Secondary battery deterioration prediction system and secondary battery deterioration prediction method

Abstract

To highly accurately diagnose an internal state of a secondary battery composed of active materials with small voltage changes due to changes in a charging state.SOLUTION: A secondary battery deterioration prediction system includes: acquisition parts 121 and 122 for acquiring a relation between a charging state of a secondary battery and an open circuit voltage and a relation between the charging state and an internal resistance of the battery under each operating condition of the secondary battery; an internal state diagnosis part 123 for diagnosing deterioration states of positive and negative electrodes of the secondary battery under each operating condition based on the relation acquired by the acquisition parts 121 and 122; a life prediction formula creation part 126 for creating a life prediction formula for predicting a life of the secondary battery based on time-series changes in the deterioration states acquired by the internal state diagnosis part 123; and a deterioration calculation part 127 for calculating the deterioration of the secondary battery based on the life prediction formula created by the life prediction formula creation part 126. Herein, the life prediction formula creation part 127 creates the life prediction formula based on the time-series changes in the deterioration states of the positive and the negative electrodes when a load is applied to the secondary battery.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a deterioration prediction system and a deterioration prediction method for a secondary battery. [Background technology]

[0002] In order to reduce carbon dioxide emissions, it is necessary to replace the energy derived from thermal power plants, which is currently the main source of supply for energy demand, with renewable energy sources such as solar and wind power. However, when a thermal power generator is disconnected from the grid, its ability to supply the adjustment power required to stabilize the grid in response to fluctuations in demand decreases. On the other hand, individual renewable energy generators may be equipped with an energy storage system (BESS: Battery Energy Storage System) that uses secondary batteries to mitigate fluctuations in their own power generation. If the capacity of these batteries can be utilized as adjustment power, it can contribute to grid stability.

[0003] The performance of a power storage system decreases due to the deterioration of secondary batteries during operation. Therefore, when introducing a power storage system with the aim of operating it for a specified period of time, it is necessary to design it by anticipating the deterioration of secondary batteries. However, if the accuracy of the prediction of the deterioration of secondary batteries is low, it is necessary to design the system with a large safety margin, and the installation of excessive batteries will result in high costs for the power storage system. Therefore, there is a demand for a deterioration prediction system that can predict the life of batteries with high accuracy.

[0004] A method for detecting internal information of a secondary battery is described in Patent Document 1. In the detection method described in Patent Document 1, the charge / discharge curve of the positive electrode and the charge / discharge curve of the negative electrode are superimposed and calculated to reproduce the charge / discharge curve of the secondary battery, and the deterioration state of the positive electrode and the negative electrode is evaluated nondestructively. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2009-080093 A Summary of the Invention [Problem to be solved by the invention]

[0006] As described above, the method described in Patent Document 1 reproduces the charge / discharge curve of a secondary battery based on the charge / discharge curves of the positive and negative electrodes. In the process of reproducing the charge / discharge curve, the effective active material amounts of the positive and negative electrodes, an index for the positional relationship of the charge / discharge curves of the positive and negative electrodes, and the open circuit potentials of the positive and negative electrodes are obtained.

[0007] Even in the conventional method described in Patent Document 1, the voltage changes depending on the change in the charging state, and the LiNi x Mn y Co z M 1-x-y-z It is possible to perform highly accurate diagnosis of a battery system consisting of an O2 positive electrode (M: any element), a LiMn2O4 positive electrode, graphite, a Si negative electrode, a SiO negative electrode, etc. However, the change in voltage due to the change in the charging state is small for the LiFePO4 positive electrode and Li4Ti5O 12 It was difficult to diagnose the battery system, which is made up of the negative electrode and other components.

[0008] In view of the above, an object of the present invention is to provide a secondary battery deterioration prediction system and a secondary battery deterioration prediction method that can diagnose with high accuracy the internal state of a secondary battery composed of an electrode active material that exhibits small voltage changes due to changes in the charging state. [Means for solving the problem]

[0009] In order to solve the above problems, for example, the configurations described in the claims are adopted. The present application includes multiple means for solving the above problems, and one example is a deterioration prediction system for a secondary battery, comprising an acquisition unit that acquires the relationship between the charge state and open circuit voltage of the secondary battery, and the relationship between the charge state and internal resistance of the battery under each operating condition of the secondary battery, an internal state diagnosis unit that diagnoses the deterioration state of the positive and negative electrodes of the secondary battery under each operating condition based on the relationship acquired by the acquisition unit, a life prediction formula creation unit that creates a life prediction formula for predicting the life of the secondary battery based on the time-series change in the deterioration state obtained by the internal state diagnosis unit, and a deterioration calculation unit that calculates the deterioration of the secondary battery based on the life prediction formula created by the life prediction formula creation unit. Here, the life prediction equation creating section creates a life prediction equation based on time-series changes in the deterioration states of the positive and negative electrodes when a load is applied to the secondary battery. Effect of the Invention

