Battery gas generation amount prediction device and operation method

Abstract

A gas generation amount prediction device according to one embodiment disclosed in this specification may include an acquisition unit that identifies measurement data relating to the voltage, current, and temperature of a battery, a calculation unit that calculates CSR (cathode side reaction) and ASR (anode side reaction) based on the measurement data, and a prediction unit that predicts the amount of gas generation from the battery based on the CSR and the ASR.

Description

[Technical Field]

[0001] The present invention claims the benefit of priority based on Korean Patent Application No. 10-2022-0182369 filed on December 22, 2022 and Korean Patent Application No. 10-2023-0153022 filed on November 7, 2023, and all contents disclosed in the documents of these Korean patent applications are incorporated herein by reference.

[0002] The embodiments disclosed herein relate to a device for predicting gas generation amount in a battery and a method for operating the same. [Background technology]

[0003] In recent years, research and development into secondary batteries has been actively conducted. Here, the term "secondary battery" refers to a battery that can be charged and discharged, and includes both conventional Ni / Cd batteries, Ni / MH batteries, and more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of having a much higher energy density than conventional Ni / Cd batteries, Ni / MH batteries, etc. Furthermore, lithium-ion batteries can be manufactured to be compact and lightweight, and are used as power sources for mobile devices. Recently, their use has expanded to include electric vehicles, and they are attracting attention as a next-generation energy storage medium.

[0004] One of the services related to such secondary batteries is a battery management system (BMS). The BMS can collect data on the voltage, current, and temperature of the battery. Based on the collected measurement data, the BMS can diagnose the presence or absence of internal disconnections, overvoltage, temperature sensor failure, venting, and other failures in the battery. Here, venting refers to a phenomenon in which gas is generated inside the battery, causing an increase in the temperature and pressure inside the battery.

[0005] If the venting phenomenon causes the internal pressure of the battery to continuously increase, the battery may release a large amount of gas, which could lead to a fire or explosion. Summary of the Invention [Problem to be solved by the invention]

[0006] The venting phenomenon can be caused by gas generated inside the battery, so it is necessary to accurately measure the amount of gas generated inside the battery.

[0007] In order to measure the amount of gas generated by a battery, a sensor must be used to directly measure the gas generated by the battery.

[0008] However, when the amount of gas generated from the battery is directly measured, there is a problem in that the venting phenomenon cannot be diagnosed early.

[0009] The technical problems of the embodiments disclosed in this specification are not limited to the above-mentioned technical problems, and other technical problems not mentioned can be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0010] A gas generation amount prediction device according to one embodiment disclosed in this specification may include an acquisition unit that identifies measurement data relating to the voltage, current, and temperature of a battery, a calculation unit that calculates CSR (cathode side reaction) and ASR (anode side reaction) based on the measurement data, and a prediction unit that predicts the amount of gas generation from the battery based on the CSR and the ASR.

[0011] In one embodiment, the prediction unit can identify a gas generation rate of the battery based on the concentration of an oxidation reactant and the rate of change of the CSR, and can predict the amount of gas generation based on the gas generation rate.

[0012] In one embodiment, the predictor may identify the rate of change of the concentration of the oxidation reactant based on the concentration of a reduction by-product and the rate of change of the ASR.

[0013] In one embodiment, the predictor can identify the concentration of the reduction by-product based on the temperature.

[0014] In one embodiment, the gas generation amount prediction device may further include a model management unit that generates a gas generation amount prediction model based on a model governing equation and changes one or more parameters included in the model governing equation, and the prediction unit may predict the gas generation amount of the battery using the gas generation amount prediction model.

[0015] In one embodiment, the gas generation amount prediction device may further include a comparison unit that compares the gas generation amount predicted by the gas generation amount prediction model with the actual gas generation amount, and the model management unit may change the one or more parameters included in the model governing equation based on the comparison result.

[0016] In one embodiment, the model governing equations may include mathematical expressions based on the interrelationship between gas production and CSR-ASR.

[0017] An operating method of a gas generation amount prediction device according to one embodiment disclosed in this specification can include the operations of acquiring measurement data relating to the voltage, current, and temperature of a battery, calculating a CSR (cathode side reaction) and an ASR (anode side reaction) based on the measurement data, and predicting the amount of gas generation of the battery based on the CSR and the ASR.

