Secondary cell life prediction method and secondary cell life prediction device

Abstract

To provide a life prediction method capable of predicting a life of a secondary cell with high accuracy.SOLUTION: A secondary cell life prediction method includes: a state diagnosis step of diagnosing a deterioration state of a secondary cell; a function determination step of determining a function form of a parameter representing the deterioration state acquired in the state diagnosis step on the basis of characteristics of a positive electrode active material and a negative electrode active material of the secondary cell; and a life prediction step of predicting the life of the secondary cell on the basis of the function form of the parameter determined in the function determination step.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a method for predicting the life of a secondary battery and a device for predicting the life of a secondary battery.

Background Art

[0002] In recent years, secondary batteries such as lithium-ion batteries have been used as power sources for driving vehicles and power storage sources for smart houses and the like. It is known that the battery characteristics of lithium-ion batteries and the like deteriorate due to repeated charge and discharge and storage in a high-temperature environment. Since the power sources for driving and power storage have a long usage time, it is required to minimize deterioration and ensure reliability and safety.

[0003] The electrode active material used in a lithium-ion battery may be selected not only from the viewpoints of characteristics such as capacity and voltage but also from the viewpoints of suppressing deterioration and improving safety. For example, as the positive electrode active material, Li(Ni,Mn,Co)O 2 (NMC), LiFePO 4 (LFP), etc. are used. As the negative electrode active material, graphite, Li 4 Ti 5 O 12 (LTO), etc. are used.

[0004] The deterioration of a secondary battery occurs due to changes in the characteristics of components such as the positive electrode, negative electrode, and electrolyte. As the deterioration of the secondary battery progresses, a decrease in the capacity of the secondary battery, an increase in resistance, a decrease in input / output characteristics, and a change in the charge / discharge curve occur, leading to a short product life and a decrease in safety of a device equipped with the secondary battery. Also, when a margin is provided in the mounting amount of the secondary battery to prevent a short product life, it causes an increase in cost. Therefore, a technique for accurately predicting the life of a secondary battery is required for extending the life, improving the reliability, and reducing the cost by optimizing the operation.

[0005] Patent Document 1 describes a method for predicting the life of a secondary battery. In claim 8 of Patent Document 1, there is an internal information detection method for the secondary battery described in claim 1, which uses the charge-discharge curves of the positive electrode and the charge-discharge curves of the negative electrode obtained in the second step to calculate and output the remaining life of the battery to be detected. An internal information detection method for a secondary battery is described.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] In Patent Document 1, life prediction is performed using parameters obtained by analyzing the SOC-OCV curve and SOC-R curve of a secondary battery using the SOC-OCP curve and SOC-R curve of the positive electrode alone and the negative electrode alone. As parameters, the effective amount of active material of the positive electrode and the negative electrode, the relative positional relationship of the SOC-OCP curves of the positive electrode and the negative electrode, the internal resistance of the positive electrode and the negative electrode, and the internal resistance other than the electrodes are used.

[0008] In the method of Patent Document 1, the time-dependent change data of the parameters is made into a function, and based on this, the life of the secondary battery is predicted. However, the behavior of the time-dependent change of the parameters with respect to the operating conditions of the secondary battery changes according to the types of the positive electrode active material and the negative electrode active material. Therefore, there is a risk of a decrease in prediction accuracy because a function reflecting the difference in the behavior of the time-dependent change for each active material has not been constructed.

[0009] The present invention has been made in view of the above points, and an object thereof is to provide a life prediction method and a life prediction device capable of predicting the life of a secondary battery with high accuracy.

Means for Solving the Problems

[0010] In order to solve the above problems, the method for predicting the life of a secondary battery according to the present invention analyzes the SOC-OCV curve and SOC-R curve of the secondary battery by using the SOC-OCP curves and SOC-R curves of the positive electrode alone and the negative electrode alone to obtain parameters, determines the functional form of the parameters based on the characteristics of the positive electrode active material and the negative electrode active material, and performs life prediction based on the determined functional form.

