Battery state prediction device and battery state prediction method

Abstract

A battery state prediction device disclosed in the present document comprises: a communication interface for receiving battery data from a battery; and at least one processor for generating a battery state prediction model, which includes a first prediction model for predicting lithium loss, a second prediction model for predicting active material loss in a positive electrode, and a third prediction model for predicting active material loss in a negative electrode, and acquiring battery state data by inputting the battery data to the battery state prediction model.

Description

Battery state prediction device and battery state prediction method

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority to Republic of Korea Patent Application No. 10-2023-0097583, filed July 26, 2023, and Republic of Korea Patent Application No. 10-2024-0089939, filed July 8, 2024, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a battery state prediction device and a battery state prediction method for predicting a battery state.

[0005] Recently, active research and development is being conducted on secondary batteries. Here, the term "secondary battery" refers to a rechargeable battery, encompassing both conventional Ni / Cd and Ni / MH batteries, as well as recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of a much 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 suitable for use as power sources for mobile devices. Furthermore, lithium-ion batteries are attracting attention as a next-generation energy storage medium, as their use is expanding to include power sources for electric vehicles.

[0006] The technology to accurately predict the State of Health (SOH) of these batteries is very important in terms of battery safety, performance, lifespan, economic efficiency, and environmental protection, and various SOH prediction technologies exist.

[0007] However, existing SOH prediction technologies have the limitation that they predict SOH by considering only the LLI (Loss of Lithium Inventory) of the cathode, and therefore cannot consider additional factors that occur during cycle experiments and actual battery use, rather than storage degradation.

[0008] According to one embodiment disclosed in this document, a battery state prediction model is provided that utilizes a P2D (Pesudo two dimensional) model in the process of predicting the SOH of a battery to explain the degradation occurring in a cycle experiment, and includes a prediction model that considers Loss of Active Material (LAM) in the positive and negative electrodes.

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

[0010] A battery state prediction device according to one embodiment includes a communication interface for receiving battery data from a battery, at least one processor for generating a battery state prediction model including a first prediction model for predicting lithium loss, a second prediction model for predicting active material loss in a positive electrode, and a third prediction model for predicting active material loss in a negative electrode, and for inputting the battery data into the battery state prediction model to obtain battery state data.

[0011] The at least one processor may generate the first predictive model based on an acceleration factor that quantifies the stress applied to the electrode and the solid electrolyte interphase (SEI).

[0012] The at least one processor can determine a change in the chemical ratio of the active material due to the lithium loss based on the first prediction model, and determine an open circuit potential (OCP) of the positive electrode and the negative electrode based on the change in the chemical ratio of the active material.

[0013] The at least one processor can predict the state of the battery based on the amount of change in the circuit potential.

[0014] The at least one processor may generate the second predictive model based on cation mixing and particle cracking induced volume change.

[0015] The at least one processor may input the battery data into the second prediction model to determine the degree of degradation of the battery.

[0016] The at least one processor may determine a correlation between the lithium loss and the active material loss in the negative electrode, and generate the third predictive model based on the correlation.

[0017] The at least one processor can input the battery capacity change derived by the first prediction model into the second prediction model or the third prediction model to derive a degradation coefficient, and predict the state of the battery based on the degradation coefficient.

[0018] A battery state prediction method according to one embodiment includes receiving battery data from a battery, generating a battery state prediction model including a first prediction model for predicting lithium loss, a second prediction model for predicting active material loss in a positive electrode, and a third prediction model for predicting active material loss in a negative electrode, and inputting the battery data into the battery state prediction model to obtain battery state data.

[0019] Generating the first predictive model may include generating the first predictive model based on an acceleration factor that quantifies the stress applied to the electrode and the solid electrolyte interphase (SEI).

[0020] A battery state prediction method according to one embodiment may further include determining a change in the chemical ratio of the active material due to the lithium loss based on the first prediction model, and determining an open circuit potential (OCP) of the positive electrode and the negative electrode based on the change in the chemical ratio of the active material.

[0021] A battery state prediction method according to one embodiment further comprises predicting the state of the battery based on the amount of change in the circuit potential.

[0022] Generating the second prediction model may include generating the second prediction model based on cation mixing and particle cracking induced volume change.

[0023] A battery state prediction method according to one embodiment may further include inputting the battery data into the second prediction model to determine a degree of degradation of the battery.

[0024] Generating the third prediction model may include determining a correlation between the lithium loss and the active material loss in the negative electrode, and generating the third prediction model based on the correlation.

[0025] A battery state prediction method according to one embodiment may further include inputting a battery capacity change derived by the first prediction model into the second prediction model or the third prediction model to derive a degradation coefficient, and predicting the state of the battery based on the degradation coefficient.

[0026] According to a battery state prediction device according to one embodiment, the deterioration resistance of the positive and negative electrodes occurring under each operating condition can be predicted through simulation according to the operating conditions of the battery.

[0027] According to a battery state prediction device according to one embodiment, the accuracy of diagnosing the SOH of a battery and predicting the RuL (Remaining Useful Life) can be improved.

[0028] According to a battery state prediction device according to one embodiment, the voltage of the battery can be diagnosed in the MoL (Middle of Life) state of the battery.

[0029] FIG. 1 illustrates a block diagram showing the configuration of a battery state prediction device according to one embodiment.

[0030] FIG. 2 schematically illustrates a flow of a battery state prediction device according to one embodiment predicting the state of a battery based on a prediction model.

[0031] FIG. 3 illustrates data analysis results based on a first prediction model of a battery state prediction device according to one embodiment.

[0032] FIG. 4 illustrates the results of a model considering an acceleration factor and a model not considering an acceleration factor in a battery state prediction device according to one embodiment.

[0033] FIG. 5 illustrates a correlation graph based on a second prediction model of a battery state prediction device according to one embodiment.

[0034] FIG. 6 illustrates a data analysis result based on a second prediction model in a battery state prediction device according to one embodiment.

[0035] FIG. 7 illustrates the results of a model that considers volume change and a model that does not consider volume change in a battery state prediction device according to one embodiment.

[0036] FIG. 8 illustrates a correlation graph based on a third prediction model of a battery state prediction device according to one embodiment.

[0037] FIG. 9 illustrates parameter changes based on a second prediction model and a third prediction model of a battery state prediction device according to one embodiment.

[0038] FIG. 10 illustrates a prediction result in storage degradation by a battery state prediction device according to one embodiment.

