Insurance premium calculation method and insurance premium calculation system

Abstract

Provided is an insurance premium calculation method capable of calculating a more appropriate insurance premium according to a target period and a desired battery performance. Specifically, a desired target period of an insurance contract applicant and a desired battery performance at the time when the target period elapses are acquired. Subsequently, a deterioration occurrence probability of an occurrence of performance deterioration in which a battery performance of a battery for travel that is mounted on a target vehicle is lower than a desired battery performance when the acquired target period elapses is calculated. Subsequently, on the basis of the calculated deterioration occurrence probability, an insurance premium for insurance that provides compensation, in part or in whole, for a cost required for resolving performance deterioration when performance deterioration occurs before the target period elapses is calculated.

Description

Insurance Premium Calculation Method and Insurance Premium Calculation System

[0001] The present disclosure relates to an insurance premium calculation method and an insurance premium calculation system.

[0002] Conventionally, a system for determining an insurance premium for insurance that compensates for loss of asset value due to battery deterioration when a vehicle is sold within a target period has been proposed (see, for example, Patent Document 1). In the system described in Patent Document 1, purchasers are ranked based on vehicle usage information regarding the usage of the vehicle by the vehicle purchaser, and the insurance premium for the above-mentioned insurance is determined for each rank of the purchaser.

[0003] Japanese Unexamined Patent Application Publication No. 2022-146859

[0004] Here, the inventor of the present disclosure considered providing an insurance service that compensates for part or all of the costs required for battery replacement, etc. when the battery performance deteriorates below the desired battery performance before the target period elapses. However, in the system described in Patent Document 1, an insurance premium corresponding to the target period and the desired battery performance cannot be calculated and cannot be used for providing the above-mentioned insurance service. The present disclosure aims to provide an insurance premium calculation method and an insurance premium calculation system capable of calculating a more appropriate insurance premium according to the target period and the desired battery performance.

[0005] An insurance premium calculation method according to one aspect of the present disclosure acquires the desired target period of an insurance applicant and the desired battery performance at the end of the target period, calculates the probability that performance deterioration occurs in which the battery performance of the driving battery mounted on the target vehicle deteriorates below the desired battery performance at the end of the acquired target period, and based on the calculated probability, calculates the insurance premium for insurance that compensates for part or all of the costs required to eliminate the performance deterioration when performance deterioration occurs before the target period elapses.

[0006] Furthermore, an insurance premium calculation system in one aspect of this disclosure includes: an acquisition unit that acquires a target period desired by an insurance policyholder and the desired battery performance at the end of the target period; a degradation prediction unit that calculates the probability that performance degradation will occur in which the battery performance of the traction battery installed in the target vehicle will fall below the desired battery performance at the end of the acquired target period; and an insurance premium calculation unit that calculates an insurance premium to cover part or all of the costs required to resolve performance degradation if performance degradation occurs before the end of the target period, based on the calculated probability.

[0007] According to this disclosure, it is possible to provide an insurance premium calculation method and insurance premium calculation system that can calculate a more appropriate insurance premium according to the target period and desired battery performance.

[0008] This figure shows the schematic configuration of the insurance premium calculation system according to the embodiment. This figure shows the contents of usage information, battery status information, and desired battery performance. This figure shows each function realized by the information processing unit. This flowchart shows an example of the insurance premium calculation method of this embodiment. This flowchart shows the contents of the probability calculation process. This block diagram shows the contents of the probability calculation process. This figure shows an example of the output of the vehicle usage prediction model. This figure shows an example of the output of the capacity estimation model. This figure shows an example of the estimation result of changes in battery performance. This figure shows an example of the method for calculating the probability of deterioration occurring. This figure shows the relationship between the desired mileage, target period, and the probability of deterioration occurring. This figure shows the relationship between the desired mileage, target period, and the probability of deterioration occurring. This figure shows the relationship between the desired mileage, target period, and the probability of deterioration occurring. This figure shows the insured items and insurance premium calculation results for each insurance contract applicant. This block diagram shows the contents of the probability calculation process of modified example (1). This figure shows the relationship between charging frequency, driving frequency, and number of QCs and the probability of deterioration occurring. This figure shows the relationship between charging frequency, driving frequency, and number of QCs and the probability of deterioration occurring. This figure shows the relationship between usage and the probability of deterioration occurring. This figure shows the relationship between usage and the probability of deterioration occurring. This flowchart shows an example of the insurance premium calculation method of modified example (2). This figure shows the relationship between usage pattern and the probability of deterioration occurring. This is a block diagram showing the contents of the probability calculation process for modification (3). This is a flowchart showing the contents of the second probability calculation process. This is a flowchart showing the contents of the second probability calculation process for modification (4). This is a diagram showing an example of a method for calculating the probability of deterioration occurring.

