System for predicting degradation of secondary battery and method for predicting degradation of secondary battery

Abstract

The present invention constitutes a system for predicting degradation of secondary battery, the system comprising a control unit that centrally controls the degradation prediction system and a storage unit in which information for degradation prediction is stored. The control unit includes calculation units that perform acquisition of an internal degradation parameter of the secondary battery, creation of a degradation prediction formula based on the internal degradation parameter, and storage of the created degradation prediction formula in the storage unit.

Description

Secondary battery deterioration prediction system and secondary battery deterioration prediction method

[0001] The present invention relates to a system for predicting deterioration of a secondary battery and a method for predicting deterioration of a secondary battery.

[0002] Currently, the means to reduce carbon dioxide emissions are becoming an issue. Therefore, efforts are being made to replace the currently predominantly used energy derived from thermal power generation with renewable energy. Examples of renewable energy include solar power generation and wind power generation. However, renewable energy becomes less stable against fluctuations in demand when a thermal power generator is disconnected from the grid. Therefore, the application of a battery energy storage system (BESS) in which secondary batteries are installed in each renewable energy generator is being considered. By providing a power storage system, fluctuations in the power generation value of the renewable energy generator can be mitigated and stability can be maintained.

[0003] However, the performance of a power storage system deteriorates due to the deterioration of secondary batteries during operation. Therefore, when introducing a power storage system, it is necessary to design it in anticipation of the deterioration of secondary batteries. However, if the accuracy of the deterioration prediction of secondary batteries is low, it becomes necessary to install excess batteries. As a result, the cost of the power storage system becomes high. Therefore, a secondary battery life assessment system that can predict deterioration with high accuracy has been proposed (see Patent Document 1).

[0004] Japanese Patent Application Laid-Open No. 2020-119658

[0005] The lifespan assessment system described in the aforementioned Patent Document 1 compares actual measured values ​​obtained from battery charging information with information predicted using a prediction formula based on the battery's usage. If the difference between the two values ​​exceeds a certain level, the system predicts the deterioration state of the secondary battery. However, when an electrode active material with small voltage changes depending on the state of charge is used, the accuracy of the lifespan assessment by the system may decrease. For this reason, the diagnostic accuracy of the lifespan assessment system depends on the voltage fluctuation of the electrode active material. Therefore, there is a need for a secondary battery deterioration prediction system and a secondary battery deterioration prediction method that can perform highly accurate lifespan assessment without relying on the voltage fluctuation of the electrode active material.

[0006] In order to solve the above-mentioned problems, the present invention provides a secondary battery deterioration prediction system and a secondary battery deterioration prediction method that are capable of highly accurate lifespan diagnosis without relying on the voltage fluctuation of the electrode active material.

[0007] The above and other objects of the present invention and novel features of the present invention will become apparent from the description of this specification and the accompanying drawings.

[0008] The deterioration prediction system for a secondary battery of the present invention includes a control unit that controls the deterioration prediction system for predicting deterioration of the secondary battery, and a memory unit that stores information for the 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 memory unit.

[0009] The deterioration prediction method for a secondary battery of the present invention predicts deterioration of the secondary battery by acquiring internal deterioration parameters of the secondary battery, creating a deterioration prediction formula based on the internal deterioration parameters, and storing the created deterioration prediction formula in a memory unit.

[0010] According to the present invention, a system for predicting deterioration of a secondary battery and a method for predicting deterioration of a secondary battery are provided that are capable of highly accurate life diagnosis without relying on the voltage fluctuation of an electrode active material.

[0011] Problems, configurations, and effects other than those described above will become clear from the following description of the embodiments.

[0012] 1 is a diagram showing the configuration of a deterioration prediction system for a secondary battery; FIG. 2 is a diagram showing the functional configuration of a calculation unit of the deterioration prediction system for a secondary battery; FIG. 3 is a graph showing the actual measured value and calculated value of the open circuit voltage of a secondary battery, the calculated value of the positive electrode potential, and the calculated value of the negative electrode potential; FIG. 4 is a graph showing the actual measured value and calculated value of the resistance of a secondary battery, the calculated value of the positive electrode resistance, and the calculated value of the negative electrode resistance; ｎ The time series change of and the positive electrode active material utilization rate m ｐ 1 is a graph showing the time series change of the amount of lithium ion loss δ associated with the formation of a coating on the negative electrode surface. ｎ The time series change of and the amount of lithium ion loss due to the film formation on the positive electrode surface δ ｐ 1 is a graph showing the time series change of the active material utilization rate m of the negative electrode. ｎ 1 is a graph showing the time series change of the amount of lithium ion loss δ associated with the formation of a coating on the negative electrode surface. ｎ 1 is a graph showing time series changes in the rate of change in capacity SOHQ of a secondary battery, and the rate of change in resistance SOHR of a secondary battery.

