Diagnosis Method for Secondary Battery and Diagnosis Program for Secondary Battery

Abstract

The present invention provides a secondary battery diagnostic method that can evaluate the remaining life of a secondary battery more accurately than in the past without disassembling the secondary battery. The secondary battery diagnostic method comprises: a step for estimating the characteristic parameters at the time of diagnosis of the secondary battery to be diagnosed (step S1); a step for finding the relationship between the electrolyte diffusion coefficient and the discharge capacity (step S2); a step for determining the threshold Dth of the electrolyte diffusion coefficient on the basis of the relationship between the electrolyte diffusion coefficient and the discharge capacity (step S3); and a step for establishing the difference ∆D between the threshold Dth and an electrolyte diffusion coefficient Dn at the time of diagnosis (step S4).

Description

Technical Field

[0001] The present invention relates to a method for diagnosing a secondary battery and a diagnostic program for a secondary battery.

Background Art

[0002] Secondary batteries such as lithium-ion batteries gradually decrease in discharge capacity by repeating charge and discharge. Therefore, it is preferable to diagnose the secondary battery at an appropriate time to evaluate its degree of deterioration, determine whether it can be reused, or determine the replacement time. Further, in order to reuse the secondary battery after diagnosis, it is preferable that the diagnosis can be performed nondestructively.

[0003] Japanese Unexamined Patent Application Publication No. 2017-97997 describes a method for analyzing the characteristics of a secondary battery in which characteristic values of members constituting the battery are used as parameters in a model formula, and the characteristic values of the members are estimated by fitting the voltage value of the battery represented by the model formula to measured data. In this characteristic analysis method, as the measured data, a charge / discharge pattern including an operation period consisting of either a constant current discharge period or a constant current charge period and a rest period provided subsequent to the operation period is applied to the battery to be analyzed, and the data obtained thereby is used.

[0004] The same publication also describes, as characteristic values estimated by fitting to measured data, the lithium ion diffusion coefficient in the positive electrode active material, the lithium ion diffusion coefficient in the negative electrode active material, the lithium ion diffusion coefficient in the electrolyte (electrolyte diffusion coefficient), the interfacial resistance in the positive electrode active material, the interfacial resistance in the negative electrode active material, and the lithium ion salt concentration in the electrolyte.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] Conventionally, the degree of deterioration of a secondary battery has been evaluated based on the magnitude of the discharge capacity and the magnitude of the internal resistance at the time of diagnosis. However, according to the investigations by the present inventors, it has been found that even when the magnitudes of the discharge capacity and the internal resistance at the time of diagnosis are similar, the number of times the secondary battery can be used thereafter is not necessarily the same. Specifically, among secondary batteries with similar magnitudes of the discharge capacity and the internal resistance at the time of diagnosis, there are those in which the discharge capacity rapidly decreases after a small number of charge-discharge cycles, and those in which the discharge capacity does not decrease much even after charge-discharge cycles. Therefore, simply measuring the magnitude of the discharge capacity and the magnitude of the internal resistance at the time of diagnosis cannot accurately evaluate the remaining life of the secondary battery.

[0007] The above-mentioned Japanese Patent Application Laid-Open No. 2017-97997 describes a method for estimating the characteristic values of the members constituting a secondary battery without disassembling the secondary battery. However, this publication does not describe a specific method for evaluating the remaining life of the secondary battery from these characteristic values.

[0008] An object of the present invention is to provide a method for diagnosing a secondary battery and a diagnostic program for a secondary battery that can more accurately evaluate the remaining life of the secondary battery without disassembling the secondary battery than in the prior art.

Means for Solving the Problems

[0009] According to one embodiment of the present invention, a method for diagnosing a secondary battery includes: a step of estimating characteristic parameters at a diagnosis time of the secondary battery to be diagnosed, including an electrolyte diffusion coefficient Dn at the diagnosis time of the secondary battery to be diagnosed, based on data obtained by measuring a load characteristic of the secondary battery to be diagnosed and using a predetermined model formula; a step of obtaining a discharge capacity when the electrolyte diffusion coefficient is changed and obtaining a relationship between the electrolyte diffusion coefficient and the discharge capacity based on the model formula and the estimated characteristic parameters; a step of determining a threshold value Dth of the electrolyte diffusion coefficient based on the relationship between the electrolyte diffusion coefficient and the discharge capacity; and a step of obtaining a difference ΔD between the threshold value Dth and the electrolyte diffusion coefficient Dn at the diagnosis time.

[0010] According to one embodiment of the present invention, a diagnostic program for a secondary battery causes a computer to execute: a step of estimating characteristic parameters at a diagnosis time of the secondary battery to be diagnosed, including an electrolyte diffusion coefficient Dn at the diagnosis time of the secondary battery to be diagnosed, based on data obtained by measuring a load characteristic of the secondary battery to be diagnosed and using a predetermined model formula; a step of obtaining a discharge capacity when the electrolyte diffusion coefficient is changed and obtaining a relationship between the electrolyte diffusion coefficient and the discharge capacity based on the model formula and the estimated characteristic parameters; a step of determining a threshold value Dth of the electrolyte diffusion coefficient based on the relationship between the electrolyte diffusion coefficient and the discharge capacity; and a step of obtaining a difference ΔD between the threshold value Dth and the electrolyte diffusion coefficient Dn at the diagnosis time.

Advantages of the Invention

[0011] According to the present invention, it is possible to more accurately evaluate the remaining life of a secondary battery without disassembling the secondary battery.

Brief Description of the Drawings

[0012]

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Embodiments for Carrying Out the Invention

[0013] In evaluating the remaining life of a secondary battery, the present inventors focused on the electrolyte diffusion coefficient. In the process of cycle degradation of a secondary battery, the amount of the electrolyte solution and the salt concentration in the solution decrease, approaching a state called "dry-out of the solution". At this time, it has been found that the value of the electrolyte diffusion coefficient decreases. The value Dn of the electrolyte diffusion coefficient of the secondary battery at the diagnosis time can be estimated non-destructively using the measurement data of the load characteristics and a model formula well known in this field. Also, the relationship between the electrolyte diffusion coefficient and the discharge capacity can be obtained by simulation using the above-mentioned model formula.

[0014] From the relationship between the electrolyte diffusion coefficient and the discharge capacity, the value Dth of the electrolyte diffusion coefficient when the secondary battery becomes unsuitable for reuse is determined, and the difference ΔD = Dn - Dth from the value Dn of the electrolyte diffusion coefficient at the diagnosis time is obtained. This ΔD can be used as an index of the remaining life of the secondary battery. That is, the larger ΔD is, the lower the possibility of early dry-out of the solution, and it can be evaluated that the possibility of long-term use is high. On the contrary, the smaller ΔD is, the higher the possibility of early dry-out of the solution, and it can be evaluated that the possibility of long-term use is low. There has been no method for predicting such a sharp drop in the discharge capacity due to dry-out of the solution until now. By using this method, the remaining life of the secondary battery can be evaluated more accurately than before.

