JP2022085385A - Battery deterioration determination device, battery management system, battery-equipped apparatus, method for determining deterioration of battery, and battery deterioration determination program - Google Patents

Abstract

To provide a battery deterioration determination device that properly estimates a reason of change of the resistance component of a determination target battery and determines the deterioration of a battery more correctly.SOLUTION: In an embodiment, there is provided a deterioration determination device for determining the deterioration of a determination target battery on the basis of information on the battery. The deterioration determination device has a processor. The processor estimates an aging factor that shows a reason of change of the resistance component other than the change of the capacity of an active material, on the basis of the resistance value of the resistance component of the battery in a first interval and in a second interval after the first interval and the capacitance of the active material of the battery in the first interval and in the second interval.SELECTED DRAWING: Figure 5

Description

An embodiment of the present invention relates to a battery deterioration determination device, a battery management system, a battery-mounted device, a battery deterioration determination method, and a battery deterioration determination program.

In recent years, for batteries such as secondary batteries, the internal state of the battery is estimated based on the measurement data including the measured values such as the current and voltage of the battery, and the deterioration of the battery is determined based on the estimation result of the internal state. It is carried out. In such a determination, in estimating the internal state of the battery to be determined, the positive electrode capacity of the battery, which is the capacity of the positive electrode active material of the battery, the negative electrode capacity of the battery, which is the capacity of the negative electrode active material of the battery, and the negative electrode capacity of the battery. The resistance component of the impedance (internal resistance) is estimated as the internal state parameter of the battery. Then, based on the estimated internal state parameter of the battery, the degree of deterioration of the battery, the deterioration rate, and the like are determined.

In a battery such as a secondary battery, the resistance component changes due to an increase in the resistance component of impedance or the like at the start of use or the like by repeating charging and discharging. In the determination of battery deterioration as described above, a change in the resistance component of the battery is estimated as a change in the internal state of the battery. Here, in determining the deterioration of the battery, it is required to appropriately estimate the cause of the change in the resistance component of the battery in addition to the change in the resistance component of the battery. For example, the influence of the change in the capacity of the active material of the battery on the change in the resistance component of the battery (increased resistance component), and the influence on the change in the resistance component of the battery other than the change in the capacity of the active material of the battery, etc. It is required to estimate. Then, it is required that the deterioration of the battery is determined more appropriately based on the factors of the estimated change of the resistance component and the like.

Japanese Unexamined Patent Publication No. 2019-132655 International Publication No. 2017/047192 Japanese Unexamined Patent Publication No. 2020-109367 Japanese Unexamined Patent Publication No. 2012-251806 Japanese Unexamined Patent Publication No. 2015-184146

The problem to be solved by the present invention is a battery deterioration determination device, a battery management system, in which the cause of the change in the resistance component of the battery to be determined is appropriately estimated and the deterioration of the battery is determined more appropriately. It is an object of the present invention to provide a battery-mounted device, a battery deterioration determination method, and a battery deterioration determination program.

In the embodiment, a deterioration determination device for determining deterioration of the battery based on information about the battery to be determined is provided. The deterioration determination device includes a processor. The processor is the resistance value of the resistance component of the battery in each of the first period and the second period after the first period, and the capacity of the active material of the battery in each of the first period and the second period. Based on, the aging factor that shows the influence other than the change in the capacity of the active material among the causes of the change in the resistance component is estimated.

FIG. 1 is a schematic diagram showing a battery management system according to the first embodiment. FIG. 2A is a schematic diagram showing an example of a current flowing through the battery in measuring the resistance value of the resistance component of the battery according to the first embodiment. FIG. 2B is a schematic diagram showing another example of the current flowing through the battery in the measurement of the resistance value of the resistance component of the battery according to the first embodiment, which is different from FIG. 2A. FIG. 3 is a schematic diagram showing an example of the frequency characteristics of the impedance of one of the positive electrode and the negative electrode of the battery according to the first embodiment. FIG. 4 is a schematic diagram illustrating an internal state parameter indicating the internal state of the battery. FIG. 5 is a flowchart showing a process performed by the deterioration determination device according to the first embodiment. FIG. 6 is a flowchart showing a process performed by the deterioration determination device according to the first modification. FIG. 7 is a flowchart showing a process performed by the deterioration determination device according to the second modification.

Hereinafter, embodiments will be described with reference to the drawings.

(First Embodiment)
First, as an example of the embodiment, the first embodiment will be described. FIG. 1 is a schematic diagram showing a battery management system according to the first embodiment. As shown in FIG. 1, the management system 1 includes a battery-mounted device 2 and a deterioration determination device 3. The battery-mounted device 2 is equipped with a battery 5, a measurement circuit 6, and a battery management unit (BMU) 7. Examples of the battery-equipped device 2 include a large power storage device for an electric power system, a smartphone, a vehicle, a stationary power supply device, a robot, a drone, and the like, and examples of the battery-equipped device 2 include a railroad vehicle, an electric bus, and electricity. Examples include automobiles, plug-in hybrid automobiles, electric motorcycles, and the like.

The battery 5 is a secondary battery such as a lithium ion secondary battery. The battery 5 may be formed from a single cell (single cell), or may be a battery module or a cell block formed by electrically connecting a plurality of single cells. When the battery 5 is formed of a plurality of single cells, the plurality of single cells may be electrically connected in series or the plurality of single cells may be electrically connected in parallel in the battery 5. Further, in the battery 5, both a series connection structure in which a plurality of single cells are connected in series and a parallel connection structure in which a plurality of single cells are connected in parallel may be formed. Further, the battery 5 may be any of a battery string, a battery array, and a storage battery to which a plurality of battery modules are electrically connected.

The measurement circuit 6 detects and measures parameters related to the battery 5 in a state where the battery 5 is being charged or discharged. In the measurement circuit 6, parameters are periodically detected and measured at predetermined timings in one charge or discharge of the battery 5. That is, the measurement circuit 6 measures the parameters related to the battery 5 at each of the plurality of measurement time points in charging or discharging the battery 5 once, and measures the parameters related to the battery 5 a plurality of times. The parameters related to the battery 5 include the current flowing through the battery 5, the voltage of the battery 5, the temperature of the battery 5, and the like. Therefore, the measurement circuit 6 includes an ammeter for measuring current, a voltmeter for measuring voltage, a temperature sensor for measuring temperature, and the like.

The battery management unit 7 constitutes a processing device (computer) that manages the battery 5 by controlling charging and discharging of the battery 5, and includes a processor and a storage medium. The processor includes any one of a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), a microcomputer, an FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor) and the like. The storage medium may include an auxiliary storage device in addition to a main storage device such as a memory. Examples of the storage medium include magnetic disks, optical disks (CD-ROM, CD-R, DVD, etc.), magneto-optical disks (MO, etc.), semiconductor memories, and the like. In the battery management unit 7, each of the processor and the storage medium may be one or a plurality. In the battery management unit 7, the processor performs processing by executing a program or the like stored in a storage medium or the like. Further, in the battery management unit 7, the program executed by the processor may be stored in a computer (server) connected via a network such as the Internet, or a server in a cloud environment. In this case, the processor downloads the program over the network.

The deterioration determination device 3 determines the deterioration of the battery 5 based on the information regarding the battery 5. Therefore, the battery 5 is a determination target in the deterioration determination by the deterioration determination device 3. In the present embodiment, the deterioration determination device 3 is provided outside the battery-mounted device 2. The deterioration determination device 3 includes a transmission / reception unit 11, a resistance estimation unit 12, a capacitance estimation unit 13, an aging factor estimation unit 15, and a data storage unit 16. The deterioration determination device 3 is, for example, a server capable of communicating with the battery management unit 7 via a network. In this case, the deterioration determination device 3 includes a processor and a storage medium, similarly to the battery management unit 7. Then, the transmission / reception unit 11, the resistance estimation unit 12, the capacitance estimation unit 13, and the aging factor estimation unit 15 perform a part of the processing performed by the processor of the deterioration determination device 3, and the storage medium of the deterioration determination device 3 is used. It functions as a data storage unit 16.

In one example, the deterioration determination device 3 may be a cloud server configured in a cloud environment. The infrastructure of the cloud environment is composed of a virtual processor such as a virtual CPU and a cloud memory. Therefore, when the deterioration determination device 3 is a cloud server, a part of the processing performed by the virtual processor is performed by the transmission / reception unit 11, the resistance estimation unit 12, the capacity estimation unit 13, and the aging factor estimation unit 15. Then, the cloud memory functions as the data storage unit 16.

