Computing system, battery inspection method, and battery inspection program

Abstract

In an operation system, a data acquisition unit acquires, from a plurality of individuals of the same product (for example, electric vehicles of the same vehicle type), via a network, operation data including at least voltage and SOC (State Of Charge) of a battery mounted in the product. A detection unit statistically processes the plurality of operation data acquired to detect an individual in which a battery different in specification from the product is mounted.

Description

Technical Field

[0001] This disclosure relates to a computing system for inspecting batteries installed in a product, a method for inspecting batteries, and a procedure for inspecting batteries. 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. Sometimes, defective products are mixed into the battery packs of electric vehicles, or illegitimate products are used. Examples of using illegitimate products include using batteries of a different type than those specified in the product catalog, or using batteries with a capacity less than the catalog specifications (e.g., about 10% less).

[0003] As methods for detecting battery defects and degradation, methods have been proposed for detecting battery anomalies by inputting high frequencies (e.g., see Patent Document 1) and for detecting battery degradation based on changes in the OCV (Open Circuit Voltage) curve (e.g., see Patent Document 2).

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent Application Publication No. 9-133741

[0007] Patent Document 2: Japanese Patent Application Publication No. 2010-19664 Summary of the Invention

[0008] However, it is difficult to detect situations where batteries different from those in the product catalog are being used. Furthermore, since we are evaluating batteries from other companies, not our own, it is not easy to obtain battery characteristics in advance, and obtaining these characteristics is costly and time-consuming.

[0009] This disclosure was made in view of the following circumstances, and its purpose is to provide a technology that can easily detect whether a battery different from the product specifications is being used.

[0010] To address the aforementioned issues, a computing system according to one aspect of this disclosure includes: a data acquisition unit that acquires, via a network, usage data from multiple individuals of the same product, including at least the voltage and SOC (State of Charge) of the battery installed in the product; and a detection unit that performs statistical processing on the acquired usage data to detect individuals equipped with batteries of different specifications than those of the product.

[0011] Furthermore, any combination of the above-mentioned constituent elements, or any manner in which the expression of this disclosure is transformed between methods, apparatuses, systems, computer programs, etc., is also valid as a form of this disclosure.

[0012] According to this disclosure, it is possible to easily detect whether a battery different from the product specifications is being used. Attached Figure Description

[0013] Figure 1 This is a diagram illustrating the computing system utilized by multiple operator systems in the implementation method.

[0014] Figure 2 It is a diagram used to illustrate the detailed structure of the battery system installed in an electric vehicle.

[0015] Figure 3 This is a diagram used to illustrate the estimation method used by the FCC.

[0016] Figure 4 It is shown Figure 1 The diagram shows an example of the structure of the operator system and the computing system.

[0017] Figure 5 (a)-(b) are graphs showing an example of driving data.

[0018] Figure 6 This is a flowchart illustrating the estimation process of the SOC-OCV characteristics of the battery module by the computing system.

[0019] Figure 7 This is an example of a graph showing multiple SOC-OCV characteristics of battery modules installed in multiple electric vehicles of a specific vehicle type.

[0020] Figure 8 This is an example of a histogram showing the median of the SOC-OCV characteristics and the Mahalanobis distances between each SOC-OCV characteristic for a given SOC value.

[0021] Figure 9 It shows from Figure 7 The curve is obtained by retaining the median value of the SOC-OCV characteristic and the SOC-OCV characteristic including the deviation value.

[0022] Figure 10 This is a flowchart illustrating a first processing example for detecting whether a battery module different from the product catalog specifications is installed by a computing system.

[0023] Figure 11 This is a flowchart illustrating a second processing example for detecting whether a battery module different from the product catalog specifications is installed by a computing system.

[0024] Figure 12 This is a diagram showing the shape of the line connecting the three points of the median.

[0025] Figure 13 This is a flowchart illustrating a third processing example for detecting whether a battery module different from the product catalog specifications is installed by a computing system. Detailed Implementation

[0026] Figure 1 This diagram illustrates the computing system 1 used by multiple operator systems 2 in the implementation method. Multiple operators A and B each possess multiple electric vehicles 3 and flexibly utilize these electric vehicles 3 to operate their businesses. For example, each operator flexibly utilizes the multiple electric vehicles 3 to operate a delivery business (courier business), a taxi business, a car rental business, or a car-sharing business. In this embodiment, the electric vehicle 3 is assumed to be a pure EV without an engine.

[0027] Each operator, A, and B, possesses an operator system 2. Operator system 2 is used to manage the services of each operator, A, and B. Operator system 2 consists of one or more information processing devices (e.g., servers, PCs). Some or all of the information processing devices constituting operator system 2 may reside in a data center. For example, it may consist of a combination of servers within the data center (company servers, cloud servers, or leased servers) and client PCs within the operator.

[0028] The computing system 1 consists of one or more information processing devices located in a data center. The operator system 2 can access the computing system 1 via network 5. Network 5 is a general term for communication lines such as the Internet and dedicated lines, regardless of their communication media or protocols.

[0029] Multiple electric vehicles 3 are parked in the parking lots and garages of the business offices of operators A and B during standby periods. These electric vehicles 3 are equipped with wireless communication capabilities and can communicate wirelessly with operator system 2. The electric vehicles 3 transmit driving data, including usage data of their secondary batteries, to operator system 2. During operation, the electric vehicles 3 can wirelessly transmit driving data to the server constituting operator system 2 via network 5. For example, driving data can be transmitted once every 10 seconds. Alternatively, driving data for the entire day can be transmitted in batches once a day at a predetermined time (e.g., at the end of business hours).

[0030] Furthermore, if operator system 2 consists of the company's server or PC located at the business premises, electric vehicle 3 can also send one day's driving data to the company's server or PC after being returned to the business premises after the business hours. In this case, the data can be sent wirelessly or via a wired connection to the company's server or PC. Alternatively, the data can be sent to the company's server or PC via a recording medium containing the driving data. Additionally, if operator system 2 consists of a cloud server and client PCs within the business premises, electric vehicle 3 can also send driving data to the cloud server via the client PCs within the business premises.

[0031] Figure 2 This diagram illustrates the detailed structure of the battery system 40 installed in the electric vehicle 3. The battery system 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 system 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 system 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, the rotational energy generated by deceleration is converted into AC power and supplied to the inverter 35.

[0032] The first relay RY1 is a contactor inserted into the wiring connecting the battery system 40 and the inverter 35. During operation, the vehicle control unit 30 controls the first relay RY1 to be in the ON (closed) state, electrically connecting the battery system 40 to the power system of the electric vehicle 3. When not in operation, the vehicle control unit 30, in principle, controls the first relay RY1 to be in the OFF (open) state, disconnecting the battery system 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.

[0033] The battery system 40 can be charged from the commercial power system 9 by connecting to a charger 4 located outside the electric vehicle 3 via a charging cable 38. The charger 4 is connected to the commercial power system 9 and charges the battery system 40 inside the electric vehicle 3 via the charging cable 38. In the electric vehicle 3, a second relay RY2 is inserted between the wiring connecting the battery system 40 and the charger 4. Alternatively, other types of switches, such as semiconductor switches, can be used instead of the relay. Before charging begins, the management unit 42 of the battery system 40 controls the second relay RY2 to the ON state (closed state), and after charging is completed, the management unit 42 of the battery system 40 controls the second relay RY2 to the OFF state (open state).

