System and method for diagnosing deterioration state of battery

Abstract

A diagnostic system for diagnosing a state of a battery according to various embodiments includes a battery and a diagnostic device configured to diagnose the state of the battery, in which the diagnostic device is further configured to predict a side reaction rate of an electrode based on a defined open circuit voltage (OCV) model using a state of charge (SOC) of the battery and an accumulated side reaction amount of the electrode, and predicting a deterioration state of the battery based on the side reaction rate.

Description

Technical Field

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and the benefit of Korean Patent Application No. 10-2022-0122902 filed in the Korean Intellectual Property Office on September 27, 2022, the entire contents of which are incorporated herein by reference. Technical Field

[0004] The present disclosure relates to a system and method for diagnosing a degradation state of a battery, and more particularly, to a system and method for improving the performance of diagnosing the degradation state. Background Art

[0005] Generally, electric vehicles or hybrid electric vehicles use electric energy stored in a battery as an energy source. For example, a lithium ion polymer battery is widely used as a battery for electric vehicles, and research on the battery is being actively conducted.

[0006] Since an electric vehicle or a hybrid electric vehicle is driven by energy charged in a battery, it is important to diagnose (or predict) not only the current state of the battery but also the deterioration state of the battery.

[0007] For example, the battery capacity may decrease due to side reactions (such as solid electrolyte interphase film formation), and the side reactions can be considered to diagnose the degradation state of the battery. In other words, the side reaction state of the electrode can be predicted and the degradation state of the battery can be diagnosed based on the predicted results.

[0008] When diagnosing the degradation state of a battery (or a side reaction of an electrode), various prediction models can be used. For example, physics-based models such as the Doyle Fuller Newman (DFN) model, the single particle model (SPM), and the enhanced single particle model (ESPM) can be used to diagnose the degradation state of a battery.

[0009] However, the above-mentioned physics-based model is a model that uses complex differential equations to formalize the physical movement of particles inside the battery, and can diagnose the degradation state of the battery relatively accurately, but the calculation complexity is high due to the mathematical complexity. Summary of the invention

[0010]

Technical issues

[0011] At least one of the various embodiments of the present disclosure is directed to providing a system and method for diagnosing a degradation state of a battery to improve the performance of diagnosing the degradation state.

[0012] At least one of the various embodiments of the present disclosure is directed to providing a system and method for diagnosing a degradation state of a battery to predict a side reaction state based on an equilibrium potential of an electrode.

[0013] At least one of the various embodiments of the present disclosure is directed to providing a system and method for diagnosing a degradation state of a battery to predict a side reaction state using an open circuit voltage (OCV) model having lower mathematical complexity than a physics-based model.

[0014]

Technical Solution

[0015] A diagnostic system for diagnosing a state of a battery according to various embodiments includes a battery and a diagnostic device configured to diagnose the state of the battery, wherein the diagnostic device is further configured to predict a side reaction rate of an electrode based on a defined open circuit voltage (OCV) model using a state of charge (SOC) of the battery and a cumulative side reaction amount of an electrode, and to predict a degradation state of the battery based on the side reaction rate.

[0016] According to various embodiments, the diagnostic device may be further configured to obtain an equilibrium potential of the electrode based on the OCV model, and predict a side reaction rate of the electrode based on the equilibrium potential.

[0017] According to various embodiments, the diagnostic device may be further configured to obtain an equilibrium potential of the electrode based on a concentration of solid-phase lithium ions of the electrode.

[0018] According to various embodiments, the diagnostic device may be further configured to predict the side reaction rate by using an ordinary differential equation that integrates the side reaction amount of the electrode with respect to time by using the potential of the electrode.

[0019] According to various embodiments, the diagnostic device may also be configured to predict the side reaction rate by approximating the side reaction amount of the electrode.

[0020] According to various embodiments, the diagnostic device may be further configured to obtain a state of health (SOH) and a self-discharge voltage of the battery based on the predicted side reaction rate, and predict a degradation state of the battery based on the SOH and the self-discharge voltage of the battery.

[0021] According to various embodiments, the diagnosis system may further include an output device, wherein the diagnosis apparatus is further configured to output the predicted degradation state through the output device.

[0022] According to various embodiments, the diagnostic device may be further configured to acquire information related to at least one of voltage, current and temperature of the battery, and acquire a charge state of the battery and an accumulated side reaction amount of the electrode based on the acquired information.