[0010] According to the present invention, even in a secondary battery system in which the change in voltage relative to the change in the state of charge is small, the internal state of the secondary battery can be diagnosed with high accuracy, and the lifespan can be predicted with high accuracy based on the obtained diagnosis results. Problems, configurations and effects other than those described above will become apparent from the following description of the embodiments. [Brief description of the drawings]

[0011] [Figure 1] 1 is a block diagram showing an example of the configuration of a deterioration prediction system for a secondary battery according to an embodiment of the present invention; [Diagram 2] 2 is a block diagram showing an example of a functional configuration of a calculation unit of a deterioration prediction system for a secondary battery according to an embodiment of the present invention; FIG. [Diagram 3] 5 is a diagram showing an example of an internal state diagnosis (battery voltage, positive electrode potential, negative electrode potential) of a secondary battery according to an embodiment of the present invention. FIG. [Figure 4] 5 is a diagram showing an example of an internal state diagnosis (battery resistance, positive electrode resistance, negative electrode resistance) of a secondary battery according to an embodiment of the present invention. FIG. [Diagram 5]FIG. 4 is a diagram showing an example of time series changes in deterioration parameters (active material utilization rate of a positive electrode and active material utilization rate of a negative electrode) of a secondary battery according to an embodiment of the present invention. [Figure 6] FIG. 2 is a diagram showing an example of time series changes in deterioration parameters of a secondary battery according to an embodiment of the present invention (amount of lithium ion loss associated with film formation on the positive electrode surface, and amount of lithium ion loss associated with film formation on the negative electrode surface). [Figure 7] 5 is a flowchart showing an example of a process for predicting a life span of a secondary battery according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] A secondary battery deterioration prediction system and a secondary battery deterioration prediction method according to one embodiment of the present invention (hereinafter referred to as "this example") will be described below with reference to the accompanying drawings.

[0013] 1 shows an example of the configuration of a secondary battery deterioration prediction system 10 of this embodiment. The secondary battery deterioration prediction system 10 of this embodiment includes an input unit 11, a calculation unit 12, and an output unit 13. The input unit 11 receives input factors for predicting deterioration of the secondary battery, such as operating conditions of the secondary battery, calculation end conditions, and output intervals. Operating conditions of the secondary battery include, for example, temperature and load (current). Calculation end conditions include a period (e.g., 10 years) and battery capacity decreasing to 70% or less. An example of the output interval is to output the calculation results every 10 days. These input factors are input, for example, by an operator who performs the deterioration prediction.

[0014] The calculation unit 12 performs calculation processing relating to the capacity and resistance of the secondary battery until the end condition input to the input unit 11 is satisfied. The output unit 13 outputs the calculation results of the capacity and resistance of the secondary battery obtained by the calculation unit 12 as values ​​at output intervals for the period input to the input unit 11 (eg, every 10 days).

[0015] The deterioration prediction system 10 for a secondary battery shown in Fig. 1 is configured, for example, by a computer. That is, as shown in Fig. 1, the deterioration prediction system 10 for a secondary battery configured by a computer device includes a CPU (Central Processing Unit) 1, a memory 2, a storage 3, an input device 4, a network interface 5, and an output unit 6, which are all connected to a bus.

[0016] The CPU 1 is an arithmetic processing unit that reads out from the memory 2 or the storage 3 and executes the program code of the software that realizes the functions performed by the deterioration prediction system 10 for a secondary battery. The CPU 1 reads out program codes from the memory 2 or the storage 3 and executes arithmetic processing in the work area of ​​the memory 2, whereby various processing function units shown in FIG.

[0017] For example, a large-capacity information storage medium such as a hard disk drive (HDD), a solid state drive (SSD), a memory card, etc. is used as the storage 3. The storage 3 stores software that realizes the functions of the secondary battery deterioration prediction system 10 and data obtained by executing the program.

[0018] The input device 4 includes devices such as a keyboard and a mouse, and performs input processing of various information based on the operation of the operator. The network interface 5 may be, for example, a network interface card (NIC), and receives programs from the outside and transmits processing results. The output unit 6 uses output devices such as a display and a printer, and performs output processing such as displaying and printing the results of calculations.