[0013] An example of a secondary battery deterioration prediction system and a secondary battery deterioration prediction method according to an embodiment of the present invention will be described below with reference to the drawings. Note that the present invention is not limited to the following example. In each of the drawings described below, common components are given the same reference numerals. Furthermore, in the drawings used in this specification, identical or corresponding components are given the same reference numerals, and repeated description of these components may be omitted.

[0014] [Configuration of a secondary battery deterioration prediction system] The configuration of a secondary battery deterioration prediction system is shown in Fig. 1. The secondary battery deterioration prediction system 10 shown in Fig. 1 is configured by, for example, a computing device. The deterioration 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 also includes a CPU (Central Processing Unit) 1, a memory 2, and a storage 3. Each of these components is connected via a bus so that they can communicate with each other.

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

[0016] The storage 3 is a memory unit. The storage 3 stores various processing programs executed by the CPU 1, programs such as an OS, and programs for implementing the functions of the secondary battery deterioration prediction system 10 required to execute the programs. The storage 3 also stores information required for the deterioration prediction system 10 to perform deterioration prediction. Examples of this information include information related to the functions of the deterioration prediction system 10 required to execute the programs, various data used to execute the various programs, and various data obtained by executing the various programs. Examples of data stored in the storage 3 include the open-circuit potential and resistance of the positive and negative electrodes (described below), the open-circuit voltage and resistance of the secondary battery, various internal deterioration parameters, and a battery deterioration prediction formula constituting a battery deterioration model. Examples of data stored in the storage 3 include battery deterioration test information used to create the battery deterioration prediction formula, battery operation information used to update the deterioration prediction formula, and calculation conditions for secondary battery deterioration prediction. The storage 3 is, for example, a large-capacity information storage medium such as a hard disk drive (HDD), a solid-state drive (SSD), or a memory card.

[0017] The input device 4 accepts input processing of various information to the deterioration prediction system 10 through operation by an operator. For example, a keyboard, a mouse, or other such device is used as the input device 4. The network interface (I / F) 5 receives programs and the like from the outside and transmits processing results. For example, a network interface card (NIC) or the like is used as the network interface (I / F) 5. The output device 6 displays the results of calculations by the arithmetic unit and performs output processing such as printing. For example, the output device 6 includes output equipment such as a display, a printer, or the like.

[0018] 1 is an example of a secondary battery deterioration prediction system 10. The deterioration prediction system 10 may have a configuration other than the above-described calculation device. For example, some or all of the configuration for realizing the functions of the secondary battery deterioration 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, a description will be given of the functional configuration of the control unit 20 of the deterioration prediction system 10. The control unit 20 includes an input unit 11, a calculation unit 12, and an output unit 13 as its functional configuration.

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

[0021] The battery degradation test data and operational data include, for example, the open circuit voltage and resistance of the battery, and the open circuit potential and resistance of the electrodes (positive electrode, negative electrode). Calculation conditions for predicting secondary battery degradation include, for example, the operating conditions of the secondary battery, the calculation termination conditions, and the data output interval. Among the calculation conditions for predicting secondary battery degradation, the operating conditions of the secondary battery include the temperature and the load (current). Furthermore, among the calculation conditions for predicting secondary battery degradation, the calculation termination conditions include a period (e.g., 10 years) and a decrease in battery capacity to a predetermined value (e.g., 70% or less). The data output interval is a predetermined period (e.g., every 10 days) for outputting the calculation results.

[0022] The calculation unit 12 calculates internal deterioration parameters of the secondary battery based on the various pieces of information acquired from the input unit 11. The calculation unit 12 also creates and updates a battery deterioration model (deterioration prediction formula) based on the calculated internal deterioration parameters, battery deterioration test data, battery operation data, and calculation conditions acquired from the input unit 11. Furthermore, the calculation unit 12 uses the battery internal deterioration parameters and battery deterioration model to perform calculations related to the battery deterioration state of the secondary battery to be predicted, such as the battery capacity and battery resistance.

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

[0024] [Functional Configuration of Calculation Unit] Next, the functional configuration performed by the calculation unit 12 of the above-mentioned secondary battery deterioration prediction system 10 will be described. Fig. 2 shows the functional configuration of the calculation unit 12 of the secondary battery deterioration prediction system 10. The calculation unit 12 has 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 equation creation unit 24, a function creation unit 25, a deterioration prediction equation creation unit 26, a deterioration calculation unit 27, and a deterioration prediction equation update unit 28. Each of these functional components 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 information on the open circuit potential (OCP) and resistance of the positive electrode and negative electrode, which are necessary to diagnose the internal state of the battery. The positive electrode information and negative electrode information acquisition unit 21 acquires each piece of information by reading it from the storage 3 or the like. The positive electrode information and negative electrode information acquisition unit 21 may also acquire information measured by an external device separate from the secondary battery deterioration prediction system 10.

