KR20160050889A - Tour scheduler of electric vehicles having charger selection mechanism - Google Patents

Abstract

Disclosed is a scheduler of an electronic vehicle having a charger selection standard. The scheduler includes: a local information storage unit which stores information on a plurality of destinations which an electronic vehicle can visit and a plurality of charging stations for charging the electronic vehicle; a visiting place selection unit which selects a plurality of visiting places that a user intends to visit among the plurality of destinations based on a user input; and a path determination unit which determines an optimum path among paths through which the electronic vehicle can visit both the plurality of visiting places and at least one DC charger based on a pre-calculated distance between the plurality of visiting places. According to various embodiments of the present invention, mileages of the electronic vehicle and costs required for a path calculation can be reduced, thereby effectively scheduling the path of the electronic vehicle.

Description

TOUR SCHEDULER OF ELECTRIC VEHICLES HAVING CHARGER SELECTION MECHANISM WITH ELECTRIC CHARGER SELECTION METHOD

The present invention relates to a scheduler for scheduling a traveling path of an electric automobile.

Electric vehicles are an essential element in smart grids. Electric vehicles acquire energy from batteries and even traffic systems become part of the power network. The electric vehicle-based traffic system is environmentally friendly. Since the electric capacity of the electric vehicle is limited, the charging infrastructure is required to be spread.

Conventionally, an AC charger has been used as a charging infrastructure for electric vehicles. The AC charger takes a long time to charge, but it is cheap and lowers the burden on the power grid. Long charging times can hinder the diffusion of electric vehicles.

The charging time of the DC charger is short. When using a DC charger, charging during running can be considered. Therefore, with the spread of the DC charger, determining the charging station to visit during the running of the electric vehicle can be an important problem.

It is an object of the present invention to provide a technique for effectively scheduling the path of an electric vehicle, which can reduce the traveling distance of the electric vehicle and reduce the cost required for path calculation.

According to one aspect, the route scheduler includes: a local information storage unit for storing information on a plurality of destinations to which an electric vehicle can be visited and a plurality of charging stations for charging the electric vehicle; A visit point selection unit that selects a plurality of visit points the user wants to visit among the plurality of destinations based on user input; And a route determining unit that determines an optimal route among the plurality of visit points that the electric vehicle can visit based on the distance between the plurality of visited points calculated in advance.

The optimum route may include a route for visiting at least one charging point among the plurality of charging stations, and the route determining unit may determine a route to the charging station located near the starting point, As the charging point.

The optimal route may be a route having a minimum sum of distances between a plurality of visited points.

The optimal path may be determined so that no waiting time, which is the time for charging the electric vehicle, occurs.

According to one aspect, a route scheduling method includes: storing information about a plurality of destinations that an electric vehicle can visit and a plurality of charging stations for charging the electric vehicle; Selecting a plurality of visited points the user wants to visit among the plurality of destinations based on user input; And determining an optimal route, based on the distance between the plurality of visited points calculated in advance, on a route through which the electric vehicle can visit all of the plurality of visit points.

According to various embodiments of the present specification, an optimal path for reducing the travel distance of the electric vehicle can be provided.

Further, according to various embodiments of the present disclosure, it is possible to reduce the cost required for calculating the path of the electric vehicle.

Further, according to various embodiments of the present disclosure, the waiting time required for charging the electric vehicle can be reduced.

1 is a block diagram illustrating a configuration of a path scheduler according to an embodiment.
2 is a view showing a distribution of a destination and a charging station according to an embodiment.
3 is a view for explaining a method of determining a landing point and a charging point according to an embodiment.
4 to 8 are graphs for explaining experimental results using a path scheduler according to an embodiment.
FIG. 9 is a flowchart illustrating a path scheduling method according to an embodiment.

In the following, embodiments will be described in detail with reference to the accompanying drawings. Like reference symbols in the drawings denote like elements.

Various modifications may be made to the embodiments described below. It is to be understood that the embodiments described below are not intended to limit the embodiments, but include all modifications, equivalents, and alternatives to them.

The terms used in the examples are used only to illustrate specific embodiments and are not intended to limit the embodiments. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises" or "having" and the like refer to the presence of stated features, integers, steps, operations, elements, components, or combinations thereof, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this embodiment belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the contextual meaning of the related art and are to be interpreted as either ideal or overly formal in the sense of the present application Do not.