[0011] The life prediction device for a secondary battery according to the present invention includes a diagnosis unit that diagnoses the deterioration state of the secondary battery based on input information, a function determination unit that determines the functional form of parameters based on the characteristics of the positive electrode active material and the negative electrode active material, and a prediction unit that performs life prediction of the secondary battery based on the determined functional form. The operating conditions of the secondary battery are used as input information, and the predicted performance of the secondary battery, positive electrode, and negative electrode under the operating conditions is used as output information.

Advantages of the Invention

[0012] According to the method and device for predicting the life of a secondary battery according to the present invention, the life of the secondary battery can be predicted with high accuracy without depending on the type of the active material. Further features related to the present invention will become apparent from the description of this specification and the accompanying drawings. In addition, problems, configurations, and effects other than those described above will be clarified by the description of the following embodiments.

Brief Description of the Drawings

[0013]

Figure 1

Figure 2

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Figure 5

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Figure 7

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of a method for predicting the life of a secondary battery according to an embodiment of the present invention will be described. However, the present disclosure is not limited to the following embodiments, and various modifications and application examples within the technical concept of the present disclosure are also included in its scope.

[0015] In the following figures, the same reference numerals are given to common configurations, and duplicate descriptions are omitted. The abbreviations for the positive electrode and the negative electrode are represented by p and n, respectively. When the positive electrode and the negative electrode are not distinguished, they are referred to as electrodes, and the abbreviation is represented by x. Hereinafter, the capacity of the secondary battery is Q, the capacity of the positive electrode or the negative electrode is q, and the resistance of the positive electrode is r p , the resistance of the negative electrode is r n and are denoted as such. Also, SOC (State of Charge) is an index representing the charge rate or the state of charge, and is an index obtained by normalizing the capacity, which can be defined with the fully charged state of the secondary battery being 100% and the fully discharged state being 0%. Therefore, SOC can be converted into capacity. OCV is the open circuit voltage, OCP is the open circuit potential, and R is the internal resistance.

[0016] <Device Configuration> FIG. 1 is a diagram showing a schematic configuration of a secondary battery life prediction device. The life prediction device 1 is a device that executes the life prediction method of the secondary battery in the present embodiment, and is configured by a computer system including an arithmetic device 10 such as a CPU, a memory 11 such as a RAM and a ROM, an input device 12 such as a keyboard and a mouse, an output device 13 such as a display, and an interface 14 with an external device. The life prediction device 1 is connected to a device that acquires internal information such as voltage changes from the secondary battery to be detected. The life prediction device 1 is realized by the arithmetic device 10 reading and executing a computer program stored in the memory 11.

[0017] FIG. 2 is a diagram for explaining the life prediction method of the secondary battery according to the present embodiment. As functions embodied by the execution of a computer program, the life prediction device 1 has a diagnosis unit 201, a function determination unit 202, and a prediction unit 203, as shown in FIG. 2.

[0018] The life prediction method by the life prediction device 1 includes a state diagnosis step of diagnosing the degradation state of the secondary battery based on input information in the diagnosis unit 201, a function determination step of determining the functional form of a parameter representing the degradation state acquired in the state diagnosis step based on the characteristics of the positive electrode active material and the negative electrode active material of the secondary battery in the function determination unit 202, and a life prediction step of predicting the life of the secondary battery based on the functional form of the parameter determined in the function determination step in the prediction unit 203.

[0019] The life prediction device 1 acquires, as input information, the operating conditions of the secondary battery (including at least one of temperature, current, voltage, SOC range, and current rate), the Q-OCV curve and the Q-R curve which are the characteristics of the secondary battery, and the life prediction conditions (prediction period, end conditions, etc.). When the type of the material is known as the characteristics of the active materials of the positive electrode and the negative electrode, it may be acquired as input information.

[0020] In the life prediction device 1, first, in the diagnosis unit 201, the Q-OCV curve and Q-R curve of the secondary battery are analyzed using the capacity (q)-OCP curves and q-r curves of the positive electrode alone and the negative electrode alone, and the effective active material amounts of the positive electrode and the negative electrode, the relative positional relationship of the q-OCP curves of the positive electrode and the negative electrode, the internal resistances of the positive electrode and the negative electrode, and the internal resistance other than the electrodes are calculated.

[0021] Subsequently, in the function determination unit 202, based on the characteristics of the positive electrode active material and the negative electrode active material of the secondary battery, when the type of the active material is known, the function form is determined based on the type.

