Degradation detection system, degradation detection method, and degradation detection program

Abstract

This deterioration determination system includes a data acquisition unit that acquires battery data. An SOH (state of health) specifying unit specifies the SOH of a secondary battery on the basis of battery data. A regression curve generation unit performs curve regression of a plurality of SOHs specified in a time series regarding the secondary battery and generates a deterioration regression curve for the secondary battery. A point of interest setting unit sets a point of interest on the deterioration regression curve. A regression line generation unit performs line regression of a plurality of SOHs starting from the point of interest and generates a deterioration regression line for the secondary battery starting from the point of interest. An inclination ratio calculation unit calculates the inclination ratio of the inclination of a tangent line at the point of interest on the deterioration regression curve and the inclination of the deterioration regression line starting from the point of interest. A rapid deterioration determination unit determines rapid deterioration of the secondary battery on the basis of the inclination ratio.

Description

Technical Field

[0001] The present disclosure relates to a deterioration determination system, a deterioration determination method, and a deterioration determination program for determining rapid deterioration of a battery.

Background Art

[0002] In recent years, hybrid vehicles (HV), plug-in hybrid vehicles (PHV), and electric vehicles (EV) have become widespread. 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 or at a high rate, rapid deterioration of the capacity (hereinafter referred to as rapid deterioration or tertiary deterioration) is likely to occur due to a decrease in the electrolyte solution or a decrease in the electrode plate reaction area. When the secondary battery mounted on an electric vehicle rapidly deteriorates, the driving range subsequently decreases rapidly, resulting in a decrease in convenience.

[0004] The replacement timing of in-vehicle secondary batteries is generally set based on SOH (State Of Health). The same applies to secondary batteries other than in-vehicle use (for example, secondary batteries used in stationary energy storage systems). When rapid deterioration occurs in a secondary battery, the SOH decreases to the reference value at which replacement is necessary at a pace faster than expected by the user or administrator. Therefore, when rapid deterioration occurs, a situation where the replacement timing is reached without being prepared for replacement is likely to occur.

[0005] In addition, after the occurrence of rapid deterioration, an internal short circuit of the secondary battery due to precipitation is likely to occur. Thus, after the occurrence of rapid deterioration, early replacement is also necessary from the viewpoint of safety.

[0006] As a method for detecting rapid deterioration of a secondary battery, a method has been proposed in which data of SOH input in time series is extracted in a short-time window and linearly approximated, and rapid deterioration is detected based on a change in the slope of the approximated straight line (for example, see Patent Document 1).

Prior Art Documents

[0007] [Patent Document 1] International Publication No. 17 / 098686 [Overview of the Initiative]

[0008] The State of Charge (SOC), Full Charge Capacity (FCC), and State of Health (SOH) values ​​calculated based on voltage and current measurement data of a secondary battery are affected by sensor measurement errors and noise. When the effects of errors and noise are significant, the slope of the approximation line becomes unstable. Furthermore, it is difficult to set an appropriate threshold value for detecting rapid degradation based on the slope of the approximation line.

[0009] This disclosure is made in light of these circumstances, and its purpose is to provide a technology for accurately determining the rapid degradation of secondary batteries.

[0010] To solve the above problems, a degradation determination system according to one embodiment of the present disclosure includes: a data acquisition unit that acquires battery data; an SOH identification unit that identifies the SOH of the secondary battery based on the battery data; a regression curve generation unit that generates a degradation regression curve of the secondary battery by performing curve regression on a plurality of SOH identified in the time series of the secondary battery; a point of interest setting unit that sets a point of interest on the degradation regression curve; a regression line generation unit that generates a degradation regression line of the secondary battery from the point of interest onwards by performing linear regression on a plurality of SOH from the point of interest onwards; a slope ratio calculation unit that calculates the ratio of the slope of the tangent line at the point of interest on the degradation regression curve to the slope of the degradation regression line from the point of interest onwards; and a rapid degradation determination unit that determines rapid degradation of the secondary battery based on the slope ratio.

[0011] Furthermore, any combination of the above components, as well as any conversion of the expressions of this disclosure between devices, systems, methods, computer programs, recording media on which computer programs are recorded, etc., are also valid as aspects of this disclosure.

[0012] According to this disclosure, it is possible to determine the rapid degradation of a secondary battery with high accuracy. [Brief explanation of the drawing]

[0013] [Figure 1] This is a diagram illustrating a deterioration determination system according to an embodiment. [Figure 2] This diagram illustrates the main components of an electric vehicle and battery pack. [Figure 3] This figure shows an example configuration of a deterioration determination system according to an embodiment. [Figure 4] This is a diagram illustrating the FCC estimation method. [Figure 5] This figure shows an example of time-series data for the SOH of a certain secondary battery. [Figure 6] This figure shows an example of a degradation regression curve, tangent line, and degradation regression line in a state where rapid degradation has not occurred. [Figure 7] This figure shows an example of a degradation regression curve, tangent line, and degradation regression line in a state where rapid degradation is occurring. [Figure 8] This figure shows an example of a degradation regression curve generated based on measured values ​​of the State of Health (SOH) of cells contained in a battery pack installed in a certain electric vehicle. [Figure 9] This is a diagram (part 1) illustrating the process of searching for the predicted point of rapid deterioration. [Figure 10] This is a diagram (part 2) illustrating the process of searching for the predicted point of rapid deterioration. [Figure 11] This figure shows a specific example of a method for determining rapid deterioration related to the comparative example. [Figure 12] This figure shows a specific example of the rapid deterioration detection method according to the embodiment. [Figure 13] This flowchart shows the flow of the first processing example of the rapid deterioration determination method according to the embodiment. [Figure 14] This flowchart shows the flow of the second processing example of the rapid deterioration determination method according to the embodiment. [Modes for carrying out the invention]

[0014] FIG. 1 is a diagram for explaining a deterioration determination system 1 according to an embodiment. The deterioration determination system 1 according to the embodiment is a system for determining whether or not rapid deterioration has occurred in a battery pack 40 (see FIG. 2) mounted on an electric vehicle 3. In FIG. 1, an example in which the delivery company uses the deterioration determination system 1 is shown. The deterioration determination system 1 may be constructed, for example, on a company's own server installed in the company's own facility or data center that provides an operation management support service for the electric vehicle 3. Further, the deterioration determination system 1 may be constructed on a cloud server used based on a cloud service contract. Further, the deterioration determination system 1 may be constructed on a plurality of servers installed in a distributed manner at a plurality of bases (data centers, company's own facilities). The plurality of servers may be any combination of a plurality of company's own servers, a combination of a plurality of cloud servers, or a combination of a company's own server and a cloud server.

[0015] Each delivery company has a plurality of electric vehicles 3 and at least one charger 4, and has a delivery base for parking the plurality of electric vehicles 3. The electric vehicle 3 is connected to the charger 4 by a charging cable 5, and the battery pack 40 mounted on the electric vehicle 3 is charged from the charger 4 via the charging cable 5.

