Secondary battery degradation prediction system and secondary battery degradation prediction method

Abstract

To provide a secondary battery degradation prediction system which enable highly accurate lifetime diagnosis without depending on voltage variability of an electrode active material.SOLUTION: A secondary battery degradation prediction system is provided, comprising a control unit for centrally controlling the degradation prediction system, and a storage unit for storing information for predicting degradation, the control unit comprising a computation unit for acquiring internal degradation parameters of a secondary battery, generating a degradation prediction equation based on the internal degradation parameters, and storing the generated degradation prediction equation in the storage unit.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a secondary battery degradation prediction system and a secondary battery degradation prediction method.

Background Art

[0002] Currently, means for suppressing carbon dioxide emissions have become a problem. Therefore, replacement from the energy derived from thermal power generation, which is mainly used at present, to renewable energy is being carried out. For renewable energy, for example, solar power generation, wind power generation, etc. are used. However, when a renewable energy generator is disconnected from the system, the stability against demand fluctuations decreases. Therefore, the application of a power storage system (BESS: Battery Energy Storage System) in which a secondary battery is installed in parallel with each renewable energy generator is being considered. By providing a power storage system, it is possible to mitigate fluctuations in the generated power value of the renewable energy generator and maintain stability.

[0003] However, the performance of the power storage system deteriorates due to the degradation of the secondary battery during its operation. Therefore, when introducing a power storage system, it is necessary to design it in anticipation of the degradation of the secondary battery. However, if the accuracy of predicting the degradation of the secondary battery is low, an excessive number of batteries need to be installed. As a result, the power storage system becomes costly. Therefore, a life diagnosis system for secondary batteries that enables highly accurate degradation prediction has been proposed (see Patent Document 1).

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] The life diagnosis system described in Patent Document 1 above compares the measured values obtained from the charging information of the battery with the information predicted using a prediction formula based on the usage method of the battery. And when the difference between the two becomes a certain value or more, the deterioration state of the secondary battery is predicted. However, when an electrode active material with a small voltage change due to the state of charge is used, the above life diagnosis system may have a decrease in the accuracy of life diagnosis. For this reason, the above life diagnosis system depends on the voltage variability of the electrode active material for the diagnosis accuracy. Therefore, there is a need for a secondary battery deterioration prediction system and a secondary battery deterioration prediction method that can perform high-accuracy life diagnosis without depending on the voltage variability of the electrode active material.

[0006] In order to solve the above-described problems, the present invention provides a secondary battery deterioration prediction system and a secondary battery deterioration prediction method that can perform high-accuracy life diagnosis without depending on the voltage variability of the electrode active material.

[0007] Also, the above object and other objects of the present invention and the novel features of the present invention are clarified by the description of this specification and the attached drawings.

Means for Solving the Problems

[0008] The secondary battery deterioration prediction system of the present invention includes a control unit that comprehensively controls a deterioration prediction system that performs deterioration prediction of a secondary battery, and a storage unit that stores information for deterioration prediction. The control unit includes a calculation unit that acquires internal deterioration parameters of the secondary battery, creates a deterioration prediction formula based on the internal deterioration parameters, and stores the created deterioration prediction formula in the storage unit.

[0009] Also, the secondary battery deterioration prediction method of the present invention performs deterioration prediction of a secondary battery. The secondary battery deterioration prediction method acquires internal deterioration parameters of the secondary battery, creates a deterioration prediction formula based on the internal deterioration parameters, and stores the created deterioration prediction formula in the storage unit.

Effects of the Invention

[0010] According to the present invention, there is provided a secondary battery degradation prediction system capable of highly accurate life diagnosis without depending on the voltage variability of an electrode active material, and a secondary battery degradation prediction method.

[0011] 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

[0012]

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Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of a secondary battery degradation prediction system and a secondary battery degradation prediction method according to an embodiment of the present invention will be described with reference to the drawings. Note that the present invention is not limited to the following examples. In each of the drawings described below, common members are denoted by the same reference numerals. Also, in the drawings used in this specification, the same or corresponding components are denoted by the same reference numerals, and repeated descriptions of these components may be omitted.

[0014] [Configuration of Secondary Battery Degradation Prediction System] The configuration of the secondary battery degradation prediction system is shown in FIG. 1. The secondary battery degradation prediction system 10 shown in FIG. 1 is composed of, for example, an arithmetic unit. The degradation prediction system 10 includes a control unit 20, an input device 4, a network interface 5, and an output device 6. The control unit 20 includes a CPU (Central Processing Unit) 1, a memory 2, and a storage 3. These components are connected to be mutually communicable via a bus.

[0015] The CPU 1 is an arithmetic processing unit. The CPU 1 executes the program code of software for realizing the functions of the secondary battery degradation prediction system 10. The CPU 1 reads the program code from the memory 2 or the storage 3 and executes arithmetic processing in the work area of the memory 2. Various processing functional units described later that are executed by the CPU 1 are configured in the memory 2.

[0016] Storage 3 is a memory unit. Storage 3 stores various processing programs executed by CPU 1, programs such as an OS, and programs for realizing the functions of the secondary battery degradation prediction system 10 necessary for the execution of such programs. Further, Storage 3 stores information for the degradation prediction system 10 to perform degradation prediction. As these information, for example, information regarding the functions of the degradation prediction system 10 necessary for program execution, various data used for the execution of various programs, and various data obtained by the execution of various programs are stored. As the data stored in Storage 3, for example, the open circuit potential and resistance of the positive and negative electrodes described later, the open circuit voltage and resistance of the secondary battery, various internal degradation parameters, and the battery degradation prediction formula of the battery constituting the battery degradation model, etc. are included. Also, as the data stored in Storage 3, for example, battery degradation test information used for creating a battery degradation prediction formula, battery operation information used for updating the degradation prediction formula, and calculation conditions for secondary battery degradation prediction are included. Storage 3 is a large-capacity information storage medium such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), or a memory card, for example.