[0023] An operating method of a diagnosis system for diagnosing a battery state according to various embodiments includes: predicting a side reaction rate of an electrode based on a defined open circuit voltage (OCV) model using a battery state of charge (SOC) and an accumulated side reaction amount of an electrode; and predicting a degradation state of the battery based on the side reaction rate.

[0024] According to various embodiments, the operating method may further include acquiring an equilibrium potential of the electrode based on the OCV model, and predicting a side reaction rate of the electrode based on the equilibrium potential.

[0025] According to various embodiments, the operating method may further include obtaining an equilibrium potential of the electrode based on a concentration of solid-phase lithium ions of the electrode.

[0026] According to various embodiments, the operating method may further include predicting a side reaction rate by using an ordinary differential equation that integrates an amount of a side reaction of an electrode with respect to time by using a potential of the electrode.

[0027] According to various embodiments, the operating method may further include predicting the side reaction rate by approximating the side reaction amount of the electrode.

[0028] According to various embodiments, the operating method may further include acquiring a state of health (SOH) and a self-discharge voltage of the battery based on the predicted side reaction rate, and predicting a degradation state of the battery based on the SOH and the self-discharge voltage of the battery.

[0029] According to various embodiments, the operating method may further include outputting the predicted degradation state through an output device.

[0030] According to various embodiments, the operating method may further include acquiring information related to at least one of voltage, current, and temperature of the battery, and acquiring a charge state of the battery and an accumulated side reaction amount of the electrode based on the acquired information.

[0031]

Beneficial Effects

[0032] The system and method for diagnosing a degradation state of a battery according to various embodiments disclosed herein may shorten the diagnosis time while improving the accuracy of diagnosing the degradation state by using an open circuit voltage (OCV) model having lower mathematical complexity than a physics-based model.

[0033] Effects that can be obtained from this document are not limited to the above-mentioned effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1a is a view schematically illustrating a configuration of a diagnosis system for diagnosing a state of a battery according to various embodiments.

[0035] Figure 1bis a view schematically showing a configuration of a diagnosis apparatus according to various embodiments.

[0036] Figure 2 is a view for describing an operation of acquiring the potential of an electrode by the diagnosis apparatus according to various embodiments.

[0037] Figure 3a to Figure 3g Results of performance comparison between the diagnostic apparatus according to various embodiments and the diagnostic apparatus according to a comparative embodiment are shown.

[0038] Figure 4 is a flow chart illustrating a battery diagnostic operation of a diagnostic system according to various embodiments.

[0039] Figure 5 Parameters used to describe a model for predicting side reaction rates according to various embodiments are shown. DETAILED DESCRIPTION

[0040] Below, some embodiments of the present disclosure will be described in detail with reference to the exemplary drawings. When adding reference numerals to the components of each figure, it should be noted that the same components are given the same reference numerals even if the same components are indicated in different figures. When describing the embodiments of the present disclosure, if the detailed description of the relevant well-known configuration or function obscures the subject matter of the present disclosure, its detailed description will be omitted.

[0041] To describe the components of the embodiments of the present disclosure, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only used to distinguish one component from another and do not limit the components to the nature, order, sequence, etc. of the components. Unless otherwise defined, all terms (including technical or scientific terms) used herein have the same meanings as those commonly understood by ordinary technicians in the relevant fields. In general, terms defined in commonly used dictionaries should be interpreted as having the same meanings as the contextual meanings of the relevant technology and should not be interpreted as having ideal or exaggerated meanings unless they are explicitly defined in this document.

[0042] In addition, it should be understood that the various embodiments of this document and the terms used therein are not intended to limit the technical features set forth herein to specific embodiments, and include various changes, equivalents or replacements of the corresponding embodiments. With respect to the description of the drawings, similar reference numerals may be used to refer to similar or related elements. It should be understood that the singular form of the noun corresponding to the item may include one or more of the things, unless the relevant context clearly indicates otherwise. As used herein, each such phrase 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 together in the corresponding one of the phrases. As used herein, terms such as "first (1st)" and "second (2nd)" or "first (first)" and "second (second)" may be used to simply distinguish the corresponding component from another component, and do not limit other aspects of the component (such as importance or order). It should be understood that if a component (e.g., a first component) is referred to as being “coupled with,” “coupled to,” “connected with,” or “connected to” another component (e.g., a second component) with or without the term “operably” or “communicatively,” it means that the component can be coupled to the other component directly (e.g., wired or wirelessly) or via a third component.

