WO2013118962A1

WO2013118962A1 - Method and apparatus for determining optimal operating information using optimal information

Info

Publication number: WO2013118962A1
Application number: PCT/KR2012/010370
Authority: WO
Inventors: 장영재; 고영대
Original assignee: 한국과학기술원
Priority date: 2012-02-06
Filing date: 2012-12-03
Publication date: 2013-08-15
Also published as: KR20130090691A; KR101337002B1

Abstract

The present invention relates to a method and apparatus for determining optimal operating information using optimal information, which involve setting at least one piece of information including the battery capacity of a moving body, a value depending on whether or not power feeding lines are embedded in a service route, the number of moving bodies, and stopover time information for power feeding at a station on the operating route, which are necessary for operating along the designated route and for electrically charging while moving, thereby minimizing costs.

Description

Method and apparatus for determining optimal driving information using optimal information

The present embodiment relates to a method and apparatus for determining optimal driving information using optimal information. More specifically, the battery capacity of the moving body, the value according to whether the feed line is buried on the running line, the number of moving vehicles and the dwell time for feeding the station on the running line, which are necessary to charge the electricity while driving the line. The present invention relates to a method and apparatus for determining optimal driving information using optimal information for setting at least one of the information according to a power feeding method on a route and minimizing the cost.

The contents described in this section merely provide background information on the present embodiment and do not constitute a prior art.

On-line electric vehicles (hereinafter referred to as OLEVs), unlike conventional electric vehicles, are a new concept of electric vehicles that are powered by a wireless method from a cable buried in a road while driving and stopping. When the OLEV is driving a cable-embedded section, it is supplied with electricity by a wireless method to deliver electricity to the motor, and the remaining electricity is sent to the battery to charge the battery. Like ordinary electric cars, OLEVs operate by supplying electricity from batteries.

For wireless charging / charging, an inverter that generates electricity and a cable that delivers it must be buried on the road. One inverter is installed together in one independent cable buried section, and OLEV is wirelessly supplied with cable power through a pick-up device installed at the bottom. In general, the battery is a high-cost item, so that the battery price is more than 40% of the price of the electric vehicle, the more the battery is installed in the electric vehicle, the more expensive the battery, the more the weight of the vehicle increases It also consumes more.

Electric vehicle systems are largely always powered from a cable by means of contact (eg, trains, streetcars) and when powered from their own battery without charging / charging while driving (b, eg, general electricity). Car), and can be divided into two. In the case where the electricity is always supplied from the cable in a contact manner (a), the battery capacity and cost need not be considered because the battery is not installed. Since cables must be installed in all sections of the road, no consideration is needed. However, there are aesthetic and environmental problems due to many cable constructions. (B) If the battery is supplied from its own battery without charging / charging while driving, the battery capacity should be taken into account, but this is often determined by the use of the vehicle rather than the cost side, and directly affects the selling price of the electric vehicle. For example, for short-distance driving in factories, golf courses, etc .: small battery capacity, city-type electric vehicles running only in cities: medium battery capacity, electric vehicles for general automotive use: large battery capacity). Therefore, in this case, cable construction and battery capacity determination in terms of cost are not a big consideration.

In the case of OLEV, a new concept electric vehicle, it is possible to have two types of trade-offs, which can take advantage of two types of eco-friendly electric vehicle operation system with minimum cost and many advantages. In order to maximize cost minimization, it is necessary to determine the optimal battery capacity and the charging / discharging section of the charging cable, unlike the above two types.

In order to solve the above-described problems, the present embodiment provides at least one or more information of a battery capacity of a moving object, a value according to whether a feed line is laid on a running route, a moving number of moving bodies, and dwell time information for feeding a station on a running route. The main purpose is to provide a method and apparatus for determining optimal driving information using optimal information for setting according to the power feeding method of the phase and minimizing the cost accordingly.

According to an aspect of the present embodiment to achieve the above object, the value of the on-line electric vehicle (OLEV) battery capacity value, the value according to whether the feed line is laid on the OLEV route, the number of OLEV operation and the station on the OLEV route An initial population group setting unit configured to generate initial population information in which a predetermined number of chromosomes including at least one or more values of residence time information for feeding powers as coefficients; A fitness measurer for calculating cost information by applying predetermined objective functions and constraints to each of the chromosomes; A selection unit generating minimum cost chromosome information based on at least one of the initial individual information and the cost information; A crossover unit configured to generate crossover information by crossovering a part of a value depending on whether a feed line is embedded on the OLEV line included in the least cost chromosome; A transition unit for generating transition information in which any one of the OLEV battery capacity value included in the crossover information and a value depending on whether the feed line is buried is changed to another value; An end determination unit for terminating generation of the variation information when the variation information is generated by a predetermined number and the variation information is equal to or more than a preset percentage; And at least one of an OLEV battery capacity value included in the variation information equal to or more than the preset percentage, a value according to whether a feed line is buried on an OLEV line, a number of OLEV running numbers, and dwell time information for feeding a station on an OLEV line. According to the present invention, there is provided an optimum driving information determining apparatus using the optimum information, characterized in that it comprises an optimal information determining unit for determining the optimum value as the optimum driving information.

According to another aspect of the present embodiment, at least one or more of an OLEV battery capacity value, a value according to whether a feed line is laid on an OLEV line, a number of OLEV running numbers, and dwell time information for feeding a station on an OLEV line are counted. An initial population setting process of generating initial object information in which initial object information is set to a predetermined number of chromosomes including an initial population; A fitness function measuring process of calculating cost information by applying a predetermined objective and a constraint to each of the chromosomes; A selection process of generating minimum cost chromosome information based on at least one of the initial individual information and the cost information; A crossover process of generating crossover information by crossing over a part of values according to whether a feed line is embedded on the OLEV line included in the least cost chromosome; A mutation process of generating variation information in which any one of the OLEV battery capacity value included in the crossover information and a value according to whether the feed line is buried is changed to another value; An end determination process of ending generation of the variation information when the variation information is generated by a predetermined number and the variation information is equal to or more than a preset percentage; And at least one of an OLEV battery capacity value included in the variation information equal to or more than the preset percentage, a value according to whether a feed line is buried on an OLEV line, a number of OLEV running numbers, and dwell time information for feeding a station on an OLEV line. The present invention provides a method for determining optimal driving information using optimal information, comprising: determining a value as an optimal value, and determining an optimal information as the optimal driving information.