[0022] The prediction unit 203 calculates the predicted performance of the secondary battery, the positive electrode, and the negative electrode, which is the output information, based on the function form determined in the function determination unit 202 and the life prediction conditions that are the input information. As the predicted performance, SOH (State Of Health), SOC, the effective active material amounts of the positive electrode and the negative electrode, the relative positional relationship of the SOC-OCP curves of the positive electrode and the negative electrode, the internal resistances of the positive electrode and the negative electrode, and the internal resistance other than the electrodes can be used.

[0023] <Input information> The Q-OCV curve of the secondary battery, which is one of the input information, can be obtained, for example, by performing the galvanostatic intermittent titration technique (GITT). In GITT, a cycle in which a secondary battery in a fully charged or fully discharged state is discharged or charged for a certain period of time and then rested for a certain period of time is repeated until the discharge cut-off voltage or the charge cut-off voltage. At this time, the Q-OCV curve of the secondary battery can be obtained by plotting the OCV of the secondary battery after the rest against Q.

[0024] The magnitude of the current during charging or discharging may be any magnitude, but since the rest time until the OCV is reached becomes long at a large current, it is desirable that the C rate is 1C or less. Also, R at each SOC can be calculated by dividing the difference between the OCV before the start of charging or discharging and the closed-circuit voltage at the time when a predetermined charging or discharging time has elapsed by the magnitude of the current.

[0025] The Q-OCV curve can also be obtained by estimating the OCV based on the charge or discharge curve of the secondary battery in addition to GITT. Examples of the estimation means include using the relationship between the previously obtained open circuit voltage and the OCV, using an equivalent circuit model, and using a filter such as a Kalman filter.

[0026] As the prediction conditions for the life, values such as the operating conditions of the secondary battery, any time for which the prediction performance is desired, the upper and lower limit values of the prediction performance of the secondary battery, the positive electrode, and the negative electrode can be set.

[0027] For the type of active material, a compound name, chemical formula, abbreviation, etc. can be used. For example, for Ni x Mn y Co z O 2 which is a main positive electrode material, notations such as ternary system and NMC can be used. As the active material to which the method for predicting the life of the secondary battery according to the present embodiment can be applied, Ni x Mn y Co z O 2 etc., NMC-based materials such as LFP, LiCoO 2 LiNiO 2 LiMn 2 O 4 etc., as positive electrode active materials, graphite, hard carbon, soft carbon, Li metal, Li 4 Ti 5 O 12 TiNb 2 O 7 Si, SiO 2 etc., as negative electrode active materials can be mentioned, but are not limited thereto in terms of the method of the life prediction method described later. Information on the type of active material is not necessarily required and can also be determined in the diagnosis, but including it in the input information can reduce the time required for the diagnosis.

[0028] <Diagnosis> The diagnostic unit 201 diagnoses the internal state of the secondary battery. In the state diagnosis step by the diagnostic unit 201, based on the relationship between the state of charge and the open circuit voltage of the secondary battery, the relationship between the state of charge and the internal resistance, the relationship between the state of charge and the open circuit potential of the positive electrode active material and the relationship between the state of charge and the internal resistance, the relationship between the state of charge and the open circuit potential of the negative electrode active material and the relationship between the state of charge and the internal resistance, a process for diagnosing the degradation state of the secondary battery is performed.

[0029] The diagnosis of the secondary battery can be carried out by analyzing the Q-OCV curve of the secondary battery using the q-OCP curves of the positive and negative electrodes, and by analyzing the Q-R curve of the secondary battery using the q-r curves of the positive and negative electrodes. Through the diagnosis, the internal state of the secondary battery can be expressed using quantitative parameters. As parameters, the utilization amount of the active material of the positive and negative electrodes (m x ), the relative positional relationship of the q-OCP curves of the positive and negative electrodes (δ x ), the resistivity of the positive and negative electrodes (a x ), the resistance of the secondary battery other than the electrodes (Ro), etc. can be mentioned.

