Lithium-ion secondary battery evaluation method, lithium-ion secondary battery evaluation system, and lithium-ion secondary battery recycling method

Abstract

A lithium-ion secondary battery evaluation method, the method comprising an evaluation step for evaluating the deterioration state of a lithium-ion secondary battery on the basis of the number of active material particles included in an electrode of the lithium-ion secondary battery and, among said active material particles, the number of active material particles having cracks.

Description

Evaluation methods for lithium-ion secondary batteries, evaluation systems for lithium-ion secondary batteries, and recycling methods for lithium-ion secondary batteries.

[0001] This invention relates to a method for evaluating lithium-ion secondary batteries, a system for evaluating lithium-ion secondary batteries, and a method for recycling lithium-ion secondary batteries. This application claims priority based on Japanese Patent Application No. 2024-207485, filed in Japan on November 28, 2024, the contents of which are incorporated herein by reference.

[0002] Conventionally, a method for estimating the capacity of a lithium-ion secondary battery is known to be the evaluation of the cycle characteristics of the lithium-ion secondary battery (see, for example, Patent Documents 1 and 2).

[0003] Japanese Patent Publication No. 2011-228213 Japanese Patent Publication No. 2019-140054

[0004] A method is needed to estimate the capacity of a lithium-ion secondary battery from images of the positive electrode active material.

[0005] This invention aims to provide a lithium-ion secondary battery evaluation method, a lithium-ion secondary battery evaluation system, and a lithium-ion secondary battery recycling method that can estimate the capacity of a lithium-ion secondary battery from an image of the positive electrode active material, in order to solve the above-mentioned problems. Ultimately, this will contribute to a significant reduction in waste generation.

[0006] The present invention has the following aspects: [1] A method for evaluating a lithium-ion secondary battery, comprising an evaluation step of evaluating the degradation state of the lithium-ion secondary battery based on the number of active material particles contained in the electrodes of the lithium-ion secondary battery and the number of active material particles having cracks among the active material particles.

[0007] According to the above embodiment, by substituting the number of cracked active material particles into the relational expression showing the relationship between the capacity retention rate of the lithium-ion secondary battery and the number of cracked active material particles, the capacity retention rate of the lithium-ion secondary battery can be estimated and the degree of degradation of the lithium-ion secondary battery can be evaluated.

[0008] [2] The method for evaluating a lithium-ion secondary battery according to [1], wherein in the evaluation step, the degradation state of the lithium-ion secondary battery is further evaluated based on the size of the crack.

[0009] According to the above embodiment, the degree of degradation of a lithium-ion secondary battery can be evaluated by applying the size of the cracks in the active material particles to a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the size of the cracks in the active material particles having cracks.

[0010] [3] The method for evaluating a lithium-ion secondary battery according to [1], wherein in the evaluation step, the area or volume of the active material particles having cracks is further accumulated to calculate an integrated value, and the degradation state of the lithium-ion secondary battery is evaluated based on the integrated value.

[0011] According to the above embodiment, the degree of degradation of a lithium-ion secondary battery can be evaluated by applying the integrated value to a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the area or volume of the active material particles having cracks.

[0012] [4] The method for evaluating a lithium-ion secondary battery according to [1], wherein in the evaluation step, the area or volume of the cracks of the active material particles having cracks is calculated to obtain an integrated value, and the degradation state of the lithium-ion secondary battery is evaluated based on the integrated value.

[0013] According to the above embodiment, the degree of degradation of a lithium-ion secondary battery can be evaluated by applying the integrated value to a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the area or volume of cracks in the active material particles having cracks.

[0014] [5] The method for evaluating a lithium-ion secondary battery according to [1], wherein in the evaluation step, the degradation state of the lithium-ion secondary battery is further evaluated based on the ratio of the number of cracked active material particles to the number of active material particles (number of cracked active material particles / number of active material particles).

[0015] According to the above embodiment, by applying the aforementioned ratio to the relational expression that shows the relationship between the capacity retention rate of a lithium-ion secondary battery and the ratio of the number of cracked active material particles to the number of active material particles (number of cracked active material particles / number of active material particles), the capacity retention rate of the lithium-ion secondary battery can be estimated and the degree of degradation of the lithium-ion secondary battery can be evaluated.

[0016] [6] A method for evaluating a lithium-ion secondary battery, comprising an evaluation step of evaluating the degradation state of the lithium-ion secondary battery based on the number of active material particles contained in the electrodes of the lithium-ion secondary battery and the number of active material particles among the active material particles that have cracks of a size that are judged to be degradation.

[0017] According to the above embodiment, by applying the number of active material particles having cracks of a size considered to be degraded to a relational expression that shows the relationship between the number of active material particles having cracks of a size considered to be degraded to the capacity retention rate of a lithium-ion secondary battery, the capacity retention rate of the lithium-ion secondary battery can be estimated and the degree of degradation of the lithium-ion secondary battery can be evaluated.

[0018] [7] The lithium-ion secondary battery evaluation method according to [6], wherein in the evaluation step, the electrode is further divided into a plurality of regions, and the degradation state of the lithium-ion secondary battery is evaluated based on the number of active material particles in the plurality of regions and the number of active material particles having cracks.

[0019] According to the above embodiment, by substituting the number of regions where the number of cracked active material particles is greater than or equal to a predetermined value into a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the number of regions where the number of cracked active material particles is greater than or equal to a predetermined value, the capacity retention rate of the lithium-ion secondary battery can be estimated and the degree of degradation of the lithium-ion secondary battery can be evaluated.

[0020] [8] The evaluation method for a lithium-ion secondary battery according to [6], wherein in the evaluation step, the degradation state of the lithium-ion secondary battery is further evaluated based on the ratio of the number of cracked active material particles to the number of active material particles (number of cracked active material particles / number of active material particles).

[0021] According to the above embodiment, by applying the aforementioned ratio to the relational expression that shows the relationship between the capacity retention rate of a lithium-ion secondary battery and the ratio of the number of cracked active material particles to the number of active material particles (number of cracked active material particles / number of active material particles), the capacity retention rate of the lithium-ion secondary battery can be estimated and the degree of degradation of the lithium-ion secondary battery can be evaluated.

[0022] A method for recycling lithium-ion secondary batteries, including an evaluation method for lithium-ion secondary batteries described in any of [9], [1], to [8].

[0023] According to the above embodiment, the degree of degradation of a lithium-ion secondary battery can be evaluated using the lithium-ion secondary battery evaluation method of the above embodiment, and based on the evaluation results, it is possible to distinguish between recyclable batteries and non-recyclable batteries.

[0024]

[10] A lithium-ion secondary battery evaluation system comprising: a first calculation means for calculating the number of active material particles contained in the electrodes of the lithium-ion secondary battery; a second calculation means for calculating the number of active material particles having cracks among the active material particles; and an evaluation means for evaluating the degradation state of the lithium-ion secondary battery based on the number of active material particles and the number of active material particles having cracks.

