Information processing method, information processing device, and computer program

Abstract

To provide an information processing method, an information processing device, and a computer program that allow appropriate evaluation of an electric moving body having a power storage element mounted therein, based on a use history.SOLUTION: An information processing method for calculating a value of an electric moving body after a predetermined time has passed since its use start time, which is performed by a computer, includes: calculating an evaluation value of the electric moving body based on both a use history of the electric moving body and a period from its use start time; calculating the soundness of a power storage element that is a power source, by using a correction value for each specific item from among items in the use history of the electric moving body; and correcting the calculated evaluation value of the electric moving body by the soundness of the power storage element.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing method, an information processing device, and a computer program for outputting an evaluation value for an electric vehicle equipped with an energy storage element. [Background technology]

[0002] Various methods have been proposed for estimating the degradation state of energy storage elements, and their accuracy has improved, making it easier to grasp the degradation state from their appearance (see, for example, Patent Document 1). However, the degradation of energy storage elements can be affected by factors such as quality variations between energy storage element lots, and even when electric vehicles are used in the same way, that is, under the same usage history, the progression of degradation of energy storage elements can differ. Furthermore, because energy storage element packs have high voltages and high output, assessing the progression of degradation is often difficult unless you are an energy storage element manufacturer or sales company with extensive knowledge of the elements. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 7103323 Summary of the Invention [Problem to be solved by the invention]

[0004] Even if the usage history is similar, the degree of deterioration of the storage element varies depending on the quality of the storage element, not on the owner of the electric vehicle that the storage element is installed in. However, since the purchase price of the electric vehicle varies depending on the condition of the storage element, a sense of unfairness arises among the owners. Furthermore, because it is difficult for dealers of electric vehicles to grasp the degree of deterioration of the storage element, the degree of deterioration of the storage element may not be accurately reflected in the purchase price of the electric vehicle.

[0005] An object of one embodiment of the present invention is to provide an information processing method, an information processing device, and a computer program for appropriately evaluating an electric vehicle equipped with a power storage element based on a usage history. [Means for solving the problem]

[0006] An information processing method according to one embodiment of the present disclosure is an information processing method for calculating the value of an electric vehicle after a period of time has elapsed since the start of use, in which a computer calculates an evaluation value of the electric vehicle based on the usage history of the electric vehicle and the period of time since the start of use, calculates the health of a storage element, which is the power source, using a correction value for each specific item in the usage history of the electric vehicle, and corrects the calculated evaluation value of the electric vehicle using the health of the storage element.

[0007] Here, "start of use" refers to when the vehicle is new. Furthermore, the usage history refers to the accident history of the electric vehicle, the history of vehicle inspections and maintenance, and reports on those results, the repair and replacement history of the power source, the charging conditions, the storage conditions, the driving method, etc. Furthermore, the specific items refer to the charging conditions, the storage conditions, the driving method, etc. of the storage elements, among the usage history. [Effects of the Invention]

[0008] According to an information processing method according to one aspect of the present invention, it is possible to appropriately evaluate an electric vehicle using the health status of the storage elements estimated from specific items in the usage history of the electric vehicle. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a schematic diagram of an information processing system according to a first embodiment. [Figure 2] FIG. 1 is a block diagram showing a configuration of an information processing device. [Figure 3] 10 shows an example of the contents of a table for calculating the health degree. [Figure 4] 10 shows an example of the contents of a table for calculating the health degree. [Figure 5] FIG. 2 is a block diagram showing the configuration of an information terminal device. [Figure 6] FIG. 1 is a block diagram showing a configuration of a communication device. [Figure 7] 10 is a flowchart illustrating an example of a processing procedure in the information processing system. [Figure 8] 10 shows an example of SOH estimated from the correction points. [Figure 9] 10 shows an example of an output screen for evaluation values. [Figure 10] 10 is a flowchart illustrating an example of a processing procedure performed by an information processing device according to a modified example. [Figure 11] 10 shows an example of an output screen in Modification 1. [Figure 12] 10 is a flowchart illustrating an example of a processing procedure performed by an information processing device according to Modification 2. [Figure 13] 10 shows an example of an output screen in Modification 2. [Figure 14] FIG. 10 is a block diagram showing the configuration of an information processing device according to a second embodiment. [Figure 15] 10 is a flowchart illustrating an example of a processing procedure performed by the information processing apparatus according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] First, an overview of the information processing method, information processing device, and computer program disclosed in this specification will be described.

[0011] (1) An information processing method for calculating the value of an electric vehicle after a period of time has elapsed since the start of use, in which a computer calculates an evaluation value of the electric vehicle based on the usage history of the electric vehicle and the period of time since the start of use, calculates the health of a storage element, which is the power source, using a correction value for each specific item in the usage history of the electric vehicle, and corrects the calculated evaluation value of the electric vehicle based on the health of the storage element.

[0012] In the information processing method according to this embodiment, the evaluation value of the electric vehicle calculated based on the usage history and the period since the start of use does not take into account the health of the storage element that serves as the power source, and the evaluation value is corrected based on the health of the storage element.

[0013] The electric vehicle according to this embodiment is a vehicle such as an electric car, a plug-in hybrid vehicle, an airplane, or a ship that is equipped with an electric storage element as a power source. The electric vehicle may be manned or unmanned. At least a part of the driving power source of the electric vehicle is the electric storage element, and the power source may also include other power sources such as an engine.

[0014] The health level of the energy storage device according to this embodiment may be a value corresponding to the ratio of the full charge capacity of the energy storage device at the time of evaluation to the full charge capacity of the energy storage device at the start of use. The health level may also be an estimated value of the state of health (SOH), which represents the ratio of the full charge capacity of the energy storage device at the time of evaluation to the full charge capacity of the energy storage device at the start of use, using a degradation estimation algorithm, multivariate analysis, machine learning, or the like, which uses an impact coefficient representing the influence of at least one specific item of the usage history on the degradation of the energy storage device as a correction value. If the relationship between the former value corresponding to the ratio of the full charge capacity of the energy storage device at the time of evaluation to the full charge capacity of the energy storage device at the start of use and the latter SOH is clear in advance, as described below, a period in which one state of health is unclear can be complemented by the other state of health. Furthermore, the two states of health can be compared.

[0015] The information processing method of this embodiment evaluates an electric vehicle equipped with a power storage device using the health of the power storage device calculated from data on specific items in the usage history, rather than evaluating the power storage device using actual measurement data of the power storage device at the time of appraisal, inspection, vehicle inspection, etc. The specific items may be single or multiple. While measurements of the power storage device are performed at the time of appraisal, if the health of the power storage device can be estimated from specific items in the usage history, the health of the power storage device as a whole can be evaluated at any time. Here, the usage history, particularly the history related to specific items, preferably covers the entire period from the start of use to the time of evaluation, but may also be continuous or intermittent history for any period in the past. This is because, as described above, if the relationship between the value corresponding to the ratio of the full charge capacity of the power storage device at the time of evaluation to the full charge capacity of the power storage device at the start of use and the SOH of that ratio itself is previously known, the actually measured SOH can be used to complement the former. Specifically, for a period when the SOH is unknown, the SOH of the power source is estimated using the value corresponding to the ratio of the full charge capacity of the storage element at the time of evaluation to the full charge capacity of the storage element at the start of use, or the impact coefficient that represents the influence of at least one specific item in the usage history on the deterioration of the storage element. It is possible to use the SOH of the storage element, which is the power source, estimated using characteristic data of the storage element actually measured during appraisal, inspection, vehicle inspection, etc. Here, "any timing" refers not only to the timing of a statutory vehicle inspection or inspection, but also to a timing that can be arbitrarily determined by the owner or user of the electric vehicle, or a third party such as a maintenance company or a buyer.