[0019] 1 is merely an example, and the system 10 may be configured with an arithmetic processing device other than a computer. For example, some or all of the functions performed by the system 10 may be realized by hardware such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC).

[0020] FIG. 2 shows each processing unit constituting the calculation unit 12 of the secondary battery deterioration prediction system 10 of this embodiment. The calculation unit 12 includes a positive electrode information and negative electrode information acquisition unit 121, a battery information acquisition unit 122, an internal state diagnosis unit 123, a time series change equation creation unit 124, a function creation unit 125, a life prediction equation creation unit 126, and a deterioration calculation unit 127. Each of these processing units 121 to 127 is configured in the memory 2 shown in Fig. 1 by executing software that realizes the functions performed by the secondary battery deterioration prediction system 10.

[0021] However, the positive electrode information and negative electrode information acquiring unit 121 and the battery information acquiring unit 122 may acquire information measured using equipment other than the deterioration prediction system 10 for secondary batteries. The calculations performed by the calculation unit 12 include a life prediction calculation for storage deterioration of the secondary battery and a life prediction calculation for operational deterioration of the secondary battery. First, an example of a case where the calculation unit 12 performs a life prediction calculation for storage deterioration of the secondary battery will be described.

[0022] The positive electrode information and negative electrode information acquisition unit 121 performs an acquisition process for acquiring information on the open circuit potential (OCP) and resistance of the positive electrode and negative electrode, which are necessary for diagnosing the internal state of the battery. An example of a method for acquiring information in the positive electrode information and negative electrode information acquiring unit 121 will be described below. First, a positive electrode single-electrode cell is prepared using the positive electrode and lithium metal used in the secondary battery to be predicted for life expectancy, and a negative electrode single-electrode cell is prepared using the negative electrode and lithium metal.Next, the Galvanostatic Intermittent Titration Technique (GITT) is performed on the single-electrode cell to obtain information on the open circuit potential and resistance.

[0023] In the constant current intermittent titration method, a single-electrode cell is fully charged, then discharged at a current equivalent to 1C[A] for 72 seconds, followed by a 30-minute rest, and this cycle is repeated until the discharge end potential is reached. The open circuit potential at each state of charge (SOC) is the potential 30 minutes after the discharge current is stopped. The resistance at each state of charge (SOC) is calculated by dividing the difference between the open circuit potential before the start of discharge and the open circuit potential after a specified discharge time has elapsed by the discharge current.

[0024] The battery information acquisition unit 122 performs an acquisition process for acquiring information on the secondary battery's open circuit voltage (OCV) and resistance, which are necessary for the internal state diagnosis unit 123 to diagnose the internal state of the battery. An example of a method for acquiring information in battery information acquisition section 122 will be described below. First, a storage test is conducted on the secondary battery, in which the battery is stored for a certain period of time at a specified temperature and state of charge (SOC). When carrying out a storage test, the battery information acquisition unit 122 periodically stops the test, performs a constant current intermittent titration method, and acquires information on the open circuit voltage and resistance.

[0025] In the constant current intermittent titration method, a single-electrode cell is fully charged, then discharged at a current equivalent to 1C[A] for 72 seconds, followed by a 30-minute rest, and this cycle is repeated until the discharge end potential is reached. The open circuit potential at each state of charge (SOC) is the voltage 30 minutes after the discharge current is stopped. The resistance at each state of charge (SOC) is calculated by dividing the difference between the open circuit voltage before the start of discharge and the open circuit voltage after a specified discharge time has elapsed by the discharge current.

[0026] The internal state diagnosis unit 123 uses the information obtained by the positive electrode information and negative electrode information acquisition unit 121 and the battery information acquisition unit 122 to perform an internal state diagnosis process for diagnosing the internal state of the secondary battery. That is, the internal state diagnosis unit 123 diagnoses the internal state of the secondary battery by comparing the open circuit potential and resistance data of the positive and negative electrodes obtained by the positive electrode information and negative electrode information acquisition unit 121 with the open circuit voltage and resistance data of the secondary battery obtained by the battery information acquisition unit 122. The internal state is diagnosed by measuring the positive electrode active material utilization rate m, which is correlated with battery deterioration. p , the utilization rate of the active material in the negative electrode m n , the amount of lithium ion loss associated with the formation of a coating on the positive electrode surface δ p , the amount of lithium ion loss associated with the formation of a coating on the negative electrode surface δ n , the battery solution resistance R0, and the rate of increase in the positive electrode resistance a p , and the rate of increase in the resistance of the negative electrode a n etc.