As described above, according to the present embodiment, the battery capacity of the moving body, the value according to whether the feed line is laid on the running route, the number of moving bodies and the station on the running route, which are necessary for charging electricity while moving At least one or more information of the residence time information for power supply of the line is set according to the power supply method on the route, and thus the cost can be minimized. In addition, according to the present embodiment, therefore, there is an effect that an optimal OLEV operating system can be constructed at a minimum investment cost.

1 is a block diagram schematically illustrating an apparatus for determining optimal driving information using optimal information according to the present embodiment;

2 is a flowchart illustrating a method of determining optimal driving information using optimal information according to the present embodiment;

3 is an exemplary diagram for explaining an OLEV line and a segment according to the present embodiment;

4 is an exemplary diagram for explaining initial entity setting, crossover, and variation according to the present embodiment;

5 is an exemplary view showing the speed according to the time of operation of the OLEV line according to the present embodiment.

Hereinafter, the present embodiment will be described in detail with reference to the accompanying drawings.

1 is a block diagram schematically illustrating an apparatus for determining optimum driving information using optimal information according to the present embodiment.

The field to be applied to the optimum driving information determining apparatus 100 according to the present embodiment may be an OLEV field. The general principle of the operation of OLEV is as follows. When the high-speed electric power is supplied to the power feeding device provided on the road while the online electric vehicle is traveling on the road, the electric power required for driving is supplied by the principle of electromagnetic induction between the power feeding device and the current collecting device provided in the electric vehicle.

In the field of OLEV, on-line electric vehicles are very expensive in two factors, which are necessary for embedding a feed line on a battery and a road provided in the on-line electric vehicle. Such a section of the battery provided in the online electric vehicle and the feeder line embedded in the road should be extracted optimally, but the online electric vehicle may be operated at the minimum cost. In other words, the lower the cost of embedding a feed line on a battery and a road provided in an online electric vehicle, the more the investment cost in the OLEV field can be minimized.

In the case of the online electric vehicle according to the present embodiment, it is assumed that a driving route is preset. In other words, there is a high possibility that a place where online electric vehicles can be applied may be applied to a predetermined route such as a bus lane, an amusement park train, an airport train, and an administrative city.

On the other hand, the operation principle of the on-line electric vehicle and the feed line embedded in the road according to the present embodiment are as follows. An online electric vehicle includes a current collector, which refers to a device that forms an induced electromotive force by a power supply device embedded in a road and supplies power to the online electric vehicle. That is, the current collector may be installed in a moving body (for example, an electric vehicle). The moving body is preferably a vehicle, but is not necessarily limited thereto, and may be widely applied to a bus, a train, a crane, or a motorbike that can be electrically driven. On the other hand, the current collector coupled to the moving body is equipped with a voltage regulator (Regulator). At this time, in order to obtain the DC power, the voltage adjusting unit rectifies the rectifying element and adjusts the voltage or current according to the load.

Here, the current collector includes a current collector unit, which refers to a part of the current collector that supplies power to an electric vehicle that is running. To briefly describe the current collecting unit in the electric vehicle, power is supplied from a power feeding device (electric supply path) embedded in the road while the electric vehicle is traveling. Such a power supply device (electric supply path) is a plurality of wires that are continuously installed at regular intervals in the direction of the electric vehicle, and are arranged in the gap between the wires adjacent to each other and magnetically and electrically insulates the wires adjacent to each other. An insulating magnetic body is provided. That is, the current collector includes a current collector core in which magnetic flux is induced and a current collector cable wound on the current collector core, and is supplied with an induced electromotive force formed from the power feeding device by using the current collector unit magnetically coupled by the power supply unit and the magnetic flux.

On the other hand, the power supply device may be implemented corresponding to the line of the online electric vehicle, and includes a power supply. Here, the power supply means an inverter. It may also be partially embedded in asphalt, which is the interior of the lane (ie road). That is, when a power feeding device including a power feeding core and a power supply power is embedded on the road to charge electric power required for driving the electric vehicle, electric current flows through the feed cable when the electric vehicle runs to supply power. have. On the other hand, such a feed cable can be implemented by dividing into several segments (segments), it is possible to supply power to the electric vehicle in progress. In this case, one feeding power (inverter) may be applied per continuous segment. Such a power supply device receives a power supply unit including a power supply core supplied with electric power from a provided power supply power source and providing a path of magnetic flux generated by a current flowing through a power supply cable connected to the power supply power supply, and a power supply cable wound around the power supply core. To power an online electric vehicle in a self-induced manner.

Hereinafter, an apparatus for determining optimal driving information for performing an algorithm for extracting an optimal battery capacity included in an online electric vehicle and an optimal embedding section of a feeding cable implemented corresponding to a route of an online electric vehicle It will be described in detail with respect to (100).

The optimum driving information determining apparatus 100 according to the present embodiment includes an initial population group setting unit 110, a fitness measuring unit 120, a selection unit 130, a crossover unit 140, a transition unit 150, and an end determination. The unit 160 and the optimum information determiner 170 is included. In the present exemplary embodiment, the optimum driving information determining apparatus 100 includes an initial population group setting unit 110, a fitness measuring unit 120, a selection unit 130, a crossover unit 140, a transition unit 150, and an end determination unit. Although it is described as including only the 160 and the optimum information determiner 170, which is merely illustrative of the technical idea of the present embodiment, those skilled in the art to which this embodiment belongs Various modifications and variations to the components included in the optimum driving information determining apparatus 100 may be applied without departing from the essential characteristics of the examples.

Meanwhile, the OLEV route according to the present embodiment is preset route information, which is divided into N segments, includes a preset station position value on the route information, and each segment has a different length value. Each segment has a value depending on whether a feed line is buried. At this time, the OLEV battery capacity value is set to a battery SOC that does not exceed I _high at an end point of the i ^th segment of the segment on the OLEV line and maintains I _low or more. In addition, the OLEV battery capacity value has a real number, and the value according to whether the feed line is buried on the OLEV line has a binary number. For example, the OLEV battery capacity value may have a real value of any one of 0 to 20, and a value according to whether a feed line is buried on an OLEV line may be divided by the number of segments. That is, when the OLEV line is divided into about 10 segments, a value depending on whether the feed line is buried on the OLEV line may be 10 binary numbers. In addition, the value according to whether the feed line is buried on the OLEV line has a value of 0 or 1 depending on whether a feed line for feeding power is installed for each segment. Here, an inductive cable for power feeding may be installed in the feed line.

