Battery diagnostic device and operation method thereof

Abstract

A battery diagnostic device, according to one embodiment disclosed in the present document, comprises: an interface for acquiring a first charging profile at the middle of life (MOL) of a battery cell; and at least one processor for predicting a second charging profile by using a battery physical model indicating a battery state, and an input parameter dependent on a change in a degradation index corresponding to the first charging profile, and calculating a degradation index of the battery cell corresponding to each of the first charging profile and the second charging profile.

Description

Battery diagnostic device and its operating method

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority to Korean Patent Application No. 10-2024-0037910, filed March 19, 2024, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] One embodiment disclosed in this document relates to a battery diagnostic device and an operating method thereof.

[0005] Research and development on secondary batteries has been actively underway recently. The term "secondary battery" refers to a rechargeable battery, encompassing both conventional Ni / Cd and Ni / MH batteries, as well as more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries boast a significantly higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight form, making them ideal power sources for mobile devices. Recently, their use has expanded to include power sources for electric vehicles, drawing attention as a next-generation energy storage medium.

[0006] To maintain and manage the performance of these secondary batteries, various methods exist for predicting their degradation. In particular, open circuit voltage (OCV) profiles can be used to predict secondary battery degradation during operation.

[0007] One purpose of the embodiments disclosed in this document is to provide a battery diagnostic device and an operating method thereof for predicting the degree of degradation of a battery cell by predicting the open circuit voltage profile of the battery cell.

[0008] 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 to which the present invention pertains from the description below.

[0009] A battery diagnostic device according to an embodiment disclosed in the present document may include an interface for obtaining a first charge profile at the Middle of Life (MOL) of a battery cell; and at least one processor for predicting a second charge profile using a battery physical model representing a state of the battery and input parameters dependent on a change in a degradation index corresponding to the first charge profile, and calculating a degradation index of the battery cell corresponding to each of the first charge profile and the second charge profile.

[0010] According to one embodiment, the processor may use a Markov Chain-Monte Carlo (MCMC) algorithm to calculate a degradation index corresponding to each of the first charging profile and the second charging profile.

[0011] According to one embodiment, the degradation indicator may include a Loss of Lithium Inventory (LLI) distribution, which is a parameter related to lithium loss in the MOL relative to the BOL (Beginning of Life) of the battery cell, and a Loss of Active Material (LAM) distribution, which is a parameter related to active material loss in the MOL relative to the BOL in the positive electrode of the battery cell.

[0012] According to one embodiment, the processor may predict a degree of degradation of the battery cell based on an LLI distribution and an LAM distribution of the battery cell, and may predict the degree of degradation of the battery cell based on an average value or a mode value of each of the LLI distribution and the LAM distribution.

[0013] According to one embodiment, the battery physics model may include a Pseudo two dimensional (P2D) model.

[0014] In one embodiment, the processor may calculate the degradation indicator until an error between the second charging profile and the measured open circuit voltage (OCV) profile of the battery cell reaches a threshold value.

[0015] In one embodiment, the first charging profile and the second charging profile may include data representing changes in voltage of the battery cell over time.

[0016] According to one embodiment, the first charging profile may correspond to a change in voltage of the battery cell measured based on a charging current of the battery cell in the MOL, and the second charging profile may correspond to a change in voltage of the battery cell in an open circuit state predicted by inputting the first charging profile into the battery physical model.

[0017] A battery diagnostic device according to an embodiment disclosed in the present document may include the steps of: obtaining a first charge profile at the Middle of Life (MOL) of a battery cell; calculating a first degradation index of the battery cell corresponding to the first charge profile; predicting a second charge profile of the battery cell depending on a change in the first degradation index based on a battery physical model representing a state of the battery; and calculating a second degradation index of the battery cell corresponding to the second charge profile.

[0018] According to one embodiment, the step of calculating the first degeneration index and the second degeneration index may be performed using an MCMC (Markov Chain-Monte Carlo) algorithm.

[0019] According to one embodiment, each of the first degradation indicator and the second degradation indicator may include a Loss of Lithium Inventory (LLI) distribution, which is a parameter related to lithium loss in the MOL relative to the BOL (Beginning of Life) of the battery cell, and a Loss of Active Material (LAM) distribution, which is a parameter related to active material loss in the MOL relative to the BOL in the positive electrode of the battery cell.

[0020] A method of operating a battery diagnostic device according to one embodiment may further include a step of predicting a degree of degradation of the battery cell based on an average value or a mode value of each of the LLI distribution and the LAM distribution included in the second degradation index.

[0021] According to one embodiment, the battery physics model may include a Pseudo two dimensional (P2D) model.

[0022] According to one embodiment, the step of predicting the second charge profile of the battery cell may be performed until an error between the second charge profile and a previously measured open circuit voltage (OCV) profile of the battery cell reaches a threshold value.

[0023] In one embodiment, the first charging profile and the second charging profile may include data representing changes in voltage of the battery cell over time.

[0024] According to one embodiment, the first charging profile may correspond to a change in voltage of the battery cell measured based on a charging current of the battery cell in the MOL, and the second charging profile may correspond to a change in voltage of the battery cell in an open circuit state predicted by inputting the first charging profile into the battery physical model.

[0025] According to one embodiment disclosed in this document, the degradation of a battery cell can be predicted without measuring the open circuit voltage profile of the battery cell.