The data storage unit 16 may be provided in a computer different from the battery management unit 7 and the deterioration determination device 3. In this case, the deterioration determination device 3 is connected to a computer provided with a data storage unit 16 or the like via a network. Further, the deterioration determination device 3 may be mounted on the battery-mounted device 2. In this case, the deterioration determination device 3 is composed of a processing device or the like mounted on the battery-mounted device 2. When the deterioration determination device 3 is mounted on the battery-mounted device 2, one processing device or the like mounted on the battery-mounted device 2 performs the processing of the deterioration determination device 3 described later, and charges and discharges the battery 5. The battery management unit 7 may perform processing such as control of the above. Hereinafter, the processing of the deterioration determination device 3 will be described.

The transmission / reception unit 11 communicates with a processing device or the like other than the deterioration determination device 3 via the network. The transmission / reception unit 11 receives, for example, measurement data including measurement results in the measurement circuit 6 of the above-mentioned parameters related to the battery 5 from the battery management unit 7. The measurement data is generated by the battery management unit 7 or the like based on the measurement result or the like in the measurement circuit 6. The measurement data includes the measurement values of the parameters related to the battery 5 at each of the plurality of measurement time points (multiple measurements). Further, the measurement data includes a time change (time history) of a parameter related to the battery 5. Therefore, the measurement data includes a time change of the current of the battery 5 (time history), a time change of the voltage of the battery 5 (time history), a time change of the temperature of the battery 5 (time history), and the like. The transmission / reception unit 11 writes the received measurement data into the data storage unit 16.

At least one of the processors of the battery management unit 7 and the deterioration determination device 3 has at least one of the charge amount and the SOC (state of charge) of the battery 5 based on the measurement results of the parameter measurement circuit 6 related to the battery 5. May be estimated. Then, the deterioration determination device 3 uses the above-mentioned measurement data as the time change (time history) of the charge amount of the battery 5 and the estimated value of any of the SOCs, and the charge amount of the battery 5 and the estimated value of any of the SOCs. It may be acquired as included data. Further, the measurement data may include data showing the relationship between the estimated charge amount of the battery 5 and the above-mentioned parameters related to the measured battery 5 with respect to any of the SOCs. In this case, for example, the measurement data includes data showing the relationship between the estimated charge amount of the battery 5 and the measured voltage of the battery 5 with respect to any of the SOCs.

The real-time charge amount of the battery 5 can be calculated based on the charge amount of the battery 5 at the start of charging or discharging and the time change of the current of the battery 5. In this case, the current integrated value of the current of the battery 5 from the start of charging or discharging is calculated based on the time change of the current. Then, the charge amount of the battery 5 is calculated based on the charge amount of the battery 5 at the start of charging or discharging and the calculated current integrated value.

Further, the SOC of the battery 5 is defined based on, for example, the voltage of the battery 5. In the battery 5, the lower limit voltage Vmin and the upper limit voltage Vmax are defined for the voltage. In one example, the SOC is 0% when the open circuit voltage (OCV: open circuit voltage) of the battery 5 is the lower limit voltage Vmin, and the SOC is 100 when the open circuit voltage of the battery 5 is the upper limit voltage Vmax. The state of%. Then, the discharge capacity of the battery 5 from the state where the SOC is 100% to the state where the SOC is 0%, or the charge capacity of the battery 5 from the state where the SOC is 0% to the state where the SOC is 100% is the charge capacity of the battery 5. It becomes the battery capacity. The real-time SOC of the battery 5 is the ratio of the real-time charge amount of the battery 5 to the battery capacity of the battery 5 with the charge amount in the state where the SOC is 0% as a reference (zero). Therefore, the SOC of the battery 5 can be calculated based on the battery capacity and the charge amount of the battery 5.

The resistance estimation unit 12 estimates the resistance value of the resistance component of the battery 5 to be determined based on the measurement data and the like received by the transmission / reception unit 11. In the estimation of the resistance value of the resistance component of the battery 5 by the resistance estimation unit 12, the battery management unit 7 and the like pass a current through the battery 5 with a current waveform in which the current value changes periodically. FIG. 2A is a schematic diagram showing an example of a current flowing through the battery in measuring the resistance value of the resistance component of the battery according to the first embodiment. FIG. 2B is a schematic diagram showing another example of the current flowing through the battery in the measurement of the resistance value of the resistance component of the battery according to the first embodiment, which is different from FIG. 2A. In FIGS. 2A and 2B, the horizontal axis represents time t and the vertical axis represents current I.

In an example of FIG. 2A, in estimating the resistance value of the resistance component of the battery 5, the battery management unit 7 or the like causes an alternating current to flow through the battery 5 with a current waveform I (t) whose flow direction changes periodically. On the other hand, in the example of FIG. 2B, a direct current is passed through the battery 5 with a current waveform I (t) in which the current value changes periodically around the reference reference current locus Iref (t) as a reference. The reference current locus Iref (t) is, for example, a locus of time change of the charging current set as a charging condition in charging the battery 5. Therefore, the current of the current waveform whose current value changes periodically includes, in addition to the alternating current, a direct current whose current value changes periodically around the reference current locus. It is preferable that the estimation of the resistance value of the resistance component of the battery 5 is performed in parallel with the charging of the battery 5. Therefore, the resistance value of the resistance component of the battery 5 is estimated by passing a current through the battery 5 with a current waveform in which the current value changes periodically around the reference current locus set as the locus of the time change of the charging current. Is preferable. In the reference current locus in charging, the current value of the charging current may be constant over time from the start of charging to the end of charging, or the current value of the charging current may be changed during charging. Further, in each example of FIGS. 2A and 2B, the current waveform is a sine wave, but the current waveform may be a current waveform other than a sine wave such as a triangular wave or a saw wave.

The measuring circuit 6 measures each of the current and the voltage of the battery 5 at a plurality of measurement time points in a state where the current is flowing through the battery 5 with a current waveform whose current value changes periodically. Then, the transmission / reception unit 11 of the deterioration determination device 3 obtains the measurement results of the current and the voltage of the battery 5 in a state where the current is flowing through the battery 5 with a current waveform whose current value changes periodically. Receive as measurement data. The measurement results of the current and voltage of the battery 5 in the state where the current is flowing through the battery 5 with the current waveform in which the current value changes periodically show the current and voltage of the battery 5 at each of the plurality of measurement points. Each measured value of, and each time change (time history) of the current and voltage of the battery 5 and the like are included.

The resistance estimation unit 12 calculates the frequency characteristic of the impedance of the battery 5 based on the measurement result received by the transmission / reception unit 11. Therefore, the frequency characteristic of the impedance of the battery 5 is measured by passing a current through the battery 5 with a current waveform in which the current value changes periodically. In one example, the resistance estimation unit 12 calculates the peak-peak value (fluctuation width) in the periodic change of the current of the battery 5 based on the time change of the current of the battery 5, and the change of the voltage of the battery 5 with time. Based on the above, the peak-peak value (fluctuation width) in the periodic change of the voltage of the battery 5 is calculated. Then, the resistance estimation unit 12 calculates the impedance of the battery 5 from the ratio of the peak value of the voltage to the peak value of the current. Then, by calculating the impedance of the battery 5 with a plurality of current waveforms having different frequencies from each other as described above, the frequency characteristic of the impedance of the battery 5 is measured.

Further, in another example, a current is passed through the battery with a current waveform of a reference frequency, and each time change of the current and the voltage of the battery 5 is acquired as measurement data. Then, the resistance estimation unit 12 performs Fourier transform conversion of each time change of the current and the voltage of the battery 5 as the frequency characteristics of the current and the voltage of the battery 5, respectively, and the respective frequencies of the current and the voltage of the battery 5. Calculate the spectrum and so on. In the calculated frequency spectra of the current and the voltage of the battery 5, in addition to the above-mentioned reference frequency component, a component that is an integral multiple of the reference frequency is shown. Then, the resistance estimation unit 12 has an autocorrelation function of the time change of the current of the battery 5 and the time change of the current of the battery 5 and the voltage of the battery 5 based on the respective frequency characteristics of the current and the voltage of the battery 5. Calculate the mutual correlation function with the time change. Then, the resistance estimation unit 12 calculates the frequency characteristic of the impedance of the battery 5 by using the autocorrelation function and the cross-correlation function. The frequency characteristic of the impedance of the battery 5 is calculated, for example, by dividing the cross-correlation function by the autocorrelation function.

The resistance estimation unit 12 acquires, for example, a complex impedance plot (Cole-Cole plot) of impedance as a measurement result of the frequency characteristic of the impedance of the battery 5. The complex impedance plot shows the impedance of the battery 5 for each of the plurality of frequencies. Then, in the complex impedance plot, the real number component (resistance component) and the imaginary number component (capacity component) of the impedance of the battery 5 are shown for each of the plurality of frequencies. In addition, the method of measuring the frequency characteristic of the impedance of the battery by passing a current through the battery with the current waveform whose current value changes periodically, and the complex impedance plot which is the measurement result of the frequency characteristic of the impedance of the battery are available. It is shown in Patent Document 1 (Japanese Patent Laid-Open No. 2019-132655) and Patent Document 2 (International Publication No. 2017/0472192). In the present embodiment, the frequency characteristic of the impedance of the battery 5 may be measured in the same manner as in any of Patent Document 1 and Patent Document 2.