The initial population setting unit 110 counts at least one or more of an OLEV battery capacity value, a value according to whether a feed line is laid on an OLEV route, a number of OLEV service numbers, and dwell time information for feeding a station on an OLEV route. Initial object information is set in which a predetermined number of chromosomes are included. Here, the initial population setting unit 110 may be set to about 100 chromosomes, but is not necessarily limited thereto. Meanwhile, to describe the process of setting the chromosome, the initial population setting unit 110 may set the chromosome randomly or set by a user's manipulation or command.

In addition, the initial population setting unit 110 sets the chromosome using any one of the first model method or the second model method, and arranges the coefficients included in the chromosome according to the first model method or the second model method. do. Here, the first model method refers to a method in which the OLEV secures a battery state of charge (SOC) by feeding power from a terminal station in a state where all stations on the OLEV line have been completed once. Meanwhile, the second model method refers to a method of securing battery SOC through power supply when OLEV stays at each station on an OLEV line while driving.

On the other hand, in more detail with respect to the counting arrangement included in the chromosome, the initial population setting unit 110 when the first model method using the OLEV battery capacity value, the OLEV running number value, the power supply at the destination station on the OLEV route The coefficients included in the salt sector are arranged in the order of the residence time information and the value according to whether the feed line is laid on the OLEV line. When using the second model, the initial population setting unit 110 in order of the OLEV battery capacity value, the number of OLEV operation number, the residence time information for feeding the station on the OLEV line and the value depending on whether or not the feed line is laid on the OLEV line Arrange the coefficients included in the salt sector.

On the other hand, the chromosome is a kind of initial individual information for extracting the optimum value of the OLEV battery capacity value and the value depending on whether the feed line is buried on the OLEV line, the fitness measure unit 120, the selector 130, the crossover unit 140 ), Since the corresponding value is changed through the transition unit 150, the termination determination unit 160, and the like, the present invention will be described as being defined as a chromosome.

The fitness measurer 120 calculates cost information by applying a predetermined objective function and constraints to each chromosome set through the initial population setter 110. For example, the fitness measurer 120 may apply an objective formula and a pharmaceutical formula to each of about 100 chromosomes, and calculate about 100 cost information for each of about 100 chromosomes.

On the other hand, the objective formula applied by the fitness measure unit 120 is the same as [Equation 1].

Equation 1

(k: OLEV operation, C _vehicle : OLEV base price, C _battery : OLEV _battery price per unit of capacity, I _max : OLEV battery capacity, C _inverter : price per _inverter , y (i): i ^th

segment

1, 0 when not installed, C _cable : price per meter of cable buried in the OLEV path, x (i): i ^th 1 when inductive cable is installed in segment, 0, l (i): i ^th Length information on the segment)

On the other hand, the constraint applied by the fitness measurer 120 according to the first model is as shown in [Equation 2].

Equation 2

(I (i): battery SOC at i ^th segment end, s (i): battery ^feed at i ^th segment, x (i): 1 when inductive cable is installed on i ^th segment, 0 when not installed, d (i): Battery consumption in segment i ^th , t _cs : Feed time information at destination station, I _CS : Feed amount per unit time when OLEV is on feed line (inductive cable), T: OLEV line Time required to complete all stations on the network once, t _interval : Information on the time interval of the OLEV running, L: total line length information, y (i): 1 when the inverter is installed on the i ^th segment, 0 when not installed)

On the other hand, the constraint applied by the fitness measurer 120 according to the second model is the same as [Equation 3].

Equation 3

(I (i): battery SOC at i ^th segment end, x (i): 1 with inductive cable installed on i ^th segment, 0 without, s (i): battery feed at i ^th segment, z (i): 1 in station i in segment i ^th , 0 in absence, t _cs (i): Feed time information in each station, d (i): battery consumption in segment i ^th , T: OLEV route time information required to once finish to all stations on, k: number of station daet of OLEV, t _interval: Service time interval OLEV that vehicle management information, L: entire line length information, y (i): inverter for i ^th segment 1 when installed, 0 when not installed)

The selector 130 generates minimum cost chromosome information based on at least one or more of initial object information generated by the initial population setter 110 and cost information generated by the fitness measurer 120. Here, the process of generating the minimum cost chromosome information by the selector 130 will be described in more detail as follows. The selector 130 randomly selects a first chromosome which is a plurality of chromosomes among initial individual information, and generates minimum cost chromosome information on which a chromosome having a minimum cost is selected based on each cost information of the selected first chromosome. .

That is, the selector 130 randomly selects the first chromosome, which is two chromosomes, among the initial individual information, and selects one chromosome having the minimum cost among the two chromosomes based on the cost information of each of the selected two chromosomes. The minimum cost chromosome information is generated by repeating the process of selecting the minimum cost chromosome a predetermined number of times. For example, the selector 130 randomly selects the first chromosome, which is two chromosomes, from among about 100 initial individual information, and selects one of the two chromosomes having the minimum cost based on the cost information of each of the two selected chromosomes. Selecting a chromosome may be performed about 100 times to generate minimum cost chromosome information including about 100 chromosomes.

The crossover unit 140 generates crossover information that crosses a part of values depending on whether a feed line is embedded on an OLEV line included in the minimum cost chromosome generated by the selector 130. Here, the process of generating the crossover information by the crossover unit 140 will be described in more detail as follows. The crossover unit 140 randomly selects a second chromosome, which is a pair of chromosomes, and selects a part of the OLEV battery capacity value between the pair of chromosomes and the value depending on whether the feed line is laid on the OLEV multi-line. Generates crossover information that has been crossover.