[0026] The effects according to the embodiments disclosed in this document are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art according to the disclosure of this document.

[0027] FIG. 1 is a drawing for explaining the configuration of a battery diagnostic device according to an embodiment disclosed in this document.

[0028] FIG. 2 is a drawing for explaining the operation of a battery diagnostic device according to an embodiment disclosed in this document.

[0029] FIGS. 3A to 3C are diagrams illustrating degradation indicators according to one embodiment disclosed in this document.

[0030] FIGS. 4A to 4D are diagrams illustrating data generated by a battery diagnostic device according to an embodiment disclosed in this document.

[0031] FIG. 5 is a flowchart illustrating the operation of a battery diagnostic device according to an embodiment disclosed in this document.

[0032] FIG. 6 is a drawing for explaining a computing system equipped with a battery diagnostic device according to an embodiment disclosed in this document.

[0033] Hereinafter, embodiments disclosed in this document will be described in detail with reference to exemplary drawings. When designating components in each drawing, it should be noted that, where possible, identical components are given identical reference numerals, even if they appear in different drawings. Furthermore, when describing embodiments disclosed in this document, detailed descriptions of related known structures or functions will be omitted if they are deemed to hinder understanding of the embodiments disclosed in this document.

[0034] In describing the components of the embodiments disclosed in this document, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only intended to distinguish the components from other components and do not limit the nature, order, or sequence of the components. In addition, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning they have in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined in this application.

[0035] FIG. 1 is a drawing for explaining the configuration of a battery diagnostic device according to an embodiment disclosed in this document.

[0036] The battery pack (10) may include a plurality of battery cells (11, 12, 13, 14). Referring to FIG. 1, the battery pack (10) is illustrated as including four battery cells, but is not limited thereto, and the battery pack (10) may be configured to include n (n is a natural number) battery cells. In addition, the battery pack (10) may be configured to include at least one battery module (not shown), which is a set of at least one battery cell. Here, the plurality of battery cells (11, 12, 13, 14) may be, but are not limited to, a lithium ion (Li-ion) battery, a nickel-hydrogen (Ni-H) battery, etc.

[0037] The battery pack (10) may be configured to supply power to a target device (not shown), and for this purpose, the battery pack (10) may be electrically connected to the target device (not shown). Here, the target device (not shown) may include an electrical, electronic, or mechanical device that operates by receiving power from the battery pack (10). For example, the target device (not shown) may be, but is not limited to, a two-wheeled electric vehicle such as an electric vehicle (EV) or an electric scooter. In addition, when the target device is a two-wheeled electric vehicle such as an electric scooter, the battery pack (10) mounted on the two-wheeled electric vehicle may be replaceable through a battery swapping station (BSS).

[0038] A battery diagnostic device (100) may be configured to predict the degradation level of each of a battery pack (10) and a plurality of battery cells (11, 12, 13, 14) included in the battery pack (10). Referring to FIG. 1, the battery diagnostic device (100) may include an interface (110), at least one processor (120), and a memory (130).

[0039] The interface (110) can obtain a charging profile of each of the plurality of battery cells (11, 12, 13, 14) included in the battery pack (10). Here, the charging profile may include, but is not limited to, data related to changes in the voltage of each of the plurality of battery cells (11, 12, 13, 14) over time. The interface (110) can measure and / or predict the voltage of each of the plurality of battery cells (11, 12, 13, 14) based on the current flowing in the battery pack (10) and / or the internal resistance.

[0040] According to one embodiment, the interface (110) may obtain a charging profile by applying voltage and / or current to the battery pack (10). In this case, the interface (110) may include various circuits for applying voltage and / or current to the battery pack (10) and a processor for calculating and / or processing the obtained charging profile.

[0041] According to one embodiment, the interface (110) can indirectly obtain a measured charging profile from the battery pack (10). In this case, the interface (110) may further include a communication module for communicating with the battery pack (10) via wires and / or wirelessly.

[0042] According to one embodiment, the interface (110) can obtain a charging profile of each of the plurality of battery cells (11, 12, 13, 14) when a charging / discharging operation is performed on the battery pack (10). For example, the interface (110) can measure a charging / discharging current of the battery pack (10) and / or obtain data related to a change in voltage of each of the plurality of battery cells (11, 12, 13, 14) over time based on the measured charging / discharging current.

[0043] The processor (120) can control the overall operation of the battery diagnostic device (100). Here, the processor can execute software to control at least one other component (e.g., hardware or software) of the battery diagnostic device (100), or perform operations such as processing and / or calculating various data. In addition, referring to FIG. 1, although the processor (120) is illustrated as being one, it is not limited thereto, and the battery diagnostic device (100) can be configured to include at least one processor.

[0044] The processor (120) can calculate a degradation index of each of the plurality of battery cells (11, 12, 13, 14). According to one embodiment, the processor (120) can be configured to calculate a degradation index of each of the plurality of battery cells (11, 12, 13, 14) based on a charging profile of each of the plurality of battery cells (11, 12, 13, 14).

[0045] According to one embodiment, the processor (120) may calculate a degradation index of each of the plurality of battery cells (11, 12, 13, 14) based on various internal algorithms. The degradation index may include various indices indicating how much each of the battery cells (11, 12, 13, 14) has degraded in the MOL (Middle of Life) compared to the BOL (Beginning of Life). For example, the degradation index may include, but is not limited to, an LLI (Loss of Lithium Inventory) indicating how much lithium has been lost in the MOL compared to the BOL of each of the plurality of battery cells (11, 12, 13, 14), and an LAM (Loss of Active Material) indicating how much active material has been lost in the MOL compared to the BOL.

