Secondary battery diagnosis device and method

Abstract

The secondary battery diagnostic device according to the present disclosure includes: a thickness sensor configured to sense a change in thickness of a secondary battery according to a change in volume of a negative electrode active material while the secondary battery to which the negative electrode active material containing silicon or silicon oxide is applied is being charged or discharged; and a processor configured to estimate a capacity ratio, which is a ratio of a capacity provided by the silicon or silicon oxide to a capacity of the secondary battery provided by the negative electrode active material, using a sensing result of the thickness sensor, and diagnose a state of the secondary battery based on the capacity ratio.

Description

Technical Field

[0001] This application is based on and claims the benefit of priority of Korean Patent Application No. 10-2023-0104441 filed in the Korean Intellectual Property Office on August 9, 2023, the disclosure of which is incorporated herein by reference in its entirety.

[0002] The present disclosure relates to a secondary battery diagnosis device and method, and more particularly, to a secondary battery diagnosis device and method for diagnosing a state of a secondary battery to which a negative electrode active material including silicon or silicon oxide is applied. Background Art

[0003] Generally, a secondary battery refers to a battery that can be repeatedly charged and discharged. Types of such secondary batteries include lithium secondary batteries using lithium ions, such as lithium ion batteries or lithium ion polymer batteries, nickel cadmium batteries, nickel hydrogen batteries, nickel zinc batteries, and the like.

[0004] Recently, as secondary batteries are applied to various devices requiring high output voltage and large charging capacity such as electric vehicles and ESS (Energy Storage System), lithium secondary batteries are being widely used because they have advantages of almost no memory effect, low self-discharge rate, and high energy density compared to secondary batteries using nickel.

[0005] As these secondary batteries undergo repeated charge and discharge cycles, they no longer maintain the capacity at the time of manufacture and gradually degrade. If the current state of these secondary batteries is not accurately diagnosed, it becomes difficult to measure or predict the SoC (state of charge), usable time, remaining life, and replacement time of the secondary batteries, which results in unexpected damage to users of the secondary batteries.

[0006] However, the existing technologies have problems in that diagnosing secondary batteries requires a lot of cost and has a high possibility of errors in diagnostic results because they measure various electrical factors such as voltage, current and resistance of secondary batteries and diagnose the state of the secondary batteries by substituting the measured values ​​into complex formulas.

[0007] In particular, the prior arts have a problem in that they cannot provide a method of efficiently and accurately diagnosing the state of a secondary battery that uses a negative electrode active material containing silicon or silicon oxide to increase energy density. Summary of the invention

[0008] Technical issues

[0009] The technical challenge that the present disclosure attempts to solve is to provide a secondary battery diagnostic device and method that efficiently and accurately diagnoses the state of a secondary battery to which a negative electrode active material including silicon or silicon oxide is applied.

[0010] Another technical challenge that the present disclosure attempts to solve is to provide a battery pack and a vehicle that efficiently and accurately diagnose the state of a secondary battery to which a negative electrode active material including silicon or silicon oxide is applied.

[0011] Technical Solution

[0012] A secondary battery diagnostic device according to one aspect of the present disclosure diagnoses the state of a secondary battery to which a negative electrode active material containing silicon or silicon oxide is applied, and includes: a thickness sensor configured to sense a thickness change of the secondary battery according to a volume change of the negative electrode active material while the secondary battery is being charged or discharged; and a processor configured to estimate a capacity ratio using the sensing result of the thickness sensor, and diagnose the state of the secondary battery based on the capacity ratio, the capacity ratio being a ratio of the capacity provided by silicon or silicon oxide to the capacity of the secondary battery provided by the negative electrode active material.

[0013] In one embodiment, the thickness sensor may be configured to sense a thickness variation of the secondary battery by measuring a distance from a predetermined position to a surface of the secondary battery.

[0014] In one embodiment, the processor can be configured to, before estimating the capacity ratio, use the sensing results of the thickness sensor to generate a first curve representing the thickness of the secondary battery that changes over time or according to the charging state of the secondary battery and a second curve representing the slope of the first curve that changes over time or according to the charging state of the secondary battery, and estimate the capacity ratio based on the second curve.

[0015] In one embodiment, the processor may be configured to divide the second curve into a plurality of segments, select a target segment associated with silicon or silicon oxide among the plurality of segments, and estimate the capacity ratio by comparing a total length of the plurality of segments in a first axis direction with a length of the target segment in the first axis direction.

[0016] In one embodiment, the processor may be configured to divide the second curve into a plurality of sections according to a slope change trend of the first curve shown in the second curve.

[0017] In one embodiment, the processor may be configured to select, as a target section, a section in which the state of charge of the secondary battery is within a range below a predetermined threshold and in which a slope change trend of the first curve is different from those of other sections among a plurality of sections.

[0018] In one embodiment, the thickness sensor may be configured to sense a change in thickness of the secondary battery while the secondary battery is being discharged, and the processor may be configured to select a segment among multiple segments in which a slope change trend of the first curve is changed from a decreasing trend to an increasing trend as a target segment.

[0019] In one embodiment, the processor can be configured to further estimate a weight ratio, which is the ratio of the weight of silicon or silicon oxide to the total weight of the negative electrode active material, before diagnosing the state of the secondary battery, and compare the capacity ratio with a reference capacity ratio corresponding to the weight ratio to diagnose the state of the secondary battery.

[0020] In one embodiment, the processor may be configured to estimate the weight ratio before the number of charge cycles or the number of discharge cycles of the secondary battery exceeds a predetermined number.

[0021] In one embodiment, the processor may be configured to repeatedly estimate the capacity ratio while a charge cycle or a discharge cycle of the secondary battery is repeated, and diagnose the state of the secondary battery based on a variation trend of the estimated capacity ratio.

[0022] In one embodiment, the negative electrode active material may include a mixture in which silicon or silicon oxide is mixed with graphite.

[0023] In one embodiment, the secondary battery may be a pouch-type secondary battery.

[0024] A secondary battery diagnostic method according to another aspect of the present disclosure diagnoses the state of a secondary battery to which a negative electrode active material containing silicon or silicon oxide is applied, and includes: a step A of sensing a thickness change of the secondary battery according to a volume change of the negative electrode active material while the secondary battery is being charged or discharged by a thickness sensor; a step B of estimating a capacity ratio by a processor using the sensing result of the thickness sensor, which is a ratio of the capacity provided by silicon or silicon oxide to the capacity of the secondary battery provided by the negative electrode active material; and a step C of diagnosing the state of the secondary battery based on the capacity ratio by the processor.

[0025] In one embodiment, step B may include: step B1, using the sensing result of the thickness sensor to generate a first curve representing the thickness of the secondary battery that changes over time or according to the charging state of the secondary battery; step B2, based on the first curve, generating a second curve representing the slope of the first curve that changes over time or according to the charging state of the secondary battery; and step B3, estimating the capacity ratio based on the second curve.

[0026] In one embodiment, step B3 may include dividing the second curve into multiple segments, and selecting a target segment associated with silicon or silicon oxide among the multiple segments; and estimating the capacity ratio by comparing the total length of the multiple segments in the first axis direction with the length of the target segment in the first axis direction.

