Charging amount prediction system

Abstract

To provide a charging amount prediction system that can predict the charging time and charging amount of a vehicle battery with higher accuracy than conventional systems.SOLUTION: A charging amount prediction system disclosed in the present specification includes an information collection device that collects vehicle information, including battery usage status, from multiple vehicles existing within a specified area, and a prediction device that predicts the future charging time and charge amount of each battery of the multiple vehicles on the basis of the collected vehicle information. The charging amount prediction system is based on vehicle information (including battery usage status) collected from multiple vehicles existing within the area so as to predict the charging time and charge amount of the batteries of vehicles in the area with higher accuracy than conventional systems.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent Document 1 discloses a charging system that collectively evaluates the possibility of charging all batteries installed in multiple mobile objects within a given area (within a power grid), and can accurately predict the required power. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 5327207 Summary of the Invention [Problem to be solved by the invention]

[0004] The present specification provides a charge amount prediction system that can predict the charging time and charge amount of a vehicle battery with higher accuracy than conventional systems. [Means for solving the problem]

[0005] The charge amount prediction system disclosed in this specification includes an information collection device that collects vehicle information, including battery usage status, from multiple vehicles located within a specified area, and a prediction device that predicts the future charging time and charge amount of each battery of the multiple vehicles from the collected vehicle information.

[0006] The charge amount prediction system disclosed in this specification is based on vehicle information (including battery usage status) collected from multiple vehicles within an area, and is therefore able to predict the charging time and charge amount of vehicle batteries in that area with greater accuracy than conventional methods.

[0007] The charge amount prediction system disclosed in this specification may include a notification device that notifies an entity (e.g., an electric power company) that supplies power to the area of ​​the predicted charge time and charge amount. The electric power company can reduce the possibility of a power shortage or power surplus occurring in the area by planning the power supply based on the predicted charge time and charge amount.

[0008] The charging amount prediction system disclosed in this specification may also include a recommended charging notification device that notifies at least one vehicle of a recommended charging time and recommended charging amount based on the predicted charging time and charging amount. By having the vehicle receiving charging adjust the charging time and charging amount, it is possible to level out the power demand in the area.

[0009] The charging amount prediction system may receive information on the amount of surplus power available for charging from an entity that supplies power to the area, and determine the recommended charging time and recommended charging amount based on the information on the amount of surplus power. By working in cooperation with the entity that supplies power, the charging amount prediction system can further reduce the possibility of a power shortage or power surplus occurring in the area.

[0010] Details and further improvements of the technology disclosed in this specification are described in the following "Description of Embodiments of the Invention." [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a block diagram of an area power management system including a charge amount prediction system according to an embodiment; [Figure 2] 1 is a graph illustrating advantages of using the charge amount prediction system of the embodiment (part 1). [Figure 3] 10 is a graph (part 2) illustrating the advantages of using the charge amount prediction system of the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] A charge amount prediction system 2 according to an embodiment will be described with reference to the drawings. FIG. 1 shows a block diagram of a power management system in area A, including the charge amount prediction system 2. Area A corresponds to one unit in which an electric power company 50 adjusts the power supply. A plurality of electric vehicles 40a-40d exist in area A. Each of the electric vehicles 40a-40d is equipped with a battery 41 that supplies power to an electric motor for driving the vehicle. Each of the electric vehicles 40a-40d also has a data communication module 42 that can exchange data with an information collection device 10 (described below) of the charge amount prediction system 2. Each of the electric vehicles 40a-40d also has a GPS device or a navigation device that identifies the location of the vehicle. Note that the electric vehicles 40a-40d shown in FIG. 1 are merely examples, and a larger number of electric vehicles may exist in area A. In the following, for ease of explanation, the electric vehicles 40a-40d may be referred to as "electric vehicle 40" when referring to any one of the electric vehicles 40a-40d without distinction, or when referring to the electric vehicles 40a-40d collectively.

[0013] The electric vehicle 40 may include a so-called plug-in hybrid vehicle, which has an engine and an electric motor for driving the vehicle, as well as a battery that supplies power to the electric motor and can charge the battery using an external power source.

[0014] Additionally, multiple chargers 51a-51d are located in area A. Power is supplied to chargers 51a-51d from power company 50. The thick lines in FIG. 1 represent power transmission lines. Each of the multiple chargers 51a-51d is equipped with a charging plug 52 that connects to an electric vehicle, and can supply power sent from power company 50 to the battery of the electric vehicle to which charging plug 52 is connected. That is, each of the multiple chargers 51a-51d can charge battery 41 of electric vehicle 40. Charger 51c can also supply power to house 53. Furthermore, charger 51d is equipped with a power storage device 54. Charger 51d can store power in power storage device 54. Furthermore, charger 51d can supply power from power storage device 54 to battery 41 of electric vehicle 40 to which charging plug 52 is connected, instead of power from power company 50. Chargers 51a-51d are merely examples, and more chargers may be present in area A. In the following, for ease of explanation, the chargers 51a to 51d may be referred to as charger 51 when referring to any one of them without distinction or when referring to the chargers 51a to 51d collectively.

