Recommendation information determination method and recommendation information determination device

Abstract

Provided is a recommendation information determination method capable of accurately determining a battery capacity suitable for a user. A recommendation information determination method disclosed herein comprises: a step (S2) of calculating the power consumption amount of a host vehicle for a predetermined period on the basis of at least one of usage status data of the host vehicle and usage status data of another vehicle on the assumption that the host vehicle is an electric vehicle; a step (S3) of calculating a battery deterioration prediction value for the predetermined period on the basis of at least one of the usage status data of the host vehicle and the usage status data of the other vehicle; a step (S4) of calculating a recommended battery capacity on the basis of the power consumption amount and the battery deterioration prediction value; and a step (S5) of determining recommendation information including at least one of recommended vehicle information and recommended battery information on the basis of the recommended battery capacity.

Description

Recommended Information Determination Method and Recommended Information Determination Device

[0008] ,

[0001] The present disclosure relates to a recommended information determination method and a recommended information determination device.

[0002] Patent Document 1 proposes an electric vehicle and charging facilities suitable for the usage status of a vehicle used by a user to a user who switches to an electric vehicle. Based on the usage status data of the vehicle, when it is assumed that the vehicle is an electric vehicle, the power consumption for a predetermined time is calculated, and based on the calculated power consumption, a proposed system for determining the battery capacity and charging power is disclosed.

[0003] Japanese Patent Application Laid-Open No. 2023-64429

[0004] However, in Patent Document 1, it determines the battery capacity suitable for the current usage status of the vehicle, and does not consider the influence on the user such as the change in the charging frequency and the maximum possible driving distance due to future battery deterioration. Therefore, it is difficult to accurately determine the battery capacity suitable for the user.

[0005] In view of the above problems, an object of the present disclosure is to provide a recommended information determination method and a recommended information determination device that can accurately determine a battery capacity suitable for a user.

[0006] The recommended information determination method and the recommended information determination device according to one aspect of the present disclosure calculate the power consumption for a predetermined period when it is assumed that the own vehicle is an electric vehicle based on at least one of the usage status data of the own vehicle and the usage status data of other vehicles, calculate a predicted value of battery deterioration for a predetermined period based on at least one of the usage status data of the own vehicle and the usage status data of other vehicles, calculate a recommended battery capacity based on the power consumption and the predicted value of battery deterioration, and determine recommended information including at least one of recommended vehicle information and recommended battery information based on the recommended battery capacity.

[0007] According to the present disclosure, it is possible to provide a recommended information determination method and a recommended information determination device that can accurately determine a battery capacity suitable for a user.

[0008] This is a block diagram showing a recommendation information determination system according to the first embodiment. This is a schematic diagram showing an example of vehicle usage data according to the first embodiment. This is a schematic diagram showing an example of a map according to the first embodiment. This is a schematic diagram showing an example of battery degradation prediction values ​​according to the first embodiment. This is a schematic diagram showing an example of a recommended battery capacity according to the first embodiment. This is a flowchart showing an example of a recommendation information determination method according to the first embodiment. This is a flowchart showing an example of a power consumption calculation process according to the first embodiment. This is a schematic diagram showing an example of vehicle usage data according to the second embodiment. This is a flowchart showing an example of a power consumption calculation process according to the second embodiment. This is a schematic diagram showing an example of power consumption according to the second embodiment. This is a flowchart showing an example of a battery degradation prediction value calculation process according to the second embodiment. This is a schematic diagram showing an example of a battery degradation prediction value according to the second embodiment. This is a schematic diagram showing an example of a battery degradation prediction value according to the second embodiment. This is a schematic diagram showing an example of a questionnaire data according to the third embodiment. This is a schematic diagram showing an example of a recommended battery capacity according to the third embodiment. This is a schematic diagram showing an example of a battery degradation prediction value according to the fourth embodiment. This is a schematic diagram showing an example of a recommended battery capacity calculation process according to the fifth embodiment. This is a schematic diagram showing an example of a recommended battery capacity according to the sixth embodiment. This is a schematic diagram showing an example of a battery degradation prediction value according to the sixth embodiment. This is a schematic diagram showing an example of a vehicle price according to the sixth embodiment. This is a schematic diagram showing an example of a TCO according to the sixth embodiment. This is a flowchart showing an example of a recommendation information determination method according to the seventh embodiment.

[0009] The first to seventh embodiments of this disclosure will be described below with reference to the drawings. In the drawings, identical or similar parts are denoted by the same or similar reference numerals, and redundant descriptions are omitted. The first to seventh embodiments shown below are illustrative examples of apparatus and methods for realizing the technical idea of ​​this disclosure, and the technical idea of ​​this disclosure is not limited to the structure, arrangement, etc. of the components described below.

[0010] (First Embodiment) <Recommended Information Determination System> The recommended information determination system (battery capacity recommendation system) according to the first embodiment includes a recommended information determination device (battery capacity recommendation device) 1, a vehicle owned by user 3 (hereinafter referred to as "user's vehicle") 2, vehicles other than user's vehicle 2 (hereinafter referred to as "other vehicles") 2a to 2c, and an information terminal 4 that user 3 can use. The recommended information determination device 1, user's vehicle 2, other vehicles 2a to 2c, and information terminal 4 can send and receive data or information from each other via a communication network such as the Internet.

[0011] Each of the vehicles 2 (own vehicle) and 2a-2c (other vehicles) may be an electric vehicle (EV) or a plug-in hybrid vehicle, or other electric vehicle equipped with a battery (secondary battery), or it may be a gasoline vehicle or a hybrid vehicle, or other vehicle other than an electric vehicle. Figure 1 shows three other vehicles 2a-2c as examples, but the number of other vehicles is not limited.

[0012] The vehicle 2 detects usage data, which is data related to the usage status of the vehicle 2, using various sensors on the vehicle 2, and transmits the detected usage data of the vehicle 2 to the recommendation information determination device 1. The usage data of the vehicle 2 includes at least one of the following, as shown in Figure 2: vehicle position, frequency of sudden acceleration, and distance traveled. The vehicle position may include latitude, longitude, and altitude.

[0013] Other vehicles 2a to 2c detect usage data, which is data regarding the usage status of other vehicles 2a to 2c, using various sensors on the other vehicles 2a to 2c, and transmit the detected usage data of other vehicles 2a to 2c to the recommendation information determination device 1. The usage data of other vehicles 2a to 2c is the same as the usage data of the own vehicle 2, and as shown in Figure 2, includes at least one of vehicle position, frequency of sudden acceleration, and distance traveled.