[0030] The utilization amount of the active material of the positive and negative electrodes (m x ) is an index indicating the amount of the active material that can contribute to the battery reaction among the active materials contained in the electrodes. The relative positional relationship of the q-OCP curves of the positive and negative electrodes (δ x ) is an index indicating the amount of electricity that is outside the voltage range of the secondary battery in the q-OCP curves of the positive and negative electrodes. The resistivity of the positive and negative electrodes (a x ) is an index indicating the resistance per unit mass or unit area of the electrodes. The resistance of the secondary battery other than the electrodes (Ro) is an index indicating the total amount of resistances other than the electrodes such as the resistance of the members of the secondary battery, the contact resistance, and the electrolyte resistance.

[0031] FIG. 3 is an example of the diagnostic result of the secondary battery according to the present embodiment and is the analysis result of the Q-OCV curve. FIG. 4 is an example of the diagnostic result of the secondary battery according to the present embodiment and is the analysis result of the Q-R curve.

[0032] In the diagnosis, the above parameters are made variable, the Q-OCV curve of the secondary battery is fitted with the q-OCP curves of the positive and negative electrodes, and the Q-R curve of the secondary battery is fitted with the q-r x curves of the positive and negative electrodes.

[0033] Let the capacities of the positive and negative electrodes be q p , q n respectively. Then, the capacity Q of the secondary battery is related to the utilization amounts of the active materials of the positive and negative electrodes (m x ), and the relative positional relationship (δ x ) of the q-OCP curves of the positive and negative electrodes, and can be expressed as Q = m p × q p - δ p = m n × q n - δ n ···(1) which can be represented by.

[0034] Also, the OCV (V c ) of the secondary battery can be expressed using the OCPs (V p , V n ) of the positive and negative electrodes as V c (Q) = V p (q p ) - V n (q n )···(2) which can be represented by.

[0035] The Q-OCV curve of the secondary battery is fitted with the q-OCP curves of the positive and negative electrodes so that equations (1) and (2) hold, and the values of the variable parameters at that time are taken as the diagnosis results. That is, the calculated values of the q-OCP of the positive and negative electrodes are adjusted so that the calculated value of the Q-OCV curve of the secondary battery matches the measured value of the Q-OCV curve of the secondary battery, and the values of the variable parameters at that time (the utilization amount of the active material (m x ) and the positional relationship (δ x )) are taken as the diagnosis results.

[0036] Also, using the positive electrode resistance r p , the negative electrode resistance r n , the internal resistance (Rc ) is R c (Q) = a p / m p ×r p (q p ) + a n / m n ×r n (q n ) ··· (3) can be expressed as

[0037] Fitting the Q-R curve of the secondary battery with the q-r curves of the positive and negative electrodes so that equations (1) and (3) hold, and using the values of the variable parameters at that time as the diagnosis result. That is, adjusting the calculated values of the resistances of the positive and negative electrodes so that the calculated value of the internal resistance (R x ) of the secondary battery matches the measured value of the internal resistance of the secondary battery, and using the values of the variable parameters at that time (the resistivity of the positive electrode (a c ) and the resistivity of the negative electrode (a p )) as the diagnosis result. n ) as the diagnosis result.

[0038] The q-OCP curves and q-r curves of the positive and negative electrodes can be obtained by the same means as the Q-OCV curve and Q-R curve of the secondary battery. At this time, by using a cell with metallic lithium used as the counter electrode for the positive or negative electrode, the q-OCP curves and q-r x curves of the positive and negative electrodes can be obtained. The q-OCP curves and q-r x curves of the positive and negative electrodes may be used as input information, or may be stored in the memory 11 of the life prediction device 1. x curves may be used as input information, or may be stored in the memory 11 of the life prediction device 1.

[0039] When the types of the active material materials are not given as input information, the types of the active material materials of the positive and negative electrodes are determined in the state diagnosis step. As a method thereof, the types of the positive electrode active material and the negative electrode active material are determined based on the fitting accuracy between the measured value and the measured value. For example, performing diagnosis while changing the types of the active material materials of the positive and negative electrodes and adopting the combination with the best fitting accuracy, etc. can be mentioned.