The data storage unit 16 stores a model of an equivalent circuit for the positive electrode, the negative electrode, and the separator of the battery 5. The resistance estimation unit 12 calculates the resistance value of the resistance component of the impedance (internal resistance) of the battery 5 based on the measurement result of the frequency characteristic of the impedance of the battery 5 and the model of the equivalent circuit. Here, the impedance of the battery 5 is divided into ohmic resistance, reaction resistance and diffusion resistance. The ohmic resistance includes the electric resistance of the active material and the current collector in the electrode (including the positive electrode and the negative electrode), the ionic conduction resistance of the electrolytic solution, and the like, and is expressed only by the resistance component (real number component). The reaction resistance includes charge transfer resistance at the interface of the electrode (including the positive electrode and the negative electrode), film resistance at the electrode, and the like, and is represented by a resistance component and a capacitance component (imaginary component). Diffusion resistance is an impedance related to the diffusion of ions and is represented by a resistance component and a capacitive component.

In the model of the equivalent circuit, related parameters are set for each of the ohmic resistance, the reaction resistance, and the diffusion resistance. For example, in the model of the equivalent circuit, the parameters corresponding to the ohmic resistance are set for each of the positive electrode, the negative electrode and the electrolytic solution, and the parameters corresponding to the resistance component of the reaction resistance and the reaction resistance are set for each of the positive electrode and the negative electrode. Parameters corresponding to the capacitive component and parameters related to the impedance of the diffusion resistance are set. The model of the equivalent circuit for the positive electrode, the negative electrode and the separator of the battery, the parameters set in the model of the equivalent circuit, and the like are shown in Patent Document 1. Also in this embodiment, the model of the equivalent circuit, the parameters set in the model of the equivalent circuit, and the like may be specified in the same manner as in Patent Document 1.

Further, the data storage unit 16 stores relationship data indicating the relationship between the parameters set in the model of the equivalent circuit with respect to the frequency characteristics of the impedance of the battery 5. The relational data is, for example, a function or a calculation formula showing the relation between the frequency characteristic of impedance and the parameter of the model of the equivalent circuit. The relationship between the frequency characteristics of the impedance of the battery 5 and the parameters of the model of the equivalent circuit changes according to the temperature of the battery 5 and the SOC, respectively. Therefore, in the relationship data, the relationship between the parameters of the model of the equivalent circuit with respect to the frequency characteristic of the impedance of the battery 5 is set for each of a plurality of temperatures different from each other, and is set for each of a plurality of SOCs different from each other.

The resistance estimation unit 12 estimates the frequency characteristics of the impedance based on the model of the equivalent circuit and the relational data. At this time, the frequency characteristic of impedance is estimated by using one or more of the parameters of the model of the equivalent circuit as variables. For example, the parameter corresponding to the resistance component of the reaction resistance at the positive electrode and the resistance component of the reaction resistance at the negative electrode. The frequency characteristic of impedance is estimated with the parameter corresponding to at least as a variable. Then, the resistance estimation unit 12 calculates the parameters used as variables in the estimation of the impedance frequency characteristics by performing the regression calculation (fitting calculation) using the estimation results and the measurement results for the impedance frequency characteristics. As a result, the parameters set in the model of the equivalent circuit are calculated.

Then, the resistance estimation unit 12 calculates the frequency characteristics of the impedances of the positive electrode and the negative electrode of the battery 5 based on the calculation results of the parameters set by the model of the equivalent circuit. As a result, the frequency characteristics of the impedance are calculated for each of the positive electrode and the negative electrode of the battery 5. That is, the frequency characteristic of the impedance of the positive electrode and the frequency characteristic of the impedance of the negative electrode are separated from the frequency characteristic of the impedance of the battery 5. The resistance estimation unit 12 acquires, for example, the above-mentioned complex impedance plot (Cole-Cole plot) of impedance as the measurement result of the frequency characteristics of the impedances of the positive electrode and the negative electrode.

Then, the resistance estimation unit 12 calculates the resistance value of the resistance component for each of the positive electrode and the negative electrode of the battery 5 based on the measurement results of the frequency characteristics of the impedances of the positive electrode and the negative electrode. For example, the resistance estimation unit 12 calculates the resistance value of the ohmic resistance, the resistance value of the resistance component of the reaction resistance, and the resistance value of the diffusion resistance for each of the positive electrode and the negative electrode. Further, the resistance estimation unit 12 calculates the resistance value of the entire resistance component including the ohmic resistance, the resistance component of the reaction resistance, and the resistance component of the diffusion resistance for each of the positive electrode and the negative electrode.

FIG. 3 is a schematic diagram showing an example of the frequency characteristics of the impedance of one of the positive electrode and the negative electrode of the battery according to the first embodiment. In FIG. 3, the frequency characteristics of impedance are shown in a complex impedance plot. Further, in FIG. 3, the horizontal axis shows the real number component (resistance component) ZRe of impedance, and the vertical axis shows the imaginary number component (capacity component) ZIm of impedance. Then, in FIG. 3, the frequency characteristic Z _BOL of the impedance in the first period and the frequency characteristic Z _EOL of the impedance in the second period after the first period are shown. The second period is included in, for example, one determination period in a plurality of determinations by the deterioration determination device 3. The first period is, for example, the period from immediately before the start of use of the battery 5 to immediately after the start of use, or the determination period by the deterioration determination device 3 before the determination period including the second period. ,included.

As shown in FIG. 3 and the like, in the complex impedance plot showing the frequency characteristics of the impedances of the positive electrode and the negative electrode, a semicircular shape or a substantially semicircular shape is formed. The resistance estimation unit 12 calculates the semicircular or substantially semicircular diameter of the complex impedance plot for each of the positive electrode and the negative electrode as the resistance value of the resistance component of the reaction resistance. In an example of FIG. 3, the resistance value Rct _BOL of the resistance component of the reaction resistance in the first period and the resistance value Rct _EOL of the resistance component of the reaction resistance in the second period are shown. In each of the positive electrode and the negative electrode of the battery 5, when the battery 5 deteriorates due to use, the resistance component of the reaction resistance increases. Therefore, when the battery 5 deteriorates due to use, the resistance component of the battery 5 rises and changes.

By measuring the frequency characteristics of the impedance of each of the positive and negative electrodes of the battery 5 as described above, the resistance estimation unit 12 has the ohmic resistance for each of the positive and negative electrodes as well as the resistance component of the reaction resistance. It is possible to calculate the resistance value of the above and the resistance value of the resistance component of the diffusion resistance. Then, the resistance estimation unit 12 can calculate the resistance value of the entire resistance component including the ohmic resistance, the resistance component of the reaction resistance, and the resistance component of the diffusion resistance for each of the positive electrode and the negative electrode. The relationship between each of the ohmic resistance, the resistance component of the reaction resistance, and the resistance component of the diffusion resistance and the above-mentioned complex impedance plot is shown in Patent Document 1 and Patent Document 2. In the present embodiment, the resistance estimation unit 12 may calculate the resistance value of the resistance component from the complex impedance plot for each of the positive electrode and the negative electrode by using the relationships shown in, for example, Patent Document 1 and Patent Document 2. good.

Further, the resistance estimation unit 12 can calculate the resistance value of the resistance component in the entire battery 5 based on the resistance value of the resistance component calculated for each of the positive electrode and the negative electrode. As a result, the resistance value of the ohmic resistance of the entire battery 5, the resistance value of the resistance component of the reaction resistance of the entire battery 5, the resistance value of the resistance component of the diffusion resistance of the entire battery, and the like can be calculated. The resistance estimation unit 12 writes the measurement result and the estimation result including the calculation result of the resistance value of the resistance component into the data storage unit 16.

The capacity estimation unit 13 estimates the capacity of the active material of the battery 5 based on the above-mentioned measurement data and the calculation result of the resistance component of the battery 5 by the resistance estimation unit 12. Examples of the capacity of the active material of the battery 5 include a positive electrode capacity corresponding to the capacity of the positive electrode active material, a negative electrode capacity corresponding to the capacity of the negative electrode active material, and the like. The positive electrode capacity and the negative electrode capacity are internal state parameters indicating the internal state of the battery 5. The internal state parameter includes the initial charge amount of the positive electrode and the initial charge amount of the negative electrode in addition to the positive electrode capacity and the negative electrode capacity, and may include the impedance (internal resistance) of the battery 5 described above. Further, the shift of operation window (SOW), which is the difference between the initial charge amount of the positive electrode and the initial charge amount of the negative electrode, may be included in the internal state parameter of the battery 5.

