System and method for diagnosing the state of health of a battery

Abstract

A diagnostic system for diagnosing a state of degradation of a battery according to various embodiments includes a battery and a diagnostic device that diagnoses the state of the battery, wherein the diagnostic device can be configured to predict a side reaction rate for the electrode based on an OCV model defined by the state of charge of the battery and the cumulative amount of side reactions of the electrode, and to predict a state of degradation of the battery based on the side reaction rate.

Description

[Technical Field]

[0001] The present invention claims the benefit of priority based on Korean Patent Application No. 10-2022-0122902, filed on September 27, 2022, and all contents disclosed in the documents of this Korean patent application are incorporated herein by reference.

[0002] The present invention relates to a system and method for diagnosing the state of health of a battery, and more particularly to a system and method for improving the performance of diagnosing the state of health. [Background technology]

[0003] Generally, electric vehicles or hybrid electric vehicles use electrical energy stored in batteries as their energy source. For example, lithium-ion polymer batteries are widely used as batteries for electric vehicles, and research into these batteries is actively being conducted.

[0004] Such electric vehicles or hybrid electric vehicles run on energy stored in the battery, so it is extremely important not only to diagnose (or predict) the current state of the battery, but also to diagnose the deterioration state of the battery.

[0005] For example, the capacity of a battery can be reduced by side reactions such as the formation of a solid electrolyte interphase (SEI) film, and such side reactions can be taken into consideration when diagnosing the state of battery degradation. In other words, the state of side reactions in the electrodes can be predicted, and the state of battery degradation can be diagnosed based on the prediction results.

[0006] Various predictive models can be used to diagnose the deterioration state of such batteries (or side reactions of electrodes). For example, physics-based models such as the Doyle Fuller Newman (DFN) model, the Single Particle Model (SPM) model, and the Enhanced Single Particle Model (ESPM) can be used to diagnose the deterioration state of batteries.

[0007] However, the aforementioned physics-based model is a model that mathematically represents the physical movement of particles inside a battery using complex differential equations, and although it can diagnose the state of battery degradation relatively accurately, it has the disadvantage of being highly computationally complex due to its mathematical complexity. Summary of the Invention [Problem to be solved by the invention]

[0008] At least one of various embodiments of the present invention aims to provide a system and method for diagnosing the state of health of a battery to improve the performance of diagnosing the state of health.

[0009] At least one of various embodiments of the present invention aims to provide a system and method for diagnosing the state of deterioration of a battery, which predicts a side reaction state based on the equilibrium potential of an electrode.

[0010] At least one of various embodiments of the present invention aims to provide a system and method for diagnosing the state of deterioration of a battery, which predicts side reaction states using an OCV (Open Circuit Voltage) model that has lower mathematical complexity than physics-based models. [Means for solving the problem]

[0011] A diagnostic system for diagnosing a state of degradation of a battery according to various embodiments includes a battery and a diagnostic device that diagnoses the state of the battery, wherein the diagnostic device can be configured to predict a side reaction rate for the electrode based on an OCV model defined by the state of charge of the battery and the cumulative amount of side reactions of the electrode, and to predict a state of degradation of the battery based on the side reaction rate.

[0012] According to various embodiments, the diagnostic device can be configured to obtain an equilibrium potential for the electrode based on the OCV model, and predict a side reaction rate of the electrode based on the equilibrium potential.

[0013] According to various embodiments, the diagnostic device can be configured to obtain an equilibrium potential of the electrode based on a concentration of solid-state lithium ions relative to the electrode.

[0014] According to various embodiments, the diagnostic device can be configured to predict the side reaction rate using ordinary differential equations that integrate the side reaction rate of the electrode with respect to time using the potential of the electrode. According to various embodiments, the diagnostic device can be configured to approximate the amount of side reactions at the electrode to predict the rate of the side reactions.

[0015] According to various embodiments, the diagnostic device can be configured to obtain a state of health and a self-discharge voltage of the battery based on the predicted side reaction rate, and to predict a state of health for the battery based on the state of health and the self-discharge voltage of the battery.

[0016] According to various embodiments, the diagnostic system may further include an output device, and the diagnostic device may be configured to output the predicted degradation state via the output device.

[0017] According to various embodiments, the diagnostic device may be configured to acquire information regarding at least one of voltage, current, and temperature for the battery, and to acquire a state of charge of the battery and an accumulated amount of side reactions of the electrodes based on the acquired information.