[0015] In addition, in Figure 1, the dashed lines connecting the electric power company 50 and each charger 51 represent communication lines. Information such as the maximum output power during charging is sent from the electric power company 50 to each charger 51. Multiple chargers 51 may contain a mixture of devices with different maximum output power. Supplying a large amount of power from the charger 51 to the battery 41 and completing charging in a short time is sometimes called "quick charging."

[0016] The charge amount 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 communicates with data communication modules 42 of a plurality of electric vehicles 40 and can acquire vehicle information of the electric vehicles 40. The vehicle information includes various data of each electric vehicle 40, including information on the batteries 41 installed in the electric vehicles 40. Specifically, the vehicle information includes the identifier of each electric vehicle 40, the current position of the electric vehicle 40 at each time, the vehicle speed, and the usage status of the battery 41. Here, the usage status of the battery 41 includes the SOC (remaining power) of the battery 41, etc. The information collection device 10 collects vehicle information of each electric vehicle 40 at each predetermined time.

[0017] Furthermore, when the electric vehicle 40 charges the battery 41 with the charger 51, the battery usage status includes the charging start time, charging end time, and amount of charged power. As described above, the usage status of the battery 41 is sent from the electric vehicle 40 to the information collection device 10 as part of the vehicle information. The amount of charged power is the difference between the SOC at the charging start time and the SOC at the charging end time.

[0018] 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 stored in a 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 present in area A).

[0019] The prediction device 20 is equipped with a charger database 24 that stores the locations of chargers 51, and the computer 21 of the prediction device 20 can compare the current location of the electric vehicle 40 with the charger database 24 to identify the charger 51 that has charged the electric vehicle 40. The identifier of the charger 51 that supplied power to the electric vehicle 40 is also stored in the vehicle information database 23 as additional vehicle information.

[0020] The charging amount prediction system 2 also includes a charger management device 31 that communicates with each charger 51. In Fig. 1, the dashed lines connecting the charger management device 31 and each charger 51 indicate communication lines. Information about charging performed by each charger 51 is stored in the vehicle information database 23 of the prediction device 20 via the communication lines and the charger management device 31. The charging information includes data such as the date and time when charging was performed and the amount of charging.

[0021] The computer 21 of the prediction device 20 predicts when, where, and how much power each electric vehicle 40 will charge in the future, based on vehicle information of each electric vehicle 40 from the past to the present. For example, the computer 21 of the prediction device 20 simply predicts future charging times and charge amounts by identifying the average charging interval and the amount of charge per charge from the past charging patterns of each electric vehicle 40 (how low the SOC must be before stopping at a charger 51, how long it takes from the completion of charging to the start of the next charge, etc.).

[0022] Alternatively, the computer 21 of the prediction device 20 may be equipped with an AI engine 22, which has made remarkable progress in recent years, and may predict the future charging time and charge amount of the battery 41 of each electric vehicle 40 using vehicle information about each electric vehicle 40 and AI technology. Explanation of the prediction algorithm using AI technology will be omitted.

[0023] The future charging time and charge amount (predicted charging time and predicted charge amount) of each electric vehicle 40 predicted by computer 21 are stored in prediction database 25. In this way, computer 21 of prediction device 20 predicts the future charging time and charge amount of each battery 41 of multiple electric vehicles 40 from the collected vehicle information.

[0024] When vehicle information of many electric vehicles 40 in area A can be collected, the charge amount prediction system 2 can predict when and how much charging power will be needed in area A from the predicted future charging times and charge amounts of many electric vehicles 40. The notification device 30 included in the charge amount prediction system 2 notifies the electric power company 50 of the predicted value (predicted value of when and how much power will be needed) of the amount of charging power (the amount of power for charging the batteries of the electric vehicles) in area A, which is specified based on the predicted future charging times and charge amounts of the multiple electric vehicles 40. The electric power company 50 formulates a power supply plan for area A as a single unit and supplies power to area A in accordance with the plan.

[0025] The electric power company 50 makes a plan for supplying electric power to the area A based on the predicted value of the amount of electric power for charging notified by the charging amount prediction system 2.