[0026] An example of a method for acquiring information by the positive electrode information and negative electrode information acquisition unit 21 will be described. First, a single-electrode positive electrode cell is fabricated using the positive electrode and lithium metal used in the secondary battery whose life is to be predicted. A single-electrode negative electrode cell is fabricated using the negative electrode and lithium metal used in the secondary battery whose life is to be predicted. Next, a charge / discharge test is performed on each of the single-electrode positive electrode cell and the single-electrode negative electrode cell using the galvanostatic intermittent titration technique (GITT). This acquires information on the open circuit potential and resistance of the single-electrode positive electrode cell. Also, information on the open circuit potential and resistance of the single-electrode negative electrode cell is acquired. After this, an operator or the like stores the acquired information on the open circuit potential and resistance of the single-electrode positive electrode cell and the single-electrode negative electrode cell in storage 3. Then, the positive electrode information and negative electrode information acquisition unit 21 reads information on the open circuit potential and resistance of the positive electrode single-electrode cell and information on the open circuit potential and resistance of the negative electrode single-electrode cell from the storage 3, and outputs it to the battery internal state diagnosis unit 23.

[0027] In the constant-current intermittent titration method, each single-electrode cell was fully charged, and then cycled through a 72-second discharge at a current equivalent to 1 C [A] followed by a 5-minute rest period until the discharge end potential was reached. The open-circuit potential at each state of charge (SOC) was measured 5 minutes after the discharge current was stopped. The resistance at each state of charge (SOC) was calculated by dividing the sum of the open-circuit potential before discharge and the open-circuit potential after a predetermined discharge time had 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 information is necessary for the battery internal state diagnosis unit 23 to diagnose the internal state of the battery. The battery information acquisition unit 22 reads and acquires each piece of information from the storage 3 or the like. The battery information acquisition unit 22 may also acquire information measured by an external device separate from the secondary battery deterioration prediction system 10.

[0029] An example of a method for acquiring information by the battery information acquisition unit 22 will be described. First, a predetermined temperature, a central SOC (State Of Charge), an SOC range, and current conditions are determined for the secondary battery, and a charge / discharge test is performed. The SOC range is the range of the state of charge (SOC) under the actual operating conditions of the secondary battery. The central SOC is the intermediate value between the maximum and minimum values ​​of the SOC range. When performing the charge / discharge test, the battery information acquisition unit 22 periodically stops the charge / discharge test and performs a constant current intermittent titration method to acquire information on the open circuit voltage and resistance.

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

[0031] In the charge-discharge test using the constant-current intermittent titration method, the secondary battery to be predicted is fully charged, and then cycled through 72 seconds of discharge at a current equivalent to 1 C [A] followed by a 5-minute rest until the battery reaches its end-of-discharge potential. The open-circuit potential at each state of charge (SOC) is the voltage measured 5 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 discharge and the open-circuit voltage after a specified 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 electrode information 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 on the open circuit potential and resistance of the positive electrode and the data on the open circuit potential and resistance of the negative electrode acquired by the positive electrode information and negative electrode information acquisition unit 21, and the data on the open circuit voltage and resistance of the secondary battery acquired by the battery information acquisition unit 22. In this way, the battery internal state diagnosis unit 23 diagnoses the internal state of the secondary battery.

[0033] The internal condition is diagnosed as follows: ｐ , negative electrode active material utilization rate m ｎ , the amount of lithium ion loss due to the film formation on the positive electrode surface δ ｐ , the amount of lithium ion loss due to the formation of a film on the negative electrode surface δ ｎ , the battery solution resistance R0, the positive electrode resistance increase rate a ｐ , and the resistance increase rate a of the negative electrode ｎ These diagnostic targets are internal deterioration parameters that have a correlation with battery deterioration.

[0034] Positive electrode active material utilization rate m ｐ is an index showing the ratio of the active material that can contribute to the battery reaction among the positive electrode active material contained in the electrode. ｎ is an index showing the ratio of the active material that can contribute to the battery reaction among the negative electrode active materials contained in the electrode. The amount of lithium ion loss due to the formation of a coating on the positive electrode surface, δ ｐ is an index showing the amount of electricity of the lithium ions captured in the film on the surface of the positive electrode. This film is mainly formed by the reaction of the electrolyte on the surface of the positive electrode. The amount of lithium ion loss due to the formation of the film on the surface of the negative electrode, δ ｎ is an index showing the amount of electricity of the lithium ions trapped in the film on the surface of the negative electrode. This film is mainly produced by the reaction of the electrolyte on the surface of the negative electrode. The solution resistance R of the battery ０ is an index showing the resistance of the battery components when the battery is used. ｐis an index showing the rate of increase in the resistance of the positive electrode when the battery is used. ｎ is an index showing the rate of increase in the resistance of the negative electrode when the battery is used.