[0016] With the above configuration, in addition to accurately estimating the state of the storage element for the electric vehicle through measurement, the health of the storage element and the electric vehicle can be evaluated from that health at any time based on data on items in the usage history of the electric vehicle that affect the deterioration of the storage element.

[0017] The usage history includes the distance traveled by the target electric vehicle from the past to the present. Specific items from the usage history are extracted as items that affect the deterioration of the storage element for calculating the health of the storage element. The data for the specific items may be data obtained from an on-board diagnosis (OBD) or may be data obtained during inspection.

[0018] By using the information processing method according to the present embodiment, anyone, even if they are not a company with extensive knowledge of energy storage elements that manufactures or sells energy storage elements, i.e., an owner or user of an electric vehicle such as an electric vehicle, or a third party such as a maintenance company or buyer, can obtain evaluation information for the electric vehicle that takes into account the health of the energy storage elements. Not only owners, but also dealers that sell electric vehicles and maintenance companies that maintain electric vehicles can obtain evaluation information that takes into account the health of the energy storage elements of the electric vehicle. This makes it possible to appropriately and easily obtain an evaluation for used electric vehicles, which tend to be lowered solely due to a decrease in the full charge capacity of the energy storage elements based on actually measured characteristic data of the energy storage elements, based on their usage history and regardless of variations in the quality of the energy storage elements.

[0019] (2) In the information processing method of (1) above, the computer may calculate a monetary value of the electric vehicle based on the corrected evaluation value.

[0020] Here, monetary value refers to the appraisal price, residual value, purchase price, trade-in price, lease price, etc.

[0021] With the above configuration, the monetary value of the electric vehicle is calculated by a computer that can appropriately calculate the health of the storage elements mounted on the electric vehicle. This makes it possible to appropriately evaluate the electric vehicle when the usage history of the electric vehicle can be obtained, rather than when measurements of the storage elements are taken.

[0022] The calculation of monetary value may be performed using deep learning techniques such as RNN (Recurrent Neural Network), LSTM (Long Short Term Memory), and Transformer that analyze usage history, particularly past sales prices corresponding to adjustments for specific items, and recent prices of similar electric vehicles at nearby used car dealerships.

[0023] (3) In the information processing method of (1) or (2) above, the computer may use, as the specific item, the charging conditions when charging the storage element mounted on the electric vehicle, and calculate a correction value according to the charging conditions.

[0024] The data used to calculate the health of the energy storage elements may include trends in charging methods. Charging methods vary depending on the owner or user, and the charging method affects the health of the energy storage elements. The above configuration makes it possible to appropriately calculate the health of the energy storage elements and evaluate the entire electric vehicle without necessarily measuring the energy storage elements.

[0025] (4) In the information processing method of (3) above, the charging conditions may be at least one of the temperature during charging, the type of charging method, and the magnitude of the difference in the amount of power before and after charging, and the computer may calculate a correction value according to the charging conditions.

[0026] As the tendency of the charging method, factors that have a relatively large impact on the health of the energy storage element, such as the temperature during charging, the type of charging method used, and the difference in the amount of power before and after charging, are used. By calculating the health of the energy storage element using these factors, the health of the energy storage element can be appropriately calculated without necessarily measuring the energy storage element, making it possible to evaluate the entire electric vehicle. Here, the type of charging method can be rapid charging, normal charging, etc., and can be arbitrarily determined based on the maximum power supplied to the energy storage element installed in the electric vehicle during charging, taking into account the deterioration of the energy storage element. For example, rapid charging can be determined when the maximum power (kW) during charging is equal to or greater than the nominal capacity (kWh) of the energy storage element, and normal charging can be determined when the maximum power is less than the nominal capacity. The charging classification may be divided into three or more categories, such as rapid charging when the maximum power during charging is 40 kW or more, normal charging (high) when it is less than 40 kW and 10 kW or more, and normal charging (low) when it is less than 10 kW. To simplify operation, the classification may be determined arbitrarily based on the maximum output of the charger connected to the electric vehicle or the maximum output actually supplied. For example, charging from a charger with a maximum output equal to or greater than the nominal capacity of the storage element may be determined as rapid charging, and charging from a charger with a maximum output less than that nominal capacity may be determined as normal charging. Alternatively, charging from a charger with a maximum output of 40 kW or more may be determined as rapid charging, charging from a charger with a maximum output of 10 kW or more but less than 40 kW may be determined as normal charging (high), and charging from a charger with a maximum output of less than 10 kW may be determined as normal charging (low).

[0027] (5) In any of the information processing methods (1) to (4) above, the computer may use a storage status of the electric vehicle as the specific item and calculate a correction value according to the storage status.

[0028] The data used to calculate the health of the energy storage elements may include the storage conditions of the electric vehicle. Although the storage conditions of each electric vehicle are different, the storage conditions, particularly the temperature, affect the health of the energy storage elements. With the above configuration, it is possible to appropriately calculate the health of the energy storage elements and evaluate the entire electric vehicle without necessarily measuring the energy storage elements.

[0029] (6) In the information processing method of (5) above, the computer may use the temperature during parking and the length of time the vehicle was parked at that temperature as the storage conditions, and calculate a correction value according to the storage conditions.

[0030] With the above configuration, the evaluation value of the electric vehicle can be corrected and an appropriate evaluation value can be calculated based on factors that affect the health of the storage element, such as the temperature while parked and the length of time the vehicle was exposed to that temperature, without having to perform measurements on the storage element.

[0031] (7) In any of the information processing methods (1) to (6) above, the computer may use a driving method of the electric vehicle as the specific item and calculate a correction value according to the driving method.

[0032] The data used to calculate the health of the energy storage elements may include the manner in which the electric vehicle is driven. The manner in which each electric vehicle is driven varies depending on the user, but the manner in which power is consumed from the energy storage elements affects the health of the energy storage elements. With the above configuration, even if measurements have not been performed on the energy storage elements, the health of the energy storage elements can be appropriately estimated, enabling the entire electric vehicle to be evaluated.

[0033] (8) In the information processing method of (7) above, the computer may use the cumulative travel distance of the electric vehicle and the frequency of sudden acceleration of the electric vehicle as the driving style, and calculate a correction value according to the driving style.

[0034] Acceleration can be measured directly from the acceleration value detected by an acceleration sensor attached to the electric vehicle, or the value obtained by dividing the short-term increase in speed by the traveled distance. The frequency of sudden acceleration refers to the number of times the acceleration value exceeds a predetermined acceleration value per unit time or unit traveled distance. Acceleration exceeding the predetermined acceleration value causes a large output or current to be discharged from the power source storage element, and this discharge affects the deterioration of the power source storage element. Therefore, acceleration can also be detected indirectly from the discharge current or output value from the power source storage element. A sudden acceleration can be counted when the discharge current or output value from the power source storage element exceeds a predetermined value, and the frequency of this can be used as the acceleration frequency. Furthermore, focusing on accelerator work, which controls the large output or current from the power source storage element, the frequency at which the accelerator depression exceeds the ratio of a predetermined accelerator depression to the fully depressed accelerator depression can be calculated and used.

[0035] Even without measuring the storage elements, the evaluation value of the electric vehicle can be corrected and an appropriate evaluation value can be calculated based on items that affect the health of the storage elements, such as the cumulative travel distance and the frequency of sudden acceleration.

[0036] (9) In any of the information processing methods (1) to (8) above, the computer may compare the health of the storage element calculated using a correction value calculated for each specific item that affects the deterioration of the storage element with the health of the storage element derived from the measurement results of the storage element mounted on the electric vehicle, and output a higher health as a result of the comparison.