[0034] Generally speaking, charging is performed using AC in normal charging and DC in fast charging. When charging with AC, AC power is converted to DC power by an AC / DC converter (not shown) inserted between the second relay RY2 and the battery system 40.

[0035] The battery system 40 includes a battery module 41 and a management unit 42. The battery module 41 comprises multiple cells E1-En connected in series. Alternatively, the battery module 41 can be constructed by connecting multiple battery modules in series or in parallel. The cells can be lithium-ion battery cells, nickel-metal hydride battery cells, lead-acid battery cells, etc. In the following specification, we assume the use of lithium-ion battery cells (nominal voltage: 3.6V-3.7V). The number of cells E1-En connected in series is determined by the drive voltage of the motor 34.

[0036] A shunt resistor Rs is connected in series with multiple individual 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 individual cells E1-En. Either a single temperature sensor can be provided within the battery module, or a temperature sensor can be provided for each of the multiple individual cells. The temperature sensors T1 and T2 can be, for example, thermistors.

[0037] The 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 series-connected cells E1-En 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 then sends the measured voltages of each cell E1-En to the battery control unit 46.

[0038] The voltage of the voltage measuring unit 43 is higher than the voltage of the battery control unit 46, therefore the voltage measuring unit 43 and the battery control unit 46 are connected in an insulated state via a communication line. 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.

[0039] The temperature measurement unit 44 includes voltage divider resistors and an A / D converter. The A / D converter sequentially converts multiple analog voltages 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 individual cells E1-En based on these digital values. For example, the battery control unit 46 estimates the temperature of each individual cell E1-En based on the values ​​measured by the temperature sensor closest to each individual cell E1-En.

[0040] 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 in the multiple cells E1-En based on this digital value.

[0041] Furthermore, if the battery control unit 46 is equipped with an A / D converter and has 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 the A / D converter in the battery control unit 46 can convert the analog voltage into a digital value.

[0042] The battery control unit 46 manages the state of multiple individual cells E1-En based on the voltage, temperature, and current measured by the voltage measuring unit 43, temperature measuring unit 44, and current measuring unit 45. The battery control unit 46 is connected to the vehicle control unit 30 via an in-vehicle network. For example, CAN (Controller Area Network) or LIN (Local Interconnect Network) can be used as the in-vehicle network.

[0043] The battery control unit 46 can be composed of a microcomputer and non-volatile memory (e.g., EEPROM, flash memory). A SOC-OCV mapping 46a is maintained within the microcomputer or non-volatile memory. The SOC-OCV mapping 46a describes the characteristic data of the SOC-OCV curves of multiple individual E1-En cells. The SOC-OCV curves of the multiple individual E1-En cells are pre-made by the battery manufacturer and registered in the microcomputer or non-volatile memory at the time of shipment. The battery manufacturer conducts various tests to derive the SOC-OCV curves of the individual E1-En cells.

[0044] The battery control unit 46 estimates the SOC, FCC, and SOH of each of the multiple cells E1-En. The battery control unit 46 combines the OCV method and the current integration method to estimate the SOC. The OCV method estimates the SOC based on the characteristic data of the OCV of each cell E1-En measured by the voltage measurement unit 43 and the SOC-OCV curve described in the SOC-OCV mapping 46a. The current integration method estimates the SOC based on the integrated value of the OCV at the start of charging and discharging of each cell E1-En and the current measured by the current measurement unit 45. In the current integration method, the measurement error of the current measurement unit 45 accumulates as the charging and discharging time increases. Therefore, it is preferable to use the SOC estimated by the OCV method and correct the estimated SOC by the current integration method.

[0045] The battery control unit 46 can estimate the FCC of a single cell based on the characteristic data of the SOC-OCV curve described in the SOC-OCV mapping 46a and the OCV of the single cell at two points measured by the voltage measurement unit 43.

[0046] Figure 3 This is a diagram used to illustrate the FCC estimation method. The battery control unit 46 acquires the OCV at two points on a single cell. Referring to the SOC-OCV curve, the battery control unit 46 determines the SOC at the two points corresponding to the voltages at those two points, and calculates the difference in SOC between the two points, ΔSOC. Figure 3 In the example shown, the SOC at the two points is 20% and 75%, and the ΔSOC is 55%.

[0047] The battery control unit 46 calculates the integral of the current (=charge / discharge capacity) Q between the two points of time when the OCV at the two points is obtained, based on the current shift measured by the current measuring unit 45. The battery control unit 46 can estimate the FCC by calculating the following (Equation 1).

[0048] FCC=Q / ΔSOC …(Equation 1)

[0049] 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. The battery control unit 46 can calculate the following (Equation 2) to estimate the SOH.

[0050] SOH = Current FCC / Initial FCC …(Equation 2)

[0051] Furthermore, SOH can be determined either through capacity measurements based on full charge and discharge, or by adding storage degradation and cycle degradation. Storage degradation can be estimated based on SOC, temperature, and storage degradation rate. Cycle degradation can be estimated based on the used SOC range, temperature, current rate, and cycle degradation rate. Storage degradation rate and cycle degradation rate can be derived in advance through experiments and simulations. SOC, temperature, SOC range, and current rate can be determined through measurement.

[0052] Furthermore, SOH can also be estimated based on its relationship with the internal resistance of the monomer. Internal resistance can be estimated by dividing the voltage drop generated when a specified current flows through the monomer for a specified time by that current value. Regarding internal resistance, there is a relationship that the higher the temperature, the lower the internal resistance, and vice versa, that the lower the SOH, the higher the internal resistance.

[0053] The battery control unit 46 notifies the vehicle control unit 30 of the voltage, current, temperature, SOC, FCC, and SOH of multiple individual E1-En cells via the vehicle network. The vehicle control unit 30 generates usage data for the multiple individual E1-En cells, including identification information, category information, voltage, current, temperature, SOC, and measurement time, as well as driving data including the identification information and vehicle type information of the electric vehicle 3. The usage data for the multiple individual E1-En cells does not include FCC and SOH. Furthermore, the driving data may also include data such as the speed and driving position of the electric vehicle 3.

[0054] The wireless communication unit 36 ​​performs signal processing for wireless connection with the network 5 via the antenna 36a. In this embodiment, the wireless communication unit 36 ​​wirelessly transmits the driving data acquired from the vehicle control unit 30 to the operator system 2. The wireless communication network that enables wireless connection for the electric vehicle 3 can be, for example, a mobile phone network (cellular network), wireless LAN, ETC (Electronic Toll Collection System), DSRC (Dedicated Short Range Communications), V2I (Vehicle-to-Infrastructure), or V2V (Vehicle-to-Vehicle).

