Deterioration determination system, deterioration determination method, and deterioration determination program
By generating degradation regression curves and slope ratio calculations for secondary batteries, the accuracy problem of rapid degradation determination of secondary batteries is solved, high-precision degradation trend prediction is achieved, and safety risks are reduced.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- PANASONIC INTELLECTUAL PROPERTY MANAGEMENT CO LTD
- Filing Date
- 2022-07-26
- Publication Date
- 2026-05-29
Smart Images
Figure CN117795358B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to a degradation determination system, degradation determination method, and degradation determination procedure for determining rapid degradation of a battery. Background Technology
[0002] In recent years, hybrid vehicles (HV), plug-in hybrid vehicles (PHV), and electric vehicles (EV) have become increasingly popular. These electric vehicles utilize secondary batteries, such as lithium-ion batteries, as a key component.
[0003] When secondary batteries such as lithium-ion batteries are repeatedly charged and discharged at low temperatures and high rates, their capacity is prone to rapid degradation (hereinafter referred to as rapid degradation or tertiary degradation) due to factors such as the reduction of electrolyte and the decrease in electrode reaction area. When the secondary batteries installed in electric vehicles degrade rapidly, the driving range decreases sharply, thus reducing convenience.
[0004] The replacement interval for automotive rechargeable batteries is generally set based on their State of Health (SOH). The same applies to rechargeable batteries used outside of automotive applications (e.g., those used in stationary energy storage systems). When a rechargeable battery deteriorates rapidly, its SOH drops to the replacement threshold much faster than anticipated by users and managers. Therefore, when rapid deterioration occurs, it's easy to find yourself facing replacement before you're ready to do so.
[0005] Furthermore, rapid degradation can easily lead to internal short circuits in the secondary battery due to precipitation. From a safety perspective, early replacement is necessary after such rapid degradation.
[0006] As a method for detecting rapid degradation of secondary batteries, the following method is proposed: extracting SOH data input in time series with a short time window and making a linear approximation, and detecting rapid degradation based on the change in the slope of the approximation line (for example, see Patent Document 1).
[0007] Existing technical documents
[0008] Patent documents
[0009] Patent Document 1: International Publication No. 17 / 098686 Summary of the Invention
[0010] The values of SOC (State of Charge), FCC (Full Charge Capacity), and SOH, calculated based on the voltage and current measurements of a secondary battery, are affected by sensor measurement errors and noise. When the influence of errors and noise is significant, the slope of the approximate straight line becomes unstable. Furthermore, it is difficult to set an appropriate threshold for detecting rapid degradation based on the slope of the approximate straight line.
[0011] This disclosure was made in view of this situation, and its purpose is to provide a technique for accurately determining the rapid degradation of secondary batteries.
[0012] To address the aforementioned problems, a degradation assessment system according to one aspect of this disclosure comprises: a data acquisition unit that acquires battery data; a State of Health (SOH) determination unit that determines the SOH of the secondary battery based on the battery data; a regression curve generation unit that performs curve regression on multiple SOHs determined by time series for the secondary battery to generate a degradation regression curve for the secondary battery; a focus point setting unit that sets a focus point on the degradation regression curve; a regression line generation unit that performs linear regression on multiple SOHs after the focus point to generate a degradation regression line for the secondary battery after the focus point; a slope ratio calculation unit that calculates the ratio of the slope of the tangent at the focus point on the degradation regression curve to the slope of the degradation regression line after the focus point; and a rapid degradation assessment unit that determines rapid degradation of the secondary battery based on the slope ratio.
[0013] Furthermore, any combination of the above structural elements, or any manner in which the present disclosure is presented in an apparatus, system, method, computer program, or recording medium containing a computer program, is also valid as a form of the present disclosure.
[0014] According to this disclosure, the rapid degradation of secondary batteries can be determined with high precision. Attached Figure Description
[0015] Figure 1 This is a diagram used to illustrate the degradation determination system involved in the implementation method.
[0016] Figure 2 It is a diagram used to illustrate the main structure of electric vehicles and battery packs.
[0017] Figure 3 This is a diagram illustrating a structural example of the degradation determination system according to the embodiment.
[0018] Figure 4 This is a diagram used to illustrate the estimation method used by the FCC.
[0019] Figure 5 This is a graph showing an example of the time series data of the SOH of a secondary battery.
[0020] Figure 6 This is a graph showing an example of the degradation regression curve, tangent line, and degradation regression line in a state where rapid degradation has not occurred.
[0021] Figure 7 This is a diagram showing an example of the degradation regression curve, tangent line, and degradation regression line under a state of rapid degradation.
[0022] Figure 8 This is a graph illustrating an example of generating a degradation regression curve based on measured values of the State of Harmony (SOH) of cells included in a battery pack in an electric vehicle.
[0023] Figure 9 This is a diagram (1) used to illustrate the search process for predicting the start of rapid degradation.
[0024] Figure 10 This is a diagram (Figure 2) used to illustrate the search process for predicting the start of rapid degradation.
[0025] Figure 11 This is a diagram illustrating a specific example of the rapid degradation determination method involved in the comparative example.
[0026] Figure 12 This is a diagram illustrating a specific example of the rapid degradation determination method involved in the implementation method.
[0027] Figure 13 This is a flowchart illustrating a first processing example of the rapid degradation determination method according to the embodiment.
[0028] Figure 14 This is a flowchart illustrating a second processing example of the rapid degradation determination method according to the embodiment. Detailed Implementation
[0029] Figure 1 This is a diagram illustrating the degradation determination system 1 involved in the embodiment. The degradation determination system 1 involved in the embodiment is used to determine the degradation of the battery pack 40 (see reference 3) mounted on the electric vehicle 3. Figure 2 Did it result in a rapidly deteriorating system? Figure 1The diagram illustrates an example of a delivery service provider utilizing the degradation assessment system 1. The degradation assessment system 1 can be built, for example, on a company server located in the company's facilities or data center, which is the service provider offering operation management support services for electric vehicles 3. Alternatively, the degradation assessment system 1 can be built on a cloud server utilized based on a cloud service contract. Furthermore, the degradation assessment system 1 can be built on multiple servers distributed across multiple locations (data centers, company facilities). These multiple servers can also be any combination of multiple company servers, multiple cloud servers, or a combination of company servers and cloud servers.