[0035] These seven indices are used as internal deterioration parameters, and the data on the open circuit potential and resistance of the positive and negative electrodes acquired by the positive electrode information and negative electrode information acquisition unit 21 is fitted to the data on the open circuit voltage and resistance of the secondary battery acquired by the battery information acquisition unit 22. An example of the fitting result is shown in Figures 3 and 4.

[0036] 3 is a graph showing the actual measured value d11 of the open circuit voltage (OCV) of the secondary battery, its calculated value d12, the calculated positive electrode potential d13, and the calculated negative electrode potential d14. In the graph shown in FIG. 3, the vertical axis on the left represents the battery voltage and the positive electrode potential [V], and the vertical axis on the right represents the negative electrode potential [V]. The horizontal axis represents 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. The calculated value d12 is a value calculated using the calculated positive electrode potential d13, the calculated negative electrode potential d14, and the measured open circuit voltage d11 of the secondary battery using the following method.

[0037] An example of a calculation process for the calculated value d12 of the open circuit voltage (OCV) of a 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 matches the measured value d11 of the open circuit voltage. For example, the positive electrode potential d13 is calculated by moving δ ｐ (Lithium ion loss δ ｐ ) and then translate by a factor m ｐ (Positive electrode active material utilization rate m ｐ ) and the negative electrode potential d14 is plotted along the horizontal axis from the position of capacity 0. ｎ (Lithium ion loss δ ｎ ) and then translate by a factor m ｎ (Anode active material utilization rate m ｎ The difference between the calculated positive electrode potential d13 and negative electrode potential d14 and the actual measured value d11 of the open circuit voltage of the secondary battery is evaluated.

[0038] Figure 4 is a graph showing the actual measured value d21 of the resistance of the secondary battery, its calculated value d22, the calculated positive electrode resistance d23, and the calculated negative electrode resistance d24. In the graph shown in Figure 4, the vertical axis represents resistance [mΩ] and the horizontal axis represents battery capacity [Ah]. In Figure 4, the actual measured value d21 of the resistance of the secondary battery is plotted as a white circle. The calculated value d22 is a value calculated using the positive electrode resistance d23, the negative electrode resistance d24, and the actual measured value d21 of the resistance of the secondary battery using the following method.

[0039] An example of the calculation process of the calculated resistance value d22 of the secondary battery is shown below. The calculated resistance value d22 can be processed in the same manner as the open circuit voltage of the secondary battery described above. In calculating the calculated resistance value d22, the positive electrode resistance d23, the negative electrode resistance d24, and the solution resistance R ０ The sum of these is fitted to match the actual measured resistance value d21.

[0040] For example, the positive electrode resistance d23 is set to δ from the position of capacitance 0 in the horizontal direction. ｐ (Lithium ion loss δ ｐ ) and move it by the coefficient m ｐ (Positive electrode active material utilization rate m ｐ ) and then multiply it by a coefficient a ｐ (Positive electrode resistance increase rate a ｐ ) is calculated. Also, the negative electrode resistance d24 is multiplied by δ ｎ (Lithium ion loss δ ｎ ) and move it by the coefficient m ｎ (Anode active material utilization rate m ｎ ) and then multiply it by a coefficient a ｎ (Rate of increase in resistance of negative electrode a ｎ Then, the calculated value of the positive electrode resistance d23, the calculated value of the negative electrode resistance d24, and the solution resistance R ０ The deviation between the sum of the above and the actual 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 expressed as the difference between the positive electrode potential d13 and the negative electrode potential d14, is calculated so as to approximate the actual measured value d14. ｐ , mｎ , δ ｐ , and δ ｎ These values ​​are calculated by fitting the positive electrode resistance d23, the negative electrode resistance d24, and the solution resistance R ０ m so that the calculated resistance value d22 of the secondary battery, which is the sum of ｐ , m ｎ , δ ｐ , δ ｎ , a ｐ , a ｎ , and R ０ These values ​​are obtained by fitting the following. The fitting results are stored in storage 3.

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

[0043] The straight line of the time series change d31 shown in FIG. ｎ The straight line of the time series change d32 shown in FIG. ｐ The curve of the time series change d41 shown in FIG. 6 is an approximation curve of the amount of lithium ion loss δ ｎ The curve of the time series change d42 shown in FIG. 6 is an approximation curve of the amount of lithium ion loss δ ｐIn this way, each of the internal deterioration parameters described above can be expressed 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. In Figures 5 and 6, the active material utilization rate m ｎ , the utilization rate of the active material of the positive electrode 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 Similarly, the time series change equation creating unit 24 calculates the approximate curves for only the solution resistance R0 of the battery and the resistance increase rate a of the positive electrode. ｐ , and the resistance increase rate a of the negative electrode ｎ An approximate curve is also created for

[0045] The negative electrode active material utilization rate m shown by the time series change d31 in FIG. ｎ The creation of the time series change equation will be explained using the approximate equation of the time series change d31 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 as a function of the negative electrode active material utilization rate m ｎ As a time series change formula, the formula [m ｎ In this formula, k is a coefficient and j is an intercept. ｐ The time-series change equation creating unit 24 creates a similar equation for the approximation curve of the time-series change d32.