[0025] According to the above embodiment, by substituting the number of cracked active material particles into the relational expression showing the relationship between the capacity retention rate of the lithium-ion secondary battery and the number of cracked active material particles, the capacity retention rate of the lithium-ion secondary battery can be estimated and the degree of degradation of the lithium-ion secondary battery can be evaluated.

[0026]

[11] The evaluation means further evaluates the degradation state of the lithium-ion secondary battery based on the size of the cracked active material particles, according to the evaluation system for lithium-ion secondary batteries described in

[10] .

[0027] According to the above embodiment, the degree of degradation of a lithium-ion secondary battery can be evaluated by applying the size of the cracks in the active material particles to a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the size of the cracks in the active material particles having cracks.

[0028]

[12] The evaluation means further calculates an integrated value by accumulating the area or volume of the active material particles having cracks, and evaluates the degradation state of the lithium-ion secondary battery based on the integrated value, the lithium-ion secondary battery evaluation system according to

[10] .

[0029] According to the above embodiment, the degree of degradation of a lithium-ion secondary battery can be evaluated by integrating the capacity retention rate of the lithium-ion secondary battery with the area or volume of the active material particles having cracks, and then applying the integrated value to a relational expression that shows the relationship between the integrated values.

[0030]

[13] The evaluation means further calculates an integrated value by accumulating the area or volume of the cracks in the active material particles having cracks, and evaluates the degradation state of the lithium-ion secondary battery based on the integrated value, the lithium-ion secondary battery evaluation system according to

[10] .

[0031] According to the above embodiment, the degree of degradation of a lithium-ion secondary battery can be evaluated by integrating the capacity retention rate of the lithium-ion secondary battery with the area or volume of cracks in the active material particles having cracks, and then applying the integrated value to a relational expression that shows the relationship between the integrated values.

[0032]

[14] The evaluation means further evaluates the degradation state of the lithium-ion secondary battery based on the ratio of the number of cracked active material particles to the number of active material particles (number of cracked active material particles / number of active material particles) of the evaluation means, according to

[10] .

[0033] According to the above embodiment, by applying the aforementioned ratio to the relational expression that shows the relationship between the capacity retention rate of a lithium-ion secondary battery and the ratio of the number of cracked active material particles to the number of active material particles (number of cracked active material particles / number of active material particles), the capacity retention rate of the lithium-ion secondary battery can be estimated and the degree of degradation of the lithium-ion secondary battery can be evaluated.

[0034] A lithium-ion secondary battery evaluation system comprising: a first calculation means for calculating the number of active material particles contained in an electrode of a lithium-ion secondary battery; a second calculation means for calculating the number of active material particles having cracks of a size determined to be deteriorated among the active material particles; and an evaluation means for evaluating the deterioration state of the lithium-ion secondary battery based on the number of the active material particles and the number of the active material particles having cracks.

[0035] According to the above aspect, by fitting the number of active material particles having cracks of a size determined to be deteriorated into a relational expression showing the relationship between the capacity retention rate of a lithium-ion secondary battery and the number of active material particles having cracks of a size determined to be deteriorated, the capacity retention rate of the lithium-ion secondary battery can be estimated, and the degree of deterioration of the lithium-ion secondary battery can be evaluated.

[0036] The evaluation means according to

[16] further divides the electrode into a plurality of regions, and evaluates the deterioration state of the lithium-ion secondary battery based on the number of the active material particles and the number of the active material particles having cracks in the plurality of regions. The lithium-ion secondary battery evaluation system according to

[15] .

[0037] According to the above aspect, by fitting the number of regions in which the number of active material particles having cracks is equal to or greater than a predetermined value into a relational expression showing the relationship between the capacity retention rate of a lithium-ion secondary battery and the number of regions in which the number of crack-containing active material particles is equal to or greater than a predetermined value, the capacity retention rate of the lithium-ion secondary battery can be estimated, and the degree of deterioration of the lithium-ion secondary battery can be evaluated.

[0038] The evaluation means according to

[17] further evaluates the deterioration state of the lithium-ion secondary battery based on the ratio of the number of the active material particles having cracks to the number of the active material particles (number of the active material particles having cracks / number of the active material particles). The lithium-ion secondary battery evaluation system according to

[15] .

[0039] According to the above aspect, by applying the ratio (number of cracked active material particles / number of active material particles) of the number of cracked active material particles to the number of active material particles to the relational expression showing the relationship between the capacity retention rate of the lithium ion secondary battery and the number of active material particles, the capacity retention rate of the lithium ion secondary battery can be estimated, and the degree of deterioration of the lithium ion secondary battery can be evaluated.

[0040] According to the present invention, it is possible to provide a method for evaluating a lithium ion secondary battery, an evaluation system for a lithium ion secondary battery, and a recycling method for a lithium ion secondary battery, which can estimate the capacity of the lithium ion secondary battery from an image of cracks in a positive electrode active material.

[0041] It is a block diagram showing an evaluation system for a lithium ion secondary battery according to an embodiment of the present invention. It is a block diagram showing an evaluation system for a lithium ion secondary battery according to an embodiment of the present invention. It is a block diagram showing an evaluation system for a lithium ion secondary battery according to an embodiment of the present invention. It is a block diagram showing an evaluation system for a lithium ion secondary battery according to an embodiment of the present invention. It is a block diagram showing an evaluation system for a lithium ion secondary battery according to an embodiment of the present invention. It is a block diagram showing an evaluation system for a lithium ion secondary battery according to an embodiment of the present invention. It is a block diagram showing an evaluation system for a lithium ion secondary battery according to an embodiment of the present invention. It is a block diagram showing an evaluation system for a lithium ion secondary battery according to an embodiment of the present invention. It is a diagram showing cracks in active material particles detected by AI in Example 1.

[0042] Hereinafter, an evaluation method for a lithium ion secondary battery, an evaluation system for a lithium ion secondary battery, and a recycling method for a lithium ion secondary battery according to an embodiment of the present invention will be described.

[0043] (First Embodiment) [Evaluation Method for Lithium Ion Secondary Battery] The evaluation method for a lithium ion secondary battery according to the present embodiment includes an evaluation step of evaluating the deterioration state of the lithium ion secondary battery based on the number of active material particles contained in the electrode of the lithium ion secondary battery and the number of active material particles having cracks among the active material particles.

[0044] In the lithium-ion secondary battery evaluation method of this embodiment, in the evaluation step, in order to evaluate the degradation state of the lithium-ion secondary battery, for example, the cross-section in the thickness direction of the electrode of the lithium-ion secondary battery is observed with a scanning electron microscope (SEM), and the number (total number) of active material particles present in the cross-section is calculated. Furthermore, the number of active material particles with cracks (hereinafter sometimes referred to as "cracked active material particles") among the active material particles present in the cross-section is calculated. Also, when simply referred to as "active material particles," it refers to active material particles without cracks.

