BATTERY MANAGEMENT DEVICE, COMPUTING SYSTEM, BATTERY DEGRADATION PREDICTION METHOD, AND BATTERY DEGRADATION PREDICTION PROGRAM

Abstract

A state-of-health (SOH) estimation unit (4631) estimates the SOH of a battery (E1, 41) on the basis of measurement data of the battery (E1, 41). A degradation regression curve generation unit (4614) generates a degradation regression curve of the battery (E1, 41) by performing curve regression on a plurality of time-sequentially identified SOHs of the battery (E1, 41). A rapid degradation determination unit (4615) determines whether rapid degradation of the battery (E1, 41) has occurred, on the basis of the difference or the ratio between a degradation coefficient of the degradation regression curve of the battery (E1, 41) generated on the basis of a plurality of SOHs in a first data section and a degradation coefficient of the degradation regression curve of the battery (E1, 41) generated on the basis of a plurality of SOHs in a second data section.

Description

[Technical field]

[0001] The present disclosure relates to a battery management device, a computing system, a battery degradation prediction method, and a battery degradation prediction program for predicting battery degradation. [Background technology]

[0002] In recent years, hybrid vehicles (HVs), plug-in hybrid vehicles (PHVs), and electric vehicles (EVs) have become popular. These electric vehicles are equipped with secondary batteries, such as lithium-ion batteries, as key devices.

[0003] When a secondary battery such as a lithium-ion battery is repeatedly charged and discharged at low temperatures, it is likely to experience a rapid deterioration in capacity (hereinafter referred to as rapid deterioration or tertiary deterioration). In addition, when a secondary battery is repeatedly charged and discharged at a high rate, rapid deterioration in capacity is also likely to occur. Rapid deterioration in capacity occurs due to a decrease in electrolyte, a decrease in the electrode plate reaction area, etc. After rapid deterioration in capacity, input / output performance drops significantly. In addition, rapid deterioration makes it more likely that lithium dissolved as ions will precipitate as metal. If metallic lithium precipitates, it may penetrate the separator and short-circuit the positive and negative electrodes. After rapid deterioration in capacity, the stability and safety of the secondary battery are reduced, so the use of the secondary battery is basically terminated.

[0004] As a method for detecting rapid deterioration of a secondary battery, a method has been proposed in which the full charge capacity (FCC: Full Charge Capacity) or capacity maintenance rate (SOH: State Of Health) of the secondary battery and the elapsed time of use are input, and it is determined that rapid deterioration has occurred if the change in the slope of the straight line obtained by linear regression of the change in the full charge capacity or capacity maintenance rate against the elapsed time exceeds a threshold value (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] WO 17 / 098686 Summary of the Invention

[0006] The values ​​of FCC, SOC (State Of Charge), and SOH calculated based on the measurement data of an electric vehicle in motion are affected by sensor measurement errors and noise. When the effects of errors and noise are large, the method of linearly regressing the amount of change in FCC or SOC described above is likely to result in erroneous judgment.

[0007] The present disclosure has been made in consideration of these circumstances, and has an object to provide a technique for detecting rapid deterioration of a battery with high accuracy.

[0008] In order to solve the above problems, a battery management device of one embodiment of the present disclosure includes a measurement unit that measures at least the voltage and current of a battery, a SOH estimation unit that estimates the SOH of the battery based on the measurement data of the battery, a deterioration regression curve generation unit that performs curve regression of multiple SOHs specified in a time series of the battery to generate a deterioration regression curve of the battery, and a rapid deterioration determination unit that determines whether or not rapid deterioration is occurring in the battery based on the difference or ratio between the deterioration coefficients of the deterioration regression curve of the battery generated based on the multiple SOHs in a first data interval and the deterioration coefficients of the deterioration regression curve of the battery generated based on the multiple SOHs in a second data interval.

[0009] Any combination of the above components and conversion of the present disclosure into a method, device, system, computer program, etc. are also valid aspects of the present disclosure.

[0010] According to the present disclosure, rapid deterioration of a battery can be detected with high accuracy. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram for explaining a computing system used by a business operator according to an embodiment. [Diagram 2]FIG. 2 is a diagram for explaining a detailed configuration of a battery system mounted on an electric vehicle according to an embodiment. [Diagram 3] FIG. 2 is a diagram illustrating a configuration example of a battery control unit according to the first embodiment. [Figure 4] FIG. 1 is a diagram for explaining a method for estimating an FCC. [Diagram 5] FIG. 1 is a graph showing a deterioration curve of a secondary battery. [Figure 6] FIG. 11 is a graph showing an example of a deterioration curve when rapid deterioration occurs in a secondary battery. [Figure 7] FIG. 13 is a graph showing specific examples of a plurality of deterioration curves having different data intervals. [Figure 8] FIG. 11 is a diagram showing a specific example of a first division method for a data section. [Figure 9] FIG. 11 is a diagram showing a specific example of a second division method for a data section. [Figure 10] 10 is a flowchart showing the flow of rapid deterioration determination processing of a battery module by a battery management unit. [Figure 11] FIG. 11 is a diagram illustrating a configuration example of a calculation system according to a second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Fig. 1 is a diagram for explaining a computing system 1 used by a business operator according to an embodiment. The business operator owns a plurality of electric vehicles 3 and runs a business using the plurality of electric vehicles 3. For example, the business operator runs a delivery business (home delivery business), a taxi business, a rental car business, or a car sharing business using the plurality of electric vehicles 3. In this embodiment, the electric vehicle 3 is assumed to be a pure EV without an engine.

[0013] The computing system 1 is a system for managing the business of a business operator. The computing system 1 is composed of one or more information processing devices (e.g., a server, a PC). Some or all of the information processing devices constituting the computing system 1 may be present in a data center. For example, the computing system 1 may be composed of a combination of a server in the data center (a company server, a cloud server, or a rental server) and a client PC in the business operator.