[0014] The information terminal 4 may be, for example, a mobile device such as a smartphone or tablet, or a notebook or desktop personal computer (PC). The information terminal 4 may also be a navigation device installed in the vehicle 2. The information terminal 4 is equipped with a display unit 5 that is visible to the user 3. The display unit 5 may be located separately from the information terminal 4.

[0015] The recommendation information determination device 1 may include a processor such as a central processing unit (CPU), a memory device, and an input / output device. The memory device may be a semiconductor memory device, a magnetic memory device, or an optical memory device, and may include storage media such as registers, cache memory, ROM (Read Only Memory) and RAM (Random Access Memory) used as main memory. The functions of the recommendation information determination device 1 may be configured with a single piece of hardware, or they may be configured individually with multiple pieces of hardware. The functions of the recommendation information determination device 1 are realized, for example, by the processor of the recommendation information determination device 1 executing a computer program stored in the memory device of the recommendation information determination device 1. The recommendation information determination device 1 may also be configured with dedicated hardware for performing each of the processes described below.

[0016] The recommended information determination device 1 comprises a processing unit 100 and a storage unit 102. The processing unit 100 is composed of a processor (controller). The processing unit 100 functionally includes an acquisition unit 101, a power consumption calculation unit 103, a degradation prediction unit 104, and a determination unit 105. The storage unit 102 may include any of semiconductor memory devices, magnetic memory devices, and optical memory devices, and may include storage media such as registers, cache memory, ROM (Read Only Memory) and RAM (Random Access Memory) used as main memory.

[0017] The acquisition unit 101 acquires usage data of the vehicle 2 transmitted from the vehicle 2. The usage data of the vehicle 2 acquired by the acquisition unit 101 may be stored in the storage unit 102. The acquisition unit 101 may also acquire usage data of other vehicles 2a to 2c transmitted from other vehicles 2a to 2c. The usage data of other vehicles 2a to 2c acquired by the acquisition unit 101 may be stored in the storage unit 102.

[0018] The memory unit 102 stores usage data for its own vehicle 2 and usage data for other vehicles 2a to 2c. The memory unit 102 also stores information for multiple types of batteries and vehicle information for multiple types of electric vehicles. The battery information includes battery performance information and battery cost information. The battery performance information includes battery capacity and battery resistance. The battery cost information includes battery price. The vehicle information includes vehicle performance information and vehicle cost information. The vehicle performance information includes electricity consumption, battery capacity, and battery resistance. The vehicle cost information includes vehicle price and lease price.

[0019] The power consumption calculation unit 103 calculates the power consumption for a predetermined period assuming that the vehicle 2 is an electric vehicle, based on at least one of the usage data of the vehicle 2 acquired by the acquisition unit 101, the usage data of the vehicle 2 stored in the storage unit 102, and the usage data of other vehicles 2a to 2c stored in the storage unit 102. The "predetermined period" for power consumption may be, for example, the number of days, the period until the next charge, or the duration of one operation, and can be set as appropriate.

[0020] For example, the power consumption calculation unit 103 may calculate the power consumption based on the usage data of the vehicle 2 stored in the storage unit 102 if the usage data of the vehicle 2 stored in the storage unit 102 is sufficient to calculate the power consumption. Alternatively, if the usage data of the vehicle 2 stored in the storage unit 102 is not sufficient to calculate the power consumption, the power consumption calculation unit 103 may calculate the power consumption based on the usage data of the vehicle 2 acquired by the acquisition unit 101. Furthermore, the power consumption calculation unit 103 may calculate the power consumption based on a combination of the usage data of the vehicle 2 stored in the storage unit 102 and the usage data of the vehicle 2 acquired by the acquisition unit 101.

[0021] Furthermore, the power consumption calculation unit 103 may, based on the usage data of its own vehicle 2 acquired by the acquisition unit 101, or the usage data of its own vehicle 2 stored in the storage unit 102, extract usage data of other vehicles 2a to 2c stored in the storage unit 102 that are similar to the usage data of its own vehicle 2, and use the extracted usage data of other vehicles in place of the usage data of its own vehicle 2 to calculate the power consumption. For example, the usage data of other vehicles that includes the mileage closest to the mileage of the usage data of its own vehicle 2 may be extracted as usage data of other vehicles similar to the usage data of its own vehicle 2.

[0022] For example, the power consumption calculation unit 103 may calculate the amount of power consumption required for the electric vehicle to travel a predetermined distance over a predetermined period based on the mileage traveled over a predetermined period from the usage data of the vehicle 2. Alternatively, the power consumption calculation unit 103 may generate a user 3 behavior profile based on the usage data of the vehicle 2 and calculate the amount of power consumption corresponding to the behavior profile. Furthermore, the power consumption calculation unit 103 may classify the vehicle 2 based on the mileage or usage area calculated from the vehicle location information and calculate the amount of power consumption according to the classification.

[0023] The power consumption calculation unit 103 may calculate power consumption using, for example, maps M1 to M3 as shown in Figure 3. Maps M1 to M3 have values ​​corresponding to rapid acceleration and distance traveled, values ​​corresponding to elevation difference and distance traveled calculated from vehicle position information, and values ​​corresponding to the frequency of rapid acceleration and distance traveled. Maps M1 to M3 may be created in advance based on experimental data or past driving data, etc., and stored in the storage unit 102. The power consumption calculation unit 103 may calculate power consumption by extracting values ​​corresponding to the usage data of the vehicle 2 from maps M1 to M3 and substituting the extracted values ​​into a predetermined function, etc. The power consumption calculation unit 103 may calculate higher power consumption if the distance traveled in the referenced usage data is large, the frequency of rapid acceleration is high, or the elevation difference calculated from the position information is large.

