Charging amount prediction system

Abstract

The invention provides a charge amount prediction system. The present specification provides a charge amount prediction system capable of predicting a charge timing and a charge amount of a battery of a vehicle more accurately than in the prior art. A charge amount prediction system disclosed in the present specification is provided with: an information collection device that collects vehicle information including the use status of a battery from a plurality of vehicles present in a predetermined area; and a prediction device that predicts, on the basis of the collected vehicle information, the future charging time and amount of charge of the battery of each of the plurality of vehicles. The charging amount prediction system can predict the charging time and the charging amount of a battery of a vehicle in a region with higher precision than in the prior art on the basis of vehicle information (including the utilization state of the battery) collected from a plurality of vehicles existing in the region.

Description

Technical Field

[0001] The technology disclosed in this specification relates to a system for predicting the charge level of a vehicle's battery within a predetermined area. Background Technology

[0002] Patent Document 1 discloses a charging system. This charging system can centrally evaluate the overall charging potential of batteries mounted on a large number of mobile bodies existing in a predetermined area (within a power grid) and predict the required power with high accuracy.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent No. 5327207 Summary of the Invention

[0006] This manual provides a charge prediction system that can predict the charging period and charge amount of a vehicle's battery with greater accuracy than ever before.

[0007] The charging quantity prediction system disclosed in this specification includes: an information collection device for collecting vehicle information, including battery utilization status, from multiple vehicles existing in a predetermined area; and a prediction device for predicting the charging time and charging quantity of the battery of each of the multiple vehicles based on the collected vehicle information.

[0008] The charging amount prediction system disclosed in this specification is based on vehicle information (including battery utilization status) collected from multiple vehicles present in the area, and is therefore able to predict the charging time and charging amount of the batteries of vehicles in the area with higher accuracy than ever before.

[0009] Furthermore, the charging quantity prediction system disclosed in this specification may also include a notification device that notifies the entity supplying electricity to the aforementioned area (e.g., a power company) of the predicted charging time and charging quantity. By planning to supply electricity according to the predicted charging time and charging quantity, the power company can reduce the possibility of power shortages or excesses in the aforementioned area.

[0010] Furthermore, the charging quantity prediction system disclosed in this specification can also include a recommended charging notification device that notifies at least one vehicle of a recommended charging time and amount based on the predicted charging time and amount. By adjusting the charging time and amount on the side of the vehicle receiving charging, the regional electricity demand can be balanced.

[0011] The charging capacity prediction system receives information about the remaining power available for charging from the entity supplying electricity to the area, and determines the recommended charging time and amount based on this information. Through cooperation with the electricity supply entity, the charging capacity prediction system can further reduce the possibility of power shortages or excesses in the aforementioned area.

[0012] The following “Detailed Description” describes the technology disclosed in this specification and further improvements. Attached Figure Description

[0013] Figure 1 This is a block diagram of a regional power management system, including the charging quantity prediction system of the embodiment.

[0014] Figure 2 This is a diagram (1) illustrating the advantages of using the charge prediction system of the embodiment.

[0015] Figure 3 This is a diagram (Figure 2) illustrating the advantages of using the charge prediction system of the embodiment. Detailed Implementation

[0016] Referring to the accompanying drawings, the charging quantity prediction system 2 of an embodiment will be described. Figure 1 A block diagram of a power management system within region A, including a charging quantity prediction system 2, is shown. Region A corresponds to a unit where the power company 50 adjusts the power supply. Multiple electric vehicles 40a-40d exist in region A. Each electric vehicle 40a-40d has a battery 41 that supplies power to an electric motor for driving the vehicle. Furthermore, each electric vehicle 40a-40d has a data communication module 42 capable of exchanging data with the information collection device 10 (described later) of the charging quantity prediction system 2. Moreover, each electric vehicle 40a-40d also has a GPS device, a navigation device, etc., for determining its own location. Figure 1 The electric vehicles 40a-40d shown are examples, and there may be a large number of electric vehicles in region A. For the sake of simplicity, electric vehicle 40a-40d will sometimes be referred to as electric vehicle 40 when referring to any one of them without distinction or when referring to them collectively.