[0030] Each delivery operator owns multiple electric vehicles 3 and at least one charger 4, and has delivery locations for parking the multiple electric vehicles 3. The electric vehicles 3 are connected to the charger 4 via charging cables 5, and the battery pack 40 mounted on the electric vehicle 3 is charged from the charger 4 via the charging cables 5.
[0031] An operation management terminal device 7 is installed at the delivery location of the delivery service provider. The operation management terminal device 7 is, for example, composed of a PC. The operation management terminal device 7 is used to manage multiple electric vehicles 3 belonging to the delivery location. The operation manager of the delivery service provider can use the operation management terminal device 7 to create delivery plans and charging plans for multiple electric vehicles 3. The operation management terminal device 7 can access the degradation assessment system 1 via network 2.
[0032] Network 2 is a general term for communication paths such as the Internet, dedicated lines, and VPNs (Virtual Private Networks), with no restrictions on communication media or protocols. For example, communication media can include mobile phone networks (cellular networks), wireless LANs, wired LANs, fiber optic networks, ADSL networks, and CATV networks. For communication protocols, examples include TCP (Transmission Control Protocol) / IP (Internet Protocol), UDP (User Datagram Protocol) / IP, and Ethernet (registered trademark).
[0033] Figure 2This diagram illustrates the main structure of the electric vehicle 3 and the battery pack 40. The battery pack 40 is connected to the motor 34 via a first relay RY1 and an inverter 35. During power operation, 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, it converts the AC power supplied from the motor 34 back into DC power and supplies it to the battery pack 40. The motor 34 is a three-phase AC motor that rotates according to the AC power supplied from the inverter 35 during power operation. During regeneration, it converts the rotational energy generated due to deceleration into AC power and supplies it to the inverter 35.
[0034] The first relay RY1 is a contactor inserted into the wiring connecting the battery pack 40 and the inverter 35. When the vehicle is in motion, the vehicle control unit 30 controls the first relay RY1 to be in the ON (closed) state, electrically connecting the battery pack 40 to the power system of the electric vehicle 3. When the vehicle is not in motion, the vehicle control unit 30, in principle, controls the first relay RY1 to be in the OFF (disconnected) state, electrically disconnecting the battery pack 40 from the power system of the electric vehicle 3. Alternatively, other types of switches, such as semiconductor switches, can be used instead of the relay.
[0035] The battery pack 40 can be charged from the commercial power system 6 by connecting to a charger 4 located outside the electric vehicle 3 via a charging cable 5. The charger 4 is connected to the commercial power system 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, can 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 controls the second relay RY2 to the OFF state after charging is completed.
[0036] Generally, charging is performed using AC during normal charging and DC during fast charging. When charging with AC, the AC power is converted to DC power via an on-board charger (not shown) plugged between the second relay RY2 and the battery pack 40.
[0037] The battery pack 40 includes a battery module 41 and a battery management unit 42. The battery module 41 includes multiple units E1-En connected in series. Alternatively, the battery module 41 can be constructed by connecting multiple battery modules in series or in parallel. The units can be lithium-ion battery units, nickel-metal hydride battery units, lead-acid battery units, etc. In the following example, a lithium-ion battery unit (nominal voltage: 3.6-3.7V) is assumed to be used. The number of units E1-En connected in series is determined by the drive voltage of the motor 34.
[0038] A shunt resistor Rs is connected in series with multiple cells E1-En. The shunt resistor Rs functions as a current sensing element. Alternatively, a Hall element can be used instead of the shunt resistor Rs. Furthermore, multiple temperature sensors T1 and T2 are provided within the battery module 41 for detecting the temperature of the multiple cells E1-En. A single temperature sensor can be provided within the battery module, or a temperature sensor can be provided for each of the multiple cells. The temperature sensors T1 and T2 can be, for example, thermistors.
[0039] The battery management unit 42 includes a voltage measuring unit 43, a temperature measuring unit 44, a current measuring unit 45, and a battery control unit 46. Each node of the multiple cells E1-En connected in series is connected to the voltage measuring unit 43 via multiple voltage lines. The voltage measuring unit 43 measures the voltage of each cell E1-En by measuring the voltage between two adjacent voltage lines. The voltage measuring unit 43 sends the measured voltage of each cell E1-En to the battery control unit 46.
[0040] The voltage measuring unit 43 operates at a high voltage relative to the battery control unit 46; therefore, the voltage measuring unit 43 and the battery control unit 46 are connected via a communication line in an insulated state. The voltage measuring unit 43 can be constructed from an ASIC (Application Specific Integrated Circuit) or a general-purpose analog front-end IC. The voltage measuring unit 43 includes a multiplexer and an A / D converter. The multiplexer outputs the voltages between two adjacent voltage lines sequentially from top to bottom to the A / D converter. The A / D converter converts the analog voltage input from the multiplexer into a digital value.
[0041] The temperature measurement unit 44 includes voltage divider resistors and an A / D converter. The A / D converter sequentially converts multiple analog voltages, which are obtained by voltage division by multiple temperature sensors T1, 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 values measured by the temperature sensor closest to each cell E1-En.
[0042] 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 this digital value.
[0043] Furthermore, an A / D converter is installed in the battery control unit 46. When the battery control unit 46 is provided with an analog input port, the temperature measuring unit 44 and the current measuring unit 45 can also output analog voltage to the battery control unit 46, and convert the analog voltage into a digital value through the A / D converter in the battery control unit 46.
[0044] The battery control unit 46 (also known as BMU or BMS) includes a microcontroller, a communication controller, and non-volatile memory. The battery control unit 46 is connected to the vehicle control unit 30 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.
[0045] The battery control unit 46 manages the state of the multiple units E1-En based on the voltage, temperature and current of the multiple units E1-En measured by the voltage measuring unit 43, the temperature measuring unit 44 and the current measuring unit 45.
