Layout support design apparatus, layout support design method, and computer program

Abstract

To support more appropriate layout design of charging equipment even if arrangement of fueling equipment or movement information of vehicles is unknown.SOLUTION: A layout support design apparatus for fueling equipment includes: a modeling unit which generates a model that represents a range in which a vehicle traveling with fuel can move in a facility, using at least one of a grid and a graph, the modeling unit representing a plurality of layout patterns of fueling equipment that satisfies constraint conditions, in the facility represented by the model; a simulation unit which simulates, for each layout pattern, a moving route of the vehicle and fuel consumption due to travel of the vehicle and fueling from the fueling equipment; and an evaluation unit which evaluates operational efficiency of the vehicle and the fueling equipment in the facility using the simulation of the simulation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a layout aided design apparatus, a layout aided design method, and a computer program. [Background technology]

[0002] There are known technologies for the layout design of charging facilities for electric vehicles (EVs) that perform tasks in large facilities such as airports (see, for example, Patent Documents 1 and 2). Patent Documents 1 and 2 describe technologies for adjusting power distribution and charging schedules during the operation phase for general vehicles. Patent Document 3 describes a technology for dynamically allocating tasks, including charging operations, during the design and operation phases for autonomous mobile robots. Patent Document 4 describes a layout analysis program that optimizes the layout of charging facilities for general vehicles based on battery shortage frequency information obtained through vehicle movement simulations at the design phase. Patent Document 5 describes a device that supports charger layout planning by calculating the movement history and charging facility operation history of general vehicles based on vehicle movement simulations at the design phase. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 7041719 [Patent Document 2] Patent No. 5607427 [Patent Document 3] Patent No. 7290291 [Patent Document 4] Patent No. 5889289 [Patent Document 5] Patent No. 5954584 Summary of the Invention [Problem to be solved by the invention]

[0004] The technologies described in Patent Documents 1 to 3 require the location of charging facilities as an input condition, which does not necessarily result in a desirable layout, and there is room for improvement in the layout support design. The technology described in Patent Document 4 does not consider the tasks performed by vehicles, and instead designs the location of charging facilities based on known vehicle movement information. The technology described in Patent Document 5 requires the location of charging facilities and movement information of vehicles not performing tasks as input conditions. Therefore, with the technologies described in Patent Documents 4 and 5, it is difficult to simultaneously optimize both the location of charging facilities and the movement routes of vehicles performing tasks. These issues are not limited to EVs, but apply to all fuel filling facilities within facilities that use vehicles that run on fuels such as gasoline.

[0005] The present invention has been made to solve at least some of the above-mentioned problems, and aims to support the design of a more desirable fuel filling facility layout even when the layout of the fuel filling facility and vehicle movement information are unknown. [Means for solving the problem]

[0006] The present invention has been made to solve at least part of the above-mentioned problems, and can be realized in the following forms.

[0007] (1) According to one aspect of the present invention, there is provided a layout support design device for a fuel filling facility. The layout support design device includes: a modeling unit that generates a model using at least one of a grid and a graph to represent a range within the facility within which a vehicle running on fuel can move, the modeling unit representing multiple layout patterns of the filling facility that satisfy constraints within the facility represented by the model; a simulation unit that simulates, for each of the layout patterns, the movement path of the vehicle, the fuel consumption associated with the vehicle's travel, and the filling of fuel from the filling facility; and an evaluation unit that evaluates the operability of the vehicle and the filling facility at the facility using the simulation by the simulation unit.

[0008] According to this configuration, multiple layout patterns for the facility and the filling equipment are represented as a model using at least one of a grid and a graph. The operability of the vehicles and the filling equipment is evaluated using simulation results of vehicle movement paths, fuel consumption, and fuel filling for each layout pattern. With this configuration, once the layout of the filling equipment is determined as a layout pattern, the operability of the vehicles and the filling equipment is evaluated through simulation, even without information on the driving history of the vehicles traveling within the facility. In other words, even in an unknown state where the location of the filling equipment has not been determined as input and vehicle movement information is unavailable, simulation and operability evaluation support the design of a desirable filling equipment layout. This layout design support allows the user to quantitatively grasp operability through simulation alone, thereby reducing the design costs of the filling equipment within the facility. Furthermore, if the operability of a facility where a filling equipment is currently in operation changes due to changes in the tasks performed by vehicles within the facility or changes in driving performance depending on the vehicle type, operability evaluation results can be easily obtained by simulating a different layout pattern for the filling equipment. This reduces operating costs by adopting the changed layout pattern.

[0009] (2) In the above-described aspect of the layout support design device, an acquisition unit may further be provided that acquires candidate areas for the placement of the filling equipment, tasks to be performed by the vehicle, and evaluation indices used to evaluate the operability, and the modeling unit may represent, as the model, multiple placement patterns in which the filling equipment is placed in the acquired candidates, and the simulation unit may have a schedule generation unit that generates a fuel filling reservation schedule for filling fuel from the filling equipment to the vehicle using the placement pattern and the vehicle's tasks, and a travel route generation unit that determines the travel route using the generated fuel filling reservation schedule and the vehicle's tasks. According to this configuration, possible locations for the filling equipment within the facility are provided, a location for the filling equipment is selected from the locations, and multiple location patterns with different selected locations are generated. The simulation uses tasks to be performed by the vehicle and a fuel filling schedule for the vehicle to fill up with fuel within the facility. Evaluation indices are limited in advance for evaluating operability. Therefore, in this configuration, operability is evaluated using evaluation indices according to the user's needs, using a travel route that is close to an actual vehicle performing tasks and refueling within the facility.

[0010] (3) In the layout support design device of the above aspect, the acquisition unit may acquire the layout of the facility and the size of the filling equipment relative to the layout, and the modeling unit may have a vehicle management unit that manages the inflow and outflow of vehicles into the facility represented by the model, and a model generation unit that generates the model using the layout and the size of the filling equipment. With this configuration, the size of the filling equipment is expressed in the model in relation to the layout within the facility. Furthermore, since the inflow and outflow of vehicles into and out of the facility are managed, operability can be evaluated taking into account the increase or decrease in the number of vehicles depending on the task and the placement of the filling equipment within the facility according to the type of equipment.

