Information processing method, information processing device, and computer program

Abstract

To provide an information processing method, an information processing device, and a computer program capable of simply and accurately outputting transition estimation of a deterioration state of a power storage element mounted on an electrically powered mobile body.SOLUTION: An information processing method causes a computer to perform the steps of: acquiring information including at least any one of a full charge capacity of a power storage element mounted on an electrically powered mobile body, a charging count, a charged electrical amount during each charging, an uncharged voltage, a cumulative charged electrical amount, an internal resistance, a use period and data on a movement history of the electrically powered mobile body; estimating transition of a present deterioration state from a past deterioration state of the power storage element on the basis of at least one of the acquired full charge capacity of the power storage element, the charging count, the charged electrical amount during each charging, the uncharged voltage, the cumulative charged electrical amount, the internal resistance, the use period and the data on the movement history; acquiring travel conditions on subsequent traveling of the electrically powered mobile body; and predicting and outputting the transition of subsequent performances of the electrically powered mobile body on the basis of the estimated transition of the deterioration state and the acquired travel conditions.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 estimating the progression of a degradation state of an energy storage element mounted on an electric vehicle and predicting the progression of performance of the electric vehicle on which the energy storage element is mounted. [Background technology]

[0002] Various methods have been proposed as methods for estimating the degradation state of an energy storage element, and the accuracy has improved, making it easier to grasp the degradation state from the appearance (e.g., Patent Document 1). However, for energy storage elements used as power sources for motors that drive electrically-driven moving objects such as electric vehicles, even energy storage elements with a relatively long life such as lithium-ion batteries are likely to be judged to have low performance if degradation progresses even slightly. [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 deterioration of the energy storage element is somewhat advanced, it may or may not be able to withstand long-term use thereafter, depending on the manner in which the electric vehicle is driven, the frequency of travel, etc. If the performance of the energy storage element is evaluated only based on the decrease in full charge capacity, even energy storage elements that can withstand long-term use may be uniformly subject to reuse or disposal, which would not contribute to environmental conservation or sustainability.

[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 that are capable of simply and accurately outputting an estimate of the progression of a deterioration state of an energy storage element mounted on an electric vehicle. [Means for solving the problem]

[0006] In an information processing method of one embodiment of the present disclosure, a computer acquires information including at least one of a full charge capacity, number of charges, charged electrical energy at each charge, uncharged voltage, accumulated charged electrical energy, internal resistance, and usage period of an energy storage element mounted on an electric vehicle, and data regarding the movement history of the electric vehicle, estimates a change in a deterioration state of the energy storage element from the past to the present based on the acquired at least one of the full charge capacity, number of charges, charged electrical energy at each charge, uncharged voltage, accumulated charged electrical energy, internal resistance, and usage period of the energy storage element, and the data regarding the movement history, acquires future driving conditions of the electric vehicle, predicts a change in future performance of the electric vehicle based on the estimated change in the deterioration state and the acquired driving conditions, and outputs the future performance of the electric vehicle. Effect of the Invention

[0007] According to an information processing method of one aspect of the present invention, it is possible to simply and accurately output an estimate of the progression of the deterioration state of an energy storage element mounted on an electric vehicle, and it is also possible to appropriately predict the progression of performance of an electric vehicle or other electric vehicle. [Brief description of the drawings]

[0008] [Figure 1] 1 is a schematic diagram of an information processing system according to a first embodiment. [Diagram 2] FIG. 1 is a block diagram showing a configuration of an information processing device. [Diagram 3] FIG. 2 is a block diagram showing a configuration of an information terminal device. [Figure 4] 11 is a flowchart illustrating an example of a processing procedure in the information processing system. [Diagram 5] FIG. 13 is a schematic diagram of an example of a method for estimating a transition in a deterioration state. [Figure 6] FIG. 13 is a schematic diagram of an example of a method for predicting and calculating a travelable distance. [Figure 7] 1 shows an example of an input screen. [Figure 8] 13 shows an example of an appraisal result screen. [Figure 9]1 shows an example of a used goods price table. [Figure 10] 13 shows an example of an evaluation table. [Figure 11] FIG. 1 is a diagram showing an example of insurance coverage. [Figure 12] FIG. 11 is a schematic diagram of an information processing system according to a second embodiment. [Figure 13] FIG. 2 is a block diagram showing a configuration of an in-vehicle device. [Figure 14] 13 is a flowchart illustrating an example of a processing procedure in an information processing system according to a second embodiment. [Figure 15] FIG. 11 is a schematic diagram of information provided from an information processing device in a modified example. [Figure 16] 13 is a flowchart showing an example of a matching process executed by an information processing device of the modified example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] First, an overview of the information processing method, information processing device, and computer program disclosed in this specification will be described.

[0010] (1) An information processing method in which a computer acquires information including at least one of a full charge capacity, number of charges, charged electrical quantity at each charge, uncharged voltage, accumulated charged electrical quantity, internal resistance, and usage period of an energy storage element mounted on an electric vehicle, and data regarding the movement history of the electric vehicle, estimates a change in a deterioration state of the energy storage element from the past to the present based on the acquired at least one of the full charge capacity, number of charges, charged electrical quantity at each charge, uncharged voltage, accumulated charged electrical quantity, internal resistance, and usage period of the energy storage element, and the data regarding the movement history, acquires future driving conditions of the electric vehicle, predicts a change in future performance of the electric vehicle based on the estimated change in the deterioration state and the acquired driving conditions, and outputs the future performance of the electric vehicle.

[0011] The electric vehicle according to this embodiment is a vehicle equipped with an electricity storage element, such as an electric car, a plug-in hybrid car, an airplane, or a ship. The electric vehicle may be manned or unmanned. At least a part of the driving power source of the electric vehicle is the electricity storage element, and the power source may include other power sources such as an engine.

[0012] Deterioration of the energy storage element according to this embodiment corresponds to a decrease in the full charge capacity of the energy storage element from the time of manufacture (when the vehicle was new).

[0013] In the information processing method of this embodiment, the performance of the storage element is not only estimated from the degradation state such as the instantaneous full charge capacity at the time of assessment. In the information processing method of this embodiment, the transition of the degradation state from the past to the present (time of assessment) is estimated. In the information processing method of this embodiment, the future driving conditions of the electric vehicle are used for the estimated transition of the degradation state of the storage element from the past to the present, and the computer predicts the future transition of the performance of the electric vehicle by taking into account the influence of the future driving conditions of the electric vehicle on the transition of degradation of the storage element.

[0014] With the above configuration, since the deterioration state transition due to repeated charging and discharging of the storage element and the driving method of the electric vehicle are not judged separately, the deterioration state of the storage element can be estimated more accurately than when referring to the travel history or the period of use. With the above configuration, the future transition of the performance of the electric vehicle as a whole can be accurately predicted based on the driving conditions of the electric vehicle after the storage element is installed.

[0015] The travel history includes the distance traveled by the target electric vehicle from the past to the present. The travel history may include information such as the driver's operating tendency regarding the movement of the electric vehicle. For example, the travel history may include the tendency of charging (high frequency charging, in which charging is performed immediately after a small amount of discharge, or low frequency charging, in which charging is performed only after the vehicle is nearly completely discharged). In addition, the travel history may include driving preferences such as a tendency to start suddenly or slowly, and the driving environment in which the electric vehicle has traveled, such as information on whether the vehicle is in a mountainous area with many slopes or a plain, or whether the vehicle is in a hot or cold region. Information may be obtained from on-board diagnosis (OBD), etc., or information on a map showing frequently used routes (multiple routes are possible) (preferably showing elevation differences, traffic lights, and the location of charging equipment to be used) obtained by interviewing owners of electric vehicles from the past to the present, accepting questionnaires, etc. Frequently used routes may be data obtained from a navigation device installed in the electric vehicle.

[0016] The future driving conditions include a predicted future driving distance. The driving conditions may include a tendency of charging (high frequency charging in which charging is performed immediately after a small amount of discharge, or low frequency charging in which charging is performed only after the vehicle is close to being completely discharged). The future driving conditions may include driving preferences such as frequent sudden or slow starts, and information on the driving environment in which the electric vehicle has traveled, such as whether the driving environment is a mountainous area with many slopes or a plain area, or whether the driving environment is a hot or cold area. The future driving conditions may be set to the JC08 or WLCT mode assumed by the manufacturer of the electric vehicle. In addition, the future driving conditions may be created based on the driving history, i.e., the travel history, assuming that the owner of the electric vehicle will continue to drive the vehicle as is.

