance or a decrease in voltage of the battery unit (110) in a discharge section of the battery unit (110). For example, the core parameter selection unit (542) may determine a section in which the resistance of the battery unit (110) increases by a predetermined resistance in the discharge section as the first degradation degree. Additionally, the core parameter selection unit (542) may determine a section in which the voltage of the battery unit (110) decreases by a predetermined voltage in the discharge section as the first degradation degree.

[0091] For example, the core parameter selection unit (542) may determine a case in which the voltage decrease amount of the battery unit (110) in the discharge section of the battery unit (110) is a first change amount (e.g., 0.05 V) as the first degradation degree. Referring to FIGS. 5A to 5C, the core parameter selection unit (542) may determine a range in which the voltage decrease amount of the battery unit (110) is 0.05 V, that is, a range in which the voltage of the battery unit (110) changes from 3.69 V to 3.64 V, as the first degradation degree.

[0092] According to an embodiment, the core parameter selection unit (542) may determine a range in which the value of a parameter corresponding to the first degradation degree changes as a target range. In another aspect, the target range may include a range of values ​​of a parameter corresponding to the first degradation degree of the battery unit (110).

[0093] For example, referring to FIG. 5A, the parameter E (e.g., cathode diffusivity) may have a range of values ​​(e.g., 1.25 to 0.1 (unit: 10-14)) corresponding to a voltage range (e.g., 3.69 V to 3.64 V). Accordingly, the target range may include a range in which the value of the parameter E (e.g., cathode diffusivity) is in the range of 1.25 to 0.1 (unit: 10-14).

[0094] Referring to FIG. 5b, parameter F (e.g., cathode curvature) may have a range of values ​​(e.g., 8.5 to 17.8) corresponding to a voltage range (e.g., 3.69 V to 3.64 V). Accordingly, the target range may include a range in which the values ​​of parameter F (e.g., cathode curvature) are in the range of 8.5 to 17.8.

[0095] Referring to FIG. 5c, the parameter G (e.g., cathodic rate constant) may have a range of values ​​(e.g., 1.5 to 0.1 (unit: 10-5)) corresponding to a voltage range (e.g., 3.69 V to 3.64 V). Accordingly, the target range may include a range in which the value of the parameter G (e.g., cathodic rate constant) is in the range of 1.5 to 0.1 (unit: 10-5).

[0096] Referring again to FIG. 4, in step S202, the core parameter selection unit (542) can compare the change in the state value of the battery unit (110) due to the changed value of at least one parameter with a threshold value.

[0097] According to an embodiment, the key parameter selection unit (542) can calculate a state value of the battery unit (110) based on the changed value of the parameter. Here, the state value of the battery unit (110) can include the depth of charge of the battery unit (110). In addition, the key parameter selection unit (542) can calculate the depth of charge of the battery unit (110) using a known depth of charge prediction method. Therefore, the key parameter selection unit (542) can calculate the state of charge value (e.g., depth of charge) of the battery unit (110) based on the target range (i.e., range of values ​​of the parameter) determined in FIGS. 5A to 5C. Thus, the key parameter selection unit (542) can compare the amount of change in the state value of the battery unit (110) with a threshold value. A description related to step S202 is described with reference to FIG. 6.

[0098] Referring to Fig. 6, the core parameter selection unit (542) can calculate the amount of change in the state value of the battery unit (110). Fig. 6 can mean the amount of change in the state value for a plurality of parameters (A, B, C, D, E, F, G, H, and I). According to an embodiment, parameter A can mean anode diffusivity, parameter B can mean anode tortuosity, parameter C can mean anode rate constant, parameter D can mean anode conductivity, parameter E can mean cathode diffusivity, parameter F can mean cathode tortuosity, parameter G can mean cathode rate constant, parameter H can mean electrolyte initial concentration, and parameter I can mean cathode conductivity.

[0099] According to an embodiment, the core parameter selection unit (542) can calculate the amount of change in the state value of the battery unit (110) using an analysis of variance method. For example, the core parameter selection unit (542) can calculate the amount of change in the state value of the battery unit (110) using an analysis of variance (ANOVA) analysis method. The ANOVA analysis method can be used to analyze the characteristics of two different groups by comparing the means and variances of the two groups. According to an embodiment, the amount of change in the state value of the battery unit (110) calculated by the core parameter selection unit (542) can be represented by a number indicated in the graph of FIG. 6.

[0100] According to an embodiment, the core parameter selection unit (542) can change the value of each of a plurality of parameters within a target range and calculate the amount of change in the status value of the battery unit (110). Here, the status value of the battery unit (110) can include the depth of charge of the battery unit (110).

