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CN112434881B - Charging station position screening method and system - Google Patents

Charging station position screening method and system Download PDF

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CN112434881B
CN112434881B CN202011447978.4A CN202011447978A CN112434881B CN 112434881 B CN112434881 B CN 112434881B CN 202011447978 A CN202011447978 A CN 202011447978A CN 112434881 B CN112434881 B CN 112434881B
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charging station
vehicle
population
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张昕
周超然
李青
王超伟
张莹
倪敏
赖斯莹
何敏
冯欣
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Changchun University of Science and Technology
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Abstract

The invention relates to a charging station position screening method and a charging station position screening system. The method comprises the following steps: constructing an initial antibody population, wherein the initial antibody population comprises a plurality of charging station position schemes; acquiring vehicle data under each charging station position scheme; inputting vehicle data into a position model, and screening corresponding charging station position schemes to obtain candidate antibody populations formed by the screened charging station position schemes; and screening the candidate antibody population by adopting an immune algorithm to obtain an optimal antibody population, and determining a preset charging station position in a charging station position scheme in the optimal antibody population as an optimal charging station position. The invention provides a more optimized charging station position setting method, which improves the charging efficiency and the charging amount of the new energy electric automobile.

Description

Charging station position screening method and system
Technical Field
The invention relates to the technical field of public facility site selection, in particular to a charging station position screening method and a charging station position screening system.
Background
With the rapid growth of world economy, the problems of environmental deterioration and resource shortage are increasingly highlighted by increasing the pollution gas generated by burning petroleum, coal, gasoline and other fuels year by year, and energy conservation and environmental protection are receiving global attention. The new energy automobile is a great opportunity for development, wherein the electric taxi is used as a public transport means, and the opportunity for directly experiencing the electric automobile is provided for passengers, so that the electric automobile is an effective way for triggering the popularization of the electric automobile. In recent years, the number of electric taxis is continuously increased, but the utilization rate of charging infrastructure is reduced due to unscientific position planning of charging stations, so that new energy electric vehicles are difficult to timely and reasonably charge, travel of the electric taxis is severely limited, inconvenience is brought to life of people, and application and popularization of the new energy electric vehicles are affected.
Disclosure of Invention
The invention aims to provide a charging station position screening method and a charging station position screening system, and provides a more optimized charging station position setting method, so that the charging efficiency and the charging amount of a new energy electric vehicle are improved.
In order to achieve the above object, the present invention provides the following solutions:
a charging station location screening method, comprising:
constructing an initial antibody population, wherein the initial antibody population comprises a plurality of charging station position schemes, and each charging station position scheme comprises a plurality of preset charging station positions;
acquiring vehicle data under each charging station position scheme; the vehicle data includes a stay time of the vehicle at the charging station each time, a position of the vehicle, a driving distance of the vehicle, and an average driving speed of the vehicle;
inputting the vehicle data into a position model, and screening corresponding charging station position schemes to obtain candidate antibody populations formed by the screened charging station position schemes; the location model includes an objective function constructed with a driving distance of the vehicle and an average driving speed of the vehicle, and a constraint condition constructed with the number of charging stations, a stay time of the vehicle at each charging station, a remaining capacity of the vehicle, a charging station position, and a charging state of the vehicle;
And screening the candidate antibody population by adopting an immune algorithm to obtain an optimal antibody population, and determining a preset charging station position in a charging station position scheme in the optimal antibody population as an optimal charging station position.
Optionally, the location model specifically includes:
wherein
Beta is the average speed of the vehicle on all roads, K represents the set of all trips of a vehicle on a certain day, P represents the set of vehicles, d pk Representing the mileage of the vehicle p in k strokes, t opk Indicating the residence time of vehicle p at charging station o after the end of k trips,indicating the charge amount, y, of the vehicle p after the end of k strokes o Indicating charging power +.>Representing the waiting time consumed by vehicle p at charging station o after the end of k trips, l representing the selected charging strategy, S pk Representing the remaining battery power of the vehicle p after the kth trip, S pk-1 Representing the remaining battery power of the vehicle p after the (k-1) th trip, E opk-1 Indicating the charge amount, r, of the vehicle p at the charging station o after completion of the (k-1) th trip p Represents the power consumption rate of the vehicle p, R pk Indicating the difference between the remaining power of the vehicle p after completion of the (k-1) th trip and the electric quantity of the (k) th trip, x o Indicating the position o, y of the charging station opk The charge state of the vehicle is represented, n represents the number of charging stations, and O represents the set of charging stations.