[0018] A diagnostic method for diagnosing a state of degradation of a battery according to various embodiments may include an operation of predicting a side reaction rate for the battery based on an OCV model defined by a state of charge of the battery and an accumulated amount of side reactions of an electrode, and an operation of predicting a state of degradation of the battery based on the side reaction rate.

[0019] According to various embodiments, the diagnostic method can include obtaining an equilibrium potential for the electrode based on the OCV model, and predicting a side reaction rate of the electrode based on the equilibrium potential.

[0020] According to various embodiments, the method of diagnosing can include obtaining an equilibrium potential of the electrode based on a concentration of solid-state lithium ions relative to the electrode.

[0021] According to various embodiments, the diagnostic method can include predicting the side reaction rate using ordinary differential equations that integrate the side reaction rate of the electrode with respect to time using the potential of the electrode. According to various embodiments, the method of diagnosing can include approximating the amount of a side reaction at the electrode to predict the rate of the side reaction.

[0022] According to various embodiments, the diagnostic method may include operations of obtaining a state of health and a self-discharge voltage of the battery based on the predicted side reaction rate, and predicting a state of health for the battery based on the state of health and the self-discharge voltage of the battery. According to various embodiments, the method of diagnosing can include outputting the predicted state of degradation via an output device.

[0023] According to various embodiments, the diagnostic method can include obtaining information regarding at least one of voltage, current, and temperature for the battery, and obtaining a state of charge of the battery and a cumulative amount of side reactions of the electrodes based on the obtained information. [Effects of the Invention]

[0024] The systems and methods for diagnosing the state of health of a battery according to various embodiments disclosed herein can improve the accuracy of diagnosing the state of health and reduce the time required for diagnosis by using an open circuit voltage (OCV) model, which has lower mathematical complexity than physics-based models. The effects obtained by this document are not limited to those mentioned above. [Brief explanation of the drawings]

[0025] [Figure 1a] 1 is a diagram illustrating a schematic configuration of a diagnostic system for diagnosing the state of a battery according to various embodiments. [Figure 1b] 1 is a diagram illustrating a schematic configuration of a diagnostic device according to various embodiments. [Figure 2] 10A and 10B are diagrams for explaining the operation of acquiring electrode potentials by a diagnostic device according to various embodiments. [Figure 3a] 10 shows a comparison result of performance between diagnostic devices according to various embodiments and a diagnostic device according to a comparative example. [Figure 3b]10 shows a comparison result of performance between diagnostic devices according to various embodiments and a diagnostic device according to a comparative example. [Figure 3c] 10 shows a comparison result of performance between diagnostic devices according to various embodiments and a diagnostic device according to a comparative example. [Figure 3d] 10 shows a comparison result of performance between diagnostic devices according to various embodiments and a diagnostic device according to a comparative example. [Figure 3e] 10 shows a comparison result of performance between diagnostic devices according to various embodiments and a diagnostic device according to a comparative example. [Figure 3f] 10 shows a comparison result of performance between diagnostic devices according to various embodiments and a diagnostic device according to a comparative example. [Figure 3g] 10 shows a comparison result of performance between diagnostic devices according to various embodiments and a diagnostic device according to a comparative example. [Figure 4] 1 is a flowchart illustrating a battery diagnostic operation of a diagnostic system according to various embodiments. [Figure 5] FIG. 1 shows parameters for describing a model for predicting side reaction rates according to various embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0026] Some embodiments of the present invention will be described in detail below with reference to the accompanying drawings. When designating components in each drawing, it should be noted that the same components are designated by the same reference numerals whenever possible even when they appear in other drawings. Furthermore, when describing embodiments of the present invention, if a detailed description of related well-known structures or functions is deemed to hinder understanding of the embodiments of the present invention, the detailed description will be omitted.