[0009] The embodiments of this disclosure will be described below with reference to the drawings. Note that the drawings are schematic and may differ from actual ones. Furthermore, the embodiments of this disclosure shown below are illustrative examples of devices and methods for realizing the technical concept of this disclosure, and the technical concept of this disclosure is not limited to the structure, arrangement, etc., of the components described below. The technical concept of this disclosure can be modified in various ways within the technical scope defined by the claims described in the patent claims.

[0010] (Configuration) In the first embodiment, an example is shown in which the insurance premium calculation method and insurance premium calculation system of this disclosure are used in an insurance premium calculation system 1 that calculates the insurance premium for an insurance service that compensates for part or all of the costs required to resolve the performance degradation of batteries installed in electric vehicles, hybrid vehicles, etc. Figure 1 is a diagram showing the schematic configuration of the insurance premium calculation system 1 according to this embodiment. As shown in Figure 1, the insurance premium calculation system 1 has an in-vehicle device 2, a dealer terminal 3, and a server device 4. The in-vehicle device 2 is a device installed in a plurality of vehicles C1. As the in-vehicle device 2, for example, an ECU (Electronic Control Unit) can be used. As the vehicle C1, for example, a vehicle that has a battery for driving, such as an electric vehicle or a hybrid vehicle. That is, the battery is a secondary battery that drives the drive motor (not shown) for driving the vehicle C1. As the battery (secondary battery), for example, a lithium-ion secondary battery or a nickel-metal hydride secondary battery can be used. The on-board device 2 acquires information on the usage status of vehicle C1 (hereinafter also referred to as "usage status information") and information on the status of the battery installed in vehicle C1 (hereinafter also referred to as "battery status information"). The on-board device 2 also has a wireless communication function and transmits the acquired usage status information and battery status information to the server device 4. Examples of communication methods (wireless) for the on-board device 2 include wireless communication via a network 5 such as a public mobile communication network, satellite communication, and vehicle-to-infrastructure communication.

[0011] As shown in Figure 2, the usage information includes driving information regarding the driving state of vehicle C1, environmental information regarding the surrounding environment of vehicle C1, and charging information for vehicle C1. Figure 2 is a diagram showing the contents of the usage information, battery status information, and desired battery performance. The driving information includes at least the speed, acceleration, and distance traveled of vehicle C1 (e.g., distance traveled during one trip). The environmental information includes at least the ambient temperature, or at least the location (e.g., latitude, longitude) and date. The environmental information may also include gradient, atmospheric pressure, and humidity. The charging information includes at least the charging type (e.g., normal charging, fast charging) and charging time. The battery status information includes at least the battery voltage, current, temperature, resistance, and cumulative time (e.g., cumulative value of discharge time from a new state), as shown in Figure 2.

[0012] Dealership terminal 3 is a terminal installed in a dealership that sells vehicles. For example, a PC (Personal Computer) can be used as dealership terminal 3. Dealership terminal 3 is operated by dealership staff or insurance applicant A, and accepts input of information on the desired coverage scope and information on insurance applicant A. Insurance applicant A may be, for example, a new car purchaser of a vehicle with a battery for driving, such as an electric vehicle or a hybrid vehicle (hereinafter also referred to as "Target Vehicle C2"). In addition, the information on the coverage scope can include the coverage period and the desired battery performance after the coverage period has elapsed. Desired battery performance may include, for example, the battery's SOH (State of Health), the driving distance of Target Vehicle C2 with a fully charged battery, the number of battery charges required to drive Target Vehicle C2 a predetermined distance, or the amount of dischargeable power of the battery from a fully charged state, as shown in Figure 2. Information on insurance applicant A may include, for example, the intended use of Target Vehicle C2 (e.g., leisure use, commuting use). Furthermore, the sales terminal 3 has a wireless communication function and transmits information on the scope of coverage and information on the insurance contract applicant A to the server device 4.