[0046] According to one embodiment, the processor (120) may calculate a degradation index of each of the plurality of battery cells (11, 12, 13, 14) using a Markov Chain-Monte Carlo (MCMC) algorithm. The processor (120) may input the charging profiles of each of the plurality of battery cells (11, 12, 13, 14) acquired by the interface (110) into the MCMC algorithm to calculate a degradation index of each of the plurality of battery cells (11, 12, 13, 14).

[0047] The MCMC (Markov Chain-Monte Carlo) algorithm may be an algorithm that extracts a sample with a desired static distribution by inferring a probability distribution based on the composition of a Markov Chain through Monte-Carlo simulation.

[0048] Here, the Markov chain property can represent the property of a probability distribution in which, when transitioning to a second state following a first state, the probability of the second state depends only on the probability of the first state. For example, the weather on a specific day is not unrelated to the weather on the day before that day and the day before that. If it rained the day before a specific day, the probability of rain on that day may be higher than if it did not rain the day before that day. In cases like this, in a Markov chain state, the probability distribution when a state change occurs can be expressed in the form of a conditional probability of the probability distribution of the previous state.

[0049] Here, Monte Carlo can be defined as a process of inferring infinite simulation results from finite simulation results. In other words, Monte Carlo can be a simulation method that obtains random samples by sampling an arbitrary probability distribution and probabilistically calculates the value of a function using the obtained random samples. In other words, the MCMC algorithm can be defined as an algorithm that analyzes probability distributions based on the Markov chain concept through Monte Carlo simulation.

[0050] When the MCMC algorithm is repeatedly executed, the input related to the next sample recommended by the sample generated as a result of the MCMC algorithm can be utilized as the input of the MCMC algorithm. That is, when the MCMC algorithm is repeatedly executed, the next sample recommended by the sample generated as a result of the analysis of the MCMC algorithm can be reanalyzed to find the optimal sample. According to one embodiment, the processor (120) can repeatedly analyze the charging profile of each of the plurality of battery cells (11, 12, 13, 14) acquired by the interface (110) based on the MCMC algorithm to calculate the degradation index of each of the plurality of battery cells (11, 12, 13, 14).

[0051] According to one embodiment, the result of analyzing the charging profile based on the MCMC algorithm, i.e., the degradation index of each of the plurality of battery cells (11, 12, 13, 14), may be expressed in the form of a probability distribution. According to the example described above, the result of analyzing the charging profile of each of the plurality of battery cells (11, 12, 13, 14) through the MCMC algorithm may include, but is not limited to, the distribution of the LLI and LAM of each of the plurality of battery cells (11, 12, 13, 14).

[0052] Here, the distribution of the degradation index of each of the plurality of battery cells (11, 12, 13, 14) analyzed through the MCMC algorithm may be different from the degradation degree of each of the plurality of battery cells (11, 12, 13, 14) predicted based on the open circuit voltage (OCV) of each of the plurality of battery cells (11, 12, 13, 14) predicted through the charge profile of each of the plurality of battery cells (11, 12, 13, 14). This is because the degradation degree of the battery cell has relatively high accuracy when measured when the battery cell is in an open circuit state, i.e., when no current flows through the battery cell, whereas the charge profile of each of the plurality of battery cells (11, 12, 13, 14) acquired by the interface (110) is the voltage of each of the plurality of battery cells (11, 12, 13, 14) acquired when a charge current is applied to the battery pack (10).

[0053] The processor (120) can predict and / or generate a sample, i.e., a new voltage profile, recommended by the degradation index of each of the plurality of battery cells (11, 12, 13, 14) calculated based on the MCMC algorithm.

[0054] According to one embodiment, the processor (120) can predict the charging profile of each battery cell (11, 12, 13, 14) based on a battery physical model representing the state of the battery. For the electrochemical analysis of the battery cells (11, 12, 13, 14), there are various physical models that briefly represent the state of the battery cells (11, 12, 13, 14), among which the P2D (Pseudo two-Dimensional) model is a model for analyzing the electrochemical state of the battery cells (11, 12, 13, 14) through two-dimensional analysis of the electrode direction and the electrolyte direction of the battery cells (11, 12, 13, 14), and can be expressed by various equations. For example, the P2D model can be expressed by, but is not limited to, equations representing lithium ion current and electron movement according to electric fields in electrolytes and solids based on Ohm's Law, equations representing lithium ion diffusion within active materials based on Fick's Law, etc. In one embodiment, the battery physics model may include Pseudo two-dimensional (P2D).

[0055] The processor (120) can predict the charging profile of each of the battery cells (11, 12, 13, 14) by inputting various input parameters into the P2D model. Predicting the charging profile of the battery cells (11, 12, 13, 14) may correspond to the meaning of generating an equivalent battery cell that exhibits the same voltage profile as the predicted charging profile of the battery cells (11, 12, 13, 14). That is, the processor (120) can generate a new equivalent battery cell by inputting various input parameters into the P2D model and obtain the predicted charging profile from the equivalent battery cell. Here, in order to generate the equivalent battery cell, unique parameters of each of the battery cells (11, 12, 13, 14) and degradation parameters that reflect the degree of degradation of each of the battery cells (11, 12, 13, 14) may be required.