Optionally, the selecting the candidate antibody population by adopting an immune algorithm to obtain an optimal antibody population specifically comprises:
obtaining immune data, wherein the immune data comprises vehicle data under the candidate antibody population, the number of charging stations with the same preset charging station position between two charging station position schemes in the candidate antibody population and the total number of charging station position schemes in the candidate antibody population;
calculating propagation probability of each charging station position scheme in the candidate antibody population according to the immune data;
the propagation probabilities are ordered in a descending order, parent populations formed by charging station position schemes corresponding to the first N propagation probabilities are determined, and position populations formed by charging station position schemes corresponding to the first m propagation probabilities are determined;
judging whether a charging station position scheme in the position population meets a set constraint condition or not to obtain a first judgment result, wherein the set constraint condition is that
Wherein d is pk Indicating the mileage of vehicle p in k trips, < >>Indicating the charge amount, y, of the vehicle p after the end of k strokes o Indicating charging power +.>Representing the waiting time consumed by vehicle p at charging station o after the end of k trips, l representing the selected charging strategy, S pk Representing the remaining battery power of the vehicle p after the kth trip, S pk-1 Representing the remaining battery power, r, of the vehicle p after the (k-1) th trip p Represents the power consumption rate of the vehicle p, R pk Indicating the difference between the remaining power of the vehicle p after completion of the (k-1) th trip and the electric quantity of the (k) th trip, x o Indicating the position o, y of the charging station opk Indicating the state of charge of the vehicle, n indicating the number of charging stations, t opk Indicating the residence time of the vehicle p at the charging station O after the end of k trips, O indicating the charging station set;
if the first judgment result is yes, determining the position population as the optimal antibody population;
if the first judgment result is negative, judging whether the set iteration times are reached or not, and obtaining a second judgment result;
if the second judgment result is yes, determining the position population as the optimal antibody population;
and if the second judgment result is negative, sequentially performing selection, crossing and mutation on the parent population to obtain an operation population, determining a combined population formed by combining the operation population and the position population as a candidate antibody population for the next iteration, and returning to the step of acquiring immune data.
Optionally, the operations of sequentially selecting, intersecting and mutating the parent population to obtain an operation population, specifically:
And carrying out roulette algorithm, single-point crossover algorithm and mutation operation on the parent population in sequence to obtain an operation population.
Optionally, the calculating the propagation probability of each charging station position scheme in the candidate antibody population according to the immune data specifically includes:
calculating affinities between charging station position schemes in the candidate antibody population and antigens according to the immune data, wherein the antigens are objective functions constructed by the driving mileage of a vehicle and the average driving speed of the vehicle, and constraint conditions constructed by the number of charging stations, the stay time of the vehicle at each charging station, the residual quantity of the vehicle, the charging station positions and the charging state of the vehicle;
calculating a similarity ratio between each charging station location scheme in the candidate antibody population according to the affinity between each charging station location scheme in the candidate antibody population;
and calculating the reproduction probability of each antibody according to the affinity between each charging station position scheme in the candidate antibody population and each antigen and the similarity ratio between each charging station position scheme in the candidate antibody population.
A charging station location screening system, comprising:
an initial antibody population determination module configured to construct an initial antibody population, the initial antibody population including a plurality of charging station location schemes, each of the charging station location schemes including a plurality of preset charging station locations;
the vehicle data acquisition module is used for acquiring vehicle data under each charging station position scheme; the vehicle data includes a stay time of the vehicle at the charging station each time, a position of the vehicle, a driving distance of the vehicle, and an average driving speed of the vehicle;
the candidate antibody population determining module is used for inputting the vehicle data into a position model, screening the corresponding charging station position schemes, and obtaining candidate antibody populations formed by the screened charging station position schemes; the location model includes an objective function constructed with a driving distance of the vehicle and an average driving speed of the vehicle, and a constraint condition constructed with the number of charging stations, a stay time of the vehicle at each charging station, a remaining capacity of the vehicle, a charging station position, and a charging state of the vehicle;
and the optimal charging station position determining module is used for screening the candidate antibody population by adopting an immune algorithm to obtain an optimal antibody population, and determining a preset charging station position in a charging station position scheme in the optimal antibody population as an optimal charging station position.
Optionally, the location model includes: a location model determination unit that is:
wherein
Beta is the average speed of the vehicle on all roads, K represents the set of all trips of a vehicle on a certain day, P represents the set of vehicles, d pk Representing the mileage of the vehicle p in k strokes, t opk Indicating the residence time of vehicle p at charging station o after the end of k trips,indicating the charge amount, y, of the vehicle p after the end of k strokes o Indicating charging power +.>Representing the waiting time consumed by vehicle p at charging station o after the end of k trips, l representing the selected charging strategy, S pk Representing the remaining battery power of the vehicle p after the kth trip, S pk-1 Represents the k-1 th rowResidual battery capacity of vehicle p after journey, E opk-1 Indicating the charge amount, r, of the vehicle p at the charging station o after completion of the (k-1) th trip p Represents the power consumption rate of the vehicle p, R pk Indicating the difference between the remaining power of the vehicle p after completion of the (k-1) th trip and the electric quantity of the (k) th trip, x o Indicating the position o, y of the charging station opk The charge state of the vehicle is represented, n represents the number of charging stations, and O represents the set of charging stations.