[0026] The advantages of using the charge amount prediction system 2 (advantages relating to the power supply plan of the power company 50) will be described using Figure 2. The power company 50 has traditionally made plans for power supply to area A based on the past power demand record, weather, temperature, etc. in area A. Figure 2 shows an example of the trend in power in area A. Figure 2(A) shows the trend in power in the conventional case. Graph G1 is a future power demand forecast obtained based on the past power demand record, weather, temperature, etc. in area A. The power company 50 makes a power supply plan by adding a predetermined margin to the power demand forecast. Graph G2 is the power supply plan, and is drawn as a line shifted upward from the power demand forecast graph G1. The power company 50 supplies power to area A in accordance with the power supply plan.

[0027] Graph G3 is a graph of the actual power demand for area A. If the actual power demand (graph G3) is equal to or less than the power supply plan (graph G2) and is close to graph G2, the power supply plan matches the actual demand well, and power is supplied to area A without excess or shortage.

[0028] However, if electric vehicles become popular rapidly, there is a risk that a discrepancy will occur between the actual electricity demand in Area A to date and the future electricity demand in Area A. For example, range C1 in Figure 2(A) shows a situation in which the actual electricity demand has exceeded the electricity supply plan due to the rapid spread of electric vehicles (in other words, due to a larger number of electric vehicles being charged than before).

[0029] FIG. 2(B) shows a case where the electric power company 50 uses the predicted value (prediction of when and how much power will be needed) of the amount of power for charging (the amount of power for charging the batteries of electric vehicles) in area A, which was generated by the charging amount prediction system 2. Graph G4 shows the predicted value of the amount of power for charging (the amount of power for charging the batteries of electric vehicles) in area A, which was generated by the charging amount prediction system 2. The data of graph G4 is notified to the electric power company 50. The electric power company 50 prepares this year's power demand forecast (graph G5) by adding the data of graph G4 to a conventional power demand forecast based on past power demand records, weather, and temperature. Furthermore, the electric power company 50 prepares a power supply plan by adding a predetermined margin to the power demand forecast (graph G6).

[0030] The actual power demand in area A includes the amount of charging for the newly added electric vehicle 40 (graph G7). As shown in range C2 in FIG. 2(B), by taking into account the amount of charging power predicted by the charging amount prediction system 2, the power supply plan (graph G6) of the power company 50 and the actual power demand (graph G7) match well, and power is supplied to area A without excess or shortage.

[0031] Another advantage of using the charge amount prediction system 2 will be described using Figure 3. Figure 3(A) shows an example of predicted charging times and charge amounts for three electric vehicles 40a, 40b, and 40c. The predicted charging times and charge amounts for the electric vehicles 40a, 40b, and 40c are determined by the prediction device 20 of the charge amount prediction system 2 based on vehicle information.

[0032] According to the predicted values, electric vehicle 40a will charge between time T1 and time T6, electric vehicle 40b will charge between time T2 and time T5, and electric vehicle 40c will charge between time T3 and time T4. Between time T3 and time T4, the power consumption of the charger will be P1, which is greater than the power consumption at other times. In other words, the charger will experience a large power consumption peak between time T3 and time T4.

[0033] Therefore, based on the predicted charging times and charge amounts (graphs in FIG. 3(B)), the charge amount prediction system 2 determines the recommended charging times and recommended charge amounts for each of the electric vehicles 40a, 40b, and 40c so that the charging time periods of the electric vehicles 40a, 40b, and 40c do not overlap. FIG. 3(B) shows the determined recommended charging times and recommended charge amounts. The recommended charging time for electric vehicle 40c is time T7, and the bar graph from time T7 to time T8 represents the recommended charge amount. The recommended charging time for electric vehicle 40b is time T8, and the bar graph from time T8 to time T9 represents the recommended charge amount. The recommended charging time for electric vehicle 40a is time T9, and the bar graph from time T9 to time T10 represents the recommended charge amount.

[0034] As shown in Figure 3(B), the peak power P2 of the charger is reduced to one-third of the peak power P1 in the conventional case. Note that the charge amount prediction system 2 assigns an earlier recommended charging time to electric vehicles with a low predicted charge amount. This allows the target electric vehicles 40a, 40b, and 40c to start charging as early as possible.

[0035] An information collection device 10 that 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. If the user of the electric vehicle that received the recommended charging time and recommended charging amount adopts the recommended charging time and recommended charging amount, the peak power consumption of the charger can be reduced.

[0036] Even if some of the electric vehicles do not follow the recommended charging time and recommended charge amount, the peak power consumption in the entire area A can be reduced if the remaining electric vehicles follow the recommended charging time and recommended charge amount.

[0037] In this way, by adopting the charge amount prediction system 2, it is possible to have the electric power company 50 create a power supply plan that is closer to the power demand in area A. Furthermore, by adopting the charge amount prediction system 2, it is possible to suppress the peak power of the charger (peak power of charging in area A).