[0027] When describing components of an embodiment of the present invention, terms such as "first," "second," "A," "B," "(a)," and "(b)" may be used. Such terms are merely used to distinguish the component from other components and do not limit the nature, order, or sequence of the components. Furthermore, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the present invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the context of the relevant art, and should not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0028] Furthermore, the various embodiments and terms used in this document are not intended to limit the technical features described herein to a specific embodiment, but should be understood to include various modifications, equivalents, or alternatives of the embodiment. In connection with the description of the drawings, like 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 item, unless the relevant context clearly dictates otherwise. In this document, phrases such as “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 or all possible combinations of the items listed therein. Terms such as “first,” “second,” “first,” or “second” may be used simply to distinguish a component from other components and do not limit the component in other aspects (e.g., importance or order). When a component (e.g., a first component) is referred to as being "coupled" or "connected" to another component (e.g., a second component), with or without the terms "functionally" or "communicatively," this means that the component may be coupled to the other component directly (e.g., by wire or wirelessly) or through a third component.

[0029] FIG. 1a is a diagram illustrating a schematic configuration of a diagnostic system for diagnosing the state of a battery according to various embodiments. Referring to FIG. 1 a, a diagnostic system 100 according to various embodiments can include a battery 110, a diagnostic device 120, and an output device 130.

[0030] According to various embodiments, the battery 110 includes a cell assembly in which a plurality of unit cells capable of repeated charging and discharging are connected in series or parallel. The unit cells may be electric double layer capacitors, including ultracapacitors, or known secondary batteries, such as lithium ion batteries, lithium polymer batteries, nickel cadmium batteries, nickel metal hydride batteries, or nickel zinc batteries.

[0031] According to various embodiments, output device 130 can output information related to the operation of diagnostic system 100. According to one embodiment, output device 130 can include an audio output device (e.g., a speaker) configured to output auditory information and / or a display configured to output visual information. For example, at least a portion of the visual and / or auditory information can include a diagnostic result for battery 110 (e.g., the status of battery 110). However, this is by way of example only, and various embodiments are not limited thereto. For example, output device 130 can include a haptic module (e.g., a motor, a piezoelectric element, an electrical stimulation device, etc.) configured to output tactile information.

[0032] According to various embodiments, the diagnostic device 120 can handle the overall operation of the diagnostic system 100. According to one embodiment, the diagnostic device 120 can diagnose (or predict) the state of the battery 110. The diagnostic device 120 can also output the state of the battery 110 via the output device 130. For example, the state of the battery 110 diagnosed by the diagnostic device 120 may be a deterioration state of the battery 110. However, this is merely an example, and various embodiments are not limited thereto. For example, the diagnostic device 120 may measure the state of charge (SOC) of the battery 110, a change in the initial capacity of the battery 110, etc. at specified time intervals (e.g., in real time), and then output the results as at least a part of the diagnostic results.

[0033] According to one embodiment, the diagnostic device 120 can diagnose the state of degradation of the battery 110 by using an OCV (Open Circuit Voltage) model (e.g., a lumped OCV model). The OCV model is a model that uses the functional dependency of the side reaction state (e.g., side reaction rate) on the equilibrium potential of the electrode. The OCV model is modeled based on a higher level of reduction than general physics-based models (e.g., an SPM model), and has the advantage of being less mathematically complex. Therefore, the diagnostic device 120 can accurately and quickly predict the state of side reactions of the electrode according to the equilibrium potential of the electrode using the OCV model, thereby improving the diagnostic performance for the state of degradation. The diagnostic device 120 using such an OCV model will be described in detail below with reference to FIG. 1b.

[0034] Fig. 1b is a diagram illustrating a schematic configuration of a diagnostic device according to various embodiments, and Fig. 2 is a diagram illustrating an operation of acquiring electrode potentials by the diagnostic device according to various embodiments.

[0035] Referring to FIG. 1 b, the diagnostic device 120 may be composed of a data acquisition unit 121 , a model calculation unit 123 , a deterioration estimation unit 125 , and a condition confirmation unit 127 .

[0036] According to various embodiments, the data acquisition unit 121 may acquire battery information regarding the battery 110. The battery information may include the state of charge (SOC) of the battery 110 and the amount of side reactions accumulated in the electrodes. According to one embodiment, the data acquisition unit 121 may acquire the voltage, current, and temperature of the battery 110 and acquire the battery information based thereon. However, this is merely an example, and various embodiments are not limited thereto. For example, in addition to the information described above, various information regarding the battery 110, such as charging cycle information, may also be acquired as the battery information. For example, the data acquisition unit 121 may include at least one sensor configured to acquire the battery information.

[0037] According to various embodiments, the model calculation unit 123 can acquire at least one parameter that can be used in diagnosing the battery 110, based on the battery information acquired by the data acquisition unit 121. For example, the model calculation unit 123 can acquire the side reaction state (e.g., side reaction rate) of the electrode as at least a part of the parameter.