[0039] FIG. 11 illustrates a prediction result in cycle degradation by a battery state prediction device according to one embodiment.

[0040] FIG. 12 illustrates a control flowchart of a battery state prediction method according to one embodiment.

[0041] Hereinafter, various embodiments disclosed in this document will be described in detail with reference to the attached drawings. In this document, identical components in the drawings are designated by the same reference numerals, and redundant descriptions of identical components are omitted.

[0042] With respect to the various embodiments disclosed in this document, specific structural and functional descriptions are merely illustrative for the purpose of explaining the embodiments, and the various embodiments disclosed in this document may be implemented in various forms and should not be construed as being limited to the embodiments described in this document.

[0043] The expressions "first," "second," "first," or "second" used in various embodiments may describe various components, regardless of order and / or importance, and do not limit the components. For example, without departing from the scope of the embodiments disclosed herein, a first component may be renamed a second component, and similarly, a second component may also be renamed a first component.

[0044] The terms used in this document are intended solely to describe specific embodiments and may not be intended to limit the scope of other embodiments. Singular expressions may include plural expressions unless the context clearly indicates otherwise.

[0045] All terms used herein, including technical or scientific terms, have the same meaning as commonly understood by those of ordinary skill in the art of the embodiments disclosed herein. Terms defined in commonly used dictionaries may be interpreted as having the same or similar meaning in the context of the relevant technology, and unless explicitly defined herein, they shall not be interpreted in an idealized or overly formal sense. In some cases, even if a term is defined herein, it cannot be interpreted to exclude the embodiments disclosed herein.

[0046] FIG. 1 illustrates a block diagram showing the configuration of a battery state prediction device according to one embodiment.

[0047] Referring to FIG. 1, a battery state prediction device (1) according to one embodiment includes a control unit (100) including at least one processor (110) and a memory (120) and a communication interface (200), and can predict a battery state by communicating with an external device (5) through the communication interface (200).

[0048] According to one embodiment, a battery state prediction device (1) can monitor voltage, current, temperature, etc. from a voltage sensor (2), a current sensor (3), and a temperature sensor (4), and control and manage the same to prevent overcharging and overdischarging, etc., and may include, for example, a BMS (Battery Management System).

[0049] According to one embodiment, the battery state prediction device (1) may be configured with a user terminal and / or server device capable of communicating with an external device (5).

[0050] Specifically, when the battery state prediction device (1) is a user terminal, the control unit (100) of the battery state prediction device (1) may be configured as a CPU of the user terminal so as to merge battery data from the user terminal to an on-device. At this time, the user terminal may include, but is not limited to, a personal computer, a terminal, a portable telephone, a smart phone, a handheld device, a wearable device, etc.

[0051] In addition, when the battery status prediction device (1) is a server device, the server device may be implemented as various computing devices such as a workstation, a cloud, a data drive, a data station, etc. The server device may be implemented as one or more server devices that are physically or logically separated based on function, detailed configuration of function, or data, etc., and may transmit and receive data and process the transmitted and received data through communication between the respective server devices.

[0052] A battery state prediction device (1) according to one embodiment may refer to any electronic device including a processor (110) and a memory (120), and may be mounted on a vehicle and operated. Each component of the battery state prediction device (1) will be described in detail below.

[0053] The communication interface (200) may include a wireless communication interface (210) and a wired communication interface (220) for communicating with an external device (5). The communication interface (200) may transmit and receive programs for calculating characteristic values ​​of battery cells, classifying classes, and estimating lifespan, as well as various data, from a separately provided external server.

[0054] The wireless communication interface (210) may include at least one of a short-range communication module and a long-range communication module.

[0055] The short-range communication module can communicate with an external device (5) adjacent to the battery state prediction device (1) using a short-range communication method. Here, the short-range communication module can utilize one of the following communication methods: Bluetooth, Bluetooth low energy, infrared data association (IrDA), Zigbee, Wi-Fi, Wi-Fi direct, Ultra Wideband (UWB), or near field communication (NFC).

[0056] The remote communication module may include a communication module that performs various types of remote communication and may include a mobile communication interface. The mobile communication interface may transmit and receive wireless signals with at least one of a base station, an external terminal, and an external device (5) on a mobile communication network. In addition, the remote communication module may communicate with an external device (5) or an external device (5) such as another electronic device through a surrounding access point (AP). The access point (AP) may connect a local area network (LAN) to which the battery state prediction device (1) is connected to a wide area network (WAN) to which a communication server is connected. Accordingly, the battery state prediction device (1) may be connected to the communication server through the wide area network (WAN) and communicate with the external device (5).

[0057] The wired communication interface (220) can connect to a wired communication network and communicate with an external device (5) through the wired communication network. For example, the wired communication interface (220) can connect to a wired communication network through Ethernet (IEEE 802.3 technology standard) or connect to a wired communication network through CAN communication, and transmit and receive data with external devices (5) through the wired communication network.

[0058] A battery status prediction device (1) according to one embodiment may include an input / output interface (not shown). An interface may be provided that connects an input device (not shown) such as a keyboard, mouse, or touch panel, an output device (not shown) such as a display, and a processor (110) to transmit and receive data.

[0059] The memory (120) can store various types of information required for operating the battery state prediction device (1). Specifically, the memory (120) can store an operating system and a program required for operating the battery state prediction device (1), or store data required for operating the battery state prediction device (1).

[0060] Specifically, the memory (120) can store various programs related to calculating characteristic values ​​of battery cells, classifying classes, and estimating lifespan. In addition, the memory (120) can store various data such as voltage, current, and characteristic value data of each battery cell.

[0061] Additionally, the memory (120) can store data regarding a first prediction model, a second prediction model, and a third prediction model for predicting the state of the battery.

[0062] The memory (120) may include volatile memory (120) such as Static Random Access Memory (S-RAM) and Dynamic Random Access Memory (D-RAM) for temporarily storing data. In addition, the memory (120) may include nonvolatile memory (120) such as Read Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM), and Electrically Erasable Programmable Read Only Memory (EEPROM) for long-term storage of data.

[0063] The processor (110) outputs a control signal to control the battery state prediction device (1) as a whole. The processor (110) may include one or more central processing units (CPUs) and graphics processing units (GPUs). In this case, the processor (110) may be implemented as an array of a plurality of logic gates, or may be implemented as a combination of a general-purpose microprocessor (110) and a memory (120) storing a program that can be executed on the microprocessor (110).