[0015] [Method for determining the threshold value Dth of the electrolyte diffusion coefficient] The present inventors further studied the method for determining the threshold value Dth of the electrolyte diffusion coefficient used in the above evaluation. The threshold value Dth can basically be arbitrarily determined according to the use of the secondary battery and the like. On the other hand, from the point of view of more reliably evaluating the life that can be reused, it is a reasonable idea to determine the value of the electrolyte diffusion coefficient at the initial stage when the discharge capacity begins to drop sharply as the threshold value Dth and deliberately ensure a large margin.

[0016] The relationship between the electrolyte diffusion coefficient and the discharge capacity is not linear, and shows a curve in which the decrease rate of the discharge capacity becomes larger as the electrolyte diffusion coefficient decreases. The relationship between the electrolyte diffusion coefficient D and the discharge capacity can be accurately approximated by the following formula (1). Discharge capacity = Amax - B×EXP(Dh / D) (1) Here, Amax (maximum capacity), B (decrease rate), and Dh (decrease coefficient) are parameters obtained by fitting the relationship between the electrolyte diffusion coefficient and the discharge capacity obtained by the above simulation.

[0017] When determining the value of the electrolyte diffusion coefficient at the initial stage when the discharge capacity begins to drop sharply as the threshold Dth, it is conceivable to set the threshold Dth to a value of 50 - 60% of Dh in the above formula (1). When the electrolyte diffusion coefficient is at a value of 50 - 60% of Dh, looking at the absolute value of the discharge capacity, it can be said that the discharge capacity is still maintained. On the other hand, in view of the subsequent exponential decrease of the discharge capacity, by formulating the value of the electrolyte diffusion coefficient at this time as the threshold Dth, the life that can be reused more reliably can be evaluated. For example, even when there are measurement errors in the data used at the time of diagnosis, simulation errors, etc., the life that can be reused more reliably can be evaluated.

[0018] Also, the fact that the discharge capacity begins to drop sharply indicates that the diffusibility of the electrolyte inside the secondary battery is insufficient, and all the active materials in the electrodes are starting to be unable to contribute to the reaction. When all the active materials in the electrodes are unable to contribute to the reaction, the reaction becomes localized, increasing the possibility of Li precipitation. That is, the point in time when the discharge capacity begins to drop sharply can be said to be the point in time when the possibility of Li precipitation increases. Therefore, from the perspective of considering the possibility of Li precipitation, it can be said that it is preferable to determine the threshold Dth to a value of 50 - 60% of Dh.

[0019] [Regarding the relationship between ΔD and the number of cycles] The life of a secondary battery varies greatly depending on usage conditions (e.g., discharge rate, etc.). Therefore, the evaluation of the remaining life based on the above ΔD does not necessarily mean predicting the specific number of charge-discharge cycles until it becomes unsuitable for reuse.

[0020] On the other hand, the inventors have found that in secondary batteries using general-purpose active materials such as NCM, LCO, GC, and SiO, when these secondary batteries cycle-deteriorate under certain conditions, the electrolyte diffusion coefficient decreases approximately linearly with the number of cycles. Therefore, when it is assumed that the secondary battery is used under certain conditions, based on the relationship between the electrolyte diffusion coefficient and the number of cycles, the number of cycles at which the above-described difference ΔD becomes zero can be predicted.

[0021] In the case of cycle deterioration, since a decrease in discharge capacity according to the root rule occurs as steady deterioration, it is necessary to assume that both this decrease and a sharp decrease due to a decrease in liquid diffusibility occur. So far, there has been no method to predict the "sharp drop" deviating from the root rule up to the cycle "number". By using this method, the remaining life of the secondary battery can be evaluated more accurately than before.

[0022] The inventors further analyzed the deterioration status of the secondary battery in more detail to improve the prediction accuracy.

[0023] The above-described threshold value Dth of the electrolyte diffusion coefficient is determined based on obtaining the relationship between the electrolyte diffusion coefficient and the discharge capacity from the characteristic parameters at the diagnosis time point. This is to make it possible to evaluate the remaining life by one parameter (electrolyte diffusion coefficient) by rounding the changes in characteristic parameters other than the electrolyte diffusion coefficient to the change in the electrolyte diffusion coefficient. Even in this way, the remaining life of the secondary battery can be evaluated with a certain degree of high accuracy. However, in an actual secondary battery, since characteristic parameters other than the electrolyte diffusion coefficient also change with cycle deterioration, there may be a deviation between the threshold value Dth obtained from the characteristic parameters at the diagnosis time point and the actual threshold value.

[0024] The inventors measured the load characteristics of a secondary battery that had undergone a cycle charge-discharge test under certain conditions at multiple time points with different numbers of cycles, and obtained the threshold value of the electrolyte diffusion coefficient from the data of these load characteristics in the same manner as the method described above. As a result, it was found that, similar to the case of the electrolyte diffusion coefficient, the threshold value of the electrolyte diffusion coefficient also changes approximately linearly with the number of cycles. Taking this into account, by predicting the time point when the electrolyte diffusion coefficient falls below the threshold value, the number of cycles at which the discharge capacity starts to rapidly decline can be obtained with higher accuracy.

[0025] The present invention has been completed based on the above findings. Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0026] [Diagnosis method for secondary battery] [First Embodiment] FIG. 1 is a flowchart of a diagnosis method for a secondary battery according to the first embodiment of the present invention. This diagnosis method includes a step of estimating characteristic parameters at the diagnosis time of a secondary battery to be diagnosed (hereinafter referred to as "target battery") (step S1), a step of obtaining the relationship between the electrolyte diffusion coefficient and the discharge capacity (step S2), a step of determining the threshold value Dth of the electrolyte diffusion coefficient (step S3), and a step of obtaining the difference ΔD between the threshold value Dth and the electrolyte diffusion coefficient Dn at the diagnosis time (step S4). Hereinafter, each step will be described in detail.

[0027] [Step of estimating characteristic parameters] Estimate the characteristic parameters at the diagnosis time of the target battery (step S1). More specifically, based on the data obtained by measuring the load characteristics of the target battery, the characteristic parameters at the diagnosis time of the target battery, including the electrolyte diffusion coefficient Dn at the diagnosis time of the target battery, are estimated using a predetermined model formula.

[0028] In this step, by fitting the data obtained by measuring the load characteristics of the target battery using a predetermined model formula, the characteristic parameters at the diagnosis time of the target battery are estimated. This analysis (simulation) can be performed by a computer program capable of fluid analysis, for example, by the software Battery Design Studio manufactured by Siemens.

[0029] As the model formula, those well-known in this field can be used. For example, the model formula described in Marc Doyle et al., "Modeling of Galvanostatic Charge and Discharge of the Lithium / Polymer / Insertion Cell", J. Electrochem. Soc., vol. 140, No. 6, June (1993) can be used.

[0030] The target battery is, for example, a lithium-ion battery.