[0024] The degradation prediction unit 104 predicts the future degree of battery degradation by calculating a battery degradation prediction value for a predetermined period from the initial state of the battery to a future point in time, based on at least one of the usage data of the own vehicle 2 acquired by the acquisition unit 101, the usage data of the own vehicle 2 stored in the storage unit 102, and the usage data of other vehicles 2a to 2c stored in the storage unit 102. The "predetermined period" for the battery degradation prediction value is, for example, the average lifespan of the battery (e.g., 5 to 10 years), and can be set as appropriate. The degradation prediction unit 104 calculates the change in battery remaining capacity (full charge capacity) considering battery degradation during the predetermined period as the battery degradation prediction value for the predetermined period. The degradation prediction unit 104 may also calculate the battery degradation prediction value by, for example, selecting (generating) a map or behavioral profile based on the usage data of the own vehicle 2. The degradation prediction unit 104 may calculate a lower battery degradation prediction value if the mileage in the referenced usage data is long, the frequency of sudden acceleration is high, or the elevation difference calculated from the location information is large, as this makes battery degradation more likely to progress. The degradation prediction unit 104 may calculate the battery degradation prediction value by assuming that the usage conditions in the referenced usage data will continue for a predetermined period of time. The degradation prediction unit 104 may calculate the battery degradation prediction value based on experimental data stored in the storage unit 102.

[0025] The degradation prediction unit 104 calculates the battery remaining capacity (fully charged capacity) considering battery degradation for a predetermined period T, from time t0 in the initial state of the battery to a future time t1, as a battery degradation prediction value for the predetermined period T, as shown in Figure 4. In Figure 4, three types of battery remaining capacities are shown as examples: a high-capacity battery A, a medium-capacity battery B, and a low-capacity battery C, but the number of battery remaining capacities to be calculated is not particularly limited. Battery A has a battery remaining capacity P1 at time t1 in its initial state, but as battery degradation progresses over time, at a future time t1 after the predetermined period T has elapsed, it has a battery remaining capacity P2 that is lower than the battery remaining capacity P1. The changes in the battery remaining capacities of batteries B and C also show a similar trend to battery A. The degradation prediction unit 104 calculates the battery remaining capacities of batteries A to C to be gradually reduced over the predetermined period T.

[0026] The determination unit 105 calculates the battery capacity recommended to user 3 (hereinafter referred to as "recommended battery capacity") based on the power consumption amount calculated by the power consumption calculation unit 103 and the battery degradation prediction value calculated by the degradation prediction unit 104. Here, even if the battery's initial remaining capacity is greater than or equal to the power consumption amount, the battery's remaining capacity in a future degraded state may fall below the power consumption amount, potentially having an adverse effect on user 3 in the future. Therefore, the determination unit 105 calculates the recommended battery capacity such that the battery's remaining capacity in a degraded state after a predetermined period has elapsed since the battery degradation prediction value calculated by the degradation prediction unit 104 is greater than or equal to the power consumption amount calculated by the power consumption calculation unit 103.

[0027] For example, as shown in Figure 5, the power consumption calculation unit 103 calculates the power consumption P3 for a predetermined period based on the usage data of the vehicle 2. The decision unit 105 selects battery A from among batteries A to C shown in Figure 4, where the remaining battery capacity P2 at time t1 after a predetermined period T has elapsed is equal to or greater than the power consumption P3 calculated by the power consumption calculation unit 103. The decision unit 105 calculates the difference (P2 - P1) between the remaining battery capacity P1 at time t0 in the initial state of the selected battery A and the remaining battery capacity P2 at time t1 after a predetermined period T has elapsed as the amount of energy ΔP1 to account for degradation. As shown in Figure 5, the decision unit 105 calculates the recommended battery capacity P4 (= P3 + ΔP1) by adding the amount of energy ΔP1 to account for degradation to the power consumption P3 calculated by the power consumption calculation unit 103.

[0028] The determination unit 105 determines recommended information that includes at least one of the battery information recommended to user 3 (hereinafter referred to as "recommended battery information") and vehicle information recommended to user 3 (hereinafter referred to as "recommended vehicle information") based on the calculated recommended battery capacity. For example, the determination unit 105 extracts battery information and vehicle information corresponding to the calculated recommended battery capacity from multiple types of battery information and multiple types of vehicle information stored in the storage unit 102, and determines them as recommended battery information and recommended vehicle information. The recommended battery information includes at least one of battery performance information and battery cost information. The recommended vehicle information includes at least one of vehicle performance information and vehicle cost information.

[0029] The determination unit 105, for example, extracts battery information relating to batteries with a battery capacity equal to or greater than the calculated recommended battery capacity from multiple types of battery information stored in the storage unit 102, and determines it as recommended battery information. It also extracts vehicle information relating to vehicles equipped with batteries with a battery capacity equal to or greater than the calculated recommended battery capacity from multiple types of vehicle information stored in the storage unit 102, and determines it as recommended vehicle information. If there are multiple battery information items corresponding to the calculated recommended battery capacity, the determination unit 105 may determine multiple recommended battery capacities. Furthermore, if there are multiple vehicle information items corresponding to the calculated recommended battery capacity, the determination unit 105 may determine multiple recommended vehicle information items.

[0030] The determination unit 105 displays at least one of the determined recommended battery information and recommended vehicle information on the display unit 5. The user 3 can visually confirm the recommended battery information and recommended vehicle information displayed on the display unit 5 and understand the battery information and vehicle information that are suitable for the user 3.

[0031] <Method for Determining Recommended Information> Next, an example of the method for determining recommended information according to the first embodiment will be described with reference to the flowchart in Figure 6.

[0032] In step S1, the acquisition unit 101 acquires usage data of the vehicle 2 transmitted from the vehicle 2. In the following processes, if the usage data of the vehicle 2 stored in the storage unit 102 or the usage data of other vehicles 2a to 2c is used instead of the usage data of the vehicle 2 acquired by the acquisition unit 101, then the acquisition of the usage data of the vehicle 2 by the acquisition unit 101 is unnecessary.

[0033] In step S2, the power consumption calculation unit 103 calculates the power consumption for a predetermined period assuming that the vehicle 2 is an electric vehicle, based on the usage data of the vehicle 2 acquired by the acquisition unit 101, the usage data of the vehicle 2 stored in the storage unit 102, and at least one of the usage data of other vehicles 2a to 2c stored in the storage unit 102.

[0034] The power consumption calculation process in step S2 of Figure 6 includes, for example, the processes in steps S21 to S23 of Figure 7. In step S21, the power consumption calculation unit 103 calculates the amount of electricity required to travel a predetermined distance over a predetermined period, based on the mileage of the vehicle 2's usage data acquired by the acquisition unit 101 and the electricity consumption assuming the vehicle 2 is an electric vehicle.