[0040] <Function determination> The function determination unit 202 determines the functional form of the parameters using reference data representing the relationship of the degradation behavior of the secondary battery with respect to an index using at least one of temperature, current, voltage, SOC range, and current rate, which are the operating conditions of the secondary battery. In the function determination step, a process is performed to represent the parameters acquired by the diagnosis of the diagnosis unit 201 in a functional form with the operating conditions of the secondary battery as variables. First, the time-series data of the parameters is made into a time function using the initial value and coefficients. Then, by functionalizing the dependence of the coefficients on the operating conditions of the secondary battery, the parameters can be calculated from the operating conditions of the secondary battery.

[0041] When making the time-series data of the parameters into a time function, an nth-order function, power, root rule, etc. can be set for each parameter.

[0042] When making the coefficients of the time function into a function with respect to the operating conditions of the secondary battery, it can be a polynomial using the operating conditions (temperature, voltage, current) and terms obtained by multiplying or dividing them. However, the polynomial using these arbitrary terms can only be used when the qualitative behavior of the parameters of the secondary battery for each term is the same regardless of the electrode active material. This reduces the accuracy of life prediction.

[0043] Therefore, in determining the functional form of the parameters according to this embodiment, reference data indicating the relationship of the behavior of the parameters with respect to the operating conditions is used based on the characteristics of the active material. The operating conditions of the secondary battery include at least one of temperature, current, voltage, SOC range, and current rate. Thereby, it is possible to construct a functional form that reflects the behavior of different parameters for each active material. As the reference data, maps, graphs, tables, functions, measured data, etc. that represent the relationship of the parameters with respect to the operating conditions and terms represented by their multiplication and subtraction can be used. The reference data is changed for each of the positive electrode active material and the negative electrode active material of the secondary battery. Also, feature amounts set for each active material with respect to the operating conditions and terms represented by their multiplication and subtraction can be used. For example, extreme values, the number of change points, weight coefficients, etc. can be used. The reference data may be used as input information or stored in the memory 11 of the life prediction device 1.

[0044] <Prediction> The prediction unit 203 performs life prediction of the secondary battery using the time-series data of the parameters representing the degradation states of the secondary battery, the positive electrode, and the negative electrode acquired in the diagnosis unit 201. In the prediction unit 203, in the functional form of the parameters with respect to the operating conditions determined by the function determination unit 202, values such as the operating conditions of the secondary battery, an arbitrary time for calculating the prediction performance, and the upper and lower limit values of the prediction performance of the secondary battery, the positive electrode, and the negative electrode can be set as the prediction conditions. The prediction unit 203 inputs the prediction conditions as described above and calculates the predicted values of the parameters using the functional form. The prediction unit 203 calculates the Q-OCV curve, Q-R curve of the secondary battery, and the q-OCP curve and q-r curve of the positive electrode and the negative electrode as shown in FIGS. 3 and 4 using the predicted values of the parameters, and thereby acquires the prediction performance of the secondary battery, the positive electrode, and the negative electrode as output information.

[0045] For example, in the calculated Q-OCV curve, predicted values of the capacities of the secondary battery, the positive electrode, and the negative electrode can be obtained from the voltage range of the secondary battery. Also, in the calculated Q-R curve, predicted values of the resistances of the secondary battery, the positive electrode, and the negative electrode at a predetermined SOC can be obtained from the voltage range of the secondary battery. Further, by normalizing the predicted values of the capacity and resistance with the initial values, predicted values of SOHQ (State of Health Capacity) and SOHR (State of Health Resistance) can be calculated.

[0046] Based on the calculated q-OCP curves of the positive electrode and the negative electrode, the capacities, resistances, active material utilization amounts (m x ), change amounts of the relative positional relationships (δ x ) of the q-OCP curves, resistivity (a x ), and predicted values of the resistance (R o ) of the secondary battery other than the electrodes can be obtained. Also, by normalizing the predicted values of the above performances with the initial values, predicted values of the change rates of the performances can be obtained.

[0047] <Output information> As output information of the method for predicting the life of a secondary battery according to the present disclosure, predicted values of at least one of the capacity, internal resistance, lithium ion loss amount, SOH, and SOC of the secondary battery can be output. Also, as output information of the method for predicting the life of a secondary battery according to the present disclosure, the capacity, internal resistance, active material utilization amount (m x ), relative positional relationship (δ x ) of the q-OCP curve, resistivity (a x ), internal resistance (Ro) of the secondary battery other than the electrodes, and at least one predicted value of their change rates can be output.