[0013] The server device 4 includes, for example, a communication unit 6, a vehicle information DB 7 (broadly speaking, a "storage unit"), a user information DB 8, and an information processing unit 9. The communication unit 6 is a wireless communication unit that provides wireless communication functionality between the server device 4 and external devices. Examples of communication methods for the communication unit 6 include wireless communication via a network 5 such as a public mobile communication network, satellite communication, and vehicle-to-infrastructure communication. The server device 4 transmits and receives data to and from the in-vehicle devices 2 and dealer terminals 3 of each of the multiple vehicles C1 via the communication unit 6. The vehicle information DB 7 stores usage status information and battery status information for the multiple vehicles C1. The storage and updating of usage status information and battery status information is performed by the information processing unit 9. The user information DB 8 stores information on insurance policy applicants A. The storage and updating of insurance policy applicant A's information is performed by the information processing unit 9.

[0014] The information processing unit 9 is a computer device that performs information processing on the server device 4. The information processing unit 9 comprises a processor 9a and peripheral components such as a storage device 9b for storing computer programs and the like. For example, the processor 9a can be a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). For example, the storage device 9b can be a semiconductor storage device, a magnetic storage device, or an optical storage device. The storage device 9b may also include registers, cache memory, and memory such as ROM and RAM used as main memory. Each function of the information processing unit 9 described below is realized, for example, by the processor 9a executing a computer program stored in the storage device 9b.

[0015] Next, we will explain in detail each function implemented by the information processing unit 9. As shown in Figure 3, the information processing unit 9 implements the functions of a model generation unit 10 (broadly referred to as the "acquisition unit"), a degradation prediction unit 11 (broadly referred to as the "prediction unit"), and an insurance premium calculation unit 12 (broadly referred to as the "calculation unit"). Figure 3 is a diagram showing each function implemented by the information processing unit 9. The model generation unit 10 acquires information on the coverage range transmitted by the dealer terminal 3 (S101 in Figure 4). That is, it acquires the desired battery performance and target period requested by insurance policyholder A. Figure 4 is a flowchart showing an example of the insurance premium calculation method in this embodiment. The model generation unit 10 also performs a probability calculation process to calculate the probability that performance degradation will occur in which the battery performance of the traction battery installed in the target vehicle C2 falls below the desired battery performance after the acquired target period has elapsed (hereinafter also referred to as the "degradation occurrence probability") (S102 in Figure 4).

[0016] In the probability calculation process, the model generation unit 10 acquires usage information and battery status information transmitted from each of the on-board devices 2 of multiple vehicles C1, as shown in Figure 6 (S201 in Figure 5). Figure 5 is a flowchart showing the contents of the probability calculation process. Figure 6 is a block diagram showing the contents of the probability calculation process. Next, the acquired usage information and battery status information are stored in the vehicle information DB 7 (S202 in Figure 5). Subsequently, statistical processing (hereinafter also referred to as "first statistical processing") is performed on the usage information stored in the vehicle information DB 7 to generate a vehicle usage prediction model (S203 in Figure 5). As an example, one vehicle usage prediction model may be generated for all vehicles C1. For example, a vehicle usage prediction model is generated by performing first statistical processing on the usage information of all vehicles C1. As a vehicle usage prediction model, for example, a prediction model that predicts the future usage status of multiple patterns of vehicles C1 can be adopted by inputting individual parameters (hereinafter also referred to as "first parameters"). As the first parameter, for example, a parameter that changes the output pattern of the vehicle usage prediction model (e.g., patterns of charging frequency, charging method, driving frequency, etc.) can be adopted. The first parameter may be used as input data (explanatory variables) to the vehicle usage prediction model, or as parameters (coefficients) that constitute the vehicle prediction model. Examples of prediction models include neural network models and linear regression models. Furthermore, as future usage, for example, as shown in Figure 7, a time schedule of the usage of vehicle C1 each day from the present to a certain point in the future (e.g., several years from now) (e.g., driving, parking, NC: Normal Charge, QC: Quick Charge) can be adopted. As the first statistical processing, for example, machine learning and multivariate analysis can be adopted. Figure 7 is a diagram showing an example of the output of the vehicle usage prediction model.

[0017] Next, the model generation unit 10 performs statistical processing (hereinafter also referred to as "second statistical processing") on the battery status information stored in the vehicle information DB 7 to generate a capacity estimation model (S204 in Figure 5). For example, one capacity estimation model may be generated for all vehicles C1. For example, a capacity estimation model is generated by performing second statistical processing on the battery status information of all vehicles C1. As a capacity estimation model, for example, an estimation model can be adopted that estimates multiple patterns of the transition of the fully charged capacity of the battery installed in vehicle C1 by inputting individual parameters (hereinafter also referred to as "second parameters"). Examples of second parameters include multiple patterns of future usage of vehicle C1 predicted by the vehicle usage prediction model (see Figure 7). The second parameters may be used as input data (explanatory variables) to the capacity estimation model, or as parameters (coefficients) that constitute the capacity estimation model. Examples of estimation models include neural network models and linear regression models. Furthermore, as a trend in full charge capacity, time-series data showing the change in full charge capacity from the past to the present can be used, for example, as shown in Figure 8. For the second statistical processing, machine learning and multivariate analysis can be used, for example. Figure 8 shows an example of the output of the capacity estimation model.