[0011] (4) In the layout support design device of the above aspect, the modeling unit may further include a constraint condition generation unit that generates the constraint conditions using the model generated by the model generation unit and layout constraint conditions regarding whether or not filling equipment can be placed, and a layout pattern generation unit that generates multiple layout patterns using the generated constraint conditions and the model. According to this configuration, constraints related to the model and the feasibility of arranging the filling equipment are generated, and an arrangement pattern that satisfies the constraints is generated. Therefore, simulations of unrealizable arrangement patterns that do not satisfy the constraints are not performed, and the operability of the vehicle and the filling equipment can be efficiently evaluated.

[0012] (5) In the layout support design device of the above aspect, the evaluation unit may have a calculation unit that uses a simulation by the simulation unit to calculate the execution time of the vehicle's tasks, the number of times the vehicle's fuel runs out, and the burden on the support staff moving around the facility to execute the vehicle's tasks, and a quantitative evaluation unit that uses the calculation results by the calculation unit and the evaluation index to quantitatively evaluate the operability for each layout pattern. With this configuration, the evaluation of operability takes into account not only the time between tasks performed by the vehicle and the number of times the vehicle's fuel runs out, but also the distance traveled by the support staff required to complete the tasks. This configuration also takes into account tasks that cannot be performed by the vehicle and can only be performed by the support staff, so the evaluation also considers the operability of the support staff in addition to the vehicle and fueling equipment. This allows for evaluation results that are closer to actual operation.

[0013] (6) The layout support design device of the above aspect may further include a density distribution output unit that visualizes the density distribution of the vehicles on the model expressed by at least one of the grid and the graph based on the quantitative evaluation by the quantitative evaluation unit. According to this configuration, the evaluation result of the operability is visually recognized by the user in the form of an image expressing a density distribution, etc. Therefore, the user can easily recognize the evaluation result of the operation of the vehicle and the filling equipment.

[0014] (7) In the layout support design device of the above aspect, the device may further include a layout adjustment unit that generates a new layout pattern for the layout pattern for which the quantitative evaluation has been performed by the quantitative evaluation unit by changing the installation position or number of the filling equipment on the model represented by the grid within a range that satisfies the constraints, and the new layout pattern may be simulated by the simulation unit and evaluated by the evaluation unit. According to this configuration, a new placement pattern is generated by changing the installation locations and number of filling equipment based on the placement pattern whose operability has been evaluated. The operability of the new placement pattern is evaluated in the same way as for the placement pattern whose operability has already been evaluated. As a result, a new placement pattern is generated using the optimal placement pattern based on the operational evaluation of multiple placement patterns. This may result in the design of a more preferable placement pattern, which may further reduce operational costs.

[0015] The present invention can be realized in various forms, such as a power distribution system, a power management device, a power management system, a power management method and a system including these devices, a system for managing vehicle operations, a vehicle operation management method and a system including these devices, a computer program for executing these devices, a server device for distributing this computer program, a non-transitory storage medium on which a computer program is stored, etc. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a schematic block diagram of a layout design apparatus according to an embodiment of the present invention; [Figure 2] FIG. 2 is an explanatory diagram of information stored in each database. [Figure 3] FIG. 1 is an explanatory diagram of a model expressing a layout pattern of charging facilities. [Figure 4] FIG. 10 is an explanatory diagram of an example of an evaluation index. [Figure 5] 1 is a flowchart of a placement aid design method according to the present embodiment. [Figure 6] FIG. 3 is an explanatory diagram of input information in the present embodiment. [Figure 7] FIG. 2 is an explanatory diagram of a layout pattern of charging facilities. [Figure 8] FIG. 2 is an explanatory diagram of a layout pattern of charging facilities. [Figure 9] FIG. 2 is an explanatory diagram of a layout pattern of charging facilities. [Figure 10]FIG. 2 is an explanatory diagram of a layout pattern of charging facilities. [Figure 11] FIG. 10 is an explanatory diagram of the results of quantitative evaluation of the arrangement pattern. [Figure 12] FIG. 1 is an explanatory diagram of the density distribution of EV travel routes. DETAILED DESCRIPTION OF THE INVENTION

[0017] First Embodiment FIG. 1 is a schematic block diagram of a layout support design device 100 according to one embodiment of the present invention. The layout support design device 100 of this embodiment supports the layout design of charging facilities for charging EVs, which run on electricity as fuel, when the EVs perform a predetermined task within a large-scale facility. The layout support design device 100 determines a layout pattern for charging facilities that takes into account the EV's travel route when performing the task, allowing for a quantitative evaluation of the layout pattern using simulation alone. This reduces the design and operation costs involved in placing charging facilities in a large-scale facility. This embodiment describes the layout design of EV charging facilities that perform the task of ground handling (hereinafter simply referred to as "ground handling") within an airport, which is a large-scale facility.

[0018] The layout design device 100 of this embodiment is a so-called computer. For example, a computer with higher performance than a personal computer in a data center is used as such a computer. As shown in Fig. 1, the layout design device 100 includes an input unit 50 that accepts various operations from a user, an output unit 60 that is composed of a monitor that displays various images and a speaker that outputs sound, a communication unit 40, a CPU (Central Processing Unit) 10, and a storage unit 30 that stores various information.

[0019] The input unit 50 of this embodiment is composed of a keyboard, a mouse, and a microphone that accepts voice input. The communication unit 40 acquires information about the facility where the charging equipment will be installed, information about the EV that will perform the task, information about the task, etc. via wireless communication with devices, servers, etc. other than the layout support design device 100. The communication unit 40 can also transmit the layout pattern of the charging equipment within the facility designed by the layout support design device 100 to other devices.

[0020] The storage unit 30 is configured with a hard disk drive (HDD: Hard Disk Drive) etc. The storage unit 30 includes a model database (model DB) 31, a placement database (placement DB) 32, an equipment database (equipment DB) 33, a vehicle database (vehicle DB) 34, a task database (task DB) 35, a schedule database (schedule DB) 36, an index database (index DB) 37, and a result database (result DB 38).

[0021] FIG. 2 is an explanatory diagram of information stored in each database. FIG. 2 shows a list of the contents stored in each database. As shown in FIG. 2, the model DB 31, the layout DB 32, the facility DB 33, the vehicle DB 34, the task DB 35, the schedule DB 36, and the index DB 37 store input information for evaluating the layout pattern of charging facilities. The result DB 38 stores output information related to the evaluated layout pattern.