[0046] The battery control unit 46 estimates the State of Charge (SOC) of each of the multiple cells E1-En included in the battery module 41. The battery control unit 46 combines the Open Circuit Voltage (OCV) method with the current integration method to estimate the SOC. The OCV method estimates the SOC based on the cell's OCV and the cell's SOC-OCV curve. The cell's SOC-OCV curve is pre-created based on characteristic tests conducted by the battery manufacturer and is registered in the microcontroller's internal memory at the factory.
[0047] The current integration method estimates the state of charge (SOC) based on the integral value of the initial charge-discharge velocity (OCV) of the cell and the current flowing through the cell. However, the current measurement error accumulates with increasing charge-discharge time. Therefore, it is preferable to use the SOC estimated by the OCV method to correct the SOC estimated by the current integration method.
[0048] The battery control unit 46 sends the voltage, current, temperature and SOC of the battery module 41 and each unit E1-En to the vehicle control unit 30 via the vehicle network.
[0049] The vehicle control unit 30 is the vehicle ECU (Electronic Control Unit) that controls the electric vehicle 3 as a whole. For example, it can be composed of a comprehensive VCM (Vehicle Control Module). The vehicle control unit 30 includes a communication controller for connecting to the vehicle network and a communication controller (e.g., a CAN controller) for communicating with the charger 4 via the charging cable 5.
[0050] The wireless communication unit 36 performs signal processing for wireless connection with the network 2 via the antenna 36a. The wireless communication network that the electric vehicle 3 can wirelessly connect to includes, for example, mobile phone networks (cellular networks), wireless LANs, V2I (Vehicle to Infrastructure), V2V (Vehicle to Vehicle), ETC (Electronic Toll Collection System), DSRC (Dedicated Short Range Communications), etc.
[0051] During the operation of the electric vehicle 3, the vehicle control unit 30 can use the wireless communication unit 36 to transmit driving data, including battery data, to the degradation assessment system 1 in real time. The driving data includes at least the vehicle speed of the electric vehicle 3. The battery data includes the voltage, current, temperature, and SOC of multiple cells E1-En. The vehicle control unit 30 samples this data periodically (e.g., every 10 seconds) and transmits it to the degradation assessment system 1 each time.
[0052] Furthermore, the vehicle control unit 30 can also accumulate the driving data of the electric vehicles 3 in its internal memory and send the accumulated driving data together at a predetermined time. For example, the vehicle control unit 30 can send the accumulated driving data in its memory to the operation management terminal device 7 at the end of a business day. The operation management terminal device 7 sends the driving data of multiple electric vehicles 3 to the degradation judgment system 1 at a predetermined time.
[0053] Additionally, when charging from a charger 4 equipped with network communication capabilities, the vehicle control unit 30 can also send the driving data accumulated in the memory to the charger 4 via the charging cable 5. The charger 4 then sends the received driving data to the degradation assessment system 1. This example is effective when the charger 4 has network communication capabilities and the electric vehicle 3 is not equipped with wireless communication capabilities.
[0054] Figure 3This is a diagram illustrating a structural example of the degradation determination system 1 according to the embodiment. The degradation determination system 1 includes 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 via wired or wireless means.
[0055] The processing unit 11 includes a data acquisition unit 111, a State of Health (SOH) determination unit 112, a regression curve generation unit 113, a focus point 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 degradation judgment unit 119, and a notification unit 1110. The functions of the processing unit 11 can be implemented through the cooperation of hardware and software resources, or solely through hardware resources. Hardware resources can utilize CPUs, ROMs, RAMs, GPUs (Graphics Processing Units), ASICs (Application Specific Integrated Circuits), FPGAs (Field Programmable Gate Arrays), and other LSIs. Software resources can utilize operating systems, application programs, etc.
[0056] The storage unit 12 includes a battery data retention unit 121. The storage unit 12 includes non-volatile recording media such as HDD (Hard Disk Drive) and SSD (Solid State Drive) for storing various types of data.
[0057] The data acquisition unit 111 acquires driving data (including battery data) of the electric vehicle 3 when it is in motion or when it is parked via the network 2, and saves the acquired battery data in the battery data storage unit 121. Driving data other than battery data can be saved together in the battery data storage unit 121, or it can be saved in a separate driving data storage unit (not shown).
[0058] The SOH determination unit 112 reads the battery data stored in the battery data holding unit 121 of the battery pack 40 mounted on the electric vehicle 3 at a predetermined time to calculate the SOH of each cell E1-En included in the battery pack 40. The predetermined time may be the time when the electric vehicle 3 finishes charging from the charger 4, or the time when it first starts driving after charging is completed.
[0059] The SOH determination unit 112 estimates the FCC of each cell E1-En based on the voltage (OCV) and SOC at two points (both when the battery is stopped) of each cell E1-En contained in the battery data, and estimates the SOH based on the estimated FCC.
[0060] Figure 4 This is a diagram used to illustrate the estimation method for FCC. The SOH determination unit 112 calculates the difference (ΔSOC) between the SOC corresponding to OCV1 before the start of charging and the SOC corresponding to OCV2 after the end of charging. The SOH determination unit 112 integrates the current during the period from the start of charging to the end of charging to calculate the current integral value Q.
[0061] The SOH determination unit 112 calculates the FCC using the following formula (1) and calculates the SOH using the following formula (2) based on the estimated FCC. The SOH is defined by the ratio of the current FCC to the initial FCC. The lower the value (closer to 0%), the more severe the degradation.
[0062] FCC=Q / ΔSOC…(Equation 1)
[0063] SOH = Current FCC / Initial FCC... (Equation 2)
[0064] In addition, the FCC can also estimate based on the OCV1 before the start of driving and the OCV2 after the start of driving of the electric vehicle.
[0065] The SOH determination unit 112 stores the estimated SOH of each cell E1-En in the battery data storage unit 121. Alternatively, the FCC and SOH of each cell E1-En can 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 sent from the electric vehicle 3 to the degradation assessment system 1, it is not necessary to estimate the FCC and SOH on the degradation assessment system 1 side.
