EV management device and EV management method

Abstract

This system accurately determines whether a battery will run out of power in a delivery plan that includes delivery by EV and charging of the EV's battery at a charging station. [Solution] The EV management device maintains route information indicating the route traveled by the delivery EV, including the delivery point of the cargo and the charging station where the delivery EV's battery is charged, as well as the amount of power consumed by the delivery EV as it travels along the route. Based on the charging record at the charging station indicated by the route information, the device calculates the expected charge amount of the battery at the charging station indicated by the route information. Based on the remaining capacity of the battery before departure in the delivery plan, the amount of power consumed, the expected charge amount, and the remaining capacity of the battery before the low-power warning, the device determines whether the battery will run out of power in the delivery plan.

Description

Technical Field

[0001] The present invention relates to an EV management device and an EV management method.

Background Art

[0002] As the background art in this technical field, there is Japanese Patent Application Laid-Open No. 2016-49922 (Patent Document 1). This publication describes that "a vehicle energy management device includes an energy pattern generation unit that calculates the energy consumption of an electric vehicle during travel and generates an energy pattern based on a speed pattern constituted by the speed of the electric vehicle at each position or each time on the route to the destination, and a gradient pattern constituted by the road gradient of the electric vehicle at each position or each time, and a consumption energy correction unit that generates a correction parameter for correcting the consumption energy calculated by the energy pattern generation unit based on vehicle information including at least the vehicle speed of the electric vehicle, the output of the motor, the output of the engine, and the state of charge of the battery." (See the abstract).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The technology described in Patent Document 1 calculates the energy consumption of an electric vehicle. However, in the delivery using an electric vehicle, the battery may be charged at a charging station during the intervals of delivery. Patent Document 1 does not describe the evaluation of the amount of charge of the battery at the charging station, and it is impossible to accurately determine whether an electric shortage occurs in the battery of the electric vehicle in a delivery plan including the charging of the battery at the charging station.

[0005] Therefore, one aspect of the present invention provides a method for accurately determining whether a battery will run out of power in a delivery plan that includes delivery by an EV (Electric Vehicle) and charging of the EV's battery at a charging station. [Means for solving the problem]

[0006] To solve the above problems, one aspect of the present invention adopts the following configuration. An EV management device for managing EVs comprises a processor and a memory, the memory holding charging performance information indicating charging performance at charging stations, route information indicating the route traveled by a delivery EV in a delivery plan that includes delivery of goods by a delivery EV and charging of the battery of the delivery EV, including a delivery point where the goods are delivered and a charging station where the battery of the delivery EV is charged, power consumption information indicating the amount of power consumed by the delivery EV as it travels along the route, the remaining capacity of the battery of the delivery EV before departure in the delivery plan, and the remaining capacity of the battery of the delivery EV before depletion warning, the processor extracts charging performance at charging stations indicated by the route information from the charging performance information, calculates the expected charge amount of the battery of the delivery EV at charging stations indicated by the route information based on the extracted charging performance information, and determines whether the battery of the delivery EV will run out of power in the delivery plan based on the remaining capacity before departure, the power consumption, the expected charge amount, and the remaining capacity before depletion warning. [Effects of the Invention]

[0007] According to one aspect of the present invention, it is possible to determine with high accuracy whether a battery will run out of power in a delivery plan that includes delivery by EV and charging of the EV's battery at a charging station.

[0008] Other issues, configurations, and effects not mentioned above will be clarified by the following description of the embodiments. [Brief explanation of the drawing]

[0009] [Figure 1]This is a block diagram showing an example configuration of the EV driving management system in Example 1. [Figure 2] This block diagram shows an example of the hardware configuration of the computers that make up each device included in the EV driving management system in Example 1. [Figure 3] This figure shows an example of the data structure of delivery plan information in Example 1. [Figure 4] This figure shows an example of the data structure of charging history information in Example 1. [Figure 5] This figure shows an example of the data structure of vehicle information in Example 1. [Figure 6] This flowchart shows an example of the EV driving management process in Example 1. [Figure 7] This figure shows an example of the data structure of route information in Example 1. [Figure 8] This flowchart shows an example of the expected charge amount calculation process in Example 1. [Figure 9] This is a flowchart showing an example of the power consumption calculation process in Example 1. [Figure 10] This figure shows an example of the data structure for power consumption data generated by EV driving in Example 1. [Figure 11] This flowchart shows an example of the power depletion detection result generation process in Example 1. [Figure 12] This is an explanatory diagram showing an example of the SOC transition profile in Example 1. [Modes for carrying out the invention]

[0010] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In this embodiment, the same components will be denoted by the same reference numerals in principle, and repeated descriptions will be omitted. It should be noted that this embodiment is merely one example for realizing the present invention and does not limit the technical scope of the present invention. [Examples]

[0011] FIG. 1 is a block diagram showing a configuration example of an EV (Electric Vehicle) travel management system. The EV travel management system 10 includes, for example, an EV travel management device 100, a delivery plan device 200, a plurality of EVSEs (Electric Vehicle Supply Equipments) 300, and one or more EVs 400. The EV travel management device 100 is connected to the delivery plan device 200, each of the EVSEs 300, and each of the EVs 400 via a network such as the Internet.