[0027] Here, the utilization rate of the positive electrode active material m p is an index showing the ratio of active material that can contribute to the battery reaction to the positive electrode active material contained in the electrode. Utilization rate of the active material in the negative electrode m n is an index showing the ratio of active material that can contribute to the battery reaction to the negative electrode active material contained in the electrode. Loss of lithium ions due to the formation of a film on the positive electrode surface δ p is an index showing the amount of electricity of the lithium ions taken up in the coating formed by the reaction with the electrolyte on the positive electrode surface.

[0028] The amount of lithium ion loss due to the formation of a coating on the negative electrode surface δ n is an index showing the amount of electricity of the lithium ions taken up in the coating film formed by the reaction with the electrolyte on the negative electrode surface. The solution resistance R0 of a battery is an index that indicates the resistance of the battery components when the battery is in use. Positive electrode resistance increase rate a p is an index showing the rate of increase in the resistance of the positive electrode when the battery is used. Negative electrode resistance increase rate a n is an index showing the rate of increase in the resistance of the negative electrode when the battery is used.

[0029] These seven indicators are used as variable parameters, and the data on the open circuit potential and resistance of the positive and negative electrodes obtained by the positive electrode information and negative electrode information acquisition unit 121 is fitted to the data on the open circuit voltage and resistance of the secondary battery obtained by the battery information acquisition unit 122.

[0030] 3 and 4 are diagrams showing examples of fitting results. Figure 3 shows the actual open circuit voltage (OCV) of the secondary battery (d11), its calculated value (d12), the positive electrode potential (calculated value) (d13), and the negative electrode potential (calculated value) (d14). In Figure 3, the vertical axis on the left side shows the battery voltage and the positive electrode potential [V], and the vertical axis on the right side shows the negative electrode potential [V]. The horizontal axis shows the battery capacity [Ah]. In FIG. 3, the actual measured value d11 of the open circuit voltage of the secondary battery is plotted as a white circle, and the calculated value d12 is a value calculated based on that.

[0031] Fig. 4 shows the actual resistance value d21 of the secondary battery, its calculated value d22, the positive electrode resistance (calculated value) d23, and the negative electrode resistance (calculated value) d24. In Fig. 4, the vertical axis represents resistance [mΩ], and the horizontal axis represents battery capacity [Ah]. In FIG. 4, the actual measured value d21 of the resistance of the secondary battery is plotted as a white circle, and the calculated value d22 is a value calculated based on that.

[0032] Here, we explain the formula for obtaining these values. First, the positive electrode capacity is q p , the negative electrode capacity is q n Then, the capacity of the secondary battery Q c can be expressed by the following equation: Q c =m p ×q p -δ p =m n ×q n -δ n

[0033] Next, the open circuit voltage V at each capacity (charged state) of the secondary battery c is the open circuit potential of the positive and negative electrodes (V p , V n ) can be expressed as follows: V c (Q c )=V p (q p )-V n (q n )

[0034] Furthermore, the positive resistance is r p , the negative electrode resistance is r n Then, the resistance R at each capacity (each charging state) of the secondary battery is c can be expressed by the following equation: R c (Q c )=a p / m p ×r p (q p )+a n / m n ×r n (q n )+R0

[0035] Returning to the explanation of FIG. 2, the time-series change equation creation section 124 creates a time-series change equation of the internal parameter obtained by the internal state diagnosis section 123. Figure 5 shows the negative electrode active material utilization ratio m n The time series change in d31 and the utilization rate of the positive electrode active material m p 5 shows an example of a time series change d32 of the negative electrode active material utilization rate. In FIG. 5, the horizontal axis represents time, and the vertical axis represents the negative electrode active material utilization rate.

[0036] Figure 6 shows the amount of lithium ion loss δ associated with the formation of a coating on the negative electrode surface. n The time series change in d41 and the amount of lithium ion loss due to the formation of a coating on the positive electrode surface δ p 6 shows an example of a time series change d42 of the amount of lithium ion loss due to the formation of a coating on the negative electrode surface.

[0037] The curves shown in Figures 5 and 6 are approximation curves of the time series changes of each parameter. The shape of the approximation curve can be arbitrarily set for each parameter, such as a linear curve, a quadratic curve, a root system equation, or a power system equation. In this manner, the time-series change equation creating section 124 creates a time-series change equation that indicates the time-series change of the internal parameter obtained by the internal state diagnosis section 123. For example, the time series change equation creation unit 124 calculates the active material utilization rate m n The time series change equation is as follows: m n =m0-m1×t Create a. In addition, the time-series change equation creation unit 124 calculates the amount of lithium ion loss δ associated with the formation of a coating on the negative electrode surface shown in FIG. n The time series change equation is as follows: δ n =m 0′ +m 1′ ×(t) 0.5 Create a.