[0014] The multiple electric vehicles 3 are parked in a parking lot or garage of the business office of the business operator while on standby. The multiple electric vehicles 3 have a wireless communication function and are capable of wireless communication with the calculation system 1. The multiple electric vehicles 3 transmit driving data including operation data of the secondary batteries mounted thereon to the calculation system 1. The electric vehicles 3 may wirelessly transmit the driving data to a server constituting the calculation system 1 via a network while driving. For example, the driving data may be transmitted each time at a frequency of once every 10 seconds. Also, one day's worth of driving data may be transmitted in batches at a predetermined timing once a day (for example, at the end of business hours).

[0015] Furthermore, when the calculation system 1 is configured with a company's own server or PC installed at a business office, the electric vehicle 3 may transmit one day's worth of driving data to the company's own server or PC after returning to the business office after closing. In this case, the data may be transmitted to the company's own server or PC wirelessly, or may be transmitted via a wired connection to the company's own server or PC. The data may also be transmitted to the company's own server or PC via a recording medium on which the driving data is recorded. Furthermore, when the calculation system 1 is configured with a combination of a cloud server and a client PC in the business office, the electric vehicle 3 may transmit the driving data to the cloud server via the client PC in the business office.

[0016] 2 is a diagram for explaining a detailed configuration of a battery system 40 mounted on an electric vehicle 3 according to an embodiment. The battery system 40 is connected to the motor 34 via a first relay RY1 and an inverter 35. During power running, the inverter 35 converts DC power supplied from the battery system 40 into AC power and supplies it to the motor 34. During regeneration, the inverter 35 converts AC power supplied from the motor 34 into DC power and supplies it to the battery system 40. The motor 34 is a three-phase AC motor, and during power running, it rotates according to the AC power supplied from the inverter 35. During regeneration, the rotational energy generated by deceleration is converted into AC power and supplied to the inverter 35.

[0017] The first relay RY1 is a contactor inserted between the wiring connecting the battery system 40 and the inverter 35. When the vehicle is running, the vehicle control unit 30 controls the first relay RY1 to the on state (closed state) to electrically connect the battery system 40 and the power system of the electric vehicle 3. When the vehicle is not running, the vehicle control unit 30 controls the first relay RY1 to the off state (open state) as a general rule to electrically cut off the battery system 40 and the power system of the electric vehicle 3. Note that instead of a relay, another type of switch, such as a semiconductor switch, may be used.

[0018] The battery system 40 can be charged from a commercial power grid 9 by connecting to a charger 4 installed outside the electric vehicle 3 via a charging cable 38. The charger 4 is connected to the commercial power grid 9 and charges the battery system 40 in the electric vehicle 3 via the charging cable 38. In the electric vehicle 3, a second relay RY2 is inserted between the wiring connecting the battery system 40 and the charger 4. Note that instead of a relay, other types of switches such as a semiconductor switch may be used. The battery management unit 42 of the battery system 40 controls the second relay RY2 to an on state (closed state) before charging begins, and to an off state (open state) after charging ends.

[0019] Generally, normal charging is performed with AC, and rapid charging is performed with DC. When charging with AC, AC power is converted to DC power by an AC / DC converter (not shown) inserted between the second relay RY2 and the battery system 40.

[0020] The battery system 40 includes a battery module 41 and a battery management unit 42, and the battery module 41 includes a plurality of cells E1-En connected in series. The battery module 41 may be configured by connecting a plurality of battery modules in series / series-parallel. The cells may be lithium-ion battery cells, nickel-metal hydride battery cells, lead battery cells, etc. In the following description, an example in which lithium-ion battery cells (nominal voltage: 3.6-3.7V) are used is assumed. The number of cells E1-En connected in series is determined according to the drive voltage of the motor 34.

[0021] A shunt resistor Rs is connected in series with the cells E1-En. The shunt resistor Rs functions as a current detection element. A Hall element may be used instead of the shunt resistor Rs. A plurality of temperature sensors T1, T2 are installed in the battery module 41 to detect the temperatures of the cells E1-En. One temperature sensor may be installed in the battery module, or one may be installed for each of the cells. The temperature sensors T1, T2 may be, for example, a thermistor.

[0022] The battery management unit 42 includes a voltage measurement unit 43, a temperature measurement unit 44, a current measurement unit 45, and a battery control unit 46. A plurality of voltage lines connect each node of the plurality of serially connected cells E1-En to the voltage measurement unit 43. The voltage measurement unit 43 measures the voltage of each cell E1-En by measuring the voltage between each of two adjacent voltage lines. The voltage measurement unit 43 transmits the measured voltage of each cell E1-En to the battery control unit 46.

[0023] Since the voltage measurement unit 43 has a high voltage relative to the battery control unit 46, the voltage measurement unit 43 and the battery control unit 46 are connected in an insulated state by a communication line. The voltage measurement unit 43 can be configured with an ASIC (Application Specific Integrated Circuit) or a general-purpose analog front-end IC. The voltage measurement unit 43 includes a multiplexer and an A / D converter. The multiplexer outputs the voltage between two adjacent voltage lines to the A / D converter in order from top to bottom. The A / D converter converts the analog voltage input from the multiplexer into a digital value.

[0024] The temperature measurement unit 44 includes a voltage dividing resistor and an A / D converter. The A / D converter sequentially converts a plurality of analog voltages divided by the plurality of temperature sensors T1, T2 and the plurality of voltage dividing resistors into digital values ​​and outputs the digital values ​​to the battery control unit 46. The battery control unit 46 estimates the temperatures of the plurality of cells E1-En based on the digital values. For example, the battery control unit 46 estimates the temperature of each cell E1-En based on a value measured by a temperature sensor closest to each cell E1-En.