[0017] Even if a dealer that sells electric vehicles or other electric vehicles does not sell storage elements, or a maintenance business that maintains electric vehicles, can obtain accurate performance information on storage elements by using the information processing method according to the present embodiment. This makes it possible to simply and accurately assess used electric vehicles, which tend to be lowered in value only based on the deterioration state of their storage elements.

[0018] (2) In the information processing method of (1) above, the computer may calculate, as the future performance of the electric vehicle, a distance that the electric vehicle can travel when the power storage element is brought into a fully charged state.

[0019] In a situation where there is a shortage of charging facilities for electric vehicles, one of the things that owners and potential buyers of electric vehicles are most concerned about is the distance and time that can be traveled on a single charge. As a performance indicator, a clearer performance evaluation can be made by outputting the transition of the distance that can be traveled on a single charge based on knowledge about storage elements. The distance that can be traveled is the distance that an electric vehicle can travel on a single charge, and is also called the cruising distance.

[0020] (3) In the information processing method of (1) or (2) above, the computer may calculate a selling price of the electric vehicle based on the estimated deterioration state of the storage element and the evaluated performance, and output the calculated selling price.

[0021] With the above configuration, the selling price is calculated by the information processing device that estimates the deterioration state and predicts the future performance transition of the electric vehicle. As a result, the electric vehicle is not evaluated based on the current deterioration state of the storage element, but is likely to be appropriately evaluated based on the prediction result of the future performance transition of the electric vehicle in addition to the estimation result of the transition from the past to the present of the deterioration state of the storage element. The calculation of the selling price may be performed by multivariate analysis weighted for each evaluation item, or by deep learning such as RNN (Recurrent Neural Network), LSTM (Long Short Term Memory), and Transformer that analyzes past sales prices and recent prices of the same type of electric vehicle at a nearby second-hand dealer.

[0022] (4) In any one of the information processing methods (1) to (3) above, the computer may determine a grade of the electric vehicle based on the estimated deterioration state of the storage element and the evaluated performance, calculate an insurance premium based on the determined grade, and output the calculated insurance premium.

[0023] With the above configuration, the deterioration state of the storage element can be estimated, and a grade that serves as the basis for determining the insurance premium can be output from the information processing device that predicts the future performance transition of the electric vehicle. The failure risk of the storage element of the electric vehicle can be determined based on information that accurately evaluates the performance of the storage element.

[0024] (5) In any one of the information processing methods (1) to (4) above, the computer may execute a process of identifying a difference between the predicted performance of the electric vehicle and an actual performance of the electric vehicle at a later point in time, and determining compensation content according to the identified difference.

[0025] An information processing method according to one aspect of the present invention guarantees the predicted performance of the electric vehicle, and when there is a difference between the predicted future performance transition of the electric vehicle and the actual performance transition thereafter, the compensation content may be repair including replacement or addition of an energy storage element to fill the difference, or the compensation content may be determined to be a payment of an amount to compensate for losses. As the difference becomes larger, the compensation content may be thicker, for example, the compensation amount may be increased. Note that a range may be set for the predicted transition, and it may be determined in advance which of the upper limit, lower limit, and median of the set range is to be guaranteed, and the compensation content may be determined from the difference between the actual performance transition and the upper limit, lower limit, or median of the transition to be guaranteed.

[0026] (6) In any one of the information processing methods (1) to (5) above, the computer may calculate future performance of the electric vehicle according to a plurality of different driving conditions of the electric vehicle, and output the future performance of the electric vehicle calculated for each of the plurality of driving conditions.

[0027] The deterioration of the storage element depends on the usage environment and the frequency of charging and discharging. Therefore, if the deterioration state of the storage element measured at the time of assessment and the performance are evaluated only under predetermined conditions as the driving conditions thereafter, there is a possibility that the prediction and the way of driving the electric vehicle will be inconsistent. With the above configuration, the prediction of the deterioration of the storage element and the different changes in the performance of the electric vehicle are presented under different driving conditions including the driving manner, driving type, location, etc. This allows a person who wants to purchase an electric vehicle to obtain an individual that suits him or her. Here, the predetermined driving conditions are preferably, for example, the JC08 or WLCT mode set by the government, because it is easy to compare with other electric vehicles.

[0028] (7) In any one of the information processing methods (1) to (6) above, the computer may acquire requirements from a prospective purchaser, including the price for the used electric vehicle, the current driving range, the trend in the reduction in the driving range, the intended use, the frequency of use, or the route of use, extract data on used electric vehicles for which the deterioration state of the storage element and the future performance of the electric vehicle that match the acquired requirements have been estimated and predicted, and output the data on the extracted electric vehicles.

[0029] It is possible to extract electric vehicles that meet the needs of the prospective buyer in terms of both the estimated results of the transition of the deterioration state of the storage element and the degree of wear of the vehicle based on the travel history of the electric vehicle. It is possible to present electric vehicles that are suitable for the prospective buyer based on the future service life and the difference in deterioration due to future driving conditions. Multiple extractions may be performed, and may be performed by multivariate analysis weighted by each desired item, or by deep learning such as RNN, LSTM, and Transformer that analyzes past sales prices and recent prices of similar electric vehicles at nearby second-hand dealerships.

[0030] (8) In the information processing method described above in (7), the computer may acquire, as the request, data on the past use history of the electric vehicle of the would-be purchaser.

[0031] Based on the usage history data of electric vehicles owned from the past to the present by the prospective purchaser, it will be possible to extract driving preferences such as sudden or slow starts, high-frequency charging where the battery is charged immediately even after it has been slightly discharged, or low-frequency charging where the battery is only charged when it is nearly completely discharged, and electric vehicles that are suitable for driving environments such as use in mountainous or plain areas with many slopes, or use in extremely hot or cold regions. The usage history data of the electric vehicle of the prospective purchaser may be information obtained from the on-board diagnosis (OBD) of the vehicle (not limited to electric vehicles) owned by the prospective purchaser, or information on a map showing a frequently used route (or routes) (preferably including elevation differences, traffic signals, and charging facility locations) obtained by interviewing the prospective purchaser. The frequently used route may be data obtained from a navigation device installed in the vehicle. The usage history data is preferably from an electric vehicle previously owned by the prospective purchaser, but if the prospective purchaser has never owned an electric vehicle in the past, usage history data of a vehicle using an internal combustion engine or the like that does not require charging of the battery may be used. In this case, the usage history data does not include the history of charging described above. After obtaining such usage history data as the request of the prospective purchaser, it is possible to predict the future performance transition of the electric vehicle if the prospective purchaser purchases it and output the predicted performance transition, making it easier for the prospective purchaser to make a purchase decision.

[0032] (9) In any one of the information processing methods (1) to (6) above, the electric vehicle is provided with a communication device that sequentially transmits information including at least one of the full charge capacity, number of charges, charged electricity amount at each charge, uncharged voltage, accumulated charged electricity amount, internal resistance, and usage period of the storage element, and data regarding the movement history of the electric vehicle to an external device via communication, and the computer acquires and stores the information including at least one of the full charge capacity, number of charges, charged electricity amount at each charge, uncharged voltage, accumulated charged electricity amount, internal resistance, and usage period of the storage element, and the data regarding the movement history of the electric vehicle, transmitted from the communication device, and the computer may use the stored data when estimating the deterioration state of the storage element.

[0033] In the above configuration, the computer may sequentially estimate the progression of the deterioration state of the storage element and predict the future progression of the performance of the electric vehicle based on future driving conditions of the electric vehicle, based on information including at least one of the full charge capacity, number of charges, charged electrical quantity at each charge, uncharged voltage, accumulated charged electrical quantity, internal resistance, and usage period of the storage element in the electric vehicle transmitted from the communication device, and data regarding the movement history of the electric vehicle.