[0037] Here, the comparison of health may be performed between the SOH obtained by converting a value corresponding to the ratio of the full charge capacity of the storage element at the time of evaluation to the full charge capacity of the storage element at the start of use, as described above, or the SOH value estimated using an impact coefficient that represents the influence of at least one specific item in the usage history on the deterioration of the storage element, and the SOH that represents the ratio itself of the full charge capacity of the storage element at the time of evaluation to the full charge capacity of the storage element at the start of use, which is derived based on measurement results for the storage element (for example, measured values of the discharge capacity after full charge).

[0038] If the health of the energy storage element estimated from the usage history is better than the health derived from the detailed measurement results performed on the energy storage element, the user will evaluate it as good even if it is better than the actual measurement result, because the user has high expectations for that health until the measurement is performed. Conversely, if the actual measurement result for the energy storage element is better than the health of the energy storage element derived from the usage history, this is used in the evaluation because it is beneficial to the user.

[0039] (10) In any of the information processing methods (1) to (9) above, the computer may sequentially acquire items of the usage history of the electric vehicle via a network, and at any timing, estimate the health of the storage element at a future time point or an evaluation value of the electric vehicle after correction based on the acquired items of the usage history of the electric vehicle, and output the estimated evaluation value.

[0040] Here, "any timing" may be any timing that can be determined at will by the owner or user of the electric vehicle, or a third party such as a maintenance company or purchaser, as well as the timing of a statutory vehicle inspection or maintenance service.

[0041] The transition of the health level can be calculated from specific items of the usage history that can be acquired sequentially. From the transition of the health level, an estimate of the future health level and a future rating value of the electric vehicle based on the estimated health level can be obtained. With the above configuration, the user can understand what the future rating value will be if the electric vehicle continues to be driven as is, that is, if the current user's usage history, particularly the charging conditions, storage conditions, and / or driving method, is continued as is.

[0042] (11) In the information processing method of (10) above, the computer may extract improvement items from the specific items in the acquired usage history of the electric vehicle to increase the health of the storage element at the future time point or the corrected evaluation value of the electric vehicle, and output improvement measures based on the extracted improvement items.

[0043] From specific items in the usage history that can be acquired sequentially, an estimate of the future health level and a future evaluation value of the electric vehicle based on the estimated health level can be obtained. It is possible to estimate which items in the current usage history affect the health level of the storage element and, if improved, will affect the evaluation of the electric vehicle as a whole. With the above configuration, the user can understand which items to pay attention to when driving the electric vehicle in order to increase the future evaluation of the electric vehicle, i.e., its monetary value.

[0044] (12) An information processing device that calculates the value of an electric vehicle after time has passed since the start of use includes: an acquisition unit that acquires the usage history of the electric vehicle and the period since the start of use; a first calculation unit that calculates an evaluation value of the electric vehicle based on the acquired usage history of the electric vehicle and the period since the start of use; a second calculation unit that calculates the health of a storage element, which is a power source, using a correction value for each specific item in the usage history of the electric vehicle; and a correction unit that corrects the calculated evaluation value of the electric vehicle based on the health of the storage element.

[0045] (13) A computer program causes a computer to calculate the value of an electric vehicle after a period of time has elapsed since the start of use, and causes the computer to execute a process of calculating an evaluation value of the electric vehicle based on the usage history of the electric vehicle and the period of time since the start of use, calculating the health of a storage element that is a power source using a correction value for each specific item in the usage history of the electric vehicle, and correcting the calculated evaluation value of the electric vehicle based on the health of the storage element.

[0046] An information processing method, an information processing device, and a computer program according to an aspect of the present invention will be specifically described with reference to the drawings illustrating embodiments thereof.

[0047] (First embodiment) FIG. 1 is a schematic diagram of an information processing system 100 according to a first embodiment. The information processing system 100 includes an information terminal device 2 used at various locations and an information processing device 1 that is communicatively connected to a plurality of information terminal devices 2. The information processing system 100 also includes a communication device 3 that is mounted on an electric vehicle equipped with a power storage element 4. The information terminal device 2 is used by the owner or user of the electric vehicle. The information terminal device 2 may also be used by a third party such as a dealer or maintenance company for the electric vehicle, which is an electrically powered moving object.

[0048] The information processing device 1 is managed by the manufacturer of the energy storage element 4, which is the power source for the drive motor (hereinafter also referred to as the main battery) mounted on the electric vehicle, the manufacturer of the electric vehicle in which the energy storage element 4 is mounted, or a technical support provider with knowledge about the energy storage element 4 and electric vehicles. The manufacturer or technical support provider may also be a dealer or maintenance provider for the electric vehicle. Here, management means having the authority to own the information processing device 1, set data required for the operation of the information processing device 1, store and change data referenced in processing, etc.

[0049] The information terminal device 2 is a smartphone, tablet terminal, or the like owned by the owner or user of the electric vehicle. The information terminal device 2 may also be another form of personal computer, such as a desktop or laptop. The information terminal device 2 can also function as the communication device 3 when it is brought into the electric vehicle by the owner or user and connected to the in-vehicle network wirelessly and via wired communication.

[0050] The communication device 3 is a device mounted on the electric vehicle, capable of acquiring information from on-board devices via an on-board network and transmitting data to the outside of the vehicle by wireless communication. The communication device 3 is a navigation device, a DCM (Data Communication Module), an on-board diagnostics (OBD), or a communication device connected to any of these. The communication device 3 may also be used as the information terminal device 2 brought into the electric vehicle by the owner or user.

[0051] A communication connection can be established between the multiple information terminal devices 2 and the information processing device 1 via a network N. The network N includes a network N1, which is a manufacturer's local network or a dedicated line. The network N may include a public communication network N2, which is the so-called Internet, and a carrier network N3. The network N includes a network N4 of a communication standard to which the communication device 3 can be connected. The network N4 may be the same as the carrier network N3.

[0052] In the information processing system 100 of the first embodiment, the owner or user of an electric vehicle operates the information terminal device 2 when they want to know the current evaluation of the electric vehicle. Alternatively, the owner or user requests the current evaluation of the electric vehicle from an electric vehicle dealer or maintenance company. In response, the information terminal device 2 requests the information processing device 1 to obtain an evaluation value of the electric vehicle via the network N. The evaluation value may be, for example, a monetary amount corresponding to the appraised price, a residual value, or a value corresponding to a ratio where the evaluation value of a new vehicle is set to 100.

[0053] Previously, the appraisal value of an electric vehicle (used car) after some time has passed since it began to be used has been calculated by, for example, a used car appraiser, who, at the time of appraisal, takes into account the same items as a gasoline-powered car, such as the exterior, suspension, and self-repair history, as well as the rate of decline in the full charge capacity of the storage element 4 estimated from measurement data obtained by a tester or the BMU (Battery Management Unit) of the storage element 4. Because used car appraisers do not necessarily have knowledge of the storage element 4, even if only a slight decline in full charge capacity has occurred in a car only about three years after it was new, they take into account variations in the performance of the storage element, or, because they are unable to assess the decline in capacity of the storage element 4, that is, degradation, they tend to use a large safety factor (assuming that the degradation is greater than expected), resulting in the appraisal price being reduced by about half.

[0054] In contrast, in the information processing system 100 of the first embodiment, the information processing device 1 can relatively easily calculate an evaluation value that takes into account the health of the energy storage elements 4 from the usage history of the electric vehicle, without having to measure the energy storage elements 4 of the electric vehicle using a tester or the like. The usage history data is sequentially uploaded from the communication device 3 to a database that the information processing device 1 can access, so that the evaluation value can be calculated at any timing.