[0101] According to an embodiment, the core parameter selection unit (542) can change the value of at least one parameter among the plurality of parameters within a target range in a charging section of a specific temperature (e.g., 25°C) and a specific C-rate (e.g., 1C). Here, the target range may be different for each of the plurality of parameters. For example, the range in which the value of parameter E is changed may be the same as the target range of parameter E (e.g., 1.25 to 0.1 (unit: 10-14)), the range in which the value of parameter F is changed may be the same as the target range of parameter F (e.g., 8.5 to 17.8), and the range in which the value of parameter G is changed may be the same as the target range of parameter G (e.g., 1.5 to 0.1 (unit: 10-5)). In addition, the core parameter selection unit (542) can calculate the amount of change in the state value of the battery unit (110) due to the changed value of at least one parameter.

[0102] According to an embodiment, the core parameter selection unit (542) can represent the amount of change in the state value of the battery unit (110) through a gray scale. For example, the core parameter selection unit (542) can represent the magnitude of the amount of change in the state value of the battery unit (110) through a gray scale from 0 to 10. Through this, the core parameter selection unit (542) can quantify the degree of influence of whether a specific value of at least one parameter among a plurality of parameters has influenced the state value (e.g., depth of charge) of the battery unit (110).

[0103] In an embodiment, the core parameter selection unit (542) may compare the change in the state value of the battery unit (110) with a threshold value. Here, the threshold value may be a predetermined value. In an embodiment, the threshold value may be calculated by machine learning as the number of operations of the core parameter selection unit (542) increases.

[0104] Referring back to FIG. 4, in step S203, the key parameter selection unit (542) may select a parameter as a key parameter if the amount of change in the state value of the battery cell is greater than or equal to a threshold value. Through this, the key parameter selection unit (542) may select at least one parameter that has a major influence on the state value (e.g., depth of charge) of the battery unit (110) as a key parameter. According to an embodiment, the selected key parameter may include a parameter related to the negative electrode of the battery cell. For example, the selected key parameter may include at least one of diffusivity, tortuosity, and rate constant for the negative electrode of the battery cell.

[0105] Fig. 7 is a flowchart illustrating the operation of a depth prediction unit according to one embodiment disclosed in this document. Fig. 8 is a flowchart illustrating the operation of a depth prediction unit according to one embodiment disclosed in this document.

[0106] Referring to FIG. 7, the depth prediction unit (543) may include a step (S301) of estimating the values ​​of key parameters based on the ohmic resistance and polarization resistance of the battery unit (110) and a step (S302) of predicting the depth of charge of the battery unit (110).

[0107] In an embodiment, the depth prediction unit (543) can manage the depth of charge of the battery unit (110) based on the state of the battery unit (110). In an embodiment, the depth prediction unit (543) can predict the depth of charge of the battery unit (110) based on key parameters. For example, the depth prediction unit (543) can predict the depth of charge that minimizes deterioration of the battery unit (110).

[0108] In step S301, the depth prediction unit (543) can estimate the values ​​of the key parameters selected by the key parameter selection unit (542). The key parameters may include electrochemical parameters related to the battery unit (110). For example, the key parameters may include at least one of diffusivity, tortuosity, conductivity, rate constant, and initial electrolyte concentration for the positive or negative electrode. It may be impossible to measure the values ​​of these key parameters without destroying the battery. However, the depth prediction unit (543) can estimate the values ​​of the key parameters in a non-destructive manner.

[0109] According to an embodiment, the depth prediction unit (543) may estimate the values ​​of key parameters using a simulator for the battery unit (110). For example, the depth prediction unit (543) may estimate the values ​​of key parameters using a SimBD (Simulator of Battery Dynamics) analysis method. The SimBD analysis method may include a simulator that numerically calculates a pseudo-2D governing equation. Here, the pseudo-2D governing equation may be an equation that includes factors for the depth direction of the battery cell region of the battery unit (110) and the radial direction of each lithium ion particle.

[0110] According to an embodiment, the depth prediction unit (543) may train a cost function used in the simulator based on key parameters. Here, the cost function may refer to a function for training a simulator that predicts the depth of charge. According to an embodiment, the cost function may include a plurality of parameters. For example, the cost function may include design parameters related to design values ​​of battery cells of the battery unit (110) and a plurality of parameters extracted by the parameter extraction unit (541). According to an embodiment, the cost function may include key parameters.