[0064] The aforementioned memory (120) and processor (110) may be included in the control unit (100), and the control unit (100) may control the aforementioned components to predict a state related to battery deterioration.

[0065] Specifically, the processor (110) can generate a battery state prediction model including a first prediction model, a second prediction model, and a third prediction model, and input battery data into the battery state prediction model to obtain battery state data.

[0066] Here, the first prediction model includes a model for predicting lithium loss, and the first prediction model can be defined by an acceleration factor that quantifies the stress applied to the electrode and the solid electrolyte interphase (SEI).

[0067] Additionally, the processor (110) can determine a change in the chemical ratio of the active material due to lithium loss based on the first prediction model, and determine the open circuit potential (OCP) of the positive and negative electrodes based on the change in the chemical ratio of the active material.

[0068] That is, the first prediction model can explain the change in the circuit potential of the positive and negative electrodes, and thus can accurately analyze the physical and chemical changes of the positive and negative electrodes that occur during the degradation process of the battery.

[0069] Additionally, the second prediction model includes a model for predicting active material loss at the anode, and the second prediction model can be defined by cation mixing and particle cracking induced volume change.

[0070] Additionally, the third prediction model includes a model for predicting active material loss in the negative electrode, and the third prediction model can be defined by the correlation between lithium loss and active material loss in the negative electrode.

[0071] The processor (110) can input the battery capacity change derived by the first prediction model into the second prediction model or the third prediction model to derive a decay factor. That is, the processor (110) can first calculate the capacity change predicted by the first prediction model and apply it to the decay models of the positive and negative electrodes, thereby determining the decay factors of the positive and negative electrodes.

[0072] In this way, the battery state prediction device (1) according to one embodiment has the effect of improving the accuracy of deterioration prediction based on a first prediction model that predicts lithium loss and second and third prediction models that predict active material loss in the positive and negative electrodes.

[0073] FIG. 2 schematically illustrates a flowchart of a battery condition prediction device according to one embodiment of the present invention, which predicts the condition of a battery based on a prediction model. Components 101 to 103 in FIG. 2 are implemented in the form of software blocks, stored in a memory (120), and executed by a processor (110).

[0074] Referring to FIG. 2, the processor (110) can receive battery data through a sensor unit including a voltage sensor (2), a current sensor (3), and a temperature sensor (4). Thereafter, the predictive model generation unit (101) of the control unit (100) can generate multiple predictive models and determine the degree of battery degradation.

[0075] Specifically, the first prediction model generation unit (101-1) of the control unit (100) can generate a prediction model that can explain the LLI (Loss of Lithium inventory) occurring in a cycle experiment by utilizing a P2D (Pesudo two dimensional) model.

[0076] Here, the P2D model mathematically models the behavior of lithium-ion batteries. It can be defined as a model that predicts battery performance by combining particle- and domain-level phenomena. Specifically, the P2D model integrates fine particle-level diffusion and reaction with domain-level macroscopic transport phenomena to predict overall battery performance. Furthermore, it can model realistic battery behavior, including factors such as electrode porosity, electrolyte heterogeneity, and polarization effects.

[0077] Additionally, the LLI phenomenon refers to a phenomenon in which the amount of lithium ions available within a battery decreases, and can be caused by the formation of a solid electrolyte interphase (SEI) layer, lithium metal plating, electrolyte decomposition, and side reactions.

[0078] When the LLI phenomenon occurs in a battery, problems such as capacity reduction, performance degradation, and shortened lifespan may occur. Therefore, a battery performance prediction device according to one embodiment can predict battery degradation by modeling the LLI phenomenon occurring in a battery.

[0079] Specifically, the LLI phenomenon occurring in the cycle is mainly caused by side reactions occurring at the cathode and anode, and this LLI phenomenon can be expressed by an electrochemical reaction formula of the Butler-Volmer type.

[0080] Furthermore, mechanical defects in the electrode and SEI layer may occur due to the stress applied to the cathode and SEI layer. These defects may expose the surfaces of the cathode and SEI layer to the electrolyte, leading to additional SEI deposition in the newly formed area. Accordingly, LLI occurs more frequently than storage degradation, and the storage degradation model alone cannot explain cycle degradation.

[0081] According to a battery state prediction device (1) according to one embodiment, cycle degradation can be simulated by introducing an acceleration factor based on the fact that the aforementioned stress occurs significantly under low temperature and high C-rate.

[0082] At this time, since it is experimentally difficult to directly calculate the stress applied to the electrode and SEI layer, in the first prediction model, the concentration difference from the electrode surface to the inside can be expressed as an empirical equation as in the mathematical formulas below instead of the stress.

[0083] In addition, the first prediction model introduces a mathematical model to describe lithium metal plating occurring under high C-rate and low temperature, which can simulate abnormal degradation such as sudden performance degradation due to rapidly increasing LLI.

[0084] Specifically, mathematical equations 1 to 7 below are included in the first prediction model and may mean an LLI model due to cycle degradation at the cathode.

[0085] Here, mathematical expression 1 is the total capacity reduction rate during the battery degradation process ( ) Capacity reduction rate related to SEI layer growth, which is included in three major factors: ), capacity reduction rate related to transition metal catalytic effect ( ) and capacity reduction rate related to lithium metal plating ( ) can mean that it is composed of the sum of .

[0086] Specifically, in Equation 2 can mean the acceleration factor according to the state of charge, A can mean the electrode plate area of ​​the battery, and L n can mean the thickness or length of the electrode, and a n may refer to a coefficient related to the active material of the battery, i RE may refer to the current associated with the growth of the SEI layer.

[0087] Also, in mathematical formula 3 It can mean acceleration factor according to state of charge, A can mean electrode plate area of ​​the battery, L n can mean the thickness or length of the electrode, and a n may refer to a coefficient related to the active material of the battery, i TM may refer to the current associated with the transition metal catalytic effect.

[0088] Also, in mathematical expression 4, A can mean the area of ​​the electrode plates of the battery, and L n can mean the thickness or length of the electrode, and a n may refer to a coefficient related to the active material of the battery, i LP may refer to the current associated with lithium plating.

[0089] Also, in Equation 5, i RE can mean the current related to SEI layer growth, and f RE can mean a function that determines the growth current of the SEI layer, can mean the electrode surface concentration over time t, and U n (t) can mean electrode potential over time, and T(t) can mean temperature over time.