[0031] The data obtained by measuring the load characteristics of the target battery is, for example, a discharge curve obtained by measuring the target battery at a plurality of discharge rates. This data preferably includes a discharge curve measured at a very low discharge rate (for example, 0.02C). Further, this data preferably includes a discharge curve measured at a discharge rate of 1C or more. This data preferably includes a discharge curve measured at three or more discharge rates, and more preferably includes a discharge curve measured at four or more discharge rates. The data obtained by measuring the load characteristics of the target battery may be a charge curve obtained by measuring the target battery at a plurality of charge rates.

[0032] The characteristic parameters estimated in this step (the characteristic parameters at the diagnosis time of the target battery) include at least the electrolyte diffusion coefficient Dn at the diagnosis time of the target battery. The characteristic parameters can also include, for example, the solid-phase diffusion coefficients of the positive and negative electrode active materials, the electrolyte conductivity, etc. Other specific examples of the characteristic parameters will be described later.

[0033] FIG. 2 is a flowchart showing an example of a more specific procedure of the step (step S1) of estimating characteristic parameters. In this example, the step (step S1) of estimating characteristic parameters includes a step (step S1-1) of inputting basic specifications of the target battery, a step (step S1-2) of inputting data obtained by measuring the load characteristics of the target battery, a step (step S1-3) of estimating static parameters of the target battery, and a step (step S1-4) of estimating dynamic parameters of the target battery.

[0034] Input the basic specifications of the secondary battery to be diagnosed into the analysis software (step S1-1). The basic specifications to be input can include, but are not limited to, for example, the following. · Electrode compositions of the positive and negative electrodes (constituent materials, content ratios, particle sizes, etc.) · Electrode thicknesses, densities, and curvature factors (= often about 1.5) of the positive and negative electrodes · Materials, thicknesses, and electrical conductivities of the positive and negative electrode current collectors · Thicknesses and porosities of the separators · Compositions of the electrolytes (constituent materials, content ratios) · Thermal conductivities and heat capacities of the above constituent materials (basic physical property values specific to the materials) · Electrode areas

[0035] Since the diagnosis is basically non-destructive, accurate composition information of the electrolyte at the time of diagnosis cannot be obtained. Therefore, general information of the secondary battery to be diagnosed (or specification information of a new battery) is obtained and input as parameters. Although some values need to be input when actually performing the simulation, the composition of the electrolyte itself does not have a great influence on the simulation results. The positioning of the electrolyte composition information is for reference in the diagnosis method of this embodiment.

[0036] Although it is assumed that the densities of the positive and negative electrodes also vary due to expansion from the initial state, the exact values at the time of diagnosis cannot be measured. Therefore, the initial value (such as the standard value) or a value predicted from the initial value is input. If it is completely unknown, a general value may be input. If necessary, fine adjustment may be performed in step S1-4.

[0037] Input the data obtained by measuring the load characteristics of the target battery into the analysis software (step S1-2). The data obtained by measuring the load characteristics of the target battery is, as described above, the discharge curve or the like obtained by measuring the target battery at a plurality of discharge rates. Hereinafter, the "data obtained by measuring the load characteristics of the target battery" may be referred to as "measured data".

[0038] Estimate the static parameters of the target battery from the measured data and the model formula (step S1-3). For example, adjust the static parameters of the target battery so as to match the shape of the discharge curve measured at a very low discharge rate. The discharge curve measured at a very low discharge rate (for example, 0.02C) can be regarded as generally coinciding with the voltage curve (OCV curve) when no load is connected. The static parameters include, but are not limited to, for example, the following. · Capacity per unit weight of the positive and negative electrode active materials (the discharge capacity of the used battery has decreased.) · Utilization rate of each of the positive and negative electrode active materials (They do not use the entire area together.) · Maximum voltage and minimum voltage in the target battery usage range

[0039] Estimate the dynamic parameters of the target battery from the measured data and the model formula (step S1-4). For example, perform a simulation in which the target battery is discharged at a current value equivalent to the measurement conditions of the measured data, and while comparing the result of this simulation with the measured data, adjust the dynamic parameters so that the two match. This simulation can be performed, for example, using the discharge curve prediction of the above-described Battery Design Studio. The dynamic parameters include, but are not limited to, for example, the following. ·Electrolyte conductivity ·Electrolyte diffusion coefficient ·Solid-phase diffusion coefficient of positive and negative electrode active materials ·Heat capacity of the target battery

[0040] It is preferable to set the environmental temperature during the simulation to be consistent with the environmental temperature at the time of acquiring the measured data. In the case of product batteries of medium size or larger, particularly those product batteries assumed to be used at high rates, it is preferable to consider the influence of heat generation. For this purpose, it is preferable to perform measurements at least at 1C and perform fitting with the measured data affected by heat generation. On the other hand, if the target battery is a small cell for in-flight testing or the like, the influence of heat generation may not be considered.

[0041] Through the above steps, it is possible to estimate the characteristic parameters at the diagnosis time of the target battery, including the electrolyte diffusion coefficient Dn at the diagnosis time of the target battery.

[0042] [Step of obtaining the relationship between the electrolyte diffusion coefficient and the discharge capacity] Based on the model formula used in step S1 and the characteristic parameters estimated in step S1, obtain the relationship between the electrolyte diffusion coefficient and the discharge capacity (step S2). More specifically, among the characteristic parameters estimated in step S1, while keeping other characteristic parameters constant, perform a discharge simulation by changing only the electrolyte diffusion coefficient and obtain the discharge capacity. The discharge rate and environmental temperature during the discharge simulation are preferably set according to the reuse application. For example, if it is assumed that the application is used at an average of about 1C, the discharge rate when obtaining the relationship between the electrolyte diffusion coefficient and the discharge capacity is also 1C. Since it is difficult to make all environments exactly the same, the simulation may be performed using average values.

[0043] Figure 3 is a graph showing an example of the relationship between the electrolyte diffusion coefficient and the discharge capacity. In this example, when the environmental temperature is 45°C and the discharge rate is 0.5C, the discharge capacity when the electrolyte diffusion coefficient is 4.8×10 -6 , 4.0×10 -6 , 2.95×10-6 , 1.85×10 -6 , 1.48×10 -6 , and 1.1×10 -6 cm 2 / sec were obtained for each case.

[0044] As shown in this example, generally, the smaller the electrolyte diffusion coefficient, the smaller the discharge capacity. Also, the relationship between the electrolyte diffusion coefficient and the discharge capacity is not linear, and there is a tendency to show a curve such that the decrease in the discharge capacity becomes larger as the electrolyte diffusion coefficient decreases.

[0045] [Step of determining the threshold value Dth of the electrolyte diffusion coefficient] Based on the relationship between the electrolyte diffusion coefficient and the discharge capacity obtained in step S2, the threshold value Dth of the electrolyte diffusion coefficient is determined (step S3). More specifically, referring to the relationship between the electrolyte diffusion coefficient and the discharge capacity obtained in step S2, the value of the electrolyte diffusion coefficient when the target battery becomes unsuitable for reuse is determined as the threshold value Dth. What is judged as "unsuitable for reuse" varies depending on the reuse application of the target battery. Therefore, a criterion for judging as "unsuitable for reuse" is set according to the application.