[0043] Figure 1a is a view schematically illustrating a configuration of a diagnosis system for diagnosing a state of a battery according to various embodiments.

[0044] Reference Figure 1a , the diagnostic system 100 according to various embodiments may include a battery 110 , a diagnostic device 120 , and an output device 130 .

[0045] According to various embodiments, the battery 110 may include a cell assembly in which a plurality of unit cells that can be repeatedly charged / discharged are connected in series or in parallel. The unit cells may be electric double layer capacitors including supercapacitors or well-known secondary batteries such as lithium ion batteries, lithium polymer batteries, nickel cadmium batteries, nickel hydrogen batteries, nickel zinc batteries, etc.

[0046] According to various embodiments, the output device 130 may output information related to the operation of the diagnostic system 100. According to an embodiment, the output device 130 may include a sound output device (e.g., a speaker) configured to output auditory information and / or a display configured to output visual information. For example, at least some of the visual information and / or auditory information may include a diagnostic result of the battery 110 (e.g., the state of the battery 100). However, this is merely an example, and various embodiments are not limited thereto. For example, the output device 130 may include a tactile module (e.g., a motor, a piezoelectric element, an electrical stimulation device, etc.) configured to output tactile information.

[0047] According to various embodiments, the diagnostic device 120 may handle the overall operation of the diagnostic system 100. According to an embodiment, the diagnostic device 120 may diagnose (or predict) the state of the battery 110. The diagnostic device 120 may output the state of the battery 110 through the output device 130. For example, the state of the battery 110 diagnosed by the diagnostic device 120 may be a degradation 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, the change in the initial capacity of the battery 110, etc. at a specified time interval (e.g., in real time), and output it as at least a part of the diagnostic result.

[0048] According to an embodiment, the diagnostic device 120 can diagnose the degradation state of the battery 110 by using an open circuit voltage (OCV) model (e.g., a lumped OCV model). The OCV model uses the functional dependence of the side reaction state (e.g., the side reaction rate) on the electrode equilibrium potential, and is modeled based on a higher level of contraction than a general physics-based model (e.g., SPM), which has a lower mathematical complexity. Therefore, the diagnostic device 120 can accurately and quickly predict the side reaction state of the electrode relative to the equilibrium potential of the electrode by using the OCV model, thereby improving the diagnostic performance with respect to the degradation state. Figure 1b The diagnostic apparatus 120 using the OCV model is described in detail.

[0049] Figure 1b is a view schematically showing a configuration of a diagnosis apparatus according to various embodiments. Figure 2 is a view for describing an operation of acquiring the potential of an electrode by the diagnosis apparatus according to various embodiments.

[0050] Reference Figure 1b The diagnostic device 120 may include a data acquisition unit 121 , a model calculation unit 123 , a degradation estimation unit 125 , and a state identification unit 127 .

[0051] According to various embodiments, the data acquisition unit 121 may acquire battery information related to the battery 110. The battery information may include the SOC of the battery 110 and the amount of side reactions accumulated in the electrode. According to an embodiment, the data acquisition unit 120 may acquire the voltage, current, and temperature of the battery 110 and acquire the battery information based on them. However, this is merely an example, and various embodiments are not limited thereto. For example, in addition to the above information, various information related to the battery 110 (such as charging cycle information, etc.) may be acquired as battery information. For example, the data acquisition unit 121 may include at least one sensor configured to acquire battery information.

[0052] According to various embodiments, the model calculation unit 123 may acquire at least one parameter that can be used to diagnose the battery 100 based on the battery information acquired by the data acquisition unit 121. For example, the model calculation unit 123 may acquire the side reaction state (eg, side reaction rate) of the electrode as at least a part of the parameter.

[0053] According to an embodiment, the model calculation unit 123 may acquire the potential of the electrode (e.g., the equilibrium potential of the negative electrode and the positive electrode) and the capacity difference of the equilibrium potential as part of the operation of acquiring the side reaction state. For example, the model calculation unit 123 may acquire the potential of the electrode using the SOC acquired by the data acquisition unit 121 and the amount of the side reaction accumulated in the electrode, and predict the side reaction state of the electrode based thereon.