Optionally, the optimal charging station location module includes:
an immune data acquisition unit configured to acquire immune data including vehicle data under the candidate antibody population, the number of charging stations having the same preset charging station position between two charging station position schemes in the candidate antibody population, and the total number of charging station position schemes in the candidate antibody population;
A breeding rate calculation unit for calculating the breeding probability of each charging station position scheme in the candidate antibody population according to the immune data;
the position population forming unit is used for sequencing the propagation probabilities in a descending order, determining parent populations formed by charging station position schemes corresponding to the first N propagation probabilities, and determining position populations formed by charging station position schemes corresponding to the first m propagation probabilities;
a first judging unit, configured to judge whether a charging station location scheme in the location population meets a set constraint condition, to obtain a first judging result, where the set constraint condition is that
Wherein d is pk Indicating the mileage of vehicle p in k trips, < >>Indicating the charge amount, y, of the vehicle p after the end of k strokes o Indicating charging power +.>Indicating that the vehicle p is at the charging station after the end of k tripso latency of consumption, l denotes the selected charging strategy, S pk Representing the remaining battery power of the vehicle p after the kth trip, S pk-1 Representing the remaining battery power, r, of the vehicle p after the (k-1) th trip p Represents the power consumption rate of the vehicle p, R pk Indicating the difference between the remaining power of the vehicle p after completion of the (k-1) th trip and the electric quantity of the (k) th trip, x o Indicating the position o, y of the charging station opk Indicating the state of charge of the vehicle, n indicating the number of charging stations, t opk Indicating the residence time of the vehicle p at the charging station O after the end of k trips, O indicating the charging station set;
the first result unit is used for determining that the position population is the optimal antibody population if the first judgment result is yes;
the second judging unit is used for judging whether the set iteration times are reached or not if the first judging result is negative, so as to obtain a second judging result;
the second result unit is used for determining that the position population is the optimal antibody population if the second judgment result is yes;
and a third result unit, configured to, if the second determination result is no, sequentially perform operations of selecting, intersecting, and mutating the parent population to obtain an operation population, determine a combined population formed by combining the operation population and the position population as a candidate antibody population for the next iteration, and return to the step of obtaining immune data.
Optionally, the third result unit includes:
and the operation population determining subunit is used for obtaining an operation population by sequentially adopting a roulette algorithm, a single-point crossover algorithm and a mutation operation on the parent population.
Optionally, the reproduction rate calculation unit includes:
an affinity calculation subunit for calculating, based on the immunization data, affinities between each charging station location scheme in the candidate antibody population and each antigen, the antigen being an objective function constructed with a driving distance of the vehicle and an average driving speed of the vehicle, and constraints constructed with a number of charging stations, a stay time of the vehicle at each charging station, a remaining amount of the vehicle, a charging station location, and a charging state of the vehicle;
A similarity ratio calculation subunit for calculating a similarity ratio between each charging station location scheme in the candidate antibody population according to an affinity between each charging station location scheme in the candidate antibody population;
and the breeding rate calculating subunit is used for calculating the breeding probability of each antibody according to the affinity between each charging station position scheme in the candidate antibody population and each antigen and the similarity ratio between each charging station position scheme in the candidate antibody population.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention provides a position screening method of a public charging station, which optimizes candidate positions through an immune optimization algorithm, scientifically and reasonably plans the positions of the charging stations, and improves the charging efficiency and the charging quantity of new energy electric vehicles.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a charging station location screening method according to an embodiment of the present invention;
fig. 2 is a block diagram of a charging station location screening system according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a candidate charging station scheme in a simulation scenario according to an embodiment of the present invention;
fig. 4 is an optimal charging station position of a charging station in a simulation scenario according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the present embodiment provides a charging station location screening method, which includes:
101: an initial antibody population is constructed. The initial population of antibodies includes a plurality of charging station location schemes, each of the charging station location schemes including a plurality of preset charging station locations.
102: and acquiring vehicle data under each charging station position scheme. The vehicle data includes a stay time of the vehicle at the charging station each time, a position of the vehicle, a driving range of the vehicle, and an average driving speed of the vehicle.
103: and inputting the vehicle data into a position model, and screening the corresponding charging station position schemes to obtain candidate antibody populations formed by the screened charging station position schemes. The location model includes an objective function constructed with the range of the vehicle and the average travel speed of the vehicle, and constraints constructed with the number of charging stations, the stay time of the vehicle at each charging station, the remaining charge of the vehicle, the charging station position, and yopk.
104: and screening the candidate antibody population by adopting an immune algorithm to obtain an optimal antibody population, and determining a preset charging station position in a charging station position scheme in the optimal antibody population as an optimal charging station position.
In practical application, the location model specifically includes:
wherein
Beta is the average speed of the vehicle on all roads, K represents the set of all trips of a vehicle on a certain day (the vehicle trips are calculated from the vehicle position), P represents the set of vehicles, d pk Representing the mileage of the vehicle p in k strokes, t opk Indicating the residence time of vehicle p at charging station o after the end of k trips,indicating the charge amount, y, of the vehicle p after the end of k strokes o Indicating charging power +.>Representing the waiting time consumed by vehicle p at charging station o after the end of k trips, l representing the selected charging strategy, S pk Representing the remaining battery power of the vehicle p after the kth trip, S pk-1 Representing the remaining battery power of the vehicle p after the (k-1) th trip, E opk-1 Indicating the charge amount, r, of the vehicle p at the charging station o after completion of the (k-1) th trip p Representing the power consumption rate of the vehicle p, i.e. the amount of electricity consumed per unit distance, R pk Indicating the difference between the remaining power of the vehicle p after completion of the (k-1) th trip and the electric quantity of the (k) th trip, x o Indicating the position o, y of the charging station opk The charge state of the vehicle is represented, n represents the number of charging stations, and O represents the set of charging stations.
In practical application, the candidate antibody population is screened by adopting an immune algorithm to obtain an optimal antibody population, which is specifically as follows:
and obtaining immune data, wherein the immune data comprises vehicle data under the candidate antibody population, the number of charging stations with the same preset charging station position between two charging station position schemes in the candidate antibody population and the total number of the charging station position schemes in the candidate antibody population.