[0055] Figure 4 It is shown Figure 1The diagram shows an example of the structure of the operator system 2 and the computing system 1. The operator system 2 includes a processing unit 21, a storage unit 22, a display unit 23, and an operation unit 24. The functions of the processing unit 21 can be implemented through the cooperation of hardware resources and software resources, or solely through hardware resources. As hardware resources, CPUs, GPUs (Graphics Processing Units), ROMs, RAMs, ASICs, FPGAs (Field Programmable Gate Arrays), and other LSIs can be used. As software resources, operating systems, application programs, and other programs can be used. The storage unit 22 includes a driving data storage unit 221 and a driver data storage unit 222. The storage unit 22 includes non-volatile recording media such as HDDs (Hard Disk Drives) and SSDs (Solid State Drives) to record various programs and data.

[0056] The driving data retention unit 221 retains driving data collected from multiple electric vehicles 3 owned by the operator. The driver data retention unit 222 retains data from multiple drivers belonging to the operator. For example, it manages the cumulative driving distance of each electric vehicle 3 driven by each driver.

[0057] Display unit 23 includes a liquid crystal display, an organic EL display, or other display that shows images generated by processing unit 21. Operation unit 24 is a user interface such as a keyboard, mouse, and touch panel, used to handle operations from users of operator system 2.

[0058] The operator system 2 can provide the computing system 1 with the driving data of multiple electric vehicles 3 stored in the driving data storage unit 221. This data provision is based on contracts between each operator A, B and the operating entity of the computing system 1. These contracts can be either contracts where operators A, B receive monetary consideration in return for providing the data, or contracts where operators A, B receive preferential treatment related to service utilization in return for providing the data. Alternatively, operators A, B can also provide the driving data free of charge.

[0059] The operator system 2 can delegate the inspection of whether the electric vehicles 3 purchased by the business are equipped with battery modules 41 that are different from the specifications in the product catalog to the computing system 1. The inspection service for the battery modules 41 can be either paid or free. For example, it can be a service that the operator can use free of charge in exchange for providing data.

[0060] The computing system 1 includes a processing unit 11 and a storage unit 12. The processing unit 11 includes a data acquisition unit 111, an extraction unit 112, an estimation unit 113, and a detection unit 114. 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, GPUs, ROMs, RAMs, ASICs, FPGAs, and other LSIs. Software resources can utilize operating systems, application programs, etc. The storage unit 12 includes a data storage unit 121 and a SOC-OCV characteristic storage unit 122. The storage unit 12 includes non-volatile recording media such as HDDs and SSDs, for recording various programs and data.

[0061] The driving data retention unit 121 retains the driving data of the electric vehicles 3 collected from each operator A and B. The SOC-OCV characteristic retention unit 122 retains the SOC-OCV characteristics generated by the processing unit 11 based on the collected driving data. The SOC-OCV characteristics can be retained either on a per-unit basis or on a per-battery-module basis. In this embodiment, the driving data retention unit 121 is designed to retain the SOC-OCV characteristics of the battery module 41 for each vehicle type.

[0062] The data acquisition unit 111 acquires driving data of electric vehicle 3 from multiple operators A and B, and saves the acquired driving data to the driving data holding unit 121. The extraction unit 112 extracts multiple sets of SOC and voltage at various times from the usage data of multiple battery modules 41 held in the driving data holding unit 121 as sample data. At this time, the extraction unit 112 extracts sets of SOC and voltage during periods that can be considered as periods when the voltage is OCV (i.e., periods that can be considered as periods when a single cell is in a resting state).

[0063] Furthermore, secondary batteries are electrochemical products. When a charging current flows through a secondary battery, the measured voltage rises non-linearly, and when a discharging current flows through it, the measured voltage drops non-linearly. The voltage measured when current flows through a secondary battery is the CCV (Closed Circuit Voltage), which includes an overvoltage component and deviates from the OCV. In a typical lithium-ion battery cell, after charging and discharging, the measured voltage converges to near the OCV, which does not contain an overvoltage component, within approximately 30 seconds. However, the time it takes to converge to near the OCV varies depending on the material used in the negative electrode. For example, if the negative electrode material is mixed with silicon, it may take more than an hour to converge to near the OCV.

[0064] The extraction unit 112 can also extract the group of SOC and voltage included in the period after a predetermined time (e.g., 30 seconds) from the group of SOC and voltage at multiple times when the current value is zero.

[0065] Figure 5 (a)-(b) are graphs showing an example of driving data. Figure 5 (a) is a graph showing an example of the shift in voltage and current contained in the driving data for a certain day. Figure 5 (b) is a graph depicting the current measurement points for a specific day. Small circles indicate current measurement points taken while the electric vehicle 3 is in motion. Large circles indicate current measurement points taken when the electric vehicle 3 is stationary. That is, they represent rest points where no current flows. Figure 5 As shown in (a)-(b), there are many rest points in the driving data, and a large amount of voltage, which is regarded as OCV, can be obtained from the driving data.

[0066] Furthermore, the extraction conditions can be eased to increase the number of samples that can be extracted. The extraction unit 112 can also include groups of SOC and voltage that have a current value below a set value (e.g., 1A or 0.1C) for a period of time (e.g., 1 minute) or more as extraction targets. That is, in this case, the period during which the current with a charge / discharge rate of 0.1C or less or the current of 1A or less in the battery module 41 is sustained for 1 minute is considered as a period that can be regarded as OCV or as a period during which the individual cell is in a resting state.

[0067] The estimation unit 113 generates an approximate curve based on sample data from multiple points extracted by the extraction unit 112 to estimate the SOC-OCV characteristics of the battery module 41. The estimation unit 113 saves the estimated SOC-OCV characteristics to the SOC-OCV characteristic holding unit 122.

[0068] The estimation unit 113 may also generate the SOC-OCV characteristics for charging and the SOC-OCV characteristics for discharging of the battery module 41 separately. In this case, the extraction unit 112 extracts the combination of SOC and voltage during the period that can be considered as a resting state after the charging of the battery module 41 is completed and the combination of SOC and voltage during the period that can be considered as a resting state after the discharging of the battery module 41 is completed separately.

[0069] The estimation unit 113 can also estimate the SOC-OCV characteristics based solely on sample data from battery modules 41 with a usage period shorter than the specified period (e.g., 1 year). When the battery module 41 is close to new, the accuracy of the SOC estimated by the management unit 42 of the electric vehicle 3 is relatively high. Generally, if the usage period of a single cell increases, the accuracy of the SOC estimated by the management unit 42 decreases. In particular, in management units 42 that do not have a structure to update the maintained SOC-OCV characteristics based on the usage period of the battery module 41, the accuracy of the SOC is prone to decrease.

[0070] Based on the above, the estimation accuracy of the SOC-OCV characteristic based solely on sample data from battery modules 41 with short usage periods is higher than the estimation accuracy based on sample data from the entire usage period. Furthermore, instead of using sample data from battery modules 41 with shorter usage periods than specified periods, the SOC-OCV characteristic can also be estimated based solely on sample data from individual cells of battery modules 41 where the cumulative charge / discharge current is less than a specified value.