[0054] Will refer to Figure 2 The operation of acquiring the electrode potential by the model calculation unit 123 is described in detail.

[0055] Reference Figure 2 , the model calculation unit 123 can calculate the theoretical capacity of the electrode and calculate the electrode OCV change curve predicted relative to the charge / discharge capacity by using the theoretical capacity. The theoretical capacity may include a negative theoretical capacity and a positive theoretical capacity, the negative theoretical capacity is the inherent capacity of the negative electrode (or the negative electrode active material), and the positive theoretical capacity is the inherent capacity of the positive electrode (or the positive electrode active material). The OCV change curve may be a predicted result of the change of OCV with the charge / discharge capacity.

[0056] The model calculation unit 123 may position the OCV change curve of the electrode on a charge balance axis (e.g., continuous charge balance Qccb) that defines the cumulative state of charge / discharge capacity. When a charge current is applied to the cell from time point 0 when the cell is first assembled, the value on the charge balance axis increases the charge capacity, and when a discharge current is applied to the cell, the value on the charge balance axis decreases the discharge capacity. For example, on the charge balance axis, the positive electrode OCV switch curve may shift (201) by the positive electrode side reaction amount, and the negative electrode OCV curve may shift (203) by the negative electrode side reaction amount.

[0057] The model calculation unit 123 may identify points 209 and 211 where the differences 205 and 207 between the positive electrode OCV change curve and the negative electrode OCV change curve correspond to preset values, and may acquire the potential of the electrode based on the identified points 209 and 211. For example, the preset value for identifying the position of the charge balance axis at which the potential is acquired may be the potential of the whole cell (e.g., the potential of the whole cell at 0% OCV and the potential of the whole cell at 100% OCV), and may be pre-stored inside or outside the diagnostic system 100.

[0058] In this regard, the model calculation unit 123 can output the electrode potential by using the input of the charge state of the battery 110 and the amount of the side reaction of the electrode through the OCV model obtained from [Equation 1] to [Equation 11] described below. Figure 5 Parameters shown.

[0059] For example, the model calculation unit 123 can acquire the described electrode potential through [Equation 1] provided below.

[0060] [Equation 1]

[0061]

[0062] In the above-provided [Equation 1], the concentration X of the solid-phase lithium ions in the positive electrode can be calculated based on P To obtain the positive electrode potential U P and can be based on the concentration of solid-phase lithium ions in the negative electrode X n To obtain the negative electrode potential U n .

[0063] In this regard, the model calculation unit 123 can obtain the concentration X of the solid phase lithium ions of the positive electrode. P and the solid lithium ion concentration X of the negative electrode n , as described in [Equation 2] to [Equation 7] provided below.

[0064] Regarding acquiring the concentration of lithium ions, the model calculation unit 123 may determine the positive electrode OCV variation curve and the negative electrode OCV variation curve through the OCV model described by [Equation 2] and [Equation 3].

[0065] [Equation 2]

[0066]

[0067] [Equation 3]

[0068]

[0069] In the aforementioned [Equation 2], the positive electrode OCV variation curve Qccb (X) can be determined based on the sum of the theoretical capacity of the positive electrode and the initial deviation of the positive electrode in a fresh state in which aging does not occur. p, BOL ).

[0070] In the aforementioned [Equation 3], the negative electrode OCV variation curve Qccb (X) can be determined based on the sum of the theoretical capacity of the negative electrode and the initial deviation of the negative electrode in a fresh state in which aging does not occur. n, BOL ).

[0071] In this regard, the model calculation unit 123 may calculate the initial offset of the negative electrode by the OCV model described based on [Equation 4] and [Equation 5].

[0072] [Equation 4]

[0073]

[0074] in,

[0075] [Equation 5]

[0076]

[0077] Based on the continuous charge balance Q relative to the positive electrode potential of the full cell described in [Equation 4] CCB (X p, SOCFC ) and the continuous charge balance Q relative to the negative electrode potential of the full cell CCB (X n, SOCFC ) are the same conditions as each other and the initial offset Q of the positive electrode in the fresh state described in [Equation 5] offset, p0 Assuming that the negative pole is '0', the initial offset Q can be calculated. offset, p0 .