[0027] A battery pack according to still another aspect of the present disclosure may include the above-mentioned secondary battery diagnosis device.

[0028] A vehicle according to still another aspect of the present disclosure may include the secondary battery diagnosis device described above.

[0029] Beneficial Effects

[0030] According to the present disclosure, while a secondary battery to which a negative electrode active material including silicon or silicon oxide is applied is being charged or discharged, a thickness sensor senses a thickness change of the secondary battery according to a volume change of the negative electrode active material, and a processor estimates a capacity ratio - a ratio of the capacity provided by the silicon oxide of the secondary battery to the capacity provided by the negative electrode active material - by using the sensing result of the thickness sensor on the thickness change of the secondary battery, and diagnoses the state of the secondary battery based on the capacity ratio, thereby efficiently and accurately diagnosing the state of the secondary battery to which a negative electrode active material including silicon or silicon oxide is applied.

[0031] In addition, before the processor estimates the capacity ratio, the processor uses the sensing result of the thickness sensor to sequentially generate a first curve representing the thickness of the secondary battery that changes with time or according to the charging state of the secondary battery, and a second curve representing the slope of the first curve that changes with time or according to the charging state of the secondary battery, and estimates the capacity ratio based on the second curve, thereby diagnosing the state of the secondary battery in a simplified manner independent of the electrical factors of the secondary battery.

[0032] In addition, the processor divides the second curve into multiple segments, selects a target segment exhibiting unique characteristics of silicon or silicon oxide from the multiple segments, and estimates the capacity ratio by comparing the lengths of the entire multiple segments in the first axis direction with the length of the target segment in the first axis direction, thereby reducing the amount of calculations required to diagnose the state of the secondary battery and, as a result, improving the speed of diagnosing the secondary battery.

[0033] In addition, the processor estimates a weight ratio, which is the ratio of the weight of silicon or silicon oxide to the total weight of the negative electrode active material, and compares the capacity ratio with a reference capacity ratio corresponding to the estimated weight ratio to diagnose the state of the secondary battery, thereby diagnosing the state of the target secondary battery even when the weight ratio of silicon or silicon oxide included in the negative electrode active material of the target secondary battery is unknown.

[0034] In addition, since the thickness sensor for sensing the thickness change of the secondary battery is configured to sense the thickness change of the corresponding secondary battery by measuring the distance from a predetermined position to the surface of the secondary battery, it is possible not only to sense the thickness change of the secondary battery for each secondary battery, but also to sense the overall thickness change of the secondary battery module by regarding a plurality of secondary batteries stacked in the thickness direction as one secondary battery module. That is, the secondary battery diagnostic device according to the present disclosure can be included in a battery pack or a vehicle having a plurality of secondary batteries, and the states of the plurality of secondary batteries can be diagnosed in units of secondary battery modules.

[0035] In addition, a person skilled in the art to which the present disclosure pertains will be able to clearly understand from the following description that various technical problems not mentioned above can be solved according to various embodiments of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a block diagram illustrating a secondary battery diagnosis device according to an embodiment of the present disclosure.

[0037] Figure 2 is an exploded perspective view showing an example of a secondary battery.

[0038] Figure 3 It is shown Figure 2 A front view of the secondary battery shown in FIG.

[0039] Figure 4 is a graph showing thickness change during charge and discharge of a secondary battery.

[0040] Figure 5 : are graphs showing a first curve and a second curve of a secondary battery to which a negative electrode active material not containing silicon oxide is applied.

[0041] Figure 6 : are graphs showing a first curve and a second curve of a first secondary battery to which a negative electrode active material including 3 weight % of silicon oxide is applied.

[0042] Figure 7 : are graphs showing a first curve and a second curve of a second secondary battery to which a negative electrode active material including 5 weight % of silicon oxide is applied.

[0043] Figure 8 : are graphs showing a first curve and a second curve of a third secondary battery to which a negative electrode active material including 10 weight % of silicon oxide is applied.

[0044] Fig. 9 is a graph showing thickness variation during charge and discharge of a secondary battery according to the number of charge and discharge cycles of the secondary battery.

[0045] Fig.10 is a graph showing a second curve in which the thickness of the secondary battery during discharge is differentiated with respect to time.

[0046] Fig.11 is a graph showing a second curve in which the thickness of the secondary battery during discharge is differentiated with respect to time according to the number of discharge cycles of the corresponding secondary battery.

[0047] Fig.12 is a flowchart illustrating a secondary battery diagnosis method according to an embodiment of the present disclosure.

[0048] Fig.13 is a flowchart illustrating a capacity ratio estimating process of a secondary battery diagnosis method according to an embodiment of the present disclosure.

[0049] Fig.14 is a diagram showing a vehicle according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0050] Hereinafter, in order to clarify the technical solutions corresponding to the technical challenges of the present disclosure, embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. However, when explaining the present disclosure, if the description of the related known technology obscures the main purpose of the present disclosure, its description may be omitted. In addition, the terms used in this specification are defined in consideration of the functions in the present disclosure, and these terms may vary depending on the intentions or habits of designers, manufacturers, etc. Therefore, the definitions of the terms described below should be made based on the contents of the entire specification.

[0051] Figure 1 is a block diagram illustrating a secondary battery diagnostic device 100 according to an embodiment of the present disclosure.

[0052] like Figure 1 As illustrated in , the secondary battery diagnostic device 100 according to an embodiment of the present disclosure is configured to diagnose the state of a secondary battery to which a negative electrode active material including silicon or silicon oxide is applied. To this end, the secondary battery diagnostic device 100 includes a thickness sensor 110 and a processor 120.

[0053] The thickness sensor 110 is configured to sense a thickness change of the secondary battery according to a volume change of a negative active material applied to the secondary battery while the secondary battery is being charged or discharged.

[0054] The processor 120 is configured to estimate the capacity ratio of silicon or silicon oxide contained in the negative electrode active material using the sensing result of the thickness sensor 110, and diagnose the state of the secondary battery based on the capacity ratio. Here, the capacity ratio means the ratio of the capacity provided by silicon or silicon oxide to the capacity of the secondary battery provided by the negative electrode active material.

[0055] In one embodiment, the thickness sensor 110 may be configured to sense a change in thickness of the secondary battery by measuring a distance from a predetermined position to the surface of the secondary battery. In this case, the thickness sensor 110 may be configured as a contact sensor (such as a micrometer, or a non-contact sensor (such as a displacement sensor using laser or light).

[0056] Furthermore, in one embodiment, the secondary battery diagnostic device 100 may further include a memory 130. In this case, the memory 130 may be configured to transfer previously stored data to the processor 120 or store data transferred from the processor 120.

[0057] For example, the memory 130 may be configured to store data on a reference capacity ratio that should be provided by silicon or silicon oxide included in the negative electrode active material of a normal secondary battery. In this case, the memory 130 may store data on a reference capacity ratio for each weight ratio of silicon or silicon oxide included in the negative electrode active material. In addition, the memory 130 may be configured to further store data on the capacity ratio of silicon or silicon oxide estimated by the processor 120 and data on the secondary battery diagnostic result.

