Calculation system, battery inspection method, and battery inspection program

Abstract

In this arithmetic system, a data acquisition unit acquires from a plurality of individual products via a network, operational data including at least the voltage and state of charge (SOC) of a battery mounted on the same product (for example, an electric vehicle of the same model). A detection unit statistically processes a plurality of pieces of acquired operational data to detect an individual equipped with a battery that differs from the product specifications.

Description

[Technical field]

[0001] The present disclosure relates to a computing system for inspecting a battery installed in a product, a battery inspection method, and a battery inspection program. [Background technology]

[0002] In recent years, hybrid vehicles (HVs), plug-in hybrid vehicles (PHVs), and electric vehicles (EVs) have become popular. These electric vehicles are equipped with secondary batteries, such as lithium-ion batteries, as key devices. There are cases where the battery packs installed in electric vehicles contain defective products or counterfeit products. Examples of counterfeit products include the use of a different type of battery than the catalog specifications, or the use of a battery with a lower capacity than the catalog specifications (for example, about 10% less).

[0003] Methods proposed for detecting battery defects or deterioration include a method for detecting battery abnormalities by inputting high frequency waves (see, for example, Patent Document 1) and a method for detecting battery deterioration based on changes in the OCV (Open Circuit Voltage) curve (see, for example, Patent Document 2). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 9-133741 [Patent Document 2] JP 2010-19664 A Summary of the Invention

[0005] However, it is difficult to detect if a battery that does not meet the catalog specifications is being used. Also, since it is necessary to evaluate batteries from other companies rather than the company's own batteries, it is not easy to obtain the battery characteristics in advance, and obtaining them is costly and time-consuming.

[0006] The present disclosure has been made in consideration of these circumstances, and has an objective to provide a technique that can easily detect whether a battery that differs from the product specifications is being used.

[0007] In order to solve the above problems, a computing system according to one embodiment of the present disclosure includes a data acquisition unit that acquires operational data, including at least the voltage and SOC (State Of Charge) of a battery installed in the same product, from multiple individuals of the product via a network, and a detection unit that statistically processes the multiple acquired operational data to detect individuals that are equipped with a battery that differs from the specifications of the product.

[0008] Any combination of the above components and conversion of the present disclosure into a method, device, system, computer program, etc. are also valid aspects of the present disclosure.

[0009] According to the present disclosure, it is possible to easily detect whether a battery that differs from the product specifications is being used. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating a computing system used by multiple business operators according to an embodiment. [Diagram 2] FIG. 2 is a diagram for explaining a detailed configuration of a battery system mounted on an electric vehicle. [Diagram 3] FIG. 1 is a diagram for explaining a method for estimating an FCC. [Figure 4] FIG. 2 is a diagram illustrating an example of the configuration of a business operator system and a computing system illustrated in FIG. [Diagram 5] 5(a)-(b) are diagrams showing an example of the driving data. [Figure 6] 5 is a flowchart showing the flow of a process of estimating the SOC-OCV characteristics of a battery module by a calculation system. [Figure 7]FIG. 1 is a diagram showing an example of a graph in which a plurality of SOC-OCV characteristics of battery modules mounted on a plurality of electric vehicles of a specific model are plotted. [Figure 8] FIG. 13 is a diagram showing an example of a histogram of Mahalanobis distances between the median value of the SOC-OCV characteristics and each SOC-OCV characteristic when the SOC has a certain value. [Figure 9] FIG. 8 is a graph showing the median value of the SOC-OCV characteristics and the SOC-OCV characteristics including outliers from the graph in FIG. 7. [Figure 10] 10 is a flowchart showing the flow of a first processing example for detecting whether or not a battery module different from the catalog specifications is mounted by a computing system. [Figure 11] 13 is a flowchart showing the flow of a second processing example for detecting whether or not a battery module different from the catalog specifications is mounted by the computing system. [Figure 12] FIG. 13 is a diagram showing the shape of the line connecting the three median points. [Figure 13] 13 is a flowchart showing the flow of a third processing example for detecting whether or not a battery module different from the catalog specifications is mounted by the computing system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] FIG. 1 is a diagram for explaining a computing system 1 used by a plurality of business operator systems 2 according to an embodiment. Each of a plurality of business operators A and B owns a plurality of electric vehicles 3 and operates a business using the plurality of electric vehicles 3. For example, each business operator operates a delivery business (home delivery business), a taxi business, a rental car business, or a car sharing business using the plurality of electric vehicles 3. In this embodiment, the electric vehicle 3 is assumed to be a pure EV without an engine.

[0012] Each of the businesses A and B has a business system 2. The business system 2 is a system for managing the business operations of each of the businesses A and B. The business system 2 is composed of one or more information processing devices (e.g., a server, a PC). Some or all of the information processing devices constituting the business system 2 may be present in a data center. For example, the business system may be composed of a combination of a server in the data center (a company's own server, a cloud server, or a rental server) and a client PC within the business.

[0013] The computing system 1 is configured with one or more information processing devices installed in a data center. The business operator system 2 can access the computing system 1 via a network 5. The network 5 is a general term for communication paths such as the Internet and dedicated lines, and the communication medium and protocol are not important.

[0014] When on standby, the multiple electric vehicles 3 are parked in parking lots or garages at the offices of the respective businesses A and B. The multiple electric vehicles 3 have wireless communication capabilities and are capable of wireless communication with the business operator system 2. The multiple electric vehicles 3 transmit driving data, including operational data of the secondary batteries mounted thereon, to the business operator system 2. The electric vehicles 3 may wirelessly transmit the driving data to a server constituting the business operator system 2 via the network 5 while driving. For example, the driving data may be transmitted each time, once every 10 seconds. Also, one day's worth of driving data may be transmitted in batches at a predetermined timing once a day (for example, at the end of business hours).

