Server and lease fee calculation method

Abstract

To provide a server capable of suppressing an increase in lease charges for secondary batteries caused by an environment in which electric vehicles are used.SOLUTION: A server 100 includes a communication unit 103 (acquisition unit) that acquires information on the capacity maintenance rate (degree of deterioration) of a battery 13 (secondary battery), and a processor 101 (control unit) that calculates a lease charge for the battery 13 using the information on the capacity maintenance rate. The processor 101 performs a suppression process to suppress an increase in the lease charge based on deterioration (decrease) in the capacity maintenance rate caused by an environment in which an electric vehicle 10 is used.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present disclosure relates to a server and a lease fee calculation method.

Background Art

[0002] Japanese Patent Application Laid-Open No. 2018-029430 (Patent Document 1) discloses that a data center receives information on a secondary battery from an electric vehicle and calculates the degree of deterioration of the secondary battery.

Prior Art Document

Patent Document

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Here, when the secondary battery is leased, the lease fee is set based on the degree of deterioration of the secondary battery. In this case, if the electric vehicle is frequently used in an environment where the secondary battery deteriorates easily, the secondary battery deteriorates more than usual. As a result, the lease fee for the secondary battery becomes higher than usual. Therefore, it is desired to suppress the increase in the lease fee for the secondary battery due to the environment in which the electric vehicle is used.

[0005] The present disclosure has been made to solve the above problems, and an object thereof is to provide a server and a lease fee calculation method capable of suppressing an increase in the lease fee for a secondary battery due to the environment in which an electric vehicle is used.

Means for Solving the Problems

[0006] The server relating to the first aspect of this disclosure is a server for calculating lease fees for secondary batteries leased to electric vehicles, and comprises an acquisition unit for acquiring information on the degree of degradation of the secondary batteries, and a control unit for calculating lease fees using the information on the degree of degradation. The control unit performs suppression processing to suppress an increase in lease fees due to deterioration of the degree of degradation caused by the environment in which the electric vehicle is used.

[0007] In the server relating to the first aspect of this disclosure, as described above, suppression processing is performed to suppress increases in lease fees due to deterioration caused by the environment in which the electric vehicle is used. As a result, even if the electric vehicle is used in an environment where the degree of deterioration of the secondary battery is likely to increase, the increase in the lease fee for the secondary battery is suppressed. Consequently, it is possible to suppress increases in the lease fee for the secondary battery.

[0008] In the server relating to the first phase described above, preferably, the control unit performs the suppression process by adjusting the correction coefficient for correcting the lease fee according to the environment. With this configuration, the lease fee can be appropriately adjusted according to differences in the environment by adjusting the correction coefficient.

[0009] In the server relating to the first phase described above, preferably, the control unit performs the suppression process by calculating the lease fee while ignoring the deterioration of the degree of degradation caused by the environment. With this configuration, it is possible to prevent the lease fee from increasing due to the deterioration of the degree of degradation caused by the environment.

[0010] In the server relating to the first phase described above, preferably, the control unit performs the suppression process when it determines that the electric vehicle is regularly used in cold regions. With this configuration, even if the battery deteriorates due to the temperature in cold regions, it is possible to suppress an increase in lease fees.

[0011] The lease fee calculation method relating to the second aspect of this disclosure is a lease fee calculation method for calculating the lease fee for a secondary battery leased to an electric vehicle, comprising: an acquisition step of acquiring information on the degree of degradation of the secondary battery; and a calculation step of calculating the lease fee using the information on the degree of degradation. The calculation step includes a step of suppressing an increase in lease fees due to deterioration of the environment in which the electric vehicle is used.

[0012] In the lease fee calculation method relating to the second aspect of this disclosure, as described above, a suppression process is performed to suppress the increase in lease fees due to deterioration caused by the environment in which the electric vehicle is used. This makes it possible to provide a lease fee calculation method that can suppress the increase in lease fees for secondary batteries caused by the environment in which the electric vehicle is used. [Effects of the Invention]

[0013] According to this disclosure, it is possible to suppress the increase in lease costs for secondary batteries caused by the environment in which electric vehicles are used. [Brief explanation of the drawing]

[0014] [Figure 1] This is a diagram showing the system configuration according to the first embodiment. [Figure 2] This figure shows the relationship between the battery capacity retention rate and the lease fee according to the first embodiment. [Figure 3] This diagram shows whether each region is a cold region, a temperate region, or a neutral region. [Figure 4] This figure shows the sequence control of the system according to the first embodiment. [Figure 5] This is a diagram showing the system configuration according to the second embodiment. [Figure 6] This figure shows the portion of the battery's capacity retention rate that is reduced due to environmental factors. [Figure 7] This figure shows the sequence control of the system according to the second embodiment. [Modes for carrying out the invention]

[0015] [First Embodiment] Hereinafter, the first embodiment of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are denoted by the same reference numerals and their description will not be repeated.