[0066] The regression curve generation unit 113 reads all the time-series determined SOH data of the target cells included in the battery pack 40 from the battery data holding unit 121, and performs curve regression on the read multiple SOHs to generate a degradation regression curve for the target cells. The curve regression can, for example, use the least squares method.
[0067] Figure 5 This is a graph showing an example of time-series data for the state of discharge (SOH) of a secondary battery. The horizontal axis represents the total discharge capacity [Ah], and the vertical axis represents SOH [%]. It is known that cell degradation is exacerbated proportionally to the square root of the total discharge capacity and time (root rule, 0.5 multiplied by the method) as shown in Equation 3 below.
[0068] SOH=w0+w1√t…(Equation 3)
[0069] w0 is the initial value, and w1 is the degradation coefficient.
[0070] The regression curve generation unit 113 calculates the degradation coefficient w1 in Equation 3 by performing an exponential curve regression with the total discharge capacity or time as the independent variable and the SOH as the dependent variable, raised to the power of 0.5. w0 is a common value and is usually set in the range of 1.0 to 1.1. When the actual initial capacity is consistent with the nominal value, w0 is set to 1.0. When the nominal value is set as the minimum guaranteed amount and is lower than the actual initial capacity, a value greater than 1.0 is set.
[0071] When Figure 5 As shown, when a secondary battery deteriorates rapidly, the rate of degradation changes from deterioration according to the root law to linear degradation (faster). In this embodiment, rapid degradation is detected through the following process.
[0072] The focus point setting unit 114 sets focus point A on the degradation regression curve generated by the regression curve generation unit 113. Focus point setting unit 114 sets focus point A as the point on the degradation regression curve located at the position after tracing a predetermined value from the end of multiple SOH data intervals determined by time series. For example, focus point setting unit 114 may also set focus point A as the point on the degradation regression curve that corresponds to the latest n days ago (e.g., 30 days ago, 60 days ago, 90 days ago). Alternatively, focus point setting unit 114 may also set focus point A as the point on the degradation regression curve that corresponds to the total discharge capacity after tracing α[Ah] from the latest total discharge capacity.
[0073] Alternatively, the focus point setting unit 114 can also set a point on the degradation regression curve that matches the time or total discharge capacity of data points n points before (e.g., 50 points before) the latest data as focus point A. Furthermore, the focus point setting unit 114 can also use data at positions after dividing all data intervals at a predetermined ratio (3:1) as data matching the data n points before the latest data.
[0074] The regression line generation unit 115 performs linear regression on multiple SOHs after the focus point A set by the focus point setting unit 114 to generate a degradation regression line for the target cell after the focus point A. The linear regression can, for example, use the least squares method.
[0075] It is known empirically that rapid cell degradation, as shown in Equation 4 below, tends to be linearly approximated to the total discharge capacity and time.
[0076] SOH=aT+b…(Equation 4)
[0077] a represents the degradation rate after the start of rapid degradation, and b represents the SOH at the start of rapid degradation.
[0078] The slope ratio calculation unit 116 calculates the ratio (a / a_tan) of the slope of the tangent line at point A on the degradation regression curve to the slope of the degradation regression line based on multiple SOHs after point A. The slope a_tan of the tangent line at point A on the degradation regression curve can be calculated based on the differential coefficient of point A.
[0079] The rapid degradation determination unit 119 determines the rapid degradation of the target cell based on the slope ratio (a / a_tan) calculated by the slope ratio calculation unit 116. For example, if the slope ratio (a / a_tan) exceeds the threshold th as shown in Equation 5 below, the rapid degradation determination unit 119 determines that the target cell has undergone rapid degradation.
[0080] a / a_tan>th…(Equation 5)
[0081] The threshold th can be set considering experimental and simulation results, as well as the designer's insights. 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, with the threshold th set to 2, it determines whether the predicted degradation rate at point A has increased by more than twice.
[0082] Figure 6 This is a graph showing an example of the degradation regression curve sqr, tangent line tan, and degradation regression line lin under conditions where rapid degradation has not occurred. Figure 7 This is a graph illustrating an example of the degradation regression curve sqr, tangent line tan, and degradation regression line lin under a state of rapid degradation. Figure 6 In the example shown, the slope ratio (a / a_tan) is 0.666747. Figure 7 In the example shown, the slope ratio (a / a_tan) is 5.356819. Figure 7 In the example shown, the slope ratio (a / a_tan) is greater than 2, therefore it is determined that rapid degradation has occurred.
[0083] Furthermore, in the example above, the slope ratio is set 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 can also be set 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 this case, the rapid degradation determination unit 119 determines that the target unit has experienced rapid degradation when the slope ratio (a_tan / a) is less than a threshold th (e.g., 0.5).
[0084] The regression curve evaluation unit 117 evaluates the reliability of the deteriorated regression curve based on the relationship between the deteriorated regression curve generated by the regression curve generation unit 113 and multiple SOHs (Solutions of Occurrence) that form the basis data for the deteriorated regression curve. The regression curve evaluation unit 117 can use general indicators, such as the sum of squares of the residuals, the standard deviation of the residuals, the coefficient of determination, and the correlation coefficient, as indicators of the reliability of the regression curve. The larger the deviation of the SOH, the larger the values of the sum of squares and the standard deviation of the residuals. The coefficient of determination and the correlation coefficient represent the relationship between the time-dependent factor and the SOH. When the reliability of the basis data (SOH) is low, the reliability of the deteriorated regression curve also decreases.
[0085] Figure 8 This is a graph illustrating an example of generating a degradation regression curve (deg) based on measured values of the State of Harm (SOH) of the cells included in a battery pack 40 of an electric vehicle 3. Figure 8 In the example shown, the intersection of the degradation regression line who for all SOH data and the degradation regression curve deg is set as the point of interest, and a degradation regression line reg for SOH after the point of interest is generated under the condition that the data passes through the point of interest.