[0058] The memory 130 may include at least one of RAM (Random Access Memory), SRAM (Static RAM), ROM (Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), PROM (Programmable Read Only Memory), flash memory, hard disk, SSD (Solid State Disk), and SDD (Solid State Disk Drive).

[0059] In addition, in one embodiment, the secondary battery diagnostic apparatus 100 may further include an output device 140. In this case, the output device 140 may be configured to output data or information by visual, auditory, or audio-visual means. To this end, the output device 140 may optionally include a visual display device such as a display, a light-emitting diode, etc., and an audio device such as a speaker.

[0060] For example, if the secondary battery diagnosis result indicates that the state of the secondary battery is bad, the processor 120 may control the output device 140 to output a predetermined alarm.

[0061] Figure 2 is an exploded perspective view showing an example of the secondary battery 200 .

[0062] like Figure 2As illustrated in , the secondary battery 200 may include an electrode assembly 222 in which a positive electrode and a negative electrode are stacked on each other with a separator therebetween, and a case 228 that accommodates the electrode assembly 222 together with an electrolyte material in an internal space S1.

[0063] In addition, the secondary battery 200 may further include an electrode lead 224 electrically connected to the electrode assembly 222 , and a sealing tape 226 sealing a periphery of the electrode lead 224 .

[0064] The case 228 of the secondary battery 200 may include a first case portion 228a and a second case portion 228b coupled to each other to form an internal space S1. The case 228 may be sealed by sealingly coupling edge portions of the first case portion 228a and the second case portion 228b to each other in a state where the electrode assembly 222 is accommodated in the internal space S1.

[0065] The positive electrode forming the electrode assembly 222 of the secondary battery 200 may include a positive electrode substrate made of a material including aluminum, and a positive electrode active material applied to the positive electrode substrate. The positive electrode active material may include lithium oxide. The positive electrode active material may be applied to the positive electrode substrate together with a conductive material, a binder, etc.

[0066] The negative electrode of the electrode assembly 222 may include a negative electrode substrate made of a material including copper, and a negative electrode active material applied to the negative electrode substrate. The negative electrode active material may include silicon (Si) or silicon oxide (SiO x ) of silicon-based materials, and graphite. That is, the negative electrode active material may include a mixture of silicon-based materials and graphite mixed in a predetermined weight ratio. The negative electrode active material may be applied to the negative electrode substrate together with a conductive material, a binder, and the like.

[0067] In this way, by including silicon or silicon oxide in the negative electrode active material of the secondary battery, the electric capacity provided by the same weight of the negative electrode active material can be increased, and as a result, the energy density of the secondary battery can be increased.

[0068] Meanwhile, silicon-based materials, such as silicon (Si) or silicon oxide (SiOx), have unique characteristics of high reaction potential and large volume change due to electrochemical reaction, compared to graphite mixed with the silicon-based materials.

[0069] Therefore, the secondary battery diagnostic device 100 according to the present disclosure may diagnose the state of the secondary battery, such as the degradation state, capacity loss rate, and remaining lifespan, by utilizing unique characteristics of silicon-based materials.

[0070] Figure 3 It is shown Figure 2 2 is a front view of the secondary battery 200 shown in FIG.

[0071] like Figure 3 As illustrated in , the manufactured secondary battery 200 may have a predetermined thickness (TH). In this case, the thickness (TH) of the secondary battery 200 may correspond to the thickness of the electrode assembly accommodated inside the secondary battery 200.

[0072] The thickness (TH) of the secondary battery 200 varies with a volume change of the negative electrode active material occurring during charge or discharge of the secondary battery 200 .

[0073] The secondary battery diagnostic device 100 according to the present disclosure may diagnose the state of the secondary battery by sensing a thickness change of the secondary battery.

[0074] In addition, the secondary battery that becomes the diagnosis target of the secondary battery diagnosis device 100 according to the present disclosure may be configured in various forms in which the thickness in a specific direction changes according to the volume change of the negative electrode active material during charge or discharge.

[0075] For example, Figure 2 and Figure 3 As illustrated in , the secondary battery as the diagnosis object of the secondary battery diagnostic device 100 according to the present disclosure may be configured as a pouch-type secondary battery. According to an embodiment, the secondary battery as the diagnosis object of the secondary battery diagnostic device 100 according to the present disclosure may also be configured as a secondary battery of another shape in which the thickness of a specific portion varies according to the volume change of the negative electrode active material, such as a square secondary battery or a cylindrical secondary battery.

[0076] Figure 4 is a graph showing thickness change during charge and discharge of a secondary battery.

[0077] exist Figure 4 , the horizontal axis represents the time elapsed after the start of the charge / discharge cycle, and the vertical axis represents the ratio of the surface position change value to the original thickness value of the secondary battery.

[0078] like Figure 4 As shown in FIG, the thickness change during charge and discharge of the secondary battery may vary depending on the weight ratio of silicon or silicon oxide included in the negative electrode active material of the secondary battery. Here, the weight ratio refers to the ratio of the weight of silicon or silicon oxide to the total weight of the negative electrode active material.

[0079] That is, during charge, rest, and discharge, a secondary battery including SiO, which is a type of silicon oxide, in a negative electrode active material exhibits a larger volume change than a secondary battery not including SiO.

[0080] In addition, among secondary batteries including silicon oxide in the negative electrode active material, the secondary battery including silicon oxide having a relatively large weight ratio (10%) exhibited a larger volume change than the secondary battery including silicon oxide having a relatively small weight ratio (3% or 5%).

[0081] Figure 5 1 is a graph showing a first curve (TH) and a second curve (dTH / dt) of a secondary battery to which a negative electrode active material not including silicon oxide is used.

[0082] exist Figure 5 , the horizontal axis represents the time elapsed after the start of the discharge cycle, the left vertical axis represents the ratio of the surface position change value to the original thickness value of the secondary battery, and the right vertical axis represents the slope of the first curve (TH).

[0083] In addition, the first curve (TH) represents the thickness of the secondary battery, which changes with time or according to the charge state of the secondary battery during the discharge cycle of the secondary battery, to which the negative electrode active material containing 100% by weight of graphite is applied. In addition, the second curve (dTH / dt) represents the slope of the first curve (TH), which changes with time or according to the charge state of the secondary battery. In this case, the second curve (dTH / dt) can be obtained by differentiating the first curve (TH) with respect to time (t).

[0084] like Figure 5 As shown in , the first curve (TH) of the secondary battery shows that the thickness of the secondary battery gradually decreases during discharge.

[0085] In addition, the second curve (dTH / dt) of the secondary battery shows that the slope of the first curve (TH) gradually decreases as the discharge progresses.

[0086] That is, the second curve (dTH / dt) may be divided into a plurality of sections (a, b, c). In this case, the slope may increase or decrease within each section, but the slope of each section tends to gradually decrease as it progresses from the first section (a) corresponding to the early stage of discharge to the third section (c) corresponding to the late stage of discharge.