[0046] The amount of lithium ion loss δ on the negative electrode surface shown in FIG. ｎ The 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 an equation of a root system of time t. Then, the time series change equation creation unit 24 uses the equation [δ ｎ = p + n × (t) ０．５In this equation, n is a coefficient and p is an intercept. The amount of lithium ion loss due to the formation of a coating on the positive electrode surface, δ ｐ The time-series change equation creating unit 24 similarly creates an equation for the approximation curve of the time-series change d42.

[0047] (Function Generation Unit) Next, the function generation unit 25 generates a function of the operating conditions from the coefficients of the time-series change equation of the internal deterioration parameters of each battery obtained by the time-series change equation generation unit 24. Here, an example of the function generation will be described. The function generation unit 25 generates a function of the operating conditions from the coefficients of the time-series change equation of the internal deterioration parameters of each battery obtained by the time-series change equation generation unit 24. ｎ The time series change formula [m ｎ The function generator 25 also calculates the amount of lithium ion loss δ associated with the formation of a film on the negative electrode surface, which is shown as an approximate curve in FIG. ｎ The time series change formula of [δ ｎ = p + n × (t) ０．５ ], the dependency of the coefficient n on the operating conditions is expressed as a function. Here, the operating conditions include the temperature, load (current) conditions, central SOC, SOC range, etc. when the target secondary battery is operated.

[0048] As an example of the function, the function generator 25 generates a function for the coefficient k using an additive equation using the operating conditions, such as: [k = α + β × temperature + γ × center SOC + ε × SOC range + ζ × current]. The function generator 25 also generates a function for the coefficient k using a multiplication equation using the operating conditions, such as: [k = α × (β × temperature) × (γ × center SOC) × (ε × SOC range) × (ζ × current)]. Similarly, the function generator 25 generates a function for the coefficient n using an additive equation using the operating conditions, such as: [n = α + β × temperature + γ × center SOC + ε × SOC range + ζ × current]. The function generator 25 also generates a function for the coefficient n using a multiplication equation using the operating conditions, such as: [n = α × (β × temperature) × (γ × center SOC) × (ε × SOC range) × (ζ × current)]. 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 carried out by, for example, ｎ The time series change formula [m ｎ= j - k × t] is the active material utilization m of the negative electrode shown in FIG. ｎ The coefficients α, β, γ, ε, and ζ are adjusted so as to match the time series change d31 of the amount of lithium ion loss in the negative electrode δ ｎ The time series change formula of [δ ｎ = p + n × (t) ０．５ ] is the amount of lithium ion loss δ in the negative electrode shown in FIG. ｎ The coefficients α, β, γ, ε, and ζ are adjusted so as to match the time series change d41 of

[0049] For example, a parameter table or the like in which values ​​are assigned to the coefficients α, β, γ, ε, and ζ is prepared in advance and stored in the storage 3. Then, for each condition in the parameter table, the values ​​of α, β, γ, ε, and ζ are substituted into an equation in which the coefficients are converted into functions, and the coefficients k and n are calculated. Furthermore, the calculated coefficients k and n are used to calculate the time series change equation [m ｎ = j - k × t], [δ ｎ = p + n × (t) ０．５ ] is calculated by the time series change formula ｎ , δ ｎ In the calculation of the active material utilization ratio m of the negative electrode, the deviations of the time series changes d31 and d41 between the conditions in the parameter table are compared and evaluated. Then, through this comparison and evaluation, the values ​​of α, β, γ, ε, and ζ of the most approximate condition in the parameter table are derived. ｎ , and the amount of lithium ion loss in the negative electrode δ ｎ The coefficients k and n of the time series change equation are shown. Similarly, the function generating unit 25 calculates the active material utilization rate m ｐ , the amount of lithium ion loss on the positive electrode surface δ ｐ , the battery solution resistance R0, the positive electrode resistance increase rate a ｐ , and the resistance increase rate a of the negative electrode ｎ The coefficients of each time series variation equation are also converted into functions. The function conversion unit 25 also derives the coefficients of α, β, γ, ε, and ζ of each time series variation equation.

[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 creates a deterioration prediction formula for the battery by combining the time series change formula for each internal deterioration parameter created by the time series change formula creation unit 24 and an equation in which the coefficients of each internal deterioration parameter are converted into a function created by the function creation unit 25.