[0111] According to an embodiment, the depth prediction unit (543) can estimate the values ​​of key parameters by training a cost function based on various resistances of the battery unit (110). According to an embodiment, the depth prediction unit (543) can estimate the values ​​of key parameters based on the ohmic resistance and the diffusion resistance of the battery unit (110). Here, the ohmic resistance and the diffusion resistance may refer to the resistance inside the battery unit (110). For example, the ohmic resistance may refer to the resistance generated by the movement of electrons in the current collector, separator, and electrode of the battery unit (110), and the diffusion resistance may refer to the resistance generated in the capacitor inside the battery unit (110) during an electrical / chemical reaction.

[0112] According to an embodiment, the battery management device (300) can calculate the ohmic resistance through the voltage change immediately after the current is applied to the battery unit (110) (e.g., 0 to 1 second), and can calculate the polarization resistance through the voltage change thereafter until a specific time (e.g., 1 to 30 seconds). The ohmic resistance and the polarization resistance can be calculated through a resistance measurement method (e.g., HPPC test). According to an embodiment, the battery management device (300) can calculate the ohmic resistance and the polarization resistance in the discharge section of the battery unit (110). Thus, the depth prediction unit (543) of the charge management device (2000) can obtain the ohmic resistance and the polarization resistance from the battery management device (300).

[0113] According to an embodiment, the depth prediction unit (543) can estimate the value of the key parameter by giving a greater weight to the polarization resistance of the battery unit (110) than to the ohmic resistance of the battery unit (110). For example, the depth prediction unit (543) can calculate the cost function by giving a higher weight to the polarization resistance than to the ohmic resistance by making the proportion of the polarization resistance greater than the proportion of the ohmic resistance in the cost function. Thus, the depth prediction unit (543) can estimate the value of the key parameter based on the cost function. Through this, the depth prediction unit (543) can accurately predict the depth of charge of the battery unit (110) through the voltage or resistance obtained in the discharge section.

[0114] In step S302, the depth prediction unit (543) can predict the depth of charge of the battery unit (110) based on the values ​​of the estimated key parameters. For example, the depth prediction unit (543) can predict the depth of charge of the battery unit (110) using a known depth of charge prediction method. According to an embodiment, the depth prediction unit (543) can predict the depth of metal charge that minimizes deterioration during rapid charging of the battery unit (110) based on the values ​​of the estimated key parameters. The step (S302) in which the depth prediction unit (543) predicts the depth of charge can be described in detail with reference to FIG. 8.

[0115] Referring to FIG. 8, the depth prediction unit (543) may include a step (S401) of estimating values ​​of key parameters based on the ohmic resistance and polarization resistance of the battery unit (110), a step (S402) of calculating a C-rate profile corresponding to the SOC of the battery unit (110) based on the estimated values ​​of the key parameters, and a step (S403) of predicting the depth of charge of the battery unit (110).

[0116] In step S401, the depth prediction unit (543) can estimate the values ​​of key parameters based on the ohmic resistance and polarization resistance of the battery unit. The operation of the depth prediction unit (543) to estimate the values ​​of key parameters has been described with reference to FIG. 7 and may not be repeated here.

[0117] In step S402, the depth prediction unit (543) can calculate a C-rate profile corresponding to the SOC of the battery unit (110) based on the values ​​of the estimated key parameters. According to an embodiment, the depth prediction unit (543) can calculate a C-rate (e.g., 0.5C, 1C, and 2C) corresponding to a specific SOC (e.g., 20%, 40%, 60%, 80%, and 100%, etc.). Here, the depth prediction unit (543) can calculate a C-rate profile corresponding to the SOC for each temperature (e.g., 0°C, 5°C, 10°C, 25°C, and 35°C, etc.).

[0118] In step S403, the depth prediction unit (543) can predict the depth of charge of the battery unit (110) based on the C-rate profile corresponding to the SOC of the battery unit (110). According to an embodiment, for each temperature (e.g., 10°C, 25°C, 35°C, etc.), the depth prediction unit (543) can predict how much current the battery unit (110) should be charged at at a specific SOC (e.g., 20%) based on the calculated C-rate (e.g., 0.5C, 1C, 2C, etc.) corresponding to the SOC (e.g., 20%). Through this, the charge management device (2000) can charge the battery unit (110) by applying different C-rates according to the SOC of the battery unit (110). Thus, the charge depth prediction unit (543) can manage the charge depth so that the battery unit (110) maintains optimal performance within the limit where lithium plating of the battery unit (110) does not occur.

[0119] Although all components constituting the embodiments disclosed in this document have been described as being combined or operating in combination as one, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purpose of the embodiments disclosed in this document, all of the components may be selectively combined and operated one or more times.