[0087] Figure 6 : is a graph showing a first curve (TH1) and a second curve (dTH / dt) of a first secondary battery to which a negative electrode active material including 3 weight % of silicon oxide is applied.

[0088] exist Figure 6, the horizontal axis represents the time after the start of the discharge cycle, the left vertical axis represents the ratio of the surface position change value of the first secondary battery to the original thickness value, and the right vertical axis represents the slope of the first curve (TH1). Depending on the embodiment, the horizontal axis may also represent the SoC (state of charge) of the first secondary battery.

[0089] like Figure 6 As shown in , the negative electrode active material of the first secondary battery may include a mixture of silicon or silicon oxide and graphite.

[0090] In addition, a first curve (TH1) of the first secondary battery to which a negative electrode active material including graphite and SiO in weight ratios of 97% and 3%, respectively, is applied shows the thickness of the first secondary battery changing with time during a discharge cycle of the first secondary battery or according to a charge state of the first secondary battery.

[0091] In addition, the second curve (dTH1 / dt) of the first secondary battery represents the slope of the first curve (TH1) that changes with time or according to the charge state of the first secondary battery. In this case, the second curve (dTH1 / dt) can be obtained by differentiating the first curve (TH1) with respect to time (t).

[0092] The first curve (TH1) of the first secondary battery shows that the thickness of the first secondary battery gradually decreases while the first secondary battery is being discharged.

[0093] However, the second curve (dTH1 / dt) of the first secondary battery shows that the slope of the first curve (TH1) gradually decreases while the first secondary battery is being discharged, and then increases again at a certain point.

[0094] That is, the second curve (dTH1 / dt) can be divided into a plurality of sections (a1, b1, c1, d1). In this case, the slope of each section gradually decreases as it progresses from the first section (a1) corresponding to the early stage of discharge to the second section (b1) and the third section (c1) corresponding to the middle stage of discharge, but in the fourth section (d1) corresponding to the end of discharge, the slope changes to an increasing trend.

[0095] This phenomenon is caused by the unique characteristics of silicon or silicon oxide that occurs when the SoC (State of Charge) of the secondary battery is relatively low because the reaction potential of silicon or silicon oxide is relatively higher than that of graphite.

[0096] Therefore, the secondary battery diagnostic device 100 according to the present disclosure may diagnose the state of the secondary battery, such as the degree of degradation, the capacity loss rate, and the remaining lifespan, by utilizing the unique characteristics of silicon or silicon oxide.

[0097] That is, the processor 120 of the secondary battery diagnostic device 100 according to an embodiment of the present disclosure can estimate the capacity ratio of silicon or silicon oxide by using the sensing result of the thickness sensor 110 that senses the thickness change of the secondary battery during charging or discharging, and can diagnose the state of the secondary battery based on the capacity ratio.

[0098] As mentioned above, the capacity ratio refers to the ratio of the capacity provided by silicon or silicon oxide to the capacity of the secondary battery provided by the negative electrode active material.

[0099] For example, the thickness sensor 110 may sense a thickness change of the first secondary battery while the first secondary battery is being discharged.

[0100] In addition, by using the sensing result of the thickness sensor 110, the processor 120 can generate Figure 6 A first curve (TH1) representing the thickness of the first secondary battery that varies with time or according to a charge state, and a second curve (dTH1 / dt) representing the slope of the first curve (TH1) that varies with time or according to a charge state of the first secondary battery are illustrated in FIG, and a capacity ratio of SiO corresponding to silicon oxide is estimated based on the second curve (dTH1 / dt).

[0101] Specifically, the processor 120 may divide the second curve (dTH1 / dt) into a plurality of sections (a1, b1, c1, d1), and select a target section related to silicon or silicon oxide among the plurality of sections (a1, b1, c1, d1).

[0102] In this case, the processor 120 may divide the second curve (dTH1 / dt) into a plurality of sections (a1, b1, c1, d1) according to the slope change trend of the first curve (TH1) shown in the second curve (dTH1 / dt). Figure 6 In the figure, the second curve (dTH1 / dt) is divided into four sections, but it can also be divided into two sections. That is, the second curve (dTH1 / dt) can be divided into a first section (a1, b1, c1) in which the slope change trend of the first curve (TH1) is a decreasing trend, and a second section (d1) in which the slope change trend is an increasing trend.

[0103] In addition, the processor 120 may select, as a target section, a section in which the state of charge of the first secondary battery is within a range below a predetermined threshold and in which a slope change trend of the first curve (TH1) is different from those of other sections among the plurality of sections (a1, b1, c1, d1).

[0104] For example, when the thickness sensor 110 senses a thickness change of the first secondary battery while the first secondary battery is being discharged, the processor 120 may select a segment (d1) in which a slope change trend of the first curve (TH1) has changed from a decreasing trend to an increasing trend among a plurality of segments (a1, b1, c1, d1) as a target segment.

[0105] In this case, the processor 120 can select the fourth segment (d1) as the target segment among the first to fourth segments (a1, b1, c1, d1), which is a segment in which the SoC of the first secondary battery is less than or equal to 50% and in which, unlike the first to third segments (a1, b1, c1), the slope change trend of the first curve (TH1) is an increasing trend.

[0106] The reason for selecting the target segment within the range where the state of charge of the secondary battery is lower than a predetermined threshold value is that in the case of a secondary battery using nickel as a positive electrode material, a segment in which the slope of the first curve (TH1) temporarily increases appears even in a late charging stage or an early discharging stage where the SoC of the secondary battery is high, and silicon or silicon oxide acts in an early charging stage or a late discharging stage where the SoC of the secondary battery is relatively low.

[0107] When a target segment is selected as described above, the processor 120 may estimate the capacity ratio of silicon or silicon oxide by comparing a total length (L1) of a plurality of segments (a1, b1, c1, d1) in the direction of a first axis (horizontal axis) representing time or a state of charge (SoC) with a length (L1') of the target segment in the direction of the first axis (horizontal axis).

[0108] For example, the capacity ratio may be calculated as in Formula 1.

[0109] [Formula 1]

[0110] CR = k(L t / L p )×100 [%]

[0111] In Formula 1, CR is the capacity ratio of silicon or silicon oxide, L p is the total length of the second curve, L t is the length of the target segment, and k is a predetermined proportionality constant. Therefore, Figure 6 The capacity ratio CR1 of SiO in the L1 can be calculated as k(L1′ / L1)×100[%].

[0112] When the capacity ratio is estimated in this manner, the processor 120 can diagnose the state of the first secondary battery using the estimated capacity ratio. For example, the processor 120 can diagnose the state of the first secondary battery by comparing the estimated capacity ratio with a reference capacity ratio of silicon or silicon oxide obtained in advance from a normal secondary battery.

[0113] In one embodiment, the secondary battery diagnostic device 100 according to the present disclosure may be configured to pre-store reference capacity ratio information of the diagnostic target secondary battery in the memory 130. The reference capacity ratio information may include information indicating a reference capacity ratio corresponding to a predetermined number of charging cycles or a predetermined number of discharging cycles of the diagnostic target secondary battery. In this case, the processor 120 may diagnose the state of the first secondary battery by comparing the capacity ratio estimated with respect to the first secondary battery with the reference capacity ratio of the first secondary battery pre-stored in the memory 130.