[0078] In addition, the model calculation unit 123 may acquire the concentration of solid-phase lithium ions of the electrode through the OCV model described based on [Equation 6] and [Equation 7].

[0079] [Equation 6]

[0080]

[0081] [Equation 7]

[0082]

[0083] The concentration of solid lithium ions in the positive electrode can be calculated using [Equation 6] P , and the concentration of solid lithium ions in the negative electrode can be calculated using [Equation 7] n .

[0084] The model calculation unit 123 may calculate the electrode potential by the OCV model described based on [Equation 8] to [Equation 11].

[0085] [Equation 8]

[0086]

[0087] In this regard, as can be seen from [Equation 8], the model calculation unit 123 can substitute the concentration of solid-phase lithium ions of the electrode calculated using [Equation 6] and [Equation 7] into [Equation 1].

[0088] The model calculation unit 123 can calculate the full cell potential U at 0% SOC FC, SOC0 and the full cell potential U at 100% SOC FC, SOC100 , as shown in [Equation 9] provided below.

[0089] [Equation 9]

[0090]

[0091] The model calculation unit 123 can calculate the electrode potential at a point on the charge balance axis, where the full cell potential U FC, SOC0 and the full cell potential U at 100% SOC FC, SOC100 Corresponding to the preset value, as shown in [Equation 10] and [Equation 11] provided below.

[0092] [Equation 10]

[0093]

[0094] in, and depending on and

[0095]

[0096] According to various embodiments, the model calculation unit 123 may acquire the side reaction state based on the potential of the electrode.

[0097] According to an embodiment, the model calculation unit 123 may acquire the side reaction state by using the potential of the electrode (eg, the equilibrium potential of the negative electrode and the positive electrode and the capacity difference of the equilibrium potential) and the side reaction amount of the electrode.

[0098] For example, the model calculation unit 123 can obtain the side reaction state of the electrode by using the OCV model described based on [Equation 11] provided below. For example, the model calculation unit 123 can use an ordinary differential equation that integrates the side reaction amount of the electrode with respect to time by using the potential of the electrode to obtain the side reaction state of the electrode.

[0099] [Equation 11]

[0100]

[0101]

[0102] With initial conditions

[0103]

[0104] in,

[0105]

[0106] In another example, the model calculation unit 123 may obtain the side reaction state of the electrode by using the OCV model described based on [Equation 12] provided below. For example, the model calculation unit 123 may approximate the side reaction amount based on the assumption that the equilibrium potential of the electrode does not change significantly with aging of the battery 110, and obtain the side reaction state of the electrode by using the side reaction amount.

[0107] [Equation 12]

[0108]

[0109] in,

[0110]

[0111] According to various embodiments, the degradation estimation unit 125 may estimate the degradation of the battery 110 based on the side reaction state of the electrode acquired by the model calculation unit 123 .

[0112] According to an embodiment, the degradation estimation unit 125 may acquire a state of health (SOH) and / or a self-discharge voltage of the battery 110 .

[0113] In this regard, the degradation estimation unit 125 may estimate the degradation of the battery 110 by using the OCV model described based on [Equation 13] to [Equation 15].

[0114] [Equation 13]

[0115]

[0116] [Equation 14]

[0117]

[0118] [Equation 15]

[0119]

[0120] For example, based on the relationship between the side reaction described in [Equation 13] and the capacity reduction of the battery 110 and [Equation 14], the health state of the battery 110 can be acquired, and the self-discharge voltage can be acquired based on [Equation 15].

[0121] According to various embodiments, the state recognition unit 127 may determine the state of the battery 110 based on the estimation result of the degradation estimation unit 125 .

[0122] According to an embodiment, the state recognition unit 127 may determine the degradation state of the battery 110 based on the health state and / or the self-discharge voltage of the battery 110. The determination result of the state recognition unit 127 may be output through the output device 130.

[0123] As described above, the diagnostic system 100 may include the battery 110, the diagnostic device 120, and the output device 130. However, this is merely an example, and various embodiments are not limited thereto. For example, the reference numerals may be omitted from the components of the diagnostic system 100. Figure 1a At least one of the components described, or other components other than the above components may be added as components of the diagnostic system. For example, various types of loads configured to operate as power supplied from the battery 110 may be added as components of the diagnostic system 110.