[0034] Since data that can be used to estimate the deterioration state of the storage element regarding the performance of the electric vehicle can be sequentially obtained from the communication device, the performance of the electric vehicle can be predicted at any timing. In other words, even if an electric vehicle, which is an electric vehicle, is in use, it is possible to estimate the deterioration of the storage element and predict the transition of the performance of the electric vehicle, including the driving distance, and these estimation results and prediction results are successively updated to highly accurate ones using the latest data. It is possible to avoid a situation in which the storage element is left unused and exhibited at a second-hand sales business, which encourages the wasteful deterioration of the storage element, and to allow the current owner of the electric vehicle to effectively use it for economic activities, etc. until a new owner is decided, and to sell it as a second-hand vehicle without delay when the next owner is decided.

[0035] (10) An information processing device includes a first acquisition unit that acquires information including at least one of a full charge capacity, a number of charges, a charged electric quantity at each charge, an uncharged voltage, an accumulated charged electric quantity, an internal resistance, and a period of use of an energy storage element mounted on an electric vehicle, and data related to a movement history of the electric vehicle; a deterioration estimation unit that estimates a change in a deterioration state of the energy storage element from the past to the present based on the acquired at least one of the full charge capacity, the number of charges, a charged electric quantity at each charge, an uncharged voltage, an accumulated charged electric quantity, an internal resistance, and a period of use of the energy storage element, and the data related to the movement history; a second acquisition unit that acquires future driving conditions of the electric vehicle; a performance prediction unit that predicts a change in future performance of the electric vehicle based on the estimated change in the deterioration state and the acquired future driving conditions of the electric vehicle; and an output unit that outputs the future performance of the electric vehicle.

[0036] (11) A computer program causes a computer to execute a process of acquiring information including at least one of a full charge capacity, number of charges, charged electrical quantity at each charge, uncharged voltage, accumulated charged electrical quantity, internal resistance, and usage period of an energy storage element mounted on an electric vehicle, and data regarding the movement history of the electric vehicle, estimating a change in a deterioration state of the energy storage element from the past to the present based on the acquired at least one of the full charge capacity, number of charges, charged electrical quantity at each charge, uncharged voltage, accumulated charged electrical quantity, internal resistance, and usage period of the energy storage element and the data regarding the movement history, acquiring future driving conditions of the electric vehicle, predicting a change in future performance of the electric vehicle based on the estimated change in the deterioration state and the acquired driving conditions, and outputting the future performance of the electric vehicle.

[0037] 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 the embodiments.

[0038] (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 to which a plurality of information terminal devices 2 are connected for communication. The information terminal device 2 is used by a dealer or maintenance company of an electric vehicle, which is an electric vehicle. The information terminal device 2 may be used by an owner of the electric vehicle. The information processing device 1 is managed by a manufacturer of the storage element B, which is a power source (hereinafter also referred to as a main battery) of a drive motor mounted on the electric vehicle, a manufacturer of an electric vehicle mounted with the storage element B, or a technical support company that has knowledge about storage elements and electric vehicles. The manufacturer or technical support company may be a dealer or maintenance company of an electric vehicle at the same time. 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 referred to in processing, and the like.

[0039] A network N allows communication between the information terminal devices 2 and the information processing device 1. The network N includes a network N1 that is a local network or a dedicated line of a manufacturer. The network N may include a public communication network N2, which is the so-called Internet, and a carrier network N3.

[0040] In the information processing system 100 of this embodiment, when inspecting an electric vehicle or inspecting the vehicle, a dealer or maintenance business uses the information terminal device 2 to access a Web service provided by the information processing device 1. The owner may also use the information terminal device 2 to access the Web service. The dealer or maintenance business uses the information terminal device 2 to provide vehicle information such as the identification data of the electric vehicle, the measurement results for the storage element B, which is the main battery, the specifications of the electric vehicle, and the mileage to the information processing device 1 via the Web service. As a result, the information processing device 1 estimates the transition of the deterioration state from the past to the present of the storage element B used as the main battery and the life based on the transition, and predicts and outputs the future performance transition of the electric vehicle taking into account future driving conditions. The dealer or maintenance business can issue the inspection result or assessment of the electric vehicle including the storage element using the predicted result of the future performance transition of the electric vehicle as a whole obtained via the information terminal device 2.

[0041] Here, the future driving conditions may include the predicted future driving distance and the tendency of charging (high frequency charging in which charging is performed immediately even if the battery is discharged only slightly, or low frequency charging in which charging is performed only after the battery is nearly completely discharged). The future driving conditions may include driving preferences such as frequent sudden or slow starts, and driving environments in which the electric vehicle often runs, such as information on whether the vehicle is in a mountainous area with many slopes or a plain, or whether the vehicle is in a hot or cold climate. The future driving conditions may be set to the JC08 or WLCT mode assumed by the manufacturer of the electric vehicle. In addition, the future driving conditions may use the history of driving performance up to now, that is, the movement history, assuming that the owner of the electric vehicle will continue to drive the vehicle, and the information processing device 1 may acquire the information entered or set by the owner. In the case where the future driving conditions are assumed to be transferred to another person, the driving conditions of the person who wishes to purchase the vehicle may be input and set by the person who wishes to purchase the vehicle, or may be input and set by the manufacturer or a technical support company, and acquired by the information processing device 1.

[0042] Conventionally, when assessing an electric vehicle as a used vehicle, the deterioration state of storage element B was estimated based on the state at that time, which was estimated from the measurement data obtained by a tester or the BMU (Battery Management Unit) of storage element B. If the full charge capacity of storage element B at the time of inspection has decreased compared to the full charge capacity of storage element B in a new vehicle state, the assessing company may not be able to evaluate it due to lack of knowledge about storage element B, and the assessed price of the electric vehicle may be reduced by half even when it is only three years old since it was new.

[0043] In contrast, in the information processing system 100 of this embodiment, by providing information on the energy storage element B together with the movement history of the electric vehicle and future driving conditions to the information processing device 1, it becomes possible to obtain the performance of the energy storage element B from that point onwards. This enables the dealer or maintenance business to simply and accurately assess the performance of the electric vehicle from that point onwards.

[0044] The following describes the components and processing details of the performance evaluation of the power storage element B used as the main battery of an electric vehicle by the information processing system 100.

[0045] 2 is a block diagram showing the configuration of the information processing device 1. The information processing device 1 is a server computer. In the following description, the information processing device 1 is described as being configured by one server computer, but may be configured in a manner in which a plurality of server computers are communicatively connected via a network to perform distributed processing.

[0046] 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 using 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 estimating the deterioration state of the energy storage element B and predicting the future driving distance.

[0047] The storage unit 11 uses a non-volatile 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 a storage medium 9 that is read by the processing unit 10 and copied into the storage unit 11. The information processing program P1 may be downloaded from another program server or device via the communication unit 12 and stored therein.

[0048] Data on the JC08 or WLCT mode assumed by the electric vehicle manufacturer as standard driving conditions for the electric vehicle is stored in the memory unit 11. Using the data, the processing unit 10 can predict the future transition of the drivable distance under standard driving conditions.

[0049] In the storage unit 11, a database that stores the manufacturing date, sales date and time, sales subject, etc. in association with the manufacturing number of the storage element B is constructed as a battery DB 111.

[0050] The communication unit 12 realizes communication with the information terminal device 2 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 transmit and receive data to and from the information terminal device 2 via the communication unit 12.

[0051] 3 is a block diagram showing the configuration of the information terminal device 2. The information terminal device 2 is a terminal device used by a person in charge at a dealer or a maintenance company. The information terminal device 2 is a laptop or desktop personal computer. The information terminal device 2 may be a smartphone or a tablet terminal.

[0052] The information terminal device 2 includes a processing unit 20, a storage unit 21, a communication unit 22, a display unit 23, an operation unit 24, and an imaging unit 25.

[0053] The processing unit 20 is a processor using 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.

[0054] The storage unit 21 uses a non-volatile memory such as a hard disk, a flash memory, or an SSD. The storage unit 21 stores data referenced by the processing unit 20. The storage unit 21 may store account data assigned to a dealer or a maintenance company. When the processing unit 20 establishes a communication connection with the information processing device 1 and acquires data, the processing unit 20 can automatically reference the account data from the storage unit 21. The storage unit 21 stores various programs including a terminal program P2. The terminal program P2 may be a Web browser program and a Web application program provided by the information processing device 1. The terminal program P2 may be an agent program incorporated in a Web browser. The terminal program P2 may be a terminal program P8 (program product) stored in the storage medium 8 read by the processing unit 20 and copied to the storage unit 21. The terminal program P2 may be downloaded from another program server or device via the communication unit 22 and stored.