[0016] An operation management terminal device 7 is installed at the delivery base of the delivery company. The operation management terminal device 7 is, for example, configured by a PC. The operation management terminal device 7 is used for managing a plurality of electric vehicles 3 belonging to the delivery base. An operation manager of the delivery company can create a delivery plan and a charging plan for the plurality of electric vehicles 3 using the operation management terminal device 7. The operation management terminal device 7 can access the deterioration determination system 1 via the network 2.

[0017] Network 2 is a general term for communication channels such as the Internet, dedicated lines, and VPN (Virtual Private Network), regardless of the communication medium or protocol. As the communication medium, for example, a mobile phone network (cellular network), wireless LAN, wired LAN, optical fiber network, ADSL network, CATV network, etc. can be used. As the communication protocol, for example, TCP (Transmission Control Protocol) / IP (Internet Protocol), UDP (User Datagram Protocol) / IP, Ethernet (registered trademark), etc. can be used.

[0018] Figure 2 is a diagram for explaining the main configurations of the electric vehicle 3 and the battery pack 40. The battery pack 40 is connected to the motor 34 via the first relay RY1 and the inverter 35. During power running, the inverter 35 converts the DC power supplied from the battery pack 40 into AC power and supplies it to the motor 34. During regeneration, the inverter 35 converts the AC power supplied from the motor 34 into DC power and supplies it to the battery pack 40. The motor 34 is a three-phase AC motor and rotates according to the AC power supplied from the inverter 35 during power running. During regeneration, the rotational energy due to deceleration is converted into AC power and supplied to the inverter 35.

[0019] The first relay RY1 is a contact inserted between the wirings connecting the battery pack 40 and the inverter 35. During running, the vehicle control unit 30 controls the first relay RY1 to be in the on state (closed state) and electrically connects the battery pack 40 and the power system of the electric vehicle 3. During non-running, in principle, the vehicle control unit 30 controls the first relay RY1 to be in the off state (open state) and electrically disconnects the battery pack 40 and the power system of the electric vehicle 3. Note that other types of switches such as semiconductor switches may be used instead of the relay.

[0020] The battery pack 40 can be charged from the commercial power grid 6 by connecting it to a charger 4 installed outside the electric vehicle 3 with a charging cable 5. The charger 4 is connected to the commercial power grid 6 and charges the battery pack 40 inside the electric vehicle 3 via the charging cable 5. In the electric vehicle 3, a second relay RY2 is inserted between the wiring connecting the battery pack 40 and the charger 4. Alternatively, other types of switches, such as semiconductor switches, may be used instead of the relay. The battery management unit 42 of the battery pack 40 controls the second relay RY2 to the ON state before charging begins and to the OFF state after charging is complete.

[0021] Generally, normal charging uses alternating current (AC), while fast charging uses direct current (DC). When charging with AC, an onboard charger (not shown) inserted between the second relay RY2 and the battery pack 40 converts the AC power to DC power.

[0022] The battery pack 40 comprises a battery module 41 and a battery management unit 42, and the battery module 41 includes a plurality of series-connected cells E1-En. The battery module 41 may be composed of multiple battery modules connected in series or in series-parallel. Lithium-ion battery cells, nickel-metal hydride battery cells, lead-acid battery cells, etc., can be used for the cells. Hereinafter, this specification assumes the use of lithium-ion battery cells (nominal voltage: 3.6-3.7V). The number of cells E1-En in series is determined according to the drive voltage of the motor 34.

[0023] A shunt resistor Rs is connected in series with multiple cells E1-En. The shunt resistor Rs functions as a current sensing element. A Hall element may be used instead of the shunt resistor Rs. In addition, multiple temperature sensors T1 and T2 are installed in the battery module 41 to detect the temperature of the multiple cells E1-En. One temperature sensor may be installed in the battery module, or one may be installed for each of the multiple cells. For example, thermistors can be used for the temperature sensors T1 and T2.

[0024] 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. Multiple voltage lines connect each node of the multiple series-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 two adjacent voltage lines. The voltage measurement unit 43 transmits the measured voltage of each cell E1-En to the battery control unit 46.

[0025] Since the voltage measurement unit 43 is at a high voltage relative to the battery control unit 46, the voltage measurement unit 43 and the battery control unit 46 are connected by a communication line in an isolated state. The voltage measurement unit 43 can be configured as 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 voltages 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.

[0026] The temperature measurement unit 44 includes a voltage divider resistor and an A / D converter. The A / D converter sequentially converts multiple analog voltages, each divided by multiple temperature sensors T1 and T2 and multiple voltage divider resistors, into digital values ​​and outputs them to the battery control unit 46. The battery control unit 46 estimates the temperature of multiple cells E1-En based on these digital values. For example, the battery control unit 46 estimates the temperature of each cell E1-En based on the value measured by the temperature sensor closest to each cell E1-En.

[0027] 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 multiple cells E1-En based on this digital value.

[0028] If the battery control unit 46 is equipped with an A / D converter and has an analog input port, the temperature measurement unit 44 and the current measurement unit 45 may output analog voltages to the battery control unit 46, which can then be converted to digital values ​​by the A / D converter in the battery control unit 46.

[0029] The battery control unit 46 (also referred to as BMU or BMS) includes a microcontroller, a communication controller, and non-volatile memory. The battery control unit 46 and the vehicle control unit 30 are connected via an in-vehicle network (e.g., CAN (Controller Area Network) or LIN (Local Interconnect Network)). The communication controller controls communication with the vehicle control unit 30.

[0030] The battery control unit 46 manages the state of multiple cells E1-En based on the voltage, temperature, and current of multiple cells E1-En measured by the voltage measurement unit 43, temperature measurement unit 44, and current measurement unit 45.

[0031] The battery control unit 46 estimates the State of Charge (SOC) of each of the multiple cells E1-En contained in the battery module 41. The battery control unit 46 estimates the 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 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 internal memory of the microcontroller at the time of shipment.

[0032] 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 current flowing through the cell. With the current integration method, current measurement errors accumulate as the charging and discharging time increases. Therefore, it is preferable to correct the SOC estimated by the current integration method using the SOC estimated by the OCV method.

[0033] The battery control unit 46 transmits the voltage, current, temperature, and state of charge (SOC) of the battery module 41 and each cell E1-En to the vehicle control unit 30 via the in-vehicle network.

[0034] The vehicle control unit 30 is a vehicle ECU (Electronic Control Unit) that controls the entire electric vehicle 3, and may be composed of, for example, an integrated VCM (Vehicle Control Module). The vehicle control unit 30 includes a communication controller for connecting to an in-vehicle network and a communication controller (for example, a CAN controller) for communicating with the charger 4 via the charging cable 5.

