Battery analysis system, battery analysis method, and battery analysis program

Abstract

A data acquisition unit 111 acquires time-series battery data including the voltage, current, and temperature of a secondary battery. A deterioration estimation unit 112: estimates, by referring to a plurality of deterioration maps generated in advance for respective deterioration factors of the secondary battery, the deterioration amounts for the respective deterioration factors of the secondary battery; and estimates the deterioration amount of the secondary battery by adding up the deterioration amounts for the respective deterioration factors. A first deterioration map represents capacity deterioration due to growth of a coating film formed on the surface of a negative electrode. A second deterioration map represents capacity deterioration due to a physical change of an active material caused by expansion and contraction of the active material due to charging and discharging. A third deterioration map represents capacity deterioration caused by a third factor in which capacity deterioration rapidly progresses from a certain deterioration state.

Description

Battery Analysis System, Battery Analysis Method, and Battery Analysis Program

[0001] The present disclosure relates to a battery analysis system, a battery analysis method, and a battery analysis program for estimating the deterioration of a secondary battery.

[0002] It is known that the progress of deterioration of a secondary battery varies depending on the usage method, and it is required to predict the deterioration according to the usage method. As a deterioration prediction method considering the usage method of a secondary battery, the storage deterioration / charge deterioration / discharge deterioration of the secondary battery is measured in advance, a deterioration map showing the degree of deterioration depending on the SOC (State Of Charge) band, temperature, and current rate used is created, and the deterioration is predicted by inputting the usage method in advance. However, in the case of a lithium-ion battery in which a plurality of materials are mixed (for example, a lithium-ion battery using a mixed material of graphite (C(Gr)) and silicon (Si) for the negative electrode), it is difficult to express the deterioration due to the isolation of the silicon-based negative electrode active material only by the pre-created storage deterioration map, charge deterioration map, and discharge deterioration map.

[0003] Patent Document 1 discloses a method of creating a deterioration map expressing the degree of deterioration with respect to temperature and SOC for each positive electrode active material. However, the current and deterioration state for each material are not sufficiently considered, and in the case where the isolated active material does not contribute to the deterioration due to the isolation of the active material, the accuracy of deterioration prediction decreases.

[0004] Japanese Patent Application Laid-Open No. 2018-78023

[0005] The present disclosure has been made in view of such circumstances, and an object thereof is to provide a technique for improving the accuracy of deterioration prediction of a secondary battery.

[0006] To solve the above problems, a battery analysis system according to one aspect of the present disclosure includes a data acquisition unit that acquires time-series battery data including voltage, current, and temperature of a secondary battery, and a degradation estimation unit that estimates the amount of degradation for each degradation factor of the secondary battery by referring to a plurality of degradation maps that have been generated in advance for each degradation factor of the secondary battery, and estimates the amount of degradation of the secondary battery by summing the amounts of degradation for each degradation factor. The plurality of degradation maps include a first degradation map that represents capacity degradation due to the growth of a film formed on the negative electrode surface, a second degradation map that represents capacity degradation due to physical changes in the active material accompanying the expansion and contraction of the active material due to charging and discharging, and a third degradation map that represents capacity degradation due to a third factor that causes capacity degradation to progress rapidly from a certain degradation state.

[0007] Furthermore, any combination of the above components, as well as any conversion of the expressions of this disclosure between devices, systems, methods, computer programs, etc., are also valid forms of this disclosure.

[0008] According to this disclosure, the accuracy of predicting the degradation of secondary batteries can be improved.

[0009] This figure illustrates a battery analysis system according to an embodiment. Figures 2(a) and 2(b) show an example of the configuration of a battery pack system. This figure shows an example of a storage degradation map. This figure shows an example of a charge degradation map. This figure shows an example of a discharge degradation map. This figure shows an example of cell degradation test results and degradation simulation results. This figure shows an example of an isolation map of silicon-based anode active material. This figure shows another example of an isolation map of silicon-based anode active material. This figure shows an example of a SOC-volume map of silicon-based anode active material. This figure shows an example of a current ratio map flowing through silicon-based anode active material. This figure shows another example of a current ratio map flowing through silicon-based anode active material. This figure is a graph summarizing an example of the full charge capacity of each material constituting a lithium-ion cell with a silicon-based mixed anode. This figure shows the degradation test results and degradation simulation results of the cell shown in Figure 6, with the degradation simulation results using multiple degradation maps according to this embodiment added.

[0010] Figure 1 is a diagram illustrating a battery analysis system 10 according to an embodiment. The battery analysis system 10 is a system for estimating the degradation of a battery pack 21 included in a battery pack mounted on an electric vehicle 20. The battery analysis system 10 can also predict the future degradation trend of the battery pack 21 and can provide the user with an estimate of when to replace the battery pack including the battery pack 21.

[0011] The battery analysis system 10 may be built, for example, on a server installed in the company's own facility or data center of a business that provides analysis services for battery packs installed in electric vehicles 20. Alternatively, the battery analysis system 10 may be built on a cloud server used based on a cloud service. Furthermore, the battery analysis system 10 may be built on multiple servers distributed across multiple locations (data centers, company facilities). These multiple servers may be a combination of multiple company servers, a combination of multiple cloud servers, or a combination of company servers and cloud servers.

[0012] The battery pack system 21, included in the battery pack mounted on the electric vehicle 20, supplies power to the drive motor (not shown). The battery pack system 21 includes multiple single cells or multiple cell blocks connected in series.

[0013] Figures 2(a) and 2(b) show examples of the configuration of a battery pack system 21. The battery pack system 21 shown in Figure 2(a) includes a plurality of single cells E1-Em connected in series. The battery pack system 21 shown in Figure 2(b) includes a plurality of cell blocks Eb1-Ebm connected in series. Each cell block Eb1-Ebm includes a plurality of cells E1a-E1n-Ema-Emn connected in parallel.

[0014] The cells can be lithium-ion battery cells, nickel-metal hydride battery cells, lead-acid battery cells, etc. In this specification, we will assume the use of lithium-ion battery cells. The number of single cells or cell blocks in series is determined according to the voltage of the drive motor.

[0015] The voltage sensor 22 detects the voltage across each of the series-connected single cells or cell blocks. A shunt resistor is connected in series with multiple series-connected single cells or cell blocks. The current sensor 23 detects the current flowing through the series-connected single cells or cell blocks based on the voltage across the shunt resistor. A Hall element may be used instead of the shunt resistor. Multiple temperature sensors 24 are installed in the battery pack, including the battery pack system 21. For example, a thermistor can be used as the temperature sensor 24. For example, one temperature sensor 24 may be provided for 6 to 8 single cells or cell blocks.

[0016] The control unit 25 is composed of a BMU (Battery Management Unit) and an ECU (Electronic Control Unit) working together. The BMU estimates the State of Charge (SOC) by combining the Open Circuit Voltage (OCV) method and the current integration method. The OCV method is a method of estimating the SOC based on the measured OCV of the cell and the SOC-OCV curve of the cell. The SOC-OCV curve of the cell is created in advance based on characteristic tests conducted by the battery manufacturer and is registered in the BMU at the time of shipment.