In the following description of the present invention with reference to the accompanying drawings, the same components are denoted by the same reference numerals regardless of the reference numerals, and redundant explanations thereof will be omitted. In the following description of the embodiments, a detailed description of related arts will be omitted if it is determined that the gist of the embodiments may be unnecessarily blurred.

The present invention relates to a scheduler that generates a highly responsive schedule in selecting a charging station. As a variation of the Traveling Salesman Problem (TSP), the scheduler determines the order of visits from a given set of destinations and additional charging points among a plurality of candidates. In response to an increase in the number of candidate charging stations, the scheduler recognizes the farthest destination from the starting point and selects a charger nearest to the selected destination. The number of reduced candidates has the effect that with the limited search space, the increase in mileage is small and the execution time for the driving schedule is considerably reduced. 4 to 8, the scheduler of the present invention has been shown to increase the mileage by only 2.9% and determine a reasonable schedule of 70% or more for a single candidate.

1 is a block diagram illustrating a configuration of a path scheduler according to an embodiment.

Referring to FIG. 1, a route scheduler 100 includes a local information storage unit 110, a visited point selection unit 120, a path determination unit 130, and a battery state prediction unit 140.

The local information storage unit 110 stores a plurality of destinations that the electric vehicle can visit and information about a plurality of charging stations for charging the electric vehicle. The local information storage unit 110 may store geographical and terrain information of various areas. An example of a plurality of destinations and a plurality of charging stations is shown in Fig.

2 is a view showing a distribution of a destination and a charging station according to an embodiment. 2, there are shown a plurality of destinations 10 and a plurality of charging stations 20. The plurality of destinations 10 may include a point of frequent visit by local residents or tourists. For example, The charging station 20 may include a facility for charging an electric vehicle, and a plurality of charging stations 20 may be installed in the charging station 20. The charging station 20 may include a plurality of charging stations 20, The plurality of charging stations 20 may mean a point where the DC charger for charging the electric vehicle is provided.

1, the local information storage unit 110 may receive and store information on a plurality of destinations 10 and a plurality of charging stations 20 from a management server, and may store a plurality of destinations 10 and a plurality of The information about the charging station 20 can be periodically or non-periodically updated through the management server.

The local information storage unit 110 stores the distance between the plurality of destinations 10 calculated in advance, the distance between the plurality of charging stations 20 calculated in advance, the plurality of destinations 10 calculated in advance, Can be stored. According to one aspect, the area information storage unit 110 may include a distance calculation unit for calculating the distances. The local information storage unit 110 may calculate and store the distances through the distance calculator. According to another aspect, the local information storage unit 110 may receive and store information on the distances from the management server.

The visited point selection unit 120 selects a plurality of visited points that the user desires to visit among the plurality of destinations 10 based on the user input. According to one aspect, the path scheduler 100 may include a user input for receiving user input. According to another aspect, a user input may be received via another device, such as a user terminal. A user can perform a user input for a plurality of visit points the user wants to visit through the user input unit or the other device. For example, the number of the plurality of visited points for the n plurality of destinations 10 may be n or less. Where n can be a natural number.

The route determining unit 130 determines an optimal route among the routes in which the electric vehicle can visit all of the plurality of visit points based on the distance between the plurality of visited points calculated in advance. There are a plurality of routes to visit all the plurality of visit points. The path determination unit 130 can determine an optimal path among a plurality of paths. At this time, the path determination unit 130 can use the distance between the plurality of previously calculated visited points. For example, the optimal path may be a path whose sum of distances between a plurality of visited points is the smallest. The route determining unit 130 may use a distance between a plurality of previously calculated visited points to calculate a sum of distances between a plurality of visited points for each of a plurality of paths. In this case, the time and resources required for calculating the path of the electric vehicle can be reduced.

The path determination unit 130 may determine an optimal path to include a path for visiting the charging point. In other words, the optimal path may include a path for visiting at least one of the plurality of charging stations 20 to the charging point. The charging point is the point of visit for charging the electric car. Preferably, the charging point may be a facility with a DC charger. The battery of the electric vehicle may be discharged during traveling and the waiting time may occur depending on the battery discharge, so that the determination of the charging point is required. Since there are a variety of cases for selecting a charging point, a large number of calculations may be required to select an optimum charging point. In other words, the selection of the charging point is closely related to the amount of computation of the route schedule.