[0017] The input device 4 accepts the input processing of various information to the degradation prediction system 10 by the operation of the operator. As the input device 4, for example, devices such as a keyboard and a mouse are used. The network interface (I / F) 5 receives programs from the outside and also transmits the processing results. As the network interface (I / F) 5, for example, a NIC (Network Interface Card) or the like is used. The output device 6 performs output processing such as display of the calculation results by the arithmetic unit and printing. The output device 6 includes output devices such as a display and a printer, for example.

[0018] Note that the arithmetic unit shown in FIG. 1 is an example of the secondary battery degradation prediction system 10. The degradation prediction system 10 may have other configurations other than the above arithmetic unit. For example, some or all of the configurations for realizing the functions of the secondary battery degradation prediction system 10 may be hardware such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).

[0019] [Functional Configuration of Control Unit] Next, the functional configuration of the control unit 20 of the degradation prediction system 10 will be described. The control unit 20 includes an input unit 11, a calculation unit 12, and an output unit 13 as functional configurations.

[0020] The input unit 11 reads out various information, programs, battery degradation models, and various data for performing degradation prediction of the secondary battery from the storage 3 or the like, and inputs them to the calculation unit 12. For example, the input unit 11 reads out battery degradation test data used for creating a battery degradation prediction formula, in-operation data of the battery used for updating the degradation prediction formula, and calculation conditions for secondary battery degradation prediction from the storage 3. Then, the input unit 11 inputs each piece of information read out from the storage 3 to the calculation unit 12.

[0021] The battery degradation test data and in-operation data are, for example, the open-circuit voltage and resistance of the battery, the open-circuit potential and resistance of the electrodes (positive electrode, negative electrode), etc. The calculation conditions for secondary battery degradation prediction are, for example, the operating conditions of the secondary battery, the end conditions of the calculation, and the data output interval, etc. In the calculation conditions for secondary battery degradation prediction, the operating conditions of the secondary battery are temperature and load (current). Also, the end conditions of the calculation in the calculation conditions for secondary battery degradation prediction are a period (such as 10 years), a decrease in battery capacity to a predetermined value (for example, 70% or less), etc. The data output interval is a predetermined period (for example, every 10 days) for outputting the calculation results.

[0022] The calculation unit 12 calculates the internal degradation parameters of the secondary battery based on the information acquired from the input unit 11. Further, the calculation unit 12 creates and updates a battery degradation model (degradation prediction formula) based on the calculated internal degradation parameters, the battery degradation test data acquired from the input unit 11, the in-operation data of the battery, and the calculation conditions, etc. Furthermore, the calculation unit 12 performs calculation processing regarding the degradation state of the battery, such as the battery capacity and battery resistance of the secondary battery to be predicted, using the internal degradation parameters of the battery and the battery degradation model.

[0023] The output unit 13 outputs the calculation results of the internal degradation parameters of the secondary battery obtained by the calculation in the calculation unit 12, and the calculation results of the degradation state of the battery such as the capacity and resistance of the secondary battery, to the output device 6. At this time, the output unit 13 may perform signal conversion, etc. according to the configuration of the output device 6 on the data of the calculation results to be output.

[0024] [Functional Configuration of the Calculation Unit] Next, the functional configuration performed by the calculation unit 12 of the above-described secondary battery degradation prediction system 10 will be described. FIG. 2 shows the functional configuration of the calculation unit 12 of the secondary battery degradation prediction system 10. The calculation unit 12 includes a positive electrode information and negative electrode information acquisition unit 21, a battery information acquisition unit 22, a battery internal state diagnosis unit 23, a time-series change formula creation unit 24, a functionalization unit 25, a degradation prediction formula creation unit 26, a degradation calculation unit 27, and a degradation prediction formula update unit 28. Each of these functional configurations is configured in the memory 2 by the CPU 1 executing a program stored in the storage 3.

[0025] (Positive Electrode Information and Negative Electrode Information Acquisition Unit) The positive electrode information and negative electrode information acquisition unit 21 acquires the open circuit potential (OCP) and resistance information of the positive electrode and negative electrode, which are necessary for diagnosing the internal state of the battery. The positive electrode information and negative electrode information acquisition unit 21 reads and acquires each piece of information from the storage 3 or the like. Further, the positive electrode information and negative electrode information acquisition unit 21 may acquire information measured by an external device different from the secondary battery degradation prediction system 10.

[0026] An example of the method for obtaining information by the positive electrode information and negative electrode information acquisition unit 21 will be described. First, a positive electrode single electrode cell using the positive electrode used in the secondary battery to be predicted for life and lithium metal is manufactured. Also, a negative electrode single electrode cell using the negative electrode used in the secondary battery to be predicted for life and lithium metal is manufactured. Next, for each of the positive electrode single electrode cell and the negative electrode single electrode cell, a charge and discharge test using the galvanostatic intermittent titration method (GITT) is performed. Thereby, information on the open circuit potential and resistance of the positive electrode single electrode cell is obtained. Also, information on the open circuit potential and resistance of the negative electrode single electrode cell is obtained. After that, an operator or the like stores the obtained information on the open circuit potential and resistance of the positive electrode single electrode cell and the negative electrode single electrode cell in the storage 3. Then, the positive electrode information and negative electrode information acquisition unit 21 reads out the information on the open circuit potential and resistance of the positive electrode single electrode cell and the information on the open circuit potential and resistance of the negative electrode single electrode cell from the storage 3 and outputs them to the battery internal state diagnosis unit 23.