[0022] The model DB 31 stores information about a model that uses a grid to represent the area within a facility where an EV can move. Specifically, this information includes the number of grids in the vertical and horizontal directions when represented by the grid, and areas that represent obstacle areas in the model represented by the grid. Obstacle areas are areas that an EV cannot enter while traveling, such as airplane runways, the sea, and buildings.

[0023] Grids as candidates representing areas in which charging facilities can be placed are stored in the placement DB 32. For example, there are five grids in which charging facilities can be placed, and the positions of the five grids are stored.

[0024] The facility DB 33 stores the types and numbers of facilities that can be placed on grids that are candidates for placing charging facilities, as well as the charging rates when an EV uses each facility. The charging rates, which represent the capacity to charge an EV per unit time, differ depending on the type of facility. The facility DB 33 also stores information about the size of each facility relative to the overall layout of the facility.

[0025] The vehicle DB 34 stores the type and number of EVs that will perform the ground handling task, as well as the initial position, movement speed, and discharge rate of each EV at the start of the simulation. The movement speed per unit time and the discharge rate of power consumed per unit time differ depending on the type of EV.

[0026] The task DB 35 stores the types and number of tasks that EVs perform within a facility, the time at which the tasks occur, and the start and end locations when the tasks occur. For example, a GSE (Ground Support Equipment) vehicle, which is one type of EV that performs tasks, performs tasks at a predetermined location in accordance with the departure and arrival times of airplanes. The task DB 35 stores the time at which the tasks occur, the start and end locations, and other information that corresponds to the tasks.

[0027] The schedule DB 36 stores a charging reservation schedule prepared by the user for charging an EV using a charging facility. If there is no tentative charging reservation schedule stored in the schedule DB 36, a charging reservation schedule is generated by the schedule generation unit 16, which will be described later.

[0028] The index DB 37 stores evaluation indexes used to compare the operability of the simulation results for each of multiple placement patterns. Examples of evaluation indexes include the total time required for all tasks shown in Figure 2 and the total number of times the EVs performing the tasks run out of power.

[0029] The results of evaluating multiple placement patterns using evaluation indices are stored in the result DB 38. Specifically, the stored evaluation results include scatter plots and lists showing the evaluation results, and the total time required for each task, which is an evaluation index, and the number of times each vehicle ran out of power.

[0030] The CPU 10 loads a computer program stored in a read-only memory (ROM) (not shown) into a random access memory (RAM). As a result, the CPU 10 functions as an acquisition unit (image acquisition unit) 11, a model generation unit 12, a vehicle management unit 13, a condition generation unit (constraint condition generation unit) 14, a pattern generation unit (disposition pattern generation unit) 15, a schedule generation unit 16, a path generation unit (movement path generation unit) 17, a calculation unit 18, a quantitative evaluation unit 19, a distribution output unit (density distribution output unit) 21, and a placement adjustment unit 22, in addition to controlling each unit of the layout design device 100.

[0031] The acquisition unit 11 acquires input information from each of the databases 31 to 38 of the storage unit 30. In the present embodiment, the model generation unit 12 generates a model that represents the inside of a facility using grids, using the input information acquired by the acquisition unit 11. The model generation unit 12 generates the model using the number of grids that represent the facility, area information such as obstacle areas, and the size of the charging equipment relative to the layout of the entire facility. The acquisition unit 11 may acquire input information in response to an input from a user via the input unit 50.

[0032] The vehicle management unit 13 creates management information for managing the flow of EVs into and out of the facility represented by the model. The vehicle management unit 13 manages EVs that flow into the facility from positions not represented by the model and EVs that flow out of the facility. The vehicle management unit 13 may also manage the flow of other vehicles other than EVs that do not execute tasks. The vehicle management unit 13 calculates the times when EVs flow into and out of the facility.

[0033] The condition generation unit 14 generates constraint conditions that restrict the feasibility of arranging charging equipment, etc., using the grid representation model generated by the model generation unit 12 and the constraint conditions of the charging equipment acquired by the acquisition unit 11. Examples of constraint conditions include the difficulty of arranging charging equipment on a certain grid (several levels such as easily possible, difficult but possible, impossible, etc.). The model generation unit 12 uses candidates for possible arrangement of charging equipment stored in the arrangement DB 32 acquired by the acquisition unit 11 to generate a model that expresses multiple arrangement patterns of charging equipment that satisfy the constraint conditions within the facility represented by the model.

[0034] Fig. 3 is an explanatory diagram of a model that expresses the placement patterns of multiple charging facilities. In Fig. 3, within a facility that is expressed as a grid, candidate grids where charging facilities stored in the placement DB 32 can be placed are shown as five placement candidates CTB0 to CTB4, indicated by circles. In Fig. 3, areas that EVs cannot enter, such as runways and oceans, are shown hatched. Note that in Fig. 3, areas where EVs can travel are shown without dashed grid lines.

[0035] 1 generates a plurality of arrangement patterns in which charging facilities are arranged within a facility, using the constraint conditions generated by the condition generation unit 14 and the grid-represented facility interior model generated by the model generation unit 12. The pattern generation unit 15 generates a plurality of arrangement pattern candidates that satisfy the constraint conditions, using the types and numbers of facilities stored in the facility DB 33.

[0036] The schedule generation unit 16 uses the generated multiple placement patterns and information about tasks executed by the EV stored in the task DB 35 to generate a charging reservation schedule (fuel filling reservation schedule) that indicates the times when the EV will start and finish charging using which charging facility for each placement pattern. An example of a generation method is to search for a charging reservation schedule with higher operability using the route generation unit 17 and the calculation unit 18. If a charging reservation schedule prepared by the user is stored in the schedule DB 36, the stored charging reservation schedule is used.

[0037] The route generation unit 17 uses the generated charging schedule and information about the tasks to be performed by the EVs to generate a travel route for each EV to travel within the facility to perform the tasks and charge at the charging equipment. Once the travel route is determined, a simulation result of the execution of all tasks by the EVs within the facility and the charging and discharging of each EV is generated.