[0038] Next, the function generating unit 125 generates a function of the operating conditions using the coefficients of the time series change equation of each deterioration parameter obtained by the time series change equation generating unit 124. Here, an example of the function generation will be described. The function generation unit 125 generates the negative electrode active material utilization rate m n、 The amount of lithium ion loss due to the formation of a coating on the negative electrode surface δ n Time series change equation m n =m0-m1×t δ n =m 0′ +m 1′ ×(t) 0.5 For coefficients m1 and m 1’ The dependency of the operating conditions is expressed as a function.

[0039] An example of a function is: m1(m 1′ )=α+β×temperature+γ×SOC m1(m 1′ )=α×(β×temperature)+(γ×SOC) etc. Here, α, β, and γ are coefficients whose values ​​are determined by fitting.

[0040] The above explanation is for the case where the calculation unit 12 calculates the storage deterioration of a secondary battery. Next, a case where the calculation unit 12 calculates the operational deterioration of the secondary battery will be described. First, the positive electrode information and negative electrode information acquisition unit 121 acquires information on the open circuit potential and resistance of the positive electrode and negative electrode, which are necessary for diagnosing the internal state of the battery. The acquisition method here is the same as that for calculating the storage deterioration.

[0041] Next, the battery information acquisition unit 122 acquires information on the secondary battery's open circuit voltage and resistance, which are necessary for diagnosing the internal state of the battery. Here, the battery information acquiring unit 122 first performs a cycle test on the secondary battery, in which charging and discharging are repeated at a predetermined temperature and within a predetermined range of state of charge (SOC) at a constant current. When the cycle test is carried out, the battery information acquisition unit 122 periodically stops the test, performs constant current intermittent titration (GITT), and acquires information on the open circuit voltage and resistance.

[0042] In the constant current intermittent titration method, a single-electrode cell is fully charged, then discharged at a current equivalent to 1C[A] for 72 seconds, followed by a 30-minute rest, and this cycle is repeated until the discharge end potential is reached. The open circuit potential at each state of charge (SOC) is the voltage 30 minutes after the discharge current is stopped. The resistance at each state of charge (SOC) is calculated by dividing the difference between the open circuit voltage before the start of discharge and the open circuit voltage after a specified discharge time has elapsed by the discharge current.

[0043] Then, the internal state diagnosis unit 123 uses the information obtained by the positive electrode information and negative electrode information acquisition unit 121 and the battery information acquisition unit 122 to diagnose the internal state of the secondary battery. That is, the internal state diagnosis unit 123 diagnoses the internal state of the secondary battery by comparing the open circuit potential and resistance data of the positive and negative electrodes obtained by the positive electrode information and negative electrode information acquisition unit 121 with the open circuit voltage and resistance data of the secondary battery obtained by the battery information acquisition unit 122. The internal state is diagnosed based on the positive electrode active material utilization rate m, which is correlated with battery deterioration. p , the utilization rate of the active material in the negative electrode m n , the amount of lithium ion loss associated with the formation of a coating on the positive electrode surface δ p , the amount of lithium ion loss associated with the formation of a coating on the negative electrode surface δ n , the battery solution resistance R0, and the rate of increase in the positive electrode resistance a p , the rate of increase in the resistance of the negative electrode a n etc.

[0044] The internal state diagnosis unit 123 uses these seven as variable parameters, and fits the data of the open circuit potential and resistance of the positive and negative electrodes obtained by the positive electrode information and negative electrode information acquisition unit 121 to the data of the open circuit voltage and resistance of the secondary battery obtained by the battery information acquisition unit 122. The fitting method is the same as that used when calculating the storage deterioration of a secondary battery.

[0045] Furthermore, the time series change equation creation unit 124 creates a time series change equation of the internal parameter obtained by the internal state diagnosis unit 123. Here, since the internal parameters of the operating load include the influence of the storage load, the time series change equation creation unit 124 calculates the change in the internal parameters of only the operating load by subtracting the change in parameters due to the storage load calculated using the function of each parameter of the storage load obtained when calculating the storage deterioration.

[0046] Next, the time-series change equation creation unit 124 plots the time-series changes of each parameter and draws an approximation curve. The shape of the approximation curve can be arbitrarily set for each parameter, such as a linear curve, a quadratic curve, a root system equation, or a power system equation.