That is, the crossover unit 140 generates a first random number for each of the minimum cost chromosome information, generates first probability information that selects only chromosomes within a predetermined probability among random numbers, and generates first probability information. Randomly selects two chromosomes, the second chromosome, crosses some of the values depending on whether the feed line is laid on the OLEV line between the two chromosomes, and generates crossover information by repeating the crossover a predetermined number of times; do. For example, the crossover unit 140 generates a first random number for each of the least cost chromosomal information including about 100 chromosomes, and selects only the chromosomes corresponding to within a predetermined probability (about 70%) among the random numbers. Generates first probability information, randomly selects a second chromosome from the first probability information (within about 70%), and selects a part of values according to whether or not the feed line is buried on the OLEV line between the two chromosomes ( For example, crossover of the second to fourth segments may be performed about 50 times to generate crossover information including about 100 chromosomes. Here, the first random number refers to the generation of random numbers, which is a set of numbers arranged to collect the samples without any bias and have a certain probability when collecting various samples in a disorderly scattered set.

Meanwhile, the crossover unit 140 randomly sets a range among values according to whether a feed line is embedded on an OLEV line included in each of the two chromosomes, and crossovers a value corresponding to the set range. For example, the crossover unit 140 may randomly position (range) a value of a value according to whether a feed line is embedded on an OLEV line included in each of two randomly selected chromosomes among first probability information (within about 70%). ) And crossover the value corresponding to the set segment position (range).

The transition unit 150 generates transition information in which any one of an OLEV battery capacity value included in the crossover information and a value depending on whether a feed line is embedded is changed to another value. Here, the process of generating the variation information by the variation unit 150 will be described in more detail as follows. The mutation unit 150 randomly selects a third chromosome from the crossover information generated by the crossover unit 140, and selects one of the OLEV battery capacity included in the third chromosome and a value according to whether a feed line is embedded. Generates variation information in which V is mutated to another value.

That is, the variation unit 150 generates a second random number for each of the crossover information generated through the crossover unit 140 and second probability information that selects only chromosomes corresponding to a predetermined probability among random numbers. And randomly select only one chromosome as the third chromosome among the second probability information, and change one of the OLEV battery capacity value included in the selected chromosome and the value depending on whether the feed line is buried on the OLEV line to another value. And generating variation information by repeating the variation process a predetermined number of times. Here, the second random number means the generation of a random number which is a set of numbers arranged to collect the samples without any bias and have a certain probability when collecting various samples in a disorderly scattered set. For example, the variation unit 150 generates a second random number for each of the crossover information including about 100 chromosomes, and selects only the chromosomes corresponding to within a predetermined probability (about 5%) among the random numbers. Generate probability information, randomly select one chromosome among the second probability information (chromosome information within about 5%) as the third chromosome, and embed the OLEV battery capacity value included in the selected chromosome and the feed line on the OLEV line. One of the values according to the variable is transformed to another value, and the variation information is generated by performing this variation process about 100 times.

On the other hand, to explain the process of the variation unit 150 to change the OLEV battery capacity value, the variation unit 150 corresponds to the OLEV battery capacity value when changing the OLEV battery capacity value contained in the selected chromosome to another value Mutes one of the mistakes. For example, when it is assumed that the OLEV battery capacity value has a real value of any one of 0 to 20, the variation unit 150 is '0 to 20' when the OLEV battery capacity value included in the selected chromosome is '15'. Is to change to a value other than '15'.

On the other hand, if the transition unit 150 to explain the process of changing the value according to whether the feed line is buried on the OLEV line, the transition unit 150 when the value of whether to embed the feed line on the OLEV line is changed to another value Change to another value among the set binary numbers. For example, the value according to whether the feed line is buried on the OLEV line can be divided by the number of segments, and if the value according to whether the feed line is buried on the OLEV line for each segment is assumed to have a binary value, the transition unit 150 The value according to whether the feed line embedded in the second to sixth segments of the segments on the OLEV route included in the selected chromosome may be changed to another value (0 → 1, 1 → 0).

The end determination unit 160 generates the variation information through the transition unit 150, and when the variation information is the same as the preset percentage or more, the generation of the variation information is terminated. That is, the termination determination unit 160 generates about 100 transition information, and when the OLEV battery capacity value included in the variation information and the value according to the position of the feed line embedding position on the OLEV line become equal to each other by about 95% or more, The generation of the variation information through the variation unit 150 may be stopped. If not equal to or greater than 95%, the fitness measurement unit 120 returns to the fitness measurement unit 120 based on the chromosome (group) included in the current variation information and repeats the fitness measurement, selection, crossover, and variation again.

The optimum information determiner 170 determines the OLEV battery capacity value included in the same variation information by a predetermined percentage or more, a value according to whether a feed line is laid on an OLEV line, the number of running OLEV values, and a residence time for feeding a station on an OLEV line. At least one or more values of the information are determined as optimal values, and the optimum values are determined as optimal driving information. That is, when the variation information becomes equal to about 95% or more, the optimal information determiner 170 substantially includes the initial population setter 110, the fitness measure 120, the selector 130, and the crossover 140. And the variation information generated through the operation of the transition unit 150 are substantially the same, and the value according to whether or not the OLEV battery capacity value included in the same variation information is 95% or more and whether the feed line is buried on the OLEV line. The optimal value can be determined.

2 is a flowchart illustrating a method of determining optimal driving information using optimal information according to the present embodiment.

The operation of the optimum driving information determining apparatus 100 for extracting the optimal battery capacity provided in the online electric vehicle and the optimum embedding section of the feed cable implemented in correspondence with the route of the online electric vehicle is shown in FIG. 2. Explain.

The optimum driving information determining apparatus 100 includes, as a coefficient, at least one or more of an OLEV battery capacity value, a value according to whether a feed line is laid on an OLEV line, a number of OLEV running numbers, and dwell time information for feeding a station on an OLEV line as a coefficient. Initial object information is set in which a chromosome is set to a predetermined number (S210). In operation S210, the optimum driving information determining apparatus 100 may set the number of chromosomes to about 100 but is not limited thereto. Meanwhile, the optimum driving information determining apparatus 100 may set chromosomes randomly or by a user's manipulation or command.

At this time, the optimum driving information determining apparatus 100 sets the chromosome by using any one of the first model method and the second model method, and calculates coefficients included in the chromosome according to the first model method or the second model method. Arrange. Here, the first model method refers to a method in which the OLEV secures a battery state of charge (SOC) by feeding power from a terminal station in a state where all stations on the OLEV line have been completed once. Meanwhile, the second model method refers to a method of securing battery SOC through power supply when OLEV stays at each station on an OLEV line while driving.