[0018] The degradation prediction unit 11 estimates the change in battery performance of the battery installed in the target vehicle C2, as shown in Figure 9, based on the vehicle usage prediction model and capacity estimation model generated by the model generation unit 10 (S205 in Figure 5). In Figure 9, each of the multiple curves showing the relationship between time and battery performance is obtained for each first parameter. As an example, one first parameter is selected at a time, the selected first parameter is input to the vehicle usage prediction model, the output of the vehicle usage prediction model (second parameter) is input to the capacity estimation model, the output of the capacity estimation model (see Figure 8) is obtained, and based on the obtained output of the capacity estimation model, the change in battery performance corresponding to the selected first parameter (hereinafter also called "individual battery curve 13") is estimated. For example, the individual battery curve 13 is estimated so that the rate of degradation is faster when the usage frequency and charging frequency of vehicle C1, as shown in the output of the vehicle usage prediction model (see Figure 7), are high compared to when they are low. Also, for example, if the charging frequency shown in the output of the vehicle usage prediction model is about the same, the rate of degradation is estimated so that the rate of degradation is faster when there are many rapid charges compared to when there are few. Figure 9 shows an example of the estimated results for changes in battery performance.

[0019] Next, the degradation prediction unit 11 calculates the probability (degradation occurrence probability) that the battery performance of the traction battery installed in the target vehicle C2 will deteriorate to a level lower than the desired battery performance after the target period has elapsed, as obtained in S101 of Figure 4, based on the estimated change in battery performance (see Figure 9), and then terminates this probability calculation process (S206 of Figure 5). As an example, as shown in Figure 10, the degradation occurrence probability may be calculated based on a plurality of estimated individual battery curves 13. For example, the battery performance at the end of the target period (hereinafter also referred to as "elapsed battery performance 14") is identified for each individual battery curve 13, and a probability density function 15 of a predetermined probability distribution (for example, a normal distribution) is generated by applying the elapsed battery performance 14 for each identified individual battery curve 13. The probability density function 15 is a function that shows the probability density of the event that the battery performance takes a certain value after the target period has elapsed. Figure 10 is a diagram showing an example of a method for calculating the degradation occurrence probability. Next, the degradation prediction unit 11 calculates the area 16 in the generated probability density function 15 where the battery performance is lower than the desired battery performance obtained in S101 of Figure 4 as the degradation occurrence probability. When this calculation method is adopted, for example, as shown in Figure 11, the higher the desired battery performance or the longer the target period, the greater the degradation occurrence probability (area 16). In Figure 11, the driving range of the target vehicle with a fully charged battery (hereinafter also referred to as "desired driving range") is used as the desired battery performance, and an example is shown where both the desired driving range and the target period are long. Also, for example, as shown in Figures 12 and 13, the lower the desired battery performance or the shorter the target period, the greater the degradation occurrence probability (area 16). Figure 12 shows an example where the target period is long and the desired driving range is short. Figure 13 also shows an example where the desired driving range is long and the target period is short.

[0020] When the probability calculation process is completed, the insurance premium calculation unit 12 calculates an insurance premium (S103 in Figure 4) that will cover part or all of the costs required to resolve the performance degradation if the above-mentioned performance degradation occurs before the expiration of the target period, based on the degradation occurrence probability (area 16) calculated in S206 in Figure 5. The above-mentioned performance degradation is a performance degradation in which the battery performance of the battery installed in the target vehicle C2 falls below the desired battery performance. The costs required to resolve the performance degradation include the cost of replacing the battery or the cost of replacing the target vehicle C2, as shown in Figure 14. In other words, the insurance can cover either the "battery alone" or the "entire vehicle". Figure 14 is a diagram showing the insurance coverage and insurance premium calculation results for each insurance policyholder A. As for the method of calculating the insurance premium, for example, one method is to use a calculation method that defines the relationship between the degradation occurrence probability and the insurance premium so that the higher the degradation occurrence probability, the higher the insurance premium will be.