[0046] For example, when the discharge capacity becomes equal to or less than a predetermined allowable value, it may be judged as unsuitable for reuse. In this case, the electrolyte diffusion coefficient when the discharge capacity becomes equal to or less than the predetermined allowable value is determined as the threshold value Dth. For example, in the example of FIG. 3, when the allowable value of the discharge capacity is 36.02 mAh, the threshold value Dth is 1.40×10 -6 cm 2 / sec.

[0047] Alternatively, when the discharge capacity starts to drop rapidly, it may be judged as unsuitable for reuse. In this case, the electrolyte diffusion coefficient when the discharge capacity starts to drop rapidly is determined as the threshold value Dth. For example, the electrolyte diffusion coefficient when the slope of the discharge capacity becomes equal to or greater than a predetermined magnitude may be used as the threshold value Dth. Also, as shown in FIG. 4, the point where the tangents of each curve before and after the discharge capacity starts to drop rapidly intersect may be used as the threshold value Dth.

[0048] [Step of obtaining difference ΔD between threshold value Dth and electrolyte diffusion coefficient Dn at diagnosis time point] Obtain the difference ΔD between the threshold value Dth determined in step S3 and the electrolyte diffusion coefficient Dn at the diagnosis time point estimated in step S1 (step S4). For example, if Dn = 2.22×10 -6 cm 2 / sec and Dth = 1.40×10 -6 cm 2 / sec, then ΔD = Dn - Dth = 0.82×10 -6 cm 2 / sec.

[0049] This ΔD can be used as an indicator of the remaining life of the target battery. That is, it can be evaluated that the larger ΔD is, the higher the possibility of using the target battery for a long time, and the smaller ΔD is, the lower the possibility of using the target battery for a long time. Even if the discharge capacity at the diagnosis time point is about the same, ΔD may be different. By using ΔD, the remaining life of the target battery can be evaluated more accurately compared with the conventional method of evaluating the remaining life according to the magnitude of the discharge capacity at the diagnosis time point.

[0050] As described above, since the life of the secondary battery varies greatly depending on the usage conditions, "accurate evaluation of the remaining life" does not necessarily mean predicting the specific number of charge-discharge cycles until it becomes unsuitable for reuse. However, if it is assumed that the target battery continues to be used under certain conditions, it is also possible to predict the remaining life (number of charge-discharge cycles) of the target battery from ΔD. For example, the relationship between ΔD and the remaining life may be measured in advance, and the remaining life of the target battery may be predicted based on this relationship between ΔD and the remaining life.

[0051] [Second Embodiment] The method for diagnosing a secondary battery according to the second embodiment of the present invention differs from the first embodiment in the step of determining the threshold value Dth of the electrolyte diffusion coefficient (step S3). FIG. 5 is a flowchart showing a more specific procedure of the step of determining the threshold value Dth of the electrolyte diffusion coefficient (step S3) in the method for diagnosing a secondary battery according to the present embodiment. In the present embodiment, the step of determining the threshold value Dth of the electrolyte diffusion coefficient (step S3) includes a step of fitting the relationship between the electrolyte diffusion coefficient D and the discharge capacity with the following formula (1) to obtain Amax, B, and Dh (step S3-1), and a step of determining the threshold value Dth based on Dh (step S3-2). Discharge capacity = Amax - B × EXP(Dh / D) (1)

[0052] The relationship between the electrolyte diffusion coefficient D obtained in step S2 and the discharge capacity is fitted with the above formula (1) to obtain Amax, B, and Dh (step S3-1). FIG. 6 is a graph showing the relationship between the electrolyte diffusion coefficient and the discharge capacity shown in FIG. 3 fitted with formula (1). The dashed line in FIG. 6 is the discharge capacity calculated by formula (1). In this example, Amax = 36.2 mAh, B = 0.034 mAh, and Dh = 2.51×10 -6 cm 2 / sec. As shown in FIG. 6, the relationship between the electrolyte diffusion coefficient D and the discharge capacity can be accurately approximated by formula (1).

[0053] Based on Dh obtained in step S3-1, the threshold value Dth is determined (step S3-2). In the above formula (1), Dh is a parameter characterizing the value of the electrolyte diffusion coefficient when the rapid drop of the discharge capacity starts, and it is reasonable to determine the threshold value Dth based on Dh.

[0054] Preferably, the threshold value Dth is determined to be a value of 50 to 60% of Dh.

[0055] For example, in the example of FIG. 6, when the threshold value Dth is set to 56% of Dh (= 2.51×10 -6 cm 2 / sec), the threshold value Dth is 1.40×10-6 cm 2 / sec. The discharge capacity when the electrolyte diffusion coefficient is 1.40×10 -6 cm 2 / sec is predicted to be approximately 36.0 mAh from Equation (1). This value is a decrease within 1% from Amax (= 36.2 mAh), and in terms of the absolute value of the discharge capacity, it can be said that the discharge capacity is still being maintained. On the other hand, in view of the subsequent exponential decrease in the discharge capacity, by establishing the value of the electrolyte diffusion coefficient at this point as the threshold Dth, the lifespan that can be reused more reliably can be evaluated. For example, even when there are measurement errors in the data used at the time of diagnosis, simulation errors, etc., the lifespan that can be reused more reliably can be evaluated.

[0056] Also, the fact that the discharge capacity starts to drop sharply indicates that the diffusibility of the electrolyte inside the secondary battery is insufficient, and all the active materials inside the electrodes are beginning to be unable to contribute to the reaction. When all the active materials inside the electrodes are unable to contribute to the reaction, the reaction becomes localized, increasing the possibility of Li precipitation. That is, the point at which the discharge capacity starts to drop sharply can be said to be the point at which the possibility of Li precipitation increases. Therefore, from the perspective of considering the possibility of Li precipitation, it can be said that it is preferable to determine the threshold Dth to be a value of 50 - 60% of Dh.

[0057] The larger the threshold Dth, the shorter the remaining lifespan of the target battery will be evaluated. Therefore, if the threshold Dth is set to a large value, the risk of overestimating the remaining lifespan decreases, but the risk of underestimating the remaining lifespan increases. Conversely, if the threshold Dth is set to a small value, the risk of underestimating the remaining lifespan decreases, but the risk of overestimating the remaining lifespan increases. The threshold Dth is more preferably a value of 54 - 58% of Dh.

[0058] [Third Embodiment] FIG. 7 is a flowchart of a method for diagnosing a secondary battery according to a third embodiment of the present invention. In addition to the steps (FIG. 1) included in the method for diagnosing a secondary battery according to the first embodiment, this diagnostic method further includes a step (step S5) of obtaining the number of cycles corresponding to the difference ΔD based on the relationship between the number of cycles and the electrolyte diffusion coefficient obtained in advance.