[0017] The current integration method is a method for estimating the State of Charge (SOC) based on the OCV at the start of charging and discharging of the cell and the integrated value of the measured current. In the current integration method, measurement errors of the current accumulate as the charging and discharging time increases. Therefore, it is preferable to use a weighted average of the SOC estimated by the current integration method and the SOC estimated by the OCV method.

[0018] The BMU periodically (for example, at 10-second intervals) transmits battery data, including voltage, current, temperature, and SOC of multiple single cells or cell blocks (hereinafter, both collectively referred to as cells as appropriate), to the ECU via the in-vehicle network, allowing the ECU to sample the battery data in a time-series manner. For example, a CAN (Controller Area Network) or a LIN (Local Interconnect Network) can be used as the in-vehicle network.

[0019] The cell voltages transmitted from the BMU to the ECU may be the voltages of all cells connected in series, or only the maximum and minimum cell voltages. The temperature transmitted to the ECU may be the temperatures of multiple observation points within the battery pack, or only the maximum and minimum temperatures.

[0020] The communication unit 26 has the function of performing communication signal processing with the communication unit 33 of the charging station 30, and the function of performing wireless signal processing for connecting to the network 5. The communication unit 26 can access the network 5 using, for example, a mobile phone network (cellular network), wireless LAN, V2I (Vehicle to Infrastructure), V2V (Vehicle to Vehicle), ETC system (Electronic Toll Collection System), or DSRC (Dedicated Short Range Communications).

[0021] Network 5 is a general term for communication channels such as the Internet, dedicated lines, and VPNs (Virtual Private Networks), and does not specify the communication medium or protocol. Examples of communication mediums include mobile phone networks, wireless LANs, wired LANs, fiber optic networks, ADSL networks, and CATV networks. Examples of communication protocols include TCP (Transmission Control Protocol) / IP (Internet Protocol), UDP (User Datagram Protocol) / IP, and Ethernet (registered trademark).

[0022] The ECU may transmit the sampled battery data to the battery analysis system 10 each time, or it may store the data in its internal memory and transmit the battery data stored in the memory to the battery analysis system 10 all at once at a predetermined timing. Furthermore, when the electric vehicle 20 and the charging station 30 are connected by a charging cable, the ECU may transmit the battery data stored in its memory to the battery analysis system 10 via the charging station 30.

[0023] By connecting the electric vehicle 20 to the charging station 30 with a charging cable, the battery pack system 21 inside the electric vehicle 20 can be charged from the outside. The charging station 30 is connected to the commercial power grid 2 and charges the battery pack system 21.

[0024] Generally, normal charging uses alternating current (AC), while rapid charging uses direct current (DC). When charging with AC (e.g., single-phase 100 / 200V), the charging voltage or current is controlled by a charger (not shown) inside the electric vehicle 20. When charging with DC, the charging voltage or current is controlled by the power supply unit 31 of the charging station 30. The power supply unit 31 includes a rectifier circuit, a filter, and a DC / DC converter. The rectifier circuit full-wave rectifies the AC power supplied from the commercial power system 2, and the filter smooths it to generate DC power. The DC / DC converter controls the voltage or current of the generated DC power.

[0025] For example, the following rapid charging standards can be used: CHAdeMO®, ChaoJi, GB / T, and Combo (Combined Charging System). CHAdeMO, ChaoJi, and GB / T use CAN as their communication method. Combo uses PLC (Power Line Communication) as its communication method.

[0026] A charging cable employing the CAN system includes communication lines in addition to power lines. When the electric vehicle 20 and the charging station 30 are connected via this charging cable, the control unit 25 of the electric vehicle 20 establishes a communication channel with the control unit 32 of the charging station 30. In a charging cable employing the PLC system, the communication signal is transmitted superimposed on the power lines.

[0027] The communication unit 33 of the charging station 30 has the function of performing communication signal processing with the communication unit 26 of the electric vehicle 20, and the function of performing signal processing for connecting to the network 5. The communication unit 33 can access the network 5 using, for example, a wired LAN, a wireless LAN, or a mobile phone network.

[0028] The battery analysis system 10 comprises a control unit 11, a storage unit 12, and a communication unit 13. The communication unit 13 is a communication interface (e.g., NIC: Network Interface Card) for connecting to the network 5 by wire or wireless connection.

[0029] The control unit 11 includes a data acquisition unit 111, a degradation estimation unit 112, and a degradation progression prediction unit 113. The functions of the control unit 11 can be realized through the cooperation of hardware and software resources, or solely through hardware resources. Hardware resources that can be used include a CPU, ROM, RAM, GPU (Graphics Processing Unit), NPU (Neural Network Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), and other LSIs. Software resources that can be used include operating systems and applications.

[0030] The storage unit 12 includes a non-volatile recording medium such as an HDD or SSD, and stores various types of data. The storage unit 12 also includes a battery data storage unit 121 and a map storage unit 122. The data acquisition unit 111 acquires time-series battery data, including voltage, current, temperature, and SOC of the battery pack system 21, from the electric vehicle 20 or charging station 30, and stores the acquired battery data in the battery data storage unit 121.

[0031] The degradation estimation unit 112 reads the battery data of the cell to be analyzed from the battery data holding unit 121. Based on the read battery data, the degradation estimation unit 112 refers to a plurality of degradation maps that have been pre-generated for each degradation factor of the secondary battery to estimate the amount of degradation for each degradation factor of the secondary battery, and estimates the degree of degradation of the cell to be analyzed by summing up the amounts of degradation for each degradation factor. The degree of degradation of the cell is expressed in terms of State of Health (SOH).

[0032] SOH (State of Health) is defined as the ratio of the current FCC (Full Charge Capacity) to the initial FCC (Full Charge Capacity), as shown in Equation 1 below. A lower value (closer to 0%) indicates more advanced degradation. SOH = Current FCC / Initial FCC × 100 ... (Equation 1)

[0033] The map holding unit 122 includes a storage degradation map, a charge degradation map, and a discharge degradation map. Storage degradation of a secondary battery is a degradation that progresses over time according to the temperature and state of charge (SOC) of the secondary battery at each point in time. It progresses over time regardless of whether charging or discharging is in progress. Storage degradation mainly occurs due to the formation of a film (SEI (Solid Electrolyte Interphase) film) on the negative electrode. Generally, the higher the SOC at each point in time and the higher the temperature at each point in time, the faster the storage degradation rate.

[0034] The charge / discharge degradation of secondary batteries is a type of degradation that progresses as the number of charge / discharge cycles increases. This degradation is primarily caused by structural deterioration (wear, fracture, cracking, delamination, etc.) due to the expansion and contraction of the positive electrode active material. The rate of charge / discharge degradation depends on the state of charge (SOC) range, temperature, and current rate used. Generally, the rate of charge / discharge degradation increases at lower SOC ranges. Furthermore, the rate of charge / discharge degradation increases with higher current rates and temperatures.

[0035] Figure 3 shows an example of a storage degradation map. The storage degradation map represents the capacity degradation of a cell due to the growth of a coating (SEI film) formed on the negative electrode surface of the cell. The horizontal axis shows SOC [%], and the vertical axis shows the amount of storage degradation [% / √h]. The amount of storage degradation is defined as the decrease in SOH (ΔSOH / √h) per unit time. It is generally known that storage degradation progresses approximately linearly with respect to the square root law (0.5 power) of elapsed time (h). Therefore, the unit time is set to 0.5 power of 1 hour.