[0021] Next, the premium calculation unit 12 determines whether the calculated premium is within the range desired by the insurance policyholder A (S104 in Figure 4). For example, the premium is displayed on the display of the sales terminal 3, and the insurance policyholder A operates the sales terminal 3, sending information to the server device 4 via the sales terminal 3 indicating whether the premium is within the range desired. If the premium calculation unit 12 determines that it is not within the range desired (S104 "No" in Figure 4), it returns to S101 in Figure 4 and repeats the above flow from S101 to S104. On the other hand, if it determines that it is within the range desired (S104 "Yes" in Figure 4), it terminates without repeating the above flow.

[0022] (Effects of this embodiment) (1) In this embodiment, the desired target period and desired battery performance at the end of the target period are obtained from the prospective insurer A. The probability of degradation occurring, in which the battery performance of the traction battery installed in the target vehicle C2 falls below the desired battery performance at the end of the obtained target period, is calculated. Based on the calculated degradation probability, an insurance premium is calculated that will cover part or all of the costs required to resolve the performance degradation if it occurs before the end of the target period. As a result, for example, the higher the desired battery performance or the longer the target period, the higher the probability of degradation occurring and the higher the insurance premium can be. Also, for example, the lower the desired battery performance or the shorter the target period, the lower the probability of degradation occurring and the lower the insurance premium can be. Therefore, a more appropriate insurance premium can be calculated according to the target period and desired battery performance. As a result, insurance can be provided according to the desired target period and desired battery performance of the prospective insurer A.

[0023] (2) The desired battery performance includes one of the following: the battery's State of Health (SOH), the driving range of the target vehicle C2 with a fully charged battery, the number of battery charges required to drive the target vehicle C2 a predetermined distance, and the amount of electrical energy that can be discharged from a fully charged state. In other words, it includes the main parameters related to battery degradation. This allows the insurance policyholder A to select an appropriate battery performance as their desired performance.

[0024] (3) Information on the usage status of each of the multiple vehicles C1 and information on the status of the battery installed in each vehicle C1 are obtained and stored in the vehicle information DB7. A first statistical processing is performed on the information on the usage status of each vehicle C1 stored in the vehicle information DB7 to generate a vehicle usage prediction model. A second statistical processing is performed on the information on the status of the battery stored in the vehicle information DB7 to generate a capacity estimation model. Based on the generated vehicle usage prediction model and capacity estimation model, the change in battery performance of the battery installed in the target vehicle C2 is estimated, and the probability of degradation occurring is calculated based on the estimated change in battery performance, the target period, and the desired battery performance. In other words, two models are generated from the information of multiple vehicles C1, and the change in battery performance is estimated by combining the two generated models. This makes it possible to estimate the change in battery performance while taking into account the nonlinearity and time series changes of battery degradation.

[0025] (4) The information on the usage status of vehicle C1 also includes driving information regarding the driving state of vehicle C1, environmental information regarding the environment surrounding vehicle C1, and charging information for vehicle C1. From the driving information and charging information, the vehicle's operating state—whether it is driving, stopped, or charging—can be determined. From the environmental information, the environment in which the vehicle's operating state is being performed can be determined. Therefore, by including driving information, environmental information, and charging information in the information on the usage status of vehicle C1, a more appropriate vehicle usage prediction model can be generated.

[0026] (5) The driving information also includes at least the speed, acceleration, and distance traveled of vehicle C1. Here, the speed of vehicle C1 allows us to understand the load on the battery. The acceleration of vehicle C1 allows us to understand the rate of change in the load on the battery. The distance traveled by vehicle C1 allows us to understand how long the load was continuous. Therefore, by including speed, acceleration, and distance traveled in the driving information, the degradation of battery performance due to the load can be estimated more appropriately.

[0027] (6) The environmental information also includes at least the ambient temperature, or at least the location and date. Here, ambient temperature is a parameter related to battery degradation. Location and date are parameters from which ambient temperature can be obtained by using a database that stores ambient temperature for each location and date. Therefore, including ambient temperature or location and date in the environmental information can improve the accuracy of the vehicle usage prediction model and improve the accuracy of estimating changes in battery performance.

[0028] (7) The battery status information also includes at least the battery's voltage, current, temperature, resistance, and cumulative time. Here, voltage, current, and temperature are the basic parameters of the battery, and resistance is an auxiliary parameter of the battery. Therefore, including the battery status information, voltage, current, temperature, resistance, and cumulative time allows for the generation of a more appropriate capacity estimation model. (8) The cost required to resolve performance degradation is defined as the cost required to replace the battery or the cost required to replace the subject vehicle C2. In other words, the battery or the subject vehicle C2 is made eligible for insurance. This allows for a more appropriate setting of the insurance coverage and premiums.