On the other hand, the coefficient arrangement process included in the chromosome to be described in more detail, the optimum driving information determining apparatus 100, when using the first model method, the OLEV battery capacity value, the OLEV running number value, the power supply at the destination station on the OLEV route The coefficients included in the salt sector are arranged in the order of the residence time information and the value according to whether the feed line is laid on the OLEV line. When using the second model method, the optimum driving information determining apparatus 100 determines the order of the values according to the OLEV battery capacity value, the OLEV running number value, the residence time information for feeding the station on the OLEV line, and whether the feeding line is laid on the OLEV line. Arrange the coefficients contained in the salt sector.

The optimum driving information determining apparatus 100 calculates cost information by applying the predetermined objective and constraint equations to the respective chromosomes set through step S210 (S220). For example, the optimum driving information determining apparatus 100 may apply an objective formula and a pharmaceutical formula to each of about 100 chromosomes, and calculate about 100 cost information for each of about 100 chromosomes. On the other hand, in step S220, the objective equation is the same as [Equation 1], the constraint is the same as [Equation 2]. In this case, when the chromosome does not satisfy the constraint such as [Equation 2], the optimal driving information determining apparatus 100 sets the cost information of the chromosome to a value exceeding a normal range so that the selection unit 130 performs the selection. It is possible to lower the probability of being selected as the least cost chromosome information.

The optimum driving information determining apparatus 100 generates minimum cost chromosome information based on at least one or more information among initial individual information generated in step S210 and cost information generated in step S220 (S230). In operation S230, the optimum driving information determining apparatus 100 selects a first chromosome which is a plurality of chromosomes randomly among initial individual information, and selects a chromosome having a minimum cost based on each cost information of the selected first chromosome. Generate cost chromosome information. That is, the apparatus 100 for determining optimal driving information randomly selects the first chromosome, which is two chromosomes, among the initial individual information, and based on the cost information of each of the selected two chromosomes, one device having the minimum cost among the two chromosomes. The chromosome is selected and the minimum cost chromosome information is generated by repeating the process of selecting the minimum cost chromosome a predetermined number of times. For example, the optimal driving information determining apparatus 100 randomly selects the first chromosome, which is two chromosomes, from among about 100 initial individual information, and selects the minimum cost of the two chromosomes based on the cost information of each of the selected two chromosomes. The process of selecting one chromosome having about 100 times may be performed to generate minimum cost chromosome information including about 100 chromosomes.

The apparatus 100 for determining optimal driving information generates crossover information obtained by crossovering a part of values depending on whether a feed line is embedded on an OLEV line included in the minimum cost chromosome generated in operation S230 (S240). In operation S240, the apparatus 100 for determining optimal driving information randomly selects a second chromosome which is a pair of chromosomes among the least cost chromosomes, and crosses some of the values according to whether a pair of chromosomes are embedded with a feed line on an OLEV line. Generates excess crossover information. That is, the optimum driving information determining apparatus 100 generates a first random number for each of the minimum cost chromosome information, generates first probability information that selects only chromosomes corresponding to a predetermined probability among random numbers, and generates a first random number. The second chromosome, which is randomly selected from two chromosomes, is randomly selected from the probability information, the crossover information of the two chromosomes crosses a part of the values depending on whether the feed line is laid on the OLEV line, and the crossover is repeated a predetermined number of times. Create For example, the optimum driving information determining apparatus 100 generates a first random number for each of the least cost chromosomal information including about 100 chromosomes, and only chromosomes corresponding to within a predetermined probability (about 70%) among random numbers are included. Generates the selected first probability information, randomly selects the second chromosome among the first probability information (within about 70%), and selects the second chromosome from among the values according to whether the feed lines are laid on the OLEV line between the two chromosomes. Crossover of a portion (eg, the second to fourth segments) may be performed about 50 times to generate crossover information including about 100 chromosomes.

Meanwhile, the optimum driving information determining apparatus 100 randomly sets a range among values according to whether a feed line is embedded on an OLEV line included in each of two chromosomes, and crosses over a value corresponding to the set range. For example, the crossover unit 140 may randomly position (range) a value of a value according to whether a feed line is embedded on an OLEV line included in each of two randomly selected chromosomes among first probability information (within about 70%). ) And crossover the value corresponding to the set segment position (range).

The optimum driving information determining apparatus 100 generates shift information in which any one of the OLEV battery capacity value included in the crossover information and a value according to whether the feed line is buried is changed to another value (S250). In operation S250, the apparatus 100 for determining optimal driving information randomly selects a third chromosome from the crossover information generated through operation S240, and selects an OLEV battery capacity value included in the third chromosome and a value according to whether a feed line is embedded. Generates variation information in which one is changed to another value.

That is, the optimum driving information determining apparatus 100 generates a second random number for each of the crossover information generated through step S240, and selects the second probability information that selects only chromosomes corresponding to a predetermined probability among the random numbers. And randomly select only one chromosome among the second probability information as the third chromosome, and change one of the OLEV battery capacity value included in the selected chromosome and a value depending on whether the feed line is buried on the OLEV line to another value. In addition, variation information is generated by repeating the variation process a predetermined number of times. For example, the optimum driving information determining apparatus 100 generates a second random number for each of crossover information including about 100 chromosomes, and selects only the chromosomes corresponding to within a predetermined probability (about 5%) among the random numbers. Generate one second probability information, randomly select one chromosome among the second probability information (chromosome information within about 5%) as the third chromosome, and feed the OLEV battery capacity value and the OLEV route contained in the selected chromosome. One of the values according to whether the track is buried is changed to another value, and the variation information is generated by performing the transformation process about 100 times.

Meanwhile, in operation S250, the optimum driving information determining apparatus 100 shifts the OLEV battery capacity value included in the selected chromosome to a different value by mistake of any real number corresponding to the OLEV battery capacity value. For example, when it is assumed that the OLEV battery capacity value has a real value of any one of 0 to 20, the optimum driving information determining apparatus 100 determines a value of '0' when the OLEV battery capacity value included in the selected chromosome is '15'. To 20 'except for' 15 '.