[0071] The SOC-OCV characteristics of battery module 41 depend on temperature and degradation degree. Extraction unit 112 can also extract sample data of battery module 41 by classification based on at least one of temperature classification and degradation degree classification. Estimation unit 113 can also generate the SOC-OCV characteristics of battery module 41 by mapping the sample data classified for each classification in at least one of the temperature classification and degradation degree classification. As described above, when the usage data of each battery module 41 includes identification information for uniquely identifying each battery module 41, the degradation degree of each battery module 41 can be estimated based on the usage period and the cumulative value of the charge / discharge current of each battery module 41.

[0072] Figure 6 This is a flowchart illustrating the estimation process of the SOC-OCV characteristics of the battery module 41 by the computing system 1. The data acquisition unit 111 acquires driving data (including voltage, current, SOC, and temperature of the battery module 41) from the operator system 2 (S10). The data acquisition unit 111 saves the acquired driving data to the driving data holding unit 121. The extraction unit 112 extracts sets of SOC and voltage that can be considered as periods when the battery module 41 is in a resting state from the driving data held in the driving data holding unit 121 (S11). The estimation unit 113 generates an approximate curve based on the extracted sets of SOC and voltage, and estimates the SOC-OCV characteristics (S12). The estimation unit 113 saves the estimated SOC-OCV characteristics to the SOC-OCV characteristic holding unit 122.

[0073] The following describes a method for checking whether the electric vehicle 3 purchased from the business is equipped with a battery module 41 that differs from the specifications in the product catalog. Even if the product catalog lists only one type of battery module 41, sometimes the actual purchased electric vehicle 3 is equipped with a different type of battery module 41. For example, the product catalog lists a ternary lithium-ion battery, but sometimes a lithium iron phosphate battery is actually used. Furthermore, even if the actual purchased electric vehicle 3 is equipped with the same type of battery module 41 as the one listed in the product catalog, sometimes a battery module 41 with a smaller capacity is used.

[0074] In practice, it is difficult to disassemble the battery system 40 of an electric vehicle 3 to confirm the specifications of the battery module 41. Therefore, a simple method is needed to check whether a battery module 41 different from the specifications in the product catalog is installed. The detection unit 114 of the computing system 1 performs statistical processing on multiple driving data of the same type of vehicle as the object of inspection to check whether a battery module 41 different from the specifications in the product catalog is installed. This will be explained in detail below.

[0075] Figure 7 This is an example of a graph showing multiple SOC-OCV characteristics of battery modules 41 installed in multiple electric vehicles 3 of a specific vehicle type. As described above, the extraction unit 112 extracts a set of SOC and voltage values ​​based on current, which can be considered as periods when the battery module 41 is in a resting state, from multiple sets of SOC and voltage values ​​included in the driving data for each electric vehicle 3 of that vehicle type, and uses these as sample data. The estimation unit 113 estimates the SOC-OCV characteristics of the battery modules 41 installed in each electric vehicle 3 of that vehicle type based on the extracted sample data. In order to detect battery modules 41 that are different from the product catalog specifications with high accuracy, it is desirable to collect the SOC-OCV characteristics of a large number of electric vehicles 3 (individuals) of that vehicle type.

[0076] The detector 114 determines the median of these multiple SOC-OCV characteristics. Figure 7 In the diagram, the line connecting the largest black dots represents the median of multiple SOC-OCV characteristics. The detection unit 114 calculates the distance between the median of the SOC-OCV characteristics and each individual SOC-OCV characteristic. Specifically, the detection unit 114 calculates the distance between the median of the SOC-OCV characteristics and each individual SOC-OCV characteristic for each SOC value (e.g., every 1%). This distance can be either Euclidean distance or Mahalanobis distance.

[0077] Furthermore, the following explanation illustrates an example of detecting an individual equipped with a battery different from the product specifications based on the distance between the median of multiple SOC-OCV characteristics for each SOC value and each SOC-OCV characteristic. However, it is not necessary to use the median of multiple SOC-OCV characteristics. Specifically, other representative values ​​such as the average or mode of multiple SOC-OCV characteristics can also be used for calculation. Additionally, the structure using representative values ​​such as the median of multiple SOC-OCV characteristics has the feature of being able to detect individuals equipped with batteries different from the product specifications even when the product specifications are unclear. However, it is also possible to calculate the distance between the pre-recorded SOC-OCV characteristic value and each SOC-OCV characteristic for each SOC value, provided that the SOC-OCV characteristics are pre-recorded in memory or the like as product specifications. Typically, the SOC-OCV curve for the product specifications can be derived by conducting various experiments.

[0078] The Mahalanobis distance d, as shown in Equation 3 below, refers to the distance obtained by normalizing with the standard deviation σ.

[0079] d 2 =(xm) 2 / σ 2 …(Equation 3)

[0080] x represents the Euclidean distance of each sample relative to the median of the OCV.

[0081] m represents the average Euclidean distance of each sample.

[0082] σ represents the standard deviation of the Euclidean distance for each sample.

[0083] Figure 8 This is an example of a histogram showing the median of the SOC-OCV characteristics and the Mahalanobis distance between each SOC-OCV characteristic for a given SOC value. The detection unit 114 determines values ​​exceeding a threshold as deviation values. This threshold can, for example, be a value of 3σ. If it is a normal distribution, it converges to within 3σ with 99.7% accuracy, thus allowing values ​​exceeding 3σ to be determined as deviation values. Furthermore, the optimal threshold can be adaptively changed through learning. The detection unit 114 calculates the deviation value for each SOC value and determines that the SOC-OCV characteristics containing deviation values ​​indicate an electric vehicle 3 equipped with a battery module 41 that differs from the product catalog specifications.

[0084] Figure 9 It shows from Figure 7The graph is a plot that retains both the median and the SOC-OCV characteristics, including deviations. The median SOC-OCV characteristic is an upward-convex curve, while the SOC-OCV characteristic including deviations is a downward-convex curve. That is, it is estimated that the SOC-OCV characteristic of a regular battery module 41 listed in the product catalog of a specific vehicle type is an upward-convex curve, while the SOC-OCV characteristic of an illegitimate battery module 41 not listed in that product catalog but installed in that vehicle type is a downward-convex curve.

[0085] Figure 10 This is a flowchart illustrating a first processing example of a system 1 detecting whether a battery module 41 different from the specifications in the product catalog is installed. The estimation unit 113 estimates the SOC-OCV characteristics of the multiple battery modules 41 installed in the same vehicle type based on driving data obtained from multiple electric vehicles 3 of the same type (S20). The detection unit 114 calculates the distance between the median of the multiple SOC-OCV characteristics and each SOC-OCV characteristic for each SOC value (S21). The detection unit 114 determines the deviation value from the multiple distances for each SOC value (S22). The detection unit 114 determines that the electric vehicle 3 that sends SOC-OCV characteristics including the deviation value is an electric vehicle 3 equipped with a battery module 41 that is not listed in the product catalog and has different specifications (S23).

[0086] In the above explanation, deviations from the median were searched across the entire SOC range (0-100%). Regarding this, the SOC range for searching deviations from the median can also be selected as a defined SOC range. For example, deviations can be searched within the range of 40%-70%. Depending on the type of battery, there is a tendency for the OCV difference to increase within a certain SOC range. For example, in the case of ternary lithium-ion batteries, the OCV difference tends to be larger in the middle of the SOC range and smaller at both ends of the SOC range.