[0055] The components and processing details that realize the calculation of an evaluation value for an electric vehicle equipped with the power storage device 4 used as the main battery by the information processing system 100 will be described below.

[0056] 2 is a block diagram showing the configuration of information processing device 1. Information processing device 1 is a server computer. In the following explanation, information processing device 1 is described as being configured by one server computer, but it may also be configured in a manner where multiple server computers are communicatively connected via a network to perform distributed processing.

[0057] The information processing device 1 includes a processing unit 10, a storage unit 11, and a communication unit 12. The processing unit 10 is a processor that uses a central processing unit and / or a graphics processing unit (GPU). Based on an information processing program P1 stored in the storage unit 11, the processing unit 10 executes processes such as calculating the health of the energy storage element 4 and calculating an evaluation value of the electric vehicle itself in which the energy storage element 4 is mounted.

[0058] The storage unit 11 uses a nonvolatile memory such as a hard disk, a flash memory, or an SSD (Solid State Drive). The storage unit 11 stores data referenced by the processing unit 10. The storage unit 11 stores an information processing program (program product) P1. The information processing program P1 may be an information processing program P9 stored in the storage medium 9 that is read by the processing unit 10 and copied to the storage unit 11. The information processing program P1 may also be one that the processing unit 10 has downloaded from another program server device via the communication unit 12 and stored therein.

[0059] The storage unit 11 stores the information processing program P9 as well as a server program for performing a web server function. Based on the server program, the processing unit 10 realizes a web service that presents to the information terminal device 2 an evaluation value calculated for each electric vehicle based on the information processing program P1.

[0060] The memory unit 11 stores a table 113 that describes a plurality of specific items from the usage history of the electric vehicle for calculating the health of the storage elements 4, and the correspondence between each item and a correction point (see FIGS. 3 and 4). The correction point will be described later with reference to FIG. 4. The specific item is at least one of an item related to the charging conditions when the electric vehicle was previously charged, an item related to the storage status of the electric vehicle, and an item related to the driving style. There may be only one item and only one table. The processing unit 10 can calculate the health by referring to the table stored in the memory unit 11 and depending on which range the data of a specific item from the usage history belongs to.

[0061] In the explanations that will be described later with reference to the drawings, in order to facilitate calculation of the state of health, a value corresponding to the ratio of the full charge capacity of the storage element 4 at the time of evaluation to the full charge capacity of the storage element 4 at the start of use, i.e., a correction value, is used. This state of health can also be converted into the SOH, which represents the state of health as the ratio of the full charge capacity of the storage element 4 at the time of evaluation to the full charge capacity of the storage element 4 at the start of use, as will be described later ( FIG. 8 ). Of course, an estimated value of the SOH, which represents the ratio of the full charge capacity of the storage element 4 at the time of evaluation to the full charge capacity of the storage element 4 at the start of use, may also be used as the state of health, using a degradation estimation algorithm, multivariate analysis, machine learning, or the like, which uses at least one impact coefficient of a specific item as a correction value. This SOH-represented state of health can also be converted into the state of health, which is a value corresponding to that ratio, by reversing the above-described procedure.

[0062] The storage unit 11 has a database configured as a vehicle information DB 111 that stores information about electric vehicles. The vehicle information DB 111 may be managed externally by the electric vehicle manufacturer, dealer, or the like. The processing unit 10 can acquire the driving history of each electric vehicle from the vehicle information DB 111 using vehicle identification data as a key. The processing unit 10 can acquire the appraisal value (new vehicle price, used vehicle transaction price) corresponding to the vehicle model and year of manufacture from the vehicle information DB 111.

[0063] The storage unit 11 may have a database constructed therein that stores data on the characteristics of each type of energy storage element 4, as well as the manufacturing date, sales date and time, and sales target in association with the manufacturing number of the energy storage element 4. The storage unit 11 may also store a correspondence between the manufacturing number of the energy storage element 4 and the vehicle identification data of the electric vehicle in which the energy storage element 4 is mounted. The processing unit 10 can obtain information on when the energy storage element 4 was manufactured from this database.

[0064] The communication unit 12 realizes communication with the information terminal device 2 via the network N. The communication unit 12 may be able to receive data directly from the communication device 3 via the network N. Specifically, the communication unit 12 is a network card. The communication unit 12 may be a wireless communication device that connects to the carrier network N3, or may be a wireless communication device for WiFi. The processing unit 10 can send and receive data to and from the information terminal device 2 via the communication unit 12.

[0065] 3 and 4 show an example of the contents of table 113 for calculating the health level. Table 113 for calculating the health level includes, for example, table 114 for charging conditions, table 115 for storage conditions, table 116 for cumulative travel distance as a driving style, and table 117 for frequency of sudden acceleration as a driving style. Each table defines a correction point for each condition. The total of the correction points is a value used to determine the level of health level as shown in FIG. 4.

[0066] Table 114 for each charging condition defines correction points that are assigned according to the combination of the temperature range during each charging and the type of charging. In the first embodiment, the correction points are expressed as a demerit value when the health level for a predetermined standard charging method or driving method is set to "0". The setting of the correction points is not limited to the manner shown in FIGS. 3 and 4, and may be a point-addition system.

[0067] In the specific example shown in Fig. 3, table 114 for different charging conditions defines correction points that are assigned depending on which of three ranges the average temperature during charging falls into: [less than 5°C], [5°C or higher but less than 15°C], or [15°C or higher], and whether the type of charging is [rapid charging] or [normal charging]. Table 114 for different charging conditions may assign different correction points depending on the type and capacity of the energy storage element 4. In the example shown in Fig. 3, correction points corresponding to the same condition are defined for type A and type B.

[0068] In the example shown in FIG. 3, in table 114 by charging condition, for example, when normal charging is performed under the condition that the temperature during each charging is 10°C, the correction point is "0" for both type A and type B. When rapid charging is performed under the same temperature, the correction point is "-2" for both type A and type B. Compared to type B storage elements 4, type A storage elements 4 tend to deteriorate more easily at low temperatures, and the correction point is "-1" when normal charging is performed at temperatures below 5°C, and the correction point is "-5" when rapid charging is performed.

[0069] As shown in Fig. 3, table 114 for each charging condition defines a correction point that is given based on a combination of a range of temperature during charging and a range of the increase in the amount of power due to charging. Specifically, table 114 for each charging condition defines a correction point that is given based on which of three ranges the average temperature during charging falls into: [less than 5°C], [5°C or higher but lower than 15°C], or [15°C or higher], and whether the increase due to charging falls into [50% or more increase] or "less than 50% increase" of the charge capacity. In the example shown in Fig. 3, correction points corresponding to the same condition are defined for type A and type B of energy storage elements 4.

[0070] Table 115 by storage condition defines correction points that are assigned based on a combination of the range of average temperatures during parking of the target electric vehicle and the range of the length of time the vehicle has been parked at those temperatures. Specifically, as shown in Fig. 3, table 115 by storage condition defines correction points that are assigned based on which of three ranges the average temperature during charging falls into: [below 30°C], [30°C or higher but lower than 35°C], or [35°C or higher], and whether the length of parking is [less than 1 hour], [1 hour or higher but lower than 3 hours], or [3 hours or longer]. Table 115 by storage condition may also assign different correction points depending on the type and capacity of the energy storage element 4; in Fig. 3, different correction points are assigned to Type A and Type B.