[0035] The wireless communication unit 36 ​​performs signal processing for wireless connection to the network 2 via the antenna 36a. As the wireless communication network to which the electric vehicle 3 can wirelessly connect, for example, a mobile phone network (cellular network), wireless LAN, V2I (Vehicle to Infrastructure), V2V (Vehicle to Vehicle), ETC system (Electronic Toll Collection System), DSRC (Dedicated Short Range Communications), etc. can be used.

[0036] While the electric vehicle 3 is in motion, the vehicle control unit 30 can transmit driving data, including battery data, to the degradation determination system 1 in real time using the wireless communication unit 36. The driving data includes at least the vehicle speed of the electric vehicle 3. The battery data includes the voltage, current, temperature, and state of charge (SOC) of multiple cells E1-En. The vehicle control unit 30 samples this data periodically (for example, at 10-second intervals) and transmits it to the degradation determination system 1 each time.

[0037] The vehicle control unit 30 may store the driving data of the electric vehicle 3 in its internal memory and transmit the stored driving data in a batch at a predetermined timing. For example, the vehicle control unit 30 may transmit the stored driving data to the operation management terminal device 7 in a batch after the end of business for the day. The operation management terminal device 7 transmits the driving data of multiple electric vehicles 3 to the deterioration determination system 1 at a predetermined timing.

[0038] Furthermore, when charging from a charger 4 equipped with network communication functionality, the vehicle control unit 30 may transmit the driving data stored in its memory to the charger 4 in a single batch via the charging cable 5. The charger 4 then transmits the received driving data to the degradation determination system 1. This example is effective when the charger 4 is equipped with network communication functionality and the electric vehicle 3 is not equipped with wireless communication functionality.

[0039] Figure 3 shows an example configuration of a degradation determination system 1 according to an embodiment. The degradation determination system 1 comprises a processing unit 11, a storage unit 12, and a communication unit 13. The communication unit 13 includes a communication interface (e.g., a router) for connecting to the network 2 by wire or wireless.

[0040] The processing unit 11 includes a data acquisition unit 111, a SOH (State of Health) identification unit 112, a regression curve generation unit 113, a point of interest setting unit 114, a regression line generation unit 115, a slope ratio calculation unit 116, a regression curve evaluation unit 117, a prediction point search unit 118, a rapid deterioration determination unit 119, and a notification unit 1110. The functions of the processing unit 11 can be realized through the cooperation of hardware and software resources, or solely through hardware resources. Hardware resources can include a CPU, ROM, RAM, GPU (Graphics Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), and other LSIs. Software resources can include operating systems, applications, and other programs.

[0041] The storage unit 12 includes a battery data storage unit 121. The storage unit 12 includes a non-volatile recording medium such as an HDD (Hard Disk Drive) or SSD (Solid State Drive) and stores various types of data.

[0042] The data acquisition unit 111 acquires driving data (including battery data) of the electric vehicle 3 while it is running, or battery data when it is parked, via the network 2, and stores the acquired battery data in the battery data storage unit 121. Driving data other than battery data may be stored together in the battery data storage unit 121, or may be stored in a separate driving data storage unit (not shown).

[0043] The SOH identification unit 112 reads the battery data of the battery pack 40 mounted on the electric vehicle 3 stored in the battery data holding unit 121 at a predetermined timing and calculates the SOH of each cell E1-En contained in the battery pack 40. The predetermined timing may be the timing when charging of the electric vehicle 3 from the charger 4 is completed, or the timing when the vehicle is driven for the first time after charging is completed.

[0044] The SOH identification unit 112 estimates the FCC of each cell E1-En based on the voltage (OCV) and SOC at two points (both when the vehicle is stopped) of each cell E1-En included in the battery data, and then estimates the SOH based on the estimated FCC.

[0045] Figure 4 is a diagram illustrating the FCC estimation method. The SOH identification unit 112 calculates the difference (ΔSOC) between the SOC corresponding to OCV1 before charging starts and the SOC corresponding to OCV2 after charging is complete. The SOH identification unit 112 calculates the integrated current value Q by integrating the current from the start of charging to the end of charging.

[0046] The SOH identification unit 112 estimates the FCC by calculating (Equation 1) below, and then estimates the SOH by calculating (Equation 2) below based on the estimated FCC. SOH is defined by the ratio of the current FCC to the initial FCC, and a lower value (closer to 0%) indicates that degradation is progressing.

[0047] FCC = Q / ΔSOC ... (Equation 1) SOH = Current FCC / Initial FCC ... (Equation 2) Furthermore, the FCC can also be estimated based on the OCV1 of the electric vehicle 3 before it starts running and the OCV2 after it starts running.

[0048] The SOH identification unit 112 stores the estimated SOH of E1-En for each cell in the battery data holding unit 121. Note that the FCC and SOH of each cell E1-En may also be estimated within the battery control unit 46 of the electric vehicle 3. If the FCC and SOH estimated within the battery control unit 46 are included in the battery data transmitted from the electric vehicle 3 to the degradation determination system 1, the degradation determination system 1 does not need to estimate the FCC and SOH.

[0049] The regression curve generation unit 113 reads all of the SOH data, which are specified for the time series of the target cell contained in the target battery pack 40, from the battery data holding unit 121, performs curve regression on the multiple read SOHs, and generates a degradation regression curve for the target cell. For example, the least squares method can be used for curve regression.

[0050] Figure 5 shows an example of time-series data for the State of Health (SOH) of a certain secondary battery. The horizontal axis represents the total discharge capacity [Ah], and the vertical axis represents the SOH [%]. It is known that cell degradation progresses in proportion to the total discharge capacity and the square root of time (root law, 0.5 power law), as shown in (Equation 3) below.

[0051] SOH=w0+w1√t (Equation 3) w0 is the initial value, and w1 is the degradation coefficient.

[0052] The regression curve generation unit 113 calculates the degradation coefficient w1 in (Equation 3) above by exponential regression to the power of 0.5, with total discharge capacity or time as the independent variable and SOH as the dependent variable. w0 is common and is usually set in the range of 1.0 to 1.1. If the actual initial capacity matches the nominal value, w0 is set to 1.0, and if 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.

[0053] As shown in Figure 5, when rapid degradation occurs in a secondary battery, the degradation rate changes from degradation following a square root rule to linear degradation (it becomes faster). In this embodiment, the occurrence of rapid degradation is detected by the following process.

[0054] The point of interest setting unit 114 sets point of interest A on the degradation regression curve generated by the regression curve generation unit 113. The point of interest setting unit 114 sets point of interest A on the degradation regression curve at a predetermined value backward from the end of one of the multiple SOH data intervals specified in the time series. For example, the point of interest setting unit 114 may set point of interest A on the degradation regression curve for the day corresponding to the most recent n days ago (for example, 30 days ago, 60 days ago, 90 days ago). Alternatively, the point of interest setting unit 114 may set point of interest A on the degradation regression curve corresponding to the total discharge capacity α [Ah] backward from the most recent total discharge capacity.