[0045] Methods for calculating the number of active material particles and the number of fractured active material particles include visually calculating the number of particles contained in the scanning electron microscope image of the cross-section, or visually calculating the number of particles contained in the scanning electron microscope image after image processing of the scanning electron microscope image of the cross-section. Alternatively, a machine learning model capable of distinguishing active material particles on an image may be constructed in advance by performing machine learning on a model using image data of an arbitrary number of active material particles, and the number of active material particles in the cross-sectional image may be output by inputting the cross-sectional image of the lithium-ion secondary battery to be evaluated into the machine learning model. Alternatively, a machine learning model capable of distinguishing fractured active material particles or fractured active material particles on an image may be constructed in advance by performing machine learning on a model using image data of fractured active material particles and image data of an arbitrary number of fractured active material particles, and the number of fractured active material particles in the cross-sectional image may be output by inputting the cross-sectional image of the lithium-ion secondary battery to be evaluated into the machine learning model.

[0046] Furthermore, in the evaluation process, the degradation state of the lithium-ion secondary battery is evaluated based on the calculated total number of active material particles and the calculated number of fractional active material particles.

[0047] Specifically, the capacity of the lithium-ion secondary battery is measured, and the capacity retention rate is calculated. Then, as described above, cross-sectional images of the electrodes of the lithium-ion secondary battery are taken, and the number of active material particles is calculated. The relationship between the capacity retention rate of the lithium-ion secondary battery and the number of active material particles is then derived. This allows for the establishment of a relational formula that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the number of active material particles. By applying the number of active material particles obtained from the cross-sectional images of the electrodes to this formula, the capacity retention rate of the lithium-ion secondary battery can be estimated. For example, if the estimated capacity retention rate is less than 70%, the lithium-ion secondary battery is considered to be degraded.

[0048] [Lithium-ion secondary battery evaluation system] The lithium-ion secondary battery evaluation system of this embodiment will be described below with reference to Figure 1. Figure 1 is a block diagram of the lithium-ion secondary battery evaluation system of this embodiment. The lithium-ion secondary battery evaluation system 1 of this embodiment (hereinafter sometimes abbreviated as "evaluation system") comprises a first calculation means 2, a second calculation means 3, and an evaluation means 4. The evaluation system 1 may also include an imaging means 5.

[0049] The first calculation means 2 is a means for calculating the total number of active material particles contained in the electrodes of a lithium-ion secondary battery. In the first calculation means 2, a machine learning model capable of distinguishing active material particles on an image may be constructed in advance by having the model perform machine learning using image data of an arbitrary number of active material particles, and the number of active material particles in the cross-sectional image may be output by the machine learning model by inputting a cross-sectional image of the lithium-ion secondary battery to be evaluated. Alternatively, a machine learning model capable of distinguishing between active material particles with and without cracks on an image may be constructed in advance by having the model perform machine learning using image data of an arbitrary number of cracked active material particles and image data of an arbitrary number of active material particles without cracks, and the number of active material particles with and without cracks may be output by inputting a cross-sectional image of the lithium-ion secondary battery to be evaluated by the machine learning model.

[0050] The second calculation means 3 is a means for calculating the number of cracked active material particles among the active material particles. In the second calculation means 3, a machine learning model capable of distinguishing active material particles on an image may be constructed in advance by having the model perform machine learning using image data of an arbitrary number of active material particles, and the number of active material particles in the cross-sectional image may be output by the machine learning model by inputting a cross-sectional image of the lithium-ion secondary battery to be evaluated. Alternatively, a machine learning model capable of distinguishing cracked active material particles or active material particles without cracks on an image may be constructed in advance by having the model perform machine learning using image data of an arbitrary number of cracked active material particles and image data of an arbitrary number of active material particles without cracks, and the number of cracked active material particles in the cross-sectional image may be output by the machine learning model by inputting a cross-sectional image of the lithium-ion secondary battery to be evaluated.

[0051] The evaluation means 4 is a means for evaluating the degradation state of a lithium-ion secondary battery based on the number of active material particles obtained by the first calculation means 2 and the number of fragmented active material particles obtained by the first calculation means 2, and has a first evaluation unit 4A that evaluates the degradation state based on the number of active material particles and the number of fragmented active material particles. The first evaluation unit 4A is implemented, for example, in an image evaluation system equipped with the machine learning model described above (for example, an evaluation device / system for secondary batteries recovered after primary use, an evaluation device / system for electrodes disassembled and extracted after recovery, etc.). The first evaluation unit 4A measures the capacity of the lithium-ion secondary battery and calculates the capacity retention rate, then takes a cross-sectional image of the electrodes of the lithium-ion secondary battery as described above, calculates the number of fragmented active material particles, and derives the relationship between the capacity retention rate of the lithium-ion secondary battery and the number of fragmented active material particles. This determines a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the number of fragmented active material particles. By applying the number of active material particles obtained from the cross-sectional image of the electrode to this relationship, the capacity retention rate of the lithium-ion secondary battery can be estimated.

[0052] The imaging means 5 can, for example, use a scanning electron microscope to observe a cross-sectional view of the electrode of a lithium-ion secondary battery in the thickness direction.

[0053] (Second Embodiment) [Method for Evaluating Lithium-ion Secondary Batteries] In the lithium-ion secondary battery evaluation method of this embodiment, in addition to the first embodiment, the degradation state of the lithium-ion secondary battery is further evaluated in the evaluation step based on the size of the cracks in the cracked active material particles.

[0054] A method for evaluating the degradation state of a lithium-ion secondary battery based on the size of cracks in the active material particles involves, for example, measuring the capacity of the lithium-ion secondary battery to calculate the capacity retention rate, then, as described above, taking cross-sectional images of the electrodes of the lithium-ion secondary battery to measure the size of cracks in the active material particles, and deriving the relationship between the capacity retention rate of the lithium-ion secondary battery and the size of the cracks in the active material particles. This allows for the establishment of a relational equation that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the size of the cracks in the active material particles. By applying the size of the cracks in the active material particles obtained from the cross-sectional images of the electrodes to this relational equation, the capacity retention rate of the lithium-ion secondary battery can be estimated.

[0055] [Lithium-ion secondary battery evaluation system] The lithium-ion secondary battery evaluation system of this embodiment will be described below with reference to Figure 2. Figure 2 is a block diagram showing the lithium-ion secondary battery evaluation system of this embodiment. In Figure 2, components identical to those in Figure 1 are denoted by the same reference numerals and their descriptions are omitted. The evaluation system 10 of this embodiment comprises a first calculation means 2, a second calculation means 3, and an evaluation means 4. The evaluation system 1 may also include an imaging means 5.