[0090] Also, in mathematical expression 6, i TM can mean the current related to the transition metal catalytic effect, and f TM can mean a function that determines the transition metal catalytic effect current, U p (t) may represent the electrode voltage over time, and T(t) may represent the temperature over time.

[0091] Also, in mathematical expression 7, i LP may refer to the lithium plating related current, f REcan mean a function that determines the lithium plating current, Un(t) can mean the electrode potential over time, and T(t) can mean the temperature over time.

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099] In addition, mathematical equations 8 to 10 below are included in the first prediction model and may mean an LLI model due to cycle degradation at the anode.

[0100] Specifically, in Equation 8 may refer to the capacity reduction rate related to lithium loss, may refer to the capacity reduction rate related to CEI formation. Here, CEI may refer to the interfacial layer formed between the positive electrode (cathode) and the electrolyte in a lithium-ion battery.

[0101] Also, in Equation 9 may refer to the capacity reduction rate related to CEI formation, A may refer to the electrode plate area, and L P can mean the thickness or length of the electrode, and a P can mean a specific surface area of ​​the electrode active material, i CEI may refer to the current associated with CEI formation.

[0102] Also, in Equation 10, i CEI can mean the current related to CEI formation, and f CEIcan mean a function that determines the CEI forming current, and U P (t) may represent the electrode voltage over time, and T(t) may represent the temperature over time.

[0103]

[0104]

[0105]

[0106] The second prediction model generation unit (101-2) of the control unit (100) can model degradation at the cathode based on the correlation between LAMp (Loss of Active Material of cathode) and SOC (State Of Charge) in battery storage and cycle degradation.

[0107] Specifically, LAMp refers to loss of active material at the anode, and can mean that the active material at the anode is lost or deactivated during the charging and discharging process of the battery, which is one of the main causes of battery performance degradation.

[0108] The second prediction model may include a Pseudo Cation Mixing model that simulates the phenomenon in which cations within an electrode swap positions with other cations within the electrode material, and a Particle Cracking Induced Volume Change model that simulates the phenomenon in which cracking occurs as electrode particles repeatedly expand and contract during the battery charging and discharging process.

[0109] The virtual cation mixing model included in the second prediction model can explain the anode degradation that occurs frequently in the intermediate SOC range (50-60%) by establishing a degradation model assuming a virtual cation mixing.

[0110] Specifically, in the low SOC region, nickel ions (Ni2+) within the NCM crystal structure can easily migrate to lithium ions (Li+), but cation mixing may be less likely to occur because there are fewer vacancies for lithium ions.

[0111] On the other hand, in the high SOC region, as nickel ions (Ni2+) are oxidized to nickel ions (Ni4+), the movement of lithium ions (Li+) becomes difficult, and even if there are many vacancies for lithium ions, fewer antisite defects occur.

[0112] Based on the above features, the second prediction model of the battery state prediction device (1) according to one embodiment can be configured as a cation mixing governing equation as in mathematical equations 11 and 12 below.

[0113] Specifically, in Equation 11 can mean the capacity reduction rate due to cation mixing, i CM can mean the current related to the mixing of cations, A can mean the area of ​​the electrode plate, and L P can mean the thickness or length of the electrode, and a P may refer to a specific surface area of ​​the electrode active material.

[0114] Also, in Equation 12, i CM can mean the current related to the mixing of silver cations, and f CM can mean a function that determines the current related to the mixing of silver cations, and NI Li (t) may represent lithium concentration over time, and T(t) may represent temperature over time.

[0115]

[0116]

[0117] In addition, the volume change model due to particle cracking included in the second prediction model can explain additional anode degradation occurring in the cycle by establishing a degradation model assuming particle cracking due to volume change.

[0118] Specifically, a volume change model due to particle cracking can be created by assuming that when current is applied to the battery, the volume of the positive electrode decreases during charging, causing delithiation relative to the positive electrode, and cracks occur between the initial particles, causing electrolyte to infiltrate into the resulting extra space, leading to degradation of the positive electrode.

[0119] Based on the above features, the second prediction model of the battery state prediction device (1) according to one embodiment can be configured as a particle cracking governing equation as in mathematical equations 13 and 14 below.

[0120] Specifically, in Equation 13 can mean the rate of capacity reduction due to volume change caused by particle cracking, i CR can mean the particle cracking related current, A can mean the electrode plate area, and L P can mean the thickness or length of the electrode, and a P may refer to a specific surface area of ​​the electrode active material.

[0121] Also, in Equation 14, i CR can mean the particle cracking related current, f CR can denote a function that determines the current related to particle cracking, △V(t) can denote the change in volume over time, and T(t) can denote the temperature over time.

[0122]

[0123]

[0124] The third prediction model generation unit (101-3) of the control unit (100) can model degradation in the negative electrode based on the correlation between LLI and LAMn (Loss of Active Material of anode) in battery storage and cycle degradation.

[0125] Specifically, LAMn refers to the loss of active material in the negative electrode, which is one of the main causes of battery performance degradation, and can mean that the active material in the negative electrode is lost or deactivated during the charging and discharging process of the battery.

[0126] A battery state prediction device (1) according to one embodiment assumes that pore clogging due to LLI blocks the surface site of the negative electrode where lithium can enter, leading to LAMn, and a third prediction model can be configured as in the following mathematical expression 15.

[0127] Specifically, in Equation 15, may mean loss of active material at the anode, may refer to a function that determines the loss of active material of the anode, could mean a loss of lithium inventory.

[0128]

[0129] Afterwards, the parameter adjustment unit (102) of the control unit (100) can simulate parameter changes of the physical model according to LLI and parameter changes of the physical model according to LAM.

[0130] Specifically, for the change in parameters of the physical model according to LLI, LLI is applied to the mathematical equations 16 to 20 below, and the voltage usage area (V max ,V min ) can be mathematically calculated to calculate the change in the stoichiometry usage area (cathode w0~w100, anode z0~z100) of the anode and cathode by LLI.

[0131] Accordingly, the results can be reflected in the physical model to explain the change in OCP (Open Circuit Potential) of the anode and cathode according to LLI.

[0132] Specifically, in Equation 16 can mean the stoichiometric value of the cathode in the initial state, can mean the maximum capacity of the cathode, can mean the stoichiometric value of the anode in the initial state, can mean the maximum capacity of the anode, can mean the total capacity of the battery, may mean capacity loss due to ion loss.