[0036] Figure 3 shows the storage degradation characteristics extracted and plotted at temperatures of 25°C and 45°C. The actual storage degradation map is described in a two-dimensional parameter space of temperature (°C) and SOC (%). For example, the unit width of temperature may be set to 1°C or 5°C, and the unit width of SOC may be set to 1%.

[0037] Figure 4 shows an example of a charge degradation map. The horizontal axis shows the usage range of SOC [%], and the vertical axis shows the charge degradation amount [% / √Ah]. Figure 5 shows an example of a discharge degradation map. The horizontal axis shows the usage range of SOC [%], and the vertical axis shows the discharge degradation amount [% / √Ah]. The charge / discharge degradation map is a degradation map that represents the capacity degradation due to physical changes in the positive electrode active material caused by the expansion and contraction of the positive electrode active material due to the charging and discharging of the cell.

[0038] In the charge degradation map shown in Figure 4, the amount of charge degradation of a cell is defined as the decrease in SOH per unit charge (ΔSOH / √Ah). Generally, charge degradation is known to progress approximately linearly with respect to the square root of the cumulative charge (Ah). Therefore, the unit charge is set to 1Ah raised to the power of 0.5.

[0039] Figure 4 shows the extracted charge degradation characteristics for charge rates of 0.1C, 0.3C, 0.5C, and 0.8C at room temperature, plotted on a graph. The actual charge degradation map is described in a three-dimensional parameter space of temperature (°C), SOC band (%), and charge rate (C). For example, the unit width of temperature may be set to 1°C or 5°C, the unit width of the SOC band to 10%, and the unit width of the charge rate to 0.1C.

[0040] In the discharge degradation map shown in Figure 5, the amount of cell discharge degradation is defined as the decrease in SOH per unit discharge (ΔSOH / √Ah). It is generally known that discharge degradation progresses approximately linearly with respect to the square root of the cumulative discharge amount (Ah). Therefore, the unit discharge amount is set to 1Ah raised to the power of 0.5.

[0041] Figure 5 shows the discharge degradation characteristics extracted and plotted in a graph at a charging rate of 0.1C, 0.3C, 0.5C, and 0.8C at room temperature. The actual discharge degradation map is described in a three-dimensional parameter space of temperature (°C), SOC band (%), and discharge rate (C). For example, the unit width of temperature may be set to 1°C or 5°C, the unit width of the SOC band may be set to 10%, and the unit width of the discharge rate may be set to 0.1C.

[0042] The storage degradation characteristics, charge degradation characteristics, and discharge degradation characteristics are derived in advance by experiments or simulations by the battery manufacturer, mapped, and stored in the map storage unit 122.

[0043] Figure 6 is a diagram showing an example of the degradation test results and degradation simulation results of the cell. In this test, a 18650-type cylindrical lithium-ion cell was used. Figure 6 shows the degradation test results and degradation simulation results when charging and discharging were repeated at a current rate of 0.5C between SOC = 0 and 80%. The horizontal axis represents the cumulative discharge amount [Ah], and the vertical axis represents SOH [%]. As the cumulative discharge amount [Ah] increases, the SOH decreases.

[0044] The solid line sim1 shows the degradation simulation results of a graphite-based single anode lithium-ion cell using lithium nickel cobalt aluminum oxide (LiNixCoyAlzO2) as the positive electrode active material and graphite (C) as the negative electrode active material. The simulation results shown by the solid line sim1 are obtained by creating in advance the storage degradation map, charge degradation map, and discharge degradation map of the graphite-based single anode lithium-ion cell, and referring to the created storage degradation map, charge degradation map, and discharge degradation map to simulate the progress of degradation when charging and discharging are repeated under the above conditions.

[0045] The SOH of the single anode lithium-ion cell is calculated as the total value of the cumulative value of the storage degradation amount [% / √h] estimated from the storage degradation map, the cumulative value of the charge degradation amount [% / √Ah] estimated from the charge degradation map, and the cumulative value of the discharge degradation amount [% / √Ah] estimated from the discharge degradation map.

[0046] The dotted line sim2(Si) shows the simulation results of the degradation of a lithium-ion cell with a silicon-based mixed anode, where lithium nickel cobalt aluminum oxide (LiNixCoyAlzO2) is used as the cathode active material and graphite (C) and silicon (Si) are used as the anode active material. The simulation results shown by the dotted line sim2(Si) are obtained by creating in advance the storage degradation map, charge degradation map, and discharge degradation map of the lithium-ion cell with the silicon-based mixed anode, and simulating the progress of degradation when charge and discharge are repeated under the above conditions by referring to the created storage degradation map, charge degradation map, and discharge degradation map.

[0047] The solid line test shows the degradation test results when the lithium-ion cell with the silicon-based mixed anode used in the simulation shown by the dotted line sim2(Si) is actually charged and discharged repeatedly under the above conditions. Until the cumulative discharge amount is less than 800 (Ah) (point a), the degradation test results test of the lithium-ion cell with the silicon-based mixed anode and the simulation results sim1 of the degradation of the lithium-ion cell with a graphite-based single anode are approximated.

[0048] The main factors for the degradation of the lithium-ion cell with the silicon-based mixed anode in the charge and discharge cycles from the start of the test to point a are mainly the growth of the SEI film formed on the anode surface, the wear of the cathode active material due to the expansion and contraction of the cathode active material, and the wear of silicon due to the expansion and contraction of silicon which is the anode active material. Note that compared with silicon, graphite has less expansion and contraction due to the input and output of current, and the wear of graphite can be ignored.

[0049] When the cumulative discharge amount of the lithium-ion cell with the silicon-based mixed anode reaches less than 800 (Ah) (point a), the SOH of the lithium-ion cell with the silicon-based mixed anode drops rapidly. When the electron and ion conduction path of silicon shrinks below a certain value due to the repeated expansion and contraction of silicon accompanying charge and discharge, the isolation (insulation) of the silicon-based anode active material accelerates.

[0050] When the cumulative discharge of a lithium-ion cell with a silicon-based mixed anode reaches slightly over 1000 Ah (point b), the decrease in SOH of the lithium-ion cell with a silicon-based mixed anode slows down. At point b, the electron and ion conduction pathways of the silicon-based anode active material are completely severed, and the silicon-based anode active material no longer contributes to charging and discharging. In other words, the effects of the degradation of the silicon-based anode active material are fully incorporated into the degradation of the entire cell. Beyond point b, the growth of the SEI film formed on the anode surface and the wear of the positive electrode active material due to expansion and contraction of the positive electrode active material become the main degradation factors, and the wear of silicon due to expansion and contraction of silicon is no longer a degradation factor.

[0051] Comparing the degradation test results (test) of a lithium-ion cell with a silicon-based mixed anode with the degradation simulation results (sim1) of a lithium-ion cell with a graphite-based anode, it is clear that the latter cannot represent the rapid decrease in SOH due to the isolation of the silicon-based anode active material, because the graphite-based lithium-ion cell does not contain silicon-based anode active material.