[0047] Then, the function generating unit 125 generates a function of the operating conditions from the coefficients of the time series change equation of each of the obtained deterioration parameters. As an example of the function generation, the function generation unit 125 generates the negative electrode active material utilization rate m n、 The amount of lithium ion loss due to the formation of a coating on the negative electrode surface δ n Time series change equation m n =m0-m1×t δ n =m 0′ +m 1′ ×(t) 0.5 For coefficients m1 and m 1′ The dependency of the operating conditions is expressed as a function.

[0048] An example of a function is: m1(m 1′ )=α+β×temperature+γ×center SOC+ε×SOC range+ζ×current m1(m 1′ ) = α × (β × temperature) × (γ × center SOC) × (ε × SOC range) × (ζ × current) etc. Here, α, β, γ, ε, and ζ are coefficients. The values ​​of these coefficients α, β, γ, ε, and ζ are determined by fitting.

[0049] Then, the life prediction equation creation unit 126 inputs the operating conditions of the secondary battery whose life is to be predicted into the function created by the function creation unit 125 when calculating the storage deterioration of the secondary battery, and into the function created by the function creation unit 125 when calculating the operational deterioration of the secondary battery, to create a life prediction equation for the secondary battery whose life is to be predicted. The deterioration calculation unit 127 calculates the internal deterioration parameters based on the life prediction equation created by the life prediction equation creation unit 126 .

[0050] An example of how the life prediction equation creation unit 126 creates a life prediction equation will be described. The life prediction equation creation unit 126 inputs the internal deterioration parameters into the following equation to obtain a capacity Q c , resistance R c Create a life prediction formula to calculate Q c =m p ×q p -δ p =m n ×qn -δ n R c =a p / m p ×r p +a n / m n ×r n +R0

[0051] Next, the deterioration calculation unit 127 calculates the result of the life prediction formula obtained by the life prediction formula creation unit 126 as the initial capacity Q ini and initial resistance R ini Divide by to calculate the percent change in capacitance and resistance. In this way, the deterioration calculation unit 127 can obtain the time series changes in the rate of change of the capacitance and resistance as shown in Fig. 5 and Fig. 6. Specifically, Rate of change of capacity SOHQ=Q c / Q ini ×100 Rate of change of resistance SOHR=R c / R ini ×100 It can be shown as:

[0052] FIG. 7 is a flow chart showing the calculation process of the storage deterioration and the operational deterioration of the secondary battery performed by the calculation unit 12 described above. First, the positive electrode information and negative electrode information acquisition unit 121 acquires information on the open circuit potential and resistance of the positive electrode and negative electrode, which is necessary for diagnosing the internal state of the battery (step S11). Moreover, the battery information acquisition unit 122 acquires information on the secondary battery's open circuit voltage and resistance, which are necessary for diagnosing the internal state of the battery (step S12).

[0053] Then, the internal state diagnosis unit 123 diagnoses the internal state of the secondary battery using the information on the open circuit potential and resistance of the positive and negative electrodes obtained by the positive electrode information and negative electrode information acquisition unit 121 in step S11, and the information on the secondary battery open circuit voltage and resistance obtained by the battery information acquisition unit 122 in step S12 (step S13). Based on the diagnosis of this internal state, the time-series change equation creation unit 124 creates a time-series change equation for storage deterioration of the secondary battery and a time-series change equation for operational deterioration of the secondary battery (step S14).

[0054] Furthermore, the function generator 125 generates a function based on the operating conditions of the time series change equation of the storage deterioration of the secondary battery, and generates a function based on the operating conditions of the time series change equation of the operational deterioration of the secondary battery (step S15). Once the function generation unit 125 has performed the function generation, the life prediction formula generation unit 126 performs a life prediction formula generation process in which the operating conditions of the secondary battery whose life is to be predicted are input into the function generated when calculating the storage deterioration of the secondary battery and the function generated when calculating the operational deterioration of the secondary battery, to generate a life prediction formula for the secondary battery whose life is to be predicted (step S16).

[0055] Thereafter, the deterioration calculation unit 127 calculates the internal deterioration parameters based on the life prediction equation created by the life prediction equation creation unit 126 (step S17). When calculating the internal deterioration parameters in step S17, the deterioration calculation unit 127 may, for example, acquire the operating conditions of the secondary battery to calculate the deterioration state. The calculated internal deterioration parameters are output as a graph of the deterioration state or a table of the deterioration state as shown in Fig. 5 or Fig. 6, for example.

[0056] As described above, according to the secondary battery deterioration prediction system 10 of this embodiment, even in a secondary battery system in which the change in voltage relative to the change in the charge state is small, the internal state of the secondary battery can be diagnosed with high accuracy, and the lifespan can be predicted with high accuracy based on the obtained diagnosis results.