[0059] [Step of obtaining the number of cycles corresponding to the difference ΔD] Based on the relationship between the number of cycles and the electrolyte diffusion coefficient obtained in advance, the number of cycles corresponding to the difference ΔD is obtained (step S5). As described above, since the life of the secondary battery varies greatly depending on the usage conditions, the evaluation of the remaining life based on the difference ΔD does not necessarily mean predicting the specific number of charge-discharge cycles until it becomes unsuitable for reuse. On the other hand, when the secondary battery deteriorates cyclically under certain conditions, it has been experimentally clarified that the electrolyte diffusion coefficient generally decreases linearly with the number of cycles. Therefore, when it is assumed that the target battery is used under certain conditions, the relationship between the number of cycles and the electrolyte diffusion coefficient is obtained in advance, and the number of cycles until the above-mentioned difference ΔD becomes zero can be predicted based on the relationship between the number of cycles and the electrolyte diffusion coefficient.

[0060] The relationship between the number of cycles and the electrolyte diffusion coefficient can be obtained, for example, as follows.

[0061] For a secondary battery of the same type as the target battery, a charge-discharge cycle test is performed under certain conditions to deteriorate this secondary battery. Here, the "secondary battery of the same type as the target battery" means a secondary battery in which the materials and shapes of the positive and negative electrodes, the type and amount of the electrolyte, etc. are equivalent to those of the target battery. For example, when the target battery is a product battery, the "secondary battery of the same type as the target battery" means a secondary battery having the same model number, etc. Hereinafter, this "secondary battery of the same type as the target battery" is referred to as a "measurement secondary battery".

[0062] During the charge-discharge cycle test, it is preferable to set the discharge rate, environmental temperature, etc. according to the reuse application of the target battery. For example, if an application that is expected to be used at an average of about 1C is assumed, a discharge rate of 1C is also used in the charge-discharge cycle test. Since it is difficult to exactly align all environments, the charge-discharge cycle test may be performed using an average value.

[0063] Since the relationship between the number of cycles and the electrolyte diffusion coefficient is linear, the charge-discharge cycle test does not need to be repeated until the measurement secondary battery is sufficiently deteriorated, and it may be performed up to an appropriate number of cycles.

[0064] Measure the load characteristics of the measurement secondary battery at two or more time points with different numbers of cycles, and estimate the characteristic parameters of the measurement secondary battery at each time point from the data of these load characteristics. The estimation of this characteristic parameter can be performed in the same manner as the step of estimating the characteristic parameter of the target battery (step S1).

[0065] As described above, when the secondary battery undergoes cycle degradation under certain conditions, the electrolyte diffusion coefficient generally decreases linearly with the number of cycles. Therefore, if the electrolyte diffusion coefficients at at least two time points are known, the relationship between the number of cycles and the electrolyte diffusion coefficient can be obtained. For example, when the value of the electrolyte diffusion coefficient at the number of cycles n1 is D1 and the value of the electrolyte diffusion coefficient at the number of cycles n2 is D2, the slope k of the electrolyte diffusion coefficient with respect to the number of cycles D is D obtainable from k = (D2 - D1) / (n2 - n1). Of course, the electrolyte diffusion coefficient may be estimated at three or more time points to obtain the slope k D so that the error is smaller.

[0066] When estimating the characteristic parameters after the second point, it is preferable to change only the discharge capacity, the electrolyte diffusion coefficient, and the electrolyte conductivity from the characteristic parameters estimated at the first point, and fix the other characteristic parameters to the characteristic parameters estimated at the first point for fitting. When estimating the characteristic parameters after the second point, it is also possible to change all the characteristic parameters for fitting. However, if this is done by someone other than a skilled technician, the increase in the number of parameters may lead to inaccurate fitting, and the relationship between the number of cycles and the electrolyte diffusion coefficient may become inaccurate.

[0067] Instead of the secondary battery for measurement, a charge-discharge cycle test may be performed on the target battery itself to obtain the relationship between the number of cycles and the electrolyte diffusion coefficient.

[0068] In addition, after performing charge and discharge for a predetermined number of cycles after using the target battery, the load characteristics may be measured, and the relationship between the number of cycles and the electrolyte diffusion coefficient may be updated using the data of the load characteristics at this time, and the prediction may be corrected sequentially. In this case, only the load characteristics that can be measured in a short time such as 3C or 5C may be acquired.

[0069] Based on the obtained relationship between the number of cycles and the electrolyte diffusion coefficient, the number of cycles corresponding to the difference ΔD is obtained. More specifically, the number of cycles at which the difference ΔD becomes 0 is obtained. The number of cycles N0 at which the difference ΔD becomes 0 is the slope k of the electrolyte diffusion coefficient with respect to the number of cycles D Using, N0 = ΔD / |k D | can be obtained. Thereby, the specific number of cycles at which the target battery becomes unsuitable for reuse can be predicted.

[0070] [Fourth Embodiment] FIG. 8 is a flowchart of a method for diagnosing a secondary battery according to the fourth embodiment of the present invention. This diagnostic method further includes a step (step S6) of correcting the number of cycles based on the relationship between the number of cycles and the threshold value of the electrolyte diffusion coefficient acquired in advance, in addition to the steps (FIG. 7) included in the method for diagnosing a secondary battery according to the third embodiment.

[0071] [Step of correcting the number of cycles] Based on the relationship between the number of cycles obtained in advance and the threshold value of the electrolyte diffusion coefficient, correct the number of cycles (the number of cycles N0 corresponding to the difference ΔD) obtained in step S5 (step S6). As described above, it has been experimentally clarified that the threshold value of the electrolyte diffusion coefficient changes approximately linearly with the number of cycles. Taking this into account, by predicting the point in time when the electrolyte diffusion coefficient falls below the threshold value Dth, it is possible to predict with higher accuracy the specific number of cycles at which the target battery becomes unsuitable for reuse.

[0072] The relationship between the number of cycles and the threshold value Dth of the electrolyte diffusion coefficient can be obtained, for example, as follows.

[0073] Similar to when obtaining the relationship between the number of cycles and the electrolyte diffusion coefficient, perform a charge / discharge cycle test on the measurement secondary battery under certain conditions to deteriorate the measurement secondary battery. Measure the load characteristics of the measurement secondary battery at two or more time points with different numbers of cycles, and estimate the characteristic parameters of the measurement secondary battery at each time point from the data of these load characteristics. The data of the load characteristics and the characteristic parameters may be those obtained when obtaining the relationship between the number of cycles and the electrolyte diffusion coefficient.

[0074] Using these characteristic parameters, obtain the relationship between the electrolyte diffusion coefficient and the discharge capacity at each time point, and further determine the threshold value of the electrolyte diffusion coefficient at each time point. These can be performed in the same manner as the step of obtaining the relationship between the electrolyte diffusion coefficient and the discharge capacity of the target battery (step S2) and the step of determining the threshold value Dth of the electrolyte diffusion coefficient of the target battery (step S3), respectively.

[0075] When a secondary battery undergoes cycle degradation under certain conditions, the threshold value of the electrolyte diffusion coefficient changes approximately linearly with the number of cycles. Therefore, if the threshold values of the electrolyte diffusion coefficient at at least two time points are known, the relationship between the number of cycles and the threshold value of the electrolyte diffusion coefficient can be obtained. For example, when the threshold value of the electrolyte diffusion coefficient at a cycle number of n1 is Dth1 and the threshold value of the electrolyte diffusion coefficient at a cycle number of n2 is Dth2, the slope k th of the threshold value of the electrolyte diffusion coefficient with respect to the number of cycles th can be obtained from k th =(Dth2 - Dth1) / (n2 - n1). Of course, the threshold values of the electrolyte diffusion coefficient can be determined at three or more time points, and the slope k

[0076] can be obtained so that the error is smaller.