[0052] Comparing the degradation test results (test) of lithium-ion cells with silicon-based mixed anodes with the degradation simulation results (sim2(Si)) of lithium-ion cells with silicon-based mixed anodes, there is a significant discrepancy between the two. Even when the storage degradation map, charge degradation map, and discharge degradation map of lithium-ion cells with graphite-based single-element anodes are replaced with the storage degradation map, charge degradation map, and discharge degradation map of lithium-ion cells with silicon-based mixed anodes, the degradation curve only decreases overall, and it is not possible to predict the actual degradation curve of lithium-ion cells with silicon-based mixed anodes with high accuracy.

[0053] As described above, the charge degradation map assumes that degradation progresses in proportion to the square root value of the cumulative charge amount (Ah), and the discharge degradation map assumes that degradation progresses in proportion to the square root value of the cumulative discharge amount (Ah). The charge degradation map or discharge degradation map of the silicon-based mixed negative electrode lithium-ion cell used in the degradation simulation result sim2(Si) uses the charge rate or discharge rate of the current flowing through the entire cell as the input variable, and does not take into account the distribution ratio of the current flowing through the graphite-based negative electrode active material and the silicon-based negative electrode active material, respectively, in the negative electrode.

[0054] As described above, as the isolation of the silicon-based anode active material accelerates, the current flowing through the silicon-based anode active material decreases rapidly, and eventually no current flows through it. As described above, simply creating charge degradation maps and discharge degradation maps for lithium-ion cells with silicon-based mixed anodes does not take into account the changes in the distribution ratio of current flowing through the graphite-based anode active material and the silicon-based anode active material, respectively.

[0055] Therefore, in this embodiment, in addition to a storage degradation map that shows storage degradation due to the formation of an SEI film on the negative electrode surface, and charging degradation maps and discharge degradation maps of the positive electrode active material that show charging degradation and discharge degradation due to the expansion and contraction of the positive electrode active material, a wear degradation map of the silicon-based negative electrode active material that shows wear degradation due to the expansion and contraction of the silicon-based negative electrode active material, and an isolation map of the silicon-based negative electrode active material that shows electrical isolation due to the expansion and contraction of the silicon-based negative electrode active material are created in advance.

[0056] The wear degradation map of the silicon-based anode active material consists of a charge degradation map and a discharge degradation map of the silicon-based anode active material. Similar to the charge degradation map and discharge degradation map of the positive electrode active material shown in Figures 4 and 5, they are described in a three-dimensional parameter space of temperature (°C), SOC band (%), and current rate (C), respectively. The charge degradation characteristics and discharge degradation characteristics of the silicon-based anode active material are derived in advance through experiments and simulations by the battery manufacturer, mapped, and stored in the map holding unit 122.

[0057] Figure 7 shows an example of an isolation map of silicon-based anode active material. The horizontal axis shows the cumulative degradation progress [%] of the silicon-based anode active material, and the vertical axis shows the amount of degradation due to isolation of the silicon-based anode active material [% / √Ah]. The amount of degradation due to isolation of the silicon-based anode active material is defined as the amount of SOH reduction per unit charge or unit discharge (ΔSOH / √Ah).

[0058] The cumulative degradation rate delta_soh_a2 of a silicon-based anode active material is defined as the cumulative degradation amount deg_soh_a2 of the silicon-based anode active material relative to the upper limit capacity ratio deg_soh_a2_ulim, as shown in (Equation 2) below. delta_soh_a2 = deg_soh_a2 / deg_soh_a2_ulim * 100 ... (Equation 2)

[0059] The upper limit capacity ratio deg_soh_a2_ulim of the silicon-based anode active material is defined as the ratio of the full charge capacity SiFCC of the silicon-based anode active material to the full charge capacity CellFCC of the entire cell, as shown in (Equation 3) below. deg_soh_a2_ulim = SiFCC / CellFCC * 100 ... (Equation 3)

[0060] The unit time degradation rate delta_deg_soh_a2 of the silicon-based anode active material can be calculated as the sum of the degradation rate due to wear deg_soh_cyc_a2 and the degradation rate due to isolation deg_soh_rapid_a2 per unit time, as shown in (Equation 4) below. delta_deg_soh_a2 = deg_soh_cyc_a2 + deg_soh_rapid_a2 ... (Equation 4)

[0061] The cumulative degradation amount deg_soh_a2(t) of the silicon-based anode active material is calculated by adding the most recent unit degradation amount delta_deg_soh_a2(t) of the silicon-based anode active material to the cumulative degradation amount deg_soh_a2(t-1) of the silicon-based anode active material one unit time ago (t-1), as shown in (Equation 5) below. deg_soh_a2(t) = deg_soh_a2(t-1) + delta_deg_soh_a2(t) ... (Equation 5)

[0062] The amount of degradation due to wear of the silicon-based anode active material, deg_soh_cyc_a2, can be determined from the wear degradation map (cyc_map_a2) as shown in (Equation 6) below. The degradation estimation unit 112 can obtain the amount of degradation due to wear of the silicon-based anode active material, deg_soh_cyc_a2, by inputting the current rate (C), temperature (°C), and SOC band (%) into the charge degradation map and discharge degradation map of the silicon-based anode active material. deg_soh_cyc_a2 = cyc_map_a2(I, Temp, SOC) ... (Equation 6)

[0063] The amount of degradation due to isolation of the silicon-based anode active material, deg_soh_rapid_a2, can be determined from the isolation map of the silicon-based anode active material (rapid_map_a2) (see, for example, Figure 7), as shown in (Equation 7) below. The degradation estimation unit 112 can obtain the amount of degradation due to isolation of the silicon-based anode active material, deg_soh_rapid_a2, by inputting the cumulative degradation progress delta_soh_a2, based on the cumulative degradation amount deg_soh_a2 of the silicon-based anode active material one unit time ago (t-1), into the isolation map of the silicon-based anode active material (rapid_map_a2). deg_soh_rapid_a2 = rapid_map_a2(deg_soh_a2(t-1)) ... (Equation 7)

[0064] The above explains an example of how to determine the degradation amount deg_soh_rapid_a2 due to isolation of silicon-based anode active material from the cumulative degradation progression delta_soh_a2 of the silicon-based anode active material. However, the degradation amount deg_soh_rapid_a2 due to isolation of silicon-based anode active material can also be determined from the cumulative expansion and contraction amount of the silicon-based anode active material.

[0065] Figure 8 shows another example of an isolation map for silicon-based anode active material. The horizontal axis shows the cumulative volume change of the silicon-based anode active material, and the vertical axis shows the amount of degradation due to isolation of the silicon-based anode active material per cycle [% / √Ah]. The amount of degradation due to isolation of the silicon-based anode active material per cycle is defined as the amount of SOH decrease (ΔSOH) per expansion-contraction cycle of the silicon-based anode active material.

[0066] Figure 9 shows an example of an SOC-volume map for a silicon-based anode active material. The horizontal axis represents the SOC [%] of the silicon-based anode active material, and the vertical axis represents the volume fraction of the silicon-based anode active material. The volume fraction is normalized by setting the volume at SOC = 0% to 0 and the volume at SOC = 100% to 1.