[0038] According to one embodiment, the model calculation unit 123 can acquire the electrode potential, for example, the equilibrium potential of the negative electrode and the positive electrode, and the capacity differential value of the equilibrium potential, as part of the operation of acquiring the side reaction state. For example, the model calculation unit 123 can acquire the electrode potential using the state of charge (SOC) acquired via the data acquisition unit 121 and the amount of side reaction accumulated in the electrode, and can predict the side reaction state of the electrode based on the potential.

[0039] First, the operation of the model calculation unit 123 to acquire the electrode potential will be described in detail with reference to FIG. 2, the model calculation unit 123 can calculate the theoretical capacity of the electrode and use it to calculate a predicted electrode OCV change profile depending on the charge / discharge capacity. The theoretical capacity can include a negative electrode theoretical capacity, which is the capacity inherent to the negative electrode (or negative electrode active material), and a positive electrode theoretical capacity, which is the capacity inherent to the positive electrode (or positive electrode active material). The OCV change profile can also be a predicted result of the OCV changing depending on the charge / discharge capacity.

[0040] In addition, the model calculation unit 123 can position the electrode OCV change profile on a charge balance axis (e.g., continuous charge balance (Qccb)) that defines the cumulative state of charge / discharge capacity. The charge balance axis has 0 at the time the cell is first assembled, and when a charge current is applied to the cell, the value on the charge balance axis can increase by the charge capacity, and when a discharge current is applied to the cell, the value on the charge balance axis can decrease by the discharge capacity. For example, on the charge balance axis, the positive electrode OCV change profile can be shifted by the amount of side reaction at the positive electrode to position (201), and the negative electrode OCV change profile can be shifted by the amount of side reaction at the negative electrode to position (203).

[0041] Furthermore, the model calculation unit 123 can identify points 209 and 211 on the charge balance axis where the differences 205 and 207 between the positive electrode OCV change profile and the negative electrode OCV change profile correspond to predetermined values, and can acquire the electrode potentials based on the identified points 209 and 211. For example, the predetermined value used to identify the position on the charge balance axis where the potential is acquired may be the potential of a full cell (e.g., the potential of a full cell at an OCV of 0% and the potential of a full cell at an OCV of 100%), which can be stored in advance inside or outside the diagnostic system 100.

[0042] In this regard, the model calculation unit 123 can output the electrode potential by inputting the state of charge of the battery 110 and the amount of side reactions at the electrodes using the OCV model obtained from the following <Equations 1> to <Equation 11>. In relation to the following <Equations>, the parameters shown in FIG. 5 can be referenced. For example, the model calculation unit 123 can obtain the potential of the electrode as described in the following <Equation 1>.

[0043] <Expression 1>

number

[0044] In the above formula 1, the potential of the positive electrode (U P ) is the concentration of solid-state lithium ions relative to the positive electrode (X P ) and the negative electrode potential (U n ) is the solid-state lithium ion concentration (X n ) can be obtained based on

[0045] In this regard, the model calculation unit 123 calculates the concentration of solid-phase lithium ions in the positive electrode (X P ), and the solid-state lithium ion concentration for the negative electrode (X n ) can be obtained.

[0046] In relation to obtaining the lithium ion concentration, the model calculation unit 123 can determine the positive electrode OCV change profile and the negative electrode OCV change profile using the OCV model described by the following <Equation 2> and <Equation 3>.

[0047] <Expression 2>

number

[0048] <Expression 3>

number

[0049] In the above <Equation 2>, the positive electrode OCV change profile (Qccb(X p、BOL )) can be determined based on the sum of the theoretical capacity of the positive electrode and the initial offset of the positive electrode in a fresh state where no aging has occurred.

[0050] In the above <Equation 3>, the negative electrode OCV change profile (Qccb(X n、BOL )) can be determined based on the sum of the theoretical capacity of the negative electrode and the initial offset of the negative electrode in a fresh state where no aging has occurred.

[0051] In this regard, the model calculation unit 123 can calculate the initial offset of the negative electrode using the OCV model described by the following <Equation 4> and <Equation 5>.