[0124] The components of the diagnostic device 120 may not be limited to Figure 1b For example, the components of the diagnostic system 120 may be omitted. Figure 1b At least one of the components shown in the figure, or one or more other components may be added as components of the diagnostic device 120. At least one of the above components may be integrated with other components. Such a diagnostic device 120 may be provided as a component of the battery management system, or may be provided as a separate component different from the battery management system.

[0125] Figure 3a to Figure 3g Results of performance comparison between the diagnostic apparatus according to various embodiments and the diagnostic apparatus according to a comparative embodiment are shown.

[0126] Reference Figure 3a and Figure 3bIt can be seen that the diagnostic results of the diagnostic device using the physics-based model (e.g., the SPM model) according to the comparative embodiment and the diagnostic results of the diagnostic device using the model based on a higher level of contraction than the physics-based model (e.g., the lumped OCV model) according to various embodiments are significantly similar to each other.

[0127] Specifically, at the first state of charge (e.g., SOC 50) of the battery 100, the side reaction state and the discharge voltage measured in the diagnostic device according to various embodiments are similar to the results measured in the diagnostic device according to the comparative embodiment. However, it can be seen that the measurement period of the side reaction state and the discharge voltage in the diagnostic device according to various embodiments is shorter than that in the diagnostic device according to the comparative embodiment.

[0128] Such measurement results can be equally recognized in the second state of charge (e.g., SOC 70) and the third state of charge (e.g., SOC 90) of the battery 100. From this point, it can be seen that the diagnostic apparatus according to various embodiments can derive measurement results similarly to the diagnostic apparatus according to the comparative embodiment, but derives the measurement results faster than the diagnostic apparatus according to the comparative embodiment.

[0129] Reference Figure 3d to Figure 3f , it can be seen that even in the aging process performed after the manufacture of the battery 100 , the measurement results of the diagnostic apparatus according to the various embodiments and the measurement results of the diagnostic apparatus according to the comparative embodiment are similar to each other.

[0130] For example, Figure 3d The side reaction states of the electrodes (e.g., the side reaction state of the full-cell electrode QFULLCELL, the negative electrode side reaction state QASR, and the positive electrode side reaction state QCSR) are measured under a first temperature condition (e.g., a temperature of 25°C) for various SOC levels (e.g., SOCs of 30, 50, 70, and 90) after one year of battery aging (calendar aging). Figure 3e shows the side reaction state of the electrode measured in an aged state under a second temperature condition (eg, a temperature of 45°C), and Figure 3f The side reaction state of the electrode measured in the aged state under the third temperature condition (eg, a temperature of 60°C) is shown. Figure 3d to Figure 3f , circles indicate measurement results of the diagnosis apparatus according to the comparative embodiment, solid lines indicate measurement results of the diagnosis apparatus according to various embodiments, and dotted lines indicate analysis solutions of the diagnosis apparatus according to various embodiments.

[0131] Reference Figure 3g It can be seen from the low error rate of the diagnostic apparatus according to various embodiments that the diagnostic apparatus according to various embodiments can produce excellent measurement results.

[0132] Figure 4 1 is a flowchart illustrating a battery diagnostic operation of a diagnostic system according to various embodiments. The operations in the following embodiments may be performed sequentially, but not necessarily sequentially. For example, the order of the operations may be changed and at least two operations may be performed in parallel.

[0133] Reference Figure 4 In operation 410, the diagnostic system 100 (or the diagnostic device 120) according to various embodiments may obtain battery information related to the battery 110. According to an embodiment, the diagnostic system 100 may obtain the voltage, current, temperature, and SOC of the battery 110 as battery information. In addition, the diagnostic system 100 may obtain the amount of side reactions accumulated in the electrodes as battery information.

[0134] According to various embodiments, in operation 420, the diagnostic system 100 (or the diagnostic device 120) may calculate the equilibrium potential of the electrode based on the acquired battery information. According to an embodiment, the diagnostic system 100 may obtain the equilibrium potential based on the concentration of solid-phase lithium ions of the electrode. In this regard, the diagnostic system 100 may obtain the concentration of solid-phase lithium ions of the electrode based on the OCV model (e.g., lumped OCV model) described in [Equation 2] to [Equation 10].