[0071] In the example shown in FIG. 3, for example, storage status table 115 specifies that if the temperature during parking is below 35°C, the correction point is "0" unless the parking time in that temperature environment is [3 hours or more]. If the vehicle is parked in an environment of [35°C or higher] and the parking time is [less than 1 hour], the correction point for type A is "-1" and the correction point for type B is "-2." If the vehicle is parked in the same temperature environment (35°C or higher) and the parking time is [1 hour or more but less than 3 hours], the correction point for type A is "-2" and the correction point for type B is "-3." If the vehicle is parked in the same temperature environment (35°C or higher) and the parking time is [3 hours or more], the correction point for type A is "-3" and the correction point for type B is "-5."

[0072] The table 116 by cumulative travel distance defines the correction points to be given for each range of cumulative travel distance of the target electric vehicle, each time the electric vehicle travels a predetermined distance within that range. The table 116 by cumulative travel distance defines, for example, the correction points to be added each time the electric vehicle travels an additional 1,000 km. In the specific example shown in FIG. 3, the table 116 by cumulative travel distance defines the correction points to be given for each additional predetermined distance depending on whether the cumulative travel distance of the electric vehicle falls into one of the following categories: [less than 30,000 km], [30,000 km or more but less than 50,000 km], [50,000 km or more but less than 80,000 km], or [80,000 km or more].

[0073] In the example shown in Fig. 3, the table 116 by accumulated travel distance specifies that while the accumulated travel distance of the electric vehicle is [less than 30,000 km], a point "-1" is added to the type A storage element 4 and a point "-1" is added to the type B storage element 4 for each 3,000 km increase.The table 116 by accumulated travel distance specifies that while the accumulated travel distance is in the range of [30,000 km or more but less than 50,000 km], a point "-1" is added to the type A storage element 4 and a point "-2" is added to the type B storage element 4 for each 1,000 km increase. Table 116 by cumulative travel distance specifies that while the cumulative travel distance is in the range of [50,000 km or more but less than 80,000 km], a point "-2" is added to the type A storage element 4 and a point "-4" is added to the type B storage element 4 for each 1,000 km increase. Table 116 by cumulative travel distance specifies that while the cumulative travel distance is in the range of [80,000 km or more], a point "-4" is added to the type A storage element 4 and a point "-8" is added to the type B storage element 4 for each 1,000 km increase. Therefore, for example, if the cumulative travel distance of an electric vehicle equipped with a Type A energy storage element 4 reaches 30,000 km, a correction point of "-1" is added nine times in the range of [less than 30,000 km] and a correction point of "-1" is added once in the range of [30,000 km or more but less than 50,000 km], resulting in a correction point of "-10." If the same electric vehicle then travels 20,000 km and reaches a total of 50,000 km, a correction point of "-1" is added 19 times in the range of [30,000 km or more but less than 50,000 km] and a correction point of "-2" is added once in the range of [50,000 km or more but less than 80,000 km], resulting in a correction point of "-31."

[0074] The table 117 for sudden acceleration frequencies defines correction points according to the frequency of sudden acceleration of the target electric vehicle. In the specific example shown in Fig. 3, the table 117 for sudden acceleration frequencies defines correction points according to whether the average number of times the accelerator pedal is depressed 70% or more per 100 km of travel distance of the electric vehicle falls into the range of [less than 10 times], [10 to less than 100 times], [100 to less than 1000 times], or [1000 or more]. In the example shown in Fig. 3, the table 117 for sudden acceleration frequencies defines the correction points for type A as "+1" and the correction points for type B as "+1" each time the travel distance of the electric vehicle increases by 100 km, if the number of times the accelerator pedal is depressed 70% or more during that 100 km travel is [less than 10 times]. Similarly, if the number of times the accelerator pedal is depressed 70% or more during a 100 km journey is [10 times or more but less than 100 times], the correction point is "+0" for both Type A and Type B. If the number of times the accelerator pedal is depressed 70% or more during a 100 km journey is [100 times or more but less than 1000 times], the correction point is "-1" for Type A and "-2" for Type B. If the number of times the accelerator pedal is depressed 70% or more during a 100 km journey is [1000 times or more], the correction point is "-4" for Type A and "-8" for Type B.

[0075] As shown in Fig. 4, table 113 includes table 118, which defines the correspondence between the health degree that can be calculated from tables 114-117 and the coefficient for correcting the evaluation value. The health degree is calculated, for example, as the total value of the correction points defined in tables 114-117. The coefficient is a numerical value by which the evaluation value is multiplied. The larger the total value of the correction points, or in the example of Fig. 4, the smaller the absolute value of the negative number, the higher the health degree and the higher the coefficient is set. Conversely, the smaller the total value of the correction points, or in the example of Fig. 4, the larger the absolute value of the negative number, the lower the health degree and the coefficient is set to be less than 1, resulting in a lower evaluation value.

[0076] The processing unit 10 of the information processing device 1 uses the table 113 shown in Figures 3 and 4 to calculate the healthiness of the storage elements 4 and calculate an evaluation value for the electric vehicle as a whole, without necessarily performing measurements on the storage elements 4.

[0077] Table 113 shown in FIGS. 3 and 4 may be replaced with a learning model that receives data for each item as input, outputs correction points, and is trained in advance using training data. The generation and training of the learning model may be performed using deep learning techniques such as RNN, LSTM, and Transformer. The learning model replaced by table 113 may be trained to output correction points when, for example, the average temperature during charging, the type of charging, and the average difference in the amount of power before and after charging (the increase in the amount of power) are input. The learning model may also be trained to further receive the average temperature during parking of the target electric vehicle and the average length of time the vehicle has been parked, and output the total correction points as the health level. The learning model may also be trained to receive a driving operation log as input, and output a degree to which the driving manner contributes to the deterioration of the energy storage elements 4.

[0078] 5 is a block diagram showing the configuration of the information terminal device 2. The information terminal device 2 includes a processing unit 20, a storage unit 21, a communication unit 22, a display unit 23, and an operation unit 24.

[0079] The processing unit 20 is a processor that uses a CPU and / or a GPU. The processing unit 20 executes a terminal program P2 stored in the storage unit 21. The processing unit 20 transmits and receives data to and from the information processing device 1 based on the terminal program P2. The terminal program P2 includes a web browser program.

[0080] The storage unit 21 uses a non-volatile memory such as a hard disk, flash memory, or SSD. The storage unit 21 stores data referenced by the processing unit 20. The storage unit 21 stores various programs including a terminal program P2. The terminal program P2 may be a web browser program that functions as a client of a web service provided by the information processing device 1. The terminal program P2 may also be an agent program incorporated into a web browser. The terminal program P2 may be a terminal program P8 (program product) stored in the storage medium 8 that is read by the processing unit 20 and copied to the storage unit 21. The terminal program P2 may also be a program downloaded from another program server or device via the communication unit 22 and stored therein.

[0081] The communication unit 22 realizes communication with the information processing device 1 via the network N. The communication unit 22 is, for example, a network card. The communication unit 22 may be a wireless communication device that connects to the carrier network N3, or may be a wireless communication device for WiFi. The processing unit 20 can send and receive data to and from the information processing device 1 via the communication unit 22.

[0082] The display unit 23 is a display such as a liquid crystal display or an organic EL (Electro Luminescence) display. The display unit 23 displays a GUI (Graphical User Interface) of various application programs. The processing unit 20 displays a screen on the display unit 23 based on the data provided from the information processing device 1, in accordance with the terminal program P2.

[0083] The operation unit 24 is a user interface such as a keyboard and a mouse that can input and output data to and from the processing unit 20. The operation unit 24 may be a touch panel built into the display unit 23. The operation unit 24 may be a physical button, or may be an audio input / output unit including a microphone.