[0052] <Expression 4>

number

[0053] <Formula 5>

number

[0054] The continuous charge balance (Q CCB (X p、SOCFC )) and the continuous charge balance (Q CCB (X n、SOCFC )) are the same as each other, and the initial offset (Q offset、p0 ) is "0", the initial offset (Q offset、n0 ) can be calculated.

[0055] Furthermore, the model calculation unit 123 can obtain the concentration of solid-phase lithium ions in the electrode using the OCV model described by the following <Equation 6> and <Equation 7>.

[0056] <Formula 6>

number

[0057] <Formula 7>

number

[0058] The concentration of solid-phase lithium ions in the positive electrode (X P) is calculated, and the solid-phase lithium ion concentration (X n ) can be calculated. Furthermore, the model calculation unit 123 can calculate the potential of the electrode using the OCV model described by the following <Equation 8> to <Equation 11>.

[0059] <Formula 8>

number

[0060] In relation to this, as can be seen from <Equation 8>, the model calculation unit 123 can substitute the solid-phase lithium ion concentration for the electrode calculated by <Equation 6> and <Equation 7> into <Equation 1>.

[0061] Furthermore, the model calculation unit 123 calculates the full cell potential (U FC、SOC0 ) and the full cell potential at 100% SOC (U FC、SOC100 ) can be calculated.

[0062] <Formula 9>

number

[0063] Furthermore, the model calculation unit 123 calculates the full cell potential (U FC、SOC0 ) and the full cell potential at 100% SOC (U FC、SOC100 ) corresponds to a pre-specified value, the potential of the electrode can be calculated.

[0064] <Formula 10>

number

[0065] According to various embodiments, the model calculation unit 123 can obtain the side reaction state based on the potential of the electrode. According to one embodiment, the model calculation unit 123 can acquire the side reaction state using the potential of the electrode (for example, the equilibrium potential of the negative electrode and the positive electrode, the capacitance differential value of the equilibrium potential) and the amount of the side reaction of the electrode.

[0066] For example, the model calculation unit 123 can obtain the side reaction state of the electrode using the OCV model described by the following <Equation 11>. For example, in order to obtain the side reaction state of the electrode, the model calculation unit 123 can use ordinary differential equations that integrate the amount of side reaction of the electrode with respect to time using the potential of the electrode.

[0067] <Formula 11>

number

[0068] As another example, the model calculation unit 123 may obtain the side reaction state of the electrode using an OCV model described by the following <Equation 12>. For example, the model calculation unit 123 may approximate the side reaction capacity based on the assumption that the equilibrium potential of the electrode does not change significantly due to aging of the battery 110, and use the approximated capacity to obtain the side reaction state of the electrode.

[0069] <Formula 12>

number

[0070] According to various embodiments, the deterioration estimation unit 125 can estimate the deterioration of the battery 110 based on the state of side reactions of the electrodes acquired by the model calculation unit 123.

[0071] According to one embodiment, the degradation estimation unit 125 can obtain the state of health (SOH) and / or the self-discharge voltage of the battery 110. In this regard, the deterioration estimation unit 125 can estimate the deterioration of the battery 110 using the OCV model described by the following <Equations 13> to <Equations 15>.

[0072] <Formula 13>

number

[0073] <Formula 14>

number

[0074] <Formula 15>

number

[0075] For example, the performance status of the battery 110 can be obtained from the relationship between the side reaction described in the above <Equation 13> and the capacity decrease of the battery 110 and <Equation 14>, and the self-discharge voltage can be obtained from <Equation 15>.

[0076] According to various embodiments, the state check unit 127 can determine the state of the battery 110 based on the estimation result of the deterioration estimation unit 125. According to an embodiment, the status check unit 127 may determine the deterioration state of the battery 110 based on the performance state and / or self-discharge voltage of the battery 110. The determination result of the status check unit 127 may be output via the output device 130.

[0077] As described above, diagnostic system 100 may be configured with battery 110, diagnostic device 120, and output device 130. However, this is merely an example, and various embodiments are not limited thereto. For example, at least one of the components described above with reference to FIG. 1a may be omitted from diagnostic system 100, or other components may be added to the diagnostic system in addition to the components described above. For example, various types of loads configured to operate on power supplied from battery 110 may be added to diagnostic system 100.