[0056] In the evaluation system 10 of this embodiment, the second calculation means 3 is a means for calculating the number of fractured active material particles and measuring the size of the fractures of the fractured active material particles.

[0057] In the evaluation system 10 of this embodiment, the evaluation means 4, in addition to the first evaluation unit 4A, further includes a second evaluation unit 4B that evaluates the degradation state of a lithium-ion secondary battery based on the size of the cracks in the cracked active material particles. The second evaluation unit 4B is implemented, for example, in an image evaluation system equipped with the machine learning model described above (e.g., an evaluation device / system for secondary batteries recovered after primary use, an evaluation device / system for electrodes disassembled and removed after recovery, etc.). The second evaluation unit 4B measures the capacity of the lithium-ion secondary battery and calculates the capacity retention rate. Then, as described above, it derives a relationship between the size of the cracks in the cracked active material particles measured by taking a cross-sectional image of the electrode of the lithium-ion secondary battery and the capacity retention rate of the lithium-ion secondary battery. This determines a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the size of the cracks in the cracked active material particles. By applying the size of the cracks in the cracked active material particles obtained from the cross-sectional image of the electrode to this relational expression, the capacity retention rate of the lithium-ion secondary battery can be estimated.

[0058] (Third Embodiment) [Method for Evaluating a Lithium-ion Secondary Battery] In the lithium-ion secondary battery evaluation method of this embodiment, in addition to the first embodiment, the area or volume of the active material particles having cracks is further accumulated to calculate an integrated value, and the degradation state of the lithium-ion secondary battery is evaluated based on the integrated value.

[0059] A method for evaluating the degradation state of a lithium-ion secondary battery by calculating an integrated value by accumulating the area or volume of the active material particles contained within the lithium-ion secondary battery, for example, involves measuring the capacity of the lithium-ion secondary battery to calculate the capacity retention rate, then, as described above, taking a cross-sectional image of the electrodes of the lithium-ion secondary battery to measure the area or volume of the active material particles contained within the lithium-ion secondary battery, calculating the integrated value of the area or volume of the active material particles, and deriving the relationship between the capacity retention rate of the lithium-ion secondary battery and the integrated value. This determines a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the integrated value. By applying the integrated value to this relational expression, the capacity retention rate of the lithium-ion secondary battery can be estimated.

[0060] [Lithium-ion secondary battery evaluation system] The lithium-ion secondary battery evaluation system of this embodiment will be described below with reference to Figure 3. Figure 3 is a block diagram showing the lithium-ion secondary battery evaluation system of this embodiment. In Figure 3, components identical to those in Figure 1 are denoted by the same reference numerals and their descriptions are omitted. The evaluation system 20 of this embodiment comprises a first calculation means 2, a second calculation means 3, and an evaluation means 4. The evaluation system 1 may also include an imaging means 5.

[0061] In the evaluation system 20 of this embodiment, the second calculation means 3 is a means for calculating the number of fragmented active material particles and for measuring the area or volume of the fragmented active material particles.

[0062] In the evaluation system 20 of this embodiment, the evaluation means 4 has, in addition to the first evaluation unit 4A, a third evaluation unit 4C that calculates an integrated value by integrating the area or volume of the active material particles contained in the lithium-ion secondary battery and evaluates the degradation state of the lithium-ion secondary battery based on that integrated value. The third evaluation unit 4C is implemented, for example, in an image evaluation system equipped with the machine learning model described above (for example, an evaluation device / system for secondary batteries recovered after primary use, an evaluation device / system for electrodes disassembled and removed after recovery, etc.). The third evaluation unit 4C measures the capacity of the lithium-ion secondary battery and calculates the capacity retention rate. Then, as described above, it derives the relationship between the integrated value measured by taking a cross-sectional image of the electrode of the lithium-ion secondary battery and the capacity retention rate of the lithium-ion secondary battery. This determines a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the integrated value. By applying the magnitude of the integrated value obtained from the cross-sectional image of the electrode to this relational expression, the capacity retention rate of the lithium-ion secondary battery can be estimated.

[0063] (Fourth Embodiment) [Method for Evaluating a Lithium-ion Secondary Battery] In the lithium-ion secondary battery evaluation method of this embodiment, in addition to the first embodiment, the area or volume of the cracks in the active material particles having cracks is accumulated to calculate an integrated value, and the degradation state of the lithium-ion secondary battery is evaluated based on the integrated value.

[0064] A method for evaluating the degradation state of a lithium-ion secondary battery by calculating an integrated value by accumulating the area or volume of cracks in the cracked active material particles and using the integrated value as a basis for evaluation includes, for example, measuring the capacity of the lithium-ion secondary battery to calculate the capacity retention rate, then, as described above, taking a cross-sectional image of the electrodes of the lithium-ion secondary battery, measuring the area or volume of cracks in the cracked active material particles, calculating an integrated value of the crack area or volume, and deriving the relationship between the capacity retention rate of the lithium-ion secondary battery and the integrated value. This determines a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the integrated value. By applying the integrated value to this relational expression, the capacity retention rate of the lithium-ion secondary battery can be estimated.

[0065] [Lithium-ion secondary battery evaluation system] The lithium-ion secondary battery evaluation system of this embodiment will be described below with reference to Figure 4. Figure 4 is a block diagram showing the lithium-ion secondary battery evaluation system of this embodiment. In Figure 4, components identical to those in Figure 1 are denoted by the same reference numerals and their descriptions are omitted. The evaluation system 30 of this embodiment comprises a first calculation means 2, a second calculation means 3, and an evaluation means 4. The evaluation system 1 may also include an imaging means 5.

[0066] In the evaluation system 30 of this embodiment, the second calculation means 3 is a means for calculating the number of fractured active material particles and for measuring the area or volume of fractures in the fractured active material particles.

[0067] In the evaluation system 30 of this embodiment, the evaluation means 4 further includes a fourth evaluation unit 4D that, in addition to the first evaluation unit 4A, calculates an integrated value by accumulating the area or volume of cracks in the cracked active material particles, and evaluates the degradation state of the lithium-ion secondary battery based on the integrated value. The fourth evaluation unit 4D is implemented, for example, in an image evaluation system equipped with the machine learning model described above (for example, an evaluation device / system for secondary batteries recovered after primary use, an evaluation device / system for electrodes disassembled and removed after recovery, etc.). The fourth evaluation unit 4D measures the capacity of the lithium-ion secondary battery and calculates the capacity retention rate. Then, as described above, it derives the relationship between the integrated value measured by taking a cross-sectional image of the electrode of the lithium-ion secondary battery and the capacity retention rate of the lithium-ion secondary battery. This determines a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the integrated value. By applying the magnitude of the integrated value obtained from the cross-sectional image of the electrode to this relational expression, the capacity retention rate of the lithium-ion secondary battery can be estimated.