[0057] Here, the internal state diagnosis unit 123 diagnoses the deterioration state of the positive and negative electrodes using a group of relationships between the state of charge and the open circuit potential specific to the positive electrode active material of the secondary battery, a group of relationships between the state of charge and the internal resistance, a group of relationships between the state of charge and the open circuit potential specific to the negative electrode active material of the secondary battery, and a group of relationships between the state of charge and the internal resistance. In this respect, the internal state diagnosis unit 123 can be said to contribute to the high accuracy of life prediction.

[0058] Furthermore, the secondary battery deterioration prediction system 10 of this embodiment is provided with a time series change equation creation unit 124 that creates a time series change equation for the state diagnosed by the internal state diagnosis unit 123, and a life prediction equation creation unit 126 creates a life prediction equation based on the time series change equation created by the time series change equation creation unit 124. This enables appropriate life prediction that reflects time series changes.

[0059] Furthermore, when diagnosing the deterioration states of the positive and negative electrodes, the internal state diagnosis unit 123 extracts parameters, such as the active material utilization rate of the positive electrode, the active material utilization rate of the negative electrode, the amount of lithium ion loss associated with the formation of a coating on the positive electrode surface, the amount of lithium ion loss associated with the formation of a coating on the negative electrode surface, the solution resistance of the battery, the resistance increase rate of the positive electrode, and the resistance increase rate of the negative electrode, to diagnose the internal state. In this respect, too, it can be said that the internal state diagnosis unit 123 contributes to improving the accuracy of life prediction. Note that, in the above-mentioned embodiment, extracting all of these parameters and using them to diagnose the internal state is just one example, and a part (at least one) of these parameters may be extracted and used to diagnose the internal state.

[0060] Furthermore, the time-series change equation creating unit 124 creates a battery life prediction equation based on the time-series changes of the parameters extracted by the internal state diagnosis unit 123. In this respect, the time-series change equation creating unit 124 contributes to improving the accuracy of life prediction.

[0061] Furthermore, the internal condition diagnosis unit 123 diagnoses the deterioration of the secondary battery due to storage load and deterioration due to operational load, and the life prediction formula creation unit 126 creates a battery life prediction formula by combining the deterioration due to storage load and the deterioration due to operational load, thereby making it possible to make an appropriate life prediction that reflects the time-series changes due to each type of deterioration.

[0062] Furthermore, the deterioration calculation unit 127 acquires the operating conditions of the secondary battery, and calculates and outputs the time-series changes in the capacity and resistance of the secondary battery, thereby enabling appropriate life prediction based on the operating conditions of the secondary battery.

[0063] The embodiment described above has been described in detail to clearly explain the present invention, and is not necessarily limited to those having all of the configurations described. In other words, the present invention is not limited to the above-described embodiment, and various modifications and applications within the technical concept of the present invention are also included within its scope.

[0064] For example, in the embodiment described above, the life prediction formula creation unit 126 calculates the time series changes in the deterioration states of the positive and negative electrodes for storage deterioration, which is when no load is applied to the secondary battery, and the time series changes in the deterioration states of the positive and negative electrodes for operational deterioration, which is when a load is applied to the secondary battery, and creates a life prediction formula. In contrast, the life prediction formula creation unit 126 may create a life prediction formula based on time series changes in the deterioration state of the positive and negative electrodes regarding operational deterioration when a load is applied to the secondary battery. However, in terms of the accuracy of life prediction, it is better to create a life prediction formula from time series changes when no load is applied to the secondary battery and time series changes when a load is applied to the secondary battery.

[0065] In the above-described embodiment, the deterioration prediction system 10 for a secondary battery is configured with one computer, but a similar deterioration prediction system may be configured with multiple computers. For example, some data, such as data on measurements of the positive and negative electrodes of a secondary battery, may be stored in a server connected to the deterioration prediction system 10 for a secondary battery via a network, and the deterioration prediction system 10 for a secondary battery may exchange information with the server to perform similar processing. Also, some of the components of the calculation unit 12 shown in FIG. 2 may be arranged in a separate computer and processed by the separate computer.

[0066] In addition, in the configuration diagrams shown in Figures 1 and 2, only control lines and information lines that are considered necessary for explanation are shown, and not all control lines and information lines in the product are necessarily shown. In reality, it can be considered that almost all components are connected to each other. Furthermore, the flow of processing shown in the flowchart in FIG. 7 is also one example, and as long as the processing results are the same, the order of some of the processing may be changed or multiple processing may be executed simultaneously.