Further, in each of the positive electrode and the negative electrode, the SOC is defined as in the entire battery 5. The SOC of the positive electrode is defined, for example, based on the positive electrode potential, and the SOC of the negative electrode is defined, for example, based on the negative electrode potential. In the battery 5, the lower limit potential Epmin and the upper limit potential Epmax are defined for the positive electrode potential, and the lower limit potential Enmin and the upper limit potential Enmax are defined for the negative electrode potential. In one example, for the positive electrode, the state where the open circuit potential (OCP: open circuit potential) is the lower limit potential Epmin is 0%, and the state where the open circuit potential is the upper limit potential Epmax is 100%. And. Then, regarding the negative electrode, the state where the open circuit potential becomes the upper limit potential Enmax is set to 0%, and the state where the open circuit potential becomes the lower limit potential Enminin is set to 100%.

FIG. 4 is a schematic diagram illustrating an internal state parameter indicating the internal state of the battery. In FIG. 4, the horizontal axis shows the charge amount Q, and the vertical axis shows the potential E. As shown in FIG. 4, in the positive electrode, the charge amount when the positive electrode potential becomes the lower limit potential Epmin (the state where the SOC of the positive electrode is 0%) is defined as the initial charge amount of the positive electrode, and the positive electrode potential becomes the upper limit potential Epmax. The amount of charge in such a state (the state where the SOC of the positive electrode is 100%) is defined as the upper limit charge amount of the positive electrode. The charge amount from the initial charge amount to the upper limit charge amount of the positive electrode is the positive electrode capacity mp of the battery 5. The real-time SOC of the positive electrode is the ratio of the real-time charge amount of the positive electrode to the positive electrode capacity mp with the initial charge amount of the positive electrode as a reference (zero).

Further, in the negative electrode, the charge amount when the negative electrode potential becomes the upper limit potential Enmax (the state where the SOC of the negative electrode is 0%) is defined as the initial charge amount of the negative electrode, and the negative electrode potential becomes the lower limit potential Enmin (the negative electrode). The charge amount when the SOC is 100%) is defined as the upper limit charge amount of the negative electrode. The charge amount from the initial charge amount to the upper limit charge amount of the negative electrode is the negative electrode capacity mn of the battery 5. The real-time SOC of the negative electrode is the ratio of the real-time charge amount of the negative electrode to the negative electrode capacity mn with the initial charge amount of the negative electrode as a reference (zero).

In the battery 5, when the battery 5 deteriorates due to use, the positive electrode capacity and the negative electrode capacity decrease. Therefore, when the battery 5 deteriorates due to use, the capacity of the active material of the battery 5 decreases and changes. Note that FIG. 4 shows the SOW of the battery 5 and the battery capacity mb of the battery 5 described above.

The capacity estimation unit 13 estimates the positive electrode capacity and the negative electrode capacity corresponding to the capacity of the active material of the battery 5 based on the measurement results of the current and voltage of the battery 5 included in the measurement data. The capacity estimation unit 13 estimates the positive electrode capacity and the negative electrode capacity by, for example, analyzing the data showing the time change of at least one of the current and the voltage of the battery 5 included in the measurement data. That is, the capacity of the active material is estimated by the charge curve analysis or the discharge curve analysis of the battery 5.

Here, the data storage unit 16 stores relational data indicating the relationship between the internal state parameters of the battery 5 with respect to at least one of the current and the voltage of the battery 5. The relational data is, for example, a function or a calculation formula showing the relationship between the internal state parameters with respect to at least one of the current and the voltage, and shows the relationship between the capacity of the active material such as the positive electrode capacity and the negative electrode capacity with respect to at least one of the current and the voltage. Contains data. The relationship between the internal state parameters and the current and voltage of the battery 5 changes according to the temperature of the battery 5 and the like. Therefore, in the relationship data, the relationship of the internal state parameters with respect to at least one of the current and the voltage of the battery 5 is set for each of a plurality of different temperatures.

The capacity estimation unit 13 estimates at least one of the current and the voltage of the battery 5 based on the above-mentioned relational data. At this time, at least one of the current and the voltage is estimated with the capacity of the active material of the battery 5 including the positive electrode capacity and the negative electrode capacity as at least a variable. Further, in addition to the capacity of the active material of the battery 5, at least one of the current and the voltage may be estimated with the charge amounts of the positive electrode and the negative electrode as variables. Then, the capacity estimation unit 13 performs regression calculation (fitting calculation) using the estimation result and the measurement result of the measurement data for at least one of the current and the voltage of the battery 5, in order to estimate at least one of the current and the voltage. Calculate the parameters used as variables. As a result, the capacity of the active material including the positive electrode capacity and the negative electrode capacity is calculated.

In one example, the capacity estimation unit 13 estimates the voltage V of the battery 5 based on the following equation (1). In this case, the relationship between the internal state parameters of the battery 5 and the voltage of the battery 5 shown in the equation (1) is stored in the data storage unit 16 or the like. In equation (1), V is the voltage of the battery 5, I is the current of the battery 5, and Ep (mp, Qp) is a function of the open circuit potential of the positive electrode having the positive electrode capacity mp and the charge amount Qp of the positive electrode as at least variables. En (mn, Qn) is a function of the open circuit potential of the negative electrode having the negative electrode capacity mn and the charge amount Qn of the negative electrode as at least variables, and Rp is the entire resistance component of the positive electrode (ohmic resistance, resistance component of reaction resistance and diffusion). (Including the resistance component of the resistance) indicates the resistance value, and Rn indicates the resistance value of the entire resistance component of the negative electrode.

V = Ep (mp, Qp) -En (mn, Qn) + (Rp + Rn) × I (1)

In the estimation using the equation (1), for example, the measured value of the measurement data is used for the current I, and each of the resistance values Rp and Rn is calculated, for example, by the resistance estimation unit 12 as described above. The calculated value is used. Then, the capacity estimation unit 13 performs regression calculation (fitting calculation) for the voltage of the battery 5 using the estimation result by the equation (1) and the time change (charge curve, discharge curve, etc.) of the voltage included in the measurement data. By performing the above, the parameters (mp, mn, Qp, Qn) used as variables in the estimation using the equation (1) are calculated. As a result, the positive electrode capacity mp and the negative electrode capacity mn, which are the capacities of the active material of the battery 5, are estimated by the capacity estimation unit 13.

A method for estimating the capacity of the active material of the battery such as the positive electrode capacity and the negative electrode capacity based on the measurement result of at least one of the current and the voltage of the battery is described in Patent Document 3 (Japanese Unexamined Patent Publication No. 2020-109367) and Patent. It is shown in Document 4 (Japanese Unexamined Patent Publication No. 2012-251806). Then, in Patent Document 4, the voltage of the battery is estimated with the positive electrode capacity and the negative electrode capacity as at least variables, and the regression calculation is performed for the battery voltage using the estimation result and the measurement result. Then, the positive electrode capacity and the negative electrode capacity are estimated by calculating the variables by the regression calculation. In the present embodiment, the capacity of the active material of the battery 5 such as the positive electrode capacity and the negative electrode capacity may be estimated in the same manner as in any of Patent Documents 3 and 4. The capacity estimation unit 13 writes the estimation result including the estimation result of the capacity of the active material in the data storage unit 16.

The aging factor estimation unit 15 estimates the aging factor based on the calculation result of the resistance component of the battery 5 by the resistance estimation unit 12, the estimation result of the capacity of the active material of the battery 5 by the capacity estimation unit 13, and the like. As described above, when the battery 5 deteriorates, the resistance component of the battery 5 including the resistance component of the reaction resistance in each of the positive electrode and the negative electrode changes. For example, the resistance value of the resistance component in the second period after the first period is higher than the resistance value of the resistance component in the first period. Further, when the battery 5 deteriorates, the capacities of the active material of the battery 5, such as the positive electrode capacity and the negative electrode capacity, change. For example, the volume of active material in the second period after the first period is lower than the volume of active material in the first period.

Here, as a factor of the change in the resistance component of the battery 5 (increased resistance component), in addition to the above-mentioned change in the capacity of the active material of the battery 5, there is also an influence other than the change in the capacity of the active material. The effects other than the change in the capacity of the active material on the change in the resistance component include the change in the coating resistance in the electrodes (including the positive electrode and the negative electrode) caused by the growth of the coating, and the cracking of the active material caused by the cracking of the active material. Changes in surface area and particle size and the like can be mentioned. The aging factor estimated by the aging factor estimation unit 15 shows the influence other than the change in the capacity of the active material of the battery 5 among the causes of the change in the resistance component of the battery 5.