[0018] In one embodiment, the predicting operation may further include identifying a gassing rate of the battery based on a concentration of an oxidation reactant and a rate of change of the CSR, and the amount of gassing may be predicted based on the gassing rate.

[0019] In one embodiment, the predicting operation can further include identifying a rate of change of the concentration of the oxidation reactant based on a concentration of a reduction by-product and a rate of change of the ASR.

[0020] In one embodiment, the predicting operation can further include identifying the concentration of the reduction by-product based on the temperature.

[0021] In one embodiment, the method of operating the gas generation amount prediction device may further include an operation of generating a gas generation amount prediction model based on a model governing equation, and the predicting operation may predict the gas generation amount of the battery using the gas generation amount prediction model.

[0022] In one embodiment, the method of operating the gas generation amount prediction device may further include an operation of comparing the gas generation amount predicted by the gas generation amount prediction model with the actual gas generation amount, and an operation of changing one or more parameters included in the gas generation amount prediction model based on the comparison result.

[0023] In one embodiment, the model governing equations may include mathematical expressions based on the interrelationship between gas production and CSR-ASR. [Effects of the Invention]

[0024] The battery gas generation amount prediction device and its operating method according to various embodiments disclosed in this specification can predict the amount of gas generation in a battery using only measurement data related to the battery's voltage, current, and temperature using a gas generation amount prediction model.

[0025] The battery gas generation amount prediction device and its operating method according to various embodiments disclosed in this specification can change the parameters of a gas generation amount prediction model based on the results of comparing the actual gas generation amount with the predicted gas generation amount.

[0026] As a result, the battery gas generation amount prediction device and its operating method can diagnose venting risks in advance based on the gas generation amount predicted by the gas generation amount prediction model, thereby preventing accidents caused by battery explosions.

[0027] The effects of the battery gas generation amount prediction device and its operating method disclosed in this specification are not limited to the effects described above, and other effects not mentioned can be clearly understood by those skilled in the art from the disclosure of this specification. [Brief explanation of the drawings]

[0028] [Figure 1] 1 is a block diagram of a battery gas generation amount prediction device according to an embodiment of the present disclosure. FIG. [Figure 2] 1 illustrates a coordinate system displaying predicted and actual gas generation rates according to one embodiment of the present disclosure. [Figure 3] 1 is a flowchart illustrating a method for generating and modifying a gas generation rate prediction model according to an embodiment of the present disclosure. [Figure 4] 1 is a flowchart illustrating an operation method of a gas generation amount prediction device for a battery according to an embodiment of the present disclosure.

[0029] In connection with the description of the drawings, the same or similar reference numerals may be used for the same or similar components. DETAILED DESCRIPTION OF THE INVENTION

[0030] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings, but this is not intended to limit the present invention to the particular embodiments, and should be understood to include various modifications, equivalents, and / or alternatives of the embodiments of the present invention.

[0031] The embodiments and terms used in this specification are not intended to limit the technical features described in this specification to specific embodiments, but should be understood to include various modifications, equivalents, or alternatives of the embodiments. With regard to the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the said item unless the relevant context clearly dictates otherwise.

[0032] As used herein, each of the terms "A or B," "at least one of A and B," "at least one of A or B," "A, B or C," "at least one of A, B and C," and "at least one of A, B, or C" may include any one of the items listed therein or all possible combinations thereof. Terms such as "first," "second," "primary," "second," "A," "B," "(a)," or "(b)" may be used simply to distinguish one element from other elements, and do not limit the element in other respects (e.g., importance or order) unless otherwise specified.

[0033] In this specification, when a (e.g., first) component is referred to as being "coupled," "coupled," or "connected" to another (e.g., second) component, with or without the terms "functionally" or "communicatively," or when a reference is made to "coupled" or "connected," it means that the component can be coupled to the other component directly (e.g., by wire or wirelessly) or indirectly (e.g., via a third component).

[0034] Methods according to various embodiments disclosed herein may be provided in a computer program product. The computer program product may be traded between a seller and a buyer as a commodity. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disc read-only memory, CD-ROM) or may be distributed online (e.g., downloaded or uploaded) via an application store or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store server, or an intermediary server.