[0027] In the galvanostatic intermittent titration method, after fully charging each single electrode cell, a cycle of discharging for 72 seconds at a current equivalent to 1 C[A] and resting for 5 minutes is repeated until the discharge end potential is reached. As the open circuit potential at each state of charge (SOC), the potential 5 minutes after stopping the discharge current is used. Also, the resistance at each state of charge (SOC) is calculated by dividing the sum of the open circuit potential before the start of discharge and the open circuit potential when a predetermined discharge time has elapsed by the discharge current.

[0028] (Battery information acquisition unit) The battery information acquisition unit 22 acquires information on the open circuit voltage (OCV) and resistance of the secondary battery, which are necessary for diagnosing the internal state of the battery by the battery internal state diagnosis unit 23. The battery information acquisition unit 22 reads out and acquires each piece of information from the storage 3 or the like. Also, the battery information acquisition unit 22 may acquire information measured by an external device different from the secondary battery degradation prediction system 10.

[0029] An example of the method for acquiring information by the battery information acquisition unit 22 will be described. First, for a secondary battery, predetermined temperature, center SOC (State Of Charge), SOC range, and current conditions are determined, and a charge-discharge test is performed. Note that the SOC range is the range of the charge rate (SOC) under the actual operating conditions of the secondary battery. Also, the center SOC is the value midway between the maximum and minimum values of the SOC range. When the battery information acquisition unit 22 performs a charge-discharge test, it periodically stops the charge-discharge test and performs a galvanostatic intermittent titration method to acquire information on the open-circuit voltage and resistance.

[0030] An example of the method for acquiring information by the battery information acquisition unit 22 will be described. First, a charge-discharge test using the galvanostatic intermittent titration method (GITT) is performed on the secondary battery to be predicted. Thereby, information on the open-circuit voltage and resistance of the secondary battery is acquired. After that, an operator or the like stores the acquired information on the open-circuit voltage and resistance of the secondary battery in the storage 3. Then, the battery information acquisition unit 22 reads out the information on the open-circuit voltage and resistance of the secondary battery from the storage 3 and outputs it to the battery internal state diagnosis unit 23.

[0031] In the charge-discharge test using the galvanostatic intermittent titration method, after fully charging the secondary battery to be predicted, a cycle of discharging for 72 seconds at a current equivalent to 1 C[A] and resting for 5 minutes is repeated until the discharge termination potential is reached. As the open-circuit potential at each charge rate (SOC), the voltage 5 minutes after stopping the discharge current is used. Also, the resistance at each charge rate (SOC) is calculated by dividing the difference between the open-circuit voltage before the start of discharge and the open-circuit voltage when a predetermined discharge time has elapsed by the discharge current.

[0032] (Battery internal state diagnosis unit) The battery internal state diagnosis unit 23 diagnoses the internal state of the secondary battery. The battery internal state diagnosis unit 23 diagnoses the internal state of the secondary battery based on various information acquired by the positive and negative electrode information acquisition unit 21 and the battery information acquisition unit 22. That is, the battery internal state diagnosis unit 23 compares the data of the open circuit potential and resistance of the positive electrode, the data of the open circuit potential and resistance of the negative electrode, which are acquired by the positive and negative electrode information acquisition unit 21, and the data of the open circuit voltage and resistance of the secondary battery, which are acquired by the battery information acquisition unit 22. Thereby, the battery internal state diagnosis unit 23 diagnoses the internal state of the secondary battery.

[0033] The diagnosis targets of the internal state include the utilization rate m of the active material of the positive electrode p , the utilization rate m of the active material of the negative electrode n , the lithium ion loss amount δ due to the film formation on the positive electrode surface p , the lithium ion loss amount δ due to the film formation on the negative electrode surface n , the solution resistance R0 of the battery, the resistance increase rate a of the positive electrode p , and the resistance increase rate a of the negative electrode n and so on. These diagnosis targets are internal deterioration parameters having a correlation with battery deterioration.

[0034] The utilization rate m of the active material of the positive electrode p is an index indicating the ratio of the active material that can contribute to the battery reaction among the positive electrode active materials contained in the electrode. The utilization rate m of the active material of the negative electrode n is an index indicating the ratio of the active material that can contribute to the battery reaction among the negative electrode active materials contained in the electrode. The lithium ion loss amount δ due to the film formation on the positive electrode surface p is an index indicating the amount of electricity of the lithium ions incorporated into the film on the positive electrode surface. This film is mainly generated by the reaction of the electrolytic solution on the positive electrode surface. The lithium ion loss amount δ due to the film formation on the negative electrode surface n is an index indicating the amount of electricity of the lithium ions incorporated into the film on the negative electrode surface. This film is mainly generated by the reaction of the electrolytic solution on the negative electrode surface. The solution resistance R0 of the battery is an index indicating the resistance of the battery members when the battery is used. The resistance increase rate a of the positive electrode p is an index indicating the resistance increase rate of the positive electrode when the battery is used. The resistance increase rate a of the negative electrode nis an index indicating the rate of increase in the resistance of the negative electrode when using the battery.

[0035] These seven indicators are used as internal degradation parameters, and the open circuit potential and resistance data of the positive and negative electrodes acquired by the positive and negative electrode information acquisition unit 21 are fitted with the open circuit voltage and resistance data of the secondary battery acquired by the battery information acquisition unit 22. Figures 3 and 4 show an example of the fitting results.