[0051] The deterioration prediction formula for the capacity Qc of the next battery is: ｐ , negative electrode capacity is q ｎ Then, it can be expressed by the following equation: [Qc=m ｐ ×q ｐ -δ ｐ = m ｎ ×q ｎ -δ ｎ In this formula, the positive electrode capacity q ｐ The positive electrode active material utilization rate m ｐ Furthermore, the amount of lithium ion loss due to the formation of a film on the positive electrode surface, δ ｐ The capacity Qc of the secondary battery is calculated by subtracting the negative electrode capacity q ｎ The negative electrode active material utilization rate m ｎ Furthermore, the amount of lithium ion loss due to the formation of a coating on the negative electrode surface, δ ｎ By subtracting the above, the capacity Qc of the secondary battery is calculated.

[0052] Also, the positive electrode resistance is r ｐ , the negative electrode resistance is 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 as follows: [Rc=a ｐ / m ｐ ×r ｐ +a ｎ / m ｎ ×r ｎ +R0] In this equation, first, the positive electrode resistance r ｐ , the positive electrode active material utilization rate m ｐ The resistance increase rate of the positive electrode relative to ｐ Then, multiply the negative electrode resistance by r ｎ , the negative electrode active material utilization rate m ｎThe rate of increase in the resistance of the negative electrode relative to ｎ The resistance of the negative electrode is calculated by multiplying the ratio of the positive electrode resistance, the negative electrode resistance, and the battery solution resistance R0 to calculate the resistance Rc. This formula is also used to calculate the positive electrode resistance r ｐ , negative electrode resistance r ｎ , the positive electrode capacity q ｐ , negative electrode capacity q ｎ By substituting the above, the equation for the capacitance Qc can be obtained. ｐ / m ｐ ×q ｐ +a ｎ / m ｎ ×q ｎ +R0]

[0053] The time series change equations for each internal deterioration parameter created by the time series change equation creating unit 24 and the equations in which the coefficients of each internal deterioration parameter are converted into functions created by the function creating unit 25 are substituted into the above equations, thereby making it possible to calculate the capacity Qc and resistance Rc of the secondary battery at the time of measurement.

[0054] (Deterioration Calculation Unit) The deterioration calculation unit 27 calculates the internal deterioration parameters of the secondary battery described above and calculates battery deterioration. The deterioration calculation unit 27 performs each calculation based on the time series change equation created by the time series change equation creation unit 24, the equation in which the coefficients are converted into a function created by the function creation unit 25, and the deterioration prediction equation created by the deterioration prediction equation creation unit 26.

[0055] Furthermore, the deterioration calculation unit 27 divides the capacity Qc and resistance Rc of the secondary battery at the time of measurement, which are obtained as calculation results of battery deterioration, by the initial capacity Qini and initial resistance Rini of the secondary battery to calculate the rate of change in capacity SOHQ and rate of change in resistance SOHR of the secondary battery. Specifically, the rate of change in capacity SOHQ and rate of change in resistance SOHR can be expressed as the following equations: [SOHQ = Qc / Qini x 100] and [SOHR = Rc / Rini x 100]. The deterioration calculation unit 27 calculates the rate of change in capacity SOHQ and rate of change in resistance SOHR using the above equations.

[0056] (Deterioration Prediction Formula Update Unit) The deterioration prediction formula update unit 28 updates the deterioration prediction formula created by the above-described method while the secondary battery or the equipment equipped with the secondary battery is in operation. The deterioration prediction formula update unit 28 stores the new deterioration prediction formula created by the above-described method in, for example, the storage 3. At this time, the deterioration prediction formula update unit 28 may update the previously stored deterioration prediction formula with the new deterioration prediction formula. The previous deterioration prediction formula may be deleted from the storage 3, or may be stored together with the new deterioration prediction formula as an old version of the deterioration prediction formula. In addition, the deterioration prediction formula update unit 28 may store 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 function generation unit 25 in addition to 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, internal deterioration parameters acquired by the battery internal state diagnosis unit 23, etc. in the storage 3. In addition, the degradation prediction formula update unit 28 may store in the storage 3 the time series change formula of the internal degradation parameter acquired by the time series change formula creation unit 24, and formulas and coefficients obtained by converting the coefficients of the time series change formula acquired by the function conversion unit 25 into functions.

[0057] [Secondary Battery Deterioration Prediction Method] Next, a method for predicting 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 actual operation of a secondary battery-equipped device, information on the secondary battery in operation is acquired during times when the secondary battery is not in use or when operation is temporarily suspended for maintenance, etc. For example, when the secondary battery is not in use or is temporarily suspended, the battery information acquisition unit 22 performs all or part of the constant current intermittent titration method. This acquires information on the secondary battery during operation of the secondary battery-equipped device, such as the open circuit voltage and resistance of the secondary battery.

[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 internal deterioration parameters of the battery. Next, the time series change equation creation unit 24 creates a time series change equation for the internal deterioration parameter. The time series change equation for the internal deterioration parameter is created using internal deterioration parameters obtained by diagnosing the internal state of information about the secondary battery before operation acquired in advance by a charge / discharge test and information about the secondary battery acquired during actual operation of equipment equipped with the secondary battery.