And calculating the propagation probability of each charging station position scheme in the candidate antibody population according to the immunization data.
And sequencing the propagation probabilities in a descending order, determining parent populations formed by the charging station position schemes corresponding to the first N propagation probabilities, and determining the position populations formed by the charging station position schemes corresponding to the first m propagation probabilities.
Judging whether a charging station position scheme in the position population meets a set constraint condition or not to obtain a first judgment result, wherein the set constraint condition is that
Wherein d is pk Indicating the mileage of vehicle p in k trips, < >>Indicating the charge amount, y, of the vehicle p after the end of k strokes o Indicating charging power +.>Representing the waiting time consumed by vehicle p at charging station o after the end of k trips, l representing the selected charging strategy, S pk Representing the remaining battery power of the vehicle p after the kth trip, S pk-1 Representing the remaining battery power, r, of the vehicle p after the (k-1) th trip p Representing the power consumption rate of the vehicle p, i.e. the amount of electricity consumed per unit distance, R pk Indicating the difference between the remaining power of the vehicle p after completion of the (k-1) th trip and the electric quantity of the (k) th trip, x o Indicating the position o, y of the charging station opk Indicating the state of charge of the vehicle, n indicating the number of charging stations, t opk The residence time of the vehicle p at the charging station O after the end of k strokes is represented, and O represents the charging station set.
And if the first judgment result is yes, determining the position population as the optimal antibody population.
If the first judgment result is negative, judging whether the set iteration times are reached or not, and obtaining a second judgment result.
And if the second judgment result is yes, determining the position population as the optimal antibody population.
And if the second judgment result is negative, sequentially performing selection, crossing and mutation on the parent population to obtain an operation population, determining a combined population formed by combining the operation population and the position population as a candidate antibody population for the next iteration, and returning to the step of acquiring immune data.
In practical application, the parent population is sequentially subjected to selection, crossing and mutation to obtain an operation population, specifically:
and carrying out roulette algorithm, single-point crossover algorithm and mutation operation on the parent population in sequence to obtain an operation population.
In practical application, the propagation probability of each charging station position scheme in the candidate antibody population is calculated according to the immune data, specifically:
and calculating the affinity between each charging station position scheme in the candidate antibody population and each antigen according to the immune data, wherein the antigen is an objective function constructed by the driving mileage of the vehicle and the average driving speed of the vehicle, and the constraint conditions constructed by the number of charging stations, the stay time of the vehicle at each charging station, the residual quantity of the vehicle, the charging station position and the charging state of the vehicle.
And calculating the similarity proportion between the charging station position schemes in the candidate antibody population according to the affinity between the charging station position schemes in the candidate antibody population.
And calculating the reproduction probability of each antibody according to the affinity between each charging station position scheme in the candidate antibody population and each antigen and the similarity ratio between each charging station position scheme in the candidate antibody population.
As shown in fig. 2, the present embodiment provides a charging station location screening system corresponding to the above method, where the system includes:
an initial antibody population determining module A1, configured to construct an initial antibody population, where the initial antibody population includes a plurality of charging station location schemes, and each of the charging station location schemes includes a plurality of preset charging station locations.
A vehicle data acquisition module A2 for acquiring vehicle data under each charging station location scheme; the vehicle data includes a stay time of the vehicle at the charging station each time, a position of the vehicle, a driving range of the vehicle, and an average driving speed of the vehicle.
The candidate antibody population determining module A3 is used for inputting the vehicle data into a position model, screening the corresponding charging station position schemes, and obtaining candidate antibody populations formed by the screened charging station position schemes; the location model includes an objective function constructed with the range of the vehicle and the average speed of the vehicle, and constraints constructed with the number of charging stations, the residence time of the vehicle at each charging station, the remaining charge of the vehicle, the charging station location, and the state of charge of the vehicle.
And the optimal charging station position determining module A4 is used for screening the candidate antibody population by adopting an immune algorithm to obtain an optimal antibody population, and determining a preset charging station position in a charging station position scheme in the optimal antibody population as an optimal charging station position.
As an alternative embodiment, the location model includes: a location model determination unit that is:
wherein
Beta is the average speed of the vehicle on all roads, K represents the set of all trips of a vehicle on a certain day, P represents the set of vehicles, d pk Representing the mileage of the vehicle p in k strokes, t opk Indicating the residence time of vehicle p at charging station o after the end of k trips,indicating the charge amount, y, of the vehicle p after the end of k strokes o Indicating charging power +.>Representing the waiting time consumed by vehicle p at charging station o after the end of k trips, l representing the selected charging strategy, S pk Representing the remaining battery power of the vehicle p after the kth trip, S pk-1 Representing the remaining battery power of the vehicle p after the (k-1) th trip, E opk-1 Indicating completion of the k-1 st timeCharge amount r of vehicle p at post-trip charging station o p Represents the power consumption rate of the vehicle p, R pk Indicating the difference between the remaining power of the vehicle p after completion of the (k-1) th trip and the electric quantity of the (k) th trip, x o Indicating the position o, y of the charging station opk The charge state of the vehicle is represented, n represents the number of charging stations, and O represents the set of charging stations.
As an alternative embodiment, the optimal charging station location module includes:
an immune data acquisition unit configured to acquire immune data including vehicle data under the candidate antibody population, the number of charging stations having the same preset charging station position between two charging station position schemes in the candidate antibody population, and the total number of charging station position schemes in the candidate antibody population.