[0016] FIG. 1 is a diagram showing the configuration of a system 1 according to the first embodiment. The system 1 includes a server 100 and an electric vehicle 10.

[0017] The electric vehicle 10 includes, for example, a PHEV (Plug-in Hybrid Electric Vehicle), a BEV (Battery Electric Vehicle), and an FCEV (Fuel Cell Electric Vehicle).

[0018] The electric vehicle 10 includes a navigation system 11, a communication device 12, and a battery 13. The battery 13 supplies power to electrical devices such as the navigation system 11 and the communication device 12. Note that the battery 13 is leased from a battery management company to the user of the electric vehicle 10. Also, the battery 13 is an example of the "secondary battery" of the present disclosure.

[0019] In addition, the user of the electric vehicle 10 owns a mobile terminal 14. The mobile terminal 14 is configured to be able to connect to the communication device 12 via short-range wireless communication.

[0020] The communication device 12 may include a DCM (Data Communication Module), or may include a communication I / F compatible with 5G (5th Generation Mobile Communication System).

[0021] Also, the server 100 is configured to manage information on registered electric vehicles 10 (hereinafter also referred to as "vehicle information") and information on registered users (hereinafter also referred to as "user information"). The user information and the vehicle information are distinguished by identification information (ID) and stored in a memory 102 described later.

[0022] The user ID is identification information used to identify a user, and also functions as information that identifies the mobile terminal 14 carried by the user (terminal ID). The server 100 is configured to store information received from the mobile terminal 14 separately for each user ID. The user information includes the communication address of the mobile terminal 14 and the vehicle ID of the electric vehicle 10.

[0023] The vehicle ID is identification information used to identify the electric vehicle 10. The vehicle ID may be a license plate number or a VIN (Vehicle Identification Number). The vehicle information includes the planned movements of the electric vehicle 10.

[0024] The server 100 includes a processor 101, a memory 102, and a communication unit 103. The processor 101 controls the communication unit 103. The processor 101 and the communication unit 103 are examples of the "control unit" and "acquisition unit" as defined in this disclosure, respectively.

[0025] Memory 102 stores not only the program executed by the processor 101, but also information used by the program (for example, maps, mathematical formulas, and various parameters).

[0026] Furthermore, memory 102 stores information regarding the degree of degradation of the battery 13 of the electric vehicle 10 (hereinafter referred to as degradation information). The degradation information includes information on the capacity retention rate (unit: %) of the battery 13. The degradation information stored in memory 102 is updated each time new degradation information is received by the communication unit 103. Alternatively, the degree of degradation of the battery 13 (100 - capacity retention rate [%]) may be used as the degradation information for the battery 13. Note that the capacity retention rate of the battery 13 is an example of the "degree of degradation" in this disclosure.

[0027] The capacity retention rate of battery 13 refers to the ratio (percentage) of the current full charge capacity C of battery 13 to the full charge capacity C0 of battery 13 in its initial state (for example, the state of battery 13 at the time of manufacture) (Q = C / C0). However, as degradation information for battery 13, the EV driving range (unit: km) of the electric vehicle 10 may be used instead of or in addition to the capacity retention rate of battery 13. Alternatively, the full charge capacity (unit: Ah or Wh) of battery 13 itself may be used as degradation information for battery 13. Furthermore, any two or all three of the capacity retention rate, EV driving range, and full charge capacity may be used.

[0028] The communication unit 103 of the server 100 communicates with the communication device 12 and the mobile terminal 14 of the electric vehicle 10, respectively. The communication unit 103 receives battery degradation information from the electric vehicle 10 (communication device 12). The communication unit 103 may also receive the above degradation information from the mobile terminal 14.

[0029] Alternatively, information for calculating the capacity retention rate of the battery 13 may be transmitted from the electric vehicle 10 to the communication unit 103 of the server 100, without transmitting information about the capacity retention rate of the battery 13.

[0030] The processor 101 calculates the lease fee for the battery 13 based on the battery 13's capacity retention rate.

[0031] If the electric vehicle 10 is used regularly in an environment where battery 13 degradation is likely to progress, the battery 13 will degrade more than usual. As a result, the lease fee for battery 13 will be higher than usual. Therefore, it is desirable to suppress the increase in the lease fee for battery 13 caused by the environment in which the electric vehicle 10 is used.