[0086] exist Figure 8 In the example shown, a large amount of SOH sample data was plotted above the upper limit of the degradation regression curve (deg), resulting in significant bias in the sample data. Therefore, the probability of misjudgment is high when using degradation regression curves based on such sample data for rapid degradation assessment.
[0087] If the reliability of the degradation regression curve does not meet the set conditions (for example, if the sum of squares of the residuals is greater than the set value), the rapid degradation determination unit 119 discards the degradation regression curve and does not perform rapid degradation determination.
[0088] In the above description, the point of interest A is set to a fixed value and used directly, but it is also possible to search for the optimal point of interest A. The prediction point search unit 118 dynamically changes the point of interest A to search for the prediction point at which the rapid degradation of the cell begins.
[0089] Figure 9 This is a diagram (1) used to illustrate the search process for predicting the onset of rapid degradation. Figure 10 This is a diagram (Figure 2) used to illustrate the search process for predicting the start of rapid degradation. First, the regression curve generation unit 113 generates a degradation regression curve for the target cell based on all the SOH data of the target cell. The focus point setting unit 114 determines focus point A as described above and sets focus point A as a temporarily determined focus point (initial value). The regression line generation unit 115 generates a degradation regression line based on multiple SOHs after the temporarily determined focus point A.
[0090] like Figure 9 As shown, the prediction point search unit 118 sets the intersection of the generated degradation regression curve and the generated degradation regression line as the new point of interest A'. The regression curve generation unit 113 generates a new degradation regression curve based on multiple SOHs before the new point of interest A', and the regression line generation unit 115 generates a new degradation regression line based on multiple SOHs after the new point of interest A'.
[0091] The prediction point search unit 118 sets the intersection of the newly generated degradation regression curve and the newly generated degradation regression line as the new focus point A. For example... Figure 10 As shown, the regression curve generation unit 113 generates a new degraded regression curve based on multiple SOHs prior to 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.
[0092] The prediction point search unit 118 sets the convergence point of the repeatedly updated concern point A as the prediction point where rapid degradation begins. For example, the prediction point search unit 118 can determine that concern point A has converged when the distance between concern point A before and after the update is less than a set value. Alternatively, the prediction point search unit 118 can consider concern point A to have converged when the above-mentioned update process for concern point A has been performed a set number of times. Furthermore, the prediction point search unit 118 can determine that concern point A has converged when the reliability of the newly generated degradation regression curve meets the judgment criteria (e.g., when the sum of squares of the residuals between the degradation regression curve and multiple SOHs is less than a set value). Moreover, the prediction point search unit 118 can also determine that concern point A has converged when the reliability of the newly generated degradation regression curve meets the judgment criteria, and the reliability of the newly generated degradation regression line meets the judgment criteria (e.g., when the sum of squares of the residuals between the degradation regression line and multiple SOHs is less than a set value).
[0093] Alternatively, while moving the point of interest A, the regression curve generation unit 113 and the regression line generation unit 115 cyclically generate a degradation regression curve and a degradation regression line, and the prediction point search unit 118 sets the point of interest A with the highest reliability of the degradation regression curve and degradation regression line as the prediction point for the start of rapid degradation.
[0094] The slope ratio calculation unit 116 calculates the ratio (a / a_tan) of the slope at the predicted point where rapid degradation begins, and the rapid degradation determination unit 119 determines the rapid degradation of the target unit based on the calculated slope ratio (a / a_tan).
[0095] The prediction point search unit 118 can also set a search exemption range in the search for prediction points where rapid degradation begins. For example, the prediction point search unit 118 can also set the range where the SOH is above a specified value (e.g., 80%), the range where the total discharge capacity of the cell is below a set value (e.g., 4000 Ah), or the range where the usage period of the cell is below a set value (e.g., 2 years) as the search exemption range. In addition, the concern point setting unit 114 does not set concern point A in the search exemption range.
[0096] The notification unit 1110 notifies the electric vehicle 3, which is equipped with a battery pack 40 including a unit that has been determined to have rapidly deteriorated, or the operation management terminal device 7 that manages the electric vehicle 3, of an alarm indicating that the battery pack 40 contains a unit that has rapidly deteriorated.
[0097] Figure 11 This is a diagram illustrating a specific example of the rapid degradation determination method involved in the comparative example. Figure 12 This diagram illustrates a specific example of the rapid degradation determination method according to the implementation method. In the comparative example, a regression line is generated based on the most recent n SOHs. Figure 11 The slope 'a' of the regression line at each time point is depicted in the figure.
[0098] In this implementation, 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 degradation regression curve based on all SOHs other than the most recent n SOHs. The slope ratio calculation unit 116 calculates the ratio (a / a_tan) of the slope of the tangent line at point A of interest corresponding to the nth most recent SOH on the degradation regression curve to the slope a of the degradation regression line based on the most recent n SOHs. Figure 12 The graph depicts the ratio of the slopes at each time point (a / a_tan). For example, n is set in the range of 3 to 7.
[0099] exist Figure 11 In the comparative examples shown, it is difficult to set an optimal threshold th. For example, when the threshold th is set to a low value, it is easy to misjudge rapid degradation when cell degradation progresses along a near-linear curve. Conversely, when the threshold th is set to a high value, rapid degradation is difficult to detect even if it occurs. In contrast, in Figure 12 In the embodiment shown, by setting the threshold th to around 2, it is possible to determine with high accuracy whether rapid degradation has occurred.
[0100] Alternatively, a model for rapid degradation can be generated by learning the temporal change of the slope ratio (a / a_tan) for each model of the cell. This model can be generated using machine learning and pattern recognition methods. Whenever a new SOH is added, the slope ratio calculation unit 116 calculates the slope ratio (a / a_tan). When the change in the slope ratio (a / a_tan) in the time series data satisfies the determination criteria based on the generated model, the rapid degradation determination unit 119 determines that the target cell has experienced rapid degradation.