[0025] The current measurement unit 45 includes a differential amplifier and an A / D converter. The differential amplifier amplifies the voltage across the shunt resistor Rs and outputs it to the A / D converter. The A / D converter converts the voltage input from the differential amplifier into a digital value and outputs it to the battery control unit 46. The battery control unit 46 estimates the current flowing through the multiple cells E1-En based on the digital value.

[0026] In addition, if an A / D converter is installed in the battery control unit 46 and an analog input port is provided in the battery control unit 46, the temperature measurement unit 44 and the current measurement unit 45 may output analog voltages to the battery control unit 46, which may be converted into digital values ​​by the A / D converter in the battery control unit 46.

[0027] The battery control unit 46 manages the states of the cells E1-En based on the voltages, temperatures, and currents of the cells E1-En measured by the voltage measurement unit 43, the temperature measurement unit 44, and the current measurement unit 45. The battery control unit 46 and the vehicle control unit 30 are connected via an in-vehicle network. For example, a CAN (Controller Area Network) or a LIN (Local Interconnect Network) can be used as the in-vehicle network.

[0028] 3 is a diagram illustrating a configuration example of the battery control unit 46 according to the first embodiment. The battery control unit 46 includes a processing unit 461 and a storage unit 462. The processing unit 461 includes a SOC estimation unit 4611, an FCC estimation unit 4612, an SOH estimation unit 4613, a degradation regression curve generation unit 4614, a rapid degradation determination unit 4615, and a data transmission unit 4616. The functions of the processing unit 461 can be realized by a combination of hardware resources and software resources, or by hardware resources alone. As the hardware resources, a CPU, a ROM, a RAM, an ASIC, an FPGA (Field Programmable Gate Array), and other LSIs can be used. As the software resources, a program such as firmware can be used.

[0029] The storage unit 462 includes a SOC-OCV (Open Circuit Voltage) characteristic storage unit 4621, a battery data storage unit 4622, and a time-series SOH value storage unit 4623. The storage unit 462 includes a non-volatile recording medium such as an EEPROM (Electrically Erasable Programmable Read-Only Memory) or a NAND flash memory, and stores various programs and data.

[0030] Characteristic data of the SOC-OCV curves of multiple cells E1-En is described in the SOC-OCV characteristic storage unit 4621. The SOC-OCV curves of multiple cells E1-En are created in advance by the battery manufacturer and registered in the SOC-OCV characteristic storage unit 4621 at the time of shipment. The battery manufacturer performs various tests to derive the SOC-OCV curves of cells E1-En.

[0031] The battery data holding unit 4622 records battery data including the voltages, currents, and temperatures of the cells E1-En in chronological order. The battery data may further include the SOC estimated by the SOC estimating unit 4611.

[0032] Time-series SOH value holding unit 4623 holds time-series data of SOH estimated by SOH estimation unit 4613. The time-series data of SOH is recorded, for example, once a day, once every few days, or once a week. Note that time-series SOH value holding unit 4623 and battery data holding unit 4622 may be integrated into one table.

[0033] The SOC estimation unit 4611 estimates the SOC of each of the multiple cells E1-En. The SOC estimation unit 4611 estimates the SOC using the OCV method, the current integration method, or a combination of both. The OCV method is a method of estimating the SOC based on the OCV of each cell E1-En measured by the voltage measurement unit 43 and the characteristic data of the SOC-OCV curve held in the SOC estimation unit 4611. The current integration method is a method of estimating the SOC based on the OCV at the start of charging and discharging of each cell E1-En and the integrated value of the current measured by the current measurement unit 45. In the current integration method, the measurement error of the current measurement unit 45 accumulates as the charging and discharging time becomes longer. Therefore, it is preferable to correct the SOC estimated by the current integration method using the SOC estimated by the OCV method.

[0034] The FCC estimation unit 4612 can estimate the FCC of the cell based on the characteristic data of the SOC-OCV curve held in the SOC estimation unit 4611 and the OCVs at two points of the cell measured by the voltage measurement unit 43.

[0035] Fig. 4 is a diagram for explaining a method of estimating FCC. The FCC estimation unit 4612 acquires the OCV of two points of the cell. The FCC estimation unit 4612 refers to the SOC-OCV curve, identifies two SOC points corresponding to the two voltage points, and calculates the difference ΔSOC between the SOC points. In the example shown in Fig. 4, the SOC at the two points is 20% and 75%, and ΔSOC is 55%.

[0036] The FCC estimation unit 4612 calculates the current integration amount (= charge / discharge capacity) Q for the period between the two points in time when the two OCVs were acquired, based on the transition of the current measured by the current measurement unit 45. The FCC estimation unit 4612 can estimate the FCC by calculating the following (Equation 1).

[0037] FCC = Q / ΔSOC (Equation 1) The SOH estimation unit 4613 estimates the SOH based on the estimated FCC. The SOH is defined as the ratio of the current FCC to the initial FCC, and the lower the value (the closer to 0%), the more the deterioration progresses. The SOH estimation unit 4613 can estimate the SOH by calculating the following (Equation 2).

[0038] SOH = current FCC / initial FCC (Equation 2) The SOH may be determined by measuring the capacity by complete charging and discharging, or by adding up the storage degradation and cycle degradation. The storage degradation can be estimated based on the SOC, temperature, and storage degradation rate. The cycle degradation can be estimated based on the SOC range, temperature, current rate, and cycle degradation rate used. The storage degradation rate and cycle degradation rate can be derived in advance by experiments or simulations. The SOC, temperature, SOC range, and current rate can be determined by measurement.

[0039] SOH can also be estimated based on its correlation with the cell's internal resistance. The internal resistance can be estimated by dividing the voltage drop that occurs when a certain current flows through the cell for a certain time by the current value. The internal resistance decreases as the temperature increases, and increases as the SOH decreases.

