Apparatus and method for diagnosing a battery

Abstract

An apparatus for diagnosing a battery according to an embodiment of the disclosure includes a data acquisition unit configured to acquire battery data including at least one of a voltage, a current, and a temperature of the battery, and a processor configured to estimate a rate of positive electrode active material loss of the battery based on the battery data by referring to a first curve provided to estimate the positive electrode active material loss related to cationic mixing and a second curve provided to estimate the positive electrode active material loss related to particle cracking.

Description

Technical Field

[0001] This disclosure relates to apparatus and methods for diagnosing batteries, and more specifically to apparatus and methods for diagnosing batteries for estimating the loss rate of the positive electrode active material.

[0002] This application claims priority to Korean Patent Application No. 10-2024-0163391, filed on November 15, 2024, and Korean Patent Application No. 10-2025-0043679, filed on April 3, 2025, the disclosures of which are incorporated herein by reference in their entirety. Background Technology

[0003] Recently, demand for portable electronic products such as laptops, cameras, and mobile phones has increased dramatically, and electric vehicles, energy storage batteries, robots, and satellites have seen significant development. Therefore, high-performance batteries that allow for repeated charging and discharging are being actively researched.

[0004] Currently available batteries include nickel-cadmium (NiCd), nickel-metal hydride (NiMH), nickel-zinc (NiZn), and lithium-ion batteries. Among them, lithium-ion batteries have attracted much attention because they have almost no memory effect compared to nickel-based batteries, and also have a very low self-discharge rate and high energy density.

[0005] Existing battery state estimation techniques primarily utilize pseudo-two-dimensional (P2D) models. P2D models mathematically simulate electrochemical reactions and material transport within lithium-ion batteries to predict performance, and they can explain battery behavior by combining particle-level and domain-level phenomena. Specifically, P2D models simulate lithium-ion diffusion and electrochemical reactions by reflecting the porous structure of the electrodes and ion transport within the electrolyte, and they can analyze structural deformation and performance degradation occurring during charging and discharging through changes in concentration gradients and mechanical stress within electrode particles. In this way, P2D models can model battery degradation mechanisms such as the loss of positive electrode active material.

[0006] The P2D model is a physically based, detailed electrochemical model that offers the advantage of accurate predictions based on changes in battery state. Therefore, due to its high prediction accuracy, the P2D model is suitable for detailed analysis of battery degradation mechanisms.

[0007] However, the high predictive accuracy of P2D models requires substantial computational and memory resources. In other words, due to the complex computational processes and large-scale data storage required for applying P2D models, there are limitations to their application in real-time processing system environments. In particular, applying P2D models directly to in-vehicle BMS (Battery Management System) or cloud BMS operating on a network requires significant computational resources, which is inefficient in environments where rapid battery state diagnosis and control are essential.

[0008] Therefore, it is necessary to develop a lightweight model (RM: simplified model) that reduces the complexity of the P2D model and enables real-time processing. Summary of the Invention

[0009] Technical issues

[0010] This disclosure is designed to address problems in the related art, and therefore aims to provide an apparatus and method for diagnosing batteries that can accurately estimate the loss rate of active material in the positive electrode by utilizing battery data based on a lightweight model, and improve the efficiency of battery diagnosis by ensuring real-time performance.

[0011] These and other objects and advantages of this disclosure will become apparent from the following detailed description and will become even more fully apparent from exemplary embodiments of this disclosure. Moreover, it will be readily understood that the objects and advantages of this disclosure can be achieved by the means and combinations thereof as shown in the appended claims.

[0012] Technical solution

[0013] An apparatus for diagnosing a battery according to one aspect of the present disclosure may include: a data acquisition unit configured to acquire battery data including at least one of battery voltage, current, and temperature; and a processor configured to estimate the positive electrode active material loss rate of the battery based on the battery data by referring to a first curve provided for estimating positive electrode active material loss related to cation mixing and a second curve provided for estimating positive electrode active material loss related to particle cracking.

[0014] The first curve can be configured to represent the correspondence between multiple reference battery data and multiple first reference state data.

[0015] The second curve can be configured to represent the correspondence between multiple reference battery data and multiple second reference state data.

[0016] The processor can be configured to determine first reference state data corresponding to the voltage and temperature in the battery data by referring to a first curve.

[0017] The processor can be configured to estimate a first loss rate based on the determined first reference state data.

[0018] The processor can be configured to estimate the first loss rate based on the first reference state data and the damping factor.

[0019] The processor can be configured to determine second reference state data corresponding to the voltage and current in the battery data by referring to a second curve.

[0020] The processor can be configured to estimate the second loss rate based on the determined second reference state data.

[0021] The processor can be configured to estimate the second loss rate based on the second reference state data and the damping factor.

[0022] The processor can be configured to estimate a first reference state data corresponding to the voltage and temperature in the battery data as a first loss rate by referring to a first curve.

[0023] The processor can be configured to estimate a second reference state data corresponding to the voltage and current in the battery data as a second loss rate by referring to a second curve.

[0024] The processor can be configured to estimate the loss rate of the positive electrode active material based on a first loss rate and a second loss rate.

[0025] The processor can be configured to calculate the value obtained by adding the first loss rate and the second loss rate as the loss rate of the positive electrode active material.

[0026] The processor can be configured to calculate the amount of positive active material loss of the battery during the reference time period based on the positive active material loss rate and a reference time period between the previous diagnostic time point and the current diagnostic time point.

[0027] The processor can be configured to calculate the cumulative positive active material loss up to the current diagnostic time point by adding the positive active material loss to the cumulative positive active material loss up to the previous diagnostic time point.

[0028] According to another aspect of this disclosure, the battery pack may include means for diagnosing the battery.

[0029] According to another aspect of this disclosure, a vehicle may include a device for diagnosing a battery.

[0030] According to another aspect of this disclosure, the server may include means for diagnosing the battery.

[0031] A method for diagnosing a battery according to another aspect of this disclosure may include: acquiring battery data including at least one of the battery's voltage, current, and temperature; and estimating the positive electrode active material loss rate of the battery based on the battery data by referring to a first curve provided for estimating the loss of positive electrode active material related to cation mixing and a second curve provided for estimating the loss of positive electrode active material related to volume changes due to particle cracking.

[0032] According to another aspect of this disclosure, a computer-readable storage medium may be a computer-readable storage medium storing a program for executing on a computer a method for diagnosing a battery.

[0033] Beneficial effects

[0034] According to one aspect of this disclosure, the loss rate of positive electrode active material under various operating conditions can be estimated quickly and efficiently by referring to pre-stored first and second curves.

[0035] Furthermore, according to one aspect of this disclosure, the state of a battery can be accurately diagnosed by independently analyzing the loss rate of positive electrode active material due to cation mixing and particle cracking using a first curve and a second curve based on conditions such as voltage, current, and temperature.

[0036] Furthermore, according to one aspect of this disclosure, the process of battery degradation can be comprehensively diagnosed by estimating the positive electrode active material loss rate, which reflects the total proportion of positive electrode active material consumed by the complex interactions of various factors such as cation mixing and particle cracking.

[0037] Furthermore, according to one aspect of this disclosure, the loss rate of the positive electrode active material can be quickly estimated using a curve (e.g., a lookup table, etc.) composed of data pre-calculated using a P2D model, while maintaining an accuracy close to that of the precise calculation results of the P2D model.