[0012] The delivery plan device 200 transmits delivery plan information indicating a delivery plan of goods by the EV 400 to the EV travel management device 100. Further, the delivery plan device 200 transmits information indicating the power shortage warning SOC (State Of Charge) of the EV 400 to the EV travel management device 100. The SOC is an example of the remaining capacity of the battery of the EV 400.

[0013] Each of the EVSEs 300 is arranged at one of the plurality of charging stations. The EVSE 300 charges the battery of the EV 400. In the present embodiment, for convenience of explanation, it is assumed that the charging station and the EVSE correspond one-to-one. That is, in the present embodiment, the charging station and the EVSE can be regarded as the same.

[0014] The EV travel management device 100 is an example of an EV management device and includes, for example, a route planning unit 111, a remaining SOC estimation unit 112, a power shortage determination unit 113, and a charging performance information generation unit 114, all of which are functional units. The EV travel management device 100 holds, for example, map information 121, charging performance information 122, and vehicle information 123.

[0015] The route planning unit 111 generates route information indicating the route that the EV400 will take when making a delivery, based on the map information 121 and the delivery plan information transmitted from the delivery planning device 200, and outputs it to the remaining SOC estimation unit 112. The map information 121 includes map information such as roads that the EV400 can travel on, the location of the delivery point of the cargo indicated in the delivery plan, and the location of each EVSE 300 (i.e., the location corresponding to each EVSE ID).

[0016] The remaining SOC estimation unit 112 estimates the remaining SOC of the EV used for delivery as indicated in the delivery plan. The remaining SOC estimation unit 112 includes, for example, a charging performance processing unit 115, a power consumption calculation unit 116, and an aggregation unit 117, all of which are functional units.

[0017] The charging performance processing unit 115 calculates the expected amount of charge for the EV400 in the delivery plan using the EVSE300, based on the charging performance information 122 and the information included in the route information generated by the route planning unit 111 (for example, vehicle ID, EVSE ID, estimated time required for each route, and additional charging time, etc.), and outputs the calculated expected amount of charge to the aggregation unit 117.

[0018] The power consumption calculation unit 116 calculates the power consumption of the EV400 in the delivery plan based on the vehicle information 123 and the information included in the route information generated by the route planning unit 111 (for example, vehicle ID, delivery start time, delivery end time, and route, etc.), and outputs it to the aggregation unit 117. The aggregation unit 117 aggregates the expected charge amount and the power consumption amount and outputs it to the power depletion determination unit 113.

[0019] The depletion detection unit 113 determines whether the EV400 will run out of power in the delivery plan based on the aggregation results from the aggregation unit 117, the depletion warning SOC, and the SOC notified by the EV400 before the start of delivery. The depletion detection unit 113 outputs the determination result of whether a depletion has occurred to the route planning unit 111. The route planning unit 111 transmits the determination result of whether the delivery plan can be executed based on the determination result of whether a depletion has occurred to the delivery planning device 200.

[0020] Each EVSE300 transmits information about the charging session performed by the EVSE300 (the period from when one EV400 starts to when it finishes a single battery charge) to the EV driving management device 100 (for example, information including the charging session ID, vehicle ID, EVSE ID, charging start time, charging end time, charge amount, charging output, and reason for stopping charging). Each EV400 transmits information about the battery charge performed on the EV400 to the EV driving management device 100 (for example, information including the vehicle ID, charging start time, charging end time, SOC at charging start, and SOC at charging end).

[0021] The charging performance information generation unit 114 generates charging performance information 122 by combining the charging session information received from the EVSE 300 and the battery charging information received from the EV400, using, for example, a combination of vehicle ID, charging start time, and charging end time as keys.

[0022] Figure 2 is a block diagram showing an example of the hardware configuration of the computers that make up each device (EV driving management device 100 and delivery planning device 200) included in the EV driving management system 10. The EV driving management device 100 and the delivery planning device 200 are each composed of, for example, a computer 500.

[0023] The computer 500 includes, for example, a CPU (Central Processing Unit) 501, memory 502, auxiliary storage device 503, communication device 504, input device 505, and display device 506.

[0024] CPU 501 is an example of a processor that executes programs stored in memory 502. Memory 502 includes non-volatile memory elements such as ROM (Read Only Memory) and volatile memory elements such as RAM (Random Access Memory). ROM stores immutable programs (e.g., BIOS (Basic Input / Output System)). RAM is a high-speed, volatile memory element such as DRAM (Dynamic Random Access Memory) that temporarily stores programs executed by CPU 501 and data used during program execution.