[0017] The electric vehicle 40 may also include a vehicle with an engine and an electric motor for driving the vehicle and a battery that supplies power to the electric motor, a vehicle that can be charged by an external power source, and a so-called plug-in hybrid electric vehicle.

[0018] In addition, multiple chargers 51a-51d are installed in area A. Power is supplied to chargers 51a-51d from power company 50. Figure 1The thick lines in the diagram represent power transmission lines. Each of the multiple chargers 51a-51d has a charging plug 52 that connects to the electric vehicle, enabling it to supply power from the power company 50 to the battery of the electric vehicle connected to the charging plug 52. That is, each of the multiple chargers 51a-51d can charge the battery 41 of the electric vehicle 40. In addition, charger 51c can also supply power to the home 53. Furthermore, charger 51d is equipped with a power storage device 54. Charger 51d can store power in the power storage device 54. In addition, charger 51d can supply power to the power storage device 54 for the battery 41 of the electric vehicle 40 connected to the charging plug 52, instead of power from the power company 50. Chargers 51a-51d are examples; a large number of chargers may also exist in region A. Hereinafter, for the sake of simplicity, when referring to any one of chargers 51a-51d without distinction or when referring to chargers 51a-51d collectively, it will sometimes be referred to as charger 51.

[0019] In addition, Figure 1 In the diagram, the dashed lines connecting the power company 50 and each charger 51 represent communication lines. The power company 50 sends information such as the maximum output power during charging to each charger 51. Multiple chargers 51 may also contain devices with different maximum output powers. Supplying a large amount of power from the charger 51 to the battery 41 to complete charging in a short time is sometimes referred to as "fast charging."

[0020] The charging prediction system 2 includes an information collection device 10, a prediction device 20, a notification device 30, and a charger management device 31. The information collection device 10 can communicate with the data communication modules 42 of multiple electric vehicles 40 to obtain vehicle information of the electric vehicles 40. The vehicle information includes various data of each electric vehicle 40, including information about the battery 41 installed in the electric vehicle 40. Specifically, the vehicle information includes the identifier of each electric vehicle 40, the current position of the electric vehicle 40 at each time, its speed, and the utilization status of the battery 41. Here, the utilization status of the battery 41 includes the SOC (State of Charge) of the battery 41. The information collection device 10 collects the vehicle information of each electric vehicle 40 at predetermined times.

[0021] Furthermore, when the electric vehicle 40 charges the battery 41 using the charger 51, the charging start time, charging end time, and the amount of electricity charged are included in the battery utilization status. As described above, the battery utilization status is transmitted from the electric vehicle 40 to the information collection device 10 as part of the vehicle information. The amount of electricity charged is the difference between the State of Charge (SOC) at the charging start time and the SOC at the charging end time.

[0022] The vehicle information collected by the information collection device 10 is sent to the prediction device 20. The vehicle information collected at each predetermined time is accumulated in the vehicle information database 23 of the prediction device 20. That is, the vehicle information database 23 includes vehicle information from the past to the present (vehicle information of electric vehicles existing in area A).

[0023] Furthermore, the prediction device 20 has a charger database 24 that stores the locations of chargers 51. The computer 21 of the prediction device 20 can also compare the current location of the electric vehicle 40 with the charger database 24 to determine which charger 51 has been charging the electric vehicle 40. The identifier of the charger 51 that supplied power to the electric vehicle 40 is also stored as additional vehicle information in the vehicle information database 23.

[0024] Additionally, the charging quantity prediction system 2 includes a charger management device 31 that communicates with each charger 51. Figure 1 In the diagram, the dashed lines connecting the charger management device 31 and each charger 51 represent communication lines. Charging information from each charger 51 is accumulated in the vehicle information database 23 of the prediction device 20 via the communication lines and the charger management device 31. This charging information includes data such as the date and time of charging, and the amount of charge.