[0036] Figure 3 is a graph showing the measured value d11 of the open circuit voltage (OCV) of the secondary battery, its calculated value d12, the positive electrode potential (calculated value) d13, and the negative electrode potential (calculated value) d14. In the graph shown in Figure 3, the left vertical axis represents the battery voltage and the positive electrode potential [V], and the right vertical axis represents the negative electrode potential [V]. The horizontal axis represents the battery capacity [Ah]. In Figure 3, the measured value d11 of the open circuit voltage of the secondary battery is the part plotted with white circles. The calculated value d12 is a value calculated using the positive electrode potential (calculated value) d13, the negative electrode potential (calculated value) d14, and the measured value d11 of the open circuit voltage of the secondary battery by the following method.

[0037] An example of the calculation process of the calculated value d12 of the open circuit voltage (OCV) of the secondary battery is shown. In calculating the calculated value d12 of the open circuit voltage, fitting is performed so that the difference between the positive electrode potential d13 and the negative electrode potential d14 coincides with the measured value d11 of the open circuit voltage. For example, the positive electrode potential d13 is translated parallel by δ p (lithium ion loss amount δ p ) in the horizontal axis direction from the position of zero capacity, and further multiplied by the coefficient m p (active material utilization rate m of the positive electrode p ) to obtain the positive electrode potential curve. The negative electrode potential d14 is translated parallel by δ n (lithium ion loss amount δ n ) in the horizontal axis direction from the position of zero capacity, and further multiplied by the coefficient m n (active material utilization rate m of the negative electrode n ) to obtain the negative electrode potential curve. Then, the difference between the two curves is calculated. The deviation between the difference between the positive electrode potential d13 and the negative electrode potential d14 calculated above and the measured value d11 of the open circuit voltage of the secondary battery is evaluated.

[0038] Figure 4 is a graph showing the measured value d21 of the resistance of the secondary battery, its calculated value d22, the positive electrode resistance (calculated value) d23, and the negative electrode resistance (calculated value) d24. In the graph shown in Figure 4, the vertical axis represents the resistance [mΩ], and the horizontal axis represents the battery capacity [Ah]. In Figure 4, the measured value d21 of the resistance of the secondary battery is the part plotted with white circles. Also, the calculated value d22 is a value calculated using the positive electrode resistance d23, the negative electrode resistance d24, and the measured value d21 of the resistance of the secondary battery by the following method.

[0039] An example of the calculation process of the calculated value d22 of the resistance of the secondary battery is shown. Note that the calculated value d22 of the resistance can also be processed in the same way as the open circuit voltage of the secondary battery described above. In the calculation of the calculated value d22 of the resistance, fitting is performed so that the sum of the positive electrode resistance d23, the negative electrode resistance d24, and the solution resistance R0 matches the measured value d21 of the resistance.

[0040] For example, the positive electrode resistance d23 is translated in the horizontal axis direction by δ p (lithium ion loss amount δ p ) and multiplied by the coefficient m p (active material utilization rate m of the positive electrode p ), and further multiplied by the coefficient a p (resistance increase rate a of the positive electrode p ) to calculate a value. Also, the negative electrode resistance d24 is translated in the horizontal axis direction by δ n (lithium ion loss amount δ n ) and multiplied by the coefficient m n (active material utilization rate m of the negative electrode n ), and further multiplied by the coefficient a n (resistance increase rate a of the negative electrode n ) to calculate a value. Then, the deviation between the sum of the calculated value of the positive electrode resistance d23, the calculated value of the negative electrode resistance d24, and the solution resistance R0 and the measured value d21 of the resistance of the secondary battery is evaluated.

[0041] In the process of obtaining each parameter, the calculated value d12 of the open circuit voltage (OCV) of the secondary battery, which is represented by the difference between the positive electrode potential d13 and the negative electrode potential d14, approximates the measured value d14, and m p , m n , δ p , and δ n are fitted to obtain these values. Also, the calculated value d22 of the resistance of the secondary battery, which is represented by the sum of the positive electrode resistance d23, the negative electrode resistance d24, and the solution resistance R0, approximates the measured value d21, and m p , m n , δ p , δ n , a p , a n , and R0 are fitted to obtain these values. The fitting results are stored in storage 3.

[0042] (Time-series change formula creation unit) Returning to the description of FIG. 2, the time-series change formula creation unit 24 creates a time-series change formula for the internal degradation parameters of the battery obtained by the battery internal state diagnosis unit 23. FIG. 5 is a graph showing an example of the time-series change d31 of the active material utilization rate m n of the negative electrode and the time-series change d32 of the active material utilization rate m p of the positive electrode. Also, FIG. 6 is a graph showing an example of the time-series change d41 of the lithium ion loss amount δ n associated with the film formation on the negative electrode surface and the time-series change d42 of the lithium ion loss amount δ p associated with the film formation on the positive electrode surface. In the graph shown in FIG. 5, the horizontal axis represents time, and the vertical axis represents the active material utilization rate of the negative electrode. In the graph shown in FIG. 6, the horizontal axis represents time, and the vertical axis represents the lithium ion loss amount associated with the film formation on the negative electrode surface.

[0043] The straight line of the time-series change d31 shown in FIG. 5 is an approximate curve of the value of the active material utilization rate m n . The straight line of the time-series change d32 shown in FIG. 5 is an approximate curve of the value of the active material utilization rate m p . Also, the curve of the time-series change d41 shown in FIG. 6 is the lithium ion loss amount δ nThe time series change curve d42 shown in FIG. 6 is an approximation curve of the amount of lithium ion loss δ p is an approximation curve of the values ​​of In this way, each of the above internal deterioration parameters can be expressed by using 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.