[0114] In another embodiment, the processor 120 can be configured to further estimate a weight ratio before diagnosing the state of the diagnostic target secondary battery - the weight ratio is the ratio of the weight of silicon or silicon oxide to the total weight of the negative electrode active material - and compare the estimated capacity ratio with a reference capacity ratio corresponding to the weight ratio to diagnose the state of the diagnostic target secondary battery.

[0115] In this case, the processor 120 may estimate the weight ratio before the number of charge cycles or the number of discharge cycles of the diagnosis target secondary battery exceeds a predetermined number.

[0116] For example, the processor 120 may estimate the capacity ratio of silicon oxide after the end of a charge cycle or a discharge cycle that has occurred before degradation of the diagnosis target secondary battery occurs, and estimate the weight ratio of silicon oxide corresponding to the estimated capacity ratio.

[0117] Table 1 shows capacity ratios corresponding to weight ratios of SiO included in the negative electrode active material.

[0118] [Table 1]

[0119]

[0120] As shown in Table 1, the silicon oxide included in the negative electrode active material has a high capacity ratio compared to its weight ratio. Therefore, by including silicon or silicon oxide in the negative electrode active material of the secondary battery, the energy density of the secondary battery can be improved. In this way, since the weight ratio and capacity ratio of silicon or silicon oxide are related, if one of the two is estimated, the other can also be estimated.

[0121] Therefore, the processor 120 may estimate the capacity ratio of silicon or silicon oxide included in the negative active material of the diagnosis target secondary battery at the start of use of the diagnosis target secondary battery, and estimate the weight ratio of silicon or silicon oxide through the estimated capacity ratio.

[0122] Next, the processor 120 may obtain reference capacity ratio information corresponding to the estimated weight ratio among reference capacity ratio information for each weight ratio pre-stored in the memory 130 .

[0123] Thereafter, the processor 120 may diagnose the state of the diagnosis target secondary battery by comparing the obtained reference capacity ratio information with the capacity ratio of silicon or silicon oxide repeatedly estimated with respect to the diagnosis target secondary battery.

[0124] Figure 7 : are graphs showing a first curve and a second curve of a second secondary battery to which a negative electrode active material including 5 weight % of silicon oxide is applied.

[0125] exist Figure 7 , the horizontal axis represents the time after the start of the discharge cycle, the left vertical axis represents the ratio of the surface position change value of the second secondary battery to the original thickness value, and the right vertical axis represents the slope of the first curve (TH2). Depending on the embodiment, the horizontal axis may also represent the SoC (state of charge) of the second secondary battery.

[0126] like Figure 7 The first curve (TH2) of the second secondary battery to which a negative electrode active material including graphite and SiO in weight ratios of 95% and 5%, respectively, is applied shows the thickness of the second secondary battery varying with time or according to a change in the charge state of the second secondary battery during a discharge cycle of the second secondary battery.

[0127] In addition, the second curve (dTH2 / dt) of the second secondary battery represents the slope of the first curve (TH2) that changes with time or according to the charge state of the second secondary battery. In this case, the second curve (dTH2 / dt) can be obtained by differentiating the first curve (TH2) with respect to time (t).

[0128] The first curve (TH2) of the second secondary battery shows that the thickness of the second secondary battery gradually decreases during discharge of the second secondary battery.

[0129] However, the second curve (dTH2 / dt) of the second secondary battery shows that the slope of the first curve (TH2) gradually decreases while the discharge of the second secondary battery progresses, and then increases again at a certain point.

[0130] That is, the second curve (dTH2 / dt) can be divided into a plurality of sections (a2, b2, c2, d2). In this case, the slope of each section gradually decreases as it progresses from the first section (a2) corresponding to the early stage of discharge to the second section (b2) and the third section (c2) corresponding to the middle stage of discharge, but in the fourth section (d2) corresponding to the end of discharge, the slope changes to an increasing trend.

[0131] Therefore, the processor 120 of the secondary battery diagnostic device 100 according to the present disclosure can estimate the capacity ratio of silicon or silicon oxide using the sensing result of the thickness sensor 110 that senses the thickness change of the secondary battery during charging or discharging, as shown in FIG. Figure 6 As described above, the state of the secondary battery can be diagnosed based on the capacity ratio.

[0132] That is, the thickness sensor 110 may sense a thickness change of the second secondary battery while the second secondary battery is being discharged.

[0133] In addition, the processor 120 can use the sensing result of the thickness sensor 110 to generate a first curve (TH2) representing the thickness of the second secondary battery that changes over time or according to the charge state of the second secondary battery and a second curve (dTH2 / dt) representing the slope of the first curve (TH2) that changes over time or according to the charge state of the second secondary battery, and can estimate the capacity ratio of SiO corresponding to silicon oxide based on the second curve (dTH2 / dt).

[0134] To this end, the processor 120 may divide the second curve (dTH2 / dt) into a plurality of sections (a2, b2, c2, d2), and select a target section associated with silicon or silicon oxide among the plurality of sections (a2, b2, c2, d2).

[0135] For example, the processor 120 may divide the second curve (dTH2 / dt) into first to fourth segments (a2, b2, c2, d2), and may select the fourth segment (d2) as a target segment among the first to fourth segments (a2, b2, c2, d2), the fourth segment being a segment in which the state of charge of the second secondary battery is lower than a predetermined threshold and in which the slope change trend of the first curve (TH2) is different from the slope change trends of other segments.

[0136] Then, the processor 120 can estimate the capacity ratio of silicon or silicon oxide by comparing the total length (L2) of the plurality of segments (a2, b2, c2, d2) in the first axis (horizontal axis) direction representing time or state of charge (SoC) with the length (L2') of the target segment in the first axis (horizontal axis) direction.

[0137] For example, Figure 7 The capacity ratio CR2 of SiO in L2 can be calculated according to Formula 1 as k(L2′ / L2)×100[%].

[0138] When the capacity ratio is estimated in this manner, the processor 120 may diagnose the state of the second secondary battery using the estimated capacity ratio, as shown in FIG. Figure 6 as described.

[0139] Figure 8 : are graphs showing a first curve and a second curve of a third secondary battery to which a negative electrode active material including 10 weight % of silicon oxide is applied.

[0140] exist Figure 8 , the horizontal axis represents the time after the start of the discharge cycle, the left vertical axis represents the ratio of the surface position change value of the third secondary battery to the original thickness value, and the right vertical axis represents the slope of the first curve (TH3). Depending on the embodiment, the horizontal axis may also represent the SoC (state of charge) of the third secondary battery.

[0141] like Figure 8 The first curve (TH3) of the third secondary battery including graphite and SiO in weight ratios of 90% and 10%, respectively, shown in , represents the thickness of the third secondary battery that changes with time during a discharge cycle of the third secondary battery or according to a charge state of the third secondary battery.