[0056] The processor (120) can generate input parameters for generating an equivalent battery cell based on the degradation indices of each of the plurality of battery cells (11, 12, 13, 14). The processor (120) can generate the above-described degradation parameters based on the degradation indices of each of the plurality of battery cells (11, 12, 13, 14), and input the generated degradation parameters and the unique parameters of each of the plurality of battery cells (11, 12, 13, 14) into a P2D model to generate an equivalent battery cell. Here, the degradation parameters may be dependent variables that depend on changes in the degradation indices of each of the plurality of battery cells (11, 12, 13, 14), but are not limited thereto.

[0057] According to one embodiment, the degradation index of each of the plurality of battery cells (11, 12, 13, 14) produced by the processor (120) may include the distribution of LLI and LAM of each of the plurality of battery cells (11, 12, 13, 14). The processor (120) may use, but is not limited to, an average value and / or a mode value of the LLI and LAM distribution of each of the plurality of battery cells (11, 12, 13, 14) to generate a degradation parameter based on the LLI and LAM distribution of each of the plurality of battery cells (11, 12, 13, 14).

[0058] According to one embodiment, the processor (120) can calculate a new degradation index based on the predicted charge profile of the equivalent battery cell. That is, the processor (120) can analyze the charge profile of the equivalent battery cell using an MCMC algorithm to calculate a new degradation index (LLI distribution and LAM distribution).

[0059] The processor (120) can predict the degradation degree of each of the plurality of battery cells (11, 12, 13, 14). According to one embodiment, the processor (120) can predict the degradation degree of each of the plurality of battery cells (11, 12, 13, 14) based on a degradation index calculated based on a critical charge profile. For example, the processor (120) can predict the degradation degree of each of the plurality of battery cells (11, 12, 13, 14) based on an average value and / or a mode of an LLI distribution and an LAM distribution of each of the plurality of battery cells (11, 12, 13, 14) calculated based on a critical charge profile, but is not limited thereto.

[0060] According to one embodiment, the processor (120) can predict the degree of degradation of each of the plurality of battery cells (11, 12, 13, 14) based on the degree of shift and shrink predicted based on the average value and / or mode value of the LLI distribution and LAM distribution of each of the plurality of battery cells calculated based on the critical charge profile. Here, shrink may be related to a phenomenon in which the capacity axis of the voltage profile is reduced when loss of active material occurs due to a cause such as the degree of disorder of the crystal structure inside the plurality of battery cells (11, 12, 13, 14) and / or destruction or loss of particles of the cathodes and anodes of the plurality of battery cells (11, 12, 13, 14), and shift may be related to a phenomenon in which the capacity axis of the voltage profile of each of the plurality of battery cells (11, 12, 13, 14) is shifted in a parallel direction in a positive direction when loss of lithium inventory (LLI) occurs due to a phenomenon in which lithium ions are consumed, such as thickening of the solid electrolyte interphase (SEI) layer formed on the cathode surface of the plurality of battery cells (11, 12, 13, 14), decomposition of the electrolyte, or lithium plating.

[0061] The memory (130) can store various data (e.g., commands, parameters, charging profiles, algorithms, etc.) for the operation of the battery diagnostic device (100). According to one embodiment, the memory (130) can include, but is not limited to, a volatile memory device such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), or a non-volatile memory device such as a read only memory (ROM), a programmable ROM (PROM), or a flash memory.

[0062] According to one embodiment, the battery diagnostic device (100) may be formed integrally with the battery pack (10). In this case, the battery diagnostic device (100) may be configured to be included in a BMS (Battery Management System) that controls the battery pack (10).

[0063] According to one embodiment, the battery diagnostic device (100) may be formed separately from the battery pack (10). In this case, the battery diagnostic device (100) may be connected to the battery pack (10) via a wired and / or wireless network, and the battery diagnostic device (100) may be implemented in various devices such as a cloud server, a charger, or a charger / discharger.

[0064] According to one embodiment, the battery diagnostic device (100) can transmit the degradation index, predicted degradation degree, etc. of each of the plurality of battery cells (11, 12, 13, 14) to an external device (e.g., a cloud server or a user terminal). The cloud server can be configured to provide the degradation degrees of the plurality of battery cells (11, 12, 13, 14) to a plurality of users, and the user terminal can include a terminal such as a personal computer (PC) or a smartphone.

[0065] According to one embodiment, the battery diagnostic device (100) may be included in a BSS (Battery Swapping System). Here, the BSS may be a system having a slot into which a battery pack (10) can be inserted and capable of charging the inserted battery pack (10).

[0066] FIG. 2 is a drawing for explaining the operation of a battery diagnostic device according to an embodiment disclosed in this document.

[0067] As described above in the description of FIG. 1, the interface (110, see FIG. 1) can directly or indirectly obtain the charging profile of each of the plurality of battery cells (11, 12, 13, 14, see FIG. 1) (a). Here, the charging profile obtained by the interface (110) may include data related to the change in voltage of each of the plurality of battery cells (11, 12, 13, 14) measured in a state in which a charging current flows through the plurality of battery cells (11, 12, 13, 14) over time. Hereinafter, the charging profile obtained by the interface (110) is collectively referred to as a first charging profile.