[0025] The computer 21 of the prediction device 20 predicts when, where, and how much electricity each electric vehicle 40 will need to be charged in the future, based on vehicle information from the past to the present. For example, the computer 21 of the prediction device 20 predicts future charging times and amounts by simply determining the average charging interval and the amount of charge per charge based on the past charging patterns of each electric vehicle 40 (such as how much the SOC decreases before moving closer to the charger 51, how long it takes from the completion of charging to the start of the next charging session, etc.).

[0026] Alternatively, the computer 21 of the prediction device 20 may also be equipped with an AI engine 22, which has seen significant advancements in recent years, to predict the future charging time and amount of charge for the batteries 41 of each electric vehicle 40 using vehicle information and AI technology. Description of the prediction algorithm using AI technology is omitted.

[0027] The computer 21 predicts the future charging time and charging amount for each electric vehicle 40 (predicted charging time and predicted charging amount), which is then stored in the prediction database 25. In this way, the computer 21 of the prediction device 20 predicts the future charging time and charging amount for the batteries 41 of each of the multiple electric vehicles 40 based on the collected vehicle information.

[0028] Having collected a large amount of vehicle information from electric vehicles 40 in region A, the charging quantity prediction system 2 can predict when and how much electricity will be needed for charging in region A based on the predicted future charging times and charging quantities of these electric vehicles 40. The notification device 30 of the charging quantity prediction system 2 notifies the power company 50 of the predicted charging power (electric power used for charging the batteries of electric vehicles) in region A, determined based on the predicted future charging times and charging quantities of the multiple electric vehicles 40. The power company 50 then formulates a power supply plan for region A as a unit and supplies electricity to region A according to the plan.

[0029] Based on the predicted electricity consumption for charging, which is notified from the charging quantity forecasting system 2, the power company 50 formulates a plan to supply electricity to region A.

[0030] use Figure 2 This section explains the advantages of using the charging volume prediction system 2 (advantages related to the power supply plan of power company 50). Power company 50 has previously formulated a plan to supply electricity to region A based on past electricity demand performance, weather, temperature, etc. Figure 2 This shows an example of the shift of electricity in region A. Figure 2 (A) illustrates the historical trend of electricity demand. Chart G1 is a forecast of future electricity demand based on past electricity demand data, weather, temperature, etc. Power company 50 adds a predetermined margin to the electricity demand forecast to formulate an electricity supply plan. Chart G2 is the electricity supply plan, depicting the line that shifts the electricity demand forecast chart G1 upwards. Power company 50 supplies electricity to region A according to the electricity supply plan.

[0031] Chart G3 is a chart of the actual electricity demand in Region A. If the actual electricity demand (Chart G3) is below and close to the planned electricity supply (Chart G2), then the planned electricity supply is well matched with the actual demand, and there is no oversupply or undersupply of electricity to Region A.

[0032] However, with the rapid popularization of electric vehicles, there is a possibility that the actual electricity demand in Region A in the past may diverge from its future electricity demand. For example, Figure 2 (A) C1 represents a situation where electricity demand exceeds the planned electricity supply due to the rapid proliferation of electric vehicles (in other words, due to a larger number of electric vehicles being charged than ever before).

[0033] Figure 2(B) shows the predicted power demand (power demand for charging electric vehicle batteries) in Region A generated by the charging demand forecasting system 2, generated by the power company 50 (predicting when and how much power is needed). Chart G4 represents the predicted power demand (power demand for charging electric vehicle batteries) in Region A generated by the charging demand forecasting system 2. The data in Chart G4 is communicated to the power company 50. The power company 50 incorporates the data from Chart G4 into its historical power demand forecasts based on past electricity demand performance, weather, and temperature to formulate this year's power demand forecast (Chart G5). Furthermore, the power company 50 adds a predetermined margin to the power demand forecast to formulate a power supply plan (Chart G6).