[0032] In the first embodiment, the processor 101 performs suppression processing to suppress an increase in the lease fee for the battery 13 due to the decrease in the capacity retention rate caused by the environment in which the electric vehicle 10 is used. The processor 101 suppresses the lease fee for the battery 13 used in cold or warm regions from becoming higher than the lease fee for the battery 13 used in normal temperature regions (room temperature regions).

[0033] Specifically, the processor 101 adjusts a correction coefficient for correcting the lease fee of the battery 13 according to the environment in which the electric vehicle 10 is used. This enables the suppression process described above.

[0034] As shown in Figure 2, when the electric vehicle 10 is regularly used in cold regions, a correction factor A is used to calculate the lease fee. The correction factor A is a value less than 1. Specifically, the lease fee for the battery 13 of the electric vehicle 10 regularly used in cold regions is calculated by multiplying the lease fee corresponding to the capacity retention rate of the battery 13 of the electric vehicle 10 regularly used in normal temperature regions by the above correction factor A. The correction factor A is set to increase as the capacity retention rate of the battery 13 decreases. Memory 102 stores data showing the relationship between the correction factor A and the capacity retention rate of the battery 13. Note that the correction factor A may be a constant value regardless of the magnitude of the capacity retention rate.

[0035] Furthermore, if the electric vehicle 10 is regularly used in a warm climate, a correction factor B is used to calculate the lease fee. The correction factor B is smaller than the correction factor A and less than 1. Specifically, the lease fee for the battery 13 of the electric vehicle 10 regularly used in a warm climate is calculated by multiplying the lease fee corresponding to the capacity retention rate of the electric vehicle 10 regularly used in a normal climate by the above correction factor B. The correction factor B is set to increase as the capacity retention rate of the battery 13 decreases. The memory 102 stores data showing the relationship between the correction factor B and the capacity retention rate of the battery 13. The correction factor B may be a constant value regardless of the magnitude of the capacity retention rate.

[0036] The correction coefficient A(B) may be set, for example, by the ratio of the degradation rate of the battery 13 in a normal temperature region to the degradation rate of the battery 13 in a cold (warm) region. Furthermore, the relative magnitudes of correction coefficient A and correction coefficient B are not limited to the above example. For example, correction coefficient A may be greater than or equal to correction coefficient B.

[0037] Furthermore, as shown in Figure 3, memory 102 stores data indicating whether each region of Japan falls into the category of a temperate, cold, or mild climate. Specifically, memory 102 stores data indicating that Hokkaido, Aomori Prefecture, Iwate Prefecture, and Akita Prefecture are cold climates. Memory 102 also stores data indicating that Okinawa Prefecture and Kagoshima Prefecture are mild climates. In addition, memory 102 stores data indicating that all other regions are temperate. Note that the examples of temperate, cold, and mild climates are not limited to those listed above.

[0038] Furthermore, the processor 101 identifies the area where the electric vehicle 10 is regularly used, based on the electric vehicle 10's location information and activity history. For example, the processor 101 extracts the area where the electric vehicle 10 has been located (or driven) for the longest period of time, based on the electric vehicle 10's location information and activity history. The processor 101 then identifies the extracted area as the area where the electric vehicle 10 is regularly used. Note that the method for identifying the electric vehicle 10's regularly used area is not limited to the above example. For example, the processor 101 may identify the electric vehicle 10's registered location (location of primary use) or the user's address as the area where the electric vehicle 10 is regularly used.

[0039] (Lease fee calculation method) Referring to the sequence diagram in Figure 4, the method for calculating the lease fee for the battery 13 by the server 100 will be explained.

[0040] In step S1, the server 100 (processor 101) identifies the area in which the electric vehicle 10 is regularly used, based on the location information and activity history of the electric vehicle 10.

[0041] In step S2, the electric vehicle 10 transmits information on the capacity retention rate of the battery 13 to the server 100. This process is performed at predetermined intervals. The server 100 may also obtain the information on the capacity retention rate of the battery 13 from the mobile terminal 14 or from a server that manages the capacity retention rate.

[0042] In step S3, the processor 101 calculates a baseline lease fee for the battery 13 based on the capacity retention rate of the battery 13 transmitted from the electric vehicle 10 in step S2. The baseline lease fee refers to the lease fee corresponding to the above capacity retention rate, assuming that the electric vehicle 10 is normally used in a place with a normal temperature.

[0043] In step S4, the processor 101 determines whether the usual operating location of the electric vehicle 10 identified in step S1 is a normal temperature area. If the usual operating location is a normal temperature area (Yes in S4), the process proceeds to step S8. If the usual operating location is not a normal temperature area (No in S4), the process proceeds to step S5.