[0067] The current SOC_a2(t) of silicon-based anode active material can be calculated from the following equation (Equation 8): SOC_a2(t) = SOC_a2(t-1) + ΣI_a2 / SiFCC_a2 * 100 ... (Equation 8) ΣI_a2 is the integrated value of the current I_a2 flowing through the silicon-based anode active material per unit time, where the charging current is positive and the discharging current is negative. SiFCC_a2 is the full charge capacity of the silicon-based anode active material.

[0068] The degradation estimation unit 112 can obtain the volume of the silicon-based anode active material by inputting the SOC_a2 of the silicon-based anode active material into the SOC-volume map. The cumulative volume change of the silicon-based anode active material is determined using methods such as the rainflow method. For example, if the charging and discharging of the silicon-based anode active material is repeated between SOC = 40% and 100%, the cumulative volume change will be Σ(1-0.6).

[0069] The degradation estimation unit 112 can obtain the degradation amount deg_soh_rapid_a2 per cycle due to isolation of the silicon-based anode active material by inputting the cumulative volume change of the silicon-based anode active material into the isolation map (rapid_map_a2) of the silicon-based anode active material. In the second term on the right-hand side of (Equation 4) above, deg_soh_rapid_a2 is added for each expansion and contraction cycle.

[0070] The properties related to the isolation of silicon-based anode active material are derived in advance through experiments and simulations by the battery manufacturer, mapped, and stored in the map holding unit 122. The isolation of silicon-based anode active material can be more easily suppressed by mixing carbon nanotubes (CNTs) into the anode. In this case, the degradation characteristics due to the isolation of silicon-based anode active material are improved.

[0071] In a lithium-ion cell with a silicon-based mixed anode, the current flowing through the entire cell is distributed between the graphite-based anode active material and the silicon-based anode active material. In the equivalent circuit model of a silicon-based mixed anode lithium-ion cell, the graphite-based anode active material and the silicon-based anode active material are connected in parallel.

[0072] Figure 10 shows an example of a current ratio map flowing through a silicon-based anode active material. Figure 11 shows another example of a current ratio map flowing through a silicon-based anode active material. The current ratio flowing through the silicon-based anode active material indicates the proportion of the current flowing through the silicon-based anode active material out of the total current flowing through the cell. The current ratio map is described in a three-dimensional parameter space of temperature (°C), SOC (%) of the silicon-based anode active material, and the current rate (C) flowing through the cell.

[0073] Figure 10 shows the current ratio map when the temperature is 25°C, and Figure 11 shows the current ratio map when the temperature is 10°C. In Figures 10 and 11, the current ratios for current rates of 0.5C discharge, 1.0C discharge, 0.5C charge, and 1.0C charge are extracted and depicted. In actual current ratio maps, for example, the temperature unit width may be set to 1°C or 5°C, the SOC unit width to 1% or 5%, and the current rate unit width to 0.1C.

[0074] As shown in Figures 10 and 11, no current flows through the silicon-based anode active material when its State of Core (SOC) is in the range of slightly over 30% to 100%. When the SOC of the silicon-based anode active material drops to slightly over 30%, current begins to flow through it, and the proportion of current flowing through the silicon-based anode active material increases as the SOC decreases. At SOC = 0%, the proportion of current flowing through the silicon-based anode active material becomes 1, and no current flows through the graphite-based anode active material.

[0075] Furthermore, as the silicon-based anode active material degrades, the current ratio map is updated so that the current flowing through the silicon-based anode active material decreases. When the degradation of the silicon-based anode active material exceeds a predetermined threshold, the current ratio flowing through the silicon-based anode active material becomes zero, and no current flows through it.

[0076] The current ratio characteristics are derived in advance through experiments and simulations conducted by the battery manufacturer, mapped, and stored in the map holding unit 122.

[0077] The degradation estimation unit 112 can obtain the current ratio flowing through the silicon-based anode active material by inputting the current flowing through the cell, the temperature, and the SOC of the silicon-based anode active material into the current ratio map. The current flowing through the cell and the temperature can be those included in the time-series battery data read from the battery data holding unit 121. The SOC of the silicon-based anode active material can be calculated from the above (Equation 8).

[0078] As described above, the degradation estimation unit 112 estimates the cumulative degradation amount of the positive electrode active material by summing the cumulative value of the charge degradation amount [% / √Ah] estimated from the charge degradation map for the positive electrode active material and the cumulative value of the discharge degradation amount [% / √Ah] estimated from the discharge degradation map for the positive electrode active material. The degradation estimation unit 112 estimates the cumulative degradation amount of the graphite-based negative electrode active material from the cumulative value of the storage degradation amount [% / √h] estimated from the storage degradation map.

[0079] The degradation estimation unit 112 calculates the cumulative degradation amount of the silicon-based anode active material by summing the cumulative value of the charge degradation amount [% / √Ah] estimated from the charge degradation map for the silicon-based anode active material, the cumulative value of the discharge degradation amount [% / √Ah] estimated from the discharge degradation map for the silicon-based anode active material, and the cumulative value of the degradation amount [% / √Ah] estimated from the isolation map which represents the capacity degradation due to electrical isolation caused by the expansion and contraction of the silicon-based anode active material.

[0080] The degradation estimation unit 112 calculates the SOH of the lithium-ion cell with a silicon-based mixed anode by summing the cumulative degradation amount of the positive electrode active material, the cumulative degradation amount of the graphite-based anode active material, and the cumulative degradation amount of the silicon-based anode active material.

[0081] The degradation estimation unit 112 sets upper limits on the cumulative degradation of the positive electrode active material, the graphite-based negative electrode active material, and the silicon-based negative electrode active material, based on the ratio of the full charge capacity of the positive electrode active material, the full charge capacity of the graphite-based negative electrode active material, and the full charge capacity of the silicon-based negative electrode active material to the full charge capacity of the lithium-ion cell with a silicon-based mixed negative electrode.

[0082] Figure 12 is a graph summarizing an example of the full charge capacities of each material constituting a lithium-ion cell with a silicon-based mixed anode. The lithium-ion cell with a silicon-based mixed anode shown in Figure 12 has a total full charge capacity of 3.3 Ah, a full charge capacity of 3.5 Ah for the positive electrode active material, a full charge capacity of 2.2 Ah for the graphite-based anode active material, and a full charge capacity of 1.1 Ah for the silicon-based anode active material.

[0083] In the example shown in Figure 12, the total full charge capacity of the cell is 3.3 Ah, and the capacity of the silicon-based anode active material is 1.1 Ah. Therefore, the upper limit of the cumulative degradation amount deg_soh_a2 of the silicon-based anode active material is 33%. Similarly, upper limits are set for the cumulative degradation amount of the cathode active material and the graphite-based anode active material. Note that the total full charge capacity of the cell and the full charge capacity of each material may be updated as the degradation of each material progresses.

[0084] If the estimated cumulative degradation amount of a specific material exceeds the set upper limit for the cumulative degradation amount of that material, the degradation estimation unit 112 reduces the estimated cumulative degradation amount of the specific material to the set upper limit for the cumulative degradation amount of that material, thereby limiting it so as not to exceed the upper limit for the cumulative degradation amount of that material.

[0085] The currents flowing through the graphite-based negative electrode active material and the silicon-based negative electrode active material can also be determined from an equivalent circuit model without using current ratio maps like those shown in Figures 10 and 11.