For example, as described above, the first period and the second period after the first period are defined. Then, the resistance value R _BOL of the resistance component of the battery 5 in the first period, the resistance value R _EOL of the resistance component of the battery 5 in the second period, the capacity _mBOL of the active material of the battery 5 in the first period, the first. When the capacity m _EOL of the active material of the battery 5 and the aging factor S _EOL in the period of 2 are set as parameters, the relationship of the equation (2) is established. Then, by modifying the equation (2), the aging factor S _EOL can be calculated by the equation (3).

R _EOL = R _BOL x (1 / S _EOL ) x (m _BOL / m _EOL ) (2)
S _EOL = (R _BOL / R _EOL ) × (m _BOL / m _EOL ) (3)

In the formulas (2) and (3), when R _BOL and R _EOL are the resistance values of the resistance components related to the positive electrode such as the resistance component of the reaction resistance of the positive electrode, m _BOL m _EOL is the positive electrode. The positive electrode capacity, which is the capacity of the active material, is used. In this case, the aging factor S _EOL shows an influence other than the change in the positive electrode capacity among the causes of the change in the resistance component related to the positive electrode. When R _BOL and R _EOL are the resistance values of the resistance components related to the negative electrode such as the resistance component of the reaction resistance of the negative electrode, m _BOL m _EOL is used by the negative electrode capacity which is the capacity of the negative electrode active material. Be done. In this case, the aging factor S _EOL shows an influence other than the change in the negative electrode capacity among the causes of the change in the resistance component related to the negative electrode.

The aging factor estimation unit 15 calculates the aging factor S _EOL by, for example, the equation (3). By calculating and estimating the aging factor S _EOL , the aging factor estimation unit 15 can estimate the magnitude of the influence other than the change in the capacity of the active material of the battery 5 among the causes of the change in the resistance component of the battery 5. .. In the calculation of the aging factor S _EOL , as the resistance values R _BOL and R _EOL , for example, the values calculated as described above by the resistance estimation unit 12 are used. Then, as the capacity _mBOL and _mEOL of the active material, for example, the values estimated as described above by the capacity estimation unit 13 are used. For example, when the first period is included in the period from immediately before the start of use of the battery 5 to immediately after the start of use, the battery is set as the resistance value R _BOL and the capacity m _BOL in the first period. The design value in 5 may be used. If the volume _mEOL of the active material in the second period is not estimated by the volume estimation unit 13, it is estimated by the volume estimation unit 13 after the first period and before the second period. The capacity of the active material may be substituted as the capacity of the active material m _EOL in the second period.

When the aging factor S _EOL is estimated as described above, the capacity estimation unit 13 estimates (re-estimates) the capacity m _EOL of the active material of the battery 5 in the second period based on the estimated aging factor S _EOL . The capacity estimation unit 13 uses, for example, the estimation result of at least one of the voltage and current of the battery 5 whose variable is the capacity of the active material, and the measurement result of at least one of the voltage and current of the battery 5 included in the measurement data. By performing the above-mentioned regression calculation, the capacity _mEOL of the active material in the second period is estimated. However, depending on the estimated value of the aging factor S _EOL , the capacity estimation unit 13 changes the parameters and the like related to the aging factor S _EOL from the previous regression calculation to the values corresponding to the aging factor S _EOL , and the like. Regression calculation is performed to calculate the volume _mEOL of the active material.

For example, when the capacity of the active material is calculated by the regression calculation using the voltage estimation result and the voltage measurement result by the equation (1), the aging in each of the functions Ep (mp, Qp) and En (mn, Qn) is performed. The constants and the like related to the factor S _EOL are changed from the previous regression calculation to the values corresponding to the estimated aging factor S _EOL . If necessary, the resistance values Rp and Rn, which are constants in the regression calculation, may be changed from the previous regression calculation to the values corresponding to the estimated aging factor _SEOL . The parameters related to the aging factor S _EOL include parameters related to the surface area of the active material, parameters related to the film resistance, and the like.

Here, regarding the capacity m _EOL of the active material of the battery 5 in the second period, the value used for calculating the aging factor S _EOL by the equation (3) is set to mi _EOL , and the above-mentioned is described based on the estimated aging factor S _EOL . The estimated value estimated by performing the regression calculation is mj _EOL . In the present embodiment, when the aging factor S _EOL is estimated as described above, the capacity estimation unit 13, the aging factor estimation unit 15, and the like are the aging factor with respect to the capacity m _EOL of the active material of the battery 5 in the second period. It is determined whether or not the deviation of the estimated value mj _EOL based on the aging factor S _EOL with respect to the value mi _EOL used for the estimation of S _EOL is within the reference range. When the deviation is within the reference range, the aging factor estimation unit 15 and the like determine the aging factor S _EOL based on the estimated value estimated by the equation (3) using the value mi _EOL .

On the other hand, when the deviation exceeds the reference range, the capacity estimation unit 13, the aging factor estimation unit 15, and the like estimate the capacity m _EOL of the active material of the battery 5 in the second period based on the aging factor S _EOL . Update to the value mj _EOL . Then, the aging factor estimation unit 15 re-estimates the aging factor S _EOL by the equation (3), using the updated value as the capacity m _EOL of the active material of the battery 5 in the second period. At this time, the value updated as the capacity m _EOL of the active material of the battery 5 in the second period becomes the value mi _EOL used for the recalculation of the aging factor S _EOL according to the equation (3). Then, the aging factor estimation unit 15 updates the aging factor S _EOL to the re-estimated value.

Then, the capacity estimation unit 13 re-estimates the estimated value mj _EOL based on the re-estimated aging factor S _EOL with respect to the capacity m _EOL of the active material in the second period by the above-mentioned regression calculation or the like. Then, the capacity estimation unit 13 and the aging factor estimation unit 15 and the like are based on the aging factor S _EOL with respect to the value mi _EOL used for estimating the aging factor S _EOL with respect to the capacity m _EOL of the active material of the battery 5 in the second period. It is determined whether or not the deviation of the estimated value mj _EOL is within the reference range. Then, when the deviation exceeds the reference range, the capacity estimation unit 13 and the like update the capacity _mEOL of the active material of the battery 5 in the second period to the estimated value _mjEOL based on the aging factor _SEOL . .. Then, the updated value is used as the capacity m _EOL of the active material of the battery 5 in the second period, and the aging factor S _EOL is re-estimated by the equation (3).

Since the above-mentioned processing is performed, in the present embodiment, the estimated value based on the aging factor S _EOL with respect to the value mi _EOL used for estimating the aging factor S _EOL with respect to the volume m _EOL of the active material in the second period. The determination of the deviation of mj _EOL is repeated until the deviation reaches the reference range. Then, until the deviation of the estimated value mj _EOL based on the aging factor S _EOL with respect to the value mi _EOL used for estimating the aging factor S _EOL becomes the reference range, the capacity m _EOL of the active material and the aging factor S in the second period are reached. _EOL is repeatedly estimated.

In one example, the deviation is determined to be in the reference range only when the estimated value mj _EOL based on the aging factor S _EOL is the same as the value mi _EOL used for estimating the aging factor S _EOL . Therefore, as long as the estimated value mj _EOL based on the aging factor S _EOL is different from the value mi _EOL used for estimating the aging factor S _EOL , the volume m _EOL and the aging factor S _EOL of the active material in the second period are different. Estimated repeatedly. In another example, when the above-mentioned deviation is equal to or less than the reference value, it is determined that the deviation is within the reference range. Therefore, if the deviation is greater than the reference value, the aging factor S _EOL is re-estimated.

In this embodiment, the resistance value of the resistance component is calculated for each of the positive electrode and the negative electrode, and the capacity is estimated for each of the positive electrode active material and the negative electrode active material. Therefore, the aging factor is calculated for each of the positive electrode and the negative electrode. Then, until the above-mentioned deviation regarding the capacity of the active material in the second period becomes the reference range in both the positive electrode and the negative electrode, the respective capacities of the positive electrode active material and the negative electrode active material in the second period, and the positive electrode. And the respective aging factors of the negative electrode are repeatedly estimated.

The estimation results and determination results of the resistance estimation unit 12, the capacitance estimation unit 13, the aging factor estimation unit 15, etc. are stored in the data storage unit 16 as described above, and are output to the outside through the transmission / reception unit 11. May be done. In this case, the estimation result or the like is transmitted from the transmission / reception unit 11 to the battery management unit 7 of the battery-mounted device 2, or is transmitted to a processing device other than the deterioration determination device 3 outside the battery-mounted device 2. Further, the deterioration determination device 3 may be provided with a notification unit composed of a user interface or the like, and the above-mentioned estimation result or the like may be notified to the user or the like of the battery-mounted device 2 by the notification unit. In this case, the notification is made by either voice or screen display. Further, the deterioration determination device 3 determines the degree of deterioration, the deterioration rate, and the like of the battery 5 based on the above-mentioned estimation results and the like.