[0034] The electricity demand figures for Region A include the charging volume of the newly added 40 electric vehicles (Chart G7). For example... Figure 2 As shown in C2 of (B), by adding the charging power predicted by the charging power prediction system 2, the power supply plan (Chart G6) and the actual power demand (Chart G7) of the power company 50 are well matched, and the power supply to region A is neither excessive nor insufficient.

[0035] use Figure 3 This illustrates other advantages of using the charge prediction system 2. Figure 3 (A) shows an example of the predicted charging time and charging amount for three electric vehicles 40a, 40b, and 40c. The prediction device 20 of the charging amount prediction system 2 determines the predicted charging time and charging amount for electric vehicles 40a, 40b, and 40c based on vehicle information.

[0036] According to the predictions, electric vehicle 40a is charged from time T1 to time T6, electric vehicle 40b is charged from time T2 to time T5, and electric vehicle 40c is charged from time T3 to time T4. During the period from time T3 to time T4, the charger's power consumption becomes P1, which is higher than the power consumption at other times. That is, the charger experiences a large power consumption peak during the period from time T3 to time T4.

[0037] Therefore, the charging quantity prediction system 2 predicts the charging time and charging quantity ( Figure 3 (B) chart), to determine the recommended charging time and recommended charging amount for each electric vehicle 40a, 40b, and 40c in a way that the charging time periods of electric vehicles 40a, 40b, and 40c do not overlap. Figure 3(B) shows the recommended charging time and recommended charging amount. For electric vehicle 40c, the recommended charging time is time T7, and the line graph from time T7 to time T8 represents the recommended charging amount. For electric vehicle 40b, the recommended charging time is time T8, and the line graph from time T8 to time T9 represents the recommended charging amount. For electric vehicle 40a, the recommended charging time is time T9, and the line graph from time T9 to time T10 represents the recommended charging amount.

[0038] like Figure 3 As shown in (B), the peak power P2 of the charger is suppressed to one-third of the peak power P1 in the previous case. Furthermore, the charging amount prediction system 2 allocates earlier recommended charging times for electric vehicles with predicted low charging amounts. Therefore, the target electric vehicles 40a, 40b, and 40c can begin charging at the earliest possible time.

[0039] The information collection device 10, which can communicate with the data communication module 42 of each electric vehicle 40, notifies the target electric vehicle of the recommended charging time and recommended charging amount. Users of the electric vehicles who receive the recommended charging time and recommended charging amount can suppress peak power from the charger by using the recommended charging time and recommended charging amount.

[0040] Furthermore, even if some of the multiple electric vehicles do not follow the recommended charging time and amount, as long as the remaining electric vehicles follow the recommended charging time and amount, peak power can be suppressed in region A as a whole.

[0041] In this way, by employing the charging volume prediction system 2, the power company 50 can formulate a power supply plan that more closely approximates the power demand of region A. Furthermore, by employing the charging volume prediction system 2, peak power from chargers (peak charging power in region A) can be suppressed.

[0042] Additionally, points of attention related to the technology described in the embodiments are explained. When the predicted charging time periods of multiple electric vehicles 40 overlap, the charging quantity prediction system 2 determines recommended charging times and recommended charging amounts where the charging time periods do not overlap, and notifies the target electric vehicle 40 of these times. The information collection device 10, capable of communicating with the data communication module 42 of each electric vehicle 40, notifies the target electric vehicle 40 of the recommended charging time and recommended charging amount. If the recommended charging time is later than the predicted charging time, the charging quantity prediction system 2 may also notify the target electric vehicle 40 of a certain reward along with the recommended charging time and recommended charging amount. Examples of rewards include discounts on fees corresponding to the charging amount. The later the recommended charging time, the greater the reward offered.

[0043] The charging prediction system 2 can also notify the electric vehicle 40 of the location of the charger 51 that can charge as early as possible, in order to recommend a charging time. For example, it can also notify the electric vehicle that it is eligible to charge at an early time or that it is equipped with a fast charger.