[0068] (Fifth Embodiment) [Method for Evaluating a Lithium-ion Secondary Battery] In the lithium-ion secondary battery evaluation method of this embodiment, in addition to the first embodiment, the degradation state of the lithium-ion secondary battery is further evaluated based on the ratio of the number of cracked active material particles to the number of active material particles (number of cracked active material particles / number of active material particles).

[0069] A method for evaluating the degradation state of a lithium-ion secondary battery based on the ratio of the number of fragmented active material particles to the number of active material particles (number of fragmented active material particles / number of active material particles) involves, for example, measuring the capacity of the lithium-ion secondary battery to calculate the capacity retention rate, then, as described above, taking cross-sectional images of the electrodes of the lithium-ion secondary battery to calculate the number of active material particles and fragmented active material particles, and deriving the relationship between the capacity retention rate of the lithium-ion secondary battery and the ratio of the number of fragmented active material particles to the number of active material particles (number of fragmented active material particles / number of active material particles). This establishes a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the aforementioned ratio. By applying the aforementioned ratio obtained from the cross-sectional image of the electrode to this relational expression, the capacity retention rate of the lithium-ion secondary battery can be estimated.

[0070] [Lithium-ion secondary battery evaluation system] The lithium-ion secondary battery evaluation system of this embodiment will be described below with reference to Figure 5. Figure 5 is a block diagram showing the lithium-ion secondary battery evaluation system of this embodiment. In Figure 5, components identical to those in Figure 1 are denoted by the same reference numerals and their descriptions are omitted. The evaluation system 40 of this embodiment comprises a first calculation means 2, a second calculation means 3, and an evaluation means 4. The evaluation system 1 may also include an imaging means 5.

[0071] In the evaluation system 40 of this embodiment, the evaluation means 4 further includes a fifth evaluation unit 4E that evaluates the degradation state of a lithium-ion secondary battery based on the ratio of the number of fragmented active material particles to the number of active material particles (number of fragmented active material particles / number of active material particles), in addition to the first evaluation unit 4A. The fifth evaluation unit 4E is implemented, for example, in an image evaluation system equipped with the machine learning model described above (e.g., an evaluation device / system for secondary batteries recovered after primary use, an evaluation device / system for electrodes disassembled and removed after recovery, etc.). The fifth evaluation unit 4E measures the capacity of the lithium-ion secondary battery and calculates the capacity retention rate. Then, as described above, it takes a cross-sectional image of the electrode of the lithium-ion secondary battery to calculate the number of active material particles and fragmented active material particles, and derives the relationship between the capacity retention rate of the lithium-ion secondary battery and the ratio. This determines a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the ratio. By applying the ratio obtained from the cross-sectional image of the electrode to this relational expression, the capacity retention rate of the lithium-ion secondary battery can be estimated.

[0072] (Sixth Embodiment) [Method for Evaluating a Lithium-ion Secondary Battery] The method for evaluating a lithium-ion secondary battery of this embodiment includes an evaluation step of evaluating the degradation state of the lithium-ion secondary battery based on the number of active material particles contained in the electrodes of the lithium-ion secondary battery and the number of active material particles among the active material particles that have cracks of a size that are judged to be degradation.

[0073] In the lithium-ion secondary battery evaluation method of this embodiment, in the evaluation step, in order to evaluate the degradation state of the lithium-ion secondary battery, for example, the cross-section in the thickness direction of the electrode of the lithium-ion secondary battery is observed with a scanning electron microscope (SEM), and the number (total number) of active material particles present in the cross-section is calculated. Furthermore, among the active material particles present in the cross-section, the number of active material particles having cracks of a size that is judged to be degradation (hereinafter sometimes referred to as "cracked active material particles") is calculated.

[0074] The method for calculating the number of active material particles and the number of active material particles contained in each component is the same as in the first embodiment.

[0075] Furthermore, in the evaluation process, the degradation state of the lithium-ion secondary battery is evaluated based on the calculated total number of active material particles and the calculated number of fragmented active material particles. Specifically, the capacity of the lithium-ion secondary battery is measured to calculate the capacity retention rate, and then, as described above, cross-sectional images of the electrodes of the lithium-ion secondary battery are taken to calculate the number of fragmented active material particles, thereby deriving the relationship between the capacity retention rate of the lithium-ion secondary battery and the number of fragmented active material particles. This establishes a relational formula that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the number of fragmented active material particles. By applying the number of fragmented active material particles obtained from the cross-sectional images of the electrodes to this relational formula, the capacity retention rate of the lithium-ion secondary battery can be estimated. For example, if the estimated capacity retention rate is less than 70%, the lithium-ion secondary battery is evaluated as degraded.

[0076] [Lithium-ion secondary battery evaluation system] The lithium-ion secondary battery evaluation system of this embodiment will be described below with reference to Figure 6. Figure 6 is a block diagram showing the lithium-ion secondary battery evaluation system of this embodiment. The evaluation system 50 of this embodiment includes a first calculation means 51, a second calculation means 52, and an evaluation means 53. The evaluation system 50 may also include an imaging means 54.

[0077] The first calculation means 51 is a means for calculating the total number of active material particles contained in the electrodes of a lithium-ion secondary battery. In the first calculation means 51, a machine learning model capable of distinguishing active material particles on an image may be constructed in advance by having the model perform machine learning using image data of an arbitrary number of active material particles, and the number of active material particles in the cross-sectional image may be output by the machine learning model by inputting a cross-sectional image of the lithium-ion secondary battery to be evaluated. Alternatively, a machine learning model capable of distinguishing between active material particles containing cracks or active material particles without cracks may be constructed in advance by having the model perform machine learning using image data of an arbitrary number of cracked active material particles and image data of an arbitrary number of active material particles without cracks, and the number of active material particles containing cracks in the cross-sectional image may be output by the machine learning model by inputting a cross-sectional image of the lithium-ion secondary battery to be evaluated.

[0078] The second calculation means 52 is a means for calculating the number of cracked active material particles among the active material particles. In the second calculation means 52, a machine learning model capable of distinguishing active material particles on an image may be constructed in advance by having the model perform machine learning using image data of an arbitrary number of active material particles, and the number of active material particles in the cross-sectional image may be output by the machine learning model by inputting a cross-sectional image of the lithium-ion secondary battery to be evaluated. Alternatively, a machine learning model capable of distinguishing cracked active material particles or active material particles without cracks on an image may be constructed in advance by having the model perform machine learning using image data of an arbitrary number of cracked active material particles and image data of an arbitrary number of active material particles without cracks, and the number of cracked active material particles in the cross-sectional image may be output by the machine learning model by inputting a cross-sectional image of the lithium-ion secondary battery to be evaluated.