[0055] 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 transmit and receive data to and from the information processing device 1 via the communication unit 22.

[0056] The display unit 23 is a display such as a liquid crystal display, an organic EL (Electro Luminescence) display, etc. The display unit 23 displays GUIs (Graphical User Interfaces) 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, based on the terminal program P2.

[0057] The operation unit 24 is a user interface such as a keyboard and a mouse capable of inputting and outputting 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 the operation unit 24 may be an audio input / output unit including a microphone.

[0058] Imaging unit 25 can capture an image of a sheet bearing a serial number attached to the package of energy storage element B, a two-dimensional code, or an image.

[0059] In the information processing system 100 configured as above, a process in which a dealer or maintenance business uses the information terminal device 2 to obtain information from the information processing device 1 regarding the performance of an electric vehicle using the storage element B as the main battery will be described with reference to a flowchart.

[0060] 4 is a flowchart showing an example of a processing procedure in the information processing system 100. The process starts when a person in charge at a dealer or a maintenance company uses the information terminal device 2 to obtain information on the performance of an electric vehicle equipped with the energy storage element B.

[0061] The processing unit 20 of the information terminal device 2 displays, on the display unit 23, a screen for receiving input of data for obtaining the overall performance of the electric vehicle equipped with the energy storage element B based on the terminal program P2 (step S201). In step S201, the processing unit 20 may display the screen based on screen data obtained by requesting the information processing device 1 for a Web screen for inputting parameters.

[0062] The data to be input on the screen displayed in step S201 for obtaining the future performance of the entire electric vehicle includes the measurement results performed on the storage element B, which is the main battery, data obtained from the BMU of the storage element B, data related to the driving history (movement history) of the electric vehicle obtained from an on-board diagnosis (OBD) provided in the electric vehicle, or the serial number of the storage element B. The screen includes a button for starting the imaging unit 25, and when this button is selected, the imaging unit 25 starts up, and when an image is taken, the processing unit 20 may perform character recognition and obtain the serial number. The measurement result is information for estimating the deterioration state. The measurement result includes, for example, a fully charged amount of electricity measured from the amount of power transmitted to the storage element B by completely discharging the storage element B and then charging it until it is fully charged, a charging end voltage at the time of full charge, and further a discharging end voltage and a discharging capacity (full charge capacity) obtained by completely discharging the storage element B thereafter. The measurement results may be the accumulated discharged quantity of electricity and the average discharge voltage, and the accumulated charged quantity of electricity and the average charge voltage, which are measured multiple times by repeatedly charging and discharging the storage element B to a certain extent without completely discharging it or fully charging it. From these parameters, the discharged quantity of electricity in a fully discharged state can be estimated, and the discharge capacity after full charging (full charge capacity) can be estimated. The measurement results include the accumulated number of charges, the charged quantity of electricity at each charge, the uncharged voltage, the accumulated charged quantity of electricity, the internal resistance, and the period of use, which are obtained from the BMU of the storage element B or the like via a dedicated device such as a tester or communication. The measurement results may also include a temperature record history.

[0063] The screen displayed in step S201 includes input fields for the manufacturing date, delivery date, and model of the electric vehicle as data related to the driving history of the electric vehicle.

[0064] The screen displayed in step S201 includes driving conditions for obtaining future performance of the entire electric vehicle. The screen includes an input field for the driving conditions of the electric vehicle. The driving conditions may include the distance traveled so far, driving position information, or driving electricity consumption, assuming that the owner will not change in the future. The driving conditions may be selectable for the driving method (rapid or slow starts), driving type (urban driving, suburban driving, highway driving), location (use in mountainous areas or plain areas with many slopes), etc., for driving the electric vehicle in the future. The driving type may be selectable from JC08 or WLCT mode.

[0065] The processing unit 20 acquires the data input on the screen displayed in step S201, and transmits it to the information processing device 1 (step S202).

[0066] The information processing device 1 receives data transmitted from the information terminal device 2 (step S101), and the processing unit 10 stores the received data in the battery DB 111 in association with the manufacturing number of the storage element B and the identification number of the electric vehicle contained in the data (step S102). The processing unit 10 estimates and calculates the degradation state, i.e., the rate of decrease in the full charge capacity from the time of manufacture (when the vehicle was new), based on the received data (step S103). In step S103, the processing unit 10 acquires the specification value of the full charge capacity of the storage element B when it is new from the model of the electric vehicle or the manufacturing number of the storage element B, and estimates and calculates what percentage of the specification value the measured full charge capacity is. When there is no measured value of the full charge capacity itself, the processing unit 10 may calculate the full charge capacity at the time of measurement by a predetermined algorithm using the accumulated number of charges, the charged electricity amount, the temperature record history, and the elapsed time from the start of operation.

[0067] The processing unit 10 estimates the transition of the degradation state of the storage element B from the past to the present when the electric vehicle is equipped with the storage element B based on at least one of the full charge capacity, the cumulative number of charges, the charged electric amount at each charge, the uncharged voltage, the cumulative charged electric amount, the internal resistance, and the period of use obtained from the BMU of the storage element B, the degradation state of the storage element B calculated in step S103, and data related to the movement history of the electric vehicle (step S104). For example, in step S104, the processing unit 10 estimates the transition of the decrease in the full charge capacity of the storage element B associated with the increase in the period of use of the electric vehicle, the cumulative charged electric amount, the cumulative number of charges, and the like shown in FIG. 5 described later. If the influence of factors such as the elapsed time from the start of operation, the cumulative charged electric amount, the internal resistance, and the number of charges that affect the full charge capacity is grasped in advance, for example, by a separate test of the storage element, the degradation state of the storage element B (the transition of the decrease in the full charge capacity) can be estimated by obtaining any of these factors. In addition, if the data on the full charge capacity of the storage element when the previous owner purchased the vehicle (when the vehicle was new) is unknown, or if it is difficult to measure the full charge capacity of the current storage element, it can be obtained by extrapolating the approximation curve in Figure 5, which will be described later.

[0068] The processing unit 10 predicts the future transition of the mileage of the electric vehicle based on the transition of the deterioration state of the storage element B from the past to the present estimated in step S104 and the future driving conditions of the electric vehicle (step S105). In step S105, the processing unit 10 may calculate the mileage of the electric vehicle on one charge, or may calculate the mileage of the electric vehicle until scrapping by accumulating these. For example, after estimating the decrease in the full charge capacity of the storage element with the increase in the integrated charged amount of electricity in step S104, it is possible to predict the future transition of the decrease in the full charge capacity of the storage element B when used under the same driving conditions as the current electric vehicle owner by extrapolating it. Furthermore, if the relationship between the battery capacity and the mileage per charge under the driving conditions is known in advance, it is possible to calculate the decrease in the mileage from the past to the present when used under the same driving conditions as the current electric vehicle owner, as well as the future transition of the decrease (shown in FIG. 6 described later). In step S105, the processing unit 10 calculates the transition of the possible driving distance per charge for factors such as the number of charging times, the accumulated charged amount of electricity, the driving distance, the driving time, and the driving time until the motor reaches a degraded state that is the limit of its use as a driving source for the motor, based on the driving time or the driving distance when the motor is driven under the following driving conditions. The processing unit 10 may further calculate the transition of the possible driving distance due to an increase in a plurality of different factors, based on each of the different driving conditions.

[0069] The processing unit 10 stores the deterioration state calculated in step S103 and data on the possible driving distance according to the future driving conditions calculated based on the progression of the deterioration state of the storage element B from the past to the present estimated in step S104 in the battery DB 111 in association with the manufacturing number of the storage element B (step S106), and transmits them to the information terminal device 2 (step S107).

[0070] The processing unit 20 of the information terminal device 2 receives the data on the deterioration state of the storage element B from the past to the present and the future possible driving distance of the electric vehicle (step S203), and displays them on the display unit 23 (step S204). This completes the process of obtaining information on the performance of the storage element B in the information terminal device 2.