[0084] 6 is a block diagram showing the configuration of the communication device 3. The communication device 3 includes a processing unit 30, a storage unit 31, a first communication unit 32, and a second communication unit 33. The processing unit 30 is a microcontroller. The processing unit 30 includes a CPU, a memory, etc., and executes the processing described below based on a control program stored in the storage unit 31.

[0085] The storage unit 31 is a non-volatile memory such as a flash memory, etc. The storage unit 31 stores data that the processing unit 30 acquires from the BMU of the energy storage element 4.

[0086] The first communication unit 32 is a communication device that realizes communication via an in-vehicle network. The first communication unit 32 is, for example, a communication device that supports CAN (Controller Area Network). The first communication unit 32 may also be a wireless communication device that supports Bluetooth (registered trademark). The first communication unit 32 may be incorporated into the processing unit 30. The processing unit 30 can acquire, via the in-vehicle network, data from various sensors such as a temperature sensor and an acceleration sensor that are mounted on the electric vehicle, as well as data such as a charging log, a driving history log, and a driving operation log.

[0087] The second communication unit 33 is a wireless communication device that realizes an external vehicle network, i.e., wireless communication. The second communication unit 33 may be, for example, a device for communication via the electric vehicle network N4, or may be a wireless communication device for communication based on a wireless LAN. The second communication unit 33 may be a wireless communication device compatible with Bluetooth (registered trademark). The second communication unit 33 may be a communication device compatible with a communication device provided in the charging device. The processing unit 30 can transmit data to the outside via the second communication unit 33.

[0088] During charging, whenever the travel distance increases by 100 km, or periodically, the processing unit 30 of the communication device 3 acquires usage history data including data history from sensors, a charging log, a driving history log, and a driving operation log from various in-vehicle devices connected via the in-vehicle network by the first communication unit 32. The processing unit 30 stores the acquired usage history data in the memory unit 31 in association with time information measured by a built-in timer.

[0089] When the second communication unit 33 detects that the communication device 3 is connected to the network N4, or when the communication device 3 is connected to a charging device, the processing unit 30 periodically transmits the usage history data stored in the storage unit 31 to the vehicle information DB 111. When transmitting this data, the processing unit 30 transmits the serial number and vehicle identification data of the energy storage element 4. The processing unit 30 may transmit the data to the information processing device 1 via the charging device.

[0090] When the communication device 3 transmits the usage history data to the vehicle information DB 111, the data is stored in the vehicle information DB 111 via the information processing device 1 or the manufacturer or dealer of the electric vehicle. In the vehicle information DB 111, the usage history data is stored in association with the vehicle identification data.

[0091] In the information processing system 100 configured as above, the process by which the owner or user of the electric vehicle uses the information terminal device 2 to obtain an evaluation value of the electric vehicle from the information processing device 1 will be described with reference to a flowchart.

[0092] Fig. 7 is a flowchart showing an example of a processing procedure in the information processing system 100. The processing procedure shown in Fig. 7 is started when the owner or user of the electric vehicle operates the information terminal device 2 to obtain evaluation information about the electric vehicle.

[0093] Based on the terminal program P2, the processing unit 20 of the information terminal device 2 transmits to the information processing device 1 a request to acquire an evaluation value for an electric vehicle equipped with an energy storage element 4 (step S201). In step S201, the processing unit 20 transmits the acquisition request by specifying the vehicle identification data of the target electric vehicle. The vehicle identification data of the target electric vehicle may be stored in advance in the storage unit 21 by a registration process, or the processing unit 20 may receive input via the operation unit 24 before the processing of step S201.

[0094] The processing unit 10 of the information processing device 1 receives an acquisition request from the information terminal device 2 (step S101). The processing unit 10 acquires data of the driving history log of the target electric vehicle from the vehicle information DB 111 based on the vehicle identification data specified in the received acquisition request (step S102). In step S102, the processing unit 20 may acquire information such as the manufacturing date of the energy storage element 4, which is the power source mounted on the target electric vehicle, from the manufacturer of the energy storage element 4.

[0095] The processing unit 10 calculates an evaluation value based on the length of use period of the target electric vehicle and the driving history log (step S103). In step S103, the processing unit 10 calculates a monetary value or points serving as an evaluation index according to the length of use period depending on the vehicle model, manufacturing year, grade, etc. identified by the vehicle identification data, and whether or not there have been any accidents.

[0096] In step S103, the processing unit 10 may calculate the monetary value from a correspondence table between the vehicle model, manufacturing year, grade, etc. stored in the vehicle information DB 111 and monetary values or points, or may calculate the monetary value using a learning model. As described above, the correspondence table stores monetary values or points corresponding to the length of use, whether or not there has been an accident, and whether or not there is any damage to the exterior for each vehicle model, manufacturing year, and grade, and is updated sequentially at the timing of model renewal, etc. The learning model is trained, for example, in response to input information such as the vehicle model, manufacturing year, grade, length of use, and whether or not there has been an accident, using past sales records, recent prices of similar electric vehicles at nearby used dealerships, etc. as training data. For example, the processing unit 10 may calculate the monetary value using a learning model that has been trained to output a monetary value when identification data of the vehicle model, manufacturing year, grade, length of use, and region of use are input.

[0097] The processing unit 10 extracts data of a specific item from the data of the driving history log acquired in step S102 (step S104). In step S104, the processing unit 10 may execute a process of averaging the data of the specific item over a target period, a process of extracting data for a specific period, or a process of calculating the data of the specific item from the data of the driving history log by calculation.

[0098] The processing unit 10 calculates correction points corresponding to the extracted data of the specific item using the table 113 in the storage unit 11 (step S105). In step S105, the processing unit 10 calculates correction points for each table to be used, for example. In step S105, in a first example, the processing unit 10 calculates correction points in table 114 from the average temperature (per charge) over the entire period read from the driving history log and the type of charging that occurs most frequently over the entire period, and calculates correction points in table 115 from the average temperature over the entire period when parking and the average parking time. In a second example, the processing unit 10 may narrow the target period for calculating the average to a target period such as the most recent year or the most recent three months, rather than the entire period. In a third example, the processing unit 10 may calculate and total correction points each time charging is performed or parking is performed.

[0099] The processing unit 10 calculates a value corresponding to the state of health (step S106). In step S106, the processing unit 10 calculates, for example, the sum of the correction points calculated for each table. In step S106, the processing unit 10 may calculate the SOH, which is the ratio of the full charge capacity at the time of evaluation to the full charge capacity at the start of use, by referring to the table. FIG. 8 shows an example of the SOH estimated from the correction points. As shown in FIG. 8, the table shows, for example, the correspondence between the sum of the correction points and a range of the current SOH in 5% increments. For example, if the sum of the correction points is 0 (zero), the processing unit 10 can estimate the SOH to be 80-85%, and if the sum of the correction points is +1 to +20, the processing unit 10 can estimate the SOH to be 85 to 90%. The processing unit 10 estimates the SOH to be 75 to 80% when the sum of the correction points is -1 to -20, estimates the SOH to be 70 to 75% when the sum is -21 to -100, and estimates the SOH to be 65 to 70% when the sum is -101 to -500. Of course, the health value or SOH expressed by the sum of the correction points may be further subdivided to correspond to the range of SOH.

[0100] The processing unit 10 acquires the coefficient corresponding to the soundness calculated in step S106 from the table 113 (step S107). The processing procedure from step S105 to step S107 can be replaced with processing using a learning model.

[0101] The processing unit 10 corrects the evaluation value calculated in step S102 with the coefficient acquired in step S107 (step S108). The processing unit 10 transmits the result of the correction in step S108 to the information terminal device 2 as a response to the request to acquire the evaluation value (step S109).