[0038] Furthermore, according to one aspect of this disclosure, by using a damping factor to estimate the first and second loss rates, the actual degradation mode of the battery can be simulated more accurately, and the reliability of battery condition diagnosis can be improved.

[0039] Furthermore, according to one aspect of this disclosure, the state of a battery can be accurately diagnosed and battery safety improved by comparing the amount of loss with a threshold.

[0040] The effects of this disclosure are not limited to those described above, and other effects not mentioned will be clearly understood by those skilled in the art based on the description of the claims. Attached Figure Description

[0041] The accompanying drawings illustrate preferred embodiments of the present disclosure and, together with the foregoing disclosure, serve to provide a further understanding of the technical features of the present disclosure; therefore, the present disclosure is not to be construed as limited to the drawings.

[0042] Figure 1 This is a schematic illustration of an apparatus for diagnosing a battery according to an embodiment of the present disclosure.

[0043] Figure 2 This is a schematic diagram illustrating an embodiment of the first curve.

[0044] Figure 3 This is a diagram that schematically illustrates an embodiment of the second curve.

[0045] Figure 4 This is a graph illustrating the correlation between the P2D model estimated using a device for diagnosing batteries according to embodiments of the present disclosure and the loss rate of the positive electrode active material.

[0046] Figures 5 to 7 The diagrams schematically illustrate the SOH, the loss of the first positive electrode active material, and the loss of the second positive electrode active material according to the charge and discharge cycles.

[0047] Figure 8 This is a schematic diagram illustrating a battery pack according to another embodiment of the present disclosure.

[0048] Figure 9 The diagram schematically illustrates a vehicle according to yet another embodiment of the present disclosure.

[0049] Figure 10 This is a schematic diagram illustrating a server according to yet another embodiment of the present disclosure.

[0050] Figure 11 This is a schematic illustration of a method for diagnosing a battery according to yet another embodiment of the present disclosure.

[0051] Figure 12 It is a schematic diagram. Figure 11 The diagram shows the sub-steps of step S1120. Detailed Implementation

[0052] It should be understood that the terms used in the specification and appended claims should not be construed as limited to their general and dictionary meanings, but rather as being interpreted based on the meanings and concepts corresponding to the technical aspects of this disclosure, on the basis of the principle that the inventors are allowed to define the terms appropriately for the best interpretation.

[0053] Therefore, the description presented herein is merely a preferred example for illustrative purposes and is not intended to limit the scope of this disclosure. It should be understood that other equivalents and modifications may be made thereto without departing from the scope of this disclosure.

[0054] In addition, in describing this disclosure, detailed descriptions are omitted herein when it is believed that such detailed descriptions of relevant known elements or functions would obscure the key subject matter of this disclosure.

[0055] Ordinal terms such as “first” and “second” can be used to distinguish one element from another among various elements, but are not intended to limit elements by terminology.

[0056] Throughout this specification, when a part is referred to as “comprising” or “including” any element, it means that the part may further include other elements, without excluding other elements, unless otherwise specifically stated.

[0057] Furthermore, throughout the instruction manual, when one part is referred to as "connected" to another part, it is not limited to the case where they are "directly connected," but also includes the case where they are "indirectly connected," in which another element is inserted between them.

[0058] Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0059] Figure 1 This is a schematic illustration of a device 100 for diagnosing a battery according to an embodiment of the present disclosure.

[0060] refer to Figure 1 The device 100 for diagnosing a battery may include a data acquisition unit 110 and a processor 120. The device 100 for diagnosing a battery may further include a storage unit 130.

[0061] The data acquisition unit 110 can be configured to acquire battery data including at least one of the battery's voltage, current, and temperature.

[0062] Here, a battery refers to a physically separable, individual cell with a negative and a positive terminal. For example, a lithium-ion cell or a lithium polymer cell can be considered a battery. Additionally, the type of battery can be cylindrical, prismatic, or pouch-shaped. Furthermore, a battery can refer to a battery bank, battery module, or battery pack in which multiple cells are connected in series and / or parallel. In the following text, for ease of explanation, a battery is interpreted as referring to a single, independent cell.

[0063] In one embodiment, the data acquisition unit 110 can directly measure the battery's voltage, current, and temperature. Specifically, the data acquisition unit 110 can measure the positive and negative terminal voltages via a pair of voltage sensing lines connected to the positive and negative terminals of the battery, respectively. Furthermore, the data acquisition unit 110 can measure the voltage across the two terminals of the battery based on the voltage difference between the measured positive and negative terminal voltages. The data acquisition unit 110 can be connected to the battery via a current measurement unit to measure the battery's current. For example, the current measurement unit can be a current sensor or a shunt resistor installed in the battery's charging / discharging path to measure the battery's current. Here, the battery's charging / discharging path can be a high-current path through which a charging current is applied to the battery or a discharging current is output from the battery. The data acquisition unit 110 can use a temperature sensor to measure the battery's temperature. That is, the data acquisition unit 110 can acquire battery data by directly measuring the battery's voltage, current, and temperature. Furthermore, the data acquisition unit 110 can acquire battery data by measuring the battery's voltage, current, and temperature at regular or irregular intervals.

[0064] In another embodiment, the data acquisition unit 110 can receive battery data from an external source. That is, the data acquisition unit 110 can receive battery data from an external source via a wired and / or wireless communication connection. For example, the data acquisition unit 110 can receive battery data from an external source via CAN (Controller Area Network) communication or CAN-FD (CAN with Flexible Data Rate). As another example, the data acquisition unit 110 can receive battery data from an external source via Zigbee, Bluetooth, Wi-Fi, or a mobile communication network. Of course, the type of communication protocol is not particularly limited, as long as it supports communication between the data acquisition unit 110 and the external source.

[0065] In another embodiment, the data acquisition unit 110 can receive battery data from a user (e.g., a driver, mechanic, etc.). The data acquisition unit 110 is communicatively connected to an input device (e.g., a keyboard, touch panel, microphone, etc.) that receives battery data from the user, and can receive battery data through the input device.

[0066] The data acquisition unit 110 can be connected to the processor 120 via a wired and / or wireless connection to enable communication with the processor 120. The data acquisition unit 110 can send the acquired battery data to the processor 120. The processor 120 can receive battery data from the data acquisition unit 110.

[0067] The processor 120 can be configured to estimate the positive electrode active material loss rate of the battery based on battery data by referring to a first curve provided for estimating the loss of positive electrode active material associated with cation mixing and a second curve provided for estimating the loss of positive electrode active material associated with particle cracking.

[0068] Here, the positive electrode active material loss rate represents the amount of active material lost per hour from the positive electrode of the battery, and can be used as an important indicator for assessing the battery's degradation rate. Specifically, the positive electrode active material loss rate quantitatively indicates the rate at which the active material of the positive electrode is lost during battery charging, discharging, or storage.

[0069] Positive electrode active material loss refers to the reduction of active material in the positive electrode of a lithium-ion battery, and is one of the main causes of battery degradation. Specifically, positive electrode active material loss can be caused by structural damage or decomposition of the positive electrode material during charge / discharge cycles, oxidation of the positive electrode material during long-term battery storage, or the formation of a non-active layer on the surface, resulting in a decrease in the active material in the positive electrode. If the loss of positive electrode active material accumulates, the battery capacity and output may decrease.

[0070] The first curve is a dataset (e.g., lookup table, mathematical function) used to estimate the loss rate of positive electrode active material associated with cation mixing, and represents the correspondence between at least one of battery voltage, current, and temperature and the loss rate of positive electrode active material due to cation mixing.