[0015] Furthermore, when the business operator system 2 is configured with a company's own server or PC installed at a business office, the electric vehicle 3 may transmit one day's worth of driving data to the company's own server or PC after returning to the business office after closing. In this case, the data may be transmitted to the company's own server or PC wirelessly, or may be connected to the company's own server or PC via a wired connection and transmitted via a wire. The data may also be transmitted to the company's own server or PC via a recording medium on which the driving data is recorded. Furthermore, when the business operator system 2 is configured with a combination of a cloud server and a client PC in the business operator's office, the electric vehicle 3 may transmit the driving data to the cloud server via the client PC in the business operator's office.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] The battery control unit 46 can be composed of a microcomputer and non-volatile memory (e.g., EEPROM, flash memory). An SOC-OCV map 46a is stored in the microcomputer or non-volatile memory. The SOC-OCV map 46a describes characteristic data of the SOC-OCV curves of the multiple cells E1-En. The SOC-OCV curves of the multiple cells E1-En are created in advance by the battery manufacturer and registered in the microcomputer or non-volatile memory at the time of shipment. The battery manufacturer derives the SOC-OCV curves of the cells E1-En by performing various tests.

[0029] The battery control unit 46 estimates the SOC, FCC, and SOH of each of the cells E1-En. The battery control unit 46 estimates the SOC by combining the OCV method and the current integration method. The OCV method is a method of estimating the SOC based on the OCV of each cell E1-En measured by the voltage measurement unit 43 and the characteristic data of the SOC-OCV curve described in the SOC-OCV map 46a. The current integration method is a method of estimating the SOC based on the OCV at the start of charging and discharging of each cell E1-En and the integrated value of the current measured by the current measurement unit 45. In the current integration method, the measurement error of the current measurement unit 45 accumulates as the charging and discharging time becomes longer. Therefore, it is preferable to correct the SOC estimated by the current integration method using the SOC estimated by the OCV method.

[0030] The battery control unit 46 can estimate the FCC of the cell based on the characteristic data of the SOC-OCV curve described in the SOC-OCV map 46 a and the OCVs at two points of the cell measured by the voltage measurement unit 43 .

[0031] Fig. 3 is a diagram for explaining a method for estimating FCC. The battery control unit 46 acquires the OCV of the cell at two points. The battery control unit 46 refers to the SOC-OCV curve to identify two SOC points corresponding to the two voltage points, and calculates the difference ΔSOC between the SOC at the two points. In the example shown in Fig. 3, the SOC at the two points are 20% and 75%, and ΔSOC is 55%.

[0032] The battery control unit 46 calculates the current integrated amount (= charge / discharge capacity) Q for the period between the two points in time when the two OCVs were acquired, based on the transition of the current measured by the current measurement unit 45. The battery control unit 46 can estimate the FCC by calculating the following (Equation 1).

[0033] FCC = Q / ΔSOC (Equation 1) SOH is defined as the ratio of the current FCC to the initial FCC, and the lower the value (the closer to 0%), the more the deterioration has progressed. The battery control unit 46 can estimate the SOH by calculating the following (Equation 2).

[0034] SOH = current FCC / initial FCC (Equation 2) The SOH may be determined by measuring the capacity by complete charging and discharging, or by adding up the storage degradation and cycle degradation. The storage degradation can be estimated based on the SOC, temperature, and storage degradation rate. The cycle degradation can be estimated based on the SOC range, temperature, current rate, and cycle degradation rate used. The storage degradation rate and cycle degradation rate can be derived in advance by experiments or simulations. The SOC, temperature, SOC range, and current rate can be determined by measurement.

[0035] SOH can also be estimated based on its correlation with the cell's internal resistance. The internal resistance can be estimated by dividing the voltage drop that occurs when a certain current flows through the cell for a certain time by the current value. The internal resistance decreases as the temperature increases, and increases as the SOH decreases.

[0036] The battery control unit 46 notifies the vehicle control unit 30 of the voltage, current, temperature, SOC, FCC, and SOH of the multiple cells E1-En via the in-vehicle network. The vehicle control unit 30 generates operation data of the multiple cells E1-En including the identification information, type information, voltage, current, temperature, SOC, and measurement time of the multiple cells E1-En, and driving data including the identification information and vehicle type information of the electric vehicle 3. The operation data of the multiple cells E1-En does not include FCC and SOH. Note that the driving data may include data such as the speed data and driving position data of the electric vehicle 3.

[0037] The wireless communication unit 36 ​​performs signal processing for wirelessly connecting to the network 5 via the antenna 36a. In the present embodiment, the wireless communication unit 36 ​​wirelessly transmits the driving data acquired from the vehicle control unit 30 to the business operator system 2. Examples of wireless communication networks to which the electric vehicle 3 can be wirelessly connected include a mobile phone network (cellular network), a wireless LAN, an Electronic Toll Collection System (ETC), a Dedicated Short Range Communications (DSRC), a Vehicle-to-Infrastructure (V2I), and a Vehicle-to-Vehicle (V2V).

[0038] FIG. 4 is a diagram showing an example of the configuration of the business operator system 2 and the calculation system 1 shown in FIG. 1. The business 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 realized by a combination of hardware resources and software resources, or by hardware resources alone. As hardware resources, a CPU, a GPU (Graphics Processing Unit), a ROM, a RAM, an ASIC, an FPGA (Field Programmable Gate Array), and other LSIs can be used. As software resources, programs such as an operating system and applications can be used. The storage unit 22 includes a traveling data storage unit 221 and a driver data storage unit 222. The storage unit 22 includes a non-volatile recording medium such as an HDD (Hard Disk Drive) and an SSD (Solid State Drive), and stores various programs and data.