And the breeding rate calculating unit is used for calculating the breeding probability of each charging station position scheme in the candidate antibody population according to the immune data.
The position population forming unit is used for sequencing the propagation probabilities in a descending order, determining parent populations formed by charging station position schemes corresponding to the first N propagation probabilities, and determining position populations formed by charging station position schemes corresponding to the first m propagation probabilities.
A first judging unit, configured to judge whether a charging station location scheme in the location population meets a set constraint condition, to obtain a first judging result, where the set constraint condition is that
Wherein d is pk Indicating the mileage of the vehicle p over k trips,indicating the charge amount, y, of the vehicle p after the end of k strokes o Indicating charging power +.>Indicating that the vehicle p is consumed at the charging station o after the end of k strokesI represents the selected charging strategy, S pk Representing the remaining battery power of the vehicle p after the kth trip, S pk-1 Representing the remaining battery power, r, of the vehicle p after the (k-1) th trip p Represents the power consumption rate of the vehicle p, R pk Indicating the difference between the remaining power of the vehicle p after completion of the (k-1) th trip and the electric quantity of the (k) th trip, x o Indicating the position o, y of the charging station opk Indicating the state of charge of the vehicle, n indicating the number of charging stations, t opk The residence time of the vehicle p at the charging station O after the end of k strokes is represented, and O represents the charging station set.
And the first result unit is used for determining the position population as the optimal antibody population if the first judgment result is yes.
And the second judging unit is used for judging whether the set iteration times are reached or not if the first judging result is negative, so as to obtain a second judging result.
And the second result unit is used for determining the position population as the optimal antibody population if the second judgment result is yes.
And a third result unit, configured to, if the second determination result is no, sequentially perform operations of selecting, intersecting, and mutating the parent population to obtain an operation population, determine a combined population formed by combining the operation population and the position population as a candidate antibody population for the next iteration, and return to the step of obtaining immune data.
As an alternative embodiment, the third result unit comprises:
and the operation population determining subunit is used for obtaining an operation population by sequentially adopting a roulette algorithm, a single-point crossover algorithm and a mutation operation on the parent population.
As an alternative embodiment, the reproduction rate calculation unit includes:
an affinity calculation subunit for calculating, based on the immunization data, affinities between each charging station location scheme in the candidate antibody population and each antigen, the antigen being an objective function constructed with a driving distance of the vehicle and an average driving speed of the vehicle, and constraints constructed with a number of charging stations, a stay time of the vehicle at each charging station, a remaining amount of the vehicle, a charging station location, and a charging state of the vehicle.
And the similarity ratio calculating subunit is used for calculating the similarity ratio between the charging station position schemes in the candidate antibody population according to the affinity between the charging station position schemes in the candidate antibody population.
And the breeding rate calculating subunit is used for calculating the breeding probability of each antibody according to the affinity between each charging station position scheme in the candidate antibody population and each antigen and the similarity ratio between each charging station position scheme in the candidate antibody population.
The embodiment provides a more specific charging station position screening method, which specifically comprises the following steps:
(1) And simulating the running and charging actions of the electric automobile based on daily movement data in the track data of the large-scale vehicle, establishing a simulation model, and providing candidate positions of the charging station. The method compares the common electric automobile and the electric taxi with the traditional internal combustion engine automobile to obtain the advantages and disadvantages of three types of vehicles, and the characteristic pairs of the different types of vehicles are shown in the table 1.
TABLE 1 characterization of different types of vehicles
By combining the consideration, the electric automobile has wider prospect than the traditional internal combustion automobile due to energy conservation and environmental protection, but the charging efficiency of the combined time is not as good as that of the traditional automobile, and the method combines the position plan of the public charging station, so that the utilization rate of the charging station in the automobile is improved to the greatest extent.
Given a set of charging station candidate sites "o= {1,2,..n }" and an electric taxi set p= {1,2,..m }. In view of the location planning of public charging stations, a certain number of stations need to be selected from these candidate locations. N is a given constant. We can make the following assumptions:
(1) All vehicles have sufficient battery power before the trip begins. That is, the power charge state is 100%.
(2) The end position of the last journey does not take into account the start position of the next journey, i.e. the idle condition of the taxi.
(3) The driver will decide whether to continue the next trip or not based on the state of charge remaining after the end of the current trip. If the current state of charge does not meet the power demand for the next trip, the customer trip is denied and the nearest charging station is charged. Otherwise, the next trip is performed.
Equation (1) is an objective function that maximizes the distance traveled by the vehicle on the road, where β is the average travel speed of the vehicle on all roads, K represents the set of all trips of one vehicle on a certain day, d pk The mileage (in kilometers) of the vehicle p over k trips is shown. t is t opk The vehicle P stay time at the charging station O after the completion of k trips is represented, P represents a vehicle set, and O represents a charging station set.
s.t.∑ o∈O x o =n constraint (2), constraint (2) representing the number of charging stations n to be placed in the investigation region.
Constraint (3) refers to the time of stay at the charging station, including charging time and waiting time. When the vehicle arrives at the charging station, if an idle charger exists, the vehicle immediately charges. Otherwise the vehicle needs to wait for charging. t is t opk Indicating the dwell time of vehicle p at charging station o after the end of k trips, < > >Indicating the charge amount, y, of the vehicle p after the end of k trips o Charging power (unit: kWh/h) is shown. />Indicating the waiting time of the vehicle p consumed at the charging station. l represents the selected charging strategy.