[0059] Next, a function generator 25 generates a function from the internal deterioration parameters of the secondary battery. A deterioration prediction formula generator 26 generates a deterioration prediction formula for the secondary battery. A deterioration prediction formula updater 28 updates the previous deterioration prediction formula with a new deterioration prediction formula generated based on the additional information obtained. A deterioration calculation unit 27 then calculates the internal deterioration parameters and battery deterioration of the battery using the new, updated deterioration prediction formula.

[0060] 7 to 10 are examples of the internal deterioration parameters of the battery and the predicted results of the battery deterioration output by the output unit 13 of the secondary battery deterioration prediction system 10 of this example. ｎ 8 is a graph showing the time series change of the amount of lithium ion loss δ associated with the formation of a film on the negative electrode surface, which is one of the internal deterioration parameters of the battery. ｎ 7 to 10. FIG. 9 is a graph showing time series changes in the rate of change of capacity SOHQ, which is one of the battery degradation indicators. FIG. 10 is a graph showing time series changes in the rate of change of resistance SOHR, which is one of the battery degradation indicators. The output unit 13 outputs graphs of the various battery internal degradation parameters and the predicted results of battery degradation shown in FIGS. 7 to 10. The output unit 13 may also output in the form of a table or the like instead of the graphs shown in FIGS. 7 to 10.

[0061] As described above, the deterioration prediction system 10 for a secondary battery according to the present embodiment can accurately diagnose the internal state of a secondary battery even in a case where the change in voltage due to a change in the state of charge is small, and can predict the lifespan of the secondary battery with high accuracy based on the obtained diagnosis results. ｘ Mn ｙ Co ｚ M １－ｘ－ｙ－ｚ O ２ LiMn ２ O ４ The internal state of the secondary battery can be diagnosed with high accuracy for a battery system using the LiFePO system positive electrode as the electrode active material. ４ This makes it possible to diagnose the internal state of the secondary battery with high accuracy even for battery systems that use a system positive electrode and have small voltage changes.

[0062] The battery internal state diagnosis unit 23 also diagnoses the state of deterioration of the positive electrode using a data table containing state-of-charge information and open-circuit potential information specific to the positive electrode active material, and a data table containing state-of-charge information and internal resistance information specific to the positive electrode active material. The battery internal state diagnosis unit 23 also diagnoses the state of deterioration of the negative electrode using a data table containing state-of-charge information and open-circuit potential information specific to the negative electrode active material of the secondary battery, and a data table containing state-of-charge information and internal resistance information specific to the negative electrode active material of the secondary battery. The battery internal state diagnosis unit 23 extracts internal deterioration parameters, including the positive electrode active material utilization rate, the negative electrode active material utilization rate, the amount of lithium ion loss associated with film formation on the positive electrode surface and the negative electrode surface, the battery solution resistance, the positive electrode resistance increase rate, and the negative electrode resistance increase rate. These internal deterioration parameters are then used to diagnose the states of deterioration of the positive electrode and negative electrode. The time-series change equation creation unit 24 creates a time-series change equation for the internal deterioration parameters of the secondary battery based on the internal deterioration parameters of the secondary battery diagnosed by the battery internal state diagnosis unit 23. Then, the secondary battery deterioration prediction equation creation unit 26 creates a life prediction equation based on the time-series change equation created by the time-series change equation creation unit 24. Therefore, the secondary battery deterioration prediction equation created by the above-described method can predict the deterioration of the secondary battery by taking into account changes in the deterioration internal parameters of each battery. Therefore, the created deterioration prediction equation can make predictions taking into account changes in the shape of the initial battery deterioration prediction due to changes in the rate-determining process of battery deterioration, etc. As a result, battery life can be predicted with higher accuracy than when the deterioration of the internal deterioration parameters of each battery is not taken into account.

[0063] The above-described embodiment illustrates an example in which the secondary battery deterioration prediction system 10 is configured with one arithmetic device. The secondary battery deterioration prediction system 10 may be configured with multiple arithmetic devices. For example, some data, such as data measured on the positive and negative electrodes of the secondary battery, may be stored in a server or the like connected to the secondary battery deterioration prediction system 10 via a network. The secondary battery deterioration prediction system 10 may then communicate with the server to obtain information and perform similar processing. Furthermore, some of the components of the calculation unit 12 shown in FIG. 2 may be arranged in a separate arithmetic device, and processing by this configuration may be performed by the separate arithmetic device.

[0064] 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 assumed that almost all components are interconnected. Furthermore, when the secondary battery deterioration prediction system is configured with an information processing device such as a computing device, the programs that realize each processing function may be stored in a storage unit within the computing device, or may be stored in an external storage medium such as an IC card, an SD card, or an optical disk.