[0025] The auxiliary storage device 503 is a high-capacity, non-volatile storage device such as a magnetic storage device (HDD (Hard Disk Drive)) or flash memory (SSD (Solid State Drive)), and stores the program executed by the CPU 501 and the data used when the program is executed. In other words, the program is read from the auxiliary storage device 503, loaded into memory 502, and executed by the CPU 501.

[0026] The communication device 504 is a network interface device that controls communication with other devices according to a predetermined protocol. The communication device 504 may also include a serial interface such as USB (Universal Serial Bus).

[0027] The input device 505 is a device that receives input from the operator, such as a keyboard or mouse. The display device 506 is a device that outputs the results of program execution in a format that the operator can see, such as a display or printer.

[0028] Some or all of the program executed by CPU 501 may be provided to computer 500 via a network from an external computer equipped with a non-temporary storage medium such as removable media (CD-ROM, flash memory, etc.) or non-temporary storage device, and stored in non-volatile auxiliary storage device 503, which is also a non-temporary storage medium. For this reason, computer 500 may have an interface for reading data from removable media.

[0029] The EV driving management device 100 is a computer system that operates on a single physical computer 500, or on multiple logically or physically configured computers 500. It may operate on the same computer 500 in separate threads, or it may operate on a virtual computer built on multiple physical computer resources. The same applies to the delivery planning device 200.

[0030] The CPU 501 of the computer 500 that constitutes the EV driving management device 100 includes, for example, the various functional units of the EV driving management device 100 shown in Figure 1. Encrypt it.

[0031] For example, the CPU 501 of the computer 500 that constitutes the EV driving management device 100 functions as a route planning unit 111 by operating according to a route planning program loaded into the memory 502 of the computer 500 that constitutes the EV driving management device 100, and functions as a remaining SOC estimation unit 112 by operating according to a remaining SOC estimation program loaded into the same memory 502. The relationship between programs and functional units is similar for other functional units of the EV driving management device 100. The relationship between programs and functional units is also similar for functional units (not shown) of the delivery planning device 200.

[0032] Furthermore, some or all of the functions of the functional units of the EV driving management device 100 and the delivery planning device 200 may be implemented by dedicated circuits such as ASICs (Application Specific Integrated Circuits) or FPGAs (Field-Programmable Gate Arrays).

[0033] For example, the various types of information held by the EV driving management device 100 shown in Figure 1 are stored in the auxiliary storage device 503 of the computer 500 that constitutes the EV driving management device 100. Some or all of the various types of information held by the EV driving management device 100 and the various types of information held by the delivery planning device 200 may be stored in the memory 502 of the computer 500 that constitutes the respective devices, or in an external database connected to the devices.

[0034] In this embodiment, the information used by the EV driving management system 10 is not dependent on a specific data structure and may be represented in any data structure. For example, a data structure appropriately selected from a table, list, database, or queue can store the information.

[0035] Furthermore, for example, the devices included in the EV driving management system 10 may be integrated. For example, the EV driving management device 100 and the delivery planning device 200 may be integrated, in which case communication between the EV driving management device 100 and the delivery planning device 200 is omitted.

[0036] Figure 3 shows an example of the data structure of the delivery plan information 210. As mentioned above, the delivery plan information 210 is information transmitted from the delivery planning device 200 to the EV driving management device 100.

[0037] The delivery plan information 210 indicates a delivery plan ID that identifies the delivery plan, the delivery date related to the delivery plan, and the delivery plan itself. Furthermore, for each delivery plan indicated in the delivery plan information 210, the delivery start time and the delivery order of the packages are defined for each vehicle ID that identifies the EV400 used for delivery in that delivery plan. The delivery order of the packages includes information such as the package ID that identifies the package, the destination address (i.e., delivery point) of the package, and the weight of the package.

[0038] Additionally, the delivery order of packages may also define the available time for additional charging. In the example of delivery plan information 210 in Figure 3, the EV400 with vehicle ID "EV001" has a planned additional charging time of "30 minutes" between the delivery of package ID "L9" and the delivery of package ID "L10". That is, the plan is for the EV400 with vehicle ID "EV001" to move to the charging station after completing the delivery of package ID "L9", charge the battery for "30 minutes" using the EVSE300, and then deliver the package ID "L10".

[0039] Figure 4 shows an example of the data structure of the charging performance information 122. The charging performance information 122 includes, for example, a charging session ID that identifies the charging session, the vehicle ID of the EV400 whose battery was charged in the charging session, the EVSE ID that identifies the EVSE300 that performed the charging in the charging session, the charging start time, charging end (stop) time, charging amount, charging output, and reason for stopping charging in the charging session, as well as the SOC of the EV400 at the start and end of the charging session.