Since the selected charging strategy limits the battery capacity, the charging behavior during the running of the electric vehicle is unavoidable, and if an appropriate charging strategy is selected when the charging behavior occurs, the charging time consumption can be reduced while satisfying the following customer schedule.
According to the method, two charging strategies are selected to simulate the action of charging the taxi under different conditions. It mainly comprises the following two aspects:
(1) Full charge strategy, when charging action occurs, the electric power is directly supplemented at 100% of charging speed, at this time
(2) The threshold charging strategy allows the vehicle to quickly replenish the battery charge to a specified threshold during the rapid charging phase, and when this threshold is exceeded, the charging rate gradually decreases until it is fully charged. The threshold value is set to 80% of the most reasonable
To simplify the problem, it is assumed that all vehicles have the same battery capacity (unit: kWh).
In constraint (4), S pk And S is pk-1 The remaining battery power (in kilowatt-hours) of the vehicle p after the kth and the kth-1 th strokes, respectively. E (E) opk-1 Indicating the charge amount of the vehicle p at the position o after completion of the k-1 th trip. r is (r) p The power consumption rate (unit: kWh/km) of the vehicle p, that is, the amount of electricity consumed per unit distance, is shown. R is R pk The difference between the remaining power of the vehicle p after completion of the kth-1 th trip and the electric quantity of the kth trip is represented.
The minimum charge threshold is specified in view of the charge consumption of the vehicle going to the charging station, which the method sets to 10%.
∑ o∈O y opk ≤x o Equation (5), equation (5) shows that the vehicle can be charged only after the charging station is located at position o.
Let formula (6) and formula (7) be two decision variables, x o Indicating whether a charging station is placed at location o. If y opk =1, indicating that the vehicle p is charged to the charging station o after the end of the kth journey. That is if y opk When the value is 1, the charging is performed, and when the value is 0, the charging is not performed.
(2) Based on the simulation model, the data are correspondingly processed, useless original data are removed, the vehicle ID, the moving distance, the moving start point and the end point position information of each vehicle are extracted, and the influence on the running distance of the electric vehicle and the residence time of the charging station under the influence of the charging condition of the charging station is evaluated.
The acquired scene data are track data of tens of thousands of common taxis for one week. The method extracts the track data of the common taxis to be used as simulation. The simulation strategy of the method is that the travel mode of the common taxi is not changed when the common taxi is replaced by the electric taxi.
Table 2 illustrates GPS data field information used in the present invention. The raw GPS orbit data can reflect the traffic situation of the city, but cannot be used directly, so that useful information needs to be extracted from the raw data. The specific operation procedure is as follows:
1. preprocessing recorder original data, and clearing abnormal or useless GPS points caused by GPS equipment faults, signal loss and the like, namely GPS data field information when no person is loaded, GPS data field information when no person is actually driven, and GPS data field information when the person is stopped during driving, wherein the table 2 is shown.
Table 2 GPS data field information
2. And extracting movement information of each vehicle according to the change of the occupied state value of the data, wherein the movement information is respectively a vehicle ID, a movement distance, a movement starting point and a movement ending point. During the processing, abnormal movement less than 1 km is removed, and part of short movement from 1 km to 2 km is combined with adjacent movement.
(3) Optimizing the candidate positions through an immune optimization algorithm, and finally deciding the optimal placement position of the charging station, wherein the detailed steps are as follows:
1) Randomly generating a code and antibody generation initial antibody population, initializing parameters used in an immunization algorithm, such as antibody population size N, charging station candidate O, number of charging stations to be placed N, and maximum number of iterations g max . With a simple coding scheme, each charging station location scheme forms a p-length antibody. Where the antibody length p is the number of charging stations that need to be placed, (an antibody is a solution to set the charging station position). Vehicle data in the initial antibody population is selected and input into the simulation model to obtain candidate antibody populations (candidate charging station schemes) as shown in fig. 3.
2) Each antibody in the candidate antibody population, i.e., the affinity of the antibody-antigen and the affinity of the antibody-antibody, was evaluated. The affinity between the antibody and the antigen is used for expressing the antigen recognition degree of the antibody and the affinity function A for the charging station position problem V The following is provided. Wherein F is v It is the affinity between the objective function antibody and the antigen that indicates the similarity between antibodies.
Where β is the average speed of travel of the vehicle on all roads, K represents the set of all trips of one vehicle on a certain day, d pk The mileage (in kilometers) of the vehicle p over k trips is shown. t is t opk The vehicle p stay time at charging station O after the end of k trips is indicated, and O indicates the charging station set.
The R-position method was used to calculate the affinity between antibodies. Wherein k is v,s Is the number of bits at the same charging station position in antibodies v and s, and L is the length of the antibody.
Antibody C v Is the proportion of similar antibodies in the population. Where N is the total number of antibodies and T is a predefined threshold.
By affinity A between antibody and antigen v And antibody concentration C v To determine the expected probability of reproduction for each individual in the population of antibodies. I.e.Where α is a constant. The higher the fitness of an individual, the higher the expected probability of reproduction, the higher the concentration of an individual, and the lower the expected probability of reproduction.