[0038] To evaluate the operability of the EVs and charging equipment in the facility, the calculation unit 18 uses the generated simulation results to calculate the task execution time for each EV, the number of times each EV runs out of battery while traveling, and the travel burden (travel distance, number of indoor and outdoor trips, etc.) imposed on the support staff moving around the facility. To perform tasks, the support staff assists with tasks that cannot be performed by the EV alone. The travel burden on the support staff refers to the distance required for the support staff, who are waiting at a specific location inside or outside the facility, to travel from a specific location to the task execution location to perform the work required for the task, the number of indoor and outdoor trips, etc. Note that the travel burden on the support staff also includes the burden incurred when, after completing one task, they move between task execution locations to perform another task.

[0039] The quantitative evaluation unit 19 performs a quantitative evaluation of the operability for each of the multiple placement patterns using the calculation results by the calculation unit 18 and the evaluation indexes acquired by the acquisition unit 11 from the index DB 37. The quantitative evaluation unit 19 associates the quantitative evaluation results with the core position pattern and stores them in the result DB 38. FIG. 4 is an explanatory diagram of an example of the evaluation indexes. FIG. 4 shows a list of examples of the evaluation indexes and the definitions of each evaluation index. The evaluation indexes shown in FIG. 4 are the total time required for the task, the total number of times the EV runs out of power, the distance traveled by the support staff, the peak voltage, and the installation cost.

[0040] The total task time is the time it takes for an EV to move from the start of its movement to the first task position until it moves to the final task end position. The total number of times an EV runs out of power is the sum of the number of times all EVs run out of power while executing tasks and while moving. Peak voltage is the instantaneous maximum value of the power used for charging at all charging facilities. Peak voltage tends to be higher when EVs are being charged at multiple charging facilities. Installation cost is the total cost required to install charging facilities. Even in locations where charging facilities can be installed, the cost required for installation may vary. Installation costs may also vary depending on the type of charging facility installed.

[0041] The distribution output unit 21 shown in Fig. 1 outputs the evaluation results by the quantitative evaluation unit 19 to the output unit 60 as image information of the density distribution of EVs on a model expressed by a grid shown in Fig. 3. The output results such as image information output from the distribution output unit 21 are associated with the arrangement pattern and stored in the result DB 38. Note that specific image information to be output will be described in the examples below.

[0042] The placement adjustment unit 22 generates a new placement pattern of charging facilities by changing the installation positions (grids) and number of charging facilities on a model represented by grids for the placement pattern that has been quantitatively evaluated, within a range that satisfies the constraints. The positions of the placement candidates CTB0 to CTB4 (FIG. 3) where charging facilities can be placed, as shown in FIG. 3, may be slightly movable, or multiple charging facilities may be installed. Therefore, the results of the quantitative evaluation may improve by moving the placement of charging facilities in the placement pattern or increasing or decreasing the number of charging facilities. Therefore, the placement adjustment unit 22 pursues more preferable results by providing feedback. The number of grids to which the placement adjustment unit 22 moves the charging facilities may be input by the user via the input unit 50, or may be predetermined to be 5 grids or less. The above-described simulation and quantitative evaluation are performed again on the new placement pattern generated by the placement adjustment unit 22.

[0043] <Example> Fig. 5 is a flowchart of the layout support design method of this embodiment. In the layout support design flow shown in Fig. 5, first, an acquisition step is performed in which the acquisition unit 11 acquires input information from each of the databases 31 to 38 of the storage unit 30 and from input via the input unit 50 (step S1). Fig. 6 is an explanatory diagram of input information in this embodiment. Fig. 6 shows a list of specific input information used in this embodiment.

[0044] As shown in Fig. 6, the model DB 31 stores information that a model with 70 horizontal and 30 vertical grids will be created, with each grid having a length of 50 m, and that there are five areas where charging facilities can be placed. The placement DB 32 stores information that the number of placement patterns for charging facilities to be generated is four. Note that the number of placement patterns (four) is fewer than the number of combinations of selecting three locations from five possible placement areas, and is the number of patterns that satisfies the constraints.

[0045] The facility DB 33 stores information about the number of charging facilities installed within the facility, including that each charging facility has a charging rate of 10% SOC (State Of Charge) per step. The charging rate indicates that 10% of the EV's charging capacity can be charged in one step, which is set as the unit time of the simulation.

[0046] The vehicle DB 34 stores information that the number of EVs performing the task is four, that the movement speed of each EV is one grid per step, and that the discharge rate of the power used by the EVs to perform the task and move is 1% of the charge capacity per step. In this embodiment, the four EVs are the same GSE vehicles.

[0047] The task DB 35 stores nine tasks to be executed by the EV. For each task, the task DB 35 also stores the time when the task occurs, the starting location within the facility of the task, and the ending location within the facility when the task is completed. The schedule DB 36 stores a charging reservation schedule prepared by the user, which is a schedule divided into 10 time periods at three charging facilities.

[0048] The indicator DB 37 stores the criteria for evaluating the simulation results for each charging facility layout pattern: the time required to execute all tasks is 200 steps or less, and the total number of times the EV runs out of power until all tasks are completed is 1 or less. Therefore, a charging facility layout pattern that does not meet at least one of the above two criteria is evaluated as a layout pattern that does not meet the required requirements.

[0049] The model generation unit 12 generates a model of the facility interior expressed in a grid as shown in FIG. 3 using the input information shown in FIG. 6 (step S2). The vehicle management unit 13 generates management information that manages the inflow and outflow of EVs that use charging equipment to charge electricity as fuel into the facility (step S3). The condition generation unit 14 generates constraint information using the grid-expressed model and constraint conditions related to the feasibility of placing charging equipment (step S4). The pattern generation unit 15 generates multiple placement patterns in which charging equipment is placed using the constraint conditions and the grid-expressed model of the facility interior (step S5). The processes from step S2 to step S5 correspond to the modeling process.

[0050] Each of Fig. 7 to Fig. 10 is an explanatory diagram of an example of charging facility placement patterns PT1 to PT4. Figs. 7 to 10 show grid representation models of each of the four placement patterns PT1 to PT4 corresponding to the number of placement patterns in the placement DB 32 shown in Fig. 6. In each of Figs. 7 to 10, charging facilities PTB0 to PTB2 are placed in three of the five charging facility placement candidates CTB0 to CTB4 shown in Fig. 3. Note that in each of Figs. 7 to 10, charging facilities PTB0 to PTB2 are indicated by solid-line circles, and placement candidates CTB0 to CTB4 in which charging facilities PTB0 to PTB2 are not placed are indicated by dashed-line circles.