[0039] The travel data storage unit 221 stores travel data collected from multiple electric vehicles 3 owned by the business operator. The driver data storage unit 222 stores data on multiple drivers belonging to the business operator. For example, the accumulated travel distance of each electric vehicle 3 driven by each driver is managed.

[0040] The display unit 23 includes a display such as a liquid crystal display or an organic EL display, and displays an image generated by the processing unit 21. The operation unit 24 is a user interface such as a keyboard, a mouse, a touch panel, etc., and accepts operations by a user of the business operator system 2.

[0041] The business operator system 2 can provide the travel data of the multiple electric vehicles 3 stored in the travel data storage unit 221 to the calculation system 1. This data provision is executed based on a contract between each of the business operators A and B and the operating entity of the calculation system 1. This contract may be a contract in which each of the business operators A and B receives monetary compensation in return for providing the data, or a contract in which each of the business operators A and B receives benefits related to the use of the service in return for providing the data. In addition, each of the business operators A and B may provide the travel data free of charge.

[0042] The business operator system 2 can request the calculation system 1 to inspect whether the electric vehicle 3 purchased by the business is equipped with a battery module 41 that differs from the catalog specifications. This inspection service for the installed battery module 41 may be provided for a fee or free of charge. For example, it may be a service that the business operator can use free of charge in return for providing data.

[0043] The calculation system 1 includes a processing unit 11 and a memory 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 realized by a combination of hardware resources and software resources, or by hardware resources alone. As hardware resources, a CPU, a GPU, a ROM, a RAM, an ASIC, an FPGA, and other LSIs can be used. As software resources, programs such as an operating system and applications can be used. The memory unit 12 includes a driving data storage unit 121 and a SOC-OCV characteristic storage unit 122. The memory unit 22 includes a non-volatile recording medium such as an HDD or SSD, and stores various programs and data.

[0044] The traveling data holding unit 121 holds the traveling data of the electric vehicle 3 collected from each of the businesses A and B. The SOC-OCV characteristic holding unit 122 holds the SOC-OCV characteristic created by the processing unit 11 based on the collected traveling data. The SOC-OCV characteristic may be held on a cell-by-cell basis, or may be held on a battery module 41-by-battery module 41 basis. Hereinafter, in this embodiment, it is assumed that the traveling data holding unit 121 holds the SOC-OCV characteristic of the battery module 41 for each vehicle type.

[0045] The data acquisition unit 111 acquires driving data of the electric vehicle 3 from multiple businesses A and B, and stores the acquired driving data in the driving data storage unit 121. The extraction unit 112 extracts, as sample data, pairs of SOC and voltage at multiple times included in the operation data of the multiple battery modules 41 stored in the driving data storage unit 121. In this case, the extraction unit 112 extracts pairs of SOC and voltage for a period during which the voltage can be considered to be OCV (i.e., a period during which the cell can be considered to be in a resting state).

[0046] Incidentally, secondary batteries are electrochemical products, and when a charging current flows through a secondary battery, the measured voltage rises nonlinearly, and when a discharging current flows through the secondary battery, the measured voltage drops nonlinearly. The voltage measured when a current flows through a secondary battery is a closed circuit voltage (CCV) that includes an overvoltage component, and is a value that deviates from the OCV. In a typical lithium-ion battery cell, after the end of charging and discharging, the measured voltage converges to near the OCV, which does not include an overvoltage component, in about 30 seconds. The time it takes to converge to near the OCV varies depending on the material used for the negative electrode. For example, with a negative electrode material that contains silicon, it takes more than an hour to converge to near the OCV.

[0047] The extraction unit 112 may extract, from the pairs of SOC and voltage at multiple times, pairs of SOC and voltage that are included in a period when the current value is zero and after a predetermined time (e.g., 30 seconds) has elapsed since the end of charging and discharging (from when the current value becomes zero).

[0048] 5(a)-(b) are diagrams showing an example of driving data. FIG. 5(a) is a diagram showing an example of the transition of voltage and current included in driving data for a certain day. FIG. 5(b) is a diagram plotting current measurement points for a certain day. Small circles indicate measurement points of current measured while the electric vehicle 3 is driving. Large circles indicate measurement points of current measured while the electric vehicle 3 is stopped. In other words, they indicate rest points where no current is flowing. As shown in FIG. 5(a)-(b), there are many rest points in the driving data, and it is possible to obtain many voltages that can be considered as OCVs from the driving data.

[0049] Furthermore, the extraction conditions may be relaxed to increase the number of samples that can be extracted. The extraction unit 112 may also extract pairs of SOC and voltage during which a current value equal to or less than a set value (e.g., 1 A or 0.1 C) continues for a set time (e.g., 1 minute) or more. That is, in this case, a period during which a current with a charge / discharge rate of 0.1 C or less or a current of 1 A or less continues to flow through the battery module 41 for 1 minute is regarded as a period that can be regarded as OCV or a period during which the cell can be regarded as being in a resting state.

[0050] The estimation unit 113 generates an approximation curve based on the sample data of the multiple points extracted by the extraction unit 112, and estimates the SOC-OCV characteristics of the battery module 41. The estimation unit 113 stores the estimated SOC-OCV characteristics in the SOC-OCV characteristics holding unit 122.

[0051] The estimation unit 113 may separately generate the SOC-OCV characteristic for charging and the SOC-OCV characteristic for discharging of the battery module 41. In this case, the extraction unit 112 separately extracts a set of SOC and voltage during a period that can be regarded as a resting state after charging to the battery module 41 is completed, and a set of SOC and voltage during a period that can be regarded as a resting state after discharging from the battery module 41 is completed.