[0077] Similar to when the relationship between the number of cycles and the electrolyte diffusion coefficient is obtained, instead of the secondary battery for measurement, a charge-discharge cycle test may be performed on the target battery itself to obtain the relationship between the number of cycles and the threshold value of the electrolyte diffusion coefficient. D Based on the obtained relationship between the number of cycles and the threshold value of the electrolyte diffusion coefficient, correct the number of cycles obtained in step S5. When the electrolyte diffusion coefficient changes with a slope k D with respect to the number of cycles, the electrolyte diffusion coefficient D' after N cycles from the diagnosis time point is D' = Dn + k th ×N. Similarly, when the threshold value of the electrolyte diffusion coefficient changes with a slope k th with respect to the number of cycles, the threshold value Dth' after N cycles from the diagnosis time point is Dth' = Dth + k th ×N. The number of cycles N1 at which D' and Dth' are equal can be obtained as N1 = (Dn - Dth) / (k D - k

[0078] [Diagnostic program for secondary battery, etc.] The above-described method for diagnosing a secondary battery can also be implemented as a computer program. The diagnostic program for a secondary battery according to an embodiment of the present invention measures the load characteristics of the secondary battery to be diagnosed, and based on the data obtained, uses a predetermined model formula to estimate the characteristic parameters at the time of diagnosis of the secondary battery to be diagnosed, including the electrolyte diffusion coefficient Dn at the time of diagnosis of the secondary battery to be diagnosed. And a step of obtaining the discharge capacity when the electrolyte diffusion coefficient is changed based on the model formula and the estimated characteristic parameters, and obtaining the relationship between the electrolyte diffusion coefficient and the discharge capacity, and based on the relationship between the electrolyte diffusion coefficient and the discharge capacity. A step of determining a threshold value Dth of the electrolyte diffusion coefficient, and a step of obtaining a difference ΔD between the threshold value Dth and the electrolyte diffusion coefficient Dn at the time of diagnosis are executed by a computer. Also according to this embodiment, compared with the conventional method of evaluating the remaining life according to the magnitude of the discharge capacity at the time of diagnosis, the remaining life of the target battery can be evaluated more accurately.

[0079] In the above, a computer program of an aspect corresponding to the method for diagnosing a secondary battery according to the first embodiment has been described. However, the methods for diagnosing a secondary battery according to the second to fourth embodiments can also be implemented as a computer program.

[0080] The above-described method for diagnosing an electrode of a secondary battery can also be implemented as a computer-readable recording medium recording the above computer program.

[0081] The above-described method for diagnosing a secondary battery can also be implemented as a computer system. A secondary battery diagnosis system according to an embodiment of the present invention includes a memory and a processor. The processor measures the load characteristics of the secondary battery to be diagnosed according to the program in the memory, and based on the data obtained, uses a predetermined model formula to estimate the characteristic parameters at the diagnosis time of the secondary battery to be diagnosed, including the electrolyte diffusion coefficient Dn at the diagnosis time of the secondary battery to be diagnosed. Then, based on the model formula and the estimated characteristic parameters, it obtains the discharge capacity when the electrolyte diffusion coefficient is changed, and obtains the relationship between the electrolyte diffusion coefficient and the discharge capacity. Next, based on the relationship between the electrolyte diffusion coefficient and the discharge capacity, it determines the threshold value Dth of the electrolyte diffusion coefficient. Finally, it obtains the difference ΔD between the threshold value Dth and the electrolyte diffusion coefficient Dn at the diagnosis time.

Example

[0082] Hereinafter, the present invention will be described more specifically by way of examples. The present invention is not limited to these examples.

[0083] A plurality of medium-sized laminated cells with a rated capacity of 5 Ah and a plurality of small-sized laminated cells with a rated capacity of 36 mAh were respectively manufactured.

[0084] [Medium-sized laminated cell] [Manufacture of positive electrode] 93 parts by mass of LiCoO2 as a positive electrode active material, 3 parts by mass of carbon black as a conductive assistant, and 4 parts by mass of PVDF as a binder were mixed uniformly using NMP as a solvent to prepare a positive electrode mixture-containing slurry. This positive electrode mixture-containing slurry was applied to both sides of a positive electrode current collector made of an aluminum foil with a thickness of 15 μm, dried, and then pressure-molded by a roller press. A part of the positive electrode current collector without the positive electrode mixture-containing slurry was punched out to form a tab portion, thereby manufacturing a positive electrode.

[0085] [Manufacture of negative electrode] Graphite, which is the negative electrode active material: 97.5 parts by mass, carboxymethyl cellulose, which is a binder: 1.5 parts by mass, and styrene-butadiene rubber: 1 part by mass were mixed, an appropriate amount of water was added and mixed well to prepare a negative electrode binder-containing slurry. This negative electrode binder-containing slurry was applied to both sides of a negative electrode current collector made of a copper foil with a thickness of 10 μm, dried, and then pressure-molded by a roller press. A negative electrode was fabricated by punching out a part of the negative electrode current collector where the negative electrode binder-containing slurry was not applied to form a tab portion.

[0086] <Fabrication of Battery> Seven of the positive electrodes and eight of the negative electrodes were alternately laminated via a polyolefin microporous film separator having a three-layer structure with a polyethylene layer as the intermediate layer and two polypropylene layers as the outer layers and having a thickness of 18 μm to form a laminated electrode body.

[0087] Next, the tab portions of the positive electrodes and the tab portions of the negative electrodes of the laminated electrode body were welded together, leads were connected to each, and then LiPF6 was dissolved in a solution obtained by mixing ethylene carbonate, diethyl carbonate, and methyl ethyl carbonate in a volume ratio of 1:1:1 at a concentration of 1 mol / L. After that, vinylene carbonate was further dissolved in an amount of 1% by mass, and the resulting non-aqueous electrolyte was sealed together with the non-aqueous electrolyte secondary battery having a rated capacity of 5 Ah into an outer package made of an aluminum laminate film.

[0088] [Small Laminated Cell] <Fabrication of Positive Electrode> LiCoO2, which is the positive electrode active material: 94 parts by mass, carbon black, which is a conductive assistant: 4 parts by mass, and PVDF, which is a binder: 2 parts by mass were mixed uniformly using NMP as a solvent to prepare a positive electrode binder-containing slurry. This positive electrode binder-containing slurry was applied to both sides of a positive electrode current collector made of an aluminum foil with a thickness of 15 μm, dried, and then pressure-molded by a roller press. A positive electrode was fabricated by punching out a part of the positive electrode current collector where the positive electrode binder-containing slurry was not applied to form a tab portion.