[0065] It should be noted that the present invention is not limited to the above-described embodiments and various modifications are possible. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to embodiments that include all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment. It is also possible to add the configuration of another embodiment to the configuration of one embodiment. It is also possible to delete part of the configuration of each embodiment, or to add or replace other configurations.

[0066] 1...CPU, 2...Memory, 3...Storage, 4...Input device, 5...Network interface, 6...Output device, 10...Deterioration prediction system, 11...Input unit, 12...Calculation unit, 13...Output unit, 20...Control unit, 21...Positive electrode information and negative electrode information acquisition unit, 22...Battery information acquisition unit, 23...Battery internal state diagnosis unit, 24...Time series change equation creation unit, 25...Function unit, 26...Deterioration prediction equation creation unit, 27...Deterioration calculation unit, 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 in utilization rate of active material of negative electrode, d32... time series change in utilization rate of active material of positive electrode, d41... time series change in amount of lithium ion loss at negative electrode, d42... time series change in amount of lithium ion loss at positive electrode

Claims

1. A deterioration prediction system for a secondary battery, comprising: a control unit that controls a deterioration prediction system that predicts deterioration of a secondary battery; and a memory unit in which information for the deterioration prediction is stored, wherein the control unit includes a calculation unit that acquires internal deterioration parameters of the secondary battery, creates a deterioration prediction equation based on the internal deterioration parameters, and stores the created deterioration prediction equation in the memory unit.

2. The secondary battery deterioration prediction system of claim 1, wherein the calculation unit includes: a time series change equation creation unit that creates a time series change equation of the internal deterioration parameter based on the internal deterioration parameter; a function conversion unit that creates an equation that converts coefficients of the time series change equation into a function; and a deterioration prediction equation creation unit that creates the deterioration prediction equation based on the converted equation.

3. The secondary battery deterioration prediction system according to claim 2, wherein the time series change equation creation unit creates the time series change equation based on an approximation curve of the time series change of the internal deterioration parameter.

4. The deterioration prediction system for a secondary battery according to claim 3, wherein the calculation unit includes a function conversion unit that converts the coefficients of the time series change equation into a function using operating conditions of the secondary battery.

5. The secondary battery deterioration prediction system according to claim 2, wherein the calculation unit includes a deterioration prediction formula update unit that stores the created deterioration prediction formula in the memory unit and updates it.

6. The deterioration prediction system for a secondary battery as described in claim 1, wherein the calculation unit includes a battery internal state diagnosis unit that obtains the internal deterioration parameters based on data on an open circuit potential of a positive electrode, data on a resistance of the positive electrode, data on an open circuit potential of a negative electrode, data on a resistance of the negative electrode, data on an open circuit voltage of the secondary battery, and data on a resistance of the secondary battery.

7. The secondary battery deterioration prediction system described in claim 6, wherein the memory unit stores data on the open circuit potential of the positive electrode, data on the resistance of the positive electrode, data on the open circuit potential of the negative electrode, and data on the resistance of the negative electrode that have been measured in advance, and the battery internal state diagnosis unit acquires the data on the open circuit potential of the positive electrode, data on the resistance of the positive electrode, data on the open circuit potential of the negative electrode, and data on the resistance of the negative electrode from the memory unit.

8. The secondary battery deterioration prediction system described in claim 7, wherein the calculation unit includes a battery information acquisition unit that acquires data on the open circuit voltage of the secondary battery and data on the resistance of the secondary battery, and the battery internal state diagnosis unit acquires the internal deterioration parameters based on the data on the open circuit voltage of the secondary battery and the data on the resistance of the secondary battery acquired by the battery information acquisition unit.

9. The deterioration prediction system for a secondary battery described in claim 8, wherein the battery internal state diagnosis unit acquires the internal deterioration parameters by fitting the open circuit voltage data of the secondary battery using the open circuit potential data of the positive electrode and the open circuit potential data of the negative electrode, and by fitting the resistance data of the secondary battery using the resistance data of the positive electrode and the resistance data of the negative electrode.

10. The deterioration prediction system for a secondary battery described in claim 9, wherein the battery internal state diagnosis unit acquires as the internal deterioration parameters at least one 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 in the positive electrode, the amount of lithium ion loss in the negative electrode, the solution resistance of the secondary battery, the resistance rise rate of the positive electrode, and the resistance rise rate of the negative electrode.

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

12. The secondary battery deterioration prediction system described in claim 1, wherein the calculation unit uses the deterioration prediction formula to calculate at least one of the active material utilization rate of the negative electrode, the amount of lithium ion loss in the negative electrode, the rate of change of capacity of the secondary battery, and the rate of change of resistance of the secondary battery.

13. A deterioration prediction method for predicting deterioration of a secondary battery, comprising: acquiring internal deterioration parameters of the secondary battery; creating a deterioration prediction equation based on the internal deterioration parameters; and storing the created deterioration prediction equation in a memory unit.

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