[0044] The time series change equation creating unit 24 creates a time series change equation of the internal deterioration parameter based on the approximate curves of the time series changes shown in Figures 5 and 6. Note that in Figures 5 and 6, the active material utilization rate m n , the utilization rate of the positive electrode active material m p , the amount of lithium ion loss on the negative electrode surface δ n , and the amount of lithium ion loss on the positive electrode surface δ p The time series change equation creation unit 24 similarly calculates the solution resistance R0 of the battery, the resistance increase rate a of the positive electrode, p , and the rate of increase in the resistance of the negative electrode a n An approximate curve is also created for

[0045] The time series change in the negative electrode active material utilization rate m n The creation of the time series change equation will be described using the approximate equation of the negative electrode active material utilization rate m as an example. The approximate curve of the time series change d31 shown in FIG. 5 is a linear curve. Therefore, it can be expressed as an equation of a linear function of time t. Therefore, the time series change equation creation unit 24 calculates the approximate curve of the time series change d31 based on the active material utilization rate m of the negative electrode active material utilization rate m as an example. n As a time series change equation, the equation [m n = jk × t], where k is the coefficient and j is the intercept. In addition, the utilization rate of the positive electrode active material m p For the approximation curve of the time series change d32, the time series change equation creating unit 24 creates a similar equation.

[0046] In addition, the amount of lithium ion loss on the negative electrode surface shown in Fig. 6, δ nThe approximate curve of the time series change d41 is a curve of an irrational function. Therefore, the approximate curve of the time series change d41 can be expressed as a radical formula of time t. And the time series change formula creation unit 24 creates, as the time series change formula, a formula n [δ 0.5 = p + n×(t) which is an irrational function of time t. In this formula, n is a coefficient and p is an intercept. p Also, for the approximate curve of the time series change d42 of the lithium ion loss amount δ

[0047] (Function conversion unit) Next, the function conversion unit 25 converts the coefficients of the time series change formulas of the internal degradation parameters of each battery obtained by the time series change formula creation unit 24 into functions of the operating conditions. Here, an example of the function conversion will be described. The function conversion unit 25 converts the dependence of the coefficient k on the operating conditions for the time series change formula [m n = j - k×t] of the active material utilization rate m n of the negative electrode whose approximate curve is shown in FIG. 5. Also, the function conversion unit 25 converts the dependence of the coefficient n on the operating conditions for the time series change formula [δ n = p + n×(t) n of the lithium ion loss amount δ 0.5 accompanying the film formation on the negative electrode surface whose approximate curve is shown in FIG. 6. Here, the operating conditions are the temperature when the target secondary battery is operated, the conditions of the load (current), the central SOC, the SOC range, etc.

[0048] As an example of the function, for example, the function conversion unit 25 [k = α + β×temperature + γ×central SOC + ε×SOC range + ζ×current] converts the coefficient k into a function by an addition formula using the operating conditions. Also, the function conversion unit 25 [k = α×(β×temperature)×(γ×central SOC)×(ε×SOC range)×(ζ×current)] converts the coefficient k into a function by a multiplication formula using the operating conditions. Similarly, the function conversion unit 25 converts the coefficient n [n = α + β × temperature + γ × central SOC + ε × SOC range + ζ × current] The coefficient n is functionalized by an addition formula using operating conditions such as the above. Also, the functionalization unit 25 [n = α × (β × temperature) × (γ × central SOC) × (ε × SOC range) × (ζ × current)] The coefficient n is functionalized by a multiplication formula using operating conditions such as the above. Here, α, β, γ, ε, and ζ are coefficients. The values of these coefficients α, β, γ, ε, and ζ are determined by fitting. The fitting here is, for example, the time series change formula of the negative electrode active material utilization rate m n [m n = j - k × t] as shown in FIG. 5 for the time series change d31 of the negative electrode active material utilization rate m n , and the coefficients α, β, γ, ε, and ζ are adjusted so as to match. Also, for example, the time series change formula of the lithium ion loss amount δ n in the negative electrode [δ n = p + n × (t) 0.5 as shown in FIG. 6 for the time series change d41 of the lithium ion loss amount δ n in the negative electrode, and the coefficients α, β, γ, ε, and ζ are adjusted so as to match.

[0049] The coefficients α, β, γ, ε, and ζ are, for example, prepared in advance with a parameter table or the like with values assigned to each, and stored in the storage 3. Then, for each condition of the parameter table, the values of α, β, γ, ε, and ζ are substituted into the formula in which the above coefficients are functionalized, and the coefficients k and n are calculated. Further, using the calculated coefficients k and n, the time series change formulas [m n = j - k × t], [δ n = p + n × (t) 0.5 are calculated. In the calculation of m n , δ n by the time series change formula, the deviation from the time series changes d31 and d41 is compared and evaluated between each condition in the parameter table. Then, based on this comparison and evaluation, the values of α, β, γ, ε, and ζ for the condition of the parameter table that is the most approximate are derived. In the above description, the negative electrode active material utilization rate mn and the amount of lithium ion loss δ at the negative electrode n shows the coefficients k and n of the time-series change formula. Similarly, the function generation unit 25 calculates the active material utilization rate m of the positive electrode p , the amount of lithium ion loss δ on the surface of the positive electrode p , the solution resistance R0 of the battery, the resistance increase rate a of the positive electrode p , and the resistance increase rate a of the negative electrode n also functionize the coefficients of their respective time-series change formulas. Further, the function generation unit 25 derives the coefficients α, β, γ, ε, and ζ of each time-series change formula