[0067] Furthermore, when the secondary battery deterioration prediction system described in this embodiment is configured with an information processing device such as a computer, the programs that realize each processing function may be prepared in non-volatile storage or memory within the computer device, or may be stored on a recording medium such as an external memory, an IC card, an SD card, or an optical disk, and transferred. [Explanation of symbols]

[0068] 1...CPU, 2...memory, 3...storage, 4...input device, 5...network interface, 6...output section, 10...secondary battery deterioration prediction system, 11...input section, 12...calculation section, 13...output section, 121...positive electrode information and negative electrode information acquisition section, 122...battery information acquisition section, 123...internal state diagnosis section, 124...time series change equation creation section, 125...function conversion section, 126...life prediction equation creation section, 127...deterioration calculation section

Claims

1. an acquisition unit that acquires a relationship between a state of charge and an open circuit voltage of the secondary battery and a relationship between a state of charge and an internal resistance of the battery under each operating condition of the secondary battery; an internal state diagnosis unit that diagnoses a deterioration state of a positive electrode and a negative electrode of the secondary battery under each operating condition based on the relationship acquired by the acquisition unit; a life prediction formula creation unit that creates a life prediction formula for predicting a life of the secondary battery based on a time-series change in the deterioration state obtained by the internal state diagnosis unit; a deterioration calculation unit that calculates deterioration of the secondary battery based on the life prediction equation created by the life prediction equation creation unit, The life prediction equation creation unit creates a life prediction equation based on time-series changes in deterioration states of the positive and negative electrodes when a load is applied to the secondary battery. A system for predicting deterioration of secondary batteries.

2. The life prediction formula creation unit further creates a life prediction formula based on time-series changes in the deterioration states of the positive and negative electrodes when no load is applied to the secondary battery. The deterioration prediction system for a secondary battery according to claim 1 .

3. The internal state diagnostic unit is A relationship group between the state of charge and the open circuit potential specific to the positive electrode active material of the secondary battery, and a relationship group between the state of charge and the internal resistance; A relationship group between the state of charge and the open circuit potential specific to the negative electrode active material of the secondary battery, and a relationship group between the state of charge and the internal resistance; Diagnose the deterioration state of the positive and negative electrodes using The deterioration prediction system for a secondary battery according to claim 2 .

4. a time series change equation creating unit that creates a time series change equation of the state diagnosed by the internal state diagnosis unit, The life prediction equation creation unit creates a life prediction equation based on the time series change equation created by the time series change equation creation unit. The deterioration prediction system for a secondary battery according to claim 3 .

5. Furthermore, the internal state diagnostic unit When diagnosing the deterioration state of the positive and negative electrodes, At least one parameter is extracted from among the following: the utilization rate of the active material of the positive electrode, the utilization rate of the active material of the negative electrode, the amount of lithium ion loss associated with the formation of a film on the positive electrode surface, the amount of lithium ion loss associated with the formation of a film on the negative electrode surface, the solution resistance of the battery, the rate of increase in the resistance of the positive electrode, and the rate of increase in the resistance of the negative electrode. The deterioration prediction system for a secondary battery according to claim 4 .

6. The time-series change equation creation unit creates a time-series change equation based on the time-series changes of the parameters extracted by the internal state diagnosis unit. The deterioration prediction system for a secondary battery according to claim 5 .

7. Furthermore, the internal state diagnosis unit diagnoses deterioration of the secondary battery due to a storage load and deterioration due to an operating load, The life prediction formula creation unit creates a battery life prediction formula by combining deterioration due to storage load and deterioration due to operating load. The deterioration prediction system for a secondary battery according to claim 2 .

8. The deterioration calculation unit calculates and outputs a time series change in capacity and resistance of the secondary battery based on an operating condition of the secondary battery. The deterioration prediction system for a secondary battery according to claim 2 .

9. An acquisition process for acquiring a relationship between a charge state of the secondary battery and an open circuit voltage, and a relationship between a charge state of the battery and an internal resistance, under each operating condition of the secondary battery; an internal state diagnosis process for diagnosing a deterioration state of a positive electrode and a negative electrode of the secondary battery under each operating condition based on the relationship acquired by the acquisition process; a life prediction formula creation process for creating a life prediction formula for predicting a life of the secondary battery based on a time-series change in a deterioration state obtained by the internal state diagnosis process; a deterioration calculation process for calculating deterioration of the secondary battery based on the life prediction equation created by the life prediction equation creation process, The life prediction formula created in the life prediction formula creation process is created based on the time-series changes in the deterioration state of the positive and negative electrodes when a load is applied to the secondary battery. A method for predicting deterioration of secondary batteries.

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