[0044] In step S5, the processor 101 determines whether the above-mentioned site is a cold region or not. If the above-mentioned site is a cold region (Yes in S5), the process proceeds to step S6. If the above-mentioned site is not a cold region but a warm region (No in S5), the process proceeds to step S7.

[0045] In step S6, the processor 101 corrects the reference value of the lease fee calculated in step S3 using the correction coefficient A. Specifically, the processor 101 corrects the reference value using the correction coefficient A corresponding to the capacity retention rate of the battery 13 obtained by the processing in step S2.

[0046] In step S7, the processor 101 corrects the reference value of the lease fee calculated in step S3 using the correction coefficient B. Specifically, the processor 101 corrects the reference value using the correction coefficient B corresponding to the capacity retention rate of the battery 13 obtained by the processing in step S2.

[0047] In step S8, the processor 101 notifies the user of the electric vehicle 10 of the lease fee for the battery 13 via the communication unit 103. Specifically, if the electric vehicle 10 is normally used in a region with a normal temperature, the base value of the lease fee calculated in step S3 is notified. If the electric vehicle 10 is normally used in a region with a cold temperature, the lease fee calculated (adjusted) in step S6 is notified. If the electric vehicle 10 is normally used in a region with a warm temperature, the lease fee calculated (adjusted) in step S7 is notified.

[0048] As described above, in the first embodiment, the processor 101 performs suppression processing to suppress an increase in lease fees based on the deterioration of the battery 13 (capacity retention rate) due to the environment in which the electric vehicle 10 is used. This makes it possible to suppress a surge in lease fees due to the deterioration of the battery 13 when the electric vehicle 10 is used in an environment that is harsh on the battery 13. As a result, it is possible to suppress differences in satisfaction with lease fees among users in different regions.

[0049] [Second Embodiment] Next, the server 200 and system 2 in the second embodiment will be described with reference to Figures 5 to 7. In the second embodiment, unlike the first embodiment which adjusts a correction coefficient to adjust the lease fee according to the environment in which the electric vehicle 10 is used, variations in the degree of degradation of the battery 13 due to environmental differences are ignored. Components that are the same as in the first embodiment are denoted by the same reference numerals as in the first embodiment and will not be described repeatedly.

[0050] Figure 5 shows the configuration of System 2 according to the second embodiment. System 2 comprises a server 200 and an electric vehicle 10.

[0051] The server 200 includes a processor 201, a memory 202, and a communication unit 203. The processor 201 controls the communication unit 203. The processor 201 and the communication unit 203 are examples of the "control unit" and "acquisition unit" as defined in this disclosure.

[0052] In the second embodiment, the processor 201 calculates the lease fee for the battery 13 while ignoring the deterioration of the battery 13 (capacity retention rate) due to the environment in which the electric vehicle 10 is used.

[0053] As shown in Figure 6, the degree of degradation of the battery 13 includes factors based on the environment (temperature) in which the electric vehicle 10 is used. As a result, the capacity retention rate of the battery 13 decreases when the electric vehicle 10 is used regularly in cold or warm climates compared to when it is used regularly in a normal temperature climate.

[0054] The processor 101 then estimates the capacity retention rate of the current electric vehicle 10 if it were to travel its current distance at a normal temperature. Based on the estimated capacity retention rate, the processor 101 calculates the lease fee for the battery 13.

[0055] (Lease fee calculation method) Referring to the sequence diagram in Figure 7, the method for calculating the lease fee for the battery 13 by the server 200 will be explained.

[0056] In step S11, the server 200 (processor 201) identifies the area in which the electric vehicle 10 is regularly used, based on the location information and activity history of the electric vehicle 10.

[0057] In step S12, the electric vehicle 10 transmits information about the driving distance of the battery 13 to the server 200. This process is performed at predetermined intervals. The server 200 may also obtain the driving distance information of the electric vehicle 10 from a mobile terminal 14 or a server that manages the driving distance.

[0058] In step S13, the processor 201 determines whether the usual operating location of the electric vehicle 10, which was identified in step S11, is a normal temperature area. If the usual operating location is a normal temperature area (Yes in S13), the process proceeds to step S16. If the usual operating location is determined to be a cold or warm region rather than a normal temperature area (No in S13), the process proceeds to step S14.

[0059] In step S14, the processor 201 estimates the capacity retention rate of the battery 13 based on the mileage of the electric vehicle 10 obtained in step S12. Specifically, the processor 201 estimates the capacity retention rate of the battery 13 by referring to the relationship between the mileage and battery capacity retention rate of several other electric vehicles that are regularly used in a normal temperature environment. A table showing the relationship between the mileage of electric vehicles and the battery capacity retention rate may be stored in memory 202.