[0068] The processor (120, see FIG. 1) can calculate a degradation index of each of the plurality of battery cells (11, 12, 13, 14) based on the first charging profile (b). According to one embodiment, the processor (120) can calculate a degradation index corresponding to the first charging profile of each of the plurality of battery cells (11, 12, 13, 14) using an MCMC (Markov Chain-Monte Carlo) algorithm. Here, the degradation index of each of the plurality of battery cells (11, 12, 13, 14) may include, but is not limited to, LLI (Loss of Lithium Inventory), which is a parameter related to lithium loss in MOL compared to BOL, and LAM (Loss of Active Material), which is a parameter related to active material loss in MOL compared to BOL.

[0069] According to one embodiment, the degradation index of each of the multiple battery cells (11, 12, 13, 14) calculated using the MCMC algorithm may be expressed in the form of a distribution rather than a specific value. Details related to this will be described later in the description of FIGS. 3A to 3C.

[0070] The processor (120) may generate input parameters to be input into a battery physical model representing the state of the battery (c). Here, the input parameters may be parameters that depend on changes in the degradation indices of each of the plurality of battery cells (11, 12, 13, 14) calculated using an MCMC algorithm. According to one embodiment, the processor (120) may generate the input parameters based on, but not limited to, the average value or the mode of the LLI distribution and the LAM distribution of each of the plurality of battery cells (11, 12, 13, 14). In addition, the input parameters generated by the processor (120) are not limited to specific parameters, and various parameters for inputting into the P2D model described with reference to FIG. 1 may be calculated based on the degradation indices of each of the plurality of battery cells (11, 12, 13, 14).

[0071] The processor (120) can predict a charging profile by inputting the generated input parameters into a battery physics model representing the state of the battery (d). As described above in the description of FIG. 1, the battery physics model may include, but is not limited to, a Pseudo two dimensional (P2D) model. That is, the processor (120) can generate an equivalent battery cell corresponding to the input parameters using the battery physics model, and generate a voltage profile predicted from the generated equivalent battery cell. Hereinafter, the voltage profile predicted from the equivalent battery cell is collectively referred to as a second charging profile.

[0072] The processor (120) can calculate an error between the second charging profile and the open circuit voltage (OCV) profile according to the experimental results of each of the plurality of battery cells (11, 12, 13, 14) stored in advance. Here, the error can be calculated by various methods (for example, RME, RMSE, MAPE, etc. between the second charging profile and the open circuit voltage profile), and is not limited to a specific example.

[0073] According to one embodiment, the processor (120) may generate a new degradation index corresponding to each equivalent battery cell based on the second charging profile. The processor (120) may generate input parameters based on the new degradation index corresponding to each equivalent battery cell, and input the generated input parameters into a battery physics model to predict a voltage profile corresponding to the new equivalent battery cell.

[0074] This feedback process can be repeated until the error (e.g., RME, RMSE, MAPE, etc.) between the charge profile of the equivalent battery cell and the open circuit voltage profile according to the degree of degradation measured through the experimental results of each of the plurality of battery cells (11, 12, 13, 14) stored in advance reaches a preset threshold value. Here, the preset threshold value is not limited to a specific value and can be set and changed in various ways. In addition, the above-described feedback process can be repeated for a preset threshold number of times, and the threshold number here is also not limited to a specific value and can be set and changed in various ways.

[0075] According to one embodiment, a charging profile in a state where the error between the open circuit voltage profile and the charging profile reaches a preset threshold value, i.e., a critical charging profile, may correspond to an open circuit voltage profile that reflects the degree of degradation of each of the plurality of battery cells (11, 12, 13, 14) predicted based on the above-described P2D model and MCMC algorithm. That is, the battery diagnosis device (100) according to one embodiment disclosed in the present document can predict an open circuit voltage profile with higher accuracy even in a state where a charging current flows through each of the plurality of battery cells (11, 12, 13, 14).

[0076] FIGS. 3A to 3C are diagrams illustrating degradation indicators according to one embodiment disclosed in this document.

[0077] Referring to FIGS. 3A to 3C, examples of degradation indices of each of a plurality of battery cells (11, 12, 13, 14, see FIG. 1) produced by a processor (120, see FIG. 1) are illustrated. The graphs illustrated in FIGS. 3A to 3C represent arbitrary measurement values, and the embodiments disclosed in this document are not limited to these examples.

[0078] The processor (120) can calculate a degradation index corresponding to each of the plurality of battery cells (11, 12, 13, 14) and the equivalent battery cell generated by the battery physics model. According to one embodiment, the processor (120) can calculate a degradation index corresponding to the charge profile of each of the plurality of battery cells (11, 12, 13, 14) and the equivalent battery cell generated by the battery physics model using a Markov Chain-Monte Carlo (MCMC) algorithm.

[0079] According to one embodiment, the degradation indicators may include Loss of Lithium Inventory (LLI), a parameter related to lithium loss at MOL relative to Beginning of Life (BOL) of each of the plurality of battery cells (11, 12, 13, 14) and the battery physics model, and Loss of Active Material (LAM), a parameter related to active material loss at MOL relative to BOL.

[0080] In one embodiment, the degradation indicator may be represented in the form of a distribution rather than a specific value. Referring to FIGS. 3A to 3C, examples of degradation indicators are shown: an LLI distribution of an arbitrary battery cell ( FIG. 3A ), an LAM distribution of the positive electrode of an arbitrary battery cell ( FIG. 3B ), and an LAM distribution of the negative electrode of an arbitrary battery cell ( FIG. 3C ).