[0087] Furthermore, the SOC range that is prone to OCV differences can be learned for each vehicle type. As the number of samples increases, the tendency of the SOC range that is prone to OCV differences can be grasped, thereby reducing the amount of computation by selecting the range of search deviation values ​​as the SOC range.

[0088] To minimize computational load, the following method is effective: based on the relationship between the SOC and OCV of a single point of multiple battery modules 41 obtained from multiple electric vehicles 3 of the same vehicle type, it is possible to detect whether a battery module 41 different from the product catalog specification is installed.

[0089] Figure 11 This is a flowchart illustrating a second processing example for detecting whether a battery module 41 different from the product catalog specification is installed by the computing system 1. The estimation unit 113 determines the SOC and OCV of the multiple battery modules 41 installed in the vehicle type when the SOC is a specific value (e.g., 50%) based on driving data obtained from multiple electric vehicles 3 of the same vehicle type (S30). The detection unit 114 calculates the distance between the median of the multiple OCVs when the SOC is the specific value and each OCV (S31). The detection unit 114 determines the deviation value from the multiple distances (S32). The detection unit 114 determines that the electric vehicle 3 that sends an OCV containing the deviation value is an electric vehicle 3 equipped with a battery module 41 different from the product catalog specification (S33).

[0090] In the second processing example, the following process can be omitted: the estimation unit 113 generates an approximate curve based on sample data from multiple points and estimates the SOC-OCV characteristics of each battery module 41. If the OCV value when the SOC is a specific value can be obtained for each battery module 41, it is possible to detect electric vehicles 3 equipped with battery modules 41 that are different from the product catalog specifications.

[0091] In the second processing example, the computational load can be significantly reduced compared to the first processing example, thereby enabling faster processing. However, there is a decrease in judgment accuracy. Therefore, in the third processing example, in addition to distance, angle is added as a judgment criterion. To add angle as a judgment criterion, the relationship between SOC and OCV at multiple points needs to be used for each battery module 41. The following method is explained: using three points representing the relationship between SOC and OCV, it is used to detect whether a battery module 41 different from the product catalog specifications is installed.

[0092] Figure 12 This is a diagram showing the shape of the line connecting the three points of the median. Figure 12 In this process, the OCVs of three points at SOC values ​​of 10%, 50%, and 90% are connected. Furthermore, the three points for OCV extraction are not limited to these values, but it is preferable to extract one point each from the low SOC region, medium SOC region, and high SOC region to facilitate understanding of the overall shape.

[0093] exist Figure 12 The SOC-OCV characteristics of multiple battery modules 41 are described. If three points representing the relationship between SOC and OCV are described for each battery module 41, a third processing example can be implemented.

[0094] Figure 13This is a flowchart illustrating a third processing example for detecting whether a battery module 41 different from the product catalog specification is installed by the computing system 1. The estimation unit 113 determines three points representing the relationship between SOC and OCV for each of the multiple battery modules 41 installed in the same type of electric vehicle 3, based on driving data obtained from multiple battery modules 41 of the same vehicle type (S40). For the SOC of each of the three points, the median of the multiple OCVs and the distance between each OCV are calculated (S41). The detection unit 114 determines the deviation value from the multiple distances for the SOC of each of the three points (S42).

[0095] The detection unit 114 calculates the angle between the line connecting the median values ​​of the three OCVs and the line connecting the three OCVs of each battery module 41 (S43). The detection unit 114 determines the line connecting the three OCVs whose angle deviates from a predetermined value (S44). For example, if the angle difference between the line connecting the median values ​​of the three OCVs and the line connecting the median values ​​of the three OCVs is 10° or more, the detection unit 114 determines that the line connecting the median values ​​of the three OCVs and the line connecting the three OCVs of the target battery module 41 belong to different groups of lines.

[0096] The detection unit 114 determines that the electric vehicle 3, which sends out three points of SOC and OCV that deviate in both distance and angle conditions, is an electric vehicle 3 equipped with a battery module 41 that is different from the product specifications (S45).

[0097] Furthermore, the detection unit 114 can also select samples from which angle comparisons are performed as data that contain deviation values ​​in the distance between the median and the median. That is, the detection unit 114 will not perform angle comparisons on data that does not contain deviation values ​​in the distance between the median and will determine it as data of a regular battery module 41 listed in the product catalog.

[0098] Alternatively, a threshold can be set more leniently to determine the deviation value within the distance from the median. In this case, the detection unit 114 selects the samples from which the angle comparison is performed as a portion of the data that exceeds the threshold but is close to it. The detection unit 114 unconditionally determines the remaining data that is far from the threshold as battery module 41 that is different from the product specifications.

[0099] In step S44, the difference in attributes is determined by whether the angle difference is above a specified value. However, the difference in attributes can also be determined by whether the line connecting the three points is convex upwards or downwards. Figure 12In the example shown, the line connecting the median values ​​of the three OCV points is convex upwards. Therefore, the detection unit 114 determines that the data of the battery module 41 is normal when the line connecting the three OCV points is convex, and determines that the data of the battery module 41 is different from the product specifications when it is convex downwards.

[0100] In addition, Figure 12 , Figure 13 The example shown illustrates the following: using three points representing the relationship between SOC and OCV to detect whether a battery module 41 different from the product catalog specification is installed. Alternatively, two points representing the relationship between SOC and OCV can also be used to detect whether a battery module 41 different from the product catalog specification is installed. In this case, regarding the two points for extracting OCV, it is preferable to extract one point each from the low SOC region and the medium SOC region, or one point each from the medium SOC region and the high SOC region.

[0101] As explained above, according to this embodiment, data from each battery module 41 is collected from multiple electric vehicles 3 of the same type, and the collected data is clustered through statistical processing. This allows for easy detection of electric vehicles 3 equipped with battery modules 41 that differ from the specifications in the product catalog. Detection is possible regardless of the battery manufacturer or battery type.

[0102] Furthermore, in this embodiment, only the driving data of the electric vehicle 3 is required, and no special inspection device is needed. There is also no need to add new functions to the management unit 42 of the battery system 40 of each electric vehicle 3. Additionally, there is no need to obtain the characteristics of various batteries in advance. Therefore, the monetary and time costs incurred in building and using the system can be reduced. For example, maintenance time for adding battery characteristics is also unnecessary.

[0103] The present disclosure has been described above based on the embodiments. It should be understood by those skilled in the art that the embodiments are illustrative and various modifications can be made to the combination of these constituent elements and processing procedures, and such modifications are also within the scope of the present disclosure.

[0104] In the above-described embodiments, the presence of a battery module 41 different from the catalog specifications is detected based on the relationship between the State of Charge (SOC) and the OCV at at least one point for each battery module 41. Furthermore, the presence of a battery module 41 different from the catalog specifications can also be detected based on the relationship between the SOC and internal resistance at at least one point for each battery module 41. The internal resistance is also dependent on the SOC. The relationship between SOC and internal resistance varies depending on the battery type, but generally, there is a tendency for the internal resistance to increase at both ends of the SOC range.