[0050] (Deterioration prediction formula creation unit) The deterioration prediction formula creation unit 26 creates a deterioration prediction formula for the battery. The deterioration prediction formula creation unit 26 combines the time-series change formula of each internal deterioration parameter created by the time-series change formula creation unit 24 and the formula obtained by functionizing the coefficients of each internal deterioration parameter created by the function generation unit 25 to create a deterioration prediction formula for the battery

[0051] The deterioration prediction formula for the capacity Qc of the secondary battery is expressed by the following formula, where the positive electrode capacity is q p and the negative electrode capacity is q n [Qc = m p × q p - δ p = m n × q n - δ n In this formula, by multiplying the positive electrode capacity q p by the active material utilization rate m of the positive electrode p , the capacity of the secondary battery at the time of measurement is derived. Further, by subtracting the amount of lithium ion loss δ p associated with the film formation on the positive electrode surface from the capacity of the secondary battery at the time of measurement, the capacity Qc of the secondary battery is calculated. Also, by multiplying the negative electrode capacity q n by the active material utilization rate m of the negative electrode n , the capacity of the secondary battery at the time of measurement is derived. Further, by subtracting the amount of lithium ion loss δ n associated with the film formation on the negative electrode surface from the capacity of the secondary battery at the time of measurement, the capacity Qc of the secondary battery is calculated

[0052] ​​ Also, let the positive electrode resistance be r p and the negative electrode resistance be r n Then, the deterioration prediction formula for the resistance Rcc at each capacity (each state of charge) of the secondary battery can be expressed by the following formula. [Rc = a p / m p ×r p + a n / m n ×r n + R0] In this formula, first, the positive electrode resistance r p is multiplied by the ratio of the resistance increase rate a p of the positive electrode with respect to the active material utilization rate m p of the positive electrode to calculate the resistance on the positive electrode side. Further, the negative electrode resistance r n is multiplied by the ratio of the resistance increase rate a n of the negative electrode with respect to the active material utilization rate m n of the negative electrode to calculate the resistance on the negative electrode side. Further, the resistance Rc is calculated by summing the resistance on the positive electrode side, the resistance on the negative electrode side, and the solution resistance R0 of the battery. Also, this formula can be converted into a formula for the capacity Qc by replacing the positive electrode resistance r p and the negative electrode resistance r n with the positive electrode capacity q p and the negative electrode capacity q n as follows. [Qc = a p / m p ×q p + a n / m n ×q n + R0]

[0053] Substitute the time-series change formula of each internal deterioration parameter created by the time-series change formula creation unit 24 and the formula obtained by functionalizing the coefficients of each internal deterioration parameter created by the functionalization unit 25 into the above formulas. Thereby, the capacity Qc and the resistance Rc of the secondary battery at the time of measurement can be calculated.

[0054] (Deterioration calculation unit) The degradation calculation unit 27 performs the calculation of each internal degradation parameter of the secondary battery described above and the calculation of battery degradation. The degradation calculation unit 27 performs each calculation based on the time-series change formula created by the time-series change formula creation unit 24, the formula obtained by functionalizing the coefficients created by the functionalization unit 25, and the degradation prediction formula created by the degradation prediction formula creation unit 26.

[0055] In addition, the degradation calculation unit 27 divides the measured capacity Qc and resistance Rc of the secondary battery obtained as the calculation result of battery degradation by the initial capacity Qini and initial resistance Rini of the secondary battery to calculate the rate of change SOHQ of the capacity of the secondary battery and the rate of change SOHR of the resistance. The rate of change SOHQ of the capacity and the rate of change SOHR of the resistance are specifically expressed by the following formulas [SOHQ = Qc / Qini × 100], and [SOHR = Rc / Rini × 100] and can be shown as such. The degradation calculation unit 27 calculates the rate of change SOHQ of the capacity and the rate of change SOHR of the resistance using the above formulas.

[0056] (Degradation prediction formula update unit) The degradation prediction formula update unit 28 updates the degradation prediction formula created by the above method during the operation of the secondary battery or during the operation of the equipment equipped with the secondary battery. The degradation prediction formula update unit 28 stores, for example, a new degradation prediction formula created by the above method in the storage 3 or the like. At this time, the degradation prediction formula update unit 28 may update the previously stored degradation prediction formula with the new degradation prediction formula. The previous degradation prediction formula may be deleted from the storage 3, or may be stored together with the new degradation prediction formula as an old version of the degradation prediction formula. Further, the deterioration prediction formula update unit 28 may store the information acquired and calculated by the battery information acquisition unit 22, the battery internal state diagnosis unit 23, the time series change formula creation unit 24, and the functionalization unit 25, while storing and updating the deterioration prediction formula. For example, the deterioration prediction formula update unit 28 may store the open circuit voltage and resistance of the secondary battery acquired by the battery information acquisition unit 22, the internal deterioration parameters acquired by the battery internal state diagnosis unit 23, etc. in the storage 3. Further, the deterioration prediction formula update unit 28 may store in the storage 3 the time series change formula of the internal deterioration parameters acquired by the time series change formula creation unit 24, the formula obtained by functionalizing the coefficients of the time series change formula acquired by the functionalization unit 25, the coefficients, etc.

[0057] [Method for predicting deterioration of secondary battery] Next, a method for predicting the deterioration of a secondary battery will be described. The deterioration prediction method described below is an example using the above-described secondary battery deterioration prediction system. In the deterioration prediction method, first, during the actual operation of the secondary battery-mounted device, when the secondary battery is not in use or when the operation is temporarily interrupted due to maintenance or the like, information on the secondary battery during operation is acquired. For example, when the secondary battery is not in use or is temporarily interrupted, the battery information acquisition unit 22 performs all or part of the constant current intermittent titration method. Thereby, information on the secondary battery during operation of the secondary battery-mounted device, for example, the open circuit voltage and resistance of the secondary battery are acquired.