[0135] According to various embodiments, in operation 430, the diagnostic system 100 (or the diagnostic device 120) may calculate the side reaction state (or side reaction rate) based on the equilibrium potential. According to an embodiment, the diagnostic system 100 may use the functional dependence 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 may calculate the side reaction state by the OCV model described in [Equation 11] and / or [Equation 12].

[0136] According to various embodiments, in operation 440, the diagnostic system 100 (or the diagnostic device 120) may estimate the degradation of the battery 100 based on the side reaction state of the electrode. According to an embodiment, the diagnostic system 100 may acquire the SOH and / or the self-discharge voltage of the battery 110. In this regard, the diagnostic system 100 may estimate the health state and / or the self-discharge voltage of the battery 110 by using the OCV model described in [Equation 13] to [Equation 15].

[0137] According to various embodiments, the diagnostic system 100 (or the diagnostic apparatus 120) may determine the state of the battery 110 based on the health state and / or the self-discharge voltage of the battery 110. According to an embodiment, the diagnostic system 100 may determine the degradation state of the battery 110 and output the determination result through the output device 130.

[0138] The above description merely illustrates the technical concept of the present invention, and a person skilled in the art to which the present invention pertains will be able to make various modifications and variations without departing from the essential features of the present invention.

[0139] Therefore, the embodiments disclosed in the present disclosure are intended to describe rather than limit the technical spirit of the present disclosure, and the scope of the technical spirit of the present disclosure is not limited to these embodiments. The protection scope of the present invention should be interpreted by the attached claims, and all technical spirits within the same scope should be understood to be included in the scope of the present invention.

Claims

1. A diagnostic system for diagnosing a battery status, the diagnostic system comprising: the battery; as well as a diagnostic device configured to diagnose the state of the battery, Wherein, the diagnostic device is further configured to: Predicting the side reaction rate of the electrode based on a defined open circuit voltage (OCV) model using the state of charge (SOC) of the battery and the accumulated side reaction amount of the electrode; and A degradation state of the battery is predicted based on the side reaction rate.

2. The diagnostic system according to claim 1, wherein: The diagnostic device is also configured to: Obtaining the equilibrium potential of the electrode based on the OCV model; and The side reaction rate of the electrode is predicted based on the equilibrium potential.

3. The diagnostic system according to claim 2, wherein: The diagnostic device is further configured to obtain the equilibrium potential of the electrode based on the concentration of solid-phase lithium ions of the electrode.

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

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

6. The diagnostic system according to claim 1, wherein: The diagnostic device is also configured to: Obtaining a state of health (SOH) and a self-discharge voltage of the battery based on the predicted side reaction rate; and The degradation state of the battery is predicted based on the health state and the self-discharge voltage of the battery.

7. The diagnostic system according to claim 6, further comprising an output device, in, The diagnostic apparatus is further configured to output the predicted degradation state through the output device.

8. The diagnostic system according to claim 1, wherein: The diagnostic device is also configured to: acquiring information related to at least one of a voltage, a current, and a temperature of the battery; as well as The charge state of the battery and the accumulated side reaction amount of the electrode are acquired based on the acquired information.

9. A method for operating a diagnostic system for diagnosing a battery status, the method comprising: Predicting the side reaction rate of the electrode based on a defined open circuit voltage (OCV) model using the state of charge (SOC) of the battery and the accumulated side reaction amount of the electrode; as well as A degradation state of the battery is predicted based on the side reaction rate.

10. The operating method according to claim 9, further comprising: Obtaining the equilibrium potential of the electrode based on the OCV model; as well as The side reaction rate of the electrode is predicted based on the equilibrium potential. 11 . The operating method according to claim 10 , further comprising obtaining the equilibrium potential of the electrode based on the concentration of solid-phase lithium ions of the electrode.

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

13. The operating method according to claim 10, further comprising predicting the side reaction rate by approximating the side reaction amount of the electrode.

14. The operating method according to claim 9, further comprising: Obtaining a state of health (SOH) and a self-discharge voltage of the battery based on the predicted side reaction rate; as well as The degradation state of the battery is predicted based on the health state and the self-discharge voltage of the battery. 15 . The operating method according to claim 14 , further comprising outputting the predicted degradation state through an output device.

16. The operating method according to claim 9, further comprising: acquiring information related to at least one of a voltage, a current, and a temperature of the battery; as well as The charge state of the battery and the accumulated side reaction amount of the electrode are acquired based on the acquired information.

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