[0040] In this embodiment, the charging history information 122 stores, but is not limited to, reasons defined in a predetermined protocol (for example, OCPP (Open Charge Point Protocol) 2.0.1) as reasons for stopping charging. In the example of charging history information 122 in Figure 4, "Local" for the reason for stopping charging indicates that charging stopped due to a request from the EV400 (for example, such a request may be output in accordance with the driver's operation or automatically output by the EV400 when the SOC reaches a predetermined value), "PowerLoss" indicates that charging stopped due to a power outage, and "PowerQuality" indicates that charging stopped due to a decrease in output from the EVSE300.

[0041] Therefore, "Local" indicates that the charging was completed successfully. On the other hand, "PowerLoss" and "PowerQuality" indicate that charging was stopped (abnormally stopped) due to force majeure for the EV400. Thus, there are two types of reasons for stopping charging: force majeure and non-force majeure, and it is predetermined which category each charging stop reason falls into. The charging history information 122 may, in place of or in addition to the information indicating the reason for stopping charging, hold a flag indicating, for example, whether or not the reason for stopping charging was due to force majeure.

[0042] Figure 5 shows an example of the data structure of vehicle information 123. For example, vehicle information 123 stores information indicating the vehicle weight, front area, drag coefficient, energy efficiency coefficient, and EV battery capacity of each EV400 vehicle ID.

[0043] Figure 6 is a flowchart showing an example of EV driving management processing by the EV driving management device 100. It is assumed that before the EV driving management processing starts, the delivery planning device 200 has completed the process of transmitting delivery planning information 210 to the EV driving management device 100, and the charging performance information generation unit 114 has completed the process of generating charging performance information 122. It is also assumed that before the EV driving management processing starts, the map information 121 and vehicle information 123 have been registered with the EV driving management device 100.

[0044] Furthermore, for the sake of explanation, in the example of Figure 6, it is assumed that the delivery plan information 210 describes a delivery by only one EV400. If the delivery plan information 210 describes deliveries by multiple EV400s, the EV driving management device 100 may, for example, execute the process in Figure 6 for each of the multiple EV400s, or it may terminate the EV driving management process when it has executed the process in step S610 described later for a predetermined number of EVs (for example, one).

[0045] The route planning unit 111 generates route information (S601) by searching for routes between delivery points (delivery addresses) according to the delivery order of the EV400 indicated by the delivery plan information 210, based on the map information 121, using a predetermined algorithm such as Dijkstra's algorithm.

[0046] Figure 7 shows an example of the data structure of the route information generated in step S601. The route information 700 includes, for example, the vehicle ID of EV400, the delivery start time, the load for each cargo ID, the estimated time required for route travel, and the route itself. The route information 700 also includes information indicating the link ID that identifies each link included in the route, the link length of the link, and the inclination of the link, described in the order in which EV400 reaches the links.

[0047] Returning to the explanation of Figure 6, the route planning unit 111, if the delivery order of the EV400 indicated by the delivery plan information 210 includes additional charging time, extracts charging stations that are close to the delivery points before and after additional charging (for example, the distance from the delivery point immediately before additional charging is within a predetermined length, or the distance from the delivery point immediately after additional charging is within the predetermined length), and for which charging history is recorded in the charging history information 122 (i.e., the EVSE ID is included in the charging history information 122) (S602).

[0048] The charging performance processing unit 115 determines whether there are any unselected charging stations among the charging stations extracted in step S602 (S603). If the charging performance processing unit 115 determines that there are any unselected charging stations (S603: YES), it selects one charging station from among the unselected charging stations (S604).

[0049] In step S604, the charging performance processing unit 115 may, for example, refer to the map information 121 and select the charging station among the unselected charging stations that has the smallest sum of the distance from the delivery point immediately before additional charging and the distance from the delivery point immediately after additional charging (this may be a weighted sum based on predetermined weights), or it may select the charging station with the smallest distance from the delivery point immediately before additional charging, or it may select the charging station with the smallest distance from the delivery point immediately after additional charging (that is, it may select a charging station based on at least one of the distance from the delivery point immediately before additional charging and the distance from the delivery point immediately after additional charging).

[0050] The charging performance processing unit 115 calculates the expected charge amount at the selected charging station based on the charging performance information 122 (S605). Details of the process in step S605 will be described later with reference to Figure 8. The power consumption calculation unit 116 calculates the power consumption of the EV400 due to route travel based on the route information 700 and the vehicle information 123 (S606). Details of step S606 will be described later with reference to Figure 9.

[0051] The aggregation unit 117 outputs the expected charge amount calculated in step S605 and the power consumption amount calculated in step S606 to the power depletion determination unit 113. Based on the outputted information, the power depletion determination unit 113 generates a power depletion determination result indicating whether the EV400 will run out of power on the delivery day, based on the SOC of the EV400 before the start of delivery transmitted from the delivery planning device 200, and outputs it to the route planning unit 111 (S607). Details of the power depletion determination result generation process in step S607 will be described later with reference to Figure 11.