[0035] In step S22, the power consumption calculation unit 103 corrects the power consumption calculated in step S21 using the map M3 shown in Figure 3, based on the frequency of sudden acceleration in the usage data of the vehicle 2 acquired by the acquisition unit 101. For example, the power consumption calculation unit 103 corrects the power consumption calculated in step S21 by a larger amount the higher the frequency of sudden acceleration.

[0036] In step S23, the power consumption calculation unit 103 further corrects the power consumption corrected in step S22 using the map M2 shown in Figure 3, based on the elevation difference during movement within a predetermined period calculated from the vehicle position information of the vehicle usage data acquired by the acquisition unit 101. For example, the power consumption calculation unit 103 corrects the power consumption corrected in step S22 by a larger amount the greater the elevation difference. In this way, the power consumption calculation unit 103 calculates the power consumption after correction in step S23 as the final power consumption.

[0037] In step S3 of Figure 6, the degradation prediction unit 104 calculates the change in battery remaining capacity (full charge capacity) considering battery degradation over a predetermined period, based on at least one of the usage data of its own vehicle 2 acquired by the acquisition unit 101, the usage data of its own vehicle 2 stored in the storage unit 102, and the usage data of other vehicles 2a to 2c stored in the storage unit 102, as a battery degradation prediction value for a predetermined period.

[0038] In step S4, the determination unit 105 calculates the recommended battery capacity based on the power consumption amount calculated by the power consumption calculation unit 103 and the battery degradation prediction value calculated by the degradation prediction unit 104. For example, the determination unit 105 refers to the battery degradation prediction value calculated by the degradation prediction unit 104 and calculates the difference between the remaining battery capacity at the time of the battery's initial state and the remaining battery capacity at a future point in time after a predetermined period has elapsed from the initial state as the amount of power to consider for degradation. Then, the determination unit 105 calculates the recommended battery capacity by adding the calculated amount of power to consider for degradation to the power consumption amount calculated by the power consumption calculation unit 103.

[0039] In step S5, the determination unit 105 determines at least one of the recommended battery information and recommended vehicle information based on the calculated recommended battery capacity. In step S6, the determination unit 105 displays at least one of the determined recommended battery information and recommended vehicle information on the display unit 5.

[0040] According to the first embodiment, the power consumption calculation unit 103 calculates the power consumption for a predetermined period, assuming the vehicle is an electric vehicle, based on usage data of the vehicle itself 2 or other vehicles 2a to 2c. The degradation prediction unit 104 calculates the battery degradation prediction value for a predetermined period, based on usage data of the vehicle itself 2 or other vehicles 2a to 2c. The determination unit 105 calculates the recommended battery capacity based on the power consumption and battery degradation prediction value, and determines the recommended battery information and recommended vehicle information based on the calculated recommended battery capacity. As a result, the recommended battery capacity can be calculated considering future battery degradation, and the recommended battery capacity suitable for user 3 can be calculated with high accuracy. Therefore, the recommended battery information and recommended vehicle information suitable for user 3 can be proposed to user 3.

[0041] (Second Embodiment) As a second embodiment, we will describe a case in which the vehicle 2 shown in Figure 1 is an electric vehicle, and the usage data of the vehicle 2 acquired by the acquisition unit 101 and the usage data of the vehicle 2 stored in the storage unit 102 each include information specific to electric vehicles. As shown in Figure 8, the usage data of the vehicle 2 acquired by the acquisition unit 101 and the usage data of the vehicle 2 stored in the storage unit 102 each include at least one of the following: vehicle position, frequency of sudden acceleration, mileage, amount of power consumed over a predetermined period, battery state of charge (SOC), charging frequency, charging method, and power supply frequency. Of these, the amount of power consumed over a predetermined period, battery state of charge, charging frequency, charging method, and power supply frequency are information specific to electric vehicles.

[0042] Next, an example of a method for determining recommended information according to the second embodiment will be described with reference to Figures 6 and 9 to 12. In step S1 of Figure 6, the acquisition unit 101 acquires usage data of the vehicle 2 transmitted from the vehicle 2, which includes at least one of the following: vehicle position, frequency of sudden acceleration, distance traveled, amount of power consumed over a predetermined period, battery remaining capacity, charging frequency, charging method, and power supply frequency.

[0043] The power consumption calculation process in step S2 of Figure 6 includes steps S21 and S22 of Figure 9. In step S21, the power consumption calculation unit 103 calculates the power consumption for a predetermined period based on the usage data of the vehicle 2 acquired by the acquisition unit 101, which includes at least one of the following: vehicle position, frequency of sudden acceleration, distance traveled, power consumption for a predetermined period, battery remaining capacity, charging frequency, charging method, and power supply frequency. If the usage data of the vehicle 2 includes the power consumption for a predetermined period, the power consumption calculation unit 103 may use the power consumption for a predetermined period included in the usage data as is to calculate (extract) the power consumption for a predetermined period. For example, as shown on the left side of Figure 10, the power consumption calculation unit 103 calculates the power consumption P1 for a predetermined period based on the usage data acquired by the acquisition unit 101.

[0044] For the battery of the own vehicle 2 owned by the user 3, the battery that the user 3 will replace in the future is expected to have high performance such as being difficult for battery deterioration to progress. Further, for the own vehicle 2 owned by the user 3, the vehicle that the user 3 will purchase in the future is expected to have high performance such as being difficult for battery deterioration to progress and reducing electricity costs in addition to that.

[0045] Therefore, in step S22, based on the usage data acquired by the acquisition unit 101, the power consumption calculation unit 103 corrects the power consumption calculated in step S21 so as to reduce it in consideration of the high performance of the battery or the vehicle, and sets the corrected power consumption as the final power consumption. For example, the power consumption calculation unit 103 calculates a correction value based on the electricity cost of the vehicle that the user 3 may purchase in the future among the vehicle information stored in the storage unit 102. The power consumption calculation unit 103 multiplies the calculated correction value by the power consumption calculated in step S21. As a result, as shown on the right side of FIG. 10, the power consumption P2 (= P1 - ΔP) reduced by the power consumption ΔP for high performance is calculated from the power consumption P1 calculated in step S21.