[0035] According to embodiments disclosed herein, each of the components (e.g., modules or programs) described above may include one or more entities, and some of the entities may be separately located in other components. According to embodiments disclosed herein, one or more of the components described above may be omitted, or one or more additional components or operations may be added. Alternatively, or in addition, multiple components (e.g., modules or programs) may be integrated into a single component. In such cases, the integrated component may perform one or more functions of each of the multiple components in a manner that is the same as or similar to that performed by the respective components of the multiple components before the integration. According to embodiments disclosed herein, operations performed by modules, programs, or other components may be performed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be performed in a different order, omitted, or one or more additional operations may be added.

[0036] FIG. 1 is a block diagram of a gas generation amount prediction device 12 according to an embodiment of the present disclosure.

[0037] Referring to FIG. 1, a gas generation amount prediction device 12 can be connected to an electronic device 10 and a user terminal 14 via wire and / or wireless.

[0038] In one embodiment, the connection 11 between the gas generation prediction device 12 and the electronic device 10 may be a communication connection via a wired and / or wireless network. In one embodiment, the wired network may be based on a local area network (LAN) communication or a power line communication. In one embodiment, the wireless network may be based on a short-range communication network (e.g., Bluetooth, WiFi (wireless fidelity), or IrDA (infrared data association)) or a long-range communication network (e.g., a cellular network, a 4G network, or a 5G network).

[0039] In another embodiment, the connection 11 between the gas generation amount prediction device 12 and the electronic device 10 may be a connection via an inter-device communication method (e.g., a bus, a general purpose input and output (GPIO), a serial peripheral interface (SPI), or a mobile industry processor interface (MIPI)).

[0040] In one embodiment, the connection 13 between the gas generation prediction device 12 and the user terminal 14 may be a communications connection via a wired and / or wireless network.

[0041] In one embodiment, the electronic device 10 may be a mobile device (e.g., a mobile phone, a laptop computer, a smartphone, or a smart pad), an electric vehicle (e.g., an electric vehicle (EV), a hybrid EV (HEV), a plug-in HEV (PHEV), or a fuel cell EV (FCEV)), an energy storage system (ESS), a battery swapping system (BSS), or a battery management system (BMS).

[0042] In one embodiment, the electronic device 10 may include one or more battery units 101, 103, 105. Each of the one or more battery units 101, 103, 105 may be a battery cell, a battery module, a battery pack, or a battery rack.

[0043] In one embodiment, the user terminal 14 may be a mobile device (eg, a mobile phone, a laptop computer, a smartphone, a smart pad) or a personal computer (PC).

[0044] In one embodiment, the gas generation amount prediction device 12 may include a communication circuit 120, a sensor 140, a memory 160, and a processor 180. According to an embodiment, the gas generation amount prediction device 12 shown in Fig. 1 may further include at least one component (e.g., a display, an input device, or an output device) other than the components shown in Fig. 1.

[0045] In one embodiment, the communication circuit 120 can establish a wired communication channel and / or a wireless communication channel between the gas generation amount prediction device 12 and the electronic device 10 and / or the user terminal 14, and can transmit and receive data to and from the electronic device 10 and / or the user terminal 14 via the established communication channel.

[0046] In one embodiment, the sensor 140 may obtain a value related to the status of the battery units 101, 103, 105 of the electronic device 10. In one embodiment, the status-related value may indicate one or more values ​​for the voltage, current, resistance, state of charge (SOC), state of health (SOH), or temperature of the battery units 101, 103, 105, or a combination thereof. Hereinafter, the status-related value may be referred to as a "status value."

[0047] In one embodiment, memory 160 may include volatile memory and / or non-volatile memory.

[0048] In one embodiment, the memory 160 can store data used by at least one component (e.g., the processor 180) of the gas generation amount prediction device 12. For example, the data can include software (or associated instructions), input data, or output data. In one embodiment, the instructions, when executed by the processor 180, can cause the gas generation amount prediction device 12 to perform the operations defined by the instructions.

[0049] In one embodiment, memory 160 may include one or more pieces of software (eg, an acquisition unit 162, a calculation unit 164, a prediction unit 166, a model management unit 168, and a comparison unit 170).

[0050] In one embodiment, processor 180 may include a central processing unit, an application processor, a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor.