[0071] Fig. 5 is a schematic diagram of an example of a method for estimating the transition of the deterioration state. Fig. 5A is a graph showing the change in full charge capacity (discharge capacity) versus the time elapsed since the start of operation, i.e., the period of use, Fig. 5B is a graph showing the change in full charge capacity versus the integrated charged amount of electricity, and Fig. 5C is a graph showing the change in full charge capacity versus the integrated number of charges. The change (deterioration) in full charge capacity shown in Figs. 5A to 5C is influenced by the driving history of the electric vehicle equipped with the storage element B. Different graphs may be obtained depending on the vehicle model, driving style, charging type, etc.

[0072] The graph in Fig. 5A shows a scatter plot of actual values ​​of full charge capacity versus period of use for storage elements B of the same type (model) and an approximation line for the actual values. The graph in Fig. 5B shows a scatter plot of actual values ​​of full charge capacity versus integrated charged electricity for storage elements B of the same type (model) and an approximation line for the actual values. The graph in Fig. 5C shows a scatter plot of actual values ​​of full charge capacity versus number of charges for storage elements B of the same type (model) and an approximation line for the actual values.

[0073] Even if the full charge capacity is not measured, the processing unit 10 can estimate the deterioration state of the storage element B (decrease in the full charge capacity) by obtaining any one of the factors, such as the elapsed time from the start of operation, the accumulated charged amount of electricity, the internal resistance, and the number of times of charging, by, for example, separately testing the storage element, and can know the relationship shown in any one of Figures 5A to 5C according to the type that matches the driving history. The processing unit 10 may estimate the full charge capacity from the relationship between the actual value and the change in the actual value of the full charge capacity and data other than the elapsed time from the start of operation, the accumulated charged amount of electricity, the internal resistance, and the accumulated number of times of charging. When multiple data can be obtained, the processing unit 10 may specify the most highly correlated relationship among the relationships shown in Figures 5A to 5C according to the driving conditions, such as the type of the storage element B, the model of the electric vehicle mounted thereon, the driving style, and the type of charging, and estimate the deterioration state using the data measured based on the most highly correlated relationship. When the data transmitted from the information terminal device 2 includes a temperature record history, the processing unit 10 may calculate a correction coefficient for deterioration at an average temperature in advance and correct the estimated result of the deterioration state. The processing unit 10 may also estimate the transition of the full charge capacity by a multivariate analysis or algorithm that weights the degree of influence of multiple factors such as the elapsed time from the start of operation, the accumulated charged electric quantity, the internal resistance, and the number of charging times on the full charge capacity.

[0074] FIG. 6 is a schematic diagram of an example of a method for predicting and calculating a mileage. FIG. 6 shows a graph showing a change from the present to the present by extrapolating the change in the full charge capacity from the past to the present with respect to the accumulated charge amount as shown in FIG. 5B, and a graph showing an example of a specification value of a mileage distance with one full charge with respect to the magnitude of the full charge capacity for each driving condition. As shown in FIG. 6, the processing unit 10 estimates a decrease rate of the full charge capacity, i.e., a deterioration state, from the accumulated charge amount (shown by an arrow in FIG. 6). The processing unit 10 can predict a mileage distance with one charge at the current full charge capacity from the estimated full charge capacity, as shown by an arrow. The processing unit 10 may predict a transition of the mileage distance with one charge with respect to the accumulated mileage, or may predict a change in the mileage distance with one charge with respect to the accumulated number of charges. Here, the change in the fully charged electric quantity from the present onward is predicted by extrapolating the change in the fully charged capacity from the past to the present, but the change may also be predicted according to a multivariate analysis that weights the degree of influence of multiple factors such as elapsed time, accumulated charged electric quantity, internal resistance, and number of charges, or an algorithm that takes into account the influence of multiple factors.

[0075] The processing procedure shown in Fig. 4 will be described by taking as an example a case where an owner of an electric vehicle also receives an appraisal when the vehicle is inspected at a dealership. The dealership staff member who has been contracted to perform the inspection with appraisal uses information terminal device 2 to input data for obtaining the performance of storage element B and data about the electric vehicle.

[0076] Fig. 7 shows an example of the input screen 230. Fig. 7 shows the screen displayed on the display unit 23 of the information terminal device 2 when the user logs in to the assessment service provided by the information processing device 1 with an account previously assigned to the dealer and selects a new input menu. The input screen 230 includes an input field 231 for the manufacturer, vehicle name, model, etc. of the electric vehicle, the manufacturing date, and the delivery date. The input screen 230 includes an input field 232 for the mileage of the electric vehicle. The input screen 230 includes an input field 233 for the model, serial number, measurement data, and data obtained from the BMU of the energy storage element B. The data obtained from the BMU may be read by the processing unit 20 when instructed to obtain data from a dedicated device via a communication medium such as a USB (Universal Serial Bus).

[0077] The input screen 230 includes an assessment request button 234. When the assessment request button 234 is selected by the operation unit 24, the processing unit 20 transmits the various data inputted on the input screen 230 to the information processing device 1.

[0078] The input screen 230 includes a driving condition change button 236. When the driving condition change button 236 is selected by the operation unit 24, the processing unit 20 displays another input screen (not shown) on the display unit 23 and accepts selection from the frequency of charging and discharging, driving style (rapid or slow starts), driving type (urban driving, suburban driving, highway driving), location (use in mountainous or plain areas with many slopes), etc. Selection of driving type may be accepted from the JC08 or WLCT mode. When the driving condition change button 236 is not selected, the driving conditions are assumed to be the frequency of charging and discharging, driving range, driving frequency, driving route, etc. of the previous owner, assuming that the owner will not change in the future.

[0079] FIG. 8 shows an example of the assessment result screen 235. The assessment result screen 235 includes a numerical value indicating the rate of decrease in the full charge capacity as the deterioration state of the storage element B, and a numerical value indicating the drivable distance. The assessment result screen 235 in FIG. 8 includes the drivable distance not only as a numerical value but also as a graph showing a plurality of future estimated transition results of the drivable distance under different driving conditions. In the graph shown in FIG. 8, a thick line indicates the transition of the drivable distance predicted based on the driving conditions that the owner does not change, that is, the driving conditions estimated from the travel history so far. And a dashed line indicates the transition of the drivable distance predicted based on different driving conditions. FIG. 8 shows the transition of the drivable distance under driving conditions corresponding to a daily commute of 50 km to work and a case where the vehicle is used for shopping at a nearby supermarket about 5 km away (horizontal axis: years after purchase). In FIG. 8, the dashed line further indicates the transition under the JC08 driving conditions, and the dashed line indicates the transition under the WLCT mode driving conditions (horizontal axis: drivable distance after purchase). In this way, not only the deterioration state of the storage element B from the past to the present, but also the future transition of the possible driving distance according to different driving conditions can be grasped.

[0080] The assessment result screen 235 shown in FIG. 8 may be obtained from the information terminal device 2 used by the owner of the electric vehicle.

[0081] The appraisal result screen 235 may include the appraisal price of the electric vehicle as shown in FIG. 8. The appraisal price may be calculated by the information processing device 1, or may be calculated by the information terminal device 2 having a used price table for electric vehicles. FIG. 9 shows an example of the used price table. The used price table includes, for each vehicle model, a price according to the deterioration state of the storage element B and the predicted driving distance on a single full charge after three years. This table may also simultaneously show the current driving distance on a single full charge and the reduction rate comparing this distance with the same distance three years from now, and the price may be determined by weighting these items. The used price table for electric vehicles is updated from time to time by the vehicle manufacturer or the dealer.

[0082] The used price table may include not only the price of the entire electric vehicle, but also the evaluation of the storage element B itself. FIG. 10 shows an example of the evaluation table. The evaluation table is an alphabet showing an evaluation according to the deterioration state of the storage element B and the predicted mileage on a single full charge after three years. In the evaluation table of FIG. 10, the combinations of the deterioration state of the storage element B and the predicted mileage are labeled SS, S, A, B, and C from the top. In the evaluation table of FIG. 10, the labels T and K are attached. The label T indicates that the electric fuel consumption is good for the mileage, long-distance driving and charging are performed frequently, and it is suitable for the same driving style. The label K indicates that the electric fuel consumption is poor because it is driven in the city, the mileage is short but the number of charging times is small, and if the driving style is similar, the mileage can be guaranteed to be long. In this way, different evaluations may be made depending on the driving conditions.