[0044] For example, for Figure 1 The charger 51d shown is equipped with an energy storage device 54. When the charger 51d is able to supply power to the electric vehicle 40 via the energy storage device 54, the charging amount prediction system 2 can also notify the electric vehicle 40 of the location of the charger 51d and the recommended charging time.

[0045] Home 53 is connected to charger 51c. In cases where a user can share the power of battery 41 with others, the charging prediction system 2 can also introduce charger 51c to that user. The user supplies power from the battery 41 of their electric vehicle 40 to home 53 via charger 51c. The charging prediction system 2 can also offer rewards to the user who provides the power.

[0046] The charging volume prediction system 2 can also determine the recommended charging time and recommended charging volume for each electric vehicle 40 based on the user's situation (SOC value, whether emergency charging is needed due to demand, etc.), the condition of each electric vehicle 40 (whether it can be charged quickly, SOC value, etc.), and the condition of the charger 51 (whether it can be charged quickly or whether the attached energy storage device can be used, etc.).

[0047] The charging power is equivalent to the power multiplied by the charging time. The charging amount prediction system 2 can also determine the mode of power supplied by the charger 51 to the battery 41 of the electric vehicle 40. The lower the SOC, the greater the power the battery can hold. That is, in the initial stage of charging, a large amount of power can be supplied to the battery, and as the SOC increases, the amount of power supplied to the battery decreases. The charging amount prediction system 2 can also determine the power supply mode for supplying power to the battery during charging and notify each electric vehicle 40 and each charger 51. The power supply mode refers to the time variation of the power during charging. The charger 51 supplies power to the battery 41 according to the notified power supply mode. If the power supply mode is determined, the charging end time is also determined. The charging amount prediction system 2 notifies the electric vehicle 40 of the recommended charging time and recommended charging amount, but the recommended charging amount includes the power supply mode. In the electric vehicle 40, the predetermined charging end time can be known according to the notified power supply mode.

[0048] Additionally, the charging volume prediction system 2 can also receive surplus electricity available for charging from the power company 50 (the entity managing the electricity supply to region A), and determine the recommended charging time and recommended charging volume for each electric vehicle 40 based on the surplus electricity. Specifically, the power company 50 determines the available electricity supply pattern based on past electricity demand performance and weather / temperature, and formulates a power supply plan for region A. Figure 2 (See Chart G2). Power company 50 generates a surplus power pattern by subtracting the power supply plan from the pattern of maximum available power and sends it to charging quantity prediction system 2. In charging quantity prediction system 2, recommended charging times and recommended charging amounts (recommended charging patterns) for multiple electric vehicles 40 are determined in a manner that converges to the surplus power pattern, and these are then provided to each electric vehicle 40. By adopting this structure, the power available to region A can be effectively utilized.

[0049] The specific examples of the present invention have been described in detail above, but these are merely illustrative and do not limit the scope of the claims. The technology described in the claims includes various modifications and variations of the specific examples described above. The technical elements described in this specification or drawings are technically useful individually or in various combinations, and are not limited to the combinations described in the claims at the time of application. Furthermore, the technology illustrated in this specification or drawings can achieve multiple objectives simultaneously, and achieving one of these objectives is itself technically useful.

[0050] (Symbol Explanation)

[0051] 2: Charging volume prediction system; 10: Information collection device; 20: Prediction device; 21: Computer; 22: AI engine; 23: Vehicle information database; 24: Charger database; 25: Prediction database; 30: Notification device; 31: Charger management device; 40, 40a-40d: Electric vehicle; 41: Battery; 42: Data communication module; 50: Power company; 51, 51a-51d: Charger; 52: Charging plug; 53: Home; 54: Energy storage device.

Claims

1. A charging capacity prediction system, comprising: An information collection device collects vehicle information, including battery usage status, from multiple vehicles located within a predetermined area; and The prediction device predicts the charging time and charging amount of the battery of each of the plurality of vehicles based on the collected vehicle information.

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