[0048] <Example> <Input information> In a secondary battery using an LFP positive electrode and a graphite negative electrode, a degradation test was carried out under the conditions shown in Table 1 of Fig. 6, and Q-OCV curves and Q-R curves were obtained by GITT at predetermined intervals. In this example, GITT was performed in which discharging at 1C for 72 seconds and resting for 30 minutes were repeated from the fully charged state to the fully discharged state. Then, the relationship between the OCV after a 30-minute rest and the discharge capacity was defined as the Q-OCV curve. Also, the relationship between the internal resistance after 72 seconds and the discharge capacity was defined as the Q-R curve.

[0049] The ○ plots in Fig. 3 are the measured values of the Q-OCV curve of the secondary battery before degradation, and the ○ plots in Fig. 4 are the measured values of the Q-R curve of the secondary battery before degradation.

[0050] <Diagnosis> Using a cell with a Li metal counter electrode, the q-OCP curves and q-r curves of the LFP positive electrode and the graphite negative electrode were obtained. Subsequently, using the obtained q-OCP curves and q-r curves of the LFP positive electrode and the graphite negative electrode, the Q-OCV curve and the Q-R curve of the secondary battery were analyzed respectively. In the analysis, based on equations (1) and (2), the calculated value of the Q-OCV curve of the secondary battery was calculated as the sum of the q-OCP curves of the LFP positive electrode and the graphite negative electrode. Also, based on equations (1) and (3), the calculated value of the Q-R curve of the secondary battery was calculated as the sum of the q-r curves of the LFP positive electrode and the graphite negative electrode.

[0051] Then, fitting (analysis) was performed so that the errors between the measured values and calculated values of the Q-OCV curve of the secondary battery and between the measured values and calculated values of the Q-R curve were minimized, and the values of seven variable parameters (m x , δ x , a x , Ro) were obtained. By performing the above flow for the Q-OCV curves and Q-R curves obtained at each predetermined interval of the degradation test, time-series data of the parameters were obtained.

[0052] <Function determination> Using the time-series data of the parameters obtained by the above diagnosis as reference data, it was functionalized using equation (4). Note that p 0is the initial value of each parameter, A is the coefficient, t is the test period, and K is the power. p = p 0 ±A * (t K ) ··· (4)

[0053] Subsequently, the coefficient A and the power K were functionalized using the temperature and SOC, which are the operating conditions of the secondary battery. Examples of the functional form include Equation (5) and Equation (6). Note that a 1 ~a 4 are coefficients and T is the temperature. A(K) = a 1 ×SOC + a 2 ×T + a 3 ··· (5) A(K) = a 1 ×SOC + a 2 ×T + a 3 ×SOC × T + a 4 ··· (6)

[0054] <Prediction> Using the functional form with the operating conditions of the secondary battery as the explanatory variable and the parameter as the objective variable, the predicted value of the parameter after 570 days was calculated. Subsequently, using the predicted value of the parameter, the predicted value of the Q-OCV curve of the secondary battery was calculated. The solid line in Figure 5 is the predicted value of the Q-OCV curve after 570 days of No. 9 in Table 2 shown in Figure 7, and the ○ plot is the measured value. Based on the calculated predicted value of the Q-OCV curve, the capacity corresponding to the operating voltage of the secondary battery was obtained, and the predicted value of the capacity was acquired.

[0055] <Output> The predicted value of the capacity after 570 days obtained was divided by the capacity at the non-deteriorated time to calculate SOHQ.

[0056] <Comparative Example> In the same degradation test as in the example, prediction by the root rule was carried out using the measured value of SOHQ obtained during GITT. Specifically, fitting was carried out on the time-dependent change data of SOHQ using Equation (7). Note that a is the coefficient. SOHQ = 100 - a × √t ··· (7)

[0057] Subsequently, the coefficient a was functionalized using the temperature and SOC, which are the operating conditions of the secondary battery. Equation (4) was used for the functional form. Using the functional form with the operating conditions of the fabricated secondary battery as the explanatory variables and SOHQ as the objective variable, the predicted value of SOHQ after 570 days was obtained.