[0052] The estimation unit 113 may estimate the SOC-OCV characteristics based only on sample data of battery modules 41 that have been in use for less than a predetermined period (e.g., one year). The SOC estimated by the management unit 42 of the electric vehicle 3 is relatively accurate when the battery module 41 is in a state close to new. As the period of use of the cells increases, the accuracy of the SOC estimated by the management unit 42 generally decreases. In particular, the accuracy of the SOC is likely to decrease in a management unit 42 that does not have a mechanism for updating the SOC-OCV characteristics it holds in accordance with the period of use of the battery module 41.

[0053] From the above, the accuracy of estimating the SOC-OCV characteristic based only on sample data of battery modules 41 with a short period of use is higher than that of estimating the SOC-OCV characteristic based on sample data for the entire period. Note that, instead of battery modules 41 with a period of use shorter than a predetermined period, the SOC-OCV characteristic may be estimated based only on sample data of cells of battery modules 41 with cumulative charge / discharge currents smaller than a predetermined value.

[0054] The SOC-OCV characteristic of the battery module 41 depends on the temperature and the deterioration degree. The extraction unit 112 may classify and extract the sample data of the battery module 41 based on at least one of the temperature category and the deterioration degree category. The estimation unit 113 may generate the SOC-OCV characteristic of the battery module 41 by mapping the sample data classified into at least one of the temperature category and the deterioration degree category based on the sample data classified into categories. As described above, when the operation data of each battery module 41 includes identification information for uniquely identifying each battery module 41, the deterioration degree of each battery module 41 can be estimated based on the usage period of each battery module 41 and the cumulative value of the charge / discharge current.

[0055] FIG. 6 is a flowchart showing the flow of the estimation process of the SOC-OCV characteristic of the battery module 41 by the computing system 1. The data acquisition unit 111 acquires the driving data (including the voltage, current, SOC, and temperature of the battery module 41) of the electric vehicle 3 from the business operator system 2 (S10). The data acquisition unit 111 stores the acquired driving data in the driving data storage unit 121. The extraction unit 112 extracts a set of SOC and voltage during a period in which the battery module 41 can be considered to be in a resting state from the driving data stored in the driving data storage unit 121 (S11). The estimation unit 113 generates an approximation curve based on the extracted multiple sets of SOC and voltage, and estimates the SOC-OCV characteristic (S12). The estimation unit 113 stores the estimated SOC-OCV characteristic in the SOC-OCV characteristic storage unit 122.

[0056] Hereinafter, a method for inspecting whether an electric vehicle 3 purchased by a business establishment is equipped with a battery module 41 different from the catalog specifications will be described. Even if the catalog states that only one type of battery module 41 is used, the electric vehicle 3 actually purchased may be equipped with a different type of battery module 41. For example, the catalog may state that a ternary lithium ion battery is used, but in reality, an iron phosphate lithium ion battery may be used. Also, even if the electric vehicle 3 actually purchased is equipped with the same type of battery module 41 as the battery module 41 described in the catalog, the battery module 41 used may have a smaller capacity.

[0057] It is practically difficult for a business to disassemble the battery system 40 of the electric vehicle 3 to check the specifications of the battery module 41, and a simple method of inspecting whether a battery module 41 different from the catalog specifications is installed is required. The detection unit 114 of the calculation system 1 statistically processes multiple driving data of the same vehicle model as the inspection target, and inspects whether a battery module 41 different from the catalog specifications is installed. This will be specifically described below.

[0058] FIG. 7 is a diagram showing an example of a graph in which a plurality of SOC-OCV characteristics of the battery module 41 mounted on a plurality of electric vehicles 3 of a specific model are plotted. As described above, the extraction unit 112 extracts, as sample data, a set of SOC and voltage during a period in which the battery module 41, which is identified based on the current, can be considered to be in a resting state, from the sets of SOC and voltage at a plurality of times included in the driving data for each electric vehicle 3 of the model. The estimation unit 113 estimates the SOC-OCV characteristics of the battery module 41 mounted on each electric vehicle 3 of the model based on the extracted sample data. In order to detect a battery module 41 that differs from the catalog specifications with high accuracy, it is desirable to collect the SOC-OCV characteristics of a large number of electric vehicles 3 (individuals) of the model.

[0059] The detection unit 114 identifies the median of the multiple SOC-OCV characteristics. In FIG. 7, the series of the largest black dots indicates the median of the multiple SOC-OCV characteristics. The detection unit 114 calculates the distance between the median of the SOC-OCV characteristics and each SOC-OCV characteristic. Specifically, the detection unit 114 calculates the distance between the median of the SOC-OCV characteristics and each SOC-OCV characteristic for each SOC value (for example, for each 1%). The distance may be a Euclidean distance or a Mahalanobis distance.

[0060] In the following description, an example is described in which an individual equipped with a battery different from the product specifications is detected based on the distance for each SOC value between the median value of a plurality of SOC-OCV characteristics and each SOC-OCV characteristic, but it does not necessarily have to be the median value of the plurality of SOC-OCV characteristics. Specifically, calculation may be performed using other representative values ​​such as the average value or mode value of the plurality of SOC-OCV characteristics. In addition, a configuration using a representative value such as the median value of a plurality of SOC-OCV characteristics has the characteristic that an individual equipped with a battery different from the product specifications can be detected even when the product specifications are unknown. However, when the SOC-OCV characteristics are recorded in advance in a memory or the like as the product specifications, a configuration may be used in which the distance for each SOC value between the value of the prerecorded SOC-OCV characteristics and each SOC-OCV characteristic is calculated. Typically, the SOC-OCV curve of the product specifications can be derived by performing various tests.

[0061] The Mahalanobis distance d is a distance normalized by the standard deviation σ, as shown in the following (Equation 3).

[0062] d 2 =(xm) 2 / σ 2 ...(Formula 3) x denotes the Euclidean distance of each sample from the median OCV.

[0063] x indicates the average value of the Euclidean distance for each sample.

[0064] σ denotes the standard deviation of the Euclidean distance of each sample.