[0078] Furthermore, the configuration of the diagnostic device 120 is not limited to the configuration shown in Fig. 1b. For example, at least one of the components shown in Fig. 1b may be omitted from the configuration of the diagnostic device 120, and one or more other components may be added to the configuration of the diagnostic device 120. Furthermore, at least one of the above-described components may be integrated with other components. Such a diagnostic device 120 may be provided as a component of a battery management system, or may be provided as a separate component separate from the battery management system.

[0079] 3a to 3g show the results of comparing the performance of diagnostic devices according to various embodiments with a diagnostic device according to a comparative example. 3a to 3b, it can be seen that the diagnostic results of the diagnostic device according to the comparative example using a physics-based model (e.g., an SPM model) are quite similar to the diagnostic results of the diagnostic devices according to various embodiments using a model based on a higher level of reduction than the physics-based model (e.g., a Lumped OCV model).

[0080] In particular, when the battery 110 is in a first state of charge (e.g., SOC 50), the side reaction state and discharge voltage measured by the diagnostic devices according to various embodiments are similar to the results measured by the diagnostic device according to the comparative example. However, it can be seen that the measurement cycles of the side reaction state and discharge voltage in the diagnostic devices according to various embodiments are shorter than those in the diagnostic device according to the comparative example.

[0081] Such measurement results are similarly confirmed at a second state of charge (e.g., SOC 70) and a third state of charge (e.g., SOC 90) of battery 110. That is, it is believed that the diagnostic devices according to various embodiments can derive measurement results similar to those of the diagnostic device according to the comparative example, and can derive measurement results more quickly than the diagnostic device according to the comparative example.

[0082] Referring to Figures 3d to 3f, it can be seen that even during the aging process performed after the fabrication of battery 110, the results measured by the diagnostic devices according to various embodiments are similar to the results measured by the diagnostic device according to the comparative example.

[0083] For example, FIG. 3d shows the electrode side reaction states (e.g., full-cell electrode side reaction state (QFULLCELL), anode side reaction state (QASR), and cathode side reaction state (QCSR)) measured after one year of calendar aging of batteries with various SOC levels (e.g., SOC 30, 50, 70, and 90) under a first temperature condition (e.g., 25°C). FIG. 3e shows the electrode side reaction states measured after aging under a second temperature condition (e.g., 45°C), and FIG. 3f shows the electrode side reaction states measured after aging under a third temperature condition (e.g., 60°C). In FIGS. 3d to 3f, circles represent the measurement results of a diagnostic device according to a comparative example, solid lines represent the measurement results of diagnostic devices according to various embodiments, and dashed lines represent analytical solutions of the diagnostic devices according to various embodiments.

[0084] Furthermore, referring to FIG. 3g, the diagnostic device according to various embodiments can derive excellent measurement results, as can be seen from the low error rate of the diagnostic device according to various embodiments.

[0085] 4 is a flowchart showing the battery diagnostic operation of the diagnostic system according to various embodiments. The operations in the following embodiments may be performed sequentially, but are not necessarily performed sequentially. For example, the order of the operations may be changed, or at least two operations may be performed in parallel.

[0086] 4, the diagnostic system 100 (or diagnostic device 120) according to various embodiments can acquire 410 battery information about the battery 110. In one embodiment, the diagnostic system 100 can acquire the voltage, current, temperature, and state of charge (SOC) of the battery 110 as the battery information. Additionally, the diagnostic system 100 can acquire the amount of side reactions accumulated in the electrodes as the battery information.

[0087] According to various embodiments, diagnostic system 100 (or diagnostic device 120) can calculate an equilibrium potential for the electrodes based on the acquired battery information in operation 420. According to one embodiment, diagnostic system 100 can acquire the equilibrium potential based on the concentration of solid-state lithium ions for the electrodes. In this regard, diagnostic system 100 can acquire the concentration of solid-state lithium ions for the electrodes using an OCV (Open Circuit Voltage) model (e.g., a Lumped OCV model) described in the above-described Equations 2 to 10.

[0088] According to various embodiments, the diagnostic system 100 (or the diagnostic device 120) can calculate the side reaction state (or side reaction rate) based on the equilibrium potential in operation 430. According to one embodiment, the diagnostic system 100 can use the functional dependency of the side reaction state (e.g., the side reaction rate) on the equilibrium potential of the electrode. In this regard, the diagnostic system 100 can calculate the side reaction state using the OCV model described in the above-mentioned Equation 11 and / or Equation 12.