3) Forming a parent population. It sorts the initial population in descending order according to the expected reproductive rate, selecting the first N individuals to form a parent population, and simultaneously obtaining the first m individuals and storing them in a memory bank.
4) Determining whether constraint conditions (constraint (2), constraint (3), constraint (4) and constraint (6)) are met, if yes, exiting, otherwise continuing to the next step.
5) A new population is generated. Selecting, crossing and mutating parent population to form new population according to the calculated result obtained in 3), and then taking out memory individual and new population from memory bank to form new generation population. Crossover is accomplished by a single point crossover operator, randomly selecting the mutation site of the mutation. The selection is performed according to a roulette mechanism, and the probability that an individual is selected is the calculated expected reproduction probability.
6) The termination condition is checked. When the maximum iteration number g is reached max And (5) ending and returning a final screening result. Otherwise, please return to 2) and continue the iterative computation.
As shown in fig. 4, in the present charging station location selection, the optimal charging post location selection position is solved by an immune algorithm, and the output result is a screening layout point (expressed by longitude and latitude) of the electric vehicle charging station.
Aiming at the position screening problem of the public charging station, the invention provides a position screening method of the public charging station, wherein candidate positions are optimized through an immune optimization algorithm, and the position planning of the charging station is planned scientifically and reasonably, so that the charging efficiency and the charging amount of the new energy electric vehicle are improved, and the driving distance of the vehicle in a charging interval period is improved.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.

Claims (6)

1. A charging station location screening method, comprising:
constructing an initial antibody population, wherein the initial antibody population comprises a plurality of charging station position schemes, and each charging station position scheme comprises a plurality of preset charging station positions;
acquiring vehicle data under each charging station position scheme; the vehicle data includes a stay time of the vehicle at the charging station each time, a position of the vehicle, a driving distance of the vehicle, and an average driving speed of the vehicle;
inputting the vehicle data into a position model, and screening corresponding charging station position schemes to obtain candidate antibody populations formed by the screened charging station position schemes; the location model includes an objective function constructed with a driving distance of the vehicle and an average driving speed of the vehicle, and a constraint condition constructed with the number of charging stations, a stay time of the vehicle at each charging station, a remaining capacity of the vehicle, a charging station position, and a charging state of the vehicle;
screening the candidate antibody population by adopting an immune algorithm to obtain an optimal antibody population, and determining a preset charging station position in a charging station position scheme in the optimal antibody population as an optimal charging station position;
The position model specifically comprises the following steps:
，
wherein the method comprises the steps of
Beta is the average speed of the vehicle on all roads, K represents the set of all trips of a vehicle on a certain day, P represents the set of vehicles, d pk Representing the mileage of the vehicle p in k strokes, t opk Indicating the residence time of vehicle p at charging station o after the end of k trips,indicating the charge amount, y, of the vehicle p after the end of k strokes o Indicating charging power +.>Representing the waiting time consumed by vehicle p at charging station o after the end of k trips, l representing the selected charging strategy, S pk Representing the remaining battery power of the vehicle p after the kth trip, S pk-1 Representing the remaining battery power of the vehicle p after the (k-1) th trip, E opk-1 Indicating the charge amount, r, of the vehicle p at the charging station o after completion of the (k-1) th trip p Represents the power consumption rate of the vehicle p, R pk Indicating the difference between the remaining power of the vehicle p after completion of the (k-1) th trip and the electric quantity of the (k) th trip, x o Indicating the position o, y of the charging station opk Indicating the state of charge of the vehicle, n indicating the chargeNumber of power stations, y opk Representing a state of charge of the vehicle, n representing the number of charging stations, O representing a collection of charging stations;
the method comprises the steps of adopting an immune algorithm to screen the candidate antibody population to obtain an optimal antibody population, specifically comprising the following steps:
obtaining immune data, wherein the immune data comprises vehicle data under the candidate antibody population, the number of charging stations with the same preset charging station position between two charging station position schemes in the candidate antibody population and the total number of charging station position schemes in the candidate antibody population;
Calculating propagation probability of each charging station position scheme in the candidate antibody population according to the immune data;
the propagation probabilities are ordered in a descending order, parent populations formed by charging station position schemes corresponding to the first N propagation probabilities are determined, and position populations formed by charging station position schemes corresponding to the first m propagation probabilities are determined;
judging whether a charging station position scheme in the position population meets a set constraint condition or not to obtain a first judgment result, wherein the set constraint condition is that
If the first judgment result is yes, determining the position population as the optimal antibody population;
if the first judgment result is negative, judging whether the set iteration times are reached or not, and obtaining a second judgment result;
if the second judgment result is yes, determining the position population as the optimal antibody population;
and if the second judgment result is negative, sequentially performing selection, crossing and mutation on the parent population to obtain an operation population, determining a combined population formed by combining the operation population and the position population as a candidate antibody population for the next iteration, and returning to the step of acquiring immune data.
2. The charging station location screening method according to claim 1, wherein the operations of sequentially selecting, crossing and mutating the parent population obtain an operation population, specifically:
And carrying out roulette algorithm, single-point crossover algorithm and mutation operation on the parent population in sequence to obtain an operation population.