[0055] Furthermore, the point of interest setting unit 114 may set point A as point of interest to a point on the degradation regression curve corresponding to the time or total discharge capacity of a data point that is n points prior to the latest data (for example, 50 points prior). The point of interest setting unit 114 may also use data at positions obtained by dividing the entire data interval at a predetermined ratio (3:1) as the data that is n points prior to the latest data.

[0056] The regression line generation unit 115 performs linear regression on multiple SOHs from point A onwards, set by the point of interest setting unit 114, to generate a degraded regression line for the target cell from point A onwards. For example, the least squares method can be used for linear regression.

[0057] It is empirically known that rapid cell degradation tends to be linearly approximated with respect to total discharge capacity and time, as shown in (Equation 4) below.

[0058] SOH=aT+b (Formula 4) a is the degradation rate after the onset of rapid degradation, and b is the SOH at the start of rapid degradation.

[0059] The slope ratio calculation unit 116 calculates the ratio (a / a_tan) of the slope a of the degradation regression line based on multiple SOHs after point A, which is the slope a of the tangent line at point A on the degradation regression curve. The slope a_tan of the tangent line at point A on the degradation regression curve can be calculated from the derivative at point A.

[0060] The rapid deterioration determination unit 119 determines rapid deterioration of the target cell based on the slope ratio (a / a_tan) calculated by the slope ratio calculation unit 116. For example, as shown in (Equation 5) below, the rapid deterioration determination unit 119 determines that rapid deterioration has occurred in the target cell if the slope ratio (a / a_tan) exceeds the threshold th.

[0061] a / a_tan>th ···(Equation 5) The threshold th can be set considering the results of experiments and simulations, as well as the designer's knowledge. The tangent line at point A on the degradation regression curve and the degradation regression line reflect the predicted degradation rate at point A. For example, if the threshold th is set to 2, it determines whether the predicted degradation rate at point A has increased by more than twice as much.

[0062] Figure 6 shows an example of a degradation regression curve sqr, tangent line tan, and degradation regression line lin in a state where rapid degradation is not occurring. Figure 7 shows an example of a degradation regression curve sqr, tangent line tan, and degradation regression line lin in a state where rapid degradation is occurring. In the example shown in Figure 6, the slope ratio (a / a_tan) is 0.666747, and in the example shown in Figure 7, the slope ratio (a / a_tan) is 5.356819. In the example shown in Figure 7, since the slope ratio (a / a_tan) is greater than 2, it is determined that rapid degradation is occurring.

[0063] In the example above, the slope ratio was defined as the ratio of the slope a of the degradation regression line to the slope a_tan of the tangent line at point A on the degradation regression curve (a / a_tan), but it may also be defined as the ratio of the slope a_tan of the tangent line at point A on the degradation regression curve to the slope a of the degradation regression line (a_tan / a). In that case, the rapid degradation determination unit 119 determines that rapid degradation has occurred in the target cell when the slope ratio (a_tan / a) is less than a threshold th (for example, 0.5).

[0064] The regression curve evaluation unit 117 evaluates the reliability of the degraded regression curve generated by the regression curve generation unit 113 based on its relationship with multiple SOHs that serve as its underlying data. The regression curve evaluation unit 117 can use common indicators to show the reliability of the regression curve, such as the sum of squared residuals, the standard deviation of residuals, the coefficient of determination, and the correlation coefficient. The sum of squared residuals and the standard deviation of residuals become larger as the variability of SOH increases. The coefficient of determination and the correlation coefficient show the relationship between time-series elements and SOH. If the reliability of the underlying data (SOH) is low, the reliability of the degraded regression curve also decreases.

[0065] Figure 8 shows an example of generating a degradation regression curve deg based on measured values ​​of State of Health (SOH) of cells contained in a battery pack 40 mounted on a certain electric vehicle 3. In the example shown in Figure 8, the intersection point of the degradation regression line who for the total SOH data and the degradation regression curve deg is set as a point of interest, and a degradation regression line reg based on the SOH from that point onward is generated under the condition that the curve passes through that point of interest.

[0066] In the example shown in Figure 8, many SOH sample data points are plotted above the upper limit of the degradation regression curve (deg), and the variability of the sample data is also large. Using a degradation regression curve based on such sample data for rapid degradation detection increases the probability of misjudgment.

[0067] The rapid deterioration detection unit 119 rejects the deterioration regression curve and avoids determining rapid deterioration if the reliability of the deterioration regression curve does not meet the set conditions (for example, if the sum of squares of residuals is greater than the set value).

[0068] In the above explanation, point A was used as a fixed value, but it is also possible to search for an optimal point A. The prediction point search unit 118 dynamically changes point A to search for the predicted point at which rapid deterioration of the cell begins.

[0069] Figure 9 is a diagram illustrating the process of searching for the predicted point of rapid deterioration onset (Part 1). Figure 10 is a diagram illustrating the process of searching for the predicted point of rapid deterioration onset (Part 2). First, the regression curve generation unit 113 generates a deterioration regression curve for the target cell based on all the SOH data of the target cell. The point of interest setting unit 114 determines point of interest A as described above and sets this point of interest A as a tentatively determined point of interest (initial value). The regression line generation unit 115 generates a deterioration regression line based on multiple SOHs from the tentatively determined point of interest A onward.

[0070] As shown in Figure 9, the prediction point search unit 118 sets the intersection point of the generated degraded regression curve and the generated degraded regression line as a new point of interest A'. The regression curve generation unit 113 generates a new degraded regression curve based on multiple SOHs before the new point of interest A', and the regression line generation unit 115 generates a new degraded regression line based on multiple SOHs after the new point of interest A'.

[0071] The prediction point search unit 118 sets the intersection point of the newly generated degraded regression curve and the newly generated degraded regression line as a new point of interest A''. As shown in Figure 10, the regression curve generation unit 113 generates a new degraded regression curve based on multiple SOHs before the new point of interest A'', and the regression line generation unit 115 generates a new degraded regression line based on multiple SOHs after the new point of interest A''. The above process is repeated.

[0072] The prediction point search unit 118 sets the convergence point of the repeatedly updated point of interest A as the predicted point of rapid deterioration initiation. For example, the prediction point search unit 118 may determine that point of interest A has converged when the distance between point of interest A before the update and point of interest A after the update becomes less than a set value. Alternatively, the prediction point search unit 118 may consider point of interest A to have converged after performing the update process for point of interest A described above a set number of times. Furthermore, the prediction point search unit 118 may determine that point of interest A has converged when the reliability of the newly generated deterioration regression curve meets the criteria (for example, when the sum of squares of residuals between the deterioration regression curve and multiple SOHs becomes less than a set value). In addition, the prediction point search unit 118 may determine that point of interest A has converged when the reliability of the newly generated deterioration regression curve meets the criteria AND the reliability of the newly generated deterioration regression line meets the criteria (for example, when the sum of squares of residuals between the deterioration line and multiple SOHs becomes less than a set value).