Meanwhile, in operation S250, the optimum driving information determining apparatus 100 shifts the value according to whether or not the feed line is laid on the OLEV line to another value among the set binary numbers. For example, the value according to whether the feed line is buried on the OLEV line can be divided by the number of segments, and the value according to whether the feed line is buried on the OLEV line for each segment is assumed to have a binary value, the optimum operation information determination device ( 100 may change the value depending on whether the feed line is embedded in the second to sixth segments of the segment on the OLEV route included in the selected chromosome to another value (0 → 1, 1 → 0).

In operation S250, the optimum driving information determining apparatus 100 generates the variation information as much as a predetermined number of times and when the variation information is equal to or more than a preset percentage, the generation of the variation information is terminated (S260). In operation S260, the optimum driving information determining apparatus 100 generates about 100 transition information, and the OLEV battery capacity value included in the variation information and the value according to the position of the feed line embedding position on the OLEV line are equal to or greater than about 95%. If it is, the generation of the variation information through the transition unit 150 to stop.

The optimum driving information determining apparatus 100 is a OLEV battery capacity value included in the same variation information by a predetermined percentage or more, a value according to whether the feed line is buried on the OLEV line, the number of OLEV running numbers, and a stay for feeding the station on the OLEV line. At least one or more values of the time information are determined as optimal values, and the optimal values are determined as optimal driving information (S270). In operation S270, when the variation information is equal to about 95% or more, the optimum driving information determining apparatus 100 determines that the variation information generated through the operation of steps S210 to S260 is substantially the same, and thus 95% As described above, an optimal value may be determined based on an OLEV battery capacity value included in the same variation information and a value depending on whether a feed line is laid on an OLEV line.

In FIG. 2, steps S210 to S270 are sequentially executed. However, this is merely illustrative of the technical idea of the present embodiment, and a person having ordinary knowledge in the technical field to which the present embodiment belongs may use the present embodiment. 2 may be modified and modified in various ways, such as by changing the order described in FIG. 2 or executing one or more steps of steps S210 to S270 in parallel without departing from the essential characteristics, and thus, FIG. It is not limited.

As described above, the method for determining optimal driving information using the optimal information according to the present embodiment described in FIG. 2 may be implemented in a program and recorded in a computer-readable recording medium. A computer-readable recording medium having recorded thereon a program for implementing the method of determining optimal driving information using the optimal information according to the present embodiment includes all kinds of recording devices storing data that can be read by a computer system. Examples of such computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage, and the like, and are implemented in the form of a carrier wave (for example, transmission over the Internet). It includes being. The computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion. Also, functional programs, codes, and code segments for implementing the present embodiment may be easily inferred by programmers in the art to which the present embodiment belongs.

3 is an exemplary diagram for describing an OLEV line and a segment according to the present embodiment.

As shown in (a) of FIG. 3, in the case of an online electric vehicle, a driving route may be preset. For example, a route may be a predetermined route, such as a bus-only lane, an amusement park train, an airport train, or an administrative city. In addition, as shown in (b) of FIG. 3, the OLEV route is divided into N segments as preset route information, and includes a preset station position value on the route information, and each segment is different. It has a length value, and each segment has a value depending on whether a feed line is buried. At this time, the OLEV battery capacity value is set to a battery SOC that does not exceed I _high at an end point of the i ^th segment of the segment on the OLEV line and maintains I _low or more.

In addition, as shown in (c) of FIG. 3, if the OLEV line is assumed to be divided into about 25 segments, each segment has a value according to whether a feed line is installed and has an OLEV battery capacity value. That is, each coefficient shown in (c) of FIG. 3 will be described below. l (i) is set to length information in the ith segment, and x (i) is set to 1 when inductive cable is installed in i ^th segment and 0 when not installed. Here, the OLEV battery capacity value has a real number, and the value according to whether the feed line is buried on the OLEV line has a binary number. For example, the OLEV battery capacity value may have a real value of any one of 0 to 20, and a value according to whether a feed line is buried on an OLEV line may be divided by the number of segments. That is, when the OLEV line is divided into about 25 segments, a value depending on whether the feed line is buried on the OLEV line may be 25 binary numbers. In addition, the value according to whether the feed line is embedded on the OLEV line has a value of 0 or 1 depending on whether a feed line for feeding power is installed for each segment. In this case, an inductive cable for feeding power may be installed in the feed line.

4 is an exemplary diagram for explaining initial entity setting, crossover, and variation according to the present embodiment.

As shown in (a) of FIG. 4, the apparatus 100 for determining optimal driving information initially sets the chromosome including the OLEV battery capacity value and the value according to whether or not the feed line is laid on the OLEV line as a predetermined number. Create entity information. Here, the apparatus 100 for determining optimal driving information may be set to about 100 chromosomes, but is not necessarily limited thereto. Meanwhile, to describe the process of setting the chromosome, the initial population setting unit 110 may set the optimal driving information determiner 100 at random or by a user's operation or command.

The OLEV battery capacity value of the chromosome shown in FIG. 4A has a real number, and the value according to whether the feed line is buried on the OLEV line has a binary number. For example, the OLEV battery capacity value may have a real value of any one of 0 to 20, and a value according to whether a feed line is buried on an OLEV line may be divided by the number of segments. That is, when the OLEV line is divided into about 10 segments, a value depending on whether the feed line is buried on the OLEV line may be 10 binary numbers. In addition, the value according to whether the feed line is buried on the OLEV line has a value of 0 or 1 depending on whether a feed line for feeding power is installed for each segment. In this case, an inductive cable for power feeding may be installed in the feed line.

Meanwhile, referring to the process of crossover through FIG. 4B, the optimum driving information determining apparatus 100 generates a first random number for each of the least cost chromosomal information including about 100 chromosomes. , Generating first probability information that selects only chromosomes within a predetermined probability (about 70%) among random numbers, and randomly generates two random information as shown in FIG. 4 (b) of the first probability information (about 70%). The second chromosome, which is a chromosome, is selected, and some of the values depending on whether or not the feed line is buried on the OLEV line between the two chromosomes (for example, the second to fourth segments) are crossover (shown in FIG. 4 (b)). As described above, a process of performing 0,0,0 → 1,1,1,1,1,1 → 0,0,0) may be performed about 50 times to generate crossover information including about 100 chromosomes. Here, the apparatus 100 for determining optimal driving information randomly positions a segment among values according to whether a feed line is buried on an OLEV line included in each of two chromosomes randomly selected among first probability information (within about 70%). (Range) can be set, and the value corresponding to the set segment position (range) can be crossover.