[0046] The battery degradation prediction value calculation process in step S3 of FIG. 3 includes steps S31 and S32 in FIG. 11. In step S31, the degradation prediction unit 104 calculates a battery degradation prediction value for a predetermined period based on the usage status data of the host vehicle 2 including at least one of the vehicle position, rapid acceleration frequency, driving distance, power consumption during a predetermined period, battery remaining capacity, charging frequency, charging method, and power supply frequency acquired by the acquisition unit 101. For example, the degradation prediction unit 104 may calculate a smaller battery degradation prediction value because the battery degradation is more likely to progress as the driving distance of the usage status data to be referred to is long, the rapid acceleration frequency is high, the height difference calculated from the position information is large, the power consumption during a predetermined period is large, the battery remaining capacity is small, the charging frequency is high, or the power supply frequency is high. When the charging method of the usage status data to be referred to by the degradation prediction unit 104 is rapid charging, the battery degradation is more likely to progress than in the case of normal charging, so the battery degradation prediction value may be calculated to be smaller than in the case of normal charging. For example, as shown in FIG. 12, the degradation prediction unit 104 calculates the change A in the battery remaining capacity during a predetermined period as the battery degradation prediction value.

[0047] In step S32, the degradation prediction unit 104 calculates a correction value considering the future high performance of the vehicle and the battery based on the vehicle information and battery information that the user 3 may purchase in the future among the battery information stored in the storage unit 102. The degradation prediction unit 104 multiplies the calculated correction value by the slope of the change A in the battery remaining capacity calculated in step S31 to calculate a change B in the battery remaining capacity in which the battery degradation is less likely to progress than the change A in the battery remaining capacity, as shown in FIG. 12. The processing after step S4 in FIG. 3 is the same as the recommended information determination method according to the first embodiment.

[0048] According to the second embodiment, when the host vehicle 2 is an electric vehicle, based on the usage status data including the power consumption during a predetermined period, the battery remaining capacity, the charging frequency, the charging method, and the power supply frequency in addition to the vehicle position, rapid acceleration frequency, and driving distance, the power consumption during a predetermined period and the battery degradation prediction value for a predetermined period are calculated. Thereby, by using the data specific to electric vehicles, the power consumption and the battery degradation prediction value can be calculated more accurately.

[0049] Furthermore, if the other vehicles 2a to 2c are electric vehicles, the usage data of the other vehicles 2a to 2c stored in the memory unit 102 may include at least one of the following, as shown in Figure 8: vehicle position, frequency of sudden acceleration, distance traveled, amount of power consumed over a predetermined period, battery remaining capacity, charging frequency, charging method, and power supply frequency. The power consumption calculation unit 103 may then calculate the amount of power consumed over a predetermined period based on the usage data of the other vehicles 2a to 2c stored in the memory unit 102. In addition, the degradation prediction unit 104 may calculate a battery degradation prediction value for a predetermined period based on the usage data of the other vehicles 2a to 2c stored in the memory unit 102.

[0050] (Third Embodiment) As a third embodiment, a case in which survey data obtained by conducting a survey with user 3 shown in Figure 1 is used will be described. The information terminal 4, using an application or the like, conducts a survey with user 3 regarding the desired usage (how to use) of the vehicle, in the event that user 3 replaces the battery of their own electric vehicle 2 in the future, or if user 3 purchases an electric vehicle in the future. The information terminal 4 acquires the responses to the survey from user 3 as survey data through user 3's operation and transmits the acquired survey data to the recommendation information determination device 1. As shown in Figure 13, the survey data includes at least one of the vehicle's intended use, frequency of use, mileage, desired number of years of use, and desired number of charging cycles. The vehicle's intended use includes, for example, commuting and weekend leisure, but is not limited to these. The acquisition unit 101 acquires the survey data transmitted from the information terminal 4.

[0051] Here, it is conceivable that the usage data of the own vehicle 2 stored in the memory unit 102 is insufficient to calculate the amount of power consumed, that it is difficult for the acquisition unit 101 to acquire the usage data of the own vehicle 2, that user 3 does not own vehicle 2, or that user 3's vehicle usage will change between the present and the future. In these cases, the power consumption calculation unit 103 may use the usage data of other vehicles 2a to 2c instead of the usage data of the own vehicle 2, based on the questionnaire data acquired by the acquisition unit 101, to calculate the amount of power consumed. For example, based on the questionnaire data acquired by the acquisition unit 101, the power consumption calculation unit 103 extracts usage data of other vehicles 2a to 2c stored in the memory unit 102 that most closely resembles (is similar to) the vehicle usage (behavioral history) desired by user 3. Based on the extracted usage data of other vehicles, the power consumption calculation unit 103 calculates the amount of power consumed for a predetermined period.

[0052] For example, the power consumption calculation unit 103 may extract usage data of other vehicles that have mileage and charging frequency, etc., that correspond to (match) the vehicle usage of the survey data acquired by the acquisition unit 101. In this case, if the vehicle usage of the survey data acquired by the acquisition unit 101 is commuting, the power consumption calculation unit 103 may extract usage data of other vehicles that have a longer mileage and a higher charging frequency than when the usage is weekend leisure. Alternatively, the power consumption calculation unit 103 may compare the mileage and desired charging frequency of the survey data acquired by the acquisition unit 101 with the mileage and charging frequency of the usage data of other vehicles 2a to 2c, and extract usage data of other vehicles that have mileage and charging frequency that approximate the mileage and desired charging frequency of the survey data.

[0053] The degradation prediction unit 104, similar to the power consumption calculation unit 103, may calculate the battery degradation prediction value based on the usage data of other vehicles 2a to 2c instead of the usage data of its own vehicle 2. For example, based on the questionnaire data acquired by the acquisition unit 101, the degradation prediction unit 104 extracts the usage data of other vehicles 2a to 2c stored in the storage unit 102 that best approximates the usage status of the vehicle desired by the user 3. Based on the extracted usage data of other vehicles, the degradation prediction unit 104 calculates the battery degradation prediction value for a predetermined period.

[0054] Here, if the degradation prediction unit 104 does not use the questionnaire data acquired by the acquisition unit 101, it calculates the battery degradation prediction value assuming that the usage data of the vehicle 2 or the usage data of vehicles 2a to 2c will continue for a predetermined period. If the determination unit 105 does not use the questionnaire data acquired by the acquisition unit 101, it calculates the amount of energy ΔP1 to account for degradation based on the battery degradation prediction value calculated by the degradation prediction unit 104. As shown on the left side of Figure 14, the determination unit 105 calculates the amount of energy P2 (= P1 + ΔP1) by adding the calculated amount of energy ΔP1 to account for degradation to the amount of energy P1 calculated by the amount of energy consumption calculation unit 103, and uses this as the recommended battery capacity.