[0105] The internal resistance R can be calculated using the following (Equation 4) and the following (Equation 5).

[0106] R = (CCVd - OCVd) / Id …(Equation 4)

[0107] R = (CCVc - OCVc) / Ic …(Equation 5)

[0108] CCV (Closed Circuit Voltage) d is the voltage during discharge, CCVc is the voltage during charging, Id is the discharge current, and Ic is the charging current.

[0109] When using the internal resistance instead of OCV, data from periods other than the period when the battery module 41 is in a resting state can also be used. For example, the internal resistance R can be estimated by setting the voltage measured during driving as CCVd, the current as Id, and the voltage measured immediately before driving begins as OCVd, and then substituting these values ​​into the above (Equation 4).

[0110] In the above embodiment, an example is assumed where the detection of whether the four-wheeled electric vehicle 3 is equipped with a battery module 41 that is different from the specifications in the product catalog is assumed. In this regard, the electric vehicle 3 could also be a two-wheeled electric motorcycle (electric scooter) or an electric bicycle. In addition, the electric vehicle 3 also includes low-speed electric vehicles such as golf carts and land cars used in shopping malls, entertainment facilities, etc.

[0111] Furthermore, products equipped with battery module 41 are not limited to electric vehicles 3. For example, they also include electric ships, railway vehicles, multi-rotor helicopters (drones), and other electric mobile vehicles. Additionally, products equipped with battery module 41 also include stationary energy storage systems and consumer electronic devices (smartphones, laptops, etc.). In any case, by collecting usage data for each product using battery module 41, it is possible to detect whether each entity is equipped with a battery module 41 that differs from the product specifications.

[0112] In addition, the implementation method can also be determined by the following items.

[0113] [Project 1]

[0114] A computing system (1) is characterized by comprising: a data acquisition unit (111) that acquires, via a network (5) application data from multiple individuals of the same product, including at least the voltage and SOC (State of Charge) of the battery (E1, 41) installed in the product; and a detection unit (114) that performs statistical processing on the acquired application data to detect individuals equipped with batteries (E1, 41) of different specifications than the product.

[0115] The battery (E1, 41) can be either a single E1 or a module 41.

[0116] Therefore, it is possible to easily detect products equipped with batteries (E1, 41) that are different from the specifications in the product catalog.

[0117] [Project 2]

[0118] According to the computing system (1) described in Project 2, the data acquisition unit (111) acquires application data from each body, including at least voltage, current and SOC measured at multiple times at each body of the product. The computing system (1) further includes: an extraction unit (112) that extracts a group of SOC and voltage at multiple times at each time from the group of SOC and voltage at each time included in the multiple application data, which can be regarded as the period when the battery (E1, 41) is in a resting state, determined based on the current, as sample data; and an estimation unit (113) that estimates the SOC-OCV (Open Circuit Voltage) characteristics of the multiple batteries (E1, 41) respectively mounted in the multiple bodies of the product based on the extracted sample data.

[0119] Accordingly, products equipped with batteries (E1, 41) that differ from the product catalog specifications can be easily and accurately detected based on the SOC-OCV characteristics estimated from the application data obtained from the individual products.

[0120] [Project 3]

[0121] According to the computing system (1) described in Project 2, the detection unit (114) detects an individual equipped with a battery (E1, 41) that has different specifications from the product based on the degree of deviation of the representative value of the SOC-OCV characteristics relative to the plurality of batteries (E1, 41).

[0122] The representative value can be, for example, the median, the average, or the mode.

[0123] Therefore, by referring to the degree of deviation from the representative value of the SOC-OCV characteristics, it is possible to easily and accurately detect products equipped with batteries (E1, 41) that are different from the specifications in the product catalog.

[0124] [Project 4]

[0125] According to the computing system (1) described in Project 2, the detection unit (114) detects an individual equipped with a battery (E1, 41) that is different from the specifications of the product based on the degree of deviation from a value pre-recorded relative to the SOC-OCV characteristics that are specifications of the product.

[0126] Accordingly, products equipped with batteries (E1, 41) that differ from the product catalog specifications can be easily and accurately detected by referring to the degree of deviation from the pre-recorded value of the SOC-OCV characteristic relative to the product specifications.

[0127] [Project 5]

[0128] According to the computing system (1) described in Project 2, the detection unit (114) uses data from a specified SOC range in the SOC-OCV characteristics of the plurality of batteries (E1, 41) to detect an individual equipped with a battery (E1, 41) that has a different specification from the product.

[0129] Therefore, the computational load can be reduced compared to using the entire range of data within the SOC range.

[0130] [Project 6]

[0131] According to the computing system (1) described in Project 1, the data acquisition unit (111) acquires application data from each individual, including at least one moment of voltage, current and SOC measured on each individual side of the product. The computing system (1) further includes: an extraction unit (112) that extracts a group of SOC and voltage that can be considered as the period when the battery (E1, 41) is in a resting state, determined based on the current, from at least one group of SOC and voltage contained in the plurality of application data, as sample data; and a determination unit that determines the relationship between the SOC and OCV (Open Circuit Voltage) of at least one point of the plurality of batteries (E1, 41) respectively mounted in the plurality of individuals of the product based on the extracted sample data. The detection unit (114) detects individuals equipped with batteries (E1, 41) of different specifications than the product based on the relationship between the SOC and OCV of at least one point of the plurality of batteries (E1, 41).

[0132] Accordingly, it is possible to suppress computational load and detect products equipped with batteries (E1, 41) that are different from the specifications in the product catalog.

[0133] [Project 7]

[0134] According to the computing system (1) described in Project 6, the detection unit (114) detects an individual equipped with a battery (E1, 41) that has a different specification than the product based on the degree of deviation between the distance between the representative values ​​of multiple points representing the relationship between the SOC and OCV of the multiple batteries (E1, 41) and the values ​​of the multiple points representing the relationship between the SOC and OCV of each battery (E1, 41), and the degree of deviation between the angle between the line connecting the representative values ​​of the multiple points and the line connecting the multiple points representing the relationship between the SOC and OCV of each battery (E1, 41).

[0135] Therefore, it is possible to suppress the amount of computation and detect products equipped with batteries (E1, 41) that are different from the specifications in the product catalog with relatively high accuracy.

[0136] [Project 8]

[0137] According to the computing system (1) described in Project 6, the detection unit (114) detects an individual equipped with a battery that is different from the specifications of the product based on the degree of deviation between the values ​​of multiple points representing the relationship between the SOC and OCV of the product specifications and the values ​​of multiple points representing the relationship between the SOC and OCV of each battery (E1, 41), and the degree of deviation between the angle between the line connecting the values ​​of the multiple points representing the relationship between the SOC and OCV of the product specifications and the line connecting the multiple points representing the relationship between the SOC and OCV of each battery (E1, 41).

[0138] Therefore, it is possible to suppress the amount of computation and detect products equipped with batteries (E1, 41) that are different from the specifications in the product catalog with relatively high accuracy.