[0051] In placement pattern PT1 shown in Fig. 7, charging equipment PTB0-PTB2 is placed in three placement candidates CTB0, CTB2, and CTB4 out of the five placement candidates CTB0-CTB4 shown in Fig. 3. In placement pattern PT2 shown in Fig. 8, charging equipment PTB0-PTB2 is placed in three placement candidates CTB1, CTB2, and CTB4 out of the five placement candidates CTB0-CTB4. In placement pattern PT3 shown in Fig. 9, charging equipment PTB0-PTB2 is placed in three placement candidates CTB0, CTB3, and CTB4 out of the five placement candidates CTB0-CTB4. In placement pattern PT4 shown in Fig. 10, charging equipment PTB0-PTB2 is placed in three placement candidates CTB1, CTB3, and CTB4 out of the five placement candidates CTB0-CTB4.

[0052] 5 is generated (step S5), the schedule generation unit 16 generates a charging reservation schedule for each of the arrangement patterns PT1 to PT4 using the generated arrangement patterns PT1 to PT4 and task information to be executed by the EVs (step S6). The schedule generation unit 16 generates a charging reservation schedule that specifies the charging times for each of the four EVs stored in the vehicle DB 34 and the charging equipment PTB0 to PTB2 to be used during charging. If a charging reservation schedule prepared by the user is stored in the schedule DB 36, the stored charging reservation schedule is used.

[0053] The route generation unit 17 generates a route for each EV traveling within the facility using the tasks to be performed by each EV and the charging reservation schedules for each of the generated placement patterns PT1 to PT4 (step S7). By generating the route, a simulation result of the execution of all tasks by the EVs and the charging and discharging of each EV is generated. The processing of steps S6 and S7 corresponds to a simulation step.

[0054] Using the simulation results, calculation unit 18 calculates the task execution time for each EV and the number of times each EV has run out of power, which are two evaluation indices stored in index DB 37 in FIG. 6 (step S8). Quantitative evaluation unit 19 performs a quantitative evaluation of the operability of each of the multiple placement patterns PT1 to PT4 using the calculation results of calculation unit 18 and the two evaluation indices (step S9). As the quantitative evaluation, quantitative evaluation unit 19 determines whether the total time required to execute each of the tasks in the multiple placement patterns PT1 to PT4 is 200 steps or less (FIG. 6). Furthermore, quantitative evaluation unit 19 determines whether the total number of times the EVs have run out of power is one or less. The processes of steps S8 and S9 correspond to the evaluation process.

[0055] FIG. 11 is an explanatory diagram of the quantitative evaluation results for each of the placement patterns PT1 to PT4. FIG. 11 shows a graph in which the horizontal axis represents the total time required to execute a task (Total Task Steps) and the vertical axis represents the total number of times the EV runs out of power (Total Zero-SOC Counts). The graph in FIG. 11 plots each of the placement patterns PT1 to PT4. The total number of times the EV runs out of power for each plot is shifted 0.5 steps in the positive direction of the vertical axis. For example, placement pattern PT2, which has a total number of times the EV runs out of power of 1, is plotted between "1" and "2" on the vertical axis. In FIG. 11, the areas of the placement patterns that satisfy the two evaluation indices are indicated by hatching. The total time required for placement patterns PT1 and PT4 is less than 200 steps. Furthermore, the total number of times the EV runs out of power is zero. In other words, placement patterns PT1 and PT4 satisfy the evaluation indices. On the other hand, although the total required time for arrangement pattern PT3 is less than 200 steps, the total number of times that arrangement pattern PT3 ran out of power is 7, which is more than 1. Also, although the total number of times that arrangement pattern PT2 ran out of power is 1, the total required time for arrangement pattern PT2 is more than 200 steps.

[0056] Once the quantitative evaluation unit 19 performs the quantitative evaluation (step S9 in FIG. 5), the distribution output unit 21 outputs the density distribution of EV movement paths as image information on the grid-represented model as the result of the quantitative evaluation to the monitor of the output unit 60 (step S10). FIG. 12 is an explanatory diagram of the density distribution of EV movement paths in placement pattern PT2. In FIG. 12, hatching of areas where EVs cannot enter is removed, and the density distribution of movement paths is shown by hatching. Of the two types of hatching, one grid indicated by cross-hatching has a density where a maximum of two EVs are present, while the other grid has a density where a maximum of one EV is present. In other words, the hatched grids are EV movement paths, and EVs do not travel in unhatched grids. Note that since one grid is 50 m square, multiple EVs can be present in one grid at the same time (step). The density distribution is taken into consideration when EVs travel on narrow roads between buildings or on connecting bridges between terminals.

[0057] When the density distribution of the movement routes is output by the distribution output unit 21 (step S10 in FIG. 5), the placement adjustment unit 22 determines whether or not to generate a new placement pattern in which the installation positions of the charging equipment PTB0-PTB2 are moved or the number of installations is increased or decreased for each placement pattern PT1-PT4 (step S11). If the installation positions of the placement candidates CTB0-CTB4 of the charging equipment PTB0-PTB2 cannot be moved or the number of installations cannot be increased or decreased due to constraints, it is determined not to generate a new placement pattern (step S11: NO), and the placement support design flow ends.

[0058] In the process of step S11, if it is determined that a new arrangement pattern is to be generated based on input via the input unit 50 or the like (step S11: YES), the arrangement adjustment unit 22 generates a new arrangement pattern (step S12). When a new arrangement pattern is generated, constraints may be set, such as how many generations of new arrangement patterns are to be generated from the original arrangement patterns PT1 to PT4. When a new arrangement pattern is generated (step S12), the processes from step S6 onwards are repeated for the generated new arrangement pattern.

[0059] As described above, in the layout design device 100 of this embodiment, the model generation unit 12 generates a model that represents multiple layout patterns PT1-PT4 of charging facilities PTB0-PTB2 that satisfy constraints in a model that represents the range in which EVs can move within a facility using a grid. The schedule generation unit 16 and the route generation unit 17 simulate EV movement routes, EV power consumption, and charging from charging facilities PTB0-PTB2 for each layout pattern. The quantitative evaluation unit 19 evaluates the operability of the simulation results. In this embodiment, once the layout of charging facilities PTB0-PTB2 is determined as layout patterns PT1-PT4, the operability of the EVs and charging facilities PTB0-PTB2 is evaluated through simulation, even if there is no information about the driving history of EVs traveling within the facility. In other words, even if the locations of charging facilities PTB0-PTB2 have not been determined as input and EV movement information is unknown, the simulation and operability evaluation support the design of a desirable layout of charging facilities PTB0-PTB2. The layout design support of this embodiment allows users to quantitatively grasp operability through simulation alone, thereby reducing the design costs of charging equipment PTB0-PTB2 within a facility. Furthermore, if operability changes in a facility where charging equipment PTB0-PTB2 is in operation due to changes in the tasks performed by EVs within the facility or the driving performance depending on the type of EV, evaluation results of operability can be easily obtained by running a simulation in which the layout pattern of charging equipment PTB0-PTB2 is changed. By adopting the changed layout pattern, operational costs are reduced.