[0101] For example, regarding models that have been trained to exhibit a tendency for the slope ratio (a / a_tan) to decrease by a predetermined value at a predetermined rate before rapid degradation occurs, and then increase by a predetermined value at a predetermined rate thereafter, rapid degradation can be detected at the early stage by detecting the action of this slope ratio (a / a_tan). Furthermore, detection can be performed at least in the early stages after rapid degradation occurs.
[0102] Figure 13 This is a flowchart illustrating the process of a first processing example of the rapid degradation determination method according to the embodiment. The regression curve generation unit 113 reads all the SOH data accumulated in the battery data holding unit 121 and determined by time series for the target cells included in the target battery pack 40 (S10). The regression curve generation unit 113 performs curve regression on all the read SOH data to generate a degradation regression curve for the target cells (S11).
[0103] The focus point setting unit 114 sets a focus point A on the degradation regression curve (S12). The slope ratio calculation unit 116 calculates the tangent line at focus point A on the degradation regression curve (S13). The regression line generation unit 115 performs linear regression on multiple SOHs after focus point A to generate a degradation regression line after focus point A (S14). The slope ratio calculation unit 116 calculates the ratio (a / a_tan) of the slope of the tangent line at focus point A on the degradation regression curve to the slope of the degradation regression line based on multiple SOHs after focus point A (S15).
[0104] The rapid degradation determination unit 119 compares the calculated slope ratio (a / a_tan) with the threshold th (S16). If the slope ratio (a / a_tan) exceeds the threshold th (S16 "Yes"), the notification unit 1110 notifies the electric vehicle 3 that used the target unit or the operation management terminal device 7 of the electric vehicle 3 of an alarm indicating that a unit that has started rapid degradation has been used (S17). If the slope ratio (a / a_tan) is below the threshold th (S16 "No"), no alarm is issued.
[0105] Figure 14 This is a flowchart illustrating the second processing example of the rapid degradation determination method according to the embodiment. The regression curve generation unit 113 reads all the SOH data accumulated in the battery data holding unit 121 and determined by time series for the target cells included in the target battery pack 40 (S20). The regression curve generation unit 113 performs curve regression on all the read SOH data to generate a degradation regression curve for the target cell (S21).
[0106] The concern point setting unit 114 sets an initial value for concern point A on the degradation regression curve (S22). The regression line generation unit 115 performs linear regression on multiple SOHs after the most recently set concern point A to generate a degradation regression line after concern point A (S23). The prediction point search unit 118 sets the intersection of the most recently generated degradation regression curve and the degradation regression line after the most recently set concern point A as the new concern point A (S24).
[0107] The prediction point search unit 118 determines whether the recently set interest point A meets the convergence condition (S25). If the convergence condition is not met ("No" in S25), the regression curve generation unit 113 performs curve regression on multiple SOHs before the recently set interest point A to generate a new degradation regression curve (S26). Next, the process moves to step S23 to continue updating interest point A.
[0108] If the convergence condition is met in step S25 (S25 is "Yes"), 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 this tangent line to the slope of the degradation regression lines of multiple SOHs after the most recently set point of interest A (S28).
[0109] The rapid degradation determination unit 119 compares the calculated slope ratio (a / a_tan) with the threshold th (S29). If the slope ratio (a / a_tan) exceeds the threshold th (S29 "Yes"), the notification unit 1110 notifies the electric vehicle 3 that used the target unit or the operation management terminal device 7 of the electric vehicle 3 of an alarm indicating that a unit that has started rapid degradation has been used (S210). If the slope ratio (a / a_tan) is below the threshold th (S29 "No"), no alarm is issued.
[0110] In addition, Figure 14In the example shown in the flowchart, rapid degradation is determined after first searching for the optimal point of interest A (the predicted point where rapid degradation begins). Regarding this point, the optimal point of interest A (the predicted point where rapid degradation begins) can also be searched after rapid degradation is detected. Specifically, in the initial search, after performing step S23, steps S28 and S29 are performed; if rapid degradation is detected, steps S24 and beyond are performed. In this example, the processing workload can be reduced when rapid degradation has not occurred.
[0111] As explained above, according to this embodiment, rapid degradation of the cell can be determined with high accuracy. By detecting rapid degradation, efficient replacement of the electric vehicle 3 or the battery pack 40 can be performed. Furthermore, by replacing it at the appropriate time, safety related to the use of the battery pack 40 can be improved. Internal short circuits are prone to occur after rapid degradation, so early replacement improves safety.
[0112] Furthermore, by using a degradation curve based on the root rule, robust degradation predictions, such as noise in the measurement system, can be performed during SOH estimation. By using a linear regression line after the point of interest A, the occurrence of short-term rapid degradation can be detected. Moreover, since rapid degradation is determined based on the ratio of the slopes of two lines obtained using a general regression method (a / a_tan), the increase in computational cost can be suppressed. Furthermore, since the relative ratio of the degradation rate to the typical degradation curve is used as a parameter, the threshold th can be intuitively set.
[0113] Furthermore, if the generated degradation regression curve has low reliability, it is discarded, thus avoiding the determination of rapid degradation based on unreliable SOH data. Additionally, by searching for the optimal point of interest A, the timing of the onset of rapid degradation can be estimated with high accuracy. Furthermore, by limiting the search range for the optimal point of interest A, misjudgments of rapid degradation occurring in regions with low probability of rapid degradation can be suppressed.
[0114] The present disclosure has been described above based on the embodiments. It will be readily understood by those skilled in the art that the embodiments are illustrative and various modifications can be made to the combination of their structural elements and processing techniques, and such modifications are also within the scope of the present disclosure.
[0115] The aforementioned degradation assessment system 1 can be installed in the battery control unit 46 within the electric vehicle 3. In this case, a large-capacity memory is required, but data loss can be reduced.
[0116] Furthermore, in the above embodiment, electric vehicle 3 is assumed to be a four-wheeled electric vehicle. However, this could also be an electric motorcycle (electric scooter), electric bicycle, or electric skateboard. Additionally, electric vehicles include not only standard electric vehicles but also low-speed electric vehicles such as golf carts and land cars. Furthermore, the object equipped with battery pack 40 is not limited to electric vehicle 3. Objects equipped with battery pack 40 also include electric ships, railway vehicles, multi-rotor helicopters (drones), stationary energy storage systems, and consumer electronic devices (smartphones, laptops, etc.).