[0083] The used price table may vary depending on the accuracy of the estimated deterioration state and the mileage. The larger the amount of data, such as the number of types of data used and the history of inspection results, the more accurate the estimation of the deterioration transition of the storage element and the prediction of the future performance transition of the electric vehicle are, compared with, for example, a case where data for a certain period cannot be viewed due to loss, or where the data acquisition sampling interval is very rough and only a part of the actual charging history can be viewed, or where the amount of charged electricity can be confirmed but the charging rate and date and time are unknown. Therefore, for storage elements B with the same manufacturing number, the price may be corrected so as to increase depending on the number of types of data stored in the battery DB 111 and the amount of data left as history. A grade may be determined depending on the number of types of data and the amount of data left as history, and the used price table shown in FIG. 9 and the evaluation table in FIG. 10 may be prepared for each grade. The selling price of the used electric vehicle may be determined based on the grade.

[0084] The estimated deterioration state and the mileage may affect the determination of the insurance premium for the energy storage element B mounted on a used electric vehicle. The processing unit 10 of the information processing device 1 may calculate and output the insurance premium when creating data for displaying the above-mentioned assessment result screen 235. When the processing unit 10 determines that the accuracy of the estimation of the battery deterioration and the prediction of the future transition of the performance of the electric vehicle is high as a result of a large amount of data, such as the number of types of data used and the history of inspection results, it can set a low insurance premium.

[0085] When a used electric vehicle is sold using the graph of predicted trends in the driving distance on a single full charge shown in FIG. 8 or the table shown in FIG. 9 or FIG. 10, and then brought to a dealer or maintenance company for inspection, the insurance premium may be determined based on the difference between the results of processing by the information processing device 1 at the time of sale and the results of processing by the information processing device 1 at the time of sale when the vehicle is brought in.

[0086] FIG. 11 is a diagram showing an outline of an example of insurance compensation. The graph shown by the dashed line in FIG. 11 shows a prediction result estimated at the time of appraisal for used sales. In FIG. 11, the deterioration state measured at the time of appraisal for used sales is shown by a black circle. Here, it is assumed that the appraisal and the sale were conducted at the same time. The graph shown in FIG. 11 shows a predicted transition of a decrease in the mileage of a storage element B of the same type as the storage element B of the target serial number, based on the deterioration state at the time of appraisal for used sales. In the example of FIG. 11, the median, the lower limit, and the upper limit of the predicted transition of a decrease in the mileage of the electric vehicle in the future for the storage element B of the target serial number are shown by a dashed line, a dashed line, and a solid line, respectively. The median, the lower limit, and the upper limit may be determined based on the reliability of the predicted value resulting from the number of data used for predicting the performance, etc. As described above, the future travel distance of the electric vehicle is predicted based on at least one of the full charge capacity, number of charges, amount of charged electricity at each charge, uncharged voltage, integrated amount of charged electricity, and internal resistance of the storage element B, the past to present deterioration state of the storage element B estimated based on data on the travel history, and the driving conditions. The driving conditions include driving preferences of the prospective purchaser (current owner) when selling a used vehicle, such as sudden or slow starts, frequent charging even when the vehicle is highly charged (high frequency charging) or charging only after the vehicle is nearly fully discharged (low frequency), and driving environments such as use in mountainous or plain areas with many slopes, and use in extremely hot or cold regions.

[0087] The current owner of an electric vehicle that was predicted to have the transition shown in Figure 11 at the time of second-hand sale is assumed to have paid an annual insurance amount of 30,000 yen to receive insurance coverage for at least the median mileage transition after the deterioration state measured at the time of second-hand sale appraisal, as indicated by the black circle. If the current owner subsequently has the vehicle inspected and it is estimated to have the deterioration state indicated by the white circle, the compensation amount will be determined as follows:

[0088] In the example of FIG. 11, although the electric vehicle was evaluated as being in good condition and having high performance at the time of appraisal for second-hand sales, the deterioration state of the storage element B when the vehicle was brought in was more deteriorated than predicted at the time of appraisal. The drivable distance calculated when the vehicle was brought in and inspected is less than the initially predicted drivable distance (dashed line) under the driving conditions of the current owner. The current owner who paid the insurance amount set as the median value can receive compensation such as replacement of the storage element B to make the drivable distance equal to or greater than the initially predicted drivable distance, addition of storage elements, or receipt of a sum according to losses in economic activity, etc., due to the drivable distance being less than the initially predicted drivable distance. Here, the storage element B used for replacement or addition may be either second-hand or new.

[0089] The above-described information processing system 100 accurately estimates the deterioration state and predicts the driving distance, making it possible to more accurately assess the future performance of the electric vehicle and allowing not only for a fair price for a used electric vehicle to be calculated, but also for owners to use their purchased used electric vehicle with peace of mind, as compensation is provided according to the difference between the predicted trend and the insurance amount.

[0090] Second embodiment In the second embodiment, the usage history of the energy storage element B of the electric vehicle is sequentially collected by the information processing device 1. The configuration of the information processing system 100 of the second embodiment is similar to that of the information processing system 100 of the first embodiment, except for the processing procedure described below. Therefore, among the configurations of the information processing system 100 of the second embodiment, the configurations common to the information processing system 100 of the first embodiment are denoted by the same reference numerals and detailed description thereof will be omitted.

[0091] FIG. 12 is a schematic diagram of an information processing system 100 according to the second embodiment. The information processing system 100 according to the second embodiment includes an in-vehicle device 3 that sequentially transmits the usage history of the storage element B. The in-vehicle device 3 is mounted on an electric vehicle and can acquire data from a BMU, an on-board diagnosis (OBD), or a navigation device of the storage element B, and transmit the data to the information processing device 1 via a network N. FIG. 12 shows how an electric vehicle equipped with the in-vehicle device 3 that sequentially transmits the usage history of the storage element B depicted at the bottom is effectively used for economic activities or the like by the current owner of the electric vehicle until a next owner is decided, and is sold as a second-hand vehicle without delay when the next owner is decided.

[0092] 13 is a block diagram showing the configuration of the in-vehicle device 3. The in-vehicle 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, and the like, and executes processing described below based on a control program stored in the storage unit 31.

[0093] The storage unit 31 is a non-volatile memory such as a flash memory. The storage unit 31 stores data that the processing unit 30 acquires from the BMU of the storage element B.

[0094] 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 compatible with a Controller Area Network (CAN). The first communication unit 32 may be incorporated in the processing unit 30. The processing unit 30 can acquire various data from the BMU of the storage element B via the in-vehicle network via the first communication unit 32.

[0095] The second communication unit 33 is a wireless communication device that realizes an external vehicle network, i.e., wireless communication. For example, the second communication unit 33 may be a device for communication via the carrier network N3, 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 processing unit 30 can transmit data to the outside via the second communication unit 33.

[0096] During charging or periodically, the processing unit 30 of the in-vehicle device 3 acquires data on the charging rate, the accumulated number of charging times, the accumulated charged amount of electricity, the internal resistance, and the elapsed time from the start of operation from the BMU of the storage element B via the first communication unit 32. The processing unit 30 stores the acquired data in the storage unit 31 in association with time information measured by a built-in timer.

[0097] The processing unit 30 of the in-vehicle device 3 periodically transmits data stored in the memory unit 31 to the information processing device 1 when the second communication unit 33 detects that the in-vehicle device 3 is connected to the carrier network N3 or when the in-vehicle device 3 is connected to a charging device. The processing unit 30 transmits the serial number of the storage element B or the vehicle identification data together with the data read from the memory unit 31. When transmitting the vehicle identification data, the processing unit 30 transmits data related to the driving history of the electric vehicle. The processing unit 30 may transmit the data to the information processing device 1 via the charging device.

[0098] Each time the in-vehicle device 3 transmits data on the charging rate, the accumulated number of charging times, the accumulated charged amount of electricity, the internal resistance, and the time elapsed since the start of operation from the BMU of the storage element B to the information processing device 1, the information processing device 1 stores the data in the battery DB 111 of the storage unit 11. Each time the in-vehicle device 3 transmits data on the driving history of the electric vehicle, the information processing device 1 stores the data in the battery DB 111 in association with the identification data of the vehicle. The battery DB 111 stores the identification data of the vehicle mounting the target storage element B, and the history of data on the charging rate, the accumulated number of charging times, the accumulated charged amount of electricity, the internal resistance, and the time elapsed since the start of operation, in association with the serial number of the storage element B.