[0055] In contrast, when using questionnaire data acquired by the acquisition unit 101, the determination unit 105 may, when calculating the recommended battery capacity, correct the battery degradation prediction value calculated by the degradation prediction unit 104 based on the questionnaire data acquired by the acquisition unit 101, and then calculate the recommended battery capacity based on the corrected battery degradation prediction value. For example, the determination unit 105 corrects the battery degradation prediction value by using the future usage status such as mileage from the questionnaire data acquired by the acquisition unit 101 instead of the current usage status such as mileage from the referenced usage status data. Based on the corrected battery degradation prediction value, the determination unit 105 calculates the amount of energy ΔP2 to account for degradation. As shown on the right side of Figure 14, the determination unit 105 calculates the recommended battery capacity by adding the calculated amount of energy ΔP2 to the amount of energy P1 calculated by the amount of energy consumption calculation unit 103, resulting in an amount of energy P3 (= P1 + ΔP2).

[0056] Furthermore, when calculating the recommended battery capacity, the determination unit 105 may correct the power consumption amount calculated by the power consumption calculation unit 103 based on the mileage and other data from the survey acquired by the acquisition unit 101, and then calculate the recommended battery capacity based on the corrected power consumption amount.

[0057] According to the third embodiment, based on the questionnaire data, the power consumption and battery degradation prediction values ​​are calculated using the usage data of other vehicles 2a to 2c instead of the usage data of the user's own vehicle 2. Furthermore, the power consumption and battery degradation prediction values ​​are corrected based on the questionnaire data. As a result, even if user 3 does not own vehicle 2, or if the vehicle usage changes between the present and the future, it is possible to calculate a recommended battery capacity that is more suitable for the user, taking into account user 3's future vehicle usage and adapting to changes in vehicle usage.

[0058] (Fourth Embodiment) As a fourth embodiment, a case in which the desired battery lifespan included in the questionnaire data is used will be described. The determination unit 105 shown in Figure 1 determines a recommended battery capacity that takes into account battery degradation at the desired lifespan, based on the desired lifespan in the questionnaire data acquired by the acquisition unit 101, the power consumption calculated by the power consumption calculation unit 103, and the battery degradation prediction value calculated by the degradation prediction unit 104. If there are multiple desired lifespans in the questionnaire data, the determination unit 105 may determine a recommended battery capacity for each desired lifespan.

[0059] For example, consider a case where the survey data contains multiple desired usage years T1 and T2. Desired usage year T2 is longer than desired usage year T1. As shown in Figure 15, the degradation prediction unit 104 calculates the changes in battery remaining capacity A and B over a predetermined period T as battery degradation prediction values. The change in battery remaining capacity A is P2 at the initial time t0, and at time t11, after the desired usage year T1 has elapsed, battery degradation progresses and the remaining battery capacity P1 becomes lower than the remaining battery capacity P2. The change in battery remaining capacity B is P3 at the initial time t0, and at time t12, after the desired usage year T1 has elapsed, battery degradation progresses and the remaining battery capacity P1 becomes lower than the remaining battery capacity P3.

[0060] Based on the desired usage period T1 from the questionnaire data, the determination unit 105 calculates the difference ΔP1 (= P2 - P1) between the amount of energy P2 at time t0, which is the initial state of the change in battery remaining capacity A, and the remaining battery capacity P1 at time t11, which is after the desired usage period T1 has elapsed, as the amount of energy to consider degradation. Then, as shown on the left side of Figure 16, the determination unit 105 determines the recommended battery capacity A, which is the amount of energy P2 (= P1 + ΔP1) obtained by adding the amount of energy to consider degradation ΔP1 to the amount of energy P1 calculated by the power consumption calculation unit 103.

[0061] Furthermore, the determination unit 105 calculates the difference ΔP2 (= P3 - P1) between the amount of energy P3 at time t0, which is the initial state of the change in battery remaining capacity B, and the remaining battery capacity P1 at time t12, which is after the desired number of years of use T2 has elapsed, as the amount of energy to account for degradation, based on the desired number of years of use T2 from the questionnaire data. Then, as shown on the right side of Figure 16, the determination unit 105 determines the recommended battery capacity B, which is the amount of energy P3 (= P1 + ΔP2) obtained by adding the amount of energy to account for degradation ΔP2 to the amount of energy P1 calculated by the power consumption calculation unit 103.

[0062] According to the fourth embodiment, a recommended battery capacity is determined based on the desired usage period from the survey data, taking into account battery degradation over the desired usage period. This makes it possible to determine a battery capacity that is more suitable for the user, taking into account when the user 3 will replace the battery or when the user 3 will buy a new vehicle.

[0063] (Fifth Embodiment) As a fifth embodiment, a process for correcting the battery degradation prediction value based on the future vehicle usage included in the questionnaire data will be described with reference to Figure 17. In step S60, the acquisition unit 101 acquires questionnaire data, including the future vehicle usage, transmitted from the information terminal 4.

[0064] In step S61, the determination unit 105 determines whether there has been a change in usage based on the vehicle usage data from the questionnaire. For example, the determination unit 105 classifies the current usage of the vehicle 2 into categories such as commuting and weekend leisure, based on the usage status of the vehicle 2. The determination unit 105 then determines that there has been a change in usage if the vehicle usage data from the questionnaire differs from the current usage of the vehicle 2. If it is determined in step S61 that there has been no change in usage, the process is completed without correcting the battery degradation prediction value. On the other hand, if it is determined in step S61 that there has been a change in usage, the process proceeds to step S62.

[0065] In step S62, the determination unit 105 determines whether the change in usage is from weekend leisure to commuting. If it is determined to be a change from weekend leisure to commuting, the process proceeds to step S63. When the usage changes from weekend leisure to commuting, it is expected that the mileage will increase and the number of charging cycles will increase. Therefore, in step S63, the determination unit 105 increases the number of charging cycles in a predetermined period by one and corrects the battery degradation prediction value by multiplying it by a correction value corresponding to the number of charging cycles. The "predetermined period" for the number of charging cycles may be a day, a week, or one vehicle operation. For example, the determination unit 105 may generate an activity profile based on the usage data of the vehicle 2 and calculate the battery degradation prediction value by correcting the activity profile based on questionnaire data. On the other hand, if it is determined in step S62 that the change in usage is not a change from weekend leisure to commuting, the process proceeds to step S64.