[0038] Other points to note regarding the technology described in the embodiments are described below. When the predicted charging time periods of multiple electric vehicles 40 overlap, the charging amount prediction system 2 identifies a recommended charging time and recommended charging amount that do not overlap, and notifies the target electric vehicle 40 of these. An information collection device 10 that can communicate 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. When the recommended charging time is later than the predicted charging time, the charging amount prediction system 2 may notify the target electric vehicle 40 of some kind of incentive along with the recommended charging time and recommended charging amount. An example of an incentive is a discount on the charge depending on the charging amount. The later the recommended charging time is, the greater the incentive presented.

[0039] The charging amount prediction system 2 may notify the electric vehicle 40 that has been notified of the recommended charging time of the location of a charger 51 that can charge as soon as possible. For example, the target electric vehicle may be notified of the location of a charger that can be reserved at an early time or a charger that can charge quickly.

[0040] For example, the charger 51d shown in Fig. 1 is equipped with a power storage device 54. When the charger 51d can supply the electric vehicle 40 with the power of the power storage device 54, the charge amount prediction system 2 may notify the target electric vehicle 40 of the location of the charger 51d and the recommended charging time.

[0041] A house 53 is connected to the charger 51c. If there is a user who is willing to share the amount of power in the battery 41 with others, the charge amount prediction system 2 may introduce the charger 51c to the user. The user supplies power from the battery 41 of his or her electric vehicle 40 to the house 53 via the charger 51c. The charge amount prediction system 2 may offer some kind of incentive to the user who provides power.

[0042] The charging amount prediction system 2 may determine the recommended charging time and recommended charging amount for each electric vehicle 40 according to the circumstances of the user of each electric vehicle 40 (SOC value, whether charging is required urgently, etc.), the status of each electric vehicle 40 (whether rapid charging is possible, SOC value, etc.), and the status of the charger 51 (whether rapid charging is possible, whether an associated power storage device is available, etc.).

[0043] The amount of charging energy is equivalent to power multiplied by charging time. The charging amount prediction system 2 may determine the pattern of power that the charger 51 supplies to the battery 41 of the electric vehicle 40. The lower the SOC of a battery, the more power it can accept. That is, a large amount of power can be supplied to the battery at the beginning of charging, and as the SOC increases, the amount of power that can be supplied to the battery decreases. The charging amount prediction system 2 may determine a power supply pattern to the battery during charging and notify each electric vehicle 40 and each charger 51 of the pattern. The power supply pattern refers to the change in power over time during charging. The charger 51 supplies power to the battery 41 according to the notified power supply pattern. Once the power supply pattern is determined, the charging end time is also determined. The charging amount prediction system 2 notifies the electric vehicle 40 of a recommended charging time and a recommended charging amount, and the recommended charging amount includes the power supply pattern. The electric vehicle 40 can know the scheduled charging end time from the notified power supply pattern.

[0044] Furthermore, the charge amount prediction system 2 may receive the amount of surplus power available for charging from the electric power company 50 (the entity that manages the power supplied to area A), and determine the recommended charging time and recommended charging amount for each electric vehicle 40 based on the amount of surplus power. Specifically, the electric power company 50 determines a pattern of power that can be supplied based on past power demand records, weather, and temperature, and creates a power supply plan for area A (graph G2 in FIG. 2 ). The electric power company 50 subtracts the power supply plan from the pattern of the maximum power that can be supplied to generate a surplus power pattern and sends it to the charge amount prediction system 2. The charge amount prediction system 2 determines the recommended charging time and recommended charging amount (recommended charging pattern) for multiple electric vehicles 40 so that they fit within the surplus power pattern, and presents them to each electric vehicle 40. By employing such a system, the power that can be supplied to area A can be effectively utilized.

[0045] Although specific examples of the present invention have been described in detail above, these are merely examples and do not limit the scope of the claims. The technology described in the claims includes various modifications and variations of the specific examples exemplified above. The technical elements described in this specification or drawings exhibit technical utility alone or in various combinations, and are not limited to the combinations described in the claims at the time of filing. Furthermore, the technology exemplified in this specification or drawings can achieve multiple objectives simultaneously, and achieving one of these objectives alone is technically useful. [Explanation of symbols]

[0046] 2: Charging amount 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: Electric power company 51, 51a-51d: Charger 52: Charging plug 53: House 54: Power storage device

Claims

[Claim 1] an information collection device that collects vehicle information including battery usage status from a plurality of vehicles present within a predetermined area; a prediction device that predicts a charging time and a charging amount of the battery of each of the plurality of vehicles from the collected vehicle information; A charging amount prediction system equipped with

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