[0089] <Fabrication of Negative Electrode> Graphite, which is the negative electrode active material: 94.5 parts by mass, and SiO particles (D50: 5.0 μm) with a carbon-coated surface: 3 parts by mass, carboxymethyl cellulose which is a binder: 1.5 parts by mass, and styrene butadiene rubber: 1 part by mass were mixed, an appropriate amount of water was added and mixed well to prepare a negative electrode binder-containing slurry. This negative electrode binder-containing slurry was applied to both sides of a negative electrode current collector made of a copper foil with a thickness of 10 μm, dried, and then pressure-molded by a roller press machine, and a part of the negative electrode current collector without the negative electrode binder-containing slurry applied was punched out to be a tab part, thereby fabricating a negative electrode.

[0090] <Fabrication of Battery> The positive electrode and the negative electrode were laminated via a polyolefin microporous film separator having a three-layer structure with a polyethylene layer as the intermediate layer and two polypropylene layers as the outer layers and having a thickness of 12 μm to form a laminated electrode body.

[0091] Next, after connecting leads to the tab part of the positive electrode and the tab part of the negative electrode of the laminated electrode body respectively, LiPF6 was dissolved in a solution obtained by mixing ethylene carbonate and diethyl carbonate at a volume ratio of 3:7 at a concentration of 1 mol / L, and then vinylene carbonate was further dissolved in an amount of 1% by mass. The resulting non-aqueous electrolyte was sealed together with the laminated electrode body into an exterior body made of an aluminum laminate film to fabricate a non-aqueous electrolyte secondary battery with a rated capacity of 36 mAh.

[0092] [Fabrication of Degraded Cells] For medium-sized laminated cells with a rated capacity of 5 Ah, charge-discharge cycle tests were conducted under a plurality of conditions with different charge-discharge rates and environmental temperatures, etc., and a plurality of degraded cells with the discharge capacity reduced to 4.8 Ah were fabricated. Similarly, for small-sized laminated cells with a rated capacity of 36 mAh, charge-discharge cycle tests were conducted under a plurality of conditions with different charge-discharge rates and environmental temperatures, etc., and a plurality of degraded cells with the discharge capacity reduced to 35 mAh were fabricated.

[0093] [Measurement of Load Characteristics] The load characteristics of these deteriorated cells were measured. Specifically, the discharge curves were measured at discharge rates of 0.02C, 0.2C, 0.5C, and 1C.

[0094] [Diagnosis of secondary battery] Using these deteriorated cells as target cells, the secondary battery diagnosis method described in the embodiment was implemented. The analysis (simulation) was performed using the software Battery Design Studio manufactured by Siemens. Among the basic specifications, the same values as those at the time of production were input for the solvent ratio and salt concentration (because the values at the time of diagnosis cannot be measured).

[0095] After estimating the characteristic parameters at the time of diagnosis, based on the estimated characteristic parameters, while changing the electrolyte diffusion coefficient, the discharge capacity at an environmental temperature of 45°C and a discharge rate of 0.5C was obtained, and the relationship between the electrolyte diffusion coefficient and the discharge capacity was acquired. The point where the tangents of each curve before and after the discharge capacity starts to drop sharply intersect was defined as the threshold value Dth, and the difference ΔD between the threshold value Dth and the electrolyte diffusion coefficient Dn at the time of diagnosis was obtained.

[0096] [Measurement of remaining life] For these deteriorated cells, a charge-discharge cycle test was performed under the same conditions, and the number of charge-discharge cycles until the discharge capacity drops sharply (the number of cycles at the start of the sharp drop) was measured. The relationship between ΔD obtained by diagnosis and the number of cycles at the start of the sharp drop is shown in FIG. 9. Note that in FIG. 9, the data of the cells in which Li deposition occurred, which will be described later in the charge-discharge cycle test, are excluded.

[0097] As shown in FIG. 9, even when the discharge capacity at the time of diagnosis was the same (4.8 Ah or 35 mAh), a difference was observed in ΔD. Also, it was found that the number of cycles at the start of the sharp drop changed according to ΔD, and the validity of this diagnosis method was confirmed.

[0098] [Setting of threshold value Dth using Equation (1)] FIG. 10 is a graph showing the relationship between the number of cycles and the discharge capacity of two cells (Cell No. 1 and Cell No. 3) in which Li deposition occurred. The solid line represents the measured data, and the broken line represents the predicted values by simulation.

[0099] The predicted value by simulation was calculated specifically as follows.

[0100] During the charge-discharge cycle test, the load characteristics of the deteriorated cell were measured, and the electrolyte diffusion coefficient Dm during the charge-discharge cycle test was estimated by the same method as when Dn was estimated. Assuming that the electrolyte diffusion coefficient decreases linearly with the number of cycles, the electrolyte diffusion coefficient DN after N cycles was expressed by the following formula (2). DN obtained from formula (2) was substituted into D in formula (1) to obtain the discharge capacity C(N) after N cycles. The discharge capacity C(N) after N cycles was divided by the initial discharge capacity C(0) to obtain the capacity retention rate q(N) = C(N) / C(0) after N cycles. DN = (Dm - Dn) / m × N + Dn (2) Here, m is the number of cycles when Dm was estimated.

[0101] In the case of cycle deterioration, in addition to the decrease in discharge capacity due to the decrease in liquid diffusibility, a decrease in discharge capacity according to the root law occurs as steady deterioration. The data before the sharp drop in discharge capacity among the measured data was fitted with the following formula (3) to obtain the discharge capacity R(N) after N cycles when following the root law. The product of this discharge capacity R(N) and the above-mentioned capacity retention rate q(N) was used as the predicted value by simulation. R(N) = C(0) - r × N 1 / 2 (3) Here, r is a parameter obtained by fitting.

[0102] The predicted value by simulation corresponds to the change in discharge capacity when Li deposition did not occur. In the measured data, due to Li deposition, a sharp drop in discharge capacity occurs at an earlier time than the predicted value.

[0103] The triangular mark in Fig. 10 indicates the point in time when the electrolyte diffusion coefficient reaches 56% of Dh. In the measured data, after the point in time when the electrolyte diffusion coefficient reaches 56% of Dh, the discharge capacity becomes unstable without much delay, and Li deposition begins to occur.

[0104] If it is a cell where Li deposition does not occur, it can be used for a longer time. However, considering the possibility of Li deposition, it can be said that it is desirable to set the threshold value Dth of the electrolyte diffusion coefficient to a value of 50 - 60% of Dh.

[0105] [Relationship between the number of cycles and the electrolyte diffusion coefficient] Regarding a degraded cell with a discharge capacity of 4.8 Ah, the load characteristics were measured every 100 cycles during the charge-discharge cycle test, and the electrolyte diffusion coefficient at each point in time was estimated by the same method as when estimating Dn. Fig. 11 is a graph showing the relationship between the number of cycles and the electrolyte diffusion coefficient. The dashed-dotted line indicates the threshold value Dth of the electrolyte diffusion coefficient.