[0040] The SOH estimation unit 4613 stores the estimated SOH in the time-series SOH value holding unit 4623. The SOH estimation unit 4613 estimates the SOH once a day, once every few days, or once a week, for example, and stores the estimated SOH in the time-series SOH value holding unit 4623.

[0041] The deterioration regression curve generating unit 113 performs curve regression of a plurality of SOHs specified in a time series of the battery module 41 to generate a deterioration regression curve of the battery module 41. For example, the least squares method can be used for the curve regression.

[0042] Fig. 5 is a graph showing the deterioration curve of a secondary battery. It is known that the deterioration of a secondary battery progresses in proportion to the square root of time (0.5 power law), as shown in the following (Equation 3).

[0043] SOH=w0+w1√t (Equation 3) w0 is the initial value and w1 is the degradation coefficient.

[0044] The deterioration regression curve generating unit 4614 obtains the deterioration coefficient w1 of the above (Equation 3) by exponential curve regression of 0.5 power with time t as an independent variable and SOH as a dependent variable. w0 is common and is usually set in the range of 1.0 to 1.1. When the actual initial capacity and the nominal value match, w0 is set to 1.0, and when the nominal value is set to the minimum guaranteed amount and is set lower than the actual initial capacity, a value greater than 1.0 is set.

[0045] FIG. 6 is a graph showing an example of a deterioration curve when rapid deterioration occurs in a secondary battery. FIG. 6 shows an example where rapid deterioration occurs at point P1. As described above, rapid deterioration is likely to occur when a method of use that places a heavy burden on a secondary battery, such as charging and discharging in a low or high temperature environment or charging and discharging at a high rate, is repeated. When rapid deterioration occurs, the secondary battery basically becomes unusable, and the life of the secondary battery is shortened. The main cause of rapid deterioration is a decrease in electrolyte, but in order to directly measure the amount of electrolyte, it is necessary to disassemble the secondary battery. It is not practical to disassemble each cell E1-En during use of the battery module 41, and a method of determining rapid deterioration without disassembling each cell E1-En is required. In contrast, in this embodiment, the occurrence of rapid deterioration is detected by detecting that the SOH of the battery module 41 deviates significantly from the deterioration curve.

[0046] The rapid deterioration determination unit 4615 determines whether rapid deterioration has occurred in the battery module 41 based on the difference or ratio between the deterioration coefficient w1 of the deterioration regression curve of the battery module 41 generated based on a plurality of SOHs in the first data section and the deterioration coefficient w1 of the deterioration regression curve of the battery module 41 generated based on a plurality of SOHs in the second data section. The rapid deterioration determination unit 4615 determines that rapid deterioration has occurred in the battery module 41 when the difference or ratio deviates from a predetermined range. That is, the rapid deterioration determination unit 4615 determines that rapid deterioration has occurred when the absolute value of the difference or ratio exceeds a threshold value, and determines that rapid deterioration has not occurred when the absolute value is equal to or less than the threshold value. A value derived by an experiment or a simulation can be used as the threshold value. The rapid deterioration determination may be performed on a cell-by-cell basis.

[0047] FIG. 7 is a graph showing specific examples of a plurality of deterioration curves with different data intervals. The w0 of the deterioration regression curve (SOH=w0+w1√t) shown in FIG. 7 is set to 1.05. This indicates that the actual initial capacity of the battery module 41 is greater than the nominal value. In the example shown in FIG. 7, a deterioration curve based on the SOH of the past 100 points, a deterioration curve based on the SOH of the past 200 points, a deterioration curve based on the SOH of the past 300 points, and a deterioration curve based on the SOH of all points are superimposed and drawn. The deterioration curve based on the past 200 points, the deterioration curve based on the past 300 points, and the deterioration curve based on all points are almost the same, and the deterioration coefficient w1 of each deterioration curve is also almost the same value. In contrast, the deterioration coefficient w1 of the deterioration curve based on the past 100 points is smaller than the deterioration coefficient w1 of the other three deterioration curves.

[0048] In the example shown in Figure 7, it can be seen that the SOH in the region R1 enclosed by the dotted line circle is significantly lower than the SOH in the previous region. Therefore, it can be estimated that sudden deterioration has occurred at some point in region R1. If the above threshold is set to a value corresponding to the difference in region R1 between the deterioration coefficient w1 of the deterioration curve based on the past 100 points and the deterioration coefficient w1 of the deterioration curve based on the past 200 points, region R1 can be detected by comparing the deterioration coefficients w1 of both.

[0049] FIG. 8 is a diagram showing a specific example of a first division method of a data section. In the first division method, the end points of a plurality of data sections are made common, and the number of data items going back to the past is changed. For example, the first data section is set to a section including a number of SOHs from the last identified SOH. The second data section is set to a section including b (b>a) number of SOHs from the last identified SOH. The third data section is set to a section including c (c>b>a) number of SOHs from the last identified SOH. In the example shown in FIG. 8, a=100, b=200, and c=300.

[0050] FIG. 9 is a diagram showing a specific example of a second division method of a data section. The second division method is a division method in which the number of data sections is common and the data sections are sequentially traced back to the past. The first data section is set to a section including a number of SOHs from the last identified SOH. The second data section is set to a section including a number of SOHs from the last identified SOH, excluding the SOHs included in the first data section. The third data section is set to a section including a number of SOHs from the last identified SOH, excluding the SOHs included in the first data section and the second data section. In the example shown in FIG. 9, a=100.