[0081] First, examining the LLI distribution of a random battery cell illustrated in Figure 3A, the horizontal axis represents the degree of lithium loss (LLI [Ah]), and the vertical axis represents the number of samples (Sample). In other words, the degradation index calculated by inputting the charge profile into the MCMC algorithm is expressed in the form of a distribution indicating the degree of lithium loss and the corresponding number of samples.

[0082] As a battery cell operates in a field environment, it undergoes repeated charge-discharge cycles. As the LLI distribution illustrated in Figure 3A progresses to the right, the number of charge-discharge cycles (1, 90, 180, 270) gradually increases. As the number of charge-discharge cycles increases, the battery cell deteriorates, leading to an increasing degree of lithium loss.

[0083] According to one embodiment, the processor (120) may generate input parameters based on the average value or the mode of the LLI distribution illustrated in FIG. 3A. For example, when generating input parameters based on the mode, the processor (120) may determine 0, which is the mode of the LLI distribution when the number of charge / discharge cycles is 90, as the LLI value, and may determine 1, which is the mode of the LLI distribution when the number of charge / discharge cycles is 180, as the LLI value. However, this is merely exemplary, and the embodiments disclosed in this document are not limited to these examples.

[0084] Similarly, when looking at the LAM distribution in each of the positive and negative electrodes of an arbitrary battery cell as shown in FIGS. 3B and 3C, the horizontal axis represents the degree of active material loss (LAMp,n[%]) and the vertical axis represents the number of samples (Sample).

[0085] As any battery cell is operated in a field environment, charge and discharge cycles are repeated. As the LLM distribution illustrated in FIGS. 3B and 3C progresses to the right, the number of charge and discharge cycles (1, 90, 180, 270) gradually increases, and as the number of charge and discharge cycles increases, the battery cell tends to deteriorate and the degree of active material loss increases. The processor (120) can generate input parameters based on the average value or mode of the LLM distribution illustrated in FIGS. 3B and 3C.

[0086] FIGS. 4A to 4D are diagrams illustrating data generated by a battery diagnostic device according to an embodiment disclosed in this document.

[0087] Referring to FIGS. 4A and 4B, the degradation indices and open circuit voltages of a battery cell estimated using a charge profile at 10 degrees are shown, and referencing FIGS. 4C and 4D, the degradation indices and open circuit voltages of a battery cell estimated using a charge profile at 45 degrees are shown.

[0088] According to one embodiment, in the plurality of graphs illustrated in each of FIGS. 4A and 4C, the dotted line graphs may correspond to graphs representing degradation indices of battery cells calculated according to one embodiment disclosed in the present document, and the solid line graphs may correspond to graphs representing degradation indices calculated based on any conventional method other than one embodiment disclosed in the present document.

[0089] According to an embodiment disclosed in this document, a battery diagnostic device (100, see FIG. 1) can predict the degradation degree of each of a plurality of battery cells (11, 12, 13, 14, see FIG. 1). Here, the degradation degree may include the degree of lithium loss (LLI) of each battery cell, the degree of active material loss (LAMp) at the positive electrode, the degree of active material loss (LAMn) at the negative electrode, and the State of Health (SOH).

[0090] First, referring to FIG. 4A, the LLI and LAM distributions estimated using the charging profile at 10 degrees tend to follow the LLI and LAM distributions of the battery cell estimated based on the conventional method. The degree of active material loss (LAMn) in the negative electrode differs from the value estimated based on the conventional method, because the degree of active material loss (LAMn) in the negative electrode can be more accurately predicted when measured during the discharge process of the battery cell. The battery diagnostic device (100) calculates the LLI and LAM values ​​based on the charging profile when the charging current flows in the battery cells (11, 12, 13, 14), and thus the above results are derived.

[0091] Also, referring to FIG. 4B, if we look at the open circuit voltage of each of the battery cells (11, 12, 13, 14) estimated using the charge profile at 10 degrees, it can be confirmed that the open circuit voltage (OCV) of each of the plurality of battery cells (11, 12, 13, 14) in the MOL predicted based on the MCMC algorithm does not have a large error from the open circuit voltage value of each of the battery cells (11, 12, 13, 14) in the BOL. The above-described tendency also applies to the case estimated using the charge profile at 45 degrees (FIGS. 4C and 4D). Here, since the result of the MCMC algorithm can be expressed in the form of a distribution as described above in the description of FIGS. 1 and 3, the predicted open circuit voltage profile of the plurality of battery cells (11, 12, 13, 14) in the MOL can also be expressed in the form of a probability distribution based on this distribution. According to one embodiment, the shaded portions shown in FIGS. 4B and 4D may correspond to, but are not limited to, a confidence interval having a 95% confidence level of the open circuit voltage of each of the plurality of battery cells (11, 12, 13, 14) predicted based on the MCMC algorithm.

[0092] FIG. 5 is a flowchart illustrating the operation of a battery diagnostic device according to an embodiment disclosed in this document.

[0093] In step S101, the battery diagnostic device (100) can obtain a first charging profile at the MOL (Middle of Life) of each of the plurality of battery cells (11, 12, 13, 14). According to one embodiment, the first charging profile obtained by the battery diagnostic device (100) can include data related to a change in voltage of each of the plurality of battery cells (11, 12, 13, 14) over time measured while a charging current is flowing in the plurality of battery cells (11, 12, 13, 14).