[0073] Alternatively, the regression curve generation unit 113 and the regression line generation unit 115 may generate a degraded regression curve and a degraded regression line by brute force while shifting point A, and the prediction point search unit 118 may set point A, which has the highest reliability for the degraded regression curve and the degraded regression line, as the prediction point for the onset of rapid degradation.

[0074] The slope ratio calculation unit 116 calculates the slope ratio (a / a_tan) at the set predicted point of rapid deterioration onset, and the rapid deterioration determination unit 119 determines the rapid deterioration of the target cell based on the calculated slope ratio (a / a_tan).

[0075] The prediction point search unit 118 may set an exclusion range when searching for a predicted point of rapid deterioration onset. For example, the prediction point search unit 118 may set an exclusion range to include a range where the SOH is above a predetermined value (e.g., 80%), a range where the total discharge capacity of the cell is below a set value (e.g., 4000 Ah), or a range where the usage period of the cell is below a set value (e.g., 2 years). Note that the point of interest setting unit 114 will not set point A within the exclusion range.

[0076] The notification unit 1110 notifies the electric vehicle 3 equipped with a battery pack 40 containing a cell that has been determined to have experienced rapid deterioration, or the operation management terminal device 7 managing the electric vehicle 3, of an alert indicating that the battery pack contains a cell that has experienced rapid deterioration.

[0077] Figure 11 shows a specific example of the rapid deterioration detection method according to the comparative example. Figure 12 shows a specific example of the rapid deterioration detection method according to the embodiment. In the comparative example, a regression line is generated based on the most recent n SOHs. Figure 11 plots the slope 'a' of the regression line at each time point.

[0078] In this embodiment, the regression line generation unit 115 generates a regression line based on the most recent n SOHs. The regression curve generation unit 113 generates a degraded regression curve based on the entire SOH excluding the most recent n SOHs. The slope ratio calculation unit 116 calculates the ratio (a / a_tan) of the slope a of the degraded regression line based on the most recent n SOHs between the slope a_tan of the tangent line at point A corresponding to the n previous SOHs on the degraded regression curve. Figure 12 plots the slope ratio (a / a_tan) at each time point. For example, n is set in the range of 3 to 7.

[0079] In the comparative example shown in Figure 11, it is difficult to set a suitable threshold th. For example, if the threshold th is set to a low value, it is easy to misjudge that rapid deterioration has occurred when the deterioration of the cell progresses along a curve that is close to linear. Conversely, if the threshold th is set to a high value, it is difficult to detect rapid deterioration even if it occurs in the cell. In contrast, in the embodiment shown in Figure 12, by setting the threshold th to about 2, it is possible to determine with high accuracy whether or not rapid deterioration has occurred.

[0080] For each cell model, the change in the slope ratio (a / a_tan) over time may be learned to generate a model for when rapid deterioration occurs. This model can be generated using machine learning or pattern recognition techniques. The slope ratio calculation unit 116 calculates the slope ratio (a / a_tan) each time a new SOH is added. The rapid deterioration determination unit 119 determines that rapid deterioration has occurred in the target cell when the behavior of the slope ratio (a / a_tan) in the time-series data of the slope ratio (a / a_tan) satisfies the determination conditions based on the generated model.

[0081] For example, in a model that has learned that the slope ratio (a / a_tan) tends to decrease by a predetermined value at a predetermined speed or higher, and then increase by a predetermined value at a predetermined speed or higher, the behavior of the slope ratio (a / a_tan) can be detected to predict the sudden deterioration. Furthermore, it can be detected at least early after the sudden deterioration has occurred.

[0082] Figure 13 is a flowchart showing the flow of a first processing example of the rapid degradation determination method according to the embodiment. The regression curve generation unit 113 reads all of the SOH data, which is specified in the time series of the target cell included in the target battery pack 40 and is stored in the battery data holding unit 121 (S10). The regression curve generation unit 113 performs curve regression on all of the read SOH data to generate a degradation regression curve for the target cell (S11).

[0083] The point of interest setting unit 114 sets point A on the degraded regression curve (S12). The slope ratio calculation unit 116 calculates the tangent line at point A on the degraded regression curve (S13). The regression line generation unit 115 performs linear regression on multiple SOHs after point A to generate a degraded regression line after point A (S14). The slope ratio calculation unit 116 calculates the ratio (a / a_tan) of the slope of the tangent line at point A on the degraded regression curve to the slope of the degraded regression line based on multiple SOHs after point A (S15).

[0084] The rapid deterioration determination unit 119 compares the calculated slope ratio (a / a_tan) with a threshold th (S16). If the slope ratio (a / a_tan) exceeds the threshold th (Y in S16), the notification unit 1110 notifies the electric vehicle 3 or its operation management terminal device 7 using the target cell with an alert indicating that it is using a cell that has begun to rapidly deteriorate (S17). If the slope ratio (a / a_tan) is less than or equal to the threshold th (N in S16), the alert is not notified.

[0085] Figure 14 is a flowchart showing the flow of a second processing example of the rapid degradation determination method according to the embodiment. The regression curve generation unit 113 reads all of the SOH data, which is specified in the time series of the target cell included in the target battery pack 40 and is stored in the battery data holding unit 121 (S20). The regression curve generation unit 113 performs curve regression on all of the read SOH data to generate a degradation regression curve for the target cell (S21).

[0086] The point of interest setting unit 114 sets the initial value of point of interest A on the degraded regression curve (S22). The regression line generation unit 115 performs linear regression on multiple SOHs from point of interest A onwards, which were set most recently, to generate a degraded regression line from point of interest A onwards (S23). The prediction point search unit 118 sets the intersection point of the most recently generated degraded regression curve and the degraded regression line from point of interest A onwards, which were set most recently, as the new point of interest A (S24).

[0087] The prediction point search unit 118 determines whether the most recently set point of interest A satisfies the convergence condition (S25). If the convergence condition is not satisfied (N in S25), the regression curve generation unit 113 performs curve regression on multiple SOHs prior to the most recently set point of interest A to generate a new degraded regression curve (S26). The process then proceeds to step S23, and the update of point of interest A continues.

[0088] If the convergence condition is satisfied in step S25 (Y in S25), the slope ratio calculation unit 116 calculates the tangent line at the most recently set point of interest A on the most recently generated degradation regression curve (S27). The slope ratio calculation unit 116 calculates the ratio (a / a_tan) of the slope of the tangent line to the slopes of the degradation regression lines based on multiple SOHs from the most recently set point of interest A onwards (S28).

[0089] The rapid deterioration determination unit 119 compares the calculated slope ratio (a / a_tan) with a threshold th (S29). If the slope ratio (a / a_tan) exceeds the threshold th (Y in S29), the notification unit 1110 notifies the electric vehicle 3 or its operation management terminal device 7 using the target cell with an alert indicating that it is using a cell that has begun to rapidly deteriorate (S210). If the slope ratio (a / a_tan) is less than or equal to the threshold th (N in S29), the alert is not sent.