[0102] The processing unit 20 of the information terminal device 2 receives a response to the acquisition request (step S202), displays the corrected evaluation value included in the response on the display unit 23 (step S203), and ends the process.

[0103] In the processing procedure shown in Fig. 7, the information processing device 1 acquires the usage history data by reading out the usage history data uploaded to the vehicle information DB 111 using the vehicle identification data specified by the information terminal device 2. The method by which the information processing device 1 acquires the usage history data is not limited to this. The information processing device 1 may also acquire the usage history data by receiving it at the information terminal device 2 through an operation by the owner or user, and receiving this together with an acquisition request (S201). In this case, it is possible to calculate an evaluation value for an electric vehicle that does not have a communication device 3 installed.

[0104] FIG. 9 shows an example of an evaluation value output screen 230. The output screen 230 includes information on the manufacturer, model, manufacturing date, and delivery date, which can be referenced from the electric vehicle's vehicle identification data. These data may be pre-stored in the storage unit 21 of the information terminal device 2. The output screen 230 also includes information on the manufacturer, model number, and manufacturing date of the energy storage device 4 installed in the electric vehicle. The output screen 230 also displays, as text, an estimate of the cost of approximately *** yen if the electric vehicle were sold secondhand. The output screen 230 displays the overall health of the electric vehicle using pictograms. The pictograms representing the health are selected based on the correspondence between the range of the calculated correction points and the type of pictogram. The example in FIG. 9 shows that the health is in a standard state, "normal," which corresponds to the second of three levels. The health level can also be selected from a level that warns of "improvement required" or a level that indicates a slower rate of deterioration than standard and a longer lifespan. The owner or user can refer to the output price and get a sense of whether the price is higher or lower than expected. The owner or user can visually check the health status on the output screen 230 and understand the current condition of the electric vehicle.

[0105] (Variation 1) The processing unit 10 of the information processing device 1 may execute a process of estimating a future health level following the process of step S108 of the process procedure shown in Fig. 7 of the first embodiment. Fig. 10 is a flowchart showing an example of the process procedure by the information processing device 1 in Modification 1. The processing unit 10 of the information processing device 1 executes the following process between step S108 and step S109.

[0106] The processing unit 10 calculates an evaluation value for the target electric vehicle at a future time point (step S181). The processing in step S181 calculates monetary value (assessed price, residual value, purchase price, trade-in price, etc.) or points that serve as an evaluation index based on the length of use of the target electric vehicle at each time point, such as one year from now, two years from now, etc., and whether or not there have been any accidents to date.

[0107] The processing unit 10 calculates the transition of the health level of the electric vehicle from the past to the present based on the driving history log of the electric vehicle acquired in step S102 (step S182). In step S182, the processing unit 10 divides the data of the driving history log into predetermined time periods and calculates the health level for each predetermined period. If the predetermined period is, for example, one year and three years have passed since the target electric vehicle was new, the processing unit 10 calculates the health level after one year has passed, the health level after two years have passed, and the health level after three years have passed. The processing unit 10 calculates the health level after one year has passed based on the driving history log for one year since the electric vehicle was new, and calculates the health level after two years has passed based on the driving history log for two years since the electric vehicle was new.

[0108] The processing unit 10 predicts the transition of the electric vehicle's health state at a future point in time based on the transition of the electric vehicle's health state from the past to the present estimated in step S182 (step S183). In step S183, the processing unit 10 may calculate the health state using table 113, assuming that the driving history log will show the same driving style and charging conditions one year from now and two years from now. In step S183, the processing unit 10 may obtain an approximation function from the transition of the health state from the past to the present calculated in step S182, and predict the health state one year from now and two years from now. Furthermore, the processing unit 10 may predict the health state using a deterioration prediction algorithm for the power storage elements, multivariate analysis, machine learning, or the like, that uses part or all of the usage history of gasoline-powered vehicles and other electric vehicles currently or previously driven by a new owner or user of the electric vehicle.

[0109] In step S183, the processing unit 10 may calculate the SOH as the health level and predict the SOH based on the past performance.

[0110] The processing unit 10 corrects the evaluation value at a future time point based on the transition of the health level predicted in step S183 (step S184). The processing unit 10 ends the processing procedure in the modified example and proceeds to step S109. In this case, the processing unit 10 transmits the health level at a future time point and the corrected evaluation value to the information terminal device 2.

[0111] Fig. 11 shows an example of an output screen 230 in Modification 1. Like Fig. 9, the output screen 230 shown in Fig. 11 includes information on the manufacturer, model, manufacturing date, and delivery date that can be referenced from the vehicle identification data of the electric vehicle. Like Fig. 9, the output screen 230 includes information on the manufacturer, model number, and manufacturing date of the energy storage element 4. The output screen 230 includes, as text, an estimate that if the electric vehicle were to be sold second-hand, it would cost approximately *** yen.

[0112] In the first modification, the output screen 230 includes a graph showing the progress of the SOH at a future point in time. The horizontal axis of the graph shows the number of years since purchase, and the vertical axis shows the magnitude of the SOH. The vertical axis may also show the magnitude of the SOH estimated from the total of the correction points.

[0113] By making it possible to visually grasp the progress of the health level at a future point in time, as in the output screen 230 shown in FIG. 11, the owner or user can understand what the evaluation value will be in the future if they continue to drive the electric vehicle as is.

[0114] (Variation 2) The processing unit 10 of the information processing device 1 may execute a process of presenting a remedy following the process of step S184 of the process procedure shown in Fig. 10 of Modification 1. Fig. 12 is a flowchart showing an example of the process procedure by the information processing device 1 in Modification 2. The processing unit 10 of the information processing device 1 executes the following process after the processes of steps S181 to S184 shown in Fig. 10.

[0115] The processing unit 10 identifies an item that will improve the health level calculated in step S182 (step S185). In step S185, the processing unit 10 identifies, for example, an item that corresponds to a range adjacent to the corresponding range in the table 113 in order to reduce the absolute value of the correction point, which is a negative number. Specifically, when the processing unit 10 has determined and calculated the correction point to correspond to [5°C or higher but lower than 15°C] and [rapid charge] in the table 114 for different charging conditions, the processing unit 10 identifies [15°C or higher] or [normal charge], which will be an improvement direction. When the processing unit 10 has determined and calculated the correction point to correspond to [35°C or higher] and [1 hour or higher but lower than 3 hours] in the table 114 for different storage conditions, the processing unit 10 identifies [30°C or higher but lower than 35°C] or [less than 1 hour], which will be an improvement direction.

[0116] The processing unit 10 provisionally calculates correction points when the items identified in step S185 are improved (step S186), and predicts a transition in the health level of the electric vehicle when the improvements are made (step S187). The processing unit 10 ends the processing procedure in Modification 2, and proceeds to step S109. In this case, the processing unit 10 transmits to the information terminal device 2 the health level at a future point in time, the corrected evaluation value, the improvement items, and the predicted health level when the improvements are made.

[0117] Fig. 13 shows an example of an output screen 230 in Modification 2. Similar to Figs. 9 and 11, the output screen 230 shown in Fig. 13 includes information on the manufacturer, model, manufacturing date, and delivery date that can be referenced from the vehicle identification data of the electric vehicle. Similar to Figs. 9 and 11, the output screen 230 includes information on the manufacturer, product number, and manufacturing date of the energy storage element 4. The output screen 230 includes, as text, an estimate that if the electric vehicle were to be sold second-hand, it would cost approximately *** yen.

[0118] In the second modification, the output screen 230 includes a graph showing the progress of the health level at a future point in time, as well as the progress of the health level if the improvement items are improved. The horizontal axis of the graph shows the number of years since purchase, and the vertical axis shows the magnitude of the health level. The vertical axis may also show the magnitude of the SOH estimated from the total of the correction points. The graph is accompanied by text indicating the items to be improved.