[0079] The evaluation means 53 is a means for evaluating the degradation state of a lithium-ion secondary battery based on the number of active material particles obtained by the first calculation means 51 and the number of fragmented active material particles obtained by the second calculation means 52, and has a first evaluation unit 53A that evaluates the degradation state based on the number of active material particles and the number of fragmented active material particles. The first evaluation unit 53A is implemented, for example, in an image evaluation system equipped with the machine learning model described above (for example, an evaluation device / system for secondary batteries recovered after primary use, an evaluation device / system for electrodes disassembled and extracted after recovery, etc.). The first evaluation unit 53A measures the capacity of the lithium-ion secondary battery and calculates the capacity retention rate, then takes a cross-sectional image of the electrodes of the lithium-ion secondary battery as described above, calculates the number of fragmented active material particles, and derives the relationship between the capacity retention rate of the lithium-ion secondary battery and the number of fragmented active material particles. This determines a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the number of fragmented active material particles. By applying the number of active material particles obtained from the cross-sectional image of the electrode to this relationship, the capacity retention rate of the lithium-ion secondary battery can be estimated.

[0080] The imaging means 54 can, for example, use a scanning electron microscope to observe a cross-sectional view of the electrode of a lithium-ion secondary battery in the thickness direction.

[0081] (Seventh Embodiment) [Method for Evaluating a Lithium-ion Secondary Battery] In the lithium-ion secondary battery evaluation method of this embodiment, in addition to the sixth embodiment, the electrode is further divided into a plurality of regions in the evaluation step, and the degradation state of the lithium-ion secondary battery is evaluated based on the number of active material particles in the plurality of regions and the number of active material particles having cracks.

[0082] A method for evaluating the degradation state of a lithium-ion secondary battery by dividing the electrode into multiple regions and evaluating the number of active material particles in each region and the number of fragmented active material particles can be used as follows: For example, the capacity of the lithium-ion secondary battery is measured to calculate the capacity retention rate, then, as described above, cross-sectional images of the electrodes of the lithium-ion secondary battery are taken to calculate the number of fragmented active material particles, and the relationship between the capacity retention rate of the lithium-ion secondary battery and the number of regions where the number of fragmented active material particles is greater than or equal to a predetermined value is derived. This allows for the determination of a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the number of regions where the number of fragmented active material particles is greater than or equal to a predetermined value. By substituting the number of regions where the number of fragmented active material particles obtained from the cross-sectional images of the electrodes is greater than or equal to a predetermined value into this relational expression, the capacity retention rate of the lithium-ion secondary battery can be estimated.

[0083] [Lithium-ion secondary battery evaluation system] The lithium-ion secondary battery evaluation system of this embodiment will be described below with reference to Figure 7. Figure 7 is a block diagram showing the lithium-ion secondary battery evaluation system of this embodiment. In Figure 7, components identical to those in Figure 6 are denoted by the same reference numerals and their descriptions are omitted. The evaluation system 60 of this embodiment comprises a first calculation means 51, a second calculation means 52, and an evaluation means 53. The evaluation system 60 may also include an imaging means 54.

[0084] In the evaluation system 60 of this embodiment, the evaluation means 53, in addition to the first evaluation unit 53A, further divides the electrode into multiple regions and evaluates the degradation state of the lithium-ion secondary battery based on the number of active material particles in the multiple regions and the number of active material particles having cracks. The evaluation means has a second evaluation unit 53B based on the number of regions where the number of cracked active material particles is greater than or equal to a predetermined value. The second evaluation unit 53B is implemented, for example, in an image evaluation system equipped with the machine learning model described above (for example, an evaluation device / system for secondary batteries recovered after primary use, an evaluation device / system for electrodes disassembled and removed after recovery, etc.). The second evaluation unit 53B measures the capacity of the lithium-ion secondary battery and calculates the capacity retention rate. Then, as described above, it takes a cross-sectional image of the electrode of the lithium-ion secondary battery to calculate the number of cracked active material particles and derives the relationship between the capacity retention rate of the lithium-ion secondary battery and the number of regions where the number of cracked active material particles is greater than or equal to a predetermined value. This determines a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the number of regions where the number of cracked active material particles is greater than or equal to a predetermined value. By substituting the number of regions where the number of active material particles obtained from cross-sectional images of electrodes exceeds a predetermined value into this relationship, the capacity retention rate of a lithium-ion secondary battery can be estimated.

[0085] (Eighth Embodiment) [Method for Evaluating a Lithium-ion Secondary Battery] In the lithium-ion secondary battery evaluation method of this embodiment, in addition to the sixth embodiment, the degradation state of the lithium-ion secondary battery is further evaluated in the evaluation step based on the ratio of the number of cracked active material particles to the number of active material particles (number of cracked active material particles / number of active material particles).

[0086] A method for evaluating the degradation state of a lithium-ion secondary battery based on the ratio of the number of cracked active material particles to the total number of active material particles (number of cracked active material particles / total number of active material particles) involves, for example, measuring the capacity of the lithium-ion secondary battery to calculate the capacity retention rate, then, as described above, taking a cross-sectional image of the electrode of the lithium-ion secondary battery to calculate the number of active material particles and cracked active material particles, and deriving the relationship between the capacity retention rate of the lithium-ion secondary battery and the ratio of the number of cracked active material particles to the total number of active material particles (number of cracked active material particles / total number of active material particles). This establishes a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the aforementioned ratio. By applying the aforementioned ratio obtained from the cross-sectional image of the electrode to this relational expression, the capacity retention rate of the lithium-ion secondary battery can be estimated.

[0087] [Lithium-ion secondary battery evaluation system] The lithium-ion secondary battery evaluation system of this embodiment will be described below with reference to Figure 8. Figure 8 is a block diagram showing the lithium-ion secondary battery evaluation system of this embodiment. In Figure 8, components identical to those in Figure 6 are denoted by the same reference numerals and their descriptions are omitted. The evaluation system 70 of this embodiment includes a first calculation means 51, a second calculation means 52, and an evaluation means 53. The evaluation system 70 may also include an imaging means 54.

[0088] In the evaluation system 70 of this embodiment, the evaluation means 53, in addition to the first evaluation unit 53A, is a means for evaluating the degradation state of a lithium-ion secondary battery based on the ratio of the number of cracked active material particles to the number of active material particles (number of cracked active material particles / number of active material particles), and has a third evaluation unit 53C based on the size of the cracks. The third evaluation unit 53C is implemented, for example, in an image evaluation system equipped with the machine learning model described above (for example, an evaluation device / system for secondary batteries recovered after primary use, an evaluation device / system for electrodes disassembled and removed after recovery, etc.). The third evaluation unit 53C measures the capacity of the lithium-ion secondary battery and calculates the capacity retention rate. Then, as described above, it takes a cross-sectional image of the electrode of the lithium-ion secondary battery to calculate the number of active material particles and cracked active material particles, and derives the relationship between the capacity retention rate of the lithium-ion secondary battery and the ratio. This determines a relational expression that shows the relationship between the capacity retention rate of the lithium-ion secondary battery and the ratio. By applying the aforementioned ratio obtained from the cross-sectional image of the electrode to this relationship, the capacity retention rate of the lithium-ion secondary battery can be estimated.