FIG. 5 is a flowchart showing a process performed by the deterioration determination device according to the first embodiment. The process of FIG. 5 is performed by the deterioration determination device 3 for each deterioration determination of the battery 5. In one example, the deterioration determination of the battery 5 including the process of FIG. 5 is automatically performed at a predetermined timing. In another example, the deterioration determination of the battery 5 including the process of FIG. 5 is performed based on the input of the operation command in the user interface by the user or the like of the battery-mounted device 2.

When the process of FIG. 5 is started, the aging factor estimation unit 15 acquires the above-mentioned measurement data (S101). Then, the aging factor estimation unit 15 acquires the resistance value R _BOL of the resistance component of the battery 5 in the first period and the capacity _mBOL of the active material of the battery 5 in the first period (S102). Then, the resistance estimation unit 12 calculates the resistance value R _EOL of the resistance component of the battery 5 in the second period using any of the above-mentioned methods (S103), and the aging factor estimation unit 15 calculates the resistance value. Acquire the calculation result of R _EOL . Then, the capacity estimation unit 13 estimates the capacity m _EOL of the active material of the battery 5 in the second period by using any of the above-mentioned methods such as the regression calculation described above (S104), and the aging factor estimation unit 13. 15 acquires the estimation result of the capacity _mEOL . At this time, the aging factor estimation unit 15 applies the capacity of the active material estimated by the capacity estimation unit 13 after the first period and before the second period to the active material in the second period. It may be acquired as the capacity m _EOL .

Then, the aging factor estimation unit 15 estimates the aging factor S _EOL by, for example, the calculation using the above-mentioned equation (3) (S105). Then, the capacity estimation unit 13 again estimates the capacity m _EOL of the active material of the battery 5 in the second period based on the estimated aging factor S _EOL (S106). Then, the capacity estimation unit 13 and the aging factor estimation unit 15 and the like are based on the aging factor S _EOL with respect to the value mi _EOL used for estimating the aging factor S _EOL with respect to the capacity m _EOL of the active material of the battery 5 in the second period. It is determined whether or not the deviation of the estimated value mj _EOL is within the reference range (S107). When the deviation is within the reference range (S107-Yes), the processing is completed, and the aging factor S _EOL is determined as the estimated value estimated by the equation (3) or the like using the value mi _EOL .

On the other hand, when the deviation exceeds the reference range (S107-No), the capacity estimation unit 13, the aging factor estimation unit 15, and the like set the capacity _mEOL of the active material of the battery 5 in the second period to the aging factor S. Update to the estimated value mj _EOL based on _EOL (S108). Then, the processing returns to S105, and the aging factor estimation unit 15 and the like sequentially perform the processing after S105. Therefore, the aging factor estimation unit 15 and the like re-estimate the aging factor S _EOL by the equation (3) and the like, using the updated value as the capacity m _EOL of the active material of the battery 5 in the second period.

In the present embodiment, the resistance value R _BOL of the resistance component of the battery 5 in the first period and the capacity _mBOL of the active material, and the resistance value R ( _EOL and activity) of the resistance component of the battery 5 in the second period. Based on the capacity m _EOL of the material, the processor or the like of the deterioration determination device 3 estimates the aging factor S _EOL which shows the influence other than the capacity change of the active material of the battery 5 among the causes of the change of the resistance component of the battery 5. The aging factor S _EOL is estimated, for example, by the above equation (3). By estimating the aging factor S _EOL , the active material of the battery 5 with respect to the change in the resistance component of the battery 5 (increased resistance component). The effects other than the change in the capacitance of the battery are appropriately estimated, and the factors of the change in the resistance component are appropriately estimated. For example, by estimating the aging factor S _EOL , the film resistance in the electrode due to the growth of the film is estimated. The effect on the change in the resistance component such as the change in the surface area and the particle size of the active material due to the change in the active material and the cracking of the active material is appropriately estimated. Based on the above, the deterioration of the battery 5 is determined more appropriately.

Further, in the present embodiment, the frequency characteristic of the impedance of the battery 5 is measured by passing a current through the battery 5 with a current waveform in which the current value changes periodically. Then, the processor or the like of the deterioration determination device 3 calculates the resistance value of the resistance component of the battery 5 based on the result of the frequency characteristic of the impedance. Therefore, it is not necessary to measure the resistance deterioration coefficient or the like when calculating the resistance value of the resistance component. Therefore, the process of calculating the resistance value of the resistance component of the battery 5 is not complicated, and the process of estimating the aging factor S _EOL is not complicated.

Further, since the resistance value of the resistance component is calculated as described above, the calculation calculated as the resistance value of the resistance component in the above-mentioned regression calculation for estimating the internal state parameter including the capacity (mp, mn) of the active material. Values can be used. That is, it is not necessary to use the resistance value of the resistance component as a variable in the regression calculation for estimating the capacity (mp, mn) of the active material. This makes it possible to reduce the number of variables in the regression calculation described above, and improves the estimation accuracy of the parameters that are variables including the capacity of the active material.

Further, the processor and the like of the deterioration determination device 3 deviate from the estimated value mj _EOL based on the aging factor S _EOL with respect to the value mi _EOL used for estimating the aging factor S _EOL with respect to the capacity m _EOL of the active material in the second period. Make a judgment about. Then, the processor and the like have a capacity m _EOL of the active material in the second period until the deviation of the estimated value mj _EOL based on the aging factor S _EOL with respect to the value mi _EOL used for estimating the aging factor S _EOL becomes the reference range. And the aging factor S _EOL is repeatedly estimated. Therefore, in the present embodiment, the volume m _EOL of the active material and the aging factor S _EOL in the second period are estimated with high accuracy.

(Modification example)
In the first modification, the data storage unit 16 stores relational data showing the relationship between the material property values of the battery 5 and the voltage of the battery 5. The relational data is, for example, a function or a calculation formula showing the relation of the material property value with respect to the voltage. Here, the battery 5 is a lithium ion secondary battery. In this case, the material property values of the battery 5 include the concentration of lithium in the solid phase and the liquid phase, the ionic conductivity in the electrolytic solution, and the diffusion coefficient of lithium in the solid phase and the liquid phase, respectively. , Exchange current density, film resistance, weight of each active material, surface area and particle size of each active material, etc. are included. Therefore, the relational data includes data showing the relationship of the lithium concentration of the battery 5 with respect to the voltage of the battery 5, and for example, a function showing the relationship of the lithium concentration in the solid phase with respect to the equilibrium potential in the solid phase, and. It contains a function that indicates the relationship between the equilibrium potential in the liquid phase and the concentration of lithium in the liquid phase. The relationship between the material property values and the voltage of the battery 5 changes according to the temperature of the battery 5, the SOC, and the like. Therefore, in the relationship data, the relationship between the material property values with respect to the voltage of the battery 5 is set for each of a plurality of temperatures different from each other, and is set for each of a plurality of SOCs different from each other.

In this modification, the capacity estimation unit 13 estimates the voltage of the battery 5 based on the above-mentioned relational data. At this time, the voltage is estimated with at least the concentration of lithium in the battery 5 including the concentration of lithium in the solid phase and the concentration of lithium in the liquid phase as variables. In addition, the voltage is estimated with a part of the material property values such as the surface area (particle size) and film resistance of each active material as a constant. Then, the capacity estimation unit 13 calculates the parameter used as a variable in the voltage estimation by performing a regression calculation (fitting calculation) using the estimation result and the measurement result of the measurement data for the voltage of the battery 5. As a result, the concentration of lithium in the battery 5, which is one of the material property values, is calculated. Further, in this modification, the capacity estimation unit 13 estimates the capacity of the active material of the battery 5 including the positive electrode capacity and the negative electrode capacity based on the calculated lithium concentration of the battery 5.

In Patent Document 5 (Japanese Unexamined Patent Publication No. 2015-184146), the voltage of the battery is estimated with the lithium concentration of the battery as at least a variable, and the regression calculation is performed for the voltage of the battery using the estimation result and the measurement result. .. Then, the lithium concentration is estimated by calculating the variable by the regression calculation. In this modification, the lithium concentration of the battery 5 may be estimated in the same manner as in Patent Document 5.

In this modification, when the aging factor S _EOL is estimated as described above, the capacity estimation unit 13 estimates the lithium concentration c of the battery 5 in the second period based on the estimated aging factor S _EOL . The capacity estimation unit 13 performs the above-mentioned regression calculation using, for example, the estimation result of the voltage of the battery 5 in which the concentration of lithium is a variable, and the measurement result of the voltage of the battery 5 included in the measurement data. The lithium concentration c in the period 2 is estimated. Here, the capacity estimation unit 13 sets a parameter or the like related to the aging factor S _EOL to a value corresponding to the aging factor S _EOL , performs a regression calculation, and calculates the lithium concentration c. As described above, the parameters related to the aging factor S _EOL include parameters related to the surface area of the active material, parameters related to the film resistance, and the like. Therefore, the regression calculation is performed by setting the constants such as the surface area (particle size) of each active material and the film resistance to the values corresponding to the estimated aging factor _SEOL .