[0065] FIG. 8 is a diagram showing an example of a histogram of the Mahalanobis distance between the median of the SOC-OCV characteristics and each SOC-OCV characteristic for a certain SOC value. The detection unit 114 determines that a value exceeding a threshold is an outlier. For example, a value of 3σ may be used as the threshold. In a normal distribution, 99.7% falls within 3σ, so that a value exceeding 3σ can be determined as an outlier. The optimum threshold may be adaptively changed by learning. The detection unit 114 calculates the outlier for each SOC value, and determines that the SOC-OCV characteristics including the outlier indicate that the electric vehicle 3 is equipped with a battery module 41 that differs from the catalog specifications.

[0066] Fig. 9 is a graph showing the median SOC-OCV characteristic and the SOC-OCV characteristic including outliers from the graph in Fig. 7. The median SOC-OCV characteristic is an upward convex curve, and the SOC-OCV characteristic including outliers is a downward convex curve. In other words, it is estimated that the SOC-OCV characteristic of a genuine battery module 41 listed in a catalog for a specific vehicle model is an upward convex curve, and the SOC-OCV characteristic of an unauthorized battery module 41 not listed in the catalog but installed in the vehicle model is a downward convex curve.

[0067] FIG. 10 is a flowchart showing a flow of a first processing example for detecting whether or not a battery module 41 different from the catalog specifications is mounted by the calculation system 1. The estimation unit 113 estimates the SOC-OCV characteristics of the multiple battery modules 41 mounted on the multiple electric vehicles 3 of the same vehicle model based on the driving data of the multiple battery modules 41 acquired from the multiple electric vehicles 3 of the same vehicle model (S20). The detection unit 114 calculates the distance between the median value of the multiple SOC-OCV characteristics and each SOC-OCV characteristic for each SOC value (S21). The detection unit 114 identifies an outlier from the multiple distances for each SOC value (S22). The detection unit 114 determines that the electric vehicle 3 that has transmitted the SOC-OCV characteristics including the outlier is an electric vehicle 3 mounted with a battery module 41 different from the product specifications not listed in the catalog (S23).

[0068] In the above explanation, outliers in the distance from the median were searched for in the entire SOC range (0-100%). In this regard, the SOC range in which outliers in the distance from the median are searched for may be narrowed down to a specified SOC range. For example, outliers may be searched for in the range of 40-70%. Depending on the type of battery, there is a tendency for the SOC range to have a large OCV difference. For example, in the case of a ternary lithium-ion battery, the OCV difference tends to be large in the center of the SOC range, and small at both ends of the SOC range.

[0069] It is also possible to learn the SOC range in which OCV differences are likely to occur for each vehicle model. As the number of samples increases, it becomes possible to grasp the tendency of SOC ranges in which OCV differences are likely to occur, and the amount of calculations can be reduced by narrowing the range for searching for outliers to that SOC range.

[0070] In order to reduce the amount of calculations as much as possible, it is effective to detect whether a battery module 41 different from the catalog specifications is installed based on the relationship between SOC and OCV at one point of multiple battery modules 41 obtained from multiple electric vehicles 3 of the same model.

[0071] 11 is a flowchart showing a flow of a second processing example for detecting whether a battery module 41 different from the catalog specifications is mounted by the calculation system 1. Based on the driving data of the battery modules 41 acquired from the electric vehicles 3 of the same vehicle model, the estimation unit 113 identifies the SOC and OCV of the battery modules 41 mounted on the vehicle model when the SOC is a specific value (e.g., 50%) (S30). The detection unit 114 calculates the median value of the OCVs when the SOC is a specific value and the distance between each OCV (S31). The detection unit 114 identifies an outlier from among the distances (S32). The detection unit 114 determines that the electric vehicle 3 that has transmitted the OCV including the outlier is an electric vehicle 3 mounted with a battery module 41 different from the product specifications (S33).

[0072] In the second processing example, it is possible to omit the process in which the estimation unit 113 generates an approximation curve based on sample data at multiple points and estimates the SOC-OCV characteristics for each battery module 41. If it is possible to obtain the OCV value when the SOC is a specific value for each battery module 41, it is possible to detect an electric vehicle 3 equipped with a battery module 41 that differs from the catalog specifications.

[0073] In the second processing example, the amount of calculation can be significantly reduced compared to the first processing example, and processing can be accelerated. However, there are cases where the accuracy of the judgment decreases. Therefore, in the third processing example, in addition to the distance, an angle is added to the judgment criteria. To add the angle to the judgment criteria, it is necessary to use the relationship between SOC and OCV at multiple points for each battery module 41. Below, a method of detecting whether or not a battery module 41 that differs from the catalog specifications is installed using three points that indicate the relationship between SOC and OCV will be described.

[0074] Fig. 12 shows the shape of the line connecting the three median points. In Fig. 12, three OCV points are connected when the SOC is 10%, 50%, and 90%. Note that the three points used to extract the OCV are not limited to these values, but it is preferable to extract one point each from the low SOC region, the medium SOC region, and the high SOC region so that the overall shape can be easily understood.

[0075] In FIG. 12, the SOC-OCV characteristics of a plurality of battery modules 41 are depicted, but the third processing example can be implemented as long as three points showing the relationship between SOC and OCV are plotted for each battery module 41.

[0076] 13 is a flowchart showing the flow of a third processing example for detecting whether a battery module 41 different from the catalog specifications is installed by the calculation system 1. Based on the travel data of the battery modules 41 acquired from the electric vehicles 3 of the same vehicle model, the estimation unit 113 identifies three points indicating the relationship between SOC and OCV for each of the battery modules 41 installed in the vehicle model (S40). For each of the three SOC points, the distance between the median value of the multiple OCVs and each OCV is calculated (S41). The detection unit 114 identifies an outlier from the multiple distances for each of the three SOC points (S42).