[0060] Furthermore, the model generation unit 12 of this embodiment generates a model that represents multiple placement patterns PT1 to PT4 of the charging equipment PTB0 to PTB2 that satisfy the constraints within the facility represented by the model, using placement candidates CTB0 to CTB4 in which the charging equipment PTB0 to PTB2 can be placed and stored in the placement DB 32 acquired by the acquisition unit 11. The schedule generation unit 16 generates a charging reservation schedule for each placement pattern PT1 to PT4 using the generated multiple placement patterns PT1 to PT4 and information on tasks performed by EVs. If a charging reservation schedule is stored in the schedule DB 36, the stored charging reservation schedule is used. The route generation unit 17 generates a travel route within the facility for each EV to perform the task and charge at the charging equipment PTB0 to PTB2, using the generated charging reservation schedule and information on the tasks performed by the EVs. The simulation uses the tasks performed by the EVs and the charging reservation schedule for the EVs to charge within the facility. The total time required for all tasks and the total number of times the EV runs out of power are acquired as evaluation indices used to evaluate operability. In this embodiment, the operability is evaluated using an evaluation index according to needs, using a travel route that is similar to an actual EV that performs tasks and charges within the facility.

[0061] Furthermore, the model generation unit 12 of this embodiment generates a model using the number of grids representing the facility, area information such as obstacle areas, and the size of the charging facility relative to the layout of the entire facility. In this embodiment, the size of the charging facilities PTB0 to PTB2 is expressed on the model expressed as grids as a size relationship relative to the layout within the facility. Furthermore, because the flow of EVs into and out of the facility is managed, operability is evaluated taking into account the increase or decrease in the number of EVs according to tasks and the placement within the facility according to the type of charging facility PTB0 to PTB2.

[0062] Furthermore, the condition generating unit 14 of this embodiment generates constraint conditions that restrict the feasibility of arranging charging equipment, etc., using a grid-represented model and the constraint conditions of the charging equipment acquired by the acquiring unit 11. The pattern generating unit 15 generates a plurality of arrangement patterns PT1 to PT4 in which charging equipment PTB0 to PTB2 are arranged within the facility, using the generated constraint conditions and a grid-represented model of the facility. In this embodiment, constraint conditions related to the grid-represented model and the feasibility of arranging charging equipment in the grid are generated, and a plurality of arrangement patterns PT1 to PT4 that satisfy the constraint conditions are generated. Therefore, simulations of unrealizable arrangement patterns that do not satisfy the constraint conditions are not performed, and the operability of EVs and charging equipment PTB0 to PTB2 can be efficiently evaluated.

[0063] Furthermore, the calculation unit 18 of this embodiment uses the generated simulation results to calculate the task execution time of each EV, the number of times each EV runs out of power while traveling, and the travel distance of the support staff moving within the facility. The quantitative evaluation unit 19 uses the calculation results by the calculation unit 18 and the evaluation index acquired by the acquisition unit 11 to perform a quantitative evaluation of the operability for each of the multiple placement patterns PT1 to PT4. In this embodiment, in addition to the task execution time of the EVs and the number of times the EVs run out of power, the travel distance of the support staff required to perform the tasks is also taken into account in the evaluation of operability. That is, this embodiment also takes into account tasks that cannot be performed by EVs and can only be performed by support staff, so the operability of the support staff is evaluated in addition to the EVs and charging facilities PTB0 to PTB2. This allows for evaluation results that are closer to actual operation.

[0064] Furthermore, the distribution output unit 21 of this embodiment outputs the evaluation results by the quantitative evaluation unit 19 to the output unit 60 as image information of the density distribution of EVs on a model expressed by a grid as shown in Fig. 3. In this embodiment, the evaluation results of operability are visually recognized by the user as an image expressing the density distribution as shown in Fig. 12. This makes it easy for the user to recognize the operation evaluation of the EVs and charging facilities PTB0 to PTB2.

[0065] Furthermore, the placement adjustment unit 22 of this embodiment generates a new placement pattern of charging equipment PTB0 to PTB2 by changing the installation positions and number of charging equipment PTB0 to PTB2 on a model expressed by a grid for the placement patterns PT1 to PT4 that have been quantitatively evaluated, within a range that satisfies the constraints. In this embodiment, the newly generated placement pattern is evaluated for operability in the same way as the placement patterns PT1 to PT4 whose operability has already been evaluated. As a result, a new placement pattern is generated using an optimal placement pattern P4 based on the operation evaluation of the multiple placement patterns PT1 to PT4. This may result in the design of a more preferable placement pattern, which may further reduce operating costs.

[0066] <Modifications of the embodiment> The present invention is not limited to the above-described embodiment, and can be implemented in various forms without departing from the spirit of the present invention, including, for example, the following modifications: In the above-described embodiment, part of the configuration realized by hardware may be replaced by software, and conversely, part of the configuration realized by software may be replaced by hardware.

[0067] In the above embodiment and example, the layout support design device 100 for charging facilities PTB0-PTB2 that supply electricity as fuel to EVs has been described as an example, but the configuration of the layout support design device 100 can be modified. For example, vehicles running within the facility are not limited to EVs that run on electricity, but may be vehicles that run on gasoline, hydrogen fuel, or biofuel. There may be a plurality of vehicles that run on different fuels within the facility. Electrically powered vehicles may be EVs other than GSE vehicles. Furthermore, facilities whose simulations and operability are evaluated by the layout support design device 100 may be facilities other than airports.