[0071] Specifically, the first curve reflects the effect of cation mixing on the loss of positive electrode active material and can be used to estimate the loss rate of positive electrode active material under various battery conditions.

[0072] The second curve is a dataset (e.g., lookup table, mathematical function) used to estimate the loss rate of positive electrode active material associated with particle cracking, and represents the correspondence between at least one of battery voltage, current, and temperature and the loss rate of positive electrode active material due to particle cracking.

[0073] Specifically, the second curve reflects the impact of stress accumulation and crack formation within the particles due to repeated volume changes during charging and discharging on the loss of positive electrode active material, and can be used to estimate the loss rate of positive electrode active material under various battery conditions.

[0074] The device 100 for diagnosing batteries has the advantage of being able to quickly and efficiently estimate the loss rate of positive electrode active material of the battery under various operating conditions by referring to a pre-stored first curve and a second curve.

[0075] Meanwhile, the data acquisition unit 110 and / or processor 120 included in the battery diagnostic device 100 may optionally include application-specific integrated circuits (ASICs), other chipsets, logic circuits, registers, communication modems, data processing devices, etc., known in the art, to execute the various diagnostic logics performed in this disclosure. Additionally, when the control logic is implemented as software, the data acquisition unit 110 and / or processor 120 can be implemented as a collection of program modules. In this case, the program modules can be stored in memory and executed by the data acquisition unit 110 and / or processor 120. The memory can be internal or external to the data acquisition unit 110 and / or processor 120, and can be connected to the data acquisition unit 110 and / or processor 120 by various known means.

[0076] Furthermore, the device 100 for diagnosing the battery may further include a storage unit 130. The storage unit 130 may store data necessary for the operation and function of each component of the device 100 for diagnosing the battery, data generated during the execution of operations or functions, etc. There are no particular limitations on the type of storage unit 130, as long as it is a known information storage device capable of recording, erasing, updating, and retrieving data. As examples, the information storage device may include RAM, flash memory, ROM, EEPROM, registers, etc. In addition, the storage unit 130 may store program code that defines the procedures that can be executed by the data acquisition unit 110 and / or the processor 120.

[0077] Specifically, storage unit 130 can store information necessary for processor 120 to estimate the loss rate of the positive electrode active material of the battery. For example, storage unit 130 can store battery data, a first curve, a second curve, etc. Furthermore, processor 120 can access storage unit 130 to obtain information necessary for processor 120 to estimate the loss rate of the positive electrode active material of the battery. For example, battery data acquired by data acquisition unit 110 is stored in storage unit 130, and processor 120 can access storage unit 130 to obtain the stored battery data.

[0078] The first and second curves will be described in detail below.

[0079] Figure 2 It is a schematic diagram illustrating an example of the first curve, and Figure 3 This is a schematic diagram illustrating an example of the second curve.

[0080] The first curve can be configured to represent the correspondence between multiple reference battery data and multiple first reference state data. Specifically, the first curve can be configured based on a preset first model.

[0081] The second curve can be configured to represent the correspondence between multiple reference battery data and multiple second reference state data. Specifically, the second curve can be configured based on a preset second model.

[0082] Here, the reference battery data is the basic data input to the first and second models, and includes the voltage, current, and temperature ranges while the battery is being charged and discharged. For example, the reference battery data is the voltage, current, and temperature data of a reference battery of the same type as the battery to be diagnosed. Preferably, the reference battery data can be set to the voltage, current, and temperature data when the reference battery is in a BOL state.

[0083] The first reference state data is an output value obtained by inputting at least one of the voltage, current, and temperature of a reference battery of the same type as the target battery into the first model. Preferably, the first reference state data can be set as the loss rate of the positive electrode active material based on cation mixing, obtained by inputting battery data when the reference battery is in the BOL state into the first model.

[0084] The second reference state data is the output value obtained by inputting at least one of the voltage, current, and temperature of a reference battery of the same type as the target battery into the second model. Preferably, the second reference state data can be set as the loss rate of positive electrode active material due to particle cracking, obtained by inputting battery data when the reference battery is in the BOL state into the second model.

[0085] The first and second models can be models that can use the P2D model to estimate the loss rate of the positive electrode active material.

[0086] The first model can be defined as a model for estimating the loss rate of cathode active material associated with cation mixing. The first model estimates the loss rate of cathode active material by reflecting the cation mixing phenomenon that occurs under specific voltage, current, and temperature conditions of the battery.

[0087] The first model could be a pseudo-cation mixing model that simulates the cation mixing phenomenon at the positive electrode. Specifically, cation mixing refers to the exchange of lithium ions and transition metal ions (e.g., nickel, cobalt) at the positive electrode of a lithium-ion battery, and it is one of the main causes of battery performance degradation and deterioration.

[0088] The first model assumes cation mixing to explain cathode degradation that mainly occurs in the intermediate SOC region (SOC 50% to 60%).

[0089] Specifically, under low SOC conditions, nickel ions (Ni 2+ Lithium ions (Li) are more likely to migrate into the NCM crystal structure.+ However, since lithium ion sites are relatively scarce in this region, the actual occurrence of cation mixing may be limited.

[0090] On the other hand, under high SOC conditions, nickel ions (Ni 2+ It is oxidized and converted into nickel ions (Ni). 4+ This reduces the tendency for nickel ions to migrate to lithium ion sites. Therefore, in the high SOC region, even if there are many vacancies for lithium ions, the probability of antisite defects occurring is relatively low.

[0091] Based on the above characteristics, the first model can be composed of cation mixing control equations, such as Equations 1 and 2 below.

[0092] [Equation 1]

[0093] here, This is due to the loss rate of the positive electrode active material caused by cation mixing. CM (t) is the current density related to cation mixing at a specific time t, where A is the electrode plate area, and L is the current density. p It is the thickness of the positive electrode, and a p It is the specific surface area of ​​the positive electrode interface.

[0094] [Equation 2]

[0095] Here, i CM (t) is the current density associated with cation mixing at a specific time t, and f CM It is a function that determines the current density associated with cation mixing, indicating that the current density associated with cation mixing depends on the lithium-ion concentration and temperature. Ni Li T(t) is the lithium-ion concentration in the electrode at a specific time t, and T(t) is the temperature of the battery at a specific time t.

[0096] The second model can be defined as a model for estimating the loss rate of cathode active material associated with particle cracking. The second model estimates the loss rate of cathode active material by reflecting particle cracking that occurs under specific voltage, current, and temperature conditions of the battery.

[0097] The second model can be a pseudo-particle cracking model simulating particle cracking caused by volume changes in the cathode. Specifically, particle cracking caused by volume changes can refer to the accumulation of stress within the particles due to repeated expansion and contraction caused by lithium-ion insertion and extraction during charging and discharging processes, and the occurrence of cracking within the particles when this stress exceeds a threshold. Such cracking leads to physical loss of active material in the cathode, which is one of the main causes of battery performance degradation and deterioration.

[0098] The second model assumes that when current is applied to the battery, the volume of the positive electrode decreases due to delithiation during charging, which causes cracks between the primary particles, allowing the electrolyte to permeate into the extra space between them, leading to the degradation of the positive electrode.

[0099] Based on the above characteristics, the second model can be composed of particle cracking control equations, such as Equations 3 and 4 below.

[0100] [Equation 3]

[0101] here, This is due to the loss rate of the positive electrode active material caused by particle cracking. CR (t) is the current density associated with particle cracking at a specific time t, where A is the area of ​​the electrode plate, and L is the current density. p It is the thickness of the positive electrode, and a p It is the specific surface area of ​​the positive electrode interface.

[0102] [Equation 4]

[0103] Here, i CR (t) is the current density associated with particle cracking, and f CR This is a function that determines the current density associated with particle cracking, indicating that the current density associated with particle cracking depends on the lithium-ion concentration and temperature. Among these, T(t) is the volume change of the positive electrode at a specific time t, and T(t) is the temperature of the battery at a specific time t.

[0104] The processor 120 can determine first reference state data corresponding to the battery data by referring to a first curve, and estimate a first loss rate based on the determined first reference state data.

[0105] The first loss rate can refer to the loss rate of the positive electrode active material related to the cation mixing phenomenon at the positive electrode of the battery.

[0106] The processor 120 can be configured to determine first reference state data corresponding to voltage and temperature in battery data by referring to a first curve, and to estimate a first loss rate based on the determined first reference state data.

[0107] Voltage is closely related to the state of charge (SOC) of a battery, and depending on the SOC and voltage conditions, there may be specific voltage ranges where cation mixing can occur. Within these voltage ranges, the loss of positive electrode active material due to cation mixing may be accelerated. Furthermore, because voltage indicates the progress of electrochemical reactions within the battery, degradation mechanisms such as cation mixing may become more active at certain voltages. Therefore, voltage information can be useful in estimating the state of loss of positive electrode active material in a battery.

[0108] Temperature affects the rate of chemical reactions within a battery, and elevated temperatures can accelerate degradation. Lithium-ion batteries are particularly sensitive to temperature, and high temperatures can exacerbate the loss of positive electrode active materials. Therefore, temperature information can be used as a key indicator for evaluating battery degradation.

[0109] Current represents the rate of battery charging and discharging, which affects the rate at which lithium ions move within the battery. High current increases the rate of lithium ion movement within the battery electrodes, which can promote cation mixing. In particular, high current increases the likelihood of physical stress on the electrode structure, which can accelerate additional degradation mechanisms. Therefore, current information can serve as a key factor in estimating the loss rate of positive electrode active material associated with cation mixing.

[0110] In one embodiment, the processor 120 may estimate the determined first reference state data as a first loss rate.

[0111] refer to Figure 2 The X-axis represents voltage ([V]), and the Y-axis represents the first reference state data (dLAM). P , CM / dt [Ah / sec]), and the legend distinguishes them by line type to represent various temperature conditions. For example, if the battery data represents a voltage of 3.6V and a temperature of 30°C, the processor 120 can estimate the y-axis value of 0.42 [Ah / sec] corresponding to the battery data as a first loss rate by referring to the first curve.

[0112] In another embodiment, processor 120 may estimate a first loss rate based on the determined first reference state data and a damping factor. Specifically, processor 120 may estimate the first loss rate as a value obtained by multiplying the first reference state data by the damping factor. For ease of illustration, a specific embodiment using a damping factor to estimate the first loss rate is described below.

[0113] The processor 120 can determine the second reference state data corresponding to the battery data by referring to the second curve, and estimate the second loss rate based on the determined second reference state data.

[0114] The second loss rate can refer to the loss rate of positive electrode active material related to particle cracking at the positive electrode of the battery.

[0115] The processor 120 can be configured to determine second reference state data corresponding to the voltage and current in the battery data by referring to a second curve, and to estimate a second loss rate based on the determined second reference state data.

[0116] Voltage is closely related to the state of charge of a battery and represents the potential difference across the electrodes during the battery's charging / discharging process. Within certain voltage ranges, electrode particles may undergo repeated expansion and contraction as lithium ions are inserted into or extracted from the electrodes. This can lead to stress accumulation within the particles, potentially accelerating particle cracking. Therefore, voltage information can be a crucial criterion for estimating the battery's degradation process and the rate of loss of positive electrode active material due to particle cracking.

[0117] Current represents the charging and discharging rate of a battery, which affects the rate at which lithium ions move through the electrodes. Under high current conditions, electrode particles undergo rapid expansion and contraction, increasing internal particle stress and potentially promoting particle cracking. Therefore, current information can be a crucial factor in estimating the second loss rate.

[0118] Temperature controls the rate of chemical reactions within a battery, and higher temperatures accelerate these reactions, potentially leading to accelerated particle cracking. High temperatures can also exacerbate stress and volume changes within the electrodes, potentially weakening the electrode structure, and these conditions can accelerate particle cracking and the loss of positive electrode active material. Therefore, temperature information can be a useful indicator for evaluating the state of degradation caused by particle cracking.

[0119] In one embodiment, the processor 120 may estimate a second loss rate based on second reference state data determined from a second curve based on battery data.

[0120] refer to Figure 3 The X-axis represents voltage ([V]), and the Y-axis represents the second reference state data (dLAM). P,CR / dt, [Ah / second]), and the legend distinguishes them by line type to represent various current conditions. For example, if the battery data represents a voltage of 3.6V and a current of 0.5C, the processor 120 can estimate the y-axis value of 2.5 corresponding to the battery data as a second loss rate by referring to the second curve.

[0121] In another embodiment, processor 120 may estimate a second loss rate based on second reference state data determined from a second curve based on battery data and a damping factor. Specifically, processor 120 may estimate the second loss rate as a value obtained by multiplying the second reference state data by the damping factor.

[0122] The device 100 for diagnosing batteries has the advantage of being able to accurately diagnose the state of the battery by independently analyzing the loss rate of positive electrode active material related to cation mixing and particle cracking using a first curve and a second curve based on conditions such as voltage, current and temperature.

[0123] The processor 120 can be configured to estimate the loss rate of the positive electrode active material based on a first loss rate and a second loss rate.

[0124] Here, the loss rate of positive electrode active material can refer to the value calculated by comprehensively reflecting the various active material loss factors that occur in the positive electrode of the battery.

[0125] For example, processor 120 can be configured to calculate the positive electrode active material loss rate as a value obtained by adding a first loss rate to a second loss rate. In this case, the positive electrode active material loss rate represents the positive electrode active material loss rate associated with cation mixing and particle cracking phenomena.

[0126] The device 100 for diagnosing batteries has the advantage of being able to comprehensively diagnose the process of battery degradation by estimating the positive active material loss rate, which reflects the total proportion of positive active material consumed by the complex interactions of various factors such as cation mixing and particle cracking.

[0127] The following describes in detail the method by which the processor 120 calculates the loss of the positive electrode active material.

[0128] The processor 120 can be configured to calculate the amount of positive active material loss of the battery during the reference time period based on the positive active material loss rate and a reference time period between the previous diagnostic time point and the current diagnostic time point.

[0129] The amount of positive electrode active material loss during the reference period indicates how much the positive electrode active material of the battery has decreased during the reference period, and can be used as an important indicator for assessing the battery's degradation state and degradation rate.

[0130] Specifically, the reference time period can refer to the time interval between the previous diagnosis time point and the current diagnosis time point. For example, if the time interval between the previous diagnosis time point and the current diagnosis time point is 0.1 seconds, then the reference time period is 0.1 seconds.

[0131] The loss of positive electrode active material can refer to the amount of active material that has been reduced based on the loss rate of positive electrode active material during the reference period.

[0132] In one embodiment, the processor 120 can calculate the loss of positive electrode active material as a value obtained by multiplying a reference time period by the loss rate of positive electrode active material. For example, if the reference time period is 0.1 seconds and the loss rate of positive electrode active material is 0.5 Ah / second, the loss of positive electrode active material during the reference time period can be calculated as 0.05 Ah.

[0133] In another embodiment, the processor 120 can calculate the amount of positive active material loss during a reference period by integrating the positive active material loss rate over time.

[0134] The device 100 for diagnosing batteries can be used to track the degradation process of batteries and predict battery performance by calculating the amount of positive active material loss in a specific period based on a reference time period and the loss rate of positive active material.

[0135] The following describes in detail a method for calculating the cumulative loss of positive electrode active material using a device 100 for diagnosing batteries.

[0136] The processor 120 can estimate the cumulative positive active material loss up to the current diagnosis time based on the amount of positive active material loss and the cumulative positive active material loss up to the previous diagnosis time.

[0137] Specifically, the processor 120 can be configured to estimate the cumulative positive active material loss up to the current diagnostic time point by adding the positive active material loss to the cumulative positive active material loss up to the previous diagnostic time point.

[0138] Here, the cumulative loss of positive electrode active material can be expressed as the total loss of positive electrode active material accumulated over time.

[0139] The cumulative loss of positive electrode active material up to the previous diagnosis time point refers to the cumulative loss of positive electrode active material up to the previous diagnosis time point, and if a reference period is set, it can indicate the cumulative loss before the start of the reference period.

[0140] For example, the processor 120 can estimate the cumulative positive electrode active material loss up to the nth time point by adding the amount of positive electrode active material loss between the (n-1)th time point and the nth time point to the cumulative amount of positive electrode active material loss up to the (n-1)th time point.

[0141] As a more specific example, if the cumulative loss of positive electrode active material up to the previous diagnostic time point is 5 Ah / m 3Furthermore, the loss of positive electrode active material during the current reference period is 0.5 Ah / m. 3 Then processor 120 can reduce the loss (0.5 Ah / m) 3 ) and the cumulative loss of positive electrode active material up to the previous diagnosis time point (5 Ah / m 3 The values ​​are added together to determine the cumulative loss of positive electrode active material up to the current diagnostic time point, which is 5.5 Ah / m. 3 .

[0142] The device 100 for diagnosing batteries can effectively diagnose the long-term degradation of battery performance by tracking the cumulative degradation of the battery over time by updating the cumulative loss of positive electrode active material at each reference time period.

[0143] Figure 4 The diagram illustrates the correlation between a P2D model estimated using a device 100 for diagnosing batteries according to an embodiment of the present disclosure and the cumulative loss of positive electrode active material.

[0144] exist Figure 4 In the middle, the horizontal axis represents the cumulative loss of positive electrode active material (LAM) estimated by the P2D model. P [P2D]), and the vertical axis represents the cumulative loss of positive electrode active material (LAM) estimated by the device 100 used for diagnosing the battery. P [RM]).

[0145] Each data point represents the degree of consistency between the estimates of the P2D model and the RM model, and the closer the points are to the distribution along the linear trend line, the higher the degree of consistency between the two models.

[0146] refer to Figure 4 The cumulative loss of positive electrode active material (LAM) estimated by the device 100 used for diagnosing the battery can be confirmed. P [RM] shows the cumulative loss of positive electrode active material (LAM) estimated by the P2D model. P [P2D]) linear relationship. That is, the data points of the curve are located close to the linear relationship trend line, indicating the cumulative positive electrode active material loss (LAM) of the device 100 used for diagnosing the battery. P [RM] shows the cumulative loss of positive electrode active material (LAM) compared to the P2D model. P The high degree of consistency of [P2D]).

[0147] In other words, the device 100 for diagnosing batteries can be configured to provide faster computation speeds while maintaining a high level of accuracy similar to that of the P2D model using a lightweight model based on an approximate P2D model. Specifically, the device 100 for diagnosing batteries has the advantage of rapidly estimating the cumulative loss of positive electrode active material using curves (e.g., lookup tables, etc.) composed of data pre-calculated using the P2D model while maintaining accuracy close to that of the P2D model.

[0148] Below, we describe a specific example of using the damping factor to estimate the loss rate of the positive electrode active material.

[0149] Figures 5 to 7 The diagrams schematically illustrate the SOH, the loss of the first positive electrode active material, and the loss of the second positive electrode active material according to the charge and discharge cycles.

[0150] exist Figure 5 In the diagram, the horizontal axis represents the charge / discharge cycle, and the vertical axis represents the SOH (%) of the battery.

[0151] refer to Figure 5 This confirms that the battery's State of Health (SOH) gradually decreases as the number of charge and discharge cycles increases.

[0152] exist Figure 6 In the diagram, the horizontal axis represents the charge / discharge cycle, and the vertical axis represents the loss of the first positive electrode active material (LAM). P,CM [Ah]). Here, the first loss of positive electrode active material can refer to the cumulative loss of positive electrode active material related to cation mixing.

[0153] refer to Figure 6 It can be confirmed that the loss of the first positive electrode active material gradually increases with the number of charge / discharge cycles. In other words, the loss of the first positive electrode active material increases rapidly in the initial charge / discharge cycles, but the rate of increase slows down as the cycles continue.

[0154] exist Figure 7 In the diagram, the horizontal axis represents charge / discharge cycles, and the vertical axis represents the loss of the second positive electrode active material (LAM). P,CR [Ah]). Here, the second positive electrode active material loss can refer to the cumulative positive electrode active material loss related to particle cracking.

[0155] refer to Figure 7 It can be confirmed that the loss of the second positive electrode active material gradually increases with the number of charge / discharge cycles. In other words, the loss of the second positive electrode active material increases rapidly in the initial charge / discharge cycles, but the rate of increase slows down as the cycles continue.

[0156] The device 100 for diagnosing batteries can use a damping factor to estimate the loss rate of the positive electrode active material, thus more accurately reflecting the battery's degradation state. In other words, the device 100 can model saturation characteristics where the loss rate of the positive electrode active material gradually slows down over time by applying a damping factor.

[0157] For example, the damping factor can be set in the form of exp(-γ), where γ is the damping coefficient. The damping coefficient reflects the characteristic that the loss rate of the positive electrode active material gradually slows down as the battery deteriorates. In this case, the larger the damping coefficient, the faster the initial loss rate slows down, and the smaller the damping coefficient, the slower the loss rate slows down.

[0158] Furthermore, in the early lifetime state (SOH 100%), the initial damping factor can be set to 0 to calculate the initial loss rate without applying a damping effect. In this case, when the initial damping factor is set to 0, the initial loss rate of the cathode active material is calculated without a damping effect, and by subsequently setting the damping factor and applying a damping term in the form of exp(-γ), the effect of mitigating the increase in loss rate over time can be reflected.

[0159] The damping factor can be set based on a negative correlation with the state of equilibrium (SOH) of the battery. For example, data indicating a negative correlation between SOH and the damping factor can be pre-stored in the storage unit 130, and the processor 120 can determine the damping factor based on the data and the SOH value of the battery.

[0160] In other words, as the state of harmonics (SOH) of the battery decreases, the damping coefficient can be set to increase, and as a result, the damping factor, which has a negative relationship with the damping coefficient, gradually decreases as degradation progresses.

[0161] The processor 120 can determine the damping factor corresponding to the state of harmonics (SOH) of the battery.

[0162] Specifically, processor 120 can acquire the state of harmonics (SOH) of the battery. For example, processor 120 can estimate the SOH of the battery based on battery data. As another example, data acquisition unit 110 can receive SOH data from an external source. Additionally, processor 120 can receive SOH data from data acquisition unit 110. Furthermore, processor 120 can determine the damping factor corresponding to the SOH of the battery based on pre-stored relational data.

[0163] The processor 120 can be configured to estimate the first loss rate based on the first reference state data and the damping factor.

[0164] For example, processor 120 can calculate the first loss rate by multiplying first reference state data determined from the first curve based on battery data with a damping factor. In other words, processor 120 can reflect the slowing effect of the loss rate of the positive electrode active material due to cation mixing over time by multiplying the first reference state data with the damping factor.

[0165] The processor 120 can be configured to estimate the second loss rate based on the second reference state data and the damping factor.

[0166] For example, processor 120 can calculate the second loss rate by multiplying second reference state data determined from the second curve based on battery data with a damping factor. In other words, processor 120 can reflect the slowing effect of the loss rate of positive electrode active material due to particle cracking over time by multiplying the second reference state data with a damping factor.

[0167] Meanwhile, the damping factor used to estimate the first loss rate and the damping factor used to estimate the second loss rate can be set to the same value or different values.

[0168] For example, the damping factor can be set by taking into account the variation pattern of the loss rate of the positive electrode active material due to cation mixing and the variation pattern of the loss rate of the positive electrode active material due to particle cracking.

[0169] As another example, the damping factor can be set by independently considering the variation patterns of the positive electrode active material loss rate due to cation mixing and the variation patterns due to particle cracking. In this case, the positive electrode active material loss rate due to each degradation mechanism can be estimated more accurately.

[0170] The device 100 for diagnosing batteries has the advantage of more accurately simulating the actual degradation mode of the battery and improving the reliability of battery condition diagnosis by estimating the first and second loss rates using a damping factor.

[0171] The following describes a specific embodiment of the processor 120 diagnosing the state of the battery.

[0172] The processor 120 can diagnose the state of the battery based on estimates related to the loss of active material at the positive electrode.

[0173] For example, the processor 120 can compare the amount of loss (including the amount of loss of positive electrode active material and the cumulative amount of loss of positive electrode active material) with the corresponding threshold and diagnose the state of the battery based on the comparison results.

[0174] Here, the threshold is a value compared to the amount of loss and is a criterion used to distinguish the battery state as normal or abnormal.

[0175] Specifically, the processor 120 can compare the amount of loss at the positive electrode with the corresponding threshold and diagnose the state of the battery based on the comparison result.

[0176] The processor 120 can compare the amount of positive electrode active material loss with a preset first threshold. If the amount of positive electrode active material loss is greater than or equal to the first threshold, the processor 120 can diagnose the battery state as abnormal. Conversely, if the amount of positive electrode active material loss is less than the first threshold, the processor 120 can diagnose the battery state as normal.

[0177] The processor 120 can compare the cumulative loss of positive electrode active material with a preset second threshold. If the cumulative loss of positive electrode active material is greater than or equal to the second threshold, the processor 120 can diagnose the battery state as abnormal. Conversely, if the cumulative loss of positive electrode active material is less than the second threshold, the processor 120 can diagnose the battery state as normal.

[0178] The device 100 for diagnosing batteries has the advantage of being able to accurately diagnose the state of the battery by comparing the amount of loss with a threshold and improving the safety of the battery.

[0179] The cumulative loss of positive electrode active material can be used to calculate the state of harmonics (SOH) of the battery.

[0180] In one embodiment, the processor 120 may calculate the SOH of the battery by taking into account the cumulative loss of positive electrode active material, the cumulative loss of negative electrode active material, and the cumulative loss of available lithium.

[0181] Negative electrode active material loss refers to the reduction of active material at the negative electrode of a lithium-ion battery, and is a major cause of battery degradation. The cumulative amount of negative electrode active material loss can be defined as the difference between the initial capacity of the negative electrode active material and its current capacity.

[0182] Available lithium loss refers to the loss of lithium ions that could be used for charging and discharging within the battery. Cumulative available lithium loss can be defined as the difference between the initial amount of available lithium ions and the current amount of available lithium ions.

[0183] The processor 120 can calculate the state of equilibrium (SOH) by adjusting pre-stored criterion cathode and criterion anode curves based on the cumulative loss of positive electrode active material, the cumulative loss of negative electrode active material, and the cumulative loss of available lithium. The cumulative loss of negative electrode active material and the cumulative loss of available lithium can be obtained by various known methods.

[0184] The standard positive electrode curve is a curve showing the relationship between the capacity and voltage of a standard positive electrode cell that is pre-designated as the positive electrode of the battery. For example, the standard positive electrode cell can be the positive electrode of a button cell or a three-electrode cell. Similarly, the standard negative electrode curve is a curve showing the relationship between the capacity and voltage of a standard negative electrode cell that is pre-designated as the negative electrode of the battery. For example, the standard negative electrode cell can be the negative electrode of a button cell or a three-electrode cell.

[0185] The processor 120 can shrink the criterion positive electrode curve by considering the cumulative loss of positive electrode active material, and can also shrink the criterion negative electrode curve by considering the cumulative loss of negative electrode active material. The shrinkage of the criterion positive electrode curve and the cumulative loss of positive electrode active material can have a predetermined positive correlation. The shrinkage of the criterion negative electrode curve and the cumulative loss of negative electrode active material can have a predetermined positive correlation.

[0186] Then, the processor 120 can shift the contracted criterion positive and negative electrode curves based on the accumulated available lithium loss to generate corrected positive and negative electrode curves. For example, the processor 120 can shift the contracted criterion positive and negative electrode curves in the negative direction (i.e., toward the low capacity side) on the capacity axis based on the accumulated available lithium loss to generate corrected positive and negative electrode curves. The parallel shift amount and the accumulated available lithium loss can have a predetermined positive correspondence.

[0187] The processor 120 can then generate a full-cell curve based on the difference between the calibrated positive and negative electrode curves. The full-cell curve represents the relationship between the battery's voltage and capacity.

[0188] The processor 120 can estimate the state of harmonics (SOH) of the battery by comparing the generated full-cell curve with a pre-stored criterion full-cell curve.

[0189] Here, the criterion full-cell curve represents the correspondence between voltage and capacity obtained for a battery in the beginning-of-life (BOL) state. The criterion full-cell curve can be pre-stored in a memory or the like based on the difference between the potential of the criterion positive electrode curve and the criterion negative electrode curve within a predetermined capacity range. For example, the processor 120 can calculate the SOH of the battery by calculating the ratio between the size of the capacity segment of the generated full-cell curve and the size of the capacity segment of the criterion full-cell curve.

[0190] The apparatus 100 for diagnosing batteries according to embodiments of the present disclosure can estimate the state of harmonics (SOH) of the battery based on the cumulative loss of positive electrode active material, thereby effectively diagnosing battery performance degradation.

[0191] The device 100 for diagnosing batteries can be configured to set usage conditions based on the results of battery state diagnosis.

[0192] In one embodiment, the processor 120 can be configured to set the battery usage conditions based on at least one of positive electrode active material loss rate, positive electrode active material loss amount, cumulative positive electrode active material loss amount, battery state, and SOH.

[0193] For example, if the battery state is diagnosed as normal, the processor 120 can maintain the existing usage conditions. Conversely, if the battery state is diagnosed as abnormal, the processor 120 can change the existing usage conditions. For example, if the battery state is diagnosed as abnormal, the processor 120 can be configured to reduce the maximum charge / discharge rate. As another example, if the battery state is diagnosed as abnormal, the processor 120 can be configured to reduce the charging termination voltage. Here, the charging termination voltage may refer to the maximum voltage allowed when charging the battery. As another example, if the battery state is diagnosed as abnormal, the processor 120 can increase the discharging termination voltage. Here, the discharging termination voltage may refer to the minimum voltage allowed when discharging the battery.

[0194] In another embodiment, the processor 120 can set the battery usage conditions according to a preset protocol, which defines the correspondence between the battery's positive electrode active material loss rate, positive electrode active material loss amount, cumulative positive electrode active material loss amount, state, and SOH and the usage conditions.

[0195] At the same time, it goes without saying that various methods that can be readily applied by those skilled in the art to which this disclosure pertains can be used in specific embodiments for setting the battery's operating conditions.

[0196] The device 100 for diagnosing a battery according to an embodiment of the present disclosure can mitigate battery performance degradation and improve its lifespan and stability by appropriately controlling the battery's usage conditions by taking into account the battery's state diagnosis results.

[0197] The battery diagnostic apparatus 100 according to this disclosure can be applied to a battery management system (BMS). That is, a BMS according to this disclosure may include the aforementioned battery diagnostic apparatus 100. In this configuration, at least some of the components of the battery diagnostic apparatus 100 can be implemented by supplementing or adding the functionality of components included in a conventional BMS. For example, the data acquisition unit 110 and processor 120 of the battery diagnostic apparatus can be implemented as components of the BMS.

[0198] Figure 8This is a schematic illustration of a battery pack 10 according to another embodiment of the present disclosure.

[0199] The battery diagnostic device 100 according to this disclosure can be disposed in the battery pack 10. That is, the battery pack 10 according to this disclosure may include the aforementioned battery diagnostic device 100 and at least one battery cell. In addition, the battery pack 10 may further include electrical components (relays, fuses, etc.) and a housing.

[0200] The positive terminal of battery 11 can be connected to the positive terminal P+ of battery pack 10, and the negative terminal of battery 11 can be connected to the negative terminal P- of battery pack 10.

[0201] The measuring unit 12 can be connected to a first sensing line SL1, a second sensing line SL2, and a third sensing line SL3. Specifically, the measuring unit 12 can be connected to the positive terminal of the battery 11 via the first sensing line SL1 and to the negative terminal of the battery 11 via the second sensing line SL2. The measuring unit 12 can measure the voltage of the battery 11 based on the voltage measured at each of the first sensing line SL1 and the second sensing line SL2.

[0202] Furthermore, the measurement unit 12 can be connected to the current measurement unit A via the third sensing line SL3. For example, the current measurement unit A can be an ammeter or a shunt resistor capable of measuring the charging current and discharging current of the battery 11. The measurement unit 12 can measure the charging current of the battery 11 via the third sensing line SL3 to calculate the amount of charge. Additionally, the measurement unit 12 can measure the discharging current of the battery 11 via the third sensing line SL3 to calculate the amount of discharge.

[0203] The data acquisition unit 110 can be connected to the measurement unit 12 via a wired and / or wireless connection to enable communication. The data acquisition unit 110 can receive voltage, current, and / or temperature information of the battery 11 from the measurement unit 12.

[0204] Figure 9 The diagram schematically illustrates a vehicle 1 according to yet another embodiment of the present disclosure.

[0205] refer to Figure 9 The above reference Figure 8 The described battery pack 10 can be included in a vehicle 1 such as an electric vehicle (EV) or a hybrid vehicle (HV). Furthermore, the battery pack 10 can supply power to a motor in the vehicle 1 via an inverter located in the vehicle 1 to drive the vehicle 1. Here, the battery pack 10 may include a device 100 for diagnosing the battery. In this case, the device 100 for diagnosing the battery may be an on-board device included in the vehicle 1.

[0206] Figure 10This is a schematic diagram of server 2 according to yet another embodiment of the present disclosure.

[0207] refer to Figure 10 The device 100 for diagnosing batteries according to this disclosure can be equipped on a server 2. The server 2 provides high-performance computing resources and data storage capabilities to estimate the loss rate of the positive electrode active material.

[0208] The device 100 for diagnosing batteries, installed on server 2, can independently acquire battery data from multiple BMS 3, analyze it in real time, and continuously monitor the positive electrode active material loss rate of each battery. Specifically, the device 100 for diagnosing batteries running on server 2 can estimate the first loss rate and the second loss rate of the battery based on the battery data by referring to a first curve and a second curve. Furthermore, the device 100 for diagnosing batteries running on server 2 can estimate the positive electrode active material loss rate, the amount of positive electrode active material lost during a reference period, and the cumulative amount of positive electrode active material lost. Additionally, the device 100 for diagnosing batteries running on server 2 can diagnose the battery's state based on estimates related to the loss of positive electrode active material.

[0209] The battery diagnostic device 100, located in server 2, can perform integrated management of a battery system comprising multiple batteries by linking to multiple BMS 3, user terminals 4, and / or vehicle control systems 5. Server 2 can be wired and / or wirelessly connected to enable communication with multiple BMS 3 and / or user terminals 4.

[0210] Server 2 can link with BMS 3 to send estimates related to the loss of positive electrode active material and / or diagnostic results of the battery to the corresponding BMS 3 in real time. Alternatively, if the battery condition is diagnosed as abnormal, server 2 can send a warning or control signal to the corresponding BMS 3.

[0211] Server 2 can be linked to user terminal 4 so that the user can remotely monitor the battery status. The user can use a dedicated application to check the battery status in real time.

[0212] Figure 11 This is a schematic illustration of a method for diagnosing a battery according to yet another embodiment of the present disclosure. Figure 12 It is a schematic diagram. Figure 11 The diagram shows the sub-steps of step S1120.

[0213] Each step of the method for diagnosing a battery can be performed by the device 100 for diagnosing a battery. In the following text, for ease of explanation, content overlapping with the previously described material will be briefly described or omitted.

[0214] refer to Figure 11 The method for diagnosing a battery includes steps S1110 and S1120. The method for diagnosing a battery may further include steps S1130 and S1140.

[0215] Step S1110 is a step of acquiring battery data including at least one of the battery's voltage, current, and temperature, and this step can be performed by the data acquisition unit 110.

[0216] Step S1120 is a step of estimating the positive electrode active material loss rate of the battery based on battery data by referring to a first curve provided for estimating the loss of positive electrode active material related to cation mixing and a second curve provided for estimating the loss of positive electrode active material related to particle cracking, and this step can be executed by processor 120.

[0217] refer to Figure 12 Step S1120 may include steps S1122, S1124 and S1126.

[0218] Step S1122 is the step of estimating the first loss rate by referring to the first curve, and this step can be executed by the processor 120.

[0219] In one embodiment, the processor 120 may estimate the determined first reference state data as a first loss rate.

[0220] In another embodiment, the processor 120 may estimate the first loss rate based on the determined first reference state data and the damping factor.

[0221] Specifically, the processor 120 can estimate the value obtained by multiplying the first reference state data and the damping factor as the first loss rate.

[0222] Step S1124 is the step of estimating the second loss rate by referring to the second curve, and this step can be executed by the processor 120.

[0223] In one embodiment, the processor 120 may estimate a second loss rate based on second reference state data determined from a second curve based on battery data.

[0224] In another embodiment, the processor 120 may estimate a second loss rate based on second reference state data, which is determined from a second curve based on battery data and a damping factor.

[0225] Specifically, the processor 120 can estimate the second loss rate as a value obtained by multiplying the second reference state data by the damping factor.

[0226] Step S1126 is a step of calculating the loss rate of the positive electrode active material by adding the first loss rate to the second loss rate, and this step can be executed by the processor 120.

[0227] Step S1130 is a step of calculating the amount of positive electrode active material loss of the battery during the reference time period based on the loss rate of positive electrode active material and the reference time period between the previous diagnosis time point and the current diagnosis time point. This step can be executed by the processor 120.

[0228] Step S1140 is a step of calculating the cumulative positive electrode active material loss up to the current diagnosis time by adding the loss of positive electrode active material to the cumulative loss of positive electrode active material up to the previous diagnosis time point, and this step can be executed by processor 120.

[0229] Another embodiment of this disclosure may provide a computer-readable storage medium having programs recorded thereon for executing the various embodiments described above on a computer.

[0230] A program can be implemented as a hardware component, a software component, and / or a combination of hardware and software components. The program can be executed by any system capable of executing computer-readable instructions.

[0231] Software may include computer programs, code, instructions, or combinations thereof, which may configure processing equipment to perform desired operations or may independently or jointly command processing equipment.

[0232] Software can be implemented as a computer program that includes instructions stored on a computer-readable storage medium. Examples of computer-readable storage media include magnetic storage media (e.g., read-only memory (ROM), random access memory (RAM), floppy disks, hard disks, etc.) and optically readable media (e.g., CD-ROM, DVD: digital multifunction disc). Computer-readable storage media can be distributed across network-connected computer systems, allowing computer-readable code to be stored and executed in a distributed manner. The storage medium can be read by a computer, stored in memory, and executed by a processor.

[0233] Computer-readable storage media may be provided in the form of non-transitory storage media. Here, the term "non-transitory storage media" simply means that it is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently on the storage medium and cases where data is temporarily stored. For example, a "non-transitory storage medium" may include a buffer in which data is temporarily stored.

[0234] In addition, the program can be provided as part of a computer program product. The computer program product can be traded as a commodity between a seller and a buyer.

[0235] A computer program product may include a software program and a computer-readable storage medium storing the software program. For example, a computer program product may include a product in the form of a software program (e.g., a downloadable application) distributed electronically by a manufacturer of an electronic device or through an electronic marketplace. For electronic distribution, at least a portion of the software program may be stored on the storage medium or temporarily generated. In this case, the storage medium may be the storage medium of a server belonging to the manufacturer of the electronic device, a server of an electronic marketplace, or a relay server temporarily storing the software program.

[0236] The embodiments of this disclosure described above can be implemented not only by apparatus and methods, but also by a program that implements functions corresponding to the configuration of the embodiments of this disclosure, or by a storage medium on which the program is recorded. This program or storage medium can be readily implemented by those skilled in the art based on the above description of the embodiments.

[0237] This disclosure has been described in detail. However, it should be understood that while the detailed description and specific examples indicate preferred embodiments of this disclosure, they are given by way of illustration only, as various changes and modifications within the scope of this disclosure will become apparent to those skilled in the art from the detailed description.

[0238] Furthermore, without departing from the technical aspects of this disclosure, those skilled in the art can make many substitutions, modifications and changes to the disclosure described above, and this disclosure is not limited to the above embodiments and drawings, and each embodiment can be selectively combined in part or in whole to allow various modifications.

[0239] (Explanation of reference numerals in the attached diagram)

[0240] 1: Vehicle

[0241] 2: Server

[0242] 3: BMS

[0243] 4: User terminal

[0244] 10: Battery Pack

[0245] 11: Battery

[0246] 12: Measurement Unit

[0247] 100: Devices for diagnosing batteries

[0248] 110: Data Acquisition Unit

[0249] 120: Processor

[0250] 130: Storage unit

Claims

1. A device for diagnosing a battery, comprising: A data acquisition unit configured to acquire battery data including at least one of the battery's voltage, current, and temperature; as well as A processor configured to estimate the positive electrode active material loss rate of the battery based on the battery data by referring to a first curve provided for estimating the loss of positive electrode active material associated with cation mixing and a second curve provided for estimating the loss of positive electrode active material associated with particle cracking.

2. The device for diagnosing batteries according to claim 1, wherein The first curve is configured to represent the correspondence between multiple reference battery data and multiple first reference state data.

3. The device for diagnosing batteries according to claim 1, wherein The second curve is configured to represent the correspondence between multiple reference battery data and multiple second reference state data.

4. The device for diagnosing batteries according to claim 1, wherein The processor is configured to determine first reference state data corresponding to the voltage and temperature in the battery data by referring to the first curve, and to estimate a first loss rate based on the determined first reference state data.

5. The device for diagnosing batteries according to claim 4, wherein The processor is configured to estimate the first loss rate based on the first reference state data and the damping factor.

6. The apparatus for diagnosing batteries according to claim 1, wherein The processor is configured to determine second reference state data corresponding to the voltage and current in the battery data by referring to the second curve, and to estimate a second loss rate based on the determined second reference state data.

7. The apparatus for diagnosing batteries according to claim 6, wherein The processor is configured to estimate the second loss rate based on the second reference state data and the damping factor.

8. The apparatus for diagnosing batteries according to claim 1, wherein The processor is configured to estimate a first reference state data corresponding to the voltage and temperature in the battery data as a first loss rate by referring to the first curve, to estimate a second reference state data corresponding to the voltage and current in the battery data as a second loss rate by referring to the second curve, and to estimate the loss rate of the positive electrode active material based on the first loss rate and the second loss rate.

9. The apparatus for diagnosing batteries according to claim 8, wherein The processor is configured to calculate the value obtained by adding the first loss rate and the second loss rate as the loss rate of the positive electrode active material.

10. The apparatus for diagnosing batteries according to claim 1, in, The processor is configured to calculate the amount of positive active material lost by the battery during the reference time period based on the loss rate of the positive active material and a reference time period between the previous diagnostic time point and the current diagnostic time point.

11. The apparatus for diagnosing batteries according to claim 10, in, The processor is configured to calculate the cumulative positive electrode active material loss up to the current diagnostic time point by adding the loss of the positive electrode active material to the cumulative loss of the positive electrode active material up to the previous diagnostic time point.

12. A battery pack comprising means for diagnosing the battery according to any one of claims 1 to 11.

13. A vehicle comprising a device for diagnosing a battery according to any one of claims 1 to 11.

14. A server comprising means for diagnosing a battery according to any one of claims 1 to 11.

15. A method for diagnosing a battery, comprising: Acquire battery data including at least one of the battery's voltage, current, and temperature; as well as The positive electrode active material loss rate of the battery is estimated based on the battery data by referring to a first curve provided for estimating the loss of positive electrode active material related to cation mixing and a second curve provided for estimating the loss of positive electrode active material related to volume changes due to particle cracking.

16. A computer-readable storage medium storing a program for performing on a computer the method for diagnosing a battery according to claim 15.

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