Meanwhile, referring to (c) of FIG. 4, the optimal driving information determining apparatus 100 generates a second random number for each of crossover information including about 100 chromosomes, and generates a random number. Generate second probability information that selects only chromosomes within a predetermined probability (about 5%), and randomly selects one of the second probability information (chromosome information within about 5%) as shown in FIG. Only the chromosome is selected as the third chromosome, and one of the OLEV battery capacity values included in the selected chromosome and the value depending on whether or not the feed line is laid on the OLEV line is changed to another value, and the mutation process is performed about 100 times. Generate information.

That is, the optimum driving information determining apparatus 100 may shift the OLEV battery capacity value included in the selected chromosome to any other real number corresponding to the OLEV battery capacity value. For example, when it is assumed that the OLEV battery capacity value has a real value of any one of 0 to 20, the variation unit 150 is '0 to 20' when the OLEV battery capacity value included in the selected chromosome is '15'. Is to change to a value other than '15'.

In addition, the optimum driving information determining apparatus 100 may change the value according to whether the feed line is buried on the OLEV line to another value among the set binary numbers. For example, the value according to whether the feed line is buried on the OLEV line can be divided by the number of segments, and if the value according to whether the feed line is buried on the OLEV line for each segment is assumed to have a binary value, the transition unit 150 The value depending on whether or not the feed line embedded in the second to sixth segments of the segments on the OLEV route included in the selected chromosome is set to another value (as shown in (c) of FIG. 4, 0,1,0,1, 1 → 1,0,1,0,0).

FIG. 5 is a graph showing speed according to time when a mobile vehicle (online electric vehicle) runs on an OLEV line, and the mobile vehicle (online electric vehicle) may achieve the same speed as the graph shown according to a preset route of the OLEV route. That is, the OLEV route is preset route information, and includes a preset station position value on the route information and starts again after a certain time stops (stays) for each corresponding station.

The above description is merely illustrative of the technical idea of the present embodiment, and those skilled in the art to which the present embodiment belongs may make various modifications and changes without departing from the essential characteristics of the present embodiment. Therefore, the present embodiments are not intended to limit the technical idea of the present embodiment but to describe the present invention, and the scope of the technical idea of the present embodiment is not limited by these embodiments. The scope of protection of the present embodiment should be interpreted by the following claims, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present embodiment.

(Explanation of the sign)

100: optimum driving information determination device

110: initial population set unit 120: fitness measure unit

130: selection section 140: crossover section

150: mutation unit 160: termination determination unit

170: optimal information determination unit

CROSS-REFERENCE TO RELATED APPLICATION

This patent application claims priority under Patent Application No. 10-2012-0012010 No. 10-2012-0012010 filed to South Korea on February 06, 2012, pursuant to 35 USC § 119 (a). All content is incorporated by reference in this patent application. In addition, if this patent application claims priority for the same reason for countries other than the United States, all its contents are incorporated into this patent application by reference.

Claims

On-Line Electric Vehicle (OLEV) battery capacity value, the value according to whether the feed line is laid on the OLEV route, the number of OLEV service, and the residence time information for feeding the station on the OLEV line as a coefficient An initial population group setting unit for generating initial population information in which a predetermined number of chromosomes is included;

A fitness measurer for calculating cost information by applying predetermined objective functions and constraints to each of the chromosomes;

A selection unit generating minimum cost chromosome information based on at least one of the initial individual information and the cost information;

A crossover unit configured to generate crossover information by crossovering a part of a value depending on whether a feed line is embedded on the OLEV line included in the least cost chromosome;

A transition unit for generating transition information in which any one of the OLEV battery capacity value included in the crossover information and a value depending on whether the feed line is buried is changed to another value;

An end determination unit for terminating generation of the variation information when the variation information is generated by a predetermined number and the variation information is equal to or more than a preset percentage; And

At least one or more of an OLEV battery capacity value included in the variation information equal to or more than the preset percentage, a value according to whether a feed line is laid on an OLEV line, a number of OLEV running numbers, and dwell time information for feeding a station on an OLEV line Is determined to be an optimum value, and the optimum information determination unit for determining the optimum value as the optimum driving information

Apparatus for determining optimal driving information using optimal information comprising a.
The method of claim 1,

The initial population group setting unit,

The chromosome is set using any one of a first model method and a second model method, and the coefficients included in the chromosome are arranged according to the first model method or the second model method. Device for determining optimal driving information using information.
The method of claim 2,

The initial population group setting unit,

When using the first model method, the salt in the order of the value according to the OLEV battery capacity value, the OLEV running number value, the residence time information for power supply at the end station on the OLEV line and whether the feed line is buried on the OLEV line An apparatus for determining optimal driving information using optimal information, comprising arranging coefficients included in a sector.
The method of claim 2,

The first model method,

And a method of securing a battery state of charge (SOC) by feeding power from a termination station in a state where the OLEV completes all stations on the OLEV line once.
The method of claim 2,

The constraint according to the first model is,

(I (i): battery SOC at i th segment end, s (i): battery feed at i th segment, x (i): 1 when inductive cable is installed on i th segment, 0 when not installed, d (i): Battery consumption in segment i th , t cs : Feed time information at destination station, I CS : Feed amount per unit time when OLEV exists on feed line, T: All stations on the OLEV line Information on the time required to complete one time, t interval : Information on the time interval of the OLEV running, L: Overall line length information, y (i): 1 when the inverter is installed on the i th segment, 0 when not installed)

An apparatus for determining optimal driving information using optimal information, characterized in that.
The method of claim 2,

The initial population group setting unit,

In the case of using the second model method, the salt sector in the order of the OLEV battery capacity value, the OLEV running number value, the residence time information for feeding the station on the OLEV line, and the value according to whether the feed line is buried on the OLEV line. Optimal driving information determining apparatus using the optimum information, characterized in that the coefficients included in the arrangement.
The method of claim 2,