[0058] Next, the battery internal state diagnosis unit 23 diagnoses the internal state of the secondary battery from the information acquired by the battery information acquisition unit 22. Then, the battery internal state diagnosis unit 23 calculates the internal deterioration parameters of the battery. Next, the time series change formula creation unit 24 creates a time series change formula for the internal deterioration parameters. The time series change formula for the internal deterioration parameters is created using the information of the secondary battery before operation acquired in advance by a charge and discharge test and the internal deterioration parameters obtained by diagnosing the internal state of the secondary battery information acquired during the actual operation of the secondary battery-mounted device, respectively.

[0059] Next, in the functionalization unit 25, the internal degradation parameters of the secondary battery are functionalized. Then, in the degradation prediction formula creation unit 26, a degradation prediction formula for the secondary battery is created. Furthermore, in the degradation prediction formula update unit 28, the previous degradation prediction formula is updated with a new degradation prediction formula created based on the additionally obtained information. Then, the degradation calculation unit 27 calculates the internal degradation parameters and battery degradation of the battery using the updated new degradation prediction formula.

[0060] Figs. 7 to 10 are examples of the prediction results of the internal degradation parameters and battery degradation of the battery output by the output unit 13 of the secondary battery degradation prediction system 10 of this example. Fig. 7 is a graph showing the time-series change of m, the active material utilization rate of the negative electrode, which is one of the internal degradation parameters of the battery. n Fig. 8 is a graph showing the time-series change of δ, the amount of lithium ion loss accompanying the film formation on the surface of the negative electrode, which is one of the internal degradation parameters of the battery. n Fig. 9 is a graph showing the time-series change of SOHQ, the change rate of the capacity, which is one of the battery degradations. Fig. 10 is a graph showing the time-series change of SOHR, the change rate of the resistance, which is one of the battery degradations. The output unit 13 outputs graphs of the internal degradation parameters and prediction results of battery degradation of various batteries shown in Figs. 7 to 10. Further, the output unit 13 may output in a form such as a table instead of the graphs shown in Figs. 7 to 10.

[0061] As described above, according to the secondary battery degradation prediction system 10 of this example, even in a secondary battery with a small voltage change with respect to the change in the charge state, the internal state of the secondary battery can be diagnosed with high accuracy, and the life can be predicted with high accuracy based on the obtained diagnosis result. For example, LiNi with a large voltage change due to the change in the charge state x Mn y Co z M 1-x-y-zThe internal state of a secondary battery can be accurately diagnosed for a battery system using an O2-based positive electrode (M: any element) or a LiMn2O4-based positive electrode as the electrode active material. Furthermore, the internal state of a secondary battery can also be accurately diagnosed for a battery system with a small voltage change using a LiFePO4-based positive electrode or the like.

[0062] In addition, the battery internal state diagnosis unit 23 diagnoses the degradation state of the positive electrode using a data table including information on the charge state and open circuit potential unique to the positive electrode active material, and a data table including information on the charge state and internal resistance unique to the positive electrode active material. The battery internal state diagnosis unit 23 diagnoses the degradation state of the negative electrode using a data table including information on the charge state and open circuit potential unique to the negative electrode active material of the secondary battery, a data table including information on the charge state and internal resistance unique to the negative electrode active material of the secondary battery, and the like. The battery internal state diagnosis unit 23 extracts internal degradation 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 due to film formation on the positive electrode surface and 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. Then, using these internal degradation parameters, the degradation states of the positive electrode and the negative electrode are diagnosed. The time-series change formula creation unit 24 creates a time-series change formula for the internal degradation parameters of the secondary battery based on the internal degradation parameters of the secondary battery diagnosed by the battery internal state diagnosis unit 23. Then, based on the time-series change formula created by the time-series change formula creation unit 24, the battery degradation prediction formula creation unit 26 creates a life prediction formula. Therefore, the secondary battery degradation prediction formula created by the above method can predict the degradation of the secondary battery in consideration of the changes in the internal degradation parameters of each battery. Therefore, the created degradation prediction formula can predict including changes in the shape of the initial battery degradation prediction due to changes in the rate-determining process of battery degradation and the like. As a result, the battery life can be predicted with higher accuracy than when not considering the degradation of the internal degradation parameters of each battery.

[0063] Note that the above-described embodiment shows an example in which the secondary battery degradation prediction system 10 is configured by one arithmetic unit. The secondary battery degradation prediction system 10 may be configured by a plurality of arithmetic units. For example, some data such as data obtained by measuring the positive electrode or negative electrode of the secondary battery may be held in a server or the like connected to the secondary battery degradation prediction system 10 via a network. Then, the secondary battery degradation prediction system 10 may communicate with the server to acquire information and perform the same processing. Also, a part of the configuration included in the calculation unit 12 shown in FIG. 2 may be arranged in another arithmetic unit, and the processing according to this configuration may be performed by the other arithmetic unit.

[0064] Also, in the configuration diagrams shown in FIGS. 1 and 2, only the control lines and information lines considered necessary for explanation are shown, and not all control lines and information lines are necessarily shown in the product. In practice, it may be considered that almost all configurations are interconnected. Also, when the secondary battery degradation prediction system is configured by an information processing device such as an arithmetic unit, the program for realizing each processing function may be stored not only in the storage unit in the arithmetic unit but also in a recording medium such as an external memory, an IC card, an SD card, or an optical disk.

[0065] Note that the present invention is not limited to the above-described embodiment, and various modifications are possible. For example, the above-described embodiment has been described in detail for easy understanding of the present invention, and the present invention is not necessarily limited to the aspect including all the configurations described. Also, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment. Also, the configuration of another embodiment can be added to the configuration of one embodiment. Also, a part of the configuration of each embodiment can be deleted, or another configuration can be added or replaced.