The route scheduler 100 can select a charging station as a variant of the TSP. The complexity of the TSP increases exponentially with the number of destinations. The TSP determines the visit order for a fixed destination of n. On the other hand, in the present invention, the destination of n + 1 can be selected from candidates of m, and the schedule with the shortest travel distance can be selected. Therefore, the complexity of the irradiation space is O (m x (n + 1)!). It is also inefficient to designate one candidate charging station and apply a scheduling algorithm to each candidate charging station. Therefore, it is important to reduce the number of candidates for m.

At this time, the determination unit 130 can determine a charging station located at a periphery of a plurality of visiting points, which is the farthest from the starting point, as the charging point. A method of determining the visit point and the charging point will be described in detail with reference to FIG.

3 is a view for explaining a method of determining a landing point and a charging point according to an embodiment. Referring to Fig. 3, a starting point 11, a plurality of landing points 12, a landing point 13, a plurality of charging stations 20 and a charging point 21 are shown. The visiting point 13 is the most distant point of the starting point 12 from the starting point 11.

The route determining unit 130 can determine a charging station located at the vicinity of the visiting point 13 farthest from the starting point among the plurality of the visiting points 12 as the charging point 21. According to one aspect, the route determining unit 130 can determine a charging station located within a predetermined distance from the landing point 13 as the charging point 21. According to another aspect, the route determining unit 130 may determine a charging station located closest to the landing point 13 among the plurality of charging stations 20 as the charging point 21.

Referring again to FIG. 1, the path determining unit 130 may determine an optimal path so that a standby time, which is a time for charging the electric vehicle, does not occur. In other words, the optimal path can be determined so that no waiting time, which is the time for charging the electric vehicle, occurs. The waiting time means the time that the user waits for charging of the electric vehicle only. According to one aspect, the route determining unit 130 can determine an optimal route so that the sum of distances between a plurality of visited points becomes minimum. In other words, the optimal path may be a path whose sum of distances between a plurality of visited points is the minimum. According to another aspect, the route determining unit 130 can determine an optimal route so that the time required for the electric vehicle to visit all the plurality of visit points is minimized. In other words, the optimal path may be the path where the time it takes for the electric vehicle to visit all of the plurality of landing points is the minimum.

The battery state predicting unit 140 predicts the remaining battery power of the electric vehicle. The battery condition predicting unit 140 may include a communication unit for communicating with the ECU of the electric vehicle. The battery state predicting unit 140 may receive information on the current state of charge (SoC) of the battery of the electric vehicle through the communication unit. The battery state predicting unit 140 can predict the battery remaining amount based on the current SoC and the SoC to be fluctuated along the traveling path. For example, the battery state predicting unit 140 can determine a traveling route in which the SoC is below a predetermined level. At this time, the route determining unit 130 can determine the optimum route so that the waiting time, which is the time for charging the electric vehicle, does not occur based on the battery remaining amount.

According to one aspect, the path determination unit 130 can generate one node from the starting point 11 for path determination. The path determination unit 130 can determine an optimal path based on all possible combinations of nodes. When a waiting time occurs in the process of generating a node, the path determining unit 130 may exclude the path from the optimal path.

The plurality of landing points 10 may include a first landing point and a second landing point for the electric vehicle to visit after the landing of the first landing point. At this time, the route determining unit 130 determines, based on the distance between the plurality of previously calculated visited points, a visit point closest to the first visit point among the plurality of visit points 10, .

According to another aspect, in consideration of the occurrence of the waiting time, the route determining unit 130 determines that the charging of the electric vehicle is completed on the route that the electric vehicle visits the second landing point after the visit of the first landing point If it is determined that the electric motor vehicle is to be required, the electric motor vehicle can determine a visit point closest to the second most distant from the plurality of the visit points (10) as a visit point to visit after the first electric motor vehicle have.

4 to 8 are graphs for explaining experimental results using a path scheduler according to an embodiment.

Referring to FIGS. 4 to 8, the probability that an optimum route will be derived according to the effect of the number of candidates on the mileage, the effect of the number of destinations on the mileage, the relationship between the mileage and the TSP distance, Are sequentially shown. 4-8, it can be seen that the scheduler of the present invention is shown to increase the mileage by only 2.9% and determine a reasonable schedule of 70% or more for a single candidate.

FIG. 9 is a flowchart illustrating a path scheduling method according to an embodiment.