[0117] In addition, the implementation method can also be determined by the following items.
[0118] [Project 1]
[0119] A degradation judgment system (1), characterized in that it comprises:
[0120] The data acquisition unit (111) acquires battery data;
[0121] The SOH determination unit (112) determines the SOH of the secondary battery (E1) based on the battery data;
[0122] The regression curve generation unit (113) performs curve regression on multiple SOH values of the secondary battery (E1) determined by time series to generate a degradation regression curve of the secondary battery (E1).
[0123] The attention setting unit (114) sets attention points on the degradation regression curve;
[0124] The regression line generation unit (115) performs linear regression on multiple SOHs after the point of interest to generate a degradation regression line after the point of interest of the secondary battery (E1).
[0125] The slope ratio calculation unit (116) 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 after the point of interest; and
[0126] The rapid degradation determination unit (119) determines the rapid degradation of the secondary battery (E1) based on the ratio of the slope.
[0127] Based on this, the rapid degradation of the secondary battery (E1) can be determined with high accuracy.
[0128] [Project 2]
[0129] According to the degradation assessment system (1) described in Project 1, the characteristic is that,
[0130] The focus setting unit (114) sets the point on the degradation regression curve as the focus point, which is the position after tracing back a predetermined value from the last of the multiple SOH data intervals determined by the time series.
[0131] Therefore, by setting temporary change points for the degradation rate, it is possible to achieve high-precision and rapid degradation determination.
[0132] [Project 3]
[0133] According to the degradation assessment system (1) described in item 1 or 2, the system is characterized by further comprising:
[0134] The regression curve evaluation unit (117) evaluates the reliability of the degradation regression curve based on the relationship between the degradation regression curve and multiple SOHs that form the basis of the degradation regression curve.
[0135] If the reliability of the degradation regression curve does not meet the set conditions, the rapid degradation determination unit (119) will not determine the rapid degradation of the secondary battery (E1).
[0136] Therefore, it is possible to quickly determine the degradation of data without relying on the unreliable SOH data.
[0137] [Project 4]
[0138] According to any one of items 1 to 3, the degradation determination system (1) is characterized in that,
[0139] It also includes a prediction point search unit (118), which dynamically changes the point of interest to search for prediction points where the secondary battery (E1) begins to deteriorate rapidly.
[0140] The rapid degradation determination unit (119) determines the rapid degradation of the secondary battery (E1) based on the ratio of the slope at the predicted point.
[0141] Therefore, it is possible to set a point of concern that is consistent with or similar to the starting point of rapid degradation.
[0142] [Project 5]
[0143] According to the degradation judgment system (1) described in Project 4, the characteristic is that,
[0144] The regression curve generation unit (113) performs curve regression on multiple SOHs within multiple SOHs determined by the time series of the secondary battery (E1) before the point of interest to generate the degradation regression curve of the secondary battery (E1).
[0145] The prediction point search unit (118) sets the convergence point of the intersection between the degradation regression curves of multiple SOHs before the point of interest and the degradation regression lines of multiple SOHs after the point of interest as the prediction point at which the secondary battery (E1) begins to degrade rapidly.
[0146] Therefore, it is possible to predict with high accuracy the starting point of rapid degradation that should be a focus of attention.
[0147] [Project 6]
[0148] According to the degradation assessment system (1) described in Project 5, the characteristic is that,
[0149] The prediction point search unit (118) sets the range where SOH is above a first set value, the range where the total discharge capacity of the secondary battery (E1) is below a second set value, or the range where the usage period of the secondary battery (E1) is below a third set value as outside the search range of the prediction point.
[0150] Accordingly, by limiting the search range for the starting point of rapid degradation that should be the focus, it is possible to prevent setting the focus in a range where the probability of rapid degradation is low.
[0151] [Project 7]
[0152] According to any one of items 1 to 5, the degradation determination system (1) is characterized in that,
[0153] When the ratio of the slope of the degradation regression line after the point of interest on the degradation regression curve to the slope of the tangent at the point of interest exceeds a threshold, or when the ratio of the slope of the tangent at the point of interest on the degradation regression curve to the slope of the degradation regression line after the point of interest is lower than a threshold, the rapid degradation determination unit (119) determines that rapid degradation of the secondary battery (E1) has occurred.
[0154] Therefore, by understanding whether the rate of degradation changes drastically, rapid degradation can be accurately determined.
[0155] [Project 8]
[0156] According to any one of items 1 to 5, the degradation determination system (1) is characterized in that,
[0157] When the slope ratio in the time series data satisfies the judgment condition generated based on learning, the rapid degradation judgment unit (119) determines that rapid degradation or a sign of rapid degradation has occurred in the secondary battery (E1).
[0158] Therefore, rapid degradation can be detected beforehand or immediately after it occurs.
[0159] [Project 9]
[0160] A degradation determination method, characterized by comprising the following steps:
[0161] Obtain battery data;
[0162] The state of harmonics (SOH) of the secondary battery (E1) is determined based on the battery data.
[0163] Curve regression is performed on multiple SOH values determined by time series for the secondary battery (E1) to generate the degradation regression curve of the secondary battery (E1).
[0164] Set points of interest on the degradation regression curve;
[0165] Linear regression is performed on multiple SOHs after the point of interest to generate the degradation regression line of the secondary battery (E1) after the point of interest;
[0166] Calculate 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 after the point of interest; and
[0167] The rapid degradation of the secondary battery (E1) is determined based on the ratio of the slope.
[0168] Based on this, the rapid degradation of the secondary battery (E1) can be determined with high accuracy.
[0169] [Project 10]
[0170] A degradation assessment procedure, characterized in that it causes a computer to perform the following processing:
[0171] Obtain battery data;
[0172] The state of harmonics (SOH) of the secondary battery (E1) is determined based on the battery data.