[0090] In the example shown in the flowchart of Figure 14, the optimal point of interest A (predicted point of rapid deterioration onset) was searched for first, and then the rapid deterioration was determined. However, it is also possible to search for the optimal point of interest A (predicted point of rapid deterioration onset) after rapid deterioration has been detected. Specifically, in the initial search, after executing the process in step S23, the processes in steps S28 and S29 are executed, and if rapid deterioration is detected, the processes from step S24 onwards are executed. In this example, the amount of processing can be reduced when rapid deterioration does not occur.

[0091] As described above, this embodiment allows for highly accurate detection of rapid cell degradation. Detecting rapid degradation enables efficient replacement of the electric vehicle 3 or battery pack 40. Furthermore, replacing the battery pack 40 at the appropriate time improves safety. Since internal short circuits are more likely to occur after rapid degradation, early replacement improves safety.

[0092] Furthermore, by using a degradation curve based on the square root rule, robust degradation prediction can be performed against noise in the measurement system during SOH estimation. From point A onward, a linear regression line can be used to detect the occurrence of short-term rapid degradation. In addition, since rapid degradation is determined based on the ratio of the slopes of two lines (a / a_tan) obtained using a general regression method, the increase in computational load can be suppressed. Moreover, since the relative ratio to the degradation rate of the normal degradation curve is used as a parameter, the threshold th can be set intuitively.

[0093] Furthermore, if the generated degradation regression curve is unreliable, rejecting the degradation regression curve can avoid determining abrupt degradation based on unreliable SOH data. Additionally, searching for the optimal point of point A allows for highly accurate estimation of when abrupt degradation began. Moreover, limiting the search range for the optimal point of point A can suppress the misjudgment of abrupt degradation occurring in regions where the probability of abrupt degradation is low.

[0094] The present disclosure has been described above based on embodiments. The embodiments are illustrative, and it will be readily apparent to 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.

[0095] The degradation detection system 1 described above may be implemented in the battery control unit 46 within the electric vehicle 3. In this case, a large memory capacity is required, but data loss can be reduced.

[0096] [Item 1] Secondary battery (E1) A data acquisition unit (111) that acquires battery data, Based on the aforementioned battery data, the SOH identification unit (112) identifies the SOH of the secondary battery (E1), A regression curve generation unit (113) generates a degradation regression curve of the secondary battery (E1) by performing a curve regression on multiple SOH values ​​identified in the time series of the secondary battery (E1), A point of interest setting unit (114) sets a point of interest on the aforementioned degradation regression curve, A regression line generation unit (115) generates a degradation regression line for the secondary battery (E1) from the point of interest onward by performing linear regression on multiple SOHs from the point of interest onward, A slope ratio calculation unit (116) calculates the ratio between the slope of the tangent line at the point of interest on the degradation regression curve and the slope of the degradation regression line beyond the point of interest, Based on the aforementioned slope ratio, a rapid deterioration determination unit (119) determines the rapid deterioration of the secondary battery (E1), A deterioration determination system (1) characterized by comprising the following: According to this method, rapid degradation of the secondary battery (E1) can be determined with high accuracy. [Item 2] The deterioration determination system (1) according to item 1 is characterized in that the point of interest setting unit (114) sets a point on the deterioration regression curve at a position that is a predetermined value backward from the end of a plurality of SOH data intervals specified in time series as the point of interest. According to this method, by setting a hypothetical change point in the rate of deterioration, it is possible to achieve highly accurate detection of rapid deterioration. [Item 3] The system further includes a regression curve evaluation unit (117) that evaluates the reliability of the degradation regression curve based on the relationship between the degradation regression curve and a plurality of underlying SOHs. The deterioration determination system (1) according to item 1 or 2, characterized in that the rapid deterioration determination unit (119) avoids determining rapid deterioration of the secondary battery (E1) if the reliability of the deterioration regression curve does not meet the set conditions. This approach avoids the need to determine rapid degradation based on unreliable SOH data. [Item 4] The system further includes a prediction point search unit (118) that dynamically changes the aforementioned points of interest to search for a predicted point at which rapid degradation of the secondary battery (E1) will begin. The rapid deterioration determination unit (119) is characterized by determining the rapid deterioration of the secondary battery (E1) based on the ratio of the slope at the prediction point. or 2 The deterioration determination system described in (1). According to this, it is possible to set a point of interest that coincides with or approximates the starting point of abrupt deterioration. [Item 5] The regression curve generation unit (113) generates a degradation regression curve for the secondary battery (E1) by performing curve regression on multiple SOHs prior to the point of interest among multiple SOHs identified in the time series of the secondary battery (E1). The degradation determination system (1) according to item 4, characterized in that the prediction point search unit (118) sets the convergence point of the intersection of a plurality of degradation regression curves based on SOH before the point of interest and a plurality of degradation regression lines based on SOH after the point of interest as the predicted point for the onset of rapid degradation of the secondary battery (E1). According to this method, it is possible to predict with high accuracy the starting point of rapid deterioration, which should be a point of interest. [Item 6] The deterioration determination system (1) according to item 5, characterized in that the prediction point search unit (118) sets the range in which SOH is greater than or equal to a first set value, the range in which the total discharge capacity of the secondary battery (E1) is less than or equal to a second set value, or the range in which the usage period of the secondary battery (E1) is less than or equal to a third set value outside the search range of the prediction point. According to this method, by limiting the search range for the starting point of abrupt deterioration, which should be the point of focus, it is possible to prevent the point of focus from being set in a range where the probability of abrupt deterioration occurring is low. [Item 7] The rapid deterioration determination unit (119) is characterized in that it determines that rapid deterioration of the secondary battery (E1) has occurred when the ratio of the slope of the deterioration regression line after the point of interest to the slope of the tangent line at the point of interest on the deterioration regression curve exceeds a threshold, or when the ratio of the slope of the tangent line at the point of interest on the deterioration regression curve to the slope of the deterioration regression line after the point of interest falls below a threshold. or 2 The deterioration determination system described in (1). According to this method, by detecting whether or not the rate of deterioration has changed abruptly, it is possible to determine rapid deterioration with high accuracy. [Item 8] The rapid degradation determination unit (119) is characterized in that, when the behavior of the slope ratio in the time series data of the slope ratio satisfies the determination conditions generated based on learning, it determines that rapid degradation or signs of rapid degradation have occurred in the secondary battery (E1). or 2 The deterioration determination system described in (1). According to this method, rapid deterioration can be detected in advance or immediately after it occurs. [Item 9] Secondary battery (E1) Steps to acquire battery data, Based on the aforementioned battery data, the step of identifying the SOH of the secondary battery (E1) is performed. The steps include generating a degradation regression curve for the secondary battery (E1) by performing curve regression on multiple SOH values ​​identified in the time series of the secondary battery (E1), The steps include setting a point of interest on the aforementioned degradation regression curve, The steps include generating a degradation regression line for the secondary battery (E1) from the point of interest onward by performing linear regression on multiple SOHs from the point of interest onward, A step of calculating the ratio between the slope of the tangent line at the point of interest on the degradation regression curve and the slope of the degradation regression line beyond the point of interest, A step of determining the rapid deterioration of the secondary battery (E1) based on the ratio of the aforementioned slope, A method for determining deterioration, characterized by having the following features. According to this method, rapid degradation of the secondary battery (E1) can be determined with high accuracy. [Item 10] Secondary battery (E1) The process of acquiring battery data, Based on the aforementioned secondary battery data, a process is performed to identify the SOH of the secondary battery (E1), A process to generate a degradation regression curve for the secondary battery (E1) by performing curve regression on multiple SOH values ​​identified in the time series of the secondary battery (E1), The process of setting a point of interest on the aforementioned degradation regression curve, A process to generate a degradation regression line for the secondary battery (E1) from the point of interest onward by performing linear regression on multiple SOHs from the point of interest onward, A process for calculating the ratio between the slope of the tangent line at the point of interest on the degradation regression curve and the slope of the degradation regression line beyond the point of interest, A process to determine the rapid deterioration of the secondary battery (E1) based on the aforementioned slope ratio, A degradation detection program characterized by being executed by a computer. According to this method, rapid degradation of the secondary battery (E1) can be determined with high accuracy.