Referring to FIG. 9, in step 200, the route scheduler stores information about a plurality of destinations that the electric vehicle can visit and a plurality of charging stations for charging electric vehicles. Multiple destinations may include points of frequent visits by locals or tourists. A plurality of charging stations means a point where a facility for charging electric vehicles is installed. Preferably, the plurality of charging stations may mean a point where the DC charger for charging the electric vehicle is provided.

In step 210, the route scheduler selects a plurality of visited points that the user desires to visit among the plurality of destinations based on the user input. According to one aspect, the path scheduler may include a user input for receiving user input. According to another aspect, a user input may be received via another device, such as a user terminal. A user can perform a user input for a plurality of visit points the user wants to visit through the user input unit or the other device.

In step 220, the route scheduler determines an optimal route, based on the distance between a plurality of pre-calculated landing points, in a path where the electric vehicle can visit all of the plurality of landing points. According to one aspect, the optimal path may include a path to visit a charging point that is at least one of a plurality of charging stations. At this time, the step 220 may include a step of determining, as a charging point, a charging station located at a periphery of a starting point and a starting point which are farthest from the starting point among the plurality of landing points.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (12)

A local information storage unit for storing a plurality of destinations that the electric vehicle can visit and information on a plurality of charging stations for charging the electric vehicle;
A visit point selection unit that selects a plurality of visit points the user wants to visit among the plurality of destinations based on user input; And
A route determining unit that determines an optimal route among the plurality of visit points that the electric vehicle can visit based on the distance between the plurality of visited points calculated in advance,
And a route scheduler for the electric vehicle. The method according to claim 1,
The optimal path may be,
And a route for visiting at least one charging point among the plurality of charging stations,
Wherein the path determining unit comprises:
Determining a charging station located at a periphery of a point of departure farthest from the starting point among the plurality of visiting points as the charging point,
Route scheduler for electric vehicles. The method according to claim 1,
The optimal path may be,
Which is determined so that a waiting time, which is a time for charging the electric vehicle, does not occur,
Route scheduler for electric vehicles. The method according to claim 1,
A battery state predicting unit for predicting a remaining battery level of the electric vehicle,
And a route scheduler for the electric vehicle. 5. The method of claim 4,
Wherein the path determining unit comprises:
Determining an optimal path so that a waiting time, which is a time for charging the electric vehicle, does not occur based on the battery remaining amount,
Route scheduler for electric vehicles. The method according to claim 1,
Wherein the plurality of visited points include:
A first landing point and a second landing point for the electric vehicle to visit after the visit of the first landing point,
Wherein the path determining unit comprises:
Determining a second visit point as a visit point closest to the first visit point among the plurality of visit points based on a distance between a plurality of pre-calculated visit points,
Route scheduler for electric vehicles. The method according to claim 6,
Wherein the path determining unit comprises:
When the electric vehicle is judged to be required to charge the electric vehicle on a route on which the electric vehicle visits the second landing point after the visit of the first landing point, Determining a second closest landing point as a landing point for the electric vehicle to visit after the visit of the first landing point,
Route scheduler for electric vehicles. The method according to claim 1,
The local-
A distance between the plurality of destinations calculated in advance, a distance between the plurality of charging stations calculated in advance, and a distance between the plurality of destinations calculated in advance and the plurality of charging stations,
Route scheduler for electric vehicles. The method according to claim 1,
The optimal path may be,
Wherein a sum of distances between a plurality of visited points is a minimum,
Route scheduler for electric vehicles. The method according to claim 1,
The optimal path may be,
Wherein the time required for the electric vehicle to visit all of the plurality of visit points is a minimum,
Route scheduler for electric vehicles. Storing information on a plurality of destinations to which the electric vehicle can be visited and a plurality of charging stations for charging the electric vehicle;
Selecting a plurality of visited points the user wants to visit among the plurality of destinations based on user input; And
Determining an optimal path among the paths that the electric vehicle can visit all of the plurality of visit points based on the distance between the plurality of visited points calculated in advance
Wherein the path scheduling method comprises the steps of: 12. The method of claim 11,
The optimal path may be,
And a route for visiting at least one charging point among the plurality of charging stations,
Wherein determining the optimal path comprises:
Determining, as the charging point, a charging station located in the vicinity of a starting point that is farthest from the starting point among the plurality of landing points.
A method of scheduling an electric vehicle path.

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