[0058] <Results> Table 2 shown in Fig. 7 shows the prediction accuracy of SOHQ after 570 days in the examples and comparative examples. It shows the difference obtained by subtracting the predicted value of SOHQ from the measured value of SOHQ. The unit [pt] is the percentage indicating the ratio of the capacity before and after deterioration. As shown in Table 2, it can be understood that, except for #3, #5, and #6, the accuracy of SOHQ is higher in the examples than in the comparative examples, and the life of the secondary battery can be predicted with high accuracy.

[0059] As described above, the embodiments of the present invention have been described in detail. However, the present invention is not limited to the above-described embodiments, and various design changes can be made without departing from the spirit of the present invention described in the claims. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Also, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Furthermore, for a part of the configuration of each embodiment, addition, deletion, or replacement with other configurations is possible.

Explanation of Reference Numerals

[0060] 1 ··· Life prediction device, 201 ··· Diagnosis unit, 202 ··· Function determination unit, 203 ··· Prediction unit

Claims

1. A method for predicting the life of a secondary battery, comprising: a state diagnosis step of diagnosing the degradation state of the secondary battery based on input information; a function determination step of determining a functional form of a parameter representing the degradation state obtained in the state diagnosis step based on the characteristics of the positive electrode active material and the negative electrode active material of the secondary battery; a life prediction step of predicting the life of the secondary battery based on the functional form of the parameter determined in the function determination step; A method for predicting the life of a secondary battery, characterized by including the above steps.

2. In the function determination step, the functional form of the parameter is determined using reference data representing the relationship of the degradation behavior of the secondary battery with respect to an index using at least one of temperature, current, voltage, SOC range, and current rate, which are operating conditions of the secondary battery. The method for predicting the life of a secondary battery according to Claim 1, characterized by this.

3. In the function determination step, the reference data is changed for each type of the positive electrode active material and the negative electrode active material of the secondary battery. The method for predicting the life of a secondary battery according to Claim 2, characterized by this.

4. In the life prediction step, the life of the secondary battery is predicted using time-series data of parameters representing the degradation states of the secondary battery, the positive electrode, and the negative electrode obtained in the state diagnosis step. The method for predicting the life of a secondary battery according to Claim 1, characterized by this.

5. In the state diagnosis step, when the input information does not include information on the types of the positive electrode active material and the negative electrode active material of the secondary battery, the types of the positive electrode active material and the negative electrode active material are determined based on the fitting accuracy between the measured value and the measured value. The method for predicting the life of a secondary battery according to Claim 1, characterized by this.

6. The input information obtained in the state diagnosis step includes at least one of temperature, current, voltage, SOC range, and current rate, which are operating conditions of the secondary battery. The information predicted by the life prediction step includes at least one predicted value of the capacity, internal resistance, lithium ion loss amount, and SOH of the secondary battery, or at least one predicted value of the capacity, internal resistance, active material utilization amount, and resistivity of the positive electrode or the negative electrode. The method for predicting the life of a secondary battery according to Claim 1, characterized by this.

7. The method for predicting the life of a secondary battery according to claim 1, wherein in the state diagnosis step, the degradation state of the secondary battery is diagnosed based on the relationship between the state of charge and the open circuit voltage of the secondary battery and the relationship between the state of charge and the internal resistance.

8. The method for predicting the life of a secondary battery according to claim 1, wherein in the state diagnosis step, the degradation state of the secondary battery is diagnosed based on the relationship between the state of charge and the open circuit potential of the positive electrode active material and the relationship between the state of charge and the internal resistance, and the relationship between the state of charge and the open circuit potential of the negative electrode active material and the relationship between the state of charge and the internal resistance.

9. A diagnosis unit that diagnoses the degradation state of a secondary battery based on input information, A function determination unit that determines the functional form of parameters based on the characteristics of the positive electrode active material and the negative electrode active material, A prediction unit that predicts the life of the secondary battery based on the determined functional form, A device for predicting the life of a secondary battery, characterized in that the operating conditions of the secondary battery are used as input information, and the predicted performance of the secondary battery, positive electrode, and negative electrode under the operating conditions is used as output information.

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