[0051] In one embodiment, the processor 180 can execute software (e.g., the acquisition unit 162, the calculation unit 164, the prediction unit 166, the model management unit 168, and the comparison unit 170) to control at least one other component (e.g., a hardware or software component) of the gas generation amount prediction device 12 connected to the processor 180, and can perform various data processing or calculations.

[0052] The operations performed by the acquisition unit 162, the calculation unit 164, the prediction unit 166, the model management unit 168, and the comparison unit 170 will be described below.

[0053] Generation of gas generation rate model The model management unit 168 can generate a gas generation amount prediction model, which can predict the amount of gas generation of the battery based on measurement data, which can include state values ​​related to the battery's voltage, current, temperature, SOC, SOH, or a combination thereof.

[0054] The model management unit 168 may establish a model governing equation. The model management unit 168 may establish the model governing equation to generate a gas generation amount prediction model. The model management unit 168 may establish the model governing equation based on experimental data. The model management unit 168 may establish the model governing equation that serves as the basis for the gas generation amount prediction model based on the experimental data. Here, the experimental data may include measurement data including state values ​​for a degraded experimental battery. The experimental data may include CSR (cathode side reaction) and ASR (anode side reaction) calculated based on measurement data for multiple experimental batteries. The experimental data may include a correlation between the gas generation amount and CSR, and a correlation between the gas generation amount and ASR. Here, CSR may be the amount of a side reaction at the positive electrode of the battery, and ASR may be the amount of a side reaction at the negative electrode.

[0055] According to an embodiment, the CSR and ASR may be calculated based on experimental data. The CSR and ASR may be calculated based on measurement data for a degraded test battery included in the experimental data. Here, the experimental data may include data previously derived using a battery of the same type as the test battery. For example, the experimental data may include information on the positive electrode open circuit potential (OCP), negative electrode OCP, and full cell OCV (Open Circuit Voltage) for the battery. The experimental data may include information on the value of the OCV (and / or OCP) with respect to the SOC. The experimental data may include information on the rate of change of the OCV (and / or OCP) with respect to the SOC.

[0056] In one embodiment, the ASR (anode side reaction) and the CSR (cathode side reaction) can be calculated based on the theoretical capacity of the full cell, the change in OCV, and the change in SOH. Specifically, the ASR and the CSR can be calculated based on Equation 1.

[0057] [Mathematical formula 1]

number

[0058] In mathematical formula 1, ΔC ASR indicates the change in the amount of side reaction at the negative electrode between the first time point (time point k-1) and the second time point (time point k), and ΔC CSR can indicate the amount of change in the amount of side reactions at the positive electrode.

[0059] In mathematical formula 1,

number

number

number

number

[0060] In mathematical formula 1, ΔSOH OCV can indicate the amount of change in SOH at a specified voltage.

number

[0061] In one embodiment,

number

[0062] [Mathematical formula 2]

number

[0063] Referring to mathematical formula 2,

number

number

number

[0064] In one embodiment,

number

[0065] [Mathematical formula 3]

number

[0066] Referring to mathematical formula 3,

number

number

number

[0067] In one embodiment, the ASR and the CSR are calculated by the slope (

number

number

[0068] In one embodiment, the ASR and CSR are calculated based on the theoretical capacity (

number

[0069] The model governing equations may include one or more mathematical expressions, where the one or more mathematical expressions may be expressed as Equation 4 and Equation 5.

[0070] [Mathematical formula 4]

number

[0071] [Mathematical formula 5]

number

[0072] where C gas is the amount of gas generated, C OR is the concentration of the oxidation reactant, C CSR is the concentration of CSR, C ASR k can be the concentration of ASR. OR , k RP , k T , k g are the reaction rate coefficients for the oxidation reactant, reduction byproduct, temperature, and gas generation rate, respectively. T is a variable for temperature, and T R can be a constant with respect to temperature.

[0073] How to change parameters The model management unit 168 can change the gas generation amount prediction model. The model management unit 168 can change the model governing equation that is the basis of the gas generation amount prediction model. The model management unit 168 can change one or more parameters included in the model governing equation.