[0133] Also, in Equation 17, can mean the stoichiometric value of the cathode in the final state, can mean the stoichiometric value of the anode in the final state. Also, can mean the maximum capacity of the cathode, can mean the maximum capacity of the anode, can mean the total capacity of the battery, may mean capacity loss due to ion loss.

[0134] Also, in Equation 18, can mean the positive potential in the initial state, can mean the negative potential in the initial state, may refer to the minimum operating voltage of the battery.

[0135] Also, in Equation 19, can mean the positive potential in the final state, can mean the negative potential in the final state, may refer to the maximum operating voltage of the battery.

[0136] Also, in Equation 20, represents the total capacity of the battery, and mathematical expression 20 can mean that the total capacity of the battery is expressed as the product of the change in the stoichiometric values ​​of the negative and positive electrodes and their maximum capacities.

[0137]

[0138]

[0139]

[0140]

[0141]

[0142] In addition, in the case of parameter changes in the physical model due to LAM, shrinkage of the OCP profiles of the anode and cathode occurs as LAM occurs, and the change in OCV shape becomes the main cause. From this, parameter changes due to LAM can be simulated by introducing a decay factor, and a detailed explanation thereof is provided later in Fig. 9.

[0143] Thereafter, the prediction result derivation unit (103) of the control unit (100) can predict the state of the battery considering the degree of battery degradation from at least one of the first prediction model, the second prediction model, and the third prediction model and transmit the result to an external device (5).

[0144] FIG. 3 illustrates data analysis results based on a first prediction model of a battery state prediction device according to one embodiment.

[0145] Referring to Fig. 3, it can be confirmed that LLI occurs significantly at low temperatures during cycle degradation, as shown in the virtual trend line (a), and the battery condition prediction device (1) according to one embodiment can predict the condition of the battery by considering LLI that occurs significantly at low temperatures. The horizontal axis of Fig. 3 may represent the temperature of the battery, and the vertical axis may represent the amount of reduction in battery capacity according to LLI.

[0146] Specifically, the Loss of Lithium Inventory (LLI), which occurs significantly at low temperatures, can be caused by increased SEI formation, lithium metal precipitation, and deterioration of electrolyte performance.

[0147] Regarding SEI formation, which is one of the causes of the increase in LLI, SEI formation on the electrode surface may increase at low temperatures, and since the SEI layer is a phenomenon in which lithium ions react with the electrolyte to form a solid layer on the electrode surface, lithium ions may be consumed in this process, reducing the amount of available lithium ions.

[0148] SEI formation occurs primarily during the initial cycling of a battery, but at lower temperatures this process can be more active, resulting in a thicker SEI layer and trapping more lithium ions, which can contribute to battery degradation.

[0149] Regarding lithium metal precipitation, which is one of the causes of increased LLI, at low temperatures, lithium ions are more likely to be precipitated in the form of lithium metal rather than being evenly inserted into the negative electrode, and this lithium metal cannot be reused, which can increase LLI.

[0150] Among the causes of increased LLI, electrolyte degradation is related to reduced ionic conductivity at low temperatures, which can slow lithium ion movement and increase the battery's internal resistance. Consequently, this degradation in electrolyte performance can lead to uneven lithium ion exchange with the electrode, increasing LLI.

[0151] A battery state prediction device according to one embodiment can simulate abnormal degradation such as sudden performance degradation due to rapidly increasing LLI by introducing a mathematical model for explaining lithium metal plating occurring under high C-rate and low temperature as in Equations 1 to 10.

[0152] Figure 4 illustrates the results of a model that considers and does not consider an acceleration factor in a battery state prediction device according to one embodiment. The horizontal axis of Figure 4 may represent a predicted value of LLI, and the vertical axis may represent a simulated value of LLI.

[0153] Referring to FIG. 4, it can be confirmed that the predicted value of LLI predicted by the processor (110) of the battery state prediction device (1) according to one embodiment shows a linear relationship with the simulation value.

[0154] Specifically, (a) of FIG. 4 may refer to a first prediction model when the acceleration factor is not considered, and (b) of FIG. 4 may refer to a first prediction model when the acceleration factor is considered, as in mathematical expressions 1 to 10.

[0155] In Fig. 4 (a), the first learning model did not consider the acceleration factor that quantifies the stress applied to the cathode and SEI, so the graph may be expressed relatively far from the linear trend line at all temperatures.

[0156] On the other hand, in (b) of Fig. 4, the first learning model considers the acceleration factor that quantifies the stress applied to the cathode and SEI as a mathematical formula, so that the graph can be expressed relatively close to the trend line of the linear relationship at all temperatures.

[0157] That is, the fact that the predicted value of LLI and the simulated value show a linear relationship means that the prediction model accurately reflects the change in LLI, and it can mean that the first prediction model well reproduces the operation of the actual battery system.

[0158] In addition, as a result of an experiment conducted under the same conditions with only the application of the acceleration factor different, the root mean square error (RMSE) in Fig. 4 (a) is 1.97 Ah, and the root mean square error in Fig. 4 (b) is 1.38 Ah. It can be confirmed that the prediction performance of Fig. 4 (b) with the acceleration factor applied is superior even by the root mean square error, which measures the difference between the model's predicted value and the simulated value.

[0159] Accordingly, the battery state prediction device (1) according to one embodiment can quantify the stress applied to the electrode and SEI by introducing an acceleration factor, unlike the conventional technology, and thus has the effect of improving the ability to simulate the degradation of an actual battery system.

[0160] FIG. 5 illustrates a correlation graph based on a second prediction model of a battery state prediction device according to one embodiment.

[0161] Figure 5 (a) may mean the correlation between LAMp and SOC related to storage degradation in the second prediction model, and Figure 5 (b) may mean the correlation between LAMp and SOC related to cycle degradation in the second prediction model.

[0162] Specifically, in the low SOC region of Fig. 5, nickel ions (Ni2+) within the NCM crystal structure can easily migrate to lithium ions (Li+), but cation mixing may occur less because there are fewer vacancies for lithium ions.

[0163] On the other hand, in the high SOC region, as nickel ions (Ni2+) are oxidized to nickel ions (Ni4+), the movement of lithium ions (Li+) becomes difficult, and even if there are many vacancies for lithium ions, fewer antisite defects occur.

[0164] In this way, although it is difficult to confirm with trend lines (a-1) and (b-1), a volcano-shaped tendency may occur in which the active material loss (LAMp) of the positive electrode is large in the intermediate SOC region, such as regions (a-2) and (b-2), because the movement of lithium vacancies and nickel ions according to the state of charge is in conflict (trade off).