[0120] In addition, terms such as "include," "comprise," or "have" described above, unless specifically stated otherwise, mean that the corresponding component can be included, and therefore should be interpreted to include other components rather than excluding other components. All terms, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document belong, unless otherwise defined. Commonly used terms, such as terms defined in a dictionary, should be interpreted to be consistent with the contextual meaning of the relevant technology, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

[0121] The foregoing disclosure outlines features of several embodiments to enable those skilled in the art to better understand the aspects of the present disclosure. Those skilled in the art will readily appreciate that the present disclosure can be readily used as a basis for designing or modifying other structures to achieve the same purposes or advantages of the embodiments introduced herein. Furthermore, those skilled in the art will recognize that such equivalent structures do not depart from the scope of the present disclosure, and that various changes, substitutions, and modifications can be made herein without departing from the scope of the present disclosure.

Claims

1. A parameter extraction unit that extracts multiple parameters related to a battery cell; A key parameter selection unit for selecting one or more key parameters among the above plurality of parameters; and A charge management device including a depth prediction unit that predicts the depth of charge of the battery cell based on the above key parameters.

2. In claim 1, The above key parameter selection section is, In an environment where the battery cell is charged based on a specific temperature and a specific C-rate, Changing the value of at least one parameter among the above plurality of parameters within the target range, A charging management device that selects at least one parameter as the core parameter when the amount of change in the state value of the battery cell due to the changed value of at least one parameter is greater than or equal to a threshold value.

3. In claim 2, A charge management device wherein the target range is a range of values ​​of at least one parameter corresponding to a first degradation degree of the battery cell.

4. In claim 3, The above key parameter selection section is, A charge management device that determines the first degradation level based on the amount of resistance increase or voltage decrease of the battery cell during the discharge period of the battery cell.

5. In claim 1, A charge management device wherein the above key parameters include at least one of diffusivity, tortuosity and rate constant for the negative electrode of the battery cell.

6. In claim 1, The above depth prediction unit, Estimating the values ​​of the key parameters based on the ohmic resistance and polarization resistance of the battery cell in the discharge section of the battery cell, A charge management device that predicts the depth of charge of the battery cell based on the values ​​of the estimated key parameters.

7. In claim 6, The above depth prediction unit, A charge management device that estimates the value of the key parameter by considering the weight of the polarization resistance of the battery cell greater than the ohmic resistance of the battery cell.

8. In claim 6, The above depth prediction unit, A charge management device that calculates a C-rate profile corresponding to the SOC of the battery cell based on the values ​​of the estimated key parameters and predicts the depth of charge of the battery cell based on the profile.

9. A step of extracting multiple parameters related to a battery cell; A step of selecting one or more core parameters among the above plurality of parameters; and A charge management method comprising a step of predicting the depth of charge of the battery cell based on the above key parameters.

10. In claim 9, The step of selecting one or more core parameters among the above multiple parameters is: In an environment where the battery cell is charged based on a specific temperature and a specific C-rate, A step of changing the value of at least one parameter among the plurality of parameters within a target range; and A charging management method comprising a step of selecting at least one parameter as the core parameter when the amount of change in the state value of the battery cell due to the changed value of at least one parameter is greater than or equal to a threshold value.

11. In claim 10, A charge management method wherein the target range is a range of values ​​of the at least one parameter corresponding to a first degradation level of the battery cell.

12. In claim 11, The step of selecting one or more core parameters among the above multiple parameters is: A charge management method further comprising a step of determining the first degradation degree based on an increase in resistance or a decrease in voltage of the battery cell during a discharge period of the battery cell.

13. In claim 9, A charge management method wherein the above key parameters include at least one of diffusivity, tortuosity and rate constant for the negative electrode of the battery cell.

14. In claim 9, The step of predicting the depth of charge of the above battery cell is: A step of estimating the values ​​of the key parameters based on the ohmic resistance and polarization resistance of the battery cell in the discharge section of the battery cell; and A charge management method comprising a step of predicting a depth of charge of the battery cell based on values ​​of estimated key parameters.

15. In claim 14, The step of predicting the depth of charge of the above battery cell is: A charge management method comprising a step of estimating the value of the key parameter by considering a greater weight of the polarization resistance of the battery cell than the ohmic resistance of the battery cell.

16. In claim 14, The step of predicting the depth of charge of the above battery cell is: A charge management method further comprising a step of calculating a C-rate profile corresponding to the SOC of the battery cell based on the values ​​of the estimated key parameters.

17. In claim 16, The step of predicting the depth of charge of the battery cell based on the values ​​of the estimated key parameters is as follows: A charging management method for predicting the charging depth based on the above profile.

Images