[0052] The route planning unit 111 determines whether the power depletion determination result generated in step S607 indicates that power depletion has occurred (S608). If the route planning unit 111 determines that power depletion will not occur (S608: NO), it sends a notification to the delivery planning device 200 indicating that the delivery plan shown in the delivery plan information 210 is executable (S609), and terminates the EV driving management process. In addition, in step S609, the route planning unit 111 may also send to the delivery planning device 200 information indicating the charging station at the time it was determined that power depletion would not occur, and information indicating the route between delivery points (including the charging station), in addition to the notification indicating that the delivery plan is executable.

[0053] If the route planning unit 111 determines that a power depletion will occur (S608: YES), it returns to step S603. If the charging performance processing unit 115 determines that there are no unselected charging stations (S603: NO), the route planning unit 111 sends a notification to the delivery planning device 200 indicating that the delivery plan shown in the delivery plan information 210 is not feasible (S610), and terminates the EV driving management process.

[0054] Figure 8 is a flowchart showing an example of the expected charge amount calculation process in step S605. The charging performance processing unit 115 extracts a record of charging performance from the charging performance information 122 based on the search key (S801). The search key includes at least the EVSE ID corresponding to the selected charging station. In addition to the EVSE ID corresponding to the selected charging station, the search key may also include the vehicle ID of the EV400.

[0055] The estimated time required for the EV400 to travel the route from the delivery point immediately preceding the selected charging station to the selected charging station is calculated, for example, by the route planning unit 111 generating the route from the delivery point immediately preceding the additional charging time to the selected charging station using map information 121 and a predetermined algorithm such as Dijkstra's algorithm.

[0056] The charging record processing unit 115 excludes records of charging that were stopped due to force majeure on the EV400 side from the records extracted in step S601 (S802). As mentioned above, the force majeure reasons for charging stoppage are predetermined, and "Powerloss" and "PowerQuality" are examples of force majeure reasons for charging stoppage. By excluding charging records where charging was stopped due to force majeure reasons, the reliability and charging capacity of the EVSE300 can be accurately evaluated.

[0057] The charging performance processing unit 115 calculates the average value of the charging output using the record after the exclusion process in step S802 and the EV battery capacity indicated by the vehicle information 123 of the EV400 whose battery was charged in the charging performance indicated by the record (S803).

[0058] Specifically, for example, the charging history processing unit 115 is {Σ (充電実績) (SOC at end of charging - SOC at start of charging) × EV battery capacity} / {Σ (充電実績) The average charging output is calculated using the formula: (charging end time - charging start time).

[0059] For example, from the charging performance information 122 in Figure 4, after the exclusion process in step S802, records with a charging session ID of "XXXXX" and records with a charging session ID of "VVVVV" are extracted, and if the EV battery capacity of both the EV400 with vehicle ID "EV001" and the EV400 with vehicle ID "EV0003" is 15 [kWh], the charging performance processing unit 115 calculates {(72%-40%)×15kWh+(82%-50%)×15kWh} / (1Hr+1Hr)=4.8 [kW] as the average value of the charging output.

[0060] The charging performance processing unit 115 calculates the expected charge amount (S804) by multiplying the average value of the charging output calculated in step S803 by the additional charging time, and then terminates the expected charge amount calculation process.

[0061] Figure 9 is a flowchart showing an example of the power consumption calculation process in step S606. The route planning unit 111 calculates the route from the delivery point immediately before the additional charging time to the selected charging station, and the route from the selected charging station to the delivery point immediately after the additional charging time, using map information 121 and a predetermined algorithm such as Dijkstra's algorithm, and includes them in the route information 700. The power consumption calculation unit 116 estimates the speed v, acceleration a, rolling resistance coefficient μ, air density ρ, and load of the EV400 at each link included in each route between each delivery point indicated by the route information 700 (in Figure 9, the selected charging station is also treated as a delivery point) (S901).

[0062] The power consumption calculation unit 116 determines the speed v of each link included in the route, for example, the maximum speed of road links included in the map information 121, the travel speed by prefecture and road type from the Ministry of Land, Infrastructure, Transport and Tourism's National Road and Street Traffic Survey (formerly the Road Traffic Census) (for example, obtained from an external database), or the average speed aggregated from probe information (for example, obtained from an external database).

[0063] The power consumption calculation unit 116 estimates the acceleration a of each link included in the route by, for example, averaging the accelerations indicated by probe information. The power consumption calculation unit 116 estimates the rolling resistance coefficient μ by, for example, from road surface temperature, air temperature, and weather conditions (for example, obtained from weather information stored in an external database) on the delivery plan day (the estimated arrival time at each stop (for example, obtained from the delivery start time and estimated duration)).

[0064] Since air density ρ is known to be a function of atmospheric pressure and temperature according to the laws of physics (the ideal gas law), the power consumption calculation unit 116 estimates it from the atmospheric pressure and temperature at each link (for example, obtained from weather information stored in an external database).