[0099] 14 is a flowchart showing an example of a processing procedure in the information processing system 100 in the second embodiment. The process starts when a dealer or a maintenance company uses the information terminal device 2 to obtain an evaluation of an energy storage element B mounted on an electric vehicle.

[0100] The processing unit 20 of the information terminal device 2 displays a screen for receiving input of the serial number of the energy storage element B or the identification data of the electric vehicle to be subjected to performance evaluation on the display unit 23 based on the terminal program P2 (step S221). In step S221, the processing unit 20 may display the screen based on screen data obtained by requesting, from the information processing device 1, a Web screen for specifying the electric vehicle to be subjected to performance evaluation.

[0101] The processing unit 20 receives the serial number of the energy storage element B to be subjected to performance evaluation or the identification data of the electric vehicle (step S222). The processing unit 20 receives input of data related to the driving history of the electric vehicle on the screen displayed in step S221 (step S223). When the data related to the driving history of the electric vehicle is periodically transmitted from the in-vehicle device 3, the reception of the input may be omitted.

[0102] The processing unit 20 transmits the received serial number or identification data and data related to the driving history of the electric vehicle to the information processing device 1 (step S224).

[0103] The information processing device 1 receives the serial number of the storage element B or the identification data of the electric vehicle and data related to the driving history of the electric vehicle transmitted from the information terminal device 2 (step S121). The processing unit 10 reads out the stored data of the storage element B's charging rate, the accumulated number of charging times, the accumulated charged amount of electricity, the internal resistance, and the elapsed time since the start of operation from the battery DB 111 based on the received serial number or identification data (step S122).

[0104] The processing unit 10 calculates the degradation state, i.e., the rate of decrease of the full charge capacity from the time of manufacture (when the vehicle was new) based on the data read in step S122 (step S123). The processing content in step S123 is similar to that in step S103 of the processing procedure shown in FIG. 4 of the first embodiment.

[0105] The processing unit 10 estimates the progress of deterioration of the storage element B from the past to the present based on at least one of the full charge capacity, the cumulative number of charging times, the cumulative charged electricity amount, the temperature record history, the internal resistance, and the elapsed time obtained from the BMU of the storage element B, and the deterioration state calculated in step S123, and then extrapolates it to predict the progress of the decrease in the full charge capacity of the storage element B in the future when used under the same driving conditions as the current electric vehicle owner. Furthermore, if the relationship between the battery capacity and the mileage per charge under the driving conditions is known in advance, the progress of the decrease in the mileage in the future can be calculated as well as the progress from the past to the present when used under the same driving conditions as the current electric vehicle owner (step S124). The processing content in step S124 is the same as steps S104 and S105 of the processing procedure shown in FIG. 4 of the first embodiment.

[0106] The processing unit 10 stores the deterioration state of the storage element B calculated in step S123, the estimated deterioration trend of the storage element B calculated in step S124, and the predicted trend of the driving distance on a single full charge, which is the performance of the electric vehicle, in the battery DB 111 in association with the manufacturing number of the storage element B (step S125), and transmits them to the information terminal device 2 (step S126).

[0107] The processing unit 20 of the information terminal device 2 receives the data on the deterioration state of the storage element B and the driving distance (step S225), and displays them on the display unit 23 (step S226). This completes the process of obtaining a performance evaluation of the storage element B in the information terminal device 2 of the second embodiment.

[0108] In the second embodiment, the accumulated number of charging times, accumulated charged electricity amount, temperature record history, internal resistance, and elapsed time obtained from the BMU of the storage element B are stored in the battery DB 111 not only at the time of inspection but also divided into a plurality of time points as a progression. The driving history including the driving position information is also divided into a plurality of time points and stored in the information processing device 1. Since it becomes possible to grasp the driving history in more detail, such as whether the electric vehicle was frequently driven far away or was driven round trips of short distances, it is expected that the accuracy of estimating the deterioration state and predicting future performance will improve.

[0109] In the second embodiment, the usage history of the storage element B of the electric vehicle is successively acquired. As a result, the information processing device 1 successively updates the results of the estimation of the degradation state of the storage element and the results of the prediction of the transition of the future performance degradation under predetermined driving conditions. As a result, the owner of the electric vehicle can decide the time of selling the electric vehicle while taking into consideration the driving distance required for the use of the electric vehicle and the above-mentioned used price table (FIG. 9) based on the prediction result of the transition of the performance degradation. Furthermore, it becomes possible to ride the electric vehicle currently being used until a buyer appears. There is also an advantage that the electric vehicle dealer does not need to secure a space to keep the used electric vehicle in the store until a buyer appears. In addition, the buyer can obtain the latest estimation result of the degradation state of the storage element and the latest prediction result of the transition of the future performance degradation under predetermined driving conditions without worrying about the degradation of the storage element B while it is being taken over by the used dealer and displayed in the store without being used. In addition, since there is no need to go through the procedure of having the vehicle picked up by a used electric vehicle dealer, the prospective buyer can select an electric vehicle that matches his or her requirements from not only used electric vehicles displayed in multiple stores, but also from electric vehicles owned (or used) by owners scattered throughout the country who are connected to the information processing system 100 and the information terminal device 2.

[0110] (Modification) In the modified example, a used electric vehicle that matches the needs of the prospective purchaser is proposed. FIG. 15 is a schematic diagram of information provided by the information processing device 1 in the modified example. As described in the first or second embodiment, the information processing device 1 stores in the battery DB 111 the degradation state estimated and calculated at the time of inspection or assessment of the electric vehicle and data on the subsequent mileage of the electric vehicle. The information processing device 1 stores data on the degradation state and subsequent mileage of a plurality of electric vehicles. Based on the stored degradation state of the storage element B and the estimated subsequent mileage, the information processing device 1 extracts, as sales candidates, used electric vehicles that match the conditions of the prospective purchaser, such as the degree of degradation, mileage range, presence or absence of charging facilities, mileage, and price.

[0111] FIG. 15 shows three examples of electric vehicles. The first electric vehicle is equipped with a storage element B whose deterioration progresses in a normal manner and whose full charge capacity has decreased to 88% compared to when it was new. The second electric vehicle is equipped with a storage element B whose full charge capacity is 83% despite being several years old and whose deterioration is estimated to progress more slowly than that of a standard storage element B. The third electric vehicle is equipped with a storage element B whose full charge capacity is 96% and whose deterioration is estimated to progress rapidly after a certain amount of time has passed. Here, FIG. 15 shows the predicted results of the future performance transition of the electric vehicle under the driving conditions of JC08.

[0112] The information processing device 1 can determine whether or not each electric vehicle equipped with a power storage element B whose future performance has been evaluated meets the needs of the prospective purchaser and make a proposal. If the prospective purchaser has conditions such as not needing to drive the vehicle for a short period of time, not having a charging facility in a place that can be used as a garage, and having a long driving distance, the information processing device 1 extracts the third electric vehicle with higher priority. If the prospective purchaser has needs such as not driving long distances much, but driving frequently in the vicinity of the home, the information processing device 1 extracts the second electric vehicle with a power storage element B whose deterioration is relatively slow.

[0113] 16 is a flowchart showing an example of a matching process executed by the information processing device 1 of the modified example. The following process is started when a dealer listens to a request from a prospective purchaser and uses the information terminal device 2 to search for a used electric vehicle. The process may also be started when the prospective purchaser himself / herself transmits a request to search for a used electric vehicle to the information processing device 1 using the information terminal device 2.

[0114] The processing unit 20 of the information terminal device 2 displays a screen for accepting input of conditions of a prospective purchaser on the display unit 23 based on the terminal program P2 (step S231). In step S231, the processing unit 20 may display the screen based on screen data obtained by requesting a Web screen for searching used electric vehicles from the information processing device 1.

[0115] The processing unit 20 accepts conditions (requests) from the prospective purchaser, including at least one of the frequency of use, the range of use (driving range), the presence or absence of charging facilities in the garage (parking lot), the desired number of years, and the desired price (step S232). The processing unit 20 transmits the accepted conditions to the information processing device 1 (step S233).