[0029] (Modified Version) (1) In this embodiment, when calculating the probability of deterioration, an example is shown in which the target period and desired battery performance are used as information of the insurance policyholder A, but other configurations can also be adopted. For example, as shown in Figure 15, the usage pattern of the target vehicle C2 by the insurance policyholder A may be acquired, and the probability of deterioration (area 16) may be calculated based on the acquired usage pattern of the target vehicle C2, the target period, and the desired battery performance. The usage pattern of the target vehicle C2 includes at least the charging frequency of the battery of the target vehicle C2, the charging method of the battery of the target vehicle C2 (for example, the number of QCs indicating the ratio of QCs to the total number of charging cycles), the use of the target vehicle C2 (for example, for commuting, for leisure, etc.), and the driving frequency of the target vehicle C2. Figure 15 is a flowchart of the probability calculation process according to a modified version obtained by replacing S206 in Figure 5 with S301 to S302. Here, the dealer terminal 3 receives input of information on the usage pattern of the target vehicle C2 by the insurance policyholder A and transmits it to the server device 4. The degradation prediction unit 11 then acquires information on the usage pattern transmitted by the retailer terminal 3 (S301 in Figure 15).

[0030] Furthermore, the degradation prediction unit 11 calculates the probability of degradation occurring (area 16) based on the acquired usage pattern of the target vehicle C2, the target period, and the desired battery performance (S302 in Figure 15). As an example, as shown in Figures 16, 17, 18, and 19, when individual battery curves 13 are estimated for each second parameter (future usage of vehicle C1) in S205 of Figure 15, the individual battery curve 13 corresponding to the second parameter (future usage of vehicle C1) that matches the acquired usage pattern is selected from the multiple estimated individual battery curves 13. Figures 16, 17, 18, and 19 illustrate the case where the desired driving distance is used as the desired battery performance. Figure 16 illustrates the case where an individual battery curve 13 corresponding to a second parameter (future usage of vehicle C1) where the charging frequency, driving frequency, and QC count are greater than predetermined thresholds is selected. Figure 17 illustrates the case where an individual battery curve 13 corresponding to a second parameter where the charging frequency, driving frequency, and QC count are less than predetermined thresholds is selected. Furthermore, Figure 18 illustrates the case where an individual battery curve 13 corresponding to a second parameter that indicates the usage is for commuting is selected. Also, Figure 19 illustrates the case where an individual battery curve 13 corresponding to a second parameter that indicates the usage is for leisure is selected.

[0031] Next, the degradation prediction unit 11 identifies the battery performance (elapsed battery performance 14) at the end of the target period for each selected individual battery curve 13, and applies the elapsed battery performance 14 for each identified individual battery curve 13 to generate a probability density function 15 of a predetermined probability distribution. Subsequently, the degradation prediction unit 11 calculates the area 16 in the generated probability density function 15 where the battery performance is lower than the desired battery performance obtained in S101 as the degradation occurrence probability. Figures 16 and 18 illustrate the case where an individual battery curve 13 with a fast degradation rate is selected, resulting in a large degradation occurrence probability (area 16). Figures 17 and 19 illustrate the case where an individual battery curve 13 with a slow degradation rate is selected, resulting in a small degradation occurrence probability (area 16). In this way, the degradation occurrence probability can be determined considering the usage pattern of the target vehicle C2 by the insurance policyholder A, and a more appropriate insurance premium can be calculated according to the usage pattern.

[0032] (2) When adopting the configuration shown in the above modified example (1) (see Figure 15), for example, as shown in Figure 20, the configuration may be such that a usage pattern for the target vehicle C2 that can reduce the insurance premium more than the insurance premium calculated based on the acquired usage pattern for the target vehicle C2 is presented to the insurance policyholder A. The insurance premium calculated based on the usage pattern is the insurance premium calculated in S103 of Figure 20. Figure 20 is a flowchart relating to a modified example obtained by adding S401 to S402 between S103 and S104 in Figure 4. In S401, the insurance premium calculation unit 12 analyzes the usage pattern for the target vehicle C2 acquired in S301 of Figure 15 and identifies factors that increase the insurance premium calculated in S103 of Figure 20. As an example, as shown in Figure 21, it determines whether the charging frequency, driving frequency, or number of QCs is greater than a predetermined threshold, and identifies the element that is determined to be greater as a factor that increases the insurance premium. Figure 21 illustrates the case where the charging frequency is determined to be greater than the threshold and the charging frequency is identified as a factor.