[0077] The detection unit 114 calculates the angle between the line connecting the three OCV medians and the line connecting the three OCVs of each battery module 41 (S43). The detection unit 114 identifies the lines connecting the three OCVs that deviate from each other by an angle equal to or greater than a predetermined value (S44). For example, when at least one of the angle difference between the line connecting the three OCV medians on the low SOC side and the angle difference between the line connecting the three OCV medians on the high SOC side is 10° or greater, the detection unit 114 determines that the line connecting the three OCV medians and the line connecting the three OCVs of the target battery module 41 are lines of groups with different attributes.

[0078] The detection unit 114 determines that the electric vehicle 3 that has transmitted three SOCs and OCVs that are out of compliance with both the distance condition and the angle condition is an electric vehicle 3 equipped with a battery module 41 that does not comply with the product specifications (S45).

[0079] The detection unit 114 may limit the samples for angle comparison to data that includes outliers in the distance from the median. That is, the detection unit 114 determines that data that does not include outliers in the distance from the median is data of a genuine battery module 41 listed in the catalog, without comparing angles.

[0080] In addition, a loose threshold may be set for identifying outliers in terms of distance from the median. In this case, the detection unit 114 may narrow down the samples for comparing angles to a portion of data that exceeds the threshold and is close to the threshold. The detection unit 114 unconditionally determines that the remaining data that is far from the threshold is a battery module 41 that does not meet the product specifications.

[0081] In step S44, whether the attributes differ is determined depending on whether the angle difference is equal to or greater than a predetermined value, but whether the attributes differ may also be determined depending on whether the line connecting the three points is upwardly convex or downwardly convex. In the example shown in Fig. 12, since the line connecting the median values ​​of the OCVs of the three points is upwardly convex, the detection unit 114 determines that the data is for a regular battery module 41 when the line connecting the three target OCV points is upwardly convex, and determines that the data is for a battery module 41 that does not conform to the product specifications when the line is downwardly convex.

[0082] 12 and 13, an example has been described in which three points indicating the relationship between SOC and OCV are used to detect whether a battery module 41 different from the catalog specifications is installed. In this regard, two points indicating the relationship between SOC and OCV may be used to detect whether a battery module 41 different from the catalog specifications is installed. In this case, it is preferable that the two points for extracting OCV are one each from the low SOC region and the middle SOC region, or one each from the middle SOC region and the high SOC region.

[0083] As described above, according to this embodiment, data on each battery module 41 is collected from multiple electric vehicles 3 of the same model, and the collected data is subjected to cluster analysis through statistical processing, making it possible to easily detect electric vehicles 3 equipped with battery modules 41 that differ from the catalog specifications. Detection is possible regardless of the battery manufacturer or battery type.

[0084] Furthermore, in this embodiment, the traveling data of the electric vehicle 3 is sufficient, and no special inspection device is required. There is no need to add new functions to the management unit 42 of the battery system 40 of each electric vehicle 3. There is also no need to acquire the characteristics of various batteries in advance. This makes it possible to reduce the financial and time costs involved in building and operating the system. For example, there is no need for maintenance time to add battery characteristics.

[0085] The present disclosure has been described above based on the embodiments. The embodiments are merely examples, and it will be understood by those skilled in the art that various modifications are possible in the combination of each component and each processing step, and that such modifications are also within the scope of the present disclosure.

[0086] In the above-described embodiment, whether or not a battery module 41 different from the catalog specifications is installed is detected based on the relationship between SOC and OCV at at least one point for each battery module 41. In this regard, whether or not a battery module 41 different from the catalog specifications is installed can also be detected based on the relationship between SOC and internal resistance at at least one point for each battery module 41. The internal resistance also depends on the SOC. The relationship between SOC and internal resistance differs depending on the type of battery, but in general, the internal resistance tends to be high at both ends of the SOC range.

[0087] The internal resistance R can be calculated by the following (Equation 4) and (Equation 5).

[0088] R=(CCVd-OCVd) / Id (Formula 4) R = (CCVc - OCVc) / Ic (Equation 5) CCV (Closed Circuit Voltage)d is the voltage during discharging, CCVc is the voltage during charging, Id is the discharge current, and Ic is the charge current.

[0089] When the internal resistance is used instead of the OCV, data other than that during the period in which the battery module 41 is considered to be in a resting state can also be used. For example, the internal resistance R can be estimated by substituting the voltage measured during driving as CCVd, the current as Id, and the voltage measured immediately before the start of driving as OCVd into the above (Equation 4).

[0090] In the above embodiment, an example is assumed in which it is detected whether or not a four-wheeled electric vehicle 3 is equipped with a battery module 41 that differs from the catalog specifications. In this regard, the electric vehicle 3 may be a two-wheeled electric motorcycle (electric scooter) or an electric bicycle. In addition, the electric vehicle 3 also includes low-speed electric vehicles 3 such as golf carts and land cars used in shopping malls, entertainment facilities, etc.

[0091] Furthermore, products in which the battery module 41 is mounted are not limited to electric vehicles 3. For example, electric vehicles such as electric ships, railway vehicles, and multicopters (drones) are also included. Products in which the battery module 41 is mounted also include stationary power storage systems and consumer electronic devices (smartphones, notebook PCs, etc.). In any case, by collecting operation data of the battery module 41 for each identical product, it is possible to detect whether each individual product is mounted with a battery module 41 that differs from the product specifications.

[0092] The embodiment may be specified by the following items.

[0093] [Item 1] a data acquisition unit (111) that acquires operational data including at least a voltage and a SOC (State Of Charge) of a battery (E1, 41) mounted in a single product (3) from a plurality of individual products (3) via a network (5); a detection unit (114) that performs statistical processing of the acquired operation data and detects an individual having a battery (E1, 41) that does not meet the specifications of the product (3); A computing system (1) comprising:

[0094] The battery (E1, 41) may be a cell E1 or a module 41.