[0142] In addition, the second curve (dTH3 / dt) of the third secondary battery represents the slope of the first curve (TH3) that changes with time or according to the charge state of the third secondary battery. In this case, the second curve (dTH3 / dt) can be obtained by differentiating the first curve (TH3) with respect to time (t).

[0143] The first curve (TH3) of the third secondary battery shows that the thickness of the third secondary battery gradually decreases during discharge of the third secondary battery.

[0144] However, the second curve (dTH3 / dt) of the third secondary battery shows that the slope of the first curve (TH3) gradually decreases while the third secondary battery is being discharged, and then increases again at a certain point.

[0145] That is, the second curve (dTH3 / dt) can be divided into a plurality of sections (a3, b3, c3, d3). In this case, the slope of each section gradually decreases as it progresses from the first section (a3) ​​corresponding to the early stage of discharge to the second section (b3) and the third section (c3) corresponding to the middle stage of discharge, but in the fourth section (d3) corresponding to the end of discharge, the slope changes to an increasing trend.

[0146] Therefore, the processor 120 of the secondary battery diagnostic device 100 according to the present disclosure can estimate the capacity ratio of silicon or silicon oxide using the sensing result of the thickness sensor 110 that senses the thickness change of the secondary battery during charging or discharging, as shown in FIG. Figure 6 As described, the state of the corresponding secondary battery can be diagnosed based on the capacity ratio.

[0147] That is, the thickness sensor 110 may sense a thickness change of the third secondary battery while the third secondary battery is being discharged.

[0148] In addition, the processor 120 can use the sensing result of the thickness sensor 110 to generate a first curve (TH3) representing the thickness of the third secondary battery that changes with time or according to the charge state of the third secondary battery and a second curve (dTH3 / dt) representing the slope of the first curve (TH3) that changes with time or according to the charge state of the third secondary battery, and can estimate the capacity ratio of SiO corresponding to silicon oxide based on the second curve (dTH3 / dt).

[0149] To this end, the processor 120 may divide the second curve (dTH3 / dt) into a plurality of sections (a3, b3, c3, d3), and select a target section associated with silicon or silicon oxide among the plurality of sections (a3, b3, c3, d3).

[0150] For example, the processor 120 may divide the second curve (dTH3 / dt) into first to fourth segments (a3, b3, c3, d3), and may select the fourth segment (d3) as a target segment among the first to fourth segments (a3, b3, c3, d3), the fourth segment being a segment in which the state of charge of the third secondary battery is lower than a predetermined threshold and in which the slope change trend of the first curve (TH3) is different from the slope change trends of other segments.

[0151] Then, the processor 120 can estimate the capacity ratio of silicon or silicon oxide by comparing the total length (L3) of the plurality of segments (a3, b3, c3, d3) in the first axis (horizontal axis) direction representing time or state of charge (SoC) with the length (L3') of the target segment in the first axis (horizontal axis) direction.

[0152] For example, Figure 8 The capacity ratio CR3 of SiO in L3 can be calculated according to Formula 1 as k(L3′ / L3)×100[%].

[0153] When the capacity ratio is estimated in this manner, the processor 120 may diagnose the state of the third secondary battery using the estimated capacity ratio, as shown in FIG. Figure 6 as described.

[0154] Fig. 9 is a graph showing thickness variation during charge and discharge of a secondary battery according to the number of charge and discharge cycles of the secondary battery.

[0155] like Fig. 9 As shown in FIG. 1 , the thickness of the secondary battery gradually increases as the number of charge and discharge cycles increases. In addition, when the pause time between the charge end time (t1) and the discharge start time (t2) is the same in all charge and discharge cycles, the total charge and discharge time gradually decreases as the number of charge and discharge cycles increases.

[0156] Therefore, the first curve showing the thickness of the secondary battery that changes over time or according to the charging state and the second curve obtained by differentiating the first curve with respect to time or SoC also change according to the number of cycles of the secondary battery, and if the diagnostic target secondary battery is a normal secondary battery, the curve shows a similar change pattern according to the number of cycles.

[0157] Fig.10 is a graph showing a second curve in which the thickness of the secondary battery during discharge is differentiated with respect to time.

[0158] like Fig.10 As illustrated in , a second curve obtained by differentiating thickness with respect to time during discharge of a secondary battery to which a negative electrode active material including silicon or silicon oxide is applied gradually decreases as discharge progresses, and then increases at a target section (TS) at the end of discharge.

[0159] The length of the target segment (TS) in the horizontal axis direction varies depending on the number of discharge cycles of the secondary battery.

[0160] Fig.11 is a graph showing a second curve obtained by differentiating the thickness of the secondary battery with respect to time during discharge according to the number of discharge cycles of the secondary battery.

[0161] like Fig.11 As shown in FIG. 1 , the pattern of the second curve and the length of the target segment may vary depending on the number of discharge cycles of the secondary battery. That is, as the number of discharge cycles of the secondary battery increases and the secondary battery degrades, the pattern of the second curve may show a trend of gradually shrinking. In addition, the position and length of the target segment may also vary depending on the change in the pattern of the second curve.

[0162] For example, when a secondary battery to which a negative electrode active material containing graphite and SiO2 at weight ratios of 90% and 10%, respectively, is applied is in a normal state, a target segment (TS1) of the second curve corresponding to the second discharge cycle may have a length corresponding to a capacity ratio of 25%. In addition, target segments (TS2, TS3, TS4) of the second curve corresponding to the 50th, 100th, and 150th discharge cycles gradually become longer according to the number of discharge cycles, respectively, and may have a length corresponding to a capacity ratio close to 29.1%.

[0163] On the other hand, if the state of the secondary battery is abnormal, such as expansion occurring in the corresponding secondary battery as the discharge cycle is repeated or loss of the conductive path due to pulverization of SiO, the target segments (TS1, TS2, TS3, TS4) of the second curve gradually become longer according to the number of discharge cycles, and then suddenly shorten.

[0164] Therefore, according to the processor 120 of the secondary battery diagnostic device 100 of the present disclosure, the capacity ratio of silicon or silicon oxide included in the negative electrode active material can be repeatedly estimated while the charging cycle or discharging cycle of the diagnostic target secondary battery is repeated, and the state of the diagnostic target secondary battery can be diagnosing based on the change trend of the estimated capacity ratio.

[0165] Fig.12 is a flowchart illustrating a secondary battery diagnosis method according to an embodiment of the present disclosure.

[0166] refer to Fig.12 , the detailed operation of the secondary battery diagnosis device 100 according to the present disclosure may be explained in time series.

[0167] When a charge and / or discharge cycle of a secondary battery to which a negative electrode active material including silicon or silicon oxide is applied is initiated, the thickness sensor 110 of the secondary battery diagnostic device 100 senses a thickness change of the secondary battery according to a volume change of the negative electrode active material while the secondary battery is being charged or discharged ( S10 , S20 ).

[0168] Next, the processor 120 of the secondary battery diagnostic device 100 estimates a capacity ratio, which is a ratio of the capacity provided by silicon or silicon oxide to the capacity of the secondary battery provided by the negative electrode active material, using the sensing result of the thickness sensor 110. In this case, the estimated capacity ratio may be stored in the memory 130 of the secondary battery diagnostic device 100 (S30).