[0097] The embodiments may be specified by the following items.

[0098] [Item 1] A data acquisition unit (111) that acquires battery data, Based on the aforementioned battery data, the SOH identification unit (112) identifies the SOH of the secondary battery (E1), A regression curve generation unit (113) generates a degradation regression curve of the secondary battery (E1) by performing a curve regression on multiple SOH values ​​identified in the time series of the secondary battery (E1), A point of interest setting unit (114) sets a point of interest on the aforementioned degradation regression curve, A regression line generation unit (115) generates a degradation regression line for the secondary battery (E1) from the point of interest onward by performing linear regression on multiple SOHs from the point of interest onward, A slope ratio calculation unit (116) calculates the ratio between the slope of the tangent line at the point of interest on the degradation regression curve and the slope of the degradation regression line beyond the point of interest, Based on the aforementioned slope ratio, a rapid deterioration determination unit (119) determines the rapid deterioration of the secondary battery (E1), A deterioration determination system (1) characterized by comprising the following:

[0099] According to this method, rapid degradation of the secondary battery (E1) can be determined with high accuracy.

[0100] [Item 2] The deterioration determination system (1) according to item 1 is characterized in that the point of interest setting unit (114) sets a point on the deterioration regression curve at a position that is a predetermined value backward from the end of a plurality of SOH data intervals specified in time series as the point of interest.

[0101] According to this method, by setting a hypothetical change point in the rate of deterioration, it is possible to achieve highly accurate detection of rapid deterioration.

[0102] [Item 3] The system further includes a regression curve evaluation unit (117) that evaluates the reliability of the degradation regression curve based on the relationship between the degradation regression curve and a plurality of underlying SOHs. The deterioration determination system (1) according to item 1 or 2, characterized in that the rapid deterioration determination unit (119) avoids determining rapid deterioration of the secondary battery (E1) if the reliability of the deterioration regression curve does not meet the set conditions.

[0103] This approach avoids the need to determine rapid degradation based on unreliable SOH data.

[0104] [Item 4] The system further includes a prediction point search unit (118) that dynamically changes the aforementioned points of interest to search for a predicted point at which rapid degradation of the secondary battery (E1) will begin. The deterioration determination system (1) according to any one of items 1 to 3, characterized in that the rapid deterioration determination unit (119) determines the rapid deterioration of the secondary battery (E1) based on the ratio of the slope at the prediction point.

[0105] According to this, it is possible to set a point of interest that coincides with or approximates the starting point of abrupt deterioration.

[0106] [Item 5] The regression curve generation unit (113) generates a degradation regression curve for the secondary battery (E1) by performing curve regression on multiple SOHs prior to the point of interest among multiple SOHs identified in the time series of the secondary battery (E1). The degradation determination system (1) according to item 4, characterized in that the prediction point search unit (118) sets the convergence point of the intersection of a plurality of degradation regression curves based on SOH before the point of interest and a plurality of degradation regression lines based on SOH after the point of interest as the predicted point for the onset of rapid degradation of the secondary battery (E1).

[0107] According to this method, it is possible to predict with high accuracy the starting point of rapid deterioration, which should be a point of interest.

[0108] [Item 6] The deterioration determination system (1) according to item 5, characterized in that the prediction point search unit (118) sets the range in which SOH is greater than or equal to a first set value, the range in which the total discharge capacity of the secondary battery (E1) is less than or equal to a second set value, or the range in which the usage period of the secondary battery (E1) is less than or equal to a third set value outside the search range of the prediction point.

[0109] According to this method, by limiting the search range for the starting point of abrupt deterioration, which should be the point of focus, it is possible to prevent the point of focus from being set in a range where the probability of abrupt deterioration occurring is low.

[0110] [Item 7] The deterioration determination system (1) according to any one of items 1 to 5, characterized in that the rapid deterioration determination unit (119) determines that rapid deterioration of the secondary battery (E1) has occurred when the ratio of the slope of the deterioration regression line after the point of interest to the slope of the tangent line at the point of interest on the deterioration regression curve exceeds a threshold, or when the ratio of the slope of the tangent line at the point of interest on the deterioration regression curve to the slope of the deterioration regression line after the point of interest falls below a threshold.

[0111] According to this method, by detecting whether or not the rate of deterioration has changed abruptly, it is possible to determine rapid deterioration with high accuracy.

[0112] [Item 8] The deterioration determination system (1) according to any one of items 1 to 5, characterized in that the rapid deterioration determination unit (119) determines that rapid deterioration or signs of rapid deterioration have occurred in the secondary battery (E1) when the behavior of the slope ratio in the time series data of the slope ratio satisfies the determination conditions generated based on learning.

[0113] According to this method, rapid deterioration can be detected in advance or immediately after it occurs.

[0114] [Item 9] Steps to acquire battery data, Based on the aforementioned battery data, the step of identifying the SOH of the secondary battery (E1) is performed. The steps include generating a degradation regression curve for the secondary battery (E1) by performing curve regression on multiple SOH values ​​identified in the time series of the secondary battery (E1), The steps include setting a point of interest on the aforementioned degradation regression curve, The steps include generating a degradation regression line for the secondary battery (E1) from the point of interest onward by performing linear regression on multiple SOHs from the point of interest onward, A step of calculating the ratio between the slope of the tangent line at the point of interest on the degradation regression curve and the slope of the degradation regression line beyond the point of interest, A step of determining the rapid deterioration of the secondary battery (E1) based on the ratio of the aforementioned slope, A method for determining deterioration, characterized by having the following features.