[0119] As shown in output screen 230 in Figure 13, by making it possible to visually grasp the progress of the health level at a future point in time and the progress of the health level if improvement measures are implemented, the owner or user can understand which items to pay attention to when driving the electric vehicle in order to increase the future evaluation of the electric vehicle, i.e., its monetary value.

[0120] (Second embodiment) The information processing device 1 of the second embodiment is capable of acquiring measurement data of the energy storage elements 4 of an electric vehicle. The configuration of the information processing system 100 of the second embodiment is the same as that of the information processing system 100 of the first embodiment, except for the storage of the measurement data and the processing procedure using the measurement data, which will be described below. Therefore, among the configuration of the information processing system 100 of the second embodiment, the configuration common to the information processing system 100 of the first embodiment is assigned the same reference numerals, and detailed description thereof will be omitted.

[0121] 14 is a block diagram showing the configuration of an information processing device 1 in the second embodiment. In the second embodiment, the information processing device 1 has a battery DB 112 built in the storage unit 11. The battery DB 112 stores measurement data read from the BMU of the power storage element 4 mounted on the electric vehicle as a power source during inspection of the electric vehicle. The measurement data may be sequentially transmitted from the communication device 3 and received and stored in the information processing device 1.

[0122] Fig. 15 is a flowchart showing an example of a processing procedure by the information processing device 1 of the second embodiment. Among the processing procedures shown in Fig. 15, steps common to the processing procedures shown in Fig. 7 of the first embodiment are assigned the same step numbers, and detailed descriptions thereof will be omitted.

[0123] In the second embodiment, the processing unit 10 of the information processing device 1 calculates a value corresponding to the state of health using measurement data stored in the battery DB 112 for the value corresponding to the state of health calculated in step S106 based on data of a specific item in the driving history log (step S121). In the second embodiment, the processing unit 10 calculates the SOH in both steps S106 and S121. The method of calculating the SOH in step S121 and the type of measurement data used for the calculation may be any known method. For example, the processing unit 10 can estimate the SOH from the progression of changes in the charging rate and its relationship with the output voltage.

[0124] The processing unit 10 selects the value corresponding to the soundness degree calculated in step S106 and the value corresponding to the soundness degree calculated in step S121, whichever is higher (step S122).

[0125] The processing unit 10 uses the selected value to refer to the table 118 or obtains the coefficient by some other method (S107), and executes the processes from step S108 onwards.

[0126] The display example displayed on the information terminal device 2 as a result of processing by the information processing device 1 of the second embodiment is the same as that of the first embodiment and its modifications 1 and 2. Therefore, an example and detailed description of the output screen 230 will be omitted.

[0127] In the second embodiment, the health score calculated based on the usage history (driving history log) is adopted because, even though it is less accurate than the health score calculated based on the measurement data of the energy storage element 4, the owner or user expects the results of the health score calculated by simple processing. For example, if the health score calculated from the usage history is frequently displayed on the display unit 23 of the information terminal device 2 used as a navigation device, this could suddenly become disadvantageous. Therefore, the information processing system 100 outputs a health score with a more favorable result to avoid disadvantages to the owner or user. Alternatively, the information processing device 1 outputs an accurate health score while clearly indicating that a properly calculated value is used for practical values such as used car appraisal prices and insurance amount calculations.

[0128] In the above-described embodiment, an electric vehicle has been described as an example of an electric vehicle. The electric vehicle is not limited to an electric vehicle, and may be a vehicle such as a plug-in hybrid vehicle, an airplane, or a ship that uses an energy storage device 4 as a power source. The electric vehicle may be manned or unmanned. At least a portion of the driving power source of the electric vehicle is the energy storage device 4, and the power source may also include other power sources such as an engine.

[0129] The embodiments disclosed above are illustrative in all respects and are not restrictive. The scope of the present invention is defined by the claims, and includes all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0130] 100 Information Processing Systems 1. Information processing equipment 10 Processing section 11 Storage section 111 Vehicle Information DB 113,114,115,116,117,118 Table P1 Information processing program (computer program) 2. Information terminal device 20 Processing section 23 Display section 230 Output Screen 3. Communications equipment 4. Energy storage element

Claims

1. An information processing method for calculating the value of an electric vehicle after a period of time has elapsed since the start of use, The computer calculating an evaluation value of the electric vehicle based on a usage history of the electric vehicle and a period of time since the start of usage; calculating a health state of a power storage element that is a power source using a correction value for each specific item among the items of the usage history of the electric vehicle; The calculated evaluation value of the electric vehicle is corrected based on the health of the storage element. Information processing methods.

2. The computer calculates the monetary value of the electric vehicle based on the corrected evaluation value. The information processing method according to claim 1 .

3. The computer As the specific item, a charging condition for charging a storage element mounted on the electric vehicle is used, Calculating a correction value according to the charging conditions The information processing method according to claim 1 .

4. the charging condition is at least one of an air temperature during charging, a type of charging method, and a magnitude of a difference in the amount of power before and after charging; The computer calculates a correction value according to the charging condition. The information processing method according to claim 3 .

5. The computer The storage status of the electric vehicle is used as the specific item, Calculating a correction value according to the storage condition The information processing method according to claim 1 .

6. The computer calculates a correction value according to the storage condition using the temperature during parking and the length of time the vehicle was parked at that temperature as the storage condition. The information processing method according to claim 5 .

7. The computer The specific item is a manner of driving the electric vehicle, Calculate a correction value according to the driving style The information processing method according to claim 1 .

8. The computer calculates a correction value according to the driving style using the cumulative travel distance of the electric vehicle and the frequency of sudden acceleration of the electric vehicle as the driving style. The information processing method according to claim 7.

9. The computer comparing the health of the storage element calculated using a correction value calculated for each specific item that affects degradation of the storage element with the health of the storage element derived from a measurement result of the storage element mounted on the electric vehicle; The comparison results in a higher health rating. The information processing method according to any one of claims 1 to 8.

10. The computer sequentially acquiring items of the usage history of the electric vehicle via a network; At any timing, based on the items of the usage history of the electric vehicle, estimate the health of the storage element at a future time point or a corrected evaluation value of the electric vehicle; Output the estimated evaluation value The information processing method according to any one of claims 1 to 8.

11. The computer extracting, from the specific items in the acquired usage history of the electric vehicle, improvement items for increasing the health of the storage element at the future time point or the corrected evaluation value of the electric vehicle; Output improvement measures based on the extracted improvement items The information processing method according to claim 10.

12. An information processing device for calculating a value of an electric vehicle after a period of time has elapsed since the start of use, an acquisition unit that acquires a usage history of the electric vehicle and a period of time since the start of usage; a first calculation unit that calculates an evaluation value of the electric vehicle based on the acquired usage history of the electric vehicle and the period since the start of usage; a second calculation unit that calculates a state of health of a power storage element that is a power source using a correction value for each specific item among items of a usage history of the electric vehicle; a correction unit that corrects the calculated evaluation value of the electric vehicle based on the health status of the storage element; An information processing device comprising:

13. A computer program that causes a computer to calculate the value of an electric vehicle after a period of time has elapsed since the start of use, The computer, calculating an evaluation value of the electric vehicle based on a usage history of the electric vehicle and a period of time since the start of usage; calculating a health state of a power storage element that is a power source using a correction value for each specific item among the items of the usage history of the electric vehicle; The calculated evaluation value of the electric vehicle is corrected based on the health of the storage element. A computer program that executes a process.

Images