[0074] The comparison unit 170 may compare the predicted gas generation rate with the actual gas generation rate in order to modify one or more parameters. The comparison unit 170 may compare the gas generation rate predicted by the prediction unit 166 with the actual gas generation rate. Here, the predicted gas generation rate may be a value predicted to be the actual gas generation rate by the gas generation rate prediction model. The actual gas generation rate may be an actual value measured by a test device to determine the actual gas generation rate of the battery. Here, the comparison result between the predicted gas generation rate and the actual gas generation rate may include a root mean square deviation (RMSE), a coefficient of determination (r-square), or a combination thereof. The root mean square deviation and the coefficient of determination may be indicators capable of evaluating the performance of the gas generation rate prediction model.

[0075] The model management unit 168 can change one or more parameters included in the model governing equation based on the comparison result between the predicted gas generation rate and the actual gas generation rate. The model management unit 168 can change one or more parameters based on the comparison result so that the difference between the predicted gas generation rate and the actual gas generation rate decreases. Here, the one or more parameters can be k OR , k RP , k T , k g , T R , or a combination thereof.

[0076] The model management unit 168 can change the gas generation rate prediction model based on the one or more changed parameters. The model management unit 168 can change the gas generation rate prediction model based on the model governing equations in which the one or more changed parameters are reflected. The model management unit 168 can generate a new gas generation rate prediction model based on the model governing equations in which the one or more changed parameters are reflected.

[0077] Gas generation amount prediction method The acquisition unit 162 can acquire data related to the battery, such as measurement data related to the voltage, current, and temperature of the battery.

[0078] The calculation unit 164 can calculate the CSR and the ASR. The calculation unit 164 can calculate the CSR and the ASR based on the measurement data.

[0079] The prediction unit 166 can predict the amount of gas generated in the battery. The prediction unit 166 can predict the amount of gas generated in the battery based on the gas generation rate. The prediction unit 166 can predict the amount of gas generated in the battery by integrating the gas generation rate.

[0080] The prediction unit 166 can identify the gas generation rate of the battery to predict the amount of gas generation of the battery. The prediction unit 166 can identify the gas generation rate of the battery based on the concentration of oxidation reactants and the rate of change of CSR. Here, the gas generation rate can be expressed by the above-mentioned Equation 4.

[0081] The prediction unit 166 can identify the rate of change in the concentration of the oxidation reactant. The prediction unit 166 can identify the rate of change in the concentration of the oxidation reactant based on the rate of change in the concentration of the reduction by-product and the ASR. The rate of change in the concentration of the oxidation reactant can be expressed by Equation 6.

[0082] [Mathematical formula 6]

number

[0083] where C OR is the concentration of the oxidation reactant, C RP is the concentration of the reduction by-product, C ASR k can be the concentration of ASR. RP may be reaction rate coefficients for the respective reduction by-products.

[0084] Referring to Equation 6, the concentration change rate of the oxidation reactant is proportional to the concentration of the reduction by-product and the electrochemical reaction rate. However, the concentration change rate of the oxidation reactant can be reduced by the amount of gas generated by the reduction by-product. The concentration change rate of the oxidation reactant excluding the amount of gas generated by the reduction by-product in Equation 6 can be expressed by Equation 7.

[0085] [Mathematical formula 7]

number

[0086] where C OR is the concentration of the oxidation reactant, C RP is the concentration of the reduction by-product, C ASR is the concentration of ASR, C gas k can be the concentration of gas generation. RP , k g are the reaction rate coefficients for the reduction by-products and gas generation, respectively.

[0087] The prediction unit 166 can identify the concentration of the reduction by-product. The prediction unit 166 can identify the concentration of the reduction by-product based on the temperature. The concentration of the reduction by-product can be expressed by Equation 8.

[0088] [Mathematical formula 8]

number

[0089] where C RP k can be the concentration of the reduction by-product. T is the reaction rate coefficient for temperature. T is the temperature variable, and T R can be a constant with respect to temperature.

[0090] The prediction unit 166 can predict the amount of gas generated in the battery using the gas generation amount prediction model. The prediction unit 166 can predict the amount of gas generated in the battery using the gas generation amount prediction model based on the CSR and the ASR.

[0091] FIG. 2 illustrates a coordinate system 200 displaying predicted and actual gas production rates according to one embodiment of the present disclosure.

[0092] Referring to FIG. 2, the x-axis of coordinate system 200 may be actual gas production and the y-axis may be predicted gas production.

[0093] The coordinates of the points that make up the reference line 202 included in the coordinate system 200 may have the same x and y values.