[0139] [Project 9]

[0140] According to the computing system (1) described in Project 1, the data acquisition unit (111) acquires application data from each individual, including at least voltage, current and SOC measured at at least one moment on each individual side of the product. The computing system (1) also includes a determination unit that determines the relationship between the SOC and internal resistance of at least one specific value of the multiple batteries (E1, 41) respectively mounted in the multiple individuals of the product based on the multiple application data. The detection unit (114) detects individuals equipped with batteries (E1, 41) of different specifications than the product based on the relationship between the SOC and internal resistance of at least one specific value of the multiple batteries (E1, 41).

[0141] [Project 10]

[0142] A method for inspecting a battery, characterized by comprising the following steps: acquiring, via a network (5) usage data from multiple individuals of the same product, including at least the voltage and SOC (State of Charge) of the battery (E1, 41) mounted in the product; and performing statistical processing on the acquired usage data to detect individuals mounted with batteries (E1, 41) of different specifications than the product.

[0143] Therefore, it is possible to easily detect products equipped with batteries (E1, 41) that are different from the specifications in the product catalog.

[0144] [Project 11]

[0145] A battery inspection procedure is characterized by having a computer perform the following processing: acquiring, via a network (5) usage data from multiple individuals of the same product, including at least the voltage and SOC (State of Charge) of the batteries (E1, 41) installed in the product; and performing statistical processing on the acquired usage data to detect individuals equipped with batteries (E1, 41) of different specifications than those of the product.

[0146] Therefore, it is possible to easily detect products equipped with batteries (E1, 41) that are different from the specifications in the product catalog.

[0147] Explanation of reference numerals in the attached figures

[0148] 1: Computing system; 2: Operator system; E1-En: Individual unit; T1, T2: Temperature sensors; RY1, RY2: Relays; 3: Electric vehicle; 4: Charger; 5: Network; 11: Processing unit; 111: Data acquisition unit; 112: Extraction unit; 113: Estimation unit; 114: Detection unit; 12: Storage unit; 121: Driving data retention unit; 122: SOC-OCV characteristic retention unit; 21: Processing unit; 22: Storage unit; 221: Driving data retention unit; 222: Driver data retention unit; 23: Display unit; 24: Operation unit; 30: Vehicle control unit; 34: Motor; 35: Inverter; 36: Wireless communication unit; 36a: Antenna; 38: Charging cable; 40: Battery system; 41: Battery module; 42: Management unit; 43: Voltage measurement unit; 44: Temperature measurement unit; 45: Current measurement unit; 46: Battery control unit; 46a: SOC-OCV mapping.

Claims

1. A computing system, characterized in that, have: The data acquisition unit acquires, via a network, usage data from multiple individuals of the same product, including at least the voltage and state of charge (SOC) of the battery installed in the product; and The detection unit performs statistical processing on multiple sets of operational data to detect individuals equipped with batteries of different specifications than those in the product. The data acquisition unit acquires usage data from each body, including voltage, current, and SOC measured at multiple times at each body side of the product. The computing system also has: The extraction unit extracts, from the multiple sets of SOC and voltage at multiple times contained in the multiple application data respectively, the set of SOC and voltage at which the battery can be considered to be in a resting state based on the current, and uses it as sample data. as well as The estimation unit, based on the extracted sample data, estimates the state-of-charge-open-circuit voltage characteristics (SOC-OCV) of multiple batteries installed in multiple individual units of the product. The detection unit detects individuals equipped with batteries of different specifications than those of the product based on the degree of deviation from the representative value of the SOC-OCV characteristics relative to the plurality of batteries, wherein the representative value is the average, mode, or median of the plurality of SOC-OCV characteristics.

2. The computing system according to claim 1, characterized in that, The data acquisition unit acquires usage data from each body, including at least one moment's voltage, current, and SOC measured on each body side of the product. The computing system also has: The extraction unit extracts, from at least one set of SOC and voltage contained in the plurality of application data respectively, a set of SOC and voltage that can be considered as the period when the battery is in a resting state, determined based on the current, as sample data. as well as The determination unit, based on extracted sample data, determines the relationship between the State of Charge (SOC) and the Open Circuit Voltage (OCV) at at least one specific point for multiple batteries installed in multiple individual units of the product. The detection unit detects individuals equipped with batteries of different specifications than those in the product based on the relationship between the SOC and OCV of at least one specific point of the plurality of batteries.

3. The computing system according to claim 2, characterized in that, The detection unit detects individuals equipped with batteries of different specifications than those of the product based on the degree of deviation between the representative values ​​of multiple points representing the relationship between the SOC and OCV of the multiple batteries and the values ​​of multiple points representing the relationship between the SOC and OCV of each battery, and the degree of deviation between the angle between the line connecting the representative values ​​of the multiple points and the line connecting the multiple points representing the relationship between the SOC and OCV of each battery.

4. The computing system according to claim 2, characterized in that, The detection unit detects individuals equipped with batteries that are different from the specifications of the product based on the degree of deviation between the values ​​of multiple points representing the relationship between the SOC and OCV of the product specifications and the values ​​of multiple points representing the relationship between the SOC and OCV of each battery, and the degree of deviation between the angle between the line connecting the values ​​of the multiple points representing the relationship between the SOC and OCV of the product specifications and the line connecting the multiple points representing the relationship between the SOC and OCV of each battery.

5. The computing system according to claim 1, characterized in that, The data acquisition unit acquires usage data from each body, including at least one moment's voltage, current, and SOC measured on each body side of the product. The computing system further includes a determination unit that, based on the plurality of application data, determines the relationship between the state of charge (SOC) and internal resistance of at least one specific point for each of the plurality of batteries mounted in the plurality of individual units of the product. The detection unit detects individuals equipped with batteries of different specifications than those in the product based on the relationship between the SOC and internal resistance of at least one specific value of the plurality of batteries at the point of origin.

6. A computing system, characterized in that, have: The data acquisition unit acquires, via a network, usage data from multiple individuals of the same product, including at least the voltage and state of charge (SOC) of the battery installed in the product; and The detection unit performs statistical processing on multiple sets of operational data to detect individuals equipped with batteries of different specifications than those in the product. The data acquisition unit acquires usage data from each body, including voltage, current, and SOC measured at multiple times at each body side of the product. The computing system also has: The extraction unit extracts, from the multiple sets of SOC and voltage at multiple times contained in the multiple application data respectively, the set of SOC and voltage at which the battery can be considered to be in a resting state based on the current, and uses it as sample data. as well as The estimation unit, based on the extracted sample data, estimates the state-of-charge-open-circuit voltage characteristics (SOC-OCV) of multiple batteries installed in multiple individual units of the product. The detection unit detects individuals equipped with batteries that are different from the specifications of the product based on the degree of deviation from a pre-recorded value relative to the SOC-OCV characteristics that are specifications of the product.