[0051] Return to FIG. 3. The data transmission unit 4616 of the battery control unit 46 notifies the vehicle control unit 30 of the voltage, current, temperature, SOC, FCC, and SOH of the multiple cells E1-En via the in-vehicle network. The vehicle control unit 30 generates driving data including battery data and vehicle data. The battery data includes the voltage, current, and temperature of the multiple cells E1-En. Note that some battery systems 40 can include SOC in addition to voltage, current, and temperature in the battery data. Furthermore, some battery systems can include at least one of FCC and SOH in addition to voltage, current, temperature, and SOC. The vehicle data can include average speed, mileage, driving route, etc.

[0052] When the rapid deterioration determination unit 4615 detects rapid deterioration of the battery module 41, the data transmission unit 4616 notifies the vehicle control unit 30 of a rapid deterioration detection signal via the in-vehicle network. When the vehicle control unit 30 receives the rapid deterioration detection signal of the battery module 41, it turns on a warning lamp indicating an abnormality in the battery module 41 in a meter panel provided at the driver's seat, thereby notifying the driver of the abnormality in the battery module 41. The vehicle control unit 30 may also notify the driver of the abnormality in the battery module 41 by outputting a synthesized voice.

[0053] The wireless communication unit 36 ​​performs signal processing for wirelessly connecting to a network via the antenna 36a. In the present embodiment, the wireless communication unit 36 ​​wirelessly transmits the driving data acquired from the vehicle control unit 30 to the calculation system 1. The wireless communication unit 36 ​​also wirelessly transmits the rapid deterioration detection signal of the battery module 41 acquired from the vehicle control unit 30 to the calculation system 1. Examples of wireless communication networks to which the electric vehicle 3 can be wirelessly connected include a mobile phone network (cellular network), a wireless LAN, an Electronic Toll Collection System (ETC), a Dedicated Short Range Communications (DSRC), a Vehicle-to-Infrastructure (V2I), and a Vehicle-to-Vehicle (V2V).

[0054] 10 is a flowchart showing the flow of rapid deterioration determination processing of the battery module 41 by the battery management unit 42. The SOH estimation unit 4613 estimates the SOH of the battery module 41 based on the measurement data of the battery module 41 (S10).

[0055] At the timing for determining whether or not the battery module 41 has rapidly deteriorated, the deterioration regression curve generating unit 4614 performs curve regression of the multiple SOHs in the first data interval of the battery module 41 to generate a first deterioration regression curve of the battery module 41 (S11). At the same time, the deterioration regression curve generating unit 4614 performs curve regression of the multiple SOHs in the second data interval of the battery module 41 to generate a second deterioration regression curve of the battery module 41 (S12).

[0056] The rapid deterioration determination unit 4615 calculates the difference between the deterioration coefficient w1 of the first deterioration regression curve and the deterioration coefficient w1 of the second deterioration regression curve (S13). When the absolute value of the difference is equal to or less than a threshold value (N of S14), the rapid deterioration determination unit 4615 determines that rapid deterioration has not occurred in the battery module 41 (S15). When the absolute value of the difference exceeds the threshold value (Y of S14), the rapid deterioration determination unit 4615 determines that rapid deterioration has occurred in the battery module 41 (S16).

[0057] In the above-described first embodiment, the battery management unit 42 performs the rapid deterioration determination process for the battery module 41. In this regard, the computing system 1 may perform the rapid deterioration determination process for the battery module 41.

[0058] 11 is a diagram showing a configuration example of a computing system 1 according to a second embodiment. The computing system 1 includes a processing unit 11, a storage unit 12, a display unit 13, and an operation unit 14. The processing unit 11 includes a data acquisition unit 111, a SOH identification unit 112, a deterioration regression curve generation unit 113, a rapid deterioration determination unit 114, an operation reception unit 115, and a display control unit 116. The functions of the processing unit 11 can be realized by a combination of hardware resources and software resources, or by hardware resources alone. As the hardware resources, a CPU, a GPU (Graphics Processing Unit), a ROM, a RAM, an ASIC, an FPGA, and other LSIs can be used. As the software resources, a program such as an operating system and an application can be used.

[0059] The storage unit 12 includes a driving data storage unit 121, a driver data storage unit 122, a SOC-OCV characteristic storage unit 123, and a time-series SOH value storage unit 124. The storage unit 12 includes a non-volatile recording medium such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and stores various programs and data.

[0060] The travel data storage unit 121 stores travel data collected from multiple electric vehicles 3 owned by the business operator. The driver data storage unit 122 stores data on multiple drivers belonging to the business operator. For example, the accumulated travel distance of each electric vehicle 3 driven by each driver is managed.

[0061] The SOC-OCV characteristics holding unit 123 holds the SOC-OCV characteristics of the multiple battery modules 41 mounted on the multiple electric vehicles 3 owned by the business operator. The SOC-OCV characteristics of the battery modules 41 may be acquired from each electric vehicle 3, or may be estimated based on the driving data collected from each electric vehicle 3.

[0062] In the latter case, the SOC-OCV characteristic estimation unit (not shown) of the processing unit 11 extracts a set of SOC and voltage (≈OCV) during a period in which the battery module 41 can be considered to be in a resting state from the sets of SOC and voltage at multiple times included in the acquired battery data, and approximates the SOC-OCV characteristic based on the multiple sets of SOC and OCV extracted. The SOC-OCV characteristic estimation unit may generate a common SOC-OCV characteristic for the battery modules 41 of the same type based on the set data of SOC and OCV acquired from multiple electric vehicles 3 equipped with the same type of battery modules 41. The SOC-OCV characteristic may be held on a cell-by-cell basis.

[0063] The time-series SOH value storage unit 124 stores time-series data of the SOH for each battery module 41. The time-series data of the SOH is recorded, for example, once a day, once every few days, or once a week.

[0064] The display unit 13 includes a display such as a liquid crystal display or an organic EL display, and displays an image generated by the processing unit 11. The operation unit 14 is a user interface such as a keyboard, a mouse, a touch panel, etc., and accepts operations of the user of the computing system 1.