[0086] Since the equivalent circuits of the graphite-based anode active material and the silicon-based anode active material are parallel circuits, the following relationships (Equation 9) and (Equation 10) hold true. In other words, the current flowing through the graphite-based anode active material and the current flowing through the silicon-based anode active material can be determined from a composite model of the parallel circuits of the equivalent circuits of the graphite-based and silicon-based anode active materials. OCV_c(t)+Ic(t)*Roc(t)+Vpc(t-1)=OCV_si(t)+Isi(t)*Rosi(t)+Vpsi(t-1) ... (Equation 9) Ic(t)+Isi(t)=I(t) ... (Equation 10)

[0087] Ic: Current flowing through the graphite-based negative electrode active material, Roc: DC resistance component of the graphite-based negative electrode active material, Vpc: Polarization voltage of the graphite-based negative electrode active material, Isi: Current flowing through the silicon-based negative electrode active material, Rosi: DC resistance component of the silicon-based negative electrode active material, Vpsi: Polarization voltage of the silicon-based negative electrode active material.

[0088] To simplify the calculations, the polarization voltage Vpc of the graphite-based anode active material and the polarization voltage Vpsi of the silicon-based anode active material are based on the previous values.

[0089] A capacity-OCV_c map for the graphite-based anode active material and a capacity-OCV_si map for the silicon-based anode active material are created in advance. As the degradation of the graphite-based anode active material progresses, the capacity-OCV_c map for the graphite-based anode active material is updated to a capacity-OCV_c map with reduced capacity. The smaller the capacity, the smaller the current flowing through the graphite-based anode active material. It can also be said that the degradation estimation unit 112 calculates the estimated OCV value of the graphite-based anode active material by referring to the capacity-OCV_c map for the graphite-based anode active material so that the current flowing through the graphite-based anode active material becomes smaller.

[0090] Similarly, as the silicon-based anode active material degrades, its capacitance-OCV_si map is updated to a capacitance-OCV_si map with reduced capacitance. The smaller the capacitance, the smaller the current flowing through the silicon-based anode active material. The degradation estimation unit 112 can be said to be calculating the estimated OCV value of the silicon-based anode active material by referring to its capacitance-OCV_si map, so that the current flowing through the silicon-based anode active material becomes smaller. The capacitance of the silicon-based anode active material's capacitance-OCV_si map asymptotically approaches 0. When the capacitance becomes 0, no current flows through the silicon-based anode active material, and the equivalent circuit of the silicon-based anode active material becomes high impedance.

[0091] Figure 13 shows the degradation test results and degradation simulation results of the cell shown in Figure 6, with the addition of degradation simulation results sim3 using multiple degradation maps according to this embodiment. The simulation results sim3 (solid line) simulate the degradation progression when charging and discharging are repeated under the same conditions as the degradation test shown in solid line test, by referring to multiple degradation maps previously created for the lithium-ion cell with a silicon-based mixed negative electrode used in the degradation test shown in solid line test.

[0092] Multiple degradation maps created for lithium-ion cells with silicon-based mixed negative electrodes include the storage degradation map, charge degradation map for positive electrode active material, discharge degradation map for positive electrode active material, charge degradation map for silicon-based negative electrode active material, discharge degradation map for silicon-based negative electrode active material, isolation map for silicon-based negative electrode active material, and current ratio map, as described above.

[0093] Comparing the degradation test results (test) of a lithium-ion cell with a silicon-based mixed anode with the degradation simulation results (sim3) of a lithium-ion cell with a silicon-based mixed anode, the two are similar. In other words, by creating the storage degradation map, charge degradation map for the positive electrode active material, discharge degradation map for the positive electrode active material, charge degradation map for the silicon-based anode active material, discharge degradation map for the silicon-based anode active material, isolation map for the silicon-based anode active material, and current ratio map described above, it is possible to predict the actual degradation curve of a lithium-ion cell with a silicon-based mixed anode with high accuracy.

[0094] In this manner, the degradation estimation unit 112 estimates the degradation curve of the cell from the start of use to the present time based on the changes in voltage, current, and temperature from the start of use to the present time, which are included in the battery data of the cell to be analyzed read from the battery data holding unit 121. If the cell to be analyzed is a lithium-ion cell with a silicon-based mixed negative electrode, the degradation estimation unit 112 estimates the degradation curve of the lithium-ion cell with a silicon-based mixed negative electrode from the start of use to the present time by referring to the storage degradation map, the charge degradation map for the positive electrode active material, the discharge degradation map for the positive electrode active material, the charge degradation map for the silicon-based negative electrode active material, the discharge degradation map for the silicon-based negative electrode active material, the isolation map for the silicon-based negative electrode active material, and the current ratio map described above.

[0095] The degradation progression prediction unit 113 generates a charge / discharge pattern profile of the cell from the voltage, current, and temperature changes of the cell from the start of use to the present time, which are included in the battery data of the cell to be analyzed read from the battery data holding unit 121. Based on the generated charge / discharge pattern profile, the degradation progression prediction unit 113 predicts the degradation curve of the cell from the present time to the future, assuming that the use of the cell will continue.

[0096] If the cell to be analyzed is a lithium-ion cell with a silicon-based mixed anode, the degradation progression prediction unit 113 predicts the progression of the degradation curve of the lithium-ion cell with a silicon-based mixed anode from the present time by referring to the storage degradation map, the charge degradation map for the positive electrode active material, the discharge degradation map for the positive electrode active material, the charge degradation map for the silicon-based anode active material, the discharge degradation map for the silicon-based anode active material, the isolation map for the silicon-based anode active material, and the current ratio map described above.

[0097] If the cell to be analyzed is a cell included in a battery pack installed in an electric vehicle 20, the degradation progression prediction unit 113 identifies the cumulative discharge amount at which the State of Health (SOH) of the cell to be analyzed reaches its end-of-use point (e.g., 60-70%), based on the predicted degradation curve. Based on the identified cumulative discharge amount and the charge-discharge pattern profile of the cell, the degradation progression prediction unit 113 predicts when the cell will reach the end of its lifespan.

[0098] As described above, this embodiment makes it possible to improve the accuracy of predicting cell degradation. For example, in a lithium-ion cell having a mixed negative electrode or mixed positive electrode containing multiple materials, by estimating the current corresponding to the degradation state of each material, it is possible to predict the degradation of the lithium-ion cell with high accuracy, even when a particular material degrades quickly. Since a degradation map based on the degradation factors of each material is created without changing the conventional full-cell test pattern, degradation can be predicted with high accuracy when a particular material degrades.

[0099] The present disclosure has been described above based on embodiments. The embodiments are illustrative, and it will be understood by those skilled in the art that various modifications are possible in combinations of their components and processing processes, and that such modifications are also within the scope of the present disclosure.

[0100] As described above, isolation of silicon-based negative electrode active material leads to a rapid progression of capacity degradation from a certain degradation state. In the embodiment described above, the degradation in which capacity degradation progresses rapidly from a certain degradation state was represented by creating an isolation map. In this respect, electrolyte depletion is also a factor that causes rapid progression of capacity degradation from a certain degradation state. For example, an electrolyte degradation map may be created in advance with input parameters of elapsed time (h), cumulative charge amount (Ah), or cumulative discharge amount (Ah), and output of electrolyte degradation amount (%), and included in the degradation map for estimating cell degradation. When the degradation estimation unit 112 calculates the SOH of the cell, it also adds the electrolyte degradation amount.