[0060] In step S15, the processor 201 calculates the lease fee for battery 13 corresponding to the capacity retention rate of battery 13 estimated in step S14.

[0061] In other words, the processor 201 calculates the lease fee for the battery 13 based on the mileage of the electric vehicle 10, regardless of the degree of degradation of the battery 13 due to temperature in cold or warm regions.

[0062] In step S16, the processor 201 notifies the user of the electric vehicle 10 of the lease fee for the battery 13 via the communication unit 203.

[0063] The other configurations in the second embodiment are the same as those in the first embodiment described above, so no further explanation will be given.

[0064] In the first embodiment described above, an example was shown in which the lease fee for the battery 13 is multiplied by a correction factor, but the disclosure is not limited thereto. The capacity retention rate of the battery 13 may be corrected based on a predetermined correction factor. In this case, the lease fee for the battery 13 is calculated based on the corrected capacity retention rate of the battery 13. As a result, the lease fee for the battery 13 is corrected based on the predetermined correction factor.

[0065] In the second embodiment described above, an example was shown in which the lease fee for the battery 13 is calculated based on the mileage of the electric vehicle 10, but the disclosure is not limited thereto. In addition to (or instead of) the mileage of the electric vehicle 10, the lease fee for the battery 13 may be calculated based on the usage period of the battery 13 (for example, the time elapsed since the battery 13 was manufactured).

[0066] In the first embodiment described above, an example was shown in which either correction factor A or B is used based on the usual location of the electric vehicle 10, but the disclosure is not limited thereto. If the electric vehicle 10 is used in multiple locations, the correction factor may be adjusted based on the ratio of time spent in each location.

[0067] In the first and second embodiments described above, examples were shown in which cold regions, warm regions, and normal temperature regions were defined for each prefecture, but this disclosure is not limited thereto. For example, the above settings may be made based on the average temperature over a predetermined period in the past for each region. Furthermore, the above settings may also be applied to regions other than Japan.

[0068] In the first embodiment described above, an example was shown in which the lease fee is adjusted when the electric vehicle 10 is normally used in a cold region or a warm region, but the disclosure is not limited thereto. The lease fee may be adjusted when the electric vehicle 10 is normally used in either a cold region or a warm region.

[0069] The first and second embodiments described above illustrate an example in which a process is performed to suppress the increase in lease fees based on temperature differences between regions, but this disclosure is not limited thereto. For example, the above process may be performed based on differences in precipitation, snowfall, and humidity between regions.

[0070] Furthermore, the configurations (processes) of the above embodiments and each of the above modified examples may be combined with each other.

[0071] The embodiments disclosed herein should be considered in all respects to be illustrative and not restrictive. The scope of this disclosure is indicated by the claims rather than by the description of the embodiments above, and all modifications within the meaning and scope equivalent to the claims are intended to be included. [Explanation of Symbols]

[0072] 1, 2 Systems, 10 Electric Vehicles, 13 Batteries (Rechargeable Batteries), 100, 200 Servers, 101, 201 Processors (Control Units), 103, 203 Communication Units (Acquisition Units).

Claims

1. A server that calculates the lease fee for secondary batteries leased to electric vehicles, An acquisition unit that acquires information on the degree of degradation of the secondary battery, The system includes a control unit that calculates the lease fee using the information on the degree of deterioration, The control unit performs a lease fee correction when the electric vehicle is used in the first environment, compared to when the electric vehicle is used in the second environment, to suppress the increase in the lease fee due to the worsening of the degree of deterioration. The first environment is an environment in which the degree of degradation is likely to increase. The second environment is a server in which the degree of degradation is less likely to increase.

2. The server according to claim 1, wherein the control unit adjusts the correction coefficient for correcting the lease fee according to the environment when performing the correction of the lease fee.

3. The server according to claim 1 or 2, wherein the first environment is a cold region.

4. A method for calculating lease fees for secondary batteries leased to electric vehicles, The acquisition process involves a server acquiring information on the degree of degradation of the aforementioned secondary battery, The system includes a calculation step in which the server calculates the lease fee using the information on the degree of deterioration, The calculation process is a process of performing a lease fee adjustment to suppress the increase in lease fees due to the deterioration of the degree of deterioration when the electric vehicle is used in the first environment compared to when the electric vehicle is used in the second environment. The first environment is an environment in which the degree of degradation is likely to increase. The second environment is an environment in which the degree of deterioration is less likely to increase, and this is a lease fee calculation method.

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