[0116] The processing unit 10 of the information processing device 1 receives the conditions of the potential purchaser transmitted from the information terminal device 2 (step S131). In step S131, the processing unit 10 may receive driving history (movement history) data of the potential purchaser's past driving as the conditions of the potential purchaser. The driving history data includes either driving frequency or driving range (location). The data may be information obtained from an on-board diagnosis (OBD) of a vehicle owned by the potential purchaser, or information obtained by interviewing the potential purchaser (information on driving methods such as sudden starts, leisurely starts, frequent charging, and infrequent charging, a charging station map, and information on locations such as driving in plains or mountain areas, and in extremely hot or cold areas).

[0117] Furthermore, the processing unit 10 predicts and outputs future trends in the electric vehicle's performance (driving distance) using the above data, rather than predictions based on pre-set driving conditions such as JC08, thereby enabling highly accurate matching.

[0118] The processing unit 10 extracts electric vehicles equipped with the storage element B that meet the conditions based on the deterioration state of the storage element B at the time of assessment stored in the battery DB 111 and the mileage on one full charge at the time of assessment and thereafter, according to the received conditions (step S132). The extraction may be single or multiple, and may be performed by multivariate analysis weighted for each item of the desired condition (request), or by deep learning using an RNN, LSTM, or Transformer that analyzes past sales prices, recent prices of similar electric vehicles at nearby used car dealerships, and the like.

[0119] In step S132, for example, in a case where the conditions of the prospective purchaser are that the range of use is within 50 kilometers, the intended period of use is about 5 years, and there is a charging facility in the garage, the processing unit 10 extracts electric vehicles equipped with a power storage element B that is estimated to have a range of 100 kilometers or more for 5 years or more if driven in the same manner, even if the possible driving distance on a single full charge is short. In another case where the range of use is in a cold region, the range of use is within 100 kilometers, the intended period of use is about 3 years, and there is a charging facility in the garage, the processing unit 10 extracts electric vehicles equipped with a power storage element B of a similar cold region type that is estimated to have a range of 200 kilometers or more for 3 years or more on a single full charge.

[0120] The processing unit 10 transmits the extracted electric vehicle identification data, vehicle information about the electric vehicle, and the deterioration state and mileage of the mounted energy storage element B to the information terminal device 2 as data about the electric vehicle as a candidate for sale (step S133). There may be one or more candidates.

[0121] The processing unit 20 of the information terminal device 2 receives the data of the sales candidate (step S234), and displays it on the display unit 23 (step S235). This ends the output process of the matching result from the information processing device 1.

[0122] By simplifying and accurately evaluating the performance of the energy storage element B, used electric vehicles equipped with the energy storage element B that meet the needs of the prospective purchaser can be appropriately evaluated by the processing procedure shown in Fig. 16. Since the manufacturer accurately estimates not only the current state of deterioration of the energy storage element B equipped in the used electric vehicle, but also the future progress of changes in the mileage, the satisfaction of owners and prospective purchasers of used electric vehicles is improved, allowing them to drive with peace of mind.

[0123] When driving history data of the prospective purchaser can be acquired, the information processing device 1 may extract identification data of an electric vehicle equipped with a storage element whose driving range transition matches the acquired driving history data. The information processing device 1 may use a neural network to train a learning model so as to output a degree of match when the driving history data of the prospective purchaser and the predicted driving range transition data are input.

[0124] 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]

[0125] 1. Information processing device 10 Processing section 11 Storage section 111 Battery DB P1 Information processing program (computer program) 2 Information terminal device 20 Processing section 23 Display section

Claims

1. The computer Acquire information including at least one of a full charge capacity, a number of charges, a charged electric quantity at each charge, a non-charged voltage, an integrated charged electric quantity, an internal resistance, and a period of use of an electric storage element mounted on the electric vehicle, and data regarding a movement history of the electric vehicle; estimating a transition of a deterioration state of the energy storage element from the past to the present based on at least one of the acquired full charge capacity, the number of charges, the amount of charged electricity at each charge, the uncharged voltage, the integrated amount of charged electricity, the internal resistance, and the period of use of the energy storage element, and the data related to the movement history; Obtaining future driving conditions of the electric vehicle; predicting a future transition in performance of the electric vehicle based on the estimated transition in the deterioration state and the acquired driving conditions; Outputting the future performance of the electric vehicle Information processing methods.

2. The computer calculates, as a future performance of the electric vehicle, a distance that the electric vehicle can travel when the power storage element is in a fully charged state. The information processing method according to claim 1 .

3. The computer includes: Calculating a selling price of the electric vehicle based on the estimated deterioration state of the energy storage element and the evaluated performance; Output the calculated selling price The information processing method according to claim 1 .

4. The computer includes: determining a grade of the electric vehicle based on the estimated deterioration state of the storage element and the evaluated performance; Calculate the insurance premium based on the determined grade, Print the calculated insurance premium The information processing method according to claim 1 .

5. The computer includes: determining a difference between the predicted performance of the electric vehicle and an actual performance of the electric vehicle at a later time; Determine compensation based on the identified difference The information processing method according to claim 1 .

6. The computer includes: Calculating a future performance of the electric vehicle according to a plurality of different driving conditions of the electric vehicle; Outputting the future performance of the electric vehicle calculated for each of the plurality of driving conditions The information processing method according to claim 1 .

7. The computer includes: The buyer's requests for a used electric vehicle are obtained, including the price, current driving range, the decline in driving range, intended use, frequency of use, and route of use. Extracting data on used electric vehicles in which the deterioration state of the storage element and the future performance of the electric vehicle that match the acquired requirements are estimated and predicted; Output the extracted data of the electric vehicle The information processing method according to claim 1 .

8. The computer acquires, as the request, data on the past use history of the electric vehicle of the purchaser. The information processing method according to claim 7.

9. The electric vehicle includes a communication device that sequentially transmits information including at least one of a full charge capacity of the storage element, a number of charges, a charged electric amount at each charge, a non-charged voltage, an integrated charged electric amount, an internal resistance, and a period of use, and data related to a movement history of the electric vehicle to an outside via communication; the computer acquires and stores information including at least one of the full charge capacity, the number of charges, the amount of charged electricity at each charge, the uncharged voltage, the integrated amount of charged electricity, the internal resistance, and the period of use of the storage element transmitted from the communication device, and data related to the movement history of the electric vehicle; The computer uses the stored data when estimating the deterioration state of the storage element. The information processing method according to claim 1 .

10. a first acquisition unit that acquires information including at least one of a full charge capacity, a number of charges, a charged electric quantity at each charge, a non-charged voltage, an integrated charged electric quantity, an internal resistance, and a period of use of an electric storage element mounted on the electric vehicle, and data related to a movement history of the electric vehicle; a degradation estimation unit that estimates a transition of a degradation state of the storage element from the past to the present based on at least one of the acquired full charge capacity, the number of charges, the amount of charged electricity at each charge, the uncharged voltage, the integrated amount of charged electricity, the internal resistance, and the period of use of the storage element, and data related to the movement history; A second acquisition unit that acquires a future driving condition of the electric vehicle; a performance prediction unit that predicts a future transition in performance of the electric vehicle based on the estimated transition in the deterioration state and the acquired future driving conditions of the electric vehicle; an output unit for outputting the subsequent performance of the electric vehicle; An information processing device comprising:

11. On the computer, Acquire information including at least one of a full charge capacity, a number of charges, a charged electric quantity at each charge, a non-charged voltage, an integrated charged electric quantity, an internal resistance, and a period of use of an electric storage element mounted on the electric vehicle, and data regarding a movement history of the electric vehicle; estimating a transition of a deterioration state of the energy storage element from the past to the present based on at least one of the acquired full charge capacity, the number of charges, the amount of charged electricity at each charge, the uncharged voltage, the integrated amount of charged electricity, the internal resistance, and the period of use of the energy storage element, and the data related to the movement history; Obtaining future driving conditions of the electric vehicle; predicting a future transition in performance of the electric vehicle based on the estimated transition in the deterioration state and the acquired driving conditions; Outputting the future performance of the electric vehicle A computer program that executes a process.

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