[0173] Curve regression is performed on multiple SOH values determined by time series for the secondary battery (E1) to generate the degradation regression curve of the secondary battery (E1).
[0174] Set points of interest on the degradation regression curve;
[0175] Linear regression is performed on multiple SOHs after the point of interest to generate the degradation regression line of the secondary battery (E1) after the point of interest;
[0176] Calculate 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 after the point of interest; and
[0177] The rapid degradation of the secondary battery (E1) is determined based on the ratio of the slope.
[0178] Based on this, the rapid degradation of the secondary battery (E1) can be determined with high accuracy.
[0179] Explanation of reference numerals in the attached figures
[0180] 1: Degradation assessment 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 determination unit; 113: Regression curve generation unit; 114: Focus point 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 assessment unit; 110: Notification Department; 12: Storage Department; 121: Battery Data Retention Department; 13: Communication Department; 30: Vehicle Control Department; 34: Motor; 35: Inverter; 36: Wireless Communication Department; 36a: Antenna; 40: Battery Pack; 41: Battery Module; 42: Battery Management Department; 43: Voltage Measurement Department; 44: Temperature Measurement Department; 45: Current Measurement Department; 46: Battery Control Department; E1-En: Unit; T1-T2: Temperature Sensor; RY1-RY2: Relay.
Claims
1. A degradation judgment system, characterized in that, have: The data acquisition department acquires battery data from the secondary batteries; The SOH determination unit determines the health status, i.e., SOH, of the secondary battery based on the battery data. The regression curve generation unit performs curve regression on multiple SOH values determined by the time series of the secondary battery to generate the degradation regression curve of the secondary battery. The attention setting unit sets attention points on the degradation regression curve; The regression line generation unit performs linear regression on multiple SOHs after the point of interest to generate a degradation regression line for the secondary battery after the point of interest. The slope ratio calculation unit 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 after the point of interest. as well as The rapid degradation determination unit determines the rapid degradation of the secondary battery based on the ratio of the slope.
2. The degradation determination system according to claim 1, characterized in that, The focus setting unit sets the point on the degradation regression curve as the focus point, which is the position after tracing a predetermined value from the end of the multiple SOH data intervals determined by the time series.
3. The degradation determination system according to claim 1 or 2, characterized in that, It also has: The regression curve evaluation unit evaluates the reliability of the degradation regression curve based on the relationship between the degradation regression curve and multiple SOHs that form the basis of the degradation regression curve. If the reliability of the degradation regression curve does not meet the set conditions, the rapid degradation determination unit will not determine the rapid degradation of the secondary battery.
4. The degradation determination system according to claim 1 or 2, characterized in that, It also includes a prediction point search unit, which dynamically changes the point of interest to search for the prediction point at which the secondary battery begins to deteriorate rapidly. The rapid degradation determination unit determines the rapid degradation of the secondary battery based on the ratio of the slope at the predicted point.
5. The degradation determination system according to claim 4, characterized in that, The regression curve generation unit performs curve regression on multiple SOHs within multiple SOHs determined by the time series of the secondary battery, prior to the point of interest, to generate the degradation regression curve of the secondary battery. The prediction point search unit sets the convergence point of the intersection between the degradation regression curves of multiple SOHs before the point of interest and the degradation regression lines of multiple SOHs after the point of interest as the prediction point at which the secondary battery begins to degrade rapidly.
6. The degradation determination system according to claim 5, characterized in that, The prediction point search unit sets the range outside the prediction point search range as the range where SOH is above a first set value, the range where the total discharge capacity of the secondary battery is below a second set value, or the range where the usage period of the secondary battery is below a third set value.
7. The degradation determination system according to claim 1 or 2, characterized in that, When the ratio of the slope of the degradation regression line after the point of interest on the degradation regression curve to the slope of the tangent line at the point of interest exceeds a threshold, or when 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 after the point of interest is lower than a threshold, the rapid degradation determination unit determines that rapid degradation of the secondary battery has occurred.
8. The degradation determination system according to claim 1 or 2, characterized in that, When the slope ratio in the time series data satisfies the judgment condition generated based on learning, the rapid degradation judgment unit determines that rapid degradation or a precursor of rapid degradation of the secondary battery has occurred.
9. A method for determining degradation, characterized in that, Includes the following steps: Obtain battery data for secondary batteries; The state of health (SOH) of the secondary battery is determined based on the battery data. Curve regression is performed on multiple SOH values determined by time series for the secondary battery to generate the degradation regression curve of the secondary battery; Set points of interest on the degradation regression curve; Linear regression is performed on multiple SOHs after the point of interest to generate a degradation regression line for the secondary battery after the point of interest. Calculate 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 after the point of interest. as well as The rapid degradation of the secondary battery is determined based on the ratio of the slope.
10. A degradation assessment procedure product, comprising a degradation assessment procedure, characterized in that, The degradation assessment procedure causes the computer to perform the following processes: Obtain battery data for secondary batteries; The state of health (SOH) of the secondary battery is determined based on the battery data. Curve regression is performed on multiple SOH values determined by time series for the secondary battery to generate the degradation regression curve of the secondary battery; Set points of interest on the degradation regression curve; Linear regression is performed on multiple SOHs after the point of interest to generate a degradation regression line for the secondary battery after the point of interest. Calculate 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 after the point of interest. as well as The rapid degradation of the secondary battery is determined based on the ratio of the slope.
11. A recording medium that records a degradation assessment procedure, characterized in that, The degradation assessment procedure causes the computer to perform the following processes: Obtain battery data for secondary batteries; The state of health (SOH) of the secondary battery is determined based on the battery data. Curve regression is performed on multiple SOH values determined by time series for the secondary battery to generate the degradation regression curve of the secondary battery; Set points of interest on the degradation regression curve; Linear regression is performed on multiple SOHs after the point of interest to generate a degradation regression line for the secondary battery after the point of interest. Calculate 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 after the point of interest. as well as The rapid degradation of the secondary battery is determined based on the ratio of the slope.