[0106] As shown in Fig. 11, it can be seen that the electrolyte diffusion coefficient generally decreases linearly with respect to the number of cycles. Also, in Fig. 11, at the 700-cycle point, ΔD = 0.3×10 -6 cm 2 / sec, and after that, when extrapolating the change in the electrolyte diffusion coefficient, it can be predicted that it will start to drop rapidly from around 800 cycles, that is, it will start to deviate from the square root rule.

[0107] Fig. 12 is a diagram showing the results of the cycle test of the degraded cell used for the estimation in Fig. 11. The solid line is the measured value, and the dashed line is the line showing the cycle curve according to the square root rule. As shown in Fig. 12, dissociation from the square root rule starts from around 900 cycles, and although there is a slight prediction error, it was confirmed that a rapid decline actually occurs.

[0108] Conventionally, there has been no method to predict a "sharp drop" that deviates from the root rule up to the cycle "number", and this method is an entirely new method. Furthermore, the predicted number of cycles dissociates by only about 100 cycles with respect to the final life of about 1000 cycles, and it can be said that the prediction is generally reasonable.

[0109] [Relationship between the number of cycles and the threshold value of the electrolyte diffusion coefficient] Next, the relationship between the electrolyte diffusion coefficient and the discharge capacity was obtained from the characteristic parameters estimated at the 400-cycle and 700-cycle points, and further, the threshold values of the electrolyte diffusion coefficient at the 400-cycle and 700-cycle points were obtained. FIG. 13 is a graph in which the relationship between the number of cycles and the threshold value of the electrolyte diffusion coefficient is added to the graph of FIG. 11. The white marks in the figure are the threshold values of the electrolyte diffusion coefficient.

[0110] As shown in FIG. 13, it can be seen that the threshold value of the electrolyte diffusion coefficient also decreases approximately linearly with respect to the number of cycles, similar to the electrolyte diffusion coefficient. It can be predicted that the straight line obtained by extrapolating the change in the electrolyte diffusion coefficient and the straight line obtained by extrapolating the change in the threshold value of the electrolyte diffusion coefficient intersect at about the 900-cycle point. This is in good agreement with the number of cycles at which the deviation from the root rule starts in FIG. 12. It is confirmed that this prediction result can reproduce the actually measured cycle capacity decrease with very high accuracy and is recognized as effective.

[0111] As described above, the embodiments of the present invention have been explained, but the present invention is not limited to the above-described embodiments only, and various modifications are possible within the scope of the invention.

Claims

1. Based on data obtained by measuring the load characteristics of a secondary battery to be diagnosed, using a predetermined model formula, estimating characteristic parameters at the diagnosis time of the secondary battery to be diagnosed, including the electrolyte diffusion coefficient Dn at the diagnosis time of the secondary battery to be diagnosed; Based on the model formula and the estimated characteristic parameters, obtaining the discharge capacity when the electrolyte diffusion coefficient is changed, and obtaining the relationship between the electrolyte diffusion coefficient and the discharge capacity; Based on the relationship between the electrolyte diffusion coefficient and the discharge capacity, determining a threshold value Dth of the electrolyte diffusion coefficient; A method for diagnosing a secondary battery, comprising: obtaining a difference ΔD between the threshold value Dth and the electrolyte diffusion coefficient Dn at the diagnosis time.

2. The method for diagnosing a secondary battery according to claim 1, wherein the step of determining the threshold value Dth is a step of determining the threshold value Dth as the electrolyte diffusion coefficient when the discharge capacity becomes equal to or less than a predetermined allowable value.

3. The method for diagnosing a secondary battery according to claim 1, wherein the step of determining the threshold value Dth is a step of determining the threshold value Dth as the electrolyte diffusion coefficient when the discharge capacity starts to drop rapidly.

4. The method for diagnosing a secondary battery according to claim 1, wherein the step of determining the threshold value Dth includes: fitting the relationship between the electrolyte diffusion coefficient D and the discharge capacity with the following formula (1) to obtain Amax, B, and Dh; determining the threshold value Dth based on Dh. Discharge capacity = Amax - B × EXP(Dh / D) (1)

5. The method for diagnosing a secondary battery according to claim 4, wherein the threshold value Dth is set to a value of 50% to 60% of Dh.

6. The method for diagnosing a secondary battery according to any one of claims 1 to 5, further comprising: predicting the remaining life of the secondary battery to be diagnosed based on the relationship between the previously measured difference ΔD and the remaining life.

7. The method for diagnosing a secondary battery according to any one of claims 1 to 5, further comprising: obtaining the number of cycles corresponding to the difference ΔD based on the relationship between the number of cycles and the electrolyte diffusion coefficient obtained in advance.

8. The method for diagnosing a secondary battery according to claim 7, wherein A method for diagnosing a secondary battery, which approximates the relationship between the number of cycles and the electrolyte diffusion coefficient by a straight line to obtain the number of cycles corresponding to the difference ΔD.

9. The method for diagnosing a secondary battery according to claim 7, wherein the relationship between the number of cycles and the electrolyte diffusion coefficient is obtained from data obtained for secondary batteries of the same type as the secondary battery to be diagnosed. A method for diagnosing a secondary battery.

10. The method for diagnosing a secondary battery according to claim 7, wherein the relationship between the number of cycles and the electrolyte diffusion coefficient is obtained from data obtained for the secondary battery to be diagnosed. A method for diagnosing a secondary battery.

11. The method for diagnosing a secondary battery according to claim 7, wherein the method further comprises a step of correcting the number of cycles corresponding to the difference ΔD based on the relationship between the number of cycles and the threshold value of the electrolyte diffusion coefficient obtained in advance. A method for diagnosing a secondary battery.

12. The method for diagnosing a secondary battery according to claim 11, wherein the relationship between the number of cycles and the threshold value of the electrolyte diffusion coefficient is approximated by a straight line to correct the number of cycles corresponding to the difference ΔD. A method for diagnosing a secondary battery.

13. The method for diagnosing a secondary battery according to any one of claims 1 to 5, wherein the data obtained by measuring the load characteristics includes a discharge curve obtained by measuring the secondary battery to be diagnosed at a plurality of discharge rates. A method for diagnosing a secondary battery.

14. The method for diagnosing a secondary battery according to any one of claims 1 to 5, wherein the secondary battery to be diagnosed is a lithium-ion battery. A method for diagnosing a secondary battery.

15. Based on the data obtained by measuring the load characteristics of the secondary battery to be diagnosed, using a predetermined model formula, to estimate the characteristic parameters at the time of diagnosing the secondary battery to be diagnosed, including the electrolyte diffusion coefficient Dn at the time of diagnosing the secondary battery to be diagnosed; Based on the model formula and the estimated characteristic parameters, obtaining the discharge capacity when the electrolyte diffusion coefficient is changed, and obtaining the relationship between the electrolyte diffusion coefficient and the discharge capacity; Based on the relationship between the electrolyte diffusion coefficient and the discharge capacity, determining a threshold value Dth of the electrolyte diffusion coefficient; A secondary battery diagnosis program that causes a computer to execute a step of obtaining a difference ΔD between the threshold value Dth and the electrolyte diffusion coefficient Dn at the time of diagnosis.

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