[0089] According to various embodiments, the diagnostic system 100 (or the diagnostic device 120) can estimate the deterioration of the battery 110 based on the side reaction state in operation 440. According to one embodiment, the diagnostic system 100 can obtain the state of health (SOH) and / or self-discharge voltage of the battery 110. In this regard, the diagnostic system 100 can obtain the state of health and / or self-discharge voltage of the battery 110 using the OCV model described in the above-mentioned <Equations 13> to <Equations 15>.

[0090] According to various embodiments, diagnostic system 100 (or diagnostic device 120) can determine the state of battery 110 based on the performance state and / or self-discharge voltage of battery 110. According to one embodiment, diagnostic system 100 can determine the state of health of battery 110 and output the determination via output device 130.

[0091] The above description is merely an illustrative example of the technical concept of the present invention, and various modifications and variations can be made by a person having ordinary knowledge in the technical field to which the present invention pertains without departing from the essential characteristics of the present invention.

[0092] Therefore, the embodiments disclosed in the present invention are for illustrative purposes only and are not intended to limit the technical idea of ​​the present invention, and the scope of the technical idea of ​​the present invention should not be limited by such embodiments. The scope of protection of the present invention should be interpreted according to the claims set forth below, and all technical ideas within the scope equivalent thereto should be interpreted as being included in the scope of the present invention.

Claims

1. A diagnostic system for diagnosing a battery condition, comprising: Batteries and a diagnostic device for diagnosing the state of the battery; Including, The diagnostic device comprises: predicting a side reaction rate for the electrode based on an OCV model defined by the state of charge of the battery and the cumulative amount of side reactions of the electrode; A diagnostic system configured to predict a state of health for the battery based on the side reaction rate.

2. The diagnostic device comprises: obtaining an equilibrium potential for the electrode based on the OCV model; The diagnostic system of claim 1 , configured to predict a side reaction rate of the electrode based on the equilibrium potential.

3. The diagnostic device comprises: The diagnostic system of claim 2 , configured to obtain an equilibrium potential of the electrode based on a concentration of solid-state lithium ions relative to the electrode.

4. The diagnostic device comprises: The diagnostic system according to claim 2 , configured to predict the side reaction rate using an ordinary differential equation that integrates the side reaction amount of the electrode with respect to time using the potential of the electrode.

5. The diagnostic device comprises: The diagnostic system according to claim 2 , configured to predict the side reaction rate by approximating the amount of the side reaction at the electrode.

6. The diagnostic device comprises: obtaining a performance state and a self-discharge voltage of the battery based on the predicted side reaction rate; The diagnostic system of claim 1 , configured to predict a state of health for the battery based on a performance state and a self-discharge voltage of the battery.

7. further including an output device; The diagnostic device comprises: The diagnostic system of claim 6 , configured to output the predicted state of degradation via the output device.

8. The diagnostic device comprises: obtaining information regarding at least one of voltage, current, and temperature for the battery; The diagnostic system according to claim 1 , configured to acquire a state of charge of the battery and an accumulated amount of side reactions of the electrodes based on the acquired information.

9. 1. A method of operating a diagnostic system for diagnosing a battery condition, comprising: an operation of predicting a side reaction rate for the battery based on an OCV model defined by the state of charge of the battery and the cumulative amount of side reactions of the electrodes; an operation of predicting a deterioration state of the battery based on the side reaction rate; A method comprising:

10. obtaining an equilibrium potential for the electrode based on the OCV model; and predicting a side reaction rate of the electrode based on the equilibrium potential.

11. 11. The method of claim 10, comprising the act of obtaining an equilibrium potential of the electrode based on a concentration of solid-state lithium ions relative to the electrode.

12. The method of claim 10 , further comprising predicting the side reaction rate using an ordinary differential equation that integrates the side reaction rate of the electrode with respect to time using the electrode potential.

13. The method of claim 10 , further comprising the step of approximating the amount of side reactions at the electrode to predict the side reaction rate.

14. obtaining a performance state and a self-discharge voltage of the battery based on the predicted side reaction rate; and predicting a state of health for the battery based on the performance state and self-discharge voltage of the battery.

15. The method of claim 14 , including the act of outputting the predicted state of degradation via an output device.

16. obtaining information regarding at least one of voltage, current, and temperature for the battery; and acquiring a state of charge of the battery and an accumulated amount of side reactions of the electrodes based on the acquired information.

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