[0068] In the above embodiment, the interior of the facility where the EV travels is represented by a grid-based model. However, it may be represented by a model using various graphs instead of a grid. An example of a graph-based model is a model represented by nodes corresponding to the grids in the above embodiment and lines connecting the nodes. In this model, the lines connecting the nodes represent roads. Each node represents a parking lot, an airplane apron, a portion of a runway, or the like. In the above embodiment, the acquisition unit 11 acquires input information from each of the databases 31 to 37 in the storage unit 30. However, the method of acquiring input information can be modified. For example, the layout design device 100 may not include the storage unit 30, and the acquisition unit 11 may acquire necessary input information via communication from another storage device, server, or the like, or may acquire information from an input via the input unit 50.

[0069] CPU 10 does not have to function as vehicle management unit 13. When vehicle management unit 13 does not manage the flow of EVs and other vehicles into and out of a facility, pattern generation unit 15 may generate multiple placement patterns PT1 to PT4, for example, on the premise that vehicles start moving from a specific location (such as a garage) within the facility. Furthermore, acquisition unit 11 does not have to acquire the layout of the facility and the sizes of charging facilities PTB0 to PTB2 relative to the layout. In this case, pattern generation unit 15 may generate placement patterns PT1 to PT4, for example, by treating candidate areas where charging facilities are to be placed as one grid regardless of the type of charging facility.

[0070] The calculation unit 18 does not need to calculate the travel distance of the support staff, etc. The calculation unit 18 is only required to calculate parameters related to the evaluation criteria by the quantitative evaluation unit 19, and does not need to calculate parameters unrelated to the evaluation criteria. On the other hand, the calculation unit 18 may calculate parameters unrelated to the evaluation criteria and store the calculation results in the result DB 38. In this case, if the evaluation criteria change, the layout support design for the fuel filling equipment may be performed using the parameters stored in the result DB 38 without performing another simulation.

[0071] The CPU 10 does not have to function as the distribution output unit 21. Image information of the vehicle density distribution does not have to be output, and only the evaluation results of the operability as shown in FIG. 11 may be stored in the result DB 38. The image information output by the distribution output unit 21 is information that visualizes the vehicle density distribution on the model, and includes, for example, images, lists, and videos. The CPU 10 does not have to function as the placement adjustment unit 22. In this case, it does not have to determine whether or not placement adjustment is required as shown in steps S11 and S12 of FIG. 5, and simulation and evaluation may be performed for only the multiple placement patterns PT1 to PT4 generated by the pattern generation unit 15.

[0072] The method of moving charging equipment PTB0 to PTB2 by the placement adjustment unit 22 can be modified. For example, in placement pattern PT4 shown in FIG. 12, when the placement adjustment unit 22 selects charging equipment PTB1 as the target to be moved, the placement adjustment unit 22 may select a destination of charging equipment PTB1 other than the hatched EV movement route. Because the movement route determined by simulation is a route that results in reduced operating costs, moving charging equipment PTB1 to a location other than the movement route may further reduce the operating costs of the new placement pattern. Furthermore, the placement adjustment unit 22 may move charging equipment PTB1 into a grid adjacent to a building that EVs cannot enter. Charging equipment PTB1 needs to be connected to a power line, and from a maintenance perspective, it may be preferable to move it to a building where a support staff member is waiting.

[0073] This aspect has been described above based on embodiments and modifications. However, the above-described embodiments are intended to facilitate understanding of this aspect and are not intended to limit this aspect. This aspect may be modified or improved without departing from the spirit and scope of the claims, and equivalents thereof are included in this aspect. Furthermore, if a technical feature is not described as essential in this specification, it may be deleted as appropriate.

[0074] The present invention can also be realized in the following forms. [Application example 1] A layout support design device for a fuel filling facility, comprising: a modeling unit that generates a model that uses at least one of a grid and a graph to represent a range within a facility in which a vehicle running on fuel can move, and that represents a plurality of layout patterns of the filling equipment that satisfy constraints within the facility represented by the model; a simulation unit that simulates, for each of the arrangement patterns, a travel route of the vehicle, fuel consumption associated with the travel of the vehicle, and fuel filling from the filling facility; an evaluation unit that evaluates the operability of the vehicle and the filling equipment in the facility using a simulation by the simulation unit; A placement support design device comprising: [Application example 2] The layout support line design device according to Application Example 1 further comprises: an acquisition unit that acquires candidates for areas in which the filling equipment can be placed, tasks to be executed by the vehicle, and evaluation indices used to evaluate the operability; the modeling unit expresses, as the model, a plurality of arrangement patterns in which the filling equipment is arranged in the acquired candidates; The simulation unit a schedule generating unit that generates a fuel filling reservation schedule for filling fuel from the filling facility to the vehicle using the allocation pattern and the vehicle task; a travel route generation unit that determines the travel route using the generated fuel filling reservation schedule and the vehicle task; A placement support design device having the above. [Application example 3] The layout design apparatus according to Application Example 1 or Application Example 2, The acquisition unit acquires a layout of the facility and a size of the filling equipment relative to the layout, The modeling unit a vehicle management unit that manages the inflow and outflow of the vehicles into the facility represented by the model; a model generation unit that generates the model using the layout and the size of the filling equipment; A placement support design device having the above. [Application example 4] The layout design apparatus according to any one of Application Examples 1 to 3, The modeling unit further a constraint condition generation unit that generates the constraint condition using the model generated by the model generation unit and a placement constraint condition regarding whether or not the filling equipment can be placed; an arrangement pattern generation unit that generates a plurality of the arrangement patterns using the generated constraint conditions and the model; A placement support design device having the above. [Application example 5] The layout design apparatus according to any one of Application Examples 1 to 4, The evaluation unit a calculation unit that calculates, using a simulation by the simulation unit, an execution time of the task of the vehicle, the number of times the fuel of the vehicle will run out, and a travel burden on a support staff member who moves within the facility to execute the task of the vehicle; a quantitative evaluation unit that performs a quantitative evaluation of the operability for each of the arrangement patterns using the calculation result by the calculation unit and the evaluation index; A placement support design device having the above. [Application Example 6] The layout design apparatus according to any one of Application Examples 1 to 5, further comprising: a density distribution output unit that visualizes the density distribution of the vehicles on the model expressed by at least one of the grid and the graph based on the quantitative evaluation by the quantitative evaluation unit. [Application Example 7] The layout design apparatus according to any one of Application Examples 1 to 6, further comprising: a placement adjustment unit that generates a new placement pattern by changing the installation positions and the number of installations of the filling equipment on the model expressed by the grid, for the placement pattern that has been quantitatively evaluated by the quantitative evaluation unit, within a range that satisfies the constraint conditions; The new placement pattern is subjected to a simulation by the simulation unit and an evaluation by the evaluation unit. [Application Example 8] A layout support design method for a fuel filling facility, comprising: a modeling step of generating a model that uses at least one of a grid and a graph to represent the range within the facility within which a vehicle running on fuel can move, and a modeling step of representing multiple layout patterns of the filling equipment that satisfy constraints within the facility represented by the model; a simulation step of simulating, for each of the arrangement patterns, a travel route of the vehicle, fuel consumption accompanying the travel of the vehicle, and fuel filling from the filling facility; an evaluation step of evaluating the operability of the vehicle and the filling equipment in the facility using a simulation performed in the simulation step; A layout support design method that achieves this. [Application Example 9] A computer program comprising: a modeling function that generates a model that uses at least one of a grid and a graph to represent the range within the facility within which a vehicle running on fuel can move, and that represents multiple placement patterns of filling equipment that satisfy constraints within the facility represented by the model; a simulation function for simulating, for each of the arrangement patterns, a travel route of the vehicle, fuel consumption accompanying the travel of the vehicle, and fuel filling from the filling facility; an evaluation unit that evaluates the operability of the vehicle and the filling equipment at the facility using a simulation by the simulation function; A computer program that enables a computer to realize the above. [Explanation of symbols]