[0065] The data acquisition unit 111 acquires traveling data including battery data of the battery modules 41 mounted on each of the multiple electric vehicles 3, and stores the acquired traveling data in the traveling data storage unit 121. The SOH identification unit 112 identifies the SOH of the battery modules 41 mounted on each electric vehicle 3 based on the battery data included in the traveling data acquired by the data acquisition unit 111. The SOH identification unit 112 stores the identified SOH in the time-series SOH value storage unit 124.

[0066] If the acquired battery data includes the SOH, the SOH determination unit 112 can use the acquired SOH as is. If the acquired battery data does not include the SOH but includes the voltage, current, temperature, and SOC, the SOH can be calculated based on the above (Equation 1) and (Equation 2). That is, the SOH determination unit 112 calculates the current integration amount Q for the period between the two points of time when the two OCVs were acquired based on the transition of the current included in the battery data, and applies the calculated current integration amount Q to the above (Equation 1) to estimate the FCC. The SOH determination unit 112 calculates the SOH by applying the calculated FCC to the above (Equation 2).

[0067] If the acquired battery data does not include SOC or SOH, the SOH determination unit 112 estimates the SOC by applying the voltage (≈OCV) during a period in which the battery module 41 can be considered to be in a resting state to the SOC-OCV characteristics. Alternatively, the SOH determination unit 112 estimates the SOC by integrating the current value for a certain period. The SOH determination unit 112 uses the estimated SOC to calculate the SOH in the same way as when the battery data includes SOC.

[0068] The deterioration regression curve generating unit 113 performs curve regression of a plurality of SOHs specified in a time series of each battery module 41 to generate a deterioration regression curve of each battery module 41. The rapid deterioration determining unit 114 calculates a difference or ratio between a deterioration coefficient w1 of the deterioration regression curve of the battery module 41 generated based on a plurality of SOHs in a first data interval in the time series SOH of the specific battery module 41 and a deterioration coefficient w1 of the deterioration regression curve of the battery module 41 generated based on a plurality of SOHs in a second data interval. The rapid deterioration determining unit 114 determines whether rapid deterioration has occurred in the battery module 41 based on the calculated difference or ratio.

[0069] The operation reception unit 117 receives a user's operation on the operation unit 14. The display control unit 118 displays various information on the display unit 13. In the second embodiment, the display control unit 118 displays the rapid deterioration determination result of each battery module 41.

[0070] As described above, according to this embodiment, by referring to the difference or ratio of the deterioration coefficient w1 of a plurality of deterioration regression curves generated by changing the data interval, rapid deterioration of the battery module 41 can be detected with high accuracy without disassembling the battery module 41. Even when data including an estimation error of the SOH is used, such as the battery module 41 mounted on the electric vehicle 3, robust detection can be performed. According to the experiment by the inventor, it was found that with about 100 SOH points, the maximum error can be suppressed to about 5%. If the SOH is estimated once a day, rapid deterioration can be detected with high accuracy in a little over three months. Note that the error is reduced as the number of SOHs increases.

[0071] Another possible method is to determine rapid degradation based on the change in the slope of a line obtained by linear regression of the change in the FCC or SOH of the battery module 41. This method is considered to function effectively when the error or noise is small, but when the error or noise is large, the determination of rapid degradation may become unstable.

[0072] In contrast, in this embodiment, a degradation regression curve is generated by dividing the data interval of the time series SOH. In the case of normal degradation, there is no substantial change in the degradation coefficient w1 of the degradation regression curve. By using the degradation coefficient w1 of the degradation regression curve as a parameter, it is possible to detect a change in the degradation regression curve itself caused by sudden degradation. By detecting the change in the degradation regression curve itself, it is possible to perform more robust detection than when detecting a change in the slope of a straight line obtained by linearly regressing the amount of change in FCC or SOH.

[0073] When sudden deterioration occurs in the battery module 41 mounted on the electric vehicle 3, the driving distance of the electric vehicle 3 decreases suddenly. Detecting the sudden deterioration makes it possible to replace the electric vehicle 3 at an appropriate time or change the way it is used. Detecting the sudden deterioration of the battery module 41 in this way makes it possible to improve the safety of using the battery module 41.

[0074] In addition, if the calculation system 1 determines whether or not there is rapid deterioration based on the measurement data transmitted from the electric vehicle 3, rather than the battery management unit 42 within the electric vehicle 3, the vehicle management of businesses that own a large number of electric vehicles 3 can be made more efficient.

[0075] The present disclosure has been described above based on the embodiments. The embodiments are merely examples, and it will be understood by those skilled in the art that various modifications are possible in the combination of each component and each processing step, and that such modifications are also within the scope of the present disclosure.

[0076] In the above embodiment, an example has been described in which the data section is divided by the number of SOHs. In this regard, the data section may be divided by the number of days (e.g., 100 days). In this case, it becomes easier to set a check for sudden deterioration of the battery module 41 as one of the items in a periodic vehicle inspection.

[0077] In the above embodiment, the degradation regression curve generating unit 4614 compares the degradation coefficient w1 based on the data of the first data interval with the degradation coefficient w1 based on the data of the second data interval. In this regard, the degradation regression curve generating unit 4614 may compare the degradation coefficient w1 based on the data of the first data interval with a value (e.g., an average value, a variance value, a standard deviation value) obtained by statistically processing a plurality of degradation coefficients w1 based on data of a plurality of data intervals.

[0078] When comparing by variance, the degradation regression curve generating unit 4614 compares the squared value of the deviation of the degradation coefficient w1 based on the data of the first data interval with the variance of the multiple degradation coefficients w1 based on the data of multiple data intervals. When comparing by standard deviation, the degradation regression curve generating unit 4614 compares the absolute value of the deviation of the degradation coefficient w1 based on the data of the first data interval with the standard deviation of the multiple degradation coefficients w1 based on the data of multiple data intervals. In these cases, it is possible to detect sudden degradation with higher accuracy.