[0094] The comparison unit 170 can compare the predicted gas generation rate with the actual gas generation rate. The comparison unit 170 can compare the predicted gas generation rate with the actual gas generation rate based on the root mean square deviation, the coefficient of determination, or a combination thereof between the predicted value and the actual value included in the coordinate system 200.

[0095] The root mean square deviation between the predicted gas production rate and the actual gas production rate contained in coordinate system 200 may be 26.2567 and the coefficient of determination may be 0.7264.

[0096] The model manager 168 can modify one or more parameters included in the model governing equations based on the comparison results. The model manager 168 can modify one or more parameters included in the model governing equations based on the root mean square deviation, the coefficient of determination, or a combination thereof.

[0097] FIG. 3 is a flowchart illustrating a method for generating and modifying a gas generation rate prediction model according to one embodiment of the present disclosure.

[0098] 3, the model manager 168 may establish model governing equations in operation 300. The model manager 168 may establish model governing equations based on experimental data.

[0099] In operation 302, the model manager 168 may generate a gas generation rate prediction model. The model manager 168 may generate a gas generation rate prediction model based on the model governing equations.

[0100] In operation 304, the comparison unit 170 can compare the predicted gas generation rate with the actual gas generation rate. The comparison unit 170 can derive the root mean square deviation and the coefficient of determination as a comparison result of the predicted gas generation rate with the actual gas generation rate.

[0101] In operation 306, the model manager 168 can modify one or more parameters included in the model governing equations. The model manager 168 can modify one or more parameters included in the model governing equations based on the comparison results. The model manager 168 can modify one or more parameters based on the comparison results to reduce the difference between the predicted gas generation rate and the actual gas generation rate. The model manager 168 can modify one or more parameters included in the model governing equations based on the root mean square deviation comparing the predicted gas generation rate and the actual gas generation rate and the coefficient of determination. The model manager 168 can modify one or more parameters based on the root mean square deviation comparing the predicted gas generation rate and the actual gas generation rate and the coefficient of determination to reduce the difference between the predicted gas generation rate and the actual gas generation rate.

[0102] In operation 308, the model manager 168 can modify the gas generation rate prediction model. The model manager 168 can modify the gas generation rate prediction model based on the model governing equations that reflect the modified one or more parameters.

[0103] FIG. 4 is a flowchart showing an operation method of the battery gas generation amount prediction device 12 according to an embodiment of the present disclosure.

[0104] 4, the acquisition unit 162 may acquire data related to the battery in operation 400. The acquisition unit 162 may acquire measurement data related to the voltage, current, and temperature of the battery.

[0105] In operation 402, the calculation unit 164 can calculate a CSR (cathode side reaction) and an ASR (anode side reaction). The calculation unit 164 can calculate the CSR and the ASR based on the measurement data.

[0106] In operation 404, the prediction unit 166 can predict the amount of gas generation of the battery. The prediction unit 166 can predict the amount of gas generation of the battery based on the CSR and the ASR. Here, the amount of gas generation can be a value obtained by integrating the gas generation rate.

[0107] In operation 406, the prediction unit 166 can predict the amount of gas generation of the battery. The prediction unit 166 can predict the amount of gas generation based on the gas generation rate. The prediction unit 166 can predict the amount of gas generation of the battery by integrating the gas generation rate.

[0108] The prediction unit 166 can identify a gassing rate of the battery to predict the amount of gassing of the battery. The prediction unit 166 can identify a gassing rate of the battery based on the concentration of oxidation reactants and the rate of change of CSR.

[0109] The prediction unit 166 can identify the rate of change in the concentration of the oxidation reactant. The prediction unit 166 can identify the rate of change in the concentration of the oxidation reactant based on the rate of change in the concentration of the reduction by-product and the ASR. Here, the rate of change in the concentration of the oxidation reactant can be reduced by the amount of gas generated by the reduction by-product.

[0110] The predictor 166 can identify the concentration of the reduction by-products. The predictor 166 can identify the concentration of the reduction by-products based on the temperature.

[0111] The prediction unit 166 can predict the amount of gas generated in the battery using the gas generation amount prediction model. The prediction unit 166 can predict the amount of gas generated in the battery using the gas generation amount prediction model based on the CSR and the ASR.