[0033] Furthermore, in S402, the insurance premium calculation unit 12 presents a usage pattern that can reduce the insurance premium compared to the insurance premium calculated in S103 in Figure 20. As an example, the elements identified as factors in S401 from the usage pattern of the target vehicle C2 acquired in S301 in Figure 15 are modified to reduce the probability of deterioration occurring. For example, as shown in Figure 21, the charging frequency, driving frequency, and number of QCs are modified to be less than the threshold. Figure 21 illustrates the probability of deterioration occurring (area 16) when the charging frequency in the usage pattern is modified to be less than the threshold. The modified usage pattern is displayed on the display of the dealer terminal 3 in S104 in Figure 20, along with the insurance premium calculated in S103. As a result, if insurance applicant A accepts using the target vehicle C2 according to the displayed usage pattern, they can reduce their insurance premium by inputting that usage pattern into the dealer terminal 3.

[0034] (3) In this embodiment, an example was shown in which a new vehicle usage prediction model and a capacity estimation model are generated each time the probability of degradation occurs is calculated, but other configurations can also be adopted. For example, the generated vehicle usage prediction model and capacity estimation model may be used multiple times to calculate the probability of degradation. In this case, as shown in Figure 22, when the information stored in the vehicle information DB 7 is updated, the vehicle usage prediction model and capacity estimation model may be updated based on the updated information. As an example, as shown in Figure 23, when a new vehicle (hereinafter also referred to as "additional vehicle C3") is added to a group of vehicles C1, the process shown in Figure 23 (hereinafter also referred to as "second probability calculation process") is executed instead of the probability calculation process shown in Figure 5. In the second probability calculation process, the model generation unit 10 acquires usage information and battery status information transmitted from the on-board device 2 of the additional vehicle C3 (S501 in Figure 23). Subsequently, the acquired usage information and battery status information are stored in the vehicle information DB 7 (S502 in Figure 23). Next, the first statistical processing is performed on the usage information stored in the vehicle information DB7 to update the vehicle usage prediction model (S503 in Figure 23). Next, the second statistical processing is performed on the battery status information stored in the vehicle information DB7 to update the capacity estimation model (S504 in Figure 23). Next, the degradation prediction unit 11 estimates the change in battery performance of the battery installed in the target vehicle C2 based on the vehicle usage prediction model and capacity estimation model updated by the model generation unit 10 (S505 in Figure 23). Next, the probability of degradation occurring is calculated based on the estimated change in battery performance, the elapsed target period, and the desired battery performance (S506 in Figure 23). This allows the latest situation to be reflected in the vehicle usage prediction model and capacity estimation model. Therefore, the accuracy of the vehicle usage prediction model, capacity estimation model, and degradation occurrence probability can be improved. Note that in S505 and S506 in Figure 23, the same processing as in S205 and S206 in Figure 5 is performed.

[0035] (4) In this embodiment, an example was shown in which the vehicle usage prediction model and the capacity estimation model are generated using information from multiple vehicles C1, but other configurations can also be adopted. For example, as shown in Figure 24, a configuration may be used in which the models are generated using information from vehicles previously owned by the insurance policy applicant A (hereinafter also referred to as "past vehicles"). Figure 24 is a flowchart of the probability calculation process according to a modified example obtained by replacing S201 to S204 in Figure 5 with S601 to S603. In the probability calculation process according to the modified example, the model generation unit 10 acquires past vehicle usage information and battery information from the vehicle information DB 7 (S601 in Figure 24). Subsequently, a first statistical processing is performed on the acquired usage information to generate a vehicle usage prediction model (S602 in Figure 24). Subsequently, a second statistical processing is performed on the acquired battery status information to generate a capacity estimation model (S603 in Figure 24). Next, as shown in Figure 25, the degradation prediction unit 11 estimates the change in battery performance (individual battery curve 13) of batteries previously installed in vehicles, based on the vehicle usage prediction model and capacity estimation model generated by the model generation unit 10 (S205 in Figure 24). Figure 25 is a diagram showing an example of a method for calculating the probability of degradation occurring. Subsequently, the degradation prediction unit 11 calculates the probability of degradation occurring (area 16) based on the estimated change in battery performance, the elapsed period, and the desired battery performance (S206 in Figure 24). This makes it possible to reflect the actual vehicle usage situation of insurance policyholder A (individual user usage situation) in the vehicle usage prediction model and capacity estimation model. Therefore, the accuracy of the probability of degradation occurring for insurance policyholder A can be improved.