[0169] Then, when the number of cycles of the secondary battery reaches a predetermined reference number, the processor 120 may diagnose the state of the secondary battery based on the estimated capacity ratio ( S40 , S50 ).

[0170] On the other hand, if the cycle number of the secondary battery does not reach the reference cycle number, the processor 120 waits without diagnosing the state of the secondary battery, and when the next charge and / or discharge cycle of the secondary battery starts, the processor 120 may repeat the secondary battery thickness sensing process (S20) and the capacity ratio estimation process of silicon or silicon oxide (S40, S80, S90).

[0171] In the secondary battery state diagnosis process ( S50 ), if the state of the secondary battery is diagnosed as bad, the processor 120 may control the output device 140 to output a predetermined alarm ( S60 , S70 ).

[0172] On the other hand, in the secondary battery status diagnosis process (S50), if the status of the secondary battery is diagnosed as good, the processor 120 stores the diagnosis result in the memory 130, and then waits, and when the next charge and / or discharge cycle of the secondary battery is started, the processor 120 may repeat the above process (S20 to S50) (S60, S80, S90).

[0173] Fig.13 is a flowchart illustrating a capacity ratio estimating process of a secondary battery diagnosis method according to an embodiment of the present disclosure.

[0174] like Fig.13 As illustrated in , when the thickness sensor 110 senses a change in thickness of a secondary battery while the secondary battery is being charged and / or discharged, the processor 120 may use the sensing result of the thickness sensor 110 to generate a first curve (S32) representing the thickness of the corresponding secondary battery that changes over time or according to a change in a state of charge (SoC) of the corresponding secondary battery.

[0175] Then, the processor 120 may generate a second curve representing the slope of the first curve varying with time or according to a change in the state of charge (SoC) of the secondary battery based on the first curve (S34). In this case, the second curve may be obtained by differentiating the first curve with respect to time or SoC.

[0176] Then, the processor 120 may estimate the capacity ratio of silicon or silicon oxide based on the second curve.

[0177] Specifically, the processor 120 may divide the second curve into a plurality of sections, and select a target section related to silicon or silicon oxide among the plurality of sections ( S36 ).

[0178] Then, the processor 120 may estimate the capacity ratio by comparing the total length of the plurality of sections in the first axis direction with the length of the target section in the first axis direction ( S38 ).

[0179] In this case, the processor 120 may divide the second curve into a plurality of sections according to a slope change trend of the first curve shown in the second curve.

[0180] In addition, the processor 120 may select, as a target section, a section in which the state of charge of the secondary battery is less than a predetermined threshold and in which the slope change trend of the first curve is different from those of other sections among the plurality of sections.

[0181] For example, if the first curve is about thickness change during discharge of a secondary battery, the processor 120 may select a segment in which the slope change trend of the first curve changes from a decreasing trend to an increasing trend as a target segment among multiple segments of the second curve.

[0182] As described above, when a target section is selected, the processor 120 may estimate the capacity ratio of silicon or silicon oxide by comparing the total length of the plurality of sections in the first axis direction with the length of the target section in the first axis direction.

[0183] When the capacity ratio is estimated in this manner, the processor 120 can diagnose the state of the secondary battery using the estimated capacity ratio. For example, the processor 120 can diagnose the state of the secondary battery by comparing the estimated capacity ratio with a reference capacity ratio of silicon or silicon oxide obtained in advance from a normal secondary battery.

[0184] In one embodiment, the secondary battery diagnostic device 100 according to the present disclosure may pre-store reference capacity ratio information of the diagnostic target secondary battery in the memory 130. The reference capacity ratio information may include information indicating a reference capacity ratio corresponding to a predetermined number of charging cycles or a predetermined number of discharging cycles of the diagnostic target secondary battery. In this case, the processor 120 may diagnose the state of the secondary battery by comparing the capacity ratio estimated for the secondary battery with the reference capacity ratio pre-stored in the memory 130.

[0185] In another embodiment, before diagnosing the state of the secondary battery, the processor 120 may also estimate a weight ratio, which is the ratio of the weight of silicon or silicon oxide to the total weight of the negative electrode active material, and compare the estimated capacity ratio with a reference capacity ratio corresponding to the weight ratio, thereby diagnosing the state of the secondary battery.

[0186] In this case, the processor 120 may estimate the weight ratio before the number of charge cycles or the number of discharge cycles of the diagnosis target secondary battery exceeds a predetermined number.

[0187] For example, the processor 120 may estimate the capacity ratio of silicon oxide after the end of a charge cycle or a discharge cycle that has been performed before degradation of the diagnosis target secondary battery occurs, and estimate the weight ratio of silicon oxide corresponding to the estimated capacity ratio.

[0188] Next, the processor 120 may obtain reference capacity ratio information corresponding to the estimated weight ratio among reference capacity ratio information for each weight ratio pre-stored in the memory 130 .

[0189] Thereafter, the processor 120 may diagnose the state of the secondary battery by comparing the obtained reference capacity ratio information with the capacity ratio of silicon or silicon oxide repeatedly estimated for the secondary battery.

[0190] Fig.14 is a diagram showing a vehicle 2 according to an embodiment of the present disclosure.

[0191] like Fig.14 As illustrated in , the vehicle 2 according to the embodiment of the present disclosure may include the secondary battery diagnostic device according to the present disclosure. The secondary battery diagnostic device may be configured to diagnose the secondary battery of the battery pack 10 installed in the vehicle 2. In this case, the secondary battery diagnostic device may be configured to interact with the ECU (Electronic Control Unit) of the vehicle 2.

[0192] In another embodiment, the secondary battery diagnostic device may be included in the battery pack 10 mounted on the vehicle 2 . In this case, the secondary battery diagnostic device may be configured to interact with a BMS (Battery Management System) of the battery pack 10 .

[0193] For reference, the secondary battery diagnosis device according to the present disclosure may be applied to various electric devices or electric systems using a secondary battery other than a vehicle, and may also be applied to an ESS (Energy Storage System).

[0194] As described above, according to the present disclosure, while a secondary battery to which a negative electrode active material including silicon or silicon oxide is applied is being charged or discharged, a thickness sensor senses a thickness change of the secondary battery according to a volume change of the negative electrode active material, and a processor estimates a capacity ratio - the capacity ratio being a ratio of the capacitance provided by silicon oxide to the capacitance provided by the negative electrode active material of the secondary battery - by using the sensing result of the thickness sensor regarding the thickness change of the secondary battery, and diagnoses the state of the secondary battery based on the capacity ratio, thereby efficiently and accurately diagnosing the state of the secondary battery to which a negative electrode active material including silicon or silicon oxide is applied.

[0195] In addition, before the processor estimates the capacity ratio, the processor uses the sensing result of the thickness sensor to sequentially generate a first curve representing the thickness of the secondary battery that changes with time or according to the charging state of the secondary battery, and a second curve representing the slope of the first curve that changes with time or according to the charging state of the secondary battery, and estimates the capacity ratio based on the second curve, thereby diagnosing the state of the secondary battery in a simplified manner independent of the electrical factors of the secondary battery.