[0075] 10...CPU 11…Acquisition part 12...Model generation section 13...Vehicle Management Department 14...Condition generation unit (constraint generation unit) 15...Pattern generation unit (arrangement pattern generation unit) 16...Schedule generation section 17...Route generation unit (movement route generation unit) 18...Calculation section 19...Quantitative Evaluation Department 21...Distribution output section (density distribution output section) 22...Placement adjustment section 30...Storage section 31...Model Database 32... Placement database 33...Facility database 34...Vehicle database 35…Task database 36...Schedule database 37...Indicator Database 38...Results database 40…Communications Department 50...Input section 60...Output section 100...Placement support design device CTB0~CTB4...Position candidates PT1~PT4...Layout pattern PTB0~PTB2…Charging equipment

Claims

1. A layout support design device for a fuel filling facility, comprising: a modeling unit that generates a model that uses at least one of a grid and a graph to represent a range within a facility in which a vehicle running on fuel can move, and that represents a plurality of layout patterns of the filling equipment that satisfy constraints within the facility represented by the model; a simulation unit that simulates, for each of the arrangement patterns, a travel route of the vehicle, fuel consumption associated with the travel of the vehicle, and fuel filling from the filling facility; an evaluation unit that evaluates the operability of the vehicle and the filling equipment in the facility using a simulation by the simulation unit; A placement support design device comprising:

2. 2. The layout support line design device according to claim 1, further comprising: an acquisition unit that acquires candidates for areas in which the filling equipment can be placed, tasks to be executed by the vehicle, and evaluation indices used to evaluate the operability; the modeling unit expresses, as the model, a plurality of arrangement patterns in which the filling equipment is arranged in the acquired candidates; The simulation unit a schedule generating unit that generates a fuel filling reservation schedule for filling fuel from the filling facility to the vehicle using the allocation pattern and the vehicle task; a travel route generation unit that determines the travel route using the generated fuel filling reservation schedule and the vehicle task; A placement support design device having the above.

3. 3. The layout design apparatus according to claim 2, The acquisition unit acquires a layout of the facility and a size of the filling equipment relative to the layout, The modeling unit a vehicle management unit that manages the inflow and outflow of the vehicles into the facility represented by the model; a model generation unit that generates the model using the layout and the size of the filling equipment; A placement support design device having the above.

4. 4. The layout design apparatus according to claim 3, The modeling unit further a constraint condition generation unit that generates the constraint condition using the model generated by the model generation unit and a placement constraint condition regarding whether or not the filling equipment can be placed; an arrangement pattern generation unit that generates a plurality of the arrangement patterns using the generated constraint conditions and the model; A placement support design device having the above.

5. 5. The layout design apparatus according to claim 4, The evaluation unit a calculation unit that calculates, using a simulation by the simulation unit, an execution time of the task of the vehicle, the number of times the fuel of the vehicle will run out, and a travel burden on a support staff member who moves within the facility to execute the task of the vehicle; a quantitative evaluation unit that performs a quantitative evaluation of the operability for each of the arrangement patterns using the calculation result by the calculation unit and the evaluation index; A placement support design device having the above.

6. 6. The layout design apparatus according to claim 5, further comprising: a density distribution output unit that visualizes the density distribution of the vehicles on the model expressed by at least one of the grid and the graph based on the quantitative evaluation by the quantitative evaluation unit.

7. The layout design device according to claim 5 or 6, further comprising: a placement adjustment unit that generates a new placement pattern by changing the installation positions and the number of installations of the filling equipment on the model expressed by the grid, for the placement pattern that has been quantitatively evaluated by the quantitative evaluation unit, within a range that satisfies the constraint conditions; The new placement pattern is subjected to a simulation by the simulation unit and an evaluation by the evaluation unit.

8. A layout support design method for a fuel filling facility, comprising: a modeling step of generating a model that uses at least one of a grid and a graph to represent the range within the facility within which a vehicle running on fuel can move, and a modeling step of representing multiple layout patterns of the filling equipment that satisfy constraints within the facility represented by the model; a simulation step of simulating, for each of the arrangement patterns, a travel route of the vehicle, fuel consumption accompanying the travel of the vehicle, and fuel filling from the filling facility; an evaluation step of evaluating the operability of the vehicle and the filling equipment in the facility using a simulation performed in the simulation step; A layout support design method that achieves this.

9. A computer program comprising: a modeling function that generates a model that uses at least one of a grid and a graph to represent the range within the facility within which a vehicle running on fuel can move, and that represents multiple placement patterns of filling equipment that satisfy constraints within the facility represented by the model; a simulation function for simulating, for each of the arrangement patterns, a travel route of the vehicle, fuel consumption accompanying the travel of the vehicle, and fuel filling from the filling facility; an evaluation unit that evaluates the operability of the vehicle and the filling equipment at the facility using a simulation by the simulation function; A computer program that enables a computer to realize the above.

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