[0101] Furthermore, uneven reaction can also be a factor that causes rapid capacity degradation from a certain degradation state. Similarly, a degradation map of uneven reaction can be created in advance and included in the degradation map used for estimating cell degradation.

[0102] When a resistive layer forms on the positive electrode, lithium ions may not be able to return to the positive electrode at the previous voltage, resulting in a state where charging becomes impossible without increasing the voltage. In this case, the cell may appear to be degraded. A resistive layer performance degradation map, which illustrates the performance degradation caused by the formation of this resistive layer, may be created in advance.

[0103] The functions of the battery analysis system 10 described above may also be implemented in the control unit 25 of the electric vehicle 20 or the control unit 32 of the charging station 30.

[0104] In the above embodiment, the electric vehicle 20 is assumed to be a four-wheeled electric vehicle. However, it may also be an electric motorcycle (electric scooter), electric bicycle, or electric kick scooter. Furthermore, electric vehicles include not only full-size electric vehicles but also low-speed electric vehicles such as golf carts and land cars. In addition, the devices on which the battery pack system 21 is installed are not limited to electric vehicles 20. Devices on which the battery pack system 21 is installed include electric ships, railway vehicles, electric mobile devices such as multicopters (drones), stationary energy storage systems, and consumer electronic devices (smartphones, notebook PCs, etc.).

[0105] The embodiments may be specified by the following items.

[0106] [Item 1] A battery analysis system (10) comprising: a data acquisition unit (111) that acquires time-series battery data including voltage, current, and temperature of a secondary battery (E1); and a degradation estimation unit (112) that estimates the amount of degradation of the secondary battery (E1) for each degradation factor by referring to a plurality of degradation maps that have been generated in advance for each degradation factor of the secondary battery (E1), and sums up the amounts of degradation for each degradation factor to estimate the amount of degradation of the secondary battery (E1), wherein the plurality of degradation maps include: a first degradation map that represents capacity degradation due to the growth of a film formed on the negative electrode surface; a second degradation map that represents capacity degradation due to physical changes in the active material accompanying the expansion and contraction of the active material due to charging and discharging; and a third degradation map that represents capacity degradation due to a third factor that causes capacity degradation to progress rapidly from a certain degradation state. According to this, the degradation of the secondary battery (E1) can be estimated with high accuracy. [Item 2] The secondary battery (E1) has a mixed negative electrode, a degradation map is generated for each active material forming the mixed negative electrode, the degradation estimation unit (112) estimates the degradation amount of the first negative electrode active material forming the mixed negative electrode by referring to the first degradation map, and estimates the degradation amount of the second negative electrode active material forming the mixed negative electrode by referring to the second degradation map of the second negative electrode active material and the third degradation map which represents the capacity degradation due to electrical isolation caused by the expansion and contraction of the second negative electrode active material, as described in Item 1 (10). According to this, the degradation of the secondary battery (E1) having a mixed negative electrode can be estimated with high accuracy. [Item 3] The third degradation map which represents the capacity degradation due to electrical isolation caused by the expansion and contraction of the second negative electrode active material is a map which defines the relationship between the cumulative degradation progress based on the cumulative degradation amount due to the expansion and contraction of the second negative electrode active material caused by the current flowing through the second negative electrode active material and the capacity degradation amount of the second negative electrode active material, as described in Item 2 (10). According to this, the degradation of the second negative electrode active material due to its electrical isolation can be represented with high accuracy.[Item 4] The third degradation map, which represents the capacity degradation due to electrical isolation caused by the expansion and contraction of the second negative electrode active material, is a map that defines the relationship between the cumulative volume change due to the expansion and contraction of the second negative electrode active material caused by the current flowing through the second negative electrode active material and the amount of capacity degradation of the second negative electrode active material, as described in item 2 (10). According to this, the degradation of the second negative electrode active material due to electrical isolation of the second negative electrode active material can be represented with high accuracy. [Item 5] The second degradation map of the second negative electrode active material is a map that defines the relationship between the combination of current flowing through the second negative electrode active material, temperature, and SOC (State Of Charge) band and the degree of capacity degradation of the second negative electrode active material, as described in item 2 (10). According to this, the degradation of the second negative electrode active material due to wear of the second negative electrode active material can be represented with high accuracy. [Item 6] The battery analysis system (10) according to any one of items 3 to 5, wherein the degradation estimation unit (112) estimates the current flowing through the second negative electrode active material by referring to a current ratio map that defines the relationship between the combination of the current flowing through the secondary battery (E1), temperature, and SOC band, and the ratio of the current flowing through the second negative electrode active material. According to this, by considering the ratio of the current flowing through the first negative electrode active material and the current flowing through the second negative electrode active material, the degradation of the first negative electrode active material and the degradation of the second negative electrode active material can be estimated with higher accuracy. [Item 7] The battery analysis system (10) according to item 6, wherein the current ratio map is updated so that the current flowing through the second negative electrode active material decreases as the degradation of the second negative electrode active material progresses. According to this, the change in the ratio of the current flowing through the first negative electrode active material and the current flowing through the second negative electrode active material due to the degradation of the second negative electrode active material can be accurately reflected.[Item 8] The degradation estimation unit (112) estimates the current flowing through the second negative electrode active material based on a composite parallel circuit model of a first equivalent circuit model for the first negative electrode active material, which includes a first OCV (Open Circuit Voltage) estimate, a first DC resistance component, and a first polarization voltage as parameters, and a second equivalent circuit model for the second negative electrode active material, which includes a second OCV estimate, a second DC resistance component, and a second polarization voltage as parameters, according to any one of items 3 to 5. [Item 9] A battery analysis system (10) as described in Item 8, which calculates the first OCV estimate so that the current flowing through the first negative electrode active material decreases in accordance with the progression of deterioration of the first negative electrode active material, and calculates the second OCV estimate so that the current flowing through the second negative electrode active material decreases in accordance with the progression of deterioration of the second negative electrode active material. This system makes it possible to accurately reflect the changes in the current flowing through the first negative electrode active material and the current flowing through the second negative electrode active material due to the deterioration of the first or second negative electrode active material. [Item 10] The battery analysis system (10) described in Item 2, wherein the degradation estimation unit (112) sets upper limits on the cumulative degradation amount of the positive electrode active material, the first negative electrode active material, and the second negative electrode active material, based on the ratio of the full charge capacity of the positive electrode active material, the full charge capacity of the first negative electrode active material, and the full charge capacity of the second negative electrode active material to the full charge capacity of the secondary battery (E1). This prevents the degradation amount of each material from being overestimated.[Item 11] A battery analysis method comprising the steps of: acquiring time-series battery data including voltage, current, and temperature of a secondary battery (E1); estimating the amount of degradation of the secondary battery (E1) for each degradation factor by referring to a plurality of degradation maps that have been pre-generated for each degradation factor of the secondary battery (E1); and estimating the amount of degradation of the secondary battery (E1) by summing the amounts of degradation for each degradation factor, wherein the plurality of degradation maps include: a first degradation map that represents capacity degradation due to the growth of a film formed on the negative electrode surface; a second degradation map that represents capacity degradation due to physical changes associated with the expansion and contraction of the active material due to charging and discharging; and a third degradation map that represents capacity degradation due to a third factor that causes capacity degradation to progress rapidly from a certain degradation state. According to this method, the degradation of a secondary battery (E1) can be estimated with high accuracy. [Item 12] A battery analysis program that causes a computer to perform the following steps: acquire time-series battery data including voltage, current, and temperature of a secondary battery (E1); estimate the amount of degradation of the secondary battery (E1) for each degradation factor by referring to a plurality of degradation maps that have been pre-generated for each degradation factor of the secondary battery (E1); and estimate the amount of degradation of the secondary battery (E1) by summing the amounts of degradation for each degradation factor, wherein the plurality of degradation maps include: a first degradation map that represents capacity degradation due to the growth of a film formed on the negative electrode surface; a second degradation map that represents capacity degradation due to physical changes associated with the expansion and contraction of the active material due to charging and discharging; and a third degradation map that represents capacity degradation due to a third factor that causes capacity degradation to progress rapidly from a certain degradation state. According to this, the degradation of the secondary battery (E1) can be estimated with high accuracy.