[0079] The rapid deterioration determination method according to the above-mentioned embodiment may be used in combination with another rapid deterioration determination method. For example, there is a method in which an AC signal in a frequency band (e.g., 100 Hz to 10 kHz) to which the electrolyte reacts is applied from outside the battery module 41, an AC impedance value of the battery module 41 is measured, and rapid deterioration of the battery module 41 is detected or predicted depending on whether the measured AC impedance value is equal to or greater than a threshold value. This method requires a circuit that applies an AC signal to the battery module 41 and measures the AC impedance value. On the other hand, the rapid deterioration determination method according to the present embodiment does not require such a circuit.

[0080] If it is determined that rapid deterioration is occurring by the rapid deterioration determination method of this embodiment, the electric vehicle 3 may be moved to a facility (e.g., a car dealer) that has a circuit device capable of measuring the AC impedance value of the battery module 41, and rapid deterioration may be determined based on the AC impedance value.

[0081] The rapid deterioration determination process needs to be performed frequently in order to notify the user of a warning early after rapid deterioration occurs in the battery module 41. If the rapid deterioration determination method according to the present embodiment is performed frequently (for example, every time one piece of data is added), there is a high probability of erroneously determining that rapid deterioration has occurred when rapid deterioration has not actually occurred.

[0082] In contrast, if the rapid deterioration determination process according to the embodiment is regarded as a primary determination and the rapid deterioration determination process based on the AC impedance value is regarded as a secondary determination, the probability of erroneous determination is reduced even if the rapid deterioration determination method according to the embodiment is performed frequently. In other words, rapid deterioration of the battery module 41 can be detected early and with high accuracy.

[0083] In the above-described embodiment, an example has been considered in which rapid deterioration of the battery module 41 mounted on the electric vehicle 3 is determined. In this regard, the electric vehicle 3 may be a two-wheeled electric motorcycle (electric scooter) or an electric bicycle. In addition, the electric vehicle 3 also includes low-speed electric vehicles 3 such as golf carts and land cars used in shopping malls, entertainment facilities, and the like.

[0084] The battery module 41 may be mounted on other than the electric vehicle 3. For example, the battery module 41 may be mounted on other electric vehicles such as electric ships, railroad cars, and multicopters (drones). The battery module 41 may be mounted on other electric vehicles such as stationary power storage systems and consumer electronic devices (smartphones, notebook PCs, etc.).

[0085] The embodiment may be specified by the following items.

[0086] [Item 1] a measuring unit (43-45) for measuring at least the voltage and current of the battery (E1, 41); a SOH estimation unit (4613) for estimating a SOH (State Of Health) of the battery (E1, 41) based on measurement data of the battery (E1, 41); a deterioration regression curve generating unit (4614) that performs curve regression of a plurality of SOHs determined in a time series of the battery (E1, 41) to generate a deterioration regression curve of the battery (E1, 41); a rapid deterioration determination unit (4615) for determining whether rapid deterioration has occurred in the battery (E1, 41) based on a difference or ratio between a deterioration coefficient of a deterioration regression curve of the battery (E1, 41) generated based on a plurality of SOHs in a first data interval and a deterioration coefficient of a deterioration regression curve of the battery (E1, 41) generated based on a plurality of SOHs in a second data interval; A battery management device (42) comprising:

[0087] The battery (E1, 41) may be a cell E1 or a module 41.

[0088] This makes it possible to detect rapid deterioration of the battery (E1, 41) with high accuracy.

[0089] [Item 2] The rapid deterioration determination unit (4615) determines that rapid deterioration has occurred in the battery (E1, 41) when the difference or the ratio deviates from a predetermined range. Item 1. The battery management device (42) according to item 1.

[0090] According to this, by detecting deviations from normal deterioration, sudden deterioration can be detected with high accuracy.

[0091] [Item 3] The first data section is a section including a SOHs from the last identified SOH, The second data section is a section including the last identified SOH and the past b (b>a) SOHs. 3. The battery management device (42) according to item 1 or 2.

[0092] According to this, by overlapping the data sections, stable detection can be performed.

[0093] [Item 4] The first data section is a section including a SOHs from the last identified SOH, The second data section is a section including the last identified SOH and the past a SOHs, excluding the first data section. 3. The battery management device (42) according to item 1 or 2.

[0094] According to this, by not overlapping data sections, early detection can be achieved.

[0095] [Item 5] the second data section includes a plurality of data sections; The degradation coefficient of the second data section is a value obtained by statistically processing each degradation coefficient of the plurality of data sections. 3. The battery management device (42) according to item 1 or 2.

[0096] This makes it possible to further improve the detection accuracy.

[0097] [Item 6] A data acquisition unit (111) that acquires measurement data of a battery (E1, 41); a SOH determination unit (112) that determines the SOH of the battery (E1, 41) based on measurement data of the battery (E1, 41); a deterioration regression curve generating unit (113) that performs curve regression of a plurality of SOHs determined in a time series of the battery (E1, 41) to generate a deterioration regression curve of the battery (E1, 41); a rapid deterioration determination unit (114) that determines whether rapid deterioration has occurred in the battery (E1, 41) based on a difference or ratio between a deterioration coefficient of a deterioration curve of the battery (E1, 41) generated based on a plurality of SOHs in a first data section and a deterioration coefficient of a deterioration curve of the battery (E1, 41) generated based on a plurality of SOHs in a second data section; A computing system (1) comprising:

[0098] This makes it possible to detect rapid deterioration of the battery (E1, 41) with high accuracy.