[0165] Accordingly, the battery state prediction device (1) according to one embodiment can simulate the active material loss (LAMp) of the positive electrode that occurs significantly in the SOC middle region in FIGS. 6 and 7.

[0166] FIG. 6 illustrates a data analysis result based on a second prediction model in a battery state prediction device according to one embodiment.

[0167] Referring to Fig. 6, it can be confirmed that LAMp occurs significantly when the lift of SOC is large, as in the virtual trend line (a), and the battery state prediction device (1) according to one embodiment can predict the state of the battery by considering LAMp that occurs when the lift of SOC is large. The horizontal axis of Fig. 3 may represent the size of the lift of SOC, and the vertical axis may represent the size of LAMp.

[0168] Specifically, when the lift of SOC is large in cycle degradation, i.e., when the SOC is maintained at a high state for a longer period of time or is frequently charged to a high SOC, the loss of active material at the cathode can be caused by a combination of factors, including electrolyte decomposition, corrosion of the cathode material, thermal instability, mechanical stress, and SEI layer formation on the electrode surface.

[0169] In response to this, a battery state prediction device (1) according to one embodiment can precisely predict LAMp based on a pseudo cation mixing model and a particle cracking induced volume change model as in mathematical expressions 11 to 14.

[0170] FIG. 7 illustrates the results of a model that considers volume change and a model that does not consider volume change in a battery state prediction device according to one embodiment.

[0171] Referring to FIG. 7, it can be confirmed that the predicted value of LAMp predicted by the processor (110) of the battery state prediction device (1) according to one embodiment shows a linear relationship with the simulation value.

[0172] Specifically, (a) of FIG. 7 may refer to a prediction result graph when the processor (110) applies only the virtual cation mixing model among the second prediction models, and (b) of FIG. 7 may refer to a prediction result graph when both the virtual cation mixing model and the volume change model due to particle damage are applied.

[0173] In Fig. 7 (a), since the second learning model does not consider the volume change due to particle damage, the graph may be expressed relatively far from the linear trend line at all temperatures.

[0174] On the other hand, in (b) of Fig. 7, the second learning model considers the volume change due to the cathode and particle damage using mathematical equations 13 and 14, so that the graph can be expressed relatively close to the trend line of the linear relationship at all temperatures.

[0175] That is, the fact that the simulated and predicted values ​​of LAMp show a linear relationship means that the prediction model accurately reflects the changes in LAMp, and it can mean that the second prediction model well reproduces the operation of the actual battery system.

[0176] In addition, as a result of an experiment in which only the consideration of volume change due to particle damage was changed under the same conditions, the root mean square error in Fig. 7 (a) was 1.63 Ah, and the root mean square error in Fig. 7 (b) was 1.28 Ah. It can be confirmed that the prediction performance of Fig. 7 (b) in which volume change due to particle damage was applied was superior even by the root mean square error, which measures the difference between the model's predicted value and the simulated value.

[0177] Accordingly, the battery state prediction device (1) according to one embodiment can predict LAMp by considering both the virtual cation mixing model and the volume change model due to particle damage as shown in the following mathematical expression 21, and thus has the effect of improving the ability to simulate the degradation of an actual battery system.

[0178] Specifically, in Equation 21 can mean the rate of capacity reduction due to loss of active material of the positive electrode, can mean the rate of capacity reduction due to cation mixing, can refer to the capacity reduction rate due to volume change caused by particle damage. Accordingly, the loss of active material of the cathode (Loss of Active Material of Cathode, ) can be expressed as the sum of the capacity reduction rates caused by cation mixing (CM) and particle cracking induced volume change (CR).

[0179]

[0180] FIG. 8 illustrates a correlation graph based on a third prediction model of a battery state prediction device according to one embodiment.

[0181] Referring to FIG. 8, the processor (110) can analyze the correlation between LLI and LAMn, which is the active material loss at the cathode, and based on this, can confirm a tendency having a linear relationship as shown in FIG. 8.

[0182] Specifically, the cause of the correlation is that when a SEI layer is formed on the negative electrode surface, lithium ions are consumed, and as the SEI layer thickens, more lithium ions are irreversibly consumed, which may increase LLI. At this time, the active surface area of ​​the negative electrode decreases as the SEI layer thickens, which may lead to the occurrence of LAMn, so they may be related.

[0183] Furthermore, mechanical stress occurs as the negative electrode material expands and contracts during repeated charge-discharge cycles, which can lead to decomposition and cracking of the negative electrode material, thereby increasing LAMn. Furthermore, increased negative electrode material loss can further exacerbate LLI by reducing its ability to accommodate lithium ions.

[0184] In addition, in high SOC conditions or low-temperature environments, lithium ions are more likely to be precipitated in the form of lithium metal rather than being evenly inserted into the negative electrode, which increases lithium ion loss (LLI), and the precipitation of lithium metal can damage the negative electrode surface, thereby worsening LAMn.

[0185] From this correlation, the processor (110) can predict the state of the battery using a model such as the aforementioned mathematical expression 15, assuming that pore clogging due to LLI blocks the surface site of the negative electrode where lithium can enter, leading to LAMn.

[0186] FIG. 9 illustrates parameter changes based on the second prediction model and the third prediction model of the battery state prediction device according to one embodiment.

[0187] Referring to Fig. 9, the second and third prediction models can simulate changes in physical model parameters due to LAM. Specifically, as LAM occurs, shrinkage of the OCP profiles of the anode and cathode occurs, and LAM is the main cause of changes in the OCV shape. From this, the processor (110) can simulate parameter changes due to LAM by introducing a decay factor.

[0188] At this time, (a) of Fig. 9 is a graph change of OCV (vertical axis) versus capacity (horizontal axis) in the case where LAM does not occur, (b) of Fig. 9 is a graph change when LAMp occurs by 20%, and (c) of Fig. 9 can be assumed to be a graph change when LAMn occurs by 30%.

[0189] In order to simulate changes in graph shape like this, a degeneration coefficient can be introduced, and by introducing a degeneration coefficient, the capacity of the battery can be calculated from design information as in the following mathematical expression 22 to simulate parameter changes according to LAM.

[0190] Specifically, in mathematical expression 22, Q can mean the capacity of the battery, can mean the initial stoichiometric value minus the final stoichiometric value, may mean the volume ratio of the active material in the charged state of the electrode, L may mean the thickness or length of the electrode, A may mean the area of ​​the electrode, and F may mean a constant such as the Faraday constant, can mean maximum concentration.