Description of Reference Numerals

[0066] 1 CPU, 2 Memory, 3 Storage, 4 Input Device, 5 Network Interface, 6 Output Device, 10 Deterioration Prediction System, 11 Input Section, 12 Calculation Section, 13 Output Section, 20 Control Section, 21 Positive Electrode Information and Negative Electrode Information Acquisition Section, 22 Battery Information Acquisition Section, 23 Battery Internal State Diagnosis Section, 24 Time-Series Change Equation Creation Section, 25 Function Conversion Section, 26 Deterioration Prediction Equation Creation Section, 27 Deterioration Calculation Section, 28 Deterioration Prediction Equation Update Section, d11 Measured Value of Open-Circuit Voltage of Secondary Battery, d12 Calculated Value of Open-Circuit Voltage of Secondary Battery, d13 Positive Electrode Potential, d14 Negative Electrode Potential, d21 Measured Value of Resistance of Secondary Battery, d22 Calculated Value of Resistance of Secondary Battery, d23 Positive Electrode Resistance, d24 Negative Electrode Resistance, d31 Time-Series Change of Active Material Utilization Rate of Negative Electrode, d32 Time-Series Change of Active Material Utilization Rate of Positive Electrode, d41 Time-Series Change of Lithium Ion Loss Amount at Negative Electrode, d42 Time-Series Change of Lithium Ion Loss Amount at Positive Electrode

Claims

1. A control unit that centrally controls a degradation prediction system for performing degradation prediction of a secondary battery, and a storage unit that stores information for the degradation prediction. The control unit includes a calculation unit that acquires internal degradation parameters of the secondary battery, creates a degradation prediction formula based on the internal degradation parameters, and stores the created degradation prediction formula in the storage unit. A secondary battery degradation prediction system .

2. The calculation unit includes a time-series change formula creation unit that creates a time-series change formula of the internal degradation parameters based on the internal degradation parameters, a functionalization unit that creates a formula obtained by functionalizing the coefficients of the time-series change formula, and a degradation prediction formula creation unit that creates the degradation prediction formula based on the functionalized formula. The secondary battery degradation prediction system according to claim 1 .

3. The time-series change formula creation unit creates the time-series change formula based on an approximation curve of the time-series change of the internal degradation parameters. The secondary battery degradation prediction system according to claim 2 .

4. The calculation unit includes a functionalization unit that functionalizes the coefficients of the time-series change formula using the operating conditions of the secondary battery. The secondary battery degradation prediction system according to claim 3 .

5. The calculation unit includes a degradation prediction formula update unit that stores and updates the created degradation prediction formula in the storage unit. The secondary battery degradation prediction system according to claim 2 .

6. The calculation unit includes a battery internal state diagnosis unit that acquires the internal degradation parameters based on data of the open circuit potential of the positive electrode, data of the resistance of the positive electrode, data of the open circuit potential of the negative electrode, data of the resistance of the negative electrode, data of the open circuit voltage of the secondary battery, and data of the resistance of the secondary battery. The secondary battery degradation prediction system according to claim 1 .

7. The storage unit stores data of the open circuit potential of the positive electrode, data of the resistance of the positive electrode, data of the open circuit potential of the negative electrode, and data of the resistance of the negative electrode measured in advance, and the battery internal state diagnosis unit acquires data of the open circuit potential of the positive electrode, data of the resistance of the positive electrode, data of the open circuit potential of the negative electrode, and data of the resistance of the negative electrode from the storage unit. The secondary battery degradation prediction system according to claim 6 .

8. The calculation unit includes a battery information acquisition unit that acquires data of the open circuit voltage of the secondary battery and data of the resistance of the secondary battery. The internal battery state diagnosis unit acquires the internal degradation parameter based on the data of the open-circuit voltage of the secondary battery and the data of the resistance of the secondary battery, which are acquired by the battery information acquisition. The secondary battery degradation prediction system according to claim 7.

9. The internal battery state diagnosis unit acquires the internal degradation parameter by fitting the data of the open-circuit voltage of the secondary battery using the data of the open-circuit potential of the positive electrode and the data of the open-circuit potential of the negative electrode, and fitting the data of the resistance of the secondary battery using the data of the resistance of the positive electrode and the data of the resistance of the negative electrode. The secondary battery degradation prediction system according to claim 8.

10. The internal battery state diagnosis unit acquires at least one or more of the active material utilization rate of the positive electrode, the active material utilization rate of the negative electrode, the amount of lithium ion loss at the positive electrode, the amount of lithium ion loss at the negative electrode, the solution resistance of the secondary battery, the resistance increase rate of the positive electrode, and the resistance increase rate of the negative electrode as the internal degradation parameter. The secondary battery degradation prediction system according to claim 9.

11. The calculation unit calculates at least one or more of the capacity and the resistance of the secondary battery using the degradation prediction formula. The secondary battery degradation prediction system according to claim 1.

12. The calculation unit calculates at least one or more of the active material utilization rate of the negative electrode, the amount of lithium ion loss at the negative electrode, the change rate of the capacity of the secondary battery, and the change rate of the resistance of the secondary battery using the degradation prediction formula. The secondary battery degradation prediction system according to claim 1.

13. A degradation prediction method for performing degradation prediction of a secondary battery, comprising: acquiring the internal degradation parameter of the secondary battery; creating a degradation prediction formula based on the internal degradation parameter; storing the created degradation prediction formula in a storage unit. A degradation prediction method for a secondary battery.

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