[0196] In addition, the processor divides the second curve into multiple segments, selects a target segment exhibiting unique characteristics of silicon or silicon oxide from the multiple segments, and estimates the capacity ratio by comparing the lengths of the entire multiple segments in the first axis direction with the length of the target segment in the first axis direction, thereby reducing the amount of calculations required to diagnose the state of the secondary battery and, as a result, improving the speed of diagnosing the secondary battery.

[0197] In addition, the processor estimates a weight ratio, which is the ratio of the weight of silicon or silicon oxide to the total weight of the negative electrode active material, and compares the capacity ratio with a reference capacity ratio corresponding to the estimated weight ratio to diagnose the state of the secondary battery, thereby diagnosing the state of the target secondary battery even when the weight ratio of silicon or silicon oxide included in the negative electrode active material of the target secondary battery is unknown.

[0198] In addition, since the thickness sensor for sensing the thickness change of the secondary battery is configured to sense the thickness change of the corresponding secondary battery by measuring the distance from a predetermined position to the surface of the secondary battery, it is possible not only to sense the thickness change of the secondary battery for each secondary battery, but also to sense the overall thickness change of the secondary battery module by regarding a plurality of secondary batteries stacked in the thickness direction as one secondary battery module. That is, the secondary battery diagnostic device according to the present disclosure can be included in a battery pack or a vehicle having a plurality of secondary batteries, and the states of the plurality of secondary batteries can be diagnosed in units of secondary battery modules.

[0199] In addition, the embodiments according to the present disclosure can solve various technical problems in the corresponding technical field and the related technical field except the technical problems mentioned in the present specification.

[0200] The present disclosure has been described with reference to specific embodiments. However, it will be clearly understood by those skilled in the art that various modified embodiments can be implemented within the technical scope of the present disclosure. Therefore, the embodiments disclosed above should be considered from an illustrative rather than a restrictive perspective. In other words, the true technical scope of the present disclosure is indicated by the claims, and all differences within the scope equivalent thereto should be interpreted as being included in the present disclosure.

[0201] [Explanation of Reference Numerals]

[0202] 100: Secondary battery diagnostic device

[0203] 110: Thickness sensor

[0204] 120: Processor

[0205] 130: Memory

[0206] 140: Output devices

Claims

1. A secondary battery diagnostic device for diagnosing the state of a secondary battery, to which a negative electrode active material containing silicon or silicon oxide is applied, the secondary battery diagnostic device comprising: a thickness sensor configured to sense a thickness change of the secondary battery according to a volume change of the negative electrode active material while the secondary battery is being charged or discharged; as well as A processor configured to estimate a capacity ratio using a sensing result of the thickness sensor, and to diagnose a state of the secondary battery based on the capacity ratio, wherein the capacity ratio is a ratio of a capacity provided by the silicon or the silicon oxide to a capacity of the secondary battery provided by the negative electrode active material.

2. The secondary battery diagnostic device according to claim 1, in, The thickness sensor is configured to sense the thickness variation of the secondary battery by measuring a distance from a predetermined position to a surface of the secondary battery.

3. The secondary battery diagnostic device according to claim 1, in, The processor is configured to, before estimating the capacity ratio, use the sensing result of the thickness sensor to generate a first curve representing the thickness of the secondary battery that changes over time or according to a charge state of the secondary battery and a second curve representing the slope of the first curve that changes over time or according to a charge state of the secondary battery, and estimate the capacity ratio based on the second curve.

4. The secondary battery diagnostic device according to claim 3, in, The processor is configured to divide the second curve into a plurality of segments, select a target segment associated with the silicon or the silicon oxide among the plurality of segments, and estimate the capacity ratio by comparing a total length of the plurality of segments in a first axis direction with a length of the target segment in the first axis direction.

5. The secondary battery diagnostic device according to claim 4, in, The processor is configured to divide the second curve into the plurality of sections according to a slope change trend of the first curve shown in the second curve.

6. The secondary battery diagnostic device according to claim 4, in, The processor is configured to select, as the target section, a section in which the state of charge of the secondary battery is within a range below a predetermined threshold and in which a slope change trend of the first curve is different from those of other sections among the plurality of sections.

7. The secondary battery diagnostic device according to claim 4, in, The thickness sensor is configured to sense the thickness change of the secondary battery while the secondary battery is being discharged, and The processor is configured to select, from among the plurality of sections, a section in which a slope variation trend of the first curve is changed from a decreasing trend to an increasing trend as the target section.

8. The secondary battery diagnostic device according to claim 1, in, The processor is configured to, before diagnosing the state of the secondary battery, further estimate a weight ratio, which is the ratio of the weight of the silicon or the silicon oxide to the total weight of the negative electrode active material, and compare the capacity ratio with a reference capacity ratio corresponding to the weight ratio to diagnose the state of the secondary battery.

9. The secondary battery diagnostic device according to claim 8, in, The processor is configured to estimate the weight ratio before a number of charge cycles or a number of discharge cycles of the secondary battery exceeds a predetermined number.

10. The secondary battery diagnostic device according to claim 1, in, The processor is configured to repeatedly estimate the capacity ratio while a charge cycle or a discharge cycle of the secondary battery is repeated, and diagnose the state of the secondary battery based on a variation trend of the estimated capacity ratio.

11. The secondary battery diagnostic device according to any one of claims 1 to 10, in, The negative electrode active material includes a mixture in which the silicon or the silicon oxide is mixed with graphite.

12. The secondary battery diagnostic device according to any one of claims 1 to 10, in, The secondary battery is a pouch-type secondary battery.

13. A secondary battery diagnostic method for diagnosing a state of a secondary battery, to which a negative electrode active material containing silicon or silicon oxide is applied, the secondary battery diagnostic method comprising: A step of sensing a thickness change of the secondary battery according to a volume change of the negative electrode active material by a thickness sensor while the secondary battery is being charged or discharged; Step B, estimating a capacity ratio by a processor using a sensing result of the thickness sensor, the capacity ratio being a ratio of a capacity provided by the silicon or the silicon oxide to a capacity of the secondary battery provided by the negative electrode active material; as well as In step C, a processor is used to diagnose the state of the secondary battery based on the capacity ratio.

14. The secondary battery diagnosis method according to claim 13, in, The step B comprises: Step B1, using the sensing result of the thickness sensor to generate a first curve representing the thickness of the secondary battery that changes over time or according to a charge state of the secondary battery; Step B2, generating a second curve representing the slope of the first curve that changes with time or according to the charge state of the secondary battery based on the first curve; and In step B3, the capacity ratio is estimated based on the second curve.

15. The secondary battery diagnosis method according to claim 14, in, The B3 step includes: dividing the second curve into a plurality of sections, and selecting a target section related to the silicon or the silicon oxide among the plurality of sections; and The capacity ratio is estimated by comparing a total length of the plurality of segments in a first axial direction with a length of the target segment in the first axial direction.

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