[0099] [Item 7] Identifying the SOH of the battery (E1, 41) based on measurement data of the battery (E1, 41); A step of performing curve regression of a plurality of SOHs determined in a time series of the battery (E1, 41) to generate a deterioration regression curve of the battery (E1, 41); a step of determining whether or not rapid deterioration has occurred in the battery (E1, 41) based on a difference or ratio between a deterioration coefficient of a deterioration regression curve of the battery (E1, 41) generated based on a plurality of SOHs in a first data interval and a deterioration coefficient of a deterioration regression curve of the battery (E1, 41) generated based on a plurality of SOHs in a second data interval; A method for predicting deterioration of a battery (E1, 41), comprising:

[0100] This makes it possible to detect rapid deterioration of the battery (E1, 41) with high accuracy.

[0101] [Item 8] A process of identifying the SOH of the battery (E1, 41) based on measurement data of the battery (E1, 41); A process of performing curve regression of a plurality of SOHs determined in a time series of the battery (E1, 41) to generate a deterioration regression curve of the battery (E1, 41); a process of determining whether or not rapid deterioration has occurred in the battery (E1, 41) based on a difference or ratio between a deterioration coefficient of a deterioration regression curve of the battery (E1, 41) generated based on a plurality of SOHs in a first data section and a deterioration coefficient of a deterioration regression curve of the battery (E1, 41) generated based on a plurality of SOHs in a second data section; A deterioration prediction program for a battery (E1, 41), characterized by causing a computer to execute the above.

[0102] This makes it possible to detect rapid deterioration of the battery (E1, 41) with high accuracy. [Explanation of symbols]

[0103] 1 Calculation system, E1-En cell, T1, T2 temperature sensor, RY1, RY2 relay, 3 Electric vehicle, 4 Charger, 11 Processing unit, 111 Data acquisition unit, 112 SOH identification unit, 113 Deterioration regression curve generation unit, 114 Rapid deterioration determination unit, 115 Operation reception unit, 116 Display control unit, 12 Memory unit, 121 Driving data storage unit, 122 Driver data storage unit, 123 SOC-OCV characteristic storage unit, 124 Time series SOH value storage unit, 13 Display unit, 14 Operation unit, 30 Vehicle control unit, 34 Motor, 35 Inverter, 36 Wireless communication unit, 36a Antenna, 38 Charging cable, 40 Battery system, 41 Battery module, 42 Battery management unit, 43 Voltage measurement unit, 44 Temperature measurement unit, 45 current measurement unit, 46 battery control unit, 461 processing unit, 4611 SOC estimation unit, 4612 FCC estimation unit, 4613 SOH estimation unit, 4614 deterioration regression curve generation unit, 4615 rapid deterioration determination unit, 4616 data transmission unit, 462 memory unit, 4621 SOC-OCV characteristic storage unit, 4622 battery data storage unit, 4623 time series SOH value storage unit.

Claims

1. A measurement unit that measures at least the voltage and current of the battery; a SOH estimation unit that estimates a SOH (State Of Health) of the battery based on measurement data of the battery; a degradation regression curve generating unit that performs curve regression of a plurality of SOHs specified in a time series of the battery to generate a degradation regression curve of the battery; a rapid deterioration determination unit that determines whether rapid deterioration is occurring in the battery based on a difference or ratio between a deterioration coefficient of a deterioration regression curve of the battery generated based on a plurality of SOHs in a first data section and a deterioration coefficient of a deterioration regression curve of the battery generated based on a plurality of SOHs in a second data section; A battery management device comprising:

2. The rapid deterioration determination unit determines that rapid deterioration is occurring in the battery when the difference or the ratio is outside a predetermined range. The battery management device according to claim 1 .

3. The first data section is a section including a SOHs from the last identified SOH, The second data section is a section including the last identified SOH and the past b (b>a) SOHs.

3. The battery management device according to claim 1 or 2.

4. The first data section is a section including a SOHs from the last identified SOH, The second data section is a section including a SOHs from the last identified SOH, excluding the first data section.

3. The battery management device according to claim 1 or 2.

5. the second data section includes a plurality of data sections; The degradation coefficient of the second data section is a value obtained by statistically processing each degradation coefficient of the plurality of data sections.

3. The battery management device according to claim 1 or 2.

6. A data acquisition unit for acquiring measurement data of a battery; a SOH determination unit that determines the SOH of the battery based on measurement data of the battery; a degradation regression curve generating unit that performs curve regression of a plurality of SOHs specified in a time series of the battery to generate a degradation regression curve of the battery; a rapid deterioration determination unit that determines whether rapid deterioration is occurring in the battery based on a difference or ratio between a deterioration coefficient of a deterioration curve of the battery generated based on a plurality of SOHs in a first data section and a deterioration coefficient of a deterioration curve of the battery generated based on a plurality of SOHs in a second data section; A computing system comprising:

7. determining a SOH of the battery based on measurement data of the battery; performing a curve regression of a plurality of SOHs determined over time for the battery to generate a deterioration regression curve for the battery; determining whether or not rapid deterioration is occurring in the battery based on a difference or ratio between a deterioration coefficient of a deterioration regression curve of the battery generated based on a plurality of SOHs in a first data interval and a deterioration coefficient of a deterioration regression curve of the battery generated based on a plurality of SOHs in a second data interval; A method for predicting deterioration of a battery, comprising:

8. A process of identifying the SOH of the battery based on measurement data of the battery; A process of performing a curve regression of a plurality of SOHs specified in a time series of the battery to generate a deterioration regression curve of the battery; A process of determining whether or not rapid deterioration is occurring in the battery based on a difference or ratio between a deterioration coefficient of a deterioration regression curve of the battery generated based on a plurality of SOHs in a first data section and a deterioration coefficient of a deterioration regression curve of the battery generated based on a plurality of SOHs in a second data section; A battery deterioration prediction program that causes a computer to execute the above steps.

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