[0036] 1... Insurance premium calculation system, 4... Server device, 7... Vehicle information DB, 8... User information DB, 9... Information processing unit, 9a... Processor, 9b... Storage device, 10... Model generation unit, 11... Deterioration prediction unit, 12... Insurance premium calculation unit

Claims

1. An insurance premium calculation method that obtains the desired coverage period and desired battery performance at the end of the coverage period from the prospective insurer, calculates the probability that the battery performance of the traction battery installed in the vehicle will deteriorate to a level lower than the desired battery performance at the end of the coverage period, and calculates an insurance premium that will cover part or all of the costs required to resolve the performance deterioration if it occurs before the end of the coverage period, based on the calculated probability.

2. The insurance premium calculation method according to claim 1, wherein the desired battery performance includes any of the following: the State of Health (SOH) of the battery, the driving distance of the target vehicle with the battery in a fully charged state, the number of times the battery needs to be charged to drive the target vehicle a predetermined distance, and the amount of electrical energy that can be discharged from the fully charged state of the battery.

3. The insurance premium calculation method according to claim 1, which involves acquiring information on the usage status of each of the multiple vehicles and information on the status of the batteries installed in the vehicles from each of the multiple vehicles and storing it in a storage unit, performing a first statistical processing on the information on the usage status of the vehicles stored in the storage unit to generate a vehicle usage prediction model, performing a second statistical processing on the information on the status of the batteries stored in the storage unit to generate a capacity estimation model, estimating the change in the battery performance of the batteries installed in the target vehicles based on the generated vehicle usage prediction model and the capacity estimation model, and calculating the probability based on the estimated change in battery performance, the target period and the desired battery performance.

4. The insurance premium calculation method according to claim 3, wherein the information on the usage status of the vehicle includes driving information relating to the driving state of the vehicle, environmental information relating to the surrounding environment of the vehicle, and charging information of the vehicle.

5. The insurance premium calculation method according to claim 4, wherein the driving information includes at least the speed, acceleration, and distance traveled of the vehicle.

6. The insurance premium calculation method according to claim 4, wherein the environmental information includes temperature information around the vehicle, location information at the time the temperature information was obtained, and date information at the time the temperature information was obtained.

7. The insurance premium calculation method according to claim 3, wherein the information on the state of the battery includes at least the voltage, current, temperature, resistance, and cumulative time of the battery.

8. The insurance premium calculation method according to claim 3, wherein when the information stored in the storage unit is updated, the capacity estimation model and the vehicle usage prediction model are updated based on the updated information.

9. The method for calculating insurance premiums according to claim 1, comprising: obtaining the usage pattern of the subject vehicle by the insured person; calculating the probability based on the obtained usage pattern of the subject vehicle, the subject period, and the desired battery performance; and further, the usage pattern of the subject vehicle includes at least the charging frequency of the subject vehicle's battery, the charging method of the subject vehicle's battery, the intended use of the subject vehicle, and the driving frequency of the subject vehicle.

10. The method for calculating insurance premiums according to claim 9, which presents the prospective insurer with a usage pattern for the target vehicle that can reduce the insurance premium more than the insurance premium calculated based on the acquired usage pattern for the target vehicle.

11. The method for calculating insurance premiums according to claim 1, wherein the cost required to resolve the performance degradation is the cost required to replace the battery or the cost required to replace the vehicle in question.

12. The method for calculating an insurance premium according to claim 1, comprising: obtaining information on the usage status of a past vehicle that was previously owned by the insurance applicant and information on the battery status; performing a first statistical process on the obtained usage status information to generate a vehicle usage prediction model; performing a second statistical process on the obtained battery status information to generate a capacity estimation model; estimating the change in battery performance of the battery installed in the past vehicle based on the generated vehicle usage prediction model and capacity estimation model; and calculating the probability based on the estimated change in battery performance, the target period, and the desired battery performance.

13. An insurance premium calculation system comprising: an acquisition unit that acquires the desired target period and the desired battery performance at the end of the target period requested by the insurance policyholder; a prediction unit that calculates the probability that performance degradation will occur in which the battery performance of the traction battery installed in the target vehicle will fall below the acquired desired battery performance at the end of the target period; and a calculation unit that calculates an insurance premium that will cover part or all of the costs required to resolve the performance degradation if the performance degradation occurs before the end of the target period, based on the calculated probability.

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