[0065] The power consumption calculation unit 116 may, for example, estimate the load of each link by simply accumulating the loads of the subsequent routes indicated by the route information 700, or it may estimate the load of each link by calculating the sum of the accumulated loads of the subsequent routes and the loads that do not decrease according to a preset redelivery rate.

[0066] The power consumption calculation unit 116 calculates the amount of power consumed by EV driving in each route between delivery points (S902) based on the values ​​of each link estimated in step S901 and the vehicle information 123, and then terminates the power consumption calculation process.

[0067] Specifically, the power consumption calculation unit 116 calculates the power consumption according to the power consumption model described in step S902 of Figure 9, for example. d is the drag coefficient shown in vehicle information 123, A is the front area shown in vehicle information 123, M is the sum of the estimated load and the vehicle weight shown in vehicle information 123, g is the acceleration due to gravity, θ is the inclination of the link (obtained from map information 121, etc.), t0 is the time when the journey along the route begins, t f is the time at the end of the journey along the route, η is the energy efficiency coefficient shown in vehicle information 123, l=(l1,l2,···) is the set of links included in the route, F l The thrust F and x in the corresponding link are shown. l This indicates the corresponding link length.

[0068] Figure 10 shows an example of the data structure of the power consumption data generated by EV driving on each route, calculated in the power consumption calculation process. The power consumption data 1000 shows, for example, the power consumption of EV400 used for delivery in a delivery plan on the delivery day, for each package ID, that is, for each link to the delivery point of the package corresponding to that package ID.

[0069] In the power consumption data 1000 in Figure 10, for example, the power consumption corresponding to the package with package ID "L1," i.e., the amount of electricity consumed by EV driving on the route from the distribution center (the starting point of the EV400 on the delivery start date) to the delivery point of that package, is 0.2 kWh, and the power consumption corresponding to the package with package ID "L2," i.e., the amount of electricity consumed by EV driving on the route from the previous delivery point (the delivery point of the package with package ID "L1") to the delivery point of the package with package ID "L2," is 0.4 kWh. The distribution center, delivery point, and charging station are all examples of stopping points where the EV400 stops.

[0070] Furthermore, the power consumption data 1000 shows the power consumption from the delivery point of the package to the charging station immediately before charging, the expected charge amount at the charging station calculated in the expected charge amount calculation process, and the power consumption from the charging station to the delivery point of the package to be delivered immediately after charging.

[0071] In the power consumption data 1000 in Figure 10, for example, the power consumption corresponding to the EVSE with EVSE ID "EVSE 001," that is, the amount of electricity generated by EV driving on the route from the previous delivery point (the delivery point for the package with package ID "L9") to the charging station with EVSE ID "EVSE 001," is 0.1kWh, the expected charge at that charging station is 2.4kWh, and the power consumption corresponding to the package with package ID "L10," that is, the amount of electricity generated by EV driving on the route from the charging station with EVSE ID "EVSE001" to the delivery point for the package with package ID "L10," is 0.3kWh.

[0072] Figure 11 is a flowchart showing an example of the power depletion determination result generation process performed in step S607. The power depletion determination unit 113 creates a transition profile of the EV400's SOC based on the SOC before departure (before delivery starts) notified by the EV400 used for delivery in the delivery plan and the power consumption data 1000 (S1101).

[0073] The depletion detection unit 113 generates a depletion detection result (S1102) based on the SOC transition profile created in step S1101 and the depletion warning SOC received from the delivery planning device 200, and then terminates the depletion detection result generation process. The depletion warning SOC may be a common value for all EV400s, or it may be different for each EV400. Details of the processes in steps S1101 and S1102 will be described later with reference to Figure 12.

[0074] Figure 12 is an explanatory diagram showing an example of a SOC transition profile. The battery depletion determination unit 113 creates a transition profile showing the SOC of the delivery EV400 at the end of delivery, arrival at and departure from the charging station, and end of the workday (i.e., when all deliveries are completed and the vehicle returns to the delivery base) for each package corresponding to the package ID, through the following process.

[0075] The power depletion determination unit 113 calculates the SOC at the end of delivery and at the charging station for each package by sequentially subtracting the SOC corresponding to the power consumption of each package ID and EVSE ID until arrival at the charging station, as indicated by the power consumption data 1000, from the SOC before departure of the delivery EV400.

[0076] Furthermore, the depletion detection unit 113 calculates the State of Charge (SOC) at the time of departure from the charging station by adding the SOC corresponding to the expected charge amount to the SOC at the time of arrival at the charging station. However, if the value obtained by adding the SOC at the time of arrival at the charging station to the SOC corresponding to the expected charge amount exceeds 100%, the depletion detection unit 113 determines the SOC at the time of departure from the charging station to be 100%. Furthermore, the depletion detection unit 113 calculates the SOC at the time of completion of delivery and at the end of the workday for each package by sequentially subtracting the SOC corresponding to the amount of power consumed for each package ID since departure from the charging station from the SOC at the time of departure from the charging station.