[0094] In step S102, the battery diagnostic device (100) may calculate a first degradation index corresponding to the first charge profile. According to one embodiment, the first degradation index may include a Loss of Lithium Inventory (LLI) index related to lithium loss in MOL compared to BOL (Beginning of Life) of each of the plurality of battery cells (11, 12, 13, 14) and a Loss of Active Material (LAM) index related to active material loss in MOL compared to BOL.

[0095] According to one embodiment, the battery diagnostic device (100) may calculate a first degradation index based on the Markov Chain-Monte Carlo (MCMC) algorithm. Here, the MCMC algorithm may be an algorithm that extracts a sample having a desired static distribution by inferring a probability distribution based on the configuration of a Markov Chain through Monte Carlo simulation.

[0096] In one embodiment, the first degradation index calculated based on the MCMC algorithm may be expressed in the form of a distribution rather than a specific value. For example, the battery diagnostic device (100) may calculate the first degradation index, i.e., the LLI distribution and the LAM distribution of each of the plurality of battery cells (11, 12, 13, 14), using the MCMC algorithm.

[0097] In step S103, the battery diagnostic device (100) can predict a second charging profile by inputting input parameters into a battery physics model. According to one embodiment, the battery physics model may include, but is not limited to, a P2D (Pseudo two dimensional) model as a physical model representing the state of the battery.

[0098] According to one embodiment, the battery diagnostic device (100) may generate input parameters to be input into a battery physical model. Here, the input parameters may be dependent variables that depend on changes in the first degradation indicator calculated by the battery diagnostic device (100), and may correspond to various variables input into the battery physical model to interpret the state of the battery.

[0099] In step S104, the battery diagnostic device (100) may calculate a second degradation index corresponding to the second charging profile. According to one embodiment, the battery diagnostic device (100) may calculate the second degradation index corresponding to the second charging profile based on the second charging profile and an MCMC algorithm, and the second degradation index may also be expressed in the form of a distribution.

[0100] In step S105, the battery diagnostic device (100) can determine whether the error between the second charging profile and the open circuit voltage (OCV) profile of each of the plurality of battery cells (11, 12, 13, 14) stored in advance is less than a preset threshold value. Here, the error can be calculated based on various methods (e.g., RMSE, RME, MAPE, etc.), and the threshold value is also not limited to a specific value.

[0101] According to one embodiment, when the error between the second charging profile and the pre-stored open circuit voltage profile is greater than or equal to a threshold value (N), the battery diagnostic device (100) may calculate a second degradation index corresponding to the second charging profile and calculate an input parameter that depends on a change in the second degradation index (S106). Here, the battery diagnostic device (100) may calculate the second degradation index based on an MCMC algorithm. Thereafter, the battery diagnostic device (100) may repeat steps S103 to S106 until the error between the predicted charging profile and the pre-stored open circuit voltage profile becomes less than the threshold value.

[0102] According to one embodiment, when the error between the second charging profile and the previously stored open circuit voltage profile is less than the threshold value (N), the battery diagnostic device (100) can predict the degree of degradation of each of the plurality of battery cells (11, 12, 13, 14) based on the second degradation index. For example, the battery diagnostic device (100) can predict the degree of degradation of each of the plurality of battery cells (11, 12, 13, 14) based on the degree of shift and shrink predicted based on the average value and / or the mode value of the LLI distribution and the LAM distribution of each of the plurality of battery cells calculated based on the second charging profile, but is not limited thereto.

[0103] FIG. 6 is a drawing for explaining a computing system equipped with a battery diagnostic device according to an embodiment disclosed in this document.

[0104] Referring to FIG. 6, the computing system (600) may include an MCU (610), a memory (620), an input / output I / F (630), and a communication I / F (640).

[0105] The MCU (610) may be a processor that executes various programs (e.g., a battery cell inspection program, a battery cell degradation prediction program, etc.) stored in the memory (620), processes various data for predicting the degradation of each of a plurality of battery cells (11, 12, 13, 14, see FIG. 1) through these programs, and performs the functions of the battery diagnosis device (100) described with reference to FIG. 1.

[0106] The memory (620) can store various programs for predicting the deterioration of battery cells. In addition, the memory (620) can store information on previously stored open circuit voltage (OCV) profiles.

[0107] Such memories (620) may be provided in multiples as needed. The memories (620) may be volatile memories or non-volatile memories. As volatile memories (620), RAM, DRAM, SRAM, etc. may be used. As non-volatile memories (620), ROM, PROM, EAROM, EPROM, EEPROM, flash memories, etc. may be used. The examples of the memories (620) listed above are merely examples and are not limited to these examples.

[0108] The input / output I / F (630) can provide an interface that enables data transmission and reception between an input device (not shown) such as a keyboard, mouse, or touch panel, and an output device (not shown) such as a display and the MCU (610).

[0109] The communication I / F (640) is a component capable of transmitting and receiving various data with the server, and may be any device capable of supporting wired or wireless communication. For example, a program for predicting battery cell deterioration or various data may be transmitted and received from a separately provided external server via the communication I / F (640).

[0110] In this way, the battery management method according to one embodiment disclosed in this document can be recorded in the memory (620) and executed by the MCU (610).