[0095] This makes it easy to detect a product (3) equipped with a battery (E1, 41) that differs from the catalog specifications.

[0096] [Item 2] The data acquisition unit (111) acquires operation data from each of the products (3), the operation data including at least a voltage, a current, and a SOC measured at a plurality of times on each of the products (3); The calculation system (1) an extraction unit (112) that extracts, as sample data, a set of SOC and voltage during a period during which the battery (E1, 41) can be considered to be in a resting state, the set of SOC and voltage being specified based on the current, from sets of SOC and voltage at a plurality of times respectively included in the plurality of operation data; The calculation system (1) according to item 2, further comprising an estimation unit (113) that estimates SOC-OCV (Open Circuit Voltage) characteristics of a plurality of batteries (E1, 41) mounted on a plurality of individual units of the product (3) based on the extracted sample data.

[0097] According to this, it is possible to easily and accurately detect products (3) equipped with batteries (E1, 41) that differ from the catalog specifications, based on the SOC-OCV characteristics estimated from operational data obtained from individual products (3).

[0098] [Item 3] The computing system (1) described in item 2, wherein the detection unit (114) detects an individual having a battery (E1, 41) that differs from the specifications of the product (3) mounted thereon, based on the degree of deviation of the SOC-OCV characteristics of the plurality of batteries (E1, 41) from a representative value.

[0099] The representative value may be, for example, the median, the average, or the mode.

[0100] According to this, by referring to the degree of deviation from the representative value of the SOC-OCV characteristics, it is possible to easily and accurately detect a product (3) equipped with a battery (E1, 41) that differs from the catalog specifications.

[0101] [Item 4] The calculation system (1) described in item 2, wherein the detection unit (114) detects an individual equipped with a battery (E1, 41) that differs from the specifications of the product (3) based on the degree of deviation from a value pre-recorded as the SOC-OCV characteristic of the specifications of the product (3).

[0102] According to this, by referring to the degree of deviation from the pre-recorded value as the SOC-OCV characteristic of the product (3) specifications, it is possible to easily and accurately detect a product (3) equipped with a battery (E1, 41) that differs from the catalog specifications.

[0103] [Item 5] The computing system (1) described in item 2, wherein the detection unit (114) detects an individual having a battery (E1, 41) that does not meet the specifications of the product (3) mounted thereon by using data of a predetermined SOC range in the SOC-OCV characteristics of the plurality of batteries (E1, 41).

[0104] This makes it possible to reduce the amount of calculations compared to using data over the entire SOC range.

[0105] [Item 6] The data acquisition unit (111) acquires operation data from each of the products (3), the operation data including at least a voltage, a current, and a SOC measured at at least one time on the side of each of the products (3); The calculation system (1) an extraction unit (112) that extracts, as sample data, a pair of SOC and voltage during a period in which the battery (E1, 41) can be considered to be in a resting state, the pair being specified based on the current, from at least one pair of SOC and voltage included in each of the plurality of operation data; and a determination unit (113) that determines a relationship between at least one specific value of SOC and an OCV (Open Circuit Voltage) of a plurality of batteries (E1, 41) mounted on each of a plurality of individual products (3) based on the extracted sample data, The computing system (1) described in item 1, wherein the detection unit (114) detects an individual battery (E1, 41) that does not meet the specifications of the product (3) based on the relationship between the SOC and OCV of at least one specific value of the plurality of batteries (E1, 41).

[0106] This makes it possible to detect a product (3) equipped with a battery (E1, 41) that differs from the catalog specifications while suppressing the amount of calculation.

[0107] [Item 7] The calculation system (1) described in item 6, characterized in that the detection unit (114) detects an individual equipped with a battery (E1, 41) that differs from the specifications of the product (3) based on the degree of deviation between each representative value of a plurality of points indicating the relationship between SOC and OCV of the plurality of batteries (E1, 41) and each value of a plurality of points indicating the relationship between SOC and OCV of each battery (E1, 41), and the degree of deviation of an angle between a line connecting the representative values ​​of the plurality of points and a line connecting the plurality of points indicating the relationship between SOC and OCV of each battery (E1, 41).

[0108] This makes it possible to detect a product (3) equipped with a battery (E1, 41) that differs from the catalog specifications with a relatively high degree of accuracy while suppressing the amount of calculation.

[0109] [Item 8] The calculation system (1) described in item 6 is characterized in that the detection unit (114) detects an individual equipped with a battery that differs from the specifications of the product (3) based on the degree of deviation between each value of a plurality of points indicating the relationship between SOC and OCV in the specifications of the product (3) and each value of a plurality of points indicating the relationship between SOC and OCV of each battery (E1, 41), and the degree of deviation of an angle between a line connecting each value of a plurality of points indicating the relationship between SOC and OCV in the specifications of the product (3) and a line connecting multiple points indicating the relationship between SOC and OCV of each battery (E1, 41).

[0110] This makes it possible to detect a product (3) equipped with a battery (E1, 41) that differs from the catalog specifications with a relatively high degree of accuracy while suppressing the amount of calculation.

[0111] [Item 9] The data acquisition unit (111) acquires operation data from each of the products (3), the operation data including at least a voltage, a current, and a SOC measured at at least one time on the side of each of the products (3); The calculation system (1) and a determination unit (113) that determines a relationship between an SOC of at least one specific value and an internal resistance of a plurality of batteries (E1, 41) mounted on each of a plurality of individual products (3) based on the plurality of operation data, The computing system (1) described in item 1 is characterized in that the detection unit (114) detects an individual battery (E1, 41) that does not meet the specifications of the product (3) based on the relationship between the SOC of at least one specific value of the plurality of batteries (E1, 41) and the internal resistance.