[0077] The power depletion determination unit 113 generates a power depletion determination result indicating that the EV400 will not run out of power if the minimum value of the SOC shown in the SOC transition profile does not fall below the power depletion warning SOC, and generates a power depletion determination result indicating that the EV400 will run out of power if the minimum value falls below the power depletion warning SOC. In the example in Figure 12, the minimum value of the SOC shown in the SOC transition profile does not fall below the power depletion warning SOC (i.e., the SOC is above the power depletion warning SOC at all times), so a power depletion determination result indicating that the EV400 will not run out of power is generated.

[0078] Furthermore, the depletion detection unit 113 may generate a depletion detection result by a simple calculation as described below. Specifically, for example, the depletion detection unit 113 may calculate the actual power consumption by subtracting the expected charge amount from the sum of all power consumption amounts indicated by the power consumption data 1000, and if the difference between the SOC of the delivery EV400 before departure and the SOC corresponding to the actual power consumption is not below the depletion warning SOC, it may generate a depletion detection result indicating that the EV400 will not run out of power. If the difference is below the depletion warning SOC, it may generate a depletion detection result indicating that the EV400 will run out of power.

[0079] The power depletion determination unit 113 may include at least one of the following in the power depletion determination result: the minimum value of SOC shown in the SOC transition profile, the SOC at the end of the workday, the actual power consumption as described above, and the difference obtained by subtracting the SOC corresponding to the actual power consumption from the SOC before the departure of the delivery EV400. In this case, the route planning unit 111 may notify the delivery planning device 200 of the information included in the power depletion determination result, in addition to whether or not the plan can be executed.

[0080] As described above, the EV driving management device 100 of this embodiment can evaluate the State of Charge (SOC) of the EV400 in a delivery plan that includes charging the EV battery at a charging station. In particular, the EV driving management device 100 calculates the expected charge amount in the EVSE300 based on the charging history information 122, thus enabling a more accurate calculation of the expected charge amount. Furthermore, as described above, the EV driving management device 100 calculates the expected charge amount after excluding charging history where charging was stopped due to force majeure on the EV400 side, thus enabling a more accurate evaluation of the capability and reliability of the EVSE300.

[0081] Thus, the EV driving management device 100 of this embodiment can estimate the amount of power consumed, taking into account charging at a charging station. Therefore, the delivery planning device 200 can easily determine whether the delivery plan it has generated is highly efficient while avoiding the EV 400 running out of power.

[0082] It should be noted that the present invention is not limited to the embodiments described above, and various modifications are included. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. It is also possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations.

[0083] Furthermore, each of the above configurations, functions, processing units, and processing means may be implemented in hardware, either partially or entirely, by designing them as integrated circuits, for example. Alternatively, each of the above configurations and functions may be implemented in software by having the processor interpret and execute programs that implement each function. Information such as programs, tables, and files that implement each function can be stored in memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

[0084] Furthermore, the control lines and information lines shown are those deemed necessary for explanatory purposes, and not all control lines and information lines are necessarily shown in the actual product. In reality, it is safe to assume that almost all components are interconnected. [Explanation of symbols]

[0085] 10 EV driving management system, 100 EV driving management device, 111 Route planning unit, 112 Remaining SOC estimation unit, 113 Battery depletion determination unit, 114 Charging performance information generation unit, 115 Charging performance processing unit, 116 Power consumption calculation unit, 117 Aggregation unit, 121 Map information, 122 Charging performance information, 123 Vehicle information, 200 Delivery planning device, 300 EVSE, 400 EV, 500 Computer, 501 CPU, 502 Memory, 503 Auxiliary storage device, 504 Communication device

Claims

1. An EV management device for managing EVs (Electric Vehicles), Equipped with a processor and memory, The aforementioned memory is Charging history information showing the charging history at the charging station, In a delivery plan including the delivery of goods by a delivery EV and the charging of the battery of the delivery EV, the delivery plan includes route information indicating the route the delivery EV will travel, including the delivery point where the goods will be delivered and the charging station where the battery of the delivery EV will be charged. Power consumption information indicating the amount of power consumed by the delivery EV as it travels along the aforementioned route, The remaining battery capacity of the delivery EV before departure in the delivery plan, The remaining capacity of the battery of the aforementioned delivery EV, and the remaining capacity of the battery, are maintained. The aforementioned processor, From the aforementioned charging history information, the charging history at the charging station indicated by the route information is extracted. Based on the extracted charging history information, the expected charge amount of the delivery EV's battery at the charging station indicated by the route information is calculated. An EV management device that determines whether the battery of the delivery EV will run out of power in the delivery plan, based on the remaining capacity before departure, the amount of power consumed, the expected charge amount, and the remaining capacity for power depletion warning.