[0066] In step S64, the determination unit 105 determines whether the change in usage is from commuting to weekend leisure. If it is determined to be a change from commuting to weekend leisure, the process proceeds to step S65. In step S62, the determination unit 105 determines whether the change in usage is from weekend leisure to commuting. If it is determined to be a change from weekend leisure to commuting, the process proceeds to step S63. In step S63, the determination unit 105 corrects the battery degradation prediction value by reducing the number of charging cycles during a predetermined period by one. On the other hand, if it is determined in step S64 that the change is not from commuting to weekend leisure, the battery degradation prediction value is not corrected and the process is completed.

[0067] In Figure 17, the vehicle is exemplified as being used for commuting and weekend leisure, but it is not limited to this. The determination unit 105 may correct the battery degradation prediction value by considering the increase or decrease in the number of charging cycles over a predetermined period when there is a change in usage. Also, in steps S63 and S65, the case where the increase or decrease in the number of charging cycles is one is exemplified, but the increase or decrease in the number of charging cycles can be set appropriately according to the type and relationship of usage before and after the change.

[0068] Furthermore, the determination unit 105 may correct the power consumption calculated by the power consumption calculation unit 103 by taking into account the increase or decrease in the number of charging cycles over a predetermined period if there is a change in usage. For example, if the usage changes from commuting to weekend leisure, the determination unit 105 may reduce the number of charging cycles over a predetermined period and correct the power consumption by multiplying the power consumption calculated by the power consumption calculation unit 103 by a correction value corresponding to the number of charging cycles. In other words, the determination unit 105 may correct at least one of the battery degradation prediction value and power consumption when there is a change in usage. The determination unit 105 may calculate the recommended battery capacity based on at least one of the corrected battery degradation prediction value and power consumption.

[0069] According to the fifth embodiment, the number of charging cycles is changed as a correction value to reflect changes in the current and future use of the vehicle, thereby correcting (recalculating) at least one of the battery degradation prediction value and power consumption. This makes it possible to calculate a recommended battery capacity that corresponds to changes in the current and future use of the vehicle.

[0070] (Sixth Embodiment) As a sixth embodiment, an example of a process for determining recommended information that includes at least one of recommended vehicle information and recommended battery information will be described. The determination unit 105 determines recommended information that includes at least one of recommended vehicle information and recommended battery information based on the vehicle information and battery information stored in the storage unit 102, the battery degradation prediction value calculated by the degradation prediction unit 104, and the recommended battery capacity etc. calculated by the determination unit 105.

[0071] The recommended vehicle information includes at least one of the following: vehicle performance information and vehicle cost information. The vehicle performance information includes at least one of the following: battery resistance, battery capacity, and energy consumption. The vehicle cost information includes at least one of the following: charging costs for a predetermined period, battery price, vehicle price, lease price, insurance premium, weight-based charge price, and TCO (Total Cost of Ownership). TCO is the total cost including purchase price and maintenance costs. TCO can be calculated based on the vehicle price of the vehicle information stored in the storage unit 102, the desired charging frequency number of survey data acquired by the acquisition unit 101, and the battery degradation prediction value calculated by the degradation prediction unit 104, etc.

[0072] Recommended battery information includes at least one of battery performance information and battery cost information. Battery performance information includes at least one of battery resistance and battery capacity. Battery cost information includes at least one of charging costs for a given period and battery price.

[0073] For example, the determination unit 105 calculates recommended battery capacities A, B, and C as shown in Figure 18. Recommended battery capacity A is the case where the amount of energy P2 is obtained by adding the amount of energy to account for degradation to the amount of energy P1 calculated by the energy consumption calculation unit 103. Recommended battery capacity B is the case where the amount of energy to account for degradation is added to the amount of energy P1 calculated by the energy consumption calculation unit 103, and the resulting amount of energy P3 is greater than the amount of energy P2 of recommended battery capacity A. Recommended battery capacity C is the case where the desired number of charging cycles in the questionnaire data is greater than the expected number of charging cycles (the relationship between the desired number of charging cycles and the expected number of charging cycles will be described later in the seventh embodiment), and the amount of energy to account for the desired number of charging cycles is subtracted from the amount of energy P1 calculated by the energy consumption calculation unit 103 to obtain battery capacity P4.

[0074] As shown in Figure 19, the degradation prediction unit 104 displays battery degradation prediction values ​​corresponding to recommended battery capacities A, B, and C. Figure 19 shows time points t11 and t12 after the two desired usage years from the questionnaire data have elapsed. The battery degradation prediction value corresponding to recommended battery capacity A is P2 in the initial state and becomes P1 at time point t11 ​​after the desired usage years have elapsed. The battery degradation prediction value corresponding to recommended battery capacity B is P3 in the initial state and becomes P1 at time point t12 after the desired usage years have elapsed. The battery degradation prediction value corresponding to recommended battery capacity C is P4 in the initial state.

[0075] The profile shown in Figure 20 is an example of vehicle information stored in the memory unit 102, and represents the vehicle price according to the remaining battery capacity. Based on the vehicle information shown in Figure 20, the determination unit 105 calculates the vehicle price corresponding to the recommended battery capacities A, B, and C.

[0076] The profile shown in Figure 21 is an example of battery information stored in the memory unit 102, and represents the total cost of ownership (TCO) corresponding to the remaining battery capacity. Based on the battery information shown in Figure 21, the determination unit 105 calculates the TCO corresponding to the recommended battery capacities A, B, and C.

[0077] According to the sixth embodiment, various information regarding the battery and vehicle can be calculated and determined by determining recommended vehicle information and recommended battery information based on the recommended battery capacity.

[0078] (Seventh Embodiment) As a seventh embodiment, with reference to Figure 22, a process for correcting the battery degradation prediction value based on the desired charging frequency included in the questionnaire data will be described. As a premise, the degradation prediction unit 104 calculates the expected number of charging frequencies based on the power consumption amount calculated by the power consumption amount calculation unit 103 and the mileage, etc., of the vehicle usage data acquired by the acquisition unit 101, and calculates the battery degradation prediction value based on the calculated expected number of charging frequencies.

[0079] In step S70, the determination unit 105 calculates the expected number of charging cycles based on the power consumption calculated by the power consumption calculation unit 103 and the vehicle usage data acquired by the acquisition unit 101. In step S71, the acquisition unit 101 acquires questionnaire data, including the desired number of charging cycles, transmitted from the information terminal 4.