Then, in this modification, the capacity estimation unit 13 estimates (re-estimates) the capacity _mEOL of the active material in the second period based on the estimated lithium concentration c in addition to the aging factor _SEOL . .. Also in this modification, the capacity estimation unit 13 has, for example, an estimation result of at least one of the voltage and current of the battery 5 having the capacity of the active material as a variable, and at least one of the voltage and current of the battery 5 included in the measurement data. By performing the above-mentioned regression calculation using the measurement result of, the capacity _mEOL of the active material in the second period is estimated. However, in this modification, the capacity estimation unit 13 changes the parameters and the like related to the aging factor S _EOL from the previous regression calculation to the values corresponding to the aging factor S _EOL , and is related to the lithium concentration c. The capacity m _EOL of the active material is calculated by changing the parameters and the like from the previous regression calculation to the values corresponding to the concentration c. Therefore, for example, when the capacity of the active material is calculated by the regression calculation using the voltage estimation result and the voltage measurement result by the equation (1), the functions Ep (mp, Qp) and En (mn, Qn) are used. The constants and the like related to the lithium concentration c in each are changed from the previous regression calculation to the values corresponding to the concentration c.

FIG. 6 is a flowchart showing a process performed by the deterioration determination device according to the first modification. As shown in FIG. 6, in this modification as well, the processor and the like of the deterioration determination device 3 perform the processes of S101 to S105 and the processes of S107 and S108. However, in this modification, when the aging factor S _EOL is estimated in S105, the capacity estimation unit 13 estimates the lithium concentration c of the battery 5 in the second period based on the estimated aging factor S _EOL . (S109). Then, the capacity estimation unit 13 again estimates the capacity m _EOL of the active material of the battery 5 based on the estimated aging factor S _EOL and the concentration c of lithium (S106A). Then, the processing after S107 is performed in the same manner as in the above-described embodiment and the like.

Also in this modification, the processor or the like of the deterioration determination device 3 estimates the aging factor S _EOL , as in the above-described embodiment. Then, the processor and the like are active in the second period until the deviation of the estimated value mj _EOL based on the aging factor S _EOL and the lithium concentration c with respect to the value mi _EOL used for estimating the aging factor S _EOL becomes the reference range. The volume m _EOL and aging factor S _EOL of the substance are repeatedly estimated. Therefore, even in this modification, the same operation and effect as those of the above-described embodiment and the like can be obtained.

In the second modification, the data storage unit 16 stores the relationship data showing the relationship between the current, the temperature, and the SOC of the battery 5 with respect to the aging factor _SEOL of the battery 5 described above. The relationship data is, for example, a function or a calculation formula showing the relationship between the current, temperature, and SOC with respect to the aging factor _SEOL . In this modification, the aging factor estimation unit 15 is based on the above-mentioned relational data and the measurement results of the current, temperature, and SOC in the measurement data, and the corresponding value corresponding to the relational data and the measurement data about the aging factor S _EOL . Acquire _S'EOL .

Then, the capacity estimation unit 13 estimates the capacity m _EOL of the active material of the battery 5 in the second period by using the corresponding value _S'EOL of the aging factor S _EOL . Here, the resistance value R _BOL of the resistance component of the battery 5 in the first period, the resistance value R _EOL of the resistance component of the battery 5 in the second period, and the capacity _mBOL of the active material of the battery 5 in the first period. Further, an estimated value _m'EOL based on the corresponding value _S'EOL of the capacity m _EOL of the active material of the battery 5 in the second period is specified. The capacity estimation unit 13 estimates the capacity m _EOL of the active material of the battery 5 in the second period by the equation (4) using the corresponding value _S'EOL of the aging factor S _EOL .

_m'EOL = m _BOL ) x (R _BOL / R _EOL ) x (1 / _S'EOL ) (4)

Further, in this modification, the capacity estimation unit 13 uses a method different from the above equation (4) based on the corresponding value _S'EOL of the aging factor S _EOL , and the capacity estimation unit 13 is the capacity m of the active material in the second period. Estimate _EOL . At this time, the capacity estimation unit 13 measures, for example, the estimation result of at least one of the voltage and the current of the battery 5 whose variable is the capacity of the active material, and the measurement of at least one of the voltage and the current of the battery 5 included in the measurement data. By performing the above-mentioned regression calculation using the result, the capacity _mEOL of the active material in the second period is estimated. Here, the capacity estimation unit 13 sets the parameters related to the aging factor S _EOL to the values corresponding to the corresponding value _S'EOL of the aging factor S _EOL , performs regression calculation, and calculates the capacity m _EOL of the active material. do. For example, when calculating the capacity of the active material by the regression calculation using the voltage estimation result and the voltage measurement result by the equation (1), the aging in each of the functions Ep (mp, Qp) and En (mn, Qn) is performed. A constant or the like related to the factor S _EOL is set to a value corresponding to the corresponding value _S'EOL of the aging factor S _EOL .

Here, regarding the capacity m _EOL of the active material of the battery 5 in the second period, the estimated value using the equation (4) is set as _m'EOL , and the estimated value by a method different from the equation (4) such as regression calculation. Let be mj _EOL . In the present embodiment, the capacity estimation unit 13, the aging factor estimation unit 15, and the like have an equation for the estimated value _m'EOL estimated using the equation (4) with respect to the capacity m _EOL of the active material of the battery 5 in the second period. It is determined whether or not the deviation of the estimated value mj _EOL by a method different from (4) is within the reference range. When the deviation is within the reference range, the aging factor estimation unit 15 and the like determine the aging factor S _EOL in the corresponding value _S'EOL described above.

On the other hand, when the deviation exceeds the reference range, the capacity estimation unit 13 and the aging factor estimation unit 15 and the like calculate the capacity _mEOL of the active material of the battery 5 in the second period by the equation (4) such as regression calculation. It is set to the estimated value mj _EOL by another method. Then, the aging factor estimation unit 15 estimates the aging factor S _EOL by the equation (3), using the set value as the capacity m _EOL of the active material of the battery 5 in the second period. At this time, the value set as the capacity m _EOL of the active material of the battery 5 in the second period becomes the value mi _EOL used for the calculation of the aging factor S _EOL according to the equation (3). Then, the aging factor estimation unit 15 updates the aging factor S _EOL to the value estimated by the equation (3). Then, when the aging factor S _EOL is estimated by the equation (3), the same processing as that of the above-described embodiment is performed.

Here, in one example, the deviation is in the reference range only when the estimated value mj _EOL by a method different from the equation (4) becomes the same value as the estimated value _m'EOL using the equation (4). It is determined that there is. In another example, when the above-mentioned deviation is equal to or less than the reference value, it is determined that the deviation is within the reference range.

FIG. 7 is a flowchart showing a process performed by the deterioration determination device according to the second modification. As shown in FIG. 7, in this modification as well, the processor and the like of the deterioration determination device 3 perform the processes of S101 to S103 and the processes of S105 to S108. However, in this modification, when the resistance value R _EOL of the resistance component in the second period is calculated in S103, the aging factor estimation unit 15 describes the above-mentioned relationship data and the battery 5 with respect to the aging factor S _EOL . Acquire the corresponding value _S'EOL corresponding to the current, temperature and SOC measurement results (S111). Then, the capacity estimation unit 13 estimates the capacity m _EOL of the active material of the battery 5 in the second period using the equation (4) based on the corresponding value _S'EOL of the aging factor S _EOL (S112). .. Further, the capacity estimation unit 13 estimates the capacity m _EOL of the active material of the battery 5 in the second period by a method different from the equation (4) based on the corresponding value _S'EOL of the aging factor S _EOL . (S113).

Then, the capacity estimation unit 13 and the aging factor estimation unit 15 and the like have the same as the equation (4) for the estimated value _m'EOL using the equation (4) with respect to the capacity m _EOL of the active material of the battery 5 in the second period. It is determined whether or not the deviation of the estimated value mj _EOL by another method is within the reference range (S114). If the deviation is within the reference range (S114-Yes), the process ends, and the aging factor S _EOL is determined in the corresponding value _S'EOL described above. On the other hand, when the deviation exceeds the reference range (S114-No), the capacity estimation unit 13, the aging factor estimation unit 15, and the like calculate the capacity _mEOL of the active material of the battery 5 in the second period by the equation (4). ) Is set to the estimated value mj _EOL by another method (S115). Then, the processing after S105 is performed using the set capacity _mEOL . The processing after S105 is performed in the same manner as in the above-described embodiment and the like.