2. An EV management device according to claim 1, The aforementioned charging history information indicates the reason for stopping charging in the aforementioned charging history. The aforementioned reason for stopping charging includes the reason for stopping charging due to force majeure on the EV side, The processor is an EV management device that extracts charging records from the charging record information, excluding charging records indicating the reason for charging stoppage due to force majeure, from the charging record information at the charging station indicated by the route information.

3. An EV management device according to claim 1, The charging history in the aforementioned charging history information indicates the EVs whose batteries have been charged. The processor is an EV management device that extracts from the charging history information the charging history of the delivery EV's battery at the charging station indicated by the route information.

4. An EV management device according to claim 1, The charging history information in the aforementioned charging history information indicates the charging start time, charging end time, the remaining battery capacity of the EV at the start of charging, and the remaining battery capacity of the EV at the end of charging. The memory holds vehicle information indicating the battery capacity of each EV as shown in the charging history information. The aforementioned processor, For each of the extracted charging records, a first value is calculated by subtracting the battery capacity of the EV at the start of charging from the remaining battery capacity of the EV at the end of charging as indicated by the charging record, and multiplying this by the battery capacity of the EV as indicated by the vehicle information. For each of the extracted charging records, a second value is calculated by subtracting the charging start time from the charging end time indicated by that charging record. An EV management device that calculates the expected charge amount based on the value obtained by dividing the sum of the first values ​​by the sum of the second values.

5. An EV management device according to claim 4, The aforementioned processor, The system receives charging session information from the charging station, including a charging station identifier that identifies the charging station, an EV identifier that identifies the EV whose battery was charged at the charging station, the charging start time, and the charging end time. The system receives charging information from the EV, including the EV identifier of the EV, the charging start time of the EV's battery, the charging end time of the EV's battery, the remaining battery capacity of the EV at the start of charging, and the remaining battery capacity of the EV at the end of charging. An EV management device that generates charging performance information by combining the charging session information and the charging information, using the combination of the EV identifier, the charging start time, and the charging end time as a key.

6. An EV management device according to claim 1, The route information indicates the order in which the delivery plan reaches the delivery point and the charging station. The aforementioned power consumption information indicates the amount of power consumed by the delivery EV during its operation in each of the routes on which the delivery EV travels in the delivery plan, which are divided by stopping points including the delivery base, the delivery point, and the charging station. The aforementioned processor, A profile of the remaining battery capacity of the aforementioned delivery EV is created, In creating the aforementioned transition profile, By referring to the aforementioned power consumption information, the remaining capacity of the delivery EV's battery at the time of arrival at each of the aforementioned stopping points reached before reaching the charging station is calculated by sequentially subtracting the capacity corresponding to the power consumption at each of the aforementioned stopping points reached before reaching the charging station, in the order described above, from the remaining capacity before departure, and this is included in the transition profile. The remaining capacity of the delivery EV at the time of departure from the charging station is calculated by adding the capacity corresponding to the expected charge amount to the remaining capacity of the delivery EV at the time of arrival at the charging station, and this is included in the transition profile. By referring to the aforementioned power consumption information, the remaining capacity of the delivery EV at each of the stopping points reached after arriving at the charging station is calculated by sequentially subtracting the capacity corresponding to the power consumption at each of the stopping points reached after arriving at the charging station, in the order described above, from the remaining capacity of the delivery EV at the time of departure from the charging station, and this is included in the transition profile. An EV management device that determines whether a power shortage will occur by comparing the minimum remaining capacity included in the transition profile with the remaining capacity for the power shortage warning.

7. An EV management device according to claim 1, The processor is an EV management device that determines whether a power shortage will occur by comparing the value obtained by subtracting the amount of power consumption indicated by the power consumption information from the remaining capacity before departure and adding the expected charge amount with the remaining capacity for power shortage warning.

8. An EV management method using an EV management device for managing EVs (Electric Vehicles), The EV management device comprises a processor and memory, The aforementioned memory is Charging history information showing the charging history at the charging station, In a delivery plan including the delivery of goods by a delivery EV and the charging of the battery of the delivery EV, the delivery plan includes route information indicating the route the delivery EV will travel, including the delivery point where the goods will be delivered and the charging station where the battery of the delivery EV will be charged. Power consumption information indicating the amount of power consumed by the delivery EV as it travels along the aforementioned route, The remaining battery capacity of the delivery EV before departure in the delivery plan, The remaining capacity of the battery of the aforementioned delivery EV, and the remaining capacity of the battery, are maintained. The aforementioned EV management method is The processor extracts the charging history at the charging station indicated by the route information from the charging history information, The processor calculates the expected charge amount of the delivery EV's battery at the charging station indicated by the route information based on the extracted charging history information. An EV management method in which the processor determines whether the battery of the delivery EV will run out of power in the delivery plan based on the remaining capacity before departure, the amount of power consumed, the expected charge amount, and the remaining capacity for power depletion warning.

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