[0080] In step S72, the determination unit 105 compares the desired number of charging frequencies obtained by the acquisition unit 101 in step S71 with the expected number of charging frequencies calculated by the determination unit 105 in step S70 to determine whether the desired number of charging frequencies is greater than the expected number of charging frequencies. If it is determined in step S72 that the desired number of charging frequencies is less than or equal to the expected number of charging frequencies, the process ends. On the other hand, if it is determined in step S72 that the desired number of charging frequencies is greater than the expected number of charging frequencies, the process proceeds to step S73.

[0081] In step S73, the determination unit 105 changes the number of charge cycles used to calculate the battery degradation prediction value to the desired number of charge cycles and corrects (recalculates) the battery degradation prediction value based on the desired number of charge cycles. Alternatively, the determination unit 105 may change the number of charge cycles used to calculate the battery degradation prediction value to a number of charge cycles increased or decreased by ±1 based on the desired number of charge cycles and correct the battery degradation prediction value based on the changed number of charge cycles. The amount of increase or decrease in the number of charge cycles is not limited to ±1 and can be set as appropriate. Based on the corrected battery degradation prediction value, the determination unit 105 calculates the recommended battery capacity. In step S74, the determination unit 105 determines the recommended vehicle information and recommended battery information based on the recommended battery capacity.

[0082] Furthermore, if the determination unit 105 determines that the desired number of charging cycles is greater than the expected number of charging cycles, it may correct the power consumption amount calculated by the power consumption calculation unit 103 to a smaller value. That is, if the determination unit 105 determines that the desired number of charging cycles is greater than the expected number of charging cycles, it may correct at least one of the battery degradation prediction value and the power consumption amount. Based on the corrected battery degradation prediction value and at least one of the power consumption amount, the determination unit 105 may calculate the recommended battery capacity.

[0083] According to the seventh embodiment, at least one of the battery degradation prediction value and power consumption is corrected based on the desired charging frequency count from the questionnaire data. This makes it possible to calculate a recommended battery capacity that is more suitable for the user.

[0084] (Other Embodiments) As described above, this disclosure is based on the first to seventh embodiments, but the descriptions and drawings that constitute part of this disclosure should not be understood as limiting this disclosure. Various alternative embodiments, examples, and operational techniques will become apparent to those skilled in the art from this disclosure. For example, the configurations disclosed in the first to seventh embodiments can be combined as appropriate to the extent that they do not cause contradictions. Thus, this disclosure naturally includes various embodiments and the like that are not described herein. Therefore, the technical scope of this disclosure is determined solely by the inventive features relating to the claims that are appropriate from the above description.

[0085] 1...Recommended information determination device, 2...Own vehicle, 2a-2c...Other vehicles, 3...User, 4...Information terminal, 5...Display unit, 100...Processing unit, 101...Acquisition unit, 102...Storage unit, 103...Power consumption calculation unit, 104...Degradation prediction unit, 105...Determination unit,

Claims

1. A method for determining recommended information, comprising the steps of: calculating the amount of power consumed over a predetermined period assuming that the vehicle is an electric vehicle, based on usage data of the vehicle itself and usage data of other vehicles; calculating a predicted battery degradation value over a predetermined period, based on usage data of the vehicle itself and usage data of other vehicles; calculating a recommended battery capacity based on the amount of power consumed and the predicted battery degradation value; and determining recommended information, including at least one of recommended vehicle information and recommended battery information, based on the recommended battery capacity.

2. The method for determining recommended information according to claim 1, wherein, if the vehicle is an electric vehicle, the vehicle usage data includes at least one of the following: vehicle position, frequency of sudden acceleration, distance traveled, amount of power consumed over a predetermined period, battery remaining capacity, charging frequency, charging method, and power supply frequency, the step of calculating the amount of power consumed is to calculate the amount of power consumed based on the vehicle usage data, and the step of calculating the battery degradation prediction value is to calculate the battery degradation prediction value based on the vehicle usage data.

3. A method for determining recommended battery capacity according to claim 1 or 2, further comprising the step of obtaining questionnaire data including at least one of the vehicle's intended use, frequency of use, mileage, desired years of use, and desired number of charging cycles, wherein the step of calculating the recommended battery capacity involves correcting the battery degradation prediction value based on the questionnaire data, and calculating the recommended battery capacity based on the corrected degradation prediction value.

4. The method for determining recommended information according to claim 3, wherein the questionnaire data includes the desired number of years of use, and the step of calculating the recommended battery capacity is to calculate the recommended battery capacity taking into account the battery degradation over the desired number of years of use.

5. The method for determining recommended battery capacity according to claim 3, wherein the questionnaire data includes the intended use of the vehicle, and the step of calculating the recommended battery capacity involves correcting at least one of the power consumption and the battery degradation prediction value based on the intended use of the vehicle, and determining the recommended battery capacity based on at least one of the corrected power consumption and the battery degradation prediction value.

6. The method for determining recommended information according to claim 1 or 2, wherein the recommended battery information includes at least one of battery performance information, which includes at least one of battery resistance and battery capacity, and battery cost information, which includes at least one of charging costs and battery price for a predetermined period, and the recommended vehicle information includes at least one of vehicle performance information, which includes at least one of battery resistance, battery capacity, and energy consumption, and vehicle cost information, which includes at least one of charging costs, battery price, vehicle price, lease price, insurance premium, weight charge price, and TCO for a predetermined period.

7. The method for determining recommended battery capacity according to claim 3, wherein the questionnaire data includes the desired number of charging frequencies, and the step of calculating the recommended battery capacity is to calculate the expected number of charging frequencies based on the power consumption and usage data, and if the desired number of charging frequencies is greater than the expected number of charging frequencies, to correct at least one of the power consumption and the battery degradation prediction value based on the desired number of charging frequencies, and to calculate the recommended battery capacity based on at least one of the corrected power consumption and the battery degradation prediction value.

8. A recommendation information determination device comprising: a power consumption calculation unit that calculates the amount of power consumed over a predetermined period assuming the vehicle is an electric vehicle, based on at least one of the usage data of the vehicle itself and the usage data of other vehicles; a degradation prediction unit that calculates a predicted battery degradation value over a predetermined period, based on at least one of the usage data of the vehicle itself and the usage data of other vehicles; and a determination unit that calculates a recommended battery capacity based on the power consumption and the predicted battery degradation value, and determines at least one of recommended vehicle information and recommended battery information based on the recommended battery capacity.

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