The second model method is

And a method for securing battery SOC through power supply when the OLEV stays at each station on the OLEV line during operation.
The method of claim 2,

The constraint according to the second model is,

(I (i): battery SOC at i th segment end, x (i): 1 with inductive cable installed on i th segment, 0 without, s (i): battery feed at i th segment, z (i): 1 when station is present in segment i th , 0 when not present, t cs (i): feeding time information at each station, d (i): battery consumption in segment i th , T: OLEV Time information for one-time completion of all stations on the route, k: number of OLEVs operated, t interval : time interval information of OLEVs operated, L: total line length information, y (i): i th segment 1 when inverter is installed, 0 when not installed

An apparatus for determining optimal driving information using optimal information, characterized in that.
The method of claim 1,

The OLEV route is divided into N segments as preset route information, and includes a preset station position value on the route information, and each of the segments has a different length value, and each of the segments Apparatus for determining optimal running information using optimum information, characterized in that it has a value according to whether the feed line is embedded.
The method of claim 9,

The OLEV battery capacity value is,

And a battery SOC that does not exceed I high at an end point of an i th segment among the segments and maintains I low or more.
The method of claim 1,

The objective formula is,

(k: OLEV operation, C vehicle : OLEV base price, C battery : price per capacity of the OLEV battery per OLEV unit, I max : OLEV battery capacity, C inverter : price per inverter , y (i): i th 1 when the inverter is installed on the segment, 0 when not installed, C cable : price per meter of cable buried in the OLEV path, x (i): i th 1 when inductive cable is installed on the segment, 0, l (i): length information in i th segment)

An apparatus for determining optimal driving information using optimal information, characterized in that.
A predetermined number of chromosomes including at least one of the capacity of the OLEV battery, the value of whether or not the feed line is laid on the OLEV line, the number of OLEV operations, and the residence time information for feeding the station on the OLEV line are set to a predetermined number. Initial population setting process for generating initial object information (Initial Population);

A fitness function measuring process of calculating cost information by applying a predetermined objective and a constraint to each of the chromosomes;

A selection process of generating minimum cost chromosome information based on at least one of the initial individual information and the cost information;

A crossover process of generating crossover information by crossing over a part of values according to whether a feed line is embedded on the OLEV line included in the least cost chromosome;

A mutation process of generating variation information in which any one of the OLEV battery capacity value included in the crossover information and a value according to whether the feed line is buried is changed to another value;

An end determination process of ending generation of the variation information when the variation information is generated by a predetermined number and the variation information is equal to or more than a preset percentage; And

At least one or more of an OLEV battery capacity value included in the variation information equal to or more than the preset percentage, a value according to whether a feed line is laid on an OLEV line, a number of OLEV running numbers, and dwell time information for feeding a station on an OLEV line Determining the optimal value and determining the optimal value as the optimal operation information

Optimal driving information determination method using the optimal information comprising a.
The method of claim 12,

The initial population setting process,

Setting the chromosome using any one of a first model method and a second model method, and arranging coefficients included in the chromosome according to the first model method or the second model method. A method of determining optimal driving information using the optimal information characterized in that.
The method of claim 13,

The initial population setting process,

When using the first model method, the salt in the order of the value according to the OLEV battery capacity value, the OLEV running number value, the residence time information for power supply at the end station on the OLEV line and whether the feed line is buried on the OLEV line A method of determining optimal driving information using optimal information, comprising the step of arranging coefficients included in a sector.
The method of claim 13,

The first model method,

And a method of securing a battery state of charge (SOC) by feeding power from a termination station while the OLEV completes all stations on the OLEV line once.
The method of claim 13,

The constraint according to the first model is,

(I (i): battery SOC at i th segment end, s (i): battery feed at i th segment, x (i): 1 when inductive cable is installed on i th segment, 0 when not installed, d (i): Battery consumption in segment i th , t cs : Feed time information at destination station, I CS : Feed amount per unit time when OLEV exists on feed line, T: All stations on the OLEV line Information on the time required to complete one time, t interval : Information on the time interval of the OLEV running, L: Overall line length information, y (i): 1 when the inverter is installed on the i th segment, 0 when not installed)

Optimal driving information determination method using the optimal information, characterized in that.
The method of claim 13,

The initial population setting process,

When using the second model method, the salt sector in the order of the value according to the OLEV battery capacity value, the OLEV running number value, the residence time information for power supply of the station on the OLEV line and the value of whether or not to feed the feed line on the OLEV line Optimal driving information determination method using the optimal information comprising the step of arranging the coefficients included in.
The method of claim 13,

The second model method is

And a method of securing battery SOC through power supply when the OLEV stays for each station on the OLEV line during operation.
The method of claim 13,

The constraint according to the second model is,

(I (i): battery SOC at i th segment end, x (i): 1 with inductive cable installed on i th segment, 0 without, s (i): battery feed at i th segment, z (i): 1 when station is present in segment i th , 0 when not present, t cs (i): feeding time information at each station, d (i): battery consumption in segment i th , T: OLEV Time information for one-time completion of all stations on the route, k: number of OLEVs operated, t interval : time interval information of OLEVs operated, L: total line length information, y (i): i th segment 1 when inverter is installed, 0 when not installed

Optimal driving information determination method using the optimal information, characterized in that.
The method of claim 12,

The OLEV route is divided into N segments as preset route information, and includes a preset station position value on the route information, and each of the segments has a different length value, and each of the segments Optimal driving information determination method using the optimum information, characterized in that having a value depending on whether the feed line is embedded.
The method of claim 21,

The OLEV battery capacity value is,

And a battery SOC that does not exceed I high and maintains I low at an end point of the i th segment among the segments.
The method of claim 12,

The objective formula is,

(k: OLEV operation, C vehicle : OLEV base price, C battery : price per capacity of the OLEV battery per OLEV unit, I max : OLEV battery capacity, C inverter : price per inverter , y (i): i th 1 when the inverter is installed on the segment, 0 when not installed, C cable : price per meter of cable buried in the OLEV path, x (i): i th 1 when inductive cable is installed on the segment, 0, l (i): length information in i th segment)

Optimal driving information determination method using the optimal information, characterized in that.