3. The charging station location screening method according to claim 1, wherein the calculating the propagation probability of each charging station location scheme in the candidate antibody population according to the immunization data specifically comprises:
calculating affinities between charging station position schemes in the candidate antibody population and antigens according to the immune data, wherein the antigens are objective functions constructed by the driving mileage of a vehicle and the average driving speed of the vehicle, and constraint conditions constructed by the number of charging stations, the stay time of the vehicle at each charging station, the residual quantity of the vehicle, the charging station positions and the charging state of the vehicle;
calculating a similarity ratio between each charging station location scheme in the candidate antibody population according to the affinity between each charging station location scheme in the candidate antibody population;
and calculating the reproduction probability of each antibody according to the affinity between each charging station position scheme in the candidate antibody population and each antigen and the similarity ratio between each charging station position scheme in the candidate antibody population.
4. A charging station location screening system, comprising:
an initial antibody population determination module configured to construct an initial antibody population, the initial antibody population including a plurality of charging station location schemes, each of the charging station location schemes including a plurality of preset charging station locations;
the vehicle data acquisition module is used for acquiring vehicle data under each charging station position scheme; the vehicle data includes a stay time of the vehicle at the charging station each time, a position of the vehicle, a driving distance of the vehicle, and an average driving speed of the vehicle;
the candidate antibody population determining module is used for inputting the vehicle data into a position model, screening the corresponding charging station position schemes, and obtaining candidate antibody populations formed by the screened charging station position schemes; the location model includes an objective function constructed with a driving distance of the vehicle and an average driving speed of the vehicle, and a constraint condition constructed with the number of charging stations, a stay time of the vehicle at each charging station, a remaining capacity of the vehicle, a charging station position, and a charging state of the vehicle;
the optimal charging station position determining module is used for screening the candidate antibody population by adopting an immune algorithm to obtain an optimal antibody population, and determining a preset charging station position in a charging station position scheme in the optimal antibody population as an optimal charging station position;
The location model includes: a location model determination unit that is:
，
wherein the method comprises the steps of
Beta is the average speed of the vehicle on all roads, K represents the set of all trips of a vehicle on a certain day, P represents the set of vehicles, d pk Representing the mileage of the vehicle p in k strokes, t opk Indicating the residence time of vehicle p at charging station o after the end of k trips,indicating the charge amount, y, of the vehicle p after the end of k strokes o Indicating charging power +.>Representing the waiting time consumed by vehicle p at charging station o after the end of k trips, l representing the selected charging strategy, S pk Representing the remaining battery power of the vehicle p after the kth trip，S pk-1 Representing the remaining battery power of the vehicle p after the (k-1) th trip, E opk-1 Indicating the charge amount, r, of the vehicle p at the charging station o after completion of the (k-1) th trip p Represents the power consumption rate of the vehicle p, R pk Indicating the difference between the remaining power of the vehicle p after completion of the (k-1) th trip and the electric quantity of the (k) th trip, x o Indicating the position o, y of the charging station opk Representing a state of charge of the vehicle, n representing the number of charging stations, O representing a collection of charging stations;
the optimal charging station location module includes:
an immune data acquisition unit configured to acquire immune data including vehicle data under the candidate antibody population, the number of charging stations having the same preset charging station position between two charging station position schemes in the candidate antibody population, and the total number of charging station position schemes in the candidate antibody population;
A breeding rate calculation unit for calculating the breeding probability of each charging station position scheme in the candidate antibody population according to the immune data;
the position population forming unit is used for sequencing the propagation probabilities in a descending order, determining parent populations formed by charging station position schemes corresponding to the first N propagation probabilities, and determining position populations formed by charging station position schemes corresponding to the first m propagation probabilities;
a first judging unit, configured to judge whether a charging station location scheme in the location population meets a set constraint condition, to obtain a first judging result, where the set constraint condition is that
The first result unit is used for determining that the position population is the optimal antibody population if the first judgment result is yes;
the second judging unit is used for judging whether the set iteration times are reached or not if the first judging result is negative, so as to obtain a second judging result;
the second result unit is used for determining that the position population is the optimal antibody population if the second judgment result is yes;
and a third result unit, configured to, if the second determination result is no, sequentially perform operations of selecting, intersecting, and mutating the parent population to obtain an operation population, determine a combined population formed by combining the operation population and the position population as a candidate antibody population for the next iteration, and return to the step of obtaining immune data.
5. The charging station location screening system of claim 4, wherein the third outcome unit comprises:
and the operation population determining subunit is used for obtaining an operation population by sequentially adopting a roulette algorithm, a single-point crossover algorithm and a mutation operation on the parent population.
6. The charging station location screening system according to claim 4, wherein the proliferation rate calculation unit comprises:
an affinity calculation subunit for calculating, based on the immunization data, affinities between each charging station location scheme in the candidate antibody population and each antigen, the antigen being an objective function constructed with a driving distance of the vehicle and an average driving speed of the vehicle, and constraints constructed with a number of charging stations, a stay time of the vehicle at each charging station, a remaining amount of the vehicle, a charging station location, and a charging state of the vehicle;
a similarity ratio calculation subunit for calculating a similarity ratio between each charging station location scheme in the candidate antibody population according to an affinity between each charging station location scheme in the candidate antibody population;
And the breeding rate calculating subunit is used for calculating the breeding probability of each antibody according to the affinity between each charging station position scheme in the candidate antibody population and each antigen and the similarity ratio between each charging station position scheme in the candidate antibody population.
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