[0089] The lithium-ion secondary battery evaluation method of this embodiment can evaluate the state of degradation of the positive electrode active material contained in the positive electrode and the negative electrode active material contained in the negative electrode.

[0090] (Ninth Embodiment) [Method for Recycling Lithium-ion Secondary Batteries] A method for recycling lithium-ion secondary batteries according to one embodiment of the present invention includes a method for evaluating lithium-ion secondary batteries according to the above embodiment.

[0091] In the lithium-ion secondary battery recycling method of this embodiment, the degree of degradation of the lithium-ion secondary battery is evaluated using the lithium-ion secondary battery evaluation method according to the above embodiment, and based on the evaluation results, the batteries are distinguished into recyclable and non-recyclable batteries. Recyclable batteries are sent to the recycling process, and non-recyclable batteries are disposed of as industrial waste.

[0092] The present invention will be described in more detail below with reference to examples, but the present invention is not limited to the following examples.

[0093] [Example 1] The cross-section of the positive electrode of a lithium-ion secondary battery in the thickness direction was observed using a scanning electron microscope (SEM). Cracks in the active material particles were detected from the obtained scanning electron microscope image using artificial intelligence (AI). Figure 9 shows the cracks in the active material particles detected by AI. Furthermore, the proportion of active material particles with cracks to the total number of active material particles was detected from the scanning electron microscope image using AI. For example, when the capacity retention rate was 90% when the proportion of active material particles with cracks was 30%, and when the capacity retention rate was 75% when the proportion of active material particles with cracks was 80%, a relational expression showing the relationship between the capacity retention rate of the lithium-ion secondary battery and the proportion of active material particles with cracks was determined. Examples of such relational expressions are the following equations (1) and (2). Capacity retention rate = 100% - A × percentage of active material particles with cracks (%) (1) Capacity retention rate = 50% + A × (100% - A × percentage of active material particles with cracks (%)) (2) The capacity retention rate of the lithium-ion secondary battery was calculated from the above formulas (1) and (2) and the degree of degradation of the lithium-ion secondary battery was evaluated.

[0094] [Example 2] In the same manner as in Example 1, artificial intelligence (AI) was used to detect cracks in active material particles from scanning electron microscope images of the cross-section in the thickness direction of the positive electrode of a lithium-ion secondary battery. The number of active material particles with cracks and the area of ​​the active material particles with cracks were histogrammed. The capacity retention rate of the lithium-ion secondary battery was estimated from this histogram. The capacity retention rate of a lithium-ion secondary battery with no active material particles with cracks (no cracks at all) was set to 100%, and the capacity retention rate of the lithium-ion secondary battery was estimated from the number of active material particles with cracks and the total area of ​​the active material particles with cracks.

[0095] [Example 3] Similar to Example 1, cracks in the positive active material particles were detected by artificial intelligence (AI) from a scanning electron microscope image of a cross-section in the thickness direction of the positive electrode of a lithium-ion secondary battery. The area of the cracks of the positive active material particles having cracks and the area of the positive active material particles having cracks were made into a histogram. From this histogram, the capacity retention rate of the lithium-ion secondary battery was estimated. The capacity retention rate of the lithium-ion secondary battery in which there are no positive active material particles having cracks (no cracks at all) was set to 100%, and the capacity retention rate of the lithium-ion secondary battery was estimated from the total area of the cracks of the positive active material particles having cracks and the total area of the positive active material particles having cracks.

[0096] [Example 4] Similar to Example 1, cracks in the positive active material particles were detected by artificial intelligence (AI) from a scanning electron microscope image of a cross-section in the thickness direction of the positive electrode of a lithium-ion secondary battery. The area of the cracks of the positive active material particles having cracks and the area of the positive active material particles having cracks were made into a histogram. From this histogram, the capacity retention rate of the lithium-ion secondary battery was estimated. (1) The area of the positive active material particles was made into a histogram, and the area of the positive active material particles was integrated. For example, around 20% near an area of 10 nm ２ around 34% near 30 nm ２ ... = total area 7850 nm ２ (2) The area of the crack size was made into a histogram, and the area of the crack size was integrated. For example, around 5% near an area of 5 nm ２ around 10% near 10 nm ２ around 24% near 30 nm ２ ... = total area 930 nm ２ (3), (4) The area of the positive active material particles and the area of the crack size were integrated. For example, around 20% near an area of 10 nm of the positive active material particles (80% without cracks, 10% of cracks near 5 nm ２ 10% of cracks near 10 nm) Around 34% near an area of 30 nm of the positive active material particles (75% without cracks, 10% of cracks near 10 nm ２ 15% of cracks near 15 nm) Around 53% near an area of 50 nm of the positive active material particles (95% without cracks, 10 nm ２ 10% of cracks near) Around 34% near an area of 30 nm of the positive active material particles (75% without cracks, 10% of cracks near 10 nm ２ 15% of cracks near 15 nm) Around 53% near an area of 50 nm of the positive active material particles (95% without cracks, 10 nm ２ 10% of cracks near 10 nm, 15% of cracks near 15 nm) Around 53% near an area of 50 nm of the positive active material particles (95% without cracks, 10 nm ２ 10% of cracks near 10 nm, 15% of cracks near 15 nm) Around 53% near an area of 50 nm of the positive active material particles (95% without cracks, 10 nm ２ 10% of cracks near 10 nm, 15% of cracks near 15 nm) Around 53% near an area of 50 nm of the positive active material particles (95% without cracks, 10 nm ２Nearby cracks: 2%, 15 nm ２ (1) In the case of (3%) cracks in the vicinity, the capacity retention rate of the lithium-ion secondary battery was evaluated as 100%. (2) If there are cracks, the capacity retention rate of the lithium-ion secondary battery was calculated as (area of ​​(1) - area of ​​(2)) / area of ​​(1). (3) and (4) were used as correction factors for the area of ​​the cracks in (2) to calculate the capacity retention rate of the lithium-ion secondary battery.