[0112] [Item 10] acquiring operational data including at least a voltage and a State Of Charge (SOC) of a battery (E1, 41) mounted in a single product (3) from a plurality of individual products (3) via a network (5); A step of statistically processing the acquired operation data to detect an individual having a battery (E1, 41) that does not conform to the specifications of the product (3); A battery inspection method comprising the steps of:

[0113] This makes it easy to detect a product (3) equipped with a battery (E1, 41) that differs from the catalog specifications.

[0114] [Item 11] A process of acquiring operational data including at least a voltage and a State Of Charge (SOC) of a battery (E1, 41) mounted in a single product (3) from a plurality of individual products (3) via a network (5); A process of statistically processing the acquired operation data to detect an individual having a battery (E1, 41) that does not meet the specifications of the product (3); A battery inspection program that causes a computer to execute the above steps.

[0115] This makes it easy to detect a product (3) equipped with a battery (E1, 41) that differs from the catalog specifications. [Explanation of symbols]

[0116] 1 Calculation system, 2 Operator system, E1-En cell, T1, T2 Temperature sensor, RY1, RY2 Relay, 3 Electric vehicle, 4 Charger, 5 Network, 11 Processing unit, 111 Data acquisition unit, 112 Extraction unit, 113 Estimation unit, 114 detection unit, 12 memory unit, 121 driving data storage unit, 122 SOC-OCV characteristic storage unit, 21 processing unit, 22 memory unit, 221 driving data storage unit, 222 driver data storage 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 map.

Claims

1. a data acquisition unit that acquires operational data including at least the voltage and SOC (State Of Charge) of a battery mounted in the same product from a plurality of individual products via a network; a detection unit that performs statistical processing of the acquired operation data to detect an individual having a battery that does not conform to the product specifications; A computing system comprising:

2. The data acquisition unit acquires operation data from each of the products, the operation data including at least a voltage, a current, and a SOC measured at a plurality of times on each of the products; The computing system includes: an extraction unit that extracts, as sample data, a pair of SOC and voltage during a period during which the battery can be considered to be in a resting state, the pair being specified based on the current, from pairs of SOC and voltage at a plurality of times respectively included in the plurality of operation data; The computing system according to claim 1, further comprising an estimation unit that estimates SOC-OCV (Open Circuit Voltage) characteristics of a plurality of batteries mounted on each of the plurality of individual products based on the extracted sample data.

3. The computing system according to claim 2, wherein the detection unit detects an individual battery that does not meet the product specifications based on the degree of deviation of the SOC-OCV characteristics of the plurality of batteries from a representative value.

4. The computing system according to claim 2, wherein the detection unit detects an individual equipped with a battery that differs from the product specifications based on a degree of deviation from a value pre-recorded as an SOC-OCV characteristic of the product specifications.

5. 3. The computing system according to claim 2, wherein the detection unit detects an individual having a battery that does not meet the product specifications by using data of a predetermined SOC range in the SOC-OCV characteristics of the plurality of batteries.

6. The data acquisition unit acquires operation data from each of the products, the operation data including at least a voltage, a current, and a SOC measured at at least one time on each of the products; The computing system includes: an extraction unit that extracts, as sample data, a pair of SOC and voltage during a period during which the battery can be considered to be in a resting state, the pair being specified based on the current, from at least one pair of SOC and voltage included in each of the plurality of operational data; and a determination unit that determines a relationship between an SOC and an OCV (Open Circuit Voltage) of at least one specific value of a plurality of batteries mounted on each of the plurality of individual products based on the extracted sample data, 2. The computing system according to claim 1, wherein the detection unit detects an individual battery having a battery different from the product specifications based on a relationship between an SOC of at least one specific value and an OCV of the plurality of batteries.

7. The computing system according to claim 6, characterized in that the detection unit detects an individual equipped with a battery that differs from the product specifications based on the degree of deviation between each representative value of a plurality of points indicating the relationship between SOC and OCV of the plurality of batteries and each value of the plurality of points indicating the relationship between SOC and OCV of each battery, and the degree of deviation of an angle between a line connecting the representative values ​​of the plurality of points and a line connecting the plurality of points indicating the relationship between SOC and OCV of each battery.

8. The computing system according to claim 6, characterized in that the detection unit detects an individual equipped with a battery that differs from the product specifications based on the degree of deviation between each of the values ​​of the multiple points indicating the relationship between SOC and OCV of the product specifications and each of the values ​​of the multiple points indicating the relationship between SOC and OCV of each battery, and the degree of deviation of an angle between a line connecting each of the values ​​of the multiple points indicating the relationship between SOC and OCV of the product specifications and a line connecting the multiple points indicating the relationship between SOC and OCV of each battery.

9. The data acquisition unit acquires operation data from each of the products, the operation data including at least a voltage, a current, and a SOC measured at at least one time on each of the products; The computing system includes: and a determination unit that determines a relationship between an SOC of at least one specific value and an internal resistance of a plurality of batteries mounted on each of the plurality of individual products based on the plurality of operational data, The computing system according to claim 1, wherein the detection unit detects an individual battery that is equipped with a battery that does not meet the product specifications based on the relationship between the SOC of at least one specific value and the internal resistance of the plurality of batteries.

10. acquiring operational data including at least the voltage and SOC (State Of Charge) of a battery mounted in a single product from a plurality of individual products via a network; A step of statistically processing the acquired operational data to detect an individual product equipped with a battery that does not conform to the product specifications; A battery inspection method comprising the steps of:

11. A process of acquiring operational data including at least the voltage and SOC (State Of Charge) of a battery mounted in a single product from a plurality of individual units of the product via a network; A process of statistically processing the acquired operational data to detect an individual product equipped with a battery that does not comply with the product specifications; A battery inspection program that causes a computer to execute the above steps.

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