[0107] This disclosure can be used to estimate the degradation of secondary batteries.

[0108] 2 Commercial power grid, 5 Network, 10 Battery analysis system, 11 Control unit, 12 Memory unit, 13 Communication unit, 20 Electric vehicle, 21 Battery system, 22 Voltage sensor, 23 Current sensor, 24 Temperature sensor, 25 Control unit, 26 Communication unit, 30 Charging station, 31 Power supply unit, 32 Control unit, 33 Communication unit, 111 Data acquisition unit, 112 Degradation estimation unit, 113 Degradation progression prediction unit, 121 Battery data storage unit, 122 Map storage unit.

Claims

1. A battery analysis system comprising: a data acquisition unit that acquires time-series battery data including voltage, current, and temperature of a secondary battery; and a degradation estimation unit that estimates the amount of degradation for each degradation factor of the secondary battery by referring to a plurality of degradation maps that have been pre-generated for each degradation factor of the secondary battery, and estimates the amount of degradation of the secondary battery by summing the amounts of degradation for each degradation factor, wherein the plurality of degradation maps include: a first degradation map that represents capacity degradation due to the growth of a film formed on the negative electrode surface; a second degradation map that represents capacity degradation due to physical changes in the active material accompanying the expansion and contraction of the active material due to charging and discharging; and a third degradation map that represents capacity degradation due to a third factor that causes capacity degradation to progress rapidly from a certain degradation state.

2. The battery analysis system according to claim 1, wherein the secondary battery has a mixed negative electrode, a degradation map is generated for each active material forming the mixed negative electrode, the degradation estimation unit estimates the amount of degradation of the first negative electrode active material forming the mixed negative electrode by referring to the first degradation map, and estimates the amount of degradation of the second negative electrode active material forming the mixed negative electrode by referring to the second degradation map of the second negative electrode active material and the third degradation map which represents the capacity degradation due to electrical isolation caused by the expansion and contraction of the second negative electrode active material.

3. The battery analysis system according to claim 2, wherein the third degradation map, which represents the capacity degradation due to electrical isolation caused by the expansion and contraction of the second negative electrode active material, is a map that defines the relationship between the cumulative degradation progress based on the cumulative degradation amount due to the expansion and contraction of the second negative electrode active material caused by the current flowing through the second negative electrode active material and the amount of capacity degradation of the second negative electrode active material.

4. The battery analysis system according to claim 2, wherein the third degradation map, which represents the capacity degradation due to electrical isolation caused by the expansion and contraction of the second negative electrode active material, is a map that defines the relationship between the cumulative volume change due to the expansion and contraction of the second negative electrode active material caused by the current flowing through the second negative electrode active material and the amount of capacity degradation of the second negative electrode active material.

5. The battery analysis system according to claim 2, wherein the second degradation map of the second negative electrode active material is a map that defines the relationship between the combination of current, temperature, and SOC (State of Charge) band flowing through the second negative electrode active material and the degree of capacity degradation of the second negative electrode active material.

6. The battery analysis system according to any one of claims 3 to 5, wherein the degradation estimation unit estimates the current flowing through the second negative electrode active material by referring to a current ratio map that defines the relationship between the combination of the current flowing through the secondary battery, the temperature, and the SOC band, and the ratio of the current flowing through the second negative electrode active material.

7. The battery analysis system according to claim 6, wherein the current ratio map is updated so that the current flowing through the second negative electrode active material decreases as the degradation of the second negative electrode active material progresses.

8. The battery analysis system according to any one of claims 3 to 5, wherein the degradation estimation unit estimates the current flowing through the second negative electrode active material based on a composite parallel circuit model of a first equivalent circuit model for a first negative electrode active material that includes a first OCV (Open Circuit Voltage) estimate, a first DC resistance component, and a first polarization voltage as parameters, and a second equivalent circuit model for a second negative electrode active material that includes a second OCV estimate, a second DC resistance component, and a second polarization voltage as parameters.

9. The battery analysis system according to claim 8, comprising: calculating the first OCV estimate value such that the current flowing through the first negative electrode active material decreases in accordance with the progression of deterioration of the first negative electrode active material; and calculating the second OCV estimate value such that the current flowing through the second negative electrode active material decreases in accordance with the progression of deterioration of the second negative electrode active material.

10. The battery analysis system according to claim 2, wherein the degradation estimation unit sets upper limits on the cumulative degradation amount of the positive electrode active material, the cumulative degradation amount of the first negative electrode active material, and the cumulative degradation amount of the second negative electrode active material based on the ratio of the full charge capacity of the positive electrode active material, the full charge capacity of the first negative electrode active material, and the full charge capacity of the second negative electrode active material to the full charge capacity of the secondary battery.

11. A battery analysis method comprising the steps of: acquiring time-series battery data including voltage, current, and temperature of a secondary battery; estimating the amount of degradation for each degradation factor of the secondary battery by referring to a plurality of degradation maps pre-generated for each degradation factor of the secondary battery, and estimating the amount of degradation of the secondary battery by summing the amounts of degradation for each degradation factor, wherein the plurality of degradation maps include: a first degradation map representing capacity degradation due to the growth of a film formed on the negative electrode surface; a second degradation map representing capacity degradation due to physical changes in the active material accompanying the expansion and contraction of the active material due to charging and discharging; and a third degradation map representing capacity degradation due to a third factor that causes capacity degradation to progress rapidly from a certain degradation state.

12. A battery analysis program that causes a computer to perform the following steps: acquire time-series battery data including voltage, current, and temperature of a secondary battery; estimate the amount of degradation for each degradation factor of the secondary battery by referring to a plurality of degradation maps that have been pre-generated for each degradation factor of the secondary battery, and estimate the amount of degradation of the secondary battery by summing the amounts of degradation for each degradation factor, wherein the plurality of degradation maps include: a first degradation map that represents capacity degradation due to the growth of a film formed on the negative electrode surface; a second degradation map that represents capacity degradation due to physical changes in the active material accompanying the expansion and contraction of the active material due to charging and discharging; and a third degradation map that represents capacity degradation due to a third factor that causes capacity degradation to progress rapidly from a certain degradation state.

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