[0111] In the above, all components constituting the embodiments have been described as being combined or operating in combination as one. However, this is not necessarily limited to such embodiments, and within the scope of the purpose, all components may be selectively combined and operated in one or more combinations. Furthermore, terms such as "include," "comprise," or "have" described above, unless specifically stated to the contrary, imply that the corresponding component may be inherent, and therefore should be interpreted to include other components rather than excluding other components.

[0112] The above description is merely an example of the technical idea disclosed in this document, and those skilled in the art to which the embodiments disclosed in this document pertain may make various modifications and variations without departing from the essential characteristics of the embodiments disclosed in this document.

[0113] Accordingly, the embodiments disclosed in this document are intended to illustrate, rather than limit, the technical concepts disclosed in this document, and the scope of the technical concepts disclosed in this document is not limited by these embodiments. The scope of protection of the technical concepts disclosed in this document should be interpreted by the claims below, and all technical concepts within the equivalent scope should be interpreted as being included within the scope of the rights of this document.

[0114] [Explanation of symbols]

[0115] 10: Battery pack

[0116] 11, 12, 13, 14: Battery cells

[0117] 100: Battery Diagnostic Device

[0118] 110: Interface

[0119] 120: Processor

[0120] 130: Memory

Claims

1. An interface for obtaining a first charge profile at the MOL (Middle of Life) of a battery cell; and A battery diagnostic device comprising at least one processor for predicting a second charging profile using a battery physical model representing a state of a battery and input parameters that depend on changes in a degradation index corresponding to the first charging profile, and calculating a degradation index of the battery cell corresponding to each of the first charging profile and the second charging profile.

2. In the first paragraph, the processor, A battery diagnostic device that calculates a degradation index corresponding to each of the first charging profile and the second charging profile using an MCMC (Markov Chain-Monte Carlo) algorithm.

3. In the second paragraph, the degradation indicator is: A battery diagnostic device comprising a distribution of Loss of Lithium Inventory (LLI), which is a parameter related to lithium loss in the MOL compared to the BOL (Beginning of Life) of the battery cell, and a distribution of Loss of Active Material (LAM), which is a parameter related to active material loss in the MOL compared to the BOL in the positive electrode of the battery cell.

4. In the third paragraph, the processor, A battery diagnostic device that predicts the degree of degradation of the battery cell based on the LLI distribution and the LAM distribution of the battery cell, and predicts the degree of degradation of the battery cell based on the average value or the mode value of each of the LLI distribution and the LAM distribution.

5. In the first paragraph, the battery physical model is, A battery diagnostic device including a P2D (Pseudo two dimensional) model.

6. In the fifth paragraph, the processor, A battery diagnostic device that calculates the degradation index until the error between the second charging profile and the measured open circuit voltage (OCV) profile of the battery cell reaches a threshold value.

7. In the first paragraph, the first charging profile and the second charging profile, A battery diagnostic device comprising data representing changes in voltage of the battery cell over time.

8. In paragraph 7, The first charging profile corresponds to a change in the voltage of the battery cell measured based on the charging current of the battery cell in the MOL, The above second charging profile is, A battery diagnostic device that inputs the first charging profile into the battery physical model to correspond to a change in voltage in the open circuit state of the battery cell predicted.

9. Step of obtaining the first charge profile at the MOL (Middle of Life) of the battery cell; A step of calculating a first degradation index of the battery cell corresponding to the first charging profile; A step of predicting a second charge profile of the battery cell depending on a change in the first degradation indicator in a battery physical model representing the state of the battery; and A method of operating a battery diagnostic device, comprising: a step of calculating a second degradation index of the battery cell corresponding to the second charging profile.

10. In the 9th paragraph, the step of calculating the first degradation index and the second degradation index is as follows: An operating method of a battery diagnosis device performed using the MCMC (Markov Chain-Monte Carlo) algorithm.

11. In the 10th paragraph, each of the first degradation indicator and the second degradation indicator is An operating method of a battery diagnostic device including a distribution of Loss of Lithium Inventory (LLI), which is a parameter related to lithium loss in the MOL compared to the BOL (Beginning of Life) of the battery cell, and a distribution of Loss of Active Material (LAM), which is related to active material loss in the MOL compared to the BOL in the positive electrode of the battery cell.

12. In paragraph 11, A method of operating a battery diagnosis device, further comprising: a step of predicting the degree of degradation of the battery cell based on the average value or the mode value of each of the LLI distribution and the LAM distribution included in the second degradation index.

13. In the 9th paragraph, the battery physical model is, An operating method of a battery diagnostic device including a P2D (Pseudo two dimensional) model.

14. In the 13th paragraph, the step of predicting the second charging profile of the battery cell comprises: An operating method of a battery diagnostic device, wherein the operation is performed until the error between the second charging profile and the measured open circuit voltage (OCV) profile of the battery cell reaches a threshold value.

15. In the 9th paragraph, the first charging profile and the second charging profile are, A method of operating a battery diagnostic device comprising data representing changes in voltage of the battery cell over time.

16. In paragraph 15, The first charging profile corresponds to a change in the voltage of the battery cell measured based on the charging current of the battery cell in the MOL, The above second charging profile is, A method of operating a battery diagnostic device corresponding to a change in voltage in an open circuit state of the battery cell predicted by inputting the first charging profile into the battery physical model.

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