[0115] According to this method, rapid degradation of the secondary battery (E1) can be determined with high accuracy.

[0116] [Item 10] The process of acquiring battery data, Based on the aforementioned secondary battery data, a process is performed to identify the SOH of the secondary battery (E1), A process to generate a degradation regression curve for the secondary battery (E1) by performing curve regression on multiple SOH values ​​identified in the time series of the secondary battery (E1), The process of setting a point of interest on the aforementioned degradation regression curve, A process to generate a degradation regression line for the secondary battery (E1) from the point of interest onward by performing linear regression on multiple SOHs from the point of interest onward, A process for calculating the ratio between the slope of the tangent line at the point of interest on the degradation regression curve and the slope of the degradation regression line beyond the point of interest, A process to determine the rapid deterioration of the secondary battery (E1) based on the aforementioned slope ratio, A degradation detection program characterized by being executed by a computer.

[0117] According to this method, rapid degradation of the secondary battery (E1) can be determined with high accuracy. [Explanation of symbols]

[0118] 1 Degradation Judgment System, 2 Network, 3 Electric Vehicle, 4 Charger, 5 Charging Cable, 6 Commercial Power System, 7 Operation Management Terminal Device, 11 Processing Unit, 111 Data Acquisition Unit, 112 SOH Identification Unit, 113 Regression Curve Generation Unit, 114 Point of Interest Setting Unit, 115 Regression Line Generation Unit, 116 Slope Ratio Calculation Unit, 117 Regression Curve Evaluation Unit, 118 Prediction Point Search Unit, 119 Rapid Degradation Judgment Unit, 1110 Notification Unit, 12 Storage Unit, 121 Battery Data Holding Unit, 13 Communication Unit, 30 Vehicle Control Unit, 34 Motor, 35 Inverter, 36 Wireless Communication Unit, 36a Antenna, 40 Battery Pack, 41 Battery Module, 42 Battery Management Unit, 43 Voltage Measurement Unit, 44 Temperature Measurement Unit, 45 Current measurement unit, 46 battery control unit, E1-En cell, T1-T2 temperature sensor, RY1-RY2 relay.

Claims

1. A data acquisition unit for acquiring battery data of a secondary battery, Based on the aforementioned battery data, the SOH (State of Health) identification unit identifies the SOH of the secondary battery, A regression curve generation unit generates a degradation regression curve for the secondary battery by performing a curve regression on multiple SOH values ​​specified in the time series of the secondary battery. A point of focus setting unit for setting points of focus on the aforementioned degradation regression curve, A regression line generation unit generates a regression line for the secondary battery from the point of interest onward by performing linear regression on multiple SOHs after the aforementioned point of interest, A slope ratio calculation unit calculates the ratio between the slope of the tangent line at the point of interest on the degradation regression curve and the slope of the degradation regression line beyond the point of interest. A rapid deterioration determination unit that determines the rapid deterioration of the secondary battery based on the aforementioned slope ratio, A deterioration determination system characterized by comprising the following features.

2. The deterioration determination system according to claim 1, characterized in that the point of interest setting unit sets a point on the deterioration regression curve located at a predetermined value backward from the end of a plurality of SOH data intervals specified in time series as the point of interest.

3. The system further includes a regression curve evaluation unit that evaluates the reliability of the degradation regression curve based on the relationship between the degradation regression curve and a plurality of underlying SOHs. The degradation determination system according to claim 1 or 2, characterized in that the rapid degradation determination unit avoids determining the rapid degradation of the secondary battery if the reliability of the degradation regression curve does not meet the set conditions.

4. The system further includes a prediction point search unit that dynamically changes the aforementioned points of interest to search for a predicted point at which rapid degradation of the secondary battery will begin. The deterioration determination system according to claim 1 or 2, characterized in that the rapid deterioration determination unit determines the rapid deterioration of the secondary battery based on the ratio of the slope at the prediction point.

5. The regression curve generation unit generates a degradation regression curve for the secondary battery by performing curve regression on multiple SOHs prior to the point of interest among multiple SOHs specified in the time series of the secondary battery. The degradation determination system according to claim 4, characterized in that the prediction point search unit sets the convergence point of the intersection of a plurality of degradation regression curves based on SOH before the point of interest and a plurality of degradation regression lines based on SOH after the point of interest as the predicted point for the onset of rapid degradation of the secondary battery.

6. The deterioration determination system according to claim 5, characterized in that the prediction point search unit sets the range in which SOH is greater than or equal to a first set value, the range in which the total discharge capacity of the secondary battery is less than or equal to a second set value, or the range in which the usage period of the secondary battery is less than or equal to a third set value outside the search range of the prediction point.

7. The deterioration determination system according to claim 1 or 2, characterized in that the rapid deterioration determination unit determines that rapid deterioration of the secondary battery has occurred when the ratio of the slope of the deterioration regression line after the point of interest to the slope of the tangent line at the point of interest on the deterioration regression curve exceeds a threshold, or when the ratio of the slope of the tangent line at the point of interest on the deterioration regression curve to the slope of the deterioration regression line after the point of interest falls below a threshold.

8. The deterioration determination system according to claim 1 or 2, characterized in that the rapid deterioration determination unit determines that rapid deterioration or signs of rapid deterioration have occurred in the secondary battery when the behavior of the slope ratio in the time series data of the slope ratio satisfies the determination conditions generated based on learning.

9. A step of acquiring battery data of a secondary battery, Based on the aforementioned battery data, the step of identifying the State of Health (SOH) of the secondary battery, The steps include generating a degradation regression curve for the secondary battery by performing curve regression on multiple SOH values ​​identified in the time series of the secondary battery, The steps include setting a point of interest on the aforementioned degradation regression curve, The steps include: generating a degradation regression line for the secondary battery from the point of interest onwards by performing linear regression on multiple SOHs after the aforementioned point of interest; A step of calculating the ratio between the slope of the tangent line at the point of interest on the degradation regression curve and the slope of the degradation regression line beyond the point of interest, A step of determining the rapid deterioration of the secondary battery based on the aforementioned slope ratio, A method for determining deterioration, characterized by having the following features.

10. A process for acquiring battery data of a secondary battery, Based on the aforementioned battery data, a process is performed to identify the State of Health (SOH) of the secondary battery, A process to generate a degradation regression curve for the secondary battery by performing curve regression on multiple SOH values ​​identified in the time series of the secondary battery, The process of setting a point of interest on the aforementioned degradation regression curve, A process to generate a degradation regression line for the secondary battery from the point of interest onwards by performing linear regression on multiple SOHs after the aforementioned point of interest, A process for calculating the ratio between the slope of the tangent line at the point of interest on the degradation regression curve and the slope of the degradation regression line beyond the point of interest, A process to determine the rapid deterioration of the secondary battery based on the aforementioned slope ratio, A degradation detection program characterized by being executed by a computer.

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