7. A computing system, characterized in that, have: The data acquisition unit acquires, via a network, usage data from multiple individuals of the same product, including at least the voltage and state of charge (SOC) of the battery installed in the product; and The detection unit performs statistical processing on multiple sets of operational data to detect individuals equipped with batteries of different specifications than those in the product. The data acquisition unit acquires usage data from each body, including voltage, current, and SOC measured at multiple times at each body side of the product. The computing system also has: The extraction unit extracts, from the multiple sets of SOC and voltage at multiple times contained in the multiple application data respectively, the set of SOC and voltage at which the battery can be considered to be in a resting state based on the current, and uses it as sample data. as well as The estimation unit, based on the extracted sample data, estimates the state-of-charge-open-circuit voltage characteristics (SOC-OCV) of multiple batteries installed in multiple individual units of the product. The detection unit uses data from a specified SOC range in the SOC-OCV characteristics of the plurality of batteries to detect individuals equipped with batteries of different specifications than those in the product.

8. A method for inspecting a battery, characterized in that, Includes the following steps: The application data, including at least the voltage and state of charge (SOC) of the battery installed in the product, is obtained from multiple individuals of the same product via a network. as well as Statistical processing is performed on multiple sets of application data to detect individuals equipped with batteries of different specifications than those in the product. In the step of acquiring the application data, application data including voltage, current, and SOC measured at multiple times at each body of the product are acquired from each body. The battery inspection method also includes the following steps: From the multiple sets of SOC and voltage at multiple times contained in the multiple application data, extract the sets of SOC and voltage during periods when the battery is considered to be in a dormant state, determined based on the current, and use these sets as sample data; and Based on the extracted sample data, the state-of-charge-open-circuit voltage characteristics, i.e., SOC-OCV characteristics, of the multiple batteries installed in multiple individual units of the product are estimated. In the step of detecting an individual equipped with a battery that is different from the specifications of the product, the individual equipped with a battery that is different from the specifications of the product is detected based on the degree of deviation from the representative value of the SOC-OCV characteristics of the plurality of batteries, wherein the representative value is the average, mode, or median of the plurality of SOC-OCV characteristics.

9. A method for inspecting a battery, characterized in that, Includes the following steps: The application data, including at least the voltage and state of charge (SOC) of the battery installed in the product, is obtained from multiple individuals of the same product via a network. as well as Statistical processing is performed on multiple sets of application data to detect individuals equipped with batteries of different specifications than those in the product. In the step of acquiring the application data, application data including voltage, current, and SOC measured at multiple times at each body of the product are acquired from each body. The battery inspection method also includes the following steps: Extract the group of SOC and voltage at multiple times from the multiple application data sets that can be considered as the period when the battery is in a resting state, based on the current, and use it as sample data. as well as Based on the extracted sample data, the state-of-charge-open-circuit voltage characteristics, i.e., the SOC-OCV characteristics, of the multiple batteries installed in multiple individual units of the product are estimated. In the step of detecting an individual equipped with a battery that is different from the specifications of the product, the individual equipped with a battery that is different from the specifications of the product is detected based on the degree of deviation from a value pre-recorded relative to the SOC-OCV characteristic that is the specification of the product.

10. A method for inspecting a battery, characterized in that, Includes the following steps: The application data, including at least the voltage and state of charge (SOC) of the battery installed in the product, is obtained from multiple individuals of the same product via a network. as well as Statistical processing is performed on multiple sets of application data to detect individuals equipped with batteries of different specifications than those in the product. In the step of acquiring the application data, application data including voltage, current, and SOC measured at multiple times at each body of the product are acquired from each body. The battery inspection method also includes the following steps: Extract the group of SOC and voltage at multiple times from the multiple application data sets that can be considered as the period when the battery is in a resting state, based on the current, and use it as sample data. as well as Based on the extracted sample data, the state-of-charge-open-circuit voltage characteristics, i.e., the SOC-OCV characteristics, of the multiple batteries installed in multiple individual units of the product are estimated. In the step of detecting an individual equipped with a battery that is different from the specifications of the product, data from the specified SOC range in the SOC-OCV characteristics of the plurality of batteries are used to detect the individual equipped with a battery that is different from the specifications of the product.

11. A recording medium recording a battery inspection procedure, characterized in that, The battery inspection procedure causes the computer to perform the following processes: Using data acquired via a network from multiple individuals of the same product, including at least the voltage and state of charge (SOC) of the battery installed in the product; and Statistical processing is performed on multiple sets of application data to detect individuals equipped with batteries of different specifications than those in the product. In the process of acquiring the application data, application data including voltage, current, and SOC measured at multiple times at each body of the product are obtained from each body. The battery inspection procedure also causes the computer to perform the following processes: Extract the group of SOC and voltage at multiple times from the multiple application data sets that can be considered as the period when the battery is in a resting state, based on the current, and use it as sample data. as well as Based on the extracted sample data, the state-of-charge-open-circuit voltage characteristics, i.e., the SOC-OCV characteristics, of the multiple batteries installed in multiple individual units of the product are estimated. In the process of detecting individuals equipped with batteries of different specifications than those of the product, the individuals equipped with batteries of different specifications than those of the product are detected based on the degree of deviation from the representative values ​​of the SOC-OCV characteristics of the plurality of batteries, wherein the representative value is the average, mode, or median of the plurality of SOC-OCV characteristics.

12. A recording medium recording a battery inspection procedure, characterized in that, The battery inspection procedure causes the computer to perform the following processes: Using data acquired via a network from multiple individuals of the same product, including at least the voltage and state of charge (SOC) of the battery installed in the product; and Statistical processing is performed on multiple sets of application data to detect individuals equipped with batteries of different specifications than those in the product. In the process of acquiring the application data, application data including voltage, current, and SOC measured at multiple times at each body of the product are obtained from each body. The battery inspection procedure also causes the computer to perform the following processes: Extract the group of SOC and voltage at multiple times from the multiple application data sets that can be considered as the period when the battery is in a resting state, based on the current, and use it as sample data. as well as Based on the extracted sample data, the state-of-charge-open-circuit voltage characteristics, i.e., the SOC-OCV characteristics, of the multiple batteries installed in multiple individual units of the product are estimated. In the process of detecting individuals equipped with batteries that are different from the specifications of the product, the individuals equipped with batteries that are different from the specifications of the product are detected based on the degree of deviation from the value of the SOC-OCV characteristic that is a specification of the product.

13. A recording medium recording a battery inspection procedure, characterized in that, The battery inspection procedure causes the computer to perform the following processes: Using data acquired via a network from multiple individuals of the same product, including at least the voltage and state of charge (SOC) of the battery installed in the product; and Statistical processing is performed on multiple sets of application data to detect individuals equipped with batteries of different specifications than those in the product. In the process of acquiring the application data, application data including voltage, current, and SOC measured at multiple times at each body of the product are obtained from each body. The battery inspection procedure also causes the computer to perform the following processes: Extract the group of SOC and voltage at multiple times from the multiple application data sets that can be considered as the period when the battery is in a resting state, based on the current, and use it as sample data. as well as Based on the extracted sample data, the state-of-charge-open-circuit voltage characteristics, i.e., the SOC-OCV characteristics, of the multiple batteries installed in multiple individual units of the product are estimated. In the process of detecting individuals equipped with batteries that are different from the specifications of the product, data from the specified SOC range in the SOC-OCV characteristics of the plurality of batteries are used to detect individuals equipped with batteries that are different from the specifications of the product.

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