[0097] [Example 5] In the same manner as in Example 1, artificial intelligence (AI) was used to detect cracks in active material particles from a scanning electron microscope image of a cross-section in the thickness direction of the positive electrode of a lithium-ion secondary battery. The capacity retention rate during lithium-ion doping recovery was estimated from the "percentage of active material particles with cracks" in the crack diagnosis result of the AI ​​logic. For example, if the percentage of active material particles with cracks is 0% or more and 35% or less, the capacity retention rate during lithium-ion doping recovery was set to 100%. If the percentage of active material particles with cracks is more than 35% and 50% or less, the capacity retention rate during lithium-ion doping recovery was set to 95% or more and less than 100%. If the percentage of active material particles with cracks is more than 50% and 70% or less, the capacity retention rate during lithium-ion doping recovery was set to 85% or more and less than 95%. By calculating the percentage of active material particles with cracks in this way, the capacity retention rate of the lithium-ion secondary battery was estimated.

[0098] [Example 6] In the same manner as in Example 1, artificial intelligence (AI) was used to detect cracks in active material particles from a scanning electron microscope image of a cross-section in the thickness direction of the positive electrode of a lithium-ion secondary battery. The pass / fail status of a new product was determined from the "percentage of active material particles with cracks" in the crack diagnosis result of the AI ​​logic. For example, the pass line was set as a percentage of active material particles with cracks of 0% or more and 25%. A percentage of active material particles with cracks of 18% was considered a pass, and a percentage of active material particles with cracks of 10% was also considered a pass. A percentage of active material particles with cracks of 26% was considered a fail.

[0099] [Example 7] In the same manner as in Example 1, artificial intelligence (AI) was used to detect cracks in active material particles from a scanning electron microscope image of a cross-section in the thickness direction of the positive electrode of a lithium-ion secondary battery. The positive electrode was divided into two regions (region A and region B), and the number of active material particles with cracks in each region was histogrammed. The region on the current collector side was designated as region A, and the region on the solid electrolyte side was designated as region B. When the proportion of active material particles with cracks in region A increased, structural collapse of the active material particles was detected.

[0100] Although embodiments of the present invention have been described in detail above, the present invention is not limited to the above embodiments, and various modifications and changes are possible within the scope of the gist of the present invention as described in the claims.

[0101] 1, 10, 20, 30, 40, 50, 60, 70 Lithium-ion secondary battery evaluation system (evaluation system) 2, 51 First calculation means 3, 52 Second calculation means 4, 53 Evaluation means 4A, 53A First evaluation unit 4B, 53B Second evaluation unit 4C, 53C Third evaluation unit 4D Fourth evaluation unit 4E Fifth evaluation unit 5, 54 Imaging means

Claims

A method for evaluating a lithium-ion secondary battery, comprising an evaluation step of evaluating the degradation state of the lithium-ion secondary battery based on the number of active material particles contained in the electrodes of the lithium-ion secondary battery and the number of active material particles having cracks among the active material particles. 　 The method for evaluating a lithium-ion secondary battery according to claim 1, further comprising the evaluation step, wherein the degradation state of the lithium-ion secondary battery is evaluated based on the size of the crack. 　 The method for evaluating a lithium-ion secondary battery according to claim 1, further comprising the evaluation step of accumulating the area or volume of the active material particles having cracks to calculate an integrated value, and evaluating the degradation state of the lithium-ion secondary battery based on the integrated value. 　 The method for evaluating a lithium-ion secondary battery according to claim 1, further comprising the evaluation step of accumulating the area or volume of the cracks in the active material particles having cracks to calculate an integrated value, and evaluating the degradation state of the lithium-ion secondary battery based on the integrated value. 　 The method for evaluating a lithium-ion secondary battery according to claim 1, further comprising the evaluation step, wherein the degradation state of the lithium-ion secondary battery is evaluated based on the ratio of the number of cracked active material particles to the number of active material particles (number of cracked active material particles / number of active material particles). 　 A method for evaluating a lithium-ion secondary battery, comprising an evaluation step of evaluating the degradation state of the lithium-ion secondary battery based on the number of active material particles contained in the electrodes of the lithium-ion secondary battery and the number of active material particles among the active material particles that have cracks of a size that are judged to be degradation. 　 The method for evaluating a lithium-ion secondary battery according to claim 6, further comprising the evaluation step, wherein the electrode is divided into a plurality of regions, and the degradation state of the lithium-ion secondary battery is evaluated based on the number of active material particles in the plurality of regions and the number of active material particles having cracks. 　 The method for evaluating a lithium-ion secondary battery according to claim 6, further comprising the evaluation step, wherein the degradation state of the lithium-ion secondary battery is evaluated based on the ratio of the number of cracked active material particles to the number of active material particles (number of cracked active material particles / number of active material particles). 　 A method for recycling a lithium-ion secondary battery, comprising the evaluation method for a lithium-ion secondary battery described in any one of claims 1 to 8. 　 A lithium-ion secondary battery evaluation system comprising: a first calculation means for calculating the number of active material particles contained in the electrodes of a lithium-ion secondary battery; a second calculation means for calculating the number of active material particles having cracks among the active material particles; and an evaluation means for evaluating the degradation state of the lithium-ion secondary battery based on the number of active material particles and the number of active material particles having cracks. 　 The lithium-ion secondary battery evaluation system according to claim 10, wherein the evaluation means further evaluates the degradation state of the lithium-ion secondary battery based on the size of the active material particles having cracks. 　 The evaluation means further calculates an integrated value by accumulating the area or volume of the active material particles having cracks, and evaluates the degradation state of the lithium-ion secondary battery based on the integrated value, as described in claim 10. 　 The lithium-ion secondary battery evaluation system according to claim 10, further comprising: the evaluation means calculating an integrated value by accumulating the area or volume of the cracks in the active material particles having the cracks; and evaluating the degradation state of the lithium-ion secondary battery based on the integrated value. 　 The lithium-ion secondary battery evaluation system according to claim 10, wherein the evaluation means further evaluates the degradation state of the lithium-ion secondary battery based on the ratio of the number of cracked active material particles to the number of active material particles (number of cracked active material particles / number of active material particles). 　 A lithium-ion secondary battery evaluation system comprising: a first calculation means for calculating the number of active material particles contained in the electrodes of a lithium-ion secondary battery; a second calculation means for calculating the number of active material particles among the active material particles that have cracks of a size that are judged to be degraded; and an evaluation means for evaluating the degradation state of the lithium-ion secondary battery based on the number of active material particles and the number of active material particles with cracks. 　 The lithium-ion secondary battery evaluation system according to claim 15, wherein the evaluation means further divides the electrode into a plurality of regions and evaluates the degradation state of the lithium-ion secondary battery based on the number of active material particles in the plurality of regions and the number of active material particles having cracks. 　 The lithium-ion secondary battery evaluation system according to claim 15, wherein the evaluation means further evaluates the degradation state of the lithium-ion secondary battery based on the ratio of the number of cracked active material particles to the number of active material particles (number of cracked active material particles / number of active material particles).

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