Also in this modification, the processor or the like of the deterioration determination device 3 estimates the aging factor S _EOL , as in the above-described embodiment. Then, the processor and the like have a capacity m _EOL of the active material in the second period until the deviation of the estimated value mj _EOL based on the aging factor S _EOL with respect to the value mi _EOL used for estimating the aging factor S _EOL becomes the reference range. And the aging factor S _EOL is repeatedly estimated. Therefore, even in this modification, the same operation and effect as those of the above-described embodiment and the like can be obtained.

Further, in this modification, the processor or the like of the deterioration determination device 3 acquires the corresponding value _S'EOL corresponding to the current, temperature, and SOC measurement results of the battery 5 for the aging factor S _EOL . Then, the processor or the like obtains an estimated value mj _EOL by a method different from the equation (4) with respect to the estimated value _m'EOL using the equation (4) with respect to the capacity m _EOL of the active material of the battery 5 in the second period. It is determined whether or not the deviation is within the reference range. Then, when the deviation is within the reference range, the aging factor S _EOL is determined in the corresponding value _S'EOL . Therefore, the regression calculation described above makes it possible to reduce the number of times the capacity m _EOL of the active material of the battery 5 is estimated in the second period, further simplifying the process of estimating the aging factor S _EOL .

In the above-described embodiment or the like, the frequency characteristic of the impedance of the battery 5 is measured by passing a current through the battery 5 with a current waveform in which the current value changes periodically, and the battery is based on the frequency characteristic of the measured impedance. The resistance value of the resistance component of 5 is calculated, but the method for estimating the resistance value of the resistance component is not limited to this. In a certain modification, a direct current may be passed through the battery 5 in a state where the current value becomes constant over time, and the resistance value of the resistance component of the battery 5 may be calculated from the time changes of the current and the voltage. Further, in another modification, the estimation result of at least one of the voltage and the current of the battery 5 whose variable is the capacity of the active material, and the measurement result of at least one of the voltage and the current of the battery 5 included in the measurement data. The resistance value of the resistance component of the battery 5 may be estimated by performing the above-mentioned regression calculation using the above. In this case, for example, in the equation (1), the voltage of the battery 5 is estimated by using the resistance value of the resistance component as a variable in addition to the capacity of the active material. Then, the resistance value of the resistance component of the battery 5 is estimated by calculating the variable by the regression calculation.

In at least one embodiment or embodiment described above, the resistance value of the resistance component of the battery in each of the first period and the second period after the first period, and the first period and the second period. Based on the capacity of the active material of the battery in each of the above, the aging factor showing the influence other than the capacity change of the active material among the causes of the change of the resistance component is estimated. As a result, the cause of the change in the resistance component of the battery to be determined is appropriately estimated, and the deterioration of the battery is determined more appropriately. The battery deterioration determination device, the battery management system, the battery-equipped device, and the battery deterioration. A determination method and a battery deterioration determination program can be provided.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other embodiments, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are also included in the scope of the invention described in the claims and the equivalent scope thereof.

1 ... Management system, 2 ... Battery-equipped equipment, 3 ... Deterioration judgment device, 5 ... Battery, 6 ... Measurement circuit, 7 ... Battery management unit, 11 ... Transmission / reception unit, 12 ... Resistance estimation unit, 13 ... Capacity estimation unit, 15 … Aging factor estimation unit, 16 data storage unit.

Claims (13)

It is a deterioration determination device that determines the deterioration of the battery based on the information on the battery to be determined.
The resistance value of the resistance component of the battery in each of the first period and the second period after the first period, and the active material of the battery in each of the first period and the second period. A deterioration determination device comprising a processor that estimates an aging factor that indicates an effect other than the change in the capacity of the active material among the causes of the change in the resistance component based on the capacity of the active material. The deterioration determination device according to claim 1, wherein the processor calculates the aging factor by the following formula (A).
S _EOL = (R _BOL / R _EOL ) × (m _BOL / m _EOL ) (A)
Here, S _EOL represents the aging factor. _RBOL represents the resistance value of the resistance component of the battery in the first period. _REOL represents the resistance value of the resistance component of the battery in the second period. _mBOL represents the capacity of the active material of the battery in the first period. m _EOL represents the capacity of the active material of the battery in the second period. The deterioration determination device according to claim 1 or 2, wherein the processor estimates the capacity of the active material of the battery in the second period based on the estimated aging factor. The processor
With respect to the capacity of the active material of the battery in the second period, it is determined whether or not the deviation of the estimated value based on the aging factor from the value used for estimating the aging factor is within the reference range.
When the deviation exceeds the reference range, the aging factor is re-estimated and the aging factor is re-estimated by using the estimated value based on the aging factor as the capacity of the active material of the battery in the second period. Update the factor to the re-estimated value,
The deterioration determination device of claim 3. The processor
Measurement data including measurement results for the current, temperature and SOC of the battery, and relational data indicating the relationship between the current, the temperature and the SOC of the battery with respect to the aging factor of the battery are acquired.
For the aging factor of the battery, the corresponding values corresponding to the measurement data and the related data are acquired, and the corresponding values are acquired.
Based on the corresponding value of the aging factor of the battery, the capacity of the active material of the battery in the second period is estimated by the following formula (B).
The deterioration determination device according to any one of claims 1 to 4.
_m'EOL = m _BOL x (R _BOL / R _EOL ) x (1 / _S'EOL ) (B)
Here, _S'EOL represents the corresponding value of the aging factor corresponding to the measured data and the related data. _RBOL represents the resistance value of the resistance component of the battery in the first period. _REOL represents the resistance value of the resistance component of the battery in the second period. _mBOL represents the capacity of the active material of the battery in the first period. _m'EOL represents the estimated value of the capacity of the active material of the battery in the second period according to the formula (B). The processor
Based on the corresponding value of the aging factor, the capacity of the active material of the battery in the second period is estimated by a method different from the formula (B).
Regarding the capacity of the active material of the battery in the second period, is the deviation of the estimated value by the method different from the formula (B) from the estimated value using the formula (B) within the reference range? Judge whether or not,
When the deviation exceeds the reference range, the aging factor is used as the capacity of the active material of the battery in the second period, using the estimated value by the method different from the formula (B) as the capacity of the active material of the battery in the second period. Re-estimate and update the aging factor to the re-estimated value,
The deterioration determination device of claim 5. The processor has the resistance component of the battery in the second period based on the frequency characteristic of the impedance of the battery measured by passing a current through the battery with a current waveform whose current value changes periodically. The deterioration determination device according to any one of claims 1 to 6, which calculates the resistance value. The processor
Measurement data including measurement results for at least one of the current and voltage of the battery, and internal state parameters of the battery including the capacity of the active material of the battery with respect to at least one of the current and voltage of the battery. Get the relationship data showing the relationship and
Based on the relational data, at least one of the current and the voltage of the battery is estimated with the capacity of the active material of the battery as at least a variable.
The battery in the second period is calculated by calculating the variable including the capacity of the active material of the battery by the estimation result based on the relational data and the regression calculation using the measurement result of the measurement data. Estimate the capacity of the active material of
The deterioration determination device according to any one of claims 1 to 7. The processor
The measurement data including the measurement result about the voltage of the battery and the relational data showing the relationship between the material property value of the battery including the concentration of lithium of the battery with the voltage of the battery are acquired.
Based on the relational data, the voltage of the battery is estimated with the concentration of lithium as at least a variable.
The variable including the concentration of the lithium of the battery was calculated by the estimation result based on the relational data and the regression calculation using the measurement result of the measurement data.
Based on the calculated concentration of lithium in the battery, the capacity of the active material in the battery in the second period is estimated.
The deterioration determination device according to any one of claims 1 to 8. The deterioration determination device according to any one of claims 1 to 9,
The battery in which the deterioration determination device is used to determine the deterioration, and the battery.
The battery management system comprising. The deterioration determination device according to any one of claims 1 to 9,
The battery in which the deterioration determination device is used to determine the deterioration, and the battery.
Battery-equipped device equipped with. It is a deterioration determination method for determining the deterioration of the battery based on the information on the battery to be determined.
The resistance value of the resistance component of the battery in each of the first period and the second period after the first period, and the active material of the battery in each of the first period and the second period. A deterioration determination method comprising estimating an aging factor indicating an influence other than a change in the capacity of the active material among the causes of change in the resistance component based on the capacity of the active material. It is a deterioration determination program that determines the deterioration of the battery based on the information about the battery to be determined, and is used by the computer.
The resistance value of the resistance component of the battery in each of the first period and the second period after the first period, and the active material of the battery in each of the first period and the second period. A deterioration determination program that estimates an aging factor that indicates an effect other than the change in the capacity of the active material among the causes of the change in the resistance component based on the capacity of the active material.

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