[0112] The prediction unit 166 can predict the amount of gas generated in the battery using the gas generation amount prediction model. The prediction unit 166 can predict the amount of gas generated in the battery using the gas generation amount prediction model based on the CSR and the ASR. The prediction unit 166 can predict the amount of gas generated in the battery using the gas generation amount prediction model based on the model governing equations based on the CSR and the ASR.

[0113] As used above, terms such as "comprise," "comprise," or "have," unless otherwise specified, mean that the relevant element can be contained within the term, and therefore should be interpreted as not excluding other elements but as including other elements. 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 herein belong, unless otherwise defined. Commonly used terms, such as dictionary-defined terms, should be interpreted as consistent with the contextual meaning of the relevant art, and should not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0114] The above description is merely an illustrative example of the technical concepts disclosed in this specification, and various modifications and variations are possible by a person skilled in the art to which the embodiments disclosed in this specification pertain, without departing from the essential characteristics of the embodiments disclosed in this specification. Therefore, the embodiments disclosed in this specification are intended to illustrate, not limit, the technical concepts of the embodiments disclosed in this specification, and such embodiments do not limit the scope of the technical concepts disclosed in this specification. The scope of protection of the technical concepts disclosed in this specification should be interpreted by the scope of the following claims, and all technical concepts within the scope equivalent thereto should be interpreted as being within the scope of the present specification.

Claims

1. an acquisition unit that acquires measurement data related to the voltage, current, and temperature of the battery; a calculation unit that calculates CSR and ASR based on the measurement data; a prediction unit that predicts the amount of gas generated in the battery based on the CSR and the ASR.

2. The prediction unit identifying a gassing rate for the battery based on a concentration of an oxidation reactant and a rate of change of the CSR; The gas generation amount prediction device according to claim 1 , wherein the gas generation amount is predicted based on the gas generation rate.

3. The prediction unit The gas generation amount prediction device according to claim 2 , wherein the rate of change of the concentration of the oxidation reactant is identified based on the rate of change of the concentration of a reduction by-product and the ASR.

4. The prediction unit The gas generation amount prediction device according to claim 3 , wherein the concentration of the reduction by-product is identified based on the temperature.

5. The method further includes a model management unit that generates a gas generation amount prediction model based on a model governing equation and changes one or more parameters included in the model governing equation; The gas generation amount prediction device according to claim 1 , wherein the prediction unit predicts the amount of gas generated in the battery using a gas generation amount prediction model.

6. a comparison unit that compares the gas generation amount predicted by the gas generation amount prediction model with the actual gas generation amount, The gas generation amount prediction device according to claim 5 , wherein the model management unit changes the one or more parameters included in the model governing equation based on a result of the comparison.

7. 6. The gas generation rate prediction device according to claim 5, wherein the model governing equation includes a mathematical expression based on an interrelationship between gas generation rate and CSR-ASR.

8. obtaining measurement data relating to the voltage, current, and temperature of the battery; calculating CSR and ASR based on the measurement data; and an operation of predicting the amount of gas generated in the battery based on the CSR and the ASR.

9. The predicted action is: further comprising the act of identifying a gassing rate of the battery based on a concentration of an oxidation reactant and a rate of change of the CSR; The method for operating the gas generation amount predicting device according to claim 8 , further comprising predicting the gas generation amount based on the gas generation rate.

10. The predicted action is: The method of claim 9 , further comprising identifying a rate of change of the concentration of the oxidation reactant based on a rate of change of a concentration of a reduction by-product and the ASR.

11. The predicted action is: The method of claim 10 , further comprising the act of identifying the concentration of the reduction by-product based on the temperature.

12. Further, the method includes generating a gas generation amount prediction model based on the model governing equations; The method for operating the gas generation amount prediction device according to claim 8 , wherein the predicting operation predicts the amount of gas generated in the battery using the gas generation amount prediction model.

13. an operation of comparing the gas generation amount predicted by the gas generation amount prediction model with the actual gas generation amount; The method of claim 12 , further comprising: modifying one or more parameters included in the gas generation rate prediction model based on a result of the comparison.

14. The method for operating a gas generation rate prediction device according to claim 12, wherein the model governing equations include a mathematical expression based on an interrelationship between gas generation rate and CSR-ASR.

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