[0191]

[0192] At this time, the shrinkage of the open circuit voltage (OCV) when loss of active material (LAM) occurs can be interpreted as maintaining the usable range of stoichiometry (z), but changing the stoichiometry over time.

[0193] According to the LAM generated from the capacity relationship above, it is possible to reflect LAM by reducing the maximum concentration (C_max) value or reducing the volume fraction (epss) value.

[0194] For example, i) if the C_max value decreases, the z value increases compared to the initial state (BoL) for the same surface concentration from the relationship z=C_surf(t) / C_max. In addition, ii) if the volume fraction decreases, the current density flowing through the electrode particles increases even if the same current as in the initial state is applied because the electrochemical specific surface area decreases. This results in a large change in stoichiometry.

[0195] In the case of the positive electrode, the loss of positive active material (LAMp) due to cation mixing is assumed to be the main cause of degradation, and the change in crystal structure due to cation mixing is assumed to change the theoretical capacity of the electrode particles, thereby adjusting the maximum concentration (C_max) value among the parameters of the physical model.

[0196] Additionally, for the negative electrode, the volume fraction value can be adjusted from the assumption that the electrochemical surface area is reduced rather than from the perspective of a single particle, assuming that the negative active material loss (LAMn) occurs from pore clogging due to lithium inventory loss (LLI).

[0197] By first calculating and applying the change in capacity as in mathematical equation 22 with the above assumptions, the degradation coefficients of the positive and negative electrodes can be defined as in mathematical equations 23 to 26 below.

[0198] Specifically, in Equation 23 can mean the degeneration coefficient of the cathode, can mean the value of the capacity at the middle of the life divided by the initial capacity, can mean the value obtained by subtracting the initial stoichiometry value from the final stoichiometry value of the cathode in the initial state, can mean the final stoichiometric value of the cathode minus the initial stoichiometric value at the middle of its life.

[0199] Also, in Equation 24 can mean the degeneration coefficient of the anode, can mean the value of the capacity at the middle of the life divided by the initial capacity, can mean the value obtained by subtracting the initial stoichiometric value from the final stoichiometric value of the anode in the initial state, can mean the final stoichiometric value of the anode minus the initial stoichiometric value at the middle of its life.

[0200] Also, in Equation 25 can mean the volume ratio of the active material of the cathode in the current state, can mean the volume ratio of the active material of the cathode in the initial state, may refer to the degeneration coefficient of the cathode.

[0201] Also, in Equation 26 can mean the maximum concentration of the anode in the current state, can mean the maximum concentration of the anode in the initial state, can mean the degeneration coefficient of the anode.

[0202]

[0203]

[0204]

[0205]

[0206] FIG. 10 illustrates a prediction result in storage degradation by a battery state prediction device according to one embodiment, and FIG. 11 illustrates a prediction result in cycle degradation by a battery state prediction device according to one embodiment.

[0207] In (a) of FIGS. 10 and 11, the horizontal axis represents time and the vertical axis represents the State of Health (SOH), which is the state of health of the battery. In (b) of FIGS. 10 and 11, the horizontal axis represents time and the vertical axis represents LLI, which is the lithium ion reduction. In addition, in (c) of FIGS. 10 and 11, the horizontal axis represents time and the vertical axis represents LAMp, which is the active material loss in the positive electrode. In (d) of FIGS. 10 and 11, the horizontal axis represents time and the vertical axis represents LAMn, which is the active material loss in the negative electrode.

[0208] In addition, points expressed as figures in (a) to (d) of FIGS. 10 and 11 may mean experimental values ​​obtained through experiments, and graphs expressed as lines may mean simulation values ​​obtained through simulations.

[0209] In addition, the experimental value (a-1), which is a point expressed as a figure in (a), (b), (c), and (d) of FIGS. 10 and 11, may mean a result derived under the same conditions as the simulation value (a-2), which is a graph expressed as a line. In addition, the experimental value (b-1) may mean a result derived under the same conditions as the simulation value (b-2).

[0210] That is, in Fig. 10, (a-1) and (a-2) are results derived under the same conditions, (b-1) and (b-2) are results derived under the same conditions, (c-1) and (c-2) are results derived under the same conditions, (d-1) and (d-2) are results derived under the same conditions, and (e-1) and (e-2) can mean results derived under the same conditions.

[0211] Likewise, in Fig. 11, (a-1) and (a-2) are results derived under the same conditions, and (b-1) and (b-2), (c-1) and (c-2), (d-1) and (d-2), (e-1) and (e-2), (f-1) and (f-2), (g-1) and (g-2) may each mean results derived under the same conditions.

[0212] In this way, according to the battery state prediction device according to one embodiment, since the trend lines of the results grouped with the same alphabet in FIGS. 10 and 11 are obtained with similar shapes, it can be analyzed that the theory and assumptions used in the prediction model are valid even in an actual environment.

[0213] Specifically, FIG. 10 includes results predicting SOH, LLI, LAMp and LAMn in storage degradation, and FIG. 11 includes results predicting SOH, LLI, LAMp and LAMn in cycle degradation.

[0214] First, the battery state prediction device (1) according to one embodiment can increase the prediction accuracy of LLI by introducing an acceleration factor that is not considered in the conventional technology, as shown in (b) of FIG. 10, and the battery state prediction device (1) according to one embodiment can increase the prediction accuracy of LAMp by introducing a cation mixing model and a volume change model due to particle damage that are not considered in the conventional technology, as shown in (c) of FIG. 10.

[0215] In addition, the battery state prediction device (1) according to one embodiment can increase the prediction accuracy of LAMn by considering the correlation between LLI and LAMn, which is not considered in the conventional technology, as shown in (d) of FIG. 10, and consequently, can improve the SOH estimation accuracy as shown in (a) of FIG. 10.

[0216] Next, the battery state prediction device (1) according to one embodiment can model the degradation of a battery by considering cycle degradation, unlike the conventional technology that only considers the storage degradation model as shown in (a) to (d) of FIG. 11.

[0217] Accordingly, the battery state prediction device (1) according to one embodiment has the effect of improving the accuracy and precision of estimating SOH for predicting the state of the battery, as shown in FIGS. 10 and 11.

[0218] FIG. 12 illustrates a control flowchart of a battery state prediction method according to one embodiment.

[0219] Referring to FIG. 12, at least one processor (110) may receive battery data of a battery (1200). Thereafter, the processor (110) may generate a first prediction model related to LLI that predicts lithium loss (1210).

[0220] Additionally, the processor (110) can generate a second predictive model related to LAMp that predicts active material loss at the anode (1220), and can generate a third predictive model related to LAMn that predicts active material loss at the cathode (1230).

[0221] Here, the processor (110) is described as generating the first to third prediction models, but the first to third prediction models may also mean models that are stored in the memory (120) and generated in advance.

[0222] Thereafter, the processor (110) may input battery data into one of the prediction models to obtain battery status data (1240). At this time, the processor (110) may input battery data into one of the prediction models and input the output value into another prediction model to obtain battery status data.

[0223] Specifically, the processor (110) can input the battery capacity change derived by the first prediction model into the second prediction model or the third prediction model to derive a degradation coefficient, and predict the state of the battery based on the degradation coefficient.

[0224] That is, the processor (110) can first calculate the change in capacity due to LLI from the first prediction model, input it into the second prediction model and the third prediction model, and derive the degradation coefficient based on mathematical expressions 23 to 26.

[0225] Thereafter, the processor (110) can determine whether a command to transmit battery status data has been received (1250), and if the processor (110) determines that a command to transmit battery status data has been received (example of 1250), the processor (110) can transmit the battery status data to an external device (5) (1260).

[0226] In this way, the battery status prediction device (1) according to one embodiment can predict battery deterioration to ensure the stability of battery use, provide an integrated model for reflection in battery warranty, and have the effect of securing data for analyzing the cause of battery failure.

[0227] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium storing computer-executable instructions. The instructions may be stored in the form of program code, and when executed by a processor, may generate program modules to perform the operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0228] Computer-readable storage media include all types of storage media that store instructions that can be deciphered by a computer. Examples include read-only memory (ROM), random access memory (RAM), magnetic tape, magnetic disks, flash memory, and optical data storage devices.

[0229] Additionally, a computer-readable recording medium may be provided in the form of a non-transitory storage medium. Here, the term "non-transitory storage medium" simply means a tangible device that does not contain signals (e.g., electromagnetic waves). This term does not distinguish between cases where data is permanently stored in 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.

[0230] According to one embodiment, the method according to various embodiments disclosed in the present document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable recording medium (e.g., compact disc read only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) via an application store (e.g., Play Store™) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product (e.g., a downloadable app) may be temporarily stored or temporarily generated on a machine-readable recording medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0231] Although all components constituting the embodiments disclosed in this document have been described as being combined or operating in combination as one, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purpose of the embodiments disclosed in this document, all of the components may be selectively combined and operated one or more times.

[0232] Furthermore, terms such as "include," "comprise," or "have" described above, unless specifically stated otherwise, imply that the corresponding component may be present, and therefore should be interpreted to include other components rather than excluding other components. All terms, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as terms defined in dictionaries, should be interpreted to be consistent with the contextual meaning of the relevant technology, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

[0233] 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. Therefore, the embodiments disclosed in this document are not intended to limit the technical idea of ​​the embodiments disclosed in this document, but to explain it, and the scope of the technical idea disclosed in this document is not limited by these embodiments. The scope of protection of the technical idea disclosed in this document should be interpreted by the claims below, and all technical ideas within a scope equivalent thereto should be interpreted as being included in the scope of rights of this document.

Claims

1. A communication interface for receiving battery data from the battery; A battery state prediction device comprising at least one processor for generating a battery state prediction model including a first prediction model for predicting lithium loss, a second prediction model for predicting active material loss in the positive electrode, and a third prediction model for predicting active material loss in the negative electrode, and for inputting the battery data into the battery state prediction model to obtain battery state data.

2. In claim 1, At least one processor, A battery state prediction device that generates the first prediction model based on an acceleration factor that quantifies the stress applied to an electrode and a solid electrolyte interphase (SEI).

3. In claim 2, At least one processor, A battery state prediction device that determines a change in the chemical ratio of the active material due to the lithium loss based on the first prediction model, and determines the open circuit potential (OCP) of the positive electrode and the negative electrode based on the change in the chemical ratio of the active material.

4. In claim 3, At least one processor, A battery state prediction device that predicts the state of the battery based on the amount of change in the above circuit potential.

5. In claim 1, At least one processor, A battery state prediction device that generates the second prediction model based on cation mixing and particle cracking induced volume change.

6. In claim 5, At least one processor, A battery condition prediction device that inputs the above battery data into the second prediction model to determine the degree of degradation of the battery.

7. In claim 1, At least one processor, A battery state prediction device that determines a correlation between the lithium loss and the active material loss in the negative electrode, and generates the third prediction model based on the correlation.

8. In claim 1, At least one processor, A battery state prediction device that inputs a change in battery capacity derived by the first prediction model into the second prediction model or the third prediction model to derive a degradation coefficient, and predicts the state of the battery based on the degradation coefficient.

9. Receive battery data from the battery; Generating a battery state prediction model including a first prediction model predicting lithium loss, a second prediction model predicting active material loss in the positive electrode, and a third prediction model predicting active material loss in the negative electrode; A battery state prediction method comprising: inputting the above battery data into a battery state prediction model to obtain battery state data.

10. In claim 9, Generating the above first prediction model comprises: A battery state prediction method comprising: generating the first prediction model based on an acceleration factor that quantifies the stress applied to an electrode and a solid electrolyte interphase (SEI).

11. In claim 10, A battery state prediction method further comprising: determining a change in the chemical ratio of the active material due to the lithium loss based on the first prediction model, and determining an open circuit potential (OCP) of the positive electrode and the negative electrode based on the change in the chemical ratio of the active material.

12. In claim 11, A battery state prediction method further comprising: predicting the state of the battery based on the amount of change in the circuit potential.

13. In claim 9, Generating the second prediction model is as follows: A battery state prediction method comprising: generating the second prediction model based on cation mixing and particle cracking induced volume change.

14. In claim 13, A battery state prediction method further comprising: inputting the battery data into the second prediction model to determine the degree of degradation of the battery.

15. In claim 9, Creating the third prediction model above is: A battery state prediction method comprising: determining a correlation between the lithium loss and the active material loss in the negative electrode, and generating the third prediction model based on the correlation.

16. In claim 9, A battery state prediction method further comprising: inputting a battery capacity change derived by the first prediction model into the second prediction model or the third prediction model to derive